From 3ef8e6dbbeb6262b7ed6c51faa74b83c23f4c6a1 Mon Sep 17 00:00:00 2001 From: Jas Date: Fri, 30 Oct 2020 11:44:02 +0800 Subject: [PATCH] Support Posetrack18 (#220) * add posetrack * update posetrack18 * add config * init posetrack dataset * update posetrack18 * fix dependencies * make pred dir * fix dependencies * fix docs * fix build error * rm dataclass * rm unnecessary cpu/cuda mapping * add @ * fix clone to copy * fix return values * add posetrack test * use url model path * add hrnet * use gt bbox * add pre-trained models * fix build * update modelzoo * add tests * add tests * add tests * compress the jpeg images --- configs/top_down/hrnet/README.md | 18 + .../hrnet_w32_posetrack18_256x192.py | 174 ++ configs/top_down/resnet/README.md | 18 +- .../posetrack18/res50_posetrack18_256x192.py | 144 + docs/data_preparation.md | 74 + mmpose/datasets/datasets/__init__.py | 9 +- mmpose/datasets/datasets/top_down/__init__.py | 4 +- .../datasets/top_down/topdown_coco_dataset.py | 12 +- .../top_down/topdown_posetrack18_dataset.py | 299 +++ mmpose/models/detectors/top_down.py | 15 +- requirements.txt | 1 + requirements/optional.txt | 2 + requirements/runtime.txt | 1 - requirements/tests.txt | 2 + setup.cfg | 2 +- setup.py | 1 + .../images/val/003418_mpii_test/000000.jpg | Bin 0 -> 246146 bytes .../images/val/009473_mpii_test/000000.jpg | Bin 0 -> 139224 bytes .../images/val/012834_mpii_test/000000.jpg | Bin 0 -> 247959 bytes .../mask/val/003418_mpii_test/000000.jpg | Bin 0 -> 96631 bytes .../mask/val/009473_mpii_test/000000.jpg | Bin 0 -> 64287 bytes .../mask/val/012834_mpii_test/000000.jpg | Bin 0 -> 111561 bytes tests/data/posetrack18/test_posetrack18.json | 2382 +++++++++++++++++ .../test_posetrack18_human_detections.json | 1102 ++++++++ tests/test_datasets/test_top_down_dataset.py | 127 + 25 files changed, 4366 insertions(+), 21 deletions(-) create mode 100644 configs/top_down/hrnet/posetrack18/hrnet_w32_posetrack18_256x192.py create mode 100644 configs/top_down/resnet/posetrack18/res50_posetrack18_256x192.py create mode 100644 mmpose/datasets/datasets/top_down/topdown_posetrack18_dataset.py create mode 100644 requirements/optional.txt create mode 100644 tests/data/posetrack18/images/val/003418_mpii_test/000000.jpg create mode 100644 tests/data/posetrack18/images/val/009473_mpii_test/000000.jpg create mode 100644 tests/data/posetrack18/images/val/012834_mpii_test/000000.jpg create mode 100755 tests/data/posetrack18/mask/val/003418_mpii_test/000000.jpg create mode 100755 tests/data/posetrack18/mask/val/009473_mpii_test/000000.jpg create mode 100755 tests/data/posetrack18/mask/val/012834_mpii_test/000000.jpg create mode 100644 tests/data/posetrack18/test_posetrack18.json create mode 100644 tests/data/posetrack18/test_posetrack18_human_detections.json diff --git a/configs/top_down/hrnet/README.md b/configs/top_down/hrnet/README.md index c471ddd199..4b33c26030 100644 --- a/configs/top_down/hrnet/README.md +++ b/configs/top_down/hrnet/README.md @@ -53,3 +53,21 @@ | Arch | Input Size | AP | AP50 | AP75 | AP (E) | AP (M) | AP (H) | ckpt | log | | :----------------- | :-----------: | :------: | :------: | :------: | :------: | :------: |:------: |:------: | :------: | | [pose_hrnet_w32](/configs/top_down/hrnet/crowdpose/hrnet_w32_crowdpose_256x192.py) | 256x192 | 0.675 | 0.825 | 0.729 | 0.768 | 0.687 | 0.554 | [ckpt](https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w32_crowdpose_256x192-9b538d47_20201017.pth) | [log](https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w32_crowdpose_256x192_20201017.log.json) | + + +#### Results on PoseTrack2018 val with ground-truth bounding boxes. + +| Arch | Input Size | Head | Shou | Elb | Wri | Hip | Knee | Ankl | Total | ckpt | log | +| :--- | :--------: | :------: |:------: |:------: |:------: |:------: |:------: | :------: | :------: |:------: |:------: | +| [pose_hrnet_w32](/configs/top_down/hrnet/posetrack18/hrnet_w32_posetrack18_256x192.py) | 256x192 | 87.4 | 88.6 | 84.3 | 78.5 | 79.7 | 81.8 | 78.8 | 83.0 | [ckpt](https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w32_posetrack18_256x192-1ee951c4_20201028.pth) | [log](https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w32_posetrack18_256x192_20201028.log.json) | + +The models are first pre-trained on COCO dataset, and then fine-tuned on PoseTrack18. + + +#### Results