From c7bfc9e461a4f084b9d1de059410c012307782ca Mon Sep 17 00:00:00 2001 From: Tau Date: Thu, 6 Apr 2023 16:51:48 +0800 Subject: [PATCH 1/7] Bump version to v1.0.0 (#2169) (#2170) --- mmpose/version.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mmpose/version.py b/mmpose/version.py index 390f9399d3..73312cc28d 100644 --- a/mmpose/version.py +++ b/mmpose/version.py @@ -1,6 +1,6 @@ # Copyright (c) Open-MMLab. All rights reserved. -__version__ = '1.0.0rc1' +__version__ = '1.0.0' short_version = __version__ From cf47b71c3235a0dddc8ae664edf27bef404640b3 Mon Sep 17 00:00:00 2001 From: Yining Li Date: Mon, 17 Apr 2023 11:49:06 +0800 Subject: [PATCH 2/7] [Fix] add .owners.yml (#2243) --- .owners.yml | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) create mode 100644 .owners.yml diff --git a/.owners.yml b/.owners.yml new file mode 100644 index 0000000000..2050b43c10 --- /dev/null +++ b/.owners.yml @@ -0,0 +1,16 @@ +assign: + issues: enabled + pull_requests: disabled + strategy: + # random + daily-shift-based + scedule: + '*/1 * * * *' + assignees: + - Tau-J + - LareinaM + - Ben-Louis + - LareinaM + - Ben-Louis + - Tau-J + - Tau-J From b2ffafacb14a66cde9a906c9066f62da55bf219c Mon Sep 17 00:00:00 2001 From: juxuan27 Date: Sun, 23 Apr 2023 23:21:48 +0800 Subject: [PATCH 3/7] add dataset: Human-Art --- README.md | 1 + README_CN.md | 1 + configs/_base_/datasets/humanart.py | 181 +++++++++++ configs/_base_/datasets/humanart_aic.py | 205 +++++++++++++ ...e_hrnet-w32_8xb24-300e_humanart-512x512.py | 159 ++++++++++ ...d_hrnet-w32_8xb20-140e_humanart-512x512.py | 164 ++++++++++ ...d_hrnet-w48_8xb20-140e_humanart-512x512.py | 164 ++++++++++ ...r_hrnet-w32_8xb10-140e_humanart-512x512.py | 186 ++++++++++++ ...r_hrnet-w48_8xb10-140e_humanart-640x640.py | 187 ++++++++++++ .../ipr_res50_8xb64-210e_humanart-256x256.py | 134 +++++++++ ...es50_debias-8xb64-210e_humanart-256x256.py | 136 +++++++++ ..._res50_dsnt-8xb64-210e_humanart-256x256.py | 134 +++++++++ ...pose-l_8xb256-420e_aic-humanart-256x192.py | 272 +++++++++++++++++ ...pose-l_8xb256-420e_aic-humanart-384x288.py | 272 +++++++++++++++++ .../rtmpose-l_8xb256-420e_humanart-256x192.py | 232 ++++++++++++++ ...pose-m_8xb256-420e_aic-humanart-256x192.py | 272 +++++++++++++++++ ...pose-m_8xb256-420e_aic-humanart-384x288.py | 272 +++++++++++++++++ .../rtmpose-m_8xb256-420e_humanart-256x192.py | 232 ++++++++++++++ ...pose-s_8xb256-420e_aic-humanart-256x192.py | 272 +++++++++++++++++ .../rtmpose-s_8xb256-420e_humanart-256x192.py | 232 ++++++++++++++ ...pose-t_8xb256-420e_aic-humanart-256x192.py | 273 +++++++++++++++++ .../rtmpose-t_8xb256-420e_humanart-256x192.py | 233 ++++++++++++++ ...2_wo-deconv-8xb64-210e_humanart-256x192.py | 124 ++++++++ ...simcc_res50_8xb32-140e_humanart-384x288.py | 120 ++++++++ ...simcc_res50_8xb64-210e_humanart-256x192.py | 114 +++++++ ...vipnas-mbv3_8xb64-210e_humanart-256x192.py | 119 ++++++++ ...-l_udp_8xb256-210e_aic-humanart-256x192.py | 284 ++++++++++++++++++ ...next-l_udp_8xb256-210e_humanart-256x192.py | 214 +++++++++++++ ...-m_udp_8xb256-210e_aic-humanart-256x192.py | 284 ++++++++++++++++++ ...next-m_udp_8xb256-210e_humanart-256x192.py | 214 +++++++++++++ ...-s_udp_8xb256-210e_aic-humanart-256x192.py | 284 ++++++++++++++++++ ...next-s_udp_8xb256-210e_humanart-256x192.py | 214 +++++++++++++ ...ny_udp_8xb256-210e_aic-humanart-256x192.py | 284 ++++++++++++++++++ ...t-tiny_udp_8xb256-210e_humanart-256x192.py | 214 +++++++++++++ .../humanart/hrnet_humanart.md | 44 +++ ...hm_2xmspn50_8xb32-210e_humanart-256x192.py | 152 ++++++++++ ...-hm_2xrsn50_8xb32-210e_humanart-256x192.py | 154 ++++++++++ ...hm_3xmspn50_8xb32-210e_humanart-256x192.py | 152 ++++++++++ ...-hm_3xrsn50_8xb32-210e_humanart-256x192.py | 154 ++++++++++ ...hm_4xmspn50_8xb32-210e_humanart-256x192.py | 152 ++++++++++ ...base-simple_8xb64-210e_humanart-256x192.py | 153 ++++++++++ ...iTPose-base_8xb64-210e_humanart-256x192.py | 150 +++++++++ ...huge-simple_8xb64-210e_humanart-256x192.py | 153 ++++++++++ ...iTPose-huge_8xb64-210e_humanart-256x192.py | 150 +++++++++ ...arge-simple_8xb64-210e_humanart-256x192.py | 153 ++++++++++ ...TPose-large_8xb64-210e_humanart-256x192.py | 150 +++++++++ ...mall-simple_8xb64-210e_humanart-256x192.py | 158 ++++++++++ ...TPose-small_8xb64-210e_humanart-256x192.py | 155 ++++++++++ ...-hm_alexnet_8xb64-210e_humanart-256x192.py | 117 ++++++++ .../td-hm_cpm_8xb32-210e_humanart-384x288.py | 125 ++++++++ .../td-hm_cpm_8xb64-210e_humanart-256x192.py | 125 ++++++++ ...hourglass52_8xb32-210e_humanart-256x256.py | 122 ++++++++ ...hourglass52_8xb32-210e_humanart-384x384.py | 122 ++++++++ ...former-base_8xb32-210e_humanart-256x192.py | 174 +++++++++++ ...former-base_8xb32-210e_humanart-384x288.py | 174 +++++++++++ ...ormer-small_8xb32-210e_humanart-256x192.py | 174 +++++++++++ ...ormer-small_8xb32-210e_humanart-384x288.py | 174 +++++++++++ ...m_hrnet-w32_8xb64-210e_humanart-256x192.py | 150 +++++++++ ...m_hrnet-w32_8xb64-210e_humanart-384x288.py | 150 +++++++++ ...8xb64-210e_humanart-aic-256x192-combine.py | 221 ++++++++++++++ ...2_8xb64-210e_humanart-aic-256x192-merge.py | 187 ++++++++++++ ...arsedropout-8xb64-210e_humanart-256x192.py | 165 ++++++++++ ...et-w32_dark-8xb64-210e_humanart-256x192.py | 154 ++++++++++ ...et-w32_dark-8xb64-210e_humanart-384x288.py | 154 ++++++++++ ...et-w32_fp16-8xb64-210e_humanart-256x192.py | 7 + ...32_gridmask-8xb64-210e_humanart-256x192.py | 162 ++++++++++ ...photometric-8xb64-210e_humanart-256x192.py | 153 ++++++++++ ...net-w32_udp-8xb64-210e_humanart-256x192.py | 150 +++++++++ ...net-w32_udp-8xb64-210e_humanart-384x288.py | 150 +++++++++ ...udp-regress-8xb64-210e_humanart-256x192.py | 155 ++++++++++ ...m_hrnet-w48_8xb32-210e_humanart-256x192.py | 150 +++++++++ ...m_hrnet-w48_8xb32-210e_humanart-384x288.py | 150 +++++++++ ...et-w48_dark-8xb32-210e_humanart-256x192.py | 154 ++++++++++ ...et-w48_dark-8xb32-210e_humanart-384x288.py | 154 ++++++++++ ...net-w48_udp-8xb32-210e_humanart-256x192.py | 150 +++++++++ ...net-w48_udp-8xb32-210e_humanart-384x288.py | 150 +++++++++ ...itehrnet-18_8xb32-210e_humanart-384x288.py | 140 +++++++++ ...itehrnet-18_8xb64-210e_humanart-256x192.py | 140 +++++++++ ...itehrnet-30_8xb32-210e_humanart-384x288.py | 140 +++++++++ ...itehrnet-30_8xb64-210e_humanart-256x192.py | 140 +++++++++ ...mobilenetv2_8xb64-210e_humanart-256x192.py | 124 ++++++++ ...mobilenetv2_8xb64-210e_humanart-384x288.py | 124 ++++++++ ...d-hm_mspn50_8xb32-210e_humanart-256x192.py | 152 ++++++++++ ...td-hm_pvt-s_8xb64-210e_humanart-256x192.py | 127 ++++++++ ...hm_pvtv2-b2_8xb64-210e_humanart-256x192.py | 128 ++++++++ ...d-hm_res101_8xb32-210e_humanart-384x288.py | 121 ++++++++ ...d-hm_res101_8xb64-210e_humanart-256x192.py | 121 ++++++++ ...res101_dark-8xb64-210e_humanart-256x192.py | 125 ++++++++ ...res101_dark-8xb64-210e_humanart-384x288.py | 125 ++++++++ ...d-hm_res152_8xb32-210e_humanart-256x192.py | 121 ++++++++ ...d-hm_res152_8xb32-210e_humanart-384x288.py | 121 ++++++++ ...res152_dark-8xb32-210e_humanart-256x192.py | 125 ++++++++ ...res152_dark-8xb32-210e_humanart-384x288.py | 126 ++++++++ ...td-hm_res50_8xb64-210e_humanart-256x192.py | 121 ++++++++ ...td-hm_res50_8xb64-210e_humanart-384x288.py | 121 ++++++++ ..._res50_dark-8xb64-210e_humanart-256x192.py | 125 ++++++++ ..._res50_dark-8xb64-210e_humanart-384x288.py | 125 ++++++++ ..._res50_fp16-8xb64-210e_humanart-256x192.py | 7 + ..._resnest101_8xb32-210e_humanart-384x288.py | 121 ++++++++ ..._resnest101_8xb64-210e_humanart-256x192.py | 121 ++++++++ ..._resnest200_8xb16-210e_humanart-384x288.py | 121 ++++++++ ..._resnest200_8xb64-210e_humanart-256x192.py | 121 ++++++++ ..._resnest269_8xb16-210e_humanart-384x288.py | 121 ++++++++ ..._resnest269_8xb32-210e_humanart-256x192.py | 121 ++++++++ ...m_resnest50_8xb64-210e_humanart-256x192.py | 121 ++++++++ ...m_resnest50_8xb64-210e_humanart-384x288.py | 121 ++++++++ ...esnetv1d101_8xb32-210e_humanart-384x288.py | 121 ++++++++ ...esnetv1d101_8xb64-210e_humanart-256x192.py | 121 ++++++++ ...esnetv1d152_8xb32-210e_humanart-256x192.py | 121 ++++++++ ...esnetv1d152_8xb48-210e_humanart-384x288.py | 121 ++++++++ ...resnetv1d50_8xb64-210e_humanart-256x192.py | 121 ++++++++ ...resnetv1d50_8xb64-210e_humanart-384x288.py | 121 ++++++++ ..._resnext101_8xb32-210e_humanart-384x288.py | 122 ++++++++ ..._resnext101_8xb64-210e_humanart-256x192.py | 122 ++++++++ ..._resnext152_8xb32-210e_humanart-256x192.py | 122 ++++++++ ..._resnext152_8xb48-210e_humanart-384x288.py | 122 ++++++++ ...m_resnext50_8xb64-210e_humanart-256x192.py | 121 ++++++++ ...m_resnext50_8xb64-210e_humanart-384x288.py | 121 ++++++++ ...td-hm_rsn18_8xb32-210e_humanart-256x192.py | 154 ++++++++++ ...td-hm_rsn50_8xb32-210e_humanart-256x192.py | 154 ++++++++++ ...hm_scnet101_8xb32-210e_humanart-256x192.py | 124 ++++++++ ...hm_scnet101_8xb48-210e_humanart-384x288.py | 124 ++++++++ ...-hm_scnet50_8xb32-210e_humanart-384x288.py | 124 ++++++++ ...-hm_scnet50_8xb64-210e_humanart-256x192.py | 124 ++++++++ ...seresnet101_8xb32-210e_humanart-384x288.py | 121 ++++++++ ...seresnet101_8xb64-210e_humanart-256x192.py | 121 ++++++++ ...seresnet152_8xb32-210e_humanart-256x192.py | 120 ++++++++ ...seresnet152_8xb48-210e_humanart-384x288.py | 120 ++++++++ ..._seresnet50_8xb64-210e_humanart-256x192.py | 121 ++++++++ ..._seresnet50_8xb64-210e_humanart-384x288.py | 121 ++++++++ ...hufflenetv1_8xb64-210e_humanart-256x192.py | 121 ++++++++ ...hufflenetv1_8xb64-210e_humanart-384x288.py | 121 ++++++++ ...hufflenetv2_8xb64-210e_humanart-256x192.py | 121 ++++++++ ...hufflenetv2_8xb64-210e_humanart-384x288.py | 121 ++++++++ ...win-b-p4-w7_8xb32-210e_humanart-256x192.py | 139 +++++++++ ...win-b-p4-w7_8xb32-210e_humanart-384x288.py | 139 +++++++++ ...win-l-p4-w7_8xb32-210e_humanart-256x192.py | 148 +++++++++ ...win-l-p4-w7_8xb32-210e_humanart-384x288.py | 148 +++++++++ ...win-t-p4-w7_8xb32-210e_humanart-256x192.py | 139 +++++++++ ...hm_vgg16-bn_8xb64-210e_humanart-256x192.py | 122 ++++++++ ...vipnas-mbv3_8xb64-210e_humanart-256x192.py | 122 ++++++++ ...ipnas-res50_8xb64-210e_humanart-256x192.py | 120 ++++++++ ...-pretrained-8xb64-210e_humanart-256x192.py | 126 ++++++++ ...-reg_res101_8xb64-210e_humanart-256x192.py | 120 ++++++++ ..._res101_rle-8xb64-210e_humanart-256x192.py | 121 ++++++++ ...-reg_res152_8xb64-210e_humanart-256x192.py | 120 ++++++++ ..._res152_rle-8xb64-210e_humanart-256x192.py | 121 ++++++++ ..._res152_rle-8xb64-210e_humanart-384x288.py | 121 ++++++++ ...d-reg_res50_8xb64-210e_humanart-256x192.py | 120 ++++++++ ...g_res50_rle-8xb64-210e_humanart-256x192.py | 121 ++++++++ ...-pretrained-8xb64-210e_humanart-256x192.py | 125 ++++++++ docs/en/dataset_zoo/2d_body_keypoint.md | 52 ++++ docs/zh_cn/dataset_zoo/2d_body_keypoint.md | 52 ++++ mmpose/datasets/datasets/body/__init__.py | 3 +- .../datasets/body/humanart_dataset.py | 73 +++++ 155 files changed, 22372 insertions(+), 1 deletion(-) create mode 100644 configs/_base_/datasets/humanart.py create mode 100644 configs/_base_/datasets/humanart_aic.py create mode 100644 configs/body_2d_keypoint/associative_embedding/humanart/ae_hrnet-w32_8xb24-300e_humanart-512x512.py create mode 100644 configs/body_2d_keypoint/cid/humanart/cid_hrnet-w32_8xb20-140e_humanart-512x512.py create mode 100644 configs/body_2d_keypoint/cid/humanart/cid_hrnet-w48_8xb20-140e_humanart-512x512.py create mode 100644 configs/body_2d_keypoint/dekr/humanart/dekr_hrnet-w32_8xb10-140e_humanart-512x512.py create mode 100644 configs/body_2d_keypoint/dekr/humanart/dekr_hrnet-w48_8xb10-140e_humanart-640x640.py create mode 100644 configs/body_2d_keypoint/integral_regression/humanart/ipr_res50_8xb64-210e_humanart-256x256.py create mode 100644 configs/body_2d_keypoint/integral_regression/humanart/ipr_res50_debias-8xb64-210e_humanart-256x256.py create mode 100644 configs/body_2d_keypoint/integral_regression/humanart/ipr_res50_dsnt-8xb64-210e_humanart-256x256.py create mode 100644 configs/body_2d_keypoint/rtmpose/humanart/rtmpose-l_8xb256-420e_aic-humanart-256x192.py create mode 100644 configs/body_2d_keypoint/rtmpose/humanart/rtmpose-l_8xb256-420e_aic-humanart-384x288.py create mode 100644 configs/body_2d_keypoint/rtmpose/humanart/rtmpose-l_8xb256-420e_humanart-256x192.py create mode 100644 configs/body_2d_keypoint/rtmpose/humanart/rtmpose-m_8xb256-420e_aic-humanart-256x192.py create mode 100644 configs/body_2d_keypoint/rtmpose/humanart/rtmpose-m_8xb256-420e_aic-humanart-384x288.py create mode 100644 configs/body_2d_keypoint/rtmpose/humanart/rtmpose-m_8xb256-420e_humanart-256x192.py create mode 100644 configs/body_2d_keypoint/rtmpose/humanart/rtmpose-s_8xb256-420e_aic-humanart-256x192.py create mode 100644 configs/body_2d_keypoint/rtmpose/humanart/rtmpose-s_8xb256-420e_humanart-256x192.py create mode 100644 configs/body_2d_keypoint/rtmpose/humanart/rtmpose-t_8xb256-420e_aic-humanart-256x192.py create mode 100644 configs/body_2d_keypoint/rtmpose/humanart/rtmpose-t_8xb256-420e_humanart-256x192.py create mode 100644 configs/body_2d_keypoint/simcc/humanart/simcc_mobilenetv2_wo-deconv-8xb64-210e_humanart-256x192.py create mode 100644 configs/body_2d_keypoint/simcc/humanart/simcc_res50_8xb32-140e_humanart-384x288.py create mode 100644 configs/body_2d_keypoint/simcc/humanart/simcc_res50_8xb64-210e_humanart-256x192.py create mode 100644 configs/body_2d_keypoint/simcc/humanart/simcc_vipnas-mbv3_8xb64-210e_humanart-256x192.py create mode 100644 configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-l_udp_8xb256-210e_aic-humanart-256x192.py create mode 100644 configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-l_udp_8xb256-210e_humanart-256x192.py create mode 100644 configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-m_udp_8xb256-210e_aic-humanart-256x192.py create mode 100644 configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-m_udp_8xb256-210e_humanart-256x192.py create mode 100644 configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-s_udp_8xb256-210e_aic-humanart-256x192.py create mode 100644 configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-s_udp_8xb256-210e_humanart-256x192.py create mode 100644 configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-tiny_udp_8xb256-210e_aic-humanart-256x192.py create mode 100644 configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-tiny_udp_8xb256-210e_humanart-256x192.py create mode 100644 configs/body_2d_keypoint/topdown_heatmap/humanart/hrnet_humanart.md create mode 100644 configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_2xmspn50_8xb32-210e_humanart-256x192.py create mode 100644 configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_2xrsn50_8xb32-210e_humanart-256x192.py create mode 100644 configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_3xmspn50_8xb32-210e_humanart-256x192.py create mode 100644 configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_3xrsn50_8xb32-210e_humanart-256x192.py create mode 100644 configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_4xmspn50_8xb32-210e_humanart-256x192.py create mode 100644 configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-base-simple_8xb64-210e_humanart-256x192.py create mode 100644 configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-base_8xb64-210e_humanart-256x192.py create mode 100644 configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-huge-simple_8xb64-210e_humanart-256x192.py create mode 100644 configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-huge_8xb64-210e_humanart-256x192.py create mode 100644 configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-large-simple_8xb64-210e_humanart-256x192.py create mode 100644 configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-large_8xb64-210e_humanart-256x192.py create mode 100644 configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-small-simple_8xb64-210e_humanart-256x192.py create mode 100644 configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-small_8xb64-210e_humanart-256x192.py create mode 100644 configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_alexnet_8xb64-210e_humanart-256x192.py create mode 100644 configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_cpm_8xb32-210e_humanart-384x288.py create mode 100644 configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_cpm_8xb64-210e_humanart-256x192.py create mode 100644 configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hourglass52_8xb32-210e_humanart-256x256.py create mode 100644 configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hourglass52_8xb32-210e_humanart-384x384.py create mode 100644 configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrformer-base_8xb32-210e_humanart-256x192.py create mode 100644 configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrformer-base_8xb32-210e_humanart-384x288.py create mode 100644 configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrformer-small_8xb32-210e_humanart-256x192.py create mode 100644 configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrformer-small_8xb32-210e_humanart-384x288.py create mode 100644 configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_8xb64-210e_humanart-256x192.py create mode 100644 configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_8xb64-210e_humanart-384x288.py create mode 100644 configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_8xb64-210e_humanart-aic-256x192-combine.py create mode 100644 configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_8xb64-210e_humanart-aic-256x192-merge.py create mode 100644 configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_coarsedropout-8xb64-210e_humanart-256x192.py create mode 100644 configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_dark-8xb64-210e_humanart-256x192.py create mode 100644 configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_dark-8xb64-210e_humanart-384x288.py create mode 100644 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@@ -284,6 +284,7 @@ A summary can be found in the [Model Zoo](https://mmpose.readthedocs.io/en/lates - [x] [InterHand2.6M](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/datasets.html#interhand2-6m-eccv-2020) \[[homepage](https://mks0601.github.io/InterHand2.6M/)\] (ECCV'2020) - [x] [AP-10K](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/datasets.html#ap-10k-neurips-2021) \[[homepage](https://github.com/AlexTheBad/AP-10K)\] (NeurIPS'2021) - [x] [Horse-10](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/datasets.html#horse-10-wacv-2021) \[[homepage](http://www.mackenziemathislab.org/horse10)\] (WACV'2021) +- [x] [Human-Art](#todo) \[[homepage](https://idea-research.github.io/HumanArt/)\] (CVPR'2023) diff --git a/README_CN.md b/README_CN.md index ff5f14c50b..bf07f1120d 100644 --- a/README_CN.md +++ b/README_CN.md @@ -282,6 +282,7 @@ MMPose v1.0.0 是一个重大更新，包括了大量的 API 和配置文件的 - [x] [InterHand2.6M](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/datasets.html#interhand2-6m-eccv-2020) \[[主页](https://mks0601.github.io/InterHand2.6M/)\] (ECCV'2020) - [x] [AP-10K](https://mmpose.readthedocs.io/en/latest/model_zoo_papers/datasets.html#ap-10k-neurips-2021) \[[主页](https://github.com/AlexTheBad/AP-10K)\] (NeurIPS'2021) - [x] [Horse-10](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/datasets.html#horse-10-wacv-2021) \[[主页](http://www.mackenziemathislab.org/horse10)\] (WACV'2021) +- [x] [Human-Art](#todo) \[[homepage](https://idea-research.github.io/HumanArt/)\] (CVPR'2023) diff --git a/configs/_base_/datasets/humanart.py b/configs/_base_/datasets/humanart.py new file mode 100644 index 0000000000..b549269b69 --- /dev/null +++ b/configs/_base_/datasets/humanart.py @@ -0,0 +1,181 @@ +dataset_info = dict( + dataset_name='Human-Art', + paper_info=dict( + author='Ju, Xuan and Zeng, Ailing and ' + 'Wang, Jianan and Xu, Qiang and Zhang, Lei', + title='Human-Art: A Versatile Human-Centric Dataset ' + 'Bridging Natural and Artificial Scenes', + container='Proceedings of the IEEE/CVF Conference on ' + 'Computer Vision and Pattern Recognition', + year='2023', + homepage='https://idea-research.github.io/HumanArt/', + ), + keypoint_info={ + 0: + dict(name='nose', id=0, color=[51, 153, 255], type='upper', swap=''), + 1: + dict( + name='left_eye', + id=1, + color=[51, 153, 255], + type='upper', + swap='right_eye'), + 2: + dict( + name='right_eye', + id=2, + color=[51, 153, 255], + type='upper', + swap='left_eye'), + 3: + dict( + name='left_ear', + id=3, + color=[51, 153, 255], + type='upper', + swap='right_ear'), + 4: + dict( + name='right_ear', + id=4, + color=[51, 153, 255], + type='upper', + swap='left_ear'), + 5: + dict( + name='left_shoulder', + id=5, + color=[0, 255, 0], + type='upper', + swap='right_shoulder'), + 6: + dict( + name='right_shoulder', + id=6, + color=[255, 128, 0], + type='upper', + swap='left_shoulder'), + 7: + dict( + name='left_elbow', + id=7, + color=[0, 255, 0], + type='upper', + swap='right_elbow'), + 8: + dict( + name='right_elbow', + id=8, + color=[255, 128, 0], + type='upper', + swap='left_elbow'), + 9: + dict( + name='left_wrist', + id=9, + color=[0, 255, 0], + type='upper', + swap='right_wrist'), + 10: + dict( + name='right_wrist', + id=10, + color=[255, 128, 0], + type='upper', + swap='left_wrist'), + 11: + dict( + name='left_hip', + id=11, + color=[0, 255, 0], + type='lower', + swap='right_hip'), + 12: + dict( + name='right_hip', + id=12, + color=[255, 128, 0], + type='lower', + swap='left_hip'), + 13: + dict( + name='left_knee', + id=13, + color=[0, 255, 0], + type='lower', + swap='right_knee'), + 14: + dict( + name='right_knee', + id=14, + color=[255, 128, 0], + type='lower', + swap='left_knee'), + 15: + dict( + name='left_ankle', + id=15, + color=[0, 255, 0], + type='lower', + swap='right_ankle'), + 16: + dict( + name='right_ankle', + id=16, + color=[255, 128, 0], + type='lower', + swap='left_ankle') + }, + skeleton_info={ + 0: + dict(link=('left_ankle', 'left_knee'), id=0, color=[0, 255, 0]), + 1: + dict(link=('left_knee', 'left_hip'), id=1, color=[0, 255, 0]), + 2: + dict(link=('right_ankle', 'right_knee'), id=2, color=[255, 128, 0]), + 3: + dict(link=('right_knee', 'right_hip'), id=3, color=[255, 128, 0]), + 4: + dict(link=('left_hip', 'right_hip'), id=4, color=[51, 153, 255]), + 5: + dict(link=('left_shoulder', 'left_hip'), id=5, color=[51, 153, 255]), + 6: + dict(link=('right_shoulder', 'right_hip'), id=6, color=[51, 153, 255]), + 7: + dict( + link=('left_shoulder', 'right_shoulder'), + id=7, + color=[51, 153, 255]), + 8: + dict(link=('left_shoulder', 'left_elbow'), id=8, color=[0, 255, 0]), + 9: + dict( + link=('right_shoulder', 'right_elbow'), id=9, color=[255, 128, 0]), + 10: + dict(link=('left_elbow', 'left_wrist'), id=10, color=[0, 255, 0]), + 11: + dict(link=('right_elbow', 'right_wrist'), id=11, color=[255, 128, 0]), + 12: + dict(link=('left_eye', 'right_eye'), id=12, color=[51, 153, 255]), + 13: + dict(link=('nose', 'left_eye'), id=13, color=[51, 153, 255]), + 14: + dict(link=('nose', 'right_eye'), id=14, color=[51, 153, 255]), + 15: + dict(link=('left_eye', 'left_ear'), id=15, color=[51, 153, 255]), + 16: + dict(link=('right_eye', 'right_ear'), id=16, color=[51, 153, 255]), + 17: + dict(link=('left_ear', 'left_shoulder'), id=17, color=[51, 153, 255]), + 18: + dict( + link=('right_ear', 'right_shoulder'), id=18, color=[51, 153, 255]) + }, + joint_weights=[ + 1., 1., 1., 1., 1., 1., 1., 1.2, 1.2, 1.5, 1.5, 1., 1., 1.2, 1.2, 1.5, + 1.5 + ], + sigmas=[ + 0.026, 0.025, 0.025, 0.035, 0.035, 0.079, 0.079, 0.072, 0.072, 0.062, + 0.062, 0.107, 0.107, 0.087, 0.087, 0.089, 0.089 + ]) diff --git a/configs/_base_/datasets/humanart_aic.py b/configs/_base_/datasets/humanart_aic.py new file mode 100644 index 0000000000..e999427536 --- /dev/null +++ b/configs/_base_/datasets/humanart_aic.py @@ -0,0 +1,205 @@ +dataset_info = dict( + dataset_name='humanart', + paper_info=[ + dict( + author='Ju, Xuan and Zeng, Ailing and ' + 'Wang, Jianan and Xu, Qiang and Zhang, ' + 'Lei', + title='Human-Art: A Versatile Human-Centric Dataset ' + 'Bridging Natural and Artificial Scenes', + container='CVPR', + year='2023', + homepage='https://idea-research.github.io/HumanArt/', + ), + dict( + author='Wu, Jiahong and Zheng, He and Zhao, Bo and ' + 'Li, Yixin and Yan, Baoming and Liang, Rui and ' + 'Wang, Wenjia and Zhou, Shipei and Lin, Guosen and ' + 'Fu, Yanwei and others', + title='Ai challenger: A large-scale dataset for going ' + 'deeper in image understanding', + container='arXiv', + year='2017', + homepage='https://github.com/AIChallenger/AI_Challenger_2017', + ), + ], + keypoint_info={ + 0: + dict(name='nose', id=0, color=[51, 153, 255], type='upper', swap=''), + 1: + dict( + name='left_eye', + id=1, + color=[51, 153, 255], + type='upper', + swap='right_eye'), + 2: + dict( + name='right_eye', + id=2, + color=[51, 153, 255], + type='upper', + swap='left_eye'), + 3: + dict( + name='left_ear', + id=3, + color=[51, 153, 255], + type='upper', + swap='right_ear'), + 4: + dict( + name='right_ear', + id=4, + color=[51, 153, 255], + type='upper', + swap='left_ear'), + 5: + dict( + name='left_shoulder', + id=5, + color=[0, 255, 0], + type='upper', + swap='right_shoulder'), + 6: + dict( + name='right_shoulder', + id=6, + color=[255, 128, 0], + type='upper', + swap='left_shoulder'), + 7: + dict( + name='left_elbow', + id=7, + color=[0, 255, 0], + type='upper', + swap='right_elbow'), + 8: + dict( + name='right_elbow', + id=8, + color=[255, 128, 0], + type='upper', + swap='left_elbow'), + 9: + dict( + name='left_wrist', + id=9, + color=[0, 255, 0], + type='upper', + swap='right_wrist'), + 10: + dict( + name='right_wrist', + id=10, + color=[255, 128, 0], + type='upper', + swap='left_wrist'), + 11: + dict( + name='left_hip', + id=11, + color=[0, 255, 0], + type='lower', + swap='right_hip'), + 12: + dict( + name='right_hip', + id=12, + color=[255, 128, 0], + type='lower', + swap='left_hip'), + 13: + dict( + name='left_knee', + id=13, + color=[0, 255, 0], + type='lower', + swap='right_knee'), + 14: + dict( + name='right_knee', + id=14, + color=[255, 128, 0], + type='lower', + swap='left_knee'), + 15: + dict( + name='left_ankle', + id=15, + color=[0, 255, 0], + type='lower', + swap='right_ankle'), + 16: + dict( + name='right_ankle', + id=16, + color=[255, 128, 0], + type='lower', + swap='left_ankle'), + 17: + dict( + name='head_top', + id=17, + color=[51, 153, 255], + type='upper', + swap=''), + 18: + dict(name='neck', id=18, color=[51, 153, 255], type='upper', swap='') + }, + skeleton_info={ + 0: + dict(link=('left_ankle', 'left_knee'), id=0, color=[0, 255, 0]), + 1: + dict(link=('left_knee', 'left_hip'), id=1, color=[0, 255, 0]), + 2: + dict(link=('right_ankle', 'right_knee'), id=2, color=[255, 128, 0]), + 3: + dict(link=('right_knee', 'right_hip'), id=3, color=[255, 128, 0]), + 4: + dict(link=('left_hip', 'right_hip'), id=4, color=[51, 153, 255]), + 5: + dict(link=('left_shoulder', 'left_hip'), id=5, color=[51, 153, 255]), + 6: + dict(link=('right_shoulder', 'right_hip'), id=6, color=[51, 153, 255]), + 7: + dict( + link=('left_shoulder', 'right_shoulder'), + id=7, + color=[51, 153, 255]), + 8: + dict(link=('left_shoulder', 'left_elbow'), id=8, color=[0, 255, 0]), + 9: + dict( + link=('right_shoulder', 'right_elbow'), id=9, color=[255, 128, 0]), + 10: + dict(link=('left_elbow', 'left_wrist'), id=10, color=[0, 255, 0]), + 11: + dict(link=('right_elbow', 'right_wrist'), id=11, color=[255, 128, 0]), + 12: + dict(link=('left_eye', 'right_eye'), id=12, color=[51, 153, 255]), + 13: + dict(link=('nose', 'left_eye'), id=13, color=[51, 153, 255]), + 14: + dict(link=('nose', 'right_eye'), id=14, color=[51, 153, 255]), + 15: + dict(link=('left_eye', 'left_ear'), id=15, color=[51, 153, 255]), + 16: + dict(link=('right_eye', 'right_ear'), id=16, color=[51, 153, 255]), + 17: + dict(link=('left_ear', 'left_shoulder'), id=17, color=[51, 153, 255]), + 18: + dict( + link=('right_ear', 'right_shoulder'), id=18, color=[51, 153, 255]), + 19: + dict(link=('head_top', 'neck'), id=11, color=[51, 153, 255]), + }, + joint_weights=[ + 1., 1., 1., 1., 1., 1., 1., 1.2, 1.2, 1.5, 1.5, 1., 1., 1.2, 1.2, 1.5, + 1.5, 1.5 + ], + sigmas=[ + 0.026, 0.025, 0.025, 0.035, 0.035, 0.079, 0.079, 0.072, 0.072, 0.062, + 0.062, 0.107, 0.107, 0.087, 0.087, 0.089, 0.089, 0.026, 0.026 + ]) diff --git a/configs/body_2d_keypoint/associative_embedding/humanart/ae_hrnet-w32_8xb24-300e_humanart-512x512.py b/configs/body_2d_keypoint/associative_embedding/humanart/ae_hrnet-w32_8xb24-300e_humanart-512x512.py new file mode 100644 index 0000000000..53f474e2f4 --- /dev/null +++ b/configs/body_2d_keypoint/associative_embedding/humanart/ae_hrnet-w32_8xb24-300e_humanart-512x512.py @@ -0,0 +1,159 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=300, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=1.5e-3, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=300, + milestones=[200, 260], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=192) + +# hooks +default_hooks = dict( + checkpoint=dict(save_best='coco/AP', rule='greater', interval=50)) + +# codec settings +codec = dict( + type='AssociativeEmbedding', + input_size=(512, 512), + heatmap_size=(128, 128), + sigma=2, + decode_keypoint_order=[ + 0, 1, 2, 3, 4, 5, 6, 11, 12, 7, 8, 9, 10, 13, 14, 15, 16 + ], + decode_max_instances=30) + +# model settings +model = dict( + type='BottomupPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='HRNet', + in_channels=3, + extra=dict( + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(4, ), + num_channels=(64, )), + stage2=dict( + num_modules=1, + num_branches=2, + block='BASIC', + num_blocks=(4, 4), + num_channels=(32, 64)), + stage3=dict( + num_modules=4, + num_branches=3, + block='BASIC', + num_blocks=(4, 4, 4), + num_channels=(32, 64, 128)), + stage4=dict( + num_modules=3, + num_branches=4, + block='BASIC', + num_blocks=(4, 4, 4, 4), + num_channels=(32, 64, 128, 256))), + init_cfg=dict( + type='Pretrained', + checkpoint='https://download.openmmlab.com/mmpose/' + 'pretrain_models/hrnet_w32-36af842e.pth'), + ), + head=dict( + type='AssociativeEmbeddingHead', + in_channels=32, + num_keypoints=17, + tag_dim=1, + tag_per_keypoint=True, + deconv_out_channels=None, + keypoint_loss=dict(type='KeypointMSELoss', use_target_weight=True), + tag_loss=dict(type='AssociativeEmbeddingLoss', loss_weight=0.001), + # The heatmap will be resized to the input size before decoding + # if ``restore_heatmap_size==True`` + decoder=dict(codec, heatmap_size=codec['input_size'])), + test_cfg=dict( + multiscale_test=False, + flip_test=True, + shift_heatmap=True, + restore_heatmap_size=True, + align_corners=False)) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'bottomup' +data_root = 'data/' + +# pipelines +train_pipeline = [] +val_pipeline = [ + dict(type='LoadImage'), + dict( + type='BottomupResize', + input_size=codec['input_size'], + size_factor=32, + resize_mode='expand'), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=24, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=1, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json', + nms_mode='none', + score_mode='keypoint', +) +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/cid/humanart/cid_hrnet-w32_8xb20-140e_humanart-512x512.py b/configs/body_2d_keypoint/cid/humanart/cid_hrnet-w32_8xb20-140e_humanart-512x512.py new file mode 100644 index 0000000000..7150772111 --- /dev/null +++ b/configs/body_2d_keypoint/cid/humanart/cid_hrnet-w32_8xb20-140e_humanart-512x512.py @@ -0,0 +1,164 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=140, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=1e-3, +)) + +# learning policy +param_scheduler = [ + dict( + type='MultiStepLR', + begin=0, + end=140, + milestones=[90, 120], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=160) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='DecoupledHeatmap', input_size=(512, 512), heatmap_size=(128, 128)) + +# model settings +model = dict( + type='BottomupPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='HRNet', + in_channels=3, + extra=dict( + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(4, ), + num_channels=(64, )), + stage2=dict( + num_modules=1, + num_branches=2, + block='BASIC', + num_blocks=(4, 4), + num_channels=(32, 64)), + stage3=dict( + num_modules=4, + num_branches=3, + block='BASIC', + num_blocks=(4, 4, 4), + num_channels=(32, 64, 128)), + stage4=dict( + num_modules=3, + num_branches=4, + block='BASIC', + num_blocks=(4, 4, 4, 4), + num_channels=(32, 64, 128, 256), + multiscale_output=True)), + init_cfg=dict( + type='Pretrained', + checkpoint='https://download.openmmlab.com/mmpose/' + 'pretrain_models/hrnet_w32-36af842e.pth'), + ), + neck=dict( + type='FeatureMapProcessor', + concat=True, + ), + head=dict( + type='CIDHead', + in_channels=480, + num_keypoints=17, + gfd_channels=32, + coupled_heatmap_loss=dict(type='FocalHeatmapLoss', loss_weight=1.0), + decoupled_heatmap_loss=dict(type='FocalHeatmapLoss', loss_weight=4.0), + contrastive_loss=dict( + type='InfoNCELoss', temperature=0.05, loss_weight=1.0), + decoder=codec, + ), + train_cfg=dict(max_train_instances=200), + test_cfg=dict( + multiscale_test=False, + flip_test=True, + shift_heatmap=False, + align_corners=False)) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'bottomup' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='BottomupRandomAffine', input_size=codec['input_size']), + dict(type='RandomFlip', direction='horizontal'), + dict(type='GenerateTarget', encoder=codec), + dict(type='BottomupGetHeatmapMask'), + dict(type='PackPoseInputs'), +] +val_pipeline = [ + dict(type='LoadImage'), + dict( + type='BottomupResize', + input_size=codec['input_size'], + size_factor=64, + resize_mode='expand'), + dict( + type='PackPoseInputs', + meta_keys=('id', 'img_id', 'img_path', 'crowd_index', 'ori_shape', + 'img_shape', 'input_size', 'input_center', 'input_scale', + 'flip', 'flip_direction', 'flip_indices', 'raw_ann_info', + 'skeleton_links')) +] + +# data loaders +train_dataloader = dict( + batch_size=20, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=1, + num_workers=1, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json', + nms_thr=0.8, + score_mode='keypoint', +) +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/cid/humanart/cid_hrnet-w48_8xb20-140e_humanart-512x512.py b/configs/body_2d_keypoint/cid/humanart/cid_hrnet-w48_8xb20-140e_humanart-512x512.py new file mode 100644 index 0000000000..b8e61a3189 --- /dev/null +++ b/configs/body_2d_keypoint/cid/humanart/cid_hrnet-w48_8xb20-140e_humanart-512x512.py @@ -0,0 +1,164 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=140, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=1e-3, +)) + +# learning policy +param_scheduler = [ + dict( + type='MultiStepLR', + begin=0, + end=140, + milestones=[90, 120], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=160) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='DecoupledHeatmap', input_size=(512, 512), heatmap_size=(128, 128)) + +# model settings +model = dict( + type='BottomupPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='HRNet', + in_channels=3, + extra=dict( + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(4, ), + num_channels=(64, )), + stage2=dict( + num_modules=1, + num_branches=2, + block='BASIC', + num_blocks=(4, 4), + num_channels=(48, 96)), + stage3=dict( + num_modules=4, + num_branches=3, + block='BASIC', + num_blocks=(4, 4, 4), + num_channels=(48, 96, 192)), + stage4=dict( + num_modules=3, + num_branches=4, + block='BASIC', + num_blocks=(4, 4, 4, 4), + num_channels=(48, 96, 192, 384), + multiscale_output=True)), + init_cfg=dict( + type='Pretrained', + checkpoint='https://download.openmmlab.com/mmpose/' + 'pretrain_models/hrnet_w48-8ef0771d.pth'), + ), + neck=dict( + type='FeatureMapProcessor', + concat=True, + ), + head=dict( + type='CIDHead', + in_channels=720, + num_keypoints=17, + gfd_channels=48, + coupled_heatmap_loss=dict(type='FocalHeatmapLoss', loss_weight=1.0), + decoupled_heatmap_loss=dict(type='FocalHeatmapLoss', loss_weight=4.0), + contrastive_loss=dict( + type='InfoNCELoss', temperature=0.05, loss_weight=1.0), + decoder=codec, + ), + train_cfg=dict(max_train_instances=200), + test_cfg=dict( + multiscale_test=False, + flip_test=True, + shift_heatmap=False, + align_corners=False)) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'bottomup' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='BottomupRandomAffine', input_size=codec['input_size']), + dict(type='RandomFlip', direction='horizontal'), + dict(type='GenerateTarget', encoder=codec), + dict(type='BottomupGetHeatmapMask'), + dict(type='PackPoseInputs'), +] +val_pipeline = [ + dict(type='LoadImage'), + dict( + type='BottomupResize', + input_size=codec['input_size'], + size_factor=64, + resize_mode='expand'), + dict( + type='PackPoseInputs', + meta_keys=('id', 'img_id', 'img_path', 'crowd_index', 'ori_shape', + 'img_shape', 'input_size', 'input_center', 'input_scale', + 'flip', 'flip_direction', 'flip_indices', 'raw_ann_info', + 'skeleton_links')) +] + +# data loaders +train_dataloader = dict( + batch_size=20, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=1, + num_workers=1, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json', + nms_thr=0.8, + score_mode='keypoint', +) +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/dekr/humanart/dekr_hrnet-w32_8xb10-140e_humanart-512x512.py b/configs/body_2d_keypoint/dekr/humanart/dekr_hrnet-w32_8xb10-140e_humanart-512x512.py new file mode 100644 index 0000000000..17e96ebdb2 --- /dev/null +++ b/configs/body_2d_keypoint/dekr/humanart/dekr_hrnet-w32_8xb10-140e_humanart-512x512.py @@ -0,0 +1,186 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=140, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=1e-3, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=140, + milestones=[90, 120], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=80) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='SPR', + input_size=(512, 512), + heatmap_size=(128, 128), + sigma=(4, 2), + minimal_diagonal_length=32**0.5, + generate_keypoint_heatmaps=True, + decode_max_instances=30) + +# model settings +model = dict( + type='BottomupPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='HRNet', + in_channels=3, + extra=dict( + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(4, ), + num_channels=(64, )), + stage2=dict( + num_modules=1, + num_branches=2, + block='BASIC', + num_blocks=(4, 4), + num_channels=(32, 64)), + stage3=dict( + num_modules=4, + num_branches=3, + block='BASIC', + num_blocks=(4, 4, 4), + num_channels=(32, 64, 128)), + stage4=dict( + num_modules=3, + num_branches=4, + block='BASIC', + num_blocks=(4, 4, 4, 4), + num_channels=(32, 64, 128, 256), + multiscale_output=True)), + init_cfg=dict( + type='Pretrained', + checkpoint='https://download.openmmlab.com/mmpose/' + 'pretrain_models/hrnet_w32-36af842e.pth'), + ), + neck=dict( + type='FeatureMapProcessor', + concat=True, + ), + head=dict( + type='DEKRHead', + in_channels=480, + num_keypoints=17, + heatmap_loss=dict(type='KeypointMSELoss', use_target_weight=True), + displacement_loss=dict( + type='SoftWeightSmoothL1Loss', + use_target_weight=True, + supervise_empty=False, + beta=1 / 9, + loss_weight=0.002, + ), + decoder=codec, + rescore_cfg=dict( + in_channels=74, + norm_indexes=(5, 6), + init_cfg=dict( + type='Pretrained', + checkpoint='https://download.openmmlab.com/mmpose/' + 'pretrain_models/kpt_rescore_coco-33d58c5c.pth')), + ), + test_cfg=dict( + multiscale_test=False, + flip_test=True, + nms_dist_thr=0.05, + shift_heatmap=True, + align_corners=False)) + +# enable DDP training when rescore net is used +find_unused_parameters = True + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'bottomup' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='BottomupRandomAffine', input_size=codec['input_size']), + dict(type='RandomFlip', direction='horizontal'), + dict(type='GenerateTarget', encoder=codec), + dict(type='BottomupGetHeatmapMask'), + dict(type='PackPoseInputs'), +] +val_pipeline = [ + dict(type='LoadImage'), + dict( + type='BottomupResize', + input_size=codec['input_size'], + size_factor=32, + resize_mode='expand'), + dict( + type='PackPoseInputs', + meta_keys=('id', 'img_id', 'img_path', 'crowd_index', 'ori_shape', + 'img_shape', 'input_size', 'input_center', 'input_scale', + 'flip', 'flip_direction', 'flip_indices', 'raw_ann_info', + 'skeleton_links')) +] + +# data loaders +train_dataloader = dict( + batch_size=10, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=1, + num_workers=1, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json', + nms_mode='none', + score_mode='keypoint', +) +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/dekr/humanart/dekr_hrnet-w48_8xb10-140e_humanart-640x640.py b/configs/body_2d_keypoint/dekr/humanart/dekr_hrnet-w48_8xb10-140e_humanart-640x640.py new file mode 100644 index 0000000000..c1b4df97de --- /dev/null +++ b/configs/body_2d_keypoint/dekr/humanart/dekr_hrnet-w48_8xb10-140e_humanart-640x640.py @@ -0,0 +1,187 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=140, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=1e-3, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=140, + milestones=[90, 120], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=80) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='SPR', + input_size=(640, 640), + heatmap_size=(160, 160), + sigma=(4, 2), + minimal_diagonal_length=32**0.5, + generate_keypoint_heatmaps=True, + decode_max_instances=30) + +# model settings +model = dict( + type='BottomupPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='HRNet', + in_channels=3, + extra=dict( + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(4, ), + num_channels=(64, )), + stage2=dict( + num_modules=1, + num_branches=2, + block='BASIC', + num_blocks=(4, 4), + num_channels=(48, 96)), + stage3=dict( + num_modules=4, + num_branches=3, + block='BASIC', + num_blocks=(4, 4, 4), + num_channels=(48, 96, 192)), + stage4=dict( + num_modules=3, + num_branches=4, + block='BASIC', + num_blocks=(4, 4, 4, 4), + num_channels=(48, 96, 192, 384), + multiscale_output=True)), + init_cfg=dict( + type='Pretrained', + checkpoint='https://download.openmmlab.com/mmpose/' + 'pretrain_models/hrnet_w48-8ef0771d.pth'), + ), + neck=dict( + type='FeatureMapProcessor', + concat=True, + ), + head=dict( + type='DEKRHead', + in_channels=720, + num_keypoints=17, + num_heatmap_filters=48, + heatmap_loss=dict(type='KeypointMSELoss', use_target_weight=True), + displacement_loss=dict( + type='SoftWeightSmoothL1Loss', + use_target_weight=True, + supervise_empty=False, + beta=1 / 9, + loss_weight=0.002, + ), + decoder=codec, + rescore_cfg=dict( + in_channels=74, + norm_indexes=(5, 6), + init_cfg=dict( + type='Pretrained', + checkpoint='https://download.openmmlab.com/mmpose/' + 'pretrain_models/kpt_rescore_coco-33d58c5c.pth')), + ), + test_cfg=dict( + multiscale_test=False, + flip_test=True, + nms_dist_thr=0.05, + shift_heatmap=True, + align_corners=False)) + +# enable DDP training when rescore net is used +find_unused_parameters = True + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'bottomup' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='BottomupRandomAffine', input_size=codec['input_size']), + dict(type='RandomFlip', direction='horizontal'), + dict(type='GenerateTarget', encoder=codec), + dict(type='BottomupGetHeatmapMask'), + dict(type='PackPoseInputs'), +] +val_pipeline = [ + dict(type='LoadImage'), + dict( + type='BottomupResize', + input_size=codec['input_size'], + size_factor=32, + resize_mode='expand'), + dict( + type='PackPoseInputs', + meta_keys=('id', 'img_id', 'img_path', 'crowd_index', 'ori_shape', + 'img_shape', 'input_size', 'input_center', 'input_scale', + 'flip', 'flip_direction', 'flip_indices', 'raw_ann_info', + 'skeleton_links')) +] + +# data loaders +train_dataloader = dict( + batch_size=10, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=1, + num_workers=1, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json', + nms_mode='none', + score_mode='keypoint', +) +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/integral_regression/humanart/ipr_res50_8xb64-210e_humanart-256x256.py b/configs/body_2d_keypoint/integral_regression/humanart/ipr_res50_8xb64-210e_humanart-256x256.py new file mode 100644 index 0000000000..85368b20be --- /dev/null +++ b/configs/body_2d_keypoint/integral_regression/humanart/ipr_res50_8xb64-210e_humanart-256x256.py @@ -0,0 +1,134 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=train_cfg['max_epochs'], + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# codec settings +codec = dict( + type='IntegralRegressionLabel', + input_size=(256, 256), + heatmap_size=(64, 64), + sigma=2.0, + normalize=True) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='ResNet', + depth=50, + ), + head=dict( + type='DSNTHead', + in_channels=2048, + in_featuremap_size=(8, 8), + num_joints=17, + loss=dict( + type='MultipleLossWrapper', + losses=[ + dict(type='SmoothL1Loss', use_target_weight=True), + dict(type='KeypointMSELoss', use_target_weight=True) + ]), + decoder=codec), + test_cfg=dict( + flip_test=True, + shift_coords=True, + shift_heatmap=True, + ), + init_cfg=dict( + type='Pretrained', + checkpoint='https://download.openmmlab.com/mmpose/' + 'pretrain_models/td-hm_res50_8xb64-210e_coco-256x192.pth')) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +test_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=test_pipeline, + )) +test_dataloader = val_dataloader + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=f'{data_root}HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/integral_regression/humanart/ipr_res50_debias-8xb64-210e_humanart-256x256.py b/configs/body_2d_keypoint/integral_regression/humanart/ipr_res50_debias-8xb64-210e_humanart-256x256.py new file mode 100644 index 0000000000..7ed6122914 --- /dev/null +++ b/configs/body_2d_keypoint/integral_regression/humanart/ipr_res50_debias-8xb64-210e_humanart-256x256.py @@ -0,0 +1,136 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=train_cfg['max_epochs'], + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# codec settings +codec = dict( + type='IntegralRegressionLabel', + input_size=(256, 256), + heatmap_size=(64, 64), + sigma=2.0, + normalize=True) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='ResNet', + depth=50, + ), + head=dict( + type='DSNTHead', + in_channels=2048, + in_featuremap_size=(8, 8), + num_joints=17, + debias=True, + beta=10., + loss=dict( + type='MultipleLossWrapper', + losses=[ + dict(type='SmoothL1Loss', use_target_weight=True), + dict(type='JSDiscretLoss', use_target_weight=True) + ]), + decoder=codec), + test_cfg=dict( + flip_test=True, + shift_coords=True, + shift_heatmap=True, + ), + init_cfg=dict( + type='Pretrained', + checkpoint='https://download.openmmlab.com/mmpose/' + 'pretrain_models/td-hm_res50_8xb64-210e_coco-256x192.pth')) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +test_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=test_pipeline, + )) +test_dataloader = val_dataloader + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=f'{data_root}HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/integral_regression/humanart/ipr_res50_dsnt-8xb64-210e_humanart-256x256.py b/configs/body_2d_keypoint/integral_regression/humanart/ipr_res50_dsnt-8xb64-210e_humanart-256x256.py new file mode 100644 index 0000000000..5078e5ee12 --- /dev/null +++ b/configs/body_2d_keypoint/integral_regression/humanart/ipr_res50_dsnt-8xb64-210e_humanart-256x256.py @@ -0,0 +1,134 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=train_cfg['max_epochs'], + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# codec settings +codec = dict( + type='IntegralRegressionLabel', + input_size=(256, 256), + heatmap_size=(64, 64), + sigma=2.0, + normalize=True) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='ResNet', + depth=50, + ), + head=dict( + type='DSNTHead', + in_channels=2048, + in_featuremap_size=(8, 8), + num_joints=17, + loss=dict( + type='MultipleLossWrapper', + losses=[ + dict(type='SmoothL1Loss', use_target_weight=True), + dict(type='JSDiscretLoss', use_target_weight=True) + ]), + decoder=codec), + test_cfg=dict( + flip_test=True, + shift_coords=True, + shift_heatmap=True, + ), + init_cfg=dict( + type='Pretrained', + checkpoint='https://download.openmmlab.com/mmpose/' + 'pretrain_models/td-hm_res50_8xb64-210e_coco-256x192.pth')) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +test_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=test_pipeline, + )) +test_dataloader = val_dataloader + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=f'{data_root}HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-l_8xb256-420e_aic-humanart-256x192.py b/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-l_8xb256-420e_aic-humanart-256x192.py new file mode 100644 index 0000000000..3eb94c1ed2 --- /dev/null +++ b/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-l_8xb256-420e_aic-humanart-256x192.py @@ -0,0 +1,272 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +max_epochs = 420 +stage2_num_epochs = 30 +base_lr = 4e-3 + +train_cfg = dict(max_epochs=max_epochs, val_interval=10) +randomness = dict(seed=21) + +# optimizer +optim_wrapper = dict( + type='OptimWrapper', + optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.05), + paramwise_cfg=dict( + norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True)) + +# learning rate +param_scheduler = [ + dict( + type='LinearLR', + start_factor=1.0e-5, + by_epoch=False, + begin=0, + end=1000), + dict( + # use cosine lr from 210 to 420 epoch + type='CosineAnnealingLR', + eta_min=base_lr * 0.05, + begin=max_epochs // 2, + end=max_epochs, + T_max=max_epochs // 2, + by_epoch=True, + convert_to_iter_based=True), +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=1024) + +# codec settings +codec = dict( + type='SimCCLabel', + input_size=(192, 256), + sigma=(4.9, 5.66), + simcc_split_ratio=2.0, + normalize=False, + use_dark=False) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + _scope_='mmdet', + type='CSPNeXt', + arch='P5', + expand_ratio=0.5, + deepen_factor=1., + widen_factor=1., + out_indices=(4, ), + channel_attention=True, + norm_cfg=dict(type='SyncBN'), + act_cfg=dict(type='SiLU'), + init_cfg=dict( + type='Pretrained', + prefix='backbone.', + checkpoint='https://download.openmmlab.com/mmpose/v1/projects/' + 'rtmpose/cspnext-l_udp-aic-coco_210e-256x192-273b7631_20230130.pth' # noqa + )), + head=dict( + type='RTMCCHead', + in_channels=1024, + out_channels=17, + input_size=codec['input_size'], + in_featuremap_size=(6, 8), + simcc_split_ratio=codec['simcc_split_ratio'], + final_layer_kernel_size=7, + gau_cfg=dict( + hidden_dims=256, + s=128, + expansion_factor=2, + dropout_rate=0., + drop_path=0., + act_fn='SiLU', + use_rel_bias=False, + pos_enc=False), + loss=dict( + type='KLDiscretLoss', + use_target_weight=True, + beta=10., + label_softmax=True), + decoder=codec), + test_cfg=dict(flip_test=True, )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +backend_args = dict(backend='local') +# backend_args = dict( +# backend='petrel', +# path_mapping=dict({ +# f'{data_root}': 's3://openmmlab/datasets/', +# f'{data_root}': 's3://openmmlab/datasets/' +# })) + +# pipelines +train_pipeline = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict( + type='RandomBBoxTransform', scale_factor=[0.6, 1.4], rotate_factor=80), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='mmdet.YOLOXHSVRandomAug'), + dict( + type='Albumentation', + transforms=[ + dict(type='Blur', p=0.1), + dict(type='MedianBlur', p=0.1), + dict( + type='CoarseDropout', + max_holes=1, + max_height=0.4, + max_width=0.4, + min_holes=1, + min_height=0.2, + min_width=0.2, + p=1.0), + ]), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +train_pipeline_stage2 = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict( + type='RandomBBoxTransform', + shift_factor=0., + scale_factor=[0.75, 1.25], + rotate_factor=60), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='mmdet.YOLOXHSVRandomAug'), + dict( + type='Albumentation', + transforms=[ + dict(type='Blur', p=0.1), + dict(type='MedianBlur', p=0.1), + dict( + type='CoarseDropout', + max_holes=1, + max_height=0.4, + max_width=0.4, + min_holes=1, + min_height=0.2, + min_width=0.2, + p=0.5), + ]), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] + +# train datasets +dataset_humanart = dict( + type='RepeatDataset', + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=[], + ), + times=3) + +dataset_aic = dict( + type='AicDataset', + data_root=data_root, + data_mode=data_mode, + ann_file='aic/annotations/aic_train.json', + data_prefix=dict(img='pose/ai_challenge/ai_challenger_keypoint' + '_train_20170902/keypoint_train_images_20170902/'), + pipeline=[ + dict( + type='KeypointConverter', + num_keypoints=17, + mapping=[ + (0, 6), + (1, 8), + (2, 10), + (3, 5), + (4, 7), + (5, 9), + (6, 12), + (7, 14), + (8, 16), + (9, 11), + (10, 13), + (11, 15), + ]) + ], +) + +# data loaders +train_dataloader = dict( + batch_size=256, + num_workers=10, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type='CombinedDataset', + metainfo=dict(from_file='configs/_base_/datasets/humanart.py'), + datasets=[dataset_humanart, dataset_aic], + pipeline=train_pipeline, + test_mode=False, + )) +val_dataloader = dict( + batch_size=64, + num_workers=10, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + # bbox_file='data/coco/person_detection_results/' + # 'COCO_val2017_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# hooks +default_hooks = dict( + checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=1)) + +custom_hooks = [ + dict( + type='EMAHook', + ema_type='ExpMomentumEMA', + momentum=0.0002, + update_buffers=True, + priority=49), + dict( + type='mmdet.PipelineSwitchHook', + switch_epoch=max_epochs - stage2_num_epochs, + switch_pipeline=train_pipeline_stage2) +] + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-l_8xb256-420e_aic-humanart-384x288.py b/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-l_8xb256-420e_aic-humanart-384x288.py new file mode 100644 index 0000000000..ace553e1f8 --- /dev/null +++ b/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-l_8xb256-420e_aic-humanart-384x288.py @@ -0,0 +1,272 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +max_epochs = 420 +stage2_num_epochs = 30 +base_lr = 4e-3 + +train_cfg = dict(max_epochs=max_epochs, val_interval=10) +randomness = dict(seed=21) + +# optimizer +optim_wrapper = dict( + type='OptimWrapper', + optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.05), + paramwise_cfg=dict( + norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True)) + +# learning rate +param_scheduler = [ + dict( + type='LinearLR', + start_factor=1.0e-5, + by_epoch=False, + begin=0, + end=1000), + dict( + # use cosine lr from 210 to 420 epoch + type='CosineAnnealingLR', + eta_min=base_lr * 0.05, + begin=max_epochs // 2, + end=max_epochs, + T_max=max_epochs // 2, + by_epoch=True, + convert_to_iter_based=True), +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=1024) + +# codec settings +codec = dict( + type='SimCCLabel', + input_size=(288, 384), + sigma=(6., 6.93), + simcc_split_ratio=2.0, + normalize=False, + use_dark=False) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + _scope_='mmdet', + type='CSPNeXt', + arch='P5', + expand_ratio=0.5, + deepen_factor=1., + widen_factor=1., + out_indices=(4, ), + channel_attention=True, + norm_cfg=dict(type='SyncBN'), + act_cfg=dict(type='SiLU'), + init_cfg=dict( + type='Pretrained', + prefix='backbone.', + checkpoint='https://download.openmmlab.com/mmpose/v1/projects/' + 'rtmpose/cspnext-l_udp-aic-coco_210e-256x192-273b7631_20230130.pth' # noqa + )), + head=dict( + type='RTMCCHead', + in_channels=1024, + out_channels=17, + input_size=codec['input_size'], + in_featuremap_size=(9, 12), + simcc_split_ratio=codec['simcc_split_ratio'], + final_layer_kernel_size=7, + gau_cfg=dict( + hidden_dims=256, + s=128, + expansion_factor=2, + dropout_rate=0., + drop_path=0., + act_fn='SiLU', + use_rel_bias=False, + pos_enc=False), + loss=dict( + type='KLDiscretLoss', + use_target_weight=True, + beta=10., + label_softmax=True), + decoder=codec), + test_cfg=dict(flip_test=True, )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +backend_args = dict(backend='local') +# backend_args = dict( +# backend='petrel', +# path_mapping=dict({ +# f'{data_root}': 's3://openmmlab/datasets/', +# f'{data_root}': 's3://openmmlab/datasets/' +# })) + +# pipelines +train_pipeline = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict( + type='RandomBBoxTransform', scale_factor=[0.6, 1.4], rotate_factor=80), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='mmdet.YOLOXHSVRandomAug'), + dict( + type='Albumentation', + transforms=[ + dict(type='Blur', p=0.1), + dict(type='MedianBlur', p=0.1), + dict( + type='CoarseDropout', + max_holes=1, + max_height=0.4, + max_width=0.4, + min_holes=1, + min_height=0.2, + min_width=0.2, + p=1.0), + ]), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +train_pipeline_stage2 = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict( + type='RandomBBoxTransform', + shift_factor=0., + scale_factor=[0.75, 1.25], + rotate_factor=60), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='mmdet.YOLOXHSVRandomAug'), + dict( + type='Albumentation', + transforms=[ + dict(type='Blur', p=0.1), + dict(type='MedianBlur', p=0.1), + dict( + type='CoarseDropout', + max_holes=1, + max_height=0.4, + max_width=0.4, + min_holes=1, + min_height=0.2, + min_width=0.2, + p=0.5), + ]), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] + +# train datasets +dataset_humanart = dict( + type='RepeatDataset', + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=[], + ), + times=3) + +dataset_aic = dict( + type='AicDataset', + data_root=data_root, + data_mode=data_mode, + ann_file='aic/annotations/aic_train.json', + data_prefix=dict(img='pose/ai_challenge/ai_challenger_keypoint' + '_train_20170902/keypoint_train_images_20170902/'), + pipeline=[ + dict( + type='KeypointConverter', + num_keypoints=17, + mapping=[ + (0, 6), + (1, 8), + (2, 10), + (3, 5), + (4, 7), + (5, 9), + (6, 12), + (7, 14), + (8, 16), + (9, 11), + (10, 13), + (11, 15), + ]) + ], +) + +# data loaders +train_dataloader = dict( + batch_size=256, + num_workers=10, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type='CombinedDataset', + metainfo=dict(from_file='configs/_base_/datasets/humanart.py'), + datasets=[dataset_humanart, dataset_aic], + pipeline=train_pipeline, + test_mode=False, + )) +val_dataloader = dict( + batch_size=64, + num_workers=10, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + # bbox_file='data/coco/person_detection_results/' + # 'COCO_val2017_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# hooks +default_hooks = dict( + checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=1)) + +custom_hooks = [ + dict( + type='EMAHook', + ema_type='ExpMomentumEMA', + momentum=0.0002, + update_buffers=True, + priority=49), + dict( + type='mmdet.PipelineSwitchHook', + switch_epoch=max_epochs - stage2_num_epochs, + switch_pipeline=train_pipeline_stage2) +] + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-l_8xb256-420e_humanart-256x192.py b/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-l_8xb256-420e_humanart-256x192.py new file mode 100644 index 0000000000..60586f584a --- /dev/null +++ b/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-l_8xb256-420e_humanart-256x192.py @@ -0,0 +1,232 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +max_epochs = 420 +stage2_num_epochs = 30 +base_lr = 4e-3 + +train_cfg = dict(max_epochs=max_epochs, val_interval=10) +randomness = dict(seed=21) + +# optimizer +optim_wrapper = dict( + type='OptimWrapper', + optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.05), + paramwise_cfg=dict( + norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True)) + +# learning rate +param_scheduler = [ + dict( + type='LinearLR', + start_factor=1.0e-5, + by_epoch=False, + begin=0, + end=1000), + dict( + # use cosine lr from 210 to 420 epoch + type='CosineAnnealingLR', + eta_min=base_lr * 0.05, + begin=max_epochs // 2, + end=max_epochs, + T_max=max_epochs // 2, + by_epoch=True, + convert_to_iter_based=True), +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=1024) + +# codec settings +codec = dict( + type='SimCCLabel', + input_size=(192, 256), + sigma=(4.9, 5.66), + simcc_split_ratio=2.0, + normalize=False, + use_dark=False) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + _scope_='mmdet', + type='CSPNeXt', + arch='P5', + expand_ratio=0.5, + deepen_factor=1., + widen_factor=1., + out_indices=(4, ), + channel_attention=True, + norm_cfg=dict(type='SyncBN'), + act_cfg=dict(type='SiLU'), + init_cfg=dict( + type='Pretrained', + prefix='backbone.', + checkpoint='https://download.openmmlab.com/mmpose/v1/projects/' + 'rtmpose/cspnext-l_udp-aic-coco_210e-256x192-273b7631_20230130.pth' # noqa + )), + head=dict( + type='RTMCCHead', + in_channels=1024, + out_channels=17, + input_size=codec['input_size'], + in_featuremap_size=(6, 8), + simcc_split_ratio=codec['simcc_split_ratio'], + final_layer_kernel_size=7, + gau_cfg=dict( + hidden_dims=256, + s=128, + expansion_factor=2, + dropout_rate=0., + drop_path=0., + act_fn='SiLU', + use_rel_bias=False, + pos_enc=False), + loss=dict( + type='KLDiscretLoss', + use_target_weight=True, + beta=10., + label_softmax=True), + decoder=codec), + test_cfg=dict(flip_test=True)) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +backend_args = dict(backend='local') +# backend_args = dict( +# backend='petrel', +# path_mapping=dict({ +# f'{data_root}': 's3://openmmlab/datasets/detection/coco/', +# f'{data_root}': 's3://openmmlab/datasets/detection/coco/' +# })) + +# pipelines +train_pipeline = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict( + type='RandomBBoxTransform', scale_factor=[0.6, 1.4], rotate_factor=80), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='mmdet.YOLOXHSVRandomAug'), + dict( + type='Albumentation', + transforms=[ + dict(type='Blur', p=0.1), + dict(type='MedianBlur', p=0.1), + dict( + type='CoarseDropout', + max_holes=1, + max_height=0.4, + max_width=0.4, + min_holes=1, + min_height=0.2, + min_width=0.2, + p=1.), + ]), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +train_pipeline_stage2 = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict( + type='RandomBBoxTransform', + shift_factor=0., + scale_factor=[0.75, 1.25], + rotate_factor=60), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='mmdet.YOLOXHSVRandomAug'), + dict( + type='Albumentation', + transforms=[ + dict(type='Blur', p=0.1), + dict(type='MedianBlur', p=0.1), + dict( + type='CoarseDropout', + max_holes=1, + max_height=0.4, + max_width=0.4, + min_holes=1, + min_height=0.2, + min_width=0.2, + p=0.5), + ]), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=256, + num_workers=10, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=64, + num_workers=10, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + # bbox_file=f'{data_root}person_detection_results/' + # 'COCO_val2017_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# hooks +default_hooks = dict( + checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=1)) + +custom_hooks = [ + dict( + type='EMAHook', + ema_type='ExpMomentumEMA', + momentum=0.0002, + update_buffers=True, + priority=49), + dict( + type='mmdet.PipelineSwitchHook', + switch_epoch=max_epochs - stage2_num_epochs, + switch_pipeline=train_pipeline_stage2) +] + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-m_8xb256-420e_aic-humanart-256x192.py b/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-m_8xb256-420e_aic-humanart-256x192.py new file mode 100644 index 0000000000..d1d3e3086f --- /dev/null +++ b/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-m_8xb256-420e_aic-humanart-256x192.py @@ -0,0 +1,272 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +max_epochs = 420 +stage2_num_epochs = 30 +base_lr = 4e-3 + +train_cfg = dict(max_epochs=max_epochs, val_interval=10) +randomness = dict(seed=21) + +# optimizer +optim_wrapper = dict( + type='OptimWrapper', + optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.05), + paramwise_cfg=dict( + norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True)) + +# learning rate +param_scheduler = [ + dict( + type='LinearLR', + start_factor=1.0e-5, + by_epoch=False, + begin=0, + end=1000), + dict( + # use cosine lr from 210 to 420 epoch + type='CosineAnnealingLR', + eta_min=base_lr * 0.05, + begin=max_epochs // 2, + end=max_epochs, + T_max=max_epochs // 2, + by_epoch=True, + convert_to_iter_based=True), +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=1024) + +# codec settings +codec = dict( + type='SimCCLabel', + input_size=(192, 256), + sigma=(4.9, 5.66), + simcc_split_ratio=2.0, + normalize=False, + use_dark=False) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + _scope_='mmdet', + type='CSPNeXt', + arch='P5', + expand_ratio=0.5, + deepen_factor=0.67, + widen_factor=0.75, + out_indices=(4, ), + channel_attention=True, + norm_cfg=dict(type='SyncBN'), + act_cfg=dict(type='SiLU'), + init_cfg=dict( + type='Pretrained', + prefix='backbone.', + checkpoint='https://download.openmmlab.com/mmpose/v1/projects/' + 'rtmpose/cspnext-m_udp-aic-coco_210e-256x192-f2f7d6f6_20230130.pth' # noqa + )), + head=dict( + type='RTMCCHead', + in_channels=768, + out_channels=17, + input_size=codec['input_size'], + in_featuremap_size=(6, 8), + simcc_split_ratio=codec['simcc_split_ratio'], + final_layer_kernel_size=7, + gau_cfg=dict( + hidden_dims=256, + s=128, + expansion_factor=2, + dropout_rate=0., + drop_path=0., + act_fn='SiLU', + use_rel_bias=False, + pos_enc=False), + loss=dict( + type='KLDiscretLoss', + use_target_weight=True, + beta=10., + label_softmax=True), + decoder=codec), + test_cfg=dict(flip_test=True, )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +backend_args = dict(backend='local') +# backend_args = dict( +# backend='petrel', +# path_mapping=dict({ +# f'{data_root}': 's3://openmmlab/datasets/', +# f'{data_root}': 's3://openmmlab/datasets/' +# })) + +# pipelines +train_pipeline = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict( + type='RandomBBoxTransform', scale_factor=[0.6, 1.4], rotate_factor=80), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='mmdet.YOLOXHSVRandomAug'), + dict( + type='Albumentation', + transforms=[ + dict(type='Blur', p=0.1), + dict(type='MedianBlur', p=0.1), + dict( + type='CoarseDropout', + max_holes=1, + max_height=0.4, + max_width=0.4, + min_holes=1, + min_height=0.2, + min_width=0.2, + p=1.0), + ]), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +train_pipeline_stage2 = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict( + type='RandomBBoxTransform', + shift_factor=0., + scale_factor=[0.75, 1.25], + rotate_factor=60), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='mmdet.YOLOXHSVRandomAug'), + dict( + type='Albumentation', + transforms=[ + dict(type='Blur', p=0.1), + dict(type='MedianBlur', p=0.1), + dict( + type='CoarseDropout', + max_holes=1, + max_height=0.4, + max_width=0.4, + min_holes=1, + min_height=0.2, + min_width=0.2, + p=0.5), + ]), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] + +# train datasets +dataset_humanart = dict( + type='RepeatDataset', + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=[], + ), + times=3) + +dataset_aic = dict( + type='AicDataset', + data_root=data_root, + data_mode=data_mode, + ann_file='aic/annotations/aic_train.json', + data_prefix=dict(img='pose/ai_challenge/ai_challenger_keypoint' + '_train_20170902/keypoint_train_images_20170902/'), + pipeline=[ + dict( + type='KeypointConverter', + num_keypoints=17, + mapping=[ + (0, 6), + (1, 8), + (2, 10), + (3, 5), + (4, 7), + (5, 9), + (6, 12), + (7, 14), + (8, 16), + (9, 11), + (10, 13), + (11, 15), + ]) + ], +) + +# data loaders +train_dataloader = dict( + batch_size=128 * 2, + num_workers=10, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type='CombinedDataset', + metainfo=dict(from_file='configs/_base_/datasets/humanart.py'), + datasets=[dataset_humanart, dataset_aic], + pipeline=train_pipeline, + test_mode=False, + )) +val_dataloader = dict( + batch_size=64, + num_workers=10, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + # bbox_file='data/coco/person_detection_results/' + # 'COCO_val2017_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# hooks +default_hooks = dict( + checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=1)) + +custom_hooks = [ + dict( + type='EMAHook', + ema_type='ExpMomentumEMA', + momentum=0.0002, + update_buffers=True, + priority=49), + dict( + type='mmdet.PipelineSwitchHook', + switch_epoch=max_epochs - stage2_num_epochs, + switch_pipeline=train_pipeline_stage2) +] + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-m_8xb256-420e_aic-humanart-384x288.py b/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-m_8xb256-420e_aic-humanart-384x288.py new file mode 100644 index 0000000000..22ebbd7b45 --- /dev/null +++ b/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-m_8xb256-420e_aic-humanart-384x288.py @@ -0,0 +1,272 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +max_epochs = 420 +stage2_num_epochs = 30 +base_lr = 4e-3 + +train_cfg = dict(max_epochs=max_epochs, val_interval=10) +randomness = dict(seed=21) + +# optimizer +optim_wrapper = dict( + type='OptimWrapper', + optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.05), + paramwise_cfg=dict( + norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True)) + +# learning rate +param_scheduler = [ + dict( + type='LinearLR', + start_factor=1.0e-5, + by_epoch=False, + begin=0, + end=1000), + dict( + # use cosine lr from 210 to 420 epoch + type='CosineAnnealingLR', + eta_min=base_lr * 0.05, + begin=max_epochs // 2, + end=max_epochs, + T_max=max_epochs // 2, + by_epoch=True, + convert_to_iter_based=True), +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=1024) + +# codec settings +codec = dict( + type='SimCCLabel', + input_size=(288, 384), + sigma=(6., 6.93), + simcc_split_ratio=2.0, + normalize=False, + use_dark=False) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + _scope_='mmdet', + type='CSPNeXt', + arch='P5', + expand_ratio=0.5, + deepen_factor=0.67, + widen_factor=0.75, + out_indices=(4, ), + channel_attention=True, + norm_cfg=dict(type='SyncBN'), + act_cfg=dict(type='SiLU'), + init_cfg=dict( + type='Pretrained', + prefix='backbone.', + checkpoint='https://download.openmmlab.com/mmpose/v1/projects/' + 'rtmpose/cspnext-m_udp-aic-coco_210e-256x192-f2f7d6f6_20230130.pth' # noqa + )), + head=dict( + type='RTMCCHead', + in_channels=768, + out_channels=17, + input_size=codec['input_size'], + in_featuremap_size=(9, 12), + simcc_split_ratio=codec['simcc_split_ratio'], + final_layer_kernel_size=7, + gau_cfg=dict( + hidden_dims=256, + s=128, + expansion_factor=2, + dropout_rate=0., + drop_path=0., + act_fn='SiLU', + use_rel_bias=False, + pos_enc=False), + loss=dict( + type='KLDiscretLoss', + use_target_weight=True, + beta=10., + label_softmax=True), + decoder=codec), + test_cfg=dict(flip_test=True, )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +backend_args = dict(backend='local') +# backend_args = dict( +# backend='petrel', +# path_mapping=dict({ +# f'{data_root}': 's3://openmmlab/datasets/', +# f'{data_root}': 's3://openmmlab/datasets/' +# })) + +# pipelines +train_pipeline = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict( + type='RandomBBoxTransform', scale_factor=[0.6, 1.4], rotate_factor=80), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='mmdet.YOLOXHSVRandomAug'), + dict( + type='Albumentation', + transforms=[ + dict(type='Blur', p=0.1), + dict(type='MedianBlur', p=0.1), + dict( + type='CoarseDropout', + max_holes=1, + max_height=0.4, + max_width=0.4, + min_holes=1, + min_height=0.2, + min_width=0.2, + p=1.0), + ]), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +train_pipeline_stage2 = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict( + type='RandomBBoxTransform', + shift_factor=0., + scale_factor=[0.75, 1.25], + rotate_factor=60), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='mmdet.YOLOXHSVRandomAug'), + dict( + type='Albumentation', + transforms=[ + dict(type='Blur', p=0.1), + dict(type='MedianBlur', p=0.1), + dict( + type='CoarseDropout', + max_holes=1, + max_height=0.4, + max_width=0.4, + min_holes=1, + min_height=0.2, + min_width=0.2, + p=0.5), + ]), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] + +# train datasets +dataset_humanart = dict( + type='RepeatDataset', + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=[], + ), + times=3) + +dataset_aic = dict( + type='AicDataset', + data_root=data_root, + data_mode=data_mode, + ann_file='aic/annotations/aic_train.json', + data_prefix=dict(img='pose/ai_challenge/ai_challenger_keypoint' + '_train_20170902/keypoint_train_images_20170902/'), + pipeline=[ + dict( + type='KeypointConverter', + num_keypoints=17, + mapping=[ + (0, 6), + (1, 8), + (2, 10), + (3, 5), + (4, 7), + (5, 9), + (6, 12), + (7, 14), + (8, 16), + (9, 11), + (10, 13), + (11, 15), + ]) + ], +) + +# data loaders +train_dataloader = dict( + batch_size=128 * 2, + num_workers=10, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type='CombinedDataset', + metainfo=dict(from_file='configs/_base_/datasets/humanart.py'), + datasets=[dataset_humanart, dataset_aic], + pipeline=train_pipeline, + test_mode=False, + )) +val_dataloader = dict( + batch_size=64, + num_workers=10, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + # bbox_file='data/coco/person_detection_results/' + # 'COCO_val2017_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# hooks +default_hooks = dict( + checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=1)) + +custom_hooks = [ + dict( + type='EMAHook', + ema_type='ExpMomentumEMA', + momentum=0.0002, + update_buffers=True, + priority=49), + dict( + type='mmdet.PipelineSwitchHook', + switch_epoch=max_epochs - stage2_num_epochs, + switch_pipeline=train_pipeline_stage2) +] + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-m_8xb256-420e_humanart-256x192.py b/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-m_8xb256-420e_humanart-256x192.py new file mode 100644 index 0000000000..b4b96baebf --- /dev/null +++ b/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-m_8xb256-420e_humanart-256x192.py @@ -0,0 +1,232 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +max_epochs = 420 +stage2_num_epochs = 30 +base_lr = 4e-3 + +train_cfg = dict(max_epochs=max_epochs, val_interval=10) +randomness = dict(seed=21) + +# optimizer +optim_wrapper = dict( + type='OptimWrapper', + optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.05), + paramwise_cfg=dict( + norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True)) + +# learning rate +param_scheduler = [ + dict( + type='LinearLR', + start_factor=1.0e-5, + by_epoch=False, + begin=0, + end=1000), + dict( + # use cosine lr from 210 to 420 epoch + type='CosineAnnealingLR', + eta_min=base_lr * 0.05, + begin=max_epochs // 2, + end=max_epochs, + T_max=max_epochs // 2, + by_epoch=True, + convert_to_iter_based=True), +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=1024) + +# codec settings +codec = dict( + type='SimCCLabel', + input_size=(192, 256), + sigma=(4.9, 5.66), + simcc_split_ratio=2.0, + normalize=False, + use_dark=False) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + _scope_='mmdet', + type='CSPNeXt', + arch='P5', + expand_ratio=0.5, + deepen_factor=0.67, + widen_factor=0.75, + out_indices=(4, ), + channel_attention=True, + norm_cfg=dict(type='SyncBN'), + act_cfg=dict(type='SiLU'), + init_cfg=dict( + type='Pretrained', + prefix='backbone.', + checkpoint='https://download.openmmlab.com/mmpose/v1/projects/' + 'rtmpose/cspnext-m_udp-aic-coco_210e-256x192-f2f7d6f6_20230130.pth' # noqa + )), + head=dict( + type='RTMCCHead', + in_channels=768, + out_channels=17, + input_size=codec['input_size'], + in_featuremap_size=(6, 8), + simcc_split_ratio=codec['simcc_split_ratio'], + final_layer_kernel_size=7, + gau_cfg=dict( + hidden_dims=256, + s=128, + expansion_factor=2, + dropout_rate=0., + drop_path=0., + act_fn='SiLU', + use_rel_bias=False, + pos_enc=False), + loss=dict( + type='KLDiscretLoss', + use_target_weight=True, + beta=10., + label_softmax=True), + decoder=codec), + test_cfg=dict(flip_test=True)) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +backend_args = dict(backend='local') +# backend_args = dict( +# backend='petrel', +# path_mapping=dict({ +# f'{data_root}': 's3://openmmlab/datasets/detection/coco/', +# f'{data_root}': 's3://openmmlab/datasets/detection/coco/' +# })) + +# pipelines +train_pipeline = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict( + type='RandomBBoxTransform', scale_factor=[0.6, 1.4], rotate_factor=80), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='mmdet.YOLOXHSVRandomAug'), + dict( + type='Albumentation', + transforms=[ + dict(type='Blur', p=0.1), + dict(type='MedianBlur', p=0.1), + dict( + type='CoarseDropout', + max_holes=1, + max_height=0.4, + max_width=0.4, + min_holes=1, + min_height=0.2, + min_width=0.2, + p=1.), + ]), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +train_pipeline_stage2 = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict( + type='RandomBBoxTransform', + shift_factor=0., + scale_factor=[0.75, 1.25], + rotate_factor=60), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='mmdet.YOLOXHSVRandomAug'), + dict( + type='Albumentation', + transforms=[ + dict(type='Blur', p=0.1), + dict(type='MedianBlur', p=0.1), + dict( + type='CoarseDropout', + max_holes=1, + max_height=0.4, + max_width=0.4, + min_holes=1, + min_height=0.2, + min_width=0.2, + p=0.5), + ]), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=256, + num_workers=10, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=64, + num_workers=10, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + # bbox_file=f'{data_root}person_detection_results/' + # 'COCO_val2017_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# hooks +default_hooks = dict( + checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=1)) + +custom_hooks = [ + dict( + type='EMAHook', + ema_type='ExpMomentumEMA', + momentum=0.0002, + update_buffers=True, + priority=49), + dict( + type='mmdet.PipelineSwitchHook', + switch_epoch=max_epochs - stage2_num_epochs, + switch_pipeline=train_pipeline_stage2) +] + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-s_8xb256-420e_aic-humanart-256x192.py b/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-s_8xb256-420e_aic-humanart-256x192.py new file mode 100644 index 0000000000..3aad883095 --- /dev/null +++ b/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-s_8xb256-420e_aic-humanart-256x192.py @@ -0,0 +1,272 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +max_epochs = 420 +stage2_num_epochs = 30 +base_lr = 4e-3 + +train_cfg = dict(max_epochs=max_epochs, val_interval=10) +randomness = dict(seed=21) + +# optimizer +optim_wrapper = dict( + type='OptimWrapper', + optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.0), + paramwise_cfg=dict( + norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True)) + +# learning rate +param_scheduler = [ + dict( + type='LinearLR', + start_factor=1.0e-5, + by_epoch=False, + begin=0, + end=1000), + dict( + # use cosine lr from 210 to 420 epoch + type='CosineAnnealingLR', + eta_min=base_lr * 0.05, + begin=max_epochs // 2, + end=max_epochs, + T_max=max_epochs // 2, + by_epoch=True, + convert_to_iter_based=True), +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=1024) + +# codec settings +codec = dict( + type='SimCCLabel', + input_size=(192, 256), + sigma=(4.9, 5.66), + simcc_split_ratio=2.0, + normalize=False, + use_dark=False) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + _scope_='mmdet', + type='CSPNeXt', + arch='P5', + expand_ratio=0.5, + deepen_factor=0.33, + widen_factor=0.5, + out_indices=(4, ), + channel_attention=True, + norm_cfg=dict(type='SyncBN'), + act_cfg=dict(type='SiLU'), + init_cfg=dict( + type='Pretrained', + prefix='backbone.', + checkpoint='https://download.openmmlab.com/mmpose/v1/projects/' + 'rtmpose/cspnext-s_udp-aic-coco_210e-256x192-92f5a029_20230130.pth' # noqa + )), + head=dict( + type='RTMCCHead', + in_channels=512, + out_channels=17, + input_size=codec['input_size'], + in_featuremap_size=(6, 8), + simcc_split_ratio=codec['simcc_split_ratio'], + final_layer_kernel_size=7, + gau_cfg=dict( + hidden_dims=256, + s=128, + expansion_factor=2, + dropout_rate=0., + drop_path=0., + act_fn='SiLU', + use_rel_bias=False, + pos_enc=False), + loss=dict( + type='KLDiscretLoss', + use_target_weight=True, + beta=10., + label_softmax=True), + decoder=codec), + test_cfg=dict(flip_test=True, )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +backend_args = dict(backend='local') +# backend_args = dict( +# backend='petrel', +# path_mapping=dict({ +# f'{data_root}': 's3://openmmlab/datasets/', +# f'{data_root}': 's3://openmmlab/datasets/' +# })) + +# pipelines +train_pipeline = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict( + type='RandomBBoxTransform', scale_factor=[0.6, 1.4], rotate_factor=80), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='mmdet.YOLOXHSVRandomAug'), + dict( + type='Albumentation', + transforms=[ + dict(type='Blur', p=0.1), + dict(type='MedianBlur', p=0.1), + dict( + type='CoarseDropout', + max_holes=1, + max_height=0.4, + max_width=0.4, + min_holes=1, + min_height=0.2, + min_width=0.2, + p=1.0), + ]), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +train_pipeline_stage2 = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict( + type='RandomBBoxTransform', + shift_factor=0., + scale_factor=[0.75, 1.25], + rotate_factor=60), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='mmdet.YOLOXHSVRandomAug'), + dict( + type='Albumentation', + transforms=[ + dict(type='Blur', p=0.1), + dict(type='MedianBlur', p=0.1), + dict( + type='CoarseDropout', + max_holes=1, + max_height=0.4, + max_width=0.4, + min_holes=1, + min_height=0.2, + min_width=0.2, + p=0.5), + ]), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] + +# train datasets +dataset_humanart = dict( + type='RepeatDataset', + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=[], + ), + times=3) + +dataset_aic = dict( + type='AicDataset', + data_root=data_root, + data_mode=data_mode, + ann_file='aic/annotations/aic_train.json', + data_prefix=dict(img='pose/ai_challenge/ai_challenger_keypoint' + '_train_20170902/keypoint_train_images_20170902/'), + pipeline=[ + dict( + type='KeypointConverter', + num_keypoints=17, + mapping=[ + (0, 6), + (1, 8), + (2, 10), + (3, 5), + (4, 7), + (5, 9), + (6, 12), + (7, 14), + (8, 16), + (9, 11), + (10, 13), + (11, 15), + ]) + ], +) + +# data loaders +train_dataloader = dict( + batch_size=128 * 2, + num_workers=10, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type='CombinedDataset', + metainfo=dict(from_file='configs/_base_/datasets/humanart.py'), + datasets=[dataset_humanart, dataset_aic], + pipeline=train_pipeline, + test_mode=False, + )) +val_dataloader = dict( + batch_size=64, + num_workers=10, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + # bbox_file='data/coco/person_detection_results/' + # 'COCO_val2017_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# hooks +default_hooks = dict( + checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=1)) + +custom_hooks = [ + dict( + type='EMAHook', + ema_type='ExpMomentumEMA', + momentum=0.0002, + update_buffers=True, + priority=49), + dict( + type='mmdet.PipelineSwitchHook', + switch_epoch=max_epochs - stage2_num_epochs, + switch_pipeline=train_pipeline_stage2) +] + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-s_8xb256-420e_humanart-256x192.py b/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-s_8xb256-420e_humanart-256x192.py new file mode 100644 index 0000000000..3f5c37fee7 --- /dev/null +++ b/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-s_8xb256-420e_humanart-256x192.py @@ -0,0 +1,232 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +max_epochs = 420 +stage2_num_epochs = 30 +base_lr = 4e-3 + +train_cfg = dict(max_epochs=max_epochs, val_interval=10) +randomness = dict(seed=21) + +# optimizer +optim_wrapper = dict( + type='OptimWrapper', + optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.), + paramwise_cfg=dict( + norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True)) + +# learning rate +param_scheduler = [ + dict( + type='LinearLR', + start_factor=1.0e-5, + by_epoch=False, + begin=0, + end=1000), + dict( + # use cosine lr from 210 to 420 epoch + type='CosineAnnealingLR', + eta_min=base_lr * 0.05, + begin=max_epochs // 2, + end=max_epochs, + T_max=max_epochs // 2, + by_epoch=True, + convert_to_iter_based=True), +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=1024) + +# codec settings +codec = dict( + type='SimCCLabel', + input_size=(192, 256), + sigma=(4.9, 5.66), + simcc_split_ratio=2.0, + normalize=False, + use_dark=False) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + _scope_='mmdet', + type='CSPNeXt', + arch='P5', + expand_ratio=0.5, + deepen_factor=0.33, + widen_factor=0.5, + out_indices=(4, ), + channel_attention=True, + norm_cfg=dict(type='SyncBN'), + act_cfg=dict(type='SiLU'), + init_cfg=dict( + type='Pretrained', + prefix='backbone.', + checkpoint='https://download.openmmlab.com/mmpose/v1/projects/' + 'rtmpose/cspnext-s_udp-aic-coco_210e-256x192-92f5a029_20230130.pth' # noqa + )), + head=dict( + type='RTMCCHead', + in_channels=512, + out_channels=17, + input_size=codec['input_size'], + in_featuremap_size=(6, 8), + simcc_split_ratio=codec['simcc_split_ratio'], + final_layer_kernel_size=7, + gau_cfg=dict( + hidden_dims=256, + s=128, + expansion_factor=2, + dropout_rate=0., + drop_path=0., + act_fn='SiLU', + use_rel_bias=False, + pos_enc=False), + loss=dict( + type='KLDiscretLoss', + use_target_weight=True, + beta=10., + label_softmax=True), + decoder=codec), + test_cfg=dict(flip_test=True)) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +backend_args = dict(backend='local') +# backend_args = dict( +# backend='petrel', +# path_mapping=dict({ +# f'{data_root}': 's3://openmmlab/datasets/detection/coco/', +# f'{data_root}': 's3://openmmlab/datasets/detection/coco/' +# })) + +# pipelines +train_pipeline = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict( + type='RandomBBoxTransform', scale_factor=[0.6, 1.4], rotate_factor=80), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='mmdet.YOLOXHSVRandomAug'), + dict( + type='Albumentation', + transforms=[ + dict(type='Blur', p=0.1), + dict(type='MedianBlur', p=0.1), + dict( + type='CoarseDropout', + max_holes=1, + max_height=0.4, + max_width=0.4, + min_holes=1, + min_height=0.2, + min_width=0.2, + p=1.), + ]), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +train_pipeline_stage2 = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict( + type='RandomBBoxTransform', + shift_factor=0., + scale_factor=[0.75, 1.25], + rotate_factor=60), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='mmdet.YOLOXHSVRandomAug'), + dict( + type='Albumentation', + transforms=[ + dict(type='Blur', p=0.1), + dict(type='MedianBlur', p=0.1), + dict( + type='CoarseDropout', + max_holes=1, + max_height=0.4, + max_width=0.4, + min_holes=1, + min_height=0.2, + min_width=0.2, + p=0.5), + ]), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=256, + num_workers=10, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=64, + num_workers=10, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + # bbox_file=f'{data_root}person_detection_results/' + # 'COCO_val2017_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# hooks +default_hooks = dict( + checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=1)) + +custom_hooks = [ + dict( + type='EMAHook', + ema_type='ExpMomentumEMA', + momentum=0.0002, + update_buffers=True, + priority=49), + dict( + type='mmdet.PipelineSwitchHook', + switch_epoch=max_epochs - stage2_num_epochs, + switch_pipeline=train_pipeline_stage2) +] + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-t_8xb256-420e_aic-humanart-256x192.py b/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-t_8xb256-420e_aic-humanart-256x192.py new file mode 100644 index 0000000000..e12f2b456a --- /dev/null +++ b/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-t_8xb256-420e_aic-humanart-256x192.py @@ -0,0 +1,273 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +max_epochs = 420 +stage2_num_epochs = 30 +base_lr = 4e-3 + +train_cfg = dict(max_epochs=max_epochs, val_interval=10) +randomness = dict(seed=21) + +# optimizer +optim_wrapper = dict( + type='OptimWrapper', + optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.), + paramwise_cfg=dict( + norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True)) + +# learning rate +param_scheduler = [ + dict( + type='LinearLR', + start_factor=1.0e-5, + by_epoch=False, + begin=0, + end=1000), + dict( + # use cosine lr from 210 to 420 epoch + type='CosineAnnealingLR', + eta_min=base_lr * 0.05, + begin=max_epochs // 2, + end=max_epochs, + T_max=max_epochs // 2, + by_epoch=True, + convert_to_iter_based=True), +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=1024) + +# codec settings +codec = dict( + type='SimCCLabel', + input_size=(192, 256), + sigma=(4.9, 5.66), + simcc_split_ratio=2.0, + normalize=False, + use_dark=False) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + _scope_='mmdet', + type='CSPNeXt', + arch='P5', + expand_ratio=0.5, + deepen_factor=0.167, + widen_factor=0.375, + out_indices=(4, ), + channel_attention=True, + norm_cfg=dict(type='SyncBN'), + act_cfg=dict(type='SiLU'), + init_cfg=dict( + type='Pretrained', + prefix='backbone.', + checkpoint='https://download.openmmlab.com/mmpose/v1/projects/' + 'rtmpose/cspnext-tiny_udp-aic-coco_210e-256x192-cbed682d_20230130.pth' # noqa + )), + head=dict( + type='RTMCCHead', + in_channels=384, + out_channels=17, + input_size=codec['input_size'], + in_featuremap_size=(6, 8), + simcc_split_ratio=codec['simcc_split_ratio'], + final_layer_kernel_size=7, + gau_cfg=dict( + hidden_dims=256, + s=128, + expansion_factor=2, + dropout_rate=0., + drop_path=0., + act_fn='SiLU', + use_rel_bias=False, + pos_enc=False), + loss=dict( + type='KLDiscretLoss', + use_target_weight=True, + beta=10., + label_softmax=True), + decoder=codec), + test_cfg=dict(flip_test=True, )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +backend_args = dict(backend='local') +# backend_args = dict( +# backend='petrel', +# path_mapping=dict({ +# f'{data_root}': 's3://openmmlab/datasets/', +# f'{data_root}': 's3://openmmlab/datasets/' +# })) + +# pipelines +train_pipeline = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict( + type='RandomBBoxTransform', scale_factor=[0.6, 1.4], rotate_factor=80), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='mmdet.YOLOXHSVRandomAug'), + dict( + type='Albumentation', + transforms=[ + dict(type='Blur', p=0.1), + dict(type='MedianBlur', p=0.1), + dict( + type='CoarseDropout', + max_holes=1, + max_height=0.4, + max_width=0.4, + min_holes=1, + min_height=0.2, + min_width=0.2, + p=1.0), + ]), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +train_pipeline_stage2 = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict( + type='RandomBBoxTransform', + shift_factor=0., + scale_factor=[0.75, 1.25], + rotate_factor=60), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='mmdet.YOLOXHSVRandomAug'), + dict( + type='Albumentation', + transforms=[ + dict(type='Blur', p=0.1), + dict(type='MedianBlur', p=0.1), + dict( + type='CoarseDropout', + max_holes=1, + max_height=0.4, + max_width=0.4, + min_holes=1, + min_height=0.2, + min_width=0.2, + p=0.5), + ]), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] + +# train datasets +dataset_humanart = dict( + type='RepeatDataset', + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=[], + ), + times=3) + +dataset_aic = dict( + type='AicDataset', + data_root=data_root, + data_mode=data_mode, + ann_file='aic/annotations/aic_train.json', + data_prefix=dict(img='pose/ai_challenge/ai_challenger_keypoint' + '_train_20170902/keypoint_train_images_20170902/'), + pipeline=[ + dict( + type='KeypointConverter', + num_keypoints=17, + mapping=[ + (0, 6), + (1, 8), + (2, 10), + (3, 5), + (4, 7), + (5, 9), + (6, 12), + (7, 14), + (8, 16), + (9, 11), + (10, 13), + (11, 15), + ]) + ], +) + +# data loaders +train_dataloader = dict( + batch_size=256, + num_workers=10, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type='CombinedDataset', + metainfo=dict(from_file='configs/_base_/datasets/humanart.py'), + datasets=[dataset_humanart, dataset_aic], + pipeline=train_pipeline, + test_mode=False, + )) +val_dataloader = dict( + batch_size=64, + num_workers=10, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + # bbox_file='data/coco/person_detection_results/' + # 'COCO_val2017_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# hooks +default_hooks = dict( + checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=1)) + +custom_hooks = [ + # Turn off EMA while training the tiny model + # dict( + # type='EMAHook', + # ema_type='ExpMomentumEMA', + # momentum=0.0002, + # update_buffers=True, + # priority=49), + dict( + type='mmdet.PipelineSwitchHook', + switch_epoch=max_epochs - stage2_num_epochs, + switch_pipeline=train_pipeline_stage2) +] + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-t_8xb256-420e_humanart-256x192.py b/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-t_8xb256-420e_humanart-256x192.py new file mode 100644 index 0000000000..273532aae4 --- /dev/null +++ b/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-t_8xb256-420e_humanart-256x192.py @@ -0,0 +1,233 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +max_epochs = 420 +stage2_num_epochs = 30 +base_lr = 4e-3 + +train_cfg = dict(max_epochs=max_epochs, val_interval=10) +randomness = dict(seed=21) + +# optimizer +optim_wrapper = dict( + type='OptimWrapper', + optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.), + paramwise_cfg=dict( + norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True)) + +# learning rate +param_scheduler = [ + dict( + type='LinearLR', + start_factor=1.0e-5, + by_epoch=False, + begin=0, + end=1000), + dict( + # use cosine lr from 210 to 420 epoch + type='CosineAnnealingLR', + eta_min=base_lr * 0.05, + begin=max_epochs // 2, + end=max_epochs, + T_max=max_epochs // 2, + by_epoch=True, + convert_to_iter_based=True), +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=1024) + +# codec settings +codec = dict( + type='SimCCLabel', + input_size=(192, 256), + sigma=(4.9, 5.66), + simcc_split_ratio=2.0, + normalize=False, + use_dark=False) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + _scope_='mmdet', + type='CSPNeXt', + arch='P5', + expand_ratio=0.5, + deepen_factor=0.167, + widen_factor=0.375, + out_indices=(4, ), + channel_attention=True, + norm_cfg=dict(type='SyncBN'), + act_cfg=dict(type='SiLU'), + init_cfg=dict( + type='Pretrained', + prefix='backbone.', + checkpoint='https://download.openmmlab.com/mmpose/v1/projects/' + 'rtmpose/cspnext-tiny_udp-aic-coco_210e-256x192-cbed682d_20230130.pth' # noqa + )), + head=dict( + type='RTMCCHead', + in_channels=384, + out_channels=17, + input_size=codec['input_size'], + in_featuremap_size=(6, 8), + simcc_split_ratio=codec['simcc_split_ratio'], + final_layer_kernel_size=7, + gau_cfg=dict( + hidden_dims=256, + s=128, + expansion_factor=2, + dropout_rate=0., + drop_path=0., + act_fn='SiLU', + use_rel_bias=False, + pos_enc=False), + loss=dict( + type='KLDiscretLoss', + use_target_weight=True, + beta=10., + label_softmax=True), + decoder=codec), + test_cfg=dict(flip_test=True)) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +backend_args = dict(backend='local') +# backend_args = dict( +# backend='petrel', +# path_mapping=dict({ +# f'{data_root}': 's3://openmmlab/datasets/detection/coco/', +# f'{data_root}': 's3://openmmlab/datasets/detection/coco/' +# })) + +# pipelines +train_pipeline = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict( + type='RandomBBoxTransform', scale_factor=[0.6, 1.4], rotate_factor=80), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='mmdet.YOLOXHSVRandomAug'), + dict( + type='Albumentation', + transforms=[ + dict(type='Blur', p=0.1), + dict(type='MedianBlur', p=0.1), + dict( + type='CoarseDropout', + max_holes=1, + max_height=0.4, + max_width=0.4, + min_holes=1, + min_height=0.2, + min_width=0.2, + p=1.), + ]), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +train_pipeline_stage2 = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict( + type='RandomBBoxTransform', + shift_factor=0., + scale_factor=[0.75, 1.25], + rotate_factor=60), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='mmdet.YOLOXHSVRandomAug'), + dict( + type='Albumentation', + transforms=[ + dict(type='Blur', p=0.1), + dict(type='MedianBlur', p=0.1), + dict( + type='CoarseDropout', + max_holes=1, + max_height=0.4, + max_width=0.4, + min_holes=1, + min_height=0.2, + min_width=0.2, + p=0.5), + ]), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=256, + num_workers=10, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=64, + num_workers=10, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + # bbox_file=f'{data_root}person_detection_results/' + # 'COCO_val2017_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# hooks +default_hooks = dict( + checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=1)) + +custom_hooks = [ + # Turn off EMA while training the tiny model + # dict( + # type='EMAHook', + # ema_type='ExpMomentumEMA', + # momentum=0.0002, + # update_buffers=True, + # priority=49), + dict( + type='mmdet.PipelineSwitchHook', + switch_epoch=max_epochs - stage2_num_epochs, + switch_pipeline=train_pipeline_stage2) +] + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/simcc/humanart/simcc_mobilenetv2_wo-deconv-8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/simcc/humanart/simcc_mobilenetv2_wo-deconv-8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..6650f03ad8 --- /dev/null +++ b/configs/body_2d_keypoint/simcc/humanart/simcc_mobilenetv2_wo-deconv-8xb64-210e_humanart-256x192.py @@ -0,0 +1,124 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=train_cfg['max_epochs'], + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# codec settings +codec = dict( + type='SimCCLabel', input_size=(192, 256), sigma=6.0, simcc_split_ratio=2.0) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='MobileNetV2', + widen_factor=1., + out_indices=(7, ), + init_cfg=dict( + type='Pretrained', + checkpoint='mmcls://mobilenet_v2', + )), + head=dict( + type='SimCCHead', + in_channels=1280, + out_channels=17, + input_size=codec['input_size'], + in_featuremap_size=(6, 8), + simcc_split_ratio=codec['simcc_split_ratio'], + deconv_out_channels=None, + loss=dict(type='KLDiscretLoss', use_target_weight=True), + decoder=codec), + test_cfg=dict(flip_test=True, )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/simcc/humanart/simcc_res50_8xb32-140e_humanart-384x288.py b/configs/body_2d_keypoint/simcc/humanart/simcc_res50_8xb32-140e_humanart-384x288.py new file mode 100644 index 0000000000..7afb8d69b6 --- /dev/null +++ b/configs/body_2d_keypoint/simcc/humanart/simcc_res50_8xb32-140e_humanart-384x288.py @@ -0,0 +1,120 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=140, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=1e-3, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=train_cfg['max_epochs'], + milestones=[90, 120], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# codec settings +codec = dict( + type='SimCCLabel', input_size=(288, 384), sigma=6.0, simcc_split_ratio=2.0) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='ResNet', + depth=50, + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'), + ), + head=dict( + type='SimCCHead', + in_channels=2048, + out_channels=17, + input_size=codec['input_size'], + in_featuremap_size=(9, 12), + simcc_split_ratio=codec['simcc_split_ratio'], + loss=dict(type='KLDiscretLoss', use_target_weight=True), + decoder=codec), + test_cfg=dict(flip_test=True)) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +test_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=test_pipeline, + )) +test_dataloader = val_dataloader + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/simcc/humanart/simcc_res50_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/simcc/humanart/simcc_res50_8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..1c29181998 --- /dev/null +++ b/configs/body_2d_keypoint/simcc/humanart/simcc_res50_8xb64-210e_humanart-256x192.py @@ -0,0 +1,114 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=1e-3, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict(type='MultiStepLR', milestones=[170, 200], gamma=0.1, by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# codec settings +codec = dict( + type='SimCCLabel', input_size=(192, 256), sigma=6.0, simcc_split_ratio=2.0) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='ResNet', + depth=50, + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'), + ), + head=dict( + type='SimCCHead', + in_channels=2048, + out_channels=17, + input_size=codec['input_size'], + in_featuremap_size=(6, 8), + simcc_split_ratio=codec['simcc_split_ratio'], + loss=dict(type='KLDiscretLoss', use_target_weight=True), + decoder=codec), + test_cfg=dict(flip_test=True)) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +test_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=test_pipeline, + )) +test_dataloader = val_dataloader + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/simcc/humanart/simcc_vipnas-mbv3_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/simcc/humanart/simcc_vipnas-mbv3_8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..119bf63b57 --- /dev/null +++ b/configs/body_2d_keypoint/simcc/humanart/simcc_vipnas-mbv3_8xb64-210e_humanart-256x192.py @@ -0,0 +1,119 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=train_cfg['max_epochs'], + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# codec settings +codec = dict( + type='SimCCLabel', input_size=(192, 256), sigma=6.0, simcc_split_ratio=2.0) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict(type='ViPNAS_MobileNetV3'), + head=dict( + type='SimCCHead', + in_channels=160, + out_channels=17, + input_size=codec['input_size'], + in_featuremap_size=(6, 8), + simcc_split_ratio=codec['simcc_split_ratio'], + deconv_type='vipnas', + deconv_out_channels=(160, 160, 160), + deconv_num_groups=(160, 160, 160), + loss=dict(type='KLDiscretLoss', use_target_weight=True), + decoder=codec), + test_cfg=dict(flip_test=True, )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=data_root + 'HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-l_udp_8xb256-210e_aic-humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-l_udp_8xb256-210e_aic-humanart-256x192.py new file mode 100644 index 0000000000..6a54bc5dca --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-l_udp_8xb256-210e_aic-humanart-256x192.py @@ -0,0 +1,284 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +max_epochs = 210 +stage2_num_epochs = 30 +base_lr = 4e-3 + +train_cfg = dict(max_epochs=max_epochs, val_interval=10) +randomness = dict(seed=21) + +# optimizer +optim_wrapper = dict( + type='OptimWrapper', + optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.05), + paramwise_cfg=dict( + norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True)) + +# learning rate +param_scheduler = [ + dict( + type='LinearLR', + start_factor=1.0e-5, + by_epoch=False, + begin=0, + end=1000), + dict( + # use cosine lr from 105 to 210 epoch + type='CosineAnnealingLR', + eta_min=base_lr * 0.05, + begin=max_epochs // 2, + end=max_epochs, + T_max=max_epochs // 2, + by_epoch=True, + convert_to_iter_based=True), +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=1024) + +# codec settings +codec = dict( + type='UDPHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# keypoint mappings +keypoint_mapping_humanart = [ + (0, 0), + (1, 1), + (2, 2), + (3, 3), + (4, 4), + (5, 5), + (6, 6), + (7, 7), + (8, 8), + (9, 9), + (10, 10), + (11, 11), + (12, 12), + (13, 13), + (14, 14), + (15, 15), + (16, 16), +] + +keypoint_mapping_aic = [ + (0, 6), + (1, 8), + (2, 10), + (3, 5), + (4, 7), + (5, 9), + (6, 12), + (7, 14), + (8, 16), + (9, 11), + (10, 13), + (11, 15), + (12, 17), + (13, 18), +] + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + _scope_='mmdet', + type='CSPNeXt', + arch='P5', + expand_ratio=0.5, + deepen_factor=1., + widen_factor=1., + out_indices=(4, ), + channel_attention=True, + norm_cfg=dict(type='SyncBN'), + act_cfg=dict(type='SiLU'), + init_cfg=dict( + type='Pretrained', + prefix='backbone.', + checkpoint='https://download.openmmlab.com/mmdetection/v3.0/' + 'rtmdet/cspnext_rsb_pretrain/' + 'cspnext-l_8xb256-rsb-a1-600e_in1k-6a760974.pth')), + head=dict( + type='HeatmapHead', + in_channels=1024, + out_channels=19, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=False, + output_keypoint_indices=[ + target for _, target in keypoint_mapping_humanart + ])) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +backend_args = dict(backend='local') +# backend_args = dict( +# backend='petrel', +# path_mapping=dict({ +# f'{data_root}': 's3://openmmlab/datasets/', +# f'{data_root}': 's3://openmmlab/datasets/' +# })) + +# pipelines +train_pipeline = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict( + type='RandomBBoxTransform', scale_factor=[0.6, 1.4], rotate_factor=80), + dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), + dict(type='mmdet.YOLOXHSVRandomAug'), + dict( + type='Albumentation', + transforms=[ + dict(type='Blur', p=0.1), + dict(type='MedianBlur', p=0.1), + dict( + type='CoarseDropout', + max_holes=1, + max_height=0.4, + max_width=0.4, + min_holes=1, + min_height=0.2, + min_width=0.2, + p=1.), + ]), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), + dict(type='PackPoseInputs') +] + +train_pipeline_stage2 = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict( + type='RandomBBoxTransform', + shift_factor=0., + scale_factor=[0.75, 1.25], + rotate_factor=60), + dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), + dict(type='mmdet.YOLOXHSVRandomAug'), + dict( + type='Albumentation', + transforms=[ + dict(type='Blur', p=0.1), + dict(type='MedianBlur', p=0.1), + dict( + type='CoarseDropout', + max_holes=1, + max_height=0.4, + max_width=0.4, + min_holes=1, + min_height=0.2, + min_width=0.2, + p=0.5), + ]), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] + +# train datasets +dataset_humanart = dict( + type='RepeatDataset', + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=[ + dict( + type='KeypointConverter', + num_keypoints=19, + mapping=keypoint_mapping_humanart) + ], + ), + times=3) + +dataset_aic = dict( + type='AicDataset', + data_root=data_root, + data_mode=data_mode, + ann_file='aic/annotations/aic_train.json', + data_prefix=dict(img='pose/ai_challenge/ai_challenger_keypoint' + '_train_20170902/keypoint_train_images_20170902/'), + pipeline=[ + dict( + type='KeypointConverter', + num_keypoints=19, + mapping=keypoint_mapping_aic) + ], +) + +# data loaders +train_dataloader = dict( + batch_size=256, + num_workers=10, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type='CombinedDataset', + metainfo=dict(from_file='configs/_base_/datasets/humanart_aic.py'), + datasets=[dataset_humanart, dataset_aic], + pipeline=train_pipeline, + test_mode=False, + )) +val_dataloader = dict( + batch_size=64, + num_workers=10, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + # bbox_file='data/coco/person_detection_results/' + # 'COCO_val2017_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# hooks +default_hooks = dict( + checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=1)) + +custom_hooks = [ + dict( + type='EMAHook', + ema_type='ExpMomentumEMA', + momentum=0.0002, + update_buffers=True, + priority=49), + dict( + type='mmdet.PipelineSwitchHook', + switch_epoch=max_epochs - stage2_num_epochs, + switch_pipeline=train_pipeline_stage2) +] + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-l_udp_8xb256-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-l_udp_8xb256-210e_humanart-256x192.py new file mode 100644 index 0000000000..0870973244 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-l_udp_8xb256-210e_humanart-256x192.py @@ -0,0 +1,214 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +max_epochs = 210 +stage2_num_epochs = 30 +base_lr = 4e-3 + +train_cfg = dict(max_epochs=max_epochs, val_interval=10) +randomness = dict(seed=21) + +# optimizer +optim_wrapper = dict( + type='OptimWrapper', + optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.05), + paramwise_cfg=dict( + norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True)) + +# learning rate +param_scheduler = [ + dict( + type='LinearLR', + start_factor=1.0e-5, + by_epoch=False, + begin=0, + end=1000), + dict( + # use cosine lr from 105 to 210 epoch + type='CosineAnnealingLR', + eta_min=base_lr * 0.05, + begin=max_epochs // 2, + end=max_epochs, + T_max=max_epochs // 2, + by_epoch=True, + convert_to_iter_based=True), +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=1024) + +# codec settings +codec = dict( + type='UDPHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + _scope_='mmdet', + type='CSPNeXt', + arch='P5', + expand_ratio=0.5, + deepen_factor=1., + widen_factor=1., + out_indices=(4, ), + channel_attention=True, + norm_cfg=dict(type='SyncBN'), + act_cfg=dict(type='SiLU'), + init_cfg=dict( + type='Pretrained', + prefix='backbone.', + checkpoint='https://download.openmmlab.com/mmdetection/v3.0/' + 'rtmdet/cspnext_rsb_pretrain/' + 'cspnext-l_8xb256-rsb-a1-600e_in1k-6a760974.pth')), + head=dict( + type='HeatmapHead', + in_channels=1024, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=False, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +backend_args = dict(backend='local') +# backend_args = dict( +# backend='petrel', +# path_mapping=dict({ +# f'{data_root}': 's3://openmmlab/datasets/detection/coco/', +# f'{data_root}': 's3://openmmlab/datasets/detection/coco/' +# })) + +# pipelines +train_pipeline = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict( + type='RandomBBoxTransform', scale_factor=[0.6, 1.4], rotate_factor=80), + dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), + dict(type='mmdet.YOLOXHSVRandomAug'), + dict( + type='Albumentation', + transforms=[ + dict(type='Blur', p=0.1), + dict(type='MedianBlur', p=0.1), + dict( + type='CoarseDropout', + max_holes=1, + max_height=0.4, + max_width=0.4, + min_holes=1, + min_height=0.2, + min_width=0.2, + p=1.), + ]), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), + dict(type='PackPoseInputs') +] + +train_pipeline_stage2 = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict( + type='RandomBBoxTransform', + shift_factor=0., + scale_factor=[0.75, 1.25], + rotate_factor=60), + dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), + dict(type='mmdet.YOLOXHSVRandomAug'), + dict( + type='Albumentation', + transforms=[ + dict(type='Blur', p=0.1), + dict(type='MedianBlur', p=0.1), + dict( + type='CoarseDropout', + max_holes=1, + max_height=0.4, + max_width=0.4, + min_holes=1, + min_height=0.2, + min_width=0.2, + p=0.5), + ]), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=256, + num_workers=10, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=64, + num_workers=10, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + # bbox_file='data/coco/person_detection_results/' + # 'COCO_val2017_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# hooks +default_hooks = dict( + checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=1)) + +custom_hooks = [ + dict( + type='EMAHook', + ema_type='ExpMomentumEMA', + momentum=0.0002, + update_buffers=True, + priority=49), + dict( + type='mmdet.PipelineSwitchHook', + switch_epoch=max_epochs - stage2_num_epochs, + switch_pipeline=train_pipeline_stage2) +] + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-m_udp_8xb256-210e_aic-humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-m_udp_8xb256-210e_aic-humanart-256x192.py new file mode 100644 index 0000000000..1e42debdbb --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-m_udp_8xb256-210e_aic-humanart-256x192.py @@ -0,0 +1,284 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +max_epochs = 210 +stage2_num_epochs = 30 +base_lr = 4e-3 + +train_cfg = dict(max_epochs=max_epochs, val_interval=10) +randomness = dict(seed=21) + +# optimizer +optim_wrapper = dict( + type='OptimWrapper', + optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.05), + paramwise_cfg=dict( + norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True)) + +# learning rate +param_scheduler = [ + dict( + type='LinearLR', + start_factor=1.0e-5, + by_epoch=False, + begin=0, + end=1000), + dict( + # use cosine lr from 105 to 210 epoch + type='CosineAnnealingLR', + eta_min=base_lr * 0.05, + begin=max_epochs // 2, + end=max_epochs, + T_max=max_epochs // 2, + by_epoch=True, + convert_to_iter_based=True), +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=1024) + +# codec settings +codec = dict( + type='UDPHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# keypoint mappings +keypoint_mapping_humanart = [ + (0, 0), + (1, 1), + (2, 2), + (3, 3), + (4, 4), + (5, 5), + (6, 6), + (7, 7), + (8, 8), + (9, 9), + (10, 10), + (11, 11), + (12, 12), + (13, 13), + (14, 14), + (15, 15), + (16, 16), +] + +keypoint_mapping_aic = [ + (0, 6), + (1, 8), + (2, 10), + (3, 5), + (4, 7), + (5, 9), + (6, 12), + (7, 14), + (8, 16), + (9, 11), + (10, 13), + (11, 15), + (12, 17), + (13, 18), +] + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + _scope_='mmdet', + type='CSPNeXt', + arch='P5', + expand_ratio=0.5, + deepen_factor=0.67, + widen_factor=0.75, + out_indices=(4, ), + channel_attention=True, + norm_cfg=dict(type='SyncBN'), + act_cfg=dict(type='SiLU'), + init_cfg=dict( + type='Pretrained', + prefix='backbone.', + checkpoint='https://download.openmmlab.com/mmdetection/v3.0/' + 'rtmdet/cspnext_rsb_pretrain/' + 'cspnext-m_8xb256-rsb-a1-600e_in1k-ecb3bbd9.pth')), + head=dict( + type='HeatmapHead', + in_channels=768, + out_channels=19, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=False, + output_keypoint_indices=[ + target for _, target in keypoint_mapping_humanart + ])) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +backend_args = dict(backend='local') +# backend_args = dict( +# backend='petrel', +# path_mapping=dict({ +# f'{data_root}': 's3://openmmlab/datasets/', +# f'{data_root}': 's3://openmmlab/datasets/' +# })) + +# pipelines +train_pipeline = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict( + type='RandomBBoxTransform', scale_factor=[0.6, 1.4], rotate_factor=80), + dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), + dict(type='mmdet.YOLOXHSVRandomAug'), + dict( + type='Albumentation', + transforms=[ + dict(type='Blur', p=0.1), + dict(type='MedianBlur', p=0.1), + dict( + type='CoarseDropout', + max_holes=1, + max_height=0.4, + max_width=0.4, + min_holes=1, + min_height=0.2, + min_width=0.2, + p=1.), + ]), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), + dict(type='PackPoseInputs') +] + +train_pipeline_stage2 = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict( + type='RandomBBoxTransform', + shift_factor=0., + scale_factor=[0.75, 1.25], + rotate_factor=60), + dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), + dict(type='mmdet.YOLOXHSVRandomAug'), + dict( + type='Albumentation', + transforms=[ + dict(type='Blur', p=0.1), + dict(type='MedianBlur', p=0.1), + dict( + type='CoarseDropout', + max_holes=1, + max_height=0.4, + max_width=0.4, + min_holes=1, + min_height=0.2, + min_width=0.2, + p=0.5), + ]), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] + +# train datasets +dataset_humanart = dict( + type='RepeatDataset', + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=[ + dict( + type='KeypointConverter', + num_keypoints=19, + mapping=keypoint_mapping_humanart) + ], + ), + times=3) + +dataset_aic = dict( + type='AicDataset', + data_root=data_root, + data_mode=data_mode, + ann_file='aic/annotations/aic_train.json', + data_prefix=dict(img='pose/ai_challenge/ai_challenger_keypoint' + '_train_20170902/keypoint_train_images_20170902/'), + pipeline=[ + dict( + type='KeypointConverter', + num_keypoints=19, + mapping=keypoint_mapping_aic) + ], +) + +# data loaders +train_dataloader = dict( + batch_size=256, + num_workers=10, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type='CombinedDataset', + metainfo=dict(from_file='configs/_base_/datasets/humanart_aic.py'), + datasets=[dataset_humanart, dataset_aic], + pipeline=train_pipeline, + test_mode=False, + )) +val_dataloader = dict( + batch_size=64, + num_workers=10, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + # bbox_file='data/coco/person_detection_results/' + # 'COCO_val2017_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# hooks +default_hooks = dict( + checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=1)) + +custom_hooks = [ + dict( + type='EMAHook', + ema_type='ExpMomentumEMA', + momentum=0.0002, + update_buffers=True, + priority=49), + dict( + type='mmdet.PipelineSwitchHook', + switch_epoch=max_epochs - stage2_num_epochs, + switch_pipeline=train_pipeline_stage2) +] + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-m_udp_8xb256-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-m_udp_8xb256-210e_humanart-256x192.py new file mode 100644 index 0000000000..7589652a94 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-m_udp_8xb256-210e_humanart-256x192.py @@ -0,0 +1,214 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +max_epochs = 210 +stage2_num_epochs = 30 +base_lr = 4e-3 + +train_cfg = dict(max_epochs=max_epochs, val_interval=10) +randomness = dict(seed=21) + +# optimizer +optim_wrapper = dict( + type='OptimWrapper', + optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.05), + paramwise_cfg=dict( + norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True)) + +# learning rate +param_scheduler = [ + dict( + type='LinearLR', + start_factor=1.0e-5, + by_epoch=False, + begin=0, + end=1000), + dict( + # use cosine lr from 105 to 210 epoch + type='CosineAnnealingLR', + eta_min=base_lr * 0.05, + begin=max_epochs // 2, + end=max_epochs, + T_max=max_epochs // 2, + by_epoch=True, + convert_to_iter_based=True), +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=1024) + +# codec settings +codec = dict( + type='UDPHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + _scope_='mmdet', + type='CSPNeXt', + arch='P5', + expand_ratio=0.5, + deepen_factor=0.67, + widen_factor=0.75, + out_indices=(4, ), + channel_attention=True, + norm_cfg=dict(type='SyncBN'), + act_cfg=dict(type='SiLU'), + init_cfg=dict( + type='Pretrained', + prefix='backbone.', + checkpoint='https://download.openmmlab.com/mmdetection/v3.0/' + 'rtmdet/cspnext_rsb_pretrain/' + 'cspnext-m_8xb256-rsb-a1-600e_in1k-ecb3bbd9.pth')), + head=dict( + type='HeatmapHead', + in_channels=768, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=False, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +backend_args = dict(backend='local') +# backend_args = dict( +# backend='petrel', +# path_mapping=dict({ +# f'{data_root}': 's3://openmmlab/datasets/detection/coco/', +# f'{data_root}': 's3://openmmlab/datasets/detection/coco/' +# })) + +# pipelines +train_pipeline = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict( + type='RandomBBoxTransform', scale_factor=[0.6, 1.4], rotate_factor=80), + dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), + dict(type='mmdet.YOLOXHSVRandomAug'), + dict( + type='Albumentation', + transforms=[ + dict(type='Blur', p=0.1), + dict(type='MedianBlur', p=0.1), + dict( + type='CoarseDropout', + max_holes=1, + max_height=0.4, + max_width=0.4, + min_holes=1, + min_height=0.2, + min_width=0.2, + p=1.), + ]), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), + dict(type='PackPoseInputs') +] + +train_pipeline_stage2 = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict( + type='RandomBBoxTransform', + shift_factor=0., + scale_factor=[0.75, 1.25], + rotate_factor=60), + dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), + dict(type='mmdet.YOLOXHSVRandomAug'), + dict( + type='Albumentation', + transforms=[ + dict(type='Blur', p=0.1), + dict(type='MedianBlur', p=0.1), + dict( + type='CoarseDropout', + max_holes=1, + max_height=0.4, + max_width=0.4, + min_holes=1, + min_height=0.2, + min_width=0.2, + p=0.5), + ]), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=256, + num_workers=10, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=64, + num_workers=10, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + # bbox_file='data/coco/person_detection_results/' + # 'COCO_val2017_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# hooks +default_hooks = dict( + checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=1)) + +custom_hooks = [ + dict( + type='EMAHook', + ema_type='ExpMomentumEMA', + momentum=0.0002, + update_buffers=True, + priority=49), + dict( + type='mmdet.PipelineSwitchHook', + switch_epoch=max_epochs - stage2_num_epochs, + switch_pipeline=train_pipeline_stage2) +] + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-s_udp_8xb256-210e_aic-humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-s_udp_8xb256-210e_aic-humanart-256x192.py new file mode 100644 index 0000000000..49770fb71b --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-s_udp_8xb256-210e_aic-humanart-256x192.py @@ -0,0 +1,284 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +max_epochs = 210 +stage2_num_epochs = 30 +base_lr = 4e-3 + +train_cfg = dict(max_epochs=max_epochs, val_interval=10) +randomness = dict(seed=21) + +# optimizer +optim_wrapper = dict( + type='OptimWrapper', + optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.0), + paramwise_cfg=dict( + norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True)) + +# learning rate +param_scheduler = [ + dict( + type='LinearLR', + start_factor=1.0e-5, + by_epoch=False, + begin=0, + end=1000), + dict( + # use cosine lr from 105 to 210 epoch + type='CosineAnnealingLR', + eta_min=base_lr * 0.05, + begin=max_epochs // 2, + end=max_epochs, + T_max=max_epochs // 2, + by_epoch=True, + convert_to_iter_based=True), +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=1024) + +# codec settings +codec = dict( + type='UDPHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# keypoint mappings +keypoint_mapping_humanart = [ + (0, 0), + (1, 1), + (2, 2), + (3, 3), + (4, 4), + (5, 5), + (6, 6), + (7, 7), + (8, 8), + (9, 9), + (10, 10), + (11, 11), + (12, 12), + (13, 13), + (14, 14), + (15, 15), + (16, 16), +] + +keypoint_mapping_aic = [ + (0, 6), + (1, 8), + (2, 10), + (3, 5), + (4, 7), + (5, 9), + (6, 12), + (7, 14), + (8, 16), + (9, 11), + (10, 13), + (11, 15), + (12, 17), + (13, 18), +] + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + _scope_='mmdet', + type='CSPNeXt', + arch='P5', + expand_ratio=0.5, + deepen_factor=0.33, + widen_factor=0.5, + out_indices=(4, ), + channel_attention=True, + norm_cfg=dict(type='SyncBN'), + act_cfg=dict(type='SiLU'), + init_cfg=dict( + type='Pretrained', + prefix='backbone.', + checkpoint='https://download.openmmlab.com/mmdetection/v3.0/' + 'rtmdet/cspnext_rsb_pretrain/' + 'cspnext-s_imagenet_600e-ea671761.pth')), + head=dict( + type='HeatmapHead', + in_channels=512, + out_channels=19, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=False, + output_keypoint_indices=[ + target for _, target in keypoint_mapping_humanart + ])) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +backend_args = dict(backend='local') +# backend_args = dict( +# backend='petrel', +# path_mapping=dict({ +# f'{data_root}': 's3://openmmlab/datasets/', +# f'{data_root}': 's3://openmmlab/datasets/' +# })) + +# pipelines +train_pipeline = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict( + type='RandomBBoxTransform', scale_factor=[0.6, 1.4], rotate_factor=80), + dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), + dict(type='mmdet.YOLOXHSVRandomAug'), + dict( + type='Albumentation', + transforms=[ + dict(type='Blur', p=0.1), + dict(type='MedianBlur', p=0.1), + dict( + type='CoarseDropout', + max_holes=1, + max_height=0.4, + max_width=0.4, + min_holes=1, + min_height=0.2, + min_width=0.2, + p=1.), + ]), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), + dict(type='PackPoseInputs') +] + +train_pipeline_stage2 = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict( + type='RandomBBoxTransform', + shift_factor=0., + scale_factor=[0.75, 1.25], + rotate_factor=60), + dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), + dict(type='mmdet.YOLOXHSVRandomAug'), + dict( + type='Albumentation', + transforms=[ + dict(type='Blur', p=0.1), + dict(type='MedianBlur', p=0.1), + dict( + type='CoarseDropout', + max_holes=1, + max_height=0.4, + max_width=0.4, + min_holes=1, + min_height=0.2, + min_width=0.2, + p=0.5), + ]), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] + +# train datasets +dataset_humanart = dict( + type='RepeatDataset', + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=[ + dict( + type='KeypointConverter', + num_keypoints=19, + mapping=keypoint_mapping_humanart) + ], + ), + times=3) + +dataset_aic = dict( + type='AicDataset', + data_root=data_root, + data_mode=data_mode, + ann_file='aic/annotations/aic_train.json', + data_prefix=dict(img='pose/ai_challenge/ai_challenger_keypoint' + '_train_20170902/keypoint_train_images_20170902/'), + pipeline=[ + dict( + type='KeypointConverter', + num_keypoints=19, + mapping=keypoint_mapping_aic) + ], +) + +# data loaders +train_dataloader = dict( + batch_size=256, + num_workers=10, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type='CombinedDataset', + metainfo=dict(from_file='configs/_base_/datasets/humanart_aic.py'), + datasets=[dataset_humanart, dataset_aic], + pipeline=train_pipeline, + test_mode=False, + )) +val_dataloader = dict( + batch_size=64, + num_workers=10, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + # bbox_file='data/coco/person_detection_results/' + # 'COCO_val2017_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# hooks +default_hooks = dict( + checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=1)) + +custom_hooks = [ + dict( + type='EMAHook', + ema_type='ExpMomentumEMA', + momentum=0.0002, + update_buffers=True, + priority=49), + dict( + type='mmdet.PipelineSwitchHook', + switch_epoch=max_epochs - stage2_num_epochs, + switch_pipeline=train_pipeline_stage2) +] + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-s_udp_8xb256-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-s_udp_8xb256-210e_humanart-256x192.py new file mode 100644 index 0000000000..ae9eadff52 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-s_udp_8xb256-210e_humanart-256x192.py @@ -0,0 +1,214 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +max_epochs = 210 +stage2_num_epochs = 30 +base_lr = 4e-3 + +train_cfg = dict(max_epochs=max_epochs, val_interval=10) +randomness = dict(seed=21) + +# optimizer +optim_wrapper = dict( + type='OptimWrapper', + optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.05), + paramwise_cfg=dict( + norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True)) + +# learning rate +param_scheduler = [ + dict( + type='LinearLR', + start_factor=1.0e-5, + by_epoch=False, + begin=0, + end=1000), + dict( + # use cosine lr from 105 to 210 epoch + type='CosineAnnealingLR', + eta_min=base_lr * 0.05, + begin=max_epochs // 2, + end=max_epochs, + T_max=max_epochs // 2, + by_epoch=True, + convert_to_iter_based=True), +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=1024) + +# codec settings +codec = dict( + type='UDPHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + _scope_='mmdet', + type='CSPNeXt', + arch='P5', + expand_ratio=0.5, + deepen_factor=0.33, + widen_factor=0.5, + out_indices=(4, ), + channel_attention=True, + norm_cfg=dict(type='SyncBN'), + act_cfg=dict(type='SiLU'), + init_cfg=dict( + type='Pretrained', + prefix='backbone.', + checkpoint='https://download.openmmlab.com/mmdetection/v3.0/' + 'rtmdet/cspnext_rsb_pretrain/' + 'cspnext-s_imagenet_600e-ea671761.pth')), + head=dict( + type='HeatmapHead', + in_channels=512, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=False, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +backend_args = dict(backend='local') +# backend_args = dict( +# backend='petrel', +# path_mapping=dict({ +# f'{data_root}': 's3://openmmlab/datasets/detection/coco/', +# f'{data_root}': 's3://openmmlab/datasets/detection/coco/' +# })) + +# pipelines +train_pipeline = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict( + type='RandomBBoxTransform', scale_factor=[0.6, 1.4], rotate_factor=80), + dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), + dict(type='mmdet.YOLOXHSVRandomAug'), + dict( + type='Albumentation', + transforms=[ + dict(type='Blur', p=0.1), + dict(type='MedianBlur', p=0.1), + dict( + type='CoarseDropout', + max_holes=1, + max_height=0.4, + max_width=0.4, + min_holes=1, + min_height=0.2, + min_width=0.2, + p=1.), + ]), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), + dict(type='PackPoseInputs') +] + +train_pipeline_stage2 = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict( + type='RandomBBoxTransform', + shift_factor=0., + scale_factor=[0.75, 1.25], + rotate_factor=60), + dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), + dict(type='mmdet.YOLOXHSVRandomAug'), + dict( + type='Albumentation', + transforms=[ + dict(type='Blur', p=0.1), + dict(type='MedianBlur', p=0.1), + dict( + type='CoarseDropout', + max_holes=1, + max_height=0.4, + max_width=0.4, + min_holes=1, + min_height=0.2, + min_width=0.2, + p=0.5), + ]), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=256, + num_workers=10, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=64, + num_workers=10, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + # bbox_file='data/coco/person_detection_results/' + # 'COCO_val2017_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# hooks +default_hooks = dict( + checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=1)) + +custom_hooks = [ + dict( + type='EMAHook', + ema_type='ExpMomentumEMA', + momentum=0.0002, + update_buffers=True, + priority=49), + dict( + type='mmdet.PipelineSwitchHook', + switch_epoch=max_epochs - stage2_num_epochs, + switch_pipeline=train_pipeline_stage2) +] + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-tiny_udp_8xb256-210e_aic-humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-tiny_udp_8xb256-210e_aic-humanart-256x192.py new file mode 100644 index 0000000000..9a4efa6361 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-tiny_udp_8xb256-210e_aic-humanart-256x192.py @@ -0,0 +1,284 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +max_epochs = 210 +stage2_num_epochs = 30 +base_lr = 4e-3 + +train_cfg = dict(max_epochs=max_epochs, val_interval=10) +randomness = dict(seed=21) + +# optimizer +optim_wrapper = dict( + type='OptimWrapper', + optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.0), + paramwise_cfg=dict( + norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True)) + +# learning rate +param_scheduler = [ + dict( + type='LinearLR', + start_factor=1.0e-5, + by_epoch=False, + begin=0, + end=1000), + dict( + # use cosine lr from 105 to 210 epoch + type='CosineAnnealingLR', + eta_min=base_lr * 0.05, + begin=max_epochs // 2, + end=max_epochs, + T_max=max_epochs // 2, + by_epoch=True, + convert_to_iter_based=True), +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=1024) + +# codec settings +codec = dict( + type='UDPHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# keypoint mappings +keypoint_mapping_humanart = [ + (0, 0), + (1, 1), + (2, 2), + (3, 3), + (4, 4), + (5, 5), + (6, 6), + (7, 7), + (8, 8), + (9, 9), + (10, 10), + (11, 11), + (12, 12), + (13, 13), + (14, 14), + (15, 15), + (16, 16), +] + +keypoint_mapping_aic = [ + (0, 6), + (1, 8), + (2, 10), + (3, 5), + (4, 7), + (5, 9), + (6, 12), + (7, 14), + (8, 16), + (9, 11), + (10, 13), + (11, 15), + (12, 17), + (13, 18), +] + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + _scope_='mmdet', + type='CSPNeXt', + arch='P5', + expand_ratio=0.5, + deepen_factor=0.167, + widen_factor=0.375, + out_indices=(4, ), + channel_attention=True, + norm_cfg=dict(type='SyncBN'), + act_cfg=dict(type='SiLU'), + init_cfg=dict( + type='Pretrained', + prefix='backbone.', + checkpoint='https://download.openmmlab.com/mmdetection/v3.0/' + 'rtmdet/cspnext_rsb_pretrain/' + 'cspnext-tiny_imagenet_600e-3a2dd350.pth')), + head=dict( + type='HeatmapHead', + in_channels=384, + out_channels=19, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=False, + output_keypoint_indices=[ + target for _, target in keypoint_mapping_humanart + ])) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +backend_args = dict(backend='local') +# backend_args = dict( +# backend='petrel', +# path_mapping=dict({ +# f'{data_root}': 's3://openmmlab/datasets/', +# f'{data_root}': 's3://openmmlab/datasets/' +# })) + +# pipelines +train_pipeline = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict( + type='RandomBBoxTransform', scale_factor=[0.6, 1.4], rotate_factor=80), + dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), + dict(type='mmdet.YOLOXHSVRandomAug'), + dict( + type='Albumentation', + transforms=[ + dict(type='Blur', p=0.1), + dict(type='MedianBlur', p=0.1), + dict( + type='CoarseDropout', + max_holes=1, + max_height=0.4, + max_width=0.4, + min_holes=1, + min_height=0.2, + min_width=0.2, + p=1.), + ]), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), + dict(type='PackPoseInputs') +] + +train_pipeline_stage2 = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict( + type='RandomBBoxTransform', + shift_factor=0., + scale_factor=[0.75, 1.25], + rotate_factor=60), + dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), + dict(type='mmdet.YOLOXHSVRandomAug'), + dict( + type='Albumentation', + transforms=[ + dict(type='Blur', p=0.1), + dict(type='MedianBlur', p=0.1), + dict( + type='CoarseDropout', + max_holes=1, + max_height=0.4, + max_width=0.4, + min_holes=1, + min_height=0.2, + min_width=0.2, + p=0.5), + ]), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] + +# train datasets +dataset_humanart = dict( + type='RepeatDataset', + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=[ + dict( + type='KeypointConverter', + num_keypoints=19, + mapping=keypoint_mapping_humanart) + ], + ), + times=3) + +dataset_aic = dict( + type='AicDataset', + data_root=data_root, + data_mode=data_mode, + ann_file='aic/annotations/aic_train.json', + data_prefix=dict(img='pose/ai_challenge/ai_challenger_keypoint' + '_train_20170902/keypoint_train_images_20170902/'), + pipeline=[ + dict( + type='KeypointConverter', + num_keypoints=19, + mapping=keypoint_mapping_aic) + ], +) + +# data loaders +train_dataloader = dict( + batch_size=256, + num_workers=10, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type='CombinedDataset', + metainfo=dict(from_file='configs/_base_/datasets/humanart_aic.py'), + datasets=[dataset_humanart, dataset_aic], + pipeline=train_pipeline, + test_mode=False, + )) +val_dataloader = dict( + batch_size=64, + num_workers=10, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + # bbox_file='data/coco/person_detection_results/' + # 'COCO_val2017_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# hooks +default_hooks = dict( + checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=1)) + +custom_hooks = [ + # dict( + # type='EMAHook', + # ema_type='ExpMomentumEMA', + # momentum=0.0002, + # update_buffers=True, + # priority=49), + dict( + type='mmdet.PipelineSwitchHook', + switch_epoch=max_epochs - stage2_num_epochs, + switch_pipeline=train_pipeline_stage2) +] + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-tiny_udp_8xb256-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-tiny_udp_8xb256-210e_humanart-256x192.py new file mode 100644 index 0000000000..d94061485f --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-tiny_udp_8xb256-210e_humanart-256x192.py @@ -0,0 +1,214 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +max_epochs = 210 +stage2_num_epochs = 30 +base_lr = 4e-3 + +train_cfg = dict(max_epochs=max_epochs, val_interval=10) +randomness = dict(seed=21) + +# optimizer +optim_wrapper = dict( + type='OptimWrapper', + optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.05), + paramwise_cfg=dict( + norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True)) + +# learning rate +param_scheduler = [ + dict( + type='LinearLR', + start_factor=1.0e-5, + by_epoch=False, + begin=0, + end=1000), + dict( + # use cosine lr from 105 to 210 epoch + type='CosineAnnealingLR', + eta_min=base_lr * 0.05, + begin=max_epochs // 2, + end=max_epochs, + T_max=max_epochs // 2, + by_epoch=True, + convert_to_iter_based=True), +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=1024) + +# codec settings +codec = dict( + type='UDPHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + _scope_='mmdet', + type='CSPNeXt', + arch='P5', + expand_ratio=0.5, + deepen_factor=0.167, + widen_factor=0.375, + out_indices=(4, ), + channel_attention=True, + norm_cfg=dict(type='SyncBN'), + act_cfg=dict(type='SiLU'), + init_cfg=dict( + type='Pretrained', + prefix='backbone.', + checkpoint='https://download.openmmlab.com/mmdetection/v3.0/' + 'rtmdet/cspnext_rsb_pretrain/' + 'cspnext-tiny_imagenet_600e-3a2dd350.pth')), + head=dict( + type='HeatmapHead', + in_channels=384, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=False, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +backend_args = dict(backend='local') +# backend_args = dict( +# backend='petrel', +# path_mapping=dict({ +# f'{data_root}': 's3://openmmlab/datasets/detection/coco/', +# f'{data_root}': 's3://openmmlab/datasets/detection/coco/' +# })) + +# pipelines +train_pipeline = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict( + type='RandomBBoxTransform', scale_factor=[0.6, 1.4], rotate_factor=80), + dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), + dict(type='mmdet.YOLOXHSVRandomAug'), + dict( + type='Albumentation', + transforms=[ + dict(type='Blur', p=0.1), + dict(type='MedianBlur', p=0.1), + dict( + type='CoarseDropout', + max_holes=1, + max_height=0.4, + max_width=0.4, + min_holes=1, + min_height=0.2, + min_width=0.2, + p=1.), + ]), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), + dict(type='PackPoseInputs') +] + +train_pipeline_stage2 = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict( + type='RandomBBoxTransform', + shift_factor=0., + scale_factor=[0.75, 1.25], + rotate_factor=60), + dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), + dict(type='mmdet.YOLOXHSVRandomAug'), + dict( + type='Albumentation', + transforms=[ + dict(type='Blur', p=0.1), + dict(type='MedianBlur', p=0.1), + dict( + type='CoarseDropout', + max_holes=1, + max_height=0.4, + max_width=0.4, + min_holes=1, + min_height=0.2, + min_width=0.2, + p=0.5), + ]), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=256, + num_workers=10, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=64, + num_workers=10, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + # bbox_file='data/coco/person_detection_results/' + # 'COCO_val2017_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# hooks +default_hooks = dict( + checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=1)) + +custom_hooks = [ + # dict( + # type='EMAHook', + # ema_type='ExpMomentumEMA', + # momentum=0.0002, + # update_buffers=True, + # priority=49), + dict( + type='mmdet.PipelineSwitchHook', + switch_epoch=max_epochs - stage2_num_epochs, + switch_pipeline=train_pipeline_stage2) +] + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/hrnet_humanart.md b/configs/body_2d_keypoint/topdown_heatmap/humanart/hrnet_humanart.md new file mode 100644 index 0000000000..fe3d0989ec --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/hrnet_humanart.md @@ -0,0 +1,44 @@ + + +
