From c23b7ca094a5b4feb2aad89c1f2cb0a0d348394f Mon Sep 17 00:00:00 2001 From: xin-li-67 Date: Sun, 23 Apr 2023 09:30:51 +0800 Subject: [PATCH 1/6] fix naming issue --- configs/face_2d_keypoint/topdown_regression/README.md | 2 +- configs/face_2d_keypoint/topdown_regression/wflw/resnet_wflw.md | 2 +- .../face_2d_keypoint/topdown_regression/wflw/resnet_wflw.yml | 2 +- ..._wflw-256x256.py => td-reg_res50_8xb64-210e_wflw-256x256.py} | 0 4 files changed, 3 insertions(+), 3 deletions(-) rename configs/face_2d_keypoint/topdown_regression/wflw/{td-reg_res50_8x64e-210e_wflw-256x256.py => td-reg_res50_8xb64-210e_wflw-256x256.py} (100%) diff --git a/configs/face_2d_keypoint/topdown_regression/README.md b/configs/face_2d_keypoint/topdown_regression/README.md index 030ee6b8fa..fef409acd3 100644 --- a/configs/face_2d_keypoint/topdown_regression/README.md +++ b/configs/face_2d_keypoint/topdown_regression/README.md @@ -14,4 +14,4 @@ Result on WFLW test set | Model | Input Size | NME | ckpt | log | | :-------------------------------------------------------------- | :--------: | :--: | :------------------------------------------------------------: | :-----------------------------------------------------------: | -| [ResNet-50](/configs/face_2d_keypoint/topdown_regression/wflw/td-reg_res50_8x64e-210e_wflw-256x256.py) | 256x256 | 4.88 | [ckpt](https://download.openmmlab.com/mmpose/face/deeppose/deeppose_res50_wflw_256x256-92d0ba7f_20210303.pth) | [log](https://download.openmmlab.com/mmpose/face/deeppose/deeppose_res50_wflw_256x256_20210303.log.json) | +| [ResNet-50](/configs/face_2d_keypoint/topdown_regression/wflw/td-reg_res50_8xb64-210e_wflw-256x256.py) | 256x256 | 4.88 | [ckpt](https://download.openmmlab.com/mmpose/face/deeppose/deeppose_res50_wflw_256x256-92d0ba7f_20210303.pth) | [log](https://download.openmmlab.com/mmpose/face/deeppose/deeppose_res50_wflw_256x256_20210303.log.json) | diff --git a/configs/face_2d_keypoint/topdown_regression/wflw/resnet_wflw.md b/configs/face_2d_keypoint/topdown_regression/wflw/resnet_wflw.md index afaac002e9..1ec3e76dba 100644 --- a/configs/face_2d_keypoint/topdown_regression/wflw/resnet_wflw.md +++ b/configs/face_2d_keypoint/topdown_regression/wflw/resnet_wflw.md @@ -55,4 +55,4 @@ The model is trained on WFLW train set. | Model | Input Size | NME | ckpt | log | | :-------------------------------------------------------------- | :--------: | :--: | :------------------------------------------------------------: | :-----------------------------------------------------------: | -| [ResNet-50](/configs/face_2d_keypoint/topdown_regression/wflw/td-reg_res50_8x64e-210e_wflw-256x256.py) | 256x256 | 4.88 | [ckpt](https://download.openmmlab.com/mmpose/face/deeppose/deeppose_res50_wflw_256x256-92d0ba7f_20210303.pth) | [log](https://download.openmmlab.com/mmpose/face/deeppose/deeppose_res50_wflw_256x256_20210303.log.json) | +| [ResNet-50](/configs/face_2d_keypoint/topdown_regression/wflw/td-reg_res50_8xb64-210e_wflw-256x256.py) | 256x256 | 4.88 | [ckpt](https://download.openmmlab.com/mmpose/face/deeppose/deeppose_res50_wflw_256x256-92d0ba7f_20210303.pth) | [log](https://download.openmmlab.com/mmpose/face/deeppose/deeppose_res50_wflw_256x256_20210303.log.json) | diff --git a/configs/face_2d_keypoint/topdown_regression/wflw/resnet_wflw.yml b/configs/face_2d_keypoint/topdown_regression/wflw/resnet_wflw.yml index 113cb33fc3..81c7b79a7e 100644 --- a/configs/face_2d_keypoint/topdown_regression/wflw/resnet_wflw.yml +++ b/configs/face_2d_keypoint/topdown_regression/wflw/resnet_wflw.yml @@ -1,5 +1,5 @@ Models: -- Config: configs/face_2d_keypoint/topdown_regression/wflw/td-reg_res50_8x64e-210e_wflw-256x256.py +- Config: configs/face_2d_keypoint/topdown_regression/wflw/td-reg_res50_8xb64-210e_wflw-256x256.py In Collection: ResNet Metadata: Architecture: diff --git a/configs/face_2d_keypoint/topdown_regression/wflw/td-reg_res50_8x64e-210e_wflw-256x256.py b/configs/face_2d_keypoint/topdown_regression/wflw/td-reg_res50_8xb64-210e_wflw-256x256.py similarity index 100% rename from configs/face_2d_keypoint/topdown_regression/wflw/td-reg_res50_8x64e-210e_wflw-256x256.py rename to configs/face_2d_keypoint/topdown_regression/wflw/td-reg_res50_8xb64-210e_wflw-256x256.py From d7711cbe42a06b864f13bfc45ce594f679a0415e Mon Sep 17 00:00:00 2001 From: Tau Date: Sun, 23 Apr 2023 12:13:09 +0800 Subject: [PATCH 2/6] [Feature] Support LaPa Dataset (#2281) --- configs/_base_/datasets/lapa.py | 688 ++++++++++++++++++ configs/face_2d_keypoint/rtmpose/README.md | 8 + .../lapa/rtmpose-m_8xb64-120e_lapa-256x256.py | 247 +++++++ .../rtmpose/lapa/rtmpose_lapa.md | 40 + .../rtmpose/lapa/rtmpose_lapa.yml | 15 + demo/topdown_demo_with_mmdet.py | 3 +- docs/en/dataset_zoo/2d_face_keypoint.md | 56 ++ mmpose/datasets/datasets/face/__init__.py | 3 +- mmpose/datasets/datasets/face/lapa_dataset.py | 54 ++ mmpose/datasets/transforms/converting.py | 79 +- projects/rtmpose/README.md | 48 +- projects/rtmpose/README_CN.md | 54 +- ...y => rtmpose-m_8xb64-120e_lapa-256x256.py} | 45 +- tests/data/lapa/10773046825_0.jpg | Bin 0 -> 363612 bytes tests/data/lapa/13609937564_5.jpg | Bin 0 -> 59876 bytes tests/data/lapa/test_lapa.json | 39 + .../test_face_datasets/test_lapa_dataset.py | 93 +++ .../test_transforms/test_converting.py | 37 + tools/dataset_converters/lapa2coco.py | 104 +++ 19 files changed, 1543 insertions(+), 70 deletions(-) create mode 100644 configs/_base_/datasets/lapa.py create mode 100644 configs/face_2d_keypoint/rtmpose/lapa/rtmpose-m_8xb64-120e_lapa-256x256.py create mode 100644 configs/face_2d_keypoint/rtmpose/lapa/rtmpose_lapa.md create mode 100644 configs/face_2d_keypoint/rtmpose/lapa/rtmpose_lapa.yml create mode 100644 mmpose/datasets/datasets/face/lapa_dataset.py rename projects/rtmpose/rtmpose/face_2d_keypoint/{rtmpose-m_8xb32-60e_coco-wholebody-face-256x256.py => rtmpose-m_8xb64-120e_lapa-256x256.py} (84%) create mode 100644 tests/data/lapa/10773046825_0.jpg create mode 100644 tests/data/lapa/13609937564_5.jpg create mode 100644 tests/data/lapa/test_lapa.json create mode 100644 tests/test_datasets/test_datasets/test_face_datasets/test_lapa_dataset.py create mode 100644 tools/dataset_converters/lapa2coco.py diff --git a/configs/_base_/datasets/lapa.py b/configs/_base_/datasets/lapa.py new file mode 100644 index 0000000000..26a0843404 --- /dev/null +++ b/configs/_base_/datasets/lapa.py @@ -0,0 +1,688 @@ +dataset_info = dict( + dataset_name='lapa', + paper_info=dict( + author='Liu, Yinglu and Shi, Hailin and Shen, Hao and Si, ' + 'Yue and Wang, Xiaobo and Mei, Tao', + title='A New Dataset and Boundary-Attention Semantic ' + 'Segmentation for Face Parsing.', + container='Proceedings of the AAAI Conference on ' + 'Artificial Intelligence 2020', + year='2020', + homepage='https://github.com/JDAI-CV/lapa-dataset', + ), + keypoint_info={ + 0: + dict( + name='kpt-0', id=0, color=[255, 0, 0], type='upper', + swap='kpt-32'), + 1: + dict( + name='kpt-1', id=1, color=[255, 0, 0], type='upper', + swap='kpt-31'), + 2: + dict( + name='kpt-2', id=2, color=[255, 0, 0], type='upper', + swap='kpt-30'), + 3: + dict( + name='kpt-3', id=3, color=[255, 0, 0], type='lower', + swap='kpt-29'), + 4: + dict( + name='kpt-4', id=4, color=[255, 0, 0], type='lower', + swap='kpt-28'), + 5: + dict( + name='kpt-5', id=5, color=[255, 0, 0], type='lower', + swap='kpt-27'), + 6: + dict( + name='kpt-6', id=6, color=[255, 0, 0], type='lower', + swap='kpt-26'), + 7: + dict( + name='kpt-7', id=7, color=[255, 0, 0], type='lower', + swap='kpt-25'), + 8: + dict( + name='kpt-8', id=8, color=[255, 0, 0], type='lower', + swap='kpt-24'), + 9: + dict( + name='kpt-9', id=9, color=[255, 0, 0], type='lower', + swap='kpt-23'), + 10: + dict( + name='kpt-10', + id=10, + color=[255, 0, 0], + type='lower', + swap='kpt-22'), + 11: + dict( + name='kpt-11', + id=11, + color=[255, 0, 0], + type='lower', + swap='kpt-21'), + 12: + dict( + name='kpt-12', + id=12, + color=[255, 0, 0], + type='lower', + swap='kpt-20'), + 13: + dict( + name='kpt-13', + id=13, + color=[255, 0, 0], + type='lower', + swap='kpt-19'), + 14: + dict( + name='kpt-14', + id=14, + color=[255, 0, 0], + type='lower', + swap='kpt-18'), + 15: + dict( + name='kpt-15', + id=15, + color=[255, 0, 0], + type='lower', + swap='kpt-17'), + 16: + dict(name='kpt-16', id=16, color=[255, 0, 0], type='lower', swap=''), + 17: + dict( + name='kpt-17', + id=17, + color=[255, 0, 0], + type='lower', + swap='kpt-15'), + 18: + dict( + name='kpt-18', + id=18, + color=[255, 0, 0], + type='lower', + swap='kpt-14'), + 19: + dict( + name='kpt-19', + id=19, + color=[255, 0, 0], + type='lower', + swap='kpt-13'), + 20: + dict( + name='kpt-20', + id=20, + color=[255, 0, 0], + type='lower', + swap='kpt-12'), + 21: + dict( + name='kpt-21', + id=21, + color=[255, 0, 0], + type='lower', + swap='kpt-11'), + 22: + dict( + name='kpt-22', + id=22, + color=[255, 0, 0], + type='lower', + swap='kpt-10'), + 23: + dict( + name='kpt-23', + id=23, + color=[255, 0, 0], + type='lower', + swap='kpt-9'), + 24: + dict( + name='kpt-24', + id=24, + color=[255, 0, 0], + type='lower', + swap='kpt-8'), + 25: + dict( + name='kpt-25', + id=25, + color=[255, 0, 0], + type='lower', + swap='kpt-7'), + 26: + dict( + name='kpt-26', + id=26, + color=[255, 0, 0], + type='lower', + swap='kpt-6'), + 27: + dict( + name='kpt-27', + id=27, + color=[255, 0, 0], + type='lower', + swap='kpt-5'), + 28: + dict( + name='kpt-28', + id=28, + color=[255, 0, 0], + type='lower', + swap='kpt-4'), + 29: + dict( + name='kpt-29', + id=29, + color=[255, 0, 0], + type='lower', + swap='kpt-3'), + 30: + dict( + name='kpt-30', + id=30, + color=[255, 0, 0], + type='upper', + swap='kpt-2'), + 31: + dict( + name='kpt-31', + id=31, + color=[255, 0, 0], + type='upper', + swap='kpt-1'), + 32: + dict( + name='kpt-32', + id=32, + color=[255, 0, 0], + type='upper', + swap='kpt-0'), + 33: + dict( + name='kpt-33', + id=33, + color=[255, 0, 0], + type='upper', + swap='kpt-46'), + 34: + dict( + name='kpt-34', + id=34, + color=[255, 0, 0], + type='upper', + swap='kpt-45'), + 35: + dict( + name='kpt-35', + id=35, + color=[255, 0, 0], + type='upper', + swap='kpt-44'), + 36: + dict( + name='kpt-36', + id=36, + color=[255, 0, 0], + type='upper', + swap='kpt-43'), + 37: + dict( + name='kpt-37', + id=37, + color=[255, 0, 0], + type='upper', + swap='kpt-42'), + 38: + dict( + name='kpt-38', + id=38, + color=[255, 0, 0], + type='upper', + swap='kpt-50'), + 39: + dict( + name='kpt-39', + id=39, + color=[255, 0, 0], + type='upper', + swap='kpt-49'), + 40: + dict( + name='kpt-40', + id=40, + color=[255, 0, 0], + type='upper', + swap='kpt-48'), + 41: + dict( + name='kpt-41', + id=41, + color=[255, 0, 0], + type='upper', + swap='kpt-47'), + 42: + dict( + name='kpt-42', + id=42, + color=[255, 0, 0], + type='upper', + swap='kpt-37'), + 43: + dict( + name='kpt-43', + id=43, + color=[255, 0, 0], + type='upper', + swap='kpt-36'), + 44: + dict( + name='kpt-44', + id=44, + color=[255, 0, 0], + type='upper', + swap='kpt-35'), + 45: + dict( + name='kpt-45', + id=45, + color=[255, 0, 0], + type='upper', + swap='kpt-34'), + 46: + dict( + name='kpt-46', + id=46, + color=[255, 0, 0], + type='upper', + swap='kpt-33'), + 47: + dict( + name='kpt-47', + id=47, + color=[255, 0, 0], + type='upper', + swap='kpt-41'), + 48: + dict( + name='kpt-48', + id=48, + color=[255, 0, 0], + type='upper', + swap='kpt-40'), + 49: + dict( + name='kpt-49', + id=49, + color=[255, 0, 0], + type='upper', + swap='kpt-39'), + 50: + dict( + name='kpt-50', + id=50, + color=[255, 0, 0], + type='upper', + swap='kpt-38'), + 51: + dict(name='kpt-51', id=51, color=[255, 0, 0], type='upper', swap=''), + 52: + dict(name='kpt-52', id=52, color=[255, 0, 0], type='upper', swap=''), + 53: + dict(name='kpt-53', id=53, color=[255, 0, 0], type='lower', swap=''), + 54: + dict(name='kpt-54', id=54, color=[255, 0, 0], type='lower', swap=''), + 55: + dict( + name='kpt-55', + id=55, + color=[255, 0, 0], + type='upper', + swap='kpt-65'), + 56: + dict( + name='kpt-56', + id=56, + color=[255, 0, 0], + type='lower', + swap='kpt-64'), + 57: + dict( + name='kpt-57', + id=57, + color=[255, 0, 0], + type='lower', + swap='kpt-63'), + 58: + dict( + name='kpt-58', + id=58, + color=[255, 0, 0], + type='lower', + swap='kpt-62'), + 59: + dict( + name='kpt-59', + id=59, + color=[255, 0, 0], + type='lower', + swap='kpt-61'), + 60: + dict(name='kpt-60', id=60, color=[255, 0, 0], type='lower', swap=''), + 61: + dict( + name='kpt-61', + id=61, + color=[255, 0, 0], + type='lower', + swap='kpt-59'), + 62: + dict( + name='kpt-62', + id=62, + color=[255, 0, 0], + type='lower', + swap='kpt-58'), + 63: + dict( + name='kpt-63', + id=63, + color=[255, 0, 0], + type='lower', + swap='kpt-57'), + 64: + dict( + name='kpt-64', + id=64, + color=[255, 0, 0], + type='lower', + swap='kpt-56'), + 65: + dict( + name='kpt-65', + id=65, + color=[255, 0, 0], + type='upper', + swap='kpt-55'), + 66: + dict( + name='kpt-66', + id=66, + color=[255, 0, 0], + type='upper', + swap='kpt-79'), + 67: + dict( + name='kpt-67', + id=67, + color=[255, 0, 0], + type='upper', + swap='kpt-78'), + 68: + dict( + name='kpt-68', + id=68, + color=[255, 0, 0], + type='upper', + swap='kpt-77'), + 69: + dict( + name='kpt-69', + id=69, + color=[255, 0, 0], + type='upper', + swap='kpt-76'), + 70: + dict( + name='kpt-70', + id=70, + color=[255, 0, 0], + type='upper', + swap='kpt-75'), + 71: + dict( + name='kpt-71', + id=71, + color=[255, 0, 0], + type='upper', + swap='kpt-82'), + 72: + dict( + name='kpt-72', + id=72, + color=[255, 0, 0], + type='upper', + swap='kpt-81'), + 73: + dict( + name='kpt-73', + id=73, + color=[255, 0, 0], + type='upper', + swap='kpt-80'), + 74: + dict( + name='kpt-74', + id=74, + color=[255, 0, 0], + type='upper', + swap='kpt-83'), + 75: + dict( + name='kpt-75', + id=75, + color=[255, 0, 0], + type='upper', + swap='kpt-70'), + 76: + dict( + name='kpt-76', + id=76, + color=[255, 0, 0], + type='upper', + swap='kpt-69'), + 77: + dict( + name='kpt-77', + id=77, + color=[255, 0, 0], + type='upper', + swap='kpt-68'), + 78: + dict( + name='kpt-78', + id=78, + color=[255, 0, 0], + type='upper', + swap='kpt-67'), + 79: + dict( + name='kpt-79', + id=79, + color=[255, 0, 0], + type='upper', + swap='kpt-66'), + 80: + dict( + name='kpt-80', + id=80, + color=[255, 0, 0], + type='upper', + swap='kpt-73'), + 81: + dict( + name='kpt-81', + id=81, + color=[255, 0, 0], + type='upper', + swap='kpt-72'), + 82: + dict( + name='kpt-82', + id=82, + color=[255, 0, 0], + type='upper', + swap='kpt-71'), + 83: + dict( + name='kpt-83', + id=83, + color=[255, 0, 0], + type='upper', + swap='kpt-74'), + 84: + dict( + name='kpt-84', + id=84, + color=[255, 0, 0], + type='lower', + swap='kpt-90'), + 85: + dict( + name='kpt-85', + id=85, + color=[255, 0, 0], + type='lower', + swap='kpt-89'), + 86: + dict( + name='kpt-86', + id=86, + color=[255, 0, 0], + type='lower', + swap='kpt-88'), + 87: + dict(name='kpt-87', id=87, color=[255, 0, 0], type='lower', swap=''), + 88: + dict( + name='kpt-88', + id=88, + color=[255, 0, 0], + type='lower', + swap='kpt-86'), + 89: + dict( + name='kpt-89', + id=89, + color=[255, 0, 0], + type='lower', + swap='kpt-85'), + 90: + dict( + name='kpt-90', + id=90, + color=[255, 0, 0], + type='lower', + swap='kpt-84'), + 91: + dict( + name='kpt-91', + id=91, + color=[255, 0, 0], + type='lower', + swap='kpt-95'), + 92: + dict( + name='kpt-92', + id=92, + color=[255, 0, 0], + type='lower', + swap='kpt-94'), + 93: + dict(name='kpt-93', id=93, color=[255, 0, 0], type='lower', swap=''), + 94: + dict( + name='kpt-94', + id=94, + color=[255, 0, 0], + type='lower', + swap='kpt-92'), + 95: + dict( + name='kpt-95', + id=95, + color=[255, 0, 0], + type='lower', + swap='kpt-91'), + 96: + dict( + name='kpt-96', + id=96, + color=[255, 0, 0], + type='lower', + swap='kpt-100'), + 97: + dict( + name='kpt-97', + id=97, + color=[255, 0, 0], + type='lower', + swap='kpt-99'), + 98: + dict(name='kpt-98', id=98, color=[255, 0, 0], type='lower', swap=''), + 99: + dict( + name='kpt-99', + id=99, + color=[255, 0, 0], + type='lower', + swap='kpt-97'), + 100: + dict( + name='kpt-100', + id=100, + color=[255, 0, 0], + type='lower', + swap='kpt-96'), + 101: + dict( + name='kpt-101', + id=101, + color=[255, 0, 0], + type='lower', + swap='kpt-103'), + 102: + dict(name='kpt-102', id=102, color=[255, 0, 0], type='lower', swap=''), + 103: + dict( + name='kpt-103', + id=103, + color=[255, 0, 0], + type='lower', + swap='kpt-101'), + 104: + dict( + name='kpt-104', + id=104, + color=[255, 0, 0], + type='upper', + swap='kpt-105'), + 105: + dict( + name='kpt-105', + id=105, + color=[255, 0, 0], + type='upper', + swap='kpt-104') + }, + skeleton_info={}, + joint_weights=[ + 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, + 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, + 0.8, 0.8, 0.8, 0.8, 0.8, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, + 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, + 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, + 2.0, 2.0, 2.0, 2.0, 1.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 1.0, + 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, + 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.0, 1.0 + ], + sigmas=[]) diff --git a/configs/face_2d_keypoint/rtmpose/README.md b/configs/face_2d_keypoint/rtmpose/README.md index d309696bed..5381e966f6 100644 --- a/configs/face_2d_keypoint/rtmpose/README.md +++ b/configs/face_2d_keypoint/rtmpose/README.md @@ -22,3 +22,11 @@ Results on WFLW dataset | Model | Input Size | NME | Details and Download | | :-------: | :--------: | :--: | :---------------------------------------: | | RTMPose-m | 256x256 | 4.01 | [rtmpose_wflw.md](./wflw/rtmpose_wflw.md) | + +### LaPa Dataset + +Results on LaPa dataset + +| Model | Input Size | NME | Details and Download | +| :-------: | :--------: | :--: | :---------------------------------------: | +| RTMPose-m | 256x256 | 1.29 | [rtmpose_lapa.md](./wflw/rtmpose_lapa.md) | diff --git a/configs/face_2d_keypoint/rtmpose/lapa/rtmpose-m_8xb64-120e_lapa-256x256.py b/configs/face_2d_keypoint/rtmpose/lapa/rtmpose-m_8xb64-120e_lapa-256x256.py new file mode 100644 index 0000000000..97b7104e9a --- /dev/null +++ b/configs/face_2d_keypoint/rtmpose/lapa/rtmpose-m_8xb64-120e_lapa-256x256.py @@ -0,0 +1,247 @@ +_base_ = ['../../../_base_/default_runtime.py'] + +# runtime +max_epochs = 120 +stage2_num_epochs = 10 +base_lr = 4e-3 + +train_cfg = dict(max_epochs=max_epochs, val_interval=1) +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 150 to 300 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=512) + +# codec settings +codec = dict( + type='SimCCLabel', + input_size=(256, 256), + sigma=(5.66, 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/' + 'rtmposev1/cspnext-m_udp-aic-coco_210e-256x192-f2f7d6f6_20230130.pth' # noqa + )), + head=dict( + type='RTMCCHead', + in_channels=768, + out_channels=106, + input_size=codec['input_size'], + in_featuremap_size=(8, 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 = 'LapaDataset' +data_mode = 'topdown' +data_root = 'data/LaPa/' + +backend_args = dict(backend='local') +# backend_args = dict( +# backend='petrel', +# path_mapping=dict({ +# f'{data_root}': 's3://openmmlab/datasets/pose/LaPa/', +# f'{data_root}': 's3://openmmlab/datasets/pose/LaPa/' +# })) + +# 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=[0.5, 1.5], rotate_factor=80), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='mmdet.YOLOXHSVRandomAug'), + dict(type='PhotometricDistortion'), + dict( + type='Albumentation', + transforms=[ + dict(type='Blur', p=0.2), + dict(type='MedianBlur', p=0.2), + 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') +] + +# data loaders +train_dataloader = dict( + batch_size=32, + 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='annotations/lapa_train.json', + data_prefix=dict(img='train/images/'), + pipeline=train_pipeline, + )) +val_dataloader = dict( + batch_size=32, + 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='annotations/lapa_val.json', + data_prefix=dict(img='val/images/'), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = dict( + batch_size=32, + 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='annotations/lapa_test.json', + data_prefix=dict(img='test/images/'), + test_mode=True, + pipeline=val_pipeline, + )) + +# hooks +default_hooks = dict( + checkpoint=dict( + save_best='NME', rule='less', max_keep_ckpts=1, interval=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='NME', + norm_mode='keypoint_distance', +) +test_evaluator = val_evaluator diff --git a/configs/face_2d_keypoint/rtmpose/lapa/rtmpose_lapa.md b/configs/face_2d_keypoint/rtmpose/lapa/rtmpose_lapa.md new file mode 100644 index 0000000000..62a3f25157 --- /dev/null +++ b/configs/face_2d_keypoint/rtmpose/lapa/rtmpose_lapa.md @@ -0,0 +1,40 @@ + + +
+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} +} +``` + +
+ + + +
+LaPa (AAAI'2020) + +```bibtex +@inproceedings{liu2020new, + title={A New Dataset and Boundary-Attention Semantic Segmentation for Face Parsing.}, + author={Liu, Yinglu and Shi, Hailin and Shen, Hao and Si, Yue and Wang, Xiaobo and Mei, Tao}, + booktitle={AAAI}, + pages={11637--11644}, + year={2020} +} +``` + +
+ +Results on COCO-WholeBody-Face val set + +| Arch | Input Size | NME | ckpt | log | +| :------------------------------------------------------------- | :--------: | :--: | :------------------------------------------------------------: | :------------------------------------------------------------: | +| [pose_rtmpose_m](/configs/face_2d_keypoint/rtmpose/lapa/rtmpose-m_8xb64-120e_lapa-256x256.py) | 256x256 | 1.29 | [ckpt](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-lapa_pt-aic-coco_120e-256x256-762b1ae2_20230422.pth) | [log](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-lapa_pt-aic-coco_120e-256x256-762b1ae2_20230422.json) | diff --git a/configs/face_2d_keypoint/rtmpose/lapa/rtmpose_lapa.yml b/configs/face_2d_keypoint/rtmpose/lapa/rtmpose_lapa.yml new file mode 100644 index 0000000000..96acff8de6 --- /dev/null +++ b/configs/face_2d_keypoint/rtmpose/lapa/rtmpose_lapa.yml @@ -0,0 +1,15 @@ +Models: +- Config: configs/face_2d_keypoint/rtmpose/lapa/rtmpose-m_8xb64-120e_lapa-256x256.py + In Collection: RTMPose + Alias: face + Metadata: + Architecture: + - RTMPose + Training Data: LaPa + Name: rtmpose-m_8xb64-120e_lapa-256x256 + Results: + - Dataset: WFLW + Metrics: + NME: 1.29 + Task: Face 2D Keypoint + Weights: https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-lapa_pt-aic-coco_120e-256x256-762b1ae2_20230422.pth diff --git a/demo/topdown_demo_with_mmdet.py b/demo/topdown_demo_with_mmdet.py index c2e3d8d714..cd001e8db6 100644 --- a/demo/topdown_demo_with_mmdet.py +++ b/demo/topdown_demo_with_mmdet.py @@ -214,7 +214,8 @@ def main(): if output_file: img_vis = visualizer.get_image() - mmcv.imwrite(mmcv.rgb2bgr(img_vis), output_file) + if args.show: + mmcv.imwrite(mmcv.rgb2bgr(img_vis), output_file) elif input_type in ['webcam', 'video']: from mmpose.visualization import FastVisualizer diff --git a/docs/en/dataset_zoo/2d_face_keypoint.md b/docs/en/dataset_zoo/2d_face_keypoint.md index 17eb823954..1fe40273db 100644 --- a/docs/en/dataset_zoo/2d_face_keypoint.md +++ b/docs/en/dataset_zoo/2d_face_keypoint.md @@ -10,6 +10,7 @@ MMPose supported datasets: - [AFLW](#aflw-dataset) \[ [Homepage](https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/aflw/) \] - [COFW](#cofw-dataset) \[ [Homepage](http://www.vision.caltech.edu/xpburgos/ICCV13/) \] - [COCO-WholeBody-Face](#coco-wholebody-face) \[ [Homepage](https://github.com/jin-s13/COCO-WholeBody/) \] +- [LaPa](#lapa-dataset) \[ [Homepage](https://github.com/JDAI-CV/lapa-dataset) \] ## 300W Dataset @@ -325,3 +326,58 @@ mmpose Please also install the latest version of [Extended COCO API](https://github.com/jin-s13/xtcocoapi) to support COCO-WholeBody evaluation: `pip install xtcocotools` + +## LaPa + + + +
+LaPa (AAAI'2020) + +```bibtex +@inproceedings{liu2020new, + title={A New Dataset and Boundary-Attention Semantic Segmentation for Face Parsing.}, + author={Liu, Yinglu and Shi, Hailin and Shen, Hao and Si, Yue and Wang, Xiaobo and Mei, Tao}, + booktitle={AAAI}, + pages={11637--11644}, + year={2020} +} +``` + +
+ +
+ +
+ +For [LaPa](https://github.com/JDAI-CV/lapa-dataset) dataset, images can be downloaded from [their github page](https://github.com/JDAI-CV/lapa-dataset). + +Download and extract them under $MMPOSE/data, and use our `tools/dataset_converters/lapa2coco.py` to make them look like this: + +```text +mmpose +├── mmpose +├── docs +├── tests +├── tools +├── configs +`── data + │── LaPa + │-- annotations + │ │-- lapa_train.json + │ |-- lapa_val.json + │ |-- lapa_test.json + │-- train + │ │-- images + │ │-- labels + │ │-- landmarks + │-- val + │ │-- images + │ │-- labels + │ │-- landmarks + `-- test + │ │-- images + │ │-- labels + │ │-- landmarks + +``` diff --git a/mmpose/datasets/datasets/face/__init__.py b/mmpose/datasets/datasets/face/__init__.py index e0a725cd0e..700cb605f7 100644 --- a/mmpose/datasets/datasets/face/__init__.py +++ b/mmpose/datasets/datasets/face/__init__.py @@ -3,9 +3,10 @@ from .coco_wholebody_face_dataset import CocoWholeBodyFaceDataset from .cofw_dataset import COFWDataset from .face_300w_dataset import Face300WDataset +from .lapa_dataset import LapaDataset from .wflw_dataset import WFLWDataset __all__ = [ 'Face300WDataset', 'WFLWDataset', 'AFLWDataset', 'COFWDataset', - 'CocoWholeBodyFaceDataset' + 'CocoWholeBodyFaceDataset', 'LapaDataset' ] diff --git a/mmpose/datasets/datasets/face/lapa_dataset.py b/mmpose/datasets/datasets/face/lapa_dataset.py new file mode 100644 index 0000000000..1a5bdc4ec0 --- /dev/null +++ b/mmpose/datasets/datasets/face/lapa_dataset.py @@ -0,0 +1,54 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from mmpose.registry import DATASETS +from ..base import BaseCocoStyleDataset + + +@DATASETS.register_module() +class LapaDataset(BaseCocoStyleDataset): + """LaPa dataset for face keypoint localization. + + "A New Dataset and Boundary-Attention Semantic Segmentation + for Face Parsing", AAAI'2020. + + The landmark annotations follow the 106 points mark-up. The definition + can be found in `https://github.com/JDAI-CV/lapa-dataset/`__ . + + 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/lapa.py') diff --git a/mmpose/datasets/transforms/converting.py b/mmpose/datasets/transforms/converting.py index 0730808967..38dcea0994 100644 --- a/mmpose/datasets/transforms/converting.py +++ b/mmpose/datasets/transforms/converting.py @@ -1,5 +1,5 @@ # Copyright (c) OpenMMLab. All rights reserved. -from typing import List, Tuple +from typing import List, Tuple, Union import numpy as np from mmcv.transforms import BaseTransform @@ -25,11 +25,66 @@ class KeypointConverter(BaseTransform): num_keypoints (int): The number of keypoints in target dataset. mapping (list): A list containing mapping indexes. Each element has format (source_index, target_index) + + Example: + >>> import numpy as np + >>> # case 1: 1-to-1 mapping + >>> # (0, 0) means target[0] = source[0] + >>> self = KeypointConverter( + >>> num_keypoints=3, + >>> mapping=[ + >>> (0, 0), (1, 1), (2, 2), (3, 3) + >>> ]) + >>> results = dict( + >>> keypoints=np.arange(34).reshape(2, 3, 2), + >>> keypoints_visible=np.arange(34).reshape(2, 3, 2) % 2) + >>> results = self(results) + >>> assert np.equal(results['keypoints'], + >>> np.arange(34).reshape(2, 3, 2)).all() + >>> assert np.equal(results['keypoints_visible'], + >>> np.arange(34).reshape(2, 3, 2) % 2).all() + >>> + >>> # case 2: 2-to-1 mapping + >>> # ((1, 2), 0) means target[0] = (source[1] + source[2]) / 2 + >>> self = KeypointConverter( + >>> num_keypoints=3, + >>> mapping=[ + >>> ((1, 2), 0), (1, 1), (2, 2) + >>> ]) + >>> results = dict( + >>> keypoints=np.arange(34).reshape(2, 3, 2), + >>> keypoints_visible=np.arange(34).reshape(2, 3, 2) % 2) + >>> results = self(results) """ - def __init__(self, num_keypoints: int, mapping: List[Tuple[int, int]]): + def __init__(self, num_keypoints: int, + mapping: Union[List[Tuple[int, int]], List[Tuple[Tuple, + int]]]): self.num_keypoints = num_keypoints self.mapping = mapping + source_index, target_index = zip(*mapping) + + src1, src2 = [], [] + interpolation = False + for x in source_index: + if isinstance(x, (list, tuple)): + assert len(x) == 2, 'source_index should be a list/tuple of ' \ + 'length 2' + src1.append(x[0]) + src2.append(x[1]) + interpolation = True + else: + src1.append(x) + src2.append(x) + + # When paired source_indexes are input, + # keep a self.source_index2 for interpolation + if interpolation: + self.source_index2 = src2 + + self.source_index = src1 + self.target_index = target_index + self.interpolation = interpolation def transform(self, results: dict) -> dict: num_instances = results['keypoints'].shape[0] @@ -37,10 +92,22 @@ def transform(self, results: dict) -> dict: keypoints = np.zeros((num_instances, self.num_keypoints, 2)) keypoints_visible = np.zeros((num_instances, self.num_keypoints)) - source_index, target_index = zip(*self.mapping) - keypoints[:, target_index] = results['keypoints'][:, source_index] - keypoints_visible[:, target_index] = results[ - 'keypoints_visible'][:, source_index] + # When paired source_indexes are input, + # perform interpolation with self.source_index and self.source_index2 + if self.interpolation: + keypoints[:, self.target_index] = 0.5 * ( + results['keypoints'][:, self.source_index] + + results['keypoints'][:, self.source_index2]) + + keypoints_visible[:, self.target_index] = results[ + 'keypoints_visible'][:, self.source_index] * \ + results['keypoints_visible'][:, self.source_index2] + else: + keypoints[:, + self.target_index] = results['keypoints'][:, self. + source_index] + keypoints_visible[:, self.target_index] = results[ + 'keypoints_visible'][:, self.source_index] results['keypoints'] = keypoints results['keypoints_visible'] = keypoints_visible diff --git a/projects/rtmpose/README.md b/projects/rtmpose/README.md index bdb1db13d1..d697af3dcc 100644 --- a/projects/rtmpose/README.md +++ b/projects/rtmpose/README.md @@ -157,14 +157,14 @@ Feel free to join our community group for more help: ### Body 2d (17 Keypoints) -| Config | Input Size | AP