on PoseTrack2018 val with [MMDetection](https://github.com/open-mmlab/mmdetection) pre-trained [Cascade R-CNN](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco/cascade_rcnn_x101_64x4d_fpn_20e_coco_20200509_224357-051557b1.pth) (X-101-64x4d-FPN) human detector. + +| Arch | Input Size | Head | Shou | Elb | Wri | Hip | Knee | Ankl | Total | ckpt | log | +| :--- | :--------: | :------: |:------: |:------: |:------: |:------: |:------: | :------: | :------: |:------: |:------: | +| [pose_hrnet_w32](/configs/top_down/hrnet/posetrack18/hrnet_w32_posetrack18_256x192.py) | 256x192 | 78.0 | 82.9 | 79.5 | 73.8 | 76.9 | 76.6 | 70.2 | 76.9 | [ckpt](https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w32_posetrack18_256x192-1ee951c4_20201028.pth) | [log](https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w32_posetrack18_256x192_20201028.log.json) | + +The models are first pre-trained on COCO dataset, and then fine-tuned on PoseTrack18. diff --git a/configs/top_down/hrnet/posetrack18/hrnet_w32_posetrack18_256x192.py b/configs/top_down/hrnet/posetrack18/hrnet_w32_posetrack18_256x192.py new file mode 100644 index 0000000000..a75fc6a1c3 --- /dev/null +++ b/configs/top_down/hrnet/posetrack18/hrnet_w32_posetrack18_256x192.py @@ -0,0 +1,174 @@ +log_level = 'INFO' +load_from = 'https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w32_coco_256x192-c78dce93_20200708.pth' # noqa: E501 +resume_from = None +dist_params = dict(backend='nccl') +workflow = [('train', 1)] +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metric='mAP', key_indicator='Total AP') + +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=[10, 15]) +total_epochs = 20 +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=None, + 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=0.4, + bbox_file='data/posetrack18/annotations/' + 'posetrack18_val_human_detections.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/posetrack18' +data = dict( + samples_per_gpu=64, + workers_per_gpu=2, + train=dict( + type='TopDownPoseTrack18Dataset', + ann_file=f'{data_root}/annotations/posetrack18_train.json', + img_prefix=f'{data_root}/', + data_cfg=data_cfg, + pipeline=train_pipeline), + val=dict( + type='TopDownPoseTrack18Dataset', + ann_file=f'{data_root}/annotations/posetrack18_val.json', + img_prefix=f'{data_root}/', + data_cfg=data_cfg, + pipeline=val_pipeline), + test=dict( + type='TopDownPoseTrack18Dataset', + ann_file=f'{data_root}/annotations/posetrack18_val.json', + img_prefix=f'{data_root}/', + data_cfg=data_cfg, + pipeline=val_pipeline), +) diff --git a/configs/top_down/resnet/README.md b/configs/top_down/resnet/README.md index e0e059c76f..20c9e1c049 100644 --- a/configs/top_down/resnet/README.md +++ b/configs/top_down/resnet/README.md @@ -88,7 +88,7 @@ Following the common setting, the models are trained on COCO train dataset, and | [pose_resnet_152](/configs/top_down/resnet/mpii_trb/res152_mpii_trb_256x256.py) | 256x256 | 0.897 | 0.868 | 0.879 | [ckpt](https://download.openmmlab.com/mmpose/top_down/resnet/res152_mpii_trb_256x256-dd369ce6_20200812.pth) | [log](https://download.openmmlab.com/mmpose/top_down/resnet/res152_mpii_trb_256x256_20200812.log.json) | -### Results on CrowdPose test with [YOLOv3](https://github.com/eriklindernoren/PyTorch-YOLOv3) human detector. +#### Results on CrowdPose test with [YOLOv3](https://github.com/eriklindernoren/PyTorch-YOLOv3) human detector. | Arch | Input Size | AP | AP50 | AP75 | AP (E) | AP (M) | AP (H) | ckpt | log | | :----------------- | :-----------: | :------: | :------: | :------: | :------: | :------: |:------: |:------: | :------: | @@ -97,6 +97,22 @@ Following the common setting, the models are trained on COCO train dataset, and | [pose_resnet_101](/configs/top_down/resnet/crowdpose/res101_crowdpose_320x256.py) | 320x256 | 0.659 | 0.819 | 0.713 | 0.757 | 0.669 | 0.536 | [ckpt](https://download.openmmlab.com/mmpose/top_down/resnet/res101_crowdpose_320x256-7723ede3_20201017.pth) | [log](https://download.openmmlab.com/mmpose/top_down/resnet/res101_crowdpose_320x256_20201017.log.json) | | [pose_resnet_152](/configs/top_down/resnet/crowdpose/res152_crowdpose_256x192.py) | 256x192 | 0.653 | 0.815 | 0.710 | 0.751 | 0.665 | 0.526 | [ckpt](https://download.openmmlab.com/mmpose/top_down/resnet/res152_crowdpose_256x192-a6507ad0_20201017.pth) | [log](https://download.openmmlab.com/mmpose/top_down/resnet/res152_crowdpose_256x192_20201017.log.json) | +#### Results on PoseTrack2018 val with ground-truth bounding boxes. + +| Arch | Input Size | Head | Shou | Elb | Wri | Hip | Knee | Ankl | Total | ckpt | log | +| :--- | :--------: | :------: |:------: |:------: |:------: |:------: |:------: | :------: | :------: |:------: |:------: | +| [pose_resnet_50](/configs/top_down/resnet/posetrack18/res50_posetrack18_256x192.py) | 256x192 | 86.5 | 87.5 | 82.3 | 75.6 | 79.9 | 78.6 | 74.0 | 81.0 | [ckpt](https://download.openmmlab.com/mmpose/top_down/resnet/res50_posetrack18_256x192-a62807c7_20201028.pth) | [log](https://download.openmmlab.com/mmpose/top_down/resnet/res50_posetrack18_256x192_20201028.log.json) | + +The models are first pre-trained on COCO dataset, and then fine-tuned on PoseTrack18. + +#### Results on PoseTrack2018 val with [MMDetection](https://github.com/open-mmlab/mmdetection) pre-trained [Cascade R-CNN](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco/cascade_rcnn_x101_64x4d_fpn_20e_coco_20200509_224357-051557b1.pth) (X-101-64x4d-FPN) human detector. + +| Arch | Input Size | Head | Shou | Elb | Wri | Hip | Knee | Ankl | Total | ckpt | log | +| :--- | :--------: | :------: |:------: |:------: |:------: |:------: |:------: | :------: | :------: |:------: |:------: | +| [pose_resnet_50](/configs/top_down/resnet/posetrack18/res50_posetrack18_256x192.py) | 256x192 | 78.9 | 81.9 | 77.8 | 70.8 | 75.3 | 73.2 | 66.4 | 75.2 | [ckpt](https://download.openmmlab.com/mmpose/top_down/resnet/res50_posetrack18_256x192-a62807c7_20201028.pth) | [log](https://download.openmmlab.com/mmpose/top_down/resnet/res50_posetrack18_256x192_20201028.log.json) | + +The models are first pre-trained on COCO dataset, and then fine-tuned on PoseTrack18. + ### 2d Hand Pose Estimation diff --git a/configs/top_down/resnet/posetrack18/res50_posetrack18_256x192.py b/configs/top_down/resnet/posetrack18/res50_posetrack18_256x192.py new file mode 100644 index 0000000000..bbea40de4e --- /dev/null +++ b/configs/top_down/resnet/posetrack18/res50_posetrack18_256x192.py @@ -0,0 +1,144 @@ +log_level = 'INFO' +load_from = 'https://download.openmmlab.com/mmpose/top_down/resnet/res50_coco_256x192-ec54d7f3_20200709.pth' # noqa: E501 +resume_from = None +dist_params = dict(backend='nccl') +workflow = [('train', 1)] +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metric='mAP', key_indicator='Total AP') + +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=[10, 15]) +total_epochs = 20 +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=None, + backbone=dict(type='ResNet', depth=50), + 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=0.4, + bbox_file='data/posetrack18/annotations/' + 'posetrack18_val_human_detections.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/posetrack18' +data = dict( + samples_per_gpu=64, + workers_per_gpu=2, + train=dict( + type='TopDownPoseTrack18Dataset', + ann_file=f'{data_root}/annotations/posetrack18_train.json', + img_prefix=f'{data_root}/', + data_cfg=data_cfg, + pipeline=train_pipeline), + val=dict( + type='TopDownPoseTrack18Dataset', + ann_file=f'{data_root}/annotations/posetrack18_val.json', + img_prefix=f'{data_root}/', + data_cfg=data_cfg, + pipeline=val_pipeline), + test=dict( + type='TopDownPoseTrack18Dataset', + ann_file=f'{data_root}/annotations/posetrack18_val.json', + img_prefix=f'{data_root}/', + data_cfg=data_cfg, + pipeline=val_pipeline), +) diff --git a/docs/data_preparation.md b/docs/data_preparation.md index 7b1f0631ce..0c8807aa7d 100644 --- a/docs/data_preparation.md +++ b/docs/data_preparation.md @@ -206,6 +206,80 @@ mmpose │-- ... ``` +## PoseTrack18 + +For PoseTrack18 data, please download from [PoseTrack18](https://posetrack.net/users/download.php). +Please download the annotation files from [posetrack18_annotations](https://download.openmmlab.com/mmpose/datasets/posetrack18_annotations.tar). +We have merged the video-wise separated official annotation files into two json files (posetrack18_train & posetrack18_val.json). We also generate the [mask files](https://download.openmmlab.com/mmpose/datasets/posetrack18_mask.tar) to speed up training. +For top-down approaches, we use [MMDetection](https://github.com/open-mmlab/mmdetection) pre-trained [Cascade