+HRNet (CVPR'2019) + +```bibtex +@inproceedings{sun2019deep, + title={Deep high-resolution representation learning for human pose estimation}, + author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong}, + booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition}, + pages={5693--5703}, + year={2019} +} +``` + +
+ + + +
+Human-Art (CVPR'2023) + +```bibtex +@inproceedings{ju2023human, + title={Human-Art: A Versatile Human-Centric Dataset Bridging Natural and Artificial Scenes}, + author={Ju, Xuan and Zeng, Ailing and Wang, Jianan and Xu, Qiang and Zhang, Lei}, + booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, + year={2023}, +} +``` + +
+ +Results on Human-Art validation set with ground-truth bounding box + +| Arch | Input Size | AP | AP50 | AP75 | AR | AR50 | ckpt | log | +| :-------------------------------------------------------------- | :--------: | :---: | :-------------: | :-------------: | :---: | :-------------: | :-------------------------------------------------------------: | :-------: | +| [pose_hrnet_w48](/configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/humanart/hrnet_w48_humanart_256x192.py) | 256x192 | 0.764 | 0.906 | 0.824 | 0.794 | 0.918 | [ckpt](https://drive.google.com/file/d/1gs1RCxRcItUHwA5N8P5_9mKcgwLiBjOO/view?usp=share_link) | [log](<>) | + +Results on COCO val2017 set with ground-truth bounding box + +| Arch | Input Size | AP | AP50 | AP75 | AR | AR50 | ckpt | log | +| :-------------------------------------------------------------- | :--------: | :---: | :-------------: | :-------------: | :---: | :-------------: | :-------------------------------------------------------------: | :-------: | +| [pose_hrnet_w48](/configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/humanart/hrnet_w48_humanart_256x192.py) | 256x192 | 0.772 | 0.936 | 0.847 | 0.800 | 0.942 | [ckpt](https://drive.google.com/file/d/1gs1RCxRcItUHwA5N8P5_9mKcgwLiBjOO/view?usp=share_link) | [log](<>) | diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_2xmspn50_8xb32-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_2xmspn50_8xb32-210e_humanart-256x192.py new file mode 100644 index 0000000000..7fbdeafabc --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_2xmspn50_8xb32-210e_humanart-256x192.py @@ -0,0 +1,152 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-3, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=256) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +# multiple kernel_sizes of heatmap gaussian for 'Megvii' approach. +kernel_sizes = [15, 11, 9, 7, 5] +codec = [ + dict( + type='MegviiHeatmap', + input_size=(192, 256), + heatmap_size=(48, 64), + kernel_size=kernel_size) for kernel_size in kernel_sizes +] + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='MSPN', + unit_channels=256, + num_stages=2, + num_units=4, + num_blocks=[3, 4, 6, 3], + norm_cfg=dict(type='BN'), + init_cfg=dict( + type='Pretrained', + checkpoint='torchvision://resnet50', + )), + head=dict( + type='MSPNHead', + out_shape=(64, 48), + unit_channels=256, + out_channels=17, + num_stages=2, + num_units=4, + norm_cfg=dict(type='BN'), + # each sub list is for a stage + # and each element in each list is for a unit + level_indices=[0, 1, 2, 3] + [1, 2, 3, 4], + loss=([ + dict( + type='KeypointMSELoss', + use_target_weight=True, + loss_weight=0.25) + ] * 3 + [ + dict( + type='KeypointOHKMMSELoss', + use_target_weight=True, + loss_weight=1.) + ]) * 2, + decoder=codec[-1]), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=False, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec[0]['input_size']), + dict(type='GenerateTarget', multilevel=True, encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec[0]['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=32, + num_workers=4, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=4, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'annotations/person_keypoints_val2017.json', + nms_mode='none') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_2xrsn50_8xb32-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_2xrsn50_8xb32-210e_humanart-256x192.py new file mode 100644 index 0000000000..5215e422bf --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_2xrsn50_8xb32-210e_humanart-256x192.py @@ -0,0 +1,154 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-3, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=256) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +# multiple kernel_sizes of heatmap gaussian for 'Megvii' approach. +kernel_sizes = [15, 11, 9, 7, 5] +codec = [ + dict( + type='MegviiHeatmap', + input_size=(192, 256), + heatmap_size=(48, 64), + kernel_size=kernel_size) for kernel_size in kernel_sizes +] + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='RSN', + unit_channels=256, + num_stages=2, + num_units=4, + num_blocks=[3, 4, 6, 3], + num_steps=4, + norm_cfg=dict(type='BN'), + ), + head=dict( + type='MSPNHead', + out_shape=(64, 48), + unit_channels=256, + out_channels=17, + num_stages=2, + num_units=4, + norm_cfg=dict(type='BN'), + # each sub list is for a stage + # and each element in each list is for a unit + level_indices=[0, 1, 2, 3] + [1, 2, 3, 4], + loss=([ + dict( + type='KeypointMSELoss', + use_target_weight=True, + loss_weight=0.25) + ] * 3 + [ + dict( + type='KeypointOHKMMSELoss', + use_target_weight=True, + loss_weight=1.) + ]) * 2, + decoder=codec[-1]), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=False, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec[0]['input_size']), + dict(type='GenerateTarget', multilevel=True, encoder=codec), + dict(type='PackPoseInputs') +] + +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec[0]['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=32, + num_workers=4, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=4, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'annotations/person_keypoints_val2017.json', + nms_mode='none') +test_evaluator = val_evaluator + +# fp16 settings +fp16 = dict(loss_scale='dynamic') diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_3xmspn50_8xb32-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_3xmspn50_8xb32-210e_humanart-256x192.py new file mode 100644 index 0000000000..4ea845985e --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_3xmspn50_8xb32-210e_humanart-256x192.py @@ -0,0 +1,152 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-3, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=256) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +# multiple kernel_sizes of heatmap gaussian for 'Megvii' approach. +kernel_sizes = [15, 11, 9, 7, 5] +codec = [ + dict( + type='MegviiHeatmap', + input_size=(192, 256), + heatmap_size=(48, 64), + kernel_size=kernel_size) for kernel_size in kernel_sizes +] + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='MSPN', + unit_channels=256, + num_stages=3, + num_units=4, + num_blocks=[3, 4, 6, 3], + norm_cfg=dict(type='BN'), + init_cfg=dict( + type='Pretrained', + checkpoint='torchvision://resnet50', + )), + head=dict( + type='MSPNHead', + out_shape=(64, 48), + unit_channels=256, + out_channels=17, + num_stages=3, + num_units=4, + norm_cfg=dict(type='BN'), + # each sub list is for a stage + # and each element in each list is for a unit + level_indices=[0, 1, 2, 3] * 2 + [1, 2, 3, 4], + loss=([ + dict( + type='KeypointMSELoss', + use_target_weight=True, + loss_weight=0.25) + ] * 3 + [ + dict( + type='KeypointOHKMMSELoss', + use_target_weight=True, + loss_weight=1.) + ]) * 3, + decoder=codec[-1]), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=False, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec[0]['input_size']), + dict(type='GenerateTarget', multilevel=True, encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec[0]['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=32, + num_workers=4, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=4, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'annotations/person_keypoints_val2017.json', + nms_mode='none') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_3xrsn50_8xb32-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_3xrsn50_8xb32-210e_humanart-256x192.py new file mode 100644 index 0000000000..3286db101f --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_3xrsn50_8xb32-210e_humanart-256x192.py @@ -0,0 +1,154 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-3, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=256) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +# multiple kernel_sizes of heatmap gaussian for 'Megvii' approach. +kernel_sizes = [15, 11, 9, 7, 5] +codec = [ + dict( + type='MegviiHeatmap', + input_size=(192, 256), + heatmap_size=(48, 64), + kernel_size=kernel_size) for kernel_size in kernel_sizes +] + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='RSN', + unit_channels=256, + num_stages=3, + num_units=4, + num_blocks=[3, 4, 6, 3], + num_steps=4, + norm_cfg=dict(type='BN'), + ), + head=dict( + type='MSPNHead', + out_shape=(64, 48), + unit_channels=256, + out_channels=17, + num_stages=3, + num_units=4, + norm_cfg=dict(type='BN'), + # each sub list is for a stage + # and each element in each list is for a unit + level_indices=[0, 1, 2, 3] * 2 + [1, 2, 3, 4], + loss=([ + dict( + type='KeypointMSELoss', + use_target_weight=True, + loss_weight=0.25) + ] * 3 + [ + dict( + type='KeypointOHKMMSELoss', + use_target_weight=True, + loss_weight=1.) + ]) * 3, + decoder=codec[-1]), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=False, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec[0]['input_size']), + dict(type='GenerateTarget', multilevel=True, encoder=codec), + dict(type='PackPoseInputs') +] + +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec[0]['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=32, + num_workers=4, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=4, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'annotations/person_keypoints_val2017.json', + nms_mode='none') +test_evaluator = val_evaluator + +# fp16 settings +fp16 = dict(loss_scale='dynamic') diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_4xmspn50_8xb32-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_4xmspn50_8xb32-210e_humanart-256x192.py new file mode 100644 index 0000000000..43f2705bf1 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_4xmspn50_8xb32-210e_humanart-256x192.py @@ -0,0 +1,152 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-3, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=256) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +# multiple kernel_sizes of heatmap gaussian for 'Megvii' approach. +kernel_sizes = [15, 11, 9, 7, 5] +codec = [ + dict( + type='MegviiHeatmap', + input_size=(192, 256), + heatmap_size=(48, 64), + kernel_size=kernel_size) for kernel_size in kernel_sizes +] + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='MSPN', + unit_channels=256, + num_stages=4, + num_units=4, + num_blocks=[3, 4, 6, 3], + norm_cfg=dict(type='BN'), + init_cfg=dict( + type='Pretrained', + checkpoint='torchvision://resnet50', + )), + head=dict( + type='MSPNHead', + out_shape=(64, 48), + unit_channels=256, + out_channels=17, + num_stages=4, + num_units=4, + norm_cfg=dict(type='BN'), + # each sub list is for a stage + # and each element in each list is for a unit + level_indices=[0, 1, 2, 3] * 3 + [1, 2, 3, 4], + loss=([ + dict( + type='KeypointMSELoss', + use_target_weight=True, + loss_weight=0.25) + ] * 3 + [ + dict( + type='KeypointOHKMMSELoss', + use_target_weight=True, + loss_weight=1.) + ]) * 4, + decoder=codec[-1]), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=False, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec[0]['input_size']), + dict(type='GenerateTarget', multilevel=True, encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec[0]['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=32, + num_workers=4, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=4, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'annotations/person_keypoints_val2017.json', + nms_mode='none') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-base-simple_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-base-simple_8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..f61aaa4bf7 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-base-simple_8xb64-210e_humanart-256x192.py @@ -0,0 +1,153 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +custom_imports = dict( + imports=['mmpose.engine.optim_wrappers.layer_decay_optim_wrapper'], + allow_failed_imports=False) + +optim_wrapper = dict( + optimizer=dict( + type='AdamW', lr=5e-4, betas=(0.9, 0.999), weight_decay=0.1), + paramwise_cfg=dict( + num_layers=12, + layer_decay_rate=0.75, + custom_keys={ + 'bias': dict(decay_multi=0.0), + 'pos_embed': dict(decay_mult=0.0), + 'relative_position_bias_table': dict(decay_mult=0.0), + 'norm': dict(decay_mult=0.0), + }, + ), + constructor='LayerDecayOptimWrapperConstructor', + clip_grad=dict(max_norm=1., norm_type=2), +) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict( + checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=1)) + +# codec settings +codec = dict( + type='UDPHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='mmcls.VisionTransformer', + arch='base', + img_size=(256, 192), + patch_size=16, + qkv_bias=True, + drop_path_rate=0.3, + with_cls_token=False, + output_cls_token=False, + patch_cfg=dict(padding=2), + init_cfg=dict( + type='Pretrained', + checkpoint='https://download.openmmlab.com/mmpose/' + 'v1/pretrained_models/mae_pretrain_vit_base.pth'), + ), + neck=dict(type='FeatureMapProcessor', scale_factor=4.0, apply_relu=True), + head=dict( + type='HeatmapHead', + in_channels=768, + out_channels=17, + deconv_out_channels=[], + deconv_kernel_sizes=[], + final_layer=dict(kernel_size=3, padding=1), + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec, + ), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=False, + )) + +# base dataset settings +data_root = 'data/' +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=4, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=4, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-base_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-base_8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..6f08f404fb --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-base_8xb64-210e_humanart-256x192.py @@ -0,0 +1,150 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +custom_imports = dict( + imports=['mmpose.engine.optim_wrappers.layer_decay_optim_wrapper'], + allow_failed_imports=False) + +optim_wrapper = dict( + optimizer=dict( + type='AdamW', lr=5e-4, betas=(0.9, 0.999), weight_decay=0.1), + paramwise_cfg=dict( + num_layers=12, + layer_decay_rate=0.75, + custom_keys={ + 'bias': dict(decay_multi=0.0), + 'pos_embed': dict(decay_mult=0.0), + 'relative_position_bias_table': dict(decay_mult=0.0), + 'norm': dict(decay_mult=0.0), + }, + ), + constructor='LayerDecayOptimWrapperConstructor', + clip_grad=dict(max_norm=1., norm_type=2), +) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict( + checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=1)) + +# codec settings +codec = dict( + type='UDPHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='mmcls.VisionTransformer', + arch='base', + img_size=(256, 192), + patch_size=16, + qkv_bias=True, + drop_path_rate=0.3, + with_cls_token=False, + output_cls_token=False, + patch_cfg=dict(padding=2), + init_cfg=dict( + type='Pretrained', + checkpoint='https://download.openmmlab.com/mmpose/' + 'v1/pretrained_models/mae_pretrain_vit_base.pth'), + ), + head=dict( + type='HeatmapHead', + in_channels=768, + out_channels=17, + deconv_out_channels=(256, 256), + deconv_kernel_sizes=(4, 4), + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=False, + )) + +# base dataset settings +data_root = 'data/' +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=4, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=4, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-huge-simple_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-huge-simple_8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..2b8dd0bc2b --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-huge-simple_8xb64-210e_humanart-256x192.py @@ -0,0 +1,153 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +custom_imports = dict( + imports=['mmpose.engine.optim_wrappers.layer_decay_optim_wrapper'], + allow_failed_imports=False) + +optim_wrapper = dict( + optimizer=dict( + type='AdamW', lr=5e-4, betas=(0.9, 0.999), weight_decay=0.1), + paramwise_cfg=dict( + num_layers=32, + layer_decay_rate=0.85, + custom_keys={ + 'bias': dict(decay_multi=0.0), + 'pos_embed': dict(decay_mult=0.0), + 'relative_position_bias_table': dict(decay_mult=0.0), + 'norm': dict(decay_mult=0.0), + }, + ), + constructor='LayerDecayOptimWrapperConstructor', + clip_grad=dict(max_norm=1., norm_type=2), +) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict( + checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=1)) + +# codec settings +codec = dict( + type='UDPHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='mmcls.VisionTransformer', + arch='huge', + img_size=(256, 192), + patch_size=16, + qkv_bias=True, + drop_path_rate=0.55, + with_cls_token=False, + output_cls_token=False, + patch_cfg=dict(padding=2), + init_cfg=dict( + type='Pretrained', + checkpoint='https://download.openmmlab.com/mmpose/' + 'v1/pretrained_models/mae_pretrain_vit_huge.pth'), + ), + neck=dict(type='FeatureMapProcessor', scale_factor=4.0, apply_relu=True), + head=dict( + type='HeatmapHead', + in_channels=1280, + out_channels=17, + deconv_out_channels=[], + deconv_kernel_sizes=[], + final_layer=dict(kernel_size=3, padding=1), + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec, + ), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=False, + )) + +# base dataset settings +data_root = 'data/' +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=4, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=4, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-huge_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-huge_8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..925f68e3d1 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-huge_8xb64-210e_humanart-256x192.py @@ -0,0 +1,150 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +custom_imports = dict( + imports=['mmpose.engine.optim_wrappers.layer_decay_optim_wrapper'], + allow_failed_imports=False) + +optim_wrapper = dict( + optimizer=dict( + type='AdamW', lr=5e-4, betas=(0.9, 0.999), weight_decay=0.1), + paramwise_cfg=dict( + num_layers=32, + layer_decay_rate=0.85, + custom_keys={ + 'bias': dict(decay_multi=0.0), + 'pos_embed': dict(decay_mult=0.0), + 'relative_position_bias_table': dict(decay_mult=0.0), + 'norm': dict(decay_mult=0.0), + }, + ), + constructor='LayerDecayOptimWrapperConstructor', + clip_grad=dict(max_norm=1., norm_type=2), +) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict( + checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=1)) + +# codec settings +codec = dict( + type='UDPHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='mmcls.VisionTransformer', + arch='huge', + img_size=(256, 192), + patch_size=16, + qkv_bias=True, + drop_path_rate=0.55, + with_cls_token=False, + output_cls_token=False, + patch_cfg=dict(padding=2), + init_cfg=dict( + type='Pretrained', + checkpoint='https://download.openmmlab.com/mmpose/' + 'v1/pretrained_models/mae_pretrain_vit_huge.pth'), + ), + head=dict( + type='HeatmapHead', + in_channels=1280, + out_channels=17, + deconv_out_channels=(256, 256), + deconv_kernel_sizes=(4, 4), + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=False, + )) + +# base dataset settings +data_root = 'data/' +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=4, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=4, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-large-simple_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-large-simple_8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..9352f615c2 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-large-simple_8xb64-210e_humanart-256x192.py @@ -0,0 +1,153 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +custom_imports = dict( + imports=['mmpose.engine.optim_wrappers.layer_decay_optim_wrapper'], + allow_failed_imports=False) + +optim_wrapper = dict( + optimizer=dict( + type='AdamW', lr=5e-4, betas=(0.9, 0.999), weight_decay=0.1), + paramwise_cfg=dict( + num_layers=24, + layer_decay_rate=0.8, + custom_keys={ + 'bias': dict(decay_multi=0.0), + 'pos_embed': dict(decay_mult=0.0), + 'relative_position_bias_table': dict(decay_mult=0.0), + 'norm': dict(decay_mult=0.0), + }, + ), + constructor='LayerDecayOptimWrapperConstructor', + clip_grad=dict(max_norm=1., norm_type=2), +) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict( + checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=1)) + +# codec settings +codec = dict( + type='UDPHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='mmcls.VisionTransformer', + arch='large', + img_size=(256, 192), + patch_size=16, + qkv_bias=True, + drop_path_rate=0.5, + with_cls_token=False, + output_cls_token=False, + patch_cfg=dict(padding=2), + init_cfg=dict( + type='Pretrained', + checkpoint='https://download.openmmlab.com/mmpose/' + 'v1/pretrained_models/mae_pretrain_vit_large.pth'), + ), + neck=dict(type='FeatureMapProcessor', scale_factor=4.0, apply_relu=True), + head=dict( + type='HeatmapHead', + in_channels=1024, + out_channels=17, + deconv_out_channels=[], + deconv_kernel_sizes=[], + final_layer=dict(kernel_size=3, padding=1), + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec, + ), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=False, + )) + +# base dataset settings +data_root = 'data/' +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=4, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=4, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-large_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-large_8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..7ea9dbf395 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-large_8xb64-210e_humanart-256x192.py @@ -0,0 +1,150 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +custom_imports = dict( + imports=['mmpose.engine.optim_wrappers.layer_decay_optim_wrapper'], + allow_failed_imports=False) + +optim_wrapper = dict( + optimizer=dict( + type='AdamW', lr=5e-4, betas=(0.9, 0.999), weight_decay=0.1), + paramwise_cfg=dict( + num_layers=24, + layer_decay_rate=0.8, + custom_keys={ + 'bias': dict(decay_multi=0.0), + 'pos_embed': dict(decay_mult=0.0), + 'relative_position_bias_table': dict(decay_mult=0.0), + 'norm': dict(decay_mult=0.0), + }, + ), + constructor='LayerDecayOptimWrapperConstructor', + clip_grad=dict(max_norm=1., norm_type=2), +) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict( + checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=1)) + +# codec settings +codec = dict( + type='UDPHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='mmcls.VisionTransformer', + arch='large', + img_size=(256, 192), + patch_size=16, + qkv_bias=True, + drop_path_rate=0.5, + with_cls_token=False, + output_cls_token=False, + patch_cfg=dict(padding=2), + init_cfg=dict( + type='Pretrained', + checkpoint='https://download.openmmlab.com/mmpose/' + 'v1/pretrained_models/mae_pretrain_vit_large.pth'), + ), + head=dict( + type='HeatmapHead', + in_channels=1024, + out_channels=17, + deconv_out_channels=(256, 256), + deconv_kernel_sizes=(4, 4), + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=False, + )) + +# base dataset settings +data_root = 'data/' +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=4, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=4, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-small-simple_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-small-simple_8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..a0e2c9f849 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-small-simple_8xb64-210e_humanart-256x192.py @@ -0,0 +1,158 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +custom_imports = dict( + imports=['mmpose.engine.optim_wrappers.layer_decay_optim_wrapper'], + allow_failed_imports=False) + +optim_wrapper = dict( + optimizer=dict( + type='AdamW', lr=5e-4, betas=(0.9, 0.999), weight_decay=0.1), + paramwise_cfg=dict( + num_layers=12, + layer_decay_rate=0.8, + custom_keys={ + 'bias': dict(decay_multi=0.0), + 'pos_embed': dict(decay_mult=0.0), + 'relative_position_bias_table': dict(decay_mult=0.0), + 'norm': dict(decay_mult=0.0), + }, + ), + constructor='LayerDecayOptimWrapperConstructor', + clip_grad=dict(max_norm=1., norm_type=2), +) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict( + checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=1)) + +# codec settings +codec = dict( + type='UDPHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='mmcls.VisionTransformer', + arch={ + 'embed_dims': 384, + 'num_layers': 12, + 'num_heads': 12, + 'feedforward_channels': 384 * 4 + }, + img_size=(256, 192), + patch_size=16, + qkv_bias=True, + drop_path_rate=0.1, + with_cls_token=False, + output_cls_token=False, + patch_cfg=dict(padding=2), + init_cfg=dict( + type='Pretrained', + checkpoint='https://download.openmmlab.com/mmpose/' + 'v1/pretrained_models/mae_pretrain_vit_small.pth'), + ), + neck=dict(type='FeatureMapProcessor', scale_factor=4.0, apply_relu=True), + head=dict( + type='HeatmapHead', + in_channels=384, + out_channels=17, + deconv_out_channels=[], + deconv_kernel_sizes=[], + final_layer=dict(kernel_size=3, padding=1), + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec, + ), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=False, + )) + +# base dataset settings +data_root = 'data/' +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=4, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=4, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-small_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-small_8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..6daf87cc90 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-small_8xb64-210e_humanart-256x192.py @@ -0,0 +1,155 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +custom_imports = dict( + imports=['mmpose.engine.optim_wrappers.layer_decay_optim_wrapper'], + allow_failed_imports=False) + +optim_wrapper = dict( + optimizer=dict( + type='AdamW', lr=5e-4, betas=(0.9, 0.999), weight_decay=0.1), + paramwise_cfg=dict( + num_layers=12, + layer_decay_rate=0.8, + custom_keys={ + 'bias': dict(decay_multi=0.0), + 'pos_embed': dict(decay_mult=0.0), + 'relative_position_bias_table': dict(decay_mult=0.0), + 'norm': dict(decay_mult=0.0), + }, + ), + constructor='LayerDecayOptimWrapperConstructor', + clip_grad=dict(max_norm=1., norm_type=2), +) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict( + checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=1)) + +# codec settings +codec = dict( + type='UDPHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='mmcls.VisionTransformer', + arch={ + 'embed_dims': 384, + 'num_layers': 12, + 'num_heads': 12, + 'feedforward_channels': 384 * 4 + }, + img_size=(256, 192), + patch_size=16, + qkv_bias=True, + drop_path_rate=0.1, + with_cls_token=False, + output_cls_token=False, + patch_cfg=dict(padding=2), + init_cfg=dict( + type='Pretrained', + checkpoint='https://download.openmmlab.com/mmpose/' + 'v1/pretrained_models/mae_pretrain_vit_small.pth'), + ), + head=dict( + type='HeatmapHead', + in_channels=384, + out_channels=17, + deconv_out_channels=(256, 256), + deconv_kernel_sizes=(4, 4), + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=False, + )) + +# base dataset settings +data_root = 'data/' +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=4, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=4, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_alexnet_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_alexnet_8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..08935037f4 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_alexnet_8xb64-210e_humanart-256x192.py @@ -0,0 +1,117 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(40, 56), sigma=2) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict(type='AlexNet', num_classes=-1), + head=dict( + type='HeatmapHead', + in_channels=256, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_cpm_8xb32-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_cpm_8xb32-210e_humanart-384x288.py new file mode 100644 index 0000000000..33f521d47f --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_cpm_8xb32-210e_humanart-384x288.py @@ -0,0 +1,125 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(36, 48), sigma=3) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='CPM', + in_channels=3, + out_channels=17, + feat_channels=128, + num_stages=6), + head=dict( + type='CPMHead', + in_channels=17, + out_channels=17, + num_stages=6, + deconv_out_channels=None, + final_layer=None, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_cpm_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_cpm_8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..7ff91cf1ce --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_cpm_8xb64-210e_humanart-256x192.py @@ -0,0 +1,125 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(24, 32), sigma=2) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='CPM', + in_channels=3, + out_channels=17, + feat_channels=128, + num_stages=6), + head=dict( + type='CPMHead', + in_channels=17, + out_channels=17, + num_stages=6, + deconv_out_channels=None, + final_layer=None, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hourglass52_8xb32-210e_humanart-256x256.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hourglass52_8xb32-210e_humanart-256x256.py new file mode 100644 index 0000000000..8bcfec9abf --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hourglass52_8xb32-210e_humanart-256x256.py @@ -0,0 +1,122 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(256, 256), heatmap_size=(64, 64), sigma=2) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='HourglassNet', + num_stacks=1, + ), + head=dict( + type='CPMHead', + in_channels=256, + out_channels=17, + num_stages=1, + deconv_out_channels=None, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hourglass52_8xb32-210e_humanart-384x384.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hourglass52_8xb32-210e_humanart-384x384.py new file mode 100644 index 0000000000..d49e4688fa --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hourglass52_8xb32-210e_humanart-384x384.py @@ -0,0 +1,122 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(384, 384), heatmap_size=(96, 96), sigma=3) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='HourglassNet', + num_stacks=1, + ), + head=dict( + type='CPMHead', + in_channels=256, + out_channels=17, + num_stages=1, + deconv_out_channels=None, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrformer-base_8xb32-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrformer-base_8xb32-210e_humanart-256x192.py new file mode 100644 index 0000000000..c8227aaea2 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrformer-base_8xb32-210e_humanart-256x192.py @@ -0,0 +1,174 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict( + optimizer=dict( + type='AdamW', + lr=5e-4, + betas=(0.9, 0.999), + weight_decay=0.01, + ), + paramwise_cfg=dict( + custom_keys={'relative_position_bias_table': dict(decay_mult=0.)})) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=256) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='HRFormer', + in_channels=3, + norm_cfg=norm_cfg, + extra=dict( + drop_path_rate=0.2, + with_rpe=True, + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(2, ), + num_channels=(64, ), + num_heads=[2], + mlp_ratios=[4]), + stage2=dict( + num_modules=1, + num_branches=2, + block='HRFORMERBLOCK', + num_blocks=(2, 2), + num_channels=(78, 156), + num_heads=[2, 4], + mlp_ratios=[4, 4], + window_sizes=[7, 7]), + stage3=dict( + num_modules=4, + num_branches=3, + block='HRFORMERBLOCK', + num_blocks=(2, 2, 2), + num_channels=(78, 156, 312), + num_heads=[2, 4, 8], + mlp_ratios=[4, 4, 4], + window_sizes=[7, 7, 7]), + stage4=dict( + num_modules=2, + num_branches=4, + block='HRFORMERBLOCK', + num_blocks=(2, 2, 2, 2), + num_channels=(78, 156, 312, 624), + num_heads=[2, 4, 8, 16], + mlp_ratios=[4, 4, 4, 4], + window_sizes=[7, 7, 7, 7])), + init_cfg=dict( + type='Pretrained', + checkpoint='https://download.openmmlab.com/mmpose/' + 'pretrain_models/hrformer_base-32815020_20220226.pth'), + ), + head=dict( + type='HeatmapHead', + in_channels=78, + out_channels=17, + deconv_out_channels=None, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] + +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator + +# fp16 settings +fp16 = dict(loss_scale='dynamic') diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrformer-base_8xb32-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrformer-base_8xb32-210e_humanart-384x288.py new file mode 100644 index 0000000000..d58d2236e5 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrformer-base_8xb32-210e_humanart-384x288.py @@ -0,0 +1,174 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict( + optimizer=dict( + type='AdamW', + lr=5e-4, + betas=(0.9, 0.999), + weight_decay=0.01, + ), + paramwise_cfg=dict( + custom_keys={'relative_position_bias_table': dict(decay_mult=0.)})) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=256) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) + +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='HRFormer', + in_channels=3, + norm_cfg=norm_cfg, + extra=dict( + drop_path_rate=0.2, + with_rpe=True, + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(2, ), + num_channels=(64, ), + num_heads=[2], + mlp_ratios=[4]), + stage2=dict( + num_modules=1, + num_branches=2, + block='HRFORMERBLOCK', + num_blocks=(2, 2), + num_channels=(78, 156), + num_heads=[2, 4], + mlp_ratios=[4, 4], + window_sizes=[7, 7]), + stage3=dict( + num_modules=4, + num_branches=3, + block='HRFORMERBLOCK', + num_blocks=(2, 2, 2), + num_channels=(78, 156, 312), + num_heads=[2, 4, 8], + mlp_ratios=[4, 4, 4], + window_sizes=[7, 7, 7]), + stage4=dict( + num_modules=2, + num_branches=4, + block='HRFORMERBLOCK', + num_blocks=(2, 2, 2, 2), + num_channels=(78, 156, 312, 624), + num_heads=[2, 4, 8, 16], + mlp_ratios=[4, 4, 4, 4], + window_sizes=[7, 7, 7, 7])), + init_cfg=dict( + type='Pretrained', + checkpoint='https://download.openmmlab.com/mmpose/' + 'pretrain_models/hrformer_base-32815020_20220226.pth'), + ), + head=dict( + type='HeatmapHead', + in_channels=78, + out_channels=17, + deconv_out_channels=None, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] + +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator + +# fp16 settings +fp16 = dict(loss_scale='dynamic') diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrformer-small_8xb32-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrformer-small_8xb32-210e_humanart-256x192.py new file mode 100644 index 0000000000..e41b3be2ac --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrformer-small_8xb32-210e_humanart-256x192.py @@ -0,0 +1,174 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict( + optimizer=dict( + type='AdamW', + lr=5e-4, + betas=(0.9, 0.999), + weight_decay=0.01, + ), + paramwise_cfg=dict( + custom_keys={'relative_position_bias_table': dict(decay_mult=0.)})) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=256) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='HRFormer', + in_channels=3, + norm_cfg=norm_cfg, + extra=dict( + drop_path_rate=0.1, + with_rpe=True, + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(2, ), + num_channels=(64, ), + num_heads=[2], + num_mlp_ratios=[4]), + stage2=dict( + num_modules=1, + num_branches=2, + block='HRFORMERBLOCK', + num_blocks=(2, 2), + num_channels=(32, 64), + num_heads=[1, 2], + mlp_ratios=[4, 4], + window_sizes=[7, 7]), + stage3=dict( + num_modules=4, + num_branches=3, + block='HRFORMERBLOCK', + num_blocks=(2, 2, 2), + num_channels=(32, 64, 128), + num_heads=[1, 2, 4], + mlp_ratios=[4, 4, 4], + window_sizes=[7, 7, 7]), + stage4=dict( + num_modules=2, + num_branches=4, + block='HRFORMERBLOCK', + num_blocks=(2, 2, 2, 2), + num_channels=(32, 64, 128, 256), + num_heads=[1, 2, 4, 8], + mlp_ratios=[4, 4, 4, 4], + window_sizes=[7, 7, 7, 7])), + init_cfg=dict( + type='Pretrained', + checkpoint='https://download.openmmlab.com/mmpose/' + 'pretrain_models/hrformer_small-09516375_20220226.pth'), + ), + head=dict( + type='HeatmapHead', + in_channels=32, + out_channels=17, + deconv_out_channels=None, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] + +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator + +# fp16 settings +fp16 = dict(loss_scale='dynamic') diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrformer-small_8xb32-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrformer-small_8xb32-210e_humanart-384x288.py new file mode 100644 index 0000000000..05ff951d84 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrformer-small_8xb32-210e_humanart-384x288.py @@ -0,0 +1,174 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict( + optimizer=dict( + type='AdamW', + lr=5e-4, + betas=(0.9, 0.999), + weight_decay=0.01, + ), + paramwise_cfg=dict( + custom_keys={'relative_position_bias_table': dict(decay_mult=0.)})) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=256) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) + +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='HRFormer', + in_channels=3, + norm_cfg=norm_cfg, + extra=dict( + drop_path_rate=0.1, + with_rpe=True, + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(2, ), + num_channels=(64, ), + num_heads=[2], + num_mlp_ratios=[4]), + stage2=dict( + num_modules=1, + num_branches=2, + block='HRFORMERBLOCK', + num_blocks=(2, 2), + num_channels=(32, 64), + num_heads=[1, 2], + mlp_ratios=[4, 4], + window_sizes=[7, 7]), + stage3=dict( + num_modules=4, + num_branches=3, + block='HRFORMERBLOCK', + num_blocks=(2, 2, 2), + num_channels=(32, 64, 128), + num_heads=[1, 2, 4], + mlp_ratios=[4, 4, 4], + window_sizes=[7, 7, 7]), + stage4=dict( + num_modules=2, + num_branches=4, + block='HRFORMERBLOCK', + num_blocks=(2, 2, 2, 2), + num_channels=(32, 64, 128, 256), + num_heads=[1, 2, 4, 8], + mlp_ratios=[4, 4, 4, 4], + window_sizes=[7, 7, 7, 7])), + init_cfg=dict( + type='Pretrained', + checkpoint='https://download.openmmlab.com/mmpose/' + 'pretrain_models/hrformer_small-09516375_20220226.pth'), + ), + head=dict( + type='HeatmapHead', + in_channels=32, + out_channels=17, + deconv_out_channels=None, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] + +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator + +# fp16 settings +fp16 = dict(loss_scale='dynamic') diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..bf9fa25beb --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_8xb64-210e_humanart-256x192.py @@ -0,0 +1,150 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='HRNet', + in_channels=3, + extra=dict( + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(4, ), + num_channels=(64, )), + stage2=dict( + num_modules=1, + num_branches=2, + block='BASIC', + num_blocks=(4, 4), + num_channels=(32, 64)), + stage3=dict( + num_modules=4, + num_branches=3, + block='BASIC', + num_blocks=(4, 4, 4), + num_channels=(32, 64, 128)), + stage4=dict( + num_modules=3, + num_branches=4, + block='BASIC', + num_blocks=(4, 4, 4, 4), + num_channels=(32, 64, 128, 256))), + init_cfg=dict( + type='Pretrained', + checkpoint='https://download.openmmlab.com/mmpose/' + 'pretrain_models/hrnet_w32-36af842e.pth'), + ), + head=dict( + type='HeatmapHead', + in_channels=32, + out_channels=17, + deconv_out_channels=None, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_8xb64-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_8xb64-210e_humanart-384x288.py new file mode 100644 index 0000000000..7f49e9708f --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_8xb64-210e_humanart-384x288.py @@ -0,0 +1,150 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='HRNet', + in_channels=3, + extra=dict( + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(4, ), + num_channels=(64, )), + stage2=dict( + num_modules=1, + num_branches=2, + block='BASIC', + num_blocks=(4, 4), + num_channels=(32, 64)), + stage3=dict( + num_modules=4, + num_branches=3, + block='BASIC', + num_blocks=(4, 4, 4), + num_channels=(32, 64, 128)), + stage4=dict( + num_modules=3, + num_branches=4, + block='BASIC', + num_blocks=(4, 4, 4, 4), + num_channels=(32, 64, 128, 256))), + init_cfg=dict( + type='Pretrained', + checkpoint='https://download.openmmlab.com/mmpose/' + 'pretrain_models/hrnet_w32-36af842e.pth'), + ), + head=dict( + type='HeatmapHead', + in_channels=32, + out_channels=17, + deconv_out_channels=None, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_8xb64-210e_humanart-aic-256x192-combine.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_8xb64-210e_humanart-aic-256x192-combine.py new file mode 100644 index 0000000000..2fa71699cb --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_8xb64-210e_humanart-aic-256x192-combine.py @@ -0,0 +1,221 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict( + checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=3)) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# keypoint mappings +keypoint_mapping_humanart = [ + (0, 0), + (1, 1), + (2, 2), + (3, 3), + (4, 4), + (5, 5), + (6, 6), + (7, 7), + (8, 8), + (9, 9), + (10, 10), + (11, 11), + (12, 12), + (13, 13), + (14, 14), + (15, 15), + (16, 16), +] + +keypoint_mapping_aic = [ + (0, 6), + (1, 8), + (2, 10), + (3, 5), + (4, 7), + (5, 9), + (6, 12), + (7, 14), + (8, 16), + (9, 11), + (10, 13), + (11, 15), + (12, 17), + (13, 18), +] + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + metainfo=dict(from_file='configs/_base_/datasets/humanart_aic.py'), + backbone=dict( + type='HRNet', + in_channels=3, + extra=dict( + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(4, ), + num_channels=(64, )), + stage2=dict( + num_modules=1, + num_branches=2, + block='BASIC', + num_blocks=(4, 4), + num_channels=(32, 64)), + stage3=dict( + num_modules=4, + num_branches=3, + block='BASIC', + num_blocks=(4, 4, 4), + num_channels=(32, 64, 128)), + stage4=dict( + num_modules=3, + num_branches=4, + block='BASIC', + num_blocks=(4, 4, 4, 4), + num_channels=(32, 64, 128, 256))), + init_cfg=dict( + type='Pretrained', + checkpoint='https://download.openmmlab.com/mmpose/' + 'pretrain_models/hrnet_w32-36af842e.pth'), + ), + head=dict( + type='HeatmapHead', + in_channels=32, + out_channels=19, + deconv_out_channels=None, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + output_keypoint_indices=[ + target for _, target in keypoint_mapping_humanart + ])) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# train datasets +dataset_humanart = dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=[ + dict( + type='KeypointConverter', + num_keypoints=19, + mapping=keypoint_mapping_humanart) + ], +) + +dataset_aic = dict( + type='AicDataset', + data_root='data/aic/', + data_mode=data_mode, + ann_file='annotations/aic_train.json', + data_prefix=dict(img='ai_challenger_keypoint_train_20170902/' + 'keypoint_train_images_20170902/'), + pipeline=[ + dict( + type='KeypointConverter', + num_keypoints=19, + mapping=keypoint_mapping_aic) + ], +) + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type='CombinedDataset', + metainfo=dict(from_file='configs/_base_/datasets/humanart_aic.py'), + datasets=[dataset_humanart, dataset_aic], + pipeline=train_pipeline, + test_mode=False, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_8xb64-210e_humanart-aic-256x192-merge.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_8xb64-210e_humanart-aic-256x192-merge.py new file mode 100644 index 0000000000..b7e287565c --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_8xb64-210e_humanart-aic-256x192-merge.py @@ -0,0 +1,187 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='HRNet', + in_channels=3, + extra=dict( + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(4, ), + num_channels=(64, )), + stage2=dict( + num_modules=1, + num_branches=2, + block='BASIC', + num_blocks=(4, 4), + num_channels=(32, 64)), + stage3=dict( + num_modules=4, + num_branches=3, + block='BASIC', + num_blocks=(4, 4, 4), + num_channels=(32, 64, 128)), + stage4=dict( + num_modules=3, + num_branches=4, + block='BASIC', + num_blocks=(4, 4, 4, 4), + num_channels=(32, 64, 128, 256))), + init_cfg=dict( + type='Pretrained', + checkpoint='https://download.openmmlab.com/mmpose/' + 'pretrain_models/hrnet_w32-36af842e.pth'), + ), + head=dict( + type='HeatmapHead', + in_channels=32, + out_channels=17, + deconv_out_channels=None, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# train datasets +dataset_humanart = dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=[], +) + +dataset_aic = dict( + type='AicDataset', + data_root='data/aic/', + data_mode=data_mode, + ann_file='annotations/aic_train.json', + data_prefix=dict(img='ai_challenger_keypoint_train_20170902/' + 'keypoint_train_images_20170902/'), + pipeline=[ + dict( + type='KeypointConverter', + num_keypoints=17, + mapping=[ + (0, 6), + (1, 8), + (2, 10), + (3, 5), + (4, 7), + (5, 9), + (6, 12), + (7, 14), + (8, 16), + (9, 11), + (10, 13), + (11, 15), + ]) + ], +) + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type='CombinedDataset', + metainfo=dict(from_file='configs/_base_/datasets/coco.py'), + datasets=[dataset_humanart, dataset_aic], + pipeline=train_pipeline, + test_mode=False, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_coarsedropout-8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_coarsedropout-8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..2eae18218e --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_coarsedropout-8xb64-210e_humanart-256x192.py @@ -0,0 +1,165 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='HRNet', + in_channels=3, + extra=dict( + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(4, ), + num_channels=(64, )), + stage2=dict( + num_modules=1, + num_branches=2, + block='BASIC', + num_blocks=(4, 4), + num_channels=(32, 64)), + stage3=dict( + num_modules=4, + num_branches=3, + block='BASIC', + num_blocks=(4, 4, 4), + num_channels=(32, 64, 128)), + stage4=dict( + num_modules=3, + num_branches=4, + block='BASIC', + num_blocks=(4, 4, 4, 4), + num_channels=(32, 64, 128, 256))), + init_cfg=dict( + type='Pretrained', + prefix='backbone.', + checkpoint='https://download.openmmlab.com/mmpose/v1/' + 'body_2d_keypoint/topdown_heatmap/coco/' + 'td-hm_hrnet-w32_8xb64-210e_coco-256x192-81c58e40_20220909.pth'), + ), + head=dict( + type='HeatmapHead', + in_channels=32, + out_channels=17, + deconv_out_channels=None, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict( + type='Albumentation', + transforms=[ + dict( + type='CoarseDropout', + max_holes=8, + max_height=40, + max_width=40, + min_holes=1, + min_height=10, + min_width=10, + p=0.5), + ]), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_dark-8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_dark-8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..1a95b32cc4 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_dark-8xb64-210e_humanart-256x192.py @@ -0,0 +1,154 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', + input_size=(192, 256), + heatmap_size=(48, 64), + sigma=2, + unbiased=True) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='HRNet', + in_channels=3, + extra=dict( + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(4, ), + num_channels=(64, )), + stage2=dict( + num_modules=1, + num_branches=2, + block='BASIC', + num_blocks=(4, 4), + num_channels=(32, 64)), + stage3=dict( + num_modules=4, + num_branches=3, + block='BASIC', + num_blocks=(4, 4, 4), + num_channels=(32, 64, 128)), + stage4=dict( + num_modules=3, + num_branches=4, + block='BASIC', + num_blocks=(4, 4, 4, 4), + num_channels=(32, 64, 128, 256))), + init_cfg=dict( + type='Pretrained', + checkpoint='https://download.openmmlab.com/mmpose/' + 'pretrain_models/hrnet_w32-36af842e.pth'), + ), + head=dict( + type='HeatmapHead', + in_channels=32, + out_channels=17, + deconv_out_channels=None, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_dark-8xb64-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_dark-8xb64-210e_humanart-384x288.py new file mode 100644 index 0000000000..09639a8b40 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_dark-8xb64-210e_humanart-384x288.py @@ -0,0 +1,154 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', + input_size=(288, 384), + heatmap_size=(72, 96), + sigma=3, + unbiased=True) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='HRNet', + in_channels=3, + extra=dict( + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(4, ), + num_channels=(64, )), + stage2=dict( + num_modules=1, + num_branches=2, + block='BASIC', + num_blocks=(4, 4), + num_channels=(32, 64)), + stage3=dict( + num_modules=4, + num_branches=3, + block='BASIC', + num_blocks=(4, 4, 4), + num_channels=(32, 64, 128)), + stage4=dict( + num_modules=3, + num_branches=4, + block='BASIC', + num_blocks=(4, 4, 4, 4), + num_channels=(32, 64, 128, 256))), + init_cfg=dict( + type='Pretrained', + checkpoint='https://download.openmmlab.com/mmpose/' + 'pretrain_models/hrnet_w32-36af842e.pth'), + ), + head=dict( + type='HeatmapHead', + in_channels=32, + out_channels=17, + deconv_out_channels=None, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_fp16-8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_fp16-8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..306d0aeb44 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_fp16-8xb64-210e_humanart-256x192.py @@ -0,0 +1,7 @@ +_base_ = ['./td-hm_hrnet-w32_8xb64-210e_coco-256x192.py'] + +# fp16 settings +optim_wrapper = dict( + type='AmpOptimWrapper', + loss_scale='dynamic', +) diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_gridmask-8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_gridmask-8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..70ea1fb597 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_gridmask-8xb64-210e_humanart-256x192.py @@ -0,0 +1,162 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='HRNet', + in_channels=3, + extra=dict( + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(4, ), + num_channels=(64, )), + stage2=dict( + num_modules=1, + num_branches=2, + block='BASIC', + num_blocks=(4, 4), + num_channels=(32, 64)), + stage3=dict( + num_modules=4, + num_branches=3, + block='BASIC', + num_blocks=(4, 4, 4), + num_channels=(32, 64, 128)), + stage4=dict( + num_modules=3, + num_branches=4, + block='BASIC', + num_blocks=(4, 4, 4, 4), + num_channels=(32, 64, 128, 256))), + init_cfg=dict( + type='Pretrained', + prefix='backbone.', + checkpoint='https://download.openmmlab.com/mmpose/v1/' + 'body_2d_keypoint/topdown_heatmap/coco/' + 'td-hm_hrnet-w32_8xb64-210e_coco-256x192-81c58e40_20220909.pth'), + ), + head=dict( + type='HeatmapHead', + in_channels=32, + out_channels=17, + deconv_out_channels=None, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict( + type='Albumentation', + transforms=[ + dict( + type='GridDropout', + unit_size_min=10, + unit_size_max=40, + random_offset=True, + p=0.5), + ]), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_photometric-8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_photometric-8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..156d012fb3 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_photometric-8xb64-210e_humanart-256x192.py @@ -0,0 +1,153 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='HRNet', + in_channels=3, + extra=dict( + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(4, ), + num_channels=(64, )), + stage2=dict( + num_modules=1, + num_branches=2, + block='BASIC', + num_blocks=(4, 4), + num_channels=(32, 64)), + stage3=dict( + num_modules=4, + num_branches=3, + block='BASIC', + num_blocks=(4, 4, 4), + num_channels=(32, 64, 128)), + stage4=dict( + num_modules=3, + num_branches=4, + block='BASIC', + num_blocks=(4, 4, 4, 4), + num_channels=(32, 64, 128, 256))), + init_cfg=dict( + type='Pretrained', + prefix='backbone.', + checkpoint='https://download.openmmlab.com/mmpose/v1/' + 'body_2d_keypoint/topdown_heatmap/coco/' + 'td-hm_hrnet-w32_8xb64-210e_coco-256x192-81c58e40_20220909.pth'), + ), + head=dict( + type='HeatmapHead', + in_channels=32, + out_channels=17, + deconv_out_channels=None, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PhotometricDistortion'), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_udp-8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_udp-8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..68e227f8a8 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_udp-8xb64-210e_humanart-256x192.py @@ -0,0 +1,150 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='UDPHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='HRNet', + in_channels=3, + extra=dict( + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(4, ), + num_channels=(64, )), + stage2=dict( + num_modules=1, + num_branches=2, + block='BASIC', + num_blocks=(4, 4), + num_channels=(32, 64)), + stage3=dict( + num_modules=4, + num_branches=3, + block='BASIC', + num_blocks=(4, 4, 4), + num_channels=(32, 64, 128)), + stage4=dict( + num_modules=3, + num_branches=4, + block='BASIC', + num_blocks=(4, 4, 4, 4), + num_channels=(32, 64, 128, 256))), + init_cfg=dict( + type='Pretrained', + checkpoint='https://download.openmmlab.com/mmpose/' + 'pretrain_models/hrnet_w32-36af842e.pth'), + ), + head=dict( + type='HeatmapHead', + in_channels=32, + out_channels=17, + deconv_out_channels=None, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=False, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_udp-8xb64-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_udp-8xb64-210e_humanart-384x288.py new file mode 100644 index 0000000000..52672824ff --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_udp-8xb64-210e_humanart-384x288.py @@ -0,0 +1,150 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='UDPHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='HRNet', + in_channels=3, + extra=dict( + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(4, ), + num_channels=(64, )), + stage2=dict( + num_modules=1, + num_branches=2, + block='BASIC', + num_blocks=(4, 4), + num_channels=(32, 64)), + stage3=dict( + num_modules=4, + num_branches=3, + block='BASIC', + num_blocks=(4, 4, 4), + num_channels=(32, 64, 128)), + stage4=dict( + num_modules=3, + num_branches=4, + block='BASIC', + num_blocks=(4, 4, 4, 4), + num_channels=(32, 64, 128, 256))), + init_cfg=dict( + type='Pretrained', + checkpoint='https://download.openmmlab.com/mmpose/' + 'pretrain_models/hrnet_w32-36af842e.pth'), + ), + head=dict( + type='HeatmapHead', + in_channels=32, + out_channels=17, + deconv_out_channels=None, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=False, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_udp-regress-8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_udp-regress-8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..b47e21d75d --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_udp-regress-8xb64-210e_humanart-256x192.py @@ -0,0 +1,155 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='UDPHeatmap', + input_size=(192, 256), + heatmap_size=(48, 64), + sigma=2, + heatmap_type='combined') + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='HRNet', + in_channels=3, + extra=dict( + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(4, ), + num_channels=(64, )), + stage2=dict( + num_modules=1, + num_branches=2, + block='BASIC', + num_blocks=(4, 4), + num_channels=(32, 64)), + stage3=dict( + num_modules=4, + num_branches=3, + block='BASIC', + num_blocks=(4, 4, 4), + num_channels=(32, 64, 128)), + stage4=dict( + num_modules=3, + num_branches=4, + block='BASIC', + num_blocks=(4, 4, 4, 4), + num_channels=(32, 64, 128, 256))), + init_cfg=dict( + type='Pretrained', + checkpoint='https://download.openmmlab.com/mmpose/' + 'pretrain_models/hrnet_w32-36af842e.pth'), + ), + head=dict( + type='HeatmapHead', + in_channels=32, + out_channels=3 * 17, + deconv_out_channels=None, + loss=dict(type='CombinedTargetMSELoss', use_target_weight=True), + decoder=codec), + train_cfg=dict(compute_acc=False), + test_cfg=dict( + flip_test=True, + flip_mode='udp_combined', + shift_heatmap=False, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w48_8xb32-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w48_8xb32-210e_humanart-256x192.py new file mode 100644 index 0000000000..6a5ae0707c --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w48_8xb32-210e_humanart-256x192.py @@ -0,0 +1,150 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='HRNet', + in_channels=3, + extra=dict( + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(4, ), + num_channels=(64, )), + stage2=dict( + num_modules=1, + num_branches=2, + block='BASIC', + num_blocks=(4, 4), + num_channels=(48, 96)), + stage3=dict( + num_modules=4, + num_branches=3, + block='BASIC', + num_blocks=(4, 4, 4), + num_channels=(48, 96, 192)), + stage4=dict( + num_modules=3, + num_branches=4, + block='BASIC', + num_blocks=(4, 4, 4, 4), + num_channels=(48, 96, 192, 384))), + init_cfg=dict( + type='Pretrained', + checkpoint='https://download.openmmlab.com/mmpose/' + 'pretrain_models/hrnet_w48-8ef0771d.pth'), + ), + head=dict( + type='HeatmapHead', + in_channels=48, + out_channels=17, + deconv_out_channels=None, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w48_8xb32-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w48_8xb32-210e_humanart-384x288.py new file mode 100644 index 0000000000..e75349bac6 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w48_8xb32-210e_humanart-384x288.py @@ -0,0 +1,150 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='HRNet', + in_channels=3, + extra=dict( + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(4, ), + num_channels=(64, )), + stage2=dict( + num_modules=1, + num_branches=2, + block='BASIC', + num_blocks=(4, 4), + num_channels=(48, 96)), + stage3=dict( + num_modules=4, + num_branches=3, + block='BASIC', + num_blocks=(4, 4, 4), + num_channels=(48, 96, 192)), + stage4=dict( + num_modules=3, + num_branches=4, + block='BASIC', + num_blocks=(4, 4, 4, 4), + num_channels=(48, 96, 192, 384))), + init_cfg=dict( + type='Pretrained', + checkpoint='https://download.openmmlab.com/mmpose/' + 'pretrain_models/hrnet_w48-8ef0771d.pth'), + ), + head=dict( + type='HeatmapHead', + in_channels=48, + out_channels=17, + deconv_out_channels=None, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w48_dark-8xb32-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w48_dark-8xb32-210e_humanart-256x192.py new file mode 100644 index 0000000000..0b7d619fe2 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w48_dark-8xb32-210e_humanart-256x192.py @@ -0,0 +1,154 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', + input_size=(192, 256), + heatmap_size=(48, 64), + sigma=2, + unbiased=True) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='HRNet', + in_channels=3, + extra=dict( + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(4, ), + num_channels=(64, )), + stage2=dict( + num_modules=1, + num_branches=2, + block='BASIC', + num_blocks=(4, 4), + num_channels=(48, 96)), + stage3=dict( + num_modules=4, + num_branches=3, + block='BASIC', + num_blocks=(4, 4, 4), + num_channels=(48, 96, 192)), + stage4=dict( + num_modules=3, + num_branches=4, + block='BASIC', + num_blocks=(4, 4, 4, 4), + num_channels=(48, 96, 192, 384))), + init_cfg=dict( + type='Pretrained', + checkpoint='https://download.openmmlab.com/mmpose/' + 'pretrain_models/hrnet_w48-8ef0771d.pth'), + ), + head=dict( + type='HeatmapHead', + in_channels=48, + out_channels=17, + deconv_out_channels=None, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w48_dark-8xb32-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w48_dark-8xb32-210e_humanart-384x288.py new file mode 100644 index 0000000000..8b3b572e6e --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w48_dark-8xb32-210e_humanart-384x288.py @@ -0,0 +1,154 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', + input_size=(288, 384), + heatmap_size=(72, 96), + sigma=3, + unbiased=True) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='HRNet', + in_channels=3, + extra=dict( + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(4, ), + num_channels=(64, )), + stage2=dict( + num_modules=1, + num_branches=2, + block='BASIC', + num_blocks=(4, 4), + num_channels=(48, 96)), + stage3=dict( + num_modules=4, + num_branches=3, + block='BASIC', + num_blocks=(4, 4, 4), + num_channels=(48, 96, 192)), + stage4=dict( + num_modules=3, + num_branches=4, + block='BASIC', + num_blocks=(4, 4, 4, 4), + num_channels=(48, 96, 192, 384))), + init_cfg=dict( + type='Pretrained', + checkpoint='https://download.openmmlab.com/mmpose/' + 'pretrain_models/hrnet_w48-8ef0771d.pth'), + ), + head=dict( + type='HeatmapHead', + in_channels=48, + out_channels=17, + deconv_out_channels=None, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w48_udp-8xb32-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w48_udp-8xb32-210e_humanart-256x192.py new file mode 100644 index 0000000000..c58a57a1da --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w48_udp-8xb32-210e_humanart-256x192.py @@ -0,0 +1,150 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='UDPHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='HRNet', + in_channels=3, + extra=dict( + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(4, ), + num_channels=(64, )), + stage2=dict( + num_modules=1, + num_branches=2, + block='BASIC', + num_blocks=(4, 4), + num_channels=(48, 96)), + stage3=dict( + num_modules=4, + num_branches=3, + block='BASIC', + num_blocks=(4, 4, 4), + num_channels=(48, 96, 192)), + stage4=dict( + num_modules=3, + num_branches=4, + block='BASIC', + num_blocks=(4, 4, 4, 4), + num_channels=(48, 96, 192, 384))), + init_cfg=dict( + type='Pretrained', + checkpoint='https://download.openmmlab.com/mmpose/' + 