Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

OChuman dataset #70

Merged
merged 11 commits into from
Aug 17, 2020
Merged

OChuman dataset #70

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
11 commits
Select commit Hold shift + click to select a range
2ba81ea
add refs
jin-s13 Aug 16, 2020
9bd23aa
add ochuman
jin-s13 Aug 16, 2020
d4749d1
use gt bbox for ochuman
jin-s13 Aug 16, 2020
6a6864e
update ochuman dataset
jin-s13 Aug 16, 2020
b16a6ee
update ochuman dataset
jin-s13 Aug 16, 2020
7efaf76
update ochuman
jin-s13 Aug 16, 2020
b4644a3
update ochuman dataset init
jin-s13 Aug 16, 2020
30ff12c
add modelzoo
jin-s13 Aug 16, 2020
bacd146
resolve comments
jin-s13 Aug 17, 2020
239246a
list to array
jin-s13 Aug 17, 2020
de09406
update readme for data
jin-s13 Aug 17, 2020
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
16 changes: 16 additions & 0 deletions configs/top_down/resnet/README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -25,6 +25,22 @@
| [pose_resnet_152](/configs/top_down/resnet/coco/res152_coco_384x288.py) | 384x288 | 0.750 | 0.908 | 0.821 | 0.800 | 0.942 | [ckpt](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmpose/top_down/resnet/res152_coco_384x288-3860d4c9_20200709.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmpose/top_down/resnet/res152_coco_384x288_20200709.log.json) |



### Results on OCHuman test dataset with ground-truth bounding boxes

Following the common setting, the models are trained on COCO train dataset, and evaluate on OCHuman dataset.

| Arch | Input Size | AP | AP<sup>50</sup> | AP<sup>75</sup> | AR | AR<sup>50</sup> | ckpt | log |
| :-------------- | :-----------: | :------: | :------: | :------: | :------: | :------: |:------: |:------: |
| [pose_resnet_50](/configs/top_down/resnet/coco/res50_coco_256x192.py) | 256x192 | 0.546 | 0.726 | 0.593 | 0.592 | 0.755 | [ckpt](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmpose/top_down/resnet/res50_coco_256x192-ec54d7f3_20200709.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmpose/top_down/resnet/res50_coco_256x192_20200709.log.json) |
| [pose_resnet_50](/configs/top_down/resnet/coco/res50_coco_384x288.py) | 384x288 | 0.539 | 0.723 | 0.574 | 0.588 | 0.756 | [ckpt](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmpose/top_down/resnet/res50_coco_384x288-e6f795e9_20200709.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmpose/top_down/resnet/res50_coco_384x288_20200709.log.json) |
| [pose_resnet_101](/configs/top_down/resnet/coco/res101_coco_256x192.py) | 256x192 | 0.559 | 0.724 | 0.606 | 0.605 | 0.751 | [ckpt](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmpose/top_down/resnet/res101_coco_256x192-6e6babf0_20200708.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmpose/top_down/resnet/res101_coco_256x192_20200708.log.json) |
| [pose_resnet_101](/configs/top_down/resnet/coco/res101_coco_384x288.py) | 384x288 | 0.571 | 0.715 | 0.615 | 0.615 | 0.748 | [ckpt](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmpose/top_down/resnet/res101_coco_384x288-8c71bdc9_20200709.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmpose/top_down/resnet/res101_coco_384x288_20200709.log.json) |
| [pose_resnet_152](/configs/top_down/resnet/coco/res152_coco_256x192.py) | 256x192 | 0.570 | 0.725 | 0.617 | 0.616 | 0.754 | [ckpt](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmpose/top_down/resnet/res152_coco_256x192-f6e307c2_20200709.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmpose/top_down/resnet/res152_coco_256x192_20200709.log.json) |
| [pose_resnet_152](/configs/top_down/resnet/coco/res152_coco_384x288.py) | 384x288 | 0.582 | 0.723 | 0.627 | 0.627 | 0.752 | [ckpt](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmpose/top_down/resnet/res152_coco_384x288-3860d4c9_20200709.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmpose/top_down/resnet/res152_coco_384x288_20200709.log.json) |



### Results on MPII val set.

| Arch | Input Size | Mean | Mean@0.1 | ckpt | log |
Expand Down
146 changes: 146 additions & 0 deletions configs/top_down/resnet/ochuman/res101_ochuman_256x192.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,146 @@
log_level = 'INFO'
load_from = None
resume_from = None
dist_params = dict(backend='nccl')
workflow = [('train', 1)]
checkpoint_config = dict(interval=10)
evaluation = dict(interval=5, metric='mAP')

optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[170, 200])
total_epochs = 210
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])

channel_cfg = dict(
num_output_channels=17,
dataset_joints=17,
dataset_channel=[
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16
])