R-CNN](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco/cascade_rcnn_x101_64x4d_fpn_20e_coco_20200509_224357-051557b1.pth) (X-101-64x4d-FPN) to generate the detected human bounding boxes. +Please download and extract them under $MMPOSE/data, and make them look like this: + +``` +mmpose +├── mmpose +├── docs +├── tests +├── tools +├── configs +`── data + │── posetrack18 + │-- annotations + │ │-- posetrack18_train.json + │ │-- posetrack18_val.json + │ │-- posetrack18_val_human_detections.json + │ │-- train + │ │ │-- 000001_bonn_train.json + │ │ │-- 000002_bonn_train.json + │ │ │-- ... + │ │-- val + │ │ │-- 000342_mpii_test.json + │ │ │-- 000522_mpii_test.json + │ │ │-- ... + │ `-- test + │ │-- 000001_mpiinew_test.json + │ │-- 000002_mpiinew_test.json + │ │-- ... + │ + `-- images + │ │-- train + │ │ │-- 000001_bonn_train + │ │ │ │-- 000000.jpg + │ │ │ │-- 000001.jpg + │ │ │ │-- ... + │ │ │-- ... + │ │-- val + │ │ │-- 000342_mpii_test + │ │ │ │-- 000000.jpg + │ │ │ │-- 000001.jpg + │ │ │ │-- ... + │ │ │-- ... + │ `-- test + │ │-- 000001_mpiinew_test + │ │ │-- 000000.jpg + │ │ │-- 000001.jpg + │ │ │-- ... + │ │-- ... + `-- mask + │-- train + │ │-- 000002_bonn_train + │ │ │-- 000000.jpg + │ │ │-- 000001.jpg + │ │ │-- ... + │ │-- ... + `-- val + │-- 000522_mpii_test + │ │-- 000000.jpg + │ │-- 000001.jpg + │ │-- ... + │-- ... +``` + +The official evaluation tool for PoseTrack should be installed from GitHub. +``` +pip install git+https://github.com/svenkreiss/poseval.git +``` + + ## OCHuman For OCHuman data, please download the images and annotations from [OCHuman](https://github.com/liruilong940607/OCHumanApi), diff --git a/mmpose/datasets/datasets/__init__.py b/mmpose/datasets/datasets/__init__.py index b758e0c5f6..29adfa5ef9 100644 --- a/mmpose/datasets/datasets/__init__.py +++ b/mmpose/datasets/datasets/__init__.py @@ -5,14 +5,15 @@ TopDownCocoWholeBodyDataset, TopDownCrowdPoseDataset, TopDownFreiHandDataset, TopDownMpiiDataset, TopDownMpiiTrbDataset, TopDownOCHumanDataset, - TopDownOneHand10KDataset, TopDownPanopticDataset) + TopDownOneHand10KDataset, TopDownPanopticDataset, + TopDownPoseTrack18Dataset) __all__ = [ - 'TopDownCocoDataset', 'BottomUpCocoDataset', 'TopDownMpiiDataset', - 'TopDownMpiiTrbDataset', 'TopDownOneHand10KDataset', + 'TopDownCocoDataset', 'BottomUpCocoDataset', 'BottomUpCrowdPoseDataset', + 'TopDownMpiiDataset', 'TopDownMpiiTrbDataset', 'TopDownOneHand10KDataset', 'TopDownPanopticDataset', 'TopDownFreiHandDataset', 'TopDownOCHumanDataset', 'TopDownAicDataset', 'TopDownCocoWholeBodyDataset', 'MeshH36MDataset', 'MeshMixDataset', 'MoshDataset', 'MeshAdversarialDataset', 'TopDownCrowdPoseDataset', - 'BottomUpCrowdPoseDataset' + 'TopDownPoseTrack18Dataset' ] diff --git a/mmpose/datasets/datasets/top_down/__init__.py b/mmpose/datasets/datasets/top_down/__init__.py index 64dbd8d337..49f2fa657f 100644 --- a/mmpose/datasets/datasets/top_down/__init__.py +++ b/mmpose/datasets/datasets/top_down/__init__.py @@ -8,10 +8,12 @@ from .topdown_ochuman_dataset import TopDownOCHumanDataset from .topdown_onehand10k_dataset import TopDownOneHand10KDataset from .topdown_panoptic_dataset import TopDownPanopticDataset +from .topdown_posetrack18_dataset import TopDownPoseTrack18Dataset __all__ = [ 'TopDownCocoDataset', 'TopDownMpiiTrbDataset', 'TopDownMpiiDataset', 'TopDownOneHand10KDataset', 'TopDownFreiHandDataset', 'TopDownPanopticDataset', 'TopDownOCHumanDataset', 'TopDownAicDataset', - 'TopDownCocoWholeBodyDataset', 'TopDownCrowdPoseDataset' + 'TopDownCocoWholeBodyDataset', 'TopDownCrowdPoseDataset', + 'TopDownPoseTrack18Dataset' ] diff --git a/mmpose/datasets/datasets/top_down/topdown_coco_dataset.py b/mmpose/datasets/datasets/top_down/topdown_coco_dataset.py index e8c8100c01..e32f6d29ed 100644 --- a/mmpose/datasets/datasets/top_down/topdown_coco_dataset.py +++ b/mmpose/datasets/datasets/top_down/topdown_coco_dataset.py @@ -170,6 +170,8 @@ def _load_coco_keypoint_annotation_kernel(self, img_id): # sanitize bboxes valid_objs = [] for obj in objs: + if 'bbox' not in obj: + continue x, y, w, h = obj['bbox'] x1 = max(0, x) y1 = max(0, y) @@ -182,8 +184,12 @@ def _load_coco_keypoint_annotation_kernel(self, img_id): rec = [] for obj in objs: + if 'keypoints' not in obj: + continue if max(obj['keypoints']) == 0: continue + if 'num_keypoints' in obj and obj['num_keypoints'] == 