'pretrain_models/hrnet_w48-8ef0771d.pth'), + ), + head=dict( + type='HeatmapHead', + in_channels=48, + out_channels=17, + deconv_out_channels=None, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=False, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w48_udp-8xb32-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w48_udp-8xb32-210e_humanart-384x288.py new file mode 100644 index 0000000000..509341b9a3 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w48_udp-8xb32-210e_humanart-384x288.py @@ -0,0 +1,150 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='UDPHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='HRNet', + in_channels=3, + extra=dict( + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(4, ), + num_channels=(64, )), + stage2=dict( + num_modules=1, + num_branches=2, + block='BASIC', + num_blocks=(4, 4), + num_channels=(48, 96)), + stage3=dict( + num_modules=4, + num_branches=3, + block='BASIC', + num_blocks=(4, 4, 4), + num_channels=(48, 96, 192)), + stage4=dict( + num_modules=3, + num_branches=4, + block='BASIC', + num_blocks=(4, 4, 4, 4), + num_channels=(48, 96, 192, 384))), + init_cfg=dict( + type='Pretrained', + checkpoint='https://download.openmmlab.com/mmpose/' + 'pretrain_models/hrnet_w48-8ef0771d.pth'), + ), + head=dict( + type='HeatmapHead', + in_channels=48, + out_channels=17, + deconv_out_channels=None, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=False, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_litehrnet-18_8xb32-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_litehrnet-18_8xb32-210e_humanart-384x288.py new file mode 100644 index 0000000000..0448a55017 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_litehrnet-18_8xb32-210e_humanart-384x288.py @@ -0,0 +1,140 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='LiteHRNet', + in_channels=3, + extra=dict( + stem=dict(stem_channels=32, out_channels=32, expand_ratio=1), + num_stages=3, + stages_spec=dict( + num_modules=(2, 4, 2), + num_branches=(2, 3, 4), + num_blocks=(2, 2, 2), + module_type=('LITE', 'LITE', 'LITE'), + with_fuse=(True, True, True), + reduce_ratios=(8, 8, 8), + num_channels=( + (40, 80), + (40, 80, 160), + (40, 80, 160, 320), + )), + with_head=True, + )), + head=dict( + type='HeatmapHead', + in_channels=40, + out_channels=17, + deconv_out_channels=None, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict( + type='RandomBBoxTransform', + rotate_factor=60, + scale_factor=(0.75, 1.25)), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_litehrnet-18_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_litehrnet-18_8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..a58a880168 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_litehrnet-18_8xb64-210e_humanart-256x192.py @@ -0,0 +1,140 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='LiteHRNet', + in_channels=3, + extra=dict( + stem=dict(stem_channels=32, out_channels=32, expand_ratio=1), + num_stages=3, + stages_spec=dict( + num_modules=(2, 4, 2), + num_branches=(2, 3, 4), + num_blocks=(2, 2, 2), + module_type=('LITE', 'LITE', 'LITE'), + with_fuse=(True, True, True), + reduce_ratios=(8, 8, 8), + num_channels=( + (40, 80), + (40, 80, 160), + (40, 80, 160, 320), + )), + with_head=True, + )), + head=dict( + type='HeatmapHead', + in_channels=40, + out_channels=17, + deconv_out_channels=None, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict( + type='RandomBBoxTransform', + rotate_factor=60, + scale_factor=(0.75, 1.25)), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_litehrnet-30_8xb32-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_litehrnet-30_8xb32-210e_humanart-384x288.py new file mode 100644 index 0000000000..1af1e659b6 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_litehrnet-30_8xb32-210e_humanart-384x288.py @@ -0,0 +1,140 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='LiteHRNet', + in_channels=3, + extra=dict( + stem=dict(stem_channels=32, out_channels=32, expand_ratio=1), + num_stages=3, + stages_spec=dict( + num_modules=(3, 8, 3), + num_branches=(2, 3, 4), + num_blocks=(2, 2, 2), + module_type=('LITE', 'LITE', 'LITE'), + with_fuse=(True, True, True), + reduce_ratios=(8, 8, 8), + num_channels=( + (40, 80), + (40, 80, 160), + (40, 80, 160, 320), + )), + with_head=True, + )), + head=dict( + type='HeatmapHead', + in_channels=40, + out_channels=17, + deconv_out_channels=None, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict( + type='RandomBBoxTransform', + rotate_factor=60, + scale_factor=(0.75, 1.25)), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_litehrnet-30_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_litehrnet-30_8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..253416c2c1 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_litehrnet-30_8xb64-210e_humanart-256x192.py @@ -0,0 +1,140 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='LiteHRNet', + in_channels=3, + extra=dict( + stem=dict(stem_channels=32, out_channels=32, expand_ratio=1), + num_stages=3, + stages_spec=dict( + num_modules=(3, 8, 3), + num_branches=(2, 3, 4), + num_blocks=(2, 2, 2), + module_type=('LITE', 'LITE', 'LITE'), + with_fuse=(True, True, True), + reduce_ratios=(8, 8, 8), + num_channels=( + (40, 80), + (40, 80, 160), + (40, 80, 160, 320), + )), + with_head=True, + )), + head=dict( + type='HeatmapHead', + in_channels=40, + out_channels=17, + deconv_out_channels=None, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict( + type='RandomBBoxTransform', + rotate_factor=60, + scale_factor=(0.75, 1.25)), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_mobilenetv2_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_mobilenetv2_8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..e068b246d6 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_mobilenetv2_8xb64-210e_humanart-256x192.py @@ -0,0 +1,124 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='MobileNetV2', + widen_factor=1., + out_indices=(7, ), + init_cfg=dict( + type='Pretrained', + checkpoint='mmcls://mobilenet_v2', + )), + head=dict( + type='HeatmapHead', + in_channels=1280, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_mobilenetv2_8xb64-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_mobilenetv2_8xb64-210e_humanart-384x288.py new file mode 100644 index 0000000000..57f5c1bb91 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_mobilenetv2_8xb64-210e_humanart-384x288.py @@ -0,0 +1,124 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='MobileNetV2', + widen_factor=1., + out_indices=(7, ), + init_cfg=dict( + type='Pretrained', + checkpoint='mmcls://mobilenet_v2', + )), + head=dict( + type='HeatmapHead', + in_channels=1280, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_mspn50_8xb32-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_mspn50_8xb32-210e_humanart-256x192.py new file mode 100644 index 0000000000..28b1778176 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_mspn50_8xb32-210e_humanart-256x192.py @@ -0,0 +1,152 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-3, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=256) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +# multiple kernel_sizes of heatmap gaussian for 'Megvii' approach. +kernel_sizes = [11, 9, 7, 5] +codec = [ + dict( + type='MegviiHeatmap', + input_size=(192, 256), + heatmap_size=(48, 64), + kernel_size=kernel_size) for kernel_size in kernel_sizes +] + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='MSPN', + unit_channels=256, + num_stages=1, + num_units=4, + num_blocks=[3, 4, 6, 3], + norm_cfg=dict(type='BN'), + init_cfg=dict( + type='Pretrained', + checkpoint='torchvision://resnet50', + )), + head=dict( + type='MSPNHead', + out_shape=(64, 48), + unit_channels=256, + out_channels=17, + num_stages=1, + num_units=4, + norm_cfg=dict(type='BN'), + # each sub list is for a stage + # and each element in each list is for a unit + level_indices=[0, 1, 2, 3], + loss=[ + dict( + type='KeypointMSELoss', + use_target_weight=True, + loss_weight=0.25) + ] * 3 + [ + dict( + type='KeypointOHKMMSELoss', + use_target_weight=True, + loss_weight=1.) + ], + decoder=codec[-1]), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=False, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec[0]['input_size']), + dict(type='GenerateTarget', multilevel=True, encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec[0]['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=32, + num_workers=4, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=4, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'annotations/person_keypoints_val2017.json', + nms_mode='none') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_pvt-s_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_pvt-s_8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..e49eb22f49 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_pvt-s_8xb64-210e_humanart-256x192.py @@ -0,0 +1,127 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='PyramidVisionTransformer', + num_layers=[3, 4, 6, 3], + init_cfg=dict( + type='Pretrained', + checkpoint='https://github.com/whai362/PVT/' + 'releases/download/v2/pvt_small.pth'), + ), + neck=dict(type='FeatureMapProcessor', select_index=3), + head=dict( + type='HeatmapHead', + in_channels=512, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] + +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_pvtv2-b2_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_pvtv2-b2_8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..bf30e834bd --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_pvtv2-b2_8xb64-210e_humanart-256x192.py @@ -0,0 +1,128 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='PyramidVisionTransformerV2', + embed_dims=64, + num_layers=[3, 4, 6, 3], + init_cfg=dict( + type='Pretrained', + checkpoint='https://github.com/whai362/PVT/' + 'releases/download/v2/pvt_v2_b2.pth'), + ), + neck=dict(type='FeatureMapProcessor', select_index=3), + head=dict( + type='HeatmapHead', + in_channels=512, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] + +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res101_8xb32-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res101_8xb32-210e_humanart-384x288.py new file mode 100644 index 0000000000..657ef05821 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res101_8xb32-210e_humanart-384x288.py @@ -0,0 +1,121 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=256) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='ResNet', + depth=101, + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet101'), + ), + head=dict( + type='HeatmapHead', + in_channels=2048, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res101_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res101_8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..d42f787e93 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res101_8xb64-210e_humanart-256x192.py @@ -0,0 +1,121 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='ResNet', + depth=101, + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet101'), + ), + head=dict( + type='HeatmapHead', + in_channels=2048, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res101_dark-8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res101_dark-8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..1f594f2fae --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res101_dark-8xb64-210e_humanart-256x192.py @@ -0,0 +1,125 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', + input_size=(192, 256), + heatmap_size=(48, 64), + sigma=2, + unbiased=True) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='ResNet', + depth=101, + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet101'), + ), + head=dict( + type='HeatmapHead', + in_channels=2048, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res101_dark-8xb64-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res101_dark-8xb64-210e_humanart-384x288.py new file mode 100644 index 0000000000..e8f075c75c --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res101_dark-8xb64-210e_humanart-384x288.py @@ -0,0 +1,125 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', + input_size=(288, 384), + heatmap_size=(72, 96), + sigma=3, + unbiased=True) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='ResNet', + depth=101, + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet101'), + ), + head=dict( + type='HeatmapHead', + in_channels=2048, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res152_8xb32-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res152_8xb32-210e_humanart-256x192.py new file mode 100644 index 0000000000..c2e5334357 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res152_8xb32-210e_humanart-256x192.py @@ -0,0 +1,121 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='ResNet', + depth=152, + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet152'), + ), + head=dict( + type='HeatmapHead', + in_channels=2048, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res152_8xb32-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res152_8xb32-210e_humanart-384x288.py new file mode 100644 index 0000000000..c60681d3b1 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res152_8xb32-210e_humanart-384x288.py @@ -0,0 +1,121 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='ResNet', + depth=152, + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet152'), + ), + head=dict( + type='HeatmapHead', + in_channels=2048, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res152_dark-8xb32-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res152_dark-8xb32-210e_humanart-256x192.py new file mode 100644 index 0000000000..b0a4b3258f --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res152_dark-8xb32-210e_humanart-256x192.py @@ -0,0 +1,125 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=256) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', + input_size=(192, 256), + heatmap_size=(48, 64), + sigma=2, + unbiased=True) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='ResNet', + depth=152, + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet152'), + ), + head=dict( + type='HeatmapHead', + in_channels=2048, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res152_dark-8xb32-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res152_dark-8xb32-210e_humanart-384x288.py new file mode 100644 index 0000000000..8ed8884023 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res152_dark-8xb32-210e_humanart-384x288.py @@ -0,0 +1,126 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=256) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', + input_size=(288, 384), + heatmap_size=(72, 96), + sigma=3, + unbiased=True, + blur_kernel_size=17) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='ResNet', + depth=152, + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet152'), + ), + head=dict( + type='HeatmapHead', + in_channels=2048, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res50_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res50_8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..06bc5283c9 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res50_8xb64-210e_humanart-256x192.py @@ -0,0 +1,121 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='ResNet', + depth=50, + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'), + ), + head=dict( + type='HeatmapHead', + in_channels=2048, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res50_8xb64-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res50_8xb64-210e_humanart-384x288.py new file mode 100644 index 0000000000..e209c63f85 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res50_8xb64-210e_humanart-384x288.py @@ -0,0 +1,121 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='ResNet', + depth=50, + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'), + ), + head=dict( + type='HeatmapHead', + in_channels=2048, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res50_dark-8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res50_dark-8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..a0770247f9 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res50_dark-8xb64-210e_humanart-256x192.py @@ -0,0 +1,125 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', + input_size=(192, 256), + heatmap_size=(48, 64), + sigma=2, + unbiased=True) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='ResNet', + depth=50, + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'), + ), + head=dict( + type='HeatmapHead', + in_channels=2048, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res50_dark-8xb64-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res50_dark-8xb64-210e_humanart-384x288.py new file mode 100644 index 0000000000..60575438ad --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res50_dark-8xb64-210e_humanart-384x288.py @@ -0,0 +1,125 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', + input_size=(288, 384), + heatmap_size=(72, 96), + sigma=3, + unbiased=True) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='ResNet', + depth=50, + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'), + ), + head=dict( + type='HeatmapHead', + in_channels=2048, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res50_fp16-8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res50_fp16-8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..66a6a27822 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res50_fp16-8xb64-210e_humanart-256x192.py @@ -0,0 +1,7 @@ +_base_ = ['./td-hm_res50_8xb64-210e_coco-256x192.py'] + +# fp16 settings +optim_wrapper = dict( + type='AmpOptimWrapper', + loss_scale='dynamic', +) diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnest101_8xb32-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnest101_8xb32-210e_humanart-384x288.py new file mode 100644 index 0000000000..edd42dceb9 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnest101_8xb32-210e_humanart-384x288.py @@ -0,0 +1,121 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=256) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='ResNeSt', + depth=101, + init_cfg=dict(type='Pretrained', checkpoint='mmcls://resnest101'), + ), + head=dict( + type='HeatmapHead', + in_channels=2048, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnest101_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnest101_8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..7f415346dd --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnest101_8xb64-210e_humanart-256x192.py @@ -0,0 +1,121 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='ResNeSt', + depth=101, + init_cfg=dict(type='Pretrained', checkpoint='mmcls://resnest101'), + ), + head=dict( + type='HeatmapHead', + in_channels=2048, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnest200_8xb16-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnest200_8xb16-210e_humanart-384x288.py new file mode 100644 index 0000000000..2f916b41e5 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnest200_8xb16-210e_humanart-384x288.py @@ -0,0 +1,121 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=128) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='ResNeSt', + depth=200, + init_cfg=dict(type='Pretrained', checkpoint='mmcls://resnest200'), + ), + head=dict( + type='HeatmapHead', + in_channels=2048, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=16, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=16, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnest200_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnest200_8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..c29cc30444 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnest200_8xb64-210e_humanart-256x192.py @@ -0,0 +1,121 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='ResNeSt', + depth=200, + init_cfg=dict(type='Pretrained', checkpoint='mmcls://resnest200'), + ), + head=dict( + type='HeatmapHead', + in_channels=2048, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnest269_8xb16-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnest269_8xb16-210e_humanart-384x288.py new file mode 100644 index 0000000000..367331a325 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnest269_8xb16-210e_humanart-384x288.py @@ -0,0 +1,121 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=128) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='ResNeSt', + depth=269, + init_cfg=dict(type='Pretrained', checkpoint='mmcls://resnest269'), + ), + head=dict( + type='HeatmapHead', + in_channels=2048, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=16, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=16, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnest269_8xb32-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnest269_8xb32-210e_humanart-256x192.py new file mode 100644 index 0000000000..dd334bb58b --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnest269_8xb32-210e_humanart-256x192.py @@ -0,0 +1,121 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='ResNeSt', + depth=269, + init_cfg=dict(type='Pretrained', checkpoint='mmcls://resnest269'), + ), + head=dict( + type='HeatmapHead', + in_channels=2048, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnest50_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnest50_8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..c0eb815008 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnest50_8xb64-210e_humanart-256x192.py @@ -0,0 +1,121 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='ResNeSt', + depth=50, + init_cfg=dict(type='Pretrained', checkpoint='mmcls://resnest50'), + ), + head=dict( + type='HeatmapHead', + in_channels=2048, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnest50_8xb64-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnest50_8xb64-210e_humanart-384x288.py new file mode 100644 index 0000000000..11958c0947 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnest50_8xb64-210e_humanart-384x288.py @@ -0,0 +1,121 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='ResNeSt', + depth=50, + init_cfg=dict(type='Pretrained', checkpoint='mmcls://resnest50'), + ), + head=dict( + type='HeatmapHead', + in_channels=2048, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnetv1d101_8xb32-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnetv1d101_8xb32-210e_humanart-384x288.py new file mode 100644 index 0000000000..20a1b17d6e --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnetv1d101_8xb32-210e_humanart-384x288.py @@ -0,0 +1,121 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=256) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='ResNetV1d', + depth=101, + init_cfg=dict(type='Pretrained', checkpoint='mmcls://resnet101_v1d'), + ), + head=dict( + type='HeatmapHead', + in_channels=2048, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnetv1d101_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnetv1d101_8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..9bb2ec99bf --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnetv1d101_8xb64-210e_humanart-256x192.py @@ -0,0 +1,121 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='ResNetV1d', + depth=101, + init_cfg=dict(type='Pretrained', checkpoint='mmcls://resnet101_v1d'), + ), + head=dict( + type='HeatmapHead', + in_channels=2048, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnetv1d152_8xb32-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnetv1d152_8xb32-210e_humanart-256x192.py new file mode 100644 index 0000000000..16754f8066 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnetv1d152_8xb32-210e_humanart-256x192.py @@ -0,0 +1,121 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='ResNetV1d', + depth=152, + init_cfg=dict(type='Pretrained', checkpoint='mmcls://resnet152_v1d'), + ), + head=dict( + type='HeatmapHead', + in_channels=2048, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnetv1d152_8xb48-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnetv1d152_8xb48-210e_humanart-384x288.py new file mode 100644 index 0000000000..9d9ec562b0 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnetv1d152_8xb48-210e_humanart-384x288.py @@ -0,0 +1,121 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=384) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='ResNetV1d', + depth=152, + init_cfg=dict(type='Pretrained', checkpoint='mmcls://resnet152_v1d'), + ), + head=dict( + type='HeatmapHead', + in_channels=2048, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=48, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnetv1d50_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnetv1d50_8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..74b206e66f --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnetv1d50_8xb64-210e_humanart-256x192.py @@ -0,0 +1,121 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='ResNetV1d', + depth=50, + init_cfg=dict(type='Pretrained', checkpoint='mmcls://resnet50_v1d'), + ), + head=dict( + type='HeatmapHead', + in_channels=2048, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnetv1d50_8xb64-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnetv1d50_8xb64-210e_humanart-384x288.py new file mode 100644 index 0000000000..193fe55052 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnetv1d50_8xb64-210e_humanart-384x288.py @@ -0,0 +1,121 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='ResNetV1d', + depth=50, + init_cfg=dict(type='Pretrained', checkpoint='mmcls://resnet50_v1d'), + ), + head=dict( + type='HeatmapHead', + in_channels=2048, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnext101_8xb32-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnext101_8xb32-210e_humanart-384x288.py new file mode 100644 index 0000000000..43fb721866 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnext101_8xb32-210e_humanart-384x288.py @@ -0,0 +1,122 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=256) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='ResNeXt', + depth=101, + init_cfg=dict( + type='Pretrained', checkpoint='mmcls://resnext101_32x4d'), + ), + head=dict( + type='HeatmapHead', + in_channels=2048, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnext101_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnext101_8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..65f3b2bbb3 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnext101_8xb64-210e_humanart-256x192.py @@ -0,0 +1,122 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='ResNeXt', + depth=101, + init_cfg=dict( + type='Pretrained', checkpoint='mmcls://resnext101_32x4d'), + ), + head=dict( + type='HeatmapHead', + in_channels=2048, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnext152_8xb32-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnext152_8xb32-210e_humanart-256x192.py new file mode 100644 index 0000000000..7315855cb6 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnext152_8xb32-210e_humanart-256x192.py @@ -0,0 +1,122 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='ResNeXt', + depth=152, + init_cfg=dict( + type='Pretrained', checkpoint='mmcls://resnext152_32x4d'), + ), + head=dict( + type='HeatmapHead', + in_channels=2048, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnext152_8xb48-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnext152_8xb48-210e_humanart-384x288.py new file mode 100644 index 0000000000..af23d16379 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnext152_8xb48-210e_humanart-384x288.py @@ -0,0 +1,122 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=384) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='ResNeXt', + depth=152, + init_cfg=dict( + type='Pretrained', checkpoint='mmcls://resnext152_32x4d'), + ), + head=dict( + type='HeatmapHead', + in_channels=2048, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=48, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnext50_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnext50_8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..5ecba30aa4 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnext50_8xb64-210e_humanart-256x192.py @@ -0,0 +1,121 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='ResNeXt', + depth=50, + init_cfg=dict(type='Pretrained', checkpoint='mmcls://resnext50_32x4d'), + ), + head=dict( + type='HeatmapHead', + in_channels=2048, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnext50_8xb64-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnext50_8xb64-210e_humanart-384x288.py new file mode 100644 index 0000000000..51d30d0394 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnext50_8xb64-210e_humanart-384x288.py @@ -0,0 +1,121 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='ResNeXt', + depth=50, + init_cfg=dict(type='Pretrained', checkpoint='mmcls://resnext50_32x4d'), + ), + head=dict( + type='HeatmapHead', + in_channels=2048, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_rsn18_8xb32-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_rsn18_8xb32-210e_humanart-256x192.py new file mode 100644 index 0000000000..06257d41fd --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_rsn18_8xb32-210e_humanart-256x192.py @@ -0,0 +1,154 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=2e-2, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 190, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=256) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +# multiple kernel_sizes of heatmap gaussian for 'Megvii' approach. +kernel_sizes = [11, 9, 7, 5] +codec = [ + dict( + type='MegviiHeatmap', + input_size=(192, 256), + heatmap_size=(48, 64), + kernel_size=kernel_size) for kernel_size in kernel_sizes +] + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='RSN', + unit_channels=256, + num_stages=1, + num_units=4, + num_blocks=[2, 2, 2, 2], + num_steps=4, + norm_cfg=dict(type='BN'), + ), + head=dict( + type='MSPNHead', + out_shape=(64, 48), + unit_channels=256, + out_channels=17, + num_stages=1, + num_units=4, + norm_cfg=dict(type='BN'), + # each sub list is for a stage + # and each element in each list is for a unit + level_indices=[0, 1, 2, 3], + loss=[ + dict( + type='KeypointMSELoss', + use_target_weight=True, + loss_weight=0.25) + ] * 3 + [ + dict( + type='KeypointOHKMMSELoss', + use_target_weight=True, + loss_weight=1.) + ], + decoder=codec[-1]), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=False, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec[0]['input_size']), + dict(type='GenerateTarget', multilevel=True, encoder=codec), + dict(type='PackPoseInputs') +] + +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec[0]['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=32, + num_workers=4, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=4, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'annotations/person_keypoints_val2017.json', + nms_mode='none') +test_evaluator = val_evaluator + +# fp16 settings +fp16 = dict(loss_scale='dynamic') diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_rsn50_8xb32-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_rsn50_8xb32-210e_humanart-256x192.py new file mode 100644 index 0000000000..e73d6aeab3 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_rsn50_8xb32-210e_humanart-256x192.py @@ -0,0 +1,154 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-3, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=256) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +# multiple kernel_sizes of heatmap gaussian for 'Megvii' approach. +kernel_sizes = [11, 9, 7, 5] +codec = [ + dict( + type='MegviiHeatmap', + input_size=(192, 256), + heatmap_size=(48, 64), + kernel_size=kernel_size) for kernel_size in kernel_sizes +] + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='RSN', + unit_channels=256, + num_stages=1, + num_units=4, + num_blocks=[3, 4, 6, 3], + num_steps=4, + norm_cfg=dict(type='BN'), + ), + head=dict( + type='MSPNHead', + out_shape=(64, 48), + unit_channels=256, + out_channels=17, + num_stages=1, + num_units=4, + norm_cfg=dict(type='BN'), + # each sub list is for a stage + # and each element in each list is for a unit + level_indices=[0, 1, 2, 3], + loss=[ + dict( + type='KeypointMSELoss', + use_target_weight=True, + loss_weight=0.25) + ] * 3 + [ + dict( + type='KeypointOHKMMSELoss', + use_target_weight=True, + loss_weight=1.) + ], + decoder=codec[-1]), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=False, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec[0]['input_size']), + dict(type='GenerateTarget', multilevel=True, encoder=codec), + dict(type='PackPoseInputs') +] + +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec[0]['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=32, + num_workers=4, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=4, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'annotations/person_keypoints_val2017.json', + nms_mode='none') +test_evaluator = val_evaluator + +# fp16 settings +fp16 = dict(loss_scale='dynamic') diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_scnet101_8xb32-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_scnet101_8xb32-210e_humanart-256x192.py new file mode 100644 index 0000000000..ac99362488 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_scnet101_8xb32-210e_humanart-256x192.py @@ -0,0 +1,124 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='SCNet', + depth=101, + init_cfg=dict( + type='Pretrained', + checkpoint='https://download.openmmlab.com/mmpose/' + 'pretrain_models/scnet101-94250a77.pth'), + ), + head=dict( + type='HeatmapHead', + in_channels=2048, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=32, + num_workers=1, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=1, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_scnet101_8xb48-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_scnet101_8xb48-210e_humanart-384x288.py new file mode 100644 index 0000000000..6aa29f6edd --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_scnet101_8xb48-210e_humanart-384x288.py @@ -0,0 +1,124 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='SCNet', + depth=101, + init_cfg=dict( + type='Pretrained', + checkpoint='https://download.openmmlab.com/mmpose/' + 'pretrain_models/scnet101-94250a77.pth'), + ), + head=dict( + type='HeatmapHead', + in_channels=2048, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=48, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_scnet50_8xb32-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_scnet50_8xb32-210e_humanart-384x288.py new file mode 100644 index 0000000000..5e7dd7ceae --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_scnet50_8xb32-210e_humanart-384x288.py @@ -0,0 +1,124 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=256) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='SCNet', + depth=50, + init_cfg=dict( + type='Pretrained', + checkpoint='https://download.openmmlab.com/mmpose/' + 'pretrain_models/scnet50-7ef0a199.pth'), + ), + head=dict( + type='HeatmapHead', + in_channels=2048, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=32, + num_workers=1, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=1, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_scnet50_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_scnet50_8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..3423c14470 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_scnet50_8xb64-210e_humanart-256x192.py @@ -0,0 +1,124 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='SCNet', + depth=50, + init_cfg=dict( + type='Pretrained', + checkpoint='https://download.openmmlab.com/mmpose/' + 'pretrain_models/scnet50-7ef0a199.pth'), + ), + head=dict( + type='HeatmapHead', + in_channels=2048, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_seresnet101_8xb32-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_seresnet101_8xb32-210e_humanart-384x288.py new file mode 100644 index 0000000000..b56a119ca1 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_seresnet101_8xb32-210e_humanart-384x288.py @@ -0,0 +1,121 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=256) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='SEResNet', + depth=101, + init_cfg=dict(type='Pretrained', checkpoint='mmcls://se-resnet101'), + ), + head=dict( + type='HeatmapHead', + in_channels=2048, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_seresnet101_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_seresnet101_8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..8d9b3185ca --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_seresnet101_8xb64-210e_humanart-256x192.py @@ -0,0 +1,121 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='SEResNet', + depth=101, + init_cfg=dict(type='Pretrained', checkpoint='mmcls://se-resnet101'), + ), + head=dict( + type='HeatmapHead', + in_channels=2048, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_seresnet152_8xb32-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_seresnet152_8xb32-210e_humanart-256x192.py new file mode 100644 index 0000000000..2714d8f3a0 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_seresnet152_8xb32-210e_humanart-256x192.py @@ -0,0 +1,120 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='SEResNet', + depth=152, + ), + head=dict( + type='HeatmapHead', + in_channels=2048, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_seresnet152_8xb48-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_seresnet152_8xb48-210e_humanart-384x288.py new file mode 100644 index 0000000000..ba4c41d100 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_seresnet152_8xb48-210e_humanart-384x288.py @@ -0,0 +1,120 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=384) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='SEResNet', + depth=152, + ), + head=dict( + type='HeatmapHead', + in_channels=2048, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=48, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_seresnet50_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_seresnet50_8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..e48954cbac --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_seresnet50_8xb64-210e_humanart-256x192.py @@ -0,0 +1,121 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='SEResNet', + depth=50, + init_cfg=dict(type='Pretrained', checkpoint='mmcls://se-resnet50'), + ), + head=dict( + type='HeatmapHead', + in_channels=2048, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_seresnet50_8xb64-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_seresnet50_8xb64-210e_humanart-384x288.py new file mode 100644 index 0000000000..90716e2158 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_seresnet50_8xb64-210e_humanart-384x288.py @@ -0,0 +1,121 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='SEResNet', + depth=50, + init_cfg=dict(type='Pretrained', checkpoint='mmcls://se-resnet50'), + ), + head=dict( + type='HeatmapHead', + in_channels=2048, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_shufflenetv1_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_shufflenetv1_8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..407bf748ab --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_shufflenetv1_8xb64-210e_humanart-256x192.py @@ -0,0 +1,121 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='ShuffleNetV1', + groups=3, + init_cfg=dict(type='Pretrained', checkpoint='mmcls://shufflenet_v1'), + ), + head=dict( + type='HeatmapHead', + in_channels=960, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_shufflenetv1_8xb64-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_shufflenetv1_8xb64-210e_humanart-384x288.py new file mode 100644 index 0000000000..e65682b415 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_shufflenetv1_8xb64-210e_humanart-384x288.py @@ -0,0 +1,121 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='ShuffleNetV1', + groups=3, + init_cfg=dict(type='Pretrained', checkpoint='mmcls://shufflenet_v1'), + ), + head=dict( + type='HeatmapHead', + in_channels=960, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_shufflenetv2_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_shufflenetv2_8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..31001638ea --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_shufflenetv2_8xb64-210e_humanart-256x192.py @@ -0,0 +1,121 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='ShuffleNetV2', + widen_factor=1.0, + init_cfg=dict(type='Pretrained', checkpoint='mmcls://shufflenet_v2'), + ), + head=dict( + type='HeatmapHead', + in_channels=1024, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_shufflenetv2_8xb64-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_shufflenetv2_8xb64-210e_humanart-384x288.py new file mode 100644 index 0000000000..c9febd94c8 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_shufflenetv2_8xb64-210e_humanart-384x288.py @@ -0,0 +1,121 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='ShuffleNetV2', + widen_factor=1.0, + init_cfg=dict(type='Pretrained', checkpoint='mmcls://shufflenet_v2'), + ), + head=dict( + type='HeatmapHead', + in_channels=1024, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_swin-b-p4-w7_8xb32-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_swin-b-p4-w7_8xb32-210e_humanart-256x192.py new file mode 100644 index 0000000000..fbdba98074 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_swin-b-p4-w7_8xb32-210e_humanart-256x192.py @@ -0,0 +1,139 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=256) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='SwinTransformer', + embed_dims=128, + depths=[2, 2, 18, 2], + num_heads=[4, 8, 16, 32], + window_size=7, + mlp_ratio=4, + qkv_bias=True, + qk_scale=None, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0.3, + patch_norm=True, + out_indices=(3, ), + with_cp=False, + convert_weights=True, + init_cfg=dict( + type='Pretrained', + checkpoint='https://github.com/SwinTransformer/storage/releases/' + 'download/v1.0.0/swin_base_patch4_window7_224_22k.pth'), + ), + head=dict( + type='HeatmapHead', + in_channels=1024, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] + +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_swin-b-p4-w7_8xb32-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_swin-b-p4-w7_8xb32-210e_humanart-384x288.py new file mode 100644 index 0000000000..7b9f6bac77 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_swin-b-p4-w7_8xb32-210e_humanart-384x288.py @@ -0,0 +1,139 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=256) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=2) + +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='SwinTransformer', + embed_dims=128, + depths=[2, 2, 18, 2], + num_heads=[4, 8, 16, 32], + window_size=12, + mlp_ratio=4, + qkv_bias=True, + qk_scale=None, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0.3, + patch_norm=True, + out_indices=(3, ), + with_cp=False, + convert_weights=True, + init_cfg=dict( + type='Pretrained', + checkpoint='https://github.com/SwinTransformer/storage/releases/' + 'download/v1.0.0/swin_base_patch4_window12_384_22k.pth'), + ), + head=dict( + type='HeatmapHead', + in_channels=1024, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] + +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_swin-l-p4-w7_8xb32-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_swin-l-p4-w7_8xb32-210e_humanart-256x192.py new file mode 100644 index 0000000000..c34d8441a1 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_swin-l-p4-w7_8xb32-210e_humanart-256x192.py @@ -0,0 +1,148 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict( + optimizer=dict( + type='AdamW', + lr=5e-4, + betas=(0.9, 0.999), + weight_decay=0.01, + ), + paramwise_cfg=dict( + custom_keys={ + 'absolute_pos_embed': dict(decay_mult=0.), + 'relative_position_bias_table': dict(decay_mult=0.), + 'norm': dict(decay_mult=0.) + })) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=256) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='SwinTransformer', + embed_dims=192, + depths=[2, 2, 18, 2], + num_heads=[6, 12, 24, 48], + window_size=7, + mlp_ratio=4, + qkv_bias=True, + qk_scale=None, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0.5, + patch_norm=True, + out_indices=(3, ), + with_cp=False, + convert_weights=True, + init_cfg=dict( + type='Pretrained', + checkpoint='https://github.com/SwinTransformer/storage/releases/' + 'download/v1.0.0/swin_base_patch4_window7_224_22k.pth'), + ), + head=dict( + type='HeatmapHead', + in_channels=1536, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] + +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_swin-l-p4-w7_8xb32-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_swin-l-p4-w7_8xb32-210e_humanart-384x288.py new file mode 100644 index 0000000000..51d49afbba --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_swin-l-p4-w7_8xb32-210e_humanart-384x288.py @@ -0,0 +1,148 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict( + optimizer=dict( + type='AdamW', + lr=5e-4, + betas=(0.9, 0.999), + weight_decay=0.01, + ), + paramwise_cfg=dict( + custom_keys={ + 'absolute_pos_embed': dict(decay_mult=0.), + 'relative_position_bias_table': dict(decay_mult=0.), + 'norm': dict(decay_mult=0.) + })) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=256) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=2) + +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='SwinTransformer', + embed_dims=192, + depths=[2, 2, 18, 2], + num_heads=[6, 12, 24, 48], + window_size=7, + mlp_ratio=4, + qkv_bias=True, + qk_scale=None, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0.5, + patch_norm=True, + out_indices=(3, ), + with_cp=False, + convert_weights=True, + init_cfg=dict( + type='Pretrained', + checkpoint='https://github.com/SwinTransformer/storage/releases/' + 'download/v1.0.0/swin_base_patch4_window12_384_22k.pth'), + ), + head=dict( + type='HeatmapHead', + in_channels=1536, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] + +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_swin-t-p4-w7_8xb32-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_swin-t-p4-w7_8xb32-210e_humanart-256x192.py new file mode 100644 index 0000000000..16d34b1e8a --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_swin-t-p4-w7_8xb32-210e_humanart-256x192.py @@ -0,0 +1,139 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=256) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='SwinTransformer', + embed_dims=96, + depths=[2, 2, 6, 2], + num_heads=[3, 6, 12, 24], + window_size=7, + mlp_ratio=4, + qkv_bias=True, + qk_scale=None, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0.2, + patch_norm=True, + out_indices=(3, ), + with_cp=False, + convert_weights=True, + init_cfg=dict( + type='Pretrained', + checkpoint='https://github.com/SwinTransformer/storage/releases/' + 'download/v1.0.0/swin_tiny_patch4_window7_224.pth'), + ), + head=dict( + type='HeatmapHead', + in_channels=768, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] + +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_vgg16-bn_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_vgg16-bn_8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..0a8722670b --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_vgg16-bn_8xb64-210e_humanart-256x192.py @@ -0,0 +1,122 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='VGG', + depth=16, + norm_cfg=dict(type='BN'), + init_cfg=dict(type='Pretrained', checkpoint='mmcls://vgg16_bn'), + ), + head=dict( + type='HeatmapHead', + in_channels=512, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_vipnas-mbv3_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_vipnas-mbv3_8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..a94e01c97e --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_vipnas-mbv3_8xb64-210e_humanart-256x192.py @@ -0,0 +1,122 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict(type='ViPNAS_MobileNetV3'), + head=dict( + type='ViPNASHead', + in_channels=160, + out_channels=17, + deconv_out_channels=(160, 160, 160), + deconv_num_groups=(160, 160, 160), + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict( + type='RandomBBoxTransform', + rotate_factor=60, + scale_factor=(0.75, 1.25)), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_vipnas-res50_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_vipnas-res50_8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..b06189da21 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_vipnas-res50_8xb64-210e_humanart-256x192.py @@ -0,0 +1,120 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=210, + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# codec settings +codec = dict( + type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict(type='ViPNAS_ResNet', depth=50), + head=dict( + type='ViPNASHead', + in_channels=608, + out_channels=17, + loss=dict(type='KeypointMSELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + flip_mode='heatmap', + shift_heatmap=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict( + type='RandomBBoxTransform', + rotate_factor=60, + scale_factor=(0.75, 1.25)), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_mobilenetv2_rle-pretrained-8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_mobilenetv2_rle-pretrained-8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..8396ea1b26 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_mobilenetv2_rle-pretrained-8xb64-210e_humanart-256x192.py @@ -0,0 +1,126 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=1e-3, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=train_cfg['max_epochs'], + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# codec settings +codec = dict(type='RegressionLabel', input_size=(192, 256)) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='MobileNetV2', + widen_factor=1., + out_indices=(7, ), + init_cfg=dict( + type='Pretrained', + prefix='backbone.', + checkpoint='https://download.openmmlab.com/mmpose/top_down/' + 'mobilenetv2/mobilenetv2_coco_256x192-d1e58e7b_20200727.pth')), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='RLEHead', + in_channels=1280, + num_joints=17, + loss=dict(type='RLELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + shift_coords=True, + ), +) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json', + score_mode='bbox_rle') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_res101_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_res101_8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..5901d7dc9b --- /dev/null +++ b/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_res101_8xb64-210e_humanart-256x192.py @@ -0,0 +1,120 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=train_cfg['max_epochs'], + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# codec settings +codec = dict(type='RegressionLabel', input_size=(192, 256)) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='ResNet', + depth=101, + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet101'), + ), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='RegressionHead', + in_channels=2048, + num_joints=17, + loss=dict(type='SmoothL1Loss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + shift_coords=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=f'{data_root}annotations/person_keypoints_val2017.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_res101_rle-8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_res101_rle-8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..189792780e --- /dev/null +++ b/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_res101_rle-8xb64-210e_humanart-256x192.py @@ -0,0 +1,121 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=train_cfg['max_epochs'], + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# codec settings +codec = dict(type='RegressionLabel', input_size=(192, 256)) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='ResNet', + depth=101, + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet101'), + ), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='RLEHead', + in_channels=2048, + num_joints=17, + loss=dict(type='RLELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + shift_coords=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json', + score_mode='bbox_rle') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_res152_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_res152_8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..6589b8e56e --- /dev/null +++ b/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_res152_8xb64-210e_humanart-256x192.py @@ -0,0 +1,120 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=train_cfg['max_epochs'], + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# codec settings +codec = dict(type='RegressionLabel', input_size=(192, 256)) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='ResNet', + depth=152, + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet152'), + ), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='RegressionHead', + in_channels=2048, + num_joints=17, + loss=dict(type='SmoothL1Loss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + shift_coords=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=f'{data_root}annotations/person_keypoints_val2017.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_res152_rle-8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_res152_rle-8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..77c3a0f1df --- /dev/null +++ b/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_res152_rle-8xb64-210e_humanart-256x192.py @@ -0,0 +1,121 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=train_cfg['max_epochs'], + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# codec settings +codec = dict(type='RegressionLabel', input_size=(192, 256)) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='ResNet', + depth=152, + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet152'), + ), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='RLEHead', + in_channels=2048, + num_joints=17, + loss=dict(type='RLELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + shift_coords=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json', + score_mode='bbox_rle') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_res152_rle-8xb64-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_res152_rle-8xb64-210e_humanart-384x288.py new file mode 100644 index 0000000000..eff463d3e1 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_res152_rle-8xb64-210e_humanart-384x288.py @@ -0,0 +1,121 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=train_cfg['max_epochs'], + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# codec settings +codec = dict(type='RegressionLabel', input_size=(288, 384)) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='ResNet', + depth=152, + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet152'), + ), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='RLEHead', + in_channels=2048, + num_joints=17, + loss=dict(type='RLELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + shift_coords=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json', + score_mode='bbox_rle') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_res50_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_res50_8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..006c0fee44 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_res50_8xb64-210e_humanart-256x192.py @@ -0,0 +1,120 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=5e-4, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=train_cfg['max_epochs'], + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# codec settings +codec = dict(type='RegressionLabel', input_size=(192, 256)) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='ResNet', + depth=50, + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'), + ), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='RegressionHead', + in_channels=2048, + num_joints=17, + loss=dict(type='SmoothL1Loss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + shift_coords=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=f'{data_root}annotations/person_keypoints_val2017.json') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_res50_rle-8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_res50_rle-8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..cf8b681560 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_res50_rle-8xb64-210e_humanart-256x192.py @@ -0,0 +1,121 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=1e-3, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=train_cfg['max_epochs'], + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# codec settings +codec = dict(type='RegressionLabel', input_size=(192, 256)) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='ResNet', + depth=50, + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'), + ), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='RLEHead', + in_channels=2048, + num_joints=17, + loss=dict(type='RLELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + shift_coords=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json', + score_mode='bbox_rle') +test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_res50_rle-pretrained-8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_res50_rle-pretrained-8xb64-210e_humanart-256x192.py new file mode 100644 index 0000000000..c208491ae8 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_res50_rle-pretrained-8xb64-210e_humanart-256x192.py @@ -0,0 +1,125 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +train_cfg = dict(max_epochs=210, val_interval=10) + +# optimizer +optim_wrapper = dict(optimizer=dict( + type='Adam', + lr=1e-3, +)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', begin=0, end=500, start_factor=0.001, + by_epoch=False), # warm-up + dict( + type='MultiStepLR', + begin=0, + end=train_cfg['max_epochs'], + milestones=[170, 200], + gamma=0.1, + by_epoch=True) +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# codec settings +codec = dict(type='RegressionLabel', input_size=(192, 256)) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + type='ResNet', + depth=50, + init_cfg=dict( + type='Pretrained', + prefix='backbone.', + checkpoint='https://download.openmmlab.com/mmpose/' + 'pretrain_models/td-hm_res50_8xb64-210e_coco-256x192.pth'), + ), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='RLEHead', + in_channels=2048, + num_joints=17, + loss=dict(type='RLELoss', use_target_weight=True), + decoder=codec), + test_cfg=dict( + flip_test=True, + shift_coords=True, + )) + +# base dataset settings +dataset_type = 'HumanArtDataset' +data_mode = 'topdown' +data_root = 'data/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict(type='RandomBBoxTransform'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +test_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=64, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/training_humanart_coco.json', + data_prefix=dict(img=''), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + num_workers=2, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file='HumanArt/annotations/validation_humanart.json', + bbox_file=f'{data_root}HumanArt/person_detection_results/' + 'HumanArt_validation_detections_AP_H_56_person.json', + data_prefix=dict(img=''), + test_mode=True, + pipeline=test_pipeline, + )) +test_dataloader = val_dataloader + +# hooks +default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) + +# evaluators +val_evaluator = dict( + type='CocoMetric', + ann_file=data_root + 'HumanArt/annotations/validation_humanart.json', + score_mode='bbox_rle') +test_evaluator = val_evaluator diff --git a/docs/en/dataset_zoo/2d_body_keypoint.md b/docs/en/dataset_zoo/2d_body_keypoint.md index c5bf70a3f8..4448ebe8f4 100644 --- a/docs/en/dataset_zoo/2d_body_keypoint.md +++ b/docs/en/dataset_zoo/2d_body_keypoint.md @@ -13,6 +13,7 @@ MMPose supported datasets: - [CrowdPose](#crowdpose) \[ [Homepage](https://github.com/Jeff-sjtu/CrowdPose) \] - [OCHuman](#ochuman) \[ [Homepage](https://github.com/liruilong940607/OCHumanApi) \] - [MHP](#mhp) \[ [Homepage](https://lv-mhp.github.io/dataset) \] + - [Human-Art](#humanart) \[ [Homepage](https://idea-research.github.io/HumanArt/) \] - Videos - [PoseTrack18](#posetrack18) \[ [Homepage](https://posetrack.net/users/download.php) \] - [sub-JHMDB](#sub-jhmdb-dataset) \[ [Homepage](http://jhmdb.is.tue.mpg.de/dataset) \] @@ -386,6 +387,57 @@ mmpose │ │ │-- ...~~~~ ``` +## Human-Art dataset + + + +
+Human-Art (CVPR'2023) + +```bibtex +@inproceedings{ju2023humanart, + title={Human-Art: A Versatile Human-Centric Dataset Bridging Natural and Artificial Scenes}, + author={Ju, Xuan and Zeng, Ailing and Jianan, Wang and Qiang, Xu and Lei, Zhang}, + booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), + year={2023}} +``` + +
+ +
+ +
+ +For [Human-Art](https://idea-research.github.io/HumanArt/) data, please download the images and annotation files from [its website](https://idea-research.github.io/HumanArt/). You need to fill in the [data form](https://docs.google.com/forms/d/e/1FAIpQLScroT_jvw6B9U2Qca1_cl5Kmmu1ceKtlh6DJNmWLte8xNEhEw/viewform) to get access to the data. +Move them under $MMPOSE/data, and make them look like this: + +```text +mmpose +├── mmpose +├── docs +├── tests +├── tools +├── configs +|── data + │── HumanArt + │-- images + │ │-- 2D_virtual_human + │ │ |-- cartoon + │ │ | |-- 000000000000.jpg + │ │ | |-- ... + │ │ |-- digital_art + │ │ |-- ... + │ |-- 3D_virtual_human + │ |-- real_human + |-- annotations + │ │-- validation_humanart.json + │ │-- training_humanart_coco.json + |-- person_detection_results + │ │-- HumanArt_validation_detections_AP_H_56_person.json +``` + +You can choose whether to download other annotation files in Human-Art. If you want to use additional annotation files (e.g. validation set of cartoon), you need to edit the corresponding code in config file. + ## PoseTrack18 diff --git a/docs/zh_cn/dataset_zoo/2d_body_keypoint.md b/docs/zh_cn/dataset_zoo/2d_body_keypoint.md index c5bf70a3f8..4448ebe8f4 100644 --- a/docs/zh_cn/dataset_zoo/2d_body_keypoint.md +++ b/docs/zh_cn/dataset_zoo/2d_body_keypoint.md @@ -13,6 +13,7 @@ MMPose supported datasets: - [CrowdPose](#crowdpose) \[ [Homepage](https://github.com/Jeff-sjtu/CrowdPose) \] - [OCHuman](#ochuman) \[ [Homepage](https://github.com/liruilong940607/OCHumanApi) \] - [MHP](#mhp) \[ [Homepage](https://lv-mhp.github.io/dataset) \] + - [Human-Art](#humanart) \[ [Homepage](https://idea-research.github.io/HumanArt/) \] - Videos - [PoseTrack18](#posetrack18) \[ [Homepage](https://posetrack.net/users/download.php) \] - [sub-JHMDB](#sub-jhmdb-dataset) \[ [Homepage](http://jhmdb.is.tue.mpg.de/dataset) \] @@ -386,6 +387,57 @@ mmpose │ │ │-- ...~~~~ ``` +## Human-Art dataset + + + +
+Human-Art (CVPR'2023) + +```bibtex +@inproceedings{ju2023humanart, + title={Human-Art: A Versatile Human-Centric Dataset Bridging Natural and Artificial Scenes}, + author={Ju, Xuan and Zeng, Ailing and Jianan, Wang and Qiang, Xu and Lei, Zhang}, + booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), + year={2023}} +``` + +
+ +
+ +
