Aic dataset

README.md

@@ -32,6 +32,11 @@ The master branch works with **PyTorch 1.3+**.
   We achieve faster training speed and higher accuracy than other popular codebases, such as [HRNet](https://github.com/leoxiaobin/deep-high-resolution-net.pytorch).
   See [benchmark.md](docs/benchmark.md) for more information.
 
+- **Support for various datasets**
+
+  The toolbox directly supports multiple datasets, COCO, AIC, MPII, MPII-TRB, OCHuman etc.
+  See [data_preparation.md](docs/data_preparation.md) for more information.
+
 - **Well tested and documented**
 
   We provide detailed documentation and API reference, as well as unittests.
@@ -66,6 +71,7 @@ More details about the benchmark are available on [benchmark.md](docs/benchmark.
 Supported backbones for human pose estimation:
 
 - [x] [AlexNet](configs/top_down/alexnet/README.md)
+- [x] [CPM](configs/top_down/cpm/README.md)
 - [x] [Hourglass](configs/top_down/hourglass/README.md)
 - [x] [HRNet](configs/top_down/hrnet/README.md)
 - [x] [MobilenetV2](configs/top_down/mobilenet_v2/README.md)
@@ -77,6 +83,7 @@ Supported backbones for human pose estimation:
 - [x] [ShufflenetV1](configs/top_down/shufflenet_v1/README.md)
 
 Supported methods for human pose estimation:
+- [x] [CPM](configs/top_down/cpm/README.md)
 - [x] [SimpleBaseline](configs/top_down/resnet/README.md)
 - [x] [HRNet](configs/top_down/hrnet/README.md)
 - [x] [Hourglass](configs/top_down/hourglass/README.md)
@@ -94,9 +101,15 @@ If you have any feature requests, please feel free to leave a comment in [Issues
 
 Please refer to [install.md](docs/install.md) for installation.
 
+## Data Preparation
+
+Please refer to [data_preparation.md](docs/data_preparation.md) for a general knowledge of data preparation.
+
 ## Get Started
 
 Please see [getting_started.md](docs/getting_started.md) for the basic usage of MMPose.
+There are also tutorials for [finetuning model](tutorials/finetune.md),
+[adding new dataset](tutorials/new_dataset.md), [adding new modules](tutorials/new_modules.md).
 
 ## License
 

configs/top_down/hrnet/README.md

@@ -22,6 +22,12 @@
 | [pose_hrnet_w48](/configs/top_down/hrnet/coco/hrnet_w48_coco_256x192.py)  | 256x192 | 0.756 | 0.907 | 0.825 | 0.806 | 0.942 | [ckpt](https://openmmlab.oss-accelerate.aliyuncs.com/mmpose/top_down/hrnet/hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth) | [log](https://openmmlab.oss-accelerate.aliyuncs.com/mmpose/top_down/hrnet/hrnet_w48_coco_256x192_20200708.log.json) |
 | [pose_hrnet_w48](/configs/top_down/hrnet/coco/hrnet_w48_coco_384x288.py)  | 384x288 | 0.767 | 0.910 | 0.831 | 0.816 | 0.946 | [ckpt](https://openmmlab.oss-accelerate.aliyuncs.com/mmpose/top_down/hrnet/hrnet_w48_coco_384x288-314c8528_20200708.pth) | [log](https://openmmlab.oss-accelerate.aliyuncs.com/mmpose/top_down/hrnet/hrnet_w48_coco_384x288_20200708.log.json) |
 
+### Results on AIC val set.
+
+| Arch  | Input Size | AP | AP<sup>50</sup> | AP<sup>75</sup> | AR | AR<sup>50</sup> | ckpt | log |
+| :-------------- | :-----------: | :------: | :------: | :------: | :------: | :------: |:------: |:------: |
+| [pose_hrnet_w32](/configs/top_down/hrnet/aic/hrnet_w32_aic_256x192.py) | 256x192 | 0.675 | 0.957 | 0.751 | 0.703 | 0.961 | [ckpt](https://openmmlab.oss-accelerate.aliyuncs.com/mmpose/top_down/hrnet/hrnet_w32_aic_256x192-30a4e465_20200826.pth) | [log](https://openmmlab.oss-accelerate.aliyuncs.com/mmpose/top_down/hrnet/hrnet_w32_aic_256x192_20200826.log.json) |
+
 
 ### Results on MPII val set.
 

configs/top_down/hrnet/aic/hrnet_w32_aic_256x192.py

@@ -0,0 +1,175 @@
+log_level = 'INFO'
+load_from = None
+resume_from = None
+dist_params = dict(backend='nccl')
+workflow = [('train', 1)]
+checkpoint_config = dict(interval=1)
+evaluation = dict(interval=1, 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=14,
+    dataset_joints=14,
+    dataset_channel=[
+        [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13],
+    ],
+    inference_channel=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13])
+
+# model settings
+model = dict(
+    type='TopDown',
+    pretrained='models/pytorch/imagenet/hrnet_w32-36af842e.pth',
+    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))),
+    ),
+    keypoint_head=dict(
+        type='TopDownSimpleHead',
+        in_channels=32,
+        out_channels=channel_cfg['num_output_channels'],
+        num_deconv_layers=0,
+        extra=dict(final_conv_kernel=1, ),
+    ),
+    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/coco/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'
+        ]),
+]
+
+valid_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 = valid_pipeline
+
+data_root = 'data/aic'
+data = dict(
+    samples_per_gpu=64,
+    workers_per_gpu=2,
+    train=dict(
+        type='TopDownAicDataset',
+        ann_file=f'{data_root}/annotations/aic_train.json',
+        img_prefix=f'{data_root}/ai_challenger_keypoint_train_20170902/'
+        'keypoint_train_images_20170902/',
+        data_cfg=data_cfg,
+        pipeline=train_pipeline),
+    val=dict(
+        type='TopDownAicDataset',
+        ann_file=f'{data_root}/annotations/aic_val.json',
+        img_prefix=f'{data_root}/ai_challenger_keypoint_validation_20170911/'
+        'keypoint_validation_images_20170911/',
+        data_cfg=data_cfg,
+        pipeline=valid_pipeline),
+    test=dict(
+        type='TopDownAicDataset',
+        ann_file=f'{data_root}/annotations/aic_val.json',
+        img_prefix=f'{data_root}/ai_challenger_keypoint_validation_20170911/'
+        'keypoint_validation_images_20170911/',
+        data_cfg=data_cfg,
+        pipeline=valid_pipeline),
+)