diff --git a/.circleci/config.yml b/.circleci/config.yml
new file mode 100644
index 0000000000..a7e79d73df
--- /dev/null
+++ b/.circleci/config.yml
@@ -0,0 +1,169 @@
+# Use the latest 2.1 version of CircleCI pipeline process engine.
+# See: https://circleci.com/docs/2.0/configuration-reference
+version: 2.1
+
+# Define a job to be invoked later in a workflow.
+# See: https://circleci.com/docs/2.0/configuration-reference/#jobs
+jobs:
+ lint:
+ docker:
+ - image: cimg/python:3.7.4
+ # Add steps to the job
+ # See: https://circleci.com/docs/2.0/configuration-reference/#steps
+ steps:
+ - checkout
+ - run:
+ name: Install dependencies
+ command: |
+ sudo apt-add-repository ppa:brightbox/ruby-ng -y
+ sudo apt-get update
+ sudo apt-get install -y ruby2.7
+ - run:
+ name: Install pre-commit hook
+ command: |
+ pip install pre-commit
+ pre-commit install
+ - run:
+ name: Linting
+ command: pre-commit run --all-files
+ - run:
+ name: Check docstring coverage
+ command: |
+ pip install interrogate
+ interrogate -v --ignore-init-method --ignore-module --ignore-nested-functions --ignore-regex "__repr__" --fail-under 80 mmpose
+ build_cpu:
+ parameters:
+ # The python version must match available image tags in
+ # https://circleci.com/developer/images/image/cimg/python
+ python:
+ type: string
+ default: "3.7.4"
+ torch:
+ type: string
+ torchvision:
+ type: string
+ env:
+ type: string
+ default: ""
+ docker:
+ - image: cimg/python:<< parameters.python >>
+ resource_class: large
+ steps:
+ - checkout
+ - run:
+ name: Install Libraries
+ command: |
+ sudo apt-get update
+ sudo apt-get install -y ffmpeg libsm6 libxext6 git ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6 libturbojpeg
+ - run:
+ name: Configure Python & pip
+ command: |
+ python -m pip install --upgrade pip
+ python -m pip install wheel
+ - run:
+ name: Install PyTorch
+ command: |
+ python -V
+ python -m pip install torch==<< parameters.torch >>+cpu torchvision==<< parameters.torchvision >>+cpu -f https://download.pytorch.org/whl/torch_stable.html
+ - when:
+ condition:
+ equal: [ "3.9.0", << parameters.python >> ]
+ steps:
+ - run: python -m pip install protobuf && sudo apt-get update && sudo apt-get -y install libprotobuf-dev protobuf-compiler cmake
+ - run:
+ name: Install mmpose dependencies
+ command: |
+ python -m pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cpu/torch<< parameters.torch >>/index.html
+ python -m pip install -r requirements.txt
+ - run:
+ name: Build and install
+ command: |
+ python -m pip install -e .
+ - run:
+ name: Run unittests
+ command: |
+ python -m coverage run --branch --source mmpose -m pytest tests/
+ python -m coverage xml
+ python -m coverage report -m
+ build_cu101:
+ machine:
+ image: ubuntu-1604-cuda-10.1:201909-23
+ resource_class: gpu.nvidia.small
+ steps:
+ - checkout
+ - run:
+ name: Configure Python & pip
+ command: |
+ pyenv global 3.7.0
+ python -m pip install --upgrade pip
+ python -m pip install wheel
+ - run:
+ name: Install Libraries
+ command: |
+ sudo apt-get update
+ sudo apt-get install -y ffmpeg libsm6 libxext6 git ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6 libturbojpeg
+ - run:
+ name: Install PyTorch
+ command: |
+ python -V
+ pip install torch==1.6.0+cu101 torchvision==0.7.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html
+ - run:
+ name: Install mmpose dependencies
+ # pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu101/torch${{matrix.torch_version}}/index.html
+ command: |
+ python -m pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu101/torch1.6.0/index.html
+ python -m pip install -r requirements.txt
+ python -c 'import mmcv; print(mmcv.__version__)'
+ - run:
+ name: Build and install
+ command: |
+ python -m pip install -e .
+ - run:
+ name: Run unittests
+ command: |
+ python -m pytest tests/
+
+workflows:
+ unit_tests:
+ jobs:
+ - lint
+ - build_cpu:
+ name: build_cpu_th1.6
+ torch: 1.6.0
+ torchvision: 0.7.0
+ requires:
+ - lint
+ - build_cpu:
+ name: build_cpu_th1.7
+ torch: 1.7.0
+ torchvision: 0.8.1
+ requires:
+ - lint
+ - build_cpu:
+ name: build_cpu_th1.8_py3.9
+ torch: 1.8.0
+ torchvision: 0.9.0
+ python: "3.9.0"
+ requires:
+ - lint
+ - build_cpu:
+ name: build_cpu_th1.9_py3.8
+ torch: 1.9.0
+ torchvision: 0.10.0
+ python: "3.8.12"
+ requires:
+ - lint
+ - build_cpu:
+ name: build_cpu_th1.9_py3.9
+ torch: 1.9.0
+ torchvision: 0.10.0
+ python: "3.9.0"
+ requires:
+ - lint
+ - build_cu101:
+ requires:
+ - build_cpu_th1.6
+ - build_cpu_th1.7
+ - build_cpu_th1.8_py3.9
+ - build_cpu_th1.9_py3.8
+ - build_cpu_th1.9_py3.9
diff --git a/.dev_scripts/github/update_model_index.py b/.dev_scripts/github/update_model_index.py
index 1b1498e039..a4c38d4835 100755
--- a/.dev_scripts/github/update_model_index.py
+++ b/.dev_scripts/github/update_model_index.py
@@ -123,6 +123,7 @@ def parse_config_path(path):
'2d_kpt_sview_rgb_img': '2D Keypoint',
'2d_kpt_sview_rgb_vid': '2D Keypoint',
'3d_kpt_sview_rgb_img': '3D Keypoint',
+ '3d_kpt_mview_rgb_img': '3D Keypoint',
'3d_kpt_sview_rgb_vid': '3D Keypoint',
'3d_mesh_sview_rgb_img': '3D Mesh',
None: None
diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index 6bfa694aa4..c9521023a5 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -1,4 +1,4 @@
-# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
+# This workflow will install Python dependencies, run tests with a variety of Python versions
# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
name: build
@@ -29,25 +29,6 @@ concurrency:
cancel-in-progress: true
jobs:
- lint:
- runs-on: ubuntu-18.04
- steps:
- - uses: actions/checkout@v2
- - name: Set up Python 3.7
- uses: actions/setup-python@v2
- with:
- python-version: 3.7
- - name: Install pre-commit hook
- run: |
- pip install pre-commit
- pre-commit install
- - name: Linting
- run: pre-commit run --all-files
- - name: Check docstring coverage
- run: |
- pip install interrogate
- interrogate -v --ignore-init-method --ignore-module --ignore-nested-functions --ignore-regex "__repr__" --fail-under 80 mmpose
-
build_cpu:
runs-on: ubuntu-18.04
strategy:
diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml
new file mode 100644
index 0000000000..34e8c4d403
--- /dev/null
+++ b/.github/workflows/lint.yml
@@ -0,0 +1,34 @@
+
+# This workflow will run lint
+
+name: lint
+
+on: [push, pull_request]
+
+concurrency:
+ group: ${{ github.workflow }}-${{ github.ref }}
+ cancel-in-progress: true
+
+jobs:
+ lint:
+ runs-on: ubuntu-18.04
+ steps:
+ - uses: actions/checkout@v2
+ - name: Set up Python 3.7
+ uses: actions/setup-python@v2
+ with:
+ python-version: 3.7
+ - name: Install pre-commit hook
+ run: |
+ # markdownlint requires ruby >= 2.7
+ sudo apt-add-repository ppa:brightbox/ruby-ng -y
+ sudo apt-get update
+ sudo apt-get install -y ruby2.7
+ pip install pre-commit
+ pre-commit install
+ - name: Linting
+ run: pre-commit run --all-files
+ - name: Check docstring coverage
+ run: |
+ pip install interrogate
+ interrogate -v --ignore-init-method --ignore-module --ignore-nested-functions --ignore-regex "__repr__" --fail-under 80 mmpose
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index d667bd9afb..e54caec467 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -29,8 +29,8 @@ repos:
args: ["--remove"]
- id: mixed-line-ending
args: ["--fix=lf"]
- - repo: https://github.com/jumanjihouse/pre-commit-hooks
- rev: 2.1.4
+ - repo: https://github.com/markdownlint/markdownlint
+ rev: v0.11.0
hooks:
- id: markdownlint
args: [ "-r", "~MD002,~MD007,~MD013,~MD024,~MD029,~MD033,~MD034,~MD036,~MD041" ]
diff --git a/README.md b/README.md
index 706f671c27..925e81922f 100644
--- a/README.md
+++ b/README.md
@@ -77,6 +77,8 @@ Supported techniques:
(click to collapse)
- [x] [Wingloss](https://mmpose.readthedocs.io/en/latest/papers/techniques.html#wingloss-cvpr-2018) (CVPR'2018)
+- [x] [AdaptiveWingloss](https://mmpose.readthedocs.io/en/latest/papers/techniques.html#adaptivewingloss-iccv-2019) (ICCV'2019)
+- [x] [SoftWingloss](https://mmpose.readthedocs.io/en/latest/papers/techniques.html#softwingloss-tip-2021) (TIP'2021)
- [x] [DarkPose](https://mmpose.readthedocs.io/en/latest/papers/techniques.html#darkpose-cvpr-2020) (CVPR'2020)
- [x] [UDP](https://mmpose.readthedocs.io/en/latest/papers/techniques.html#udp-cvpr-2020) (CVPR'2020)
- [x] [FP16](https://mmpose.readthedocs.io/en/latest/papers/techniques.html#fp16-arxiv-2017) (ArXiv'2017)
diff --git a/README_CN.md b/README_CN.md
index f8835d1b4a..5486508b7b 100644
--- a/README_CN.md
+++ b/README_CN.md
@@ -77,6 +77,8 @@ https://user-images.githubusercontent.com/15977946/124654387-0fd3c500-ded1-11eb-
(click to collapse)
- [x] [Wingloss](https://mmpose.readthedocs.io/zh_CN/latest/papers/techniques.html#wingloss-cvpr-2018) (CVPR'2018)
+- [x] [AdaptiveWingloss](https://mmpose.readthedocs.io/zh_CN/latest/papers/techniques.html#adaptivewingloss-iccv-2019) (ICCV'2019)
+- [x] [SoftWingloss](https://mmpose.readthedocs.io/zh_CN/latest/papers/techniques.html#softwingloss-tip-2021) (TIP'2021)
- [x] [DarkPose](https://mmpose.readthedocs.io/zh_CN/latest/papers/techniques.html#darkpose-cvpr-2020) (CVPR'2020)
- [x] [UDP](https://mmpose.readthedocs.io/zh_CN/latest/papers/techniques.html#udp-cvpr-2020) (CVPR'2020)
- [x] [FP16](https://mmpose.readthedocs.io/zh_CN/latest/papers/techniques.html#fp16-arxiv-2017) (ArXiv'2017)
diff --git a/configs/_base_/datasets/panoptic_body3d.py b/configs/_base_/datasets/panoptic_body3d.py
new file mode 100644
index 0000000000..e3b19ac462
--- /dev/null
+++ b/configs/_base_/datasets/panoptic_body3d.py
@@ -0,0 +1,160 @@
+dataset_info = dict(
+ dataset_name='panoptic_pose_3d',
+ paper_info=dict(
+ author='Joo, Hanbyul and Simon, Tomas and Li, Xulong'
+ 'and Liu, Hao and Tan, Lei and Gui, Lin and Banerjee, Sean'
+ 'and Godisart, Timothy and Nabbe, Bart and Matthews, Iain'
+ 'and Kanade, Takeo and Nobuhara, Shohei and Sheikh, Yaser',
+ title='Panoptic Studio: A Massively Multiview System '
+ 'for Interaction Motion Capture',
+ container='IEEE Transactions on Pattern Analysis'
+ ' and Machine Intelligence',
+ year='2017',
+ homepage='http://domedb.perception.cs.cmu.edu',
+ ),
+ keypoint_info={
+ 0:
+ dict(name='neck', id=0, color=[51, 153, 255], type='upper', swap=''),
+ 1:
+ dict(name='nose', id=1, color=[51, 153, 255], type='upper', swap=''),
+ 2:
+ dict(name='mid_hip', id=2, color=[0, 255, 0], type='lower', swap=''),
+ 3:
+ dict(
+ name='left_shoulder',
+ id=3,
+ color=[0, 255, 0],
+ type='upper',
+ swap='right_shoulder'),
+ 4:
+ dict(
+ name='left_elbow',
+ id=4,
+ color=[0, 255, 0],
+ type='upper',
+ swap='right_elbow'),
+ 5:
+ dict(
+ name='left_wrist',
+ id=5,
+ color=[0, 255, 0],
+ type='upper',
+ swap='right_wrist'),
+ 6:
+ dict(
+ name='left_hip',
+ id=6,
+ color=[0, 255, 0],
+ type='lower',
+ swap='right_hip'),
+ 7:
+ dict(
+ name='left_knee',
+ id=7,
+ color=[0, 255, 0],
+ type='lower',
+ swap='right_knee'),
+ 8:
+ dict(
+ name='left_ankle',
+ id=8,
+ color=[0, 255, 0],
+ type='lower',
+ swap='right_ankle'),
+ 9:
+ dict(
+ name='right_shoulder',
+ id=9,
+ color=[255, 128, 0],
+ type='upper',
+ swap='left_shoulder'),
+ 10:
+ dict(
+ name='right_elbow',
+ id=10,
+ color=[255, 128, 0],
+ type='upper',
+ swap='left_elbow'),
+ 11:
+ dict(
+ name='right_wrist',
+ id=11,
+ color=[255, 128, 0],
+ type='upper',
+ swap='left_wrist'),
+ 12:
+ dict(
+ name='right_hip',
+ id=12,
+ color=[255, 128, 0],
+ type='lower',
+ swap='left_hip'),
+ 13:
+ dict(
+ name='right_knee',
+ id=13,
+ color=[255, 128, 0],
+ type='lower',
+ swap='left_knee'),
+ 14:
+ dict(
+ name='right_ankle',
+ id=14,
+ color=[255, 128, 0],
+ type='lower',
+ swap='left_ankle'),
+ 15:
+ dict(
+ name='left_eye',
+ id=15,
+ color=[51, 153, 255],
+ type='upper',
+ swap='right_eye'),
+ 16:
+ dict(
+ name='left_ear',
+ id=16,
+ color=[51, 153, 255],
+ type='upper',
+ swap='right_ear'),
+ 17:
+ dict(
+ name='right_eye',
+ id=17,
+ color=[51, 153, 255],
+ type='upper',
+ swap='left_eye'),
+ 18:
+ dict(
+ name='right_ear',
+ id=18,
+ color=[51, 153, 255],
+ type='upper',
+ swap='left_ear')
+ },
+ skeleton_info={
+ 0: dict(link=('nose', 'neck'), id=0, color=[51, 153, 255]),
+ 1: dict(link=('neck', 'left_shoulder'), id=1, color=[0, 255, 0]),
+ 2: dict(link=('neck', 'right_shoulder'), id=2, color=[255, 128, 0]),
+ 3: dict(link=('left_shoulder', 'left_elbow'), id=3, color=[0, 255, 0]),
+ 4: dict(
+ link=('right_shoulder', 'right_elbow'), id=4, color=[255, 128, 0]),
+ 5: dict(link=('left_elbow', 'left_wrist'), id=5, color=[0, 255, 0]),
+ 6:
+ dict(link=('right_elbow', 'right_wrist'), id=6, color=[255, 128, 0]),
+ 7: dict(link=('left_ankle', 'left_knee'), id=7, color=[0, 255, 0]),
+ 8: dict(link=('left_knee', 'left_hip'), id=8, color=[0, 255, 0]),
+ 9: dict(link=('right_ankle', 'right_knee'), id=9, color=[255, 128, 0]),
+ 10: dict(link=('right_knee', 'right_hip'), id=10, color=[255, 128, 0]),
+ 11: dict(link=('mid_hip', 'left_hip'), id=11, color=[0, 255, 0]),
+ 12: dict(link=('mid_hip', 'right_hip'), id=12, color=[255, 128, 0]),
+ 13: dict(link=('mid_hip', 'neck'), id=13, color=[51, 153, 255]),
+ },
+ joint_weights=[
+ 1.0, 1.0, 1.0, 1.0, 1.2, 1.5, 1.0, 1.2, 1.5, 1.0, 1.2, 1.5, 1.0, 1.2,
+ 1.5, 1.0, 1.0, 1.0, 1.0
+ ],
+ sigmas=[
+ 0.026, 0.026, 0.107, 0.079, 0.072, 0.062, 0.107, 0.087, 0.089, 0.079,
+ 0.072, 0.062, 0.107, 0.087, 0.089, 0.025, 0.035, 0.025, 0.035
+ ])
diff --git a/configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/res50_coco_256x192_awing.py b/configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/res50_coco_256x192_awing.py
new file mode 100644
index 0000000000..e3f328b87a
--- /dev/null
+++ b/configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/res50_coco_256x192_awing.py
@@ -0,0 +1,145 @@
+_base_ = ['../../../../_base_/datasets/coco.py']
+log_level = 'INFO'
+load_from = None
+resume_from = None
+dist_params = dict(backend='nccl')
+workflow = [('train', 1)]
+checkpoint_config = dict(interval=10)
+evaluation = dict(interval=10, metric='mAP', save_best='AP')
+
+optimizer = dict(
+ type='Adam',
+ lr=5e-4,
+)
+optimizer_config = dict(grad_clip=None)
+# learning policy
+lr_config = dict(
+ policy='step',
+ warmup='linear',
+ warmup_iters=500,
+ warmup_ratio=0.001,
+ step=[170, 200])
+total_epochs = 210
+log_config = dict(
+ interval=50,
+ hooks=[
+ dict(type='TextLoggerHook'),
+ # dict(type='TensorboardLoggerHook')
+ ])
+
+channel_cfg = dict(
+ num_output_channels=17,
+ dataset_joints=17,
+ dataset_channel=[
+ [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
+ ],
+ inference_channel=[
+ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16
+ ])
+
+# model settings
+model = dict(
+ type='TopDown',
+ pretrained='torchvision://resnet50',
+ backbone=dict(type='ResNet', depth=50),
+ keypoint_head=dict(
+ type='TopdownHeatmapSimpleHead',
+ in_channels=2048,
+ out_channels=channel_cfg['num_output_channels'],
+ loss_keypoint=dict(type='AdaptiveWingLoss', use_target_weight=True)),
+ train_cfg=dict(),
+ test_cfg=dict(
+ flip_test=True,
+ post_process='default',
+ shift_heatmap=True,
+ modulate_kernel=11))
+
+data_cfg = dict(
+ image_size=[192, 256],
+ heatmap_size=[48, 64],
+ num_output_channels=channel_cfg['num_output_channels'],
+ num_joints=channel_cfg['dataset_joints'],
+ dataset_channel=channel_cfg['dataset_channel'],
+ inference_channel=channel_cfg['inference_channel'],
+ soft_nms=False,
+ nms_thr=1.0,
+ oks_thr=0.9,
+ vis_thr=0.2,
+ use_gt_bbox=False,
+ det_bbox_thr=0.0,
+ bbox_file='data/coco/person_detection_results/'
+ 'COCO_val2017_detections_AP_H_56_person.json',
+)
+
+train_pipeline = [
+ dict(type='LoadImageFromFile'),
+ dict(type='TopDownRandomFlip', flip_prob=0.5),
+ dict(
+ type='TopDownHalfBodyTransform',
+ num_joints_half_body=8,
+ prob_half_body=0.3),
+ dict(
+ type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5),
+ dict(type='TopDownAffine'),
+ dict(type='ToTensor'),
+ dict(
+ type='NormalizeTensor',
+ mean=[0.485, 0.456, 0.406],
+ std=[0.229, 0.224, 0.225]),
+ dict(type='TopDownGenerateTarget', sigma=2),
+ dict(
+ type='Collect',
+ keys=['img', 'target', 'target_weight'],
+ meta_keys=[
+ 'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
+ 'rotation', 'bbox_score', 'flip_pairs'
+ ]),
+]
+
+val_pipeline = [
+ dict(type='LoadImageFromFile'),
+ dict(type='TopDownAffine'),
+ dict(type='ToTensor'),
+ dict(
+ type='NormalizeTensor',
+ mean=[0.485, 0.456, 0.406],
+ std=[0.229, 0.224, 0.225]),
+ dict(
+ type='Collect',
+ keys=['img'],
+ meta_keys=[
+ 'image_file', 'center', 'scale', 'rotation', 'bbox_score',
+ 'flip_pairs'
+ ]),
+]
+
+test_pipeline = val_pipeline
+
+data_root = 'data/coco'
+data = dict(
+ samples_per_gpu=64,
+ workers_per_gpu=2,
+ val_dataloader=dict(samples_per_gpu=32),
+ test_dataloader=dict(samples_per_gpu=32),
+ train=dict(
+ type='TopDownCocoDataset',
+ ann_file=f'{data_root}/annotations/person_keypoints_train2017.json',
+ img_prefix=f'{data_root}/train2017/',
+ data_cfg=data_cfg,
+ pipeline=train_pipeline,
+ dataset_info={{_base_.dataset_info}}),
+ val=dict(
+ type='TopDownCocoDataset',
+ ann_file=f'{data_root}/annotations/person_keypoints_val2017.json',
+ img_prefix=f'{data_root}/val2017/',
+ data_cfg=data_cfg,
+ pipeline=val_pipeline,
+ dataset_info={{_base_.dataset_info}}),
+ test=dict(
+ type='TopDownCocoDataset',
+ ann_file=f'{data_root}/annotations/person_keypoints_val2017.json',
+ img_prefix=f'{data_root}/val2017/',
+ data_cfg=data_cfg,
+ pipeline=test_pipeline,
+ dataset_info={{_base_.dataset_info}}),
+)
diff --git a/configs/body/3d_kpt_mview_rgb_img/README.md b/configs/body/3d_kpt_mview_rgb_img/README.md
new file mode 100644
index 0000000000..706a780fd8
--- /dev/null
+++ b/configs/body/3d_kpt_mview_rgb_img/README.md
@@ -0,0 +1,8 @@
+# Multi-view 3D Human Body Pose Estimation
+
+Multi-view 3D human body pose estimation targets at predicting the X, Y, Z coordinates of human body joints from multi-view RGB images.
+For this task, we currently support [VoxelPose](configs/body/3d_kpt_mview_rgb_img/voxelpose).
+
+## Data preparation
+
+Please follow [DATA Preparation](/docs/tasks/3d_body_keypoint.md) to prepare data.
diff --git a/configs/body/3d_kpt_mview_rgb_img/voxelpose/README.md b/configs/body/3d_kpt_mview_rgb_img/voxelpose/README.md
new file mode 100644
index 0000000000..f3160f5b92
--- /dev/null
+++ b/configs/body/3d_kpt_mview_rgb_img/voxelpose/README.md
@@ -0,0 +1,23 @@
+# VoxelPose: Towards Multi-Camera 3D Human Pose Estimation in Wild Environment
+
+
+
+
+VoxelPose (ECCV'2020)
+
+```bibtex
+@inproceedings{tumultipose,
+ title={VoxelPose: Towards Multi-Camera 3D Human Pose Estimation in Wild Environment},
+ author={Tu, Hanyue and Wang, Chunyu and Zeng, Wenjun},
+ booktitle={ECCV},
+ year={2020}
+}
+```
+
+
+
+VoxelPose proposes to break down the task of 3d human pose estimation into 2 stages: (1) Human center detection by Cuboid Proposal Network
+(2) Human pose regression by Pose Regression Network.
+
+The networks in the two stages are all based on 3D convolution. And the input feature volumes are generated by projecting each voxel to
+multi-view images and sampling at the projected location on the 2D heatmaps.
diff --git a/configs/body/3d_kpt_mview_rgb_img/voxelpose/panoptic/voxelpose_prn64x64x64_cpn80x80x20_panoptic_cam5.md b/configs/body/3d_kpt_mview_rgb_img/voxelpose/panoptic/voxelpose_prn64x64x64_cpn80x80x20_panoptic_cam5.md
new file mode 100644
index 0000000000..a71ad8e6a0
--- /dev/null
+++ b/configs/body/3d_kpt_mview_rgb_img/voxelpose/panoptic/voxelpose_prn64x64x64_cpn80x80x20_panoptic_cam5.md
@@ -0,0 +1,37 @@
+
+
+
+VoxelPose (ECCV'2020)
+
+```bibtex
+@inproceedings{tumultipose,
+ title={VoxelPose: Towards Multi-Camera 3D Human Pose Estimation in Wild Environment},
+ author={Tu, Hanyue and Wang, Chunyu and Zeng, Wenjun},
+ booktitle={ECCV},
+ year={2020}
+}
+```
+
+
+
+
+
+
+CMU Panoptic (ICCV'2015)
+
+```bibtex
+@Article = {joo_iccv_2015,
+author = {Hanbyul Joo, Hao Liu, Lei Tan, Lin Gui, Bart Nabbe, Iain Matthews, Takeo Kanade, Shohei Nobuhara, and Yaser Sheikh},
+title = {Panoptic Studio: A Massively Multiview System for Social Motion Capture},
+booktitle = {ICCV},
+year = {2015}
+}
+```
+
+
+
+Results on CMU Panoptic dataset.
+
+| Arch | mAP | mAR | MPJPE | Recall@500mm| ckpt | log |
+| :--- | :---: | :---: | :---: | :---: | :---: | :---: |
+| [prn64_cpn80_res50](/configs/body/3d_kpt_mview_rgb_img/voxelpose/panoptic/voxelpose_prn64x64x64_cpn80x80x20_panoptic_cam5.py) | 97.31 | 97.99 | 17.57| 99.85| [ckpt](https://download.openmmlab.com/mmpose/body3d/voxelpose/voxelpose_prn64x64x64_cpn80x80x20_panoptic_cam5-545c150e_20211103.pth) | [log](https://download.openmmlab.com/mmpose/body3d/voxelpose/voxelpose_prn64x64x64_cpn80x80x20_panoptic_cam5_20211103.log.json) |
diff --git a/configs/body/3d_kpt_mview_rgb_img/voxelpose/panoptic/voxelpose_prn64x64x64_cpn80x80x20_panoptic_cam5.py b/configs/body/3d_kpt_mview_rgb_img/voxelpose/panoptic/voxelpose_prn64x64x64_cpn80x80x20_panoptic_cam5.py
new file mode 100644
index 0000000000..a444a0da1d
--- /dev/null
+++ b/configs/body/3d_kpt_mview_rgb_img/voxelpose/panoptic/voxelpose_prn64x64x64_cpn80x80x20_panoptic_cam5.py
@@ -0,0 +1,223 @@
+_base_ = ['../../../../_base_/datasets/panoptic_body3d.py']
+log_level = 'INFO'
+load_from = None
+resume_from = None
+dist_params = dict(backend='nccl')
+workflow = [('train', 1)]
+checkpoint_config = dict(interval=1)
+evaluation = dict(interval=1, metric='mAP', save_best='mAP')
+
+optimizer = dict(
+ type='Adam',
+ lr=0.0001,
+)
+optimizer_config = dict(grad_clip=None)
+# learning policy
+lr_config = dict(
+ policy='step',
+ warmup='linear',
+ warmup_iters=500,
+ warmup_ratio=0.001,
+ step=[8, 9])
+total_epochs = 15
+log_config = dict(
+ interval=50, hooks=[
+ dict(type='TextLoggerHook'),
+ ])
+
+space_size = [8000, 8000, 2000]
+space_center = [0, -500, 800]
+cube_size = [80, 80, 20]
+sub_space_size = [2000, 2000, 2000]
+sub_cube_size = [64, 64, 64]
+image_size = [960, 512]
+heatmap_size = [240, 128]
+num_joints = 15
+
+train_data_cfg = dict(
+ image_size=image_size,
+ heatmap_size=[heatmap_size],
+ num_joints=num_joints,
+ seq_list=[
+ '160422_ultimatum1', '160224_haggling1', '160226_haggling1',
+ '161202_haggling1', '160906_ian1', '160906_ian2', '160906_ian3',
+ '160906_band1', '160906_band2'
+ ],
+ cam_list=[(0, 12), (0, 6), (0, 23), (0, 13), (0, 3)],
+ num_cameras=5,
+ seq_frame_interval=3,
+ subset='train',
+ root_id=2,
+ max_num=10,
+ space_size=space_size,
+ space_center=space_center,
+ cube_size=cube_size,
+)
+
+test_data_cfg = train_data_cfg.copy()
+test_data_cfg.update(
+ dict(
+ seq_list=[
+ '160906_pizza1',
+ '160422_haggling1',
+ '160906_ian5',
+ '160906_band4',
+ ],
+ seq_frame_interval=12,
+ subset='validation'))
+
+# model settings
+detector_2d = dict(
+ type='AssociativeEmbedding',
+ pretrained=None,
+ backbone=dict(type='ResNet', depth=50),
+ keypoint_head=dict(
+ type='DeconvHead',
+ in_channels=2048,
+ out_channels=num_joints,
+ num_deconv_layers=3,
+ num_deconv_filters=(256, 256, 256),
+ num_deconv_kernels=(4, 4, 4),
+ loss_keypoint=dict(
+ type='MultiLossFactory',
+ num_joints=15,
+ num_stages=1,
+ ae_loss_type='exp',
+ with_ae_loss=[False],
+ push_loss_factor=[0.001],
+ pull_loss_factor=[0.001],
+ with_heatmaps_loss=[True],
+ heatmaps_loss_factor=[1.0],
+ )),
+ train_cfg=dict(),
+ test_cfg=dict(
+ num_joints=num_joints,
+ nms_kernel=None,
+ nms_padding=None,
+ tag_per_joint=None,
+ max_num_people=None,
+ detection_threshold=None,
+ tag_threshold=None,
+ use_detection_val=None,
+ ignore_too_much=None,
+ ))
+
+model = dict(
+ type='VoxelPose',
+ detector_2d=detector_2d,
+ pretrained='checkpoints/resnet_50_deconv.pth.tar',
+ space_3d=dict(
+ space_size=space_size,
+ space_center=space_center,
+ cube_size=cube_size,
+ sub_space_size=sub_space_size,
+ sub_cube_size=sub_cube_size),
+ project_layer=dict(image_size=image_size, heatmap_size=heatmap_size),
+ center_net=dict(
+ type='V2VNet', input_channels=num_joints, output_channels=1),
+ center_head=dict(
+ type='CuboidCenterHead',
+ cfg=dict(
+ space_size=space_size,
+ space_center=space_center,
+ cube_size=cube_size,
+ max_num=10,
+ max_pool_kernel=3)),
+ pose_net=dict(
+ type='V2VNet', input_channels=num_joints, output_channels=num_joints),
+ pose_head=dict(type='CuboidPoseHead', beta=100.0),
+ train_cfg=dict(dist_threshold=500.0),
+ test_cfg=dict(center_threshold=0.3))
+
+train_pipeline = [
+ dict(
+ type='MultiItemProcess',
+ pipeline=[
+ dict(type='LoadImageFromFile'),
+ dict(
+ type='BottomUpRandomAffine',
+ rot_factor=0,
+ scale_factor=[1.0, 1.0],
+ scale_type='long',
+ trans_factor=0),
+ dict(type='ToTensor'),
+ dict(
+ type='NormalizeTensor',
+ mean=[0.485, 0.456, 0.406],
+ std=[0.229, 0.224, 0.225]),
+ ]),
+ dict(
+ type='DiscardDuplicatedItems',
+ keys_list=[
+ 'joints_3d', 'joints_3d_visible', 'ann_info', 'roots_3d',
+ 'num_persons', 'sample_id'
+ ]),
+ dict(type='GenerateVoxel3DHeatmapTarget', sigma=200.0, joint_indices=[2]),
+ dict(
+ type='Collect',
+ keys=['img', 'targets_3d'],
+ meta_keys=[
+ 'num_persons', 'joints_3d', 'camera', 'center', 'scale',
+ 'joints_3d_visible', 'roots_3d'
+ ]),
+]
+
+val_pipeline = [
+ dict(
+ type='MultiItemProcess',
+ pipeline=[
+ dict(type='LoadImageFromFile'),
+ dict(
+ type='BottomUpRandomAffine',
+ rot_factor=0,
+ scale_factor=[1.0, 1.0],
+ scale_type='long',
+ trans_factor=0),
+ dict(type='ToTensor'),
+ dict(
+ type='NormalizeTensor',
+ mean=[0.485, 0.456, 0.406],
+ std=[0.229, 0.224, 0.225]),
+ ]),
+ dict(
+ type='DiscardDuplicatedItems',
+ keys_list=[
+ 'joints_3d', 'joints_3d_visible', 'ann_info', 'roots_3d',
+ 'num_persons', 'sample_id'
+ ]),
+ dict(
+ type='Collect',
+ keys=['img'],
+ meta_keys=['sample_id', 'camera', 'center', 'scale']),
+]
+
+test_pipeline = val_pipeline
+
+data_root = 'data/panoptic/'
+data = dict(
+ samples_per_gpu=1,
+ workers_per_gpu=4,
+ val_dataloader=dict(samples_per_gpu=2),
+ test_dataloader=dict(samples_per_gpu=2),
+ train=dict(
+ type='Body3DMviewDirectPanopticDataset',
+ ann_file=None,
+ img_prefix=data_root,
+ data_cfg=train_data_cfg,
+ pipeline=train_pipeline,
+ dataset_info={{_base_.dataset_info}}),
+ val=dict(
+ type='Body3DMviewDirectPanopticDataset',
+ ann_file=None,
+ img_prefix=data_root,
+ data_cfg=test_data_cfg,
+ pipeline=val_pipeline,
+ dataset_info={{_base_.dataset_info}}),
+ test=dict(
+ type='Body3DMviewDirectPanopticDataset',
+ ann_file=None,
+ img_prefix=data_root,
+ data_cfg=test_data_cfg,
+ pipeline=test_pipeline,
+ dataset_info={{_base_.dataset_info}}),
+)
diff --git a/configs/body/3d_kpt_mview_rgb_img/voxelpose/panoptic/voxelpose_prn64x64x64_cpn80x80x20_panoptic_cam5.yml b/configs/body/3d_kpt_mview_rgb_img/voxelpose/panoptic/voxelpose_prn64x64x64_cpn80x80x20_panoptic_cam5.yml
new file mode 100644
index 0000000000..078ffa1edd
--- /dev/null
+++ b/configs/body/3d_kpt_mview_rgb_img/voxelpose/panoptic/voxelpose_prn64x64x64_cpn80x80x20_panoptic_cam5.yml
@@ -0,0 +1,22 @@
+Collections:
+- Name: VoxelPose
+ Paper:
+ Title: 'VoxelPose: Towards Multi-Camera 3D Human Pose Estimation in Wild Environment'
+ URL: https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123460188.pdf
+Models:
+- Config: configs/body/3d_kpt_mview_rgb_img/voxelpose/panoptic/voxelpose_prn64x64x64_cpn80x80x20_panoptic_cam5.py
+ In Collection: VoxelPose
+ Metadata:
+ Architecture:
+ - VoxelPose
+ Training Data: CMU Panoptic
+ Name: voxelpose_voxelpose_prn64x64x64_cpn80x80x20_panoptic_cam5
+ README: configs/body/3d_kpt_mview_rgb_img/voxelpose/panoptic/voxelpose_prn64x64x64_cpn80x80x20_panoptic_cam5.md
+ Results:
+ - Dataset: CMU Panoptic
+ Metrics:
+ MPJPE: 17.57
+ mAP: 97.31
+ mAR: 97.99
+ Task: Body 3D Keypoint
+ Weights: https://download.openmmlab.com/mmpose/body3d/voxelpose/voxelpose_prn64x64x64_cpn80x80x20_panoptic_cam5-545c150e_20211103.pth
diff --git a/configs/face/2d_kpt_sview_rgb_img/deeppose/wflw/res50_wflw_256x256_softwingloss.py b/configs/face/2d_kpt_sview_rgb_img/deeppose/wflw/res50_wflw_256x256_softwingloss.py
new file mode 100644
index 0000000000..e11b760d99
--- /dev/null
+++ b/configs/face/2d_kpt_sview_rgb_img/deeppose/wflw/res50_wflw_256x256_softwingloss.py
@@ -0,0 +1,125 @@
+_base_ = ['../../../../_base_/datasets/wflw.py']
+log_level = 'INFO'
+load_from = None
+resume_from = None
+dist_params = dict(backend='nccl')
+workflow = [('train', 1)]
+checkpoint_config = dict(interval=10)
+evaluation = dict(interval=1, metric=['NME'], save_best='NME')
+
+optimizer = dict(
+ type='Adam',
+ lr=5e-4,
+)
+optimizer_config = dict(grad_clip=None)
+# learning policy
+lr_config = dict(
+ policy='step',
+ warmup='linear',
+ warmup_iters=500,
+ warmup_ratio=0.001,
+ step=[170, 200])
+total_epochs = 210
+log_config = dict(
+ interval=5,
+ hooks=[
+ dict(type='TextLoggerHook'),
+ # dict(type='TensorboardLoggerHook')
+ ])
+
+channel_cfg = dict(
+ num_output_channels=98,
+ dataset_joints=98,
+ dataset_channel=[
+ list(range(98)),
+ ],
+ inference_channel=list(range(98)))
+
+# model settings
+model = dict(
+ type='TopDown',
+ pretrained='torchvision://resnet50',
+ backbone=dict(type='ResNet', depth=50, num_stages=4, out_indices=(3, )),
+ neck=dict(type='GlobalAveragePooling'),
+ keypoint_head=dict(
+ type='DeepposeRegressionHead',
+ in_channels=2048,
+ num_joints=channel_cfg['num_output_channels'],
+ loss_keypoint=dict(type='SoftWingLoss', use_target_weight=True)),
+ train_cfg=dict(),
+ test_cfg=dict(flip_test=True))
+
+data_cfg = dict(
+ image_size=[256, 256],
+ heatmap_size=[64, 64],
+ num_output_channels=channel_cfg['num_output_channels'],
+ num_joints=channel_cfg['dataset_joints'],
+ dataset_channel=channel_cfg['dataset_channel'],
+ inference_channel=channel_cfg['inference_channel'])
+
+train_pipeline = [
+ dict(type='LoadImageFromFile'),
+ dict(type='TopDownRandomFlip', flip_prob=0.5),
+ dict(
+ type='TopDownGetRandomScaleRotation', rot_factor=30,
+ scale_factor=0.25),
+ dict(type='TopDownAffine'),
+ dict(type='ToTensor'),
+ dict(
+ type='NormalizeTensor',
+ mean=[0.485, 0.456, 0.406],
+ std=[0.229, 0.224, 0.225]),
+ dict(type='TopDownGenerateTargetRegression'),
+ dict(
+ type='Collect',
+ keys=['img', 'target', 'target_weight'],
+ meta_keys=[
+ 'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
+ 'rotation', 'flip_pairs'
+ ]),
+]
+
+val_pipeline = [
+ dict(type='LoadImageFromFile'),
+ dict(type='TopDownAffine'),
+ dict(type='ToTensor'),
+ dict(
+ type='NormalizeTensor',
+ mean=[0.485, 0.456, 0.406],
+ std=[0.229, 0.224, 0.225]),
+ dict(
+ type='Collect',
+ keys=['img'],
+ meta_keys=['image_file', 'center', 'scale', 'rotation', 'flip_pairs']),
+]
+
+test_pipeline = val_pipeline
+
+data_root = 'data/wflw'
+data = dict(
+ samples_per_gpu=64,
+ workers_per_gpu=2,
+ val_dataloader=dict(samples_per_gpu=32),
+ test_dataloader=dict(samples_per_gpu=32),
+ train=dict(
+ type='FaceWFLWDataset',
+ ann_file=f'{data_root}/annotations/face_landmarks_wflw_train.json',
+ img_prefix=f'{data_root}/images/',
+ data_cfg=data_cfg,
+ pipeline=train_pipeline,
+ dataset_info={{_base_.dataset_info}}),
+ val=dict(
+ type='FaceWFLWDataset',
+ ann_file=f'{data_root}/annotations/face_landmarks_wflw_test.json',
+ img_prefix=f'{data_root}/images/',
+ data_cfg=data_cfg,
+ pipeline=val_pipeline,
+ dataset_info={{_base_.dataset_info}}),
+ test=dict(
+ type='FaceWFLWDataset',
+ ann_file=f'{data_root}/annotations/face_landmarks_wflw_test.json',
+ img_prefix=f'{data_root}/images/',
+ data_cfg=data_cfg,
+ pipeline=test_pipeline,
+ dataset_info={{_base_.dataset_info}}),
+)
diff --git a/configs/face/2d_kpt_sview_rgb_img/deeppose/wflw/resnet_softwingloss_wflw.md b/configs/face/2d_kpt_sview_rgb_img/deeppose/wflw/resnet_softwingloss_wflw.md
new file mode 100644
index 0000000000..e7bad57043
--- /dev/null
+++ b/configs/face/2d_kpt_sview_rgb_img/deeppose/wflw/resnet_softwingloss_wflw.md
@@ -0,0 +1,75 @@
+
+
+
+DeepPose (CVPR'2014)
+
+```bibtex
+@inproceedings{toshev2014deeppose,
+ title={Deeppose: Human pose estimation via deep neural networks},
+ author={Toshev, Alexander and Szegedy, Christian},
+ booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
+ pages={1653--1660},
+ year={2014}
+}
+```
+
+
+
+
+
+
+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}
+}
+```
+
+
+
+
+
+
+SoftWingloss (TIP'2021)
+
+```bibtex
+@article{lin2021structure,
+ title={Structure-Coherent Deep Feature Learning for Robust Face Alignment},
+ author={Lin, Chunze and Zhu, Beier and Wang, Quan and Liao, Renjie and Qian, Chen and Lu, Jiwen and Zhou, Jie},
+ journal={IEEE Transactions on Image Processing},
+ year={2021},
+ publisher={IEEE}
+}
+```
+
+
+
+
+
+
+WFLW (CVPR'2018)
+
+```bibtex
+@inproceedings{wu2018look,
+ title={Look at boundary: A boundary-aware face alignment algorithm},
+ author={Wu, Wayne and Qian, Chen and Yang, Shuo and Wang, Quan and Cai, Yici and Zhou, Qiang},
+ booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
+ pages={2129--2138},
+ year={2018}
+}
+```
+
+
+
+Results on WFLW dataset
+
+The model is trained on WFLW train.