+ +For [Human-Art](https://idea-research.github.io/HumanArt/) data, please download the images and annotation files from [its website](https://idea-research.github.io/HumanArt/). You need to fill in the [data form](https://docs.google.com/forms/d/e/1FAIpQLScroT_jvw6B9U2Qca1_cl5Kmmu1ceKtlh6DJNmWLte8xNEhEw/viewform) to get access to the data. +Move them under $MMPOSE/data, and make them look like this: + +```text +mmpose +├── mmpose +├── docs +├── tests +├── tools +├── configs +|── data + │── HumanArt + │-- images + │ │-- 2D_virtual_human + │ │ |-- cartoon + │ │ | |-- 000000000000.jpg + │ │ | |-- ... + │ │ |-- digital_art + │ │ |-- ... + │ |-- 3D_virtual_human + │ |-- real_human + |-- annotations + │ │-- validation_humanart.json + │ │-- training_humanart_coco.json + |-- person_detection_results + │ │-- HumanArt_validation_detections_AP_H_56_person.json +``` + +You can choose whether to download other annotation files in Human-Art. If you want to use additional annotation files (e.g. validation set of cartoon), you need to edit the corresponding code in config file. + ## PoseTrack18 diff --git a/mmpose/datasets/datasets/body/__init__.py b/mmpose/datasets/datasets/body/__init__.py index a4aeef8519..1405b0d675 100644 --- a/mmpose/datasets/datasets/body/__init__.py +++ b/mmpose/datasets/datasets/body/__init__.py @@ -2,6 +2,7 @@ from .aic_dataset import AicDataset from .coco_dataset import CocoDataset from .crowdpose_dataset import CrowdPoseDataset +from .humanart_dataset import HumanArtDataset from .jhmdb_dataset import JhmdbDataset from .mhp_dataset import MhpDataset from .mpii_dataset import MpiiDataset @@ -13,5 +14,5 @@ __all__ = [ 'CocoDataset', 'MpiiDataset', 'MpiiTrbDataset', 'AicDataset', 'CrowdPoseDataset', 'OCHumanDataset', 'MhpDataset', 'PoseTrack18Dataset', - 'JhmdbDataset', 'PoseTrack18VideoDataset' + 'JhmdbDataset', 'PoseTrack18VideoDataset', 'HumanArtDataset' ] diff --git a/mmpose/datasets/datasets/body/humanart_dataset.py b/mmpose/datasets/datasets/body/humanart_dataset.py new file mode 100644 index 0000000000..719f35fc9e --- /dev/null +++ b/mmpose/datasets/datasets/body/humanart_dataset.py @@ -0,0 +1,73 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from mmpose.registry import DATASETS +from ..base import BaseCocoStyleDataset + + +@DATASETS.register_module() +class HumanArtDataset(BaseCocoStyleDataset): + """Human-Art dataset for pose estimation. + + "Human-Art: A Versatile Human-Centric Dataset + Bridging Natural and Artificial Scenes", CVPR'2023. + More details can be found in the `paper + `__ . + + Human-Art keypoints:: + + 0: 'nose', + 1: 'left_eye', + 2: 'right_eye', + 3: 'left_ear', + 4: 'right_ear', + 5: 'left_shoulder', + 6: 'right_shoulder', + 7: 'left_elbow', + 8: 'right_elbow', + 9: 'left_wrist', + 10: 'right_wrist', + 11: 'left_hip', + 12: 'right_hip', + 13: 'left_knee', + 14: 'right_knee', + 15: 'left_ankle', + 16: 'right_ankle' + + Args: + ann_file (str): Annotation file path. Default: ''. + bbox_file (str, optional): Detection result file path. If + ``bbox_file`` is set, detected bboxes loaded from this file will + be used instead of ground-truth bboxes. This setting is only for + evaluation, i.e., ignored when ``test_mode`` is ``False``. + Default: ``None``. + data_mode (str): Specifies the mode of data samples: ``'topdown'`` or + ``'bottomup'``. In ``'topdown'`` mode, each data sample contains + one instance; while in ``'bottomup'`` mode, each data sample + contains all instances in a image. Default: ``'topdown'`` + metainfo (dict, optional): Meta information for dataset, such as class + information. Default: ``None``. + data_root (str, optional): The root directory for ``data_prefix`` and + ``ann_file``. Default: ``None``. + data_prefix (dict, optional): Prefix for training data. Default: + ``dict(img=None, ann=None)``. + filter_cfg (dict, optional): Config for filter data. Default: `None`. + indices (int or Sequence[int], optional): Support using first few + data in annotation file to facilitate training/testing on a smaller + dataset. Default: ``None`` which means using all ``data_infos``. + serialize_data (bool, optional): Whether to hold memory using + serialized objects, when enabled, data loader workers can use + shared RAM from master process instead of making a copy. + Default: ``True``. + pipeline (list, optional): Processing pipeline. Default: []. + test_mode (bool, optional): ``test_mode=True`` means in test phase. + Default: ``False``. + lazy_init (bool, optional): Whether to load annotation during + instantiation. In some cases, such as visualization, only the meta + information of the dataset is needed, which is not necessary to + load annotation file. ``Basedataset`` can skip load annotations to + save time by set ``lazy_init=False``. Default: ``False``. + max_refetch (int, optional): If ``Basedataset.prepare_data`` get a + None img. The maximum extra number of cycles to get a valid + image. Default: 1000. + """ + + METAINFO: dict = dict(from_file='configs/_base_/datasets/humanart.py') From 165428e99790823f2148698f57c4a11b1059d7fb Mon Sep 17 00:00:00 2001 From: juxuan27 Date: Sun, 23 Apr 2023 23:46:13 +0800 Subject: [PATCH 4/7] add dataset: Human-Art --- tests/data/humanart/000000000785.jpg | Bin 0 -> 133674 bytes tests/data/humanart/000000040083.jpg | Bin 0 -> 104152 bytes tests/data/humanart/000000196141.jpg | Bin 0 -> 135345 bytes tests/data/humanart/000000197388.jpg | Bin 0 -> 167407 bytes tests/data/humanart/test_humanart.json | 2423 +++++++++++++++++ .../humanart/test_humanart_det_AP_H_56.json | 1300 +++++++++ .../test_humanart_dataset.py | 160 ++ 7 files changed, 3883 insertions(+) create mode 100644 tests/data/humanart/000000000785.jpg create mode 100644 tests/data/humanart/000000040083.jpg create mode 100644 tests/data/humanart/000000196141.jpg create mode 100644 tests/data/humanart/000000197388.jpg create mode 100644 tests/data/humanart/test_humanart.json create 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--- /dev/null +++ b/tests/test_datasets/test_datasets/test_body_datasets/test_humanart_dataset.py @@ -0,0 +1,160 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from unittest import TestCase + +import numpy as np + +from mmpose.datasets.datasets.body import HumanArtDataset + + +class TestHumanartDataset(TestCase): + + def build_humanart_dataset(self, **kwargs): + + cfg = dict( + ann_file='test_humanart.json', + bbox_file=None, + data_mode='topdown', + data_root='tests/data/humanart', + pipeline=[], + test_mode=False) + + cfg.update(kwargs) + return HumanArtDataset(**cfg) + + def check_data_info_keys(self, + data_info: dict, + data_mode: str = 'topdown'): + if data_mode == 'topdown': + expected_keys = dict( + img_id=int, + img_path=str, + bbox=np.ndarray, + bbox_score=np.ndarray, + keypoints=np.ndarray, + keypoints_visible=np.ndarray, + id=int) + elif data_mode == 'bottomup': + expected_keys = dict( + img_id=int, + img_path=str, + bbox=np.ndarray, + bbox_score=np.ndarray, + keypoints=np.ndarray, + keypoints_visible=np.ndarray, + invalid_segs=list, + id=list) + else: + raise ValueError(f'Invalid data_mode {data_mode}') + + for key, type_ in expected_keys.items(): + self.assertIn(key, data_info) + self.assertIsInstance(data_info[key], type_, key) + + def check_metainfo_keys(self, metainfo: dict): + expected_keys = dict( + dataset_name=str, + num_keypoints=int, + keypoint_id2name=dict, + keypoint_name2id=dict, + upper_body_ids=list, + lower_body_ids=list, + flip_indices=list, + flip_pairs=list, + keypoint_colors=np.ndarray, + num_skeleton_links=int, + skeleton_links=list, + skeleton_link_colors=np.ndarray, + dataset_keypoint_weights=np.ndarray) + + for key, type_ in expected_keys.items(): + self.assertIn(key, metainfo) + self.assertIsInstance(metainfo[key], type_, key) + + def test_metainfo(self): + dataset = self.build_humanart_dataset() + self.check_metainfo_keys(dataset.metainfo) + # test dataset_name + self.assertEqual(dataset.metainfo['dataset_name'], 'humanart') + + # test number of keypoints + num_keypoints = 17 + self.assertEqual(dataset.metainfo['num_keypoints'], num_keypoints) + self.assertEqual( + len(dataset.metainfo['keypoint_colors']), num_keypoints) + self.assertEqual( + len(dataset.metainfo['dataset_keypoint_weights']), num_keypoints) + # note that len(sigmas) may be zero if dataset.metainfo['sigmas'] = [] + self.assertEqual(len(dataset.metainfo['sigmas']), num_keypoints) + + # test some extra metainfo + self.assertEqual( + len(dataset.metainfo['skeleton_links']), + len(dataset.metainfo['skeleton_link_colors'])) + + def test_topdown(self): + # test topdown training + dataset = self.build_humanart_dataset(data_mode='topdown') + self.assertEqual(len(dataset), 12) + self.check_data_info_keys(dataset[0], data_mode='topdown') + + # test topdown testing + dataset = self.build_humanart_dataset( + data_mode='topdown', test_mode=True) + self.assertEqual(len(dataset), 12) + self.check_data_info_keys(dataset[0], data_mode='topdown') + + # test topdown testing with bbox file + dataset = self.build_humanart_dataset( + data_mode='topdown', + test_mode=True, + bbox_file='tests/data/humanart/test_humanart_det_AP_H_56.json') + self.assertEqual(len(dataset), 118) + self.check_data_info_keys(dataset[0], data_mode='topdown') + + # test topdown testing with filter config + dataset = self.build_humanart_dataset( + data_mode='topdown', + test_mode=True, + bbox_file='tests/data/humanart/test_humanart_det_AP_H_56.json', + filter_cfg=dict(bbox_score_thr=0.3)) + self.assertEqual(len(dataset), 33) + + def test_bottomup(self): + # test bottomup training + dataset = self.build_humanart_dataset(data_mode='bottomup') + self.assertEqual(len(dataset), 4) + self.check_data_info_keys(dataset[0], data_mode='bottomup') + + # test bottomup testing + dataset = self.build_humanart_dataset( + data_mode='bottomup', test_mode=True) + self.assertEqual(len(dataset), 4) + self.check_data_info_keys(dataset[0], data_mode='bottomup') + + def test_exceptions_and_warnings(self): + + with self.assertRaisesRegex(ValueError, 'got invalid data_mode'): + _ = self.build_humanart_dataset(data_mode='invalid') + + with self.assertRaisesRegex( + ValueError, + '"bbox_file" is only supported when `test_mode==True`'): + _ = self.build_humanart_dataset( + data_mode='topdown', + test_mode=False, + bbox_file='tests/data/humanart/test_humanart_det_AP_H_56.json') + + with self.assertRaisesRegex( + ValueError, '"bbox_file" is only supported in topdown mode'): + _ = self.build_humanart_dataset( + data_mode='bottomup', + test_mode=True, + bbox_file='tests/data/humanart/test_humanart_det_AP_H_56.json') + + with self.assertRaisesRegex( + ValueError, + '"bbox_score_thr" is only supported in topdown mode'): + _ = self.build_humanart_dataset( + data_mode='bottomup', + test_mode=True, + filter_cfg=dict(bbox_score_thr=0.3)) From dcd1eb8752c600e205d44e7a8b8a520dcedc22b8 Mon Sep 17 00:00:00 2001 From: juxuan27 Date: Mon, 12 Jun 2023 00:22:43 +0800 Subject: [PATCH 5/7] add vitpose-s vitpose-b rtmpose-l rtmpose-b rtmpose-s --- ...e_hrnet-w32_8xb24-300e_humanart-512x512.py | 159 ---------- ...d_hrnet-w32_8xb20-140e_humanart-512x512.py | 164 ---------- ...d_hrnet-w48_8xb20-140e_humanart-512x512.py | 164 ---------- ...r_hrnet-w32_8xb10-140e_humanart-512x512.py | 186 ------------ ...r_hrnet-w48_8xb10-140e_humanart-640x640.py | 187 ------------ .../ipr_res50_8xb64-210e_humanart-256x256.py | 134 --------- ...es50_debias-8xb64-210e_humanart-256x256.py | 136 --------- ..._res50_dsnt-8xb64-210e_humanart-256x256.py | 134 --------- configs/body_2d_keypoint/rtmpose/README.md | 18 ++ ...pose-l_8xb256-420e_aic-humanart-256x192.py | 272 ----------------- ...pose-l_8xb256-420e_aic-humanart-384x288.py | 272 ----------------- .../rtmpose-l_8xb256-420e_humanart-256x192.py | 4 +- ...pose-m_8xb256-420e_aic-humanart-256x192.py | 272 ----------------- ...pose-m_8xb256-420e_aic-humanart-384x288.py | 272 ----------------- .../rtmpose-m_8xb256-420e_humanart-256x192.py | 4 +- ...pose-s_8xb256-420e_aic-humanart-256x192.py | 272 ----------------- .../rtmpose-s_8xb256-420e_humanart-256x192.py | 4 +- ...pose-t_8xb256-420e_aic-humanart-256x192.py | 273 ----------------- .../rtmpose-t_8xb256-420e_humanart-256x192.py | 233 -------------- .../rtmpose/humanart/rtmpose_humanart.md | 110 +++++++ .../rtmpose/humanart/rtmpose_humanart.yml | 106 +++++++ ...2_wo-deconv-8xb64-210e_humanart-256x192.py | 124 -------- ...simcc_res50_8xb32-140e_humanart-384x288.py | 120 -------- ...simcc_res50_8xb64-210e_humanart-256x192.py | 114 ------- ...vipnas-mbv3_8xb64-210e_humanart-256x192.py | 119 -------- .../topdown_heatmap/README.md | 16 + ...-l_udp_8xb256-210e_aic-humanart-256x192.py | 284 ------------------ ...next-l_udp_8xb256-210e_humanart-256x192.py | 214 ------------- ...-m_udp_8xb256-210e_aic-humanart-256x192.py | 284 ------------------ ...next-m_udp_8xb256-210e_humanart-256x192.py | 214 ------------- ...-s_udp_8xb256-210e_aic-humanart-256x192.py | 284 ------------------ ...next-s_udp_8xb256-210e_humanart-256x192.py | 214 ------------- ...ny_udp_8xb256-210e_aic-humanart-256x192.py | 284 ------------------ ...t-tiny_udp_8xb256-210e_humanart-256x192.py | 214 ------------- .../humanart/hrnet_humanart.md | 44 --- ...hm_2xmspn50_8xb32-210e_humanart-256x192.py | 152 ---------- ...-hm_2xrsn50_8xb32-210e_humanart-256x192.py | 154 ---------- ...hm_3xmspn50_8xb32-210e_humanart-256x192.py | 152 ---------- ...-hm_3xrsn50_8xb32-210e_humanart-256x192.py | 154 ---------- ...hm_4xmspn50_8xb32-210e_humanart-256x192.py | 152 ---------- ...base-simple_8xb64-210e_humanart-256x192.py | 153 ---------- ...huge-simple_8xb64-210e_humanart-256x192.py | 153 ---------- ...iTPose-huge_8xb64-210e_humanart-256x192.py | 150 --------- ...arge-simple_8xb64-210e_humanart-256x192.py | 153 ---------- ...TPose-large_8xb64-210e_humanart-256x192.py | 150 --------- ...mall-simple_8xb64-210e_humanart-256x192.py | 158 ---------- ...-hm_alexnet_8xb64-210e_humanart-256x192.py | 117 -------- .../td-hm_cpm_8xb32-210e_humanart-384x288.py | 125 -------- .../td-hm_cpm_8xb64-210e_humanart-256x192.py | 125 -------- ...hourglass52_8xb32-210e_humanart-256x256.py | 122 -------- ...hourglass52_8xb32-210e_humanart-384x384.py | 122 -------- ...former-base_8xb32-210e_humanart-256x192.py | 174 ----------- ...former-base_8xb32-210e_humanart-384x288.py | 174 ----------- ...ormer-small_8xb32-210e_humanart-256x192.py | 174 ----------- ...ormer-small_8xb32-210e_humanart-384x288.py | 174 ----------- ...m_hrnet-w32_8xb64-210e_humanart-256x192.py | 150 --------- ...m_hrnet-w32_8xb64-210e_humanart-384x288.py | 150 --------- ...8xb64-210e_humanart-aic-256x192-combine.py | 221 -------------- ...2_8xb64-210e_humanart-aic-256x192-merge.py | 187 ------------ ...arsedropout-8xb64-210e_humanart-256x192.py | 165 ---------- ...et-w32_dark-8xb64-210e_humanart-256x192.py | 154 ---------- ...et-w32_dark-8xb64-210e_humanart-384x288.py | 154 ---------- ...et-w32_fp16-8xb64-210e_humanart-256x192.py | 7 - ...32_gridmask-8xb64-210e_humanart-256x192.py | 162 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-)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=300, - milestones=[200, 260], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=192) - -# hooks -default_hooks = dict( - checkpoint=dict(save_best='coco/AP', rule='greater', interval=50)) - -# codec settings -codec = dict( - type='AssociativeEmbedding', - input_size=(512, 512), - heatmap_size=(128, 128), - sigma=2, - decode_keypoint_order=[ - 0, 1, 2, 3, 4, 5, 6, 11, 12, 7, 8, 9, 10, 13, 14, 15, 16 - ], - decode_max_instances=30) - -# model settings -model = dict( - type='BottomupPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='HRNet', - in_channels=3, - extra=dict( - stage1=dict( - num_modules=1, - num_branches=1, - block='BOTTLENECK', - num_blocks=(4, ), - num_channels=(64, )), - stage2=dict( - num_modules=1, - num_branches=2, - block='BASIC', - num_blocks=(4, 4), - num_channels=(32, 64)), - stage3=dict( - num_modules=4, - num_branches=3, - block='BASIC', - num_blocks=(4, 4, 4), - num_channels=(32, 64, 128)), - stage4=dict( - num_modules=3, - num_branches=4, - block='BASIC', - num_blocks=(4, 4, 4, 4), - num_channels=(32, 64, 128, 256))), - init_cfg=dict( - type='Pretrained', - checkpoint='https://download.openmmlab.com/mmpose/' - 'pretrain_models/hrnet_w32-36af842e.pth'), - ), - head=dict( - type='AssociativeEmbeddingHead', - in_channels=32, - num_keypoints=17, - tag_dim=1, - tag_per_keypoint=True, - deconv_out_channels=None, - keypoint_loss=dict(type='KeypointMSELoss', use_target_weight=True), - tag_loss=dict(type='AssociativeEmbeddingLoss', loss_weight=0.001), - # The heatmap will be resized to the input size before decoding - # if ``restore_heatmap_size==True`` - decoder=dict(codec, heatmap_size=codec['input_size'])), - test_cfg=dict( - multiscale_test=False, - flip_test=True, - shift_heatmap=True, - restore_heatmap_size=True, - align_corners=False)) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'bottomup' -data_root = 'data/' - -# pipelines -train_pipeline = [] -val_pipeline = [ - dict(type='LoadImage'), - dict( - type='BottomupResize', - input_size=codec['input_size'], - size_factor=32, - resize_mode='expand'), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=24, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=1, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json', - nms_mode='none', - score_mode='keypoint', -) -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/cid/humanart/cid_hrnet-w32_8xb20-140e_humanart-512x512.py b/configs/body_2d_keypoint/cid/humanart/cid_hrnet-w32_8xb20-140e_humanart-512x512.py deleted file mode 100644 index 7150772111..0000000000 --- a/configs/body_2d_keypoint/cid/humanart/cid_hrnet-w32_8xb20-140e_humanart-512x512.py +++ /dev/null @@ -1,164 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=140, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=1e-3, -)) - -# learning policy -param_scheduler = [ - dict( - type='MultiStepLR', - begin=0, - end=140, - milestones=[90, 120], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=160) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='DecoupledHeatmap', input_size=(512, 512), heatmap_size=(128, 128)) - -# model settings -model = dict( - type='BottomupPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='HRNet', - in_channels=3, - extra=dict( - stage1=dict( - num_modules=1, - num_branches=1, - block='BOTTLENECK', - num_blocks=(4, ), - num_channels=(64, )), - stage2=dict( - num_modules=1, - num_branches=2, - block='BASIC', - num_blocks=(4, 4), - num_channels=(32, 64)), - stage3=dict( - num_modules=4, - num_branches=3, - block='BASIC', - num_blocks=(4, 4, 4), - num_channels=(32, 64, 128)), - stage4=dict( - num_modules=3, - num_branches=4, - block='BASIC', - num_blocks=(4, 4, 4, 4), - num_channels=(32, 64, 128, 256), - multiscale_output=True)), - init_cfg=dict( - type='Pretrained', - checkpoint='https://download.openmmlab.com/mmpose/' - 'pretrain_models/hrnet_w32-36af842e.pth'), - ), - neck=dict( - type='FeatureMapProcessor', - concat=True, - ), - head=dict( - type='CIDHead', - in_channels=480, - num_keypoints=17, - gfd_channels=32, - coupled_heatmap_loss=dict(type='FocalHeatmapLoss', loss_weight=1.0), - decoupled_heatmap_loss=dict(type='FocalHeatmapLoss', loss_weight=4.0), - contrastive_loss=dict( - type='InfoNCELoss', temperature=0.05, loss_weight=1.0), - decoder=codec, - ), - train_cfg=dict(max_train_instances=200), - test_cfg=dict( - multiscale_test=False, - flip_test=True, - shift_heatmap=False, - align_corners=False)) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'bottomup' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='BottomupRandomAffine', input_size=codec['input_size']), - dict(type='RandomFlip', direction='horizontal'), - dict(type='GenerateTarget', encoder=codec), - dict(type='BottomupGetHeatmapMask'), - dict(type='PackPoseInputs'), -] -val_pipeline = [ - dict(type='LoadImage'), - dict( - type='BottomupResize', - input_size=codec['input_size'], - size_factor=64, - resize_mode='expand'), - dict( - type='PackPoseInputs', - meta_keys=('id', 'img_id', 'img_path', 'crowd_index', 'ori_shape', - 'img_shape', 'input_size', 'input_center', 'input_scale', - 'flip', 'flip_direction', 'flip_indices', 'raw_ann_info', - 'skeleton_links')) -] - -# data loaders -train_dataloader = dict( - batch_size=20, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=1, - num_workers=1, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json', - nms_thr=0.8, - score_mode='keypoint', -) -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/cid/humanart/cid_hrnet-w48_8xb20-140e_humanart-512x512.py b/configs/body_2d_keypoint/cid/humanart/cid_hrnet-w48_8xb20-140e_humanart-512x512.py deleted file mode 100644 index b8e61a3189..0000000000 --- a/configs/body_2d_keypoint/cid/humanart/cid_hrnet-w48_8xb20-140e_humanart-512x512.py +++ /dev/null @@ -1,164 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=140, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=1e-3, -)) - -# learning policy -param_scheduler = [ - dict( - type='MultiStepLR', - begin=0, - end=140, - milestones=[90, 120], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=160) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='DecoupledHeatmap', input_size=(512, 512), heatmap_size=(128, 128)) - -# model settings -model = dict( - type='BottomupPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='HRNet', - in_channels=3, - extra=dict( - stage1=dict( - num_modules=1, - num_branches=1, - block='BOTTLENECK', - num_blocks=(4, ), - num_channels=(64, )), - stage2=dict( - num_modules=1, - num_branches=2, - block='BASIC', - num_blocks=(4, 4), - num_channels=(48, 96)), - stage3=dict( - num_modules=4, - num_branches=3, - block='BASIC', - num_blocks=(4, 4, 4), - num_channels=(48, 96, 192)), - stage4=dict( - num_modules=3, - num_branches=4, - block='BASIC', - num_blocks=(4, 4, 4, 4), - num_channels=(48, 96, 192, 384), - multiscale_output=True)), - init_cfg=dict( - type='Pretrained', - checkpoint='https://download.openmmlab.com/mmpose/' - 'pretrain_models/hrnet_w48-8ef0771d.pth'), - ), - neck=dict( - type='FeatureMapProcessor', - concat=True, - ), - head=dict( - type='CIDHead', - in_channels=720, - num_keypoints=17, - gfd_channels=48, - coupled_heatmap_loss=dict(type='FocalHeatmapLoss', loss_weight=1.0), - decoupled_heatmap_loss=dict(type='FocalHeatmapLoss', loss_weight=4.0), - contrastive_loss=dict( - type='InfoNCELoss', temperature=0.05, loss_weight=1.0), - decoder=codec, - ), - train_cfg=dict(max_train_instances=200), - test_cfg=dict( - multiscale_test=False, - flip_test=True, - shift_heatmap=False, - align_corners=False)) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'bottomup' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='BottomupRandomAffine', input_size=codec['input_size']), - dict(type='RandomFlip', direction='horizontal'), - dict(type='GenerateTarget', encoder=codec), - dict(type='BottomupGetHeatmapMask'), - dict(type='PackPoseInputs'), -] -val_pipeline = [ - dict(type='LoadImage'), - dict( - type='BottomupResize', - input_size=codec['input_size'], - size_factor=64, - resize_mode='expand'), - dict( - type='PackPoseInputs', - meta_keys=('id', 'img_id', 'img_path', 'crowd_index', 'ori_shape', - 'img_shape', 'input_size', 'input_center', 'input_scale', - 'flip', 'flip_direction', 'flip_indices', 'raw_ann_info', - 'skeleton_links')) -] - -# data loaders -train_dataloader = dict( - batch_size=20, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=1, - num_workers=1, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json', - nms_thr=0.8, - score_mode='keypoint', -) -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/dekr/humanart/dekr_hrnet-w32_8xb10-140e_humanart-512x512.py b/configs/body_2d_keypoint/dekr/humanart/dekr_hrnet-w32_8xb10-140e_humanart-512x512.py deleted file mode 100644 index 17e96ebdb2..0000000000 --- a/configs/body_2d_keypoint/dekr/humanart/dekr_hrnet-w32_8xb10-140e_humanart-512x512.py +++ /dev/null @@ -1,186 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=140, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=1e-3, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=140, - milestones=[90, 120], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=80) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='SPR', - input_size=(512, 512), - heatmap_size=(128, 128), - sigma=(4, 2), - minimal_diagonal_length=32**0.5, - generate_keypoint_heatmaps=True, - decode_max_instances=30) - -# model settings -model = dict( - type='BottomupPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='HRNet', - in_channels=3, - extra=dict( - stage1=dict( - num_modules=1, - num_branches=1, - block='BOTTLENECK', - num_blocks=(4, ), - num_channels=(64, )), - stage2=dict( - num_modules=1, - num_branches=2, - block='BASIC', - num_blocks=(4, 4), - num_channels=(32, 64)), - stage3=dict( - num_modules=4, - num_branches=3, - block='BASIC', - num_blocks=(4, 4, 4), - num_channels=(32, 64, 128)), - stage4=dict( - num_modules=3, - num_branches=4, - block='BASIC', - num_blocks=(4, 4, 4, 4), - num_channels=(32, 64, 128, 256), - multiscale_output=True)), - init_cfg=dict( - type='Pretrained', - checkpoint='https://download.openmmlab.com/mmpose/' - 'pretrain_models/hrnet_w32-36af842e.pth'), - ), - neck=dict( - type='FeatureMapProcessor', - concat=True, - ), - head=dict( - type='DEKRHead', - in_channels=480, - num_keypoints=17, - heatmap_loss=dict(type='KeypointMSELoss', use_target_weight=True), - displacement_loss=dict( - type='SoftWeightSmoothL1Loss', - use_target_weight=True, - supervise_empty=False, - beta=1 / 9, - loss_weight=0.002, - ), - decoder=codec, - rescore_cfg=dict( - in_channels=74, - norm_indexes=(5, 6), - init_cfg=dict( - type='Pretrained', - checkpoint='https://download.openmmlab.com/mmpose/' - 'pretrain_models/kpt_rescore_coco-33d58c5c.pth')), - ), - test_cfg=dict( - multiscale_test=False, - flip_test=True, - nms_dist_thr=0.05, - shift_heatmap=True, - align_corners=False)) - -# enable DDP training when rescore net is used -find_unused_parameters = True - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'bottomup' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='BottomupRandomAffine', input_size=codec['input_size']), - dict(type='RandomFlip', direction='horizontal'), - dict(type='GenerateTarget', encoder=codec), - dict(type='BottomupGetHeatmapMask'), - dict(type='PackPoseInputs'), -] -val_pipeline = [ - dict(type='LoadImage'), - dict( - type='BottomupResize', - input_size=codec['input_size'], - size_factor=32, - resize_mode='expand'), - dict( - type='PackPoseInputs', - meta_keys=('id', 'img_id', 'img_path', 'crowd_index', 'ori_shape', - 'img_shape', 'input_size', 'input_center', 'input_scale', - 'flip', 'flip_direction', 'flip_indices', 'raw_ann_info', - 'skeleton_links')) -] - -# data loaders -train_dataloader = dict( - batch_size=10, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=1, - num_workers=1, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json', - nms_mode='none', - score_mode='keypoint', -) -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/dekr/humanart/dekr_hrnet-w48_8xb10-140e_humanart-640x640.py b/configs/body_2d_keypoint/dekr/humanart/dekr_hrnet-w48_8xb10-140e_humanart-640x640.py deleted file mode 100644 index c1b4df97de..0000000000 --- a/configs/body_2d_keypoint/dekr/humanart/dekr_hrnet-w48_8xb10-140e_humanart-640x640.py +++ /dev/null @@ -1,187 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=140, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=1e-3, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=140, - milestones=[90, 120], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=80) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='SPR', - input_size=(640, 640), - heatmap_size=(160, 160), - sigma=(4, 2), - minimal_diagonal_length=32**0.5, - generate_keypoint_heatmaps=True, - decode_max_instances=30) - -# model settings -model = dict( - type='BottomupPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='HRNet', - in_channels=3, - extra=dict( - stage1=dict( - num_modules=1, - num_branches=1, - block='BOTTLENECK', - num_blocks=(4, ), - num_channels=(64, )), - stage2=dict( - num_modules=1, - num_branches=2, - block='BASIC', - num_blocks=(4, 4), - num_channels=(48, 96)), - stage3=dict( - num_modules=4, - num_branches=3, - block='BASIC', - num_blocks=(4, 4, 4), - num_channels=(48, 96, 192)), - stage4=dict( - num_modules=3, - num_branches=4, - block='BASIC', - num_blocks=(4, 4, 4, 4), - num_channels=(48, 96, 192, 384), - multiscale_output=True)), - init_cfg=dict( - type='Pretrained', - checkpoint='https://download.openmmlab.com/mmpose/' - 'pretrain_models/hrnet_w48-8ef0771d.pth'), - ), - neck=dict( - type='FeatureMapProcessor', - concat=True, - ), - head=dict( - type='DEKRHead', - in_channels=720, - num_keypoints=17, - num_heatmap_filters=48, - heatmap_loss=dict(type='KeypointMSELoss', use_target_weight=True), - displacement_loss=dict( - type='SoftWeightSmoothL1Loss', - use_target_weight=True, - supervise_empty=False, - beta=1 / 9, - loss_weight=0.002, - ), - decoder=codec, - rescore_cfg=dict( - in_channels=74, - norm_indexes=(5, 6), - init_cfg=dict( - type='Pretrained', - checkpoint='https://download.openmmlab.com/mmpose/' - 'pretrain_models/kpt_rescore_coco-33d58c5c.pth')), - ), - test_cfg=dict( - multiscale_test=False, - flip_test=True, - nms_dist_thr=0.05, - shift_heatmap=True, - align_corners=False)) - -# enable DDP training when rescore net is used -find_unused_parameters = True - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'bottomup' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='BottomupRandomAffine', input_size=codec['input_size']), - dict(type='RandomFlip', direction='horizontal'), - dict(type='GenerateTarget', encoder=codec), - dict(type='BottomupGetHeatmapMask'), - dict(type='PackPoseInputs'), -] -val_pipeline = [ - dict(type='LoadImage'), - dict( - type='BottomupResize', - input_size=codec['input_size'], - size_factor=32, - resize_mode='expand'), - dict( - type='PackPoseInputs', - meta_keys=('id', 'img_id', 'img_path', 'crowd_index', 'ori_shape', - 'img_shape', 'input_size', 'input_center', 'input_scale', - 'flip', 'flip_direction', 'flip_indices', 'raw_ann_info', - 'skeleton_links')) -] - -# data loaders -train_dataloader = dict( - batch_size=10, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=1, - num_workers=1, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json', - nms_mode='none', - score_mode='keypoint', -) -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/integral_regression/humanart/ipr_res50_8xb64-210e_humanart-256x256.py b/configs/body_2d_keypoint/integral_regression/humanart/ipr_res50_8xb64-210e_humanart-256x256.py deleted file mode 100644 index 85368b20be..0000000000 --- a/configs/body_2d_keypoint/integral_regression/humanart/ipr_res50_8xb64-210e_humanart-256x256.py +++ /dev/null @@ -1,134 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=train_cfg['max_epochs'], - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# codec settings -codec = dict( - type='IntegralRegressionLabel', - input_size=(256, 256), - heatmap_size=(64, 64), - sigma=2.0, - normalize=True) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='ResNet', - depth=50, - ), - head=dict( - type='DSNTHead', - in_channels=2048, - in_featuremap_size=(8, 8), - num_joints=17, - loss=dict( - type='MultipleLossWrapper', - losses=[ - dict(type='SmoothL1Loss', use_target_weight=True), - dict(type='KeypointMSELoss', use_target_weight=True) - ]), - decoder=codec), - test_cfg=dict( - flip_test=True, - shift_coords=True, - shift_heatmap=True, - ), - init_cfg=dict( - type='Pretrained', - checkpoint='https://download.openmmlab.com/mmpose/' - 'pretrain_models/td-hm_res50_8xb64-210e_coco-256x192.pth')) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -test_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=test_pipeline, - )) -test_dataloader = val_dataloader - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=f'{data_root}HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/integral_regression/humanart/ipr_res50_debias-8xb64-210e_humanart-256x256.py b/configs/body_2d_keypoint/integral_regression/humanart/ipr_res50_debias-8xb64-210e_humanart-256x256.py deleted file mode 100644 index 7ed6122914..0000000000 --- a/configs/body_2d_keypoint/integral_regression/humanart/ipr_res50_debias-8xb64-210e_humanart-256x256.py +++ /dev/null @@ -1,136 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=train_cfg['max_epochs'], - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# codec settings -codec = dict( - type='IntegralRegressionLabel', - input_size=(256, 256), - heatmap_size=(64, 64), - sigma=2.0, - normalize=True) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='ResNet', - depth=50, - ), - head=dict( - type='DSNTHead', - in_channels=2048, - in_featuremap_size=(8, 8), - num_joints=17, - debias=True, - beta=10., - loss=dict( - type='MultipleLossWrapper', - losses=[ - dict(type='SmoothL1Loss', use_target_weight=True), - dict(type='JSDiscretLoss', use_target_weight=True) - ]), - decoder=codec), - test_cfg=dict( - flip_test=True, - shift_coords=True, - shift_heatmap=True, - ), - init_cfg=dict( - type='Pretrained', - checkpoint='https://download.openmmlab.com/mmpose/' - 'pretrain_models/td-hm_res50_8xb64-210e_coco-256x192.pth')) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -test_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=test_pipeline, - )) -test_dataloader = val_dataloader - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=f'{data_root}HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/integral_regression/humanart/ipr_res50_dsnt-8xb64-210e_humanart-256x256.py b/configs/body_2d_keypoint/integral_regression/humanart/ipr_res50_dsnt-8xb64-210e_humanart-256x256.py deleted file mode 100644 index 5078e5ee12..0000000000 --- a/configs/body_2d_keypoint/integral_regression/humanart/ipr_res50_dsnt-8xb64-210e_humanart-256x256.py +++ /dev/null @@ -1,134 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=train_cfg['max_epochs'], - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# codec settings -codec = dict( - type='IntegralRegressionLabel', - input_size=(256, 256), - heatmap_size=(64, 64), - sigma=2.0, - normalize=True) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='ResNet', - depth=50, - ), - head=dict( - type='DSNTHead', - in_channels=2048, - in_featuremap_size=(8, 8), - num_joints=17, - loss=dict( - type='MultipleLossWrapper', - losses=[ - dict(type='SmoothL1Loss', use_target_weight=True), - dict(type='JSDiscretLoss', use_target_weight=True) - ]), - decoder=codec), - test_cfg=dict( - flip_test=True, - shift_coords=True, - shift_heatmap=True, - ), - init_cfg=dict( - type='Pretrained', - checkpoint='https://download.openmmlab.com/mmpose/' - 'pretrain_models/td-hm_res50_8xb64-210e_coco-256x192.pth')) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -test_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=test_pipeline, - )) -test_dataloader = val_dataloader - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=f'{data_root}HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/rtmpose/README.md b/configs/body_2d_keypoint/rtmpose/README.md index 3037974917..38fd938376 100644 --- a/configs/body_2d_keypoint/rtmpose/README.md +++ b/configs/body_2d_keypoint/rtmpose/README.md @@ -37,3 +37,21 @@ Results on CrowdPose test with [YOLOv3](https://github.com/eriklindernoren/PyTor | Model | Input Size | AP | AR | Details and Download | | :-------: | :--------: | :---: | :---: | :------------------------------------------------------: | | RTMPose-m | 256x192 | 0.706 | 0.788 | [rtmpose_crowdpose.md](./crowdpose/rtmpose_crowdpose.md) | + +### Human-Art Dataset + +Results on Human-Art validation dataset with detector having human AP of 56.2 on Human-Art validation dataset + +| Model | Input Size | AP | AR | Details and Download | +| :-------: | :--------: | :---: | :---: | :---------------------------------------------------: | +| RTMPose-s | 256x192 | 0.311 | 0.381 | [rtmpose_humanart.md](./humanart/rtmpose_humanart.md) | +| RTMPose-m | 256x192 | 0.355 | 0.417 | [rtmpose_humanart.md](./humanart/rtmpose_humanart.md) | +| RTMPose-l | 256x192 | 0.378 | 0.442 | [rtmpose_humanart.md](./humanart/rtmpose_humanart.md) | + +Results on Human-Art validation dataset with ground-truth bounding-box + +| Model | Input Size | AP | AR | Details and Download | +| :-------: | :--------: | :---: | :---: | :---------------------------------------------------: | +| RTMPose-s | 256x192 | 0.698 | 0.732 | [rtmpose_humanart.md](./humanart/rtmpose_humanart.md) | +| RTMPose-m | 256x192 | 0.728 | 0.759 | [rtmpose_humanart.md](./humanart/rtmpose_humanart.md) | +| RTMPose-l | 256x192 | 0.753 | 0.783 | [rtmpose_humanart.md](./humanart/rtmpose_humanart.md) | diff --git a/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-l_8xb256-420e_aic-humanart-256x192.py b/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-l_8xb256-420e_aic-humanart-256x192.py deleted file mode 100644 index 3eb94c1ed2..0000000000 --- a/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-l_8xb256-420e_aic-humanart-256x192.py +++ /dev/null @@ -1,272 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -max_epochs = 420 -stage2_num_epochs = 30 -base_lr = 4e-3 - -train_cfg = dict(max_epochs=max_epochs, val_interval=10) -randomness = dict(seed=21) - -# optimizer -optim_wrapper = dict( - type='OptimWrapper', - optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.05), - paramwise_cfg=dict( - norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True)) - -# learning rate -param_scheduler = [ - dict( - type='LinearLR', - start_factor=1.0e-5, - by_epoch=False, - begin=0, - end=1000), - dict( - # use cosine lr from 210 to 420 epoch - type='CosineAnnealingLR', - eta_min=base_lr * 0.05, - begin=max_epochs // 2, - end=max_epochs, - T_max=max_epochs // 2, - by_epoch=True, - convert_to_iter_based=True), -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=1024) - -# codec settings -codec = dict( - type='SimCCLabel', - input_size=(192, 256), - sigma=(4.9, 5.66), - simcc_split_ratio=2.0, - normalize=False, - use_dark=False) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - _scope_='mmdet', - type='CSPNeXt', - arch='P5', - expand_ratio=0.5, - deepen_factor=1., - widen_factor=1., - out_indices=(4, ), - channel_attention=True, - norm_cfg=dict(type='SyncBN'), - act_cfg=dict(type='SiLU'), - init_cfg=dict( - type='Pretrained', - prefix='backbone.', - checkpoint='https://download.openmmlab.com/mmpose/v1/projects/' - 'rtmpose/cspnext-l_udp-aic-coco_210e-256x192-273b7631_20230130.pth' # noqa - )), - head=dict( - type='RTMCCHead', - in_channels=1024, - out_channels=17, - input_size=codec['input_size'], - in_featuremap_size=(6, 8), - simcc_split_ratio=codec['simcc_split_ratio'], - final_layer_kernel_size=7, - gau_cfg=dict( - hidden_dims=256, - s=128, - expansion_factor=2, - dropout_rate=0., - drop_path=0., - act_fn='SiLU', - use_rel_bias=False, - pos_enc=False), - loss=dict( - type='KLDiscretLoss', - use_target_weight=True, - beta=10., - label_softmax=True), - decoder=codec), - test_cfg=dict(flip_test=True, )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -backend_args = dict(backend='local') -# backend_args = dict( -# backend='petrel', -# path_mapping=dict({ -# f'{data_root}': 's3://openmmlab/datasets/', -# f'{data_root}': 's3://openmmlab/datasets/' -# })) - -# pipelines -train_pipeline = [ - dict(type='LoadImage', backend_args=backend_args), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict( - type='RandomBBoxTransform', scale_factor=[0.6, 1.4], rotate_factor=80), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='mmdet.YOLOXHSVRandomAug'), - dict( - type='Albumentation', - transforms=[ - dict(type='Blur', p=0.1), - dict(type='MedianBlur', p=0.1), - dict( - type='CoarseDropout', - max_holes=1, - max_height=0.4, - max_width=0.4, - min_holes=1, - min_height=0.2, - min_width=0.2, - p=1.0), - ]), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage', backend_args=backend_args), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -train_pipeline_stage2 = [ - dict(type='LoadImage', backend_args=backend_args), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict( - type='RandomBBoxTransform', - shift_factor=0., - scale_factor=[0.75, 1.25], - rotate_factor=60), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='mmdet.YOLOXHSVRandomAug'), - dict( - type='Albumentation', - transforms=[ - dict(type='Blur', p=0.1), - dict(type='MedianBlur', p=0.1), - dict( - type='CoarseDropout', - max_holes=1, - max_height=0.4, - max_width=0.4, - min_holes=1, - min_height=0.2, - min_width=0.2, - p=0.5), - ]), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] - -# train datasets -dataset_humanart = dict( - type='RepeatDataset', - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=[], - ), - times=3) - -dataset_aic = dict( - type='AicDataset', - data_root=data_root, - data_mode=data_mode, - ann_file='aic/annotations/aic_train.json', - data_prefix=dict(img='pose/ai_challenge/ai_challenger_keypoint' - '_train_20170902/keypoint_train_images_20170902/'), - pipeline=[ - dict( - type='KeypointConverter', - num_keypoints=17, - mapping=[ - (0, 6), - (1, 8), - (2, 10), - (3, 5), - (4, 7), - (5, 9), - (6, 12), - (7, 14), - (8, 16), - (9, 11), - (10, 13), - (11, 15), - ]) - ], -) - -# data loaders -train_dataloader = dict( - batch_size=256, - num_workers=10, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type='CombinedDataset', - metainfo=dict(from_file='configs/_base_/datasets/humanart.py'), - datasets=[dataset_humanart, dataset_aic], - pipeline=train_pipeline, - test_mode=False, - )) -val_dataloader = dict( - batch_size=64, - num_workers=10, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - # bbox_file='data/coco/person_detection_results/' - # 'COCO_val2017_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# hooks -default_hooks = dict( - checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=1)) - -custom_hooks = [ - dict( - type='EMAHook', - ema_type='ExpMomentumEMA', - momentum=0.0002, - update_buffers=True, - priority=49), - dict( - type='mmdet.PipelineSwitchHook', - switch_epoch=max_epochs - stage2_num_epochs, - switch_pipeline=train_pipeline_stage2) -] - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-l_8xb256-420e_aic-humanart-384x288.py b/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-l_8xb256-420e_aic-humanart-384x288.py deleted file mode 100644 index ace553e1f8..0000000000 --- a/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-l_8xb256-420e_aic-humanart-384x288.py +++ /dev/null @@ -1,272 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -max_epochs = 420 -stage2_num_epochs = 30 -base_lr = 4e-3 - -train_cfg = dict(max_epochs=max_epochs, val_interval=10) -randomness = dict(seed=21) - -# optimizer -optim_wrapper = dict( - type='OptimWrapper', - optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.05), - paramwise_cfg=dict( - norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True)) - -# learning rate -param_scheduler = [ - dict( - type='LinearLR', - start_factor=1.0e-5, - by_epoch=False, - begin=0, - end=1000), - dict( - # use cosine lr from 210 to 420 epoch - type='CosineAnnealingLR', - eta_min=base_lr * 0.05, - begin=max_epochs // 2, - end=max_epochs, - T_max=max_epochs // 2, - by_epoch=True, - convert_to_iter_based=True), -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=1024) - -# codec settings -codec = dict( - type='SimCCLabel', - input_size=(288, 384), - sigma=(6., 6.93), - simcc_split_ratio=2.0, - normalize=False, - use_dark=False) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - _scope_='mmdet', - type='CSPNeXt', - arch='P5', - expand_ratio=0.5, - deepen_factor=1., - widen_factor=1., - out_indices=(4, ), - channel_attention=True, - norm_cfg=dict(type='SyncBN'), - act_cfg=dict(type='SiLU'), - init_cfg=dict( - type='Pretrained', - prefix='backbone.', - checkpoint='https://download.openmmlab.com/mmpose/v1/projects/' - 'rtmpose/cspnext-l_udp-aic-coco_210e-256x192-273b7631_20230130.pth' # noqa - )), - head=dict( - type='RTMCCHead', - in_channels=1024, - out_channels=17, - input_size=codec['input_size'], - in_featuremap_size=(9, 12), - simcc_split_ratio=codec['simcc_split_ratio'], - final_layer_kernel_size=7, - gau_cfg=dict( - hidden_dims=256, - s=128, - expansion_factor=2, - dropout_rate=0., - drop_path=0., - act_fn='SiLU', - use_rel_bias=False, - pos_enc=False), - loss=dict( - type='KLDiscretLoss', - use_target_weight=True, - beta=10., - label_softmax=True), - decoder=codec), - test_cfg=dict(flip_test=True, )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -backend_args = dict(backend='local') -# backend_args = dict( -# backend='petrel', -# path_mapping=dict({ -# f'{data_root}': 's3://openmmlab/datasets/', -# f'{data_root}': 's3://openmmlab/datasets/' -# })) - -# pipelines -train_pipeline = [ - dict(type='LoadImage', backend_args=backend_args), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict( - type='RandomBBoxTransform', scale_factor=[0.6, 1.4], rotate_factor=80), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='mmdet.YOLOXHSVRandomAug'), - dict( - type='Albumentation', - transforms=[ - dict(type='Blur', p=0.1), - dict(type='MedianBlur', p=0.1), - dict( - type='CoarseDropout', - max_holes=1, - max_height=0.4, - max_width=0.4, - min_holes=1, - min_height=0.2, - min_width=0.2, - p=1.0), - ]), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage', backend_args=backend_args), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -train_pipeline_stage2 = [ - dict(type='LoadImage', backend_args=backend_args), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict( - type='RandomBBoxTransform', - shift_factor=0., - scale_factor=[0.75, 1.25], - rotate_factor=60), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='mmdet.YOLOXHSVRandomAug'), - dict( - type='Albumentation', - transforms=[ - dict(type='Blur', p=0.1), - dict(type='MedianBlur', p=0.1), - dict( - type='CoarseDropout', - max_holes=1, - max_height=0.4, - max_width=0.4, - min_holes=1, - min_height=0.2, - min_width=0.2, - p=0.5), - ]), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] - -# train datasets -dataset_humanart = dict( - type='RepeatDataset', - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=[], - ), - times=3) - -dataset_aic = dict( - type='AicDataset', - data_root=data_root, - data_mode=data_mode, - ann_file='aic/annotations/aic_train.json', - data_prefix=dict(img='pose/ai_challenge/ai_challenger_keypoint' - '_train_20170902/keypoint_train_images_20170902/'), - pipeline=[ - dict( - type='KeypointConverter', - num_keypoints=17, - mapping=[ - (0, 6), - (1, 8), - (2, 10), - (3, 5), - (4, 7), - (5, 9), - (6, 12), - (7, 14), - (8, 16), - (9, 11), - (10, 13), - (11, 15), - ]) - ], -) - -# data loaders -train_dataloader = dict( - batch_size=256, - num_workers=10, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type='CombinedDataset', - metainfo=dict(from_file='configs/_base_/datasets/humanart.py'), - datasets=[dataset_humanart, dataset_aic], - pipeline=train_pipeline, - test_mode=False, - )) -val_dataloader = dict( - batch_size=64, - num_workers=10, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - # bbox_file='data/coco/person_detection_results/' - # 'COCO_val2017_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# hooks -default_hooks = dict( - checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=1)) - -custom_hooks = [ - dict( - type='EMAHook', - ema_type='ExpMomentumEMA', - momentum=0.0002, - update_buffers=True, - priority=49), - dict( - type='mmdet.PipelineSwitchHook', - switch_epoch=max_epochs - stage2_num_epochs, - switch_pipeline=train_pipeline_stage2) -] - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-l_8xb256-420e_humanart-256x192.py b/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-l_8xb256-420e_humanart-256x192.py index 60586f584a..384a712d95 100644 --- a/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-l_8xb256-420e_humanart-256x192.py +++ b/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-l_8xb256-420e_humanart-256x192.py @@ -200,8 +200,8 @@ data_root=data_root, data_mode=data_mode, ann_file='HumanArt/annotations/validation_humanart.json', - # bbox_file=f'{data_root}person_detection_results/' - # 'COCO_val2017_detections_AP_H_56_person.json', + # bbox_file=f'{data_root}HumanArt/person_detection_results/' + # 'HumanArt_validation_detections_AP_H_56_person.json', data_prefix=dict(img=''), test_mode=True, pipeline=val_pipeline, diff --git a/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-m_8xb256-420e_aic-humanart-256x192.py b/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-m_8xb256-420e_aic-humanart-256x192.py deleted file mode 100644 index d1d3e3086f..0000000000 --- a/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-m_8xb256-420e_aic-humanart-256x192.py +++ /dev/null @@ -1,272 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -max_epochs = 420 -stage2_num_epochs = 30 -base_lr = 4e-3 - -train_cfg = dict(max_epochs=max_epochs, val_interval=10) -randomness = dict(seed=21) - -# optimizer -optim_wrapper = dict( - type='OptimWrapper', - optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.05), - paramwise_cfg=dict( - norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True)) - -# learning rate -param_scheduler = [ - dict( - type='LinearLR', - start_factor=1.0e-5, - by_epoch=False, - begin=0, - end=1000), - dict( - # use cosine lr from 210 to 420 epoch - type='CosineAnnealingLR', - eta_min=base_lr * 0.05, - begin=max_epochs // 2, - end=max_epochs, - T_max=max_epochs // 2, - by_epoch=True, - convert_to_iter_based=True), -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=1024) - -# codec settings -codec = dict( - type='SimCCLabel', - input_size=(192, 256), - sigma=(4.9, 5.66), - simcc_split_ratio=2.0, - normalize=False, - use_dark=False) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - _scope_='mmdet', - type='CSPNeXt', - arch='P5', - expand_ratio=0.5, - deepen_factor=0.67, - widen_factor=0.75, - out_indices=(4, ), - channel_attention=True, - norm_cfg=dict(type='SyncBN'), - act_cfg=dict(type='SiLU'), - init_cfg=dict( - type='Pretrained', - prefix='backbone.', - checkpoint='https://download.openmmlab.com/mmpose/v1/projects/' - 'rtmpose/cspnext-m_udp-aic-coco_210e-256x192-f2f7d6f6_20230130.pth' # noqa - )), - head=dict( - type='RTMCCHead', - in_channels=768, - out_channels=17, - input_size=codec['input_size'], - in_featuremap_size=(6, 8), - simcc_split_ratio=codec['simcc_split_ratio'], - final_layer_kernel_size=7, - gau_cfg=dict( - hidden_dims=256, - s=128, - expansion_factor=2, - dropout_rate=0., - drop_path=0., - act_fn='SiLU', - use_rel_bias=False, - pos_enc=False), - loss=dict( - type='KLDiscretLoss', - use_target_weight=True, - beta=10., - label_softmax=True), - decoder=codec), - test_cfg=dict(flip_test=True, )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -backend_args = dict(backend='local') -# backend_args = dict( -# backend='petrel', -# path_mapping=dict({ -# f'{data_root}': 's3://openmmlab/datasets/', -# f'{data_root}': 's3://openmmlab/datasets/' -# })) - -# pipelines -train_pipeline = [ - dict(type='LoadImage', backend_args=backend_args), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict( - type='RandomBBoxTransform', scale_factor=[0.6, 1.4], rotate_factor=80), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='mmdet.YOLOXHSVRandomAug'), - dict( - type='Albumentation', - transforms=[ - dict(type='Blur', p=0.1), - dict(type='MedianBlur', p=0.1), - dict( - type='CoarseDropout', - max_holes=1, - max_height=0.4, - max_width=0.4, - min_holes=1, - min_height=0.2, - min_width=0.2, - p=1.0), - ]), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage', backend_args=backend_args), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -train_pipeline_stage2 = [ - dict(type='LoadImage', backend_args=backend_args), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict( - type='RandomBBoxTransform', - shift_factor=0., - scale_factor=[0.75, 1.25], - rotate_factor=60), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='mmdet.YOLOXHSVRandomAug'), - dict( - type='Albumentation', - transforms=[ - dict(type='Blur', p=0.1), - dict(type='MedianBlur', p=0.1), - dict( - type='CoarseDropout', - max_holes=1, - max_height=0.4, - max_width=0.4, - min_holes=1, - min_height=0.2, - min_width=0.2, - p=0.5), - ]), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] - -# train datasets -dataset_humanart = dict( - type='RepeatDataset', - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=[], - ), - times=3) - -dataset_aic = dict( - type='AicDataset', - data_root=data_root, - data_mode=data_mode, - ann_file='aic/annotations/aic_train.json', - data_prefix=dict(img='pose/ai_challenge/ai_challenger_keypoint' - '_train_20170902/keypoint_train_images_20170902/'), - pipeline=[ - dict( - type='KeypointConverter', - num_keypoints=17, - mapping=[ - (0, 6), - (1, 8), - (2, 10), - (3, 5), - (4, 7), - (5, 9), - (6, 12), - (7, 14), - (8, 16), - (9, 11), - (10, 13), - (11, 15), - ]) - ], -) - -# data loaders -train_dataloader = dict( - batch_size=128 * 2, - num_workers=10, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type='CombinedDataset', - metainfo=dict(from_file='configs/_base_/datasets/humanart.py'), - datasets=[dataset_humanart, dataset_aic], - pipeline=train_pipeline, - test_mode=False, - )) -val_dataloader = dict( - batch_size=64, - num_workers=10, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - # bbox_file='data/coco/person_detection_results/' - # 'COCO_val2017_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# hooks -default_hooks = dict( - checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=1)) - -custom_hooks = [ - dict( - type='EMAHook', - ema_type='ExpMomentumEMA', - momentum=0.0002, - update_buffers=True, - priority=49), - dict( - type='mmdet.PipelineSwitchHook', - switch_epoch=max_epochs - stage2_num_epochs, - switch_pipeline=train_pipeline_stage2) -] - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-m_8xb256-420e_aic-humanart-384x288.py b/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-m_8xb256-420e_aic-humanart-384x288.py deleted file mode 100644 index 22ebbd7b45..0000000000 --- a/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-m_8xb256-420e_aic-humanart-384x288.py +++ /dev/null @@ -1,272 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -max_epochs = 420 -stage2_num_epochs = 30 -base_lr = 4e-3 - -train_cfg = dict(max_epochs=max_epochs, val_interval=10) -randomness = dict(seed=21) - -# optimizer -optim_wrapper = dict( - type='OptimWrapper', - optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.05), - paramwise_cfg=dict( - norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True)) - -# learning rate -param_scheduler = [ - dict( - type='LinearLR', - start_factor=1.0e-5, - by_epoch=False, - begin=0, - end=1000), - dict( - # use cosine lr from 210 to 420 epoch - type='CosineAnnealingLR', - eta_min=base_lr * 0.05, - begin=max_epochs // 2, - end=max_epochs, - T_max=max_epochs // 2, - by_epoch=True, - convert_to_iter_based=True), -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=1024) - -# codec settings -codec = dict( - type='SimCCLabel', - input_size=(288, 384), - sigma=(6., 6.93), - simcc_split_ratio=2.0, - normalize=False, - use_dark=False) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - _scope_='mmdet', - type='CSPNeXt', - arch='P5', - expand_ratio=0.5, - deepen_factor=0.67, - widen_factor=0.75, - out_indices=(4, ), - channel_attention=True, - norm_cfg=dict(type='SyncBN'), - act_cfg=dict(type='SiLU'), - init_cfg=dict( - type='Pretrained', - prefix='backbone.', - checkpoint='https://download.openmmlab.com/mmpose/v1/projects/' - 'rtmpose/cspnext-m_udp-aic-coco_210e-256x192-f2f7d6f6_20230130.pth' # noqa - )), - head=dict( - type='RTMCCHead', - in_channels=768, - out_channels=17, - input_size=codec['input_size'], - in_featuremap_size=(9, 12), - simcc_split_ratio=codec['simcc_split_ratio'], - final_layer_kernel_size=7, - gau_cfg=dict( - hidden_dims=256, - s=128, - expansion_factor=2, - dropout_rate=0., - drop_path=0., - act_fn='SiLU', - use_rel_bias=False, - pos_enc=False), - loss=dict( - type='KLDiscretLoss', - use_target_weight=True, - beta=10., - label_softmax=True), - decoder=codec), - test_cfg=dict(flip_test=True, )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -backend_args = dict(backend='local') -# backend_args = dict( -# backend='petrel', -# path_mapping=dict({ -# f'{data_root}': 's3://openmmlab/datasets/', -# f'{data_root}': 's3://openmmlab/datasets/' -# })) - -# pipelines -train_pipeline = [ - dict(type='LoadImage', backend_args=backend_args), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict( - type='RandomBBoxTransform', scale_factor=[0.6, 1.4], rotate_factor=80), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='mmdet.YOLOXHSVRandomAug'), - dict( - type='Albumentation', - transforms=[ - dict(type='Blur', p=0.1), - dict(type='MedianBlur', p=0.1), - dict( - type='CoarseDropout', - max_holes=1, - max_height=0.4, - max_width=0.4, - min_holes=1, - min_height=0.2, - min_width=0.2, - p=1.0), - ]), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage', backend_args=backend_args), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -train_pipeline_stage2 = [ - dict(type='LoadImage', backend_args=backend_args), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict( - type='RandomBBoxTransform', - shift_factor=0., - scale_factor=[0.75, 1.25], - rotate_factor=60), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='mmdet.YOLOXHSVRandomAug'), - dict( - type='Albumentation', - transforms=[ - dict(type='Blur', p=0.1), - dict(type='MedianBlur', p=0.1), - dict( - type='CoarseDropout', - max_holes=1, - max_height=0.4, - max_width=0.4, - min_holes=1, - min_height=0.2, - min_width=0.2, - p=0.5), - ]), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] - -# train datasets -dataset_humanart = dict( - type='RepeatDataset', - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=[], - ), - times=3) - -dataset_aic = dict( - type='AicDataset', - data_root=data_root, - data_mode=data_mode, - ann_file='aic/annotations/aic_train.json', - data_prefix=dict(img='pose/ai_challenge/ai_challenger_keypoint' - '_train_20170902/keypoint_train_images_20170902/'), - pipeline=[ - dict( - type='KeypointConverter', - num_keypoints=17, - mapping=[ - (0, 6), - (1, 8), - (2, 10), - (3, 5), - (4, 7), - (5, 9), - (6, 12), - (7, 14), - (8, 16), - (9, 11), - (10, 13), - (11, 15), - ]) - ], -) - -# data loaders -train_dataloader = dict( - batch_size=128 * 2, - num_workers=10, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type='CombinedDataset', - metainfo=dict(from_file='configs/_base_/datasets/humanart.py'), - datasets=[dataset_humanart, dataset_aic], - pipeline=train_pipeline, - test_mode=False, - )) -val_dataloader = dict( - batch_size=64, - num_workers=10, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - # bbox_file='data/coco/person_detection_results/' - # 'COCO_val2017_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# hooks -default_hooks = dict( - checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=1)) - -custom_hooks = [ - dict( - type='EMAHook', - ema_type='ExpMomentumEMA', - momentum=0.0002, - update_buffers=True, - priority=49), - dict( - type='mmdet.PipelineSwitchHook', - switch_epoch=max_epochs - stage2_num_epochs, - switch_pipeline=train_pipeline_stage2) -] - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-m_8xb256-420e_humanart-256x192.py b/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-m_8xb256-420e_humanart-256x192.py index b4b96baebf..30178cbb6d 100644 --- a/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-m_8xb256-420e_humanart-256x192.py +++ b/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-m_8xb256-420e_humanart-256x192.py @@ -200,8 +200,8 @@ data_root=data_root, data_mode=data_mode, ann_file='HumanArt/annotations/validation_humanart.json', - # bbox_file=f'{data_root}person_detection_results/' - # 'COCO_val2017_detections_AP_H_56_person.json', + # bbox_file=f'{data_root}HumanArt/person_detection_results/' + # 'HumanArt_validation_detections_AP_H_56_person.json', data_prefix=dict(img=''), test_mode=True, pipeline=val_pipeline, diff --git a/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-s_8xb256-420e_aic-humanart-256x192.py b/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-s_8xb256-420e_aic-humanart-256x192.py deleted file mode 100644 index 3aad883095..0000000000 --- a/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-s_8xb256-420e_aic-humanart-256x192.py +++ /dev/null @@ -1,272 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -max_epochs = 420 -stage2_num_epochs = 30 -base_lr = 4e-3 - -train_cfg = dict(max_epochs=max_epochs, val_interval=10) -randomness = dict(seed=21) - -# optimizer -optim_wrapper = dict( - type='OptimWrapper', - optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.0), - paramwise_cfg=dict( - norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True)) - -# learning rate -param_scheduler = [ - dict( - type='LinearLR', - start_factor=1.0e-5, - by_epoch=False, - begin=0, - end=1000), - dict( - # use cosine lr from 210 to 420 epoch - type='CosineAnnealingLR', - eta_min=base_lr * 0.05, - begin=max_epochs // 2, - end=max_epochs, - T_max=max_epochs // 2, - by_epoch=True, - convert_to_iter_based=True), -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=1024) - -# codec settings -codec = dict( - type='SimCCLabel', - input_size=(192, 256), - sigma=(4.9, 5.66), - simcc_split_ratio=2.0, - normalize=False, - use_dark=False) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - _scope_='mmdet', - type='CSPNeXt', - arch='P5', - expand_ratio=0.5, - deepen_factor=0.33, - widen_factor=0.5, - out_indices=(4, ), - channel_attention=True, - norm_cfg=dict(type='SyncBN'), - act_cfg=dict(type='SiLU'), - init_cfg=dict( - type='Pretrained', - prefix='backbone.', - checkpoint='https://download.openmmlab.com/mmpose/v1/projects/' - 'rtmpose/cspnext-s_udp-aic-coco_210e-256x192-92f5a029_20230130.pth' # noqa - )), - head=dict( - type='RTMCCHead', - in_channels=512, - out_channels=17, - input_size=codec['input_size'], - in_featuremap_size=(6, 8), - simcc_split_ratio=codec['simcc_split_ratio'], - final_layer_kernel_size=7, - gau_cfg=dict( - hidden_dims=256, - s=128, - expansion_factor=2, - dropout_rate=0., - drop_path=0., - act_fn='SiLU', - use_rel_bias=False, - pos_enc=False), - loss=dict( - type='KLDiscretLoss', - use_target_weight=True, - beta=10., - label_softmax=True), - decoder=codec), - test_cfg=dict(flip_test=True, )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -backend_args = dict(backend='local') -# backend_args = dict( -# backend='petrel', -# path_mapping=dict({ -# f'{data_root}': 's3://openmmlab/datasets/', -# f'{data_root}': 's3://openmmlab/datasets/' -# })) - -# pipelines -train_pipeline = [ - dict(type='LoadImage', backend_args=backend_args), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict( - type='RandomBBoxTransform', scale_factor=[0.6, 1.4], rotate_factor=80), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='mmdet.YOLOXHSVRandomAug'), - dict( - type='Albumentation', - transforms=[ - dict(type='Blur', p=0.1), - dict(type='MedianBlur', p=0.1), - dict( - type='CoarseDropout', - max_holes=1, - max_height=0.4, - max_width=0.4, - min_holes=1, - min_height=0.2, - min_width=0.2, - p=1.0), - ]), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage', backend_args=backend_args), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -train_pipeline_stage2 = [ - dict(type='LoadImage', backend_args=backend_args), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict( - type='RandomBBoxTransform', - shift_factor=0., - scale_factor=[0.75, 1.25], - rotate_factor=60), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='mmdet.YOLOXHSVRandomAug'), - dict( - type='Albumentation', - transforms=[ - dict(type='Blur', p=0.1), - dict(type='MedianBlur', p=0.1), - dict( - type='CoarseDropout', - max_holes=1, - max_height=0.4, - max_width=0.4, - min_holes=1, - min_height=0.2, - min_width=0.2, - p=0.5), - ]), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] - -# train datasets -dataset_humanart = dict( - type='RepeatDataset', - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=[], - ), - times=3) - -dataset_aic = dict( - type='AicDataset', - data_root=data_root, - data_mode=data_mode, - ann_file='aic/annotations/aic_train.json', - data_prefix=dict(img='pose/ai_challenge/ai_challenger_keypoint' - '_train_20170902/keypoint_train_images_20170902/'), - pipeline=[ - dict( - type='KeypointConverter', - num_keypoints=17, - mapping=[ - (0, 6), - (1, 8), - (2, 10), - (3, 5), - (4, 7), - (5, 9), - (6, 12), - (7, 14), - (8, 16), - (9, 11), - (10, 13), - (11, 15), - ]) - ], -) - -# data loaders -train_dataloader = dict( - batch_size=128 * 2, - num_workers=10, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type='CombinedDataset', - metainfo=dict(from_file='configs/_base_/datasets/humanart.py'), - datasets=[dataset_humanart, dataset_aic], - pipeline=train_pipeline, - test_mode=False, - )) -val_dataloader = dict( - batch_size=64, - num_workers=10, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - # bbox_file='data/coco/person_detection_results/' - # 'COCO_val2017_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# hooks -default_hooks = dict( - checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=1)) - -custom_hooks = [ - dict( - type='EMAHook', - ema_type='ExpMomentumEMA', - momentum=0.0002, - update_buffers=True, - priority=49), - dict( - type='mmdet.PipelineSwitchHook', - switch_epoch=max_epochs - stage2_num_epochs, - switch_pipeline=train_pipeline_stage2) -] - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-s_8xb256-420e_humanart-256x192.py b/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-s_8xb256-420e_humanart-256x192.py index 3f5c37fee7..b4263f25e7 100644 --- a/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-s_8xb256-420e_humanart-256x192.py +++ b/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-s_8xb256-420e_humanart-256x192.py @@ -200,8 +200,8 @@ data_root=data_root, data_mode=data_mode, ann_file='HumanArt/annotations/validation_humanart.json', - # bbox_file=f'{data_root}person_detection_results/' - # 'COCO_val2017_detections_AP_H_56_person.json', + # bbox_file=f'{data_root}HumanArt/person_detection_results/' + # 'HumanArt_validation_detections_AP_H_56_person.json', data_prefix=dict(img=''), test_mode=True, pipeline=val_pipeline, diff --git a/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-t_8xb256-420e_aic-humanart-256x192.py b/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-t_8xb256-420e_aic-humanart-256x192.py deleted file mode 100644 index e12f2b456a..0000000000 --- a/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-t_8xb256-420e_aic-humanart-256x192.py +++ /dev/null @@ -1,273 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -max_epochs = 420 -stage2_num_epochs = 30 -base_lr = 4e-3 - -train_cfg = dict(max_epochs=max_epochs, val_interval=10) -randomness = dict(seed=21) - -# optimizer -optim_wrapper = dict( - type='OptimWrapper', - optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.), - paramwise_cfg=dict( - norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True)) - -# learning rate -param_scheduler = [ - dict( - type='LinearLR', - start_factor=1.0e-5, - by_epoch=False, - begin=0, - end=1000), - dict( - # use cosine lr from 210 to 420 epoch - type='CosineAnnealingLR', - eta_min=base_lr * 0.05, - begin=max_epochs // 2, - end=max_epochs, - T_max=max_epochs // 2, - by_epoch=True, - convert_to_iter_based=True), -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=1024) - -# codec settings -codec = dict( - type='SimCCLabel', - input_size=(192, 256), - sigma=(4.9, 5.66), - simcc_split_ratio=2.0, - normalize=False, - use_dark=False) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - _scope_='mmdet', - type='CSPNeXt', - arch='P5', - expand_ratio=0.5, - deepen_factor=0.167, - widen_factor=0.375, - out_indices=(4, ), - channel_attention=True, - norm_cfg=dict(type='SyncBN'), - act_cfg=dict(type='SiLU'), - init_cfg=dict( - type='Pretrained', - prefix='backbone.', - checkpoint='https://download.openmmlab.com/mmpose/v1/projects/' - 'rtmpose/cspnext-tiny_udp-aic-coco_210e-256x192-cbed682d_20230130.pth' # noqa - )), - head=dict( - type='RTMCCHead', - in_channels=384, - out_channels=17, - input_size=codec['input_size'], - in_featuremap_size=(6, 8), - simcc_split_ratio=codec['simcc_split_ratio'], - final_layer_kernel_size=7, - gau_cfg=dict( - hidden_dims=256, - s=128, - expansion_factor=2, - dropout_rate=0., - drop_path=0., - act_fn='SiLU', - use_rel_bias=False, - pos_enc=False), - loss=dict( - type='KLDiscretLoss', - use_target_weight=True, - beta=10., - label_softmax=True), - decoder=codec), - test_cfg=dict(flip_test=True, )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -backend_args = dict(backend='local') -# backend_args = dict( -# backend='petrel', -# path_mapping=dict({ -# f'{data_root}': 's3://openmmlab/datasets/', -# f'{data_root}': 's3://openmmlab/datasets/' -# })) - -# pipelines -train_pipeline = [ - dict(type='LoadImage', backend_args=backend_args), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict( - type='RandomBBoxTransform', scale_factor=[0.6, 1.4], rotate_factor=80), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='mmdet.YOLOXHSVRandomAug'), - dict( - type='Albumentation', - transforms=[ - dict(type='Blur', p=0.1), - dict(type='MedianBlur', p=0.1), - dict( - type='CoarseDropout', - max_holes=1, - max_height=0.4, - max_width=0.4, - min_holes=1, - min_height=0.2, - min_width=0.2, - p=1.0), - ]), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage', backend_args=backend_args), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -train_pipeline_stage2 = [ - dict(type='LoadImage', backend_args=backend_args), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict( - type='RandomBBoxTransform', - shift_factor=0., - scale_factor=[0.75, 1.25], - rotate_factor=60), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='mmdet.YOLOXHSVRandomAug'), - dict( - type='Albumentation', - transforms=[ - dict(type='Blur', p=0.1), - dict(type='MedianBlur', p=0.1), - dict( - type='CoarseDropout', - max_holes=1, - max_height=0.4, - max_width=0.4, - min_holes=1, - min_height=0.2, - min_width=0.2, - p=0.5), - ]), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] - -# train datasets -dataset_humanart = dict( - type='RepeatDataset', - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=[], - ), - times=3) - -dataset_aic = dict( - type='AicDataset', - data_root=data_root, - data_mode=data_mode, - ann_file='aic/annotations/aic_train.json', - data_prefix=dict(img='pose/ai_challenge/ai_challenger_keypoint' - '_train_20170902/keypoint_train_images_20170902/'), - pipeline=[ - dict( - type='KeypointConverter', - num_keypoints=17, - mapping=[ - (0, 6), - (1, 8), - (2, 10), - (3, 5), - (4, 7), - (5, 9), - (6, 12), - (7, 14), - (8, 16), - (9, 11), - (10, 13), - (11, 15), - ]) - ], -) - -# data loaders -train_dataloader = dict( - batch_size=256, - num_workers=10, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type='CombinedDataset', - metainfo=dict(from_file='configs/_base_/datasets/humanart.py'), - datasets=[dataset_humanart, dataset_aic], - pipeline=train_pipeline, - test_mode=False, - )) -val_dataloader = dict( - batch_size=64, - num_workers=10, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - # bbox_file='data/coco/person_detection_results/' - # 'COCO_val2017_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# hooks -default_hooks = dict( - checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=1)) - -custom_hooks = [ - # Turn off EMA while training the tiny model - # dict( - # type='EMAHook', - # ema_type='ExpMomentumEMA', - # momentum=0.0002, - # update_buffers=True, - # priority=49), - dict( - type='mmdet.PipelineSwitchHook', - switch_epoch=max_epochs - stage2_num_epochs, - switch_pipeline=train_pipeline_stage2) -] - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-t_8xb256-420e_humanart-256x192.py b/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-t_8xb256-420e_humanart-256x192.py deleted file mode 100644 index 273532aae4..0000000000 --- a/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-t_8xb256-420e_humanart-256x192.py +++ /dev/null @@ -1,233 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -max_epochs = 420 -stage2_num_epochs = 30 -base_lr = 4e-3 - -train_cfg = dict(max_epochs=max_epochs, val_interval=10) -randomness = dict(seed=21) - -# optimizer -optim_wrapper = dict( - type='OptimWrapper', - optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.), - paramwise_cfg=dict( - norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True)) - -# learning rate -param_scheduler = [ - dict( - type='LinearLR', - start_factor=1.0e-5, - by_epoch=False, - begin=0, - end=1000), - dict( - # use cosine lr from 210 to 420 epoch - type='CosineAnnealingLR', - eta_min=base_lr * 0.05, - begin=max_epochs // 2, - end=max_epochs, - T_max=max_epochs // 2, - by_epoch=True, - convert_to_iter_based=True), -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=1024) - -# codec settings -codec = dict( - type='SimCCLabel', - input_size=(192, 256), - sigma=(4.9, 5.66), - simcc_split_ratio=2.0, - normalize=False, - use_dark=False) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - _scope_='mmdet', - type='CSPNeXt', - arch='P5', - expand_ratio=0.5, - deepen_factor=0.167, - widen_factor=0.375, - out_indices=(4, ), - channel_attention=True, - norm_cfg=dict(type='SyncBN'), - act_cfg=dict(type='SiLU'), - init_cfg=dict( - type='Pretrained', - prefix='backbone.', - checkpoint='https://download.openmmlab.com/mmpose/v1/projects/' - 'rtmpose/cspnext-tiny_udp-aic-coco_210e-256x192-cbed682d_20230130.pth' # noqa - )), - head=dict( - type='RTMCCHead', - in_channels=384, - out_channels=17, - input_size=codec['input_size'], - in_featuremap_size=(6, 8), - simcc_split_ratio=codec['simcc_split_ratio'], - final_layer_kernel_size=7, - gau_cfg=dict( - hidden_dims=256, - s=128, - expansion_factor=2, - dropout_rate=0., - drop_path=0., - act_fn='SiLU', - use_rel_bias=False, - pos_enc=False), - loss=dict( - type='KLDiscretLoss', - use_target_weight=True, - beta=10., - label_softmax=True), - decoder=codec), - test_cfg=dict(flip_test=True)) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -backend_args = dict(backend='local') -# backend_args = dict( -# backend='petrel', -# path_mapping=dict({ -# f'{data_root}': 's3://openmmlab/datasets/detection/coco/', -# f'{data_root}': 's3://openmmlab/datasets/detection/coco/' -# })) - -# pipelines -train_pipeline = [ - dict(type='LoadImage', backend_args=backend_args), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict( - type='RandomBBoxTransform', scale_factor=[0.6, 1.4], rotate_factor=80), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='mmdet.YOLOXHSVRandomAug'), - dict( - type='Albumentation', - transforms=[ - dict(type='Blur', p=0.1), - dict(type='MedianBlur', p=0.1), - dict( - type='CoarseDropout', - max_holes=1, - max_height=0.4, - max_width=0.4, - min_holes=1, - min_height=0.2, - min_width=0.2, - p=1.), - ]), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage', backend_args=backend_args), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -train_pipeline_stage2 = [ - dict(type='LoadImage', backend_args=backend_args), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict( - type='RandomBBoxTransform', - shift_factor=0., - scale_factor=[0.75, 1.25], - rotate_factor=60), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='mmdet.YOLOXHSVRandomAug'), - dict( - type='Albumentation', - transforms=[ - dict(type='Blur', p=0.1), - dict(type='MedianBlur', p=0.1), - dict( - type='CoarseDropout', - max_holes=1, - max_height=0.4, - max_width=0.4, - min_holes=1, - min_height=0.2, - min_width=0.2, - p=0.5), - ]), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=256, - num_workers=10, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=64, - num_workers=10, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - # bbox_file=f'{data_root}person_detection_results/' - # 'COCO_val2017_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# hooks -default_hooks = dict( - checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=1)) - -custom_hooks = [ - # Turn off EMA while training the tiny model - # dict( - # type='EMAHook', - # ema_type='ExpMomentumEMA', - # momentum=0.0002, - # update_buffers=True, - # priority=49), - dict( - type='mmdet.PipelineSwitchHook', - switch_epoch=max_epochs - stage2_num_epochs, - switch_pipeline=train_pipeline_stage2) -] - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/rtmpose/humanart/rtmpose_humanart.md b/configs/body_2d_keypoint/rtmpose/humanart/rtmpose_humanart.md new file mode 100644 index 0000000000..bfd925b2c8 --- /dev/null +++ b/configs/body_2d_keypoint/rtmpose/humanart/rtmpose_humanart.md @@ -0,0 +1,110 @@ + + +