(COCO) | Params(M) | FLOPS(G) | ORT-Latency(ms)
(i7-11700) | TRT-FP16-Latency(ms)
(GTX 1660Ti) | ncnn-FP16-Latency(ms)
(Snapdragon 865) | Logs | Download | -| :---------: | :--------: | :---------------: | :-------: | :------: | :--------------------------------: | :---------------------------------------: | :--------------------------------------------: | :--------: | :------------: | -| [RTMPose-t](./rtmpose/body_2d_keypoint/rtmpose-t_8xb256-420e_coco-256x192.py) | 256x192 | 68.5 | 3.34 | 0.36 | 3.20 | 1.06 | 9.02 | [Log](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-tiny_simcc-aic-coco_pt-aic-coco_420e-256x192-cfc8f33d_20230126.json) | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-tiny_simcc-aic-coco_pt-aic-coco_420e-256x192-cfc8f33d_20230126.pth) | -| [RTMPose-s](./rtmpose/body_2d_keypoint/rtmpose-s_8xb256-420e_coco-256x192.py) | 256x192 | 72.2 | 5.47 | 0.68 | 4.48 | 1.39 | 13.89 | [Log](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-s_simcc-aic-coco_pt-aic-coco_420e-256x192-fcb2599b_20230126.json) | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-s_simcc-aic-coco_pt-aic-coco_420e-256x192-fcb2599b_20230126.pth) | -| [RTMPose-m](./rtmpose/body_2d_keypoint/rtmpose-m_8xb256-420e_coco-256x192.py) | 256x192 | 75.8 | 13.59 | 1.93 | 11.06 | 2.29 | 26.44 | [Log](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-aic-coco_pt-aic-coco_420e-256x192-63eb25f7_20230126.json) | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-aic-coco_pt-aic-coco_420e-256x192-63eb25f7_20230126.pth) | -| [RTMPose-l](./rtmpose/body_2d_keypoint/rtmpose-l_8xb256-420e_coco-256x192.py) | 256x192 | 76.5 | 27.66 | 4.16 | 18.85 | 3.46 | 45.37 | [Log](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_simcc-aic-coco_pt-aic-coco_420e-256x192-f016ffe0_20230126.json) | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_simcc-aic-coco_pt-aic-coco_420e-256x192-f016ffe0_20230126.pth) | -| [RTMPose-m](./rtmpose/body_2d_keypoint/rtmpose-m_8xb256-420e_coco-384x288.py) | 384x288 | 77.0 | 13.72 | 4.33 | 24.78 | 3.66 | - | [Log](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-aic-coco_pt-aic-coco_420e-384x288-a62a0b32_20230228.json) | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-aic-coco_pt-aic-coco_420e-384x288-a62a0b32_20230228.pth) | -| [RTMPose-l](./rtmpose/body_2d_keypoint/rtmpose-l_8xb256-420e_coco-384x288.py) | 384x288 | 77.3 | 27.79 | 9.35 | - | 6.05 | - | [Log](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_simcc-aic-coco_pt-aic-coco_420e-384x288-97d6cb0f_20230228.json) | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_simcc-aic-coco_pt-aic-coco_420e-384x288-97d6cb0f_20230228.pth) | +| Config | Input Size | AP
(COCO) | Params(M) | FLOPS(G) | ORT-Latency(ms)
(i7-11700) | TRT-FP16-Latency(ms)
(GTX 1660Ti) | ncnn-FP16-Latency(ms)
(Snapdragon 865) | Download | +| :--------------: | :--------: | :---------------: | :-------: | :------: | :--------------------------------: | :---------------------------------------: | :--------------------------------------------: | :-----------------: | +| [RTMPose-t](./rtmpose/body_2d_keypoint/rtmpose-t_8xb256-420e_coco-256x192.py) | 256x192 | 68.5 | 3.34 | 0.36 | 3.20 | 1.06 | 9.02 | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-tiny_simcc-aic-coco_pt-aic-coco_420e-256x192-cfc8f33d_20230126.pth) | +| [RTMPose-s](./rtmpose/body_2d_keypoint/rtmpose-s_8xb256-420e_coco-256x192.py) | 256x192 | 72.2 | 5.47 | 0.68 | 4.48 | 1.39 | 13.89 | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-s_simcc-aic-coco_pt-aic-coco_420e-256x192-fcb2599b_20230126.pth) | +| [RTMPose-m](./rtmpose/body_2d_keypoint/rtmpose-m_8xb256-420e_coco-256x192.py) | 256x192 | 75.8 | 13.59 | 1.93 | 11.06 | 2.29 | 26.44 | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-aic-coco_pt-aic-coco_420e-256x192-63eb25f7_20230126.pth) | +| [RTMPose-l](./rtmpose/body_2d_keypoint/rtmpose-l_8xb256-420e_coco-256x192.py) | 256x192 | 76.5 | 27.66 | 4.16 | 18.85 | 3.46 | 45.37 | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_simcc-aic-coco_pt-aic-coco_420e-256x192-f016ffe0_20230126.pth) | +| [RTMPose-m](./rtmpose/body_2d_keypoint/rtmpose-m_8xb256-420e_coco-384x288.py) | 384x288 | 77.0 | 13.72 | 4.33 | 24.78 | 3.66 | - | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-aic-coco_pt-aic-coco_420e-384x288-a62a0b32_20230228.pth) | +| [RTMPose-l](./rtmpose/body_2d_keypoint/rtmpose-l_8xb256-420e_coco-384x288.py) | 384x288 | 77.3 | 27.79 | 9.35 | - | 6.05 | - | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_simcc-aic-coco_pt-aic-coco_420e-384x288-97d6cb0f_20230228.pth) | #### Model Pruning @@ -172,29 +172,35 @@ Feel free to join our community group for more help: - Model pruning is supported by [MMRazor](https://github.com/open-mmlab/mmrazor) -| Config | Input Size | AP
(COCO) | Params(M) | FLOPS(G) | ORT-Latency(ms)
(i7-11700) | TRT-FP16-Latency(ms)
(GTX 1660Ti) | ncnn-FP16-Latency(ms)
(Snapdragon 865) | Logs | Download | -| :---------: | :--------: | :---------------: | :-------: | :------: | :--------------------------------: | :---------------------------------------: | :--------------------------------------------: | :--------: | :------------: | -| RTMPose-s-aic-coco-pruned | 256x192 | 69.4 | 3.43 | 0.35 | - | - | - | [log](https://download.openmmlab.com/mmrazor/v1/pruning/group_fisher/rtmpose-s/group_fisher_finetune_rtmpose-s_8xb256-420e_aic-coco-256x192.json) | [model](https://download.openmmlab.com/mmrazor/v1/pruning/group_fisher/rtmpose-s/group_fisher_finetune_rtmpose-s_8xb256-420e_aic-coco-256x192.pth) | +| Config | Input Size | AP
(COCO) | Params(M) | FLOPS(G) | ORT-Latency(ms)
(i7-11700) | TRT-FP16-Latency(ms)
(GTX 1660Ti) | ncnn-FP16-Latency(ms)
(Snapdragon 865) | Download | +| :--------------: | :--------: | :---------------: | :-------: | :------: | :--------------------------------: | :---------------------------------------: | :--------------------------------------------: | :-----------------: | +| RTMPose-s-aic-coco-pruned | 256x192 | 69.4 | 3.43 | 0.35 | - | - | - | [Model](https://download.openmmlab.com/mmrazor/v1/pruning/group_fisher/rtmpose-s/group_fisher_finetune_rtmpose-s_8xb256-420e_aic-coco-256x192.pth) | For more details, please refer to [GroupFisher Pruning for RTMPose](./rtmpose/pruning/README.md). ### WholeBody 2d (133 Keypoints) -| Config | Input Size | Whole AP | Whole AR | FLOPS(G) | ORT-Latency(ms)
(i7-11700) | TRT-FP16-Latency(ms)
(GTX 1660Ti) | Logs | Download | -| :----------------------------- | :--------: | :------: | :------: | :------: | :--------------------------------: | :---------------------------------------: | :--------------------------: | :-------------------------------: | -| [RTMPose-m](./rtmpose/wholebody_2d_keypoint/rtmpose-m_8xb64-270e_coco-wholebody-256x192.py) | 256x192 | 60.4 | 66.7 | 2.22 | 13.50 | 4.00 | [Log](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-coco-wholebody_pt-aic-coco_270e-256x192-cd5e845c_20230123.json) | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-coco-wholebody_pt-aic-coco_270e-256x192-cd5e845c_20230123.pth) | -| [RTMPose-l](./rtmpose/wholebody_2d_keypoint/rtmpose-l_8xb64-270e_coco-wholebody-256x192.py) | 256x192 | 63.2 | 69.4 | 4.52 | 23.41 | 5.67 | [Log](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_simcc-coco-wholebody_pt-aic-coco_270e-256x192-6f206314_20230124.json) | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_simcc-coco-wholebody_pt-aic-coco_270e-256x192-6f206314_20230124.pth) | -| [RTMPose-l](./rtmpose/wholebody_2d_keypoint/rtmpose-l_8xb32-270e_coco-wholebody-384x288.py) | 384x288 | 67.0 | 72.3 | 10.07 | 44.58 | 7.68 | [Log](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_simcc-coco-wholebody_pt-aic-coco_270e-384x288-eaeb96c8_20230125.json) | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_simcc-coco-wholebody_pt-aic-coco_270e-384x288-eaeb96c8_20230125.pth) | +| Config | Input Size | Whole AP | Whole AR | FLOPS(G) | ORT-Latency(ms)
(i7-11700) | TRT-FP16-Latency(ms)
(GTX 1660Ti) | Download | +| :------------------------------------------- | :--------: | :------: | :------: | :------: | :--------------------------------: | :---------------------------------------: | :---------------------------------------------: | +| [RTMPose-m](./rtmpose/wholebody_2d_keypoint/rtmpose-m_8xb64-270e_coco-wholebody-256x192.py) | 256x192 | 60.4 | 66.7 | 2.22 | 13.50 | 4.00 | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-coco-wholebody_pt-aic-coco_270e-256x192-cd5e845c_20230123.pth) | +| [RTMPose-l](./rtmpose/wholebody_2d_keypoint/rtmpose-l_8xb64-270e_coco-wholebody-256x192.py) | 256x192 | 63.2 | 69.4 | 4.52 | 23.41 | 5.67 | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_simcc-coco-wholebody_pt-aic-coco_270e-256x192-6f206314_20230124.pth) | +| [RTMPose-l](./rtmpose/wholebody_2d_keypoint/rtmpose-l_8xb32-270e_coco-wholebody-384x288.py) | 384x288 | 67.0 | 72.3 | 10.07 | 44.58 | 7.68 | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_simcc-coco-wholebody_pt-aic-coco_270e-384x288-eaeb96c8_20230125.pth) | ### Animal 2d (17 Keypoints) -| Config | Input Size | AP
(AP10K) | FLOPS(G) | ORT-Latency(ms)
(i7-11700) | TRT-FP16-Latency(ms)
(GTX 1660Ti) | Logs | Download | -| :---------------------------: | :--------: | :----------------: | :------: | :--------------------------------: | :---------------------------------------: | :--------------------------: | :------------------------------: | -| [RTMPose-m](./rtmpose/animal_2d_keypoint/rtmpose-m_8xb64-210e_ap10k-256x256.py) | 256x256 | 72.2 | 2.57 | 14.157 | 2.404 | [Log](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-ap10k_pt-aic-coco_210e-256x256-7a041aa1_20230206.json) | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-ap10k_pt-aic-coco_210e-256x256-7a041aa1_20230206.pth) | +| Config | Input Size | AP
(AP10K) | FLOPS(G) | ORT-Latency(ms)
(i7-11700) | TRT-FP16-Latency(ms)
(GTX 1660Ti) | Download | +| :-----------------------------------------: | :--------: | :----------------: | :------: | :--------------------------------: | :---------------------------------------: | :--------------------------------------------: | +| [RTMPose-m](./rtmpose/animal_2d_keypoint/rtmpose-m_8xb64-210e_ap10k-256x256.py) | 256x256 | 72.2 | 2.57 | 14.157 | 2.404 | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-ap10k_pt-aic-coco_210e-256x256-7a041aa1_20230206.pth) | -### Face 2d +### Face 2d (106 Keypoints) -Coming soon +
+ +
+ +| Config | Input Size | NME
(LaPa) | FLOPS(G) | ORT-Latency(ms)
(i7-11700) | TRT-FP16-Latency(ms)
(GTX 1660Ti) | Download | +| :----------------------------------------------------------------------------: | :--------: | :----------------: | :------: | :--------------------------------: | :---------------------------------------: | :---------: | +| [RTMPose-m (alpha version)](./rtmpose/face_2d_keypoint/rtmpose-m_8xb64-120e_lapa-256x256.py) | 256x256 | 1.70 | - | - | - | Coming soon | ### Hand 2d diff --git a/projects/rtmpose/README_CN.md b/projects/rtmpose/README_CN.md index 5b7979c3ec..0b25ebece2 100644 --- a/projects/rtmpose/README_CN.md +++ b/projects/rtmpose/README_CN.md @@ -148,14 +148,14 @@ RTMPose 是一个长期优化迭代的项目，致力于业务场景下的高性 ### 人体 2d 关键点 (17 Keypoints) -| Config | Input Size | AP
(COCO) | Params(M) | FLOPS(G) | ORT-Latency(ms)
(i7-11700) | TRT-FP16-Latency(ms)
(GTX 1660Ti) | ncnn-FP16-Latency(ms)
(Snapdragon 865) | Logs | Download | -| :---------: | :--------: | :---------------: | :-------: | :------: | :--------------------------------: | :---------------------------------------: | :--------------------------------------------: | :--------: | :------------: | -| [RTMPose-t](./rtmpose/body_2d_keypoint/rtmpose-t_8xb256-420e_coco-256x192.py) | 256x192 | 68.5 | 3.34 | 0.36 | 3.20 | 1.06 | 9.02 | [Log](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-tiny_simcc-aic-coco_pt-aic-coco_420e-256x192-cfc8f33d_20230126.json) | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-tiny_simcc-aic-coco_pt-aic-coco_420e-256x192-cfc8f33d_20230126.pth) | -| [RTMPose-s](./rtmpose/body_2d_keypoint/rtmpose-s_8xb256-420e_coco-256x192.py) | 256x192 | 72.2 | 5.47 | 0.68 | 4.48 | 1.39 | 13.89 | [Log](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-s_simcc-aic-coco_pt-aic-coco_420e-256x192-fcb2599b_20230126.json) | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-s_simcc-aic-coco_pt-aic-coco_420e-256x192-fcb2599b_20230126.pth) | -| [RTMPose-m](./rtmpose/body_2d_keypoint/rtmpose-m_8xb256-420e_coco-256x192.py) | 256x192 | 75.8 | 13.59 | 1.93 | 11.06 | 2.29 | 26.44 | [Log](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-aic-coco_pt-aic-coco_420e-256x192-63eb25f7_20230126.json) | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-aic-coco_pt-aic-coco_420e-256x192-63eb25f7_20230126.pth) | -| [RTMPose-l](./rtmpose/body_2d_keypoint/rtmpose-l_8xb256-420e_coco-256x192.py) | 256x192 | 76.5 | 27.66 | 4.16 | 18.85 | 3.46 | 45.37 | [Log](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_simcc-aic-coco_pt-aic-coco_420e-256x192-f016ffe0_20230126.json) | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_simcc-aic-coco_pt-aic-coco_420e-256x192-f016ffe0_20230126.pth) | -| [RTMPose-m](./rtmpose/body_2d_keypoint/rtmpose-m_8xb256-420e_coco-384x288.py) | 384x288 | 77.0 | 13.72 | 4.33 | 24.78 | 3.66 | - | [Log](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-aic-coco_pt-aic-coco_420e-384x288-a62a0b32_20230228.json) | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-aic-coco_pt-aic-coco_420e-384x288-a62a0b32_20230228.pth) | -| [RTMPose-l](./rtmpose/body_2d_keypoint/rtmpose-l_8xb256-420e_coco-384x288.py) | 384x288 | 77.3 | 27.79 | 9.35 | - | 6.05 | - | [Log](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_simcc-aic-coco_pt-aic-coco_420e-384x288-97d6cb0f_20230228.json) | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_simcc-aic-coco_pt-aic-coco_420e-384x288-97d6cb0f_20230228.pth) | +| Config | Input Size | AP
(COCO) | Params(M) | FLOPS(G) | ORT-Latency(ms)
(i7-11700) | TRT-FP16-Latency(ms)
(GTX 1660Ti) | ncnn-FP16-Latency(ms)
(Snapdragon 865) | Download | +| :--------------: | :--------: | :---------------: | :-------: | :------: | :--------------------------------: | :---------------------------------------: | :--------------------------------------------: | :-----------------: | +| [RTMPose-t](./rtmpose/body_2d_keypoint/rtmpose-t_8xb256-420e_coco-256x192.py) | 256x192 | 68.5 | 3.34 | 0.36 | 3.20 | 1.06 | 9.02 | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-tiny_simcc-aic-coco_pt-aic-coco_420e-256x192-cfc8f33d_20230126.pth) | +| [RTMPose-s](./rtmpose/body_2d_keypoint/rtmpose-s_8xb256-420e_coco-256x192.py) | 256x192 | 72.2 | 5.47 | 0.68 | 4.48 | 1.39 | 13.89 | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-s_simcc-aic-coco_pt-aic-coco_420e-256x192-fcb2599b_20230126.pth) | +| [RTMPose-m](./rtmpose/body_2d_keypoint/rtmpose-m_8xb256-420e_coco-256x192.py) | 256x192 | 75.8 | 13.59 | 1.93 | 11.06 | 2.29 | 26.44 | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-aic-coco_pt-aic-coco_420e-256x192-63eb25f7_20230126.pth) | +| [RTMPose-l](./rtmpose/body_2d_keypoint/rtmpose-l_8xb256-420e_coco-256x192.py) | 256x192 | 76.5 | 27.66 | 4.16 | 18.85 | 3.46 | 45.37 | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_simcc-aic-coco_pt-aic-coco_420e-256x192-f016ffe0_20230126.pth) | +| [RTMPose-m](./rtmpose/body_2d_keypoint/rtmpose-m_8xb256-420e_coco-384x288.py) | 384x288 | 77.0 | 13.72 | 4.33 | 24.78 | 3.66 | - | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-aic-coco_pt-aic-coco_420e-384x288-a62a0b32_20230228.pth) | +| [RTMPose-l](./rtmpose/body_2d_keypoint/rtmpose-l_8xb256-420e_coco-384x288.py) | 384x288 | 77.3 | 27.79 | 9.35 | - | 6.05 | - | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_simcc-aic-coco_pt-aic-coco_420e-384x288-97d6cb0f_20230228.pth) | #### 模型剪枝 @@ -163,37 +163,39 @@ RTMPose 是一个长期优化迭代的项目，致力于业务场景下的高性 - 模型剪枝由 [MMRazor](https://github.com/open-mmlab/mmrazor) 提供 -| Config | Input Size | AP
(COCO) | Params(M) | FLOPS(G) | ORT-Latency(ms)
(i7-11700) | TRT-FP16-Latency(ms)
(GTX 1660Ti) | ncnn-FP16-Latency(ms)
(Snapdragon 865) | Logs | Download | -| :---------: | :--------: | :---------------: | :-------: | :------: | :--------------------------------: | :---------------------------------------: | :--------------------------------------------: | :--------: | :------------: | -| RTMPose-s-aic-coco-pruned | 256x192 | 69.4 | 3.43 | 0.35 | - | - | - | [log](https://download.openmmlab.com/mmrazor/v1/pruning/group_fisher/rtmpose-s/group_fisher_finetune_rtmpose-s_8xb256-420e_aic-coco-256x192.json) | [model](https://download.openmmlab.com/mmrazor/v1/pruning/group_fisher/rtmpose-s/group_fisher_finetune_rtmpose-s_8xb256-420e_aic-coco-256x192.pth) | +| Config | Input Size | AP
(COCO) | Params(M) | FLOPS(G) | ORT-Latency(ms)
(i7-11700) | TRT-FP16-Latency(ms)
(GTX 1660Ti) | ncnn-FP16-Latency(ms)
(Snapdragon 865) | Download | +| :--------------: | :--------: | :---------------: | :-------: | :------: | :--------------------------------: | :---------------------------------------: | :--------------------------------------------: | :-----------------: | +| RTMPose-s-aic-coco-pruned | 256x192 | 69.4 | 3.43 | 0.35 | - | - | - | [Model](https://download.openmmlab.com/mmrazor/v1/pruning/group_fisher/rtmpose-s/group_fisher_finetune_rtmpose-s_8xb256-420e_aic-coco-256x192.pth) | 更多信息，请参考 [GroupFisher Pruning for RTMPose](./rtmpose/pruning/README.md). ### 人体全身 2d 关键点 (133 Keypoints) -| Config | Input Size | Whole AP | Whole AR | FLOPS(G) | ORT-Latency(ms)
(i7-11700) | TRT-FP16-Latency(ms)
(GTX 1660Ti) | Logs | Download | -| :----------------------------- | :--------: | :------: | :------: | :------: | :--------------------------------: | :---------------------------------------: | :--------------------------: | :-------------------------------: | -| [RTMPose-m](./rtmpose/wholebody_2d_keypoint/rtmpose-m_8xb64-270e_coco-wholebody-256x192.py) | 256x192 | 60.4 | 66.7 | 2.22 | 13.50 | 4.00 | [Log](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-coco-wholebody_pt-aic-coco_270e-256x192-cd5e845c_20230123.json) | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-coco-wholebody_pt-aic-coco_270e-256x192-cd5e845c_20230123.pth) | -| [RTMPose-l](./rtmpose/wholebody_2d_keypoint/rtmpose-l_8xb64-270e_coco-wholebody-256x192.py) | 256x192 | 63.2 | 69.4 | 4.52 | 23.41 | 5.67 | [Log](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_simcc-coco-wholebody_pt-aic-coco_270e-256x192-6f206314_20230124.json) | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_simcc-coco-wholebody_pt-aic-coco_270e-256x192-6f206314_20230124.pth) | -| [RTMPose-l](./rtmpose/wholebody_2d_keypoint/rtmpose-l_8xb32-270e_coco-wholebody-384x288.py) | 384x288 | 67.0 | 72.3 | 10.07 | 44.58 | 7.68 | [Log](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_simcc-coco-wholebody_pt-aic-coco_270e-384x288-eaeb96c8_20230125.json) | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_simcc-coco-wholebody_pt-aic-coco_270e-384x288-eaeb96c8_20230125.pth) | +| Config | Input Size | Whole AP | Whole AR | FLOPS(G) | ORT-Latency(ms)
(i7-11700) | TRT-FP16-Latency(ms)
(GTX 1660Ti) | Download | +| :------------------------------------------- | :--------: | :------: | :------: | :------: | :--------------------------------: | :---------------------------------------: | :---------------------------------------------: | +| [RTMPose-m](./rtmpose/wholebody_2d_keypoint/rtmpose-m_8xb64-270e_coco-wholebody-256x192.py) | 256x192 | 60.4 | 66.7 | 2.22 | 13.50 | 4.00 | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-coco-wholebody_pt-aic-coco_270e-256x192-cd5e845c_20230123.pth) | +| [RTMPose-l](./rtmpose/wholebody_2d_keypoint/rtmpose-l_8xb64-270e_coco-wholebody-256x192.py) | 256x192 | 63.2 | 69.4 | 4.52 | 23.41 | 5.67 | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_simcc-coco-wholebody_pt-aic-coco_270e-256x192-6f206314_20230124.pth) | +| [RTMPose-l](./rtmpose/wholebody_2d_keypoint/rtmpose-l_8xb32-270e_coco-wholebody-384x288.py) | 384x288 | 67.0 | 72.3 | 10.07 | 44.58 | 7.68 | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_simcc-coco-wholebody_pt-aic-coco_270e-384x288-eaeb96c8_20230125.pth) | ### 动物 2d 关键点 (17 Keypoints) -| Config | Input Size | AP
(AP10K) | FLOPS(G) | ORT-Latency(ms)
(i7-11700) | TRT-FP16-Latency(ms)
(GTX 1660Ti) | Logs | Download | -| :---------------------------: | :--------: | :----------------: | :------: | :--------------------------------: | :---------------------------------------: | :--------------------------: | :------------------------------: | -| [RTMPose-m](./rtmpose/animal_2d_keypoint/rtmpose-m_8xb64-210e_ap10k-256x256.py) | 256x256 | 72.2 | 2.57 | 14.157 | 2.404 | [Log](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-ap10k_pt-aic-coco_210e-256x256-7a041aa1_20230206.json) | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-ap10k_pt-aic-coco_210e-256x256-7a041aa1_20230206.pth) | +| Config | Input Size | AP
(AP10K) | FLOPS(G) | ORT-Latency(ms)
(i7-11700) | TRT-FP16-Latency(ms)
(GTX 1660Ti) | Download | +| :-----------------------------------------: | :--------: | :----------------: | :------: | :--------------------------------: | :---------------------------------------: | :--------------------------------------------: | +| [RTMPose-m](./rtmpose/animal_2d_keypoint/rtmpose-m_8xb64-210e_ap10k-256x256.py) | 256x256 | 72.2 | 2.57 | 14.157 | 2.404 | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-ap10k_pt-aic-coco_210e-256x256-7a041aa1_20230206.pth) | -### 脸部 2d 关键点 +### 脸部 2d 关键点 (106 Keypoints) -| Config | Input Size | NME
(COCO-WholeBody-Face) | FLOPS(G) | ORT-Latency(ms)
(i7-11700) | TRT-FP16-Latency(ms)
(GTX 1660Ti) | Logs | Download | -| :--------------------------------------------------: | :--------: | :-------------------------------: | :------: | :--------------------------------: | :---------------------------------------: | :---------: | :---------: | -| [RTMPose-m](./rtmpose/face_2d_keypoint/wflw/rtmpose-m_8xb64-60e_coco-wholebody-face-256x256.py) | 256x256 | 4.57 | - | - | - | Coming soon | Coming soon | +
+ +
+ +| Config | Input Size | NME
(LaPa) | FLOPS(G) | ORT-Latency(ms)
(i7-11700) | TRT-FP16-Latency(ms)
(GTX 1660Ti) | Download | +| :----------------------------------------------------------------------------: | :--------: | :----------------: | :------: | :--------------------------------: | :---------------------------------------: | :---------: | +| [RTMPose-m (alpha version)](./rtmpose/face_2d_keypoint/rtmpose-m_8xb64-120e_lapa-256x256.py) | 256x256 | 1.70 | - | - | - | Coming soon | ### 手部 2d 关键点 -| Config | Input Size | PCK
(COCO-WholeBody-Hand) | FLOPS(G) | ORT-Latency(ms)
(i7-11700) | TRT-FP16-Latency(ms)