0: + continue joints_3d = np.zeros((num_joints, 3), dtype=np.float32) joints_3d_visible = np.zeros((num_joints, 3), dtype=np.float32) @@ -321,7 +327,7 @@ def evaluate(self, outputs, res_folder, metric='mAP', **kwargs): kpts = defaultdict(list) for preds, boxes, image_path, _ in outputs: str_image_path = ''.join(image_path) - image_id = self.name2id[os.path.basename(str_image_path)] + image_id = self.name2id[str_image_path[len(self.img_prefix):]] kpts[image_id].append({ 'keypoints': preds[0], @@ -337,8 +343,8 @@ def evaluate(self, outputs, res_folder, metric='mAP', **kwargs): vis_thr = self.vis_thr oks_thr = self.oks_thr oks_nmsed_kpts = [] - for img in kpts.keys(): - img_kpts = kpts[img] + for image_id in kpts.keys(): + img_kpts = kpts[image_id] for n_p in img_kpts: box_score = n_p['score'] kpt_score = 0 diff --git a/mmpose/datasets/datasets/top_down/topdown_posetrack18_dataset.py b/mmpose/datasets/datasets/top_down/topdown_posetrack18_dataset.py new file mode 100644 index 0000000000..17efa9e7e4 --- /dev/null +++ b/mmpose/datasets/datasets/top_down/topdown_posetrack18_dataset.py @@ -0,0 +1,299 @@ +import os +import os.path as osp +from collections import OrderedDict, defaultdict + +import json_tricks as json +import numpy as np +from poseval import eval_helpers +from poseval.evaluateAP import evaluateAP +from xtcocotools.coco import COCO + +from ....core.post_processing import oks_nms, soft_oks_nms +from ...registry import DATASETS +from .topdown_coco_dataset import TopDownCocoDataset + + +@DATASETS.register_module() +class TopDownPoseTrack18Dataset(TopDownCocoDataset): + """PoseTrack18 dataset for top-down pose estimation. + + `Posetrack: A benchmark for human pose estimation and tracking' CVPR'2018 + More details can be found in the `paper + `_ . + + The dataset loads raw features and apply specified transforms + to return a dict containing the image tensors and other information. + + PoseTrack2018 keypoint indexes:: + 0: 'nose', + 1: 'head_bottom', + 2: 'head_top', + 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): Path to the annotation file. + img_prefix (str): Path to a directory where images are held. + Default: None. + data_cfg (dict): config + pipeline (list[dict | callable]): A sequence of data transforms. + test_mode (bool): Store True when building test or + validation dataset. Default: False. + """ + + def __init__(self, + ann_file, + img_prefix, + data_cfg, + pipeline, + test_mode=False): + super(TopDownCocoDataset, self).__init__( + ann_file, img_prefix, data_cfg, pipeline, test_mode=test_mode) + + self.use_gt_bbox = data_cfg['use_gt_bbox'] + self.bbox_file = data_cfg['bbox_file'] + self.image_thr = data_cfg['image_thr'] + + self.soft_nms = data_cfg['soft_nms'] + self.nms_thr = data_cfg['nms_thr'] + self.oks_thr = data_cfg['oks_thr'] + self.vis_thr = data_cfg['vis_thr'] + self.bbox_thr = data_cfg['bbox_thr'] + + self.ann_info['flip_pairs'] = [[3, 4], [5, 6], [7, 8], [9, 10], + [11, 12], [13, 14], [15, 16]] + + self.ann_info['upper_body_ids'] = (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10) + self.ann_info['lower_body_ids'] = (11, 12, 13, 14, 15, 16) + + self.ann_info['use_different_joint_weights'] = False + self.ann_info['joint_weights'] = np.array( + [ + 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 + ], + dtype=np.float32).reshape((self.ann_info['num_joints'], 1)) + + self.sigmas = np.array([ + .26, .25, .25, .35, .35, .79, .79, .72, .72, .62, .62, 1.07, 1.07, + .87, .87, .89, .89 + ]) / 10.0 + + self.coco = COCO(ann_file) + + cats = [ + cat['name'] for cat in self.coco.loadCats(self.coco.getCatIds()) + ] + self.classes = ['__background__'] + cats + self.num_classes = len(self.classes) + self._class_to_ind = dict(zip(self.classes, range(self.num_classes))) + self._class_to_coco_ind = dict(zip(cats, self.coco.getCatIds())) + self._coco_ind_to_class_ind = dict( + (self._class_to_coco_ind[cls], self._class_to_ind[cls]) + for cls in self.classes[1:]) + self.img_ids = self.coco.getImgIds() + self.num_images = len(self.img_ids) + self.id2name, self.name2id = self._get_mapping_id_name(self.coco.imgs) + self.dataset_name = 'posetrack18' + + self.db = self._get_db() + + print(f'=> num_images: {self.num_images}') + print(f'=> load {len(self.db)} samples') + + def evaluate(self, outputs, res_folder, metric='mAP', **kwargs): + """Evaluate coco keypoint results. The pose prediction results will be + saved in `${res_folder}/result_keypoints.json`. + + Note: + num_keypoints: K + + Args: + outputs (list(preds, boxes, image_path)) + :preds (np.ndarray[1,K,3]): The first two dimensions are + coordinates, score is the third dimension of the array. + :boxes (np.ndarray[1,6]): [center[0], center[1], scale[0] + , scale[1],area, score] + :image_path (list[str]): For example, [ '/', 'v','a', 'l', + '2', '0', '1', '7', '/', '0', '0', '0', '0', '0', + '0', '3', '9', '7', '1', '3', '3', '.', 'j', 'p', 'g'] + res_folder (str): Path of directory to save the results. + metric (str | list[str]): Metric to be performed. Defaults: 'mAP'. + + Returns: + dict: Evaluation results for evaluation metric. + """ + metrics = metric if isinstance(metric, list) else [metric] + allowed_metrics = ['mAP'] + for metric in metrics: + if metric not in allowed_metrics: + raise KeyError(f'metric {metric} is not supported') + + pred_folder = osp.join(res_folder, 'preds') + os.makedirs(pred_folder, exist_ok=True) + gt_folder = osp.join( + osp.dirname(self.annotations_path), + osp.splitext(self.annotations_path.split('_')[-1])[0]) + + kpts = defaultdict(list) + for preds, boxes, image_path, _ in outputs: + str_image_path = ''.join(image_path) + image_id = self.name2id[str_image_path[len(self.img_prefix):]] + + kpts[image_id].append({ + 'keypoints': preds[0], + 'center': boxes[0][0:2], + 'scale': boxes[0][2:4], + 'area': boxes[0][4], + 'score': boxes[0][5], + 'image_id': image_id, + }) + + # rescoring and oks nms + num_joints = self.ann_info['num_joints'] + vis_thr = self.vis_thr + oks_thr = self.oks_thr + oks_nmsed_kpts = defaultdict(list) + for image_id in kpts.keys(): + img_kpts = kpts[image_id] + for n_p in img_kpts: + box_score = n_p['score'] + kpt_score = 0 + valid_num = 0 + for n_jt in range(0, num_joints): + t_s = n_p['keypoints'][n_jt][2] + if t_s > vis_thr: + kpt_score = kpt_score + t_s + valid_num = valid_num + 1 + if valid_num != 0: + kpt_score = kpt_score / valid_num + # rescoring + n_p['score'] = kpt_score * box_score + + nms = soft_oks_nms if self.soft_nms else oks_nms + keep = nms(list(img_kpts), oks_thr, sigmas=self.sigmas) + + if len(keep) == 0: + oks_nmsed_kpts[image_id].append(img_kpts) + else: + oks_nmsed_kpts[image_id].append( + [img_kpts[_keep] for _keep in keep]) + + self._write_posetrack18_keypoint_results(oks_nmsed_kpts, gt_folder, + pred_folder) + + info_str = self._do_python_keypoint_eval(gt_folder, pred_folder) + name_value = OrderedDict(info_str) + + return name_value + + def _write_posetrack18_keypoint_results(self, keypoint_results, gt_folder, + pred_folder): + """Write results into a json file. + + Args: + keypoint_results (dict): keypoint results organized by image_id. + gt_folder (str): Path of directory for official gt files. + pred_folder (str): Path of directory to save the results. + """ + categories = [] + + cat = {} + cat['supercategory'] = 'person' + cat['id'] = 1 + cat['name'] = 'person' + cat['keypoints'] = [ + 'nose', 'head_bottom', 'head_top', 'left_ear', 'right_ear', + 'left_shoulder', 'right_shoulder', 'left_elbow', 'right_elbow', + 'left_wrist', 'right_wrist', 'left_hip', 'right_hip', 'left_knee', + 'right_knee', 'left_ankle', 'right_ankle' + ] + cat['skeleton'] = [[16, 14], [14, 12], [17, 15], [15, 13], [12, 13], + [6, 12], [7, 13], [6, 7], [6, 8], [7, 9], [8, 10], + [9, 11], [2, 3], [1, 2], [1, 3], [2, 4], [3, 5], + [4, 6], [5, 7]] + categories.append(cat) + + json_files = [ + pos for pos in os.listdir(gt_folder) if pos.endswith('.json') + ] + for json_file in json_files: + + with open(osp.join(gt_folder, json_file), 'r') as f: + gt = json.load(f) + + annotations = [] + images = [] + + for image in gt['images']: + im = {} + im['id'] = image['id'] + im['file_name'] = image['file_name'] + images.append(im) + + img_kpts = keypoint_results[im['id']] + + if len(img_kpts) == 0: + continue + for track_id, img_kpt in enumerate(img_kpts[0]): + ann = {} + ann['image_id'] = img_kpt['image_id'] + ann['keypoints'] = np.array( + img_kpt['keypoints']).reshape(-1).tolist() + ann['scores'] = np.array(ann['keypoints']).reshape( + [-1, 3])[:, 2].tolist() + ann['score'] = float(img_kpt['score']) + ann['track_id'] = track_id + annotations.append(ann) + + info = {} + info['images'] = images + info['categories'] = categories + info['annotations'] = annotations + + with open(osp.join(pred_folder, json_file), 'w') as f: + json.dump(info, f, sort_keys=True, indent=4) + + def _do_python_keypoint_eval(self, gt_folder, pred_folder): + """Keypoint evaluation using poseval.""" + + argv = ['', gt_folder + '/', pred_folder + '/'] + + print('Loading data') + gtFramesAll, prFramesAll = eval_helpers.load_data_dir(argv) + + print('# gt frames :', len(gtFramesAll)) + print('# pred frames:', len(prFramesAll)) + + # evaluate per-frame multi-person pose estimation (AP) + # compute AP + print('Evaluation of per-frame multi-person pose estimation') + apAll, preAll, recAll = evaluateAP(gtFramesAll, prFramesAll, None, + False, False) + + # print AP + print('Average Precision (AP) metric:') + eval_helpers.printTable(apAll) + + stats = eval_helpers.getCum(apAll) + + stats_names = [ + 'Head AP', 'Shou AP', 'Elb AP', 'Wri AP', 'Hip AP', 'Knee AP', + 'Ankl AP', 'Total AP' + ] + + info_str = list(zip(stats_names, stats)) + + return info_str diff --git a/mmpose/models/detectors/top_down.py b/mmpose/models/detectors/top_down.py index 209900f1ef..eb1b778a3f 100644 --- a/mmpose/models/detectors/top_down.py +++ b/mmpose/models/detectors/top_down.py @@ -3,7 +3,6 @@ import cv2 import mmcv import numpy as np -import torch import torch.nn as nn from mmcv.image import imwrite from mmcv.visualization.image import imshow @@ -191,6 +190,7 @@ def forward_test(self, img, img_metas, **kwargs): if isinstance(output, list): output = output[-1] + output_heatmap = output.detach().cpu().numpy() if self.test_cfg['flip_test']: img_flipped = img.flip(3) @@ -199,19 +199,14 @@ def forward_test(self, img, img_metas, **kwargs): output_flipped = self.keypoint_head(output_flipped) if isinstance(output_flipped, list): output_flipped = output_flipped[-1] - output_flipped = flip_back(output_flipped.cpu().numpy(), + output_flipped = flip_back(output_flipped.detach().cpu().numpy(), flip_pairs) - output_flipped = torch.from_numpy(output_flipped.copy()).to( - output.device) - # feature is not aligned, shift flipped heatmap for higher accuracy if self.test_cfg['shift_heatmap']: - output_flipped[:, :, :, 1:] = \ - output_flipped.clone()[:, :, :, 0:-1] - output = (output + output_flipped) * 0.5 + output_flipped[:, :, :, 1:] = output_flipped[:, :, :, :-1] + output_heatmap = (output_heatmap + output_flipped) * 0.5 - output_heatmap = output.detach().cpu().numpy() c = img_metas['center'].reshape(1, -1) s = img_metas['scale'].reshape(1, -1) @@ -227,7 +222,7 @@ def forward_test(self, img, img_metas, **kwargs): unbiased=self.test_cfg['unbiased_decoding'], kernel=self.test_cfg['modulate_kernel']) - all_preds = np.zeros((1, output.shape[1], 3), dtype=np.float32) + all_preds = np.zeros((1, output_heatmap.shape[1], 3), dtype=np.float32) all_boxes = np.zeros((1, 6), dtype=np.float32) image_path = [] diff --git a/requirements.txt b/requirements.txt index 5f50cbdc09..b5b5d97a6e 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,3 +1,4 @@ -r requirements/build.txt -r requirements/runtime.txt -r requirements/tests.txt +-r requirements/optional.txt diff --git a/requirements/optional.txt b/requirements/optional.txt new file mode 100644 index 0000000000..48b8d0292f --- /dev/null +++ b/requirements/optional.txt @@ -0,0 +1,2 @@ +poseval@git+https://github.com/svenkreiss/poseval.git +smplx diff --git a/requirements/runtime.txt b/requirements/runtime.txt index 7b324f8b96..80d4e19601 100644 --- a/requirements/runtime.txt +++ b/requirements/runtime.txt @@ -7,5 +7,4 @@ numpy opencv-python pillow scipy -smplx xtcocotools>=1.6 diff --git a/requirements/tests.txt b/requirements/tests.txt index 43f7b16d22..d27e654cda 100644 --- a/requirements/tests.txt +++ b/requirements/tests.txt @@ -2,7 +2,9 @@ coverage flake8 interrogate isort==4.3.21 +poseval@git+https://github.com/svenkreiss/poseval.git pytest pytest-runner +smplx xdoctest >= 0.10.0 yapf diff --git a/setup.cfg b/setup.cfg index 98cdf3d4c8..a2ac6f4bfe 100644 --- a/setup.cfg +++ b/setup.cfg @@ -17,6 +17,6 @@ line_length = 79 multi_line_output = 0 known_standard_library = pkg_resources,setuptools known_first_party = mmpose -known_third_party = cv2,json_tricks,mmcv,mmdet,munkres,numpy,pytest,scipy,smplx,torch,torchvision,xtcocotools +known_third_party = 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b/tests/test_datasets/test_top_down_dataset.py @@ -26,6 +26,26 @@ def load_json_to_output(json_name, prefix=''): return outputs +def convert_db_to_output(db): + outputs = [] + + for item in db: + keypoints = item['joints_3d'].reshape((1, -1, 3)) + center = item['center'] + scale = item['scale'] + box = np.array([ + center[0], center[1], scale[0], scale[1], + scale[0] * scale[1] * 200 * 200, 1.0 + ], + dtype=np.float32).reshape(1, -1) + img_path = [] + img_path[:0] = item['image_file'] + output = (keypoints, box, img_path, None) + outputs.append(output) + + return outputs + + def test_top_down_COCO_dataset(): dataset = 'TopDownCocoDataset' # test COCO datasets @@ -91,6 +111,81 @@ def test_top_down_COCO_dataset(): assert len(custom_dataset.img_ids) == 4 _ = custom_dataset[0] + outputs = convert_db_to_output(custom_dataset.db) + with tempfile.TemporaryDirectory() as tmpdir: + infos = custom_dataset.evaluate(outputs, tmpdir, 'mAP') + assert_almost_equal(infos['AP'], 1.0) + + with pytest.raises(KeyError): + _ = custom_dataset.evaluate(outputs, tmpdir, 'PCK') + + +def test_top_down_PoseTrack18_dataset(): + dataset = 'TopDownPoseTrack18Dataset' + # test PoseTrack datasets + dataset_class = DATASETS.get(dataset) + dataset_class.load_annotations = MagicMock() + dataset_class.coco = MagicMock() + + 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 + ]) + + 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='tests/data/posetrack18/' + 'test_posetrack18_human_detections.json', + ) + # Test det bbox + data_cfg_copy = copy.deepcopy(data_cfg) + data_cfg_copy['use_gt_bbox'] = False + _ = dataset_class( + ann_file='tests/data/posetrack18/test_posetrack18.json', + img_prefix='tests/data/posetrack18/', + data_cfg=data_cfg_copy, + pipeline=[], + test_mode=True) + + _ = dataset_class( + ann_file='tests/data/posetrack18/test_posetrack18.json', + img_prefix='tests/data/posetrack18/', + data_cfg=data_cfg_copy, + pipeline=[], + test_mode=False) + + # Test gt bbox + custom_dataset = dataset_class( + ann_file='tests/data/posetrack18/test_posetrack18.json', + img_prefix='tests/data/posetrack18/', + data_cfg=data_cfg, + pipeline=[], + test_mode=True) + + assert custom_dataset.test_mode is True + + image_id = 10128340000 + assert image_id in custom_dataset.img_ids + assert len(custom_dataset.img_ids) == 3 + _ = custom_dataset[0] + def test_top_down_CrowdPose_dataset(): dataset = 'TopDownCrowdPoseDataset' @@ -155,6 +250,14 @@ def test_top_down_CrowdPose_dataset(): assert len(custom_dataset.img_ids) == 2 _ = custom_dataset[0] + outputs = convert_db_to_output(custom_dataset.db) + with tempfile.TemporaryDirectory() as tmpdir: + infos = custom_dataset.evaluate(outputs, tmpdir, 'mAP') + assert_almost_equal(infos['AP'], 1.0) + + with pytest.raises(KeyError): + _ = custom_dataset.evaluate(outputs, tmpdir, 'PCK') + def test_top_down_COCO_wholebody_dataset(): dataset = 'TopDownCocoWholeBodyDataset' @@ -219,6 +322,14 @@ def test_top_down_COCO_wholebody_dataset(): assert len(custom_dataset.img_ids) == 4 _ = custom_dataset[0] + outputs = convert_db_to_output(custom_dataset.db) + with tempfile.TemporaryDirectory() as tmpdir: + infos = custom_dataset.evaluate(outputs, tmpdir, 'mAP') + assert_almost_equal(infos['AP'], 1.0) + + with pytest.raises(KeyError): + _ = custom_dataset.evaluate(outputs, tmpdir, 'PCK') + def test_top_down_OCHuman_dataset(): dataset = 'TopDownOCHumanDataset' @@ -280,6 +391,14 @@ def test_top_down_OCHuman_dataset(): assert len(custom_dataset.img_ids) == 3 _ = custom_dataset[0] + outputs = convert_db_to_output(custom_dataset.db) + with tempfile.TemporaryDirectory() as tmpdir: + infos = custom_dataset.evaluate(outputs, tmpdir, 'mAP') + assert_almost_equal(infos['AP'], 1.0) + + with pytest.raises(KeyError): + _ = custom_dataset.evaluate(outputs, tmpdir, 'PCK') + def test_top_down_OneHand10K_dataset(): dataset = 'TopDownOneHand10KDataset' @@ -590,3 +709,11 @@ def test_top_down_AIC_dataset(): assert image_id in custom_dataset.img_ids assert len(custom_dataset.img_ids) == 3 _ = custom_dataset[0] + + outputs = convert_db_to_output(custom_dataset.db) + with tempfile.TemporaryDirectory() as tmpdir: + infos = custom_dataset.evaluate(outputs, tmpdir, 'mAP') + assert_almost_equal(infos['AP'], 1.0) + + with pytest.raises(KeyError): + _ = custom_dataset.evaluate(outputs, tmpdir, 'PCK')