+RTMPose (arXiv'2023) + +```bibtex +@misc{https://doi.org/10.48550/arxiv.2303.07399, + doi = {10.48550/ARXIV.2303.07399}, + url = {https://arxiv.org/abs/2303.07399}, + author = {Jiang, Tao and Lu, Peng and Zhang, Li and Ma, Ningsheng and Han, Rui and Lyu, Chengqi and Li, Yining and Chen, Kai}, + keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences}, + title = {RTMPose: Real-Time Multi-Person Pose Estimation based on MMPose}, + publisher = {arXiv}, + year = {2023}, + copyright = {Creative Commons Attribution 4.0 International} +} + +``` + +
+ + + +
+RTMDet (arXiv'2022) + +```bibtex +@misc{lyu2022rtmdet, + title={RTMDet: An Empirical Study of Designing Real-Time Object Detectors}, + author={Chengqi Lyu and Wenwei Zhang and Haian Huang and Yue Zhou and Yudong Wang and Yanyi Liu and Shilong Zhang and Kai Chen}, + year={2022}, + eprint={2212.07784}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} +``` + +
+ + + +
+COCO (ECCV'2014) + +```bibtex +@inproceedings{lin2014microsoft, + title={Microsoft coco: Common objects in context}, + author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence}, + booktitle={European conference on computer vision}, + pages={740--755}, + year={2014}, + organization={Springer} +} +``` + +
+ +
+Human-Art (CVPR'2023) + +```bibtex +@inproceedings{ju2023humanart, + title={Human-Art: A Versatile Human-Centric Dataset Bridging Natural and Artificial Scenes}, + author={Ju, Xuan and Zeng, Ailing and Jianan, Wang and Qiang, Xu and Lei, Zhang}, + booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), + year={2023}} +``` + +
+ +Results on Human-Art validation dataset with detector having human AP of 56.2 on Human-Art validation dataset + +| Arch | Input Size | AP | AP50 | AP75 | AR | AR50 | ckpt | log | +| :-------------------------------------------- | :--------: | :---: | :-------------: | :-------------: | :---: | :-------------: | :-------------------------------------------: | :-------------------------------------------: | +| [rtmpose-s-coco](/configs/body_2d_keypoint/rtmpose/coco/rtmpose-s_8xb256-420e_coco-256x192.py) | 256x192 | 0.199 | 0.328 | 0.198 | 0.261 | 0.418 | [ckpt](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-s_simcc-coco_pt-aic-coco_420e-256x192-8edcf0d7_20230127.pth) | [log](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-s_simcc-coco_pt-aic-coco_420e-256x192-8edcf0d7_20230127.json) | +| [rtmpose-s-humanart-coco](/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-s_8xb256-420e_humanart-256x192.py) | 256x192 | 0.311 | 0.462 | 0.323 | 0.381 | 0.540 | [ckpt](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-s_8xb256-420e_humanart-256x192-5a3ac943_20230611.pth) | [log](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-s_8xb256-420e_humanart-256x192-5a3ac943_20230611.json) | +| [rtmpose-m-coco](/configs/body_2d_keypoint/rtmpose/coco/rtmpose-m_8xb256-420e_coco-256x192.py) | 256x192 | 0.239 | 0.372 | 0.243 | 0.302 | 0.455 | [ckpt](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-coco_pt-aic-coco_420e-256x192-d8dd5ca4_20230127.pth) | [log](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-coco_pt-aic-coco_420e-256x192-d8dd5ca4_20230127.json) | +| [rtmpose-m-humanart-coco](/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-m_8xb256-420e_humanart-256x192.py) | 256x192 | 0.355 | 0.503 | 0.377 | 0.417 | 0.568 | [ckpt](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_8xb256-420e_humanart-256x192-8430627b_20230611.pth) | [log](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_8xb256-420e_humanart-256x192-8430627b_20230611.json) | +| [rtmpose-l-coco](/configs/body_2d_keypoint/rtmpose/coco/rtmpose-l_8xb256-420e_coco-256x192.py) | 256x192 | 0.260 | 0.393 | 0.267 | 0.323 | 0.472 | [ckpt](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_simcc-coco_pt-aic-coco_420e-256x192-1352a4d2_20230127.pth) | [log](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_simcc-coco_pt-aic-coco_420e-256x192-1352a4d2_20230127.json) | +| [rtmpose-l-humanart-coco](/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-l_8xb256-420e_humanart-256x192.py) | 256x192 | 0.378 | 0.521 | 0.399 | 0.442 | 0.584 | [ckpt](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_8xb256-420e_humanart-256x192-389f2cb0_20230611.pth) | [log](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_8xb256-420e_humanart-256x192-389f2cb0_20230611.json) | + +Results on Human-Art validation dataset with ground-truth bounding-box + +| Arch | Input Size | AP | AP50 | AP75 | AR | AR50 | ckpt | log | +| :-------------------------------------------- | :--------: | :---: | :-------------: | :-------------: | :---: | :-------------: | :-------------------------------------------: | :-------------------------------------------: | +| [rtmpose-s-coco](/configs/body_2d_keypoint/rtmpose/coco/rtmpose-s_8xb256-420e_coco-256x192.py) | 256x192 | 0.480 | 0.739 | 0.498 | 0.521 | 0.763 | [ckpt](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-s_simcc-coco_pt-aic-coco_420e-256x192-8edcf0d7_20230127.pth) | [log](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-s_simcc-coco_pt-aic-coco_420e-256x192-8edcf0d7_20230127.json) | +| [rtmpose-s-humanart-coco](/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-s_8xb256-420e_humanart-256x192.py) | 256x192 | 0.698 | 0.893 | 0.768 | 0.732 | 0.903 | [ckpt](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-s_8xb256-420e_humanart-256x192-5a3ac943_20230611.pth) | [log](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-s_8xb256-420e_humanart-256x192-5a3ac943_20230611.json) | +| [rtmpose-m-coco](/configs/body_2d_keypoint/rtmpose/coco/rtmpose-m_8xb256-420e_coco-256x192.py) | 256x192 | 0.532 | 0.765 | 0.563 | 0.571 | 0.789 | [ckpt](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-coco_pt-aic-coco_420e-256x192-d8dd5ca4_20230127.pth) | [log](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-coco_pt-aic-coco_420e-256x192-d8dd5ca4_20230127.json) | +| [rtmpose-m-humanart-coco](/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-m_8xb256-420e_humanart-256x192.py) | 256x192 | 0.728 | 0.895 | 0.791 | 0.759 | 0.906 | [ckpt](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_8xb256-420e_humanart-256x192-8430627b_20230611.pth) | [log](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_8xb256-420e_humanart-256x192-8430627b_20230611.json) | +| [rtmpose-l-coco](/configs/body_2d_keypoint/rtmpose/coco/rtmpose-l_8xb256-420e_coco-256x192.py) | 256x192 | 0.564 | 0.789 | 0.602 | 0.599 | 0.808 | [ckpt](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_simcc-coco_pt-aic-coco_420e-256x192-1352a4d2_20230127.pth) | [log](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_simcc-coco_pt-aic-coco_420e-256x192-1352a4d2_20230127.json) | +| [rtmpose-l-humanart-coco](/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-l_8xb256-420e_humanart-256x192.py) | 256x192 | 0.753 | 0.905 | 0.812 | 0.783 | 0.915 | [ckpt](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_8xb256-420e_humanart-256x192-389f2cb0_20230611.pth) | [log](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_8xb256-420e_humanart-256x192-389f2cb0_20230611.json) | + +Results on COCO val2017 with detector having human AP of 56.4 on COCO val2017 dataset + +| Arch | Input Size | AP | AP50 | AP75 | AR | AR50 | ckpt | log | +| :-------------------------------------------- | :--------: | :---: | :-------------: | :-------------: | :---: | :-------------: | :-------------------------------------------: | :-------------------------------------------: | +| [rtmpose-s-coco](/configs/body_2d_keypoint/rtmpose/coco/rtmpose-s_8xb256-420e_coco-256x192.py) | 256x192 | 0.716 | 0.892 | 0.789 | 0.768 | 0.929 | [ckpt](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-s_simcc-coco_pt-aic-coco_420e-256x192-8edcf0d7_20230127.pth) | [log](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-s_simcc-coco_pt-aic-coco_420e-256x192-8edcf0d7_20230127.json) | +| [rtmpose-s-humanart-coco](/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-s_8xb256-420e_humanart-256x192.py) | 256x192 | 0.706 | 0.888 | 0.780 | 0.759 | 0.928 | [ckpt](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-s_8xb256-420e_humanart-256x192-5a3ac943_20230611.pth) | [log](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-s_8xb256-420e_humanart-256x192-5a3ac943_20230611.json) | +| [rtmpose-m-coco](/configs/body_2d_keypoint/rtmpose/coco/rtmpose-m_8xb256-420e_coco-256x192.py) | 256x192 | 0.746 | 0.899 | 0.817 | 0.795 | 0.935 | [ckpt](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-coco_pt-aic-coco_420e-256x192-d8dd5ca4_20230127.pth) | [log](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-coco_pt-aic-coco_420e-256x192-d8dd5ca4_20230127.json) | +| [rtmpose-m-humanart-coco](/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-m_8xb256-420e_humanart-256x192.py) | 256x192 | 0.725 | 0.892 | 0.795 | 0.775 | 0.929 | [ckpt](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_8xb256-420e_humanart-256x192-8430627b_20230611.pth) | [log](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_8xb256-420e_humanart-256x192-8430627b_20230611.json) | +| [rtmpose-l-coco](/configs/body_2d_keypoint/rtmpose/coco/rtmpose-l_8xb256-420e_coco-256x192.py) | 256x192 | 0.758 | 0.906 | 0.826 | 0.806 | 0.942 | [ckpt](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_simcc-coco_pt-aic-coco_420e-256x192-1352a4d2_20230127.pth) | [log](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_simcc-coco_pt-aic-coco_420e-256x192-1352a4d2_20230127.json) | +| [rtmpose-l-humanart-coco](/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-l_8xb256-420e_humanart-256x192.py) | 256x192 | 0.748 | 0.901 | 0.816 | 0.796 | 0.938 | [ckpt](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_8xb256-420e_humanart-256x192-389f2cb0_20230611.pth) | [log](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_8xb256-420e_humanart-256x192-389f2cb0_20230611.json) | + +Results on COCO val2017 with ground-truth bounding box + +| Arch | Input Size | AP | AP50 | AP75 | AR | AR50 | ckpt | log | +| :-------------------------------------------- | :--------: | :---: | :-------------: | :-------------: | :---: | :-------------: | :-------------------------------------------: | :-------------------------------------------: | +| [rtmpose-s-humanart-coco](/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-s_8xb256-420e_humanart-256x192.py) | 256x192 | 0.725 | 0.916 | 0.798 | 0.753 | 0.925 | [ckpt](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-s_8xb256-420e_humanart-256x192-5a3ac943_20230611.pth) | [log](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-s_8xb256-420e_humanart-256x192-5a3ac943_20230611.json) | +| [rtmpose-m-humanart-coco](/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-m_8xb256-420e_humanart-256x192.py) | 256x192 | 0.744 | 0.916 | 0.818 | 0.770 | 0.930 | [ckpt](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_8xb256-420e_humanart-256x192-8430627b_20230611.pth) | [log](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_8xb256-420e_humanart-256x192-8430627b_20230611.json) | +| [rtmpose-l-humanart-coco](/configs/body_2d_keypoint/rtmpose/humanart/rtmpose-l_8xb256-420e_humanart-256x192.py) | 256x192 | 0.770 | 0.927 | 0.840 | 0.794 | 0.939 | [ckpt](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_8xb256-420e_humanart-256x192-389f2cb0_20230611.pth) | [log](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_8xb256-420e_humanart-256x192-389f2cb0_20230611.json) | diff --git a/configs/body_2d_keypoint/rtmpose/humanart/rtmpose_humanart.yml b/configs/body_2d_keypoint/rtmpose/humanart/rtmpose_humanart.yml new file mode 100644 index 0000000000..f0f21b2d6f --- /dev/null +++ b/configs/body_2d_keypoint/rtmpose/humanart/rtmpose_humanart.yml @@ -0,0 +1,106 @@ +Collections: +- Name: RTMPose + Paper: + Title: "RTMPose: Real-Time Multi-Person Pose Estimation based on MMPose" + URL: https://arxiv.org/abs/2303.07399 + README: https://github.com/open-mmlab/mmpose/blob/main/projects/rtmpose/README.md +Models: +- Config: configs/body_2d_keypoint/rtmpose/humanart/rtmpose-l_8xb256-420e_humanart-256x192.py + In Collection: RTMPose + Metadata: + Architecture: &id001 + - RTMPose + Training Data: &id002 + - COCO + - Human-Art + Name: rtmpose-l_8xb256-420e_humanart-256x192 + Results: + - Dataset: COCO + Metrics: + AP: 0.748 + AP@0.5: 0.901 + AP@0.75: 0.816 + AR: 0.796 + AR@0.5: 0.938 + Task: Body 2D Keypoint + - Dataset: Human-Art + Metrics: + AP: 0.378 + AP@0.5: 0.521 + AP@0.75: 0.399 + AR: 0.442 + AR@0.5: 0.584 + Task: Body 2D Keypoint + - Dataset: Human-Art(GT) + Metrics: + AP: 0.753 + AP@0.5: 0.905 + AP@0.75: 0.812 + AR: 0.783 + AR@0.5: 0.915 + Task: Body 2D Keypoint + Weights: https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_8xb256-420e_humanart-256x192-389f2cb0_20230611.pth +- Config: configs/body_2d_keypoint/rtmpose/humanart/rtmpose-m_8xb256-420e_humanart-256x192.py + In Collection: RTMPose + Metadata: + Architecture: *id001 + Training Data: *id002 + Name: rtmpose-m_8xb256-420e_humanart-256x192 + Results: + - Dataset: COCO + Metrics: + AP: 0.725 + AP@0.5: 0.892 + AP@0.75: 0.795 + AR: 0.775 + AR@0.5: 0.929 + Task: Body 2D Keypoint + - Dataset: Human-Art + Metrics: + AP: 0.355 + AP@0.5: 0.503 + AP@0.75: 0.377 + AR: 0.417 + AR@0.5: 0.568 + Task: Body 2D Keypoint + - Dataset: Human-Art(GT) + Metrics: + AP: 0.728 + AP@0.5: 0.895 + AP@0.75: 0.791 + AR: 0.759 + AR@0.5: 0.906 + Task: Body 2D Keypoint + Weights: https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_8xb256-420e_humanart-256x192-8430627b_20230611.pth +- Config: configs/body_2d_keypoint/rtmpose/humanart/rtmpose-s_8xb256-420e_humanart-256x192.py + In Collection: RTMPose + Metadata: + Architecture: *id001 + Training Data: *id002 + Name: rtmpose-s_8xb256-420e_humanart-256x192 + Results: + - Dataset: COCO + Metrics: + AP: 0.706 + AP@0.5: 0.888 + AP@0.75: 0.780 + AR: 0.759 + AR@0.5: 0.928 + Task: Body 2D Keypoint + - Dataset: Human-Art + Metrics: + AP: 0.311 + AP@0.5: 0.462 + AP@0.75: 0.323 + AR: 0.381 + AR@0.5: 0.540 + Task: Body 2D Keypoint + - Dataset: Human-Art(GT) + Metrics: + AP: 0.698 + AP@0.5: 0.893 + AP@0.75: 0.768 + AR: 0.732 + AR@0.5: 0.903 + Task: Body 2D Keypoint + Weights: https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-s_8xb256-420e_humanart-256x192-5a3ac943_20230611.pth diff --git a/configs/body_2d_keypoint/simcc/humanart/simcc_mobilenetv2_wo-deconv-8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/simcc/humanart/simcc_mobilenetv2_wo-deconv-8xb64-210e_humanart-256x192.py deleted file mode 100644 index 6650f03ad8..0000000000 --- a/configs/body_2d_keypoint/simcc/humanart/simcc_mobilenetv2_wo-deconv-8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,124 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=train_cfg['max_epochs'], - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# codec settings -codec = dict( - type='SimCCLabel', input_size=(192, 256), sigma=6.0, simcc_split_ratio=2.0) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='MobileNetV2', - widen_factor=1., - out_indices=(7, ), - init_cfg=dict( - type='Pretrained', - checkpoint='mmcls://mobilenet_v2', - )), - head=dict( - type='SimCCHead', - in_channels=1280, - out_channels=17, - input_size=codec['input_size'], - in_featuremap_size=(6, 8), - simcc_split_ratio=codec['simcc_split_ratio'], - deconv_out_channels=None, - loss=dict(type='KLDiscretLoss', use_target_weight=True), - decoder=codec), - test_cfg=dict(flip_test=True, )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/simcc/humanart/simcc_res50_8xb32-140e_humanart-384x288.py b/configs/body_2d_keypoint/simcc/humanart/simcc_res50_8xb32-140e_humanart-384x288.py deleted file mode 100644 index 7afb8d69b6..0000000000 --- a/configs/body_2d_keypoint/simcc/humanart/simcc_res50_8xb32-140e_humanart-384x288.py +++ /dev/null @@ -1,120 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=140, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=1e-3, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=train_cfg['max_epochs'], - milestones=[90, 120], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# codec settings -codec = dict( - type='SimCCLabel', input_size=(288, 384), sigma=6.0, simcc_split_ratio=2.0) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='ResNet', - depth=50, - init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'), - ), - head=dict( - type='SimCCHead', - in_channels=2048, - out_channels=17, - input_size=codec['input_size'], - in_featuremap_size=(9, 12), - simcc_split_ratio=codec['simcc_split_ratio'], - loss=dict(type='KLDiscretLoss', use_target_weight=True), - decoder=codec), - test_cfg=dict(flip_test=True)) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -test_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=test_pipeline, - )) -test_dataloader = val_dataloader - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/simcc/humanart/simcc_res50_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/simcc/humanart/simcc_res50_8xb64-210e_humanart-256x192.py deleted file mode 100644 index 1c29181998..0000000000 --- a/configs/body_2d_keypoint/simcc/humanart/simcc_res50_8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,114 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=1e-3, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict(type='MultiStepLR', milestones=[170, 200], gamma=0.1, by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# codec settings -codec = dict( - type='SimCCLabel', input_size=(192, 256), sigma=6.0, simcc_split_ratio=2.0) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='ResNet', - depth=50, - init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'), - ), - head=dict( - type='SimCCHead', - in_channels=2048, - out_channels=17, - input_size=codec['input_size'], - in_featuremap_size=(6, 8), - simcc_split_ratio=codec['simcc_split_ratio'], - loss=dict(type='KLDiscretLoss', use_target_weight=True), - decoder=codec), - test_cfg=dict(flip_test=True)) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -test_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=test_pipeline, - )) -test_dataloader = val_dataloader - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/simcc/humanart/simcc_vipnas-mbv3_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/simcc/humanart/simcc_vipnas-mbv3_8xb64-210e_humanart-256x192.py deleted file mode 100644 index 119bf63b57..0000000000 --- a/configs/body_2d_keypoint/simcc/humanart/simcc_vipnas-mbv3_8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,119 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=train_cfg['max_epochs'], - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# codec settings -codec = dict( - type='SimCCLabel', input_size=(192, 256), sigma=6.0, simcc_split_ratio=2.0) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict(type='ViPNAS_MobileNetV3'), - head=dict( - type='SimCCHead', - in_channels=160, - out_channels=17, - input_size=codec['input_size'], - in_featuremap_size=(6, 8), - simcc_split_ratio=codec['simcc_split_ratio'], - deconv_type='vipnas', - deconv_out_channels=(160, 160, 160), - deconv_num_groups=(160, 160, 160), - loss=dict(type='KLDiscretLoss', use_target_weight=True), - decoder=codec), - test_cfg=dict(flip_test=True, )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=data_root + 'HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/README.md b/configs/body_2d_keypoint/topdown_heatmap/README.md index 9e23b874bc..47aae219e4 100644 --- a/configs/body_2d_keypoint/topdown_heatmap/README.md +++ b/configs/body_2d_keypoint/topdown_heatmap/README.md @@ -115,3 +115,19 @@ Results on PoseTrack2018 val with ground-truth bounding boxes. | HRNet-w48 | 256x192 | 84.6 | [hrnet_posetrack18.md](./posetrack18/hrnet_posetrack18.md) | | HRNet-w32 | 256x192 | 83.4 | [hrnet_posetrack18.md](./posetrack18/hrnet_posetrack18.md) | | ResNet-50 | 256x192 | 81.2 | [resnet_posetrack18.md](./posetrack18/resnet_posetrack18.md) | + +### Human-Art Dataset + +Results on Human-Art validation dataset with detector having human AP of 56.2 on Human-Art validation dataset + +| Model | Input Size | AP | AR | Details and Download | +| :-------: | :--------: | :---: | :---: | :---------------------------------------------------: | +| ViTPose-s | 256x192 | 0.381 | 0.448 | [vitpose_humanart.md](./humanart/vitpose_humanart.md) | +| ViTPose-b | 256x192 | 0.410 | 0.475 | [vitpose_humanart.md](./humanart/vitpose_humanart.md) | + +Results on Human-Art validation dataset with ground-truth bounding-box + +| Model | Input Size | AP | AR | Details and Download | +| :-------: | :--------: | :---: | :---: | :---------------------------------------------------: | +| ViTPose-s | 256x192 | 0.738 | 0.768 | [vitpose_humanart.md](./humanart/vitpose_humanart.md) | +| ViTPose-b | 256x192 | 0.759 | 0.790 | [vitpose_humanart.md](./humanart/vitpose_humanart.md) | diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-l_udp_8xb256-210e_aic-humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-l_udp_8xb256-210e_aic-humanart-256x192.py deleted file mode 100644 index 6a54bc5dca..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-l_udp_8xb256-210e_aic-humanart-256x192.py +++ /dev/null @@ -1,284 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -max_epochs = 210 -stage2_num_epochs = 30 -base_lr = 4e-3 - -train_cfg = dict(max_epochs=max_epochs, val_interval=10) -randomness = dict(seed=21) - -# optimizer -optim_wrapper = dict( - type='OptimWrapper', - optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.05), - paramwise_cfg=dict( - norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True)) - -# learning rate -param_scheduler = [ - dict( - type='LinearLR', - start_factor=1.0e-5, - by_epoch=False, - begin=0, - end=1000), - dict( - # use cosine lr from 105 to 210 epoch - type='CosineAnnealingLR', - eta_min=base_lr * 0.05, - begin=max_epochs // 2, - end=max_epochs, - T_max=max_epochs // 2, - by_epoch=True, - convert_to_iter_based=True), -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=1024) - -# codec settings -codec = dict( - type='UDPHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# keypoint mappings -keypoint_mapping_humanart = [ - (0, 0), - (1, 1), - (2, 2), - (3, 3), - (4, 4), - (5, 5), - (6, 6), - (7, 7), - (8, 8), - (9, 9), - (10, 10), - (11, 11), - (12, 12), - (13, 13), - (14, 14), - (15, 15), - (16, 16), -] - -keypoint_mapping_aic = [ - (0, 6), - (1, 8), - (2, 10), - (3, 5), - (4, 7), - (5, 9), - (6, 12), - (7, 14), - (8, 16), - (9, 11), - (10, 13), - (11, 15), - (12, 17), - (13, 18), -] - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - _scope_='mmdet', - type='CSPNeXt', - arch='P5', - expand_ratio=0.5, - deepen_factor=1., - widen_factor=1., - out_indices=(4, ), - channel_attention=True, - norm_cfg=dict(type='SyncBN'), - act_cfg=dict(type='SiLU'), - init_cfg=dict( - type='Pretrained', - prefix='backbone.', - checkpoint='https://download.openmmlab.com/mmdetection/v3.0/' - 'rtmdet/cspnext_rsb_pretrain/' - 'cspnext-l_8xb256-rsb-a1-600e_in1k-6a760974.pth')), - head=dict( - type='HeatmapHead', - in_channels=1024, - out_channels=19, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=False, - output_keypoint_indices=[ - target for _, target in keypoint_mapping_humanart - ])) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -backend_args = dict(backend='local') -# backend_args = dict( -# backend='petrel', -# path_mapping=dict({ -# f'{data_root}': 's3://openmmlab/datasets/', -# f'{data_root}': 's3://openmmlab/datasets/' -# })) - -# pipelines -train_pipeline = [ - dict(type='LoadImage', backend_args=backend_args), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict( - type='RandomBBoxTransform', scale_factor=[0.6, 1.4], rotate_factor=80), - dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), - dict(type='mmdet.YOLOXHSVRandomAug'), - dict( - type='Albumentation', - transforms=[ - dict(type='Blur', p=0.1), - dict(type='MedianBlur', p=0.1), - dict( - type='CoarseDropout', - max_holes=1, - max_height=0.4, - max_width=0.4, - min_holes=1, - min_height=0.2, - min_width=0.2, - p=1.), - ]), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage', backend_args=backend_args), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), - dict(type='PackPoseInputs') -] - -train_pipeline_stage2 = [ - dict(type='LoadImage', backend_args=backend_args), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict( - type='RandomBBoxTransform', - shift_factor=0., - scale_factor=[0.75, 1.25], - rotate_factor=60), - dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), - dict(type='mmdet.YOLOXHSVRandomAug'), - dict( - type='Albumentation', - transforms=[ - dict(type='Blur', p=0.1), - dict(type='MedianBlur', p=0.1), - dict( - type='CoarseDropout', - max_holes=1, - max_height=0.4, - max_width=0.4, - min_holes=1, - min_height=0.2, - min_width=0.2, - p=0.5), - ]), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] - -# train datasets -dataset_humanart = dict( - type='RepeatDataset', - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=[ - dict( - type='KeypointConverter', - num_keypoints=19, - mapping=keypoint_mapping_humanart) - ], - ), - times=3) - -dataset_aic = dict( - type='AicDataset', - data_root=data_root, - data_mode=data_mode, - ann_file='aic/annotations/aic_train.json', - data_prefix=dict(img='pose/ai_challenge/ai_challenger_keypoint' - '_train_20170902/keypoint_train_images_20170902/'), - pipeline=[ - dict( - type='KeypointConverter', - num_keypoints=19, - mapping=keypoint_mapping_aic) - ], -) - -# data loaders -train_dataloader = dict( - batch_size=256, - num_workers=10, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type='CombinedDataset', - metainfo=dict(from_file='configs/_base_/datasets/humanart_aic.py'), - datasets=[dataset_humanart, dataset_aic], - pipeline=train_pipeline, - test_mode=False, - )) -val_dataloader = dict( - batch_size=64, - num_workers=10, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - # bbox_file='data/coco/person_detection_results/' - # 'COCO_val2017_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# hooks -default_hooks = dict( - checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=1)) - -custom_hooks = [ - dict( - type='EMAHook', - ema_type='ExpMomentumEMA', - momentum=0.0002, - update_buffers=True, - priority=49), - dict( - type='mmdet.PipelineSwitchHook', - switch_epoch=max_epochs - stage2_num_epochs, - switch_pipeline=train_pipeline_stage2) -] - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-l_udp_8xb256-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-l_udp_8xb256-210e_humanart-256x192.py deleted file mode 100644 index 0870973244..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-l_udp_8xb256-210e_humanart-256x192.py +++ /dev/null @@ -1,214 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -max_epochs = 210 -stage2_num_epochs = 30 -base_lr = 4e-3 - -train_cfg = dict(max_epochs=max_epochs, val_interval=10) -randomness = dict(seed=21) - -# optimizer -optim_wrapper = dict( - type='OptimWrapper', - optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.05), - paramwise_cfg=dict( - norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True)) - -# learning rate -param_scheduler = [ - dict( - type='LinearLR', - start_factor=1.0e-5, - by_epoch=False, - begin=0, - end=1000), - dict( - # use cosine lr from 105 to 210 epoch - type='CosineAnnealingLR', - eta_min=base_lr * 0.05, - begin=max_epochs // 2, - end=max_epochs, - T_max=max_epochs // 2, - by_epoch=True, - convert_to_iter_based=True), -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=1024) - -# codec settings -codec = dict( - type='UDPHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - _scope_='mmdet', - type='CSPNeXt', - arch='P5', - expand_ratio=0.5, - deepen_factor=1., - widen_factor=1., - out_indices=(4, ), - channel_attention=True, - norm_cfg=dict(type='SyncBN'), - act_cfg=dict(type='SiLU'), - init_cfg=dict( - type='Pretrained', - prefix='backbone.', - checkpoint='https://download.openmmlab.com/mmdetection/v3.0/' - 'rtmdet/cspnext_rsb_pretrain/' - 'cspnext-l_8xb256-rsb-a1-600e_in1k-6a760974.pth')), - head=dict( - type='HeatmapHead', - in_channels=1024, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=False, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -backend_args = dict(backend='local') -# backend_args = dict( -# backend='petrel', -# path_mapping=dict({ -# f'{data_root}': 's3://openmmlab/datasets/detection/coco/', -# f'{data_root}': 's3://openmmlab/datasets/detection/coco/' -# })) - -# pipelines -train_pipeline = [ - dict(type='LoadImage', backend_args=backend_args), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict( - type='RandomBBoxTransform', scale_factor=[0.6, 1.4], rotate_factor=80), - dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), - dict(type='mmdet.YOLOXHSVRandomAug'), - dict( - type='Albumentation', - transforms=[ - dict(type='Blur', p=0.1), - dict(type='MedianBlur', p=0.1), - dict( - type='CoarseDropout', - max_holes=1, - max_height=0.4, - max_width=0.4, - min_holes=1, - min_height=0.2, - min_width=0.2, - p=1.), - ]), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage', backend_args=backend_args), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), - dict(type='PackPoseInputs') -] - -train_pipeline_stage2 = [ - dict(type='LoadImage', backend_args=backend_args), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict( - type='RandomBBoxTransform', - shift_factor=0., - scale_factor=[0.75, 1.25], - rotate_factor=60), - dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), - dict(type='mmdet.YOLOXHSVRandomAug'), - dict( - type='Albumentation', - transforms=[ - dict(type='Blur', p=0.1), - dict(type='MedianBlur', p=0.1), - dict( - type='CoarseDropout', - max_holes=1, - max_height=0.4, - max_width=0.4, - min_holes=1, - min_height=0.2, - min_width=0.2, - p=0.5), - ]), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=256, - num_workers=10, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=64, - num_workers=10, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - # bbox_file='data/coco/person_detection_results/' - # 'COCO_val2017_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# hooks -default_hooks = dict( - checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=1)) - -custom_hooks = [ - dict( - type='EMAHook', - ema_type='ExpMomentumEMA', - momentum=0.0002, - update_buffers=True, - priority=49), - dict( - type='mmdet.PipelineSwitchHook', - switch_epoch=max_epochs - stage2_num_epochs, - switch_pipeline=train_pipeline_stage2) -] - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-m_udp_8xb256-210e_aic-humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-m_udp_8xb256-210e_aic-humanart-256x192.py deleted file mode 100644 index 1e42debdbb..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-m_udp_8xb256-210e_aic-humanart-256x192.py +++ /dev/null @@ -1,284 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -max_epochs = 210 -stage2_num_epochs = 30 -base_lr = 4e-3 - -train_cfg = dict(max_epochs=max_epochs, val_interval=10) -randomness = dict(seed=21) - -# optimizer -optim_wrapper = dict( - type='OptimWrapper', - optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.05), - paramwise_cfg=dict( - norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True)) - -# learning rate -param_scheduler = [ - dict( - type='LinearLR', - start_factor=1.0e-5, - by_epoch=False, - begin=0, - end=1000), - dict( - # use cosine lr from 105 to 210 epoch - type='CosineAnnealingLR', - eta_min=base_lr * 0.05, - begin=max_epochs // 2, - end=max_epochs, - T_max=max_epochs // 2, - by_epoch=True, - convert_to_iter_based=True), -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=1024) - -# codec settings -codec = dict( - type='UDPHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# keypoint mappings -keypoint_mapping_humanart = [ - (0, 0), - (1, 1), - (2, 2), - (3, 3), - (4, 4), - (5, 5), - (6, 6), - (7, 7), - (8, 8), - (9, 9), - (10, 10), - (11, 11), - (12, 12), - (13, 13), - (14, 14), - (15, 15), - (16, 16), -] - -keypoint_mapping_aic = [ - (0, 6), - (1, 8), - (2, 10), - (3, 5), - (4, 7), - (5, 9), - (6, 12), - (7, 14), - (8, 16), - (9, 11), - (10, 13), - (11, 15), - (12, 17), - (13, 18), -] - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - _scope_='mmdet', - type='CSPNeXt', - arch='P5', - expand_ratio=0.5, - deepen_factor=0.67, - widen_factor=0.75, - out_indices=(4, ), - channel_attention=True, - norm_cfg=dict(type='SyncBN'), - act_cfg=dict(type='SiLU'), - init_cfg=dict( - type='Pretrained', - prefix='backbone.', - checkpoint='https://download.openmmlab.com/mmdetection/v3.0/' - 'rtmdet/cspnext_rsb_pretrain/' - 'cspnext-m_8xb256-rsb-a1-600e_in1k-ecb3bbd9.pth')), - head=dict( - type='HeatmapHead', - in_channels=768, - out_channels=19, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=False, - output_keypoint_indices=[ - target for _, target in keypoint_mapping_humanart - ])) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -backend_args = dict(backend='local') -# backend_args = dict( -# backend='petrel', -# path_mapping=dict({ -# f'{data_root}': 's3://openmmlab/datasets/', -# f'{data_root}': 's3://openmmlab/datasets/' -# })) - -# pipelines -train_pipeline = [ - dict(type='LoadImage', backend_args=backend_args), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict( - type='RandomBBoxTransform', scale_factor=[0.6, 1.4], rotate_factor=80), - dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), - dict(type='mmdet.YOLOXHSVRandomAug'), - dict( - type='Albumentation', - transforms=[ - dict(type='Blur', p=0.1), - dict(type='MedianBlur', p=0.1), - dict( - type='CoarseDropout', - max_holes=1, - max_height=0.4, - max_width=0.4, - min_holes=1, - min_height=0.2, - min_width=0.2, - p=1.), - ]), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage', backend_args=backend_args), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), - dict(type='PackPoseInputs') -] - -train_pipeline_stage2 = [ - dict(type='LoadImage', backend_args=backend_args), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict( - type='RandomBBoxTransform', - shift_factor=0., - scale_factor=[0.75, 1.25], - rotate_factor=60), - dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), - dict(type='mmdet.YOLOXHSVRandomAug'), - dict( - type='Albumentation', - transforms=[ - dict(type='Blur', p=0.1), - dict(type='MedianBlur', p=0.1), - dict( - type='CoarseDropout', - max_holes=1, - max_height=0.4, - max_width=0.4, - min_holes=1, - min_height=0.2, - min_width=0.2, - p=0.5), - ]), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] - -# train datasets -dataset_humanart = dict( - type='RepeatDataset', - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=[ - dict( - type='KeypointConverter', - num_keypoints=19, - mapping=keypoint_mapping_humanart) - ], - ), - times=3) - -dataset_aic = dict( - type='AicDataset', - data_root=data_root, - data_mode=data_mode, - ann_file='aic/annotations/aic_train.json', - data_prefix=dict(img='pose/ai_challenge/ai_challenger_keypoint' - '_train_20170902/keypoint_train_images_20170902/'), - pipeline=[ - dict( - type='KeypointConverter', - num_keypoints=19, - mapping=keypoint_mapping_aic) - ], -) - -# data loaders -train_dataloader = dict( - batch_size=256, - num_workers=10, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type='CombinedDataset', - metainfo=dict(from_file='configs/_base_/datasets/humanart_aic.py'), - datasets=[dataset_humanart, dataset_aic], - pipeline=train_pipeline, - test_mode=False, - )) -val_dataloader = dict( - batch_size=64, - num_workers=10, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - # bbox_file='data/coco/person_detection_results/' - # 'COCO_val2017_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# hooks -default_hooks = dict( - checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=1)) - -custom_hooks = [ - dict( - type='EMAHook', - ema_type='ExpMomentumEMA', - momentum=0.0002, - update_buffers=True, - priority=49), - dict( - type='mmdet.PipelineSwitchHook', - switch_epoch=max_epochs - stage2_num_epochs, - switch_pipeline=train_pipeline_stage2) -] - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-m_udp_8xb256-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-m_udp_8xb256-210e_humanart-256x192.py deleted file mode 100644 index 7589652a94..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-m_udp_8xb256-210e_humanart-256x192.py +++ /dev/null @@ -1,214 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -max_epochs = 210 -stage2_num_epochs = 30 -base_lr = 4e-3 - -train_cfg = dict(max_epochs=max_epochs, val_interval=10) -randomness = dict(seed=21) - -# optimizer -optim_wrapper = dict( - type='OptimWrapper', - optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.05), - paramwise_cfg=dict( - norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True)) - -# learning rate -param_scheduler = [ - dict( - type='LinearLR', - start_factor=1.0e-5, - by_epoch=False, - begin=0, - end=1000), - dict( - # use cosine lr from 105 to 210 epoch - type='CosineAnnealingLR', - eta_min=base_lr * 0.05, - begin=max_epochs // 2, - end=max_epochs, - T_max=max_epochs // 2, - by_epoch=True, - convert_to_iter_based=True), -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=1024) - -# codec settings -codec = dict( - type='UDPHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - _scope_='mmdet', - type='CSPNeXt', - arch='P5', - expand_ratio=0.5, - deepen_factor=0.67, - widen_factor=0.75, - out_indices=(4, ), - channel_attention=True, - norm_cfg=dict(type='SyncBN'), - act_cfg=dict(type='SiLU'), - init_cfg=dict( - type='Pretrained', - prefix='backbone.', - checkpoint='https://download.openmmlab.com/mmdetection/v3.0/' - 'rtmdet/cspnext_rsb_pretrain/' - 'cspnext-m_8xb256-rsb-a1-600e_in1k-ecb3bbd9.pth')), - head=dict( - type='HeatmapHead', - in_channels=768, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=False, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -backend_args = dict(backend='local') -# backend_args = dict( -# backend='petrel', -# path_mapping=dict({ -# f'{data_root}': 's3://openmmlab/datasets/detection/coco/', -# f'{data_root}': 's3://openmmlab/datasets/detection/coco/' -# })) - -# pipelines -train_pipeline = [ - dict(type='LoadImage', backend_args=backend_args), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict( - type='RandomBBoxTransform', scale_factor=[0.6, 1.4], rotate_factor=80), - dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), - dict(type='mmdet.YOLOXHSVRandomAug'), - dict( - type='Albumentation', - transforms=[ - dict(type='Blur', p=0.1), - dict(type='MedianBlur', p=0.1), - dict( - type='CoarseDropout', - max_holes=1, - max_height=0.4, - max_width=0.4, - min_holes=1, - min_height=0.2, - min_width=0.2, - p=1.), - ]), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage', backend_args=backend_args), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), - dict(type='PackPoseInputs') -] - -train_pipeline_stage2 = [ - dict(type='LoadImage', backend_args=backend_args), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict( - type='RandomBBoxTransform', - shift_factor=0., - scale_factor=[0.75, 1.25], - rotate_factor=60), - dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), - dict(type='mmdet.YOLOXHSVRandomAug'), - dict( - type='Albumentation', - transforms=[ - dict(type='Blur', p=0.1), - dict(type='MedianBlur', p=0.1), - dict( - type='CoarseDropout', - max_holes=1, - max_height=0.4, - max_width=0.4, - min_holes=1, - min_height=0.2, - min_width=0.2, - p=0.5), - ]), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=256, - num_workers=10, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=64, - num_workers=10, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - # bbox_file='data/coco/person_detection_results/' - # 'COCO_val2017_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# hooks -default_hooks = dict( - checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=1)) - -custom_hooks = [ - dict( - type='EMAHook', - ema_type='ExpMomentumEMA', - momentum=0.0002, - update_buffers=True, - priority=49), - dict( - type='mmdet.PipelineSwitchHook', - switch_epoch=max_epochs - stage2_num_epochs, - switch_pipeline=train_pipeline_stage2) -] - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-s_udp_8xb256-210e_aic-humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-s_udp_8xb256-210e_aic-humanart-256x192.py deleted file mode 100644 index 49770fb71b..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-s_udp_8xb256-210e_aic-humanart-256x192.py +++ /dev/null @@ -1,284 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -max_epochs = 210 -stage2_num_epochs = 30 -base_lr = 4e-3 - -train_cfg = dict(max_epochs=max_epochs, val_interval=10) -randomness = dict(seed=21) - -# optimizer -optim_wrapper = dict( - type='OptimWrapper', - optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.0), - paramwise_cfg=dict( - norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True)) - -# learning rate -param_scheduler = [ - dict( - type='LinearLR', - start_factor=1.0e-5, - by_epoch=False, - begin=0, - end=1000), - dict( - # use cosine lr from 105 to 210 epoch - type='CosineAnnealingLR', - eta_min=base_lr * 0.05, - begin=max_epochs // 2, - end=max_epochs, - T_max=max_epochs // 2, - by_epoch=True, - convert_to_iter_based=True), -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=1024) - -# codec settings -codec = dict( - type='UDPHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# keypoint mappings -keypoint_mapping_humanart = [ - (0, 0), - (1, 1), - (2, 2), - (3, 3), - (4, 4), - (5, 5), - (6, 6), - (7, 7), - (8, 8), - (9, 9), - (10, 10), - (11, 11), - (12, 12), - (13, 13), - (14, 14), - (15, 15), - (16, 16), -] - -keypoint_mapping_aic = [ - (0, 6), - (1, 8), - (2, 10), - (3, 5), - (4, 7), - (5, 9), - (6, 12), - (7, 14), - (8, 16), - (9, 11), - (10, 13), - (11, 15), - (12, 17), - (13, 18), -] - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - _scope_='mmdet', - type='CSPNeXt', - arch='P5', - expand_ratio=0.5, - deepen_factor=0.33, - widen_factor=0.5, - out_indices=(4, ), - channel_attention=True, - norm_cfg=dict(type='SyncBN'), - act_cfg=dict(type='SiLU'), - init_cfg=dict( - type='Pretrained', - prefix='backbone.', - checkpoint='https://download.openmmlab.com/mmdetection/v3.0/' - 'rtmdet/cspnext_rsb_pretrain/' - 'cspnext-s_imagenet_600e-ea671761.pth')), - head=dict( - type='HeatmapHead', - in_channels=512, - out_channels=19, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=False, - output_keypoint_indices=[ - target for _, target in keypoint_mapping_humanart - ])) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -backend_args = dict(backend='local') -# backend_args = dict( -# backend='petrel', -# path_mapping=dict({ -# f'{data_root}': 's3://openmmlab/datasets/', -# f'{data_root}': 's3://openmmlab/datasets/' -# })) - -# pipelines -train_pipeline = [ - dict(type='LoadImage', backend_args=backend_args), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict( - type='RandomBBoxTransform', scale_factor=[0.6, 1.4], rotate_factor=80), - dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), - dict(type='mmdet.YOLOXHSVRandomAug'), - dict( - type='Albumentation', - transforms=[ - dict(type='Blur', p=0.1), - dict(type='MedianBlur', p=0.1), - dict( - type='CoarseDropout', - max_holes=1, - max_height=0.4, - max_width=0.4, - min_holes=1, - min_height=0.2, - min_width=0.2, - p=1.), - ]), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage', backend_args=backend_args), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), - dict(type='PackPoseInputs') -] - -train_pipeline_stage2 = [ - dict(type='LoadImage', backend_args=backend_args), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict( - type='RandomBBoxTransform', - shift_factor=0., - scale_factor=[0.75, 1.25], - rotate_factor=60), - dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), - dict(type='mmdet.YOLOXHSVRandomAug'), - dict( - type='Albumentation', - transforms=[ - dict(type='Blur', p=0.1), - dict(type='MedianBlur', p=0.1), - dict( - type='CoarseDropout', - max_holes=1, - max_height=0.4, - max_width=0.4, - min_holes=1, - min_height=0.2, - min_width=0.2, - p=0.5), - ]), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] - -# train datasets -dataset_humanart = dict( - type='RepeatDataset', - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=[ - dict( - type='KeypointConverter', - num_keypoints=19, - mapping=keypoint_mapping_humanart) - ], - ), - times=3) - -dataset_aic = dict( - type='AicDataset', - data_root=data_root, - data_mode=data_mode, - ann_file='aic/annotations/aic_train.json', - data_prefix=dict(img='pose/ai_challenge/ai_challenger_keypoint' - '_train_20170902/keypoint_train_images_20170902/'), - pipeline=[ - dict( - type='KeypointConverter', - num_keypoints=19, - mapping=keypoint_mapping_aic) - ], -) - -# data loaders -train_dataloader = dict( - batch_size=256, - num_workers=10, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type='CombinedDataset', - metainfo=dict(from_file='configs/_base_/datasets/humanart_aic.py'), - datasets=[dataset_humanart, dataset_aic], - pipeline=train_pipeline, - test_mode=False, - )) -val_dataloader = dict( - batch_size=64, - num_workers=10, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - # bbox_file='data/coco/person_detection_results/' - # 'COCO_val2017_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# hooks -default_hooks = dict( - checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=1)) - -custom_hooks = [ - dict( - type='EMAHook', - ema_type='ExpMomentumEMA', - momentum=0.0002, - update_buffers=True, - priority=49), - dict( - type='mmdet.PipelineSwitchHook', - switch_epoch=max_epochs - stage2_num_epochs, - switch_pipeline=train_pipeline_stage2) -] - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-s_udp_8xb256-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-s_udp_8xb256-210e_humanart-256x192.py deleted file mode 100644 index ae9eadff52..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-s_udp_8xb256-210e_humanart-256x192.py +++ /dev/null @@ -1,214 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -max_epochs = 210 -stage2_num_epochs = 30 -base_lr = 4e-3 - -train_cfg = dict(max_epochs=max_epochs, val_interval=10) -randomness = dict(seed=21) - -# optimizer -optim_wrapper = dict( - type='OptimWrapper', - optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.05), - paramwise_cfg=dict( - norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True)) - -# learning rate -param_scheduler = [ - dict( - type='LinearLR', - start_factor=1.0e-5, - by_epoch=False, - begin=0, - end=1000), - dict( - # use cosine lr from 105 to 210 epoch - type='CosineAnnealingLR', - eta_min=base_lr * 0.05, - begin=max_epochs // 2, - end=max_epochs, - T_max=max_epochs // 2, - by_epoch=True, - convert_to_iter_based=True), -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=1024) - -# codec settings -codec = dict( - type='UDPHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - _scope_='mmdet', - type='CSPNeXt', - arch='P5', - expand_ratio=0.5, - deepen_factor=0.33, - widen_factor=0.5, - out_indices=(4, ), - channel_attention=True, - norm_cfg=dict(type='SyncBN'), - act_cfg=dict(type='SiLU'), - init_cfg=dict( - type='Pretrained', - prefix='backbone.', - checkpoint='https://download.openmmlab.com/mmdetection/v3.0/' - 'rtmdet/cspnext_rsb_pretrain/' - 'cspnext-s_imagenet_600e-ea671761.pth')), - head=dict( - type='HeatmapHead', - in_channels=512, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=False, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -backend_args = dict(backend='local') -# backend_args = dict( -# backend='petrel', -# path_mapping=dict({ -# f'{data_root}': 's3://openmmlab/datasets/detection/coco/', -# f'{data_root}': 's3://openmmlab/datasets/detection/coco/' -# })) - -# pipelines -train_pipeline = [ - dict(type='LoadImage', backend_args=backend_args), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict( - type='RandomBBoxTransform', scale_factor=[0.6, 1.4], rotate_factor=80), - dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), - dict(type='mmdet.YOLOXHSVRandomAug'), - dict( - type='Albumentation', - transforms=[ - dict(type='Blur', p=0.1), - dict(type='MedianBlur', p=0.1), - dict( - type='CoarseDropout', - max_holes=1, - max_height=0.4, - max_width=0.4, - min_holes=1, - min_height=0.2, - min_width=0.2, - p=1.), - ]), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage', backend_args=backend_args), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), - dict(type='PackPoseInputs') -] - -train_pipeline_stage2 = [ - dict(type='LoadImage', backend_args=backend_args), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict( - type='RandomBBoxTransform', - shift_factor=0., - scale_factor=[0.75, 1.25], - rotate_factor=60), - dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), - dict(type='mmdet.YOLOXHSVRandomAug'), - dict( - type='Albumentation', - transforms=[ - dict(type='Blur', p=0.1), - dict(type='MedianBlur', p=0.1), - dict( - type='CoarseDropout', - max_holes=1, - max_height=0.4, - max_width=0.4, - min_holes=1, - min_height=0.2, - min_width=0.2, - p=0.5), - ]), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=256, - num_workers=10, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=64, - num_workers=10, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - # bbox_file='data/coco/person_detection_results/' - # 'COCO_val2017_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# hooks -default_hooks = dict( - checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=1)) - -custom_hooks = [ - dict( - type='EMAHook', - ema_type='ExpMomentumEMA', - momentum=0.0002, - update_buffers=True, - priority=49), - dict( - type='mmdet.PipelineSwitchHook', - switch_epoch=max_epochs - stage2_num_epochs, - switch_pipeline=train_pipeline_stage2) -] - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-tiny_udp_8xb256-210e_aic-humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-tiny_udp_8xb256-210e_aic-humanart-256x192.py deleted file mode 100644 index 9a4efa6361..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-tiny_udp_8xb256-210e_aic-humanart-256x192.py +++ /dev/null @@ -1,284 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -max_epochs = 210 -stage2_num_epochs = 30 -base_lr = 4e-3 - -train_cfg = dict(max_epochs=max_epochs, val_interval=10) -randomness = dict(seed=21) - -# optimizer -optim_wrapper = dict( - type='OptimWrapper', - optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.0), - paramwise_cfg=dict( - norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True)) - -# learning rate -param_scheduler = [ - dict( - type='LinearLR', - start_factor=1.0e-5, - by_epoch=False, - begin=0, - end=1000), - dict( - # use cosine lr from 105 to 210 epoch - type='CosineAnnealingLR', - eta_min=base_lr * 0.05, - begin=max_epochs // 2, - end=max_epochs, - T_max=max_epochs // 2, - by_epoch=True, - convert_to_iter_based=True), -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=1024) - -# codec settings -codec = dict( - type='UDPHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# keypoint mappings -keypoint_mapping_humanart = [ - (0, 0), - (1, 1), - (2, 2), - (3, 3), - (4, 4), - (5, 5), - (6, 6), - (7, 7), - (8, 8), - (9, 9), - (10, 10), - (11, 11), - (12, 12), - (13, 13), - (14, 14), - (15, 15), - (16, 16), -] - -keypoint_mapping_aic = [ - (0, 6), - (1, 8), - (2, 10), - (3, 5), - (4, 7), - (5, 9), - (6, 12), - (7, 14), - (8, 16), - (9, 11), - (10, 13), - (11, 15), - (12, 17), - (13, 18), -] - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - _scope_='mmdet', - type='CSPNeXt', - arch='P5', - expand_ratio=0.5, - deepen_factor=0.167, - widen_factor=0.375, - out_indices=(4, ), - channel_attention=True, - norm_cfg=dict(type='SyncBN'), - act_cfg=dict(type='SiLU'), - init_cfg=dict( - type='Pretrained', - prefix='backbone.', - checkpoint='https://download.openmmlab.com/mmdetection/v3.0/' - 'rtmdet/cspnext_rsb_pretrain/' - 'cspnext-tiny_imagenet_600e-3a2dd350.pth')), - head=dict( - type='HeatmapHead', - in_channels=384, - out_channels=19, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=False, - output_keypoint_indices=[ - target for _, target in keypoint_mapping_humanart - ])) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -backend_args = dict(backend='local') -# backend_args = dict( -# backend='petrel', -# path_mapping=dict({ -# f'{data_root}': 's3://openmmlab/datasets/', -# f'{data_root}': 's3://openmmlab/datasets/' -# })) - -# pipelines -train_pipeline = [ - dict(type='LoadImage', backend_args=backend_args), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict( - type='RandomBBoxTransform', scale_factor=[0.6, 1.4], rotate_factor=80), - dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), - dict(type='mmdet.YOLOXHSVRandomAug'), - dict( - type='Albumentation', - transforms=[ - dict(type='Blur', p=0.1), - dict(type='MedianBlur', p=0.1), - dict( - type='CoarseDropout', - max_holes=1, - max_height=0.4, - max_width=0.4, - min_holes=1, - min_height=0.2, - min_width=0.2, - p=1.), - ]), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage', backend_args=backend_args), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), - dict(type='PackPoseInputs') -] - -train_pipeline_stage2 = [ - dict(type='LoadImage', backend_args=backend_args), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict( - type='RandomBBoxTransform', - shift_factor=0., - scale_factor=[0.75, 1.25], - rotate_factor=60), - dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), - dict(type='mmdet.YOLOXHSVRandomAug'), - dict( - type='Albumentation', - transforms=[ - dict(type='Blur', p=0.1), - dict(type='MedianBlur', p=0.1), - dict( - type='CoarseDropout', - max_holes=1, - max_height=0.4, - max_width=0.4, - min_holes=1, - min_height=0.2, - min_width=0.2, - p=0.5), - ]), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] - -# train datasets -dataset_humanart = dict( - type='RepeatDataset', - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=[ - dict( - type='KeypointConverter', - num_keypoints=19, - mapping=keypoint_mapping_humanart) - ], - ), - times=3) - -dataset_aic = dict( - type='AicDataset', - data_root=data_root, - data_mode=data_mode, - ann_file='aic/annotations/aic_train.json', - data_prefix=dict(img='pose/ai_challenge/ai_challenger_keypoint' - '_train_20170902/keypoint_train_images_20170902/'), - pipeline=[ - dict( - type='KeypointConverter', - num_keypoints=19, - mapping=keypoint_mapping_aic) - ], -) - -# data loaders -train_dataloader = dict( - batch_size=256, - num_workers=10, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type='CombinedDataset', - metainfo=dict(from_file='configs/_base_/datasets/humanart_aic.py'), - datasets=[dataset_humanart, dataset_aic], - pipeline=train_pipeline, - test_mode=False, - )) -val_dataloader = dict( - batch_size=64, - num_workers=10, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - # bbox_file='data/coco/person_detection_results/' - # 'COCO_val2017_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# hooks -default_hooks = dict( - checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=1)) - -custom_hooks = [ - # dict( - # type='EMAHook', - # ema_type='ExpMomentumEMA', - # momentum=0.0002, - # update_buffers=True, - # priority=49), - dict( - type='mmdet.PipelineSwitchHook', - switch_epoch=max_epochs - stage2_num_epochs, - switch_pipeline=train_pipeline_stage2) -] - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-tiny_udp_8xb256-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-tiny_udp_8xb256-210e_humanart-256x192.py deleted file mode 100644 index d94061485f..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/cspnext-tiny_udp_8xb256-210e_humanart-256x192.py +++ /dev/null @@ -1,214 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -max_epochs = 210 -stage2_num_epochs = 30 -base_lr = 4e-3 - -train_cfg = dict(max_epochs=max_epochs, val_interval=10) -randomness = dict(seed=21) - -# optimizer -optim_wrapper = dict( - type='OptimWrapper', - optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.05), - paramwise_cfg=dict( - norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True)) - -# learning rate -param_scheduler = [ - dict( - type='LinearLR', - start_factor=1.0e-5, - by_epoch=False, - begin=0, - end=1000), - dict( - # use cosine lr from 105 to 210 epoch - type='CosineAnnealingLR', - eta_min=base_lr * 0.05, - begin=max_epochs // 2, - end=max_epochs, - T_max=max_epochs // 2, - by_epoch=True, - convert_to_iter_based=True), -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=1024) - -# codec settings -codec = dict( - type='UDPHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - _scope_='mmdet', - type='CSPNeXt', - arch='P5', - expand_ratio=0.5, - deepen_factor=0.167, - widen_factor=0.375, - out_indices=(4, ), - channel_attention=True, - norm_cfg=dict(type='SyncBN'), - act_cfg=dict(type='SiLU'), - init_cfg=dict( - type='Pretrained', - prefix='backbone.', - checkpoint='https://download.openmmlab.com/mmdetection/v3.0/' - 'rtmdet/cspnext_rsb_pretrain/' - 'cspnext-tiny_imagenet_600e-3a2dd350.pth')), - head=dict( - type='HeatmapHead', - in_channels=384, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=False, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -backend_args = dict(backend='local') -# backend_args = dict( -# backend='petrel', -# path_mapping=dict({ -# f'{data_root}': 's3://openmmlab/datasets/detection/coco/', -# f'{data_root}': 's3://openmmlab/datasets/detection/coco/' -# })) - -# pipelines -train_pipeline = [ - dict(type='LoadImage', backend_args=backend_args), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict( - type='RandomBBoxTransform', scale_factor=[0.6, 1.4], rotate_factor=80), - dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), - dict(type='mmdet.YOLOXHSVRandomAug'), - dict( - type='Albumentation', - transforms=[ - dict(type='Blur', p=0.1), - dict(type='MedianBlur', p=0.1), - dict( - type='CoarseDropout', - max_holes=1, - max_height=0.4, - max_width=0.4, - min_holes=1, - min_height=0.2, - min_width=0.2, - p=1.), - ]), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage', backend_args=backend_args), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), - dict(type='PackPoseInputs') -] - -train_pipeline_stage2 = [ - dict(type='LoadImage', backend_args=backend_args), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict( - type='RandomBBoxTransform', - shift_factor=0., - scale_factor=[0.75, 1.25], - rotate_factor=60), - dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), - dict(type='mmdet.YOLOXHSVRandomAug'), - dict( - type='Albumentation', - transforms=[ - dict(type='Blur', p=0.1), - dict(type='MedianBlur', p=0.1), - dict( - type='CoarseDropout', - max_holes=1, - max_height=0.4, - max_width=0.4, - min_holes=1, - min_height=0.2, - min_width=0.2, - p=0.5), - ]), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=256, - num_workers=10, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=64, - num_workers=10, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - # bbox_file='data/coco/person_detection_results/' - # 'COCO_val2017_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# hooks -default_hooks = dict( - checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=1)) - -custom_hooks = [ - # dict( - # type='EMAHook', - # ema_type='ExpMomentumEMA', - # momentum=0.0002, - # update_buffers=True, - # priority=49), - dict( - type='mmdet.PipelineSwitchHook', - switch_epoch=max_epochs - stage2_num_epochs, - switch_pipeline=train_pipeline_stage2) -] - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/hrnet_humanart.md b/configs/body_2d_keypoint/topdown_heatmap/humanart/hrnet_humanart.md deleted file mode 100644 index fe3d0989ec..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/hrnet_humanart.md +++ /dev/null @@ -1,44 +0,0 @@ - - -
-HRNet (CVPR'2019) - -```bibtex -@inproceedings{sun2019deep, - title={Deep high-resolution representation learning for human pose estimation}, - author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong}, - booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition}, - pages={5693--5703}, - year={2019} -} -``` - -
- - - -
-Human-Art (CVPR'2023) - -```bibtex -@inproceedings{ju2023human, - title={Human-Art: A Versatile Human-Centric Dataset Bridging Natural and Artificial Scenes}, - author={Ju, Xuan and Zeng, Ailing and Wang, Jianan and Xu, Qiang and Zhang, Lei}, - booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, - year={2023}, -} -``` - -