# model settings
model = dict(
type='TopDown',
pretrained='models/pytorch/imagenet/resnet101-5d3b4d8f.pth',
backbone=dict(type='ResNet', depth=101),
keypoint_head=dict(
type='TopDownSimpleHead',
in_channels=2048,
out_channels=channel_cfg['num_output_channels'],
),
train_cfg=dict(),
test_cfg=dict(
flip_test=True,
post_process=True,
shift_heatmap=True,
unbiased_decoding=False,
modulate_kernel=11),
loss_pose=dict(type='JointsMSELoss', use_target_weight=True))

data_cfg = dict(
image_size=[192, 256],
heatmap_size=[48, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'],
soft_nms=False,
nms_thr=1.0,
oks_thr=0.9,
vis_thr=0.2,
bbox_thr=1.0,
use_gt_bbox=True,
image_thr=0.0,
bbox_file='data/person_detection_results/'
'COCO_val2017_detections_AP_H_56_person.json',
)

train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownRandomFlip', flip_prob=0.5),
dict(
type='TopDownHalfBodyTransform',
num_joints_half_body=8,
prob_half_body=0.3),
dict(
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(type='TopDownGenerateTarget', sigma=2),
dict(
type='Collect',
keys=['img', 'target', 'target_weight'],
meta_keys=[
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
'rotation', 'bbox_score', 'flip_pairs'
]),
]

val_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(
type='Collect',
keys=[
'img',
],
meta_keys=[
'image_file', 'center', 'scale', 'rotation', 'bbox_score',
'flip_pairs'
]),
]

test_pipeline = val_pipeline

data_root = 'data/ochuman'
data = dict(
samples_per_gpu=64,
workers_per_gpu=2,
train=dict(
type='TopDownCocoDataset',
ann_file='data/coco/annotations/person_keypoints_train2017.json',
img_prefix='data/coco//train2017/',
data_cfg=data_cfg,
pipeline=train_pipeline),
val=dict(
type='TopDownOCHumanDataset',
ann_file=f'{data_root}/annotations/'
'ochuman_coco_format_val_range_0.00_1.00.json',
img_prefix=f'{data_root}/images/',
data_cfg=data_cfg,
pipeline=val_pipeline),
test=dict(
type='TopDownOCHumanDataset',
ann_file=f'{data_root}/annotations/'
'ochuman_coco_format_test_range_0.00_1.00.json',
img_prefix=f'{data_root}/images/',
data_cfg=data_cfg,
pipeline=val_pipeline),
)
146 changes: 146 additions & 0 deletions configs/top_down/resnet/ochuman/res101_ochuman_384x288.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,146 @@
log_level = 'INFO'
load_from = None
resume_from = None
dist_params = dict(backend='nccl')
workflow = [('train', 1)]
checkpoint_config = dict(interval=10)
evaluation = dict(interval=5, metric='mAP')

optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[170, 200])
total_epochs = 210
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])

channel_cfg = dict(
num_output_channels=17,
dataset_joints=17,
dataset_channel=[
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16
])

# model settings
model = dict(
type='TopDown',
pretrained='models/pytorch/imagenet/resnet101-5d3b4d8f.pth',
backbone=dict(type='ResNet', depth=101),
keypoint_head=dict(
type='TopDownSimpleHead',
in_channels=2048,
out_channels=channel_cfg['num_output_channels'],
),
train_cfg=dict(),
test_cfg=dict(
flip_test=True,
post_process=True,
shift_heatmap=True,
unbiased_decoding=False,
modulate_kernel=11),
loss_pose=dict(type='JointsMSELoss', use_target_weight=True))

data_cfg = dict(
image_size=[288, 384],
heatmap_size=[72, 96],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'],
soft_nms=False,
nms_thr=1.0,
oks_thr=0.9,
vis_thr=0.2,
bbox_thr=1.0,
use_gt_bbox=True,
image_thr=0.0,
bbox_file='data/person_detection_results/'
'COCO_val2017_detections_AP_H_56_person.json',
)

train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownRandomFlip', flip_prob=0.5),
dict(
type='TopDownHalfBodyTransform',
num_joints_half_body=8,
prob_half_body=0.3),
dict(
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(type='TopDownGenerateTarget', sigma=3),
dict(
type='Collect',
keys=['img', 'target', 'target_weight'],
meta_keys=[
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
'rotation', 'bbox_score', 'flip_pairs'
]),
]

val_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(
type='Collect',
keys=[
'img',
],
meta_keys=[
'image_file', 'center', 'scale', 'rotation', 'bbox_score',
'flip_pairs'
]),
]

test_pipeline = val_pipeline

data_root = 'data/ochuman'
data = dict(
samples_per_gpu=32,
workers_per_gpu=2,
train=dict(
type='TopDownCocoDataset',
ann_file='data/coco/annotations/person_keypoints_train2017.json',
img_prefix='data/coco//train2017/',
data_cfg=data_cfg,
pipeline=train_pipeline),
val=dict(
type='TopDownOCHumanDataset',
ann_file=f'{data_root}/annotations/'
'ochuman_coco_format_val_range_0.00_1.00.json',
img_prefix=f'{data_root}/images/',
data_cfg=data_cfg,
pipeline=val_pipeline),
test=dict(
type='TopDownOCHumanDataset',
ann_file=f'{data_root}/annotations/'
'ochuman_coco_format_test_range_0.00_1.00.json',
img_prefix=f'{data_root}/images/',
data_cfg=data_cfg,
pipeline=val_pipeline),
)
Loading