+
+| Arch | Input Size | NME*test* | NME*pose* | NME*illumination* | NME*occlusion* | NME*blur* | NME*makeup* | NME*expression* | ckpt | log |
+| :-----| :--------: | :------------------: | :------------------: |:---------------------------: |:------------------------: | :------------------: | :--------------: |:-------------------------: |:---: | :---: |
+| [deeppose_res50_softwingloss](/configs/face/2d_kpt_sview_rgb_img/deeppose/wflw/res50_wflw_256x256_softwingloss.py) | 256x256 | 4.41 | 7.77 | 4.37 | 5.27 | 5.01 | 4.36 | 4.70 | [ckpt](https://download.openmmlab.com/mmpose/face/deeppose/deeppose_res50_wflw_256x256_softwingloss-4d34f22a_20211212.pth) | [log](https://download.openmmlab.com/mmpose/face/deeppose/deeppose_res50_wflw_256x256_softwingloss_20211212.log.json) |
diff --git a/configs/face/2d_kpt_sview_rgb_img/deeppose/wflw/resnet_softwingloss_wflw.yml b/configs/face/2d_kpt_sview_rgb_img/deeppose/wflw/resnet_softwingloss_wflw.yml
new file mode 100644
index 0000000000..805f2fb0da
--- /dev/null
+++ b/configs/face/2d_kpt_sview_rgb_img/deeppose/wflw/resnet_softwingloss_wflw.yml
@@ -0,0 +1,28 @@
+Collections:
+- Name: SoftWingloss
+ Paper:
+ Title: Structure-Coherent Deep Feature Learning for Robust Face Alignment
+ URL: https://ieeexplore.ieee.org/document/9442331/
+Models:
+- Config: configs/face/2d_kpt_sview_rgb_img/deeppose/wflw/res50_wflw_256x256_softwingloss.py
+ In Collection: SoftWingloss
+ Metadata:
+ Architecture:
+ - DeepPose
+ - ResNet
+ - SoftWingloss
+ Training Data: WFLW
+ Name: deeppose_res50_wflw_256x256_softwingloss
+ README: configs/face/2d_kpt_sview_rgb_img/deeppose/wflw/resnet_softwingloss_wflw.md
+ Results:
+ - Dataset: WFLW
+ Metrics:
+ NME blur: 5.01
+ NME expression: 4.7
+ NME illumination: 4.37
+ NME makeup: 4.36
+ NME occlusion: 5.27
+ NME pose: 7.77
+ NME test: 4.41
+ Task: Face 2D Keypoint
+ Weights: https://download.openmmlab.com/mmpose/face/deeppose/deeppose_res50_wflw_256x256_softwingloss-4d34f22a_20211212.pth
diff --git a/configs/face/2d_kpt_sview_rgb_img/topdown_heatmap/wflw/hrnetv2_awing_wflw.md b/configs/face/2d_kpt_sview_rgb_img/topdown_heatmap/wflw/hrnetv2_awing_wflw.md
new file mode 100644
index 0000000000..1930299182
--- /dev/null
+++ b/configs/face/2d_kpt_sview_rgb_img/topdown_heatmap/wflw/hrnetv2_awing_wflw.md
@@ -0,0 +1,59 @@
+
+
+
+HRNetv2 (TPAMI'2019)
+
+```bibtex
+@article{WangSCJDZLMTWLX19,
+ title={Deep High-Resolution Representation Learning for Visual Recognition},
+ author={Jingdong Wang and Ke Sun and Tianheng Cheng and
+ Borui Jiang and Chaorui Deng and Yang Zhao and Dong Liu and Yadong Mu and
+ Mingkui Tan and Xinggang Wang and Wenyu Liu and Bin Xiao},
+ journal={TPAMI},
+ year={2019}
+}
+```
+
+
+
+
+
+
+AdaptiveWingloss (ICCV'2019)
+
+```bibtex
+@inproceedings{wang2019adaptive,
+ title={Adaptive wing loss for robust face alignment via heatmap regression},
+ author={Wang, Xinyao and Bo, Liefeng and Fuxin, Li},
+ booktitle={Proceedings of the IEEE/CVF international conference on computer vision},
+ pages={6971--6981},
+ year={2019}
+}
+```
+
+
+
+
+
+
+WFLW (CVPR'2018)
+
+```bibtex
+@inproceedings{wu2018look,
+ title={Look at boundary: A boundary-aware face alignment algorithm},
+ author={Wu, Wayne and Qian, Chen and Yang, Shuo and Wang, Quan and Cai, Yici and Zhou, Qiang},
+ booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
+ pages={2129--2138},
+ year={2018}
+}
+```
+
+
+
+Results on WFLW dataset
+
+The model is trained on WFLW train.
+
+| Arch | Input Size | NME*test* | NME*pose* | NME*illumination* | NME*occlusion* | NME*blur* | NME*makeup* | NME*expression* | ckpt | log |
+| :-----| :--------: | :------------------: | :------------------: |:---------------------------: |:------------------------: | :------------------: | :--------------: |:-------------------------: |:---: | :---: |
+| [pose_hrnetv2_w18_awing](/configs/face/2d_kpt_sview_rgb_img/topdown_heatmap/wflw/hrnetv2_w18_wflw_256x256_awing.py) | 256x256 | 4.02 | 6.94 | 3.96 | 4.78 | 4.59 | 3.85 | 4.28 | [ckpt](https://download.openmmlab.com/mmpose/face/hrnetv2/hrnetv2_w18_wflw_256x256_awing-5af5055c_20211212.pth) | [log](https://download.openmmlab.com/mmpose/face/hrnetv2/hrnetv2_w18_wflw_256x256_awing_20211212.log.json) |
diff --git a/configs/face/2d_kpt_sview_rgb_img/topdown_heatmap/wflw/hrnetv2_awing_wflw.yml b/configs/face/2d_kpt_sview_rgb_img/topdown_heatmap/wflw/hrnetv2_awing_wflw.yml
new file mode 100644
index 0000000000..509da9627b
--- /dev/null
+++ b/configs/face/2d_kpt_sview_rgb_img/topdown_heatmap/wflw/hrnetv2_awing_wflw.yml
@@ -0,0 +1,27 @@
+Collections:
+- Name: HRNetv2
+ Paper:
+ Title: Deep High-Resolution Representation Learning for Visual Recognition
+ URL: https://ieeexplore.ieee.org/abstract/document/9052469/
+Models:
+- Config: configs/face/2d_kpt_sview_rgb_img/topdown_heatmap/wflw/hrnetv2_w18_wflw_256x256_awing.py
+ In Collection: HRNetv2
+ Metadata:
+ Architecture:
+ - HRNetv2
+ - AdaptiveWingloss
+ Training Data: WFLW
+ Name: topdown_heatmap_hrnetv2_w18_wflw_256x256_awing
+ README: configs/face/2d_kpt_sview_rgb_img/topdown_heatmap/wflw/hrnetv2_awing_wflw.md
+ Results:
+ - Dataset: WFLW
+ Metrics:
+ NME blur: 4.59
+ NME expression: 4.28
+ NME illumination: 3.96
+ NME makeup: 3.85
+ NME occlusion: 4.78
+ NME pose: 6.94
+ NME test: 4.02
+ Task: Face 2D Keypoint
+ Weights: https://download.openmmlab.com/mmpose/face/hrnetv2/hrnetv2_w18_wflw_256x256_awing-5af5055c_20211212.pth
diff --git a/configs/face/2d_kpt_sview_rgb_img/topdown_heatmap/wflw/hrnetv2_w18_wflw_256x256_awing.py b/configs/face/2d_kpt_sview_rgb_img/topdown_heatmap/wflw/hrnetv2_w18_wflw_256x256_awing.py
new file mode 100644
index 0000000000..c73b8cd408
--- /dev/null
+++ b/configs/face/2d_kpt_sview_rgb_img/topdown_heatmap/wflw/hrnetv2_w18_wflw_256x256_awing.py
@@ -0,0 +1,162 @@
+_base_ = ['../../../../_base_/datasets/wflw.py']
+log_level = 'INFO'
+load_from = None
+resume_from = None
+dist_params = dict(backend='nccl')
+workflow = [('train', 1)]
+checkpoint_config = dict(interval=1)
+evaluation = dict(interval=1, metric=['NME'], save_best='NME')
+
+optimizer = dict(
+ type='Adam',
+ lr=2e-3,
+)
+optimizer_config = dict(grad_clip=None)
+# learning policy
+lr_config = dict(
+ policy='step',
+ warmup='linear',
+ warmup_iters=500,
+ warmup_ratio=0.001,
+ step=[40, 55])
+total_epochs = 60
+log_config = dict(
+ interval=5,
+ hooks=[
+ dict(type='TextLoggerHook'),
+ # dict(type='TensorboardLoggerHook')
+ ])
+
+channel_cfg = dict(
+ num_output_channels=98,
+ dataset_joints=98,
+ dataset_channel=[
+ list(range(98)),
+ ],
+ inference_channel=list(range(98)))
+
+# model settings
+model = dict(
+ type='TopDown',
+ pretrained='open-mmlab://msra/hrnetv2_w18',
+ backbone=dict(
+ type='HRNet',
+ in_channels=3,
+ extra=dict(
+ stage1=dict(
+ num_modules=1,
+ num_branches=1,
+ block='BOTTLENECK',
+ num_blocks=(4, ),
+ num_channels=(64, )),
+ stage2=dict(
+ num_modules=1,
+ num_branches=2,
+ block='BASIC',
+ num_blocks=(4, 4),
+ num_channels=(18, 36)),
+ stage3=dict(
+ num_modules=4,
+ num_branches=3,
+ block='BASIC',
+ num_blocks=(4, 4, 4),
+ num_channels=(18, 36, 72)),
+ stage4=dict(
+ num_modules=3,
+ num_branches=4,
+ block='BASIC',
+ num_blocks=(4, 4, 4, 4),
+ num_channels=(18, 36, 72, 144),
+ multiscale_output=True),
+ upsample=dict(mode='bilinear', align_corners=False))),
+ keypoint_head=dict(
+ type='TopdownHeatmapSimpleHead',
+ in_channels=[18, 36, 72, 144],
+ in_index=(0, 1, 2, 3),
+ input_transform='resize_concat',
+ out_channels=channel_cfg['num_output_channels'],
+ num_deconv_layers=0,
+ extra=dict(
+ final_conv_kernel=1, num_conv_layers=1, num_conv_kernels=(1, )),
+ loss_keypoint=dict(type='AdaptiveWingLoss', use_target_weight=True)),
+ train_cfg=dict(),
+ test_cfg=dict(
+ flip_test=True,
+ post_process='default',
+ shift_heatmap=True,
+ modulate_kernel=11))
+
+data_cfg = dict(
+ image_size=[256, 256],
+ heatmap_size=[64, 64],
+ num_output_channels=channel_cfg['num_output_channels'],
+ num_joints=channel_cfg['dataset_joints'],
+ dataset_channel=channel_cfg['dataset_channel'],
+ inference_channel=channel_cfg['inference_channel'])
+
+train_pipeline = [
+ dict(type='LoadImageFromFile'),
+ dict(type='TopDownRandomFlip', flip_prob=0.5),
+ dict(
+ type='TopDownGetRandomScaleRotation', rot_factor=30,
+ scale_factor=0.25),
+ dict(type='TopDownAffine'),
+ dict(type='ToTensor'),
+ dict(
+ type='NormalizeTensor',
+ mean=[0.485, 0.456, 0.406],
+ std=[0.229, 0.224, 0.225]),
+ dict(type='TopDownGenerateTarget', sigma=2),
+ dict(
+ type='Collect',
+ keys=['img', 'target', 'target_weight'],
+ meta_keys=[
+ 'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
+ 'rotation', 'flip_pairs'
+ ]),
+]
+
+val_pipeline = [
+ dict(type='LoadImageFromFile'),
+ dict(type='TopDownAffine'),
+ dict(type='ToTensor'),
+ dict(
+ type='NormalizeTensor',
+ mean=[0.485, 0.456, 0.406],
+ std=[0.229, 0.224, 0.225]),
+ dict(
+ type='Collect',
+ keys=['img'],
+ meta_keys=['image_file', 'center', 'scale', 'rotation', 'flip_pairs']),
+]
+
+test_pipeline = val_pipeline
+
+data_root = 'data/wflw'
+data = dict(
+ samples_per_gpu=64,
+ workers_per_gpu=2,
+ val_dataloader=dict(samples_per_gpu=32),
+ test_dataloader=dict(samples_per_gpu=32),
+ train=dict(
+ type='FaceWFLWDataset',
+ ann_file=f'{data_root}/annotations/face_landmarks_wflw_train.json',
+ img_prefix=f'{data_root}/images/',
+ data_cfg=data_cfg,
+ pipeline=train_pipeline,
+ dataset_info={{_base_.dataset_info}}),
+ val=dict(
+ type='FaceWFLWDataset',
+ ann_file=f'{data_root}/annotations/face_landmarks_wflw_test.json',
+ img_prefix=f'{data_root}/images/',
+ data_cfg=data_cfg,
+ pipeline=val_pipeline,
+ dataset_info={{_base_.dataset_info}}),
+ test=dict(
+ type='FaceWFLWDataset',
+ ann_file=f'{data_root}/annotations/face_landmarks_wflw_test.json',
+ img_prefix=f'{data_root}/images/',
+ data_cfg=data_cfg,
+ pipeline=test_pipeline,
+ dataset_info={{_base_.dataset_info}}),
+)
diff --git a/docs/api.rst b/docs/api.rst
index 2856891b9f..af0ec96bb7 100644
--- a/docs/api.rst
+++ b/docs/api.rst
@@ -70,9 +70,11 @@ datasets
^^^^^^^^^^^
.. automodule:: mmpose.datasets.datasets.top_down
:members:
+ :noindex:
.. automodule:: mmpose.datasets.datasets.bottom_up
:members:
+ :noindex:
pipelines
^^^^^^^^^^^
@@ -101,7 +103,7 @@ samplers
^^^^^^^^^^^
.. automodule:: mmpose.datasets.samplers
:members:
-
+ :noindex:
mmpose.utils
---------------
diff --git a/docs/faq.md b/docs/faq.md
index 4e021ef556..c4f818b9d4 100644
--- a/docs/faq.md
+++ b/docs/faq.md
@@ -35,6 +35,17 @@ If the contents here do not cover your issue, please create an issue using the [
## Data
+- **How to convert my 2d keypoint dataset to coco-type?**
+
+ You may refer to this conversion [tool](https://github.com/open-mmlab/mmpose/blob/master/tools/dataset/parse_macaquepose_dataset.py) to prepare your data.
+ Here is an [example](https://github.com/open-mmlab/mmpose/blob/master/tests/data/macaque/test_macaque.json) of the coco-type json.
+ In the coco-type json, we need "categories", "annotations" and "images". "categories" contain some basic information of the dataset, e.g. class name and keypoint names.
+ "images" contain image-level information. We need "id", "file_name", "height", "width". Others are optional.
+ Note: (1) It is okay that "id"s are not continuous or not sorted (e.g. 1000, 40, 352, 333 ...).
+
+ "annotations" contain instance-level information. We need "image_id", "id", "keypoints", "num_keypoints", "bbox", "iscrowd", "area", "category_id". Others are optional.
+ Note: (1) "num_keypoints" means the number of visible keypoints. (2) By default, please set "iscrowd: 0". (3) "area" can be calculated using the bbox (area = w * h) (4) Simply set "category_id: 1". (5) The "image_id" in "annotations" should match the "id" in "images".
+
- **What if my custom dataset does not have bounding box label?**
We can estimate the bounding box of a person as the minimal box that tightly bounds all the keypoints.
@@ -93,6 +104,9 @@ If the contents here do not cover your issue, please create an issue using the [
Since we do not have the ground-truth for test dataset, we cannot evaluate it 'locally'.
If you would like to evaluate the performance on test set, you have to upload the pred.mat (which is generated during testing) to the official server via email, according to [the MPII guideline](http://human-pose.mpi-inf.mpg.de/#evaluation).
+- **For top-down 2d pose estimation, why predicted joint coordinates can be out of the bounding box (bbox)?**
+ We do not directly use the bbox to crop the image. bbox will be first transformed to center & scale, and the scale will be multiplied by a factor (1.25) to include some context. If the ratio of width/height is different from that of model input (possibly 192/256), we will adjust the bbox.
+
## Inference
- **How to run mmpose on CPU?**
diff --git a/docs/papers/algorithms/voxelpose.md b/docs/papers/algorithms/voxelpose.md
new file mode 100644
index 0000000000..ffec692867
--- /dev/null
+++ b/docs/papers/algorithms/voxelpose.md
@@ -0,0 +1,17 @@
+# VoxelPose: Towards Multi-Camera 3D Human Pose Estimation in Wild Environment
+
+
+
+
+VoxelPose (ECCV'2020)
+
+```bibtex
+@inproceedings{tumultipose,
+ title={VoxelPose: Towards Multi-Camera 3D Human Pose Estimation in Wild Environment},
+ author={Tu, Hanyue and Wang, Chunyu and Zeng, Wenjun},
+ booktitle={ECCV},
+ year={2020}
+}
+```
+
+
diff --git a/docs/papers/datasets/panoptic_body3d.md b/docs/papers/datasets/panoptic_body3d.md
new file mode 100644
index 0000000000..b7f45c8beb
--- /dev/null
+++ b/docs/papers/datasets/panoptic_body3d.md
@@ -0,0 +1,17 @@
+# Panoptic Studio: A Massively Multiview System for Social Motion Capture
+
+
+
+
+CMU Panoptic (ICCV'2015)
+
+```bibtex
+@Article = {joo_iccv_2015,
+author = {Hanbyul Joo, Hao Liu, Lei Tan, Lin Gui, Bart Nabbe, Iain Matthews, Takeo Kanade, Shohei Nobuhara, and Yaser Sheikh},
+title = {Panoptic Studio: A Massively Multiview System for Social Motion Capture},
+booktitle = {ICCV},
+year = {2015}
+}
+```
+
+
diff --git a/docs/papers/techniques/awingloss.md b/docs/papers/techniques/awingloss.md
new file mode 100644
index 0000000000..fa8a6f95b7
--- /dev/null
+++ b/docs/papers/techniques/awingloss.md
@@ -0,0 +1,18 @@
+# Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression
+
+
+
+
+AdaptiveWingloss (ICCV'2019)
+
+```bibtex
+@inproceedings{wang2019adaptive,
+ title={Adaptive wing loss for robust face alignment via heatmap regression},
+ author={Wang, Xinyao and Bo, Liefeng and Fuxin, Li},
+ booktitle={Proceedings of the IEEE/CVF international conference on computer vision},
+ pages={6971--6981},
+ year={2019}
+}
+```
+
+
diff --git a/docs/papers/techniques/softwingloss.md b/docs/papers/techniques/softwingloss.md
new file mode 100644
index 0000000000..94f7d3b384
--- /dev/null
+++ b/docs/papers/techniques/softwingloss.md
@@ -0,0 +1,18 @@
+# Structure-Coherent Deep Feature Learning for Robust Face Alignment
+
+
+
+
+SoftWingloss (TIP'2021)
+
+```bibtex
+@article{lin2021structure,
+ title={Structure-Coherent Deep Feature Learning for Robust Face Alignment},
+ author={Lin, Chunze and Zhu, Beier and Wang, Quan and Liao, Renjie and Qian, Chen and Lu, Jiwen and Zhou, Jie},
+ journal={IEEE Transactions on Image Processing},
+ year={2021},
+ publisher={IEEE}
+}
+```
+
+
diff --git a/docs/tasks/3d_body_keypoint.md b/docs/tasks/3d_body_keypoint.md
index 2b19df7395..82d989c717 100644
--- a/docs/tasks/3d_body_keypoint.md
+++ b/docs/tasks/3d_body_keypoint.md
@@ -6,6 +6,7 @@ If your folder structure is different, you may need to change the corresponding
MMPose supported datasets:
- [Human3.6M](#human36m) \[ [Homepage](http://vision.imar.ro/human3.6m/description.php) \]
+- [CMU Panoptic](#cmu-panoptic) \[ [Homepage](http://domedb.perception.cs.cmu.edu/) \]
## Human3.6M
@@ -73,3 +74,47 @@ mmpose
```
Please note that Human3.6M dataset is also used in the [3D_body_mesh](/docs/tasks/3d_body_mesh.md) task, where different schemes for data preprocessing and organizing are adopted.
+
+## CMU Panoptic
+
+
+CMU Panoptic (ICCV'2015)
+
+```bibtex
+@Article = {joo_iccv_2015,
+author = {Hanbyul Joo, Hao Liu, Lei Tan, Lin Gui, Bart Nabbe, Iain Matthews, Takeo Kanade, Shohei Nobuhara, and Yaser Sheikh},
+title = {Panoptic Studio: A Massively Multiview System for Social Motion Capture},
+booktitle = {ICCV},
+year = {2015}
+}
+```
+
+
+
+Please follow [voxelpose-pytorch](https://github.com/microsoft/voxelpose-pytorch) to prepare this dataset.
+
+1. Download the dataset by following the instructions in [panoptic-toolbox](https://github.com/CMU-Perceptual-Computing-Lab/panoptic-toolbox) and extract them under `$MMPOSE/data/panoptic`.
+
+2. Only download those sequences that are needed. You can also just download a subset of camera views by specifying the number of views (HD_Video_Number) and changing the camera order in `./scripts/getData.sh`. The used sequences and camera views can be found in [VoxelPose](https://arxiv.org/abs/2004.06239). Note that the sequence "160906_band3" might not be available due to errors on the server of CMU Panoptic.
+
+3. Note that we only use HD videos, calibration data, and 3D Body Keypoint in the codes. You can comment out other irrelevant codes such as downloading 3D Face data in `./scripts/getData.sh`.
+
+The directory tree should be like this:
+
+```text
+mmpose
+├── mmpose
+├── docs
+├── tests
+├── tools
+├── configs
+`── data
+ ├── panoptic
+ ├── 16060224_haggling1
+ | | ├── hdImgs
+ | | ├── hdvideos
+ | | ├── hdPose3d_stage1_coco19
+ | | ├── calibration_160224_haggling1.json
+ ├── 160226_haggling1
+ ├── ...
+```
diff --git a/docs/useful_tools.md b/docs/useful_tools.md
index 6421007adb..a9d246dfde 100644
--- a/docs/useful_tools.md
+++ b/docs/useful_tools.md
@@ -31,7 +31,7 @@ Examples:
- Plot the mse loss of some run.
```shell
- python tools/analysis/analyze_logs.py plot_curve log.json --keys mse_loss --legend mse_loss
+ python tools/analysis/analyze_logs.py plot_curve log.json --keys loss --legend loss
```
- Plot the acc of some run, and save the figure to a pdf.
diff --git a/mmpose/apis/inference.py b/mmpose/apis/inference.py
index defc118bf5..ad42f238ed 100644
--- a/mmpose/apis/inference.py
+++ b/mmpose/apis/inference.py
@@ -156,8 +156,9 @@ def _inference_single_pose_model(model,
return_heatmap=False):
"""Inference human bounding boxes.
- num_bboxes: N
- num_keypoints: K
+ Note:
+ - num_bboxes: N
+ - num_keypoints: K
Args:
model (nn.Module): The loaded pose model.
@@ -343,41 +344,46 @@ def inference_top_down_pose_model(model,
outputs=None):
"""Inference a single image with a list of person bounding boxes.
- num_people: P
- num_keypoints: K
- bbox height: H
- bbox width: W
+ Note:
+ - num_people: P
+ - num_keypoints: K
+ - bbox height: H
+ - bbox width: W
Args:
model (nn.Module): The loaded pose model.
img_or_path (str| np.ndarray): Image filename or loaded image.
- person_results (List(dict), optional): a list of detected persons that
- contains following items:
- - 'bbox' and/or 'track_id'.
- - 'bbox' (4, ) or (5, ): The person bounding box, which contains
+ person_results (list(dict), optional): a list of detected persons that
+ contains ``bbox`` and/or ``track_id``:
+
+ - ``bbox`` (4, ) or (5, ): The person bounding box, which contains
4 box coordinates (and score).
- - 'track_id' (int): The unique id for each human instance.
- If not provided, a dummy person result with a bbox covering the
- entire image will be used. Default: None.
- bbox_thr: Threshold for bounding boxes. Only bboxes with higher scores
- will be fed into the pose detector. If bbox_thr is None, ignore it.
- format: bbox format ('xyxy' | 'xywh'). Default: 'xywh'.
- 'xyxy' means (left, top, right, bottom),
- 'xywh' means (left, top, width, height).
+ - ``track_id`` (int): The unique id for each human instance. If
+ not provided, a dummy person result with a bbox covering
+ the entire image will be used. Default: None.
+ bbox_thr (float | None): Threshold for bounding boxes. Only bboxes
+ with higher scores will be fed into the pose detector.
+ If bbox_thr is None, all boxes will be used.
+ format (str): bbox format ('xyxy' | 'xywh'). Default: 'xywh'.
+
+ - `xyxy` means (left, top, right, bottom),
+ - `xywh` means (left, top, width, height).
dataset (str): Dataset name, e.g. 'TopDownCocoDataset'.
It is deprecated. Please use dataset_info instead.
dataset_info (DatasetInfo): A class containing all dataset info.
return_heatmap (bool) : Flag to return heatmap, default: False
outputs (list(str) | tuple(str)) : Names of layers whose outputs
- need to be returned, default: None
+ need to be returned. Default: None.
Returns:
- list[dict]: The bbox & pose info,
- Each item in the list is a dictionary,
- containing the bbox: (left, top, right, bottom, [score])
- and the pose (ndarray[Kx3]): x, y, score
- list[dict[np.ndarray[N, K, H, W] | torch.tensor[N, K, H, W]]]:
- Output feature maps from layers specified in `outputs`.
+ tuple:
+ - pose_results (list[dict]): The bbox & pose info. \
+ Each item in the list is a dictionary, \
+ containing the bbox: (left, top, right, bottom, [score]) \
+ and the pose (ndarray[Kx3]): x, y, score.
+ - returned_outputs (list[dict[np.ndarray[N, K, H, W] | \
+ torch.Tensor[N, K, H, W]]]): \
+ Output feature maps from layers specified in `outputs`. \
Includes 'heatmap' if `return_heatmap` is True.
"""
# get dataset info
@@ -464,12 +470,13 @@ def inference_bottom_up_pose_model(model,
pose_nms_thr=0.9,
return_heatmap=False,
outputs=None):
- """Inference a single image.
+ """Inference a single image with a bottom-up pose model.
- num_people: P
- num_keypoints: K
- bbox height: H
- bbox width: W
+ Note:
+ - num_people: P
+ - num_keypoints: K
+ - bbox height: H
+ - bbox width: W
Args:
model (nn.Module): The loaded pose model.
@@ -483,12 +490,14 @@ def inference_bottom_up_pose_model(model,
need to be returned, default: None.
Returns:
- list[ndarray]: The predicted pose info.
- The length of the list is the number of people (P).
- Each item in the list is a ndarray, containing each person's
- pose (ndarray[Kx3]): x, y, score.
- list[dict[np.ndarray[N, K, H, W] | torch.tensor[N, K, H, W]]]:
- Output feature maps from layers specified in `outputs`.
+ tuple:
+ - pose_results (list[np.ndarray]): The predicted pose info. \
+ The length of the list is the number of people (P). \
+ Each item in the list is a ndarray, containing each \
+ person's pose (np.ndarray[Kx3]): x, y, score.
+ - returned_outputs (list[dict[np.ndarray[N, K, H, W] | \
+ torch.Tensor[N, K, H, W]]]): \
+ Output feature maps from layers specified in `outputs`. \
Includes 'heatmap' if `return_heatmap` is True.
"""
# get dataset info
@@ -841,6 +850,7 @@ def process_mmdet_results(mmdet_results, cat_id=1):
Args:
mmdet_results (list|tuple): mmdet results.
cat_id (int): category id (default: 1 for human)
+
Returns:
person_results (list): a list of detected bounding boxes
"""
diff --git a/mmpose/apis/inference_3d.py b/mmpose/apis/inference_3d.py
index c670a1fd6b..c0fb83e05e 100644
--- a/mmpose/apis/inference_3d.py
+++ b/mmpose/apis/inference_3d.py
@@ -14,25 +14,26 @@ def extract_pose_sequence(pose_results, frame_idx, causal, seq_len, step=1):
fixed length.
Args:
- pose_results (List[List[Dict]]): Multi-frame pose detection results
+ pose_results (list[list[dict]]): Multi-frame pose detection results
stored in a nested list. Each element of the outer list is the
pose detection results of a single frame, and each element of the
inner list is the pose information of one person, which contains:
- keypoints (ndarray[K, 2 or 3]): x, y, [score]
- track_id (int): unique id of each person, required when
- ``with_track_id==True```
- bbox ((4, ) or (5, )): left, right, top, bottom, [score]
+
+ - keypoints (ndarray[K, 2 or 3]): x, y, [score]
+ - track_id (int): unique id of each person, required \
+ when ``with_track_id==True``.
+ - bbox ((4, ) or (5, )): left, right, top, bottom, [score]
+
frame_idx (int): The index of the frame in the original video.
causal (bool): If True, the target frame is the last frame in
- a sequence. Otherwise, the target frame is in the middle of a
- sequence.
+ a sequence. Otherwise, the target frame is in the middle of
+ a sequence.
seq_len (int): The number of frames in the input sequence.
step (int): Step size to extract frames from the video.
Returns:
- List[List[Dict]]: Multi-frame pose detection results stored in a
- nested list with a length of seq_len.
- int: The target frame index in the padded sequence.
+ list[list[dict]]: Multi-frame pose detection results stored \
+ in a nested list with a length of seq_len.
"""
if causal:
@@ -60,21 +61,23 @@ def _gather_pose_lifter_inputs(pose_results,
norm_pose_2d=False):
"""Gather input data (keypoints and track_id) for pose lifter model.
- Notes:
- T: The temporal length of the pose detection results
- N: The number of the person instances
- K: The number of the keypoints
- C: The channel number of each keypoint
+ Note:
+ - The temporal length of the pose detection results: T
+ - The number of the person instances: N
+ - The number of the keypoints: K
+ - The channel number of each keypoint: C
Args:
pose_results (List[List[Dict]]): Multi-frame pose detection results
stored in a nested list. Each element of the outer list is the
pose detection results of a single frame, and each element of the
inner list is the pose information of one person, which contains:
- keypoints (ndarray[K, 2 or 3]): x, y, [score]
- track_id (int): unique id of each person, required when
+
+ - keypoints (ndarray[K, 2 or 3]): x, y, [score]
+ - track_id (int): unique id of each person, required when
``with_track_id==True```
- bbox ((4, ) or (5, )): left, right, top, bottom, [score]
+ - bbox ((4, ) or (5, )): left, right, top, bottom, [score]
+
bbox_center (ndarray[1, 2]): x, y. The average center coordinate of the
bboxes in the dataset.
bbox_scale (int|float): The average scale of the bboxes in the dataset.
@@ -83,13 +86,14 @@ def _gather_pose_lifter_inputs(pose_results,
bbox_center. Default: False.
Returns:
- List[List[dict]]: Multi-frame pose detection results
+ list[list[dict]]: Multi-frame pose detection results
stored in a nested list. Each element of the outer list is the
pose detection results of a single frame, and each element of the
inner list is the pose information of one person, which contains:
- keypoints (ndarray[K, 2 or 3]): x, y, [score]
- track_id (int): unique id of each person, required when
- ``with_track_id==True```
+
+ - keypoints (ndarray[K, 2 or 3]): x, y, [score]
+ - track_id (int): unique id of each person, required when
+ ``with_track_id==True``
"""
sequence_inputs = []
for frame in pose_results:
@@ -122,20 +126,22 @@ def _collate_pose_sequence(pose_results, with_track_id=True, target_frame=-1):
"""Reorganize multi-frame pose detection results into individual pose
sequences.
- Notes:
- T: The temporal length of the pose detection results
- N: The number of the person instances
- K: The number of the keypoints
- C: The channel number of each keypoint
+ Note:
+ - The temporal length of the pose detection results: T
+ - The number of the person instances: N
+ - The number of the keypoints: K
+ - The channel number of each keypoint: C
Args:
pose_results (List[List[Dict]]): Multi-frame pose detection results
stored in a nested list. Each element of the outer list is the
pose detection results of a single frame, and each element of the
inner list is the pose information of one person, which contains:
- keypoints (ndarray[K, 2 or 3]): x, y, [score]
- track_id (int): unique id of each person, required when
+
+ - keypoints (ndarray[K, 2 or 3]): x, y, [score]
+ - track_id (int): unique id of each person, required when
``with_track_id==True```
+
with_track_id (bool): If True, the element in pose_results is expected
to contain "track_id", which will be used to gather the pose
sequence of a person from multiple frames. Otherwise, the pose
@@ -214,32 +220,35 @@ def inference_pose_lifter_model(model,
Args:
model (nn.Module): The loaded pose lifter model
- pose_results_2d (List[List[dict]]): The 2D pose sequences stored in a
+ pose_results_2d (list[list[dict]]): The 2D pose sequences stored in a
nested list. Each element of the outer list is the 2D pose results
of a single frame, and each element of the inner list is the 2D
pose of one person, which contains:
- - "keypoints" (ndarray[K, 2 or 3]): x, y, [score]
- - "track_id" (int)
+
+ - "keypoints" (ndarray[K, 2 or 3]): x, y, [score]
+ - "track_id" (int)
dataset (str): Dataset name, e.g. 'Body3DH36MDataset'
with_track_id: If True, the element in pose_results_2d is expected to
contain "track_id", which will be used to gather the pose sequence
of a person from multiple frames. Otherwise, the pose results in
each frame are expected to have a consistent number and order of
identities. Default is True.
- image_size (Tuple|List): image width, image height. If None, image size
+ image_size (tuple|list): image width, image height. If None, image size
will not be contained in dict ``data``.
norm_pose_2d (bool): If True, scale the bbox (along with the 2D
pose) to the average bbox scale of the dataset, and move the bbox
(along with the 2D pose) to the average bbox center of the dataset.
Returns:
- List[dict]: 3D pose inference results. Each element is the result of
+ list[dict]: 3D pose inference results. Each element is the result of \
an instance, which contains:
- - "keypoints_3d" (ndarray[K,3]): predicted 3D keypoints
- - "keypoints" (ndarray[K, 2 or 3]): from the last frame in
+
+ - "keypoints_3d" (ndarray[K, 3]): predicted 3D keypoints
+ - "keypoints" (ndarray[K, 2 or 3]): from the last frame in \
``pose_results_2d``.
- - "track_id" (int): from the last frame in ``pose_results_2d``.
- If there is no valid instance, an empty list will be returned.
+ - "track_id" (int): from the last frame in ``pose_results_2d``. \
+ If there is no valid instance, an empty list will be \
+ returned.
"""
cfg = model.cfg
test_pipeline = Compose(cfg.test_pipeline)
@@ -465,27 +474,27 @@ def inference_interhand_3d_model(model,
dataset='InterHand3DDataset'):
"""Inference a single image with a list of hand bounding boxes.
- num_bboxes: N
- num_keypoints: K
+ Note:
+ - num_bboxes: N
+ - num_keypoints: K
Args:
model (nn.Module): The loaded pose model.
img_or_path (str | np.ndarray): Image filename or loaded image.
- det_results (List[dict]): The 2D bbox sequences stored in a list.
- Each each element of the list is the bbox of one person, which
- contains:
- - "bbox" (ndarray[4 or 5]): The person bounding box,
- which contains 4 box coordinates (and score).
+ det_results (list[dict]): The 2D bbox sequences stored in a list.
+ Each each element of the list is the bbox of one person, whose
+ shape is (ndarray[4 or 5]), containing 4 box coordinates
+ (and score).
dataset (str): Dataset name.
format: bbox format ('xyxy' | 'xywh'). Default: 'xywh'.
'xyxy' means (left, top, right, bottom),
'xywh' means (left, top, width, height).
Returns:
- List[dict]: 3D pose inference results. Each element is the result of
- an instance, which contains:
- - "keypoints_3d" (ndarray[K,3]): predicted 3D keypoints
- If there is no valid instance, an empty list will be returned.
+ list[dict]: 3D pose inference results. Each element is the result \
+ of an instance, which contains the predicted 3D keypoints with \
+ shape (ndarray[K,3]). If there is no valid instance, an \
+ empty list will be returned.
"""
assert format in ['xyxy', 'xywh']
@@ -621,35 +630,43 @@ def inference_mesh_model(model,
format='xywh',
dataset='MeshH36MDataset'):
"""Inference a single image with a list of bounding boxes.
- num_bboxes: N
- num_keypoints: K
- num_vertices: V
- num_faces: F
+
+ Note:
+ - num_bboxes: N
+ - num_keypoints: K
+ - num_vertices: V
+ - num_faces: F
+
Args:
model (nn.Module): The loaded pose model.
img_or_path (str | np.ndarray): Image filename or loaded image.
- det_results (List[dict]): The 2D bbox sequences stored in a list.
- Each each element of the list is the bbox of one person, which
- contains:
- - "bbox" (ndarray[4 or 5]): The person bounding box,
- which contains 4 box coordinates (and score).
- bbox_thr: Threshold for bounding boxes. Only bboxes with higher scores
- will be fed into the pose detector. If bbox_thr is None, ignore it.
- format: bbox format ('xyxy' | 'xywh'). Default: 'xywh'.
- 'xyxy' means (left, top, right, bottom),
- 'xywh' means (left, top, width, height).
+ det_results (list[dict]): The 2D bbox sequences stored in a list.
+ Each element of the list is the bbox of one person.
+ "bbox" (ndarray[4 or 5]): The person bounding box,
+ which contains 4 box coordinates (and score).
+ bbox_thr (float | None): Threshold for bounding boxes.
+ Only bboxes with higher scores will be fed into the pose
+ detector. If bbox_thr is None, all boxes will be used.
+ format (str): bbox format ('xyxy' | 'xywh'). Default: 'xywh'.
+
+ - 'xyxy' means (left, top, right, bottom),
+ - 'xywh' means (left, top, width, height).
dataset (str): Dataset name.
+
Returns:
- List[dict]: 3D pose inference results. Each element is the result of
- an instance, which contains:
- - "bbox" (ndarray[4]): instance bounding bbox
- - "center" (ndarray[2]): bbox center
- - "scale" (ndarray[2]): bbox scale
- - "keypoints_3d" (ndarray[K,3]): predicted 3D keypoints
- - "camera" (ndarray[3]): camera parameters
- - "vertices" (ndarray[V, 3]): predicted 3D vertices
- - "faces" (ndarray[F, 3]): mesh faces
- If there is no valid instance, an empty list will be returned.
+ list[dict]: 3D pose inference results. Each element \
+ is the result of an instance, which contains:
+
+ - 'bbox' (ndarray[4]): instance bounding bbox
+ - 'center' (ndarray[2]): bbox center
+ - 'scale' (ndarray[2]): bbox scale
+ - 'keypoints_3d' (ndarray[K,3]): predicted 3D keypoints
+ - 'camera' (ndarray[3]): camera parameters
+ - 'vertices' (ndarray[V, 3]): predicted 3D vertices
+ - 'faces' (ndarray[F, 3]): mesh faces
+
+ If there is no valid instance, an empty list
+ will be returned.
"""
assert format in ['xyxy', 'xywh']
@@ -777,7 +794,7 @@ def vis_3d_mesh_result(model, result, img=None, show=False, out_file=None):
Args:
model (nn.Module): The loaded model.
- result (list[dict])
+ result (list[dict]): 3D mesh estimation results.
"""
if hasattr(model, 'module'):
model = model.module
diff --git a/mmpose/apis/inference_tracking.py b/mmpose/apis/inference_tracking.py
index ab340415e5..5dba87c9df 100644
--- a/mmpose/apis/inference_tracking.py
+++ b/mmpose/apis/inference_tracking.py
@@ -42,13 +42,13 @@ def _track_by_iou(res, results_last, thr):
Args:
res (dict): The bbox & pose results of the person instance.
results_last (list[dict]): The bbox & pose & track_id info of the
- last frame (bbox_result, pose_result, track_id).
+ last frame (bbox_result, pose_result, track_id).
thr (float): The threshold for iou tracking.
Returns:
int: The track id for the new person instance.
list[dict]: The bbox & pose & track_id info of the persons
- that have not been matched on the last frame.
+ that have not been matched on the last frame.
dict: The matched person instance on the last frame.
"""
@@ -81,13 +81,13 @@ def _track_by_oks(res, results_last, thr):
Args:
res (dict): The pose results of the person instance.
results_last (list[dict]): The pose & track_id info of the
- last frame (pose_result, track_id).
+ last frame (pose_result, track_id).
thr (float): The threshold for oks tracking.
Returns:
int: The track id for the new person instance.
list[dict]: The pose & track_id info of the persons
- that have not been matched on the last frame.
+ that have not been matched on the last frame.
dict: The matched person instance on the last frame.
"""
pose = res['keypoints'].reshape((-1))
@@ -121,10 +121,10 @@ def _get_area(results):
Args:
results (list[dict]): The pose results of the current frame
- (pose_result).
+ (pose_result).
Returns:
list[dict]: The bbox & pose info of the current frame
- (bbox_result, pose_result, area).
+ (bbox_result, pose_result, area).
"""
for result in results:
if 'bbox' in result:
@@ -152,7 +152,7 @@ def _temporal_refine(result, match_result, fps=None):
(pose_result).
match_result (dict): The pose results of the last frame
(match_result)
- return:
+ Returns:
(array): The person keypoints after refine.
"""
if 'one_euro' in match_result:
@@ -176,22 +176,23 @@ def get_track_id(results,
Args:
results (list[dict]): The bbox & pose results of the current frame
- (bbox_result, pose_result).
+ (bbox_result, pose_result).
results_last (list[dict]): The bbox & pose & track_id info of the
- last frame (bbox_result, pose_result, track_id).
+ last frame (bbox_result, pose_result, track_id).
next_id (int): The track id for the new person instance.
min_keypoints (int): Minimum number of keypoints recognized as person.
- default: 3.
+ default: 3.
use_oks (bool): Flag to using oks tracking. default: False.
tracking_thr (float): The threshold for tracking.
use_one_euro (bool): Option to use one-euro-filter. default: False.
fps (optional): Parameters that d_cutoff
- when one-euro-filter is used as a video input
+ when one-euro-filter is used as a video input
Returns:
- list[dict]: The bbox & pose & track_id info of the
- current frame (bbox_result, pose_result, track_id).
- int: The track id for the new person instance.
+ tuple:
+ - results (list[dict]): The bbox & pose & track_id info of the \
+ current frame (bbox_result, pose_result, track_id).
+ - next_id (int): The track id for the new person instance.
"""
results = _get_area(results)
@@ -239,11 +240,11 @@ def vis_pose_tracking_result(model,
model (nn.Module): The loaded detector.
img (str | np.ndarray): Image filename or loaded image.
result (list[dict]): The results to draw over `img`
- (bbox_result, pose_result).
+ (bbox_result, pose_result).
radius (int): Radius of circles.
thickness (int): Thickness of lines.
kpt_score_thr (float): The threshold to visualize the keypoints.
- skeleton (list[tuple()]): Default None.
+ skeleton (list[tuple]): Default None.
show (bool): Whether to show the image. Default True.
out_file (str|None): The filename of the output visualization image.
"""
diff --git a/mmpose/apis/train.py b/mmpose/apis/train.py
index 3db5bb8be7..36490f1ae7 100644
--- a/mmpose/apis/train.py
+++ b/mmpose/apis/train.py
@@ -27,10 +27,12 @@ def init_random_seed(seed=None, device='cuda'):
If the seed is not set, the seed will be automatically randomized,
and then broadcast to all processes to prevent some potential bugs.
+
Args:
seed (int, Optional): The seed. Default to None.
device (str): The device where the seed will be put on.
Default to 'cuda'.
+
Returns:
int: Seed to be used.
"""
diff --git a/mmpose/core/camera/__init__.py b/mmpose/core/camera/__init__.py
index 05d05bbb6b..a4a3c55265 100644
--- a/mmpose/core/camera/__init__.py
+++ b/mmpose/core/camera/__init__.py
@@ -1,5 +1,6 @@
# Copyright (c) OpenMMLab. All rights reserved.
from .camera_base import CAMERAS
from .single_camera import SimpleCamera
+from .single_camera_torch import SimpleCameraTorch
-__all__ = ['CAMERAS', 'SimpleCamera']
+__all__ = ['CAMERAS', 'SimpleCamera', 'SimpleCameraTorch']
diff --git a/mmpose/core/camera/single_camera.py b/mmpose/core/camera/single_camera.py
index b2ee42d9e6..cabd79941a 100644
--- a/mmpose/core/camera/single_camera.py
+++ b/mmpose/core/camera/single_camera.py
@@ -9,7 +9,7 @@ class SimpleCamera(SingleCameraBase):
"""Camera model to calculate coordinate transformation with given
intrinsic/extrinsic camera parameters.
- Notes:
+ Note:
The keypoint coordinate should be an np.ndarray with a shape of
[...,J, C] where J is the keypoint number of an instance, and C is
the coordinate dimension. For example:
diff --git a/mmpose/core/camera/single_camera_torch.py b/mmpose/core/camera/single_camera_torch.py
new file mode 100644
index 0000000000..22eb72f23d
--- /dev/null
+++ b/mmpose/core/camera/single_camera_torch.py
@@ -0,0 +1,118 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+
+from .camera_base import CAMERAS, SingleCameraBase
+
+
+@CAMERAS.register_module()
+class SimpleCameraTorch(SingleCameraBase):
+ """Camera model to calculate coordinate transformation with given
+ intrinsic/extrinsic camera parameters.
+
+ Notes:
+ The keypoint coordinate should be an np.ndarray with a shape of
+ [...,J, C] where J is the keypoint number of an instance, and C is
+ the coordinate dimension. For example:
+
+ [J, C]: shape of joint coordinates of a person with J joints.
+ [N, J, C]: shape of a batch of person joint coordinates.
+ [N, T, J, C]: shape of a batch of pose sequences.
+
+ Args:
+ param (dict): camera parameters including:
+ - R: 3x3, camera rotation matrix (camera-to-world)
+ - T: 3x1, camera translation (camera-to-world)
+ - K: (optional) 2x3, camera intrinsic matrix
+ - k: (optional) nx1, camera radial distortion coefficients
+ - p: (optional) mx1, camera tangential distortion coefficients
+ - f: (optional) 2x1, camera focal length
+ - c: (optional) 2x1, camera center
+ if K is not provided, it will be calculated from f and c.
+
+ Methods:
+ world_to_camera: Project points from world coordinates to camera
+ coordinates
+ camera_to_pixel: Project points from camera coordinates to pixel
+ coordinates
+ world_to_pixel: Project points from world coordinates to pixel
+ coordinates
+ """
+
+ def __init__(self, param, device):
+
+ self.param = {}
+ # extrinsic param
+ R = torch.tensor(param['R'], device=device)
+ T = torch.tensor(param['T'], device=device)
+
+ assert R.shape == (3, 3)
+ assert T.shape == (3, 1)
+ # The camera matrices are transposed in advance because the joint
+ # coordinates are stored as row vectors.
+ self.param['R_c2w'] = R.T
+ self.param['T_c2w'] = T.T
+ self.param['R_w2c'] = R
+ self.param['T_w2c'] = -self.param['T_c2w'] @ self.param['R_w2c']
+
+ # intrinsic param
+ if 'K' in param:
+ K = torch.tensor(param['K'], device=device)
+ assert K.shape == (2, 3)
+ self.param['K'] = K.T
+ self.param['f'] = torch.tensor([[K[0, 0]], [K[1, 1]]],
+ device=device)
+ self.param['c'] = torch.tensor([[K[0, 2]], [K[1, 2]]],
+ device=device)
+ elif 'f' in param and 'c' in param:
+ f = torch.tensor(param['f'], device=device)
+ c = torch.tensor(param['c'], device=device)
+ assert f.shape == (2, 1)
+ assert c.shape == (2, 1)
+ self.param['K'] = torch.cat([torch.diagflat(f), c], dim=-1).T
+ self.param['f'] = f
+ self.param['c'] = c
+ else:
+ raise ValueError('Camera intrinsic parameters are missing. '
+ 'Either "K" or "f"&"c" should be provided.')
+
+ # distortion param
+ if 'k' in param and 'p' in param:
+ self.undistortion = True
+ self.param['k'] = torch.tensor(param['k'], device=device).view(-1)
+ self.param['p'] = torch.tensor(param['p'], device=device).view(-1)
+ assert len(self.param['k']) in {3, 6}
+ assert len(self.param['p']) == 2
+ else:
+ self.undistortion = False
+
+ def world_to_camera(self, X):
+ assert isinstance(X, torch.Tensor)
+ assert X.ndim >= 2 and X.shape[-1] == 3
+ return X @ self.param['R_w2c'] + self.param['T_w2c']
+
+ def camera_to_world(self, X):
+ assert isinstance(X, torch.Tensor)
+ assert X.ndim >= 2 and X.shape[-1] == 3
+ return X @ self.param['R_c2w'] + self.param['T_c2w']
+
+ def camera_to_pixel(self, X):
+ assert isinstance(X, torch.Tensor)
+ assert X.ndim >= 2 and X.shape[-1] == 3
+
+ _X = X / X[..., 2:]
+
+ if self.undistortion:
+ k = self.param['k']
+ p = self.param['p']
+ _X_2d = _X[..., :2]
+ r2 = (_X_2d**2).sum(-1)
+ radial = 1 + sum(ki * r2**(i + 1) for i, ki in enumerate(k[:3]))
+ if k.size == 6:
+ radial /= 1 + sum(
+ (ki * r2**(i + 1) for i, ki in enumerate(k[3:])))
+
+ tangential = 2 * (p[1] * _X[..., 0] + p[0] * _X[..., 1])
+
+ _X[..., :2] = _X_2d * (radial + tangential)[..., None] + torch.ger(
+ r2, p.flip([0])).reshape(_X_2d.shape)
+ return _X @ self.param['K']
diff --git a/mmpose/core/evaluation/bottom_up_eval.py b/mmpose/core/evaluation/bottom_up_eval.py
index df8abf7909..7b37d7c98e 100644
--- a/mmpose/core/evaluation/bottom_up_eval.py
+++ b/mmpose/core/evaluation/bottom_up_eval.py
@@ -11,17 +11,19 @@ def split_ae_outputs(outputs, num_joints, with_heatmaps, with_ae,
"""Split multi-stage outputs into heatmaps & tags.
Args:
- outputs (list(torch.Tensor)): Outputs of network
+ outputs (list(Tensor)): Outputs of network
num_joints (int): Number of joints
with_heatmaps (list[bool]): Option to output
heatmaps for different stages.
with_ae (list[bool]): Option to output
ae tags for different stages.
select_output_index (list[int]): Output keep the selected index
+
Returns:
tuple: A tuple containing multi-stage outputs.
- - heatmaps (list(torch.Tensor)): multi-stage heatmaps.
- - tags (list(torch.Tensor)): multi-stage tags.
+
+ - list[Tensor]: multi-stage heatmaps.
+ - list[Tensor]: multi-stage tags.
"""
heatmaps = []
@@ -44,12 +46,13 @@ def split_ae_outputs(outputs, num_joints, with_heatmaps, with_ae,
def flip_feature_maps(feature_maps, flip_index=None):
"""Flip the feature maps and swap the channels.
- Args:
- feature_maps (list(torch.Tensor)): Feature maps.
- flip_index (list(int) | None): Channel-flip indexes. If None,
- do not flip channels.
+ Args:
+ feature_maps (list[Tensor]): Feature maps.
+ flip_index (list[int] | None): Channel-flip indexes.
+ If None, do not flip channels.
+
Returns:
- flipped_feature_maps (list(torch.Tensor)): Flipped feature_maps.
+ list[Tensor]: Flipped feature_maps.
"""
flipped_feature_maps = []
for feature_map in feature_maps:
@@ -65,15 +68,16 @@ def flip_feature_maps(feature_maps, flip_index=None):
def _resize_average(feature_maps, align_corners, index=-1, resize_size=None):
"""Resize the feature maps and compute the average.
- Args:
- feature_maps (list(torch.Tensor)): Feature maps.
+ Args:
+ feature_maps (list[Tensor]): Feature maps.
align_corners (bool): Align corners when performing interpolation.
index (int): Only used when `resize_size' is None.
If `resize_size' is None, the target size is the size
of the indexed feature maps.
- resize_size ([w, h]): The target size.
+ resize_size (list[int, int]): The target size [w, h].
+
Returns:
- feature_maps_avg (list(torch.Tensor)): Averaged feature_maps.
+ list[Tensor]: Averaged feature_maps.
"""
if feature_maps is None:
@@ -95,15 +99,16 @@ def _resize_unsqueeze_concat(feature_maps,
resize_size=None):
"""Resize, unsqueeze and concatenate the feature_maps.
- Args:
- feature_maps (list(torch.Tensor)): Feature maps.
+ Args:
+ feature_maps (list[Tensor]): Feature maps.
align_corners (bool): Align corners when performing interpolation.
index (int): Only used when `resize_size' is None.
If `resize_size' is None, the target size is the size
of the indexed feature maps.
- resize_size ([w, h]): The target size.
+ resize_size (list[int, int]): The target size [w, h].
+
Returns:
- output_feature_maps (list(torch.Tensor)): Averaged feature_maps.
+ list[Tensor]: Averaged feature_maps.
"""
if feature_maps is None:
return None
@@ -120,15 +125,16 @@ def _resize_unsqueeze_concat(feature_maps,
def _resize_concate(feature_maps, align_corners, index=-1, resize_size=None):
"""Resize and concatenate the feature_maps.
- Args:
- feature_maps (list(torch.Tensor)): Feature maps.
+ Args:
+ feature_maps (list[Tensor]): Feature maps.
align_corners (bool): Align corners when performing interpolation.
index (int): Only used when `resize_size' is None.
If `resize_size' is None, the target size is the size
of the indexed feature maps.
- resize_size ([w, h]): The target size.
+ resize_size (list[int, int]): The target size [w, h].
+
Returns:
- feature_map_list (list(torch.Tensor)): Averaged feature_maps.
+ list[Tensor]: Averaged feature_maps.
"""
if feature_maps is None:
return None
@@ -168,31 +174,30 @@ def aggregate_stage_flip(feature_maps,
resize them to base sizes.
Args:
- feature_maps (list(torch.Tensor)): feature_maps can be heatmaps,
+ feature_maps (list[Tensor]): feature_maps can be heatmaps,
tags, and pafs.
- feature_maps_flip (list(torch.Tensor) | None): flipped feature_maps.
+ feature_maps_flip (list[Tensor] | None): flipped feature_maps.
feature maps can be heatmaps, tags, and pafs.
project2image (bool): Option to resize to base scale.
- size_projected ([w, h]): Base size of heatmaps.
+ size_projected (list[int, int]): Base size of heatmaps [w, h].
align_corners (bool): Align corners when performing interpolation.
aggregate_stage (str): Methods to aggregate multi-stage feature maps.
- Options: 'concat', 'average'.
- 'concat': Concatenate the original and the flipped feature maps.
- 'average': Get the average of the original and the flipped
- feature maps..
- Default: 'concat.
+ Options: 'concat', 'average'. Default: 'concat.
+
+ - 'concat': Concatenate the original and the flipped feature maps.
+ - 'average': Get the average of the original and the flipped
+ feature maps.
aggregate_flip (str): Methods to aggregate the original and
- the flipped feature maps.
- Options: 'concat', 'average', 'none'.
- 'concat': Concatenate the original and the flipped feature maps.
- 'average': Get the average of the original and the flipped
- feature maps..
- 'none': no flipped feature maps.
+ the flipped feature maps. Options: 'concat', 'average', 'none'.
Default: 'average.
+ - 'concat': Concatenate the original and the flipped feature maps.
+ - 'average': Get the average of the original and the flipped
+ feature maps..
+ - 'none': no flipped feature maps.
+
Returns:
- - output_feature_maps (List(torch.Tensor[NxKxWxH])):
- Aggregated feature maps.
+ list[Tensor]: Aggregated feature maps with shape [NxKxWxH].
"""
if feature_maps_flip is None:
@@ -258,17 +263,18 @@ def aggregate_scale(feature_maps_list,
heatmap height: H
Args:
- feature_maps_list (list(torch.Tensor)): Aggregated feature maps.
+ feature_maps_list (list[Tensor]): Aggregated feature maps.
project2image (bool): Option to resize to base scale.
align_corners (bool): Align corners when performing interpolation.
aggregate_scale (str): Methods to aggregate multi-scale feature maps.
Options: 'average', 'unsqueeze_concat'.
- 'average': Get the average of the feature maps.
- 'unsqueeze_concat': Concatenate the feature maps along new axis.
- Default: 'average.
- Return:
- - output_feature_maps (torch.Tensor): Aggregated feature maps.
+ - 'average': Get the average of the feature maps.
+ - 'unsqueeze_concat': Concatenate the feature maps along new axis.
+ Default: 'average.
+
+ Returns:
+ Tensor: Aggregated feature maps.
"""
if aggregate_scale == 'average':
@@ -299,7 +305,7 @@ def get_group_preds(grouped_joints,
heatmap_size (np.ndarray[2, ]): Size of the destination heatmaps.
use_udp (bool): Unbiased data processing.
Paper ref: Huang et al. The Devil is in the Details: Delving into
- Unbiased Data Processing for Human Pose Estimation (CVPR 2020).
+ Unbiased Data Processing for Human Pose Estimation (CVPR'2020).
Returns:
list: List of the pose result for each person.
diff --git a/mmpose/core/evaluation/mesh_eval.py b/mmpose/core/evaluation/mesh_eval.py
index 63c6202311..683b4539b2 100644
--- a/mmpose/core/evaluation/mesh_eval.py
+++ b/mmpose/core/evaluation/mesh_eval.py
@@ -13,15 +13,15 @@ def compute_similarity_transform(source_points, target_points):
transformed 3D points source_points_hat (N x 3). i.e. solves the orthogonal
Procrutes problem.
- Notes:
+ Note:
Points number: N
Args:
- source_points (np.ndarray([N, 3])): Source point set.
- target_points (np.ndarray([N, 3])): Target point set.
+ source_points (np.ndarray): Source point set with shape [N, 3].
+ target_points (np.ndarray): Target point set with shape [N, 3].
Returns:
- source_points_hat (np.ndarray([N, 3])): Transformed source point set.
+ np.ndarray: Transformed source point set with shape [N, 3].
"""
assert target_points.shape[0] == source_points.shape[0]
diff --git a/mmpose/core/evaluation/pose3d_eval.py b/mmpose/core/evaluation/pose3d_eval.py
index 56a58dc940..545778ca74 100644
--- a/mmpose/core/evaluation/pose3d_eval.py
+++ b/mmpose/core/evaluation/pose3d_eval.py
@@ -8,28 +8,30 @@ def keypoint_mpjpe(pred, gt, mask, alignment='none'):
"""Calculate the mean per-joint position error (MPJPE) and the error after
rigid alignment with the ground truth (P-MPJPE).
- batch_size: N
- num_keypoints: K
- keypoint_dims: C
+ Note:
+ - batch_size: N
+ - num_keypoints: K
+ - keypoint_dims: C
Args:
- pred (np.ndarray[N, K, C]): Predicted keypoint location.
- gt (np.ndarray[N, K, C]): Groundtruth keypoint location.
- mask (np.ndarray[N, K]): Visibility of the target. False for invisible
- joints, and True for visible. Invisible joints will be ignored for
- accuracy calculation.
+ pred (np.ndarray): Predicted keypoint location with shape [N, K, C].
+ gt (np.ndarray): Groundtruth keypoint location with shape [N, K, C].
+ mask (np.ndarray): Visibility of the target with shape [N, K].
+ False for invisible joints, and True for visible.
+ Invisible joints will be ignored for accuracy calculation.
alignment (str, optional): method to align the prediction with the
groundtruth. Supported options are:
- - ``'none'``: no alignment will be applied
- - ``'scale'``: align in the least-square sense in scale
- - ``'procrustes'``: align in the least-square sense in scale,
- rotation and translation.
+
+ - ``'none'``: no alignment will be applied
+ - ``'scale'``: align in the least-square sense in scale
+ - ``'procrustes'``: align in the least-square sense in
+ scale, rotation and translation.
Returns:
tuple: A tuple containing joint position errors
- - mpjpe (float|np.ndarray[N]): mean per-joint position error.
- - p-mpjpe (float|np.ndarray[N]): mpjpe after rigid alignment with the
- ground truth
+ - (float | np.ndarray): mean per-joint position error (mpjpe).
+ - (float | np.ndarray): mpjpe after rigid alignment with the
+ ground truth (p-mpjpe).
"""
assert mask.any()
@@ -58,13 +60,12 @@ def keypoint_3d_pck(pred, gt, mask, alignment='none', threshold=0.15):
alignment.
Paper ref: `Monocular 3D Human Pose Estimation In The Wild Using Improved
- CNN Supervision' 3DV`2017
- More details can be found in the `paper
- `__.
+ CNN Supervision' 3DV'2017. `__ .
- batch_size: N
- num_keypoints: K
- keypoint_dims: C
+ Note:
+ - batch_size: N
+ - num_keypoints: K
+ - keypoint_dims: C
Args:
pred (np.ndarray[N, K, C]): Predicted keypoint location.
@@ -74,13 +75,16 @@ def keypoint_3d_pck(pred, gt, mask, alignment='none', threshold=0.15):
accuracy calculation.
alignment (str, optional): method to align the prediction with the
groundtruth. Supported options are:
+
- ``'none'``: no alignment will be applied
- ``'scale'``: align in the least-square sense in scale
- ``'procrustes'``: align in the least-square sense in scale,
rotation and translation.
+
threshold: If L2 distance between the prediction and the groundtruth
is less then threshold, the predicted result is considered as
correct. Default: 0.15 (m).
+
Returns:
pck: percentage of correct keypoints.
"""
@@ -112,15 +116,14 @@ def keypoint_3d_auc(pred, gt, mask, alignment='none'):
thresholds.
Paper ref: `Monocular 3D Human Pose Estimation In The Wild Using Improved
- CNN Supervision' 3DV`2017
- More details can be found in the `paper
- `__.
+ CNN Supervision' 3DV'2017. `__ .
This implementation is derived from mpii_compute_3d_pck.m, which is
provided as part of the MPI-INF-3DHP test data release.
- batch_size: N
- num_keypoints: K
- keypoint_dims: C
+ Note:
+ batch_size: N
+ num_keypoints: K
+ keypoint_dims: C
Args:
pred (np.ndarray[N, K, C]): Predicted keypoint location.
@@ -130,10 +133,12 @@ def keypoint_3d_auc(pred, gt, mask, alignment='none'):
accuracy calculation.
alignment (str, optional): method to align the prediction with the
groundtruth. Supported options are:
+
- ``'none'``: no alignment will be applied
- ``'scale'``: align in the least-square sense in scale
- ``'procrustes'``: align in the least-square sense in scale,
rotation and translation.
+
Returns:
auc: AUC computed for a range of 3DPCK thresholds.
"""
diff --git a/mmpose/core/evaluation/top_down_eval.py b/mmpose/core/evaluation/top_down_eval.py
index 2a8afb50df..d73f01fe55 100644
--- a/mmpose/core/evaluation/top_down_eval.py
+++ b/mmpose/core/evaluation/top_down_eval.py
@@ -24,8 +24,8 @@ def _calc_distances(preds, targets, mask, normalize):
normalize (np.ndarray[N, D]): Typical value is heatmap_size
Returns:
- np.ndarray[K, N]: The normalized distances.
- If target keypoints are missing, the distance is -1.
+ np.ndarray[K, N]: The normalized distances. \
+ If target keypoints are missing, the distance is -1.
"""
N, K, _ = preds.shape
# set mask=0 when normalize==0
@@ -50,8 +50,8 @@ def _distance_acc(distances, thr=0.5):
thr (float): Threshold of the distances.
Returns:
- float: Percentage of distances below the threshold.
- If all target keypoints are missing, return -1.
+ float: Percentage of distances below the threshold. \
+ If all target keypoints are missing, return -1.
"""
distance_valid = distances != -1
num_distance_valid = distance_valid.sum()
@@ -110,6 +110,7 @@ def _get_max_preds_3d(heatmaps):
Returns:
tuple: A tuple containing aggregated results.
+
- preds (np.ndarray[N, K, 3]): Predicted keypoint location.
- maxvals (np.ndarray[N, K, 1]): Scores (confidence) of the keypoints.
"""
@@ -143,10 +144,10 @@ def pose_pck_accuracy(output, target, mask, thr=0.05, normalize=None):
The threshold (thr) of the normalized distance is commonly set
as 0.05, 0.1 or 0.2 etc.
- batch_size: N
- num_keypoints: K
- heatmap height: H
- heatmap width: W
+ - batch_size: N
+ - num_keypoints: K
+ - heatmap height: H
+ - heatmap width: W
Args:
output (np.ndarray[N, K, H, W]): Model output heatmaps.
@@ -186,8 +187,8 @@ def keypoint_pck_accuracy(pred, gt, mask, thr, normalize):
The threshold (thr) of the normalized distance is commonly set
as 0.05, 0.1 or 0.2 etc.
- batch_size: N
- num_keypoints: K
+ - batch_size: N
+ - num_keypoints: K
Args:
pred (np.ndarray[N, K, 2]): Predicted keypoint location.
@@ -219,8 +220,8 @@ def keypoint_auc(pred, gt, mask, normalize, num_step=20):
averaged accuracy across all keypoints for coordinates.
Note:
- batch_size: N
- num_keypoints: K
+ - batch_size: N
+ - num_keypoints: K
Args:
pred (np.ndarray[N, K, 2]): Predicted keypoint location.
@@ -250,8 +251,8 @@ def keypoint_nme(pred, gt, mask, normalize_factor):
"""Calculate the normalized mean error (NME).
Note:
- batch_size: N
- num_keypoints: K
+ - batch_size: N
+ - num_keypoints: K
Args:
pred (np.ndarray[N, K, 2]): Predicted keypoint location.
@@ -273,8 +274,8 @@ def keypoint_epe(pred, gt, mask):
"""Calculate the end-point error.
Note:
- batch_size: N
- num_keypoints: K
+ - batch_size: N
+ - num_keypoints: K
Args:
pred (np.ndarray[N, K, 2]): Predicted keypoint location.
@@ -298,8 +299,8 @@ def _taylor(heatmap, coord):
"""Distribution aware coordinate decoding method.
Note:
- heatmap height: H
- heatmap width: W
+ - heatmap height: H
+ - heatmap width: W
Args:
heatmap (np.ndarray[H, W]): Heatmap of a particular joint type.
@@ -338,11 +339,12 @@ def post_dark_udp(coords, batch_heatmaps, kernel=3):
Representation for Human Pose Estimation (CVPR 2020).
Note:
- batch size: B
- num keypoints: K
- num persons: N
- height of heatmaps: H
- width of heatmaps: W
+ - batch size: B
+ - num keypoints: K
+ - num persons: N
+ - height of heatmaps: H
+ - width of heatmaps: W
+
B=1 for bottom_up paradigm where all persons share the same heatmap.
B=N for top_down paradigm where each person has its own heatmaps.
@@ -352,7 +354,7 @@ def post_dark_udp(coords, batch_heatmaps, kernel=3):
kernel (int): Gaussian kernel size (K) for modulation.
Returns:
- res (np.ndarray[N, K, 2]): Refined coordinates.
+ np.ndarray([N, K, 2]): Refined coordinates.
"""
if not isinstance(batch_heatmaps, np.ndarray):
batch_heatmaps = batch_heatmaps.cpu().numpy()
@@ -366,8 +368,29 @@ def post_dark_udp(coords, batch_heatmaps, kernel=3):
np.log(batch_heatmaps, batch_heatmaps)
batch_heatmaps = np.transpose(batch_heatmaps,
(2, 3, 0, 1)).reshape(H, W, -1)
- batch_heatmaps_pad = cv2.copyMakeBorder(
- batch_heatmaps, 1, 1, 1, 1, borderType=cv2.BORDER_REFLECT)
+
+ # cv2.copyMakeBorder will report an error when input dimension exceeds 512
+ batch_heatmaps_channel = batch_heatmaps.shape[2]
+ if batch_heatmaps_channel > 512:
+ total_group_number = int(np.ceil(batch_heatmaps_channel / 512))
+ splited_batch_heatmaps = []
+ for group_idx in range(total_group_number):
+ splited_batch_heatmap = batch_heatmaps[
+ ..., group_idx *
+ 512:min(batch_heatmaps_channel, (group_idx + 1) * 512)]
+ batch_heatmap_pad = cv2.copyMakeBorder(
+ splited_batch_heatmap,
+ 1,
+ 1,
+ 1,
+ 1,
+ borderType=cv2.BORDER_REFLECT)
+ splited_batch_heatmaps.append(batch_heatmap_pad)
+ batch_heatmaps_pad = np.concatenate(splited_batch_heatmaps, axis=2)
+ else:
+ batch_heatmaps_pad = cv2.copyMakeBorder(
+ batch_heatmaps, 1, 1, 1, 1, borderType=cv2.BORDER_REFLECT)
+
batch_heatmaps_pad = np.transpose(
batch_heatmaps_pad.reshape(H + 2, W + 2, B, K),
(2, 3, 0, 1)).flatten()
@@ -406,10 +429,10 @@ def _gaussian_blur(heatmaps, kernel=11):
sigma~=1 if k=3;
Note:
- batch_size: N
- num_keypoints: K
- heatmap height: H
- heatmap width: W
+ - batch_size: N
+ - num_keypoints: K
+ - heatmap height: H
+ - heatmap width: W
Args:
heatmaps (np.ndarray[N, K, H, W]): model predicted heatmaps.
@@ -418,7 +441,7 @@ def _gaussian_blur(heatmaps, kernel=11):
K=17 for sigma=3 and k=11 for sigma=2.
Returns:
- np.ndarray[N, K, H, W]: Modulated heatmap distribution.
+ np.ndarray ([N, K, H, W]): Modulated heatmap distribution.
"""
assert kernel % 2 == 1
@@ -444,8 +467,8 @@ def keypoints_from_regression(regression_preds, center, scale, img_size):
them back to the image.
Note:
- batch_size: N
- num_keypoints: K
+ - batch_size: N
+ - num_keypoints: K
Args:
regression_preds (np.ndarray[N, K, 2]): model prediction.
@@ -454,10 +477,11 @@ def keypoints_from_regression(regression_preds, center, scale, img_size):
wrt height/width.
img_size (list(img_width, img_height)): model input image size.
-
Returns:
- preds (np.ndarray[N, K, 2]): Predicted keypoint location in images.
- maxvals (np.ndarray[N, K, 1]): Scores (confidence) of the keypoints.
+ tuple:
+
+ - preds (np.ndarray[N, K, 2]): Predicted keypoint location in images.
+ - maxvals (np.ndarray[N, K, 1]): Scores (confidence) of the keypoints.
"""
N, K, _ = regression_preds.shape
preds, maxvals = regression_preds, np.ones((N, K, 1), dtype=np.float32)
@@ -484,10 +508,10 @@ def keypoints_from_heatmaps(heatmaps,
the image.
Note:
- batch size: N
- num keypoints: K
- heatmap height: H
- heatmap width: W
+ - batch size: N
+ - num keypoints: K
+ - heatmap height: H
+ - heatmap width: W
Args:
heatmaps (np.ndarray[N, K, H, W]): model predicted heatmaps.
@@ -627,11 +651,11 @@ def keypoints_from_heatmaps3d(heatmaps, center, scale):
to the image.
Note:
- batch size: N
- num keypoints: K
- heatmap depth size: D
- heatmap height: H
- heatmap width: W
+ - batch size: N
+ - num keypoints: K
+ - heatmap depth size: D
+ - heatmap height: H
+ - heatmap width: W
Args:
heatmaps (np.ndarray[N, K, D, H, W]): model predicted heatmaps.
@@ -642,8 +666,8 @@ def keypoints_from_heatmaps3d(heatmaps, center, scale):
Returns:
tuple: A tuple containing keypoint predictions and scores.
- - preds (np.ndarray[N, K, 3]): Predicted 3d keypoint location
- in images.
+ - preds (np.ndarray[N, K, 3]): Predicted 3d keypoint location \
+ in images.
- maxvals (np.ndarray[N, K, 1]): Scores (confidence) of the keypoints.
"""
N, K, D, H, W = heatmaps.shape
@@ -657,9 +681,10 @@ def keypoints_from_heatmaps3d(heatmaps, center, scale):
def multilabel_classification_accuracy(pred, gt, mask, thr=0.5):
"""Get multi-label classification accuracy.
- Notes:
- batch size: N
- label number: L
+
+ Note:
+ - batch size: N
+ - label number: L
Args:
pred (np.ndarray[N, L, 2]): model predicted labels.
@@ -668,7 +693,7 @@ def multilabel_classification_accuracy(pred, gt, mask, thr=0.5):
ground-truth labels.
Returns:
- acc (float): multi-label classification accuracy.
+ float: multi-label classification accuracy.
"""
# we only compute accuracy on the samples with ground-truth of all labels.
valid = (mask > 0).min(axis=1) if mask.ndim == 2 else (mask > 0)
diff --git a/mmpose/core/post_processing/group.py b/mmpose/core/post_processing/group.py
index c8e83f01d7..37ca6f8321 100644
--- a/mmpose/core/post_processing/group.py
+++ b/mmpose/core/post_processing/group.py
@@ -203,7 +203,7 @@ def top_k(self, heatmaps, tags):
heatmaps (torch.Tensor[NxKxHxW])
tags (torch.Tensor[NxKxHxWxL])
- Return:
+ Returns:
dict: A dict containing top_k values.
- tag_k (np.ndarray[NxKxMxL]):
@@ -289,8 +289,8 @@ def refine(heatmap, tag, keypoints, use_udp=False):
If use flip testing, L=2; else L=1.
Args:
- heatmap: np.ndarray(K, H, W).
- tag: np.ndarray(K, H, W) | np.ndarray(K, H, W, L)
+ heatmap: torch.Tensor(K, H, W).
+ tag: torch.Tensor(K, H, W) | torch.Tensor(K, H, W, L)
keypoints: np.ndarray of size (K, 3 + L)
last dim is (x, y, score, tag).
use_udp: bool-unbiased data processing
@@ -298,7 +298,6 @@ def refine(heatmap, tag, keypoints, use_udp=False):
Returns:
np.ndarray: The refined keypoints.
"""
-
K, H, W = heatmap.shape
if len(tag.shape) == 3:
tag = tag[..., None]
@@ -310,7 +309,7 @@ def refine(heatmap, tag, keypoints, use_udp=False):
x, y = keypoints[i][:2].astype(int)
x = np.clip(x, 0, W - 1)
y = np.clip(y, 0, H - 1)
- tags.append(tag[i, y, x])
+ tags.append(tag[i, y, x].cpu().numpy())
# mean tag of current detected people
prev_tag = np.mean(tags, axis=0)
@@ -319,16 +318,18 @@ def refine(heatmap, tag, keypoints, use_udp=False):
for _heatmap, _tag in zip(heatmap, tag):
# distance of all tag values with mean tag of
# current detected people
- distance_tag = (((_tag -
- prev_tag[None, None, :])**2).sum(axis=2)**0.5)
- norm_heatmap = _heatmap - np.round(distance_tag)
+ prev_tag_tensor = torch.Tensor(prev_tag[None, None, :])
+ prev_tag_tensor = prev_tag_tensor.to(_tag.device)
+ distance_tag = (((_tag - prev_tag_tensor)**2).sum(2)**0.5)
+ norm_heatmap = _heatmap - torch.round(distance_tag)
# find maximum position
- y, x = np.unravel_index(np.argmax(norm_heatmap), _heatmap.shape)
+ y, x = np.unravel_index(
+ torch.argmax(norm_heatmap).cpu().numpy(), _heatmap.shape)
xx = x.copy()
yy = y.copy()
# detection score at maximum position
- val = _heatmap[y, x]
+ val = _heatmap[y, x].cpu().numpy()
if not use_udp:
# offset by 0.5
x += 0.5
@@ -392,15 +393,13 @@ def parse(self, heatmaps, tags, adjust=True, refine=True):
if refine:
results = results[0]
- # for every detected person
+ heatmap = heatmaps[0]
+ tag = tags[0]
+ if not self.tag_per_joint:
+ tag = tag.repeat(self.params.num_joints, 1, 1, 1)
for i in range(len(results)):
- heatmap_numpy = heatmaps[0].cpu().numpy()
- tag_numpy = tags[0].cpu().numpy()
- if not self.tag_per_joint:
- tag_numpy = np.tile(tag_numpy,
- (self.params.num_joints, 1, 1, 1))
results[i] = self.refine(
- heatmap_numpy, tag_numpy, results[i], use_udp=self.use_udp)
+ heatmap, tag, results[i], use_udp=self.use_udp)
results = [results]
return results, scores
diff --git a/mmpose/core/post_processing/one_euro_filter.py b/mmpose/core/post_processing/one_euro_filter.py
index ab69c243b1..01ffa5fda9 100644
--- a/mmpose/core/post_processing/one_euro_filter.py
+++ b/mmpose/core/post_processing/one_euro_filter.py
@@ -59,8 +59,9 @@ def __init__(self,
def __call__(self, x, t_e=1.0):
"""Compute the filtered signal.
- parameter (cutoff, beta) from VNect
- (http://gvv.mpi-inf.mpg.de/projects/VNect/)
+ Hyper-parameters (cutoff, beta) are from `VNect
+ `__ .
+
Realtime Camera fps (d_cutoff) default 30.0
Args:
diff --git a/mmpose/core/post_processing/post_transforms.py b/mmpose/core/post_processing/post_transforms.py
index ad1cff9106..93063fb1c1 100644
--- a/mmpose/core/post_processing/post_transforms.py
+++ b/mmpose/core/post_processing/post_transforms.py
@@ -7,20 +7,21 @@
import cv2
import numpy as np
+import torch
def fliplr_joints(joints_3d, joints_3d_visible, img_width, flip_pairs):
"""Flip human joints horizontally.
Note:
- num_keypoints: K
+ - num_keypoints: K
Args:
joints_3d (np.ndarray([K, 3])): Coordinates of keypoints.
joints_3d_visible (np.ndarray([K, 1])): Visibility of keypoints.
img_width (int): Image width.
- flip_pairs (list[tuple()]): Pairs of keypoints which are mirrored
- (for example, left ear -- right ear).
+ flip_pairs (list[tuple]): Pairs of keypoints which are mirrored
+ (for example, left ear and right ear).
Returns:
tuple: Flipped human joints.
@@ -58,11 +59,13 @@ def fliplr_regression(regression,
"""Flip human joints horizontally.
Note:
- batch_size: N
- num_keypoint: K
+ - batch_size: N
+ - num_keypoint: K
+
Args:
regression (np.ndarray([..., K, C])): Coordinates of keypoints, where K
is the joint number and C is the dimension. Example shapes are:
+
- [N, K, C]: a batch of keypoints where N is the batch size.
- [N, T, K, C]: a batch of pose sequences, where T is the frame
number.
@@ -70,6 +73,7 @@ def fliplr_regression(regression,
(for example, left ear -- right ear).
center_mode (str): The mode to set the center location on the x-axis
to flip around. Options are:
+
- static: use a static x value (see center_x also)
- root: use a root joint (see center_index also)
center_x (float): Set the x-axis location of the flip center. Only used
@@ -78,9 +82,7 @@ def fliplr_regression(regression,
will be used as the flip center. Only used when center_mode=root.
Returns:
- tuple: Flipped human joints.
-
- - regression_flipped (np.ndarray([..., K, C])): Flipped joints.
+ np.ndarray([..., K, C]): Flipped joints.
"""
assert regression.ndim >= 2, f'Invalid pose shape {regression.shape}'
@@ -109,10 +111,10 @@ def flip_back(output_flipped, flip_pairs, target_type='GaussianHeatmap'):
"""Flip the flipped heatmaps back to the original form.
Note:
- batch_size: N
- num_keypoints: K
- heatmap height: H
- heatmap width: W
+ - batch_size: N
+ - num_keypoints: K
+ - heatmap height: H
+ - heatmap width: W
Args:
output_flipped (np.ndarray[N, K, H, W]): The output heatmaps obtained
@@ -319,7 +321,7 @@ def get_warp_matrix(theta, size_input, size_dst, size_target):
size_target (np.ndarray): Size of ROI in input plane [w, h].
Returns:
- matrix (np.ndarray): A matrix for transformation.
+ np.ndarray: A matrix for transformation.
"""
theta = np.deg2rad(theta)
matrix = np.zeros((2, 3), dtype=np.float32)
@@ -347,7 +349,7 @@ def warp_affine_joints(joints, mat):
mat (np.ndarray[3, 2]): The affine matrix.
Returns:
- matrix (np.ndarray[..., 2]): Result coordinate of joints.
+ np.ndarray[..., 2]: Result coordinate of joints.
"""
joints = np.array(joints)
shape = joints.shape
@@ -355,3 +357,10 @@ def warp_affine_joints(joints, mat):
return np.dot(
np.concatenate((joints, joints[:, 0:1] * 0 + 1), axis=1),
mat.T).reshape(shape)
+
+
+def affine_transform_torch(pts, t):
+ npts = pts.shape[0]
+ pts_homo = torch.cat([pts, torch.ones(npts, 1, device=pts.device)], dim=1)
+ out = torch.mm(t, torch.t(pts_homo))
+ return torch.t(out[:2, :])
diff --git a/mmpose/datasets/datasets/__init__.py b/mmpose/datasets/datasets/__init__.py
index 151c72a994..f3839e5eaa 100644
--- a/mmpose/datasets/datasets/__init__.py
+++ b/mmpose/datasets/datasets/__init__.py
@@ -4,7 +4,7 @@
from .animal import (AnimalATRWDataset, AnimalFlyDataset, AnimalHorse10Dataset,
AnimalLocustDataset, AnimalMacaqueDataset,
AnimalPoseDataset, AnimalZebraDataset)
-from .body3d import Body3DH36MDataset
+from .body3d import Body3DH36MDataset, Body3DMviewDirectPanopticDataset
from .bottom_up import (BottomUpAicDataset, BottomUpCocoDataset,
BottomUpCocoWholeBodyDataset, BottomUpCrowdPoseDataset,
BottomUpMhpDataset)
@@ -40,5 +40,6 @@
'Body3DH36MDataset', 'AnimalHorse10Dataset', 'AnimalMacaqueDataset',
'AnimalFlyDataset', 'AnimalLocustDataset', 'AnimalZebraDataset',
'AnimalATRWDataset', 'AnimalPoseDataset', 'TopDownH36MDataset',
- 'TopDownHalpeDataset', 'TopDownPoseTrack18VideoDataset'
+ 'TopDownHalpeDataset', 'TopDownPoseTrack18VideoDataset',
+ 'Body3DMviewDirectPanopticDataset'
]
diff --git a/mmpose/datasets/datasets/animal/animal_ap10k_dataset.py b/mmpose/datasets/datasets/animal/animal_ap10k_dataset.py
index 01651f22ed..82cd61601c 100644
--- a/mmpose/datasets/datasets/animal/animal_ap10k_dataset.py
+++ b/mmpose/datasets/datasets/animal/animal_ap10k_dataset.py
@@ -17,8 +17,8 @@
class AnimalAP10KDataset(Kpt2dSviewRgbImgTopDownDataset):
"""AP-10K dataset for animal pose estimation.
- `AP-10K: A Benchmark for Animal Pose Estimation in the Wild’
- Neurips Dataset Track'2021
+ "AP-10K: A Benchmark for Animal Pose Estimation in the Wild"
+ Neurips Dataset Track'2021.
More details can be found in the `paper
`__ .
@@ -189,24 +189,25 @@ def _load_coco_keypoint_annotation_kernel(self, img_id):
def evaluate(self, outputs, res_folder, metric='mAP', **kwargs):
"""Evaluate coco keypoint results. The pose prediction results will be
- saved in `${res_folder}/result_keypoints.json`.
+ saved in ``${res_folder}/result_keypoints.json``.
Note:
- batch_size: N
- num_keypoints: K
- heatmap height: H
- heatmap width: W
+ - batch_size: N
+ - num_keypoints: K
+ - heatmap height: H
+ - heatmap width: W
Args:
- outputs (list(dict))
- :preds (np.ndarray[N,K,3]): The first two dimensions are
+ outputs (list[dict]): Outputs containing the following items.
+
+ - preds (np.ndarray[N,K,3]): The first two dimensions are \
coordinates, score is the third dimension of the array.
- :boxes (np.ndarray[N,6]): [center[0], center[1], scale[0]
- , scale[1],area, score]
- :image_paths (list[str]): For example, ['data/coco/val2017
+ - boxes (np.ndarray[N,6]): [center[0], center[1], scale[0], \
+ scale[1],area, score]
+ - image_paths (list[str]): For example, ['data/coco/val2017\
/000000393226.jpg']
- :heatmap (np.ndarray[N, K, H, W]): model output heatmap
- :bbox_id (list(int)).
+ - heatmap (np.ndarray[N, K, H, W]): model output heatmap
+ - bbox_id (list(int)).
res_folder (str): Path of directory to save the results.
metric (str | list[str]): Metric to be performed. Defaults: 'mAP'.
diff --git a/mmpose/datasets/datasets/animal/animal_atrw_dataset.py b/mmpose/datasets/datasets/animal/animal_atrw_dataset.py
index a4f3e1cc1f..a609f4c532 100644
--- a/mmpose/datasets/datasets/animal/animal_atrw_dataset.py
+++ b/mmpose/datasets/datasets/animal/animal_atrw_dataset.py
@@ -17,8 +17,8 @@
class AnimalATRWDataset(Kpt2dSviewRgbImgTopDownDataset):
"""ATRW dataset for animal pose estimation.
- `ATRW: A Benchmark for Amur Tiger Re-identification in the Wild'
- ACM MM'2020
+ "ATRW: A Benchmark for Amur Tiger Re-identification in the Wild"
+ ACM MM'2020.
More details can be found in the `paper
`__ .
@@ -176,24 +176,25 @@ def _load_coco_keypoint_annotation_kernel(self, img_id):
def evaluate(self, outputs, res_folder, metric='mAP', **kwargs):
"""Evaluate coco keypoint results. The pose prediction results will be
- saved in `${res_folder}/result_keypoints.json`.
+ saved in ``${res_folder}/result_keypoints.json``.
Note:
- batch_size: N
- num_keypoints: K
- heatmap height: H
- heatmap width: W
+ - batch_size: N
+ - num_keypoints: K
+ - heatmap height: H
+ - heatmap width: W
Args:
- outputs (list(dict))
- :preds (np.ndarray[N,K,3]): The first two dimensions are
+ outputs (list[dict]): Outputs containing the following items.
+
+ - preds (np.ndarray[N,K,3]): The first two dimensions are \
coordinates, score is the third dimension of the array.
- :boxes (np.ndarray[N,6]): [center[0], center[1], scale[0]
- , scale[1],area, score]
- :image_paths (list[str]): For example, ['data/coco/val2017
+ - boxes (np.ndarray[N,6]): [center[0], center[1], scale[0], \
+ scale[1],area, score]
+ - image_paths (list[str]): For example, ['data/coco/val2017\
/000000393226.jpg']
- :heatmap (np.ndarray[N, K, H, W]): model output heatmap
- :bbox_id (list(int)).
+ - heatmap (np.ndarray[N, K, H, W]): model output heatmap
+ - bbox_id (list(int)).
res_folder (str): Path of directory to save the results.
metric (str | list[str]): Metric to be performed. Defaults: 'mAP'.
diff --git a/mmpose/datasets/datasets/animal/animal_fly_dataset.py b/mmpose/datasets/datasets/animal/animal_fly_dataset.py
index 0a6e274ede..c7dfee019a 100644
--- a/mmpose/datasets/datasets/animal/animal_fly_dataset.py
+++ b/mmpose/datasets/datasets/animal/animal_fly_dataset.py
@@ -14,10 +14,10 @@
class AnimalFlyDataset(Kpt2dSviewRgbImgTopDownDataset):
"""AnimalFlyDataset for animal pose estimation.
- `Fast animal pose estimation using deep neural networks'
+ "Fast animal pose estimation using deep neural networks"
Nature methods'2019. More details can be found in the `paper
- `__ .
+ `__ .
The dataset loads raw features and apply specified transforms
to return a dict containing the image tensors and other information.
@@ -143,22 +143,23 @@ def _get_db(self):
def evaluate(self, outputs, res_folder, metric='PCK', **kwargs):
"""Evaluate Fly keypoint results. The pose prediction results will be
- saved in `${res_folder}/result_keypoints.json`.
+ saved in ``${res_folder}/result_keypoints.json``.
Note:
- batch_size: N
- num_keypoints: K
- heatmap height: H
- heatmap width: W
+ - batch_size: N
+ - num_keypoints: K
+ - heatmap height: H
+ - heatmap width: W
Args:
- outputs (list(preds, boxes, image_path, output_heatmap))
- :preds (np.ndarray[N,K,3]): The first two dimensions are
+ outputs (list[dict]): Outputs containing the following items.
+
+ - preds (np.ndarray[N,K,3]): The first two dimensions are \
coordinates, score is the third dimension of the array.
- :boxes (np.ndarray[N,6]): [center[0], center[1], scale[0]
- , scale[1],area, score]
- :image_paths (list[str]): For example, ['Test/source/0.jpg']
- :output_heatmap (np.ndarray[N, K, H, W]): model outputs.
+ - boxes (np.ndarray[N,6]): [center[0], center[1], scale[0], \
+ scale[1],area, score]
+ - image_paths (list[str]): For example, ['Test/source/0.jpg']
+ - output_heatmap (np.ndarray[N, K, H, W]): model outputs.
res_folder (str): Path of directory to save the results.
metric (str | list[str]): Metric to be performed.
diff --git a/mmpose/datasets/datasets/animal/animal_horse10_dataset.py b/mmpose/datasets/datasets/animal/animal_horse10_dataset.py
index d44cf87869..5ddd328198 100644
--- a/mmpose/datasets/datasets/animal/animal_horse10_dataset.py
+++ b/mmpose/datasets/datasets/animal/animal_horse10_dataset.py
@@ -14,7 +14,7 @@
class AnimalHorse10Dataset(Kpt2dSviewRgbImgTopDownDataset):
"""AnimalHorse10Dataset for animal pose estimation.
- `Pretraining boosts out-of-domain robustness for pose estimation'
+ "Pretraining boosts out-of-domain robustness for pose estimation"
WACV'2021. More details can be found in the `paper
`__ .
@@ -137,7 +137,7 @@ def _get_normalize_factor(self, gts):
Args:
gts (np.ndarray[N, K, 2]): Groundtruth keypoint location.
- Return:
+ Returns:
np.ndarray[N, 2]: normalized factor
"""
@@ -147,23 +147,23 @@ def _get_normalize_factor(self, gts):
def evaluate(self, outputs, res_folder, metric='PCK', **kwargs):
"""Evaluate horse-10 keypoint results. The pose prediction results will
- be saved in `${res_folder}/result_keypoints.json`.
+ be saved in ``${res_folder}/result_keypoints.json``.
Note:
- batch_size: N
- num_keypoints: K
- heatmap height: H
- heatmap width: W
+ - batch_size: N
+ - num_keypoints: K
+ - heatmap height: H
+ - heatmap width: W
Args:
- outputs (list(preds, boxes, image_path, output_heatmap))
- :preds (np.ndarray[N,K,3]): The first two dimensions are
- coordinates, score is the third dimension of the array.
- :boxes (np.ndarray[N,6]): [center[0], center[1], scale[0]
- , scale[1],area, score]
- :image_paths (list[str]): For example, ['Test/source/0.jpg']
- :output_heatmap (np.ndarray[N, K, H, W]): model outputs.
+ outputs (list[dict]): Outputs containing the following items.
+ - preds (np.ndarray[N,K,3]): The first two dimensions are \
+ coordinates, score is the third dimension of the array.
+ - boxes (np.ndarray[N,6]): [center[0], center[1], scale[0], \
+ scale[1],area, score]
+ - image_paths (list[str]): For example, ['Test/source/0.jpg']
+ - output_heatmap (np.ndarray[N, K, H, W]): model outputs.
res_folder (str): Path of directory to save the results.
metric (str | list[str]): Metric to be performed.
Options: 'PCK', 'NME'.
diff --git a/mmpose/datasets/datasets/animal/animal_locust_dataset.py b/mmpose/datasets/datasets/animal/animal_locust_dataset.py
index 1815f7d729..81357d0e6b 100644
--- a/mmpose/datasets/datasets/animal/animal_locust_dataset.py
+++ b/mmpose/datasets/datasets/animal/animal_locust_dataset.py
@@ -14,9 +14,9 @@
class AnimalLocustDataset(Kpt2dSviewRgbImgTopDownDataset):
"""AnimalLocustDataset for animal pose estimation.
- `DeepPoseKit, a software toolkit for fast and robust animal
- pose estimation using deep learning'
- Elife'2019. More details can be found in the `paper.
+ "DeepPoseKit, a software toolkit for fast and robust animal
+ pose estimation using deep learning" Elife'2019.
+ More details can be found in the paper.
The dataset loads raw features and apply specified transforms
to return a dict containing the image tensors and other information.
@@ -145,23 +145,23 @@ def _get_db(self):
def evaluate(self, outputs, res_folder, metric='PCK', **kwargs):
"""Evaluate Fly keypoint results. The pose prediction results will be
- saved in `${res_folder}/result_keypoints.json`.
+ saved in ``${res_folder}/result_keypoints.json``.
Note:
- batch_size: N
- num_keypoints: K
- heatmap height: H
- heatmap width: W
+ - batch_size: N
+ - num_keypoints: K
+ - heatmap height: H
+ - heatmap width: W
Args:
- outputs (list(preds, boxes, image_path, output_heatmap))
- :preds (np.ndarray[N,K,3]): The first two dimensions are
- coordinates, score is the third dimension of the array.
- :boxes (np.ndarray[N,6]): [center[0], center[1], scale[0]
- , scale[1],area, score]
- :image_paths (list[str]): For example, ['Test/source/0.jpg']
- :output_heatmap (np.ndarray[N, K, H, W]): model outputs.
+ outputs (list[dict]): Outputs containing the following items.
+ - preds (np.ndarray[N,K,3]): The first two dimensions are \
+ coordinates, score is the third dimension of the array.
+ - boxes (np.ndarray[N,6]): [center[0], center[1], scale[0], \
+ scale[1],area, score]
+ - image_paths (list[str]): For example, ['Test/source/0.jpg']
+ - output_heatmap (np.ndarray[N, K, H, W]): model outputs.
res_folder (str): Path of directory to save the results.
metric (str | list[str]): Metric to be performed.
Options: 'PCK', 'AUC', 'EPE'.
diff --git a/mmpose/datasets/datasets/animal/animal_macaque_dataset.py b/mmpose/datasets/datasets/animal/animal_macaque_dataset.py
index abe5f21872..185c2e8a7f 100644
--- a/mmpose/datasets/datasets/animal/animal_macaque_dataset.py
+++ b/mmpose/datasets/datasets/animal/animal_macaque_dataset.py
@@ -17,8 +17,8 @@
class AnimalMacaqueDataset(Kpt2dSviewRgbImgTopDownDataset):
"""MacaquePose dataset for animal pose estimation.
- `MacaquePose: A novel ‘in the wild’ macaque monkey pose dataset
- for markerless motion capture' bioRxiv'2020
+ "MacaquePose: A novel ‘in the wild’ macaque monkey pose dataset
+ for markerless motion capture" bioRxiv'2020.
More details can be found in the `paper
`__ .
@@ -178,7 +178,7 @@ def _load_coco_keypoint_annotation_kernel(self, img_id):
def evaluate(self, outputs, res_folder, metric='mAP', **kwargs):
"""Evaluate coco keypoint results. The pose prediction results will be
- saved in `${res_folder}/result_keypoints.json`.
+ saved in ``${res_folder}/result_keypoints.json``.
Note:
batch_size: N
@@ -187,15 +187,16 @@ def evaluate(self, outputs, res_folder, metric='mAP', **kwargs):
heatmap width: W
Args:
- outputs (list(dict))
- :preds (np.ndarray[N,K,3]): The first two dimensions are
+ outputs (list[dict]): Outputs containing the following items.
+
+ - preds (np.ndarray[N,K,3]): The first two dimensions are \
coordinates, score is the third dimension of the array.
- :boxes (np.ndarray[N,6]): [center[0], center[1], scale[0]
- , scale[1],area, score]
- :image_paths (list[str]): For example, ['data/coco/val2017
+ - boxes (np.ndarray[N,6]): [center[0], center[1], scale[0], \
+ scale[1],area, score]
+ - image_paths (list[str]): For example, ['data/coco/val2017\
/000000393226.jpg']
- :heatmap (np.ndarray[N, K, H, W]): model output heatmap
- :bbox_id (list(int)).
+ - heatmap (np.ndarray[N, K, H, W]): model output heatmap
+ - bbox_id (list(int)).
res_folder (str): Path of directory to save the results.
metric (str | list[str]): Metric to be performed. Defaults: 'mAP'.
diff --git a/mmpose/datasets/datasets/animal/animal_pose_dataset.py b/mmpose/datasets/datasets/animal/animal_pose_dataset.py
index 2765365ede..c9f054237d 100644
--- a/mmpose/datasets/datasets/animal/animal_pose_dataset.py
+++ b/mmpose/datasets/datasets/animal/animal_pose_dataset.py
@@ -17,7 +17,7 @@
class AnimalPoseDataset(Kpt2dSviewRgbImgTopDownDataset):
"""Animal-Pose dataset for animal pose estimation.
- `Cross-domain Adaptation For Animal Pose Estimation’ ICCV'2019
+ "Cross-domain Adaptation For Animal Pose Estimation" ICCV'2019
More details can be found in the `paper
`__ .
@@ -115,8 +115,10 @@ def _load_coco_keypoint_annotation_kernel(self, img_id):
Note:
bbox:[x1, y1, w, h]
+
Args:
img_id: coco image id
+
Returns:
dict: db entry
"""
@@ -180,24 +182,25 @@ def _load_coco_keypoint_annotation_kernel(self, img_id):
def evaluate(self, outputs, res_folder, metric='mAP', **kwargs):
"""Evaluate coco keypoint results. The pose prediction results will be
- saved in `${res_folder}/result_keypoints.json`.
+ saved in ``${res_folder}/result_keypoints.json``.
Note:
- batch_size: N
- num_keypoints: K
- heatmap height: H
- heatmap width: W
+ - batch_size: N
+ - num_keypoints: K
+ - heatmap height: H
+ - heatmap width: W
Args:
- outputs (list(dict))
- :preds (np.ndarray[N,K,3]): The first two dimensions are
+ outputs (list[dict]): Outputs containing the following items.
+
+ - preds (np.ndarray[N,K,3]): The first two dimensions are \
coordinates, score is the third dimension of the array.
- :boxes (np.ndarray[N,6]): [center[0], center[1], scale[0]
- , scale[1],area, score]
- :image_paths (list[str]): For example, ['data/coco/val2017
+ - boxes (np.ndarray[N,6]): [center[0], center[1], scale[0], \
+ scale[1],area, score]
+ - image_paths (list[str]): For example, ['data/coco/val2017\
/000000393226.jpg']
- :heatmap (np.ndarray[N, K, H, W]): model output heatmap
- :bbox_id (list(int)).
+ - heatmap (np.ndarray[N, K, H, W]): model output heatmap
+ - bbox_id (list(int)).
res_folder (str): Path of directory to save the results.
metric (str | list[str]): Metric to be performed. Defaults: 'mAP'.
diff --git a/mmpose/datasets/datasets/animal/animal_zebra_dataset.py b/mmpose/datasets/datasets/animal/animal_zebra_dataset.py
index da922ebeef..f1c3f9bb82 100644
--- a/mmpose/datasets/datasets/animal/animal_zebra_dataset.py
+++ b/mmpose/datasets/datasets/animal/animal_zebra_dataset.py
@@ -14,9 +14,9 @@
class AnimalZebraDataset(Kpt2dSviewRgbImgTopDownDataset):
"""AnimalZebraDataset for animal pose estimation.
- `DeepPoseKit, a software toolkit for fast and robust animal
- pose estimation using deep learning'
- Elife'2019. More details can be found in the `paper.
+ "DeepPoseKit, a software toolkit for fast and robust animal
+ pose estimation using deep learning" Elife'2019.
+ More details can be found in the paper.
The dataset loads raw features and apply specified transforms
to return a dict containing the image tensors and other information.
@@ -119,22 +119,23 @@ def _get_db(self):
def evaluate(self, outputs, res_folder, metric='PCK', **kwargs):
"""Evaluate Fly keypoint results. The pose prediction results will be
- saved in `${res_folder}/result_keypoints.json`.
+ saved in ``${res_folder}/result_keypoints.json``.
Note:
- batch_size: N
- num_keypoints: K
- heatmap height: H
- heatmap width: W
+ - batch_size: N
+ - num_keypoints: K
+ - heatmap height: H
+ - heatmap width: W
Args:
- outputs (list(preds, boxes, image_path, output_heatmap))
- :preds (np.ndarray[N,K,3]): The first two dimensions are
+ outputs (list[dict]): Outputs containing the following items.
+
+ - preds (np.ndarray[N,K,3]): The first two dimensions are \
coordinates, score is the third dimension of the array.
- :boxes (np.ndarray[N,6]): [center[0], center[1], scale[0]
- , scale[1],area, score]
- :image_paths (list[str]): For example, ['Test/source/0.jpg']
- :output_heatmap (np.ndarray[N, K, H, W]): model outputs.
+ - boxes (np.ndarray[N,6]): [center[0], center[1], scale[0], \
+ scale[1],area, score]
+ - image_paths (list[str]): For example, ['Test/source/0.jpg']
+ - output_heatmap (np.ndarray[N, K, H, W]): model outputs.
res_folder (str): Path of directory to save the results.
metric (str | list[str]): Metric to be performed.
diff --git a/mmpose/datasets/datasets/base/__init__.py b/mmpose/datasets/datasets/base/__init__.py
index 9bce08aa8a..e5f9a0899c 100644
--- a/mmpose/datasets/datasets/base/__init__.py
+++ b/mmpose/datasets/datasets/base/__init__.py
@@ -5,12 +5,13 @@
Kpt2dSviewRgbImgTopDownDataset
from .kpt_2d_sview_rgb_vid_top_down_dataset import \
Kpt2dSviewRgbVidTopDownDataset
+from .kpt_3d_mview_rgb_img_direct_dataset import Kpt3dMviewRgbImgDirectDataset
from .kpt_3d_sview_kpt_2d_dataset import Kpt3dSviewKpt2dDataset
from .kpt_3d_sview_rgb_img_top_down_dataset import \
Kpt3dSviewRgbImgTopDownDataset
__all__ = [
- 'Kpt2dSviewRgbImgTopDownDataset', 'Kpt3dSviewRgbImgTopDownDataset',
- 'Kpt2dSviewRgbImgBottomUpDataset', 'Kpt3dSviewKpt2dDataset',
- 'Kpt2dSviewRgbVidTopDownDataset'
+ 'Kpt3dMviewRgbImgDirectDataset', 'Kpt2dSviewRgbImgTopDownDataset',
+ 'Kpt3dSviewRgbImgTopDownDataset', 'Kpt2dSviewRgbImgBottomUpDataset',
+ 'Kpt3dSviewKpt2dDataset', 'Kpt2dSviewRgbVidTopDownDataset'
]
diff --git a/mmpose/datasets/datasets/base/kpt_2d_sview_rgb_img_top_down_dataset.py b/mmpose/datasets/datasets/base/kpt_2d_sview_rgb_img_top_down_dataset.py
index 0c8bf6f766..10f2e7f2fc 100644
--- a/mmpose/datasets/datasets/base/kpt_2d_sview_rgb_img_top_down_dataset.py
+++ b/mmpose/datasets/datasets/base/kpt_2d_sview_rgb_img_top_down_dataset.py
@@ -164,7 +164,7 @@ def _get_normalize_factor(self, gts, *args, **kwargs):
Args:
gts (np.ndarray[N, K, 2]): Groundtruth keypoint location.
- Return:
+ Returns:
np.ndarray[N, 2]: normalized factor
"""
return np.ones([gts.shape[0], 2], dtype=np.float32)
diff --git a/mmpose/datasets/datasets/base/kpt_3d_mview_rgb_img_direct_dataset.py b/mmpose/datasets/datasets/base/kpt_3d_mview_rgb_img_direct_dataset.py
new file mode 100644
index 0000000000..42ac84be0c
--- /dev/null
+++ b/mmpose/datasets/datasets/base/kpt_3d_mview_rgb_img_direct_dataset.py
@@ -0,0 +1,144 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import copy
+from abc import ABCMeta, abstractmethod
+
+import json_tricks as json
+import numpy as np
+from torch.utils.data import Dataset
+
+from mmpose.datasets import DatasetInfo
+from mmpose.datasets.pipelines import Compose
+
+
+class Kpt3dMviewRgbImgDirectDataset(Dataset, metaclass=ABCMeta):
+ """Base class for keypoint 3D top-down pose estimation with multi-view RGB
+ images as the input.
+
+ All subclasses should overwrite:
+ Methods:`_get_db`, 'evaluate'
+
+ Args:
+ ann_file (str): Path to the annotation file.
+ img_prefix (str): Path to a directory where images are held.
+ Default: None.
+ data_cfg (dict): config
+ pipeline (list[dict | callable]): A sequence of data transforms.
+ dataset_info (DatasetInfo): A class containing all dataset info.
+ test_mode (bool): Store True when building test or
+ validation dataset. Default: False.
+ """
+
+ def __init__(self,
+ ann_file,
+ img_prefix,
+ data_cfg,
+ pipeline,
+ dataset_info=None,
+ test_mode=False):
+
+ self.image_info = {}
+ self.ann_info = {}
+
+ self.ann_file = ann_file
+ self.img_prefix = img_prefix
+ self.pipeline = pipeline
+ self.test_mode = test_mode
+
+ self.ann_info['image_size'] = np.array(data_cfg['image_size'])
+ self.ann_info['heatmap_size'] = np.array(data_cfg['heatmap_size'])
+ self.ann_info['num_joints'] = data_cfg['num_joints']
+
+ self.ann_info['space_size'] = data_cfg['space_size']
+ self.ann_info['space_center'] = data_cfg['space_center']
+ self.ann_info['cube_size'] = data_cfg['cube_size']
+ self.ann_info['scale_aware_sigma'] = data_cfg.get(
+ 'scale_aware_sigma', False)
+
+ if dataset_info is None:
+ raise ValueError(
+ 'Check https://github.com/open-mmlab/mmpose/pull/663 '
+ 'for details.')
+
+ dataset_info = DatasetInfo(dataset_info)
+
+ assert self.ann_info['num_joints'] <= dataset_info.keypoint_num
+ self.ann_info['flip_pairs'] = dataset_info.flip_pairs
+ self.ann_info['num_scales'] = 1
+ self.ann_info['flip_index'] = dataset_info.flip_index
+ self.ann_info['upper_body_ids'] = dataset_info.upper_body_ids
+ self.ann_info['lower_body_ids'] = dataset_info.lower_body_ids
+ self.ann_info['joint_weights'] = dataset_info.joint_weights
+ self.ann_info['skeleton'] = dataset_info.skeleton
+ self.sigmas = dataset_info.sigmas
+ self.dataset_name = dataset_info.dataset_name
+
+ self.load_config(data_cfg)
+
+ self.db = []
+
+ self.pipeline = Compose(self.pipeline)
+
+ def load_config(self, data_cfg):
+ """Initialize dataset attributes according to the config.
+
+ Override this method to set dataset specific attributes.
+ """
+ self.num_joints = data_cfg['num_joints']
+ self.num_cameras = data_cfg['num_cameras']
+ self.seq_frame_interval = data_cfg.get('seq_frame_interval', 1)
+ self.subset = data_cfg.get('subset', 'train')
+ self.need_2d_label = data_cfg.get('need_2d_label', False)
+ self.need_camera_param = True
+
+ @staticmethod
+ def _get_mapping_id_name(imgs):
+ """
+ Args:
+ imgs (dict): dict of image info.
+
+ Returns:
+ tuple: Image name & id mapping dicts.
+
+ - id2name (dict): Mapping image id to name.
+ - name2id (dict): Mapping image name to id.
+ """
+ id2name = {}
+ name2id = {}
+ for image_id, image in imgs.items():
+ file_name = image['file_name']
+ id2name[image_id] = file_name
+ name2id[file_name] = image_id
+
+ return id2name, name2id
+
+ @abstractmethod
+ def _get_db(self):
+ """Load dataset."""
+ raise NotImplementedError
+
+ @abstractmethod
+ def evaluate(self, cfg, outputs, res_folder, metric, *args, **kwargs):
+ """Evaluate keypoint results."""
+ raise NotImplementedError
+
+ @staticmethod
+ def _write_keypoint_results(keypoints, res_file):
+ """Write results into a json file."""
+
+ with open(res_file, 'w') as f:
+ json.dump(keypoints, f, sort_keys=True, indent=4)
+
+ def __len__(self):
+ """Get the size of the dataset."""
+ return len(self.db) // self.num_cameras
+
+ def __getitem__(self, idx):
+ """Get the sample given index."""
+ results = {}
+ # return self.pipeline(results)
+ for c in range(self.num_cameras):
+ result = copy.deepcopy(self.db[self.num_cameras * idx + c])
+ result['ann_info'] = self.ann_info
+ results[c] = result
+
+ return self.pipeline(results)
diff --git a/mmpose/datasets/datasets/body3d/__init__.py b/mmpose/datasets/datasets/body3d/__init__.py
index cdb74ae4e4..5bc25a9ebb 100644
--- a/mmpose/datasets/datasets/body3d/__init__.py
+++ b/mmpose/datasets/datasets/body3d/__init__.py
@@ -1,9 +1,11 @@
# Copyright (c) OpenMMLab. All rights reserved.
from .body3d_h36m_dataset import Body3DH36MDataset
from .body3d_mpi_inf_3dhp_dataset import Body3DMpiInf3dhpDataset
+from .body3d_mview_direct_panoptic_dataset import \
+ Body3DMviewDirectPanopticDataset
from .body3d_semi_supervision_dataset import Body3DSemiSupervisionDataset
__all__ = [
'Body3DH36MDataset', 'Body3DSemiSupervisionDataset',
- 'Body3DMpiInf3dhpDataset'
+ 'Body3DMpiInf3dhpDataset', 'Body3DMviewDirectPanopticDataset'
]
diff --git a/mmpose/datasets/datasets/body3d/body3d_h36m_dataset.py b/mmpose/datasets/datasets/body3d/body3d_h36m_dataset.py
index 9e2e01bee4..d4ad9a2d8c 100644
--- a/mmpose/datasets/datasets/body3d/body3d_h36m_dataset.py
+++ b/mmpose/datasets/datasets/body3d/body3d_h36m_dataset.py
@@ -16,12 +16,13 @@
class Body3DH36MDataset(Kpt3dSviewKpt2dDataset):
"""Human3.6M dataset for 3D human pose estimation.
- `Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human
- Sensing in Natural Environments' TPAMI`2014
+ "Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human
+ Sensing in Natural Environments", TPAMI`2014.
More details can be found in the `paper
`__.
Human3.6M keypoint indexes::
+
0: 'root (pelvis)',
1: 'right_hip',
2: 'right_knee',
@@ -274,6 +275,7 @@ def _report_mpjpe(self, keypoint_results, mode='mpjpe'):
keypoint_results (list): Keypoint predictions. See
'Body3DH36MDataset.evaluate' for details.
mode (str): Specify mpjpe variants. Supported options are:
+
- ``'mpjpe'``: Standard MPJPE.
- ``'p-mpjpe'``: MPJPE after aligning prediction to groundtruth
via a rigid transformation (scale, rotation and
diff --git a/mmpose/datasets/datasets/body3d/body3d_mpi_inf_3dhp_dataset.py b/mmpose/datasets/datasets/body3d/body3d_mpi_inf_3dhp_dataset.py
index 97cb757621..69656052ba 100644
--- a/mmpose/datasets/datasets/body3d/body3d_mpi_inf_3dhp_dataset.py
+++ b/mmpose/datasets/datasets/body3d/body3d_mpi_inf_3dhp_dataset.py
@@ -17,12 +17,13 @@
class Body3DMpiInf3dhpDataset(Kpt3dSviewKpt2dDataset):
"""MPI-INF-3DHP dataset for 3D human pose estimation.
- `Monocular 3D Human Pose Estimation In The Wild Using Improved CNN
- Supervision` 3DV'2017
+ "Monocular 3D Human Pose Estimation In The Wild Using Improved CNN
+ Supervision", 3DV'2017.
More details can be found in the `paper
`__.
MPI-INF-3DHP keypoint indexes:
+
0: 'head_top',
1: 'neck',
2: 'right_shoulder',
@@ -366,6 +367,7 @@ def _report_3d_auc(self, keypoint_results, mode='3dauc'):
keypoint_results (list): Keypoint predictions. See
'Body3DMpiInf3dhpDataset.evaluate' for details.
mode (str): Specify mpjpe variants. Supported options are:
+
- ``'3dauc'``: Standard 3DAUC.
- ``'p-3dauc'``: 3DAUC after aligning prediction to
groundtruth via a rigid transformation (scale, rotation and
diff --git a/mmpose/datasets/datasets/body3d/body3d_mview_direct_panoptic_dataset.py b/mmpose/datasets/datasets/body3d/body3d_mview_direct_panoptic_dataset.py
new file mode 100644
index 0000000000..0dcca6169d
--- /dev/null
+++ b/mmpose/datasets/datasets/body3d/body3d_mview_direct_panoptic_dataset.py
@@ -0,0 +1,480 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import copy
+import glob
+import json
+import os
+import os.path as osp
+import pickle
+import warnings
+from collections import OrderedDict
+
+import mmcv
+import numpy as np
+from mmcv import Config
+
+from mmpose.core.camera import SimpleCamera
+from mmpose.datasets.builder import DATASETS
+from mmpose.datasets.datasets.base import Kpt3dMviewRgbImgDirectDataset
+
+
+@DATASETS.register_module()
+class Body3DMviewDirectPanopticDataset(Kpt3dMviewRgbImgDirectDataset):
+ """Panoptic dataset for direct multi-view human pose estimation.
+
+ `Panoptic Studio: A Massively Multiview System for Social Motion
+ Capture' ICCV'2015
+ More details can be found in the `paper
+ `__ .
+
+ The dataset loads both 2D and 3D annotations as well as camera parameters.
+
+ Panoptic keypoint indexes::
+
+ 'neck': 0,
+ 'nose': 1,
+ 'mid-hip': 2,
+ 'l-shoulder': 3,
+ 'l-elbow': 4,
+ 'l-wrist': 5,
+ 'l-hip': 6,
+ 'l-knee': 7,
+ 'l-ankle': 8,
+ 'r-shoulder': 9,
+ 'r-elbow': 10,
+ 'r-wrist': 11,
+ 'r-hip': 12,
+ 'r-knee': 13,
+ 'r-ankle': 14,
+ 'l-eye': 15,
+ 'l-ear': 16,
+ 'r-eye': 17,
+ 'r-ear': 18,
+
+ Args:
+ ann_file (str): Path to the annotation file.
+ img_prefix (str): Path to a directory where images are held.
+ Default: None.
+ data_cfg (dict): config
+ pipeline (list[dict | callable]): A sequence of data transforms.
+ dataset_info (DatasetInfo): A class containing all dataset info.
+ test_mode (bool): Store True when building test or
+ validation dataset. Default: False.
+ """
+ ALLOWED_METRICS = {'mpjpe', 'mAP'}
+
+ def __init__(self,
+ ann_file,
+ img_prefix,
+ data_cfg,
+ pipeline,
+ dataset_info=None,
+ test_mode=False):
+
+ if dataset_info is None:
+ warnings.warn(
+ 'dataset_info is missing. '
+ 'Check https://github.com/open-mmlab/mmpose/pull/663 '
+ 'for details.', DeprecationWarning)
+ cfg = Config.fromfile('configs/_base_/datasets/panoptic_body3d.py')
+ dataset_info = cfg._cfg_dict['dataset_info']
+
+ super().__init__(
+ ann_file,
+ img_prefix,
+ data_cfg,
+ pipeline,
+ dataset_info=dataset_info,
+ test_mode=test_mode)
+
+ self.load_config(data_cfg)
+ self.ann_info['use_different_joint_weights'] = False
+
+ if ann_file is None:
+ self.db_file = os.path.join(
+ img_prefix, f'group_{self.subset}_cam{self.num_cameras}.pkl')
+ else:
+ self.db_file = ann_file
+
+ if osp.exists(self.db_file):
+ with open(self.db_file, 'rb') as f:
+ info = pickle.load(f)
+ assert info['sequence_list'] == self.seq_list
+ assert info['interval'] == self.seq_frame_interval
+ assert info['cam_list'] == self.cam_list
+ self.db = info['db']
+ else:
+ self.db = self._get_db()
+ info = {
+ 'sequence_list': self.seq_list,
+ 'interval': self.seq_frame_interval,
+ 'cam_list': self.cam_list,
+ 'db': self.db
+ }
+ with open(self.db_file, 'wb') as f:
+ pickle.dump(info, f)
+
+ self.db_size = len(self.db)
+
+ print(f'=> load {len(self.db)} samples')
+
+ def load_config(self, data_cfg):
+ """Initialize dataset attributes according to the config.
+
+ Override this method to set dataset specific attributes.
+ """
+ self.num_joints = data_cfg['num_joints']
+ assert self.num_joints <= 19
+ self.seq_list = data_cfg['seq_list']
+ self.cam_list = data_cfg['cam_list']
+ self.num_cameras = data_cfg['num_cameras']
+ assert self.num_cameras == len(self.cam_list)
+ self.seq_frame_interval = data_cfg.get('seq_frame_interval', 1)
+ self.subset = data_cfg.get('subset', 'train')
+ self.need_camera_param = True
+ self.root_id = data_cfg.get('root_id', 0)
+ self.max_persons = data_cfg.get('max_num', 10)
+
+ def _get_scale(self, raw_image_size):
+ heatmap_size = self.ann_info['heatmap_size']
+ image_size = self.ann_info['image_size']
+ assert heatmap_size[0][0] / heatmap_size[0][1] \
+ == image_size[0] / image_size[1]
+ w, h = raw_image_size
+ w_resized, h_resized = image_size
+ if w / w_resized < h / h_resized:
+ w_pad = h / h_resized * w_resized
+ h_pad = h
+ else:
+ w_pad = w
+ h_pad = w / w_resized * h_resized
+
+ scale = np.array([w_pad, h_pad], dtype=np.float32)
+
+ return scale
+
+ def _get_cam(self, seq):
+ """Get camera parameters.
+
+ Args:
+ seq (str): Sequence name.
+
+ Returns: Camera parameters.
+ """
+ cam_file = osp.join(self.img_prefix, seq,
+ 'calibration_{:s}.json'.format(seq))
+ with open(cam_file) as cfile:
+ calib = json.load(cfile)
+
+ M = np.array([[1.0, 0.0, 0.0], [0.0, 0.0, -1.0], [0.0, 1.0, 0.0]])
+ cameras = {}
+ for cam in calib['cameras']:
+ if (cam['panel'], cam['node']) in self.cam_list:
+ sel_cam = {}
+ R_w2c = np.array(cam['R']).dot(M)
+ T_w2c = np.array(cam['t']).reshape((3, 1)) * 10.0 # cm to mm
+ R_c2w = R_w2c.T
+ T_c2w = -R_w2c.T @ T_w2c
+ sel_cam['R'] = R_c2w.tolist()
+ sel_cam['T'] = T_c2w.tolist()
+ sel_cam['K'] = cam['K'][:2]
+ distCoef = cam['distCoef']
+ sel_cam['k'] = [distCoef[0], distCoef[1], distCoef[4]]
+ sel_cam['p'] = [distCoef[2], distCoef[3]]
+ cameras[(cam['panel'], cam['node'])] = sel_cam
+
+ return cameras
+
+ def _get_db(self):
+ """Get dataset base.
+
+ Returns:
+ dict: the dataset base (2D and 3D information)
+ """
+ width = 1920
+ height = 1080
+ db = []
+ sample_id = 0
+ for seq in self.seq_list:
+ cameras = self._get_cam(seq)
+ curr_anno = osp.join(self.img_prefix, seq,
+ 'hdPose3d_stage1_coco19')
+ anno_files = sorted(glob.iglob('{:s}/*.json'.format(curr_anno)))
+ print(f'load sequence: {seq}', flush=True)
+ for i, file in enumerate(anno_files):
+ if i % self.seq_frame_interval == 0:
+ with open(file) as dfile:
+ bodies = json.load(dfile)['bodies']
+ if len(bodies) == 0:
+ continue
+
+ for k, cam_param in cameras.items():
+ single_view_camera = SimpleCamera(cam_param)
+ postfix = osp.basename(file).replace('body3DScene', '')
+ prefix = '{:02d}_{:02d}'.format(k[0], k[1])
+ image_file = osp.join(seq, 'hdImgs', prefix,
+ prefix + postfix)
+ image_file = image_file.replace('json', 'jpg')
+
+ all_poses_3d = np.zeros(
+ (self.max_persons, self.num_joints, 3),
+ dtype=np.float32)
+ all_poses_vis_3d = np.zeros(
+ (self.max_persons, self.num_joints, 3),
+ dtype=np.float32)
+ all_roots_3d = np.zeros((self.max_persons, 3),
+ dtype=np.float32)
+ all_poses = np.zeros(
+ (self.max_persons, self.num_joints, 3),
+ dtype=np.float32)
+
+ cnt = 0
+ person_ids = -np.ones(self.max_persons, dtype=np.int)
+ for body in bodies:
+ if cnt >= self.max_persons:
+ break
+ pose3d = np.array(body['joints19']).reshape(
+ (-1, 4))
+ pose3d = pose3d[:self.num_joints]
+
+ joints_vis = pose3d[:, -1] > 0.1
+
+ if not joints_vis[self.root_id]:
+ continue
+
+ # Coordinate transformation
+ M = np.array([[1.0, 0.0, 0.0], [0.0, 0.0, -1.0],
+ [0.0, 1.0, 0.0]])
+ pose3d[:, 0:3] = pose3d[:, 0:3].dot(M) * 10.0
+
+ all_poses_3d[cnt] = pose3d[:, :3]
+ all_roots_3d[cnt] = pose3d[self.root_id, :3]
+ all_poses_vis_3d[cnt] = np.repeat(
+ np.reshape(joints_vis, (-1, 1)), 3, axis=1)
+
+ pose2d = np.zeros((pose3d.shape[0], 3))
+ # get pose_2d from pose_3d
+ pose2d[:, :2] = single_view_camera.world_to_pixel(
+ pose3d[:, :3])
+ x_check = np.bitwise_and(pose2d[:, 0] >= 0,
+ pose2d[:, 0] <= width - 1)
+ y_check = np.bitwise_and(
+ pose2d[:, 1] >= 0, pose2d[:, 1] <= height - 1)
+ check = np.bitwise_and(x_check, y_check)
+ joints_vis[np.logical_not(check)] = 0
+ pose2d[:, -1] = joints_vis
+
+ all_poses[cnt] = pose2d
+ person_ids[cnt] = body['id']
+ cnt += 1
+
+ if cnt > 0:
+ db.append({
+ 'image_file':
+ osp.join(self.img_prefix, image_file),
+ 'joints_3d':
+ all_poses_3d,
+ 'person_ids':
+ person_ids,
+ 'joints_3d_visible':
+ all_poses_vis_3d,
+ 'joints': [all_poses],
+ 'roots_3d':
+ all_roots_3d,
+ 'camera':
+ cam_param,
+ 'num_persons':
+ cnt,
+ 'sample_id':
+ sample_id,
+ 'center':
+ np.array((width / 2, height / 2),
+ dtype=np.float32),
+ 'scale':
+ self._get_scale((width, height))
+ })
+ sample_id += 1
+ return db
+
+ def evaluate(self, outputs, res_folder, metric='mpjpe', **kwargs):
+ """
+
+ Args:
+ outputs list(dict(pose_3d, sample_id)):
+ pose_3d (np.ndarray): predicted 3D human pose
+ sample_id (np.ndarray): sample id of a frame.
+ res_folder (str): Path of directory to save the results.
+ metric (str | list[str]): Metric to be performed.
+ Defaults: 'mpjpe'.
+ **kwargs:
+
+ Returns:
+
+ """
+ pose_3ds = np.concatenate([output['pose_3d'] for output in outputs],
+ axis=0)
+ sample_ids = []
+ for output in outputs:
+ sample_ids.extend(output['sample_id'])
+ _outputs = [
+ dict(sample_id=sample_id, pose_3d=pose_3d)
+ for (sample_id, pose_3d) in zip(sample_ids, pose_3ds)
+ ]
+ _outputs = self._sort_and_unique_outputs(_outputs, key='sample_id')
+
+ metrics = metric if isinstance(metric, list) else [metric]
+ for _metric in metrics:
+ if _metric not in self.ALLOWED_METRICS:
+ raise ValueError(
+ f'Unsupported metric "{_metric}"'
+ f'Supported metrics are {self.ALLOWED_METRICS}')
+
+ res_file = osp.join(res_folder, 'result_keypoints.json')
+
+ mmcv.dump(_outputs, res_file)
+
+ eval_list = []
+ gt_num = self.db_size // self.num_cameras
+ assert len(
+ _outputs) == gt_num, f'number mismatch: {len(_outputs)}, {gt_num}'
+
+ total_gt = 0
+ for i in range(gt_num):
+ index = self.num_cameras * i
+ db_rec = copy.deepcopy(self.db[index])
+ joints_3d = db_rec['joints_3d']
+ joints_3d_vis = db_rec['joints_3d_visible']
+
+ if joints_3d_vis.sum() < 1:
+ continue
+
+ pred = _outputs[i]['pose_3d'].copy()
+ pred = pred[pred[:, 0, 3] >= 0]
+ for pose in pred:
+ mpjpes = []
+ for (gt, gt_vis) in zip(joints_3d, joints_3d_vis):
+ vis = gt_vis[:, 0] > 0
+ if vis.sum() < 1:
+ break
+ mpjpe = np.mean(
+ np.sqrt(
+ np.sum((pose[vis, 0:3] - gt[vis])**2, axis=-1)))
+ mpjpes.append(mpjpe)
+ min_gt = np.argmin(mpjpes)
+ min_mpjpe = np.min(mpjpes)
+ score = pose[0, 4]
+ eval_list.append({
+ 'mpjpe': float(min_mpjpe),
+ 'score': float(score),
+ 'gt_id': int(total_gt + min_gt)
+ })
+
+ total_gt += (joints_3d_vis[:, :, 0].sum(-1) >= 1).sum()
+
+ mpjpe_threshold = np.arange(25, 155, 25)
+ aps = []
+ ars = []
+ for t in mpjpe_threshold:
+ ap, ar = self._eval_list_to_ap(eval_list, total_gt, t)
+ aps.append(ap)
+ ars.append(ar)
+
+ name_value_tuples = []
+ for _metric in metrics:
+ if _metric == 'mpjpe':
+ stats_names = ['RECALL 500mm', 'MPJPE 500mm']
+ info_str = list(
+ zip(stats_names, [
+ self._eval_list_to_recall(eval_list, total_gt),
+ self._eval_list_to_mpjpe(eval_list)
+ ]))
+ elif _metric == 'mAP':
+ stats_names = [
+ 'AP 25', 'AP 50', 'AP 75', 'AP 100', 'AP 125', 'AP 150',
+ 'mAP', 'AR 25', 'AR 50', 'AR 75', 'AR 100', 'AR 125',
+ 'AR 150', 'mAR'
+ ]
+ mAP = np.array(aps).mean()
+ mAR = np.array(ars).mean()
+ info_str = list(zip(stats_names, aps + [mAP] + ars + [mAR]))
+ else:
+ raise NotImplementedError
+ name_value_tuples.extend(info_str)
+
+ return OrderedDict(name_value_tuples)
+
+ @staticmethod
+ def _eval_list_to_ap(eval_list, total_gt, threshold):
+ """Get Average Precision (AP) and Average Recall at a certain
+ threshold."""
+
+ eval_list.sort(key=lambda k: k['score'], reverse=True)
+ total_num = len(eval_list)
+
+ tp = np.zeros(total_num)
+ fp = np.zeros(total_num)
+ gt_det = []
+ for i, item in enumerate(eval_list):
+ if item['mpjpe'] < threshold and item['gt_id'] not in gt_det:
+ tp[i] = 1
+ gt_det.append(item['gt_id'])
+ else:
+ fp[i] = 1
+ tp = np.cumsum(tp)
+ fp = np.cumsum(fp)
+ recall = tp / (total_gt + 1e-5)
+ precise = tp / (tp + fp + 1e-5)
+ for n in range(total_num - 2, -1, -1):
+ precise[n] = max(precise[n], precise[n + 1])
+
+ precise = np.concatenate(([0], precise, [0]))
+ recall = np.concatenate(([0], recall, [1]))
+ index = np.where(recall[1:] != recall[:-1])[0]
+ ap = np.sum((recall[index + 1] - recall[index]) * precise[index + 1])
+
+ return ap, recall[-2]
+
+ @staticmethod
+ def _eval_list_to_mpjpe(eval_list, threshold=500):
+ """Get MPJPE within a certain threshold."""
+ eval_list.sort(key=lambda k: k['score'], reverse=True)
+ gt_det = []
+
+ mpjpes = []
+ for i, item in enumerate(eval_list):
+ if item['mpjpe'] < threshold and item['gt_id'] not in gt_det:
+ mpjpes.append(item['mpjpe'])
+ gt_det.append(item['gt_id'])
+
+ return np.mean(mpjpes) if len(mpjpes) > 0 else np.inf
+
+ @staticmethod
+ def _eval_list_to_recall(eval_list, total_gt, threshold=500):
+ """Get Recall at a certain threshold."""
+ gt_ids = [e['gt_id'] for e in eval_list if e['mpjpe'] < threshold]
+
+ return len(np.unique(gt_ids)) / total_gt
+
+ def __getitem__(self, idx):
+ """Get the sample given index."""
+ results = {}
+ for c in range(self.num_cameras):
+ result = copy.deepcopy(self.db[self.num_cameras * idx + c])
+ result['ann_info'] = self.ann_info
+ width = 1920
+ height = 1080
+ result['mask'] = [np.ones((height, width), dtype=np.float32)]
+ results[c] = result
+
+ return self.pipeline(results)
+
+ @staticmethod
+ def _sort_and_unique_outputs(outputs, key='sample_id'):
+ """sort outputs and remove the repeated ones."""
+ outputs = sorted(outputs, key=lambda x: x[key])
+ num_outputs = len(outputs)
+ for i in range(num_outputs - 1, 0, -1):
+ if outputs[i][key] == outputs[i - 1][key]:
+ del outputs[i]
+
+ return outputs
diff --git a/mmpose/datasets/datasets/bottom_up/bottom_up_aic.py b/mmpose/datasets/datasets/bottom_up/bottom_up_aic.py
index f3cbd246e4..e56b72586f 100644
--- a/mmpose/datasets/datasets/bottom_up/bottom_up_aic.py
+++ b/mmpose/datasets/datasets/bottom_up/bottom_up_aic.py
@@ -13,13 +13,16 @@
class BottomUpAicDataset(BottomUpCocoDataset):
"""Aic dataset for bottom-up pose estimation.
- `AI Challenger : A Large-scale Dataset for Going Deeper
- in Image Understanding `__
+ "AI Challenger : A Large-scale Dataset for Going Deeper
+ in Image Understanding", arXiv'2017.
+ More details can be found in the `paper
+ `__
The dataset loads raw features and apply specified transforms
to return a dict containing the image tensors and other information.
AIC keypoint indexes::
+
0: "right_shoulder",
1: "right_elbow",
2: "right_wrist",
diff --git a/mmpose/datasets/datasets/bottom_up/bottom_up_coco.py b/mmpose/datasets/datasets/bottom_up/bottom_up_coco.py
index a0057cfb3f..d054d818c3 100644
--- a/mmpose/datasets/datasets/bottom_up/bottom_up_coco.py
+++ b/mmpose/datasets/datasets/bottom_up/bottom_up_coco.py
@@ -141,21 +141,21 @@ def _get_joints(self, anno):
def evaluate(self, outputs, res_folder, metric='mAP', **kwargs):
"""Evaluate coco keypoint results. The pose prediction results will be
- saved in `${res_folder}/result_keypoints.json`.
+ saved in ``${res_folder}/result_keypoints.json``.
Note:
- num_people: P
- num_keypoints: K
+ - num_people: P
+ - num_keypoints: K
Args:
- outputs (list(preds, scores, image_path, heatmap)):
-
- * preds (list[np.ndarray(P, K, 3+tag_num)]):
- Pose predictions for all people in images.
- * scores (list[P]):
- * image_path (list[str]): For example, ['coco/images/
- val2017/000000397133.jpg']
- * heatmap (np.ndarray[N, K, H, W]): model outputs.
+ outputs (list[dict]): Outputs containing the following items.
+
+ - preds (list[np.ndarray(P, K, 3+tag_num)]): \
+ Pose predictions for all people in images.
+ - scores (list[P]): List of person scores.
+ - image_path (list[str]): For example, ['coco/images/\
+ val2017/000000397133.jpg']
+ - heatmap (np.ndarray[N, K, H, W]): model outputs.
res_folder (str): Path of directory to save the results.
metric (str | list[str]): Metric to be performed. Defaults: 'mAP'.
diff --git a/mmpose/datasets/datasets/bottom_up/bottom_up_coco_wholebody.py b/mmpose/datasets/datasets/bottom_up/bottom_up_coco_wholebody.py
index 5be8f6bf06..363d2efb2e 100644
--- a/mmpose/datasets/datasets/bottom_up/bottom_up_coco_wholebody.py
+++ b/mmpose/datasets/datasets/bottom_up/bottom_up_coco_wholebody.py
@@ -13,7 +13,7 @@
class BottomUpCocoWholeBodyDataset(BottomUpCocoDataset):
"""CocoWholeBodyDataset dataset for bottom-up pose estimation.
- `Whole-Body Human Pose Estimation in the Wild' ECCV'2020
+ `Whole-Body Human Pose Estimation in the Wild', ECCV'2020.
More details can be found in the `paper
`__ .
@@ -23,9 +23,10 @@ class BottomUpCocoWholeBodyDataset(BottomUpCocoDataset):
In total, we have 133 keypoints for wholebody pose estimation.
COCO-WholeBody keypoint indexes::
- 0-16: 17 body keypoints
- 17-22: 6 foot keypoints
- 23-90: 68 face keypoints
+
+ 0-16: 17 body keypoints,
+ 17-22: 6 foot keypoints,
+ 23-90: 68 face keypoints,
91-132: 42 hand keypoints
Args:
diff --git a/mmpose/datasets/datasets/bottom_up/bottom_up_crowdpose.py b/mmpose/datasets/datasets/bottom_up/bottom_up_crowdpose.py
index acff96e70f..ebabf3e1dd 100644
--- a/mmpose/datasets/datasets/bottom_up/bottom_up_crowdpose.py
+++ b/mmpose/datasets/datasets/bottom_up/bottom_up_crowdpose.py
@@ -13,6 +13,11 @@
class BottomUpCrowdPoseDataset(BottomUpCocoDataset):
"""CrowdPose dataset for bottom-up pose estimation.
+ "CrowdPose: Efficient Crowded Scenes Pose Estimation and
+ A New Benchmark", CVPR'2019.
+ More details can be found in the `paper
+ `__.
+
The dataset loads raw features and apply specified transforms
to return a dict containing the image tensors and other information.
diff --git a/mmpose/datasets/datasets/bottom_up/bottom_up_mhp.py b/mmpose/datasets/datasets/bottom_up/bottom_up_mhp.py
index 2b09870867..1438123325 100644
--- a/mmpose/datasets/datasets/bottom_up/bottom_up_mhp.py
+++ b/mmpose/datasets/datasets/bottom_up/bottom_up_mhp.py
@@ -13,11 +13,10 @@
class BottomUpMhpDataset(BottomUpCocoDataset):
"""MHPv2.0 dataset for top-down pose estimation.
- `The Multi-Human Parsing project of Learning and Vision (LV) Group,
- National University of Singapore (NUS) is proposed to push the frontiers
- of fine-grained visual understanding of humans in crowd scene.
- `
-
+ "Understanding Humans in Crowded Scenes: Deep Nested Adversarial
+ Learning and A New Benchmark for Multi-Human Parsing", ACM MM'2018.
+ More details can be found in the `paper
+ `__
The dataset loads raw features and apply specified transforms
to return a dict containing the image tensors and other information.
diff --git a/mmpose/datasets/datasets/face/face_300w_dataset.py b/mmpose/datasets/datasets/face/face_300w_dataset.py
index 1bff83d909..cf54db2b73 100644
--- a/mmpose/datasets/datasets/face/face_300w_dataset.py
+++ b/mmpose/datasets/datasets/face/face_300w_dataset.py
@@ -14,8 +14,8 @@
class Face300WDataset(Kpt2dSviewRgbImgTopDownDataset):
"""Face300W dataset for top-down face keypoint localization.
- `300 faces In-the-wild challenge: Database and results.
- Image and Vision Computing (IMAVIS) 2019`.
+ "300 faces In-the-wild challenge: Database and results",
+ Image and Vision Computing (IMAVIS) 2019.
The dataset loads raw images and apply specified transforms
to return a dict containing the image tensors and other information.
@@ -115,7 +115,7 @@ def _get_normalize_factor(self, gts, *args, **kwargs):
Args:
gts (np.ndarray[N, K, 2]): Groundtruth keypoint location.
- Return:
+ Returns:
np.ndarray[N, 2]: normalized factor
"""
@@ -125,24 +125,24 @@ def _get_normalize_factor(self, gts, *args, **kwargs):
def evaluate(self, outputs, res_folder, metric='NME', **kwargs):
"""Evaluate freihand keypoint results. The pose prediction results will
- be saved in `${res_folder}/result_keypoints.json`.
+ be saved in ``${res_folder}/result_keypoints.json``.
Note:
- batch_size: N
- num_keypoints: K
- heatmap height: H
- heatmap width: W
+ - batch_size: N
+ - num_keypoints: K
+ - heatmap height: H
+ - heatmap width: W
Args:
- outputs (list(preds, boxes, image_path, output_heatmap))
- :preds (np.ndarray[1,K,3]): The first two dimensions are
+ outputs (list[dict]): Outputs containing the following items.
+
+ - preds (np.ndarray[1,K,3]): The first two dimensions are \
coordinates, score is the third dimension of the array.
- :boxes (np.ndarray[1,6]): [center[0], center[1], scale[0]
- , scale[1],area, score]
- :image_path (list[str]): For example, ['3', '0', '0', 'W', '/',
- 'i', 'b', 'u', 'g', '/', 'i', 'm', 'a', 'g', 'e', '_', '0',
- '1', '8', '.', 'j', 'p', 'g']
- :output_heatmap (np.ndarray[N, K, H, W]): model outputs.
+ - boxes (np.ndarray[1,6]): [center[0], center[1], scale[0], \
+ scale[1],area, score]
+ - image_path (list[str]): For example, ['300W/ibug/\
+ image_018.jpg']
+ - output_heatmap (np.ndarray[N, K, H, W]): model outputs.
res_folder (str): Path of directory to save the results.
metric (str | list[str]): Metric to be performed.
Options: 'NME'.
diff --git a/mmpose/datasets/datasets/face/face_aflw_dataset.py b/mmpose/datasets/datasets/face/face_aflw_dataset.py
index 4e4de6161d..ad231021ea 100644
--- a/mmpose/datasets/datasets/face/face_aflw_dataset.py
+++ b/mmpose/datasets/datasets/face/face_aflw_dataset.py
@@ -14,10 +14,10 @@
class FaceAFLWDataset(Kpt2dSviewRgbImgTopDownDataset):
"""Face AFLW dataset for top-down face keypoint localization.
- `Annotated Facial Landmarks in the Wild: A Large-scale,
- Real-world Database for Facial Landmark Localization.
+ "Annotated Facial Landmarks in the Wild: A Large-scale,
+ Real-world Database for Facial Landmark Localization".
In Proc. First IEEE International Workshop on Benchmarking
- Facial Image Analysis Technologies, 2011`
+ Facial Image Analysis Technologies, 2011.
The dataset loads raw images and apply specified transforms
to return a dict containing the image tensors and other information.
@@ -123,7 +123,7 @@ def _get_normalize_factor(self, box_sizes, *args, **kwargs):
Args:
box_sizes (np.ndarray[N, 1]): box size
- Return:
+ Returns:
np.ndarray[N, 2]: normalized factor
"""
@@ -131,24 +131,24 @@ def _get_normalize_factor(self, box_sizes, *args, **kwargs):
def evaluate(self, outputs, res_folder, metric='NME', **kwargs):
"""Evaluate freihand keypoint results. The pose prediction results will
- be saved in `${res_folder}/result_keypoints.json`.
+ be saved in ``${res_folder}/result_keypoints.json``.
Note:
- batch_size: N
- num_keypoints: K
- heatmap height: H
- heatmap width: W
+ - batch_size: N
+ - num_keypoints: K
+ - heatmap height: H
+ - heatmap width: W
Args:
- outputs (list(preds, boxes, image_path, output_heatmap))
- :preds (np.ndarray[1,K,3]): The first two dimensions are
+ outputs (list[dict]): Outputs containing the following items.
+
+ - preds (np.ndarray[1,K,3]): The first two dimensions are \
coordinates, score is the third dimension of the array.
- :boxes (np.ndarray[1,6]): [center[0], center[1], scale[0]
- , scale[1],area, score]
- :image_path (list[str]): For example, ['3', '0', '0', 'W', '/',
- 'i', 'b', 'u', 'g', '/', 'i', 'm', 'a', 'g', 'e', '_', '0',
- '1', '8', '.', 'j', 'p', 'g']
- :output_heatmap (np.ndarray[N, K, H, W]): model outputs.
+ - boxes (np.ndarray[1,6]): [center[0], center[1], scale[0], \
+ scale[1],area, score]
+ - image_path (list[str]): For example, ['300W/ibug/\
+ image_018.jpg']
+ - output_heatmap (np.ndarray[N, K, H, W]): model outputs.
res_folder (str): Path of directory to save the results.
metric (str | list[str]): Metric to be performed.
Options: 'NME'.
diff --git a/mmpose/datasets/datasets/face/face_coco_wholebody_dataset.py b/mmpose/datasets/datasets/face/face_coco_wholebody_dataset.py
index 67efd1e80e..5ad017687a 100644
--- a/mmpose/datasets/datasets/face/face_coco_wholebody_dataset.py
+++ b/mmpose/datasets/datasets/face/face_coco_wholebody_dataset.py
@@ -14,7 +14,7 @@
class FaceCocoWholeBodyDataset(Kpt2dSviewRgbImgTopDownDataset):
"""CocoWholeBodyDataset for face keypoint localization.
- `Whole-Body Human Pose Estimation in the Wild' ECCV'2020
+ `Whole-Body Human Pose Estimation in the Wild', ECCV'2020.
More details can be found in the `paper
`__ .
@@ -113,7 +113,7 @@ def _get_normalize_factor(self, gts, *args, **kwargs):
Args:
gts (np.ndarray[N, K, 2]): Groundtruth keypoint location.
- Return:
+ Returns:
np.ndarray[N, 2]: normalized factor
"""
@@ -123,24 +123,24 @@ def _get_normalize_factor(self, gts, *args, **kwargs):
def evaluate(self, outputs, res_folder, metric='NME', **kwargs):
"""Evaluate COCO-WholeBody Face keypoint results. The pose prediction
- results will be saved in `${res_folder}/result_keypoints.json`.
+ results will be saved in ``${res_folder}/result_keypoints.json``.
Note:
- batch_size: N
- num_keypoints: K
- heatmap height: H
- heatmap width: W
+ - batch_size: N
+ - num_keypoints: K
+ - heatmap height: H
+ - heatmap width: W
Args:
- outputs (list(preds, boxes, image_path, output_heatmap))
- :preds (np.ndarray[1,K,3]): The first two dimensions are
+ outputs (list[dict]): Outputs containing the following items.
+
+ - preds (np.ndarray[1,K,3]): The first two dimensions are \
coordinates, score is the third dimension of the array.
- :boxes (np.ndarray[1,6]): [center[0], center[1], scale[0]
- , scale[1],area, score]
- :image_path (list[str]): For example, ['3', '0', '0', 'W', '/',
- 'i', 'b', 'u', 'g', '/', 'i', 'm', 'a', 'g', 'e', '_', '0',
- '1', '8', '.', 'j', 'p', 'g']
- :output_heatmap (np.ndarray[N, K, H, W]): model outputs.
+ - boxes (np.ndarray[1,6]): [center[0], center[1], scale[0], \
+ scale[1],area, score]
+ - image_path (list[str]): For example, ['300W/ibug/\
+ image_018.jpg']
+ - output_heatmap (np.ndarray[N, K, H, W]): model outputs.
res_folder (str): Path of directory to save the results.
metric (str | list[str]): Metric to be performed.
Options: 'NME'.
diff --git a/mmpose/datasets/datasets/face/face_cofw_dataset.py b/mmpose/datasets/datasets/face/face_cofw_dataset.py
index ee9d68161a..3913b11ed1 100644
--- a/mmpose/datasets/datasets/face/face_cofw_dataset.py
+++ b/mmpose/datasets/datasets/face/face_cofw_dataset.py
@@ -14,7 +14,7 @@
class FaceCOFWDataset(Kpt2dSviewRgbImgTopDownDataset):
"""Face COFW dataset for top-down face keypoint localization.
- `Robust face landmark estimation under occlusion. (ICCV) 2013`.
+ "Robust face landmark estimation under occlusion", ICCV'2013.
The dataset loads raw images and apply specified transforms
to return a dict containing the image tensors and other information.
@@ -114,7 +114,7 @@ def _get_normalize_factor(self, gts, *args, **kwargs):
Args:
gts (np.ndarray[N, K, 2]): Groundtruth keypoint location.
- Return:
+ Returns:
np.ndarray[N, 2]: normalized factor
"""
@@ -124,24 +124,24 @@ def _get_normalize_factor(self, gts, *args, **kwargs):
def evaluate(self, outputs, res_folder, metric='NME', **kwargs):
"""Evaluate freihand keypoint results. The pose prediction results will
- be saved in `${res_folder}/result_keypoints.json`.
+ be saved in ``${res_folder}/result_keypoints.json``.
Note:
- batch_size: N
- num_keypoints: K
- heatmap height: H
- heatmap width: W
+ - batch_size: N
+ - num_keypoints: K
+ - heatmap height: H
+ - heatmap width: W
Args:
- outputs (list(preds, boxes, image_path, output_heatmap))
- :preds (np.ndarray[1,K,3]): The first two dimensions are
+ outputs (list[dict]): Outputs containing the following items.
+
+ - preds (np.ndarray[1,K,3]): The first two dimensions are \
coordinates, score is the third dimension of the array.
- :boxes (np.ndarray[1,6]): [center[0], center[1], scale[0]
- , scale[1],area, score]
- :image_path (list[str]): For example, ['3', '0', '0', 'W', '/',
- 'i', 'b', 'u', 'g', '/', 'i', 'm', 'a', 'g', 'e', '_', '0',
- '1', '8', '.', 'j', 'p', 'g']
- :output_heatmap (np.ndarray[N, K, H, W]): model outputs.
+ - boxes (np.ndarray[1,6]): [center[0], center[1], scale[0], \
+ scale[1],area, score]
+ - image_path (list[str]): For example, ['300W/ibug/\
+ image_018.jpg']
+ - output_heatmap (np.ndarray[N, K, H, W]): model outputs.
res_folder (str): Path of directory to save the results.
metric (str | list[str]): Metric to be performed.
Options: 'NME'.
diff --git a/mmpose/datasets/datasets/face/face_wflw_dataset.py b/mmpose/datasets/datasets/face/face_wflw_dataset.py
index 95c930ed32..874839093d 100644
--- a/mmpose/datasets/datasets/face/face_wflw_dataset.py
+++ b/mmpose/datasets/datasets/face/face_wflw_dataset.py
@@ -14,8 +14,8 @@
class FaceWFLWDataset(Kpt2dSviewRgbImgTopDownDataset):
"""Face WFLW dataset for top-down face keypoint localization.
- `Look at Boundary: A Boundary-Aware Face Alignment Algorithm.
- CVPR'2018`
+ "Look at Boundary: A Boundary-Aware Face Alignment Algorithm",
+ CVPR'2018.
The dataset loads raw images and apply specified transforms
to return a dict containing the image tensors and other information.
@@ -115,7 +115,7 @@ def _get_normalize_factor(self, gts, *args, **kwargs):
Args:
gts (np.ndarray[N, K, 2]): Groundtruth keypoint location.
- Return:
+ Returns:
np.ndarray[N, 2]: normalized factor
"""
@@ -125,24 +125,24 @@ def _get_normalize_factor(self, gts, *args, **kwargs):
def evaluate(self, outputs, res_folder, metric='NME', **kwargs):
"""Evaluate freihand keypoint results. The pose prediction results will
- be saved in `${res_folder}/result_keypoints.json`.
+ be saved in ``${res_folder}/result_keypoints.json``.
Note:
- batch_size: N
- num_keypoints: K
- heatmap height: H
- heatmap width: W
+ - batch_size: N
+ - num_keypoints: K
+ - heatmap height: H
+ - heatmap width: W
Args:
- outputs (list(preds, boxes, image_path, output_heatmap))
- :preds (np.ndarray[1,K,3]): The first two dimensions are
+ outputs (list[dict]): Outputs containing the following items.
+
+ - preds (np.ndarray[1,K,3]): The first two dimensions are \
coordinates, score is the third dimension of the array.
- :boxes (np.ndarray[1,6]): [center[0], center[1], scale[0]
- , scale[1],area, score]
- :image_path (list[str]): For example, ['3', '0', '0', 'W', '/',
- 'i', 'b', 'u', 'g', '/', 'i', 'm', 'a', 'g', 'e', '_', '0',
- '1', '8', '.', 'j', 'p', 'g']
- :output_heatmap (np.ndarray[N, K, H, W]): model outputs.
+ - boxes (np.ndarray[1,6]): [center[0], center[1], scale[0], \
+ scale[1],area, score]
+ - image_path (list[str]): For example, ['300W/ibug/\
+ image_018.jpg']
+ - output_heatmap (np.ndarray[N, K, H, W]): model outputs.
res_folder (str): Path of directory to save the results.
metric (str | list[str]): Metric to be performed.
Options: 'NME'.
diff --git a/mmpose/datasets/datasets/fashion/deepfashion_dataset.py b/mmpose/datasets/datasets/fashion/deepfashion_dataset.py
index adbab6bbf2..764ea0c4d3 100644
--- a/mmpose/datasets/datasets/fashion/deepfashion_dataset.py
+++ b/mmpose/datasets/datasets/fashion/deepfashion_dataset.py
@@ -14,9 +14,9 @@
class DeepFashionDataset(Kpt2dSviewRgbImgTopDownDataset):
"""DeepFashion dataset (full-body clothes) for fashion landmark detection.
- `DeepFashion: Powering Robust Clothes Recognition
- and Retrieval with Rich Annotations' CVPR'2016 and
- `Fashion Landmark Detection in the Wild' ECCV'2016
+ "DeepFashion: Powering Robust Clothes Recognition
+ and Retrieval with Rich Annotations", CVPR'2016.
+ "Fashion Landmark Detection in the Wild", ECCV'2016.
The dataset loads raw features and apply specified transforms
to return a dict containing the image tensors and other information.
@@ -152,23 +152,23 @@ def _get_db(self):
def evaluate(self, outputs, res_folder, metric='PCK', **kwargs):
"""Evaluate freihand keypoint results. The pose prediction results will
- be saved in `${res_folder}/result_keypoints.json`.
+ be saved in ``${res_folder}/result_keypoints.json``.
Note:
- batch_size: N
- num_keypoints: K
- heatmap height: H
- heatmap width: W
+ - batch_size: N
+ - num_keypoints: K
+ - heatmap height: H
+ - heatmap width: W
Args:
- outputs (list(preds, boxes, image_path, output_heatmap))
- :preds (np.ndarray[N,K,3]): The first two dimensions are
- coordinates, score is the third dimension of the array.
- :boxes (np.ndarray[N,6]): [center[0], center[1], scale[0]
- , scale[1],area, score]
- :image_paths (list[str]): For example, [ 'img_00000001.jpg']
- :output_heatmap (np.ndarray[N, K, H, W]): model outputs.
+ outputs (list[dict]): Outputs containing the following items.
+ - preds (np.ndarray[N,K,3]): The first two dimensions are \
+ coordinates, score is the third dimension of the array.
+ - boxes (np.ndarray[N,6]): [center[0], center[1], scale[0], \
+ scale[1],area, score]
+ - image_paths (list[str]): For example, ['img_00000001.jpg']
+ - output_heatmap (np.ndarray[N, K, H, W]): model outputs.
res_folder (str): Path of directory to save the results.
metric (str | list[str]): Metric to be performed.
Options: 'PCK', 'AUC', 'EPE'.
diff --git a/mmpose/datasets/datasets/hand/freihand_dataset.py b/mmpose/datasets/datasets/hand/freihand_dataset.py
index 30d03517da..267b3cbe04 100644
--- a/mmpose/datasets/datasets/hand/freihand_dataset.py
+++ b/mmpose/datasets/datasets/hand/freihand_dataset.py
@@ -14,8 +14,8 @@
class FreiHandDataset(Kpt2dSviewRgbImgTopDownDataset):
"""FreiHand dataset for top-down hand pose estimation.
- `FreiHAND: A Dataset for Markerless Capture of Hand Pose
- and Shape from Single RGB Images' ICCV'2019
+ "FreiHAND: A Dataset for Markerless Capture of Hand Pose
+ and Shape from Single RGB Images", ICCV'2019.
More details can be found in the `paper
`__ .
@@ -131,24 +131,24 @@ def _get_db(self):
def evaluate(self, outputs, res_folder, metric='PCK', **kwargs):
"""Evaluate freihand keypoint results. The pose prediction results will
- be saved in `${res_folder}/result_keypoints.json`.
+ be saved in ``${res_folder}/result_keypoints.json``.
Note:
- batch_size: N
- num_keypoints: K
- heatmap height: H
- heatmap width: W
+ - batch_size: N
+ - num_keypoints: K
+ - heatmap height: H
+ - heatmap width: W
Args:
- outputs (list(preds, boxes, image_path, output_heatmap))
- :preds (np.ndarray[N,K,3]): The first two dimensions are
+ outputs (list[dict]): Outputs containing the following items.
+
+ - preds (np.ndarray[N,K,3]): The first two dimensions are \
coordinates, score is the third dimension of the array.
- :boxes (np.ndarray[N,6]): [center[0], center[1], scale[0]
- , scale[1],area, score]
- :image_paths (list[str]): For example, ['training/rgb/
+ - boxes (np.ndarray[N,6]): [center[0], center[1], scale[0], \
+ scale[1],area, score]
+ - image_paths (list[str]): For example, ['training/rgb/\
00031426.jpg']
- :output_heatmap (np.ndarray[N, K, H, W]): model outputs.
-
+ - output_heatmap (np.ndarray[N, K, H, W]): model outputs.
res_folder (str): Path of directory to save the results.
metric (str | list[str]): Metric to be performed.
Options: 'PCK', 'AUC', 'EPE'.
diff --git a/mmpose/datasets/datasets/hand/hand_coco_wholebody_dataset.py b/mmpose/datasets/datasets/hand/hand_coco_wholebody_dataset.py
index b847054368..b53e715d63 100644
--- a/mmpose/datasets/datasets/hand/hand_coco_wholebody_dataset.py
+++ b/mmpose/datasets/datasets/hand/hand_coco_wholebody_dataset.py
@@ -14,7 +14,7 @@
class HandCocoWholeBodyDataset(Kpt2dSviewRgbImgTopDownDataset):
"""CocoWholeBodyDataset for top-down hand pose estimation.
- `Whole-Body Human Pose Estimation in the Wild' ECCV'2020
+ "Whole-Body Human Pose Estimation in the Wild", ECCV'2020.
More details can be found in the `paper
`__ .
@@ -137,23 +137,23 @@ def _get_db(self):
def evaluate(self, outputs, res_folder, metric='PCK', **kwargs):
"""Evaluate COCO-WholeBody Hand keypoint results. The pose prediction
- results will be saved in `${res_folder}/result_keypoints.json`.
+ results will be saved in ``${res_folder}/result_keypoints.json``.
Note:
- batch_size: N
- num_keypoints: K
- heatmap height: H
- heatmap width: W
+ - batch_size: N
+ - num_keypoints: K
+ - heatmap height: H
+ - heatmap width: W
Args:
- outputs (list(preds, boxes, image_path, output_heatmap))
- :preds (np.ndarray[N,K,3]): The first two dimensions are
- coordinates, score is the third dimension of the array.
- :boxes (np.ndarray[N,6]): [center[0], center[1], scale[0]
- , scale[1],area, score]
- :image_paths (list[str]): For example, ['Test/source/0.jpg']
- :output_heatmap (np.ndarray[N, K, H, W]): model outputs.
+ outputs (list[dict]): Outputs containing the following items.
+ - preds (np.ndarray[N,K,3]): The first two dimensions are \
+ coordinates, score is the third dimension of the array.
+ - boxes (np.ndarray[N,6]): [center[0], center[1], scale[0], \
+ scale[1],area, score]
+ - image_paths (list[str]): For example, ['Test/source/0.jpg']
+ - output_heatmap (np.ndarray[N, K, H, W]): model outputs.
res_folder (str): Path of directory to save the results.
metric (str | list[str]): Metric to be performed.
Options: 'PCK', 'AUC', 'EPE'.
diff --git a/mmpose/datasets/datasets/hand/interhand2d_dataset.py b/mmpose/datasets/datasets/hand/interhand2d_dataset.py
index 10a0cd4cd4..6b630c2ca9 100644
--- a/mmpose/datasets/datasets/hand/interhand2d_dataset.py
+++ b/mmpose/datasets/datasets/hand/interhand2d_dataset.py
@@ -15,8 +15,8 @@
class InterHand2DDataset(Kpt2dSviewRgbImgTopDownDataset):
"""InterHand2.6M 2D dataset for top-down hand pose estimation.
- `InterHand2.6M: A Dataset and Baseline for 3D Interacting Hand Pose
- Estimation from a Single RGB Image' Moon, Gyeongsik etal. ECCV'2020
+ "InterHand2.6M: A Dataset and Baseline for 3D Interacting Hand Pose
+ Estimation from a Single RGB Image", ECCV'2020.
More details can be found in the `paper
`__ .
@@ -100,7 +100,7 @@ def _cam2pixel(cam_coord, f, c):
coordinates.
Note:
- N: number of joints
+ - N: number of joints
Args:
cam_coord (ndarray[N, 3]): 3D joints coordinates
@@ -124,7 +124,7 @@ def _world2cam(world_coord, R, T):
coordinates.
Note:
- N: number of joints
+ - N: number of joints
Args:
world_coord (ndarray[3, N]): 3D joints coordinates
@@ -143,7 +143,7 @@ def _get_db(self):
"""Load dataset.
Adapted from 'https://github.com/facebookresearch/InterHand2.6M/'
- 'blob/master/data/InterHand2.6M/dataset.py'
+ 'blob/master/data/InterHand2.6M/dataset.py'
Copyright (c) FaceBook Research, under CC-BY-NC 4.0 license.
"""
with open(self.camera_file, 'r') as f:
@@ -231,28 +231,24 @@ def _get_db(self):
def evaluate(self, outputs, res_folder, metric='PCK', **kwargs):
"""Evaluate interhand2d keypoint results. The pose prediction results
- will be saved in `${res_folder}/result_keypoints.json`.
+ will be saved in ``${res_folder}/result_keypoints.json``.
Note:
- batch_size: N
- num_keypoints: K
- heatmap height: H
- heatmap width: W
+ - batch_size: N
+ - num_keypoints: K
+ - heatmap height: H
+ - heatmap width: W
Args:
- outputs (list(preds, boxes, image_path, output_heatmap))
- :preds (np.ndarray[N,K,3]): The first two dimensions are
- coordinates, score is the third dimension of the array.
- :boxes (np.ndarray[N,6]): [center[0], center[1], scale[0]
- , scale[1],area, score]
- :image_paths (list[str]): For example, ['C', 'a', 'p', 't',
- 'u', 'r', 'e', '1', '2', '/', '0', '3', '9', '0', '_',
- 'd', 'h', '_', 't', 'o', 'u', 'c', 'h', 'R', 'O', 'M',
- '/', 'c', 'a', 'm', '4', '1', '0', '2', '0', '9', '/',
- 'i', 'm', 'a', 'g', 'e', '6', '2', '4', '3', '4', '.',
- 'j', 'p', 'g']
- :output_heatmap (np.ndarray[N, K, H, W]): model outputs.
+ outputs (list[dict]): Outputs containing the following items.
+ - preds (np.ndarray[N,K,3]): The first two dimensions are \
+ coordinates, score is the third dimension of the array.
+ - boxes (np.ndarray[N,6]): [center[0], center[1], scale[0], \
+ scale[1],area, score]
+ - image_paths (list[str]): For example, ['Capture12/\
+ 0390_dh_touchROM/cam410209/image62434.jpg']
+ - output_heatmap (np.ndarray[N, K, H, W]): model outputs.
res_folder (str): Path of directory to save the results.
metric (str | list[str]): Metric to be performed.
Options: 'PCK', 'AUC', 'EPE'.
diff --git a/mmpose/datasets/datasets/hand/interhand3d_dataset.py b/mmpose/datasets/datasets/hand/interhand3d_dataset.py
index 1bfa6a1b9f..951fc3ecea 100644
--- a/mmpose/datasets/datasets/hand/interhand3d_dataset.py
+++ b/mmpose/datasets/datasets/hand/interhand3d_dataset.py
@@ -16,8 +16,8 @@
class InterHand3DDataset(Kpt3dSviewRgbImgTopDownDataset):
"""InterHand2.6M 3D dataset for top-down hand pose estimation.
- `InterHand2.6M: A Dataset and Baseline for 3D Interacting Hand Pose
- Estimation from a Single RGB Image' Moon, Gyeongsik etal. ECCV'2020
+ "InterHand2.6M: A Dataset and Baseline for 3D Interacting Hand Pose
+ Estimation from a Single RGB Image", ECCV'2020.
More details can be found in the `paper
`__ .
@@ -147,7 +147,7 @@ def _get_db(self):
"""Load dataset.
Adapted from 'https://github.com/facebookresearch/InterHand2.6M/'
- 'blob/master/data/InterHand2.6M/dataset.py'
+ 'blob/master/data/InterHand2.6M/dataset.py'
Copyright (c) FaceBook Research, under CC-BY-NC 4.0 license.
"""
with open(self.camera_file, 'r') as f:
@@ -253,28 +253,28 @@ def _get_db(self):
def evaluate(self, outputs, res_folder, metric='MPJPE', **kwargs):
"""Evaluate interhand2d keypoint results. The pose prediction results
- will be saved in `${res_folder}/result_keypoints.json`.
+ will be saved in ``${res_folder}/result_keypoints.json``.
Note:
- batch_size: N
- num_keypoints: K
- heatmap height: H
- heatmap width: W
+ - batch_size: N
+ - num_keypoints: K
+ - heatmap height: H
+ - heatmap width: W
Args:
- outputs (list(dict))
- :preds (np.ndarray[N,K,3]): The first two dimensions are
+ outputs (list[dict]): Outputs containing the following items.
+
+ - preds (np.ndarray[N,K,3]): The first two dimensions are \
coordinates, score is the third dimension of the array.
- :hand_type (np.ndarray[N, 4]): The first two dimensions are
+ - hand_type (np.ndarray[N, 4]): The first two dimensions are \
hand type, scores is the last two dimensions.
- :rel_root_depth (np.ndarray[N]): The relative depth of left
+ - rel_root_depth (np.ndarray[N]): The relative depth of left \
wrist and right wrist.
- :boxes (np.ndarray[N,6]): [center[0], center[1], scale[0]
- , scale[1],area, score]
- :image_paths (list[str]): For example, ['Capture6/
+ - boxes (np.ndarray[N,6]): [center[0], center[1], scale[0], \
+ scale[1],area, score]
+ - image_paths (list[str]): For example, ['Capture6/\
0012_aokay_upright/cam410061/image4996.jpg']
- :output_heatmap (np.ndarray[N, K, H, W]): model outputs.
-
+ - output_heatmap (np.ndarray[N, K, H, W]): model outputs.
res_folder (str): Path of directory to save the results.
metric (str | list[str]): Metric to be performed.
Options: 'MRRPE', 'MPJPE', 'Handedness_acc'.
@@ -343,8 +343,8 @@ def _get_accuracy(outputs, gts, masks):
"""Get accuracy of multi-label classification.
Note:
- batch_size: N
- label_num: C
+ - batch_size: N
+ - label_num: C
Args:
outputs (np.array[N, C]): predicted multi-label.
@@ -353,7 +353,7 @@ def _get_accuracy(outputs, gts, masks):
accuracy calculation.
Returns:
- accuracy (float)
+ float: mean accuracy
"""
acc = (outputs == gts).all(axis=1)
return np.mean(acc[masks])
@@ -367,7 +367,7 @@ def _report_metric(self, res_file, metrics):
Options: 'MRRPE', 'MPJPE', 'Handedness_acc'.
Returns:
- List: Evaluation results for evaluation metric.
+ list: Evaluation results for evaluation metric.
"""
info_str = []
diff --git a/mmpose/datasets/datasets/hand/onehand10k_dataset.py b/mmpose/datasets/datasets/hand/onehand10k_dataset.py
index c36a6287b6..75d65dfaad 100644
--- a/mmpose/datasets/datasets/hand/onehand10k_dataset.py
+++ b/mmpose/datasets/datasets/hand/onehand10k_dataset.py
@@ -14,8 +14,8 @@
class OneHand10KDataset(Kpt2dSviewRgbImgTopDownDataset):
"""OneHand10K dataset for top-down hand pose estimation.
- `Mask-pose Cascaded CNN for 2D Hand Pose Estimation from
- Single Color Images' TCSVT'2019
+ "Mask-pose Cascaded CNN for 2D Hand Pose Estimation from
+ Single Color Images", TCSVT'2019.
More details can be found in the `paper
`__ .
@@ -132,23 +132,23 @@ def _get_db(self):
def evaluate(self, outputs, res_folder, metric='PCK', **kwargs):
"""Evaluate onehand10k keypoint results. The pose prediction results
- will be saved in `${res_folder}/result_keypoints.json`.
+ will be saved in ``${res_folder}/result_keypoints.json``.
Note:
- batch_size: N
- num_keypoints: K
- heatmap height: H
- heatmap width: W
+ - batch_size: N
+ - num_keypoints: K
+ - heatmap height: H
+ - heatmap width: W
Args:
- outputs (list(preds, boxes, image_path, output_heatmap))
- :preds (np.ndarray[N,K,3]): The first two dimensions are
- coordinates, score is the third dimension of the array.
- :boxes (np.ndarray[N,6]): [center[0], center[1], scale[0]
- , scale[1],area, score]
- :image_paths (list[str]): For example, ['Test/source/0.jpg']
- :output_heatmap (np.ndarray[N, K, H, W]): model outputs.
+ outputs (list[dict]): Outputs containing the following items.
+ - preds (np.ndarray[N,K,3]): The first two dimensions are \
+ coordinates, score is the third dimension of the array.
+ - boxes (np.ndarray[N,6]): [center[0], center[1], scale[0], \
+ scale[1],area, score]
+ - image_paths (list[str]): For example, ['Test/source/0.jpg']
+ - output_heatmap (np.ndarray[N, K, H, W]): model outputs.
res_folder (str): Path of directory to save the results.
metric (str | list[str]): Metric to be performed.
Options: 'PCK', 'AUC', 'EPE'.
diff --git a/mmpose/datasets/datasets/hand/panoptic_hand2d_dataset.py b/mmpose/datasets/datasets/hand/panoptic_hand2d_dataset.py
index 469c1d0b96..1104f15fd8 100644
--- a/mmpose/datasets/datasets/hand/panoptic_hand2d_dataset.py
+++ b/mmpose/datasets/datasets/hand/panoptic_hand2d_dataset.py
@@ -14,8 +14,8 @@
class PanopticDataset(Kpt2dSviewRgbImgTopDownDataset):
"""Panoptic dataset for top-down hand pose estimation.
- `Hand Keypoint Detection in Single Images using Multiview
- Bootstrapping' CVPR'2017
+ "Hand Keypoint Detection in Single Images using Multiview
+ Bootstrapping", CVPR'2017.
More details can be found in the `paper
`__ .
@@ -134,24 +134,24 @@ def _get_db(self):
def evaluate(self, outputs, res_folder, metric='PCKh', **kwargs):
"""Evaluate panoptic keypoint results. The pose prediction results will
- be saved in `${res_folder}/result_keypoints.json`.
+ be saved in ``${res_folder}/result_keypoints.json``.
Note:
- batch_size: N
- num_keypoints: K
- heatmap height: H
- heatmap width: W
+ - batch_size: N
+ - num_keypoints: K
+ - heatmap height: H
+ - heatmap width: W
Args:
- outputs (list(preds, boxes, image_path, output_heatmap))
- :preds (np.ndarray[N,K,3]): The first two dimensions are
- coordinates, score is the third dimension of the array.
- :boxes (np.ndarray[N,6]): [center[0], center[1], scale[0]
- , scale[1],area, score]
- :image_paths (list[str]): For example, ['hand_labels/'
- 'manual_test/000648952_02_l.jpg']
- :output_heatmap (np.ndarray[N, K, H, W]): model outputs.
+ outputs (list[dict]): Outputs containing the following items.
+ - preds (np.ndarray[N,K,3]): The first two dimensions are \
+ coordinates, score is the third dimension of the array.
+ - boxes (np.ndarray[N,6]): [center[0], center[1], scale[0], \
+ scale[1],area, score]
+ - image_paths (list[str]): For example, ['hand_labels/\
+ manual_test/000648952_02_l.jpg']
+ - output_heatmap (np.ndarray[N, K, H, W]): model outputs.
res_folder (str): Path of directory to save the results.
metric (str | list[str]): Metric to be performed.
Options: 'PCKh', 'AUC', 'EPE'.
diff --git a/mmpose/datasets/datasets/hand/rhd2d_dataset.py b/mmpose/datasets/datasets/hand/rhd2d_dataset.py
index 55c6432def..29c31c0d4a 100644
--- a/mmpose/datasets/datasets/hand/rhd2d_dataset.py
+++ b/mmpose/datasets/datasets/hand/rhd2d_dataset.py
@@ -14,7 +14,8 @@
class Rhd2DDataset(Kpt2dSviewRgbImgTopDownDataset):
"""Rendered Handpose Dataset for top-down hand pose estimation.
- `Learning to Estimate 3D Hand Pose from Single RGB Images' ICCV'2017
+ "Learning to Estimate 3D Hand Pose from Single RGB Images",
+ ICCV'2017.
More details can be found in the `paper
`__ .
@@ -130,24 +131,24 @@ def _get_db(self):
def evaluate(self, outputs, res_folder, metric='PCK', **kwargs):
"""Evaluate rhd keypoint results. The pose prediction results will be
- saved in `${res_folder}/result_keypoints.json`.
+ saved in ``${res_folder}/result_keypoints.json``.
Note:
- batch_size: N
- num_keypoints: K
- heatmap height: H
- heatmap width: W
+ - batch_size: N
+ - num_keypoints: K
+ - heatmap height: H
+ - heatmap width: W
Args:
- outputs (list(preds, boxes, image_path, output_heatmap))
- :preds (np.ndarray[N,K,3]): The first two dimensions are
- coordinates, score is the third dimension of the array.
- :boxes (np.ndarray[N,6]): [center[0], center[1], scale[0]
- , scale[1],area, score]
- :image_paths (list[str]): For example
- , ['training/rgb/00031426.jpg']
- :output_heatmap (np.ndarray[N, K, H, W]): model outputs.
+ outputs (list[dict]): Outputs containing the following items.
+ - preds (np.ndarray[N,K,3]): The first two dimensions are \
+ coordinates, score is the third dimension of the array.
+ - boxes (np.ndarray[N,6]): [center[0], center[1], scale[0], \
+ scale[1], area, score]
+ - image_paths (list[str]): For example,
+ ['training/rgb/00031426.jpg']
+ - output_heatmap (np.ndarray[N, K, H, W]): model outputs.
res_folder (str): Path of directory to save the results.
metric (str | list[str]): Metric to be performed.
Options: 'PCK', 'AUC', 'EPE'.
diff --git a/mmpose/datasets/datasets/top_down/topdown_aic_dataset.py b/mmpose/datasets/datasets/top_down/topdown_aic_dataset.py
index ba969e3794..13c41dfea9 100644
--- a/mmpose/datasets/datasets/top_down/topdown_aic_dataset.py
+++ b/mmpose/datasets/datasets/top_down/topdown_aic_dataset.py
@@ -12,13 +12,16 @@
class TopDownAicDataset(TopDownCocoDataset):
"""AicDataset dataset for top-down pose estimation.
- `AI Challenger : A Large-scale Dataset for Going Deeper
- in Image Understanding `__
+ "AI Challenger : A Large-scale Dataset for Going Deeper
+ in Image Understanding", arXiv'2017.
+ More details can be found in the `paper
+ `__
The dataset loads raw features and apply specified transforms
to return a dict containing the image tensors and other information.
AIC keypoint indexes::
+
0: "right_shoulder",
1: "right_elbow",
2: "right_wrist",
diff --git a/mmpose/datasets/datasets/top_down/topdown_coco_dataset.py b/mmpose/datasets/datasets/top_down/topdown_coco_dataset.py
index 5010ce2236..1f2f9200f0 100644
--- a/mmpose/datasets/datasets/top_down/topdown_coco_dataset.py
+++ b/mmpose/datasets/datasets/top_down/topdown_coco_dataset.py
@@ -17,7 +17,7 @@
class TopDownCocoDataset(Kpt2dSviewRgbImgTopDownDataset):
"""CocoDataset dataset for top-down pose estimation.
- `Microsoft COCO: Common Objects in Context' ECCV'2014
+ "Microsoft COCO: Common Objects in Context", ECCV'2014.
More details can be found in the `paper
`__ .
@@ -115,8 +115,10 @@ def _load_coco_keypoint_annotation_kernel(self, img_id):
Note:
bbox:[x1, y1, w, h]
+
Args:
img_id: coco image id
+
Returns:
dict: db entry
"""
@@ -226,24 +228,25 @@ def _load_coco_person_detection_results(self):
def evaluate(self, outputs, res_folder, metric='mAP', **kwargs):
"""Evaluate coco keypoint results. The pose prediction results will be
- saved in `${res_folder}/result_keypoints.json`.
+ saved in ``${res_folder}/result_keypoints.json``.
Note:
- batch_size: N
- num_keypoints: K
- heatmap height: H
- heatmap width: W
+ - batch_size: N
+ - num_keypoints: K
+ - heatmap height: H
+ - heatmap width: W
Args:
- outputs (list(dict))
- :preds (np.ndarray[N,K,3]): The first two dimensions are
+ outputs (list[dict]): Outputs containing the following items.
+
+ - preds (np.ndarray[N,K,3]): The first two dimensions are \
coordinates, score is the third dimension of the array.
- :boxes (np.ndarray[N,6]): [center[0], center[1], scale[0]
- , scale[1],area, score]
- :image_paths (list[str]): For example, ['data/coco/val2017
+ - boxes (np.ndarray[N,6]): [center[0], center[1], scale[0], \
+ scale[1],area, score]
+ - image_paths (list[str]): For example, ['data/coco/val2017\
/000000393226.jpg']
- :heatmap (np.ndarray[N, K, H, W]): model output heatmap
- :bbox_id (list(int)).
+ - heatmap (np.ndarray[N, K, H, W]): model output heatmap
+ - bbox_id (list(int)).
res_folder (str): Path of directory to save the results.
metric (str | list[str]): Metric to be performed. Defaults: 'mAP'.
diff --git a/mmpose/datasets/datasets/top_down/topdown_coco_wholebody_dataset.py b/mmpose/datasets/datasets/top_down/topdown_coco_wholebody_dataset.py
index 126a0ce77a..791a3c5790 100644
--- a/mmpose/datasets/datasets/top_down/topdown_coco_wholebody_dataset.py
+++ b/mmpose/datasets/datasets/top_down/topdown_coco_wholebody_dataset.py
@@ -14,21 +14,22 @@
class TopDownCocoWholeBodyDataset(TopDownCocoDataset):
"""CocoWholeBodyDataset dataset for top-down pose estimation.
- `Whole-Body Human Pose Estimation in the Wild' ECCV'2020
+ "Whole-Body Human Pose Estimation in the Wild", ECCV'2020.
More details can be found in the `paper
`__ .
The dataset loads raw features and apply specified transforms
to return a dict containing the image tensors and other information.
- In total, we have 133 keypoints for wholebody pose estimation.
-
COCO-WholeBody keypoint indexes::
- 0-16: 17 body keypoints
- 17-22: 6 foot keypoints
- 23-90: 68 face keypoints
+
+ 0-16: 17 body keypoints,
+ 17-22: 6 foot keypoints,
+ 23-90: 68 face keypoints,
91-132: 42 hand keypoints
+ In total, we have 133 keypoints for wholebody pose estimation.
+
Args:
ann_file (str): Path to the annotation file.
img_prefix (str): Path to a directory where images are held.
diff --git a/mmpose/datasets/datasets/top_down/topdown_crowdpose_dataset.py b/mmpose/datasets/datasets/top_down/topdown_crowdpose_dataset.py
index 6283188e35..b9b196f744 100644
--- a/mmpose/datasets/datasets/top_down/topdown_crowdpose_dataset.py
+++ b/mmpose/datasets/datasets/top_down/topdown_crowdpose_dataset.py
@@ -12,6 +12,11 @@
class TopDownCrowdPoseDataset(TopDownCocoDataset):
"""CrowdPoseDataset dataset for top-down pose estimation.
+ "CrowdPose: Efficient Crowded Scenes Pose Estimation and
+ A New Benchmark", CVPR'2019.
+ More details can be found in the `paper
+ `__.
+
The dataset loads raw features and apply specified transforms
to return a dict containing the image tensors and other information.
diff --git a/mmpose/datasets/datasets/top_down/topdown_h36m_dataset.py b/mmpose/datasets/datasets/top_down/topdown_h36m_dataset.py
index addfa9a9a6..fdec2c04fd 100644
--- a/mmpose/datasets/datasets/top_down/topdown_h36m_dataset.py
+++ b/mmpose/datasets/datasets/top_down/topdown_h36m_dataset.py
@@ -15,12 +15,13 @@
class TopDownH36MDataset(Kpt2dSviewRgbImgTopDownDataset):
"""Human3.6M dataset for top-down 2D pose estimation.
- `Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human
- Sensing in Natural Environments' TPAMI`2014
+ "Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human
+ Sensing in Natural Environments", TPAMI`2014.
More details can be found in the `paper
`__.
Human3.6M keypoint indexes::
+
0: 'root (pelvis)',
1: 'right_hip',
2: 'right_knee',
@@ -127,21 +128,22 @@ def evaluate(self, outputs, res_folder, metric, **kwargs):
will be saved in `${res_folder}/result_keypoints.json`.
Note:
- batch_size: N
- num_keypoints: K
- heatmap height: H
- heatmap width: W
+ - batch_size: N
+ - num_keypoints: K
+ - heatmap height: H
+ - heatmap width: W
Args:
- outputs (list(dict))
- :preds (np.ndarray[N,K,3]): The first two dimensions are
+ outputs (list[dict]): Outputs containing the following items.
+
+ - preds (np.ndarray[N,K,3]): The first two dimensions are
coordinates, score is the third dimension of the array.
- :boxes (np.ndarray[N,6]): [center[0], center[1], scale[0]
- , scale[1],area, score]
- :image_paths (list[str]): For example, ['data/coco/val2017
+ - boxes (np.ndarray[N,6]): [center[0], center[1], scale[0],
+ scale[1],area, score]
+ - image_paths (list[str]): For example, ['data/coco/val2017
/000000393226.jpg']
- :heatmap (np.ndarray[N, K, H, W]): model output heatmap
- :bbox_id (list(int)).
+ - heatmap (np.ndarray[N, K, H, W]): model output heatmap
+ - bbox_id (list(int)).
res_folder (str): Path of directory to save the results.
metric (str | list[str]): Metric to be performed. Defaults: 'mAP'.
diff --git a/mmpose/datasets/datasets/top_down/topdown_halpe_dataset.py b/mmpose/datasets/datasets/top_down/topdown_halpe_dataset.py
index 96267a10a1..7042daa29e 100644
--- a/mmpose/datasets/datasets/top_down/topdown_halpe_dataset.py
+++ b/mmpose/datasets/datasets/top_down/topdown_halpe_dataset.py
@@ -16,14 +16,15 @@ class TopDownHalpeDataset(TopDownCocoDataset):
The dataset loads raw features and apply specified transforms
to return a dict containing the image tensors and other information.
- In total, we have 136 keypoints for wholebody pose estimation.
-
Halpe keypoint indexes::
- 0-19: 20 body keypoints
- 20-25: 6 foot keypoints
- 26-93: 68 face keypoints
+
+ 0-19: 20 body keypoints,
+ 20-25: 6 foot keypoints,
+ 26-93: 68 face keypoints,
94-135: 42 hand keypoints
+ In total, we have 136 keypoints for wholebody pose estimation.
+
Args:
ann_file (str): Path to the annotation file.
img_prefix (str): Path to a directory where images are held.
diff --git a/mmpose/datasets/datasets/top_down/topdown_jhmdb_dataset.py b/mmpose/datasets/datasets/top_down/topdown_jhmdb_dataset.py
index 2c71d73fc7..ce79b50a71 100644
--- a/mmpose/datasets/datasets/top_down/topdown_jhmdb_dataset.py
+++ b/mmpose/datasets/datasets/top_down/topdown_jhmdb_dataset.py
@@ -16,14 +16,16 @@
class TopDownJhmdbDataset(TopDownCocoDataset):
"""JhmdbDataset dataset for top-down pose estimation.
- `Towards understanding action recognition
- `__
+ "Towards understanding action recognition", ICCV'2013.
+ More details can be found in the `paper
+ `__
The dataset loads raw features and apply specified transforms
to return a dict containing the image tensors and other information.
sub-JHMDB keypoint indexes::
+
0: "neck",
1: "belly",
2: "head",
@@ -273,20 +275,20 @@ def evaluate(self, outputs, res_folder, metric='PCK', **kwargs):
will be saved in `${res_folder}/result_keypoints.json`.
Note:
- batch_size: N
- num_keypoints: K
- heatmap height: H
- heatmap width: W
+ - batch_size: N
+ - num_keypoints: K
+ - heatmap height: H
+ - heatmap width: W
Args:
- outputs (list(preds, boxes, image_path, output_heatmap))
- :preds (np.ndarray[N,K,3]): The first two dimensions are
- coordinates, score is the third dimension of the array.
- :boxes (np.ndarray[N,6]): [center[0], center[1], scale[0]
- , scale[1],area, score]
- :image_path (list[str])
- :output_heatmap (np.ndarray[N, K, H, W]): model outputs.
+ outputs (list[dict]): Outputs containing the following items.
+ - preds (np.ndarray[N,K,3]): The first two dimensions are \
+ coordinates, score is the third dimension of the array.
+ - boxes (np.ndarray[N,6]): [center[0], center[1], scale[0], \
+ scale[1],area, score]
+ - image_path (list[str])
+ - output_heatmap (np.ndarray[N, K, H, W]): model outputs.
res_folder (str): Path of directory to save the results.
metric (str | list[str]): Metric to be performed.
Options: 'PCK', 'tPCK'.
diff --git a/mmpose/datasets/datasets/top_down/topdown_mhp_dataset.py b/mmpose/datasets/datasets/top_down/topdown_mhp_dataset.py
index 78bf70a8ab..050824a88a 100644
--- a/mmpose/datasets/datasets/top_down/topdown_mhp_dataset.py
+++ b/mmpose/datasets/datasets/top_down/topdown_mhp_dataset.py
@@ -12,10 +12,10 @@
class TopDownMhpDataset(TopDownCocoDataset):
"""MHPv2.0 dataset for top-down pose estimation.
- `The Multi-Human Parsing project of Learning and Vision (LV) Group,
- National University of Singapore (NUS) is proposed to push the frontiers
- of fine-grained visual understanding of humans in crowd scene.
- `
+ "Understanding Humans in Crowded Scenes: Deep Nested Adversarial
+ Learning and A New Benchmark for Multi-Human Parsing", ACM MM'2018.
+ More details can be found in the `paper
+ `__
Note that, the evaluation metric used here is mAP (adapted from COCO),
which may be different from the official evaluation codes.
diff --git a/mmpose/datasets/datasets/top_down/topdown_mpii_dataset.py b/mmpose/datasets/datasets/top_down/topdown_mpii_dataset.py
index a5e6f6bf39..0c99c722e7 100644
--- a/mmpose/datasets/datasets/top_down/topdown_mpii_dataset.py
+++ b/mmpose/datasets/datasets/top_down/topdown_mpii_dataset.py
@@ -16,8 +16,8 @@
class TopDownMpiiDataset(Kpt2dSviewRgbImgTopDownDataset):
"""MPII Dataset for top-down pose estimation.
- `2D Human Pose Estimation: New Benchmark and State of the Art Analysis'
- CVPR'2014. More details can be found in the `paper
+ "2D Human Pose Estimation: New Benchmark and State of the Art Analysis"
+ ,CVPR'2014. More details can be found in the `paper
`__ .
The dataset loads raw features and apply specified transforms
@@ -144,22 +144,21 @@ def evaluate(self, outputs, res_folder, metric='PCKh', **kwargs):
Copyright (c) Microsoft, under the MIT License.
Note:
- batch_size: N
- num_keypoints: K
- heatmap height: H
- heatmap width: W
+ - batch_size: N
+ - num_keypoints: K
+ - heatmap height: H
+ - heatmap width: W
Args:
- outputs(list(preds, boxes, image_path, heatmap)):
-
- * preds (np.ndarray[N,K,3]): The first two dimensions are
- coordinates, score is the third dimension of the array.
- * boxes (np.ndarray[N,6]): [center[0], center[1], scale[0]
- , scale[1],area, score]
- * image_paths (list[str]): For example, ['/val2017/000000
- 397133.jpg']
- * heatmap (np.ndarray[N, K, H, W]): model output heatmap.
-
+ outputs (list[dict]): Outputs containing the following items.
+
+ - preds (np.ndarray[N,K,3]): The first two dimensions are \
+ coordinates, score is the third dimension of the array.
+ - boxes (np.ndarray[N,6]): [center[0], center[1], scale[0], \
+ scale[1],area, score]
+ - image_paths (list[str]): For example, ['/val2017/000000\
+ 397133.jpg']
+ - heatmap (np.ndarray[N, K, H, W]): model output heatmap.
res_folder(str): Path of directory to save the results.
metric (str | list[str]): Metrics to be performed.
Defaults: 'PCKh'.
diff --git a/mmpose/datasets/datasets/top_down/topdown_mpii_trb_dataset.py b/mmpose/datasets/datasets/top_down/topdown_mpii_trb_dataset.py
index 25daca66e9..3399bc2690 100644
--- a/mmpose/datasets/datasets/top_down/topdown_mpii_trb_dataset.py
+++ b/mmpose/datasets/datasets/top_down/topdown_mpii_trb_dataset.py
@@ -17,8 +17,8 @@
class TopDownMpiiTrbDataset(Kpt2dSviewRgbImgTopDownDataset):
"""MPII-TRB Dataset dataset for top-down pose estimation.
- `TRB: A Novel Triplet Representation for Understanding 2D Human Body`
- ICCV'2019 More details can be found in the `paper
+ "TRB: A Novel Triplet Representation for Understanding 2D Human Body",
+ ICCV'2019. More details can be found in the `paper
`__ .
The dataset loads raw features and apply specified transforms
@@ -190,22 +190,22 @@ def evaluate(self, outputs, res_folder, metric='PCKh', **kwargs):
"""Evaluate PCKh for MPII-TRB dataset.
Note:
- batch_size: N
- num_keypoints: K
- heatmap height: H
- heatmap width: W
+ - batch_size: N
+ - num_keypoints: K
+ - heatmap height: H
+ - heatmap width: W
Args:
- outputs(list(preds, boxes, image_paths, heatmap)):
-
- * preds (np.ndarray[N,K,3]): The first two dimensions are
- coordinates, score is the third dimension of the array.
- * boxes (np.ndarray[N,6]): [center[0], center[1], scale[0]
- , scale[1],area, score]
- * image_paths (list[str]): For example, ['/val2017/000000
- 397133.jpg']
- * heatmap (np.ndarray[N, K, H, W]): model output heatmap.
- * bbox_ids (list[str]): For example, ['27407']
+ outputs (list[dict]): Outputs containing the following items.
+
+ - preds (np.ndarray[N,K,3]): The first two dimensions are \
+ coordinates, score is the third dimension of the array.
+ - boxes (np.ndarray[N,6]): [center[0], center[1], scale[0], \
+ scale[1],area, score]
+ - image_paths (list[str]): For example, ['/val2017/\
+ 000000397133.jpg']
+ - heatmap (np.ndarray[N, K, H, W]): model output heatmap.
+ - bbox_ids (list[str]): For example, ['27407'].
res_folder(str): Path of directory to save the results.
metric (str | list[str]): Metrics to be performed.
Defaults: 'PCKh'.
diff --git a/mmpose/datasets/datasets/top_down/topdown_ochuman_dataset.py b/mmpose/datasets/datasets/top_down/topdown_ochuman_dataset.py
index 473e95d957..0ad6b81405 100644
--- a/mmpose/datasets/datasets/top_down/topdown_ochuman_dataset.py
+++ b/mmpose/datasets/datasets/top_down/topdown_ochuman_dataset.py
@@ -11,7 +11,7 @@
class TopDownOCHumanDataset(TopDownCocoDataset):
"""OChuman dataset for top-down pose estimation.
- `Pose2Seg: Detection Free Human Instance Segmentation' CVPR'2019
+ "Pose2Seg: Detection Free Human Instance Segmentation", CVPR'2019.
More details can be found in the `paper
`__ .
diff --git a/mmpose/datasets/datasets/top_down/topdown_posetrack18_dataset.py b/mmpose/datasets/datasets/top_down/topdown_posetrack18_dataset.py
index 635ccdbff9..f9723caf85 100644
--- a/mmpose/datasets/datasets/top_down/topdown_posetrack18_dataset.py
+++ b/mmpose/datasets/datasets/top_down/topdown_posetrack18_dataset.py
@@ -24,14 +24,15 @@
class TopDownPoseTrack18Dataset(TopDownCocoDataset):
"""PoseTrack18 dataset for top-down pose estimation.
- `Posetrack: A benchmark for human pose estimation and tracking' CVPR'2018
+ "Posetrack: A benchmark for human pose estimation and tracking", CVPR'2018.
More details can be found in the `paper
- `_ .
+ `__ .
The dataset loads raw features and apply specified transforms
to return a dict containing the image tensors and other information.
PoseTrack2018 keypoint indexes::
+
0: 'nose',
1: 'head_bottom',
2: 'head_top',
@@ -101,21 +102,22 @@ def __init__(self,
def evaluate(self, outputs, res_folder, metric='mAP', **kwargs):
"""Evaluate posetrack keypoint results. The pose prediction results
- will be saved in `${res_folder}/result_keypoints.json`.
+ will be saved in ``${res_folder}/result_keypoints.json``.
Note:
- num_keypoints: K
+ - num_keypoints: K
Args:
- outputs (list(preds, boxes, image_paths))
- :preds (np.ndarray[N,K,3]): The first two dimensions are
+ outputs (list[dict]): Outputs containing the following items.
+
+ - preds (np.ndarray[N,K,3]): The first two dimensions are \
coordinates, score is the third dimension of the array.
- :boxes (np.ndarray[N,6]): [center[0], center[1], scale[0]
- , scale[1],area, score]
- :image_paths (list[str]): For example, ['val/010016_mpii_test
+ - boxes (np.ndarray[N,6]): [center[0], center[1], scale[0], \
+ scale[1],area, score]
+ - image_paths (list[str]): For example, ['val/010016_mpii_test\
/000024.jpg']
- :heatmap (np.ndarray[N, K, H, W]): model output heatmap.
- :bbox_id (list(int))
+ - heatmap (np.ndarray[N, K, H, W]): model output heatmap.
+ - bbox_id (list(int))
res_folder (str): Path of directory to save the results.
metric (str | list[str]): Metric to be performed. Defaults: 'mAP'.
diff --git a/mmpose/datasets/datasets/top_down/topdown_posetrack18_video_dataset.py b/mmpose/datasets/datasets/top_down/topdown_posetrack18_video_dataset.py
index 43e1c4f1ff..77f230317b 100644
--- a/mmpose/datasets/datasets/top_down/topdown_posetrack18_video_dataset.py
+++ b/mmpose/datasets/datasets/top_down/topdown_posetrack18_video_dataset.py
@@ -23,14 +23,15 @@
class TopDownPoseTrack18VideoDataset(Kpt2dSviewRgbVidTopDownDataset):
"""PoseTrack18 dataset for top-down pose estimation.
- `Posetrack: A benchmark for human pose estimation and tracking' CVPR'2018
+ "Posetrack: A benchmark for human pose estimation and tracking", CVPR'2018.
More details can be found in the `paper
- `_ .
+ `__ .
The dataset loads raw features and apply specified transforms
to return a dict containing the image tensors and other information.
PoseTrack2018 keypoint indexes::
+
0: 'nose',
1: 'head_bottom',
2: 'head_top',
@@ -333,21 +334,22 @@ def _load_posetrack_person_detection_results(self):
def evaluate(self, outputs, res_folder, metric='mAP', **kwargs):
"""Evaluate posetrack keypoint results. The pose prediction results
- will be saved in `${res_folder}/result_keypoints.json`.
+ will be saved in ``${res_folder}/result_keypoints.json``.
Note:
- num_keypoints: K
+ - num_keypoints: K
Args:
- outputs (list(preds, boxes, image_paths))
- :preds (np.ndarray[N,K,3]): The first two dimensions are
+ outputs (list[dict]): Outputs containing the following items.
+
+ - preds (np.ndarray[N,K,3]): The first two dimensions are \
coordinates, score is the third dimension of the array.
- :boxes (np.ndarray[N,6]): [center[0], center[1], scale[0]
- , scale[1],area, score]
- :image_paths (list[str]): For example, ['val/010016_mpii_test
+ - boxes (np.ndarray[N,6]): [center[0], center[1], scale[0], \
+ scale[1],area, score]
+ - image_paths (list[str]): For example, ['val/010016_mpii_test\
/000024.jpg']
- :heatmap (np.ndarray[N, K, H, W]): model output heatmap.
- :bbox_id (list(int))
+ - heatmap (np.ndarray[N, K, H, W]): model output heatmap.
+ - bbox_id (list(int))
res_folder (str): Path of directory to save the results.
metric (str | list[str]): Metric to be performed. Defaults: 'mAP'.
diff --git a/mmpose/datasets/pipelines/bottom_up_transform.py b/mmpose/datasets/pipelines/bottom_up_transform.py
index adc6f8226d..275e8b4d0a 100644
--- a/mmpose/datasets/pipelines/bottom_up_transform.py
+++ b/mmpose/datasets/pipelines/bottom_up_transform.py
@@ -151,7 +151,7 @@ def __init__(self, output_size, num_joints, sigma=-1, use_udp=False):
self.output_size = output_size
else:
self.output_size = np.array([output_size, output_size],
- dtype=np.int)
+ dtype=np.int32)
self.num_joints = num_joints
if sigma < 0:
sigma = self.output_size.prod()**0.5 / 64
@@ -231,21 +231,21 @@ def __init__(self, max_num_people, num_joints, output_size, tag_per_joint):
self.output_size = output_size
else:
self.output_size = np.array([output_size, output_size],
- dtype=np.int)
+ dtype=np.int32)
self.tag_per_joint = tag_per_joint
def __call__(self, joints):
"""
Note:
- number of people in image: N
- number of keypoints: K
- max number of people in an image: M
+ - number of people in image: N
+ - number of keypoints: K
+ - max number of people in an image: M
Args:
- joints (np.ndarray[NxKx3])
+ joints (np.ndarray[N,K,3])
Returns:
- visible_kpts (np.ndarray[MxKx2]).
+ visible_kpts (np.ndarray[M,K,2]).
"""
visible_kpts = np.zeros((self.max_num_people, self.num_joints, 2),
dtype=np.float32)
@@ -282,7 +282,7 @@ def __init__(self, output_size, limb_width, skeleton):
self.output_size = output_size
else:
self.output_size = np.array([output_size, output_size],
- dtype=np.int)
+ dtype=np.int32)
self.limb_width = limb_width
self.skeleton = skeleton
@@ -290,12 +290,12 @@ def _accumulate_paf_map_(self, pafs, src, dst, count):
"""Accumulate part affinity fields between two given joints.
Args:
- pafs (np.ndarray[2xHxW]): paf maps (2 dimensions:x axis and
+ pafs (np.ndarray[2,H,W]): paf maps (2 dimensions:x axis and
y axis) for a certain limb connection. This argument will
be modified inplace.
src (np.ndarray[2,]): coordinates of the source joint.
dst (np.ndarray[2,]): coordinates of the destination joint.
- count (np.ndarray[HxW]): count map that preserves the number
+ count (np.ndarray[H,W]): count map that preserves the number
of non-zero vectors at each point. This argument will be
modified inplace.
"""
@@ -388,7 +388,7 @@ def __call__(self, results):
assert len(_output_size) == 2
else:
_output_size = np.array([_output_size, _output_size],
- dtype=np.int)
+ dtype=np.int32)
mask[i] = mask[i][:, ::-1].copy()
joints[i] = joints[i][:, self.flip_index]
joints[i][:, :, 0] = _output_size[0] - joints[i][:, :, 0] - 1
@@ -565,8 +565,6 @@ def __call__(self, results):
results['img'], results['mask'], results[
'joints'] = image, mask, joints
- results['center'], results['scale'] = center, scale
-
return results
@@ -645,8 +643,7 @@ def __call__(self, results):
self._generate(results['ann_info']['num_joints'],
results['ann_info']['heatmap_size'])
target_list = list()
- img, mask_list, joints_list = results['img'], results['mask'], results[
- 'joints']
+ mask_list, joints_list = results['mask'], results['joints']
for scale_id in range(results['ann_info']['num_scales']):
target_t = heatmap_generator[scale_id](joints_list[scale_id])
@@ -656,8 +653,7 @@ def __call__(self, results):
mask_list[scale_id] = mask_list[scale_id].astype(np.float32)
joints_list[scale_id] = joints_t.astype(np.int32)
- results['img'], results['masks'], results[
- 'joints'] = img, mask_list, joints_list
+ results['masks'], results['joints'] = mask_list, joints_list
results['targets'] = target_list
return results
@@ -734,7 +730,7 @@ def __call__(self, results):
if input_size.size > 1:
assert len(input_size) == 2
else:
- input_size = np.array([input_size, input_size], dtype=np.int)
+ input_size = np.array([input_size, input_size], dtype=np.int32)
img = results['img']
h, w, _ = img.shape
@@ -802,7 +798,7 @@ def __call__(self, results):
if input_size.size > 1:
assert len(input_size) == 2
else:
- input_size = np.array([input_size, input_size], dtype=np.int)
+ input_size = np.array([input_size, input_size], dtype=np.int32)
test_scale_factor = results['ann_info']['test_scale_factor']
aug_data = []
diff --git a/mmpose/datasets/pipelines/hand_transform.py b/mmpose/datasets/pipelines/hand_transform.py
index e6fa095a95..b83e399c4e 100644
--- a/mmpose/datasets/pipelines/hand_transform.py
+++ b/mmpose/datasets/pipelines/hand_transform.py
@@ -12,6 +12,7 @@ class HandRandomFlip(TopDownRandomFlip):
Required keys: 'img', 'joints_3d', 'joints_3d_visible', 'center',
'hand_type', 'rel_root_depth' and 'ann_info'.
+
Modifies key: 'img', 'joints_3d', 'joints_3d_visible', 'center',
'hand_type', 'rel_root_depth'.
@@ -40,8 +41,9 @@ def __call__(self, results):
class HandGenerateRelDepthTarget:
"""Generate the target relative root depth.
- Required keys: 'rel_root_depth', 'rel_root_valid', 'ann_info'. Modified
- keys: 'target', 'target_weight'.
+ Required keys: 'rel_root_depth', 'rel_root_valid', 'ann_info'.
+
+ Modified keys: 'target', 'target_weight'.
"""
def __init__(self):
diff --git a/mmpose/datasets/pipelines/loading.py b/mmpose/datasets/pipelines/loading.py
index 24879158fb..11cf14d89c 100644
--- a/mmpose/datasets/pipelines/loading.py
+++ b/mmpose/datasets/pipelines/loading.py
@@ -1,5 +1,6 @@
# Copyright (c) OpenMMLab. All rights reserved.
import mmcv
+import numpy as np
from ..builder import PIPELINES
@@ -8,38 +9,71 @@
class LoadImageFromFile:
"""Loading image(s) from file.
+ Required key: "image_file".
+
+ Added key: "img".
+
Args:
+ to_float32 (bool): Whether to convert the loaded image to a float32
+ numpy array. If set to False, the loaded image is an uint8 array.
+ Defaults to False.
color_type (str): Flags specifying the color type of a loaded image,
candidates are 'color', 'grayscale' and 'unchanged'.
channel_order (str): Order of channel, candidates are 'bgr' and 'rgb'.
+ file_client_args (dict): Arguments to instantiate a FileClient.
+ See :class:`mmcv.fileio.FileClient` for details.
+ Defaults to ``dict(backend='disk')``.
"""
def __init__(self,
to_float32=False,
color_type='color',
- channel_order='rgb'):
+ channel_order='rgb',
+ file_client_args=dict(backend='disk')):
self.to_float32 = to_float32
self.color_type = color_type
self.channel_order = channel_order
+ self.file_client_args = file_client_args.copy()
+ self.file_client = None
def __call__(self, results):
"""Loading image(s) from file."""
+ if self.file_client is None:
+ self.file_client = mmcv.FileClient(**self.file_client_args)
+
image_file = results['image_file']
if isinstance(image_file, (list, tuple)):
imgs = []
for image in image_file:
- img = mmcv.imread(image, self.color_type, self.channel_order)
+ img_bytes = self.file_client.get(image)
+ img = mmcv.imfrombytes(
+ img_bytes,
+ flag=self.color_type,
+ channel_order=self.channel_order)
+ if self.to_float32:
+ img = img.astype(np.float32)
if img is None:
raise ValueError(f'Fail to read {image}')
imgs.append(img)
-
results['img'] = imgs
else:
- img = mmcv.imread(image_file, self.color_type, self.channel_order)
-
+ img_bytes = self.file_client.get(image_file)
+ img = mmcv.imfrombytes(
+ img_bytes,
+ flag=self.color_type,
+ channel_order=self.channel_order)
+ if self.to_float32:
+ img = img.astype(np.float32)
if img is None:
raise ValueError(f'Fail to read {image_file}')
results['img'] = img
return results
+
+ def __repr__(self):
+ repr_str = (f'{self.__class__.__name__}('
+ f'to_float32={self.to_float32}, '
+ f"color_type='{self.color_type}', "
+ f'file_client_args={self.file_client_args})')
+ return repr_str
diff --git a/mmpose/datasets/pipelines/mesh_transform.py b/mmpose/datasets/pipelines/mesh_transform.py
index c79ca4c01b..e3f32febcf 100644
--- a/mmpose/datasets/pipelines/mesh_transform.py
+++ b/mmpose/datasets/pipelines/mesh_transform.py
@@ -35,7 +35,7 @@ def _flip_smpl_pose(pose):
def _flip_iuv(iuv, uv_type='BF'):
"""Flip IUV image horizontally.
- Notes:
+ Note:
IUV image height: H
IUV image width: W
@@ -87,7 +87,7 @@ def _construct_rotation_matrix(rot, size=3):
def _rotate_joints_3d(joints_3d, rot):
"""Rotate the 3D joints in the local coordinates.
- Notes:
+ Note:
Joints number: K
Args:
diff --git a/mmpose/datasets/pipelines/pose3d_transform.py b/mmpose/datasets/pipelines/pose3d_transform.py
index c85556d907..124937861f 100644
--- a/mmpose/datasets/pipelines/pose3d_transform.py
+++ b/mmpose/datasets/pipelines/pose3d_transform.py
@@ -30,6 +30,7 @@ class GetRootCenteredPose:
Required keys:
item
+
Modified keys:
item, visible_item, root_name
"""
@@ -89,8 +90,10 @@ class NormalizeJointCoordinate:
std (array): Std values of joint coordinates in shape [K, C].
norm_param_file (str): Optionally load a dict containing `mean` and
`std` from a file using `mmcv.load`.
+
Required keys:
item
+
Modified keys:
item
"""
@@ -131,8 +134,10 @@ class ImageCoordinateNormalization:
class definition for more details. If None is given, the camera
parameter will be obtained during processing of each data sample
with the key "camera_param".
+
Required keys:
item
+
Modified keys:
item (, camera_param)
"""
@@ -190,6 +195,7 @@ class definition for more details. If None is given, the camera
Required keys:
camera_param (if camera parameters are not given in initialization)
+
Modified keys:
intrinsics
"""
@@ -229,6 +235,7 @@ class CameraProjection:
Args:
item (str): The name of the pose to apply camera projection.
mode (str): The type of camera projection, supported options are
+
- world_to_camera
- world_to_pixel
- camera_to_world
@@ -243,8 +250,10 @@ class definition for more details. If None is given, the camera
with the key "camera_param".
Required keys:
- item
- camera_param (if camera parameters are not given in initialization)
+
+ - item
+ - camera_param (if camera parameters are not given in initialization)
+
Modified keys:
output_name
"""
@@ -316,10 +325,12 @@ class RelativeJointRandomFlip:
item (str|list[str]): The name of the pose to flip.
flip_cfg (dict|list[dict]): Configurations of the fliplr_regression
function. It should contain the following arguments:
- - `center_mode`: The mode to set the center location on the
- x-axis to flip around.
- -`center_x` or `center_index`: Set the x-axis location or the
- root joint's index to define the flip center.
+
+ - ``center_mode``: The mode to set the center location on the \
+ x-axis to flip around.
+ - ``center_x`` or ``center_index``: Set the x-axis location or \
+ the root joint's index to define the flip center.
+
Please refer to the docstring of the fliplr_regression function for
more details.
visible_item (str|list[str]): The name of the visibility item which
@@ -333,6 +344,7 @@ class definition for more details. If None is given, the camera
Required keys:
item
+
Modified keys:
item (, camera_param)
"""
@@ -422,13 +434,14 @@ class PoseSequenceToTensor:
The original pose sequence should have a shape of [T,K,C] or [K,C], where
T is the sequence length, K and C are keypoint number and dimension. The
- converted pose sequence will have a shape of [K*C, T].
+ converted pose sequence will have a shape of [KxC, T].
Args:
item (str): The name of the pose sequence
Required keys:
item
+
Modified keys:
item
"""
@@ -463,7 +476,7 @@ class Generate3DHeatmapTarget:
Args:
sigma: Sigma of heatmap gaussian.
joint_indices (list): Indices of joints used for heatmap generation.
- If None (default) is given, all joints will be used.
+ If None (default) is given, all joints will be used.
max_bound (float): The maximal value of heatmap.
"""
@@ -540,3 +553,91 @@ def __call__(self, results):
results['target'] = target
results['target_weight'] = target_weight
return results
+
+
+@PIPELINES.register_module()
+class GenerateVoxel3DHeatmapTarget:
+ """Generate the target 3d heatmap.
+
+ Required keys: 'joints_3d', 'joints_3d_visible', 'ann_info_3d'.
+ Modified keys: 'target', and 'target_weight'.
+
+ Args:
+ sigma: Sigma of heatmap gaussian (mm).
+ joint_indices (list): Indices of joints used for heatmap generation.
+ If None (default) is given, all joints will be used.
+ """
+
+ def __init__(self, sigma=200.0, joint_indices=None):
+ self.sigma = sigma # mm
+ self.joint_indices = joint_indices
+
+ def __call__(self, results):
+ """Generate the target heatmap."""
+ joints_3d = results['joints_3d']
+ joints_3d_visible = results['joints_3d_visible']
+ cfg = results['ann_info']
+
+ num_people = len(joints_3d)
+ num_joints = joints_3d[0].shape[0]
+
+ if self.joint_indices is not None:
+ num_joints = len(self.joint_indices)
+ joint_indices = self.joint_indices
+ else:
+ joint_indices = list(range(num_joints))
+
+ space_size = cfg['space_size']
+ space_center = cfg['space_center']
+ cube_size = cfg['cube_size']
+ grids_x = np.linspace(-space_size[0] / 2, space_size[0] / 2,
+ cube_size[0]) + space_center[0]
+ grids_y = np.linspace(-space_size[1] / 2, space_size[1] / 2,
+ cube_size[1]) + space_center[1]
+ grids_z = np.linspace(-space_size[2] / 2, space_size[2] / 2,
+ cube_size[2]) + space_center[2]
+
+ target = np.zeros(
+ (num_joints, cube_size[0], cube_size[1], cube_size[2]),
+ dtype=np.float32)
+
+ for n in range(num_people):
+ for idx, joint_id in enumerate(joint_indices):
+ mu_x = joints_3d[n][joint_id][0]
+ mu_y = joints_3d[n][joint_id][1]
+ mu_z = joints_3d[n][joint_id][2]
+ vis = joints_3d_visible[n][joint_id][0]
+ if vis < 1:
+ continue
+ i_x = [
+ np.searchsorted(grids_x, mu_x - 3 * self.sigma),
+ np.searchsorted(grids_x, mu_x + 3 * self.sigma, 'right')
+ ]
+ i_y = [
+ np.searchsorted(grids_y, mu_y - 3 * self.sigma),
+ np.searchsorted(grids_y, mu_y + 3 * self.sigma, 'right')
+ ]
+ i_z = [
+ np.searchsorted(grids_z, mu_z - 3 * self.sigma),
+ np.searchsorted(grids_z, mu_z + 3 * self.sigma, 'right')
+ ]
+ if i_x[0] >= i_x[1] or i_y[0] >= i_y[1] or i_z[0] >= i_z[1]:
+ continue
+ kernel_xs, kernel_ys, kernel_zs = np.meshgrid(
+ grids_x[i_x[0]:i_x[1]],
+ grids_y[i_y[0]:i_y[1]],
+ grids_z[i_z[0]:i_z[1]],
+ indexing='ij')
+ g = np.exp(-((kernel_xs - mu_x)**2 + (kernel_ys - mu_y)**2 +
+ (kernel_zs - mu_z)**2) / (2 * self.sigma**2))
+ target[idx, i_x[0]:i_x[1], i_y[0]:i_y[1], i_z[0]:i_z[1]] \
+ = np.maximum(target[idx, i_x[0]:i_x[1],
+ i_y[0]:i_y[1], i_z[0]:i_z[1]], g)
+
+ target = np.clip(target, 0, 1)
+ if target.shape[0] == 1:
+ target = target[0]
+
+ results['targets_3d'] = target
+
+ return results
diff --git a/mmpose/datasets/pipelines/shared_transform.py b/mmpose/datasets/pipelines/shared_transform.py
index 4e6e202355..e4fea806ce 100644
--- a/mmpose/datasets/pipelines/shared_transform.py
+++ b/mmpose/datasets/pipelines/shared_transform.py
@@ -190,7 +190,9 @@ class Albumentation:
to get more information about pixel-level transforms.
An example of ``transforms`` is as followed:
- .. code-block::
+
+ .. code-block:: python
+
[
dict(
type='RandomBrightnessContrast',
@@ -206,6 +208,7 @@ class Albumentation:
],
p=0.1),
]
+
Args:
transforms (list[dict]): A list of Albumentation transformations
keymap (dict): Contains {'input key':'albumentation-style key'},
@@ -234,8 +237,10 @@ def albu_builder(self, cfg):
"""Import a module from albumentations.
It resembles some of :func:`build_from_cfg` logic.
+
Args:
cfg (dict): Config dict. It should at least contain the key "type".
+
Returns:
obj: The constructed object.
"""
@@ -267,9 +272,11 @@ def mapper(d, keymap):
"""Dictionary mapper.
Renames keys according to keymap provided.
+
Args:
d (dict): old dict
keymap (dict): {'old_key':'new_key'}
+
Returns:
dict: new dict.
"""
@@ -417,6 +424,50 @@ def __repr__(self):
return repr_str
+@PIPELINES.register_module()
+class MultiItemProcess:
+ """Process each item and merge multi-item results to lists.
+
+ Args:
+ pipeline (dict): Dictionary to construct pipeline for a single item.
+ """
+
+ def __init__(self, pipeline):
+ self.pipeline = Compose(pipeline)
+
+ def __call__(self, results):
+ results_ = {}
+ for idx, result in results.items():
+ single_result = self.pipeline(result)
+ for k, v in single_result.items():
+ if k in results_:
+ results_[k].append(v)
+ else:
+ results_[k] = [v]
+
+ return results_
+
+
+@PIPELINES.register_module()
+class DiscardDuplicatedItems:
+
+ def __init__(self, keys_list):
+ """Discard duplicated single-item results.
+
+ Args:
+ keys_list (list): List of keys that need to be deduplicate.
+ """
+ self.keys_list = keys_list
+
+ def __call__(self, results):
+ for k, v in results.items():
+ if k in self.keys_list:
+ assert isinstance(v, Sequence)
+ results[k] = v[0]
+
+ return results
+
+
@PIPELINES.register_module()
class MultitaskGatherTarget:
"""Gather the targets for multitask heads.
@@ -459,9 +510,9 @@ class RenameKeys:
"""Rename the keys.
Args:
- key_pairs (Sequence[tuple]): Required keys to be renamed. If a tuple
- (key_src, key_tgt) is given as an element, the item retrieved by key_src
- will be renamed as key_tgt.
+ key_pairs (Sequence[tuple]): Required keys to be renamed.
+ If a tuple (key_src, key_tgt) is given as an element,
+ the item retrieved by key_src will be renamed as key_tgt.
"""
def __init__(self, key_pairs):
diff --git a/mmpose/datasets/pipelines/top_down_transform.py b/mmpose/datasets/pipelines/top_down_transform.py
index 0dcce9266d..6fb95e6aad 100644
--- a/mmpose/datasets/pipelines/top_down_transform.py
+++ b/mmpose/datasets/pipelines/top_down_transform.py
@@ -14,6 +14,7 @@ class TopDownRandomFlip:
Required keys: 'img', 'joints_3d', 'joints_3d_visible', 'center' and
'ann_info'.
+
Modifies key: 'img', 'joints_3d', 'joints_3d_visible', 'center' and
'flipped'.
@@ -67,6 +68,7 @@ class TopDownHalfBodyTransform:
the lower body at random.
Required keys: 'joints_3d', 'joints_3d_visible', and 'ann_info'.
+
Modifies key: 'scale' and 'center'.
Args:
@@ -145,7 +147,9 @@ def __call__(self, results):
class TopDownGetRandomScaleRotation:
"""Data augmentation with random scaling & rotating.
- Required key: 'scale'. Modifies key: 'scale' and 'rotation'.
+ Required key: 'scale'.
+
+ Modifies key: 'scale' and 'rotation'.
Args:
rot_factor (int): Rotating to ``[-2*rot_factor, 2*rot_factor]``.
@@ -182,8 +186,9 @@ class TopDownAffine:
"""Affine transform the image to make input.
Required keys:'img', 'joints_3d', 'joints_3d_visible', 'ann_info','scale',
- 'rotation' and 'center'. Modified keys:'img', 'joints_3d', and
- 'joints_3d_visible'.
+ 'rotation' and 'center'.
+
+ Modified keys:'img', 'joints_3d', and 'joints_3d_visible'.
Args:
use_udp (bool): To use unbiased data processing.
@@ -253,6 +258,7 @@ class TopDownGenerateTarget:
"""Generate the target heatmap.
Required keys: 'joints_3d', 'joints_3d_visible', 'ann_info'.
+
Modified keys: 'target', and 'target_weight'.
Args:
@@ -261,7 +267,6 @@ class TopDownGenerateTarget:
encoding (str): Approach to generate target heatmaps.
Currently supported approaches: 'MSRA', 'Megvii', 'UDP'.
Default:'MSRA'
-
unbiased_encoding (bool): Option to use unbiased
encoding methods.
Paper ref: Zhang et al. Distribution-Aware Coordinate
@@ -387,6 +392,7 @@ def _megvii_generate_target(self, cfg, joints_3d, joints_3d_visible,
joints_3d: np.ndarray ([num_joints, 3])
joints_3d_visible: np.ndarray ([num_joints, 3])
kernel: Kernel of heatmap gaussian
+
Returns:
tuple: A tuple containing targets.
@@ -429,12 +435,12 @@ def _udp_generate_target(self, cfg, joints_3d, joints_3d_visible, factor,
for Human Pose Estimation (CVPR 2020).
Note:
- num keypoints: K
- heatmap height: H
- heatmap width: W
- num target channels: C
- C = K if target_type=='GaussianHeatmap'
- C = 3*K if target_type=='CombinedTarget'
+ - num keypoints: K
+ - heatmap height: H
+ - heatmap width: W
+ - num target channels: C
+ - C = K if target_type=='GaussianHeatmap'
+ - C = 3*K if target_type=='CombinedTarget'
Args:
cfg (dict): data config
@@ -651,6 +657,7 @@ def _generate_target(self, cfg, joints_3d, joints_3d_visible):
cfg (dict): data config
joints_3d: np.ndarray([num_joints, 3])
joints_3d_visible: np.ndarray([num_joints, 3])
+
Returns:
target, target_weight(1: visible, 0: invisible)
"""
@@ -691,15 +698,17 @@ def __call__(self, results):
class TopDownRandomTranslation:
"""Data augmentation with random translation.
- Required key: 'scale' and 'center'. Modifies key: 'center'.
+ Required key: 'scale' and 'center'.
+
+ Modifies key: 'center'.
- Notes:
- bbox height: H
- bbox width: W
+ Note:
+ - bbox height: H
+ - bbox width: W
Args:
trans_factor (float): Translating center to
- ``[-trans_factor, trans_factor] * [W, H] + center``.
+ ``[-trans_factor, trans_factor] * [W, H] + center``.
trans_prob (float): Probability of random translation.
"""
diff --git a/mmpose/models/backbones/__init__.py b/mmpose/models/backbones/__init__.py
index fb3bf1d01d..543a6148ce 100644
--- a/mmpose/models/backbones/__init__.py
+++ b/mmpose/models/backbones/__init__.py
@@ -19,6 +19,7 @@
from .shufflenet_v1 import ShuffleNetV1
from .shufflenet_v2 import ShuffleNetV2
from .tcn import TCN
+from .v2v_net import V2VNet
from .vgg import VGG
from .vipnas_mbv3 import ViPNAS_MobileNetV3
from .vipnas_resnet import ViPNAS_ResNet
@@ -28,5 +29,5 @@
'MobileNetV3', 'RegNet', 'ResNet', 'ResNetV1d', 'ResNeXt', 'SCNet',
'SEResNet', 'SEResNeXt', 'ShuffleNetV1', 'ShuffleNetV2', 'CPM', 'RSN',
'MSPN', 'ResNeSt', 'VGG', 'TCN', 'ViPNAS_ResNet', 'ViPNAS_MobileNetV3',
- 'LiteHRNet'
+ 'LiteHRNet', 'V2VNet'
]
diff --git a/mmpose/models/backbones/base_backbone.py b/mmpose/models/backbones/base_backbone.py
index b2e9d6e06e..18b0f2e5c0 100644
--- a/mmpose/models/backbones/base_backbone.py
+++ b/mmpose/models/backbones/base_backbone.py
@@ -38,6 +38,6 @@ def forward(self, x):
"""Forward function.
Args:
- x (tensor | tuple[tensor]): x could be a Torch.tensor or a tuple of
- Torch.tensor, containing input data for forward computation.
+ x (Tensor | tuple[Tensor]): x could be a torch.Tensor or a tuple of
+ torch.Tensor, containing input data for forward computation.
"""
diff --git a/mmpose/models/backbones/litehrnet.py b/mmpose/models/backbones/litehrnet.py
index 26667d1461..954368841e 100644
--- a/mmpose/models/backbones/litehrnet.py
+++ b/mmpose/models/backbones/litehrnet.py
@@ -739,10 +739,9 @@ class LiteHRNet(nn.Module):
"""Lite-HRNet backbone.
`Lite-HRNet: A Lightweight High-Resolution Network
- `__
+ `_.
- Code adapted from 'https://github.com/HRNet/Lite-HRNet/'
- 'blob/hrnet/models/backbones/litehrnet.py'
+ Code adapted from 'https://github.com/HRNet/Lite-HRNet'.
Args:
extra (dict): detailed configuration for each stage of HRNet.
diff --git a/mmpose/models/backbones/mspn.py b/mmpose/models/backbones/mspn.py
index 6d28a49f14..71cee34e39 100644
--- a/mmpose/models/backbones/mspn.py
+++ b/mmpose/models/backbones/mspn.py
@@ -131,7 +131,7 @@ class UpsampleUnit(nn.Module):
hourglass-like module. Default:False
norm_cfg (dict): dictionary to construct and config norm layer.
Default: dict(type='BN')
- out_channels (in): Number of channels of feature output by upsample
+ out_channels (int): Number of channels of feature output by upsample
module. Must equal to in_channels of downsample module. Default:64
"""
@@ -391,7 +391,7 @@ class MSPN(BaseBackbone):
unit_channels (int): Number of Channels in an upsample unit.
Default: 256
num_stages (int): Number of stages in a multi-stage MSPN. Default: 4
- num_units (int): NUmber of downsample/upsample units in a single-stage
+ num_units (int): Number of downsample/upsample units in a single-stage
network. Default: 4
Note: Make sure num_units == len(self.num_blocks)
num_blocks (list): Number of bottlenecks in each
diff --git a/mmpose/models/backbones/resnest.py b/mmpose/models/backbones/resnest.py
index 2c38fa2ff2..0a2d4081df 100644
--- a/mmpose/models/backbones/resnest.py
+++ b/mmpose/models/backbones/resnest.py
@@ -260,8 +260,8 @@ def _inner_forward(x):
class ResNeSt(ResNetV1d):
"""ResNeSt backbone.
- Please refer to the `paper `_ for
- details.
+ Please refer to the `paper `__
+ for details.
Args:
depth (int): Network depth, from {50, 101, 152, 200}.
diff --git a/mmpose/models/backbones/v2v_net.py b/mmpose/models/backbones/v2v_net.py
new file mode 100644
index 0000000000..a432d3ea0a
--- /dev/null
+++ b/mmpose/models/backbones/v2v_net.py
@@ -0,0 +1,256 @@
+# ------------------------------------------------------------------------------
+# Adapted from
+# https://github.com/microsoft/voxelpose-pytorch/blob/main/lib/models/v2v_net.py
+# Original licence: MIT License.
+# ------------------------------------------------------------------------------
+
+import torch.nn as nn
+import torch.nn.functional as F
+from mmcv.cnn import ConvModule
+
+from ..builder import BACKBONES
+from .base_backbone import BaseBackbone
+
+
+class Basic3DBlock(nn.Module):
+ """A basic 3D convolutional block.
+
+ Args:
+ in_channels (int): Input channels of this block.
+ out_channels (int): Output channels of this block.
+ kernel_size (int): Kernel size of the convolution operation
+ conv_cfg (dict): Dictionary to construct and config conv layer.
+ Default: dict(type='Conv3d')
+ norm_cfg (dict): Dictionary to construct and config norm layer.
+ Default: dict(type='BN3d')
+ """
+
+ def __init__(self,
+ in_channels,
+ out_channels,
+ kernel_size,
+ conv_cfg=dict(type='Conv3d'),
+ norm_cfg=dict(type='BN3d')):
+ super(Basic3DBlock, self).__init__()
+ self.block = ConvModule(
+ in_channels,
+ out_channels,
+ kernel_size,
+ stride=1,
+ padding=((kernel_size - 1) // 2),
+ conv_cfg=conv_cfg,
+ norm_cfg=norm_cfg,
+ bias=True)
+
+ def forward(self, x):
+ """Forward function."""
+ return self.block(x)
+
+
+class Res3DBlock(nn.Module):
+ """A residual 3D convolutional block.
+
+ Args:
+ in_channels (int): Input channels of this block.
+ out_channels (int): Output channels of this block.
+ kernel_size (int): Kernel size of the convolution operation
+ Default: 3
+ conv_cfg (dict): Dictionary to construct and config conv layer.
+ Default: dict(type='Conv3d')
+ norm_cfg (dict): Dictionary to construct and config norm layer.
+ Default: dict(type='BN3d')
+ """
+
+ def __init__(self,
+ in_channels,
+ out_channels,
+ kernel_size=3,
+ conv_cfg=dict(type='Conv3d'),
+ norm_cfg=dict(type='BN3d')):
+ super(Res3DBlock, self).__init__()
+ self.res_branch = nn.Sequential(
+ ConvModule(
+ in_channels,
+ out_channels,
+ kernel_size,
+ stride=1,
+ padding=((kernel_size - 1) // 2),
+ conv_cfg=conv_cfg,
+ norm_cfg=norm_cfg,
+ bias=True),
+ ConvModule(
+ out_channels,
+ out_channels,
+ kernel_size,
+ stride=1,
+ padding=((kernel_size - 1) // 2),
+ conv_cfg=conv_cfg,
+ norm_cfg=norm_cfg,
+ act_cfg=None,
+ bias=True))
+
+ if in_channels == out_channels:
+ self.skip_con = nn.Sequential()
+ else:
+ self.skip_con = ConvModule(
+ in_channels,
+ out_channels,
+ 1,
+ stride=1,
+ padding=0,
+ conv_cfg=conv_cfg,
+ norm_cfg=norm_cfg,
+ act_cfg=None,
+ bias=True)
+
+ def forward(self, x):
+ """Forward function."""
+ res = self.res_branch(x)
+ skip = self.skip_con(x)
+ return F.relu(res + skip, True)
+
+
+class Pool3DBlock(nn.Module):
+ """A 3D max-pool block.
+
+ Args:
+ pool_size (int): Pool size of the 3D max-pool layer
+ """
+
+ def __init__(self, pool_size):
+ super(Pool3DBlock, self).__init__()
+ self.pool_size = pool_size
+
+ def forward(self, x):
+ """Forward function."""
+ return F.max_pool3d(
+ x, kernel_size=self.pool_size, stride=self.pool_size)
+
+
+class Upsample3DBlock(nn.Module):
+ """A 3D upsample block.
+
+ Args:
+ in_channels (int): Input channels of this block.
+ out_channels (int): Output channels of this block.
+ kernel_size (int): Kernel size of the transposed convolution operation.
+ Default: 2
+ stride (int): Kernel size of the transposed convolution operation.
+ Default: 2
+ """
+
+ def __init__(self, in_channels, out_channels, kernel_size=2, stride=2):
+ super(Upsample3DBlock, self).__init__()
+ assert kernel_size == 2
+ assert stride == 2
+ self.block = nn.Sequential(
+ nn.ConvTranspose3d(
+ in_channels,
+ out_channels,
+ kernel_size=kernel_size,
+ stride=stride,
+ padding=0,
+ output_padding=0), nn.BatchNorm3d(out_channels), nn.ReLU(True))
+
+ def forward(self, x):
+ """Forward function."""
+ return self.block(x)
+
+
+class EncoderDecorder(nn.Module):
+ """An encoder-decoder block.
+
+ Args:
+ in_channels (int): Input channels of this block
+ """
+
+ def __init__(self, in_channels=32):
+ super(EncoderDecorder, self).__init__()
+
+ self.encoder_pool1 = Pool3DBlock(2)
+ self.encoder_res1 = Res3DBlock(in_channels, in_channels * 2)
+ self.encoder_pool2 = Pool3DBlock(2)
+ self.encoder_res2 = Res3DBlock(in_channels * 2, in_channels * 4)
+
+ self.mid_res = Res3DBlock(in_channels * 4, in_channels * 4)
+
+ self.decoder_res2 = Res3DBlock(in_channels * 4, in_channels * 4)
+ self.decoder_upsample2 = Upsample3DBlock(in_channels * 4,
+ in_channels * 2, 2, 2)
+ self.decoder_res1 = Res3DBlock(in_channels * 2, in_channels * 2)
+ self.decoder_upsample1 = Upsample3DBlock(in_channels * 2, in_channels,
+ 2, 2)
+
+ self.skip_res1 = Res3DBlock(in_channels, in_channels)
+ self.skip_res2 = Res3DBlock(in_channels * 2, in_channels * 2)
+
+ def forward(self, x):
+ """Forward function."""
+ skip_x1 = self.skip_res1(x)
+ x = self.encoder_pool1(x)
+ x = self.encoder_res1(x)
+
+ skip_x2 = self.skip_res2(x)
+ x = self.encoder_pool2(x)
+ x = self.encoder_res2(x)
+
+ x = self.mid_res(x)
+
+ x = self.decoder_res2(x)
+ x = self.decoder_upsample2(x)
+ x = x + skip_x2
+
+ x = self.decoder_res1(x)
+ x = self.decoder_upsample1(x)
+ x = x + skip_x1
+
+ return x
+
+
+@BACKBONES.register_module()
+class V2VNet(BaseBackbone):
+ """V2VNet.
+
+ Please refer to the `paper `
+ for details.
+
+ Args:
+ input_channels (int):
+ Number of channels of the input feature volume.
+ output_channels (int):
+ Number of channels of the output volume.
+ mid_channels (int):
+ Input and output channels of the encoder-decoder block.
+ """
+
+ def __init__(self, input_channels, output_channels, mid_channels=32):
+ super(V2VNet, self).__init__()
+
+ self.front_layers = nn.Sequential(
+ Basic3DBlock(input_channels, mid_channels // 2, 7),
+ Res3DBlock(mid_channels // 2, mid_channels),
+ )
+
+ self.encoder_decoder = EncoderDecorder(in_channels=mid_channels)
+
+ self.output_layer = nn.Conv3d(
+ mid_channels, output_channels, kernel_size=1, stride=1, padding=0)
+
+ self._initialize_weights()
+
+ def forward(self, x):
+ """Forward function."""
+ x = self.front_layers(x)
+ x = self.encoder_decoder(x)
+ x = self.output_layer(x)
+
+ return x
+
+ def _initialize_weights(self):
+ for m in self.modules():
+ if isinstance(m, nn.Conv3d):
+ nn.init.normal_(m.weight, 0, 0.001)
+ nn.init.constant_(m.bias, 0)
+ elif isinstance(m, nn.ConvTranspose3d):
+ nn.init.normal_(m.weight, 0, 0.001)
+ nn.init.constant_(m.bias, 0)
diff --git a/mmpose/models/backbones/vipnas_mbv3.py b/mmpose/models/backbones/vipnas_mbv3.py
index 7fef559705..ed990e3966 100644
--- a/mmpose/models/backbones/vipnas_mbv3.py
+++ b/mmpose/models/backbones/vipnas_mbv3.py
@@ -15,7 +15,7 @@
class ViPNAS_MobileNetV3(BaseBackbone):
"""ViPNAS_MobileNetV3 backbone.
- ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search.
+ "ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search"
More details can be found in the `paper
`__ .
diff --git a/mmpose/models/backbones/vipnas_resnet.py b/mmpose/models/backbones/vipnas_resnet.py
index 4ce387b2b1..81b028ed5f 100644
--- a/mmpose/models/backbones/vipnas_resnet.py
+++ b/mmpose/models/backbones/vipnas_resnet.py
@@ -343,7 +343,7 @@ def __init__(self,
class ViPNAS_ResNet(BaseBackbone):
"""ViPNAS_ResNet backbone.
- ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search.
+ "ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search"
More details can be found in the `paper
`__ .
@@ -546,7 +546,7 @@ def _freeze_stages(self):
param.requires_grad = False
def init_weights(self, pretrained=None):
-
+ """Initialize model weights."""
super().init_weights(pretrained)
if pretrained is None:
for m in self.modules():
diff --git a/mmpose/models/detectors/__init__.py b/mmpose/models/detectors/__init__.py
index 07a1dfdb1a..e0b769fe02 100644
--- a/mmpose/models/detectors/__init__.py
+++ b/mmpose/models/detectors/__init__.py
@@ -6,8 +6,9 @@
from .pose_lifter import PoseLifter
from .posewarper import PoseWarper
from .top_down import TopDown
+from .voxelpose import VoxelPose
__all__ = [
'TopDown', 'AssociativeEmbedding', 'ParametricMesh', 'MultiTask',
- 'PoseLifter', 'Interhand3D', 'PoseWarper'
+ 'PoseLifter', 'Interhand3D', 'PoseWarper', 'VoxelPose'
]
diff --git a/mmpose/models/detectors/associative_embedding.py b/mmpose/models/detectors/associative_embedding.py
index a573ab462a..100c7806d3 100644
--- a/mmpose/models/detectors/associative_embedding.py
+++ b/mmpose/models/detectors/associative_embedding.py
@@ -35,7 +35,7 @@ class AssociativeEmbedding(BasePose):
test_cfg (dict): Config for testing. Default: None.
pretrained (str): Path to the pretrained models.
loss_pose (None): Deprecated arguments. Please use
- `loss_keypoint` for heads instead.
+ ``loss_keypoint`` for heads instead.
"""
def __init__(self,
@@ -90,24 +90,27 @@ def forward(self,
**kwargs):
"""Calls either forward_train or forward_test depending on whether
return_loss is True.
+
Note:
- batch_size: N
- num_keypoints: K
- num_img_channel: C
- img_width: imgW
- img_height: imgH
- heatmaps weight: W
- heatmaps height: H
- max_num_people: M
+ - batch_size: N
+ - num_keypoints: K
+ - num_img_channel: C
+ - img_width: imgW
+ - img_height: imgH
+ - heatmaps weight: W
+ - heatmaps height: H
+ - max_num_people: M
+
Args:
- img (torch.Tensor[NxCximgHximgW]): Input image.
- targets (List(torch.Tensor[NxKxHxW])): Multi-scale target heatmaps.
- masks (List(torch.Tensor[NxHxW])): Masks of multi-scale target
- heatmaps
- joints (List(torch.Tensor[NxMxKx2])): Joints of multi-scale target
- heatmaps for ae loss
- img_metas(dict):Information about val&test
- By default this includes:
+ img (torch.Tensor[N,C,imgH,imgW]): Input image.
+ targets (list(torch.Tensor[N,K,H,W])): Multi-scale target heatmaps.
+ masks (list(torch.Tensor[N,H,W])): Masks of multi-scale target
+ heatmaps
+ joints (list(torch.Tensor[N,M,K,2])): Joints of multi-scale target
+ heatmaps for ae loss
+ img_metas (dict): Information about val & test.
+ By default it includes:
+
- "image_file": image path
- "aug_data": input
- "test_scale_factor": test scale factor
@@ -115,15 +118,14 @@ def forward(self,
- "center": center of image
- "scale": scale of image
- "flip_index": flip index of keypoints
-
- return loss(bool): Option to 'return_loss'. 'return_loss=True' for
- training, 'return_loss=False' for validation & test
+ return loss (bool): ``return_loss=True`` for training,
+ ``return_loss=False`` for validation & test.
return_heatmap (bool) : Option to return heatmap.
Returns:
- dict|tuple: if 'return_loss' is true, then return losses.
- Otherwise, return predicted poses, scores, image
- paths and heatmaps.
+ dict|tuple: if 'return_loss' is true, then return losses. \
+ Otherwise, return predicted poses, scores, image \
+ paths and heatmaps.
"""
if return_loss:
@@ -146,11 +148,11 @@ def forward_train(self, img, targets, masks, joints, img_metas, **kwargs):
max_num_people: M
Args:
- img (torch.Tensor[NxCximgHximgW]): Input image.
- targets (List(torch.Tensor[NxKxHxW])): Multi-scale target heatmaps.
- masks (List(torch.Tensor[NxHxW])): Masks of multi-scale target
+ img (torch.Tensor[N,C,imgH,imgW]): Input image.
+ targets (List(torch.Tensor[N,K,H,W])): Multi-scale target heatmaps.
+ masks (List(torch.Tensor[N,H,W])): Masks of multi-scale target
heatmaps
- joints (List(torch.Tensor[NxMxKx2])): Joints of multi-scale target
+ joints (List(torch.Tensor[N,M,K,2])): Joints of multi-scale target
heatmaps for ae loss
img_metas (dict):Information about val&test
By default this includes:
@@ -200,10 +202,10 @@ def forward_test(self, img, img_metas, return_heatmap=False, **kwargs):
"""Inference the bottom-up model.
Note:
- Batchsize = N (currently support batchsize = 1)
- num_img_channel: C
- img_width: imgW
- img_height: imgH
+ - Batchsize: N (currently support batchsize = 1)
+ - num_img_channel: C
+ - img_width: imgW
+ - img_height: imgH
Args:
flip_index (List(int)):
diff --git a/mmpose/models/detectors/base.py b/mmpose/models/detectors/base.py
index 338efdba3c..5d459b42de 100644
--- a/mmpose/models/detectors/base.py
+++ b/mmpose/models/detectors/base.py
@@ -43,9 +43,9 @@ def _parse_losses(losses):
losses and other necessary information.
Returns:
- tuple[Tensor, dict]: (loss, log_vars), loss is the loss tensor
- which may be a weighted sum of all losses, log_vars contains
- all the variables to be sent to the logger.
+ tuple[Tensor, dict]: (loss, log_vars), loss is the loss tensor \
+ which may be a weighted sum of all losses, log_vars \
+ contains all the variables to be sent to the logger.
"""
log_vars = OrderedDict()
for loss_name, loss_value in losses.items():
diff --git a/mmpose/models/detectors/interhand_3d.py b/mmpose/models/detectors/interhand_3d.py
index 615313fbbc..5a4d6bde1b 100644
--- a/mmpose/models/detectors/interhand_3d.py
+++ b/mmpose/models/detectors/interhand_3d.py
@@ -27,17 +27,17 @@ def forward(self,
return_loss=True. Note this setting will change the expected inputs.
When `return_loss=True`, img and img_meta are single-nested (i.e.
Tensor and List[dict]), and when `resturn_loss=False`, img and img_meta
- should be double nested (i.e. List[Tensor], List[List[dict]]), with
+ should be double nested (i.e. list[Tensor], list[list[dict]]), with
the outer list indicating test time augmentations.
Note:
- batch_size: N
- num_keypoints: K
- num_img_channel: C (Default: 3)
- img height: imgH
- img width: imgW
- heatmaps height: H
- heatmaps weight: W
+ - batch_size: N
+ - num_keypoints: K
+ - num_img_channel: C (Default: 3)
+ - img height: imgH
+ - img width: imgW
+ - heatmaps height: H
+ - heatmaps weight: W
Args:
img (torch.Tensor[NxCximgHximgW]): Input images.
@@ -47,22 +47,23 @@ def forward(self,
heatmaps, relative hand root depth and hand type.
img_metas (list(dict)): Information about data augmentation
By default this includes:
+
- "image_file: path to the image file
- "center": center of the bbox
- "scale": scale of the bbox
- "rotation": rotation of the bbox
- "bbox_score": score of bbox
- "heatmap3d_depth_bound": depth bound of hand keypoint 3D
- heatmap
+ heatmap
- "root_depth_bound": depth bound of relative root depth 1D
- heatmap
+ heatmap
return_loss (bool): Option to `return loss`. `return loss=True`
for training, `return loss=False` for validation & test.
Returns:
- dict|tuple: if `return loss` is true, then return losses.
- Otherwise, return predicted poses, boxes, image paths, heatmaps,
- relative hand root depth and hand type.
+ dict|tuple: if `return loss` is true, then return losses. \
+ Otherwise, return predicted poses, boxes, image paths, \
+ heatmaps, relative hand root depth and hand type.
"""
if return_loss:
return self.forward_train(img, target, target_weight, img_metas,
@@ -123,6 +124,7 @@ def show_result(self,
Args:
result (list[dict]): The pose estimation results containing:
+
- "keypoints_3d" ([K,4]): 3D keypoints
- "keypoints" ([K,3] or [T,K,3]): Optional for visualizing
2D inputs. If a sequence is given, only the last frame
diff --git a/mmpose/models/detectors/mesh.py b/mmpose/models/detectors/mesh.py
index d9611308c3..0af18e3844 100644
--- a/mmpose/models/detectors/mesh.py
+++ b/mmpose/models/detectors/mesh.py
@@ -86,9 +86,10 @@ def train_step(self, data_batch, optimizer, **kwargs):
In this function, the detector will finish the train step following
the pipeline:
- 1. get fake and real SMPL parameters
- 2. optimize discriminator (if have)
- 3. optimize generator
+
+ 1. get fake and real SMPL parameters
+ 2. optimize discriminator (if have)
+ 3. optimize generator
If `self.train_cfg.disc_step > 1`, the train step will contain multiple
iterations for optimizing discriminator with different input data and
@@ -290,15 +291,16 @@ def forward(self, img, img_metas=None, return_loss=False, **kwargs):
return_loss=True.
Note:
- batch_size: N
- num_img_channel: C (Default: 3)
- img height: imgH
- img width: imgW
+ - batch_size: N
+ - num_img_channel: C (Default: 3)
+ - img height: imgH
+ - img width: imgW
Args:
img (torch.Tensor[N x C x imgH x imgW]): Input images.
img_metas (list(dict)): Information about data augmentation
By default this includes:
+
- "image_file: path to the image file
- "center": center of the bbox
- "scale": scale of the bbox
@@ -329,6 +331,7 @@ def show_result(self,
Args:
result (list[dict]): The mesh estimation results containing:
+
- "bbox" (ndarray[4]): instance bounding bbox
- "center" (ndarray[2]): bbox center
- "scale" (ndarray[2]): bbox scale
@@ -339,8 +342,7 @@ def show_result(self,
img (str or Tensor): Optional. The image to visualize 2D inputs on.
win_name (str): The window name.
show (bool): Whether to show the image. Default: False.
- wait_time (int): Value of waitKey param.
- Default: 0.
+ wait_time (int): Value of waitKey param. Default: 0.
out_file (str or None): The filename to write the image.
Default: None.
bbox_color (str or tuple or :obj:`Color`): Color of bbox lines.
diff --git a/mmpose/models/detectors/multi_task.py b/mmpose/models/detectors/multi_task.py
index db08aea9a5..1b6f3178a4 100644
--- a/mmpose/models/detectors/multi_task.py
+++ b/mmpose/models/detectors/multi_task.py
@@ -11,8 +11,8 @@ class MultiTask(nn.Module):
Args:
backbone (dict): Backbone modules to extract feature.
- heads (List[dict]): heads to output predictions.
- necks (List[dict] | None): necks to process feature.
+ heads (list[dict]): heads to output predictions.
+ necks (list[dict] | None): necks to process feature.
head2neck (dict{int:int}): head index to neck index.
pretrained (str): Path to the pretrained models.
"""
@@ -81,20 +81,21 @@ def forward(self,
the outer list indicating test time augmentations.
Note:
- batch_size: N
- num_keypoints: K
- num_img_channel: C (Default: 3)
- img height: imgH
- img weight: imgW
- heatmaps height: H
- heatmaps weight: W
+ - batch_size: N
+ - num_keypoints: K
+ - num_img_channel: C (Default: 3)
+ - img height: imgH
+ - img weight: imgW
+ - heatmaps height: H
+ - heatmaps weight: W
Args:
- img (torch.Tensor[NxCximgHximgW]): Input images.
- target (List[torch.Tensor]): Targets.
+ img (torch.Tensor[N,C,imgH,imgW]): Input images.
+ target (list[torch.Tensor]): Targets.
target_weight (List[torch.Tensor]): Weights.
img_metas (list(dict)): Information about data augmentation
By default this includes:
+
- "image_file: path to the image file
- "center": center of the bbox
- "scale": scale of the bbox
@@ -104,9 +105,9 @@ def forward(self,
for training, `return loss=False` for validation & test.
Returns:
- dict|tuple: if `return loss` is true, then return losses.
- Otherwise, return predicted poses, boxes, image paths
- and heatmaps.
+ dict|tuple: if `return loss` is true, then return losses. \
+ Otherwise, return predicted poses, boxes, image paths \
+ and heatmaps.
"""
if return_loss:
return self.forward_train(img, target, target_weight, img_metas,
@@ -176,7 +177,7 @@ def forward_dummy(self, img):
img (torch.Tensor): Input image.
Returns:
- List[Tensor]: Outputs.
+ list[Tensor]: Outputs.
"""
features = self.backbone(img)
outputs = []
diff --git a/mmpose/models/detectors/pose_lifter.py b/mmpose/models/detectors/pose_lifter.py
index 45bb4091c6..ace6b9f3e8 100644
--- a/mmpose/models/detectors/pose_lifter.py
+++ b/mmpose/models/detectors/pose_lifter.py
@@ -149,16 +149,13 @@ def forward(self,
return_loss=True.
Note:
- Note:
- batch_size: N
- num_input_keypoints: Ki
- input_keypoint_dim: Ci
- input_sequence_len: Ti
- num_output_keypoints: Ko
- output_keypoint_dim: Co
- input_sequence_len: To
-
-
+ - batch_size: N
+ - num_input_keypoints: Ki
+ - input_keypoint_dim: Ci
+ - input_sequence_len: Ti
+ - num_output_keypoints: Ko
+ - output_keypoint_dim: Co
+ - input_sequence_len: To
Args:
input (torch.Tensor[NxKixCixTi]): Input keypoint coordinates.
@@ -169,9 +166,10 @@ def forward(self,
metas (list(dict)): Information about data augmentation
return_loss (bool): Option to `return loss`. `return loss=True`
for training, `return loss=False` for validation & test.
+
Returns:
- dict|Tensor: if `reutrn_loss` is true, return losses. Otherwise
- return predicted poses
+ dict|Tensor: If `reutrn_loss` is true, return losses. \
+ Otherwise return predicted poses.
"""
if return_loss:
return self.forward_train(input, target, target_weight, metas,
@@ -304,6 +302,7 @@ def show_result(self,
Args:
result (list[dict]): The pose estimation results containing:
+
- "keypoints_3d" ([K,4]): 3D keypoints
- "keypoints" ([K,3] or [T,K,3]): Optional for visualizing
2D inputs. If a sequence is given, only the last frame
diff --git a/mmpose/models/detectors/posewarper.py b/mmpose/models/detectors/posewarper.py
index 8aed8154ba..aa1d05f2a4 100644
--- a/mmpose/models/detectors/posewarper.py
+++ b/mmpose/models/detectors/posewarper.py
@@ -17,11 +17,10 @@
@POSENETS.register_module()
class PoseWarper(TopDown):
- """Top-down pose detectors for multi-frame settings for video inputs. Paper
- ref: Bertasius, Gedas, et al. "Learning temporal pose estimation from
- sparsely-labeled videos." arXiv preprint:1906.04016 (2019).
+ """Top-down pose detectors for multi-frame settings for video inputs.
- <``https://arxiv.org/abs/1906.04016``>
+ `"Learning temporal pose estimation from sparsely-labeled videos"
+ `_.
A child class of TopDown detector. The main difference between PoseWarper
and TopDown lies in that the former takes a list of tensors as input image
@@ -76,22 +75,23 @@ def forward(self,
the outer list indicating test time augmentations.
Note:
- number of frames: F
- batch_size: N
- num_keypoints: K
- num_img_channel: C (Default: 3)
- img height: imgH
- img width: imgW
- heatmaps height: H
- heatmaps weight: W
+ - number of frames: F
+ - batch_size: N
+ - num_keypoints: K
+ - num_img_channel: C (Default: 3)
+ - img height: imgH
+ - img width: imgW
+ - heatmaps height: H
+ - heatmaps weight: W
Args:
- imgs (list[Fxtorch.Tensor[NxCximgHximgW]]): multiple input frames
- target (torch.Tensor[NxKxHxW]): Target heatmaps for one frame.
- target_weight (torch.Tensor[NxKx1]): Weights across
+ imgs (list[F,torch.Tensor[N,C,imgH,imgW]]): multiple input frames
+ target (torch.Tensor[N,K,H,W]): Target heatmaps for one frame.
+ target_weight (torch.Tensor[N,K,1]): Weights across
different joint types.
img_metas (list(dict)): Information about data augmentation
By default this includes:
+
- "image_file: paths to multiple video frames
- "center": center of the bbox
- "scale": scale of the bbox
@@ -102,8 +102,8 @@ def forward(self,
return_heatmap (bool) : Option to return heatmap.
Returns:
- dict|tuple: if `return loss` is true, then return losses.
- Otherwise, return predicted poses, boxes, image paths
+ dict|tuple: if `return loss` is true, then return losses. \
+ Otherwise, return predicted poses, boxes, image paths \
and heatmaps.
"""
if return_loss:
@@ -214,8 +214,8 @@ def forward_dummy(self, img):
See ``tools/get_flops.py``.
Args:
- img (torch.Tensor[NxCximgHximgW], or list/tuple of tensors):
- multiple input frames, N >= 2
+ img (torch.Tensor[N,C,imgH,imgW], or list|tuple of tensors):
+ multiple input frames, N >= 2.
Returns:
Tensor: Output heatmaps.
diff --git a/mmpose/models/detectors/top_down.py b/mmpose/models/detectors/top_down.py
index 4d701bf576..af0ab51c5b 100644
--- a/mmpose/models/detectors/top_down.py
+++ b/mmpose/models/detectors/top_down.py
@@ -104,13 +104,13 @@ def forward(self,
the outer list indicating test time augmentations.
Note:
- batch_size: N
- num_keypoints: K
- num_img_channel: C (Default: 3)
- img height: imgH
- img width: imgW
- heatmaps height: H
- heatmaps weight: W
+ - batch_size: N
+ - num_keypoints: K
+ - num_img_channel: C (Default: 3)
+ - img height: imgH
+ - img width: imgW
+ - heatmaps height: H
+ - heatmaps weight: W
Args:
img (torch.Tensor[NxCximgHximgW]): Input images.
@@ -119,6 +119,7 @@ def forward(self,
different joint types.
img_metas (list(dict)): Information about data augmentation
By default this includes:
+
- "image_file: path to the image file
- "center": center of the bbox
- "scale": scale of the bbox
@@ -129,8 +130,8 @@ def forward(self,
return_heatmap (bool) : Option to return heatmap.
Returns:
- dict|tuple: if `return loss` is true, then return losses.
- Otherwise, return predicted poses, boxes, image paths
+ dict|tuple: if `return loss` is true, then return losses. \
+ Otherwise, return predicted poses, boxes, image paths \
and heatmaps.
"""
if return_loss:
diff --git a/mmpose/models/detectors/voxelpose.py b/mmpose/models/detectors/voxelpose.py
new file mode 100644
index 0000000000..35eec1b1f5
--- /dev/null
+++ b/mmpose/models/detectors/voxelpose.py
@@ -0,0 +1,606 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from mmcv.runner import load_checkpoint
+
+from mmpose.core.camera import SimpleCameraTorch
+from mmpose.core.post_processing.post_transforms import (
+ affine_transform_torch, get_affine_transform)
+from .. import builder
+from ..builder import POSENETS
+from .base import BasePose
+
+
+class CuboidProposalNet(nn.Module):
+ """Cuboid proposal net in the paper "VoxelPose: Towards Multi-Camera 3D
+ Human Pose Estimation in Wild Environment".
+
+ Args:
+ v2v_net (ConfigDict): dictionary to construct
+ the 3D convolutional layers.
+ """
+
+ def __init__(self, v2v_net):
+ super(CuboidProposalNet, self).__init__()
+ self.v2v_net = builder.build_backbone(v2v_net)
+
+ def forward(self, initial_cubes):
+ return self.v2v_net(initial_cubes)
+
+
+class PoseRegressionNet(nn.Module):
+ """Pose regression net in the paper "VoxelPose: Towards Multi-Camera 3D
+ Human Pose Estimation in Wild Environment".
+
+ Args:
+ v2v_net (ConfigDict): dictionary to construct
+ the 3D convolutional layers.
+ """
+
+ def __init__(self, v2v_net):
+ super(PoseRegressionNet, self).__init__()
+ self.v2v_net = builder.build_backbone(v2v_net)
+
+ def forward(self, cubes):
+ return self.v2v_net(cubes)
+
+
+class ProjectLayer(nn.Module):
+
+ def __init__(self, cfg):
+ """Project layer to get voxel feature. Adapted from
+ https://github.com/microsoft/voxelpose-
+ pytorch/blob/main/lib/models/project_layer.py.
+
+ Args:
+ cfg (dict):
+ image_size: input size of the 2D model
+ heatmap_size: output size of the 2D model
+ """
+ super(ProjectLayer, self).__init__()
+ self.image_size = cfg['image_size']
+ self.heatmap_size = cfg['heatmap_size']
+ if isinstance(self.image_size, int):
+ self.image_size = [self.image_size, self.image_size]
+ if isinstance(self.heatmap_size, int):
+ self.heatmap_size = [self.heatmap_size, self.heatmap_size]
+
+ def compute_grid(self, box_size, box_center, num_bins, device=None):
+ if isinstance(box_size, int) or isinstance(box_size, float):
+ box_size = [box_size, box_size, box_size]
+ if isinstance(num_bins, int):
+ num_bins = [num_bins, num_bins, num_bins]
+
+ grid_1D_x = torch.linspace(
+ -box_size[0] / 2, box_size[0] / 2, num_bins[0], device=device)
+ grid_1D_y = torch.linspace(
+ -box_size[1] / 2, box_size[1] / 2, num_bins[1], device=device)
+ grid_1D_z = torch.linspace(
+ -box_size[2] / 2, box_size[2] / 2, num_bins[2], device=device)
+ grid_x, grid_y, grid_z = torch.meshgrid(
+ grid_1D_x + box_center[0],
+ grid_1D_y + box_center[1],
+ grid_1D_z + box_center[2],
+ )
+ grid_x = grid_x.contiguous().view(-1, 1)
+ grid_y = grid_y.contiguous().view(-1, 1)
+ grid_z = grid_z.contiguous().view(-1, 1)
+ grid = torch.cat([grid_x, grid_y, grid_z], dim=1)
+
+ return grid
+
+ def get_voxel(self, heatmaps, meta, grid_size, grid_center, cube_size):
+ device = heatmaps[0].device
+ batch_size = heatmaps[0].shape[0]
+ num_joints = heatmaps[0].shape[1]
+ num_bins = cube_size[0] * cube_size[1] * cube_size[2]
+ n = len(heatmaps)
+ cubes = torch.zeros(
+ batch_size, num_joints, 1, num_bins, n, device=device)
+ w, h = self.heatmap_size
+ grids = torch.zeros(batch_size, num_bins, 3, device=device)
+ bounding = torch.zeros(batch_size, 1, 1, num_bins, n, device=device)
+ for i in range(batch_size):
+ if len(grid_center[0]) == 3 or grid_center[i][3] >= 0:
+ if len(grid_center) == 1:
+ grid = self.compute_grid(
+ grid_size, grid_center[0], cube_size, device=device)
+ else:
+ grid = self.compute_grid(
+ grid_size, grid_center[i], cube_size, device=device)
+ grids[i:i + 1] = grid
+ for c in range(n):
+ center = meta[i]['center'][c]
+ scale = meta[i]['scale'][c]
+
+ width, height = center * 2
+ trans = torch.as_tensor(
+ get_affine_transform(center, scale / 200.0, 0,
+ self.image_size),
+ dtype=torch.float,
+ device=device)
+
+ cam_param = meta[i]['camera'][c].copy()
+
+ single_view_camera = SimpleCameraTorch(
+ param=cam_param, device=device)
+ xy = single_view_camera.world_to_pixel(grid)
+
+ bounding[i, 0, 0, :, c] = (xy[:, 0] >= 0) & (
+ xy[:, 1] >= 0) & (xy[:, 0] < width) & (
+ xy[:, 1] < height)
+ xy = torch.clamp(xy, -1.0, max(width, height))
+ xy = affine_transform_torch(xy, trans)
+ xy = xy * torch.tensor(
+ [w, h], dtype=torch.float,
+ device=device) / torch.tensor(
+ self.image_size, dtype=torch.float, device=device)
+ sample_grid = xy / torch.tensor([w - 1, h - 1],
+ dtype=torch.float,
+ device=device) * 2.0 - 1.0
+ sample_grid = torch.clamp(
+ sample_grid.view(1, 1, num_bins, 2), -1.1, 1.1)
+
+ cubes[i:i + 1, :, :, :, c] += F.grid_sample(
+ heatmaps[c][i:i + 1, :, :, :],
+ sample_grid,
+ align_corners=True)
+
+ cubes = torch.sum(
+ torch.mul(cubes, bounding), dim=-1) / (
+ torch.sum(bounding, dim=-1) + 1e-6)
+ cubes[cubes != cubes] = 0.0
+ cubes = cubes.clamp(0.0, 1.0)
+
+ cubes = cubes.view(batch_size, num_joints, cube_size[0], cube_size[1],
+ cube_size[2])
+ return cubes, grids
+
+ def forward(self, heatmaps, meta, grid_size, grid_center, cube_size):
+ cubes, grids = self.get_voxel(heatmaps, meta, grid_size, grid_center,
+ cube_size)
+ return cubes, grids
+
+
+@POSENETS.register_module()
+class VoxelPose(BasePose):
+ """VoxelPose Please refer to the `paper `
+ for details.
+
+ Args:
+ detector_2d (ConfigDict): Dictionary to construct the 2D pose detector
+ space_3d (ConfigDict): Dictionary that contains 3D space information
+ space_size (list): Size of the 3D space
+ cube_size (list): Size of the input volume to the center net.
+ space_center (list): Coordinate of the center of the 3D space
+ sub_space_size (list): Size of the cuboid human proposal.
+ sub_cube_size (list): Size of the input volume to the pose net.
+ project_layer (ConfigDict): Dictionary to construct the project layer.
+ center_net (ConfigDict): Dictionary to construct the center net.
+ center_head (ConfigDict): Dictionary to construct the center head.
+ pose_net (ConfigDict): Dictionary to construct the pose net.
+ pose_head (ConfigDict): Dictionary to construct the pose head.
+ train_cfg (ConfigDict): Config for training. Default: None.
+ test_cfg (ConfigDict): Config for testing. Default: None.
+ pretrained (str): Path to the pretrained 2D model. Default: None.
+ freeze_2d (bool): Whether to freeze the 2D model in training.
+ Default: True.
+ """
+
+ def __init__(self,
+ detector_2d,
+ space_3d,
+ project_layer,
+ center_net,
+ center_head,
+ pose_net,
+ pose_head,
+ train_cfg=None,
+ test_cfg=None,
+ pretrained=None,
+ freeze_2d=True):
+ super(VoxelPose, self).__init__()
+
+ self.backbone = builder.build_posenet(detector_2d)
+ if self.training and pretrained is not None:
+ load_checkpoint(self.backbone, pretrained)
+
+ self.freeze_2d = freeze_2d
+ self.project_layer = ProjectLayer(project_layer)
+ self.root_net = CuboidProposalNet(center_net)
+ self.center_head = builder.build_head(center_head)
+ self.pose_net = PoseRegressionNet(pose_net)
+ self.pose_head = builder.build_head(pose_head)
+
+ self.space_size = space_3d['space_size']
+ self.cube_size = space_3d['cube_size']
+ self.space_center = space_3d['space_center']
+
+ self.sub_space_size = space_3d['sub_space_size']
+ self.sub_cube_size = space_3d['sub_cube_size']
+
+ self.num_joints = pose_net['output_channels']
+
+ self.train_cfg = train_cfg
+ self.test_cfg = test_cfg
+
+ @staticmethod
+ def _freeze(model):
+ """Freeze parameters."""
+ model.eval()
+ for param in model.parameters():
+ param.requires_grad = False
+
+ def train(self, mode=True):
+ """Sets the module in training mode.
+ Args:
+ mode (bool): whether to set training mode (``True``)
+ or evaluation mode (``False``). Default: ``True``.
+
+ Returns:
+ Module: self
+ """
+ super().train(mode)
+ if mode and self.freeze_2d:
+ self._freeze(self.backbone)
+
+ return self
+
+ def forward(self,
+ img,
+ img_metas,
+ return_loss=True,
+ targets=None,
+ masks=None,
+ targets_3d=None,
+ input_heatmaps=None,
+ **kwargs):
+ """
+ Note:
+ batch_size: N
+ num_keypoints: K
+ num_img_channel: C
+ img_width: imgW
+ img_height: imgH
+ heatmaps width: W
+ heatmaps height: H
+ volume_length: cubeL
+ volume_width: cubeW
+ volume_height: cubeH
+
+ Args:
+ img (list(torch.Tensor[NxCximgHximgW])):
+ Multi-camera input images to the 2D model.
+ img_metas (list(dict)):
+ Information about image, 3D groundtruth and camera parameters.
+ return_loss: Option to `return loss`. `return loss=True`
+ for training, `return loss=False` for validation & test.
+ targets (list(torch.Tensor[NxKxHxW])):
+ Multi-camera target heatmaps of the 2D model.
+ masks (list(torch.Tensor[NxHxW])):
+ Multi-camera masks of the input to the 2D model.
+ targets_3d (torch.Tensor[NxcubeLxcubeWxcubeH]):
+ Ground-truth 3D heatmap of human centers.
+ input_heatmaps (list(torch.Tensor[NxKxHxW])):
+ Multi-camera heatmaps when the 2D model is not available.
+ Default: None.
+ **kwargs:
+
+ Returns:
+ dict: if 'return_loss' is true, then return losses.
+ Otherwise, return predicted poses, human centers and sample_id
+
+ """
+ if return_loss:
+ return self.forward_train(img, img_metas, targets, masks,
+ targets_3d, input_heatmaps)
+ else:
+ return self.forward_test(img, img_metas, input_heatmaps)
+
+ def train_step(self, data_batch, optimizer, **kwargs):
+ """The iteration step during training.
+
+ This method defines an iteration step during training, except for the
+ back propagation and optimizer updating, which are done in an optimizer
+ hook. Note that in some complicated cases or models, the whole process
+ including back propagation and optimizer updating is also defined in
+ this method, such as GAN.
+
+ Args:
+ data_batch (dict): The output of dataloader.
+ optimizer (:obj:`torch.optim.Optimizer` | dict): The optimizer of
+ runner is passed to ``train_step()``. This argument is unused
+ and reserved.
+
+ Returns:
+ dict: It should contain at least 3 keys: ``loss``, ``log_vars``,
+ ``num_samples``.
+ ``loss`` is a tensor for back propagation, which can be a
+ weighted sum of multiple losses.
+ ``log_vars`` contains all the variables to be sent to the
+ logger.
+ ``num_samples`` indicates the batch size (when the model is
+ DDP, it means the batch size on each GPU), which is used for
+ averaging the logs.
+ """
+ losses = self.forward(**data_batch)
+
+ loss, log_vars = self._parse_losses(losses)
+ batch_size = data_batch['img'][0].shape[0]
+ outputs = dict(loss=loss, log_vars=log_vars, num_samples=batch_size)
+
+ return outputs
+
+ def assign2gt(self, center_candidates, gt_centers, gt_num_persons):
+ """"Assign gt id to each valid human center candidate."""
+ det_centers = center_candidates[..., :3]
+ batch_size = center_candidates.shape[0]
+ cand_num = center_candidates.shape[1]
+ cand2gt = torch.zeros(batch_size, cand_num)
+
+ for i in range(batch_size):
+ cand = det_centers[i].view(cand_num, 1, -1)
+ gt = gt_centers[None, i, :gt_num_persons[i]]
+
+ dist = torch.sqrt(torch.sum((cand - gt)**2, dim=-1))
+ min_dist, min_gt = torch.min(dist, dim=-1)
+
+ cand2gt[i] = min_gt
+ cand2gt[i][min_dist > self.train_cfg['dist_threshold']] = -1.0
+
+ center_candidates[:, :, 3] = cand2gt
+
+ return center_candidates
+
+ def forward_train(self,
+ img,
+ img_metas,
+ targets=None,
+ masks=None,
+ targets_3d=None,
+ input_heatmaps=None):
+ """
+ Note:
+ batch_size: N
+ num_keypoints: K
+ num_img_channel: C
+ img_width: imgW
+ img_height: imgH
+ heatmaps width: W
+ heatmaps height: H
+ volume_length: cubeL
+ volume_width: cubeW
+ volume_height: cubeH
+
+ Args:
+ img (list(torch.Tensor[NxCximgHximgW])):
+ Multi-camera input images to the 2D model.
+ img_metas (list(dict)):
+ Information about image, 3D groundtruth and camera parameters.
+ targets (list(torch.Tensor[NxKxHxW])):
+ Multi-camera target heatmaps of the 2D model.
+ masks (list(torch.Tensor[NxHxW])):
+ Multi-camera masks of the input to the 2D model.
+ targets_3d (torch.Tensor[NxcubeLxcubeWxcubeH]):
+ Ground-truth 3D heatmap of human centers.
+ input_heatmaps (list(torch.Tensor[NxKxHxW])):
+ Multi-camera heatmaps when the 2D model is not available.
+ Default: None.
+
+ Returns:
+ dict: losses.
+
+ """
+ if self.backbone is None:
+ assert input_heatmaps is not None
+ heatmaps = []
+ for input_heatmap in input_heatmaps:
+ heatmaps.append(input_heatmap[0])
+ else:
+ heatmaps = []
+ assert isinstance(img, list)
+ for img_ in img:
+ heatmaps.append(self.backbone.forward_dummy(img_)[0])
+ initial_cubes, _ = self.project_layer(heatmaps, img_metas,
+ self.space_size,
+ [self.space_center],
+ self.cube_size)
+ center_heatmaps_3d = self.root_net(initial_cubes)
+ num_joints = initial_cubes.shape[1]
+ center_heatmaps_3d = center_heatmaps_3d.squeeze(1)
+ center_candidates = self.center_head(center_heatmaps_3d)
+
+ device = center_candidates.device
+
+ gt_centers = torch.stack([
+ torch.tensor(img_meta['roots_3d'], device=device)
+ for img_meta in img_metas
+ ])
+ gt_num_persons = torch.stack([
+ torch.tensor(img_meta['num_persons'], device=device)
+ for img_meta in img_metas
+ ])
+ center_candidates = self.assign2gt(center_candidates, gt_centers,
+ gt_num_persons)
+ gt_3d = torch.stack([
+ torch.tensor(img_meta['joints_3d'], device=device)
+ for img_meta in img_metas
+ ])
+ gt_3d_vis = torch.stack([
+ torch.tensor(img_meta['joints_3d_visible'], device=device)
+ for img_meta in img_metas
+ ])
+ valid_preds = []
+ valid_targets = []
+ valid_weights = []
+
+ batch_size, num_candidates, _ = center_candidates.shape
+ pred = center_candidates.new_zeros(batch_size, num_candidates,
+ self.num_joints, 5)
+ pred[:, :, :, 3:] = center_candidates[:, :, None, 3:]
+
+ for n in range(num_candidates):
+ index = pred[:, n, 0, 3] >= 0
+ num_valid = index.sum()
+ if num_valid > 0:
+ pose_input_cube, coordinates \
+ = self.project_layer(heatmaps,
+ img_metas,
+ self.sub_space_size,
+ center_candidates[:, n, :3],
+ self.sub_cube_size)
+ pose_heatmaps_3d = self.pose_net(pose_input_cube)
+ pose_3d = self.pose_head(pose_heatmaps_3d[index],
+ coordinates[index])
+
+ pred[index, n, :, 0:3] = pose_3d.detach()
+ valid_targets.append(gt_3d[index, pred[index, n, 0, 3].long()])
+ valid_weights.append(gt_3d_vis[index, pred[index, n, 0,
+ 3].long(), :,
+ 0:1].float())
+ valid_preds.append(pose_3d)
+
+ losses = dict()
+ losses.update(
+ self.center_head.get_loss(center_heatmaps_3d, targets_3d))
+ if len(valid_preds) > 0:
+ valid_targets = torch.cat(valid_targets, dim=0)
+ valid_weights = torch.cat(valid_weights, dim=0)
+ valid_preds = torch.cat(valid_preds, dim=0)
+ losses.update(
+ self.pose_head.get_loss(valid_preds, valid_targets,
+ valid_weights))
+ else:
+ pose_input_cube = initial_cubes.new_zeros(batch_size, num_joints,
+ *self.sub_cube_size)
+ coordinates = initial_cubes.new_zeros(batch_size,
+ *self.sub_cube_size,
+ 3).view(batch_size, -1, 3)
+ pseudo_targets = initial_cubes.new_zeros(batch_size, num_joints, 3)
+ pseudo_weights = initial_cubes.new_zeros(batch_size, num_joints, 1)
+ pose_heatmaps_3d = self.pose_net(pose_input_cube)
+ pose_3d = self.pose_head(pose_heatmaps_3d, coordinates)
+ losses.update(
+ self.pose_head.get_loss(pose_3d, pseudo_targets,
+ pseudo_weights))
+
+ if not self.freeze_2d:
+ losses_2d = {}
+ heatmaps_tensor = torch.cat(heatmaps, dim=0)
+ targets_tensor = torch.cat(targets, dim=0)
+ masks_tensor = torch.cat(masks, dim=0)
+ losses_2d_ = self.backbone.get_loss(heatmaps_tensor,
+ targets_tensor, masks_tensor)
+ for k, v in losses_2d_.items():
+ losses_2d[k + '_2d'] = v
+ losses.update(losses_2d)
+
+ return losses
+
+ def forward_test(
+ self,
+ img,
+ img_metas,
+ input_heatmaps=None,
+ ):
+ """
+ Note:
+ batch_size: N
+ num_keypoints: K
+ num_img_channel: C
+ img_width: imgW
+ img_height: imgH
+ heatmaps width: W
+ heatmaps height: H
+ volume_length: cubeL
+ volume_width: cubeW
+ volume_height: cubeH
+
+ Args:
+ img (list(torch.Tensor[NxCximgHximgW])):
+ Multi-camera input images to the 2D model.
+ img_metas (list(dict)):
+ Information about image, 3D groundtruth and camera parameters.
+ input_heatmaps (list(torch.Tensor[NxKxHxW])):
+ Multi-camera heatmaps when the 2D model is not available.
+ Default: None.
+
+ Returns:
+ dict: predicted poses, human centers and sample_id
+
+ """
+ if self.backbone is None:
+ assert input_heatmaps is not None
+ heatmaps = []
+ for input_heatmap in input_heatmaps:
+ heatmaps.append(input_heatmap[0])
+ else:
+ heatmaps = []
+ assert isinstance(img, list)
+ for img_ in img:
+ heatmaps.append(self.backbone.forward_dummy(img_)[0])
+ initial_cubes, _ = self.project_layer(heatmaps, img_metas,
+ self.space_size,
+ [self.space_center],
+ self.cube_size)
+ center_heatmaps_3d = self.root_net(initial_cubes)
+ center_heatmaps_3d = center_heatmaps_3d.squeeze(1)
+ center_candidates = self.center_head(center_heatmaps_3d)
+
+ center_candidates[..., 3] = \
+ (center_candidates[..., 4] >
+ self.test_cfg['center_threshold']).float() - 1.0
+
+ batch_size, num_candidates, _ = center_candidates.shape
+ pred = center_candidates.new_zeros(batch_size, num_candidates,
+ self.num_joints, 5)
+ pred[:, :, :, 3:] = center_candidates[:, :, None, 3:]
+
+ for n in range(num_candidates):
+ index = pred[:, n, 0, 3] >= 0
+ num_valid = index.sum()
+ if num_valid > 0:
+ pose_input_cube, coordinates \
+ = self.project_layer(heatmaps,
+ img_metas,
+ self.sub_space_size,
+ center_candidates[:, n, :3],
+ self.sub_cube_size)
+ pose_heatmaps_3d = self.pose_net(pose_input_cube)
+ pose_3d = self.pose_head(pose_heatmaps_3d[index],
+ coordinates[index])
+
+ pred[index, n, :, 0:3] = pose_3d.detach()
+
+ result = {}
+ result['pose_3d'] = pred.cpu().numpy()
+ result['center_3d'] = center_candidates.cpu().numpy()
+ result['sample_id'] = [img_meta['sample_id'] for img_meta in img_metas]
+
+ return result
+
+ def show_result(self, **kwargs):
+ """Visualize the results."""
+ raise NotImplementedError
+
+ def forward_dummy(self, img, input_heatmaps=None, num_candidates=5):
+ """Used for computing network FLOPs."""
+ if self.backbone is None:
+ assert input_heatmaps is not None
+ heatmaps = []
+ for input_heatmap in input_heatmaps:
+ heatmaps.append(input_heatmap[0])
+ else:
+ heatmaps = []
+ assert isinstance(img, list)
+ for img_ in img:
+ heatmaps.append(self.backbone.forward_dummy(img_)[0])
+ batch_size, num_channels, _, _ = heatmaps[0].shape
+ initial_cubes = heatmaps[0].new_zeros(batch_size, num_channels,
+ *self.cube_size)
+ _ = self.root_net(initial_cubes)
+ pose_input_cube = heatmaps[0].new_zeros(batch_size, num_channels,
+ *self.sub_cube_size)
+ for n in range(num_candidates):
+ _ = self.pose_net(pose_input_cube)
diff --git a/mmpose/models/heads/__init__.py b/mmpose/models/heads/__init__.py
index 9c448e94ac..a98e91140e 100644
--- a/mmpose/models/heads/__init__.py
+++ b/mmpose/models/heads/__init__.py
@@ -12,11 +12,13 @@
TopdownHeatmapMultiStageHead)
from .topdown_heatmap_simple_head import TopdownHeatmapSimpleHead
from .vipnas_heatmap_simple_head import ViPNASHeatmapSimpleHead
+from .voxelpose_head import CuboidCenterHead, CuboidPoseHead
__all__ = [
'TopdownHeatmapSimpleHead', 'TopdownHeatmapMultiStageHead',
'TopdownHeatmapMSMUHead', 'TopdownHeatmapBaseHead',
'AEHigherResolutionHead', 'AESimpleHead', 'AEMultiStageHead',
'DeepposeRegressionHead', 'TemporalRegressionHead', 