- -Results on Human-Art validation set with ground-truth bounding box - -| Arch | Input Size | AP | AP50 | AP75 | AR | AR50 | ckpt | log | -| :-------------------------------------------------------------- | :--------: | :---: | :-------------: | :-------------: | :---: | :-------------: | :-------------------------------------------------------------: | :-------: | -| [pose_hrnet_w48](/configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/humanart/hrnet_w48_humanart_256x192.py) | 256x192 | 0.764 | 0.906 | 0.824 | 0.794 | 0.918 | [ckpt](https://drive.google.com/file/d/1gs1RCxRcItUHwA5N8P5_9mKcgwLiBjOO/view?usp=share_link) | [log](<>) | - -Results on COCO val2017 set with ground-truth bounding box - -| Arch | Input Size | AP | AP50 | AP75 | AR | AR50 | ckpt | log | -| :-------------------------------------------------------------- | :--------: | :---: | :-------------: | :-------------: | :---: | :-------------: | :-------------------------------------------------------------: | :-------: | -| [pose_hrnet_w48](/configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/humanart/hrnet_w48_humanart_256x192.py) | 256x192 | 0.772 | 0.936 | 0.847 | 0.800 | 0.942 | [ckpt](https://drive.google.com/file/d/1gs1RCxRcItUHwA5N8P5_9mKcgwLiBjOO/view?usp=share_link) | [log](<>) | diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_2xmspn50_8xb32-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_2xmspn50_8xb32-210e_humanart-256x192.py deleted file mode 100644 index 7fbdeafabc..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_2xmspn50_8xb32-210e_humanart-256x192.py +++ /dev/null @@ -1,152 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-3, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=256) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -# multiple kernel_sizes of heatmap gaussian for 'Megvii' approach. -kernel_sizes = [15, 11, 9, 7, 5] -codec = [ - dict( - type='MegviiHeatmap', - input_size=(192, 256), - heatmap_size=(48, 64), - kernel_size=kernel_size) for kernel_size in kernel_sizes -] - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='MSPN', - unit_channels=256, - num_stages=2, - num_units=4, - num_blocks=[3, 4, 6, 3], - norm_cfg=dict(type='BN'), - init_cfg=dict( - type='Pretrained', - checkpoint='torchvision://resnet50', - )), - head=dict( - type='MSPNHead', - out_shape=(64, 48), - unit_channels=256, - out_channels=17, - num_stages=2, - num_units=4, - norm_cfg=dict(type='BN'), - # each sub list is for a stage - # and each element in each list is for a unit - level_indices=[0, 1, 2, 3] + [1, 2, 3, 4], - loss=([ - dict( - type='KeypointMSELoss', - use_target_weight=True, - loss_weight=0.25) - ] * 3 + [ - dict( - type='KeypointOHKMMSELoss', - use_target_weight=True, - loss_weight=1.) - ]) * 2, - decoder=codec[-1]), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=False, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec[0]['input_size']), - dict(type='GenerateTarget', multilevel=True, encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec[0]['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=32, - num_workers=4, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=4, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'annotations/person_keypoints_val2017.json', - nms_mode='none') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_2xrsn50_8xb32-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_2xrsn50_8xb32-210e_humanart-256x192.py deleted file mode 100644 index 5215e422bf..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_2xrsn50_8xb32-210e_humanart-256x192.py +++ /dev/null @@ -1,154 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-3, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=256) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -# multiple kernel_sizes of heatmap gaussian for 'Megvii' approach. -kernel_sizes = [15, 11, 9, 7, 5] -codec = [ - dict( - type='MegviiHeatmap', - input_size=(192, 256), - heatmap_size=(48, 64), - kernel_size=kernel_size) for kernel_size in kernel_sizes -] - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='RSN', - unit_channels=256, - num_stages=2, - num_units=4, - num_blocks=[3, 4, 6, 3], - num_steps=4, - norm_cfg=dict(type='BN'), - ), - head=dict( - type='MSPNHead', - out_shape=(64, 48), - unit_channels=256, - out_channels=17, - num_stages=2, - num_units=4, - norm_cfg=dict(type='BN'), - # each sub list is for a stage - # and each element in each list is for a unit - level_indices=[0, 1, 2, 3] + [1, 2, 3, 4], - loss=([ - dict( - type='KeypointMSELoss', - use_target_weight=True, - loss_weight=0.25) - ] * 3 + [ - dict( - type='KeypointOHKMMSELoss', - use_target_weight=True, - loss_weight=1.) - ]) * 2, - decoder=codec[-1]), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=False, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec[0]['input_size']), - dict(type='GenerateTarget', multilevel=True, encoder=codec), - dict(type='PackPoseInputs') -] - -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec[0]['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=32, - num_workers=4, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=4, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'annotations/person_keypoints_val2017.json', - nms_mode='none') -test_evaluator = val_evaluator - -# fp16 settings -fp16 = dict(loss_scale='dynamic') diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_3xmspn50_8xb32-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_3xmspn50_8xb32-210e_humanart-256x192.py deleted file mode 100644 index 4ea845985e..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_3xmspn50_8xb32-210e_humanart-256x192.py +++ /dev/null @@ -1,152 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-3, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=256) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -# multiple kernel_sizes of heatmap gaussian for 'Megvii' approach. -kernel_sizes = [15, 11, 9, 7, 5] -codec = [ - dict( - type='MegviiHeatmap', - input_size=(192, 256), - heatmap_size=(48, 64), - kernel_size=kernel_size) for kernel_size in kernel_sizes -] - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='MSPN', - unit_channels=256, - num_stages=3, - num_units=4, - num_blocks=[3, 4, 6, 3], - norm_cfg=dict(type='BN'), - init_cfg=dict( - type='Pretrained', - checkpoint='torchvision://resnet50', - )), - head=dict( - type='MSPNHead', - out_shape=(64, 48), - unit_channels=256, - out_channels=17, - num_stages=3, - num_units=4, - norm_cfg=dict(type='BN'), - # each sub list is for a stage - # and each element in each list is for a unit - level_indices=[0, 1, 2, 3] * 2 + [1, 2, 3, 4], - loss=([ - dict( - type='KeypointMSELoss', - use_target_weight=True, - loss_weight=0.25) - ] * 3 + [ - dict( - type='KeypointOHKMMSELoss', - use_target_weight=True, - loss_weight=1.) - ]) * 3, - decoder=codec[-1]), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=False, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec[0]['input_size']), - dict(type='GenerateTarget', multilevel=True, encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec[0]['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=32, - num_workers=4, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=4, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'annotations/person_keypoints_val2017.json', - nms_mode='none') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_3xrsn50_8xb32-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_3xrsn50_8xb32-210e_humanart-256x192.py deleted file mode 100644 index 3286db101f..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_3xrsn50_8xb32-210e_humanart-256x192.py +++ /dev/null @@ -1,154 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-3, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=256) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -# multiple kernel_sizes of heatmap gaussian for 'Megvii' approach. -kernel_sizes = [15, 11, 9, 7, 5] -codec = [ - dict( - type='MegviiHeatmap', - input_size=(192, 256), - heatmap_size=(48, 64), - kernel_size=kernel_size) for kernel_size in kernel_sizes -] - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='RSN', - unit_channels=256, - num_stages=3, - num_units=4, - num_blocks=[3, 4, 6, 3], - num_steps=4, - norm_cfg=dict(type='BN'), - ), - head=dict( - type='MSPNHead', - out_shape=(64, 48), - unit_channels=256, - out_channels=17, - num_stages=3, - num_units=4, - norm_cfg=dict(type='BN'), - # each sub list is for a stage - # and each element in each list is for a unit - level_indices=[0, 1, 2, 3] * 2 + [1, 2, 3, 4], - loss=([ - dict( - type='KeypointMSELoss', - use_target_weight=True, - loss_weight=0.25) - ] * 3 + [ - dict( - type='KeypointOHKMMSELoss', - use_target_weight=True, - loss_weight=1.) - ]) * 3, - decoder=codec[-1]), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=False, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec[0]['input_size']), - dict(type='GenerateTarget', multilevel=True, encoder=codec), - dict(type='PackPoseInputs') -] - -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec[0]['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=32, - num_workers=4, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=4, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'annotations/person_keypoints_val2017.json', - nms_mode='none') -test_evaluator = val_evaluator - -# fp16 settings -fp16 = dict(loss_scale='dynamic') diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_4xmspn50_8xb32-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_4xmspn50_8xb32-210e_humanart-256x192.py deleted file mode 100644 index 43f2705bf1..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_4xmspn50_8xb32-210e_humanart-256x192.py +++ /dev/null @@ -1,152 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-3, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=256) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -# multiple kernel_sizes of heatmap gaussian for 'Megvii' approach. -kernel_sizes = [15, 11, 9, 7, 5] -codec = [ - dict( - type='MegviiHeatmap', - input_size=(192, 256), - heatmap_size=(48, 64), - kernel_size=kernel_size) for kernel_size in kernel_sizes -] - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='MSPN', - unit_channels=256, - num_stages=4, - num_units=4, - num_blocks=[3, 4, 6, 3], - norm_cfg=dict(type='BN'), - init_cfg=dict( - type='Pretrained', - checkpoint='torchvision://resnet50', - )), - head=dict( - type='MSPNHead', - out_shape=(64, 48), - unit_channels=256, - out_channels=17, - num_stages=4, - num_units=4, - norm_cfg=dict(type='BN'), - # each sub list is for a stage - # and each element in each list is for a unit - level_indices=[0, 1, 2, 3] * 3 + [1, 2, 3, 4], - loss=([ - dict( - type='KeypointMSELoss', - use_target_weight=True, - loss_weight=0.25) - ] * 3 + [ - dict( - type='KeypointOHKMMSELoss', - use_target_weight=True, - loss_weight=1.) - ]) * 4, - decoder=codec[-1]), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=False, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec[0]['input_size']), - dict(type='GenerateTarget', multilevel=True, encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec[0]['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=32, - num_workers=4, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=4, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'annotations/person_keypoints_val2017.json', - nms_mode='none') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-base-simple_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-base-simple_8xb64-210e_humanart-256x192.py deleted file mode 100644 index f61aaa4bf7..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-base-simple_8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,153 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -custom_imports = dict( - imports=['mmpose.engine.optim_wrappers.layer_decay_optim_wrapper'], - allow_failed_imports=False) - -optim_wrapper = dict( - optimizer=dict( - type='AdamW', lr=5e-4, betas=(0.9, 0.999), weight_decay=0.1), - paramwise_cfg=dict( - num_layers=12, - layer_decay_rate=0.75, - custom_keys={ - 'bias': dict(decay_multi=0.0), - 'pos_embed': dict(decay_mult=0.0), - 'relative_position_bias_table': dict(decay_mult=0.0), - 'norm': dict(decay_mult=0.0), - }, - ), - constructor='LayerDecayOptimWrapperConstructor', - clip_grad=dict(max_norm=1., norm_type=2), -) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict( - checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=1)) - -# codec settings -codec = dict( - type='UDPHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='mmcls.VisionTransformer', - arch='base', - img_size=(256, 192), - patch_size=16, - qkv_bias=True, - drop_path_rate=0.3, - with_cls_token=False, - output_cls_token=False, - patch_cfg=dict(padding=2), - init_cfg=dict( - type='Pretrained', - checkpoint='https://download.openmmlab.com/mmpose/' - 'v1/pretrained_models/mae_pretrain_vit_base.pth'), - ), - neck=dict(type='FeatureMapProcessor', scale_factor=4.0, apply_relu=True), - head=dict( - type='HeatmapHead', - in_channels=768, - out_channels=17, - deconv_out_channels=[], - deconv_kernel_sizes=[], - final_layer=dict(kernel_size=3, padding=1), - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec, - ), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=False, - )) - -# base dataset settings -data_root = 'data/' -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=4, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=4, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-huge-simple_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-huge-simple_8xb64-210e_humanart-256x192.py deleted file mode 100644 index 2b8dd0bc2b..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-huge-simple_8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,153 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -custom_imports = dict( - imports=['mmpose.engine.optim_wrappers.layer_decay_optim_wrapper'], - allow_failed_imports=False) - -optim_wrapper = dict( - optimizer=dict( - type='AdamW', lr=5e-4, betas=(0.9, 0.999), weight_decay=0.1), - paramwise_cfg=dict( - num_layers=32, - layer_decay_rate=0.85, - custom_keys={ - 'bias': dict(decay_multi=0.0), - 'pos_embed': dict(decay_mult=0.0), - 'relative_position_bias_table': dict(decay_mult=0.0), - 'norm': dict(decay_mult=0.0), - }, - ), - constructor='LayerDecayOptimWrapperConstructor', - clip_grad=dict(max_norm=1., norm_type=2), -) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict( - checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=1)) - -# codec settings -codec = dict( - type='UDPHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='mmcls.VisionTransformer', - arch='huge', - img_size=(256, 192), - patch_size=16, - qkv_bias=True, - drop_path_rate=0.55, - with_cls_token=False, - output_cls_token=False, - patch_cfg=dict(padding=2), - init_cfg=dict( - type='Pretrained', - checkpoint='https://download.openmmlab.com/mmpose/' - 'v1/pretrained_models/mae_pretrain_vit_huge.pth'), - ), - neck=dict(type='FeatureMapProcessor', scale_factor=4.0, apply_relu=True), - head=dict( - type='HeatmapHead', - in_channels=1280, - out_channels=17, - deconv_out_channels=[], - deconv_kernel_sizes=[], - final_layer=dict(kernel_size=3, padding=1), - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec, - ), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=False, - )) - -# base dataset settings -data_root = 'data/' -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=4, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=4, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-huge_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-huge_8xb64-210e_humanart-256x192.py deleted file mode 100644 index 925f68e3d1..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-huge_8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,150 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -custom_imports = dict( - imports=['mmpose.engine.optim_wrappers.layer_decay_optim_wrapper'], - allow_failed_imports=False) - -optim_wrapper = dict( - optimizer=dict( - type='AdamW', lr=5e-4, betas=(0.9, 0.999), weight_decay=0.1), - paramwise_cfg=dict( - num_layers=32, - layer_decay_rate=0.85, - custom_keys={ - 'bias': dict(decay_multi=0.0), - 'pos_embed': dict(decay_mult=0.0), - 'relative_position_bias_table': dict(decay_mult=0.0), - 'norm': dict(decay_mult=0.0), - }, - ), - constructor='LayerDecayOptimWrapperConstructor', - clip_grad=dict(max_norm=1., norm_type=2), -) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict( - checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=1)) - -# codec settings -codec = dict( - type='UDPHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='mmcls.VisionTransformer', - arch='huge', - img_size=(256, 192), - patch_size=16, - qkv_bias=True, - drop_path_rate=0.55, - with_cls_token=False, - output_cls_token=False, - patch_cfg=dict(padding=2), - init_cfg=dict( - type='Pretrained', - checkpoint='https://download.openmmlab.com/mmpose/' - 'v1/pretrained_models/mae_pretrain_vit_huge.pth'), - ), - head=dict( - type='HeatmapHead', - in_channels=1280, - out_channels=17, - deconv_out_channels=(256, 256), - deconv_kernel_sizes=(4, 4), - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=False, - )) - -# base dataset settings -data_root = 'data/' -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=4, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=4, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-large-simple_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-large-simple_8xb64-210e_humanart-256x192.py deleted file mode 100644 index 9352f615c2..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-large-simple_8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,153 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -custom_imports = dict( - imports=['mmpose.engine.optim_wrappers.layer_decay_optim_wrapper'], - allow_failed_imports=False) - -optim_wrapper = dict( - optimizer=dict( - type='AdamW', lr=5e-4, betas=(0.9, 0.999), weight_decay=0.1), - paramwise_cfg=dict( - num_layers=24, - layer_decay_rate=0.8, - custom_keys={ - 'bias': dict(decay_multi=0.0), - 'pos_embed': dict(decay_mult=0.0), - 'relative_position_bias_table': dict(decay_mult=0.0), - 'norm': dict(decay_mult=0.0), - }, - ), - constructor='LayerDecayOptimWrapperConstructor', - clip_grad=dict(max_norm=1., norm_type=2), -) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict( - checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=1)) - -# codec settings -codec = dict( - type='UDPHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='mmcls.VisionTransformer', - arch='large', - img_size=(256, 192), - patch_size=16, - qkv_bias=True, - drop_path_rate=0.5, - with_cls_token=False, - output_cls_token=False, - patch_cfg=dict(padding=2), - init_cfg=dict( - type='Pretrained', - checkpoint='https://download.openmmlab.com/mmpose/' - 'v1/pretrained_models/mae_pretrain_vit_large.pth'), - ), - neck=dict(type='FeatureMapProcessor', scale_factor=4.0, apply_relu=True), - head=dict( - type='HeatmapHead', - in_channels=1024, - out_channels=17, - deconv_out_channels=[], - deconv_kernel_sizes=[], - final_layer=dict(kernel_size=3, padding=1), - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec, - ), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=False, - )) - -# base dataset settings -data_root = 'data/' -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=4, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=4, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-large_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-large_8xb64-210e_humanart-256x192.py deleted file mode 100644 index 7ea9dbf395..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-large_8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,150 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -custom_imports = dict( - imports=['mmpose.engine.optim_wrappers.layer_decay_optim_wrapper'], - allow_failed_imports=False) - -optim_wrapper = dict( - optimizer=dict( - type='AdamW', lr=5e-4, betas=(0.9, 0.999), weight_decay=0.1), - paramwise_cfg=dict( - num_layers=24, - layer_decay_rate=0.8, - custom_keys={ - 'bias': dict(decay_multi=0.0), - 'pos_embed': dict(decay_mult=0.0), - 'relative_position_bias_table': dict(decay_mult=0.0), - 'norm': dict(decay_mult=0.0), - }, - ), - constructor='LayerDecayOptimWrapperConstructor', - clip_grad=dict(max_norm=1., norm_type=2), -) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict( - checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=1)) - -# codec settings -codec = dict( - type='UDPHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='mmcls.VisionTransformer', - arch='large', - img_size=(256, 192), - patch_size=16, - qkv_bias=True, - drop_path_rate=0.5, - with_cls_token=False, - output_cls_token=False, - patch_cfg=dict(padding=2), - init_cfg=dict( - type='Pretrained', - checkpoint='https://download.openmmlab.com/mmpose/' - 'v1/pretrained_models/mae_pretrain_vit_large.pth'), - ), - head=dict( - type='HeatmapHead', - in_channels=1024, - out_channels=17, - deconv_out_channels=(256, 256), - deconv_kernel_sizes=(4, 4), - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=False, - )) - -# base dataset settings -data_root = 'data/' -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=4, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=4, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-small-simple_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-small-simple_8xb64-210e_humanart-256x192.py deleted file mode 100644 index a0e2c9f849..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-small-simple_8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,158 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -custom_imports = dict( - imports=['mmpose.engine.optim_wrappers.layer_decay_optim_wrapper'], - allow_failed_imports=False) - -optim_wrapper = dict( - optimizer=dict( - type='AdamW', lr=5e-4, betas=(0.9, 0.999), weight_decay=0.1), - paramwise_cfg=dict( - num_layers=12, - layer_decay_rate=0.8, - custom_keys={ - 'bias': dict(decay_multi=0.0), - 'pos_embed': dict(decay_mult=0.0), - 'relative_position_bias_table': dict(decay_mult=0.0), - 'norm': dict(decay_mult=0.0), - }, - ), - constructor='LayerDecayOptimWrapperConstructor', - clip_grad=dict(max_norm=1., norm_type=2), -) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict( - checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=1)) - -# codec settings -codec = dict( - type='UDPHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='mmcls.VisionTransformer', - arch={ - 'embed_dims': 384, - 'num_layers': 12, - 'num_heads': 12, - 'feedforward_channels': 384 * 4 - }, - img_size=(256, 192), - patch_size=16, - qkv_bias=True, - drop_path_rate=0.1, - with_cls_token=False, - output_cls_token=False, - patch_cfg=dict(padding=2), - init_cfg=dict( - type='Pretrained', - checkpoint='https://download.openmmlab.com/mmpose/' - 'v1/pretrained_models/mae_pretrain_vit_small.pth'), - ), - neck=dict(type='FeatureMapProcessor', scale_factor=4.0, apply_relu=True), - head=dict( - type='HeatmapHead', - in_channels=384, - out_channels=17, - deconv_out_channels=[], - deconv_kernel_sizes=[], - final_layer=dict(kernel_size=3, padding=1), - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec, - ), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=False, - )) - -# base dataset settings -data_root = 'data/' -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=4, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=4, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_alexnet_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_alexnet_8xb64-210e_humanart-256x192.py deleted file mode 100644 index 08935037f4..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_alexnet_8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,117 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(40, 56), sigma=2) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict(type='AlexNet', num_classes=-1), - head=dict( - type='HeatmapHead', - in_channels=256, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_cpm_8xb32-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_cpm_8xb32-210e_humanart-384x288.py deleted file mode 100644 index 33f521d47f..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_cpm_8xb32-210e_humanart-384x288.py +++ /dev/null @@ -1,125 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(36, 48), sigma=3) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='CPM', - in_channels=3, - out_channels=17, - feat_channels=128, - num_stages=6), - head=dict( - type='CPMHead', - in_channels=17, - out_channels=17, - num_stages=6, - deconv_out_channels=None, - final_layer=None, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_cpm_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_cpm_8xb64-210e_humanart-256x192.py deleted file mode 100644 index 7ff91cf1ce..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_cpm_8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,125 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(24, 32), sigma=2) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='CPM', - in_channels=3, - out_channels=17, - feat_channels=128, - num_stages=6), - head=dict( - type='CPMHead', - in_channels=17, - out_channels=17, - num_stages=6, - deconv_out_channels=None, - final_layer=None, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hourglass52_8xb32-210e_humanart-256x256.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hourglass52_8xb32-210e_humanart-256x256.py deleted file mode 100644 index 8bcfec9abf..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hourglass52_8xb32-210e_humanart-256x256.py +++ /dev/null @@ -1,122 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(256, 256), heatmap_size=(64, 64), sigma=2) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='HourglassNet', - num_stacks=1, - ), - head=dict( - type='CPMHead', - in_channels=256, - out_channels=17, - num_stages=1, - deconv_out_channels=None, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hourglass52_8xb32-210e_humanart-384x384.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hourglass52_8xb32-210e_humanart-384x384.py deleted file mode 100644 index d49e4688fa..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hourglass52_8xb32-210e_humanart-384x384.py +++ /dev/null @@ -1,122 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(384, 384), heatmap_size=(96, 96), sigma=3) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='HourglassNet', - num_stacks=1, - ), - head=dict( - type='CPMHead', - in_channels=256, - out_channels=17, - num_stages=1, - deconv_out_channels=None, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrformer-base_8xb32-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrformer-base_8xb32-210e_humanart-256x192.py deleted file mode 100644 index c8227aaea2..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrformer-base_8xb32-210e_humanart-256x192.py +++ /dev/null @@ -1,174 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict( - optimizer=dict( - type='AdamW', - lr=5e-4, - betas=(0.9, 0.999), - weight_decay=0.01, - ), - paramwise_cfg=dict( - custom_keys={'relative_position_bias_table': dict(decay_mult=0.)})) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=256) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# model settings -norm_cfg = dict(type='SyncBN', requires_grad=True) -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='HRFormer', - in_channels=3, - norm_cfg=norm_cfg, - extra=dict( - drop_path_rate=0.2, - with_rpe=True, - stage1=dict( - num_modules=1, - num_branches=1, - block='BOTTLENECK', - num_blocks=(2, ), - num_channels=(64, ), - num_heads=[2], - mlp_ratios=[4]), - stage2=dict( - num_modules=1, - num_branches=2, - block='HRFORMERBLOCK', - num_blocks=(2, 2), - num_channels=(78, 156), - num_heads=[2, 4], - mlp_ratios=[4, 4], - window_sizes=[7, 7]), - stage3=dict( - num_modules=4, - num_branches=3, - block='HRFORMERBLOCK', - num_blocks=(2, 2, 2), - num_channels=(78, 156, 312), - num_heads=[2, 4, 8], - mlp_ratios=[4, 4, 4], - window_sizes=[7, 7, 7]), - stage4=dict( - num_modules=2, - num_branches=4, - block='HRFORMERBLOCK', - num_blocks=(2, 2, 2, 2), - num_channels=(78, 156, 312, 624), - num_heads=[2, 4, 8, 16], - mlp_ratios=[4, 4, 4, 4], - window_sizes=[7, 7, 7, 7])), - init_cfg=dict( - type='Pretrained', - checkpoint='https://download.openmmlab.com/mmpose/' - 'pretrain_models/hrformer_base-32815020_20220226.pth'), - ), - head=dict( - type='HeatmapHead', - in_channels=78, - out_channels=17, - deconv_out_channels=None, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] - -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator - -# fp16 settings -fp16 = dict(loss_scale='dynamic') diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrformer-base_8xb32-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrformer-base_8xb32-210e_humanart-384x288.py deleted file mode 100644 index d58d2236e5..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrformer-base_8xb32-210e_humanart-384x288.py +++ /dev/null @@ -1,174 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict( - optimizer=dict( - type='AdamW', - lr=5e-4, - betas=(0.9, 0.999), - weight_decay=0.01, - ), - paramwise_cfg=dict( - custom_keys={'relative_position_bias_table': dict(decay_mult=0.)})) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=256) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) - -# model settings -norm_cfg = dict(type='SyncBN', requires_grad=True) -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='HRFormer', - in_channels=3, - norm_cfg=norm_cfg, - extra=dict( - drop_path_rate=0.2, - with_rpe=True, - stage1=dict( - num_modules=1, - num_branches=1, - block='BOTTLENECK', - num_blocks=(2, ), - num_channels=(64, ), - num_heads=[2], - mlp_ratios=[4]), - stage2=dict( - num_modules=1, - num_branches=2, - block='HRFORMERBLOCK', - num_blocks=(2, 2), - num_channels=(78, 156), - num_heads=[2, 4], - mlp_ratios=[4, 4], - window_sizes=[7, 7]), - stage3=dict( - num_modules=4, - num_branches=3, - block='HRFORMERBLOCK', - num_blocks=(2, 2, 2), - num_channels=(78, 156, 312), - num_heads=[2, 4, 8], - mlp_ratios=[4, 4, 4], - window_sizes=[7, 7, 7]), - stage4=dict( - num_modules=2, - num_branches=4, - block='HRFORMERBLOCK', - num_blocks=(2, 2, 2, 2), - num_channels=(78, 156, 312, 624), - num_heads=[2, 4, 8, 16], - mlp_ratios=[4, 4, 4, 4], - window_sizes=[7, 7, 7, 7])), - init_cfg=dict( - type='Pretrained', - checkpoint='https://download.openmmlab.com/mmpose/' - 'pretrain_models/hrformer_base-32815020_20220226.pth'), - ), - head=dict( - type='HeatmapHead', - in_channels=78, - out_channels=17, - deconv_out_channels=None, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] - -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator - -# fp16 settings -fp16 = dict(loss_scale='dynamic') diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrformer-small_8xb32-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrformer-small_8xb32-210e_humanart-256x192.py deleted file mode 100644 index e41b3be2ac..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrformer-small_8xb32-210e_humanart-256x192.py +++ /dev/null @@ -1,174 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict( - optimizer=dict( - type='AdamW', - lr=5e-4, - betas=(0.9, 0.999), - weight_decay=0.01, - ), - paramwise_cfg=dict( - custom_keys={'relative_position_bias_table': dict(decay_mult=0.)})) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=256) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# model settings -norm_cfg = dict(type='SyncBN', requires_grad=True) -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='HRFormer', - in_channels=3, - norm_cfg=norm_cfg, - extra=dict( - drop_path_rate=0.1, - with_rpe=True, - stage1=dict( - num_modules=1, - num_branches=1, - block='BOTTLENECK', - num_blocks=(2, ), - num_channels=(64, ), - num_heads=[2], - num_mlp_ratios=[4]), - stage2=dict( - num_modules=1, - num_branches=2, - block='HRFORMERBLOCK', - num_blocks=(2, 2), - num_channels=(32, 64), - num_heads=[1, 2], - mlp_ratios=[4, 4], - window_sizes=[7, 7]), - stage3=dict( - num_modules=4, - num_branches=3, - block='HRFORMERBLOCK', - num_blocks=(2, 2, 2), - num_channels=(32, 64, 128), - num_heads=[1, 2, 4], - mlp_ratios=[4, 4, 4], - window_sizes=[7, 7, 7]), - stage4=dict( - num_modules=2, - num_branches=4, - block='HRFORMERBLOCK', - num_blocks=(2, 2, 2, 2), - num_channels=(32, 64, 128, 256), - num_heads=[1, 2, 4, 8], - mlp_ratios=[4, 4, 4, 4], - window_sizes=[7, 7, 7, 7])), - init_cfg=dict( - type='Pretrained', - checkpoint='https://download.openmmlab.com/mmpose/' - 'pretrain_models/hrformer_small-09516375_20220226.pth'), - ), - head=dict( - type='HeatmapHead', - in_channels=32, - out_channels=17, - deconv_out_channels=None, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] - -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator - -# fp16 settings -fp16 = dict(loss_scale='dynamic') diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrformer-small_8xb32-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrformer-small_8xb32-210e_humanart-384x288.py deleted file mode 100644 index 05ff951d84..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrformer-small_8xb32-210e_humanart-384x288.py +++ /dev/null @@ -1,174 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict( - optimizer=dict( - type='AdamW', - lr=5e-4, - betas=(0.9, 0.999), - weight_decay=0.01, - ), - paramwise_cfg=dict( - custom_keys={'relative_position_bias_table': dict(decay_mult=0.)})) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=256) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) - -# model settings -norm_cfg = dict(type='SyncBN', requires_grad=True) -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='HRFormer', - in_channels=3, - norm_cfg=norm_cfg, - extra=dict( - drop_path_rate=0.1, - with_rpe=True, - stage1=dict( - num_modules=1, - num_branches=1, - block='BOTTLENECK', - num_blocks=(2, ), - num_channels=(64, ), - num_heads=[2], - num_mlp_ratios=[4]), - stage2=dict( - num_modules=1, - num_branches=2, - block='HRFORMERBLOCK', - num_blocks=(2, 2), - num_channels=(32, 64), - num_heads=[1, 2], - mlp_ratios=[4, 4], - window_sizes=[7, 7]), - stage3=dict( - num_modules=4, - num_branches=3, - block='HRFORMERBLOCK', - num_blocks=(2, 2, 2), - num_channels=(32, 64, 128), - num_heads=[1, 2, 4], - mlp_ratios=[4, 4, 4], - window_sizes=[7, 7, 7]), - stage4=dict( - num_modules=2, - num_branches=4, - block='HRFORMERBLOCK', - num_blocks=(2, 2, 2, 2), - num_channels=(32, 64, 128, 256), - num_heads=[1, 2, 4, 8], - mlp_ratios=[4, 4, 4, 4], - window_sizes=[7, 7, 7, 7])), - init_cfg=dict( - type='Pretrained', - checkpoint='https://download.openmmlab.com/mmpose/' - 'pretrain_models/hrformer_small-09516375_20220226.pth'), - ), - head=dict( - type='HeatmapHead', - in_channels=32, - out_channels=17, - deconv_out_channels=None, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] - -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator - -# fp16 settings -fp16 = dict(loss_scale='dynamic') diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_8xb64-210e_humanart-256x192.py deleted file mode 100644 index bf9fa25beb..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,150 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='HRNet', - in_channels=3, - extra=dict( - stage1=dict( - num_modules=1, - num_branches=1, - block='BOTTLENECK', - num_blocks=(4, ), - num_channels=(64, )), - stage2=dict( - num_modules=1, - num_branches=2, - block='BASIC', - num_blocks=(4, 4), - num_channels=(32, 64)), - stage3=dict( - num_modules=4, - num_branches=3, - block='BASIC', - num_blocks=(4, 4, 4), - num_channels=(32, 64, 128)), - stage4=dict( - num_modules=3, - num_branches=4, - block='BASIC', - num_blocks=(4, 4, 4, 4), - num_channels=(32, 64, 128, 256))), - init_cfg=dict( - type='Pretrained', - checkpoint='https://download.openmmlab.com/mmpose/' - 'pretrain_models/hrnet_w32-36af842e.pth'), - ), - head=dict( - type='HeatmapHead', - in_channels=32, - out_channels=17, - deconv_out_channels=None, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_8xb64-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_8xb64-210e_humanart-384x288.py deleted file mode 100644 index 7f49e9708f..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_8xb64-210e_humanart-384x288.py +++ /dev/null @@ -1,150 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='HRNet', - in_channels=3, - extra=dict( - stage1=dict( - num_modules=1, - num_branches=1, - block='BOTTLENECK', - num_blocks=(4, ), - num_channels=(64, )), - stage2=dict( - num_modules=1, - num_branches=2, - block='BASIC', - num_blocks=(4, 4), - num_channels=(32, 64)), - stage3=dict( - num_modules=4, - num_branches=3, - block='BASIC', - num_blocks=(4, 4, 4), - num_channels=(32, 64, 128)), - stage4=dict( - num_modules=3, - num_branches=4, - block='BASIC', - num_blocks=(4, 4, 4, 4), - num_channels=(32, 64, 128, 256))), - init_cfg=dict( - type='Pretrained', - checkpoint='https://download.openmmlab.com/mmpose/' - 'pretrain_models/hrnet_w32-36af842e.pth'), - ), - head=dict( - type='HeatmapHead', - in_channels=32, - out_channels=17, - deconv_out_channels=None, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_8xb64-210e_humanart-aic-256x192-combine.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_8xb64-210e_humanart-aic-256x192-combine.py deleted file mode 100644 index 2fa71699cb..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_8xb64-210e_humanart-aic-256x192-combine.py +++ /dev/null @@ -1,221 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict( - checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=3)) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# keypoint mappings -keypoint_mapping_humanart = [ - (0, 0), - (1, 1), - (2, 2), - (3, 3), - (4, 4), - (5, 5), - (6, 6), - (7, 7), - (8, 8), - (9, 9), - (10, 10), - (11, 11), - (12, 12), - (13, 13), - (14, 14), - (15, 15), - (16, 16), -] - -keypoint_mapping_aic = [ - (0, 6), - (1, 8), - (2, 10), - (3, 5), - (4, 7), - (5, 9), - (6, 12), - (7, 14), - (8, 16), - (9, 11), - (10, 13), - (11, 15), - (12, 17), - (13, 18), -] - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - metainfo=dict(from_file='configs/_base_/datasets/humanart_aic.py'), - backbone=dict( - type='HRNet', - in_channels=3, - extra=dict( - stage1=dict( - num_modules=1, - num_branches=1, - block='BOTTLENECK', - num_blocks=(4, ), - num_channels=(64, )), - stage2=dict( - num_modules=1, - num_branches=2, - block='BASIC', - num_blocks=(4, 4), - num_channels=(32, 64)), - stage3=dict( - num_modules=4, - num_branches=3, - block='BASIC', - num_blocks=(4, 4, 4), - num_channels=(32, 64, 128)), - stage4=dict( - num_modules=3, - num_branches=4, - block='BASIC', - num_blocks=(4, 4, 4, 4), - num_channels=(32, 64, 128, 256))), - init_cfg=dict( - type='Pretrained', - checkpoint='https://download.openmmlab.com/mmpose/' - 'pretrain_models/hrnet_w32-36af842e.pth'), - ), - head=dict( - type='HeatmapHead', - in_channels=32, - out_channels=19, - deconv_out_channels=None, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - output_keypoint_indices=[ - target for _, target in keypoint_mapping_humanart - ])) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# train datasets -dataset_humanart = dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=[ - dict( - type='KeypointConverter', - num_keypoints=19, - mapping=keypoint_mapping_humanart) - ], -) - -dataset_aic = dict( - type='AicDataset', - data_root='data/aic/', - data_mode=data_mode, - ann_file='annotations/aic_train.json', - data_prefix=dict(img='ai_challenger_keypoint_train_20170902/' - 'keypoint_train_images_20170902/'), - pipeline=[ - dict( - type='KeypointConverter', - num_keypoints=19, - mapping=keypoint_mapping_aic) - ], -) - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type='CombinedDataset', - metainfo=dict(from_file='configs/_base_/datasets/humanart_aic.py'), - datasets=[dataset_humanart, dataset_aic], - pipeline=train_pipeline, - test_mode=False, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_8xb64-210e_humanart-aic-256x192-merge.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_8xb64-210e_humanart-aic-256x192-merge.py deleted file mode 100644 index b7e287565c..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_8xb64-210e_humanart-aic-256x192-merge.py +++ /dev/null @@ -1,187 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='HRNet', - in_channels=3, - extra=dict( - stage1=dict( - num_modules=1, - num_branches=1, - block='BOTTLENECK', - num_blocks=(4, ), - num_channels=(64, )), - stage2=dict( - num_modules=1, - num_branches=2, - block='BASIC', - num_blocks=(4, 4), - num_channels=(32, 64)), - stage3=dict( - num_modules=4, - num_branches=3, - block='BASIC', - num_blocks=(4, 4, 4), - num_channels=(32, 64, 128)), - stage4=dict( - num_modules=3, - num_branches=4, - block='BASIC', - num_blocks=(4, 4, 4, 4), - num_channels=(32, 64, 128, 256))), - init_cfg=dict( - type='Pretrained', - checkpoint='https://download.openmmlab.com/mmpose/' - 'pretrain_models/hrnet_w32-36af842e.pth'), - ), - head=dict( - type='HeatmapHead', - in_channels=32, - out_channels=17, - deconv_out_channels=None, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# train datasets -dataset_humanart = dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=[], -) - -dataset_aic = dict( - type='AicDataset', - data_root='data/aic/', - data_mode=data_mode, - ann_file='annotations/aic_train.json', - data_prefix=dict(img='ai_challenger_keypoint_train_20170902/' - 'keypoint_train_images_20170902/'), - pipeline=[ - dict( - type='KeypointConverter', - num_keypoints=17, - mapping=[ - (0, 6), - (1, 8), - (2, 10), - (3, 5), - (4, 7), - (5, 9), - (6, 12), - (7, 14), - (8, 16), - (9, 11), - (10, 13), - (11, 15), - ]) - ], -) - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type='CombinedDataset', - metainfo=dict(from_file='configs/_base_/datasets/coco.py'), - datasets=[dataset_humanart, dataset_aic], - pipeline=train_pipeline, - test_mode=False, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_coarsedropout-8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_coarsedropout-8xb64-210e_humanart-256x192.py deleted file mode 100644 index 2eae18218e..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_coarsedropout-8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,165 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='HRNet', - in_channels=3, - extra=dict( - stage1=dict( - num_modules=1, - num_branches=1, - block='BOTTLENECK', - num_blocks=(4, ), - num_channels=(64, )), - stage2=dict( - num_modules=1, - num_branches=2, - block='BASIC', - num_blocks=(4, 4), - num_channels=(32, 64)), - stage3=dict( - num_modules=4, - num_branches=3, - block='BASIC', - num_blocks=(4, 4, 4), - num_channels=(32, 64, 128)), - stage4=dict( - num_modules=3, - num_branches=4, - block='BASIC', - num_blocks=(4, 4, 4, 4), - num_channels=(32, 64, 128, 256))), - init_cfg=dict( - type='Pretrained', - prefix='backbone.', - checkpoint='https://download.openmmlab.com/mmpose/v1/' - 'body_2d_keypoint/topdown_heatmap/coco/' - 'td-hm_hrnet-w32_8xb64-210e_coco-256x192-81c58e40_20220909.pth'), - ), - head=dict( - type='HeatmapHead', - in_channels=32, - out_channels=17, - deconv_out_channels=None, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict( - type='Albumentation', - transforms=[ - dict( - type='CoarseDropout', - max_holes=8, - max_height=40, - max_width=40, - min_holes=1, - min_height=10, - min_width=10, - p=0.5), - ]), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_dark-8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_dark-8xb64-210e_humanart-256x192.py deleted file mode 100644 index 1a95b32cc4..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_dark-8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,154 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', - input_size=(192, 256), - heatmap_size=(48, 64), - sigma=2, - unbiased=True) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='HRNet', - in_channels=3, - extra=dict( - stage1=dict( - num_modules=1, - num_branches=1, - block='BOTTLENECK', - num_blocks=(4, ), - num_channels=(64, )), - stage2=dict( - num_modules=1, - num_branches=2, - block='BASIC', - num_blocks=(4, 4), - num_channels=(32, 64)), - stage3=dict( - num_modules=4, - num_branches=3, - block='BASIC', - num_blocks=(4, 4, 4), - num_channels=(32, 64, 128)), - stage4=dict( - num_modules=3, - num_branches=4, - block='BASIC', - num_blocks=(4, 4, 4, 4), - num_channels=(32, 64, 128, 256))), - init_cfg=dict( - type='Pretrained', - checkpoint='https://download.openmmlab.com/mmpose/' - 'pretrain_models/hrnet_w32-36af842e.pth'), - ), - head=dict( - type='HeatmapHead', - in_channels=32, - out_channels=17, - deconv_out_channels=None, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_dark-8xb64-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_dark-8xb64-210e_humanart-384x288.py deleted file mode 100644 index 09639a8b40..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_dark-8xb64-210e_humanart-384x288.py +++ /dev/null @@ -1,154 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', - input_size=(288, 384), - heatmap_size=(72, 96), - sigma=3, - unbiased=True) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='HRNet', - in_channels=3, - extra=dict( - stage1=dict( - num_modules=1, - num_branches=1, - block='BOTTLENECK', - num_blocks=(4, ), - num_channels=(64, )), - stage2=dict( - num_modules=1, - num_branches=2, - block='BASIC', - num_blocks=(4, 4), - num_channels=(32, 64)), - stage3=dict( - num_modules=4, - num_branches=3, - block='BASIC', - num_blocks=(4, 4, 4), - num_channels=(32, 64, 128)), - stage4=dict( - num_modules=3, - num_branches=4, - block='BASIC', - num_blocks=(4, 4, 4, 4), - num_channels=(32, 64, 128, 256))), - init_cfg=dict( - type='Pretrained', - checkpoint='https://download.openmmlab.com/mmpose/' - 'pretrain_models/hrnet_w32-36af842e.pth'), - ), - head=dict( - type='HeatmapHead', - in_channels=32, - out_channels=17, - deconv_out_channels=None, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_fp16-8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_fp16-8xb64-210e_humanart-256x192.py deleted file mode 100644 index 306d0aeb44..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_fp16-8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,7 +0,0 @@ -_base_ = ['./td-hm_hrnet-w32_8xb64-210e_coco-256x192.py'] - -# fp16 settings -optim_wrapper = dict( - type='AmpOptimWrapper', - loss_scale='dynamic', -) diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_gridmask-8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_gridmask-8xb64-210e_humanart-256x192.py deleted file mode 100644 index 70ea1fb597..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_gridmask-8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,162 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='HRNet', - in_channels=3, - extra=dict( - stage1=dict( - num_modules=1, - num_branches=1, - block='BOTTLENECK', - num_blocks=(4, ), - num_channels=(64, )), - stage2=dict( - num_modules=1, - num_branches=2, - block='BASIC', - num_blocks=(4, 4), - num_channels=(32, 64)), - stage3=dict( - num_modules=4, - num_branches=3, - block='BASIC', - num_blocks=(4, 4, 4), - num_channels=(32, 64, 128)), - stage4=dict( - num_modules=3, - num_branches=4, - block='BASIC', - num_blocks=(4, 4, 4, 4), - num_channels=(32, 64, 128, 256))), - init_cfg=dict( - type='Pretrained', - prefix='backbone.', - checkpoint='https://download.openmmlab.com/mmpose/v1/' - 'body_2d_keypoint/topdown_heatmap/coco/' - 'td-hm_hrnet-w32_8xb64-210e_coco-256x192-81c58e40_20220909.pth'), - ), - head=dict( - type='HeatmapHead', - in_channels=32, - out_channels=17, - deconv_out_channels=None, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict( - type='Albumentation', - transforms=[ - dict( - type='GridDropout', - unit_size_min=10, - unit_size_max=40, - random_offset=True, - p=0.5), - ]), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_photometric-8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_photometric-8xb64-210e_humanart-256x192.py deleted file mode 100644 index 156d012fb3..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_photometric-8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,153 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='HRNet', - in_channels=3, - extra=dict( - stage1=dict( - num_modules=1, - num_branches=1, - block='BOTTLENECK', - num_blocks=(4, ), - num_channels=(64, )), - stage2=dict( - num_modules=1, - num_branches=2, - block='BASIC', - num_blocks=(4, 4), - num_channels=(32, 64)), - stage3=dict( - num_modules=4, - num_branches=3, - block='BASIC', - num_blocks=(4, 4, 4), - num_channels=(32, 64, 128)), - stage4=dict( - num_modules=3, - num_branches=4, - block='BASIC', - num_blocks=(4, 4, 4, 4), - num_channels=(32, 64, 128, 256))), - init_cfg=dict( - type='Pretrained', - prefix='backbone.', - checkpoint='https://download.openmmlab.com/mmpose/v1/' - 'body_2d_keypoint/topdown_heatmap/coco/' - 'td-hm_hrnet-w32_8xb64-210e_coco-256x192-81c58e40_20220909.pth'), - ), - head=dict( - type='HeatmapHead', - in_channels=32, - out_channels=17, - deconv_out_channels=None, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PhotometricDistortion'), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_udp-8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_udp-8xb64-210e_humanart-256x192.py deleted file mode 100644 index 68e227f8a8..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_udp-8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,150 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='UDPHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='HRNet', - in_channels=3, - extra=dict( - stage1=dict( - num_modules=1, - num_branches=1, - block='BOTTLENECK', - num_blocks=(4, ), - num_channels=(64, )), - stage2=dict( - num_modules=1, - num_branches=2, - block='BASIC', - num_blocks=(4, 4), - num_channels=(32, 64)), - stage3=dict( - num_modules=4, - num_branches=3, - block='BASIC', - num_blocks=(4, 4, 4), - num_channels=(32, 64, 128)), - stage4=dict( - num_modules=3, - num_branches=4, - block='BASIC', - num_blocks=(4, 4, 4, 4), - num_channels=(32, 64, 128, 256))), - init_cfg=dict( - type='Pretrained', - checkpoint='https://download.openmmlab.com/mmpose/' - 'pretrain_models/hrnet_w32-36af842e.pth'), - ), - head=dict( - type='HeatmapHead', - in_channels=32, - out_channels=17, - deconv_out_channels=None, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=False, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_udp-8xb64-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_udp-8xb64-210e_humanart-384x288.py deleted file mode 100644 index 52672824ff..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_udp-8xb64-210e_humanart-384x288.py +++ /dev/null @@ -1,150 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='UDPHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='HRNet', - in_channels=3, - extra=dict( - stage1=dict( - num_modules=1, - num_branches=1, - block='BOTTLENECK', - num_blocks=(4, ), - num_channels=(64, )), - stage2=dict( - num_modules=1, - num_branches=2, - block='BASIC', - num_blocks=(4, 4), - num_channels=(32, 64)), - stage3=dict( - num_modules=4, - num_branches=3, - block='BASIC', - num_blocks=(4, 4, 4), - num_channels=(32, 64, 128)), - stage4=dict( - num_modules=3, - num_branches=4, - block='BASIC', - num_blocks=(4, 4, 4, 4), - num_channels=(32, 64, 128, 256))), - init_cfg=dict( - type='Pretrained', - checkpoint='https://download.openmmlab.com/mmpose/' - 'pretrain_models/hrnet_w32-36af842e.pth'), - ), - head=dict( - type='HeatmapHead', - in_channels=32, - out_channels=17, - deconv_out_channels=None, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=False, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_udp-regress-8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_udp-regress-8xb64-210e_humanart-256x192.py deleted file mode 100644 index b47e21d75d..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w32_udp-regress-8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,155 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='UDPHeatmap', - input_size=(192, 256), - heatmap_size=(48, 64), - sigma=2, - heatmap_type='combined') - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='HRNet', - in_channels=3, - extra=dict( - stage1=dict( - num_modules=1, - num_branches=1, - block='BOTTLENECK', - num_blocks=(4, ), - num_channels=(64, )), - stage2=dict( - num_modules=1, - num_branches=2, - block='BASIC', - num_blocks=(4, 4), - num_channels=(32, 64)), - stage3=dict( - num_modules=4, - num_branches=3, - block='BASIC', - num_blocks=(4, 4, 4), - num_channels=(32, 64, 128)), - stage4=dict( - num_modules=3, - num_branches=4, - block='BASIC', - num_blocks=(4, 4, 4, 4), - num_channels=(32, 64, 128, 256))), - init_cfg=dict( - type='Pretrained', - checkpoint='https://download.openmmlab.com/mmpose/' - 'pretrain_models/hrnet_w32-36af842e.pth'), - ), - head=dict( - type='HeatmapHead', - in_channels=32, - out_channels=3 * 17, - deconv_out_channels=None, - loss=dict(type='CombinedTargetMSELoss', use_target_weight=True), - decoder=codec), - train_cfg=dict(compute_acc=False), - test_cfg=dict( - flip_test=True, - flip_mode='udp_combined', - shift_heatmap=False, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w48_8xb32-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w48_8xb32-210e_humanart-256x192.py deleted file mode 100644 index 6a5ae0707c..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w48_8xb32-210e_humanart-256x192.py +++ /dev/null @@ -1,150 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='HRNet', - in_channels=3, - extra=dict( - stage1=dict( - num_modules=1, - num_branches=1, - block='BOTTLENECK', - num_blocks=(4, ), - num_channels=(64, )), - stage2=dict( - num_modules=1, - num_branches=2, - block='BASIC', - num_blocks=(4, 4), - num_channels=(48, 96)), - stage3=dict( - num_modules=4, - num_branches=3, - block='BASIC', - num_blocks=(4, 4, 4), - num_channels=(48, 96, 192)), - stage4=dict( - num_modules=3, - num_branches=4, - block='BASIC', - num_blocks=(4, 4, 4, 4), - num_channels=(48, 96, 192, 384))), - init_cfg=dict( - type='Pretrained', - checkpoint='https://download.openmmlab.com/mmpose/' - 'pretrain_models/hrnet_w48-8ef0771d.pth'), - ), - head=dict( - type='HeatmapHead', - in_channels=48, - out_channels=17, - deconv_out_channels=None, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w48_8xb32-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w48_8xb32-210e_humanart-384x288.py deleted file mode 100644 index e75349bac6..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w48_8xb32-210e_humanart-384x288.py +++ /dev/null @@ -1,150 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='HRNet', - in_channels=3, - extra=dict( - stage1=dict( - num_modules=1, - num_branches=1, - block='BOTTLENECK', - num_blocks=(4, ), - num_channels=(64, )), - stage2=dict( - num_modules=1, - num_branches=2, - block='BASIC', - num_blocks=(4, 4), - num_channels=(48, 96)), - stage3=dict( - num_modules=4, - num_branches=3, - block='BASIC', - num_blocks=(4, 4, 4), - num_channels=(48, 96, 192)), - stage4=dict( - num_modules=3, - num_branches=4, - block='BASIC', - num_blocks=(4, 4, 4, 4), - num_channels=(48, 96, 192, 384))), - init_cfg=dict( - type='Pretrained', - checkpoint='https://download.openmmlab.com/mmpose/' - 'pretrain_models/hrnet_w48-8ef0771d.pth'), - ), - head=dict( - type='HeatmapHead', - in_channels=48, - out_channels=17, - deconv_out_channels=None, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w48_dark-8xb32-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w48_dark-8xb32-210e_humanart-256x192.py deleted file mode 100644 index 0b7d619fe2..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w48_dark-8xb32-210e_humanart-256x192.py +++ /dev/null @@ -1,154 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', - input_size=(192, 256), - heatmap_size=(48, 64), - sigma=2, - unbiased=True) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='HRNet', - in_channels=3, - extra=dict( - stage1=dict( - num_modules=1, - num_branches=1, - block='BOTTLENECK', - num_blocks=(4, ), - num_channels=(64, )), - stage2=dict( - num_modules=1, - num_branches=2, - block='BASIC', - num_blocks=(4, 4), - num_channels=(48, 96)), - stage3=dict( - num_modules=4, - num_branches=3, - block='BASIC', - num_blocks=(4, 4, 4), - num_channels=(48, 96, 192)), - stage4=dict( - num_modules=3, - num_branches=4, - block='BASIC', - num_blocks=(4, 4, 4, 4), - num_channels=(48, 96, 192, 384))), - init_cfg=dict( - type='Pretrained', - checkpoint='https://download.openmmlab.com/mmpose/' - 'pretrain_models/hrnet_w48-8ef0771d.pth'), - ), - head=dict( - type='HeatmapHead', - in_channels=48, - out_channels=17, - deconv_out_channels=None, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w48_dark-8xb32-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w48_dark-8xb32-210e_humanart-384x288.py deleted file mode 100644 index 8b3b572e6e..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w48_dark-8xb32-210e_humanart-384x288.py +++ /dev/null @@ -1,154 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', - input_size=(288, 384), - heatmap_size=(72, 96), - sigma=3, - unbiased=True) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='HRNet', - in_channels=3, - extra=dict( - stage1=dict( - num_modules=1, - num_branches=1, - block='BOTTLENECK', - num_blocks=(4, ), - num_channels=(64, )), - stage2=dict( - num_modules=1, - num_branches=2, - block='BASIC', - num_blocks=(4, 4), - num_channels=(48, 96)), - stage3=dict( - num_modules=4, - num_branches=3, - block='BASIC', - num_blocks=(4, 4, 4), - num_channels=(48, 96, 192)), - stage4=dict( - num_modules=3, - num_branches=4, - block='BASIC', - num_blocks=(4, 4, 4, 4), - num_channels=(48, 96, 192, 384))), - init_cfg=dict( - type='Pretrained', - checkpoint='https://download.openmmlab.com/mmpose/' - 'pretrain_models/hrnet_w48-8ef0771d.pth'), - ), - head=dict( - type='HeatmapHead', - in_channels=48, - out_channels=17, - deconv_out_channels=None, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w48_udp-8xb32-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w48_udp-8xb32-210e_humanart-256x192.py deleted file mode 100644 index c58a57a1da..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w48_udp-8xb32-210e_humanart-256x192.py +++ /dev/null @@ -1,150 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='UDPHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='HRNet', - in_channels=3, - extra=dict( - stage1=dict( - num_modules=1, - num_branches=1, - block='BOTTLENECK', - num_blocks=(4, ), - num_channels=(64, )), - stage2=dict( - num_modules=1, - num_branches=2, - block='BASIC', - num_blocks=(4, 4), - num_channels=(48, 96)), - stage3=dict( - num_modules=4, - num_branches=3, - block='BASIC', - num_blocks=(4, 4, 4), - num_channels=(48, 96, 192)), - stage4=dict( - num_modules=3, - num_branches=4, - block='BASIC', - num_blocks=(4, 4, 4, 4), - num_channels=(48, 96, 192, 384))), - init_cfg=dict( - type='Pretrained', - checkpoint='https://download.openmmlab.com/mmpose/' - 'pretrain_models/hrnet_w48-8ef0771d.pth'), - ), - head=dict( - type='HeatmapHead', - in_channels=48, - out_channels=17, - deconv_out_channels=None, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=False, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w48_udp-8xb32-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w48_udp-8xb32-210e_humanart-384x288.py deleted file mode 100644 index 509341b9a3..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w48_udp-8xb32-210e_humanart-384x288.py +++ /dev/null @@ -1,150 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='UDPHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='HRNet', - in_channels=3, - extra=dict( - stage1=dict( - num_modules=1, - num_branches=1, - block='BOTTLENECK', - num_blocks=(4, ), - num_channels=(64, )), - stage2=dict( - num_modules=1, - num_branches=2, - block='BASIC', - num_blocks=(4, 4), - num_channels=(48, 96)), - stage3=dict( - num_modules=4, - num_branches=3, - block='BASIC', - num_blocks=(4, 4, 4), - num_channels=(48, 96, 192)), - stage4=dict( - num_modules=3, - num_branches=4, - block='BASIC', - num_blocks=(4, 4, 4, 4), - num_channels=(48, 96, 192, 384))), - init_cfg=dict( - type='Pretrained', - checkpoint='https://download.openmmlab.com/mmpose/' - 'pretrain_models/hrnet_w48-8ef0771d.pth'), - ), - head=dict( - type='HeatmapHead', - in_channels=48, - out_channels=17, - deconv_out_channels=None, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=False, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size'], use_udp=True), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_litehrnet-18_8xb32-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_litehrnet-18_8xb32-210e_humanart-384x288.py deleted file mode 100644 index 0448a55017..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_litehrnet-18_8xb32-210e_humanart-384x288.py +++ /dev/null @@ -1,140 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='LiteHRNet', - in_channels=3, - extra=dict( - stem=dict(stem_channels=32, out_channels=32, expand_ratio=1), - num_stages=3, - stages_spec=dict( - num_modules=(2, 4, 2), - num_branches=(2, 3, 4), - num_blocks=(2, 2, 2), - module_type=('LITE', 'LITE', 'LITE'), - with_fuse=(True, True, True), - reduce_ratios=(8, 8, 8), - num_channels=( - (40, 80), - (40, 80, 160), - (40, 80, 160, 320), - )), - with_head=True, - )), - head=dict( - type='HeatmapHead', - in_channels=40, - out_channels=17, - deconv_out_channels=None, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict( - type='RandomBBoxTransform', - rotate_factor=60, - scale_factor=(0.75, 1.25)), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_litehrnet-18_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_litehrnet-18_8xb64-210e_humanart-256x192.py deleted file mode 100644 index a58a880168..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_litehrnet-18_8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,140 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='LiteHRNet', - in_channels=3, - extra=dict( - stem=dict(stem_channels=32, out_channels=32, expand_ratio=1), - num_stages=3, - stages_spec=dict( - num_modules=(2, 4, 2), - num_branches=(2, 3, 4), - num_blocks=(2, 2, 2), - module_type=('LITE', 'LITE', 'LITE'), - with_fuse=(True, True, True), - reduce_ratios=(8, 8, 8), - num_channels=( - (40, 80), - (40, 80, 160), - (40, 80, 160, 320), - )), - with_head=True, - )), - head=dict( - type='HeatmapHead', - in_channels=40, - out_channels=17, - deconv_out_channels=None, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict( - type='RandomBBoxTransform', - rotate_factor=60, - scale_factor=(0.75, 1.25)), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_litehrnet-30_8xb32-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_litehrnet-30_8xb32-210e_humanart-384x288.py deleted file mode 100644 index 1af1e659b6..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_litehrnet-30_8xb32-210e_humanart-384x288.py +++ /dev/null @@ -1,140 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='LiteHRNet', - in_channels=3, - extra=dict( - stem=dict(stem_channels=32, out_channels=32, expand_ratio=1), - num_stages=3, - stages_spec=dict( - num_modules=(3, 8, 3), - num_branches=(2, 3, 4), - num_blocks=(2, 2, 2), - module_type=('LITE', 'LITE', 'LITE'), - with_fuse=(True, True, True), - reduce_ratios=(8, 8, 8), - num_channels=( - (40, 80), - (40, 80, 160), - (40, 80, 160, 320), - )), - with_head=True, - )), - head=dict( - type='HeatmapHead', - in_channels=40, - out_channels=17, - deconv_out_channels=None, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict( - type='RandomBBoxTransform', - rotate_factor=60, - scale_factor=(0.75, 1.25)), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_litehrnet-30_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_litehrnet-30_8xb64-210e_humanart-256x192.py deleted file mode 100644 index 253416c2c1..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_litehrnet-30_8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,140 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='LiteHRNet', - in_channels=3, - extra=dict( - stem=dict(stem_channels=32, out_channels=32, expand_ratio=1), - num_stages=3, - stages_spec=dict( - num_modules=(3, 8, 3), - num_branches=(2, 3, 4), - num_blocks=(2, 2, 2), - module_type=('LITE', 'LITE', 'LITE'), - with_fuse=(True, True, True), - reduce_ratios=(8, 8, 8), - num_channels=( - (40, 80), - (40, 80, 160), - (40, 80, 160, 320), - )), - with_head=True, - )), - head=dict( - type='HeatmapHead', - in_channels=40, - out_channels=17, - deconv_out_channels=None, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict( - type='RandomBBoxTransform', - rotate_factor=60, - scale_factor=(0.75, 1.25)), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_mobilenetv2_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_mobilenetv2_8xb64-210e_humanart-256x192.py deleted file mode 100644 index e068b246d6..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_mobilenetv2_8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,124 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='MobileNetV2', - widen_factor=1., - out_indices=(7, ), - init_cfg=dict( - type='Pretrained', - checkpoint='mmcls://mobilenet_v2', - )), - head=dict( - type='HeatmapHead', - in_channels=1280, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_mobilenetv2_8xb64-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_mobilenetv2_8xb64-210e_humanart-384x288.py deleted file mode 100644 index 57f5c1bb91..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_mobilenetv2_8xb64-210e_humanart-384x288.py +++ /dev/null @@ -1,124 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='MobileNetV2', - widen_factor=1., - out_indices=(7, ), - init_cfg=dict( - type='Pretrained', - checkpoint='mmcls://mobilenet_v2', - )), - head=dict( - type='HeatmapHead', - in_channels=1280, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_mspn50_8xb32-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_mspn50_8xb32-210e_humanart-256x192.py deleted file mode 100644 index 28b1778176..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_mspn50_8xb32-210e_humanart-256x192.py +++ /dev/null @@ -1,152 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-3, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=256) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -# multiple kernel_sizes of heatmap gaussian for 'Megvii' approach. -kernel_sizes = [11, 9, 7, 5] -codec = [ - dict( - type='MegviiHeatmap', - input_size=(192, 256), - heatmap_size=(48, 64), - kernel_size=kernel_size) for kernel_size in kernel_sizes -] - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='MSPN', - unit_channels=256, - num_stages=1, - num_units=4, - num_blocks=[3, 4, 6, 3], - norm_cfg=dict(type='BN'), - init_cfg=dict( - type='Pretrained', - checkpoint='torchvision://resnet50', - )), - head=dict( - type='MSPNHead', - out_shape=(64, 48), - unit_channels=256, - out_channels=17, - num_stages=1, - num_units=4, - norm_cfg=dict(type='BN'), - # each sub list is for a stage - # and each element in each list is for a unit - level_indices=[0, 1, 2, 3], - loss=[ - dict( - type='KeypointMSELoss', - use_target_weight=True, - loss_weight=0.25) - ] * 3 + [ - dict( - type='KeypointOHKMMSELoss', - use_target_weight=True, - loss_weight=1.) - ], - decoder=codec[-1]), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=False, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec[0]['input_size']), - dict(type='GenerateTarget', multilevel=True, encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec[0]['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=32, - num_workers=4, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=4, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'annotations/person_keypoints_val2017.json', - nms_mode='none') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_pvt-s_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_pvt-s_8xb64-210e_humanart-256x192.py deleted file mode 100644 index e49eb22f49..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_pvt-s_8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,127 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# model settings -norm_cfg = dict(type='SyncBN', requires_grad=True) -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='PyramidVisionTransformer', - num_layers=[3, 4, 6, 3], - init_cfg=dict( - type='Pretrained', - checkpoint='https://github.com/whai362/PVT/' - 'releases/download/v2/pvt_small.pth'), - ), - neck=dict(type='FeatureMapProcessor', select_index=3), - head=dict( - type='HeatmapHead', - in_channels=512, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] - -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_pvtv2-b2_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_pvtv2-b2_8xb64-210e_humanart-256x192.py deleted file mode 100644 index bf30e834bd..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_pvtv2-b2_8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,128 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# model settings -norm_cfg = dict(type='SyncBN', requires_grad=True) -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='PyramidVisionTransformerV2', - embed_dims=64, - num_layers=[3, 4, 6, 3], - init_cfg=dict( - type='Pretrained', - checkpoint='https://github.com/whai362/PVT/' - 'releases/download/v2/pvt_v2_b2.pth'), - ), - neck=dict(type='FeatureMapProcessor', select_index=3), - head=dict( - type='HeatmapHead', - in_channels=512, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] - -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res101_8xb32-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res101_8xb32-210e_humanart-384x288.py deleted file mode 100644 index 657ef05821..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res101_8xb32-210e_humanart-384x288.py +++ /dev/null @@ -1,121 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=256) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='ResNet', - depth=101, - init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet101'), - ), - head=dict( - type='HeatmapHead', - in_channels=2048, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res101_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res101_8xb64-210e_humanart-256x192.py deleted file mode 100644 index d42f787e93..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res101_8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,121 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='ResNet', - depth=101, - init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet101'), - ), - head=dict( - type='HeatmapHead', - in_channels=2048, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res101_dark-8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res101_dark-8xb64-210e_humanart-256x192.py deleted file mode 100644 index 1f594f2fae..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res101_dark-8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,125 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', - input_size=(192, 256), - heatmap_size=(48, 64), - sigma=2, - unbiased=True) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='ResNet', - depth=101, - init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet101'), - ), - head=dict( - type='HeatmapHead', - in_channels=2048, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res101_dark-8xb64-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res101_dark-8xb64-210e_humanart-384x288.py deleted file mode 100644 index e8f075c75c..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res101_dark-8xb64-210e_humanart-384x288.py +++ /dev/null @@ -1,125 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', - input_size=(288, 384), - heatmap_size=(72, 96), - sigma=3, - unbiased=True) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='ResNet', - depth=101, - init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet101'), - ), - head=dict( - type='HeatmapHead', - in_channels=2048, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res152_8xb32-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res152_8xb32-210e_humanart-256x192.py deleted file mode 100644 index c2e5334357..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res152_8xb32-210e_humanart-256x192.py +++ /dev/null @@ -1,121 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='ResNet', - depth=152, - init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet152'), - ), - head=dict( - type='HeatmapHead', - in_channels=2048, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res152_8xb32-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res152_8xb32-210e_humanart-384x288.py deleted file mode 100644 index c60681d3b1..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res152_8xb32-210e_humanart-384x288.py +++ /dev/null @@ -1,121 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='ResNet', - depth=152, - init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet152'), - ), - head=dict( - type='HeatmapHead', - in_channels=2048, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res152_dark-8xb32-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res152_dark-8xb32-210e_humanart-256x192.py deleted file mode 100644 index b0a4b3258f..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res152_dark-8xb32-210e_humanart-256x192.py +++ /dev/null @@ -1,125 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=256) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', - input_size=(192, 256), - heatmap_size=(48, 64), - sigma=2, - unbiased=True) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='ResNet', - depth=152, - init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet152'), - ), - head=dict( - type='HeatmapHead', - in_channels=2048, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res152_dark-8xb32-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res152_dark-8xb32-210e_humanart-384x288.py deleted file mode 100644 index 8ed8884023..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res152_dark-8xb32-210e_humanart-384x288.py +++ /dev/null @@ -1,126 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=256) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', - input_size=(288, 384), - heatmap_size=(72, 96), - sigma=3, - unbiased=True, - blur_kernel_size=17) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='ResNet', - depth=152, - init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet152'), - ), - head=dict( - type='HeatmapHead', - in_channels=2048, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res50_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res50_8xb64-210e_humanart-256x192.py deleted file mode 100644 index 06bc5283c9..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res50_8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,121 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='ResNet', - depth=50, - init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'), - ), - head=dict( - type='HeatmapHead', - in_channels=2048, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res50_8xb64-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res50_8xb64-210e_humanart-384x288.py deleted file mode 100644 index e209c63f85..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res50_8xb64-210e_humanart-384x288.py +++ /dev/null @@ -1,121 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='ResNet', - depth=50, - init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'), - ), - head=dict( - type='HeatmapHead', - in_channels=2048, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res50_dark-8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res50_dark-8xb64-210e_humanart-256x192.py deleted file mode 100644 index a0770247f9..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res50_dark-8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,125 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', - input_size=(192, 256), - heatmap_size=(48, 64), - sigma=2, - unbiased=True) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='ResNet', - depth=50, - init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'), - ), - head=dict( - type='HeatmapHead', - in_channels=2048, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res50_dark-8xb64-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res50_dark-8xb64-210e_humanart-384x288.py deleted file mode 100644 index 60575438ad..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res50_dark-8xb64-210e_humanart-384x288.py +++ /dev/null @@ -1,125 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', - input_size=(288, 384), - heatmap_size=(72, 96), - sigma=3, - unbiased=True) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='ResNet', - depth=50, - init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'), - ), - head=dict( - type='HeatmapHead', - in_channels=2048, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res50_fp16-8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res50_fp16-8xb64-210e_humanart-256x192.py deleted file mode 100644 index 66a6a27822..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_res50_fp16-8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,7 +0,0 @@ -_base_ = ['./td-hm_res50_8xb64-210e_coco-256x192.py'] - -# fp16 settings -optim_wrapper = dict( - type='AmpOptimWrapper', - loss_scale='dynamic', -) diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnest101_8xb32-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnest101_8xb32-210e_humanart-384x288.py deleted file mode 100644 index edd42dceb9..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnest101_8xb32-210e_humanart-384x288.py +++ /dev/null @@ -1,121 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=256) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='ResNeSt', - depth=101, - init_cfg=dict(type='Pretrained', checkpoint='mmcls://resnest101'), - ), - head=dict( - type='HeatmapHead', - in_channels=2048, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnest101_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnest101_8xb64-210e_humanart-256x192.py deleted file mode 100644 index 7f415346dd..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnest101_8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,121 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='ResNeSt', - depth=101, - init_cfg=dict(type='Pretrained', checkpoint='mmcls://resnest101'), - ), - head=dict( - type='HeatmapHead', - in_channels=2048, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnest200_8xb16-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnest200_8xb16-210e_humanart-384x288.py deleted file mode 100644 index 2f916b41e5..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnest200_8xb16-210e_humanart-384x288.py +++ /dev/null @@ -1,121 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=128) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='ResNeSt', - depth=200, - init_cfg=dict(type='Pretrained', checkpoint='mmcls://resnest200'), - ), - head=dict( - type='HeatmapHead', - in_channels=2048, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=16, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=16, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnest200_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnest200_8xb64-210e_humanart-256x192.py deleted file mode 100644 index c29cc30444..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnest200_8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,121 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='ResNeSt', - depth=200, - init_cfg=dict(type='Pretrained', checkpoint='mmcls://resnest200'), - ), - head=dict( - type='HeatmapHead', - in_channels=2048, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnest269_8xb16-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnest269_8xb16-210e_humanart-384x288.py deleted file mode 100644 index 367331a325..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnest269_8xb16-210e_humanart-384x288.py +++ /dev/null @@ -1,121 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=128) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='ResNeSt', - depth=269, - init_cfg=dict(type='Pretrained', checkpoint='mmcls://resnest269'), - ), - head=dict( - type='HeatmapHead', - in_channels=2048, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=16, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=16, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnest269_8xb32-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnest269_8xb32-210e_humanart-256x192.py deleted file mode 100644 index dd334bb58b..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnest269_8xb32-210e_humanart-256x192.py +++ /dev/null @@ -1,121 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='ResNeSt', - depth=269, - init_cfg=dict(type='Pretrained', checkpoint='mmcls://resnest269'), - ), - head=dict( - type='HeatmapHead', - in_channels=2048, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnest50_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnest50_8xb64-210e_humanart-256x192.py deleted file mode 100644 index c0eb815008..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnest50_8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,121 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='ResNeSt', - depth=50, - init_cfg=dict(type='Pretrained', checkpoint='mmcls://resnest50'), - ), - head=dict( - type='HeatmapHead', - in_channels=2048, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnest50_8xb64-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnest50_8xb64-210e_humanart-384x288.py deleted file mode 100644 index 11958c0947..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnest50_8xb64-210e_humanart-384x288.py +++ /dev/null @@ -1,121 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='ResNeSt', - depth=50, - init_cfg=dict(type='Pretrained', checkpoint='mmcls://resnest50'), - ), - head=dict( - type='HeatmapHead', - in_channels=2048, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnetv1d101_8xb32-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnetv1d101_8xb32-210e_humanart-384x288.py deleted file mode 100644 index 20a1b17d6e..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnetv1d101_8xb32-210e_humanart-384x288.py +++ /dev/null @@ -1,121 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=256) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='ResNetV1d', - depth=101, - init_cfg=dict(type='Pretrained', checkpoint='mmcls://resnet101_v1d'), - ), - head=dict( - type='HeatmapHead', - in_channels=2048, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnetv1d101_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnetv1d101_8xb64-210e_humanart-256x192.py deleted file mode 100644 index 9bb2ec99bf..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnetv1d101_8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,121 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='ResNetV1d', - depth=101, - init_cfg=dict(type='Pretrained', checkpoint='mmcls://resnet101_v1d'), - ), - head=dict( - type='HeatmapHead', - in_channels=2048, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnetv1d152_8xb32-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnetv1d152_8xb32-210e_humanart-256x192.py deleted file mode 100644 index 16754f8066..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnetv1d152_8xb32-210e_humanart-256x192.py +++ /dev/null @@ -1,121 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='ResNetV1d', - depth=152, - init_cfg=dict(type='Pretrained', checkpoint='mmcls://resnet152_v1d'), - ), - head=dict( - type='HeatmapHead', - in_channels=2048, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnetv1d152_8xb48-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnetv1d152_8xb48-210e_humanart-384x288.py deleted file mode 100644 index 9d9ec562b0..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnetv1d152_8xb48-210e_humanart-384x288.py +++ /dev/null @@ -1,121 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=384) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='ResNetV1d', - depth=152, - init_cfg=dict(type='Pretrained', checkpoint='mmcls://resnet152_v1d'), - ), - head=dict( - type='HeatmapHead', - in_channels=2048, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=48, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnetv1d50_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnetv1d50_8xb64-210e_humanart-256x192.py deleted file mode 100644 index 74b206e66f..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnetv1d50_8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,121 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='ResNetV1d', - depth=50, - init_cfg=dict(type='Pretrained', checkpoint='mmcls://resnet50_v1d'), - ), - head=dict( - type='HeatmapHead', - in_channels=2048, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnetv1d50_8xb64-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnetv1d50_8xb64-210e_humanart-384x288.py deleted file mode 100644 index 193fe55052..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnetv1d50_8xb64-210e_humanart-384x288.py +++ /dev/null @@ -1,121 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='ResNetV1d', - depth=50, - init_cfg=dict(type='Pretrained', checkpoint='mmcls://resnet50_v1d'), - ), - head=dict( - type='HeatmapHead', - in_channels=2048, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnext101_8xb32-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnext101_8xb32-210e_humanart-384x288.py deleted file mode 100644 index 43fb721866..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnext101_8xb32-210e_humanart-384x288.py +++ /dev/null @@ -1,122 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=256) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='ResNeXt', - depth=101, - init_cfg=dict( - type='Pretrained', checkpoint='mmcls://resnext101_32x4d'), - ), - head=dict( - type='HeatmapHead', - in_channels=2048, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnext101_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnext101_8xb64-210e_humanart-256x192.py deleted file mode 100644 index 65f3b2bbb3..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnext101_8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,122 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='ResNeXt', - depth=101, - init_cfg=dict( - type='Pretrained', checkpoint='mmcls://resnext101_32x4d'), - ), - head=dict( - type='HeatmapHead', - in_channels=2048, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnext152_8xb32-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnext152_8xb32-210e_humanart-256x192.py deleted file mode 100644 index 7315855cb6..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnext152_8xb32-210e_humanart-256x192.py +++ /dev/null @@ -1,122 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='ResNeXt', - depth=152, - init_cfg=dict( - type='Pretrained', checkpoint='mmcls://resnext152_32x4d'), - ), - head=dict( - type='HeatmapHead', - in_channels=2048, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnext152_8xb48-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnext152_8xb48-210e_humanart-384x288.py deleted file mode 100644 index af23d16379..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnext152_8xb48-210e_humanart-384x288.py +++ /dev/null @@ -1,122 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=384) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='ResNeXt', - depth=152, - init_cfg=dict( - type='Pretrained', checkpoint='mmcls://resnext152_32x4d'), - ), - head=dict( - type='HeatmapHead', - in_channels=2048, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=48, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnext50_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnext50_8xb64-210e_humanart-256x192.py deleted file mode 100644 index 5ecba30aa4..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnext50_8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,121 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='ResNeXt', - depth=50, - init_cfg=dict(type='Pretrained', checkpoint='mmcls://resnext50_32x4d'), - ), - head=dict( - type='HeatmapHead', - in_channels=2048, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnext50_8xb64-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnext50_8xb64-210e_humanart-384x288.py deleted file mode 100644 index 51d30d0394..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_resnext50_8xb64-210e_humanart-384x288.py +++ /dev/null @@ -1,121 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='ResNeXt', - depth=50, - init_cfg=dict(type='Pretrained', checkpoint='mmcls://resnext50_32x4d'), - ), - head=dict( - type='HeatmapHead', - in_channels=2048, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_rsn18_8xb32-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_rsn18_8xb32-210e_humanart-256x192.py deleted file mode 100644 index 06257d41fd..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_rsn18_8xb32-210e_humanart-256x192.py +++ /dev/null @@ -1,154 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=2e-2, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 190, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=256) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -# multiple kernel_sizes of heatmap gaussian for 'Megvii' approach. -kernel_sizes = [11, 9, 7, 5] -codec = [ - dict( - type='MegviiHeatmap', - input_size=(192, 256), - heatmap_size=(48, 64), - kernel_size=kernel_size) for kernel_size in kernel_sizes -] - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='RSN', - unit_channels=256, - num_stages=1, - num_units=4, - num_blocks=[2, 2, 2, 2], - num_steps=4, - norm_cfg=dict(type='BN'), - ), - head=dict( - type='MSPNHead', - out_shape=(64, 48), - unit_channels=256, - out_channels=17, - num_stages=1, - num_units=4, - norm_cfg=dict(type='BN'), - # each sub list is for a stage - # and each element in each list is for a unit - level_indices=[0, 1, 2, 3], - loss=[ - dict( - type='KeypointMSELoss', - use_target_weight=True, - loss_weight=0.25) - ] * 3 + [ - dict( - type='KeypointOHKMMSELoss', - use_target_weight=True, - loss_weight=1.) - ], - decoder=codec[-1]), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=False, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec[0]['input_size']), - dict(type='GenerateTarget', multilevel=True, encoder=codec), - dict(type='PackPoseInputs') -] - -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec[0]['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=32, - num_workers=4, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=4, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'annotations/person_keypoints_val2017.json', - nms_mode='none') -test_evaluator = val_evaluator - -# fp16 settings -fp16 = dict(loss_scale='dynamic') diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_rsn50_8xb32-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_rsn50_8xb32-210e_humanart-256x192.py deleted file mode 100644 index e73d6aeab3..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_rsn50_8xb32-210e_humanart-256x192.py +++ /dev/null @@ -1,154 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-3, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=256) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -# multiple kernel_sizes of heatmap gaussian for 'Megvii' approach. -kernel_sizes = [11, 9, 7, 5] -codec = [ - dict( - type='MegviiHeatmap', - input_size=(192, 256), - heatmap_size=(48, 64), - kernel_size=kernel_size) for kernel_size in kernel_sizes -] - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='RSN', - unit_channels=256, - num_stages=1, - num_units=4, - num_blocks=[3, 4, 6, 3], - num_steps=4, - norm_cfg=dict(type='BN'), - ), - head=dict( - type='MSPNHead', - out_shape=(64, 48), - unit_channels=256, - out_channels=17, - num_stages=1, - num_units=4, - norm_cfg=dict(type='BN'), - # each sub list is for a stage - # and each element in each list is for a unit - level_indices=[0, 1, 2, 3], - loss=[ - dict( - type='KeypointMSELoss', - use_target_weight=True, - loss_weight=0.25) - ] * 3 + [ - dict( - type='KeypointOHKMMSELoss', - use_target_weight=True, - loss_weight=1.) - ], - decoder=codec[-1]), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=False, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec[0]['input_size']), - dict(type='GenerateTarget', multilevel=True, encoder=codec), - dict(type='PackPoseInputs') -] - -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec[0]['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=32, - num_workers=4, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=4, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'annotations/person_keypoints_val2017.json', - nms_mode='none') -test_evaluator = val_evaluator - -# fp16 settings -fp16 = dict(loss_scale='dynamic') diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_scnet101_8xb32-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_scnet101_8xb32-210e_humanart-256x192.py deleted file mode 100644 index ac99362488..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_scnet101_8xb32-210e_humanart-256x192.py +++ /dev/null @@ -1,124 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='SCNet', - depth=101, - init_cfg=dict( - type='Pretrained', - checkpoint='https://download.openmmlab.com/mmpose/' - 'pretrain_models/scnet101-94250a77.pth'), - ), - head=dict( - type='HeatmapHead', - in_channels=2048, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=32, - num_workers=1, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=1, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_scnet101_8xb48-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_scnet101_8xb48-210e_humanart-384x288.py deleted file mode 100644 index 6aa29f6edd..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_scnet101_8xb48-210e_humanart-384x288.py +++ /dev/null @@ -1,124 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='SCNet', - depth=101, - init_cfg=dict( - type='Pretrained', - checkpoint='https://download.openmmlab.com/mmpose/' - 'pretrain_models/scnet101-94250a77.pth'), - ), - head=dict( - type='HeatmapHead', - in_channels=2048, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=48, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_scnet50_8xb32-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_scnet50_8xb32-210e_humanart-384x288.py deleted file mode 100644 index 5e7dd7ceae..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_scnet50_8xb32-210e_humanart-384x288.py +++ /dev/null @@ -1,124 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=256) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='SCNet', - depth=50, - init_cfg=dict( - type='Pretrained', - checkpoint='https://download.openmmlab.com/mmpose/' - 'pretrain_models/scnet50-7ef0a199.pth'), - ), - head=dict( - type='HeatmapHead', - in_channels=2048, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=32, - num_workers=1, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=1, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_scnet50_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_scnet50_8xb64-210e_humanart-256x192.py deleted file mode 100644 index 3423c14470..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_scnet50_8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,124 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='SCNet', - depth=50, - init_cfg=dict( - type='Pretrained', - checkpoint='https://download.openmmlab.com/mmpose/' - 'pretrain_models/scnet50-7ef0a199.pth'), - ), - head=dict( - type='HeatmapHead', - in_channels=2048, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_seresnet101_8xb32-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_seresnet101_8xb32-210e_humanart-384x288.py deleted file mode 100644 index b56a119ca1..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_seresnet101_8xb32-210e_humanart-384x288.py +++ /dev/null @@ -1,121 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=256) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='SEResNet', - depth=101, - init_cfg=dict(type='Pretrained', checkpoint='mmcls://se-resnet101'), - ), - head=dict( - type='HeatmapHead', - in_channels=2048, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_seresnet101_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_seresnet101_8xb64-210e_humanart-256x192.py deleted file mode 100644 index 8d9b3185ca..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_seresnet101_8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,121 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='SEResNet', - depth=101, - init_cfg=dict(type='Pretrained', checkpoint='mmcls://se-resnet101'), - ), - head=dict( - type='HeatmapHead', - in_channels=2048, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_seresnet152_8xb32-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_seresnet152_8xb32-210e_humanart-256x192.py deleted file mode 100644 index 2714d8f3a0..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_seresnet152_8xb32-210e_humanart-256x192.py +++ /dev/null @@ -1,120 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='SEResNet', - depth=152, - ), - head=dict( - type='HeatmapHead', - in_channels=2048, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_seresnet152_8xb48-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_seresnet152_8xb48-210e_humanart-384x288.py deleted file mode 100644 index ba4c41d100..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_seresnet152_8xb48-210e_humanart-384x288.py +++ /dev/null @@ -1,120 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=384) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='SEResNet', - depth=152, - ), - head=dict( - type='HeatmapHead', - in_channels=2048, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=48, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_seresnet50_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_seresnet50_8xb64-210e_humanart-256x192.py deleted file mode 100644 index e48954cbac..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_seresnet50_8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,121 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='SEResNet', - depth=50, - init_cfg=dict(type='Pretrained', checkpoint='mmcls://se-resnet50'), - ), - head=dict( - type='HeatmapHead', - in_channels=2048, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_seresnet50_8xb64-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_seresnet50_8xb64-210e_humanart-384x288.py deleted file mode 100644 index 90716e2158..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_seresnet50_8xb64-210e_humanart-384x288.py +++ /dev/null @@ -1,121 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='SEResNet', - depth=50, - init_cfg=dict(type='Pretrained', checkpoint='mmcls://se-resnet50'), - ), - head=dict( - type='HeatmapHead', - in_channels=2048, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_shufflenetv1_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_shufflenetv1_8xb64-210e_humanart-256x192.py deleted file mode 100644 index 407bf748ab..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_shufflenetv1_8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,121 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='ShuffleNetV1', - groups=3, - init_cfg=dict(type='Pretrained', checkpoint='mmcls://shufflenet_v1'), - ), - head=dict( - type='HeatmapHead', - in_channels=960, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_shufflenetv1_8xb64-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_shufflenetv1_8xb64-210e_humanart-384x288.py deleted file mode 100644 index e65682b415..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_shufflenetv1_8xb64-210e_humanart-384x288.py +++ /dev/null @@ -1,121 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='ShuffleNetV1', - groups=3, - init_cfg=dict(type='Pretrained', checkpoint='mmcls://shufflenet_v1'), - ), - head=dict( - type='HeatmapHead', - in_channels=960, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_shufflenetv2_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_shufflenetv2_8xb64-210e_humanart-256x192.py deleted file mode 100644 index 31001638ea..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_shufflenetv2_8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,121 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='ShuffleNetV2', - widen_factor=1.0, - init_cfg=dict(type='Pretrained', checkpoint='mmcls://shufflenet_v2'), - ), - head=dict( - type='HeatmapHead', - in_channels=1024, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_shufflenetv2_8xb64-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_shufflenetv2_8xb64-210e_humanart-384x288.py deleted file mode 100644 index c9febd94c8..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_shufflenetv2_8xb64-210e_humanart-384x288.py +++ /dev/null @@ -1,121 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=3) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='ShuffleNetV2', - widen_factor=1.0, - init_cfg=dict(type='Pretrained', checkpoint='mmcls://shufflenet_v2'), - ), - head=dict( - type='HeatmapHead', - in_channels=1024, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_swin-b-p4-w7_8xb32-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_swin-b-p4-w7_8xb32-210e_humanart-256x192.py deleted file mode 100644 index fbdba98074..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_swin-b-p4-w7_8xb32-210e_humanart-256x192.py +++ /dev/null @@ -1,139 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=256) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# model settings -norm_cfg = dict(type='SyncBN', requires_grad=True) -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='SwinTransformer', - embed_dims=128, - depths=[2, 2, 18, 2], - num_heads=[4, 8, 16, 32], - window_size=7, - mlp_ratio=4, - qkv_bias=True, - qk_scale=None, - drop_rate=0., - attn_drop_rate=0., - drop_path_rate=0.3, - patch_norm=True, - out_indices=(3, ), - with_cp=False, - convert_weights=True, - init_cfg=dict( - type='Pretrained', - checkpoint='https://github.com/SwinTransformer/storage/releases/' - 'download/v1.0.0/swin_base_patch4_window7_224_22k.pth'), - ), - head=dict( - type='HeatmapHead', - in_channels=1024, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] - -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_swin-b-p4-w7_8xb32-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_swin-b-p4-w7_8xb32-210e_humanart-384x288.py deleted file mode 100644 index 7b9f6bac77..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_swin-b-p4-w7_8xb32-210e_humanart-384x288.py +++ /dev/null @@ -1,139 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=256) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=2) - -# model settings -norm_cfg = dict(type='SyncBN', requires_grad=True) -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='SwinTransformer', - embed_dims=128, - depths=[2, 2, 18, 2], - num_heads=[4, 8, 16, 32], - window_size=12, - mlp_ratio=4, - qkv_bias=True, - qk_scale=None, - drop_rate=0., - attn_drop_rate=0., - drop_path_rate=0.3, - patch_norm=True, - out_indices=(3, ), - with_cp=False, - convert_weights=True, - init_cfg=dict( - type='Pretrained', - checkpoint='https://github.com/SwinTransformer/storage/releases/' - 'download/v1.0.0/swin_base_patch4_window12_384_22k.pth'), - ), - head=dict( - type='HeatmapHead', - in_channels=1024, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] - -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_swin-l-p4-w7_8xb32-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_swin-l-p4-w7_8xb32-210e_humanart-256x192.py deleted file mode 100644 index c34d8441a1..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_swin-l-p4-w7_8xb32-210e_humanart-256x192.py +++ /dev/null @@ -1,148 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict( - optimizer=dict( - type='AdamW', - lr=5e-4, - betas=(0.9, 0.999), - weight_decay=0.01, - ), - paramwise_cfg=dict( - custom_keys={ - 'absolute_pos_embed': dict(decay_mult=0.), - 'relative_position_bias_table': dict(decay_mult=0.), - 'norm': dict(decay_mult=0.) - })) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=256) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# model settings -norm_cfg = dict(type='SyncBN', requires_grad=True) -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='SwinTransformer', - embed_dims=192, - depths=[2, 2, 18, 2], - num_heads=[6, 12, 24, 48], - window_size=7, - mlp_ratio=4, - qkv_bias=True, - qk_scale=None, - drop_rate=0., - attn_drop_rate=0., - drop_path_rate=0.5, - patch_norm=True, - out_indices=(3, ), - with_cp=False, - convert_weights=True, - init_cfg=dict( - type='Pretrained', - checkpoint='https://github.com/SwinTransformer/storage/releases/' - 'download/v1.0.0/swin_base_patch4_window7_224_22k.pth'), - ), - head=dict( - type='HeatmapHead', - in_channels=1536, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] - -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_swin-l-p4-w7_8xb32-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_swin-l-p4-w7_8xb32-210e_humanart-384x288.py deleted file mode 100644 index 51d49afbba..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_swin-l-p4-w7_8xb32-210e_humanart-384x288.py +++ /dev/null @@ -1,148 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict( - optimizer=dict( - type='AdamW', - lr=5e-4, - betas=(0.9, 0.999), - weight_decay=0.01, - ), - paramwise_cfg=dict( - custom_keys={ - 'absolute_pos_embed': dict(decay_mult=0.), - 'relative_position_bias_table': dict(decay_mult=0.), - 'norm': dict(decay_mult=0.) - })) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=256) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(288, 384), heatmap_size=(72, 96), sigma=2) - -# model settings -norm_cfg = dict(type='SyncBN', requires_grad=True) -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='SwinTransformer', - embed_dims=192, - depths=[2, 2, 18, 2], - num_heads=[6, 12, 24, 48], - window_size=7, - mlp_ratio=4, - qkv_bias=True, - qk_scale=None, - drop_rate=0., - attn_drop_rate=0., - drop_path_rate=0.5, - patch_norm=True, - out_indices=(3, ), - with_cp=False, - convert_weights=True, - init_cfg=dict( - type='Pretrained', - checkpoint='https://github.com/SwinTransformer/storage/releases/' - 'download/v1.0.0/swin_base_patch4_window12_384_22k.pth'), - ), - head=dict( - type='HeatmapHead', - in_channels=1536, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] - -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_swin-t-p4-w7_8xb32-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_swin-t-p4-w7_8xb32-210e_humanart-256x192.py deleted file mode 100644 index 16d34b1e8a..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_swin-t-p4-w7_8xb32-210e_humanart-256x192.py +++ /dev/null @@ -1,139 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=256) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# model settings -norm_cfg = dict(type='SyncBN', requires_grad=True) -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='SwinTransformer', - embed_dims=96, - depths=[2, 2, 6, 2], - num_heads=[3, 6, 12, 24], - window_size=7, - mlp_ratio=4, - qkv_bias=True, - qk_scale=None, - drop_rate=0., - attn_drop_rate=0., - drop_path_rate=0.2, - patch_norm=True, - out_indices=(3, ), - with_cp=False, - convert_weights=True, - init_cfg=dict( - type='Pretrained', - checkpoint='https://github.com/SwinTransformer/storage/releases/' - 'download/v1.0.0/swin_tiny_patch4_window7_224.pth'), - ), - head=dict( - type='HeatmapHead', - in_channels=768, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] - -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_vgg16-bn_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_vgg16-bn_8xb64-210e_humanart-256x192.py deleted file mode 100644 index 0a8722670b..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_vgg16-bn_8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,122 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='VGG', - depth=16, - norm_cfg=dict(type='BN'), - init_cfg=dict(type='Pretrained', checkpoint='mmcls://vgg16_bn'), - ), - head=dict( - type='HeatmapHead', - in_channels=512, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_vipnas-mbv3_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_vipnas-mbv3_8xb64-210e_humanart-256x192.py deleted file mode 100644 index a94e01c97e..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_vipnas-mbv3_8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,122 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict(type='ViPNAS_MobileNetV3'), - head=dict( - type='ViPNASHead', - in_channels=160, - out_channels=17, - deconv_out_channels=(160, 160, 160), - deconv_num_groups=(160, 160, 160), - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict( - type='RandomBBoxTransform', - rotate_factor=60, - scale_factor=(0.75, 1.25)), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_vipnas-res50_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_vipnas-res50_8xb64-210e_humanart-256x192.py deleted file mode 100644 index b06189da21..0000000000 --- a/configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_vipnas-res50_8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,120 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=210, - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# codec settings -codec = dict( - type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict(type='ViPNAS_ResNet', depth=50), - head=dict( - type='ViPNASHead', - in_channels=608, - out_channels=17, - loss=dict(type='KeypointMSELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - flip_mode='heatmap', - shift_heatmap=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict( - type='RandomBBoxTransform', - rotate_factor=60, - scale_factor=(0.75, 1.25)), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/vitpose_humanart.md b/configs/body_2d_keypoint/topdown_heatmap/humanart/vitpose_humanart.md new file mode 100644 index 0000000000..1e559aa4da --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/vitpose_humanart.md @@ -0,0 +1,85 @@ +To utilize ViTPose, you'll need to have [MMClassification](https://github.com/open-mmlab/mmclassification). To install the required version, run the following command: + +```shell +mim install 'mmcls>=1.0.0rc5' +``` + + + +
+ +ViTPose (NeurIPS'2022) + +```bibtex +@inproceedings{ + xu2022vitpose, + title={Vi{TP}ose: Simple Vision Transformer Baselines for Human Pose Estimation}, + author={Yufei Xu and Jing Zhang and Qiming Zhang and Dacheng Tao}, + booktitle={Advances in Neural Information Processing Systems}, + year={2022}, +} +``` + +
+ + + +
+COCO-WholeBody (ECCV'2020) + +```bibtex +@inproceedings{jin2020whole, + title={Whole-Body Human Pose Estimation in the Wild}, + author={Jin, Sheng and Xu, Lumin and Xu, Jin and Wang, Can and Liu, Wentao and Qian, Chen and Ouyang, Wanli and Luo, Ping}, + booktitle={Proceedings of the European Conference on Computer Vision (ECCV)}, + year={2020} +} +``` + +
+ +
+Human-Art (CVPR'2023) + +```bibtex +@inproceedings{ju2023humanart, + title={Human-Art: A Versatile Human-Centric Dataset Bridging Natural and Artificial Scenes}, + author={Ju, Xuan and Zeng, Ailing and Jianan, Wang and Qiang, Xu and Lei, Zhang}, + booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), + year={2023}} +``` + +
+ +Results on Human-Art validation dataset with detector having human AP of 56.2 on Human-Art validation dataset + +> With classic decoder + +| Arch | Input Size | AP | AP50 | AP75 | AR | AR50 | ckpt | log | +| :-------------------------------------------- | :--------: | :---: | :-------------: | :-------------: | :---: | :-------------: | :-------------------------------------------: | :-------------------------------------------: | +| [ViTPose-S-coco](/configs/body_2d_keypoint/topdown_heatmap/coco/td-hm_ViTPose-small_8xb64-210e_coco-256x192.py) | 256x192 | 0.228 | 0.371 | 0.229 | 0.298 | 0.467 | [ckpt](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/coco/td-hm_ViTPose-small_8xb64-210e_coco-256x192-62d7a712_20230314.pth) | [log](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/coco/td-hm_ViTPose-small_8xb64-210e_coco-256x192-62d7a712_20230314.json) | +| [ViTPose-S-humanart-coco](configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-small_8xb64-210e_humanart-256x192.py) | 256x192 | 0.381 | 0.532 | 0.405 | 0.448 | 0.602 | [ckpt](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/human_art/td-hm_ViTPose-small_8xb64-210e_humanart-256x192-5cbe2bfc_20230611.pth) | [log](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/human_art/td-hm_ViTPose-small_8xb64-210e_humanart-256x192-5cbe2bfc_20230611.json) | +| [ViTPose-B-coco](/configs/body_2d_keypoint/topdown_heatmap/coco/td-hm_ViTPose-base_8xb64-210e_coco-256x192.py) | 256x192 | 0.270 | 0.423 | 0.272 | 0.340 | 0.510 | [ckpt](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/coco/td-hm_ViTPose-base_8xb64-210e_coco-256x192-216eae50_20230314.pth) | [log](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/coco/td-hm_ViTPose-base_8xb64-210e_coco-256x192-216eae50_20230314.json) | +| [ViTPose-B-humanart-coco](configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-base_8xb64-210e_humanart-256x192.py) | 256x192 | 0.410 | 0.549 | 0.434 | 0.475 | 0.615 | [ckpt](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/human_art/td-hm_ViTPose-base_8xb64-210e_humanart-256x192-b417f546_20230611.pth) | [log](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/human_art/td-hm_ViTPose-base_8xb64-210e_humanart-256x192-b417f546_20230611.json) | + +Results on Human-Art validation dataset with ground-truth bounding-box + +> With classic decoder + +| Arch | Input Size | AP | AP50 | AP75 | AR | AR50 | ckpt | log | +| :-------------------------------------------- | :--------: | :---: | :-------------: | :-------------: | :---: | :-------------: | :-------------------------------------------: | :-------------------------------------------: | +| [ViTPose-S-coco](/configs/body_2d_keypoint/topdown_heatmap/coco/td-hm_ViTPose-small_8xb64-210e_coco-256x192.py) | 256x192 | 0.507 | 0.758 | 0.531 | 0.551 | 0.780 | [ckpt](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/coco/td-hm_ViTPose-small_8xb64-210e_coco-256x192-62d7a712_20230314.pth) | [log](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/coco/td-hm_ViTPose-small_8xb64-210e_coco-256x192-62d7a712_20230314.json) | +| [ViTPose-S-humanart-coco](configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-small_8xb64-210e_humanart-256x192.py) | 256x192 | 0.738 | 0.905 | 0.802 | 0.768 | 0.911 | [ckpt](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/human_art/td-hm_ViTPose-small_8xb64-210e_humanart-256x192-5cbe2bfc_20230611.pth) | [log](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/human_art/td-hm_ViTPose-small_8xb64-210e_humanart-256x192-5cbe2bfc_20230611.json) | +| [ViTPose-B-coco](/configs/body_2d_keypoint/topdown_heatmap/coco/td-hm_ViTPose-base_8xb64-210e_coco-256x192.py) | 256x192 | 0.555 | 0.782 | 0.590 | 0.599 | 0.809 | [ckpt](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/coco/td-hm_ViTPose-base_8xb64-210e_coco-256x192-216eae50_20230314.pth) | [log](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/coco/td-hm_ViTPose-base_8xb64-210e_coco-256x192-216eae50_20230314.json) | +| [ViTPose-B-humanart-coco](configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-base_8xb64-210e_humanart-256x192.py) | 256x192 | 0.759 | 0.905 | 0.823 | 0.790 | 0.917 | [ckpt](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/human_art/td-hm_ViTPose-base_8xb64-210e_humanart-256x192-b417f546_20230611.pth) | [log](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/human_art/td-hm_ViTPose-base_8xb64-210e_humanart-256x192-b417f546_20230611.json) | + +Results on COCO val2017 with detector having human AP of 56.4 on COCO val2017 dataset + +> With classic decoder + +| Arch | Input Size | AP | AP50 | AP75 | AR | AR50 | ckpt | log | +| :-------------------------------------------- | :--------: | :---: | :-------------: | :-------------: | :---: | :-------------: | :-------------------------------------------: | :-------------------------------------------: | +| [ViTPose-S-coco](/configs/body_2d_keypoint/topdown_heatmap/coco/td-hm_ViTPose-small_8xb64-210e_coco-256x192.py) | 256x192 | 0.739 | 0.903 | 0.816 | 0.792 | 0.942 | [ckpt](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/coco/td-hm_ViTPose-small_8xb64-210e_coco-256x192-62d7a712_20230314.pth) | [log](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/coco/td-hm_ViTPose-small_8xb64-210e_coco-256x192-62d7a712_20230314.json) | +| [ViTPose-S-humanart-coco](configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-small_8xb64-210e_humanart-256x192.py) | 256x192 | 0.737 | 0.902 | 0.811 | 0.792 | 0.942 | [ckpt](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/human_art/td-hm_ViTPose-small_8xb64-210e_humanart-256x192-5cbe2bfc_20230611.pth) | [log](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/human_art/td-hm_ViTPose-small_8xb64-210e_humanart-256x192-5cbe2bfc_20230611.json) | +| [ViTPose-B-coco](/configs/body_2d_keypoint/topdown_heatmap/coco/td-hm_ViTPose-base_8xb64-210e_coco-256x192.py) | 256x192 | 0.757 | 0.905 | 0.829 | 0.810 | 0.946 | [ckpt](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/coco/td-hm_ViTPose-base_8xb64-210e_coco-256x192-216eae50_20230314.pth) | [log](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/coco/td-hm_ViTPose-base_8xb64-210e_coco-256x192-216eae50_20230314.json) | +| [ViTPose-B-humanart-coco](configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-base_8xb64-210e_humanart-256x192.py) | 256x192 | 0.758 | 0.906 | 0.829 | 0.812 | 0.946 | [ckpt](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/human_art/td-hm_ViTPose-base_8xb64-210e_humanart-256x192-b417f546_20230611.pth) | [log](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/human_art/td-hm_ViTPose-base_8xb64-210e_humanart-256x192-b417f546_20230611.json) | diff --git a/configs/body_2d_keypoint/topdown_heatmap/humanart/vitpose_humanart.yml b/configs/body_2d_keypoint/topdown_heatmap/humanart/vitpose_humanart.yml new file mode 100644 index 0000000000..12a557fbf6 --- /dev/null +++ b/configs/body_2d_keypoint/topdown_heatmap/humanart/vitpose_humanart.yml @@ -0,0 +1,79 @@ +Collections: +- Name: ViTPose + Paper: + Title: 'ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation' + URL: https://arxiv.org/abs/2204.12484 + README: https://github.com/open-mmlab/mmpose/blob/main/docs/src/papers/algorithms/vitpose.md + Metadata: + Training Resources: 8x A100 GPUs +Models: +- Config: configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-small_8xb64-210e_humanart-256x192.py + In Collection: ViTPose + Metadata: + Architecture: &id001 + - ViTPose + - Classic Head + Model Size: Small + Training Data: &id002 + - COCO + - Human-Art + Name: td-hm_ViTPose-small_8xb64-210e_humanart-256x192 + Results: + - Dataset: COCO + Metrics: + AP: 0.737 + AP@0.5: 0.902 + AP@0.75: 0.811 + AR: 0.792 + AR@0.5: 0.942 + Task: Body 2D Keypoint + - Dataset: Human-Art + Metrics: + AP: 0.381 + AP@0.5: 0.532 + AP@0.75: 0.405 + AR: 0.448 + AR@0.5: 0.602 + Task: Body 2D Keypoint + - Dataset: Human-Art(GT) + Metrics: + AP: 0.738 + AP@0.5: 0.905 + AP@0.75: 0.802 + AR: 0.768 + AR@0.5: 0.911 + Task: Body 2D Keypoint + Weights: https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/human_art/td-hm_ViTPose-small_8xb64-210e_humanart-256x192-5cbe2bfc_20230611.pth +- Config: configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_ViTPose-base_8xb64-210e_humanart-256x192.py + In Collection: ViTPose + Metadata: + Architecture: *id001 + Model Size: Base + Training Data: *id002 + Name: td-hm_ViTPose-base_8xb64-210e_humanart-256x192 + Results: + - Dataset: COCO + Metrics: + AP: 0.758 + AP@0.5: 0.906 + AP@0.75: 0.829 + AR: 0.812 + AR@0.5: 0.946 + Task: Body 2D Keypoint + - Dataset: Human-Art + Metrics: + AP: 0.410 + AP@0.5: 0.549 + AP@0.75: 0.434 + AR: 0.475 + AR@0.5: 0.615 + Task: Body 2D Keypoint + - Dataset: Human-Art(GT) + Metrics: + AP: 0.759 + AP@0.5: 0.905 + AP@0.75: 0.823 + AR: 0.790 + AR@0.5: 0.917 + Task: Body 2D Keypoint + Weights: https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/human_art/td-hm_ViTPose-base_8xb64-210e_humanart-256x192-b417f546_20230611.pth diff --git a/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_mobilenetv2_rle-pretrained-8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_mobilenetv2_rle-pretrained-8xb64-210e_humanart-256x192.py deleted file mode 100644 index 8396ea1b26..0000000000 --- a/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_mobilenetv2_rle-pretrained-8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,126 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=1e-3, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=train_cfg['max_epochs'], - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# codec settings -codec = dict(type='RegressionLabel', input_size=(192, 256)) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='MobileNetV2', - widen_factor=1., - out_indices=(7, ), - init_cfg=dict( - type='Pretrained', - prefix='backbone.', - checkpoint='https://download.openmmlab.com/mmpose/top_down/' - 'mobilenetv2/mobilenetv2_coco_256x192-d1e58e7b_20200727.pth')), - neck=dict(type='GlobalAveragePooling'), - head=dict( - type='RLEHead', - in_channels=1280, - num_joints=17, - loss=dict(type='RLELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - shift_coords=True, - ), -) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json', - score_mode='bbox_rle') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_res101_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_res101_8xb64-210e_humanart-256x192.py deleted file mode 100644 index 5901d7dc9b..0000000000 --- a/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_res101_8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,120 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=train_cfg['max_epochs'], - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# codec settings -codec = dict(type='RegressionLabel', input_size=(192, 256)) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='ResNet', - depth=101, - init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet101'), - ), - neck=dict(type='GlobalAveragePooling'), - head=dict( - type='RegressionHead', - in_channels=2048, - num_joints=17, - loss=dict(type='SmoothL1Loss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - shift_coords=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=f'{data_root}annotations/person_keypoints_val2017.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_res101_rle-8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_res101_rle-8xb64-210e_humanart-256x192.py deleted file mode 100644 index 189792780e..0000000000 --- a/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_res101_rle-8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,121 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=train_cfg['max_epochs'], - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# codec settings -codec = dict(type='RegressionLabel', input_size=(192, 256)) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='ResNet', - depth=101, - init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet101'), - ), - neck=dict(type='GlobalAveragePooling'), - head=dict( - type='RLEHead', - in_channels=2048, - num_joints=17, - loss=dict(type='RLELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - shift_coords=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json', - score_mode='bbox_rle') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_res152_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_res152_8xb64-210e_humanart-256x192.py deleted file mode 100644 index 6589b8e56e..0000000000 --- a/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_res152_8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,120 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=train_cfg['max_epochs'], - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# codec settings -codec = dict(type='RegressionLabel', input_size=(192, 256)) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='ResNet', - depth=152, - init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet152'), - ), - neck=dict(type='GlobalAveragePooling'), - head=dict( - type='RegressionHead', - in_channels=2048, - num_joints=17, - loss=dict(type='SmoothL1Loss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - shift_coords=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=f'{data_root}annotations/person_keypoints_val2017.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_res152_rle-8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_res152_rle-8xb64-210e_humanart-256x192.py deleted file mode 100644 index 77c3a0f1df..0000000000 --- a/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_res152_rle-8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,121 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=train_cfg['max_epochs'], - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# codec settings -codec = dict(type='RegressionLabel', input_size=(192, 256)) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='ResNet', - depth=152, - init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet152'), - ), - neck=dict(type='GlobalAveragePooling'), - head=dict( - type='RLEHead', - in_channels=2048, - num_joints=17, - loss=dict(type='RLELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - shift_coords=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json', - score_mode='bbox_rle') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_res152_rle-8xb64-210e_humanart-384x288.py b/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_res152_rle-8xb64-210e_humanart-384x288.py deleted file mode 100644 index eff463d3e1..0000000000 --- a/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_res152_rle-8xb64-210e_humanart-384x288.py +++ /dev/null @@ -1,121 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=train_cfg['max_epochs'], - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# codec settings -codec = dict(type='RegressionLabel', input_size=(288, 384)) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='ResNet', - depth=152, - init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet152'), - ), - neck=dict(type='GlobalAveragePooling'), - head=dict( - type='RLEHead', - in_channels=2048, - num_joints=17, - loss=dict(type='RLELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - shift_coords=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json', - score_mode='bbox_rle') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_res50_8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_res50_8xb64-210e_humanart-256x192.py deleted file mode 100644 index 006c0fee44..0000000000 --- a/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_res50_8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,120 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=5e-4, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=train_cfg['max_epochs'], - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# codec settings -codec = dict(type='RegressionLabel', input_size=(192, 256)) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='ResNet', - depth=50, - init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'), - ), - neck=dict(type='GlobalAveragePooling'), - head=dict( - type='RegressionHead', - in_channels=2048, - num_joints=17, - loss=dict(type='SmoothL1Loss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - shift_coords=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=f'{data_root}annotations/person_keypoints_val2017.json') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_res50_rle-8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_res50_rle-8xb64-210e_humanart-256x192.py deleted file mode 100644 index cf8b681560..0000000000 --- a/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_res50_rle-8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,121 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=1e-3, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=train_cfg['max_epochs'], - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# codec settings -codec = dict(type='RegressionLabel', input_size=(192, 256)) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='ResNet', - depth=50, - init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'), - ), - neck=dict(type='GlobalAveragePooling'), - head=dict( - type='RLEHead', - in_channels=2048, - num_joints=17, - loss=dict(type='RLELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - shift_coords=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -val_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=val_pipeline, - )) -test_dataloader = val_dataloader - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json', - score_mode='bbox_rle') -test_evaluator = val_evaluator diff --git a/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_res50_rle-pretrained-8xb64-210e_humanart-256x192.py b/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_res50_rle-pretrained-8xb64-210e_humanart-256x192.py deleted file mode 100644 index c208491ae8..0000000000 --- a/configs/body_2d_keypoint/topdown_regression/humanart/td-reg_res50_rle-pretrained-8xb64-210e_humanart-256x192.py +++ /dev/null @@ -1,125 +0,0 @@ -_base_ = ['../../../_base_/default_runtime.py'] - -# runtime -train_cfg = dict(max_epochs=210, val_interval=10) - -# optimizer -optim_wrapper = dict(optimizer=dict( - type='Adam', - lr=1e-3, -)) - -# learning policy -param_scheduler = [ - dict( - type='LinearLR', begin=0, end=500, start_factor=0.001, - by_epoch=False), # warm-up - dict( - type='MultiStepLR', - begin=0, - end=train_cfg['max_epochs'], - milestones=[170, 200], - gamma=0.1, - by_epoch=True) -] - -# automatically scaling LR based on the actual training batch size -auto_scale_lr = dict(base_batch_size=512) - -# codec settings -codec = dict(type='RegressionLabel', input_size=(192, 256)) - -# model settings -model = dict( - type='TopdownPoseEstimator', - data_preprocessor=dict( - type='PoseDataPreprocessor', - mean=[123.675, 116.28, 103.53], - std=[58.395, 57.12, 57.375], - bgr_to_rgb=True), - backbone=dict( - type='ResNet', - depth=50, - init_cfg=dict( - type='Pretrained', - prefix='backbone.', - checkpoint='https://download.openmmlab.com/mmpose/' - 'pretrain_models/td-hm_res50_8xb64-210e_coco-256x192.pth'), - ), - neck=dict(type='GlobalAveragePooling'), - head=dict( - type='RLEHead', - in_channels=2048, - num_joints=17, - loss=dict(type='RLELoss', use_target_weight=True), - decoder=codec), - test_cfg=dict( - flip_test=True, - shift_coords=True, - )) - -# base dataset settings -dataset_type = 'HumanArtDataset' -data_mode = 'topdown' -data_root = 'data/' - -# pipelines -train_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='RandomFlip', direction='horizontal'), - dict(type='RandomHalfBody'), - dict(type='RandomBBoxTransform'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='GenerateTarget', encoder=codec), - dict(type='PackPoseInputs') -] -test_pipeline = [ - dict(type='LoadImage'), - dict(type='GetBBoxCenterScale'), - dict(type='TopdownAffine', input_size=codec['input_size']), - dict(type='PackPoseInputs') -] - -# data loaders -train_dataloader = dict( - batch_size=64, - num_workers=2, - persistent_workers=True, - sampler=dict(type='DefaultSampler', shuffle=True), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/training_humanart_coco.json', - data_prefix=dict(img=''), - pipeline=train_pipeline, - )) -val_dataloader = dict( - batch_size=32, - num_workers=2, - persistent_workers=True, - drop_last=False, - sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), - dataset=dict( - type=dataset_type, - data_root=data_root, - data_mode=data_mode, - ann_file='HumanArt/annotations/validation_humanart.json', - bbox_file=f'{data_root}HumanArt/person_detection_results/' - 'HumanArt_validation_detections_AP_H_56_person.json', - data_prefix=dict(img=''), - test_mode=True, - pipeline=test_pipeline, - )) -test_dataloader = val_dataloader - -# hooks -default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) - -# evaluators -val_evaluator = dict( - type='CocoMetric', - ann_file=data_root + 'HumanArt/annotations/validation_humanart.json', - score_mode='bbox_rle') -test_evaluator = val_evaluator diff --git a/tools/dist_train.sh b/tools/dist_train.sh old mode 100644 new mode 100755 From 954f9f4fd7a15a4f392cb5902fdace26d2aa4d95 Mon Sep 17 00:00:00 2001 From: juxuan27 Date: Mon, 12 Jun 2023 01:38:12 +0800 Subject: [PATCH 6/7] change test images --- tests/data/humanart/000000000785.jpg | Bin 133674 -> 0 bytes tests/data/humanart/000000040083.jpg | Bin 104152 -> 0 bytes tests/data/humanart/000000196141.jpg | Bin 135345 -> 0 bytes tests/data/humanart/000000197388.jpg | Bin 167407 -> 0 bytes .../digital_art/000000001648.jpg | Bin 0 -> 1024164 bytes .../garage_kits/000000005603.jpg | Bin 0 -> 225722 bytes .../real_human/acrobatics/000000000590.jpg | Bin 0 -> 76762 bytes tests/data/humanart/test_humanart.json | 2745 ++++------------- .../humanart/test_humanart_det_AP_H_56.json | 1311 +------- 9 files changed, 597 insertions(+), 3459 deletions(-) delete mode 100644 tests/data/humanart/000000000785.jpg delete mode 100644 tests/data/humanart/000000040083.jpg delete mode 100644 tests/data/humanart/000000196141.jpg delete mode 100644 tests/data/humanart/000000197388.jpg create mode 100644 tests/data/humanart/2D_virtual_human/digital_art/000000001648.jpg create mode 100644 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z8nIXsn9wi^>Uz8h71@>bAB$#=*w!hg91M~(LCN+FNSFXpuxT9FgQMpu?mKN zIuTS_c*)s{Dx8_VKj%Fd5`l+c0n8l485YJtZ~9o|u<4Th|K#Z~ZW|vip8w->SF%AVz{;ap5nsG~73%>3zIc-wj?7X0a5q z9AvlSXzN^Atkys%F*+3ys_t~5woGuEAR9=ojIraY+nIH!~wLxscgcN!^G_OIu{;(mDoUsvd1BHL%Hhj zP830(#lTZAKot8BRnK8{BXP{tkJkRxHT@xl4F+k&CUy?QelG8N5`A8a<}LS%O9UT1#_LuGkT5_E_=k@9@G2vP_wh;LVJ(TBdjmD9ul-YL7bUX#D=TJqR{jbw|)BLt9 zKV)L5c#wLv#?ml(be6`a1rqe`p5I!fg`&Ydx(lVhG9lo7lnQ8Z1ChS zFU~y6i>;6z_Sy8^V7KpEmMC{S3oPzwP=Pm{nD zYzFHZ)F+^PFIIBjB8DbnL^URr zXfril8z}7?O&9uoJ`NYtyLSGuFSE}DHo)sq>6hU{>2+Gy5_tUiIj6%^_T98=!QRU` zPU(mK(&*5g+>ekYp;vkQ`-9KI*}c@P(Ov20M+3DKds>%5Wa)BmyP!?~UcV@$dv(?S zawz?82iE`1-%TFXvdPVb-S=PA{p+5@%-FH^t|$7HqchUHUmxt5B^v%gMJMf0(f&zv z_+|0?^)qIU-M}AmcdS1(^IET9hR>=AGj{lSDhBnx6u{*;=ksJpt)u15@Q!q20mWrg k44aW1khmbX&cqUF Date: Mon, 12 Jun 2023 02:12:48 +0800 Subject: [PATCH 7/7] add test Human-Art --- .../test_body_datasets/test_humanart_dataset.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/tests/test_datasets/test_datasets/test_body_datasets/test_humanart_dataset.py b/tests/test_datasets/test_datasets/test_body_datasets/test_humanart_dataset.py index e63484a4e7..dcf29ab692 100644 --- a/tests/test_datasets/test_datasets/test_body_datasets/test_humanart_dataset.py +++ b/tests/test_datasets/test_datasets/test_body_datasets/test_humanart_dataset.py @@ -74,7 +74,7 @@ def test_metainfo(self): dataset = self.build_humanart_dataset() self.check_metainfo_keys(dataset.metainfo) # test dataset_name - self.assertEqual(dataset.metainfo['dataset_name'], 'humanart') + self.assertEqual(dataset.metainfo['dataset_name'], 'Human-Art') # test number of keypoints num_keypoints = 17 @@ -94,13 +94,13 @@ def test_metainfo(self): def test_topdown(self): # test topdown training dataset = self.build_humanart_dataset(data_mode='topdown') - self.assertEqual(len(dataset), 12) + self.assertEqual(len(dataset), 4) self.check_data_info_keys(dataset[0], data_mode='topdown') # test topdown testing dataset = self.build_humanart_dataset( data_mode='topdown', test_mode=True) - self.assertEqual(len(dataset), 12) + self.assertEqual(len(dataset), 4) self.check_data_info_keys(dataset[0], data_mode='topdown') # test topdown testing with bbox file @@ -108,7 +108,7 @@ def test_topdown(self): data_mode='topdown', test_mode=True, bbox_file='tests/data/humanart/test_humanart_det_AP_H_56.json') - self.assertEqual(len(dataset), 118) + self.assertEqual(len(dataset), 13) self.check_data_info_keys(dataset[0], data_mode='topdown') # test topdown testing with filter config @@ -117,18 +117,18 @@ def test_topdown(self): test_mode=True, bbox_file='tests/data/humanart/test_humanart_det_AP_H_56.json', filter_cfg=dict(bbox_score_thr=0.3)) - self.assertEqual(len(dataset), 33) + self.assertEqual(len(dataset), 8) def test_bottomup(self): # test bottomup training dataset = self.build_humanart_dataset(data_mode='bottomup') - self.assertEqual(len(dataset), 4) + self.assertEqual(len(dataset), 3) self.check_data_info_keys(dataset[0], data_mode='bottomup') # test bottomup testing dataset = self.build_humanart_dataset( data_mode='bottomup', test_mode=True) - self.assertEqual(len(dataset), 4) + self.assertEqual(len(dataset), 3) self.check_data_info_keys(dataset[0], data_mode='bottomup') def test_exceptions_and_warnings(self):

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