(GTX 1660Ti) | Logs | Download | -| :--------------------------------------------------: | :--------: | :-------------------------------: | :------: | :--------------------------------: | :---------------------------------------: | :---------: | :---------: | -| [RTMPose-m](./rtmpose/hand_2d_keypoint/coco_wholebody_hand/rtmpose-m_8xb32-210e_coco-wholebody-hand-256x256.py) | 256x256 | 81.5 | - | - | - | Coming soon | Coming soon | +Coming soon ### 预训练模型 diff --git a/projects/rtmpose/rtmpose/face_2d_keypoint/rtmpose-m_8xb32-60e_coco-wholebody-face-256x256.py b/projects/rtmpose/rtmpose/face_2d_keypoint/rtmpose-m_8xb64-120e_lapa-256x256.py similarity index 84% rename from projects/rtmpose/rtmpose/face_2d_keypoint/rtmpose-m_8xb32-60e_coco-wholebody-face-256x256.py rename to projects/rtmpose/rtmpose/face_2d_keypoint/rtmpose-m_8xb64-120e_lapa-256x256.py index d331dac91a..309414d371 100644 --- a/projects/rtmpose/rtmpose/face_2d_keypoint/rtmpose-m_8xb32-60e_coco-wholebody-face-256x256.py +++ b/projects/rtmpose/rtmpose/face_2d_keypoint/rtmpose-m_8xb64-120e_lapa-256x256.py @@ -1,7 +1,7 @@ _base_ = ['mmpose::_base_/default_runtime.py'] # runtime -max_epochs = 60 +max_epochs = 120 stage2_num_epochs = 10 base_lr = 4e-3 @@ -74,7 +74,7 @@ head=dict( type='RTMCCHead', in_channels=768, - out_channels=68, + out_channels=106, input_size=codec['input_size'], in_featuremap_size=(8, 8), simcc_split_ratio=codec['simcc_split_ratio'], @@ -97,16 +97,16 @@ test_cfg=dict(flip_test=True, )) # base dataset settings -dataset_type = 'CocoWholeBodyFaceDataset' +dataset_type = 'LapaDataset' data_mode = 'topdown' -data_root = 'data/coco/' +data_root = 'data/LaPa/' 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/' +# f'{data_root}': 's3://openmmlab/datasets/pose/LaPa/', +# f'{data_root}': 's3://openmmlab/datasets/pose/LaPa/' # })) # pipelines @@ -114,16 +114,17 @@ dict(type='LoadImage', backend_args=backend_args), dict(type='GetBBoxCenterScale'), dict(type='RandomFlip', direction='horizontal'), - # dict(type='RandomHalfBody'), + dict(type='RandomHalfBody'), dict( - type='RandomBBoxTransform', scale_factor=[0.6, 1.4], rotate_factor=80), + type='RandomBBoxTransform', scale_factor=[0.5, 1.5], rotate_factor=80), dict(type='TopdownAffine', input_size=codec['input_size']), dict(type='mmdet.YOLOXHSVRandomAug'), + dict(type='PhotometricDistortion'), dict( type='Albumentation', transforms=[ - dict(type='Blur', p=0.1), - dict(type='MedianBlur', p=0.1), + dict(type='Blur', p=0.2), + dict(type='MedianBlur', p=0.2), dict( type='CoarseDropout', max_holes=1, @@ -185,8 +186,8 @@ type=dataset_type, data_root=data_root, data_mode=data_mode, - ann_file='annotations/coco_wholebody_train_v1.0.json', - data_prefix=dict(img='train2017/'), + ann_file='annotations/lapa_train.json', + data_prefix=dict(img='train/images/'), pipeline=train_pipeline, )) val_dataloader = dict( @@ -199,12 +200,26 @@ type=dataset_type, data_root=data_root, data_mode=data_mode, - ann_file='annotations/coco_wholebody_val_v1.0.json', - data_prefix=dict(img='val2017/'), + ann_file='annotations/lapa_val.json', + data_prefix=dict(img='val/images/'), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = dict( + batch_size=32, + 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='annotations/lapa_test.json', + data_prefix=dict(img='test/images/'), test_mode=True, pipeline=val_pipeline, )) -test_dataloader = val_dataloader # hooks default_hooks = dict( diff --git a/tests/data/lapa/10773046825_0.jpg b/tests/data/lapa/10773046825_0.jpg new file mode 100644 index 0000000000000000000000000000000000000000..ebbc0a3bc55bcf3fd06b56044538f65990f839e2 GIT 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324.0, 2.0, 263.0, 325.0, 2.0, 246.0, 320.0, 2.0, 233.0, 310.0, 2.0, 229.0, 300.0, 2.0, 246.0, 298.0, 2.0, 269.0, 302.0, 2.0, 282.0, 305.0, 2.0, 289.0, 310.0, 2.0, 280.0, 313.0, 2.0, 265.0, 313.0, 2.0, 243.0, 307.0, 2.0, 232.0, 228.0, 2.0, 297.0, 241.0, 2.0], + "image_id": 41, + "id": 41, + "num_keypoints": 106, + "bbox": [165.0, 204.0, 160.0, 161.0], + "iscrowd": 0, + "area": 25760, + "category_id": 1 + } + ] +} diff --git a/tests/test_datasets/test_datasets/test_face_datasets/test_lapa_dataset.py b/tests/test_datasets/test_datasets/test_face_datasets/test_lapa_dataset.py new file mode 100644 index 0000000000..991f285476 --- /dev/null +++ b/tests/test_datasets/test_datasets/test_face_datasets/test_lapa_dataset.py @@ -0,0 +1,93 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from unittest import TestCase + +import numpy as np + +from mmpose.datasets.datasets.face import LapaDataset + + +class TestLaPaDataset(TestCase): + + def build_lapa_dataset(self, **kwargs): + + cfg = dict( + ann_file='test_lapa.json', + bbox_file=None, + data_mode='topdown', + data_root='tests/data/lapa', + pipeline=[], + test_mode=False) + + cfg.update(kwargs) + return LapaDataset(**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) + 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_lapa_dataset() + self.check_metainfo_keys(dataset.metainfo) + # test dataset_name + self.assertEqual(dataset.metainfo['dataset_name'], 'lapa') + + # test number of keypoints + num_keypoints = 106 + 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']), 0) + + def test_topdown(self): + # test topdown training + dataset = self.build_lapa_dataset(data_mode='topdown') + self.assertEqual(dataset.data_mode, 'topdown') + self.assertEqual(dataset.bbox_file, None) + # filter invalid insances due to face_valid = false + self.assertEqual(len(dataset), 2) + self.check_data_info_keys(dataset[0]) + + # test topdown testing + dataset = self.build_lapa_dataset(data_mode='topdown', test_mode=True) + self.assertEqual(dataset.data_mode, 'topdown') + self.assertEqual(dataset.bbox_file, None) + self.assertEqual(len(dataset), 2) + self.check_data_info_keys(dataset[0]) diff --git a/tests/test_datasets/test_transforms/test_converting.py b/tests/test_datasets/test_transforms/test_converting.py index f345a44063..09f06e1e65 100644 --- a/tests/test_datasets/test_transforms/test_converting.py +++ b/tests/test_datasets/test_transforms/test_converting.py @@ -13,6 +13,7 @@ def setUp(self): img_shape=(240, 320), num_instances=4, with_bbox_cs=True) def test_transform(self): + # 1-to-1 mapping mapping = [(3, 0), (6, 1), (16, 2), (5, 3)] transform = KeypointConverter(num_keypoints=5, mapping=mapping) results = transform(self.data_info.copy()) @@ -34,3 +35,39 @@ def test_transform(self): self.assertTrue( (results['keypoints_visible'][:, target_index] == self.data_info['keypoints_visible'][:, source_index]).all()) + + # 2-to-1 mapping + mapping = [((3, 5), 0), (6, 1), (16, 2), (5, 3)] + transform = KeypointConverter(num_keypoints=5, mapping=mapping) + results = transform(self.data_info.copy()) + + # check shape + self.assertEqual(results['keypoints'].shape[0], + self.data_info['keypoints'].shape[0]) + self.assertEqual(results['keypoints'].shape[1], 5) + self.assertEqual(results['keypoints'].shape[2], 2) + self.assertEqual(results['keypoints_visible'].shape[0], + self.data_info['keypoints_visible'].shape[0]) + self.assertEqual(results['keypoints_visible'].shape[1], 5) + + # check value + for source_index, target_index in mapping: + if isinstance(source_index, tuple): + source_index, source_index2 = source_index + self.assertTrue( + (results['keypoints'][:, target_index] == 0.5 * + (self.data_info['keypoints'][:, source_index] + + self.data_info['keypoints'][:, source_index2])).all()) + self.assertTrue( + (results['keypoints_visible'][:, target_index] == + self.data_info['keypoints_visible'][:, source_index] * + self.data_info['keypoints_visible'][:, + source_index2]).all()) + else: + self.assertTrue( + (results['keypoints'][:, target_index] == + self.data_info['keypoints'][:, source_index]).all()) + self.assertTrue( + (results['keypoints_visible'][:, target_index] == + self.data_info['keypoints_visible'][:, + source_index]).all()) diff --git a/tools/dataset_converters/lapa2coco.py b/tools/dataset_converters/lapa2coco.py new file mode 100644 index 0000000000..7727bdf022 --- /dev/null +++ b/tools/dataset_converters/lapa2coco.py @@ -0,0 +1,104 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import json +import os +import os.path as osp +import time + +import cv2 +import mmengine +import numpy as np + + +def default_dump(obj): + """Convert numpy classes to JSON serializable objects.""" + if isinstance(obj, (np.integer, np.floating, np.bool_)): + return obj.item() + elif isinstance(obj, np.ndarray): + return obj.tolist() + else: + return obj + + +def convert_labpa_to_coco(ann_dir, out_file): + landmark_dir = osp.join(ann_dir, 'landmarks') + ann_list = os.listdir(landmark_dir) + + img_dir = osp.join(ann_dir, 'images') + + annotations = [] + images = [] + cnt = 0 + for idx, ann_file in enumerate(mmengine.track_iter_progress(ann_list)): + cnt += 1 + ann_path = osp.join(landmark_dir, ann_file) + file_name = ann_file[:-4] + '.jpg' + img_path = osp.join(img_dir, file_name) + data_info = open(ann_path).readlines() + + img = cv2.imread(img_path) + + keypoints = [] + for line in data_info[1:]: + x, y = line.strip().split(' ') + x, y = float(x), float(y) + keypoints.append([x, y, 2]) + keypoints = np.array(keypoints) + + x1, y1, _ = np.amin(keypoints, axis=0) + x2, y2, _ = np.amax(keypoints, axis=0) + w, h = x2 - x1, y2 - y1 + bbox = [x1, y1, w, h] + + image = {} + image['id'] = cnt + image['file_name'] = file_name + image['height'] = img.shape[0] + image['width'] = img.shape[1] + images.append(image) + + ann = {} + ann['keypoints'] = keypoints.reshape(-1).tolist() + ann['image_id'] = cnt + ann['id'] = cnt + ann['num_keypoints'] = len(keypoints) + ann['bbox'] = bbox + ann['iscrowd'] = 0 + ann['area'] = int(ann['bbox'][2] * ann['bbox'][3]) + ann['category_id'] = 1 + + annotations.append(ann) + + cocotype = {} + + cocotype['info'] = {} + cocotype['info']['description'] = 'LaPa Generated by MMPose Team' + cocotype['info']['version'] = 1.0 + cocotype['info']['year'] = time.strftime('%Y', time.localtime()) + cocotype['info']['date_created'] = time.strftime('%Y/%m/%d', + time.localtime()) + + cocotype['images'] = images + cocotype['annotations'] = annotations + cocotype['categories'] = [{ + 'supercategory': 'person', + 'id': 1, + 'name': 'face', + 'keypoints': [], + 'skeleton': [] + }] + + json.dump( + cocotype, + open(out_file, 'w'), + ensure_ascii=False, + default=default_dump) + print(f'done {out_file}') + + +if __name__ == '__main__': + if not osp.exists('data/LaPa/annotations'): + os.makedirs('data/LaPa/annotations') + for tv in ['val', 'test', 'train']: + print(f'processing {tv}') + convert_labpa_to_coco(f'data/LaPa/{tv}', + f'data/LaPa/annotations/lapa_{tv}.json') From 8c2d3fad79cd7e3438f17fe25e557e4e718945fb Mon Sep 17 00:00:00 2001 From: FishBigOcean <36850642+FishBigOcean@users.noreply.github.com> Date: Sun, 23 Apr 2023 14:09:08 +0800 Subject: [PATCH 3/6] fix and op bug (#2286) --- mmpose/evaluation/functional/nms.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mmpose/evaluation/functional/nms.py b/mmpose/evaluation/functional/nms.py index 50bbe1550b..eed4e5cf73 100644 --- a/mmpose/evaluation/functional/nms.py +++ b/mmpose/evaluation/functional/nms.py @@ -102,7 +102,7 @@ def oks_iou(g: np.ndarray, dy = yd - yg e = (dx**2 + dy**2) / vars / ((a_g + a_d[n_d]) / 2 + np.spacing(1)) / 2 if vis_thr is not None: - ind = list(vg > vis_thr) and list(vd > vis_thr) + ind = list((vg > vis_thr) & (vd > vis_thr)) e = e[ind] ious[n_d] = np.sum(np.exp(-e)) / len(e) if len(e) != 0 else 0.0 return ious From 9551fe54bda1ced2c7a66678f07e59fb0e2145ea Mon Sep 17 00:00:00 2001 From: Tau Date: Sun, 23 Apr 2023 15:15:02 +0800 Subject: [PATCH 4/6] [Fix] Fix CI and Update lowest torch version (#2288) --- .circleci/test.yml | 4 +- .github/ISSUE_TEMPLATE/1-bug-report.yml | 93 ++++++++++++++++++ .github/ISSUE_TEMPLATE/2-feature_request.yml | 37 +++++++ ...l_questions.md => 3-general_questions.yml} | 2 - .github/ISSUE_TEMPLATE/bug-report.md | 96 ------------------- .github/ISSUE_TEMPLATE/feature_request.md | 42 -------- .github/workflows/merge_stage_test.yml | 16 ++-- .github/workflows/pr_stage_test.yml | 8 +- README.md | 2 +- README_CN.md | 2 +- demo/MMPose_Tutorial.ipynb | 4 +- docker/Dockerfile | 2 +- docs/en/faq.md | 3 +- docs/en/installation.md | 2 +- docs/zh_cn/faq.md | 3 +- docs/zh_cn/installation.md | 2 +- requirements/mminstall.txt | 4 +- tools/misc/publish_model.py | 2 +- 18 files changed, 157 insertions(+), 167 deletions(-) create mode 100644 .github/ISSUE_TEMPLATE/1-bug-report.yml create mode 100644 .github/ISSUE_TEMPLATE/2-feature_request.yml rename .github/ISSUE_TEMPLATE/{general_questions.md => 3-general_questions.yml} (92%) delete mode 100644 .github/ISSUE_TEMPLATE/bug-report.md delete mode 100644 .github/ISSUE_TEMPLATE/feature_request.md diff --git a/.circleci/test.yml b/.circleci/test.yml index 534ba7fa4e..7e2f892cbf 100644 --- a/.circleci/test.yml +++ b/.circleci/test.yml @@ -62,7 +62,7 @@ jobs: pip install -U numpy pip install git+https://github.com/open-mmlab/mmengine.git@main pip install -U openmim - mim install 'mmcv >= 2.0.0rc1' + mim install 'mmcv >= 2.0.0' pip install git+https://github.com/open-mmlab/mmdetection.git@dev-3.x pip install -r requirements/tests.txt pip install -r requirements/albu.txt @@ -111,7 +111,7 @@ jobs: docker exec mmpose pip install -U numpy docker exec mmpose pip install -e /mmengine docker exec mmpose pip install -U openmim - docker exec mmpose mim install 'mmcv >= 2.0.0rc1' + docker exec mmpose mim install 'mmcv >= 2.0.0' docker exec mmpose pip install -e /mmdetection docker exec mmpose pip install -r requirements/tests.txt docker exec mmpose pip install -r requirements/albu.txt diff --git a/.github/ISSUE_TEMPLATE/1-bug-report.yml b/.github/ISSUE_TEMPLATE/1-bug-report.yml new file mode 100644 index 0000000000..23427231b8 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/1-bug-report.yml @@ -0,0 +1,93 @@ +name: "🐞 Bug report" +description: "Create a report to help us reproduce and fix the bug" +labels: bug +title: "[Bug] " + +body: + - type: markdown + attributes: + value: | + ## Note + For general usage questions or idea discussions, please post it to our [**Forum**](https://github.com/open-mmlab/mmpose/discussions) + Please fill in as **much** of the following form as you're able to. **The clearer the description, the shorter it will take to solve it.** + + - type: checkboxes + attributes: + label: Prerequisite + description: Please check the following items before creating a new issue. + options: + + - label: I have searched [Issues](https://github.com/open-mmlab/mmpose/issues) and [Discussions](https://github.com/open-mmlab/mmpose/discussions) but cannot get the expected help. + required: true + - label: The bug has not been fixed in the latest version(https://github.com/open-mmlab/mmpose). + required: true + + - type: textarea + attributes: + label: Environment + description: | + Please run following commands and and copy-paste it here: + - `python -c "from mmpose.utils import collect_env; print(collect_env())"` to collect necessary environment information. + - `pip list | grep mm` to collect repositories related to OpenMMLab. + - \[Optional\] Other environment variables that may be related (such as `$PATH`, `$LD_LIBRARY_PATH`, `$PYTHONPATH`, etc.) + validations: + required: true + + - type: textarea + attributes: + label: Reproduces the problem - code sample + description: | + Please provide a code sample that reproduces the problem you ran into. It can be a Colab link or just a code snippet. + placeholder: | + ```python + # Sample code to reproduce the problem + ``` + validations: + required: true + + - type: textarea + attributes: + label: Reproduces the problem - command or script + description: | + What command or script did you run? + placeholder: | + ```shell + The command or script you run. + ``` + validations: + required: true + + - type: textarea + attributes: + label: Reproduces the problem - error message + description: | + Please provide the error message or logs you got, with the full traceback. + + Tip: You can attach screenshots or log files by dragging them into the text area.. + placeholder: | + ``` + The error message or logs you got, with the full traceback. + ``` + validations: + required: true + + - type: textarea + attributes: + label: Additional information + description: | + Tell us anything else you think we should know. + + Tip: You can attach screenshots or log files by dragging them into the text area. + placeholder: | + 1. What's your expected result? + 2. What dataset did you use? + 3. What do you think might be the reason? + + - type: markdown + attributes: + value: | + ## Acknowledgement + Thanks for taking the time to fill out this report. + + If you have already identified the reason, we strongly appreciate you creating a new PR to fix it [**Here**](https://github.com/open-mmlab/mmpose/pulls)! + Please refer to [**Contribution Guide**](https://mmpose.readthedocs.io/en/latest/contribution_guide.html) for contributing. diff --git a/.github/ISSUE_TEMPLATE/2-feature_request.yml b/.github/ISSUE_TEMPLATE/2-feature_request.yml new file mode 100644 index 0000000000..47247a0e17 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/2-feature_request.yml @@ -0,0 +1,37 @@ +name: 🚀 Feature request +description: Suggest an idea for this project +labels: feature-request +title: "[Feature] " + +body: + - type: markdown + attributes: + value: | + ## Note + For general usage questions or idea discussions, please post it to our [**Forum**](https://github.com/open-mmlab/mmpose/discussions) + + Please fill in as **much** of the following form as you're able to. **The clearer the description, the shorter it will take to solve it.** + + - type: textarea + attributes: + label: What is the feature? + description: Tell us more about the feature and how this feature can help. + placeholder: | + E.g., It is inconvenient when \[....\]. + validations: + required: true + + - type: textarea + attributes: + label: Any other context? + description: | + Have you considered any alternative solutions or features? If so, what are they? Also, feel free to add any other context or screenshots about the feature request here. + + - type: markdown + attributes: + value: | + ## Acknowledgement + Thanks for taking the time to fill out this report. + + We strongly appreciate you creating a new PR to implement it [**Here**](https://github.com/open-mmlab/mmpose/pulls)! + Please refer to [**Contribution Guide**](https://mmpose.readthedocs.io/en/latest/contribution_guide.html) for contributing. diff --git a/.github/ISSUE_TEMPLATE/general_questions.md b/.github/ISSUE_TEMPLATE/3-general_questions.yml similarity index 92% rename from .github/ISSUE_TEMPLATE/general_questions.md rename to .github/ISSUE_TEMPLATE/3-general_questions.yml index f02dd63a80..0b1194dc2e 100644 --- a/.github/ISSUE_TEMPLATE/general_questions.md +++ b/.github/ISSUE_TEMPLATE/3-general_questions.yml @@ -1,7 +1,5 @@ ---- name: General questions about: Ask general questions to get help title: '' labels: '' assignees: '' ---- diff --git a/.github/ISSUE_TEMPLATE/bug-report.md b/.github/ISSUE_TEMPLATE/bug-report.md deleted file mode 100644 index 2f9c005993..0000000000 --- a/.github/ISSUE_TEMPLATE/bug-report.md +++ /dev/null @@ -1,96 +0,0 @@ -name: "🐞 Bug report" -description: "Create a report to help us reproduce and fix the bug" -labels: bug -title: "\[Bug\] " - -body: - -- type: markdown - attributes: - value: | - \## Note - For general usage questions or idea discussions, please post it to our [**Forum**](https://github.com/open-mmlab/mmpose/discussions) - Please fill in as **much** of the following form as you're able to. **The clearer the description, the shorter it will take to solve it.** - -- type: checkboxes - attributes: - label: Prerequisite - description: Please check the following items before creating a new issue. - options: - - - label: I have searched [Issues](https://github.com/open-mmlab/mmpose/issues) and [Discussions](https://github.com/open-mmlab/mmpose/discussions) but cannot get the expected help. - required: true - - label: The bug has not been fixed in the latest version(https://github.com/open-mmlab/mmpose). - required: true - -- type: textarea - attributes: - label: Environment - description: | - Please run following commands and and copy-paste it here: - \- `python -c "from mmpose.utils import collect_env; print(collect_env())"` to collect necessary environment information. - \- `pip list | grep mm` to collect repositories related to OpenMMLab. - \- \[Optional\] Other environment variables that may be related (such as `$PATH`, `$LD_LIBRARY_PATH`, `$PYTHONPATH`, etc.) - validations: - required: true - -- type: textarea - attributes: - label: Reproduces the problem - code sample - description: | - Please provide a code sample that reproduces the problem you ran into. It can be a Colab link or just a code snippet. - placeholder: | - `python # Sample code to reproduce the problem ` - validations: - required: true - -- type: textarea - attributes: - label: Reproduces the problem - command or script - description: | - What command or script did you run? - placeholder: | - `shell The command or script you run. ` - validations: - required: true - -- type: textarea - attributes: - label: Reproduces the problem - error message - description: | - Please provide the error message or logs you got, with the full traceback. - - ``` - Tip: You can attach screenshots or log files by dragging them into the text area.. - ``` - - placeholder: | - ` The error message or logs you got, with the full traceback. ` - validations: - required: true - -- type: textarea - attributes: - label: Additional information - description: | - Tell us anything else you think we should know. - - ``` - Tip: You can attach screenshots or log files by dragging them into the text area. - ``` - - placeholder: | - 1\. What's your expected result? - 2\. What dataset did you use? - 3\. What do you think might be the reason? - -- type: markdown - attributes: - value: | - \## Acknowledgement - Thanks for taking the time to fill out this report. - - ``` - If you have already identified the reason, we strongly appreciate you creating a new PR to fix it [**Here**](https://github.com/open-mmlab/mmpose/pulls)! - Please refer to [**Contribution Guide**](https://mmpose.readthedocs.io/en/latest/contribution_guide.html) for contributing. - ``` diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md deleted file mode 100644 index ca94a6f1de..0000000000 --- a/.github/ISSUE_TEMPLATE/feature_request.md +++ /dev/null @@ -1,42 +0,0 @@ -name: 🚀 Feature request -description: Suggest an idea for this project -labels: \[feature-request\] -title: "\[Feature\] " - -body: - -- type: markdown - attributes: - value: | - \## Note - For general usage questions or idea discussions, please post it to our [**Forum**](https://github.com/open-mmlab/mmpose/discussions) - - ``` - Please fill in as **much** of the following form as you're able to. **The clearer the description, the shorter it will take to solve it.** - ``` - -- type: textarea - attributes: - label: What is the feature? - description: Tell us more about the feature and how this feature can help. - placeholder: | - E.g., It is inconvenient when \[....\]. - validations: - required: true - -- type: textarea - attributes: - label: Any other context? - description: | - Have you considered any alternative solutions or features? If so, what are they? Also, feel free to add any other context or screenshots about the feature request here. - -- type: markdown - attributes: - value: | - \## Acknowledgement - Thanks for taking the time to fill out this report. - - ``` - We strongly appreciate you creating a new PR to implement it [**Here**](https://github.com/open-mmlab/mmpose/pulls)! - Please refer to [**Contribution Guide**](https://mmpose.readthedocs.io/en/latest/contribution_guide.html) for contributing. - ``` diff --git a/.github/workflows/merge_stage_test.yml b/.github/workflows/merge_stage_test.yml index d2a64590ae..bb60ad40fa 100644 --- a/.github/workflows/merge_stage_test.yml +++ b/.github/workflows/merge_stage_test.yml @@ -43,7 +43,7 @@ jobs: - name: Install MMCV run: | pip install -U openmim - mim install 'mmcv >= 2.0.0rc1' + mim install 'mmcv >= 2.0.0' - name: Install MMDet run: | python -m pip install --upgrade pip setuptools wheel @@ -67,12 +67,10 @@ jobs: strategy: matrix: python-version: [3.7] - torch: [1.6.0, 1.7.1, 1.8.1, 1.9.1, 1.10.1, 1.11.0, 1.12.1, 1.13.0] + torch: [1.8.0, 1.8.1, 1.9.1, 1.10.1, 1.11.0, 1.12.1, 1.13.0] include: - - torch: 1.6.0 - torchvision: 0.7.0 - - torch: 1.7.1 - torchvision: 0.8.2 + - torch: 1.8.0 + torchvision: 0.9.0 - torch: 1.8.1 torchvision: 0.9.1 - torch: 1.9.1 @@ -105,7 +103,7 @@ jobs: - name: Install MMCV run: | pip install -U openmim - mim install 'mmcv >= 2.0.0rc1' + mim install 'mmcv >= 2.0.0' - name: Install MMDet run: | python -m pip install --upgrade pip setuptools wheel @@ -167,7 +165,7 @@ jobs: pip install -U numpy pip install git+https://github.com/open-mmlab/mmengine.git@main pip install -U openmim - mim install 'mmcv >= 2.0.0rc1' + mim install 'mmcv >= 2.0.0' pip install git+https://github.com/open-mmlab/mmdetection.git@dev-3.x pip install -r requirements/tests.txt pip install -r requirements/runtime.txt @@ -212,7 +210,7 @@ jobs: python -m pip install -U numpy python -m pip install git+https://github.com/open-mmlab/mmengine.git@main python -m pip install -U openmim - mim install 'mmcv >= 2.0.0rc1' + mim install 'mmcv >= 2.0.0' python -m pip install git+https://github.com/open-mmlab/mmdetection.git@dev-3.x python -m pip install -r requirements/tests.txt python -m pip install -r requirements/runtime.txt diff --git a/.github/workflows/pr_stage_test.yml b/.github/workflows/pr_stage_test.yml index c5f99b0ebd..5ed6fc8ae7 100644 --- a/.github/workflows/pr_stage_test.yml +++ b/.github/workflows/pr_stage_test.yml @@ -41,7 +41,7 @@ jobs: pip install -U numpy pip install git+https://github.com/open-mmlab/mmengine.git@main pip install -U openmim - mim install 'mmcv >= 2.0.0rc1' + mim install 'mmcv >= 2.0.0' pip install git+https://github.com/open-mmlab/mmdetection.git@dev-3.x pip install -r requirements/tests.txt pip install -r requirements/runtime.txt @@ -95,7 +95,7 @@ jobs: pip install -U numpy pip install git+https://github.com/open-mmlab/mmengine.git@main pip install -U openmim - mim install 'mmcv >= 2.0.0rc1' + mim install 'mmcv >= 2.0.0' pip install git+https://github.com/open-mmlab/mmdetection.git@dev-3.x pip install -r requirements/tests.txt pip install -r requirements/runtime.txt @@ -135,7 +135,7 @@ jobs: pip install -U numpy pip install git+https://github.com/open-mmlab/mmengine.git@main pip install -U openmim - mim install 'mmcv >= 2.0.0rc1' + mim install 'mmcv >= 2.0.0' pip install git+https://github.com/open-mmlab/mmdetection.git@dev-3.x pip install -r requirements/tests.txt pip install -r requirements/runtime.txt @@ -180,7 +180,7 @@ jobs: python -m pip install -U numpy python -m pip install git+https://github.com/open-mmlab/mmengine.git@main python -m pip install -U openmim - mim install 'mmcv >= 2.0.0rc1' + mim install 'mmcv >= 2.0.0' python -m pip install git+https://github.com/open-mmlab/mmdetection.git@dev-3.x python -m pip install -r requirements/tests.txt python -m pip install -r requirements/albu.txt diff --git a/README.md b/README.md index 34f9caacda..951c4adf2e 100644 --- a/README.md +++ b/README.md @@ -63,7 +63,7 @@ English | [简体中文](README_CN.md) MMPose is an open-source toolbox for pose estimation based on PyTorch. It is a part of the [OpenMMLab project](https://github.com/open-mmlab). -The master branch works with **PyTorch 1.6+**. +The master branch works with **PyTorch 1.8+**. https://user-images.githubusercontent.com/15977946/124654387-0fd3c500-ded1-11eb-84f6-24eeddbf4d91.mp4 diff --git a/README_CN.md b/README_CN.md index 1b8121767d..49a956cab9 100644 --- a/README_CN.md +++ b/README_CN.md @@ -62,7 +62,7 @@ MMPose 是一款基于 PyTorch 的姿态分析的开源工具箱，是 [OpenMMLab](https://github.com/open-mmlab) 项目的成员之一。 -主分支代码目前支持 **PyTorch 1.6 以上**的版本。 +主分支代码目前支持 **PyTorch 1.8 以上**的版本。 https://user-images.githubusercontent.com/15977946/124654387-0fd3c500-ded1-11eb-84f6-24eeddbf4d91.mp4 diff --git a/demo/MMPose_Tutorial.ipynb b/demo/MMPose_Tutorial.ipynb index 1866a38aa8..1fa381c3f8 100644 --- a/demo/MMPose_Tutorial.ipynb +++ b/demo/MMPose_Tutorial.ipynb @@ -299,8 +299,8 @@ "# install MMEngine, MMCV and MMDetection using MIM\n", "%pip install -U openmim\n", "!mim install mmengine\n", - "!mim install \"mmcv>=2.0.0rc1\"\n", - "!mim install \"mmdet>=3.0.0rc0\"" + "!mim install \"mmcv>=2.0.0\"\n", + "!mim install \"mmdet>=3.0.0\"" ] }, { diff --git a/docker/Dockerfile b/docker/Dockerfile index 347af89ca8..064b803979 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -22,7 +22,7 @@ RUN pip install xtcocotools # Install MMEngine and MMCV RUN pip install openmim -RUN mim install mmengine "mmcv>=2.0.0rc1" +RUN mim install mmengine "mmcv>=2.0.0" # Install MMPose RUN conda clean --all diff --git a/docs/en/faq.md b/docs/en/faq.md index 40676cbb67..b3efa69255 100644 --- a/docs/en/faq.md +++ b/docs/en/faq.md @@ -14,6 +14,7 @@ Compatible MMPose and MMCV versions are shown as below. Please choose the correc | MMPose version | MMCV/MMEngine version | | :------------: | :-----------------------------: | +| 1.0.0 | mmcv>=2.0.0, mmengine>=0.7.0 | | 1.0.0rc1 | mmcv>=2.0.0rc4, mmengine>=0.6.0 | | 1.0.0rc0 | mmcv>=2.0.0rc0, mmengine>=0.0.1 | | 1.0.0b0 | mmcv>=2.0.0rc0, mmengine>=0.0.1 | @@ -22,7 +23,7 @@ Compatible MMPose and MMCV versions are shown as below. Please choose the correc | MMPose version | MMCV version | | :------------: | :-----------------------: | -| master | mmcv-full>=1.3.8, \<1.8.0 | +| 0.x | mmcv-full>=1.3.8, \<1.8.0 | | 0.29.0 | mmcv-full>=1.3.8, \<1.7.0 | | 0.28.1 | mmcv-full>=1.3.8, \<1.7.0 | | 0.28.0 | mmcv-full>=1.3.8, \<1.6.0 | diff --git a/docs/en/installation.md b/docs/en/installation.md index dc4a0ab386..7285982059 100644 --- a/docs/en/installation.md +++ b/docs/en/installation.md @@ -220,7 +220,7 @@ Note that within Jupyter, the exclamation mark `!` is used to call external exec We provide a [Dockerfile](https://github.com/open-mmlab/mmpose/blob/master/docker/Dockerfile) to build an image. Ensure that your [docker version](https://docs.docker.com/engine/install/) >=19.03. ```shell -# build an image with PyTorch 1.6.0, CUDA 10.1, CUDNN 7. +# build an image with PyTorch 1.8.0, CUDA 10.1, CUDNN 7. # If you prefer other versions, just modified the Dockerfile docker build -t mmpose docker/ ``` diff --git a/docs/zh_cn/faq.md b/docs/zh_cn/faq.md index 15e3fbb98d..ea929b9f91 100644 --- a/docs/zh_cn/faq.md +++ b/docs/zh_cn/faq.md @@ -14,6 +14,7 @@ Compatible MMPose and MMCV versions are shown as below. Please choose the correc | MMPose version | MMCV/MMEngine version | | :------------: | :-----------------------------: | +| 1.0.0 | mmcv>=2.0.0, mmengine>=0.7.0 | | 1.0.0rc1 | mmcv>=2.0.0rc4, mmengine>=0.6.0 | | 1.0.0rc0 | mmcv>=2.0.0rc0, mmengine>=0.0.1 | | 1.0.0b0 | mmcv>=2.0.0rc0, mmengine>=0.0.1 | @@ -22,7 +23,7 @@ Compatible MMPose and MMCV versions are shown as below. Please choose the correc | MMPose version | MMCV version | | :------------: | :-----------------------: | -| master | mmcv-full>=1.3.8, \<1.8.0 | +| 0.x | mmcv-full>=1.3.8, \<1.8.0 | | 0.29.0 | mmcv-full>=1.3.8, \<1.7.0 | | 0.28.1 | mmcv-full>=1.3.8, \<1.7.0 | | 0.28.0 | mmcv-full>=1.3.8, \<1.6.0 | diff --git a/docs/zh_cn/installation.md b/docs/zh_cn/installation.md index 1ec42fe78a..3e9a709e9e 100644 --- a/docs/zh_cn/installation.md +++ b/docs/zh_cn/installation.md @@ -227,7 +227,7 @@ MMPose 提供 [Dockerfile](https://github.com/open-mmlab/mmpose/blob/master/dock 用于构建镜像。请确保您的 [Docker 版本](https://docs.docker.com/engine/install/) >=19.03。 ```shell -# 构建默认的 PyTorch 1.6.0，CUDA 10.1 版本镜像 +# 构建默认的 PyTorch 1.8.0，CUDA 10.1 版本镜像 # 如果您希望使用其他版本，请修改 Dockerfile docker build -t mmpose docker/ ``` diff --git a/requirements/mminstall.txt b/requirements/mminstall.txt index fb0519c072..24be7462fc 100644 --- a/requirements/mminstall.txt +++ b/requirements/mminstall.txt @@ -1,3 +1,3 @@ -mmcv>=2.0.0rc1,<2.1.0 -mmdet>=3.0.0rc6,<3.1.0 +mmcv>=2.0.0,<2.1.0 +mmdet>=3.0.0,<3.1.0 mmengine>=0.4.0,<1.0.0 diff --git a/tools/misc/publish_model.py b/tools/misc/publish_model.py index 4a8338fdbd..addf4cca64 100644 --- a/tools/misc/publish_model.py +++ b/tools/misc/publish_model.py @@ -41,7 +41,7 @@ def process_checkpoint(in_file, out_file, save_keys=['meta', 'state_dict']): # if it is necessary to remove some sensitive data in checkpoint['meta'], # add the code here. - if digit_version(TORCH_VERSION) >= digit_version('1.6.0'): + if digit_version(TORCH_VERSION) >= digit_version('1.8.0'): torch.save(checkpoint, out_file, _use_new_zipfile_serialization=False) else: torch.save(checkpoint, out_file) From c3fb09cf8c010f8032e62046035c2bded1c6ce3f Mon Sep 17 00:00:00 2001 From: Hanxiao Xiang <107178092+ATang0729@users.noreply.github.com> Date: Sun, 23 Apr 2023 15:35:22 +0800 Subject: [PATCH 5/6] [Feature] Add support for deepfashion2 dataset (#2201) --- configs/_base_/datasets/deepfashion2.py | 2660 +++++++++++++++++ .../deepfashion2/res50_deepfashion2.md | 67 + .../deepfashion2/res50_deepfasion2.yml | 185 ++ ..._deepfasion2-long-sleeved-dress-256x192.py | 122 + ...50_1xb64-210e_deepfasion2-skirt-256x192.py | 122 + ...b64-210e_deepfasion2-vest-dress-256x192.py | 122 + ...2xb64-210e_deepfasion2-trousers-256x192.py | 122 + ...0_3xb64-210e_deepfasion2-shorts-256x192.py | 122 + ...deepfasion2-short-sleeved-dress-256x192.py | 122 + ...50_4xb64-210e_deepfasion2-sling-256x192.py | 122 + ...64-210e_deepfasion2-sling-dress-256x192.py | 122 + ...s50_4xb64-210e_deepfasion2-vest-256x192.py | 122 + ...deepfasion2-short-sleeved-shirt-256x192.py | 122 + ...eepfasion2-long-sleeved-outwear-256x192.py | 123 + ..._deepfasion2-long-sleeved-shirt-256x192.py | 122 + ...epfasion2-short-sleeved-outwear-256x192.py | 123 + docs/en/dataset_zoo/2d_fashion_landmark.md | 62 + docs/zh_cn/dataset_zoo/2d_fashion_landmark.md | 81 +- docs/zh_cn/user_guides/inference.md | 211 +- .../inferencers/base_mmpose_inferencer.py | 12 +- mmpose/apis/inferencers/mmpose_inferencer.py | 2 +- mmpose/apis/inferencers/pose2d_inferencer.py | 27 +- mmpose/datasets/datasets/fashion/__init__.py | 3 +- .../datasets/fashion/deepfashion2_dataset.py | 10 + model-index.yml | 1 + projects/yolox-pose/configs/_base_/datasets | 2 +- projects/yolox-pose/demo | 2 +- projects/yolox-pose/tools | 2 +- tests/data/deepfasion2/000264.jpg | Bin 0 -> 69862 bytes tests/data/deepfasion2/000265.jpg | Bin 0 -> 51574 bytes tests/data/deepfasion2/deepfasion2.json | 2404 +++++++++++++++ 31 files changed, 7218 insertions(+), 101 deletions(-) create mode 100644 configs/_base_/datasets/deepfashion2.py create mode 100644 configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/res50_deepfashion2.md create mode 100644 configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/res50_deepfasion2.yml create mode 100644 configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_1xb64-210e_deepfasion2-long-sleeved-dress-256x192.py create mode 100644 configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_1xb64-210e_deepfasion2-skirt-256x192.py create mode 100644 configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_1xb64-210e_deepfasion2-vest-dress-256x192.py create mode 100644 configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_2xb64-210e_deepfasion2-trousers-256x192.py create mode 100644 configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_3xb64-210e_deepfasion2-shorts-256x192.py create mode 100644 configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_4xb64-210e_deepfasion2-short-sleeved-dress-256x192.py create mode 100644 configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_4xb64-210e_deepfasion2-sling-256x192.py create mode 100644 configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_4xb64-210e_deepfasion2-sling-dress-256x192.py create mode 100644 configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_4xb64-210e_deepfasion2-vest-256x192.py create mode 100644 configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_6xb64-210e_deepfasion2-short-sleeved-shirt-256x192.py create mode 100644 configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_8xb64-210e_deepfasion2-long-sleeved-outwear-256x192.py create mode 100644 configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_8xb64-210e_deepfasion2-long-sleeved-shirt-256x192.py create mode 100644 configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_8xb64-210e_deepfasion2-short-sleeved-outwear-256x192.py create mode 100644 mmpose/datasets/datasets/fashion/deepfashion2_dataset.py create mode 100644 tests/data/deepfasion2/000264.jpg create mode 100644 tests/data/deepfasion2/000265.jpg create mode 100644 tests/data/deepfasion2/deepfasion2.json diff --git a/configs/_base_/datasets/deepfashion2.py b/configs/_base_/datasets/deepfashion2.py new file mode 100644 index 0000000000..f65d1bb591 --- /dev/null +++ b/configs/_base_/datasets/deepfashion2.py @@ -0,0 +1,2660 @@ +colors = dict( + sss=[255, 128, 0], # short_sleeve_shirt + lss=[255, 0, 128], # long_sleeved_shirt + sso=[128, 0, 255], # short_sleeved_outwear + lso=[0, 128, 255], # long_sleeved_outwear + vest=[0, 128, 128], # vest + sling=[0, 0, 128], # sling + shorts=[128, 128, 128], # shorts + trousers=[128, 0, 128], # trousers + skirt=[64, 128, 128], # skirt + ssd=[64, 64, 128], # short_sleeved_dress + lsd=[128, 64, 0], # long_sleeved_dress + vd=[128, 64, 255], # vest_dress + sd=[128, 64, 0], # sling_dress +) +dataset_info = dict( + dataset_name='deepfashion2', + paper_info=dict( + author='Yuying Ge and Ruimao Zhang and Lingyun Wu ' + 'and Xiaogang Wang and Xiaoou Tang and Ping Luo', + title='DeepFashion2: A Versatile Benchmark for ' + 'Detection, Pose Estimation, Segmentation and ' + 'Re-Identification of Clothing Images', + container='Proceedings of IEEE Conference on Computer ' + 'Vision and Pattern Recognition (CVPR)', + year='2019', + homepage='https://github.com/switchablenorms/DeepFashion2', + ), + keypoint_info={ + # short_sleeved_shirt + 0: + dict(name='sss_kpt1', id=0, color=colors['sss'], type='', swap=''), + 1: + dict( + name='sss_kpt2', + id=1, + color=colors['sss'], + type='', + swap='sss_kpt6'), + 2: + dict( + name='sss_kpt3', + id=2, + color=colors['sss'], + type='', + swap='sss_kpt5'), + 3: + dict(name='sss_kpt4', id=3, color=colors['sss'], type='', swap=''), + 4: + dict( + name='sss_kpt5', + id=4, + color=colors['sss'], + type='', + swap='sss_kpt3'), + 5: + dict( + name='sss_kpt6', + id=5, + color=colors['sss'], + type='', + swap='sss_kpt2'), + 6: + dict( + name='sss_kpt7', + id=6, + color=colors['sss'], + type='', + swap='sss_kpt25'), + 7: + dict( + name='sss_kpt8', + id=7, + color=colors['sss'], + type='', + swap='sss_kpt24'), + 8: + dict( + name='sss_kpt9', + id=8, + color=colors['sss'], + type='', + swap='sss_kpt23'), + 9: + dict( + name='sss_kpt10', + id=9, + color=colors['sss'], + type='', + swap='sss_kpt22'), + 10: + dict( + name='sss_kpt11', + id=10, + color=colors['sss'], + type='', + swap='sss_kpt21'), + 11: + dict( + name='sss_kpt12', + id=11, + color=colors['sss'], + type='', + swap='sss_kpt20'), + 12: + dict( + name='sss_kpt13', + id=12, + color=colors['sss'], + type='', + swap='sss_kpt19'), + 13: + dict( + name='sss_kpt14', + id=13, + color=colors['sss'], + type='', + swap='sss_kpt18'), + 14: + dict( + name='sss_kpt15', + id=14, + color=colors['sss'], + type='', + swap='sss_kpt17'), + 15: + dict(name='sss_kpt16', id=15, color=colors['sss'], type='', swap=''), + 16: + dict( + name='sss_kpt17', + id=16, + color=colors['sss'], + type='', + swap='sss_kpt15'), + 17: + dict( + name='sss_kpt18', + id=17, + color=colors['sss'], + type='', + swap='sss_kpt14'), + 18: + dict( + name='sss_kpt19', + id=18, + color=colors['sss'], + type='', + swap='sss_kpt13'), + 19: + dict( + name='sss_kpt20', + id=19, + color=colors['sss'], + type='', + swap='sss_kpt12'), + 20: + dict( + name='sss_kpt21', + id=20, + color=colors['sss'], + type='', + swap='sss_kpt11'), + 21: + dict( + name='sss_kpt22', + id=21, + color=colors['sss'], + type='', + swap='sss_kpt10'), + 22: + dict( + name='sss_kpt23', + id=22, + color=colors['sss'], + type='', + swap='sss_kpt9'), + 23: + dict( + name='sss_kpt24', + id=23, + color=colors['sss'], + type='', + swap='sss_kpt8'), + 24: + dict( + name='sss_kpt25', + id=24, + color=colors['sss'], + type='', + swap='sss_kpt7'), + # long_sleeved_shirt + 25: + dict(name='lss_kpt1', id=25, color=colors['lss'], type='', swap=''), + 26: + dict( + name='lss_kpt2', + id=26, + color=colors['lss'], + type='', + swap='lss_kpt6'), + 27: + dict( + name='lss_kpt3', + id=27, + color=colors['lss'], + type='', + swap='lss_kpt5'), + 28: + dict(name='lss_kpt4', id=28, color=colors['lss'], type='', swap=''), + 29: + dict( + name='lss_kpt5', + id=29, + color=colors['lss'], + type='', + swap='lss_kpt3'), + 30: + dict( + name='lss_kpt6', + id=30, + color=colors['lss'], + type='', + swap='lss_kpt2'), + 31: + dict( + name='lss_kpt7', + id=31, + color=colors['lss'], + type='', + swap='lss_kpt33'), + 32: + dict( + name='lss_kpt8', + id=32, + color=colors['lss'], + type='', + swap='lss_kpt32'), + 33: + dict( + name='lss_kpt9', + id=33, + color=colors['lss'], + type='', + swap='lss_kpt31'), + 34: + dict( + name='lss_kpt10', + id=34, + color=colors['lss'], + type='', + swap='lss_kpt30'), + 35: + dict( + name='lss_kpt11', + id=35, + color=colors['lss'], + type='', + swap='lss_kpt29'), + 36: + dict( + name='lss_kpt12', + id=36, + color=colors['lss'], + type='', + swap='lss_kpt28'), + 37: + dict( + name='lss_kpt13', + id=37, + color=colors['lss'], + type='', + swap='lss_kpt27'), + 38: + dict( + name='lss_kpt14', + id=38, + color=colors['lss'], + type='', + swap='lss_kpt26'), + 39: + dict( + name='lss_kpt15', + id=39, + color=colors['lss'], + type='', + swap='lss_kpt25'), + 40: + dict( + name='lss_kpt16', + id=40, + color=colors['lss'], + type='', + swap='lss_kpt24'), + 41: + dict( + name='lss_kpt17', + id=41, + color=colors['lss'], + type='', + swap='lss_kpt23'), + 42: + dict( + name='lss_kpt18', + id=42, + color=colors['lss'], + type='', + swap='lss_kpt22'), + 43: + dict( + name='lss_kpt19', + id=43, + color=colors['lss'], + type='', + swap='lss_kpt21'), + 44: + dict(name='lss_kpt20', id=44, color=colors['lss'], type='', swap=''), + 45: + dict( + name='lss_kpt21', + id=45, + color=colors['lss'], + type='', + swap='lss_kpt19'), + 46: + dict( + name='lss_kpt22', + id=46, + color=colors['lss'], + type='', + swap='lss_kpt18'), + 47: + dict( + name='lss_kpt23', + id=47, + color=colors['lss'], + type='', + swap='lss_kpt17'), + 48: + dict( + name='lss_kpt24', + id=48, + color=colors['lss'], + type='', + swap='lss_kpt16'), + 49: + dict( + name='lss_kpt25', + id=49, + color=colors['lss'], + type='', + swap='lss_kpt15'), + 50: + dict( + name='lss_kpt26', + id=50, + color=colors['lss'], + type='', + swap='lss_kpt14'), + 51: + dict( + name='lss_kpt27', + id=51, + color=colors['lss'], + type='', + swap='lss_kpt13'), + 52: + dict( + name='lss_kpt28', + id=52, + color=colors['lss'], + type='', + swap='lss_kpt12'), + 53: + dict( + name='lss_kpt29', + id=53, + color=colors['lss'], + type='', + swap='lss_kpt11'), + 54: + dict( + name='lss_kpt30', + id=54, + color=colors['lss'], + type='', + swap='lss_kpt10'), + 55: + dict( + name='lss_kpt31', + id=55, + color=colors['lss'], + type='', + swap='lss_kpt9'), + 56: + dict( + name='lss_kpt32', + id=56, + color=colors['lss'], + type='', + swap='lss_kpt8'), + 57: + dict( + name='lss_kpt33', + id=57, + color=colors['lss'], + type='', + swap='lss_kpt7'), + # short_sleeved_outwear + 58: + dict(name='sso_kpt1', id=58, color=colors['sso'], type='', swap=''), + 59: + dict( + name='sso_kpt2', + id=59, + color=colors['sso'], + type='', + swap='sso_kpt26'), + 60: + dict( + name='sso_kpt3', + id=60, + color=colors['sso'], + type='', + swap='sso_kpt5'), + 61: + dict( + name='sso_kpt4', + id=61, + color=colors['sso'], + type='', + swap='sso_kpt6'), + 62: + dict( + name='sso_kpt5', + id=62, + color=colors['sso'], + type='', + swap='sso_kpt3'), + 63: + dict( + name='sso_kpt6', + id=63, + color=colors['sso'], + type='', + swap='sso_kpt4'), + 64: + dict( + name='sso_kpt7', + id=64, + color=colors['sso'], + type='', + swap='sso_kpt25'), + 65: + dict( + name='sso_kpt8', + id=65, + color=colors['sso'], + type='', + swap='sso_kpt24'), + 66: + dict( + name='sso_kpt9', + id=66, + color=colors['sso'], + type='', + swap='sso_kpt23'), + 67: + dict( + name='sso_kpt10', + id=67, + color=colors['sso'], + type='', + swap='sso_kpt22'), + 68: + dict( + name='sso_kpt11', + id=68, + color=colors['sso'], + type='', + swap='sso_kpt21'), + 69: + dict( + name='sso_kpt12', + id=69, + color=colors['sso'], + type='', + swap='sso_kpt20'), + 70: + dict( + name='sso_kpt13', + id=70, + color=colors['sso'], + type='', + swap='sso_kpt19'), + 71: + dict( + name='sso_kpt14', + id=71, + color=colors['sso'], + type='', + swap='sso_kpt18'), + 72: + dict( + name='sso_kpt15', + id=72, + color=colors['sso'], + type='', + swap='sso_kpt17'), + 73: + dict( + name='sso_kpt16', + id=73, + color=colors['sso'], + type='', + swap='sso_kpt29'), + 74: + dict( + name='sso_kpt17', + id=74, + color=colors['sso'], + type='', + swap='sso_kpt15'), + 75: + dict( + name='sso_kpt18', + id=75, + color=colors['sso'], + type='', + swap='sso_kpt14'), + 76: + dict( + name='sso_kpt19', + id=76, + color=colors['sso'], + type='', + swap='sso_kpt13'), + 77: + dict( + name='sso_kpt20', + id=77, + color=colors['sso'], + type='', + swap='sso_kpt12'), + 78: + dict( + name='sso_kpt21', + id=78, + color=colors['sso'], + type='', + swap='sso_kpt11'), + 79: + dict( + name='sso_kpt22', + id=79, + color=colors['sso'], + type='', + swap='sso_kpt10'), + 80: + dict( + name='sso_kpt23', + id=80, + color=colors['sso'], + type='', + swap='sso_kpt9'), + 81: + dict( + name='sso_kpt24', + id=81, + color=colors['sso'], + type='', + swap='sso_kpt8'), + 82: + dict( + name='sso_kpt25', + id=82, + color=colors['sso'], + type='', + swap='sso_kpt7'), + 83: + dict( + name='sso_kpt26', + id=83, + color=colors['sso'], + type='', + swap='sso_kpt2'), + 84: + dict( + name='sso_kpt27', + id=84, + color=colors['sso'], + type='', + swap='sso_kpt30'), + 85: + dict( + name='sso_kpt28', + id=85, + color=colors['sso'], + type='', + swap='sso_kpt31'), + 86: + dict( + name='sso_kpt29', + id=86, + color=colors['sso'], + type='', + swap='sso_kpt16'), + 87: + dict( + name='sso_kpt30', + id=87, + color=colors['sso'], + type='', + swap='sso_kpt27'), + 88: + dict( + name='sso_kpt31', + id=88, + color=colors['sso'], + type='', + swap='sso_kpt28'), + # long_sleeved_outwear + 89: + dict(name='lso_kpt1', id=89, color=colors['lso'], type='', swap=''), + 90: + dict( + name='lso_kpt2', + id=90, + color=colors['lso'], + type='', + swap='lso_kpt6'), + 91: + dict( + name='lso_kpt3', + id=91, + color=colors['lso'], + type='', + swap='lso_kpt5'), + 92: + dict( + name='lso_kpt4', + id=92, + color=colors['lso'], + type='', + swap='lso_kpt34'), + 93: + dict( + name='lso_kpt5', + id=93, + color=colors['lso'], + type='', + swap='lso_kpt3'), + 94: + dict( + name='lso_kpt6', + id=94, + color=colors['lso'], + type='', + swap='lso_kpt2'), + 95: + dict( + name='lso_kpt7', + id=95, + color=colors['lso'], + type='', + swap='lso_kpt33'), + 96: + dict( + name='lso_kpt8', + id=96, + color=colors['lso'], + type='', + swap='lso_kpt32'), + 97: + dict( + name='lso_kpt9', + id=97, + color=colors['lso'], + type='', + swap='lso_kpt31'), + 98: + dict( + name='lso_kpt10', + id=98, + color=colors['lso'], + type='', + swap='lso_kpt30'), + 99: + dict( + name='lso_kpt11', + id=99, + color=colors['lso'], + type='', + swap='lso_kpt29'), + 100: + dict( + name='lso_kpt12', + id=100, + color=colors['lso'], + type='', + swap='lso_kpt28'), + 101: + dict( + name='lso_kpt13', + id=101, + color=colors['lso'], + type='', + swap='lso_kpt27'), + 102: + dict( + name='lso_kpt14', + id=102, + color=colors['lso'], + type='', + swap='lso_kpt26'), + 103: + dict( + name='lso_kpt15', + id=103, + color=colors['lso'], + type='', + swap='lso_kpt25'), + 104: + dict( + name='lso_kpt16', + id=104, + color=colors['lso'], + type='', + swap='lso_kpt24'), + 105: + dict( + name='lso_kpt17', + id=105, + color=colors['lso'], + type='', + swap='lso_kpt23'), + 106: + dict( + name='lso_kpt18', + id=106, + color=colors['lso'], + type='', + swap='lso_kpt22'), + 107: + dict( + name='lso_kpt19', + id=107, + color=colors['lso'], + type='', + swap='lso_kpt21'), + 108: + dict( + name='lso_kpt20', + id=108, + color=colors['lso'], + type='', + swap='lso_kpt37'), + 109: + dict( + name='lso_kpt21', + id=109, + color=colors['lso'], + type='', + swap='lso_kpt19'), + 110: + dict( + name='lso_kpt22', + id=110, + color=colors['lso'], + type='', + swap='lso_kpt18'), + 111: + dict( + name='lso_kpt23', + id=111, + color=colors['lso'], + type='', + swap='lso_kpt17'), + 112: + dict( + name='lso_kpt24', + id=112, + color=colors['lso'], + type='', + swap='lso_kpt16'), + 113: + dict( + name='lso_kpt25', + id=113, + color=colors['lso'], + type='', + swap='lso_kpt15'), + 114: + dict( + name='lso_kpt26', + id=114, + color=colors['lso'], + type='', + swap='lso_kpt14'), + 115: + dict( + name='lso_kpt27', + id=115, + color=colors['lso'], + type='', + swap='lso_kpt13'), + 116: + dict( + name='lso_kpt28', + id=116, + color=colors['lso'], + type='', + swap='lso_kpt12'), + 117: + dict( + name='lso_kpt29', + id=117, + color=colors['lso'], + type='', + swap='lso_kpt11'), + 118: + dict( + name='lso_kpt30', + id=118, + color=colors['lso'], + type='', + swap='lso_kpt10'), + 119: + dict( + name='lso_kpt31', + id=119, + color=colors['lso'], + type='', + swap='lso_kpt9'), + 120: + dict( + name='lso_kpt32', + id=120, + color=colors['lso'], + type='', + swap='lso_kpt8'), + 121: + dict( + name='lso_kpt33', + id=121, + color=colors['lso'], + type='', + swap='lso_kpt7'), + 122: + dict( + name='lso_kpt34', + id=122, + color=colors['lso'], + type='', + swap='lso_kpt4'), + 123: + dict( + name='lso_kpt35', + id=123, + color=colors['lso'], + type='', + swap='lso_kpt38'), + 124: + dict( + name='lso_kpt36', + id=124, + color=colors['lso'], + type='', + swap='lso_kpt39'), + 125: + dict( + name='lso_kpt37', + id=125, + color=colors['lso'], + type='', + swap='lso_kpt20'), + 126: + dict( + name='lso_kpt38', + id=126, + color=colors['lso'], + type='', + swap='lso_kpt35'), + 127: + dict( + name='lso_kpt39', + id=127, + color=colors['lso'], + type='', + swap='lso_kpt36'), + # vest + 128: + dict(name='vest_kpt1', id=128, color=colors['vest'], type='', swap=''), + 129: + dict( + name='vest_kpt2', + id=129, + color=colors['vest'], + type='', + swap='vest_kpt6'), + 130: + dict( + name='vest_kpt3', + id=130, + color=colors['vest'], + type='', + swap='vest_kpt5'), + 131: + dict(name='vest_kpt4', id=131, color=colors['vest'], type='', swap=''), + 132: + dict( + name='vest_kpt5', + id=132, + color=colors['vest'], + type='', + swap='vest_kpt3'), + 133: + dict( + name='vest_kpt6', + id=133, + color=colors['vest'], + type='', + swap='vest_kpt2'), + 134: + dict( + name='vest_kpt7', + id=134, + color=colors['vest'], + type='', + swap='vest_kpt15'), + 135: + dict( + name='vest_kpt8', + id=135, + color=colors['vest'], + type='', + swap='vest_kpt14'), + 136: + dict( + name='vest_kpt9', + id=136, + color=colors['vest'], + type='', + swap='vest_kpt13'), + 137: + dict( + name='vest_kpt10', + id=137, + color=colors['vest'], + type='', + swap='vest_kpt12'), + 138: + dict( + name='vest_kpt11', id=138, color=colors['vest'], type='', swap=''), + 139: + dict( + name='vest_kpt12', + id=139, + color=colors['vest'], + type='', + swap='vest_kpt10'), + 140: + dict( + name='vest_kpt13', id=140, color=colors['vest'], type='', swap=''), + 141: + dict( + name='vest_kpt14', + id=141, + color=colors['vest'], + type='', + swap='vest_kpt8'), + 142: + dict( + name='vest_kpt15', + id=142, + color=colors['vest'], + type='', + swap='vest_kpt7'), + # sling + 143: + dict( + name='sling_kpt1', id=143, color=colors['sling'], type='', + swap=''), + 144: + dict( + name='sling_kpt2', + id=144, + color=colors['sling'], + type='', + swap='sling_kpt6'), + 145: + dict( + name='sling_kpt3', + id=145, + color=colors['sling'], + type='', + swap='sling_kpt5'), + 146: + dict( + name='sling_kpt4', id=146, color=colors['sling'], type='', + swap=''), + 147: + dict( + name='sling_kpt5', + id=147, + color=colors['sling'], + type='', + swap='sling_kpt3'), + 148: + dict( + name='sling_kpt6', + id=148, + color=colors['sling'], + type='', + swap='sling_kpt2'), + 149: + dict( + name='sling_kpt7', + id=149, + color=colors['sling'], + type='', + swap='sling_kpt15'), + 150: + dict( + name='sling_kpt8', + id=150, + color=colors['sling'], + type='', + swap='sling_kpt14'), + 151: + dict( + name='sling_kpt9', + id=151, + color=colors['sling'], + type='', + swap='sling_kpt13'), + 152: + dict( + name='sling_kpt10', + id=152, + color=colors['sling'], + type='', + swap='sling_kpt12'), + 153: + dict( + name='sling_kpt11', + id=153, + color=colors['sling'], + type='', + swap=''), + 154: + dict( + name='sling_kpt12', + id=154, + color=colors['sling'], + type='', + swap='sling_kpt10'), + 155: + dict( + name='sling_kpt13', + id=155, + color=colors['sling'], + type='', + swap='sling_kpt9'), + 156: + dict( + name='sling_kpt14', + id=156, + color=colors['sling'], + type='', + swap='sling_kpt8'), + 157: + dict( + name='sling_kpt15', + id=157, + color=colors['sling'], + type='', + swap='sling_kpt7'), + # shorts + 158: + dict( + name='shorts_kpt1', + id=158, + color=colors['shorts'], + type='', + swap='shorts_kpt3'), + 159: + dict( + name='shorts_kpt2', + id=159, + color=colors['shorts'], + type='', + swap=''), + 160: + dict( + name='shorts_kpt3', + id=160, + color=colors['shorts'], + type='', + swap='shorts_kpt1'), + 161: + dict( + name='shorts_kpt4', + id=161, + color=colors['shorts'], + type='', + swap='shorts_kpt10'), + 162: + dict( + name='shorts_kpt5', + id=162, + color=colors['shorts'], + type='', + swap='shorts_kpt9'), + 163: + dict( + name='shorts_kpt6', + id=163, + color=colors['shorts'], + type='', + swap='shorts_kpt8'), + 164: + dict( + name='shorts_kpt7', + id=164, + color=colors['shorts'], + type='', + swap=''), + 165: + dict( + name='shorts_kpt8', + id=165, + color=colors['shorts'], + type='', + swap='shorts_kpt6'), + 166: + dict( + name='shorts_kpt9', + id=166, + color=colors['shorts'], + type='', + swap='shorts_kpt5'), + 167: + dict( + name='shorts_kpt10', + id=167, + color=colors['shorts'], + type='', + swap='shorts_kpt4'), + # trousers + 168: + dict( + name='trousers_kpt1', + id=168, + color=colors['trousers'], + type='', + swap='trousers_kpt3'), + 169: + dict( + name='trousers_kpt2', + id=169, + color=colors['trousers'], + type='', + swap=''), + 170: + dict( + name='trousers_kpt3', + id=170, + color=colors['trousers'], + type='', + swap='trousers_kpt1'), + 171: + dict( + name='trousers_kpt4', + id=171, + color=colors['trousers'], + type='', + swap='trousers_kpt14'), + 172: + dict( + name='trousers_kpt5', + id=172, + color=colors['trousers'], + type='', + swap='trousers_kpt13'), + 173: + dict( + name='trousers_kpt6', + id=173, + color=colors['trousers'], + type='', + swap='trousers_kpt12'), + 174: + dict( + name='trousers_kpt7', + id=174, + color=colors['trousers'], + type='', + swap='trousers_kpt11'), + 175: + dict( + name='trousers_kpt8', + id=175, + color=colors['trousers'], + type='', + swap='trousers_kpt10'), + 176: + dict( + name='trousers_kpt9', + id=176, + color=colors['trousers'], + type='', + swap=''), + 177: + dict( + name='trousers_kpt10', + id=177, + color=colors['trousers'], + type='', + swap='trousers_kpt8'), + 178: + dict( + name='trousers_kpt11', + id=178, + color=colors['trousers'], + type='', + swap='trousers_kpt7'), + 179: + dict( + name='trousers_kpt12', + id=179, + color=colors['trousers'], + type='', + swap='trousers_kpt6'), + 180: + dict( + name='trousers_kpt13', + id=180, + color=colors['trousers'], + type='', + swap='trousers_kpt5'), + 181: + dict( + name='trousers_kpt14', + id=181, + color=colors['trousers'], + type='', + swap='trousers_kpt4'), + # skirt + 182: + dict( + name='skirt_kpt1', + id=182, + color=colors['skirt'], + type='', + swap='skirt_kpt3'), + 183: + dict( + name='skirt_kpt2', id=183, color=colors['skirt'], type='', + swap=''), + 184: + dict( + name='skirt_kpt3', + id=184, + color=colors['skirt'], + type='', + swap='skirt_kpt1'), + 185: + dict( + name='skirt_kpt4', + id=185, + color=colors['skirt'], + type='', + swap='skirt_kpt8'), + 186: + dict( + name='skirt_kpt5', + id=186, + color=colors['skirt'], + type='', + swap='skirt_kpt7'), + 187: + dict( + name='skirt_kpt6', id=187, color=colors['skirt'], type='', + swap=''), + 188: + dict( + name='skirt_kpt7', + id=188, + color=colors['skirt'], + type='', + swap='skirt_kpt5'), + 189: + dict( + name='skirt_kpt8', + id=189, + color=colors['skirt'], + type='', + swap='skirt_kpt4'), + # short_sleeved_dress + 190: + dict(name='ssd_kpt1', id=190, color=colors['ssd'], type='', swap=''), + 191: + dict( + name='ssd_kpt2', + id=191, + color=colors['ssd'], + type='', + swap='ssd_kpt6'), + 192: + dict( + name='ssd_kpt3', + id=192, + color=colors['ssd'], + type='', + swap='ssd_kpt5'), + 193: + dict(name='ssd_kpt4', id=193, color=colors['ssd'], type='', swap=''), + 194: + dict( + name='ssd_kpt5', + id=194, + color=colors['ssd'], + type='', + swap='ssd_kpt3'), + 195: + dict( + name='ssd_kpt6', + id=195, + color=colors['ssd'], + type='', + swap='ssd_kpt2'), + 196: + dict( + name='ssd_kpt7', + id=196, + color=colors['ssd'], + type='', + swap='ssd_kpt29'), + 197: + dict( + name='ssd_kpt8', + id=197, + color=colors['ssd'], + type='', + swap='ssd_kpt28'), + 198: + dict( + name='ssd_kpt9', + id=198, + color=colors['ssd'], + type='', + swap='ssd_kpt27'), + 199: + dict( + name='ssd_kpt10', + id=199, + color=colors['ssd'], + type='', + swap='ssd_kpt26'), + 200: + dict( + name='ssd_kpt11', + id=200, + color=colors['ssd'], + type='', + swap='ssd_kpt25'), + 201: + dict( + name='ssd_kpt12', + id=201, + color=colors['ssd'], + type='', + swap='ssd_kpt24'), + 202: + dict( + name='ssd_kpt13', + id=202, + color=colors['ssd'], + type='', + swap='ssd_kpt23'), + 203: + dict( + name='ssd_kpt14', + id=203, + color=colors['ssd'], + type='', + swap='ssd_kpt22'), + 204: + dict( + name='ssd_kpt15', + id=204, + color=colors['ssd'], + type='', + swap='ssd_kpt21'), + 205: + dict( + name='ssd_kpt16', + id=205, + color=colors['ssd'], + type='', + swap='ssd_kpt20'), + 206: + dict( + name='ssd_kpt17', + id=206, + color=colors['ssd'], + type='', + swap='ssd_kpt19'), + 207: + dict(name='ssd_kpt18', id=207, color=colors['ssd'], type='', swap=''), + 208: + dict( + name='ssd_kpt19', + id=208, + color=colors['ssd'], + type='', + swap='ssd_kpt17'), + 209: + dict( + name='ssd_kpt20', + id=209, + color=colors['ssd'], + type='', + swap='ssd_kpt16'), + 210: + dict( + name='ssd_kpt21', + id=210, + color=colors['ssd'], + type='', + swap='ssd_kpt15'), + 211: + dict( + name='ssd_kpt22', + id=211, + color=colors['ssd'], + type='', + swap='ssd_kpt14'), + 212: + dict( + name='ssd_kpt23', + id=212, + color=colors['ssd'], + type='', + swap='ssd_kpt13'), + 213: + dict( + name='ssd_kpt24', + id=213, + color=colors['ssd'], + type='', + swap='ssd_kpt12'), + 214: + dict( + name='ssd_kpt25', + id=214, + color=colors['ssd'], + type='', + swap='ssd_kpt11'), + 215: + dict( + name='ssd_kpt26', + id=215, + color=colors['ssd'], + type='', + swap='ssd_kpt10'), + 216: + dict( + name='ssd_kpt27', + id=216, + color=colors['ssd'], + type='', + swap='ssd_kpt9'), + 217: + dict( + name='ssd_kpt28', + id=217, + color=colors['ssd'], + type='', + swap='ssd_kpt8'), + 218: + dict( + name='ssd_kpt29', + id=218, + color=colors['ssd'], + type='', + swap='ssd_kpt7'), + # long_sleeved_dress + 219: + dict(name='lsd_kpt1', id=219, color=colors['lsd'], type='', swap=''), + 220: + dict( + name='lsd_kpt2', + id=220, + color=colors['lsd'], + type='', + swap='lsd_kpt6'), + 221: + dict( + name='lsd_kpt3', + id=221, + color=colors['lsd'], + type='', + swap='lsd_kpt5'), + 222: + dict(name='lsd_kpt4', id=222, color=colors['lsd'], type='', swap=''), + 223: + dict( + name='lsd_kpt5', + id=223, + color=colors['lsd'], + type='', + swap='lsd_kpt3'), + 224: + dict( + name='lsd_kpt6', + id=224, + color=colors['lsd'], + type='', + swap='lsd_kpt2'), + 225: + dict( + name='lsd_kpt7', + id=225, + color=colors['lsd'], + type='', + swap='lsd_kpt37'), + 226: + dict( + name='lsd_kpt8', + id=226, + color=colors['lsd'], + type='', + swap='lsd_kpt36'), + 227: + dict( + name='lsd_kpt9', + id=227, + color=colors['lsd'], + type='', + swap='lsd_kpt35'), + 228: + dict( + name='lsd_kpt10', + id=228, + color=colors['lsd'], + type='', + swap='lsd_kpt34'), + 229: + dict( + name='lsd_kpt11', + id=229, + color=colors['lsd'], + type='', + swap='lsd_kpt33'), + 230: + dict( + name='lsd_kpt12', + id=230, + color=colors['lsd'], + type='', + swap='lsd_kpt32'), + 231: + dict( + name='lsd_kpt13', + id=231, + color=colors['lsd'], + type='', + swap='lsd_kpt31'), + 232: + dict( + name='lsd_kpt14', + id=232, + color=colors['lsd'], + type='', + swap='lsd_kpt30'), + 233: + dict( + name='lsd_kpt15', + id=233, + color=colors['lsd'], + type='', + swap='lsd_kpt29'), + 234: + dict( + name='lsd_kpt16', + id=234, + color=colors['lsd'], + type='', + swap='lsd_kpt28'), + 235: + dict( + name='lsd_kpt17', + id=235, + color=colors['lsd'], + type='', + swap='lsd_kpt27'), + 236: + dict( + name='lsd_kpt18', + id=236, + color=colors['lsd'], + type='', + swap='lsd_kpt26'), + 237: + dict( + name='lsd_kpt19', + id=237, + color=colors['lsd'], + type='', + swap='lsd_kpt25'), + 238: + dict( + name='lsd_kpt20', + id=238, + color=colors['lsd'], + type='', + swap='lsd_kpt24'), + 239: + dict( + name='lsd_kpt21', + id=239, + color=colors['lsd'], + type='', + swap='lsd_kpt23'), + 240: + dict(name='lsd_kpt22', id=240, color=colors['lsd'], type='', swap=''), + 241: + dict( + name='lsd_kpt23', + id=241, + color=colors['lsd'], + type='', + swap='lsd_kpt21'), + 242: + dict( + name='lsd_kpt24', + id=242, + color=colors['lsd'], + type='', + swap='lsd_kpt20'), + 243: + dict( + name='lsd_kpt25', + id=243, + color=colors['lsd'], + type='', + swap='lsd_kpt19'), + 244: + dict( + name='lsd_kpt26', + id=244, + color=colors['lsd'], + type='', + swap='lsd_kpt18'), + 245: + dict( + name='lsd_kpt27', + id=245, + color=colors['lsd'], + type='', + swap='lsd_kpt17'), + 246: + dict( + name='lsd_kpt28', + id=246, + color=colors['lsd'], + type='', + swap='lsd_kpt16'), + 247: + dict( + name='lsd_kpt29', + id=247, + color=colors['lsd'], + type='', + swap='lsd_kpt15'), + 248: + dict( + name='lsd_kpt30', + id=248, + color=colors['lsd'], + type='', + swap='lsd_kpt14'), + 249: + dict( + name='lsd_kpt31', + id=249, + color=colors['lsd'], + type='', + swap='lsd_kpt13'), + 250: + dict( + name='lsd_kpt32', + id=250, + color=colors['lsd'], + type='', + swap='lsd_kpt12'), + 251: + dict( + name='lsd_kpt33', + id=251, + color=colors['lsd'], + type='', + swap='lsd_kpt11'), + 252: + dict( + name='lsd_kpt34', + id=252, + color=colors['lsd'], + type='', + swap='lsd_kpt10'), + 253: + dict( + name='lsd_kpt35', + id=253, + color=colors['lsd'], + type='', + swap='lsd_kpt9'), + 254: + dict( + name='lsd_kpt36', + id=254, + color=colors['lsd'], + type='', + swap='lsd_kpt8'), + 255: + dict( + name='lsd_kpt37', + id=255, + color=colors['lsd'], + type='', + swap='lsd_kpt7'), + # vest_dress + 256: + dict(name='vd_kpt1', id=256, color=colors['vd'], type='', swap=''), + 257: + dict( + name='vd_kpt2', + id=257, + color=colors['vd'], + type='', + swap='vd_kpt6'), + 258: + dict( + name='vd_kpt3', + id=258, + color=colors['vd'], + type='', + swap='vd_kpt5'), + 259: + dict(name='vd_kpt4', id=259, color=colors['vd'], type='', swap=''), + 260: + dict( + name='vd_kpt5', + id=260, + color=colors['vd'], + type='', + swap='vd_kpt3'), + 261: + dict( + name='vd_kpt6', + id=261, + color=colors['vd'], + type='', + swap='vd_kpt2'), + 262: + dict( + name='vd_kpt7', + id=262, + color=colors['vd'], + type='', + swap='vd_kpt19'), + 263: + dict( + name='vd_kpt8', + id=263, + color=colors['vd'], + type='', + swap='vd_kpt18'), + 264: + dict( + name='vd_kpt9', + id=264, + color=colors['vd'], + type='', + swap='vd_kpt17'), + 265: + dict( + name='vd_kpt10', + id=265, + color=colors['vd'], + type='', + swap='vd_kpt16'), + 266: + dict( + name='vd_kpt11', + id=266, + color=colors['vd'], + type='', + swap='vd_kpt15'), + 267: + dict( + name='vd_kpt12', + id=267, + color=colors['vd'], + type='', + swap='vd_kpt14'), + 268: + dict(name='vd_kpt13', id=268, color=colors['vd'], type='', swap=''), + 269: + dict( + name='vd_kpt14', + id=269, + color=colors['vd'], + type='', + swap='vd_kpt12'), + 270: + dict( + name='vd_kpt15', + id=270, + color=colors['vd'], + type='', + swap='vd_kpt11'), + 271: + dict( + name='vd_kpt16', + id=271, + color=colors['vd'], + type='', + swap='vd_kpt10'), + 272: + dict( + name='vd_kpt17', + id=272, + color=colors['vd'], + type='', + swap='vd_kpt9'), + 273: + dict( + name='vd_kpt18', + id=273, + color=colors['vd'], + type='', + swap='vd_kpt8'), + 274: + dict( + name='vd_kpt19', + id=274, + color=colors['vd'], + type='', + swap='vd_kpt7'), + # sling_dress + 275: + dict(name='sd_kpt1', id=275, color=colors['sd'], type='', swap=''), + 276: + dict( + name='sd_kpt2', + id=276, + color=colors['sd'], + type='', + swap='sd_kpt6'), + 277: + dict( + name='sd_kpt3', + id=277, + color=colors['sd'], + type='', + swap='sd_kpt5'), + 278: + dict(name='sd_kpt4', id=278, color=colors['sd'], type='', swap=''), + 279: + dict( + name='sd_kpt5', + id=279, + color=colors['sd'], + type='', + swap='sd_kpt3'), + 280: + dict( + name='sd_kpt6', + id=280, + color=colors['sd'], + type='', + swap='sd_kpt2'), + 281: + dict( + name='sd_kpt7', + id=281, + color=colors['sd'], + type='', + swap='sd_kpt19'), + 282: + dict( + name='sd_kpt8', + id=282, + color=colors['sd'], + type='', + swap='sd_kpt18'), + 283: + dict( + name='sd_kpt9', + id=283, + color=colors['sd'], + type='', + swap='sd_kpt17'), + 284: + dict( + name='sd_kpt10', + id=284, + color=colors['sd'], + type='', + swap='sd_kpt16'), + 285: + dict( + name='sd_kpt11', + id=285, + color=colors['sd'], + type='', + swap='sd_kpt15'), + 286: + dict( + name='sd_kpt12', + id=286, + color=colors['sd'], + type='', + swap='sd_kpt14'), + 287: + dict(name='sd_kpt13', id=287, color=colors['sd'], type='', swap=''), + 288: + dict( + name='sd_kpt14', + id=288, + color=colors['sd'], + type='', + swap='sd_kpt12'), + 289: + dict( + name='sd_kpt15', + id=289, + color=colors['sd'], + type='', + swap='sd_kpt11'), + 290: + dict( + name='sd_kpt16', + id=290, + color=colors['sd'], + type='', + swap='sd_kpt10'), + 291: + dict( + name='sd_kpt17', + id=291, + color=colors['sd'], + type='', + swap='sd_kpt9'), + 292: + dict( + name='sd_kpt18', + id=292, + color=colors['sd'], + type='', + swap='sd_kpt8'), + 293: + dict( + name='sd_kpt19', + id=293, + color=colors['sd'], + type='', + swap='sd_kpt7'), + }, + skeleton_info={ + # short_sleeved_shirt + 0: + dict(link=('sss_kpt1', 'sss_kpt2'), id=0, color=[255, 128, 0]), + 1: + dict(link=('sss_kpt2', 'sss_kpt7'), id=1, color=[255, 128, 0]), + 2: + dict(link=('sss_kpt7', 'sss_kpt8'), id=2, color=[255, 128, 0]), + 3: + dict(link=('sss_kpt8', 'sss_kpt9'), id=3, color=[255, 128, 0]), + 4: + dict(link=('sss_kpt9', 'sss_kpt10'), id=4, color=[255, 128, 0]), + 5: + dict(link=('sss_kpt10', 'sss_kpt11'), id=5, color=[255, 128, 0]), + 6: + dict(link=('sss_kpt11', 'sss_kpt12'), id=6, color=[255, 128, 0]), + 7: + dict(link=('sss_kpt12', 'sss_kpt13'), id=7, color=[255, 128, 0]), + 8: + dict(link=('sss_kpt13', 'sss_kpt14'), id=8, color=[255, 128, 0]), + 9: + dict(link=('sss_kpt14', 'sss_kpt15'), id=9, color=[255, 128, 0]), + 10: + dict(link=('sss_kpt15', 'sss_kpt16'), id=10, color=[255, 128, 0]), + 11: + dict(link=('sss_kpt16', 'sss_kpt17'), id=11, color=[255, 128, 0]), + 12: + dict(link=('sss_kpt17', 'sss_kpt18'), id=12, color=[255, 128, 0]), + 13: + dict(link=('sss_kpt18', 'sss_kpt19'), id=13, color=[255, 128, 0]), + 14: + dict(link=('sss_kpt19', 'sss_kpt20'), id=14, color=[255, 128, 0]), + 15: + dict(link=('sss_kpt20', 'sss_kpt21'), id=15, color=[255, 128, 0]), + 16: + dict(link=('sss_kpt21', 'sss_kpt22'), id=16, color=[255, 128, 0]), + 17: + dict(link=('sss_kpt22', 'sss_kpt23'), id=17, color=[255, 128, 0]), + 18: + dict(link=('sss_kpt23', 'sss_kpt24'), id=18, color=[255, 128, 0]), + 19: + dict(link=('sss_kpt24', 'sss_kpt25'), id=19, color=[255, 128, 0]), + 20: + dict(link=('sss_kpt25', 'sss_kpt6'), id=20, color=[255, 128, 0]), + 21: + dict(link=('sss_kpt6', 'sss_kpt1'), id=21, color=[255, 128, 0]), + 22: + dict(link=('sss_kpt2', 'sss_kpt3'), id=22, color=[255, 128, 0]), + 23: + dict(link=('sss_kpt3', 'sss_kpt4'), id=23, color=[255, 128, 0]), + 24: + dict(link=('sss_kpt4', 'sss_kpt5'), id=24, color=[255, 128, 0]), + 25: + dict(link=('sss_kpt5', 'sss_kpt6'), id=25, color=[255, 128, 0]), + # long_sleeve_shirt + 26: + dict(link=('lss_kpt1', 'lss_kpt2'), id=26, color=[255, 0, 128]), + 27: + dict(link=('lss_kpt2', 'lss_kpt7'), id=27, color=[255, 0, 128]), + 28: + dict(link=('lss_kpt7', 'lss_kpt8'), id=28, color=[255, 0, 128]), + 29: + dict(link=('lss_kpt8', 'lss_kpt9'), id=29, color=[255, 0, 128]), + 30: + dict(link=('lss_kpt9', 'lss_kpt10'), id=30, color=[255, 0, 128]), + 31: + dict(link=('lss_kpt10', 'lss_kpt11'), id=31, color=[255, 0, 128]), + 32: + dict(link=('lss_kpt11', 'lss_kpt12'), id=32, color=[255, 0, 128]), + 33: + dict(link=('lss_kpt12', 'lss_kpt13'), id=33, color=[255, 0, 128]), + 34: + dict(link=('lss_kpt13', 'lss_kpt14'), id=34, color=[255, 0, 128]), + 35: + dict(link=('lss_kpt14', 'lss_kpt15'), id=35, color=[255, 0, 128]), + 36: + dict(link=('lss_kpt15', 'lss_kpt16'), id=36, color=[255, 0, 128]), + 37: + dict(link=('lss_kpt16', 'lss_kpt17'), id=37, color=[255, 0, 128]), + 38: + dict(link=('lss_kpt17', 'lss_kpt18'), id=38, color=[255, 0, 128]), + 39: + dict(link=('lss_kpt18', 'lss_kpt19'), id=39, color=[255, 0, 128]), + 40: + dict(link=('lss_kpt19', 'lss_kpt20'), id=40, color=[255, 0, 128]), + 41: + dict(link=('lss_kpt20', 'lss_kpt21'), id=41, color=[255, 0, 128]), + 42: + dict(link=('lss_kpt21', 'lss_kpt22'), id=42, color=[255, 0, 128]), + 43: + dict(link=('lss_kpt22', 'lss_kpt23'), id=43, color=[255, 0, 128]), + 44: + dict(link=('lss_kpt23', 'lss_kpt24'), id=44, color=[255, 0, 128]), + 45: + dict(link=('lss_kpt24', 'lss_kpt25'), id=45, color=[255, 0, 128]), + 46: + dict(link=('lss_kpt25', 'lss_kpt26'), id=46, color=[255, 0, 128]), + 47: + dict(link=('lss_kpt26', 'lss_kpt27'), id=47, color=[255, 0, 128]), + 48: + dict(link=('lss_kpt27', 'lss_kpt28'), id=48, color=[255, 0, 128]), + 49: + dict(link=('lss_kpt28', 'lss_kpt29'), id=49, color=[255, 0, 128]), + 50: + dict(link=('lss_kpt29', 'lss_kpt30'), id=50, color=[255, 0, 128]), + 51: + dict(link=('lss_kpt30', 'lss_kpt31'), id=51, color=[255, 0, 128]), + 52: + dict(link=('lss_kpt31', 'lss_kpt32'), id=52, color=[255, 0, 128]), + 53: + dict(link=('lss_kpt32', 'lss_kpt33'), id=53, color=[255, 0, 128]), + 54: + dict(link=('lss_kpt33', 'lss_kpt6'), id=54, color=[255, 0, 128]), + 55: + dict(link=('lss_kpt6', 'lss_kpt5'), id=55, color=[255, 0, 128]), + 56: + dict(link=('lss_kpt5', 'lss_kpt4'), id=56, color=[255, 0, 128]), + 57: + dict(link=('lss_kpt4', 'lss_kpt3'), id=57, color=[255, 0, 128]), + 58: + dict(link=('lss_kpt3', 'lss_kpt2'), id=58, color=[255, 0, 128]), + 59: + dict(link=('lss_kpt6', 'lss_kpt1'), id=59, color=[255, 0, 128]), + # short_sleeved_outwear + 60: + dict(link=('sso_kpt1', 'sso_kpt4'), id=60, color=[128, 0, 255]), + 61: + dict(link=('sso_kpt4', 'sso_kpt7'), id=61, color=[128, 0, 255]), + 62: + dict(link=('sso_kpt7', 'sso_kpt8'), id=62, color=[128, 0, 255]), + 63: + dict(link=('sso_kpt8', 'sso_kpt9'), id=63, color=[128, 0, 255]), + 64: + dict(link=('sso_kpt9', 'sso_kpt10'), id=64, color=[128, 0, 255]), + 65: + dict(link=('sso_kpt10', 'sso_kpt11'), id=65, color=[128, 0, 255]), + 66: + dict(link=('sso_kpt11', 'sso_kpt12'), id=66, color=[128, 0, 255]), + 67: + dict(link=('sso_kpt12', 'sso_kpt13'), id=67, color=[128, 0, 255]), + 68: + dict(link=('sso_kpt13', 'sso_kpt14'), id=68, color=[128, 0, 255]), + 69: + dict(link=('sso_kpt14', 'sso_kpt15'), id=69, color=[128, 0, 255]), + 70: + dict(link=('sso_kpt15', 'sso_kpt16'), id=70, color=[128, 0, 255]), + 71: + dict(link=('sso_kpt16', 'sso_kpt31'), id=71, color=[128, 0, 255]), + 72: + dict(link=('sso_kpt31', 'sso_kpt30'), id=72, color=[128, 0, 255]), + 73: + dict(link=('sso_kpt30', 'sso_kpt2'), id=73, color=[128, 0, 255]), + 74: + dict(link=('sso_kpt2', 'sso_kpt3'), id=74, color=[128, 0, 255]), + 75: + dict(link=('sso_kpt3', 'sso_kpt4'), id=75, color=[128, 0, 255]), + 76: + dict(link=('sso_kpt1', 'sso_kpt6'), id=76, color=[128, 0, 255]), + 77: + dict(link=('sso_kpt6', 'sso_kpt25'), id=77, color=[128, 0, 255]), + 78: + dict(link=('sso_kpt25', 'sso_kpt24'), id=78, color=[128, 0, 255]), + 79: + dict(link=('sso_kpt24', 'sso_kpt23'), id=79, color=[128, 0, 255]), + 80: + dict(link=('sso_kpt23', 'sso_kpt22'), id=80, color=[128, 0, 255]), + 81: + dict(link=('sso_kpt22', 'sso_kpt21'), id=81, color=[128, 0, 255]), + 82: + dict(link=('sso_kpt21', 'sso_kpt20'), id=82, color=[128, 0, 255]), + 83: + dict(link=('sso_kpt20', 'sso_kpt19'), id=83, color=[128, 0, 255]), + 84: + dict(link=('sso_kpt19', 'sso_kpt18'), id=84, color=[128, 0, 255]), + 85: + dict(link=('sso_kpt18', 'sso_kpt17'), id=85, color=[128, 0, 255]), + 86: + dict(link=('sso_kpt17', 'sso_kpt29'), id=86, color=[128, 0, 255]), + 87: + dict(link=('sso_kpt29', 'sso_kpt28'), id=87, color=[128, 0, 255]), + 88: + dict(link=('sso_kpt28', 'sso_kpt27'), id=88, color=[128, 0, 255]), + 89: + dict(link=('sso_kpt27', 'sso_kpt26'), id=89, color=[128, 0, 255]), + 90: + dict(link=('sso_kpt26', 'sso_kpt5'), id=90, color=[128, 0, 255]), + 91: + dict(link=('sso_kpt5', 'sso_kpt6'), id=91, color=[128, 0, 255]), + # long_sleeved_outwear + 92: + dict(link=('lso_kpt1', 'lso_kpt2'), id=92, color=[0, 128, 255]), + 93: + dict(link=('lso_kpt2', 'lso_kpt7'), id=93, color=[0, 128, 255]), + 94: + dict(link=('lso_kpt7', 'lso_kpt8'), id=94, color=[0, 128, 255]), + 95: + dict(link=('lso_kpt8', 'lso_kpt9'), id=95, color=[0, 128, 255]), + 96: + dict(link=('lso_kpt9', 'lso_kpt10'), id=96, color=[0, 128, 255]), + 97: + dict(link=('lso_kpt10', 'lso_kpt11'), id=97, color=[0, 128, 255]), + 98: + dict(link=('lso_kpt11', 'lso_kpt12'), id=98, color=[0, 128, 255]), + 99: + dict(link=('lso_kpt12', 'lso_kpt13'), id=99, color=[0, 128, 255]), + 100: + dict(link=('lso_kpt13', 'lso_kpt14'), id=100, color=[0, 128, 255]), + 101: + dict(link=('lso_kpt14', 'lso_kpt15'), id=101, color=[0, 128, 255]), + 102: + dict(link=('lso_kpt15', 'lso_kpt16'), id=102, color=[0, 128, 255]), + 103: + dict(link=('lso_kpt16', 'lso_kpt17'), id=103, color=[0, 128, 255]), + 104: + dict(link=('lso_kpt17', 'lso_kpt18'), id=104, color=[0, 128, 255]), + 105: + dict(link=('lso_kpt18', 'lso_kpt19'), id=105, color=[0, 128, 255]), + 106: + dict(link=('lso_kpt19', 'lso_kpt20'), id=106, color=[0, 128, 255]), + 107: + dict(link=('lso_kpt20', 'lso_kpt39'), id=107, color=[0, 128, 255]), + 108: + dict(link=('lso_kpt39', 'lso_kpt38'), id=108, color=[0, 128, 255]), + 109: + dict(link=('lso_kpt38', 'lso_kpt4'), id=109, color=[0, 128, 255]), + 110: + dict(link=('lso_kpt4', 'lso_kpt3'), id=110, color=[0, 128, 255]), + 111: + dict(link=('lso_kpt3', 'lso_kpt2'), id=111, color=[0, 128, 255]), + 112: + dict(link=('lso_kpt1', 'lso_kpt6'), id=112, color=[0, 128, 255]), + 113: + dict(link=('lso_kpt6', 'lso_kpt33'), id=113, color=[0, 128, 255]), + 114: + dict(link=('lso_kpt33', 'lso_kpt32'), id=114, color=[0, 128, 255]), + 115: + dict(link=('lso_kpt32', 'lso_kpt31'), id=115, color=[0, 128, 255]), + 116: + dict(link=('lso_kpt31', 'lso_kpt30'), id=116, color=[0, 128, 255]), + 117: + dict(link=('lso_kpt30', 'lso_kpt29'), id=117, color=[0, 128, 255]), + 118: + dict(link=('lso_kpt29', 'lso_kpt28'), id=118, color=[0, 128, 255]), + 119: + dict(link=('lso_kpt28', 'lso_kpt27'), id=119, color=[0, 128, 255]), + 120: + dict(link=('lso_kpt27', 'lso_kpt26'), id=120, color=[0, 128, 255]), + 121: + dict(link=('lso_kpt26', 'lso_kpt25'), id=121, color=[0, 128, 255]), + 122: + dict(link=('lso_kpt25', 'lso_kpt24'), id=122, color=[0, 128, 255]), + 123: + dict(link=('lso_kpt24', 'lso_kpt23'), id=123, color=[0, 128, 255]), + 124: + dict(link=('lso_kpt23', 'lso_kpt22'), id=124, color=[0, 128, 255]), + 125: + dict(link=('lso_kpt22', 'lso_kpt21'), id=125, color=[0, 128, 255]), + 126: + dict(link=('lso_kpt21', 'lso_kpt37'), id=126, color=[0, 128, 255]), + 127: + dict(link=('lso_kpt37', 'lso_kpt36'), id=127, color=[0, 128, 255]), + 128: + dict(link=('lso_kpt36', 'lso_kpt35'), id=128, color=[0, 128, 255]), + 129: + dict(link=('lso_kpt35', 'lso_kpt34'), id=129, color=[0, 128, 255]), + 130: + dict(link=('lso_kpt34', 'lso_kpt5'), id=130, color=[0, 128, 255]), + 131: + dict(link=('lso_kpt5', 'lso_kpt6'), id=131, color=[0, 128, 255]), + # vest + 132: + dict(link=('vest_kpt1', 'vest_kpt2'), id=132, color=[0, 128, 128]), + 133: + dict(link=('vest_kpt2', 'vest_kpt7'), id=133, color=[0, 128, 128]), + 134: + dict(link=('vest_kpt7', 'vest_kpt8'), id=134, color=[0, 128, 128]), + 135: + dict(link=('vest_kpt8', 'vest_kpt9'), id=135, color=[0, 128, 128]), + 136: + dict(link=('vest_kpt9', 'vest_kpt10'), id=136, color=[0, 128, 128]), + 137: + dict(link=('vest_kpt10', 'vest_kpt11'), id=137, color=[0, 128, 128]), + 138: + dict(link=('vest_kpt11', 'vest_kpt12'), id=138, color=[0, 128, 128]), + 139: + dict(link=('vest_kpt12', 'vest_kpt13'), id=139, color=[0, 128, 128]), + 140: + dict(link=('vest_kpt13', 'vest_kpt14'), id=140, color=[0, 128, 128]), + 141: + dict(link=('vest_kpt14', 'vest_kpt15'), id=141, color=[0, 128, 128]), + 142: + dict(link=('vest_kpt15', 'vest_kpt6'), id=142, color=[0, 128, 128]), + 143: + dict(link=('vest_kpt6', 'vest_kpt1'), id=143, color=[0, 128, 128]), + 144: + dict(link=('vest_kpt2', 'vest_kpt3'), id=144, color=[0, 128, 128]), + 145: + dict(link=('vest_kpt3', 'vest_kpt4'), id=145, color=[0, 128, 128]), + 146: + dict(link=('vest_kpt4', 'vest_kpt5'), id=146, color=[0, 128, 128]), + 147: + dict(link=('vest_kpt5', 'vest_kpt6'), id=147, color=[0, 128, 128]), + # sling + 148: + dict(link=('sling_kpt1', 'sling_kpt2'), id=148, color=[0, 0, 128]), + 149: + dict(link=('sling_kpt2', 'sling_kpt8'), id=149, color=[0, 0, 128]), + 150: + dict(link=('sling_kpt8', 'sling_kpt9'), id=150, color=[0, 0, 128]), + 151: + dict(link=('sling_kpt9', 'sling_kpt10'), id=151, color=[0, 0, 128]), + 152: + dict(link=('sling_kpt10', 'sling_kpt11'), id=152, color=[0, 0, 128]), + 153: + dict(link=('sling_kpt11', 'sling_kpt12'), id=153, color=[0, 0, 128]), + 154: + dict(link=('sling_kpt12', 'sling_kpt13'), id=154, color=[0, 0, 128]), + 155: + dict(link=('sling_kpt13', 'sling_kpt14'), id=155, color=[0, 0, 128]), + 156: + dict(link=('sling_kpt14', 'sling_kpt6'), id=156, color=[0, 0, 128]), + 157: + dict(link=('sling_kpt2', 'sling_kpt7'), id=157, color=[0, 0, 128]), + 158: + dict(link=('sling_kpt6', 'sling_kpt15'), id=158, color=[0, 0, 128]), + 159: + dict(link=('sling_kpt2', 'sling_kpt3'), id=159, color=[0, 0, 128]), + 160: + dict(link=('sling_kpt3', 'sling_kpt4'), id=160, color=[0, 0, 128]), + 161: + dict(link=('sling_kpt4', 'sling_kpt5'), id=161, color=[0, 0, 128]), + 162: + dict(link=('sling_kpt5', 'sling_kpt6'), id=162, color=[0, 0, 128]), + 163: + dict(link=('sling_kpt1', 'sling_kpt6'), id=163, color=[0, 0, 128]), + # shorts + 164: + dict( + link=('shorts_kpt1', 'shorts_kpt4'), id=164, color=[128, 128, + 128]), + 165: + dict( + link=('shorts_kpt4', 'shorts_kpt5'), id=165, color=[128, 128, + 128]), + 166: + dict( + link=('shorts_kpt5', 'shorts_kpt6'), id=166, color=[128, 128, + 128]), + 167: + dict( + link=('shorts_kpt6', 'shorts_kpt7'), id=167, color=[128, 128, + 128]), + 168: + dict( + link=('shorts_kpt7', 'shorts_kpt8'), id=168, color=[128, 128, + 128]), + 169: + dict( + link=('shorts_kpt8', 'shorts_kpt9'), id=169, color=[128, 128, + 128]), + 170: + dict( + link=('shorts_kpt9', 'shorts_kpt10'), + id=170, + color=[128, 128, 128]), + 171: + dict( + link=('shorts_kpt10', 'shorts_kpt3'), + id=171, + color=[128, 128, 128]), + 172: + dict( + link=('shorts_kpt3', 'shorts_kpt2'), id=172, color=[128, 128, + 128]), + 173: + dict( + link=('shorts_kpt2', 'shorts_kpt1'), id=173, color=[128, 128, + 128]), + # trousers + 174: + dict( + link=('trousers_kpt1', 'trousers_kpt4'), + id=174, + color=[128, 0, 128]), + 175: + dict( + link=('trousers_kpt4', 'trousers_kpt5'), + id=175, + color=[128, 0, 128]), + 176: + dict( + link=('trousers_kpt5', 'trousers_kpt6'), + id=176, + color=[128, 0, 128]), + 177: + dict( + link=('trousers_kpt6', 'trousers_kpt7'), + id=177, + color=[128, 0, 128]), + 178: + dict( + link=('trousers_kpt7', 'trousers_kpt8'), + id=178, + color=[128, 0, 128]), + 179: + dict( + link=('trousers_kpt8', 'trousers_kpt9'), + id=179, + color=[128, 0, 128]), + 180: + dict( + link=('trousers_kpt9', 'trousers_kpt10'), + id=180, + color=[128, 0, 128]), + 181: + dict( + link=('trousers_kpt10', 'trousers_kpt11'), + id=181, + color=[128, 0, 128]), + 182: + dict( + link=('trousers_kpt11', 'trousers_kpt12'), + id=182, + color=[128, 0, 128]), + 183: + dict( + link=('trousers_kpt12', 'trousers_kpt13'), + id=183, + color=[128, 0, 128]), + 184: + dict( + link=('trousers_kpt13', 'trousers_kpt14'), + id=184, + color=[128, 0, 128]), + 185: + dict( + link=('trousers_kpt14', 'trousers_kpt3'), + id=185, + color=[128, 0, 128]), + 186: + dict( + link=('trousers_kpt3', 'trousers_kpt2'), + id=186, + color=[128, 0, 128]), + 187: + dict( + link=('trousers_kpt2', 'trousers_kpt1'), + id=187, + color=[128, 0, 128]), + # skirt + 188: + dict(link=('skirt_kpt1', 'skirt_kpt4'), id=188, color=[64, 128, 128]), + 189: + dict(link=('skirt_kpt4', 'skirt_kpt5'), id=189, color=[64, 128, 128]), + 190: + dict(link=('skirt_kpt5', 'skirt_kpt6'), id=190, color=[64, 128, 128]), + 191: + dict(link=('skirt_kpt6', 'skirt_kpt7'), id=191, color=[64, 128, 128]), + 192: + dict(link=('skirt_kpt7', 'skirt_kpt8'), id=192, color=[64, 128, 128]), + 193: + dict(link=('skirt_kpt8', 'skirt_kpt3'), id=193, color=[64, 128, 128]), + 194: + dict(link=('skirt_kpt3', 'skirt_kpt2'), id=194, color=[64, 128, 128]), + 195: + dict(link=('skirt_kpt2', 'skirt_kpt1'), id=195, color=[64, 128, 128]), + # short_sleeved_dress + 196: + dict(link=('ssd_kpt1', 'ssd_kpt2'), id=196, color=[64, 64, 128]), + 197: + dict(link=('ssd_kpt2', 'ssd_kpt7'), id=197, color=[64, 64, 128]), + 198: + dict(link=('ssd_kpt7', 'ssd_kpt8'), id=198, color=[64, 64, 128]), + 199: + dict(link=('ssd_kpt8', 'ssd_kpt9'), id=199, color=[64, 64, 128]), + 200: + dict(link=('ssd_kpt9', 'ssd_kpt10'), id=200, color=[64, 64, 128]), + 201: + dict(link=('ssd_kpt10', 'ssd_kpt11'), id=201, color=[64, 64, 128]), + 202: + dict(link=('ssd_kpt11', 'ssd_kpt12'), id=202, color=[64, 64, 128]), + 203: + dict(link=('ssd_kpt12', 'ssd_kpt13'), id=203, color=[64, 64, 128]), + 204: + dict(link=('ssd_kpt13', 'ssd_kpt14'), id=204, color=[64, 64, 128]), + 205: + dict(link=('ssd_kpt14', 'ssd_kpt15'), id=205, color=[64, 64, 128]), + 206: + dict(link=('ssd_kpt15', 'ssd_kpt16'), id=206, color=[64, 64, 128]), + 207: + dict(link=('ssd_kpt16', 'ssd_kpt17'), id=207, color=[64, 64, 128]), + 208: + dict(link=('ssd_kpt17', 'ssd_kpt18'), id=208, color=[64, 64, 128]), + 209: + dict(link=('ssd_kpt18', 'ssd_kpt19'), id=209, color=[64, 64, 128]), + 210: + dict(link=('ssd_kpt19', 'ssd_kpt20'), id=210, color=[64, 64, 128]), + 211: + dict(link=('ssd_kpt20', 'ssd_kpt21'), id=211, color=[64, 64, 128]), + 212: + dict(link=('ssd_kpt21', 'ssd_kpt22'), id=212, color=[64, 64, 128]), + 213: + dict(link=('ssd_kpt22', 'ssd_kpt23'), id=213, color=[64, 64, 128]), + 214: + dict(link=('ssd_kpt23', 'ssd_kpt24'), id=214, color=[64, 64, 128]), + 215: + dict(link=('ssd_kpt24', 'ssd_kpt25'), id=215, color=[64, 64, 128]), + 216: + dict(link=('ssd_kpt25', 'ssd_kpt26'), id=216, color=[64, 64, 128]), + 217: + dict(link=('ssd_kpt26', 'ssd_kpt27'), id=217, color=[64, 64, 128]), + 218: + dict(link=('ssd_kpt27', 'ssd_kpt28'), id=218, color=[64, 64, 128]), + 219: + dict(link=('ssd_kpt28', 'ssd_kpt29'), id=219, color=[64, 64, 128]), + 220: + dict(link=('ssd_kpt29', 'ssd_kpt6'), id=220, color=[64, 64, 128]), + 221: + dict(link=('ssd_kpt6', 'ssd_kpt5'), id=221, color=[64, 64, 128]), + 222: + dict(link=('ssd_kpt5', 'ssd_kpt4'), id=222, color=[64, 64, 128]), + 223: + dict(link=('ssd_kpt4', 'ssd_kpt3'), id=223, color=[64, 64, 128]), + 224: + dict(link=('ssd_kpt3', 'ssd_kpt2'), id=224, color=[64, 64, 128]), + 225: + dict(link=('ssd_kpt6', 'ssd_kpt1'), id=225, color=[64, 64, 128]), + # long_sleeved_dress + 226: + dict(link=('lsd_kpt1', 'lsd_kpt2'), id=226, color=[128, 64, 0]), + 227: + dict(link=('lsd_kpt2', 'lsd_kpt7'), id=228, color=[128, 64, 0]), + 228: + dict(link=('lsd_kpt7', 'lsd_kpt8'), id=228, color=[128, 64, 0]), + 229: + dict(link=('lsd_kpt8', 'lsd_kpt9'), id=229, color=[128, 64, 0]), + 230: + dict(link=('lsd_kpt9', 'lsd_kpt10'), id=230, color=[128, 64, 0]), + 231: + dict(link=('lsd_kpt10', 'lsd_kpt11'), id=231, color=[128, 64, 0]), + 232: + dict(link=('lsd_kpt11', 'lsd_kpt12'), id=232, color=[128, 64, 0]), + 233: + dict(link=('lsd_kpt12', 'lsd_kpt13'), id=233, color=[128, 64, 0]), + 234: + dict(link=('lsd_kpt13', 'lsd_kpt14'), id=234, color=[128, 64, 0]), + 235: + dict(link=('lsd_kpt14', 'lsd_kpt15'), id=235, color=[128, 64, 0]), + 236: + dict(link=('lsd_kpt15', 'lsd_kpt16'), id=236, color=[128, 64, 0]), + 237: + dict(link=('lsd_kpt16', 'lsd_kpt17'), id=237, color=[128, 64, 0]), + 238: + dict(link=('lsd_kpt17', 'lsd_kpt18'), id=238, color=[128, 64, 0]), + 239: + dict(link=('lsd_kpt18', 'lsd_kpt19'), id=239, color=[128, 64, 0]), + 240: + dict(link=('lsd_kpt19', 'lsd_kpt20'), id=240, color=[128, 64, 0]), + 241: + dict(link=('lsd_kpt20', 'lsd_kpt21'), id=241, color=[128, 64, 0]), + 242: + dict(link=('lsd_kpt21', 'lsd_kpt22'), id=242, color=[128, 64, 0]), + 243: + dict(link=('lsd_kpt22', 'lsd_kpt23'), id=243, color=[128, 64, 0]), + 244: + dict(link=('lsd_kpt23', 'lsd_kpt24'), id=244, color=[128, 64, 0]), + 245: + dict(link=('lsd_kpt24', 'lsd_kpt25'), id=245, color=[128, 64, 0]), + 246: + dict(link=('lsd_kpt25', 'lsd_kpt26'), id=246, color=[128, 64, 0]), + 247: + dict(link=('lsd_kpt26', 'lsd_kpt27'), id=247, color=[128, 64, 0]), + 248: + dict(link=('lsd_kpt27', 'lsd_kpt28'), id=248, color=[128, 64, 0]), + 249: + dict(link=('lsd_kpt28', 'lsd_kpt29'), id=249, color=[128, 64, 0]), + 250: + dict(link=('lsd_kpt29', 'lsd_kpt30'), id=250, color=[128, 64, 0]), + 251: + dict(link=('lsd_kpt30', 'lsd_kpt31'), id=251, color=[128, 64, 0]), + 252: + dict(link=('lsd_kpt31', 'lsd_kpt32'), id=252, color=[128, 64, 0]), + 253: + dict(link=('lsd_kpt32', 'lsd_kpt33'), id=253, color=[128, 64, 0]), + 254: + dict(link=('lsd_kpt33', 'lsd_kpt34'), id=254, color=[128, 64, 0]), + 255: + dict(link=('lsd_kpt34', 'lsd_kpt35'), id=255, color=[128, 64, 0]), + 256: + dict(link=('lsd_kpt35', 'lsd_kpt36'), id=256, color=[128, 64, 0]), + 257: + dict(link=('lsd_kpt36', 'lsd_kpt37'), id=257, color=[128, 64, 0]), + 258: + dict(link=('lsd_kpt37', 'lsd_kpt6'), id=258, color=[128, 64, 0]), + 259: + dict(link=('lsd_kpt6', 'lsd_kpt5'), id=259, color=[128, 64, 0]), + 260: + dict(link=('lsd_kpt5', 'lsd_kpt4'), id=260, color=[128, 64, 0]), + 261: + dict(link=('lsd_kpt4', 'lsd_kpt3'), id=261, color=[128, 64, 0]), + 262: + dict(link=('lsd_kpt3', 'lsd_kpt2'), id=262, color=[128, 64, 0]), + 263: + dict(link=('lsd_kpt6', 'lsd_kpt1'), id=263, color=[128, 64, 0]), + # vest_dress + 264: + dict(link=('vd_kpt1', 'vd_kpt2'), id=264, color=[128, 64, 255]), + 265: + dict(link=('vd_kpt2', 'vd_kpt7'), id=265, color=[128, 64, 255]), + 266: + dict(link=('vd_kpt7', 'vd_kpt8'), id=266, color=[128, 64, 255]), + 267: + dict(link=('vd_kpt8', 'vd_kpt9'), id=267, color=[128, 64, 255]), + 268: + dict(link=('vd_kpt9', 'vd_kpt10'), id=268, color=[128, 64, 255]), + 269: + dict(link=('vd_kpt10', 'vd_kpt11'), id=269, color=[128, 64, 255]), + 270: + dict(link=('vd_kpt11', 'vd_kpt12'), id=270, color=[128, 64, 255]), + 271: + dict(link=('vd_kpt12', 'vd_kpt13'), id=271, color=[128, 64, 255]), + 272: + dict(link=('vd_kpt13', 'vd_kpt14'), id=272, color=[128, 64, 255]), + 273: + dict(link=('vd_kpt14', 'vd_kpt15'), id=273, color=[128, 64, 255]), + 274: + dict(link=('vd_kpt15', 'vd_kpt16'), id=274, color=[128, 64, 255]), + 275: + dict(link=('vd_kpt16', 'vd_kpt17'), id=275, color=[128, 64, 255]), + 276: + dict(link=('vd_kpt17', 'vd_kpt18'), id=276, color=[128, 64, 255]), + 277: + dict(link=('vd_kpt18', 'vd_kpt19'), id=277, color=[128, 64, 255]), + 278: + dict(link=('vd_kpt19', 'vd_kpt6'), id=278, color=[128, 64, 255]), + 279: + dict(link=('vd_kpt6', 'vd_kpt5'), id=279, color=[128, 64, 255]), + 280: + dict(link=('vd_kpt5', 'vd_kpt4'), id=280, color=[128, 64, 255]), + 281: + dict(link=('vd_kpt4', 'vd_kpt3'), id=281, color=[128, 64, 255]), + 282: + dict(link=('vd_kpt3', 'vd_kpt2'), id=282, color=[128, 64, 255]), + 283: + dict(link=('vd_kpt6', 'vd_kpt1'), id=283, color=[128, 64, 255]), + # sling_dress + 284: + dict(link=('sd_kpt1', 'sd_kpt2'), id=284, color=[128, 64, 0]), + 285: + dict(link=('sd_kpt2', 'sd_kpt8'), id=285, color=[128, 64, 0]), + 286: + dict(link=('sd_kpt8', 'sd_kpt9'), id=286, color=[128, 64, 0]), + 287: + dict(link=('sd_kpt9', 'sd_kpt10'), id=287, color=[128, 64, 0]), + 288: + dict(link=('sd_kpt10', 'sd_kpt11'), id=288, color=[128, 64, 0]), + 289: + dict(link=('sd_kpt11', 'sd_kpt12'), id=289, color=[128, 64, 0]), + 290: + dict(link=('sd_kpt12', 'sd_kpt13'), id=290, color=[128, 64, 0]), + 291: + dict(link=('sd_kpt13', 'sd_kpt14'), id=291, color=[128, 64, 0]), + 292: + dict(link=('sd_kpt14', 'sd_kpt15'), id=292, color=[128, 64, 0]), + 293: + dict(link=('sd_kpt15', 'sd_kpt16'), id=293, color=[128, 64, 0]), + 294: + dict(link=('sd_kpt16', 'sd_kpt17'), id=294, color=[128, 64, 0]), + 295: + dict(link=('sd_kpt17', 'sd_kpt18'), id=295, color=[128, 64, 0]), + 296: + dict(link=('sd_kpt18', 'sd_kpt6'), id=296, color=[128, 64, 0]), + 297: + dict(link=('sd_kpt6', 'sd_kpt5'), id=297, color=[128, 64, 0]), + 298: + dict(link=('sd_kpt5', 'sd_kpt4'), id=298, color=[128, 64, 0]), + 299: + dict(link=('sd_kpt4', 'sd_kpt3'), id=299, color=[128, 64, 0]), + 300: + dict(link=('sd_kpt3', 'sd_kpt2'), id=300, color=[128, 64, 0]), + 301: + dict(link=('sd_kpt2', 'sd_kpt7'), id=301, color=[128, 64, 0]), + 302: + dict(link=('sd_kpt6', 'sd_kpt19'), id=302, color=[128, 64, 0]), + 303: + dict(link=('sd_kpt6', 'sd_kpt1'), id=303, color=[128, 64, 0]), + }, + joint_weights=[1.] * 294, + sigmas=[]) diff --git a/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/res50_deepfashion2.md b/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/res50_deepfashion2.md new file mode 100644 index 0000000000..1dcfd59313 --- /dev/null +++ b/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/res50_deepfashion2.md @@ -0,0 +1,67 @@ + + +

+SimpleBaseline2D (ECCV'2018) + +```bibtex +@inproceedings{xiao2018simple, + title={Simple baselines for human pose estimation and tracking}, + author={Xiao, Bin and Wu, Haiping and Wei, Yichen}, + booktitle={Proceedings of the European conference on computer vision (ECCV)}, + pages={466--481}, + year={2018} +} +``` + +
+ + + +
+ResNet (CVPR'2016) + +```bibtex +@inproceedings{he2016deep, + title={Deep residual learning for image recognition}, + author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian}, + booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition}, + pages={770--778}, + year={2016} +} +``` + +
+ + + +
+DeepFashion2 (CVPR'2019) + +```bibtex +@article{DeepFashion2, + author = {Yuying Ge and Ruimao Zhang and Lingyun Wu and Xiaogang Wang and Xiaoou Tang and Ping Luo}, + title={A Versatile Benchmark for Detection, Pose Estimation, Segmentation and Re-Identification of Clothing Images}, + journal={CVPR}, + year={2019} +} +``` + +
+ +Results on DeepFashion2 val set + +| Set | Arch | Input Size | PCK@0.2 | AUC | EPE | ckpt | log | +| :-------------------- | :-------------------------------------------------: | :--------: | :-----: | :---: | :--: | :-------------------------------------------------: | :-------------------------------------------------: | +| short_sleeved_shirt | [pose_resnet_50](/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_6xb64-210e_deepfasion2-short-sleeved-shirt-256x192.py) | 256x192 | 0.988 | 0.703 | 10.2 | [ckpt](https://download.openmmlab.com/mmpose/fashion/resnet/res50_deepfashion2_short_sleeved_shirt_256x192-21e1c5da_20221208.pth) | [log](https://download.openmmlab.com/mmpose/fashion/resnet/res50_deepfashion2_short_sleeved_shirt_256x192_20221208.log.json) | +| long_sleeved_shirt | [pose_resnet_50](/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_8xb64-210e_deepfasion2-long-sleeved-shirt-256x192.py) | 256x192 | 0.973 | 0.587 | 16.6 | [ckpt](https://download.openmmlab.com/mmpose/fashion/resnet/res50_deepfashion2_long_sleeved_shirt_256x192-8679e7e3_20221208.pth) | [log](https://download.openmmlab.com/mmpose/fashion/resnet/res50_deepfashion2_long_sleeved_shirt_256x192_20221208.log.json) | +| short_sleeved_outwear | [pose_resnet_50](/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_8xb64-210e_deepfasion2-short-sleeved-outwear-256x192.py) | 256x192 | 0.966 | 0.408 | 24.0 | [ckpt](https://download.openmmlab.com/mmpose/fashion/resnet/res50_deepfashion2_short_sleeved_outwear_256x192-a04c1298_20221208.pth) | [log](https://download.openmmlab.com/mmpose/fashion/resnet/res50_deepfashion2_short_sleeved_outwear_256x192_20221208.log.json) | +| long_sleeved_outwear | [pose_resnet_50](/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_8xb64-210e_deepfasion2-long-sleeved-outwear-256x192.py) | 256x192 | 0.987 | 0.517 | 18.1 | [ckpt](https://download.openmmlab.com/mmpose/fashion/resnet/res50_deepfashion2_long_sleeved_outwear_256x192-31fbaecf_20221208.pth) | [log](https://download.openmmlab.com/mmpose/fashion/resnet/res50_deepfashion2_long_sleeved_outwear_256x192_20221208.log.json) | +| vest | [pose_resnet_50](/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_4xb64-210e_deepfasion2-vest-256x192.py) | 256x192 | 0.981 | 0.643 | 12.7 | [ckpt](https://download.openmmlab.com/mmpose/fashion/resnet/res50_deepfashion2_vest_256x192-4c48d05c_20221208.pth) | [log](https://download.openmmlab.com/mmpose/fashion/resnet/res50_deepfashion2_vest_256x192_20221208.log.json) | +| sling | [pose_resnet_50](/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_4xb64-210e_deepfasion2-sling-256x192.py) | 256x192 | 0.940 | 0.557 | 21.6 | [ckpt](https://download.openmmlab.com/mmpose/fashion/resnet/res50_deepfashion2_sling_256x192-ebb2b736_20221208.pth) | [log](https://download.openmmlab.com/mmpose/fashion/resnet/res50_deepfashion2_sling_256x192_20221208.log.json) | +| shorts | [pose_resnet_50](/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_3xb64-210e_deepfasion2-shorts-256x192.py) | 256x192 | 0.975 | 0.682 | 12.4 | [ckpt](https://download.openmmlab.com/mmpose/fashion/resnet/res50_deepfashion2_shorts_256x192-9ab23592_20221208.pth) | [log](https://download.openmmlab.com/mmpose/fashion/resnet/res50_deepfashion2_shorts_256x192_20221208.log.json) | +| trousers | [pose_resnet_50](/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_2xb64-210e_deepfasion2-trousers-256x192.py) | 256x192 | 0.973 | 0.625 | 14.8 | [ckpt](https://download.openmmlab.com/mmpose/fashion/resnet/res50_deepfashion2_trousers_256x192-3e632257_20221208.pth) | [log](https://download.openmmlab.com/mmpose/fashion/resnet/res50_deepfashion2_trousers_256x192_20221208.log.json) | +| skirt | [pose_resnet_50](/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_1xb64-210e_deepfasion2-skirt-256x192.py) | 256x192 | 0.952 | 0.653 | 16.6 | [ckpt](https://download.openmmlab.com/mmpose/fashion/resnet/res50_deepfashion2_skirt_256x192-09573469_20221208.pth) | [log](https://download.openmmlab.com/mmpose/fashion/resnet/res50_deepfashion2_skirt_256x192_20221208.log.json) | +| short_sleeved_dress | [pose_resnet_50](/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_4xb64-210e_deepfasion2-short-sleeved-dress-256x192.py) | 256x192 | 0.980 | 0.603 | 15.6 | [ckpt](https://download.openmmlab.com/mmpose/fashion/resnet/res50_deepfashion2_short_sleeved_dress_256x192-1345b07a_20221208.pth) | [log](https://download.openmmlab.com/mmpose/fashion/resnet/res50_deepfashion2_short_sleeved_dress_256x192_20221208.log.json) | +| long_sleeved_dress | [pose_resnet_50](/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_1xb64-210e_deepfasion2-long-sleeved-dress-256x192.py) | 256x192 | 0.976 | 0.518 | 20.1 | [ckpt](https://download.openmmlab.com/mmpose/fashion/resnet/res50_deepfashion2_long_sleeved_dress_256x192-87bac74e_20221208.pth) | [log](https://download.openmmlab.com/mmpose/fashion/resnet/res50_deepfashion2_long_sleeved_dress_256x192_20221208.log.json) | +| vest_dress | [pose_resnet_50](/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_1xb64-210e_deepfasion2-vest-dress-256x192.py) | 256x192 | 0.980 | 0.600 | 16.0 | [ckpt](https://download.openmmlab.com/mmpose/fashion/resnet/res50_deepfashion2_vest_dress_256x192-fb3fbd6f_20221208.pth) | [log](https://download.openmmlab.com/mmpose/fashion/resnet/res50_deepfashion2_vest_dress_256x192_20221208.log.json) | +| sling_dress | [pose_resnet_50](/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_4xb64-210e_deepfasion2-sling-dress-256x192.py) | 256x192 | 0.967 | 0.544 | 19.5 | [ckpt](https://download.openmmlab.com/mmpose/fashion/resnet/res50_deepfashion2_sling_dress_256x192-8ebae0eb_20221208.pth) | [log](https://download.openmmlab.com/mmpose/fashion/resnet/res50_deepfashion2_sling_dress_256x192_20221208.log.json) | diff --git a/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/res50_deepfasion2.yml b/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/res50_deepfasion2.yml new file mode 100644 index 0000000000..28825fa011 --- /dev/null +++ b/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/res50_deepfasion2.yml @@ -0,0 +1,185 @@ +Models: +- Config: configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_6xb64-210e_deepfasion2-short-sleeved-shirt-256x192.py + In Collection: SimpleBaseline2D + Metadata: + Architecture: &id001 + - SimpleBaseline2D + - ResNet + Training Data: DeepFashion2 + Name: td-hm_res50_6xb64-210e_deepfasion2-short-sleeved-shirt-256x192 + Results: + - Dataset: DeepFashion2 + Metrics: + AUC: 0.703 + EPE: 10.2 + PCK@0.2: 0.988 + Task: Fashion 2D Keypoint + Weights: https://download.openmmlab.com/mmpose/fashion/resnet/res50_deepfashion2_short_sleeved_shirt_256x192-21e1c5da_20221208.pth +- Config: configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_8xb64-210e_deepfasion2-long-sleeved-shirt-256x192.py + In Collection: SimpleBaseline2D + Metadata: + Architecture: *id001 + Training Data: DeepFashion2 + Name: td-hm_res50_8xb64-210e_deepfasion2-long-sleeved-shirt-256x192 + Results: + - Dataset: DeepFashion2 + Metrics: + AUC: 0.587 + EPE: 16.5 + PCK@0.2: 0.973 + Task: Fashion 2D Keypoint + Weights: https://download.openmmlab.com/mmpose/fashion/resnet/res50_deepfashion2_long_sleeved_shirt_256x192-8679e7e3_20221208.pth +- Config: configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_8xb64-210e_deepfasion2-short-sleeved-outwear-256x192.py + In Collection: SimpleBaseline2D + Metadata: + Architecture: *id001 + Training Data: DeepFashion2 + Name: td-hm_res50_8xb64-210e_deepfasion2-short-sleeved-outwear-256x192 + Results: + - Dataset: DeepFashion2 + Metrics: + AUC: 0.408 + EPE: 24.0 + PCK@0.2: 0.966 + Task: Fashion 2D Keypoint + Weights: https://download.openmmlab.com/mmpose/fashion/resnet/res50_deepfashion2_short_sleeved_outwear_256x192-a04c1298_20221208.pth +- Config: configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_8xb64-210e_deepfasion2-long-sleeved-outwear-256x192.py + In Collection: SimpleBaseline2D + Metadata: + Architecture: *id001 + Training Data: DeepFashion2 + Name: td-hm_res50_8xb64-210e_deepfasion2-long-sleeved-outwear-256x192 + Results: + - Dataset: DeepFashion2 + Metrics: + AUC: 0.517 + EPE: 18.1 + PCK@0.2: 0.987 + Task: Fashion 2D Keypoint + Weights: https://download.openmmlab.com/mmpose/fashion/resnet/res50_deepfashion2_long_sleeved_outwear_256x192-31fbaecf_20221208.pth +- Config: configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_4xb64-210e_deepfasion2-vest-256x192.py + In Collection: SimpleBaseline2D + Metadata: + Architecture: *id001 + Training Data: DeepFashion2 + Name: td-hm_res50_4xb64-210e_deepfasion2-vest-256x192 + Results: + - Dataset: DeepFashion2 + Metrics: + AUC: 0.643 + EPE: 12.7 + PCK@0.2: 0.981 + Task: Fashion 2D Keypoint + Weights: https://download.openmmlab.com/mmpose/fashion/resnet/res50_deepfashion2_vest_256x192-4c48d05c_20221208.pth +- Config: configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_4xb64-210e_deepfasion2-sling-256x192.py + In Collection: SimpleBaseline2D + Metadata: + Architecture: *id001 + Training Data: DeepFashion2 + Name: td-hm_res50_4xb64-210e_deepfasion2-sling-256x192 + Results: + - Dataset: DeepFashion2 + Metrics: + AUC: 0.557 + EPE: 21.6 + PCK@0.2: 0.94 + Task: Fashion 2D Keypoint + Weights: https://download.openmmlab.com/mmpose/fashion/resnet/res50_deepfashion2_sling_256x192-ebb2b736_20221208.pth +- Config: configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_3xb64-210e_deepfasion2-shorts-256x192.py + In Collection: SimpleBaseline2D + Metadata: + Architecture: *id001 + Training Data: DeepFashion2 + Name: td-hm_res50_3xb64-210e_deepfasion2-shorts-256x192 + Results: + - Dataset: DeepFashion2 + Metrics: + AUC: 0.682 + EPE: 12.4 + PCK@0.2: 0.975 + Task: Fashion 2D Keypoint + Weights: https://download.openmmlab.com/mmpose/fashion/resnet/res50_deepfashion2_shorts_256x192-9ab23592_20221208.pth +- Config: configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_2xb64-210e_deepfasion2-trousers-256x192.py + In Collection: SimpleBaseline2D + Metadata: + Architecture: *id001 + Training Data: DeepFashion2 + Name: td-hm_res50_2xb64-210e_deepfasion2-trousers-256x192 + Results: + - Dataset: DeepFashion2 + Metrics: + AUC: 0.625 + EPE: 14.8 + PCK@0.2: 0.973 + Task: Fashion 2D Keypoint + Weights: https://download.openmmlab.com/mmpose/fashion/resnet/res50_deepfashion2_trousers_256x192-3e632257_20221208.pth +- Config: configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_1xb64-210e_deepfasion2-skirt-256x192.py + In Collection: SimpleBaseline2D + Metadata: + Architecture: *id001 + Training Data: DeepFashion2 + Name: td-hm_res50_1xb64-210e_deepfasion2-skirt-256x192 + Results: + - Dataset: DeepFashion2 + Metrics: + AUC: 0.653 + EPE: 16.6 + PCK@0.2: 0.952 + Task: Fashion 2D Keypoint + Weights: https://download.openmmlab.com/mmpose/fashion/resnet/res50_deepfashion2_skirt_256x192-09573469_20221208.pth +- Config: configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_4xb64-210e_deepfasion2-short-sleeved-dress-256x192.py + In Collection: SimpleBaseline2D + Metadata: + Architecture: *id001 + Training Data: DeepFashion2 + Name: td-hm_res50_4xb64-210e_deepfasion2-short-sleeved-dress-256x192 + Results: + - Dataset: DeepFashion2 + Metrics: + AUC: 0.603 + EPE: 15.6 + PCK@0.2: 0.98 + Task: Fashion 2D Keypoint + Weights: https://download.openmmlab.com/mmpose/fashion/resnet/res50_deepfashion2_short_sleeved_dress_256x192-1345b07a_20221208.pth +- Config: configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_1xb64-210e_deepfasion2-long-sleeved-dress-256x192.py + In Collection: SimpleBaseline2D + Metadata: + Architecture: *id001 + Training Data: DeepFashion2 + Name: td-hm_res50_1xb64-210e_deepfasion2-long-sleeved-dress-256x192 + Results: + - Dataset: DeepFashion2 + Metrics: + AUC: 0.518 + EPE: 20.1 + PCK@0.2: 0.976 + Task: Fashion 2D Keypoint + Weights: https://download.openmmlab.com/mmpose/fashion/resnet/res50_deepfashion2_long_sleeved_dress_256x192-87bac74e_20221208.pth +- Config: configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_1xb64-210e_deepfasion2-vest-dress-256x192.py + In Collection: SimpleBaseline2D + Metadata: + Architecture: *id001 + Training Data: DeepFashion2 + Name: td-hm_res50_1xb64-210e_deepfasion2-vest-dress-256x192 + Results: + - Dataset: DeepFashion2 + Metrics: + AUC: 0.6 + EPE: 16.0 + PCK@0.2: 0.98 + Task: Fashion 2D Keypoint + Weights: https://download.openmmlab.com/mmpose/fashion/resnet/res50_deepfashion2_vest_dress_256x192-fb3fbd6f_20221208.pth +- Config: configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_4xb64-210e_deepfasion2-sling-dress-256x192.py + In Collection: SimpleBaseline2D + Metadata: + Architecture: *id001 + Training Data: DeepFashion2 + Name: td-hm_res50_4xb64-210e_deepfasion2-sling-dress-256x192 + Results: + - Dataset: DeepFashion2 + Metrics: + AUC: 0.544 + EPE: 19.5 + PCK@0.2: 0.967 + Task: Fashion 2D Keypoint + Weights: https://download.openmmlab.com/mmpose/fashion/resnet/res50_deepfashion2_sling_dress_256x192-8ebae0eb_20221208.pth diff --git a/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_1xb64-210e_deepfasion2-long-sleeved-dress-256x192.py b/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_1xb64-210e_deepfasion2-long-sleeved-dress-256x192.py new file mode 100644 index 0000000000..09dfaaa390 --- /dev/null +++ b/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_1xb64-210e_deepfasion2-long-sleeved-dress-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=64) + +# hooks +default_hooks = dict( + logger=dict(type='LoggerHook', interval=10), + checkpoint=dict(save_best='AUC', 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=294, + 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 = 'DeepFashion2Dataset' +data_mode = 'topdown' +data_root = 'data/deepfasion2/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + 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='train/deepfashion2_long_sleeved_dress_train.json', + data_prefix=dict(img='train/image/'), + 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='validation/deepfashion2_long_sleeved_dress_validation.json', + data_prefix=dict(img='validation/image/'), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = [ + dict(type='PCKAccuracy', thr=0.2), + dict(type='AUC'), + dict(type='EPE'), +] +test_evaluator = val_evaluator diff --git a/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_1xb64-210e_deepfasion2-skirt-256x192.py b/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_1xb64-210e_deepfasion2-skirt-256x192.py new file mode 100644 index 0000000000..f0e6f0c632 --- /dev/null +++ b/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_1xb64-210e_deepfasion2-skirt-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=64) + +# hooks +default_hooks = dict( + logger=dict(type='LoggerHook', interval=10), + checkpoint=dict(save_best='AUC', 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=294, + 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 = 'DeepFashion2Dataset' +data_mode = 'topdown' +data_root = 'data/deepfasion2/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + 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='train/deepfashion2_skirt_train.json', + data_prefix=dict(img='train/image/'), + 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='validation/deepfashion2_skirt_validation.json', + data_prefix=dict(img='validation/image/'), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = [ + dict(type='PCKAccuracy', thr=0.2), + dict(type='AUC'), + dict(type='EPE'), +] +test_evaluator = val_evaluator diff --git a/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_1xb64-210e_deepfasion2-vest-dress-256x192.py b/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_1xb64-210e_deepfasion2-vest-dress-256x192.py new file mode 100644 index 0000000000..9bed742199 --- /dev/null +++ b/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_1xb64-210e_deepfasion2-vest-dress-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=64) + +# hooks +default_hooks = dict( + logger=dict(type='LoggerHook', interval=10), + checkpoint=dict(save_best='AUC', 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=294, + 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 = 'DeepFashion2Dataset' +data_mode = 'topdown' +data_root = 'data/deepfasion2/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + 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='train/deepfashion2_vest_dress_train.json', + data_prefix=dict(img='train/image/'), + 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='validation/deepfashion2_vest_dress_validation.json', + data_prefix=dict(img='validation/image/'), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = [ + dict(type='PCKAccuracy', thr=0.2), + dict(type='AUC'), + dict(type='EPE'), +] +test_evaluator = val_evaluator diff --git a/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_2xb64-210e_deepfasion2-trousers-256x192.py b/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_2xb64-210e_deepfasion2-trousers-256x192.py new file mode 100644 index 0000000000..617e59ae74 --- /dev/null +++ b/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_2xb64-210e_deepfasion2-trousers-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=128) + +# hooks +default_hooks = dict( + logger=dict(type='LoggerHook', interval=10), + checkpoint=dict(save_best='AUC', 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=294, + 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 = 'DeepFashion2Dataset' +data_mode = 'topdown' +data_root = 'data/deepfasion2/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + 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='train/deepfashion2_trousers_train.json', + data_prefix=dict(img='train/image/'), + 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='validation/deepfashion2_trousers_validation.json', + data_prefix=dict(img='validation/image/'), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = [ + dict(type='PCKAccuracy', thr=0.2), + dict(type='AUC'), + dict(type='EPE'), +] +test_evaluator = val_evaluator diff --git a/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_3xb64-210e_deepfasion2-shorts-256x192.py b/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_3xb64-210e_deepfasion2-shorts-256x192.py new file mode 100644 index 0000000000..aa3b2774fc --- /dev/null +++ b/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_3xb64-210e_deepfasion2-shorts-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=192) + +# hooks +default_hooks = dict( + logger=dict(type='LoggerHook', interval=10), + checkpoint=dict(save_best='AUC', 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=294, + 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 = 'DeepFashion2Dataset' +data_mode = 'topdown' +data_root = 'data/deepfasion2/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + 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='train/deepfashion2_shorts_train.json', + data_prefix=dict(img='train/image/'), + 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='validation/deepfashion2_shorts_validation.json', + data_prefix=dict(img='validation/image/'), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = [ + dict(type='PCKAccuracy', thr=0.2), + dict(type='AUC'), + dict(type='EPE'), +] +test_evaluator = val_evaluator diff --git a/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_4xb64-210e_deepfasion2-short-sleeved-dress-256x192.py b/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_4xb64-210e_deepfasion2-short-sleeved-dress-256x192.py new file mode 100644 index 0000000000..0bfcabaa54 --- /dev/null +++ b/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_4xb64-210e_deepfasion2-short-sleeved-dress-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=256) + +# hooks +default_hooks = dict( + logger=dict(type='LoggerHook', interval=10), + checkpoint=dict(save_best='AUC', 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=294, + 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 = 'DeepFashion2Dataset' +data_mode = 'topdown' +data_root = 'data/deepfasion2/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + 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='train/deepfashion2_short_sleeved_dress_train.json', + data_prefix=dict(img='train/image/'), + 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='validation/deepfashion2_short_sleeved_dress_validation.json', + data_prefix=dict(img='validation/image/'), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = [ + dict(type='PCKAccuracy', thr=0.2), + dict(type='AUC'), + dict(type='EPE'), +] +test_evaluator = val_evaluator diff --git a/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_4xb64-210e_deepfasion2-sling-256x192.py b/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_4xb64-210e_deepfasion2-sling-256x192.py new file mode 100644 index 0000000000..f627eb182c --- /dev/null +++ b/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_4xb64-210e_deepfasion2-sling-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=256) + +# hooks +default_hooks = dict( + logger=dict(type='LoggerHook', interval=10), + checkpoint=dict(save_best='AUC', 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=294, + 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 = 'DeepFashion2Dataset' +data_mode = 'topdown' +data_root = 'data/deepfasion2/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + 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='train/deepfashion2_sling_train.json', + data_prefix=dict(img='train/image/'), + 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='validation/deepfashion2_sling_validation.json', + data_prefix=dict(img='validation/image/'), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = [ + dict(type='PCKAccuracy', thr=0.2), + dict(type='AUC'), + dict(type='EPE'), +] +test_evaluator = val_evaluator diff --git a/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_4xb64-210e_deepfasion2-sling-dress-256x192.py b/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_4xb64-210e_deepfasion2-sling-dress-256x192.py new file mode 100644 index 0000000000..8b59607060 --- /dev/null +++ b/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_4xb64-210e_deepfasion2-sling-dress-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=256) + +# hooks +default_hooks = dict( + logger=dict(type='LoggerHook', interval=10), + checkpoint=dict(save_best='AUC', 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=294, + 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 = 'DeepFashion2Dataset' +data_mode = 'topdown' +data_root = 'data/deepfasion2/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + 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='train/deepfashion2_sling_dress_train.json', + data_prefix=dict(img='train/image/'), + 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='validation/deepfashion2_sling_dress_validation.json', + data_prefix=dict(img='validation/image/'), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = [ + dict(type='PCKAccuracy', thr=0.2), + dict(type='AUC'), + dict(type='EPE'), +] +test_evaluator = val_evaluator diff --git a/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_4xb64-210e_deepfasion2-vest-256x192.py b/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_4xb64-210e_deepfasion2-vest-256x192.py new file mode 100644 index 0000000000..4249d5a897 --- /dev/null +++ b/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_4xb64-210e_deepfasion2-vest-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=256) + +# hooks +default_hooks = dict( + logger=dict(type='LoggerHook', interval=10), + checkpoint=dict(save_best='AUC', 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=294, + 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 = 'DeepFashion2Dataset' +data_mode = 'topdown' +data_root = 'data/deepfasion2/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + 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='train/deepfashion2_vest_train.json', + data_prefix=dict(img='train/image/'), + 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='validation/deepfashion2_vest_validation.json', + data_prefix=dict(img='validation/image/'), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = [ + dict(type='PCKAccuracy', thr=0.2), + dict(type='AUC'), + dict(type='EPE'), +] +test_evaluator = val_evaluator diff --git a/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_6xb64-210e_deepfasion2-short-sleeved-shirt-256x192.py b/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_6xb64-210e_deepfasion2-short-sleeved-shirt-256x192.py new file mode 100644 index 0000000000..4161952dcf --- /dev/null +++ b/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_6xb64-210e_deepfasion2-short-sleeved-shirt-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=384) + +# hooks +default_hooks = dict( + logger=dict(type='LoggerHook', interval=10), + checkpoint=dict(save_best='AUC', 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=294, + 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 = 'DeepFashion2Dataset' +data_mode = 'topdown' +data_root = 'data/deepfasion2/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + 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='train/deepfashion2_short_sleeved_shirt_train.json', + data_prefix=dict(img='train/image/'), + 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='validation/deepfashion2_short_sleeved_shirt_validation.json', + data_prefix=dict(img='validation/image/'), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = [ + dict(type='PCKAccuracy', thr=0.2), + dict(type='AUC'), + dict(type='EPE'), +] +test_evaluator = val_evaluator diff --git a/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_8xb64-210e_deepfasion2-long-sleeved-outwear-256x192.py b/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_8xb64-210e_deepfasion2-long-sleeved-outwear-256x192.py new file mode 100644 index 0000000000..36e0318bf7 --- /dev/null +++ b/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_8xb64-210e_deepfasion2-long-sleeved-outwear-256x192.py @@ -0,0 +1,123 @@ +_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( + logger=dict(type='LoggerHook', interval=10), + checkpoint=dict(save_best='AUC', 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=294, + 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 = 'DeepFashion2Dataset' +data_mode = 'topdown' +data_root = 'data/deepfasion2/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + 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='train/deepfashion2_long_sleeved_outwear_train.json', + data_prefix=dict(img='train/image/'), + 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='validation/' + 'deepfashion2_long_sleeved_outwear_validation.json', + data_prefix=dict(img='validation/image/'), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = [ + dict(type='PCKAccuracy', thr=0.2), + dict(type='AUC'), + dict(type='EPE'), +] +test_evaluator = val_evaluator diff --git a/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_8xb64-210e_deepfasion2-long-sleeved-shirt-256x192.py b/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_8xb64-210e_deepfasion2-long-sleeved-shirt-256x192.py new file mode 100644 index 0000000000..f82e3cb5fb --- /dev/null +++ b/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_8xb64-210e_deepfasion2-long-sleeved-shirt-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( + logger=dict(type='LoggerHook', interval=10), + checkpoint=dict(save_best='AUC', 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=294, + 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 = 'DeepFashion2Dataset' +data_mode = 'topdown' +data_root = 'data/deepfasion2/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + 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='train/deepfashion2_long_sleeved_shirt_train.json', + data_prefix=dict(img='train/image/'), + 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='validation/deepfashion2_long_sleeved_shirt_validation.json', + data_prefix=dict(img='validation/image/'), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = [ + dict(type='PCKAccuracy', thr=0.2), + dict(type='AUC'), + dict(type='EPE'), +] +test_evaluator = val_evaluator diff --git a/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_8xb64-210e_deepfasion2-short-sleeved-outwear-256x192.py b/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_8xb64-210e_deepfasion2-short-sleeved-outwear-256x192.py new file mode 100644 index 0000000000..30db99de9e --- /dev/null +++ b/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td-hm_res50_8xb64-210e_deepfasion2-short-sleeved-outwear-256x192.py @@ -0,0 +1,123 @@ +_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( + logger=dict(type='LoggerHook', interval=10), + checkpoint=dict(save_best='AUC', 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=294, + 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 = 'DeepFashion2Dataset' +data_mode = 'topdown' +data_root = 'data/deepfasion2/' + +# pipelines +train_pipeline = [ + dict(type='LoadImage'), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + 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='train/deepfashion2_short_sleeved_outwear_train.json', + data_prefix=dict(img='train/image/'), + 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='validation/' + 'deepfashion2_short_sleeved_outwear_validation.json', + data_prefix=dict(img='validation/image/'), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# evaluators +val_evaluator = [ + dict(type='PCKAccuracy', thr=0.2), + dict(type='AUC'), + dict(type='EPE'), +] +test_evaluator = val_evaluator diff --git a/docs/en/dataset_zoo/2d_fashion_landmark.md b/docs/en/dataset_zoo/2d_fashion_landmark.md index 42f213e40a..b1146b47b6 100644 --- a/docs/en/dataset_zoo/2d_fashion_landmark.md +++ b/docs/en/dataset_zoo/2d_fashion_landmark.md @@ -6,6 +6,7 @@ If your folder structure is different, you may need to change the corresponding MMPose supported datasets: - [DeepFashion](#deepfashion) \[ [Homepage](http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion/LandmarkDetection.html) \] +- [DeepFashion2](#deepfashion2) \[ [Homepage](https://github.com/switchablenorms/DeepFashion2) \] ## DeepFashion (Fashion Landmark Detection, FLD) @@ -78,3 +79,64 @@ mmpose │ │-- img_00000005.jpg │ │-- ... ``` + +## DeepFashion2 + + + +
+DeepFashion2 (CVPR'2019) + +```bibtex +@article{DeepFashion2, + author = {Yuying Ge and Ruimao Zhang and Lingyun Wu and Xiaogang Wang and Xiaoou Tang and Ping Luo}, + title={A Versatile Benchmark for Detection, Pose Estimation, Segmentation and Re-Identification of Clothing Images}, + journal={CVPR}, + year={2019} +} +``` + +
+ + + +For [DeepFashion2](https://github.com/switchablenorms/DeepFashion2) dataset, images can be downloaded from [download](https://drive.google.com/drive/folders/125F48fsMBz2EF0Cpqk6aaHet5VH399Ok?usp=sharing). +Please download the [annotation files](https://drive.google.com/file/d/1RM9l9EaB9ULRXhoCS72PkCXtJ4Cn4i6O/view?usp=share_link). These annotation files are converted by [deepfashion2_to_coco.py](https://github.com/switchablenorms/DeepFashion2/blob/master/evaluation/deepfashion2_to_coco.py). +Extract them under {MMPose}/data, and make them look like this: + +```text +mmpose +├── mmpose +├── docs +├── tests +├── tools +├── configs +`── data + │── deepfashion2 + │── train + │-- deepfashion2_short_sleeved_outwear_train.json + │-- deepfashion2_short_sleeved_dress_train.json + │-- deepfashion2_skirt_train.json + │-- deepfashion2_sling_dress_train.json + │-- ... + │-- image + │ │-- 000001.jpg + │ │-- 000002.jpg + │ │-- 000003.jpg + │ │-- 000004.jpg + │ │-- 000005.jpg + │ │-- ... + │── validation + │-- deepfashion2_short_sleeved_dress_validation.json + │-- deepfashion2_long_sleeved_shirt_validation.json + │-- deepfashion2_trousers_validation.json + │-- deepfashion2_skirt_validation.json + │-- ... + │-- image + │ │-- 000001.jpg + │ │-- 000002.jpg + │ │-- 000003.jpg + │ │-- 000004.jpg + │ │-- 000005.jpg + │ │-- ... +``` diff --git a/docs/zh_cn/dataset_zoo/2d_fashion_landmark.md b/docs/zh_cn/dataset_zoo/2d_fashion_landmark.md index 42f213e40a..25b7fd7c64 100644 --- a/docs/zh_cn/dataset_zoo/2d_fashion_landmark.md +++ b/docs/zh_cn/dataset_zoo/2d_fashion_landmark.md @@ -1,80 +1,3 @@ -# 2D Fashion Landmark Dataset +# 2D服装关键点数据集 -It is recommended to symlink the dataset root to `$MMPOSE/data`. -If your folder structure is different, you may need to change the corresponding paths in config files. - -MMPose supported datasets: - -- [DeepFashion](#deepfashion) \[ [Homepage](http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion/LandmarkDetection.html) \] - -## DeepFashion (Fashion Landmark Detection, FLD) - - - -
-DeepFashion (CVPR'2016) - -```bibtex -@inproceedings{liuLQWTcvpr16DeepFashion, - author = {Liu, Ziwei and Luo, Ping and Qiu, Shi and Wang, Xiaogang and Tang, Xiaoou}, - title = {DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations}, - booktitle = {Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, - month = {June}, - year = {2016} -} -``` - -
- - - -
-DeepFashion (ECCV'2016) - -```bibtex -@inproceedings{liuYLWTeccv16FashionLandmark, - author = {Liu, Ziwei and Yan, Sijie and Luo, Ping and Wang, Xiaogang and Tang, Xiaoou}, - title = {Fashion Landmark Detection in the Wild}, - booktitle = {European Conference on Computer Vision (ECCV)}, - month = {October}, - year = {2016} - } -``` - -
- -
- -
- -For [DeepFashion](http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion/LandmarkDetection.html) dataset, images can be downloaded from [download](http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion/LandmarkDetection.html). -Please download the annotation files from [fld_annotations](https://download.openmmlab.com/mmpose/datasets/fld_annotations.tar). -Extract them under {MMPose}/data, and make them look like this: - -```text -mmpose -├── mmpose -├── docs -├── tests -├── tools -├── configs -`── data - │── fld - │-- annotations - │ │-- fld_upper_train.json - │ |-- fld_upper_val.json - │ |-- fld_upper_test.json - │ │-- fld_lower_train.json - │ |-- fld_lower_val.json - │ |-- fld_lower_test.json - │ │-- fld_full_train.json - │ |-- fld_full_val.json - │ |-- fld_full_test.json - │-- img - │ │-- img_00000001.jpg - │ │-- img_00000002.jpg - │ │-- img_00000003.jpg - │ │-- img_00000004.jpg - │ │-- img_00000005.jpg - │ │-- ... -``` +内容建设中…… diff --git a/docs/zh_cn/user_guides/inference.md b/docs/zh_cn/user_guides/inference.md index d77ff2185c..1359be01f4 100644 --- a/docs/zh_cn/user_guides/inference.md +++ b/docs/zh_cn/user_guides/inference.md @@ -1,3 +1,210 @@ -# 模型推理 +# 使用现有模型进行推理 -中文内容建设中，暂时请查阅[英文版文档](../../en/user_guides/inference.md) +MMPose为姿态估计提供了大量可以从[模型库](https://mmpose.readthedocs.io/en/latest/model_zoo.html)中找到的预测训练模型。本指南将演示**如何执行推理**，或使用训练过的模型对提供的图像或视频运行姿态估计。 + +有关在标准数据集上测试现有模型的说明，请参阅本指南。 + +在MMPose，模型由配置文件定义，而其已计算好的参数存储在权重文件（checkpoint file）中。您可以在[模型库](https://mmpose.readthedocs.io/en/latest/model_zoo.html)中找到模型配置文件和相应的权重文件的URL。我们建议从使用HRNet模型的[配置文件](https://github.com/open-mmlab/mmpose/blob/main/configs/body_2d_keypoint/topdown_heatmap/coco/td-hm_hrnet-w32_8xb64-210e_coco-256x192.py)和[权重文件](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/coco/td-hm_hrnet-w32_8xb64-210e_coco-256x192-81c58e40_20220909.pth)开始。 + +# 推理器：统一的推理接口 + +MMPose提供了一个被称为`MMPoseInferencer`的、全面的推理API。这个API使得用户得以使用所有MMPose支持的模型来对图像和视频进行模型推理。此外，该API可以完成推理结果自动化，并方便用户保存预测结果。 + +## 基本用法 + +`MMPoseInferencer`可以在任何Python程序中被用来执行姿态估计任务。以下是在一个在Python Shell中使用预训练的人体姿态模型对给定图像进行推理的示例。 + +```python +from mmpose.apis import MMPoseInferencer + +img_path = 'tests/data/coco/000000000785.jpg' # 将img_path替换给你自己的路径 + +# 使用模型别名创建推断器 +inferencer = MMPoseInferencer('human') + +# MMPoseInferencer采用了惰性推断方法，在给定输入时创建一个预测生成器 +result_generator = inferencer(img_path, show=True) +result = next(result_generator) +``` + +如果一切正常，你将在一个新窗口中看到下图： + +![inferencer_result_coco](https://user-images.githubusercontent.com/26127467/220008302-4a57fd44-0978-408e-8351-600e5513316a.jpg) + +在上述示例中，变量`result`是一个字典，包含两个键，分别是`visualization`和`predictions`。`visualization`用于存储可视化结果，但由于没有设定参数`return_vis`，因此该列表为空。但是`predictions`保存了每个检测到的实例的、估计得到的关键点列表。 + +还可以使用用于用于推断的**命令行界面工具**（CLI, command-line interface）:`demo/inferencer_demo.py`。这个工具允许用户使用以下命令使用相同的模型和输入执行推理： + +```python +python demo/inferencer_demo.py 'tests/data/coco/000000000785.jpg' \ + --pose2d 'human' --show --pred-out-dir 'predictions' +``` + +预测结果将被保存在路径`predictions/000000000785.json`。作为一个API，`inferencer_demo.py`的输入参数与`MMPoseInferencer`的相同。前者能够处理一系列输入类型，包括以下内容： + +- 图像路径 + +- 视频路径 + +- 文件夹路径（这会导致该文件夹中的所有图像都被推断出来） + +- An image array (NA for CLI tool) + +- A list of image arrays (NA for CLI tool) + +- 摄像头（在这种情况下，输入参数应该设置为`webcam`或`webcam:{CAMERA_ID}`） + +## 自定义姿态估计模型 + +`MMPoseInferencer`提供了几种可用于自定义所使用的模型的方法： + +```python +# 使用模型别名构建推断器 +inferencer = MMPoseInferencer('human') + +# 使用模型配置名构建推断器 +inferencer = MMPoseInferencer('td-hm_hrnet-w32_8xb64-210e_coco-256x192') + +# 使用模型配置文件和权重文件的路径或URL构建推断器 +inferencer = MMPoseInferencer( + pose2d='configs/body_2d_keypoint/topdown_heatmap/coco/' \ + 'td-hm_hrnet-w32_8xb64-210e_coco-256x192.py', + pose2d_weights='https://download.openmmlab.com/mmpose/top_down/' \ + 'hrnet/hrnet_w32_coco_256x192-c78dce93_20200708.pth' +) +``` + +模型别名的完整列表可以在模型别名部分（Model Alias section）中找到。 + +此外，自顶向下的姿态估计器还需要一个对象检测模型。`MMPoseInferencer`能够推断用MMPose支持的数据集训练的模型的实例类型，然后构建必要的对象检测模型。用户也可以通过以下方式手动指定检测模型: + +```python +# 通过别名指定检测模型 +# 可用的别名包括“human”、“hand”、“face”、“animal”、 +# 以及mmdet中定义的任何其他别名 +inferencer = MMPoseInferencer( + # 假设姿态估计器是在自定义数据集上训练的 + pose2d='custom_human_pose_estimator.py', + pose2d_weights='custom_human_pose_estimator.pth', + det_model='human' +) + +# 使用模型配置名称指定检测模型 +inferencer = MMPoseInferencer( + pose2d='human', + det_model='yolox_l_8x8_300e_coco', + det_cat_ids=[0], # 指定'human'类的类别id +) + +# 使用模型配置文件和权重文件的路径或URL构建推断器 +inferencer = MMPoseInferencer( + pose2d='human', + det_model=f'{PATH_TO_MMDET}/configs/yolox/yolox_l_8x8_300e_coco.py', + det_weights='https://download.openmmlab.com/mmdetection/v2.0/' \ + 'yolox/yolox_l_8x8_300e_coco/' \ + 'yolox_l_8x8_300e_coco_20211126_140236-d3bd2b23.pth', + det_cat_ids=[0], # 指定'human'类的类别id +) +``` + +## 转储结果 + +在执行姿态估计推理任务之后，您可能希望保存结果以供进一步分析或处理。本节将指导您将预测的关键点和可视化结果保存到本地。 + +要将预测保存在JSON文件中，在运行`MMPoseInferencer`的实例`inferencer`时使用`pred_out_dir`参数: + +```python +result_generator = inferencer(img_path, pred_out_dir='predictions') +result = next(result_generator) +``` + +预测结果将以JSON格式保存在`predictions/`文件夹中，每个文件以相应的输入图像或视频的名称命名。 + +对于更高级的场景，还可以直接从`inferencer`返回的`result`字典中访问预测结果。其中，`predictions`包含输入图像或视频中每个单独实例的预测关键点列表。然后，您可以使用您喜欢的方法操作或存储这些结果。 + +请记住，如果你想将可视化图像和预测文件保存在一个文件夹中，你可以使用`out_dir`参数： + +```python +result_generator = inferencer(img_path, out_dir='output') +result = next(result_generator) +``` + +在这种情况下，可视化图像将保存在`output/visualization/`文件夹中，而预测将存储在`output/forecasts/`文件夹中。 + +## 可视化 + +推理器`inferencer`可以自动对输入的图像或视频进行预测。可视化结果可以显示在一个新的窗口中，并保存在本地。 + +要在新窗口中查看可视化结果，请使用以下代码： + +请注意： + +- 如果输入视频来自网络摄像头，默认情况下将在新窗口中显示可视化结果，以此让用户看到输入 + +- 如果平台上没有GUI，这个步骤可能会卡住 + +要将可视化结果保存在本地，可以像这样指定`vis_out_dir`参数: + +```python +result_generator = inferencer(img_path, vis_out_dir='vis_results') +result = next(result_generator) +``` + +输入图片或视频的可视化预测结果将保存在`vis_results/`文件夹中 + +在开头展示的滑雪图中，姿态的可视化估计结果由关键点（用实心圆描绘）和骨架（用线条表示）组成。这些视觉元素的默认大小可能不会产生令人满意的结果。用户可以使用`radius`和`thickness`参数来调整圆的大小和线的粗细，如下所示： + +```python +result_generator = inferencer(img_path, show=True, radius=4, thickness=2) +result = next(result_generator) +``` + +## 推理器参数 + +`MMPoseInferencer`提供了各种自定义姿态估计、可视化和保存预测结果的参数。下面是初始化推断器时可用的参数列表及对这些参数的描述： + +| Argument | Description | +| ---------------- | ---------------------------------------------------------- | +| `pose2d` | 指定2D姿态估计模型的模型别名、配置文件名称或配置文件路径。 | +| `pose2d_weights` | 指定2D姿态估计模型权重文件的URL或本地路径。 | +| `det_model` | 指定对象检测模型的模型别名、配置文件名或配置文件路径。 | +| `det_weights` | 指定对象检测模型权重文件的URL或本地路径。 | +| `det_cat_ids` | 指定与要检测的对象类对应的类别id列表。 | +| `device` | 执行推理的设备。如果为`None`，推理器将选择最合适的一个。 | +| `scope` | 定义模型模块的名称空间 | + +推理器设计用于处理预测的可视化和保存。下面是使用`MMPoseInferencer`执行推理时可用的参数列表： + +| Argument | Description | +| ------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `show` | 确定图像或视频的预测结果是否应在弹出窗口中显示。 | +| `radius` | 设置关键点半径。 | +| `thickness` | 设置骨架（线条）粗细。 | +| `return_vis` | 确定返回结果`result`中是否应包括可视化结果列表`visualization`。 | +| `vis_out_dir` | 指定保存可视化图像的文件夹路径。如果未设置，将不会保存可视化图像。 | +| `return_datasample` | 确定是否以`PoseDataSample`的形式返回预测。 | +| `pred_out_dir` | 指定保存预测结果`predictions`的文件夹路径。如果不设置，预测结果将不会被保存。 | +| `out_dir` | 如果指定了输出路径参数`out_dir`，但未设置`vis_out_dir`或`pred_out_dir`，则分别将`vis_out_dir`或`pred_out_dir`设置为`f'{out_dir}/visualization'`或` f'{out_dir}/ forecasts'`。 | + +### 模型别名 + +MMPose为常用模型提供了一组预定义的别名。在初始化`MMPoseInferencer`时，这些别名可以用作简略的表达方式，而不是指定完整的模型配置名称。下面是可用的模型别名及其对应的配置名称的列表： + +| 别名 | 配置文件名称 | 对应任务 | 姿态估计模型 | 检测模型 | +| --------- | -------------------------------------------------- | ------------------------------- | ------------- | ------------------- | +| animal | rtmpose-m_8xb64-210e_ap10k-256x256 | Animal pose estimation | RTMPose-m | RTMDet-m | +| human | rtmpose-m_8xb256-420e_aic-coco-256x192 | Human pose estimation | RTMPose-m | RTMDet-m | +| face | rtmpose-m_8xb64-60e_wflw-256x256 | Face keypoint detection | RTMPose-m | yolox-s | +| hand | rtmpose-m_8xb32-210e_coco-wholebody-hand-256x256 | Hand keypoint detection | RTMPose-m | ssdlite_mobilenetv2 | +| wholebody | rtmpose-m_8xb64-270e_coco-wholebody-256x192 | Human wholebody pose estimation | RTMPose-m | RTMDet-m | +| vitpose | td-hm_ViTPose-base-simple_8xb64-210e_coco-256x192 | Human pose estimation | ViTPose-base | RTMDet-m | +| vitpose-s | td-hm_ViTPose-small-simple_8xb64-210e_coco-256x192 | Human pose estimation | ViTPose-small | RTMDet-m | +| vitpose-b | td-hm_ViTPose-base-simple_8xb64-210e_coco-256x192 | Human pose estimation | ViTPose-base | RTMDet-m | +| vitpose-l | td-hm_ViTPose-large-simple_8xb64-210e_coco-256x192 | Human pose estimation | ViTPose-large | RTMDet-m | +| vitpose-h | td-hm_ViTPose-huge-simple_8xb64-210e_coco-256x192 | Human pose estimation | ViTPose-huge | RTMDet-m | + +此外，用户可以使用CLI工具显示所有可用的别名，使用以下命令: + +```shell +python demo/inferencer_demo.py --show-alias +``` diff --git a/mmpose/apis/inferencers/base_mmpose_inferencer.py b/mmpose/apis/inferencers/base_mmpose_inferencer.py index 167b30276a..15312c6bb7 100644 --- a/mmpose/apis/inferencers/base_mmpose_inferencer.py +++ b/mmpose/apis/inferencers/base_mmpose_inferencer.py @@ -35,7 +35,7 @@ class BaseMMPoseInferencer(BaseInferencer): """The base class for MMPose inferencers.""" - preprocess_kwargs: set = {'bbox_thr', 'nms_thr'} + preprocess_kwargs: set = {'bbox_thr', 'nms_thr', 'bboxes'} forward_kwargs: set = set() visualize_kwargs: set = { 'return_vis', @@ -220,7 +220,11 @@ def _init_pipeline(self, cfg: ConfigType) -> Callable: """ return Compose(cfg.test_dataloader.dataset.pipeline) - def preprocess(self, inputs: InputsType, batch_size: int = 1, **kwargs): + def preprocess(self, + inputs: InputsType, + batch_size: int = 1, + bboxes: Optional[List] = None, + **kwargs): """Process the inputs into a model-feedable format. Args: @@ -233,7 +237,9 @@ def preprocess(self, inputs: InputsType, batch_size: int = 1, **kwargs): """ for i, input in enumerate(inputs): - data_infos = self.preprocess_single(input, index=i, **kwargs) + bbox = bboxes[i] if bboxes is not None else [] + data_infos = self.preprocess_single( + input, index=i, bboxes=bbox, **kwargs) # only supports inference with batch size 1 yield self.collate_fn(data_infos), [input] diff --git a/mmpose/apis/inferencers/mmpose_inferencer.py b/mmpose/apis/inferencers/mmpose_inferencer.py index 845b3d066a..3d5ac222bb 100644 --- a/mmpose/apis/inferencers/mmpose_inferencer.py +++ b/mmpose/apis/inferencers/mmpose_inferencer.py @@ -55,7 +55,7 @@ class MMPoseInferencer(BaseMMPoseInferencer): config will be used. Default is None. """ - preprocess_kwargs: set = {'bbox_thr', 'nms_thr'} + preprocess_kwargs: set = {'bbox_thr', 'nms_thr', 'bboxes'} forward_kwargs: set = set() visualize_kwargs: set = { 'return_vis', diff --git a/mmpose/apis/inferencers/pose2d_inferencer.py b/mmpose/apis/inferencers/pose2d_inferencer.py index adf80543a5..b35abddb19 100644 --- a/mmpose/apis/inferencers/pose2d_inferencer.py +++ b/mmpose/apis/inferencers/pose2d_inferencer.py @@ -66,7 +66,7 @@ class Pose2DInferencer(BaseMMPoseInferencer): config will be used. Default is None. """ - preprocess_kwargs: set = {'bbox_thr', 'nms_thr'} + preprocess_kwargs: set = {'bbox_thr', 'nms_thr', 'bboxes'} forward_kwargs: set = set() visualize_kwargs: set = { 'return_vis', @@ -102,13 +102,18 @@ def __init__(self, # initialize detector for top-down models if self.cfg.data_mode == 'topdown': - if det_model != 'whole_image': + object_type = DATASETS.get(self.cfg.dataset_type).__module__.split( + 'datasets.')[-1].split('.')[0].lower() + + if det_model in ('whole_image', 'whole-image') or \ + (det_model is None and + object_type not in default_det_models): + self.detector = None + + else: det_scope = 'mmdet' if det_model is None: - det_model = DATASETS.get( - self.cfg.dataset_type).__module__.split( - 'datasets.')[-1].split('.')[0].lower() - det_info = default_det_models[det_model] + det_info = default_det_models[object_type] det_model, det_weights, det_cat_ids = det_info[ 'model'], det_info['weights'], det_info['cat_ids'] elif os.path.exists(det_model): @@ -120,15 +125,13 @@ def __init__(self, det_model, det_weights, device=device, scope=det_scope) else: raise RuntimeError( - 'MMDetection (v3.0.0 or above) is required to build ' + 'MMDetection (v3.0.0 or above) is required to build ' 'inferencers for top-down pose estimation models.') if isinstance(det_cat_ids, (tuple, list)): self.det_cat_ids = det_cat_ids else: self.det_cat_ids = (det_cat_ids, ) - else: - self.detector = None self._video_input = False @@ -136,7 +139,9 @@ def preprocess_single(self, input: InputType, index: int, bbox_thr: float = 0.3, - nms_thr: float = 0.3): + nms_thr: float = 0.3, + bboxes: Union[List[List], List[np.ndarray], + np.ndarray] = []): """Process a single input into a model-feedable format. Args: @@ -174,8 +179,6 @@ def preprocess_single(self, bboxes = bboxes[np.logical_and( label_mask, pred_instance.scores > bbox_thr)] bboxes = bboxes[nms(bboxes, nms_thr)] - else: - bboxes = [] data_infos = [] if len(bboxes) > 0: diff --git a/mmpose/datasets/datasets/fashion/__init__.py b/mmpose/datasets/datasets/fashion/__init__.py index 575d6ed4af..8be25dede3 100644 --- a/mmpose/datasets/datasets/fashion/__init__.py +++ b/mmpose/datasets/datasets/fashion/__init__.py @@ -1,4 +1,5 @@ # Copyright (c) OpenMMLab. All rights reserved. +from .deepfashion2_dataset import DeepFashion2Dataset from .deepfashion_dataset import DeepFashionDataset -__all__ = ['DeepFashionDataset'] +__all__ = ['DeepFashionDataset', 'DeepFashion2Dataset'] diff --git a/mmpose/datasets/datasets/fashion/deepfashion2_dataset.py b/mmpose/datasets/datasets/fashion/deepfashion2_dataset.py new file mode 100644 index 0000000000..c3cde9bf97 --- /dev/null +++ b/mmpose/datasets/datasets/fashion/deepfashion2_dataset.py @@ -0,0 +1,10 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from mmpose.registry import DATASETS +from ..base import BaseCocoStyleDataset + + +@DATASETS.register_module(name='DeepFashion2Dataset') +class DeepFashion2Dataset(BaseCocoStyleDataset): + """DeepFashion2 dataset for fashion landmark detection.""" + + METAINFO: dict = dict(from_file='configs/_base_/datasets/deepfashion2.py') diff --git a/model-index.yml b/model-index.yml index d5cb0e28d4..33fb73aa4a 100644 --- a/model-index.yml +++ b/model-index.yml @@ -117,3 +117,4 @@ Import: - configs/wholebody_2d_keypoint/topdown_heatmap/coco-wholebody/vipnas_coco-wholebody.yml - configs/wholebody_2d_keypoint/topdown_heatmap/coco-wholebody/vipnas_dark_coco-wholebody.yml - configs/wholebody_2d_keypoint/topdown_heatmap/coco-wholebody/cspnext_udp_coco-wholebody.yml +- configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/res50_deepfasion2.yml diff --git a/projects/yolox-pose/configs/_base_/datasets b/projects/yolox-pose/configs/_base_/datasets index 8feca66d56..bc9c713221 120000 --- a/projects/yolox-pose/configs/_base_/datasets +++ b/projects/yolox-pose/configs/_base_/datasets @@ -1 +1 @@ -../../../../configs/_base_/datasets \ No newline at end of file +../../../../configs/_base_/datasets diff --git a/projects/yolox-pose/demo b/projects/yolox-pose/demo index bf71256cd3..14e5d68e95 120000 --- a/projects/yolox-pose/demo +++ b/projects/yolox-pose/demo @@ -1 +1 @@ -../../demo \ No newline at end of file +../../demo diff --git a/projects/yolox-pose/tools b/projects/yolox-pose/tools index 31941e941d..682f2b4528 120000 --- a/projects/yolox-pose/tools +++ b/projects/yolox-pose/tools @@ -1 +1 @@ -../../tools \ No newline at end of file +../../tools diff --git a/tests/data/deepfasion2/000264.jpg b/tests/data/deepfasion2/000264.jpg new file mode 100644 index 0000000000000000000000000000000000000000..2bf7429024c123f1bf904888ec5ec8eb915a3f6e GIT binary patch literal 69862 zcmbTdbyQSg8$CKOAYD2r-3SAsbT?8%4~>9ygLF%Z$VjJBQo~R~4~^0#-7Sr@bjuyT 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00:00:00 2001 From: Peng Lu Date: Sun, 23 Apr 2023 15:43:01 +0800 Subject: [PATCH 6/6] [Enhance] Add Opencv backend support for Visualizer (#2283) --- demo/bottomup_demo.py | 47 +- demo/topdown_demo_with_mmdet.py | 63 +-- .../inferencers/base_mmpose_inferencer.py | 18 +- mmpose/visualization/__init__.py | 3 +- mmpose/visualization/fast_visualizer.py | 78 --- mmpose/visualization/local_visualizer.py | 73 +-- .../opencv_backend_visualizer.py | 444 ++++++++++++++++++ .../test_fast_visualizer.py | 71 --- 8 files changed, 515 insertions(+), 282 deletions(-) delete mode 100644 mmpose/visualization/fast_visualizer.py create mode 100644 mmpose/visualization/opencv_backend_visualizer.py delete mode 100644 tests/test_visualization/test_fast_visualizer.py diff --git a/demo/bottomup_demo.py b/demo/bottomup_demo.py index 4616f54b3e..3d6fee7a03 100644 --- a/demo/bottomup_demo.py +++ b/demo/bottomup_demo.py @@ -11,6 +11,7 @@ import numpy as np from mmpose.apis import inference_bottomup, init_model +from mmpose.registry import VISUALIZERS from mmpose.structures import split_instances @@ -128,20 +129,18 @@ def main(): device=args.device, cfg_options=cfg_options) + # build visualizer + model.cfg.visualizer.radius = args.radius + model.cfg.visualizer.line_width = args.thickness + visualizer = VISUALIZERS.build(model.cfg.visualizer) + visualizer.set_dataset_meta(model.dataset_meta) + if args.input == 'webcam': input_type = 'webcam' else: input_type = mimetypes.guess_type(args.input)[0].split('/')[0] if input_type == 'image': - # init visualizer - from mmpose.registry import VISUALIZERS - - model.cfg.visualizer.radius = args.radius - model.cfg.visualizer.line_width = args.thickness - visualizer = VISUALIZERS.build(model.cfg.visualizer) - visualizer.set_dataset_meta(model.dataset_meta) - # inference pred_instances = process_one_image( args, args.input, model, visualizer, show_interval=0) @@ -154,22 +153,6 @@ def main(): mmcv.imwrite(mmcv.rgb2bgr(img_vis), output_file) elif input_type in ['webcam', 'video']: - from mmpose.visualization import FastVisualizer - - visualizer = FastVisualizer( - model.dataset_meta, - radius=args.radius, - line_width=args.thickness, - kpt_thr=args.kpt_thr) - - if args.draw_heatmap: - # init Localvisualizer - from mmpose.registry import VISUALIZERS - - model.cfg.visualizer.radius = args.radius - model.cfg.visualizer.line_width = args.thickness - local_visualizer = VISUALIZERS.build(model.cfg.visualizer) - local_visualizer.set_dataset_meta(model.dataset_meta) if args.input == 'webcam': cap = cv2.VideoCapture(0) @@ -187,15 +170,8 @@ def main(): if not success: break - # bottom-up pose estimation - if args.draw_heatmap: - pred_instances = process_one_image(args, frame, model, - local_visualizer, 0.001) - else: - pred_instances = process_one_image(args, frame, model) - # visualization - visualizer.draw_pose(frame, pred_instances) - cv2.imshow('MMPose Demo [Press ESC to Exit]', frame) + pred_instances = process_one_image(args, frame, model, visualizer, + 0.001) if args.save_predictions: # save prediction results @@ -206,10 +182,7 @@ def main(): # output videos if output_file: - if args.draw_heatmap: - frame_vis = local_visualizer.get_image() - else: - frame_vis = frame.copy()[:, :, ::-1] + frame_vis = visualizer.get_image() if video_writer is None: fourcc = cv2.VideoWriter_fourcc(*'mp4v') diff --git a/demo/topdown_demo_with_mmdet.py b/demo/topdown_demo_with_mmdet.py index cd001e8db6..a143795693 100644 --- a/demo/topdown_demo_with_mmdet.py +++ b/demo/topdown_demo_with_mmdet.py @@ -13,6 +13,7 @@ from mmpose.apis import inference_topdown from mmpose.apis import init_model as init_pose_estimator from mmpose.evaluation.functional import nms +from mmpose.registry import VISUALIZERS from mmpose.structures import merge_data_samples, split_instances from mmpose.utils import adapt_mmdet_pipeline @@ -186,24 +187,22 @@ def main(): cfg_options=dict( model=dict(test_cfg=dict(output_heatmaps=args.draw_heatmap)))) + # build visualizer + pose_estimator.cfg.visualizer.radius = args.radius + pose_estimator.cfg.visualizer.alpha = args.alpha + pose_estimator.cfg.visualizer.line_width = args.thickness + visualizer = VISUALIZERS.build(pose_estimator.cfg.visualizer) + # the dataset_meta is loaded from the checkpoint and + # then pass to the model in init_pose_estimator + visualizer.set_dataset_meta( + pose_estimator.dataset_meta, skeleton_style=args.skeleton_style) + if args.input == 'webcam': input_type = 'webcam' else: input_type = mimetypes.guess_type(args.input)[0].split('/')[0] if input_type == 'image': - # init visualizer - from mmpose.registry import VISUALIZERS - - pose_estimator.cfg.visualizer.radius = args.radius - pose_estimator.cfg.visualizer.alpha = args.alpha - pose_estimator.cfg.visualizer.line_width = args.thickness - visualizer = VISUALIZERS.build(pose_estimator.cfg.visualizer) - - # the dataset_meta is loaded from the checkpoint and - # then pass to the model in init_pose_estimator - visualizer.set_dataset_meta( - pose_estimator.dataset_meta, skeleton_style=args.skeleton_style) # inference pred_instances = process_one_image(args, args.input, detector, @@ -218,28 +217,6 @@ def main(): mmcv.imwrite(mmcv.rgb2bgr(img_vis), output_file) elif input_type in ['webcam', 'video']: - from mmpose.visualization import FastVisualizer - - visualizer = FastVisualizer( - pose_estimator.dataset_meta, - radius=args.radius, - line_width=args.thickness, - kpt_thr=args.kpt_thr) - - if args.draw_heatmap: - # init Localvisualizer - from mmpose.registry import VISUALIZERS - - pose_estimator.cfg.visualizer.radius = args.radius - pose_estimator.cfg.visualizer.alpha = args.alpha - pose_estimator.cfg.visualizer.line_width = args.thickness - local_visualizer = VISUALIZERS.build(pose_estimator.cfg.visualizer) - - # the dataset_meta is loaded from the checkpoint and - # then pass to the model in init_pose_estimator - local_visualizer.set_dataset_meta( - pose_estimator.dataset_meta, - skeleton_style=args.skeleton_style) if args.input == 'webcam': cap = cv2.VideoCapture(0) @@ -258,16 +235,9 @@ def main(): break # topdown pose estimation - if args.draw_heatmap: - pred_instances = process_one_image(args, frame, detector, - pose_estimator, - local_visualizer, 0.001) - else: - pred_instances = process_one_image(args, frame, detector, - pose_estimator) - # visualization - visualizer.draw_pose(frame, pred_instances) - cv2.imshow('MMPose Demo [Press ESC to Exit]', frame) + pred_instances = process_one_image(args, frame, detector, + pose_estimator, visualizer, + 0.001) if args.save_predictions: # save prediction results @@ -278,10 +248,7 @@ def main(): # output videos if output_file: - if args.draw_heatmap: - frame_vis = local_visualizer.get_image() - else: - frame_vis = frame.copy()[:, :, ::-1] + frame_vis = visualizer.get_image() if video_writer is None: fourcc = cv2.VideoWriter_fourcc(*'mp4v') diff --git a/mmpose/apis/inferencers/base_mmpose_inferencer.py b/mmpose/apis/inferencers/base_mmpose_inferencer.py index 15312c6bb7..86e61463b6 100644 --- a/mmpose/apis/inferencers/base_mmpose_inferencer.py +++ b/mmpose/apis/inferencers/base_mmpose_inferencer.py @@ -159,6 +159,9 @@ def _get_webcam_inputs(self, inputs: str) -> Generator: Raises: ValueError: If the inputs string is not in the expected format. """ + assert getattr(self.visualizer, 'backend', None) == 'opencv', \ + 'Visualizer must utilize the OpenCV backend in order to ' \ + 'support webcam inputs.' # Ensure the inputs string is in the expected format. inputs = inputs.lower() @@ -187,12 +190,9 @@ def _get_webcam_inputs(self, inputs: str) -> Generator: self.video_info = dict( fps=10, name='webcam.mp4', writer=None, predictions=[]) - # Set up webcam reader generator function. - self._window_closing = False - def _webcam_reader() -> Generator: while True: - if self._window_closing: + if cv2.waitKey(5) & 0xFF == 27: vcap.release() break @@ -322,16 +322,6 @@ def visualize(self, kpt_thr=kpt_thr) results.append(visualization) - if show and not hasattr(self, '_window_close_cid'): - if window_close_event_handler is None: - window_close_event_handler = \ - self._visualization_window_on_close - self._window_close_cid = \ - self.visualizer.manager.canvas.mpl_connect( - 'close_event', - window_close_event_handler - ) - if vis_out_dir: out_img = mmcv.rgb2bgr(visualization) diff --git a/mmpose/visualization/__init__.py b/mmpose/visualization/__init__.py index 73fbd645a9..357d40a707 100644 --- a/mmpose/visualization/__init__.py +++ b/mmpose/visualization/__init__.py @@ -1,5 +1,4 @@ # Copyright (c) OpenMMLab. All rights reserved. -from .fast_visualizer import FastVisualizer from .local_visualizer import PoseLocalVisualizer -__all__ = ['PoseLocalVisualizer', 'FastVisualizer'] +__all__ = ['PoseLocalVisualizer'] diff --git a/mmpose/visualization/fast_visualizer.py b/mmpose/visualization/fast_visualizer.py deleted file mode 100644 index fa0cb38527..0000000000 --- a/mmpose/visualization/fast_visualizer.py +++ /dev/null @@ -1,78 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import cv2 - - -class FastVisualizer: - """MMPose Fast Visualizer. - - A simple yet fast visualizer for video/webcam inference. - - Args: - metainfo (dict): pose meta information - radius (int, optional)): Keypoint radius for visualization. - Defaults to 6. - line_width (int, optional): Link width for visualization. - Defaults to 3. - kpt_thr (float, optional): Threshold for keypoints' confidence score, - keypoints with score below this value will not be drawn. - Defaults to 0.3. - """ - - def __init__(self, metainfo, radius=6, line_width=3, kpt_thr=0.3): - self.radius = radius - self.line_width = line_width - self.kpt_thr = kpt_thr - - self.keypoint_id2name = metainfo['keypoint_id2name'] - self.keypoint_name2id = metainfo['keypoint_name2id'] - self.keypoint_colors = metainfo['keypoint_colors'] - self.skeleton_links = metainfo['skeleton_links'] - self.skeleton_link_colors = metainfo['skeleton_link_colors'] - - def draw_pose(self, img, instances): - """Draw pose estimations on the given image. - - This method draws keypoints and skeleton links on the input image - using the provided instances. - - Args: - img (numpy.ndarray): The input image on which to - draw the pose estimations. - instances (object): An object containing detected instances' - information, including keypoints and keypoint_scores. - - Returns: - None: The input image will be modified in place. - """ - - if instances is None: - print('no instance detected') - return - - keypoints = instances.keypoints - scores = instances.keypoint_scores - - for kpts, score in zip(keypoints, scores): - for sk_id, sk in enumerate(self.skeleton_links): - if score[sk[0]] < self.kpt_thr or score[sk[1]] < self.kpt_thr: - # skip the link that should not be drawn - continue - - pos1 = (int(kpts[sk[0], 0]), int(kpts[sk[0], 1])) - pos2 = (int(kpts[sk[1], 0]), int(kpts[sk[1], 1])) - - color = self.skeleton_link_colors[sk_id].tolist() - cv2.line(img, pos1, pos2, color, thickness=self.line_width) - - for kid, kpt in enumerate(kpts): - if score[kid] < self.kpt_thr: - # skip the point that should not be drawn - continue - - x_coord, y_coord = int(kpt[0]), int(kpt[1]) - - color = self.keypoint_colors[kid].tolist() - cv2.circle(img, (int(x_coord), int(y_coord)), self.radius, - color, -1) - cv2.circle(img, (int(x_coord), int(y_coord)), self.radius, - (255, 255, 255)) diff --git a/mmpose/visualization/local_visualizer.py b/mmpose/visualization/local_visualizer.py index b19e89dea6..205993c006 100644 --- a/mmpose/visualization/local_visualizer.py +++ b/mmpose/visualization/local_visualizer.py @@ -8,11 +8,11 @@ import torch from mmengine.dist import master_only from mmengine.structures import InstanceData, PixelData -from mmengine.visualization import Visualizer from mmpose.datasets.datasets.utils import parse_pose_metainfo from mmpose.registry import VISUALIZERS from mmpose.structures import PoseDataSample +from .opencv_backend_visualizer import OpencvBackendVisualizer from .simcc_vis import SimCCVisualizer @@ -42,7 +42,7 @@ def _get_adaptive_scales(areas: np.ndarray, @VISUALIZERS.register_module() -class PoseLocalVisualizer(Visualizer): +class PoseLocalVisualizer(OpencvBackendVisualizer): """MMPose Local Visualizer. Args: @@ -115,8 +115,15 @@ def __init__(self, line_width: Union[int, float] = 1, radius: Union[int, float] = 3, show_keypoint_weight: bool = False, + backend: str = 'opencv', alpha: float = 0.8): - super().__init__(name, image, vis_backends, save_dir) + super().__init__( + name=name, + image=image, + vis_backends=vis_backends, + save_dir=save_dir, + backend=backend) + self.bbox_color = bbox_color self.kpt_color = kpt_color self.link_color = link_color @@ -297,35 +304,6 @@ def _draw_instances_kpts(self, f'({len(self.kpt_color)}) does not matches ' f'that of keypoints ({len(kpts)})') - # draw each point on image - for kid, kpt in enumerate(kpts): - if score[kid] < kpt_thr or not visible[ - kid] or kpt_color[kid] is None: - # skip the point that should not be drawn - continue - - color = kpt_color[kid] - if not isinstance(color, str): - color = tuple(int(c) for c in color) - transparency = self.alpha - if self.show_keypoint_weight: - transparency *= max(0, min(1, score[kid])) - self.draw_circles( - kpt, - radius=np.array([self.radius]), - face_colors=color, - edge_colors=color, - alpha=transparency, - line_widths=self.radius) - if show_kpt_idx: - self.draw_texts( - str(kid), - kpt, - colors=color, - font_sizes=self.radius * 3, - vertical_alignments='bottom', - horizontal_alignments='center') - # draw links if self.skeleton is not None and self.link_color is not None: if self.link_color is None or isinstance( @@ -385,6 +363,37 @@ def _draw_instances_kpts(self, self.draw_lines( X, Y, color, line_widths=self.line_width) + # draw each point on image + for kid, kpt in enumerate(kpts): + if score[kid] < kpt_thr or not visible[ + kid] or kpt_color[kid] is None: + # skip the point that should not be drawn + continue + + color = kpt_color[kid] + if not isinstance(color, str): + color = tuple(int(c) for c in color) + transparency = self.alpha + if self.show_keypoint_weight: + transparency *= max(0, min(1, score[kid])) + self.draw_circles( + kpt, + radius=np.array([self.radius]), + face_colors=color, + edge_colors=color, + alpha=transparency, + line_widths=self.radius) + if show_kpt_idx: + kpt[0] += self.radius + kpt[1] -= self.radius + self.draw_texts( + str(kid), + kpt, + colors=color, + font_sizes=self.radius * 3, + vertical_alignments='bottom', + horizontal_alignments='center') + return self.get_image() def _draw_instance_heatmap( diff --git a/mmpose/visualization/opencv_backend_visualizer.py b/mmpose/visualization/opencv_backend_visualizer.py new file mode 100644 index 0000000000..66a7731c76 --- /dev/null +++ b/mmpose/visualization/opencv_backend_visualizer.py @@ -0,0 +1,444 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from typing import List, Optional, Union + +import cv2 +import mmcv +import numpy as np +import torch +from mmengine.dist import master_only +from mmengine.visualization import Visualizer + + +class OpencvBackendVisualizer(Visualizer): + """Base visualizer with opencv backend support. + + Args: + name (str): Name of the instance. Defaults to 'visualizer'. + image (np.ndarray, optional): the origin image to draw. The format + should be RGB. Defaults to None. + vis_backends (list, optional): Visual backend config list. + Defaults to None. + save_dir (str, optional): Save file dir for all storage backends. + If it is None, the backend storage will not save any data. + fig_save_cfg (dict): Keyword parameters of figure for saving. + Defaults to empty dict. + fig_show_cfg (dict): Keyword parameters of figure for showing. + Defaults to empty dict. + backend (str): Backend used to draw elements on the image and display + the image. Defaults to 'matplotlib'. + """ + + def __init__(self, + name='visualizer', + backend: str = 'matplotlib', + *args, + **kwargs): + super().__init__(name, *args, **kwargs) + assert backend in ('opencv', 'matplotlib'), f'the argument ' \ + f'\'backend\' must be either \'opencv\' or \'matplotlib\', ' \ + f'but got \'{backend}\'.' + self.backend = backend + + @master_only + def set_image(self, image: np.ndarray) -> None: + """Set the image to draw. + + Args: + image (np.ndarray): The image to draw. + backend (str): The backend to save the image. + """ + assert image is not None + image = image.astype('uint8') + self._image = image + self.width, self.height = image.shape[1], image.shape[0] + self._default_font_size = max( + np.sqrt(self.height * self.width) // 90, 10) + + if self.backend == 'matplotlib': + # add a small 1e-2 to avoid precision lost due to matplotlib's + # truncation (https://github.com/matplotlib/matplotlib/issues/15363) # noqa + self.fig_save.set_size_inches( # type: ignore + (self.width + 1e-2) / self.dpi, + (self.height + 1e-2) / self.dpi) + # self.canvas = mpl.backends.backend_cairo.FigureCanvasCairo(fig) + self.ax_save.cla() + self.ax_save.axis(False) + self.ax_save.imshow( + image, + extent=(0, self.width, self.height, 0), + interpolation='none') + + @master_only + def get_image(self) -> np.ndarray: + """Get the drawn image. The format is RGB. + + Returns: + np.ndarray: the drawn image which channel is RGB. + """ + assert self._image is not None, 'Please set image using `set_image`' + if self.backend == 'matplotlib': + return super().get_image() + else: + return self._image + + @master_only + def draw_circles(self, + center: Union[np.ndarray, torch.Tensor], + radius: Union[np.ndarray, torch.Tensor], + face_colors: Union[str, tuple, List[str], + List[tuple]] = 'none', + **kwargs) -> 'Visualizer': + """Draw single or multiple circles. + + Args: + center (Union[np.ndarray, torch.Tensor]): The x coordinate of + each line' start and end points. + radius (Union[np.ndarray, torch.Tensor]): The y coordinate of + each line' start and end points. + edge_colors (Union[str, tuple, List[str], List[tuple]]): The + colors of circles. ``colors`` can have the same length with + lines or just single value. If ``colors`` is single value, + all the lines will have the same colors. Reference to + https://matplotlib.org/stable/gallery/color/named_colors.html + for more details. Defaults to 'g. + line_styles (Union[str, List[str]]): The linestyle + of lines. ``line_styles`` can have the same length with + texts or just single value. If ``line_styles`` is single + value, all the lines will have the same linestyle. + Reference to + https://matplotlib.org/stable/api/collections_api.html?highlight=collection#matplotlib.collections.AsteriskPolygonCollection.set_linestyle + for more details. Defaults to '-'. + line_widths (Union[Union[int, float], List[Union[int, float]]]): + The linewidth of lines. ``line_widths`` can have + the same length with lines or just single value. + If ``line_widths`` is single value, all the lines will + have the same linewidth. Defaults to 2. + face_colors (Union[str, tuple, List[str], List[tuple]]): + The face colors. Defaults to None. + alpha (Union[int, float]): The transparency of circles. + Defaults to 0.8. + """ + if self.backend == 'matplotlib': + super().draw_circles( + center=center, + radius=radius, + face_colors=face_colors, + **kwargs) + elif self.backend == 'opencv': + if isinstance(face_colors, str): + face_colors = mmcv.color_val(face_colors) + self._image = cv2.circle(self._image, + (int(center[0]), int(center[1])), + int(radius), face_colors, -1) + else: + raise ValueError(f'got unsupported backend {self.backend}') + + @master_only + def draw_texts( + self, + texts: Union[str, List[str]], + positions: Union[np.ndarray, torch.Tensor], + font_sizes: Optional[Union[int, List[int]]] = None, + colors: Union[str, tuple, List[str], List[tuple]] = 'g', + vertical_alignments: Union[str, List[str]] = 'top', + horizontal_alignments: Union[str, List[str]] = 'left', + bboxes: Optional[Union[dict, List[dict]]] = None, + **kwargs, + ) -> 'Visualizer': + """Draw single or multiple text boxes. + + Args: + texts (Union[str, List[str]]): Texts to draw. + positions (Union[np.ndarray, torch.Tensor]): The position to draw + the texts, which should have the same length with texts and + each dim contain x and y. + font_sizes (Union[int, List[int]], optional): The font size of + texts. ``font_sizes`` can have the same length with texts or + just single value. If ``font_sizes`` is single value, all the + texts will have the same font size. Defaults to None. + colors (Union[str, tuple, List[str], List[tuple]]): The colors + of texts. ``colors`` can have the same length with texts or + just single value. If ``colors`` is single value, all the + texts will have the same colors. Reference to + https://matplotlib.org/stable/gallery/color/named_colors.html + for more details. Defaults to 'g. + vertical_alignments (Union[str, List[str]]): The verticalalignment + of texts. verticalalignment controls whether the y positional + argument for the text indicates the bottom, center or top side + of the text bounding box. + ``vertical_alignments`` can have the same length with + texts or just single value. If ``vertical_alignments`` is + single value, all the texts will have the same + verticalalignment. verticalalignment can be 'center' or + 'top', 'bottom' or 'baseline'. Defaults to 'top'. + horizontal_alignments (Union[str, List[str]]): The + horizontalalignment of texts. Horizontalalignment controls + whether the x positional argument for the text indicates the + left, center or right side of the text bounding box. + ``horizontal_alignments`` can have + the same length with texts or just single value. + If ``horizontal_alignments`` is single value, all the texts + will have the same horizontalalignment. Horizontalalignment + can be 'center','right' or 'left'. Defaults to 'left'. + font_families (Union[str, List[str]]): The font family of + texts. ``font_families`` can have the same length with texts or + just single value. If ``font_families`` is single value, all + the texts will have the same font family. + font_familiy can be 'serif', 'sans-serif', 'cursive', 'fantasy' + or 'monospace'. Defaults to 'sans-serif'. + bboxes (Union[dict, List[dict]], optional): The bounding box of the + texts. If bboxes is None, there are no bounding box around + texts. ``bboxes`` can have the same length with texts or + just single value. If ``bboxes`` is single value, all + the texts will have the same bbox. Reference to + https://matplotlib.org/stable/api/_as_gen/matplotlib.patches.FancyBboxPatch.html#matplotlib.patches.FancyBboxPatch + for more details. Defaults to None. + font_properties (Union[FontProperties, List[FontProperties]], optional): + The font properties of texts. FontProperties is + a ``font_manager.FontProperties()`` object. + If you want to draw Chinese texts, you need to prepare + a font file that can show Chinese characters properly. + For example: `simhei.ttf`, `simsun.ttc`, `simkai.ttf` and so on. + Then set ``font_properties=matplotlib.font_manager.FontProperties(fname='path/to/font_file')`` + ``font_properties`` can have the same length with texts or + just single value. If ``font_properties`` is single value, + all the texts will have the same font properties. + Defaults to None. + `New in version 0.6.0.` + """ # noqa: E501 + + if self.backend == 'matplotlib': + super().draw_texts( + texts=texts, + positions=positions, + font_sizes=font_sizes, + colors=colors, + vertical_alignments=vertical_alignments, + horizontal_alignments=horizontal_alignments, + bboxes=bboxes, + **kwargs) + + elif self.backend == 'opencv': + font_scale = max(0.1, font_sizes / 30) + thickness = max(1, font_sizes // 15) + + text_size, text_baseline = cv2.getTextSize(texts, + cv2.FONT_HERSHEY_DUPLEX, + font_scale, thickness) + + x = int(positions[0]) + if horizontal_alignments == 'right': + x = max(0, x - text_size[0]) + y = int(positions[1]) + if vertical_alignments == 'top': + y = min(self.height, y + text_size[1]) + + if bboxes is not None: + bbox_color = bboxes[0]['facecolor'] + if isinstance(bbox_color, str): + bbox_color = mmcv.color_val(bbox_color) + + y = y - text_baseline // 2 + self._image = cv2.rectangle( + self._image, (x, y - text_size[1] - text_baseline // 2), + (x + text_size[0], y + text_baseline // 2), bbox_color, + cv2.FILLED) + + self._image = cv2.putText(self._image, texts, (x, y), + cv2.FONT_HERSHEY_SIMPLEX, font_scale, + colors, thickness - 1) + else: + raise ValueError(f'got unsupported backend {self.backend}') + + @master_only + def draw_bboxes(self, + bboxes: Union[np.ndarray, torch.Tensor], + edge_colors: Union[str, tuple, List[str], + List[tuple]] = 'g', + line_widths: Union[Union[int, float], + List[Union[int, float]]] = 2, + **kwargs) -> 'Visualizer': + """Draw single or multiple bboxes. + + Args: + bboxes (Union[np.ndarray, torch.Tensor]): The bboxes to draw with + the format of(x1,y1,x2,y2). + edge_colors (Union[str, tuple, List[str], List[tuple]]): The + colors of bboxes. ``colors`` can have the same length with + lines or just single value. If ``colors`` is single value, all + the lines will have the same colors. Refer to `matplotlib. + colors` for full list of formats that are accepted. + Defaults to 'g'. + line_styles (Union[str, List[str]]): The linestyle + of lines. ``line_styles`` can have the same length with + texts or just single value. If ``line_styles`` is single + value, all the lines will have the same linestyle. + Reference to + https://matplotlib.org/stable/api/collections_api.html?highlight=collection#matplotlib.collections.AsteriskPolygonCollection.set_linestyle + for more details. Defaults to '-'. + line_widths (Union[Union[int, float], List[Union[int, float]]]): + The linewidth of lines. ``line_widths`` can have + the same length with lines or just single value. + If ``line_widths`` is single value, all the lines will + have the same linewidth. Defaults to 2. + face_colors (Union[str, tuple, List[str], List[tuple]]): + The face colors. Defaults to None. + alpha (Union[int, float]): The transparency of bboxes. + Defaults to 0.8. + """ + if self.backend == 'matplotlib': + super().draw_bboxes( + bboxes=bboxes, + edge_colors=edge_colors, + line_widths=line_widths, + **kwargs) + + elif self.backend == 'opencv': + self._image = mmcv.imshow_bboxes( + self._image, + bboxes, + edge_colors, + top_k=-1, + thickness=line_widths, + show=False) + else: + raise ValueError(f'got unsupported backend {self.backend}') + + @master_only + def draw_lines(self, + x_datas: Union[np.ndarray, torch.Tensor], + y_datas: Union[np.ndarray, torch.Tensor], + colors: Union[str, tuple, List[str], List[tuple]] = 'g', + line_widths: Union[Union[int, float], + List[Union[int, float]]] = 2, + **kwargs) -> 'Visualizer': + """Draw single or multiple line segments. + + Args: + x_datas (Union[np.ndarray, torch.Tensor]): The x coordinate of + each line' start and end points. + y_datas (Union[np.ndarray, torch.Tensor]): The y coordinate of + each line' start and end points. + colors (Union[str, tuple, List[str], List[tuple]]): The colors of + lines. ``colors`` can have the same length with lines or just + single value. If ``colors`` is single value, all the lines + will have the same colors. Reference to + https://matplotlib.org/stable/gallery/color/named_colors.html + for more details. Defaults to 'g'. + line_styles (Union[str, List[str]]): The linestyle + of lines. ``line_styles`` can have the same length with + texts or just single value. If ``line_styles`` is single + value, all the lines will have the same linestyle. + Reference to + https://matplotlib.org/stable/api/collections_api.html?highlight=collection#matplotlib.collections.AsteriskPolygonCollection.set_linestyle + for more details. Defaults to '-'. + line_widths (Union[Union[int, float], List[Union[int, float]]]): + The linewidth of lines. ``line_widths`` can have + the same length with lines or just single value. + If ``line_widths`` is single value, all the lines will + have the same linewidth. Defaults to 2. + """ + if self.backend == 'matplotlib': + super().draw_lines( + x_datas=x_datas, + y_datas=y_datas, + colors=colors, + line_widths=line_widths, + **kwargs) + + elif self.backend == 'opencv': + + self._image = cv2.line( + self._image, (x_datas[0], y_datas[0]), + (x_datas[1], y_datas[1]), + colors, + thickness=line_widths) + else: + raise ValueError(f'got unsupported backend {self.backend}') + + @master_only + def draw_polygons(self, + polygons: Union[Union[np.ndarray, torch.Tensor], + List[Union[np.ndarray, torch.Tensor]]], + edge_colors: Union[str, tuple, List[str], + List[tuple]] = 'g', + **kwargs) -> 'Visualizer': + """Draw single or multiple bboxes. + + Args: + polygons (Union[Union[np.ndarray, torch.Tensor],\ + List[Union[np.ndarray, torch.Tensor]]]): The polygons to draw + with the format of (x1,y1,x2,y2,...,xn,yn). + edge_colors (Union[str, tuple, List[str], List[tuple]]): The + colors of polygons. ``colors`` can have the same length with + lines or just single value. If ``colors`` is single value, + all the lines will have the same colors. Refer to + `matplotlib.colors` for full list of formats that are accepted. + Defaults to 'g. + line_styles (Union[str, List[str]]): The linestyle + of lines. ``line_styles`` can have the same length with + texts or just single value. If ``line_styles`` is single + value, all the lines will have the same linestyle. + Reference to + https://matplotlib.org/stable/api/collections_api.html?highlight=collection#matplotlib.collections.AsteriskPolygonCollection.set_linestyle + for more details. Defaults to '-'. + line_widths (Union[Union[int, float], List[Union[int, float]]]): + The linewidth of lines. ``line_widths`` can have + the same length with lines or just single value. + If ``line_widths`` is single value, all the lines will + have the same linewidth. Defaults to 2. + face_colors (Union[str, tuple, List[str], List[tuple]]): + The face colors. Defaults to None. + alpha (Union[int, float]): The transparency of polygons. + Defaults to 0.8. + """ + if self.backend == 'matplotlib': + super().draw_polygons( + polygons=polygons, edge_colors=edge_colors, **kwargs) + + elif self.backend == 'opencv': + + self._image = cv2.fillConvexPoly(self._image, polygons, + edge_colors) + else: + raise ValueError(f'got unsupported backend {self.backend}') + + @master_only + def show(self, + drawn_img: Optional[np.ndarray] = None, + win_name: str = 'image', + wait_time: float = 0., + continue_key=' ') -> None: + """Show the drawn image. + + Args: + drawn_img (np.ndarray, optional): The image to show. If drawn_img + is None, it will show the image got by Visualizer. Defaults + to None. + win_name (str): The image title. Defaults to 'image'. + wait_time (float): Delay in seconds. 0 is the special + value that means "forever". Defaults to 0. + continue_key (str): The key for users to continue. Defaults to + the space key. + """ + if self.backend == 'matplotlib': + super().show( + drawn_img=drawn_img, + win_name=win_name, + wait_time=wait_time, + continue_key=continue_key) + + elif self.backend == 'opencv': + # Keep images are shown in the same window, and the title of window + # will be updated with `win_name`. + if not hasattr(self, win_name): + self._cv_win_name = win_name + cv2.namedWindow(winname=f'{id(self)}') + cv2.setWindowTitle(f'{id(self)}', win_name) + else: + cv2.setWindowTitle(f'{id(self)}', win_name) + shown_img = self.get_image() if drawn_img is None else drawn_img + cv2.imshow(str(id(self)), mmcv.bgr2rgb(shown_img)) + cv2.waitKey(int(np.ceil(wait_time * 1000))) + else: + raise ValueError(f'got unsupported backend {self.backend}') diff --git a/tests/test_visualization/test_fast_visualizer.py b/tests/test_visualization/test_fast_visualizer.py deleted file mode 100644 index f4a24ca1f9..0000000000 --- a/tests/test_visualization/test_fast_visualizer.py +++ /dev/null @@ -1,71 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -from unittest import TestCase - -import numpy as np - -from mmpose.visualization import FastVisualizer - - -class TestFastVisualizer(TestCase): - - def setUp(self): - self.metainfo = { - 'keypoint_id2name': { - 0: 'nose', - 1: 'left_eye', - 2: 'right_eye' - }, - 'keypoint_name2id': { - 'nose': 0, - 'left_eye': 1, - 'right_eye': 2 - }, - 'keypoint_colors': np.array([[255, 0, 0], [0, 255, 0], [0, 0, - 255]]), - 'skeleton_links': [(0, 1), (1, 2)], - 'skeleton_link_colors': np.array([[255, 255, 0], [255, 0, 255]]) - } - self.visualizer = FastVisualizer(self.metainfo) - - def test_init(self): - self.assertEqual(self.visualizer.radius, 6) - self.assertEqual(self.visualizer.line_width, 3) - self.assertEqual(self.visualizer.kpt_thr, 0.3) - self.assertEqual(self.visualizer.keypoint_id2name, - self.metainfo['keypoint_id2name']) - self.assertEqual(self.visualizer.keypoint_name2id, - self.metainfo['keypoint_name2id']) - np.testing.assert_array_equal(self.visualizer.keypoint_colors, - self.metainfo['keypoint_colors']) - self.assertEqual(self.visualizer.skeleton_links, - self.metainfo['skeleton_links']) - np.testing.assert_array_equal(self.visualizer.skeleton_link_colors, - self.metainfo['skeleton_link_colors']) - - def test_draw_pose(self): - img = np.zeros((480, 640, 3), dtype=np.uint8) - instances = type('Instances', (object, ), {})() - instances.keypoints = np.array([[[100, 100], [200, 200], [300, 300]]], - dtype=np.float32) - instances.keypoint_scores = np.array([[0.5, 0.5, 0.5]], - dtype=np.float32) - - self.visualizer.draw_pose(img, instances) - - # Check if keypoints are drawn - self.assertNotEqual(img[100, 100].tolist(), [0, 0, 0]) - self.assertNotEqual(img[200, 200].tolist(), [0, 0, 0]) - self.assertNotEqual(img[300, 300].tolist(), [0, 0, 0]) - - # Check if skeleton links are drawn - self.assertNotEqual(img[150, 150].tolist(), [0, 0, 0]) - self.assertNotEqual(img[250, 250].tolist(), [0, 0, 0]) - - def test_draw_pose_with_none_instances(self): - img = np.zeros((480, 640, 3), dtype=np.uint8) - instances = None - - self.visualizer.draw_pose(img, instances) - - # Check if the image is still empty (black) - self.assertEqual(np.count_nonzero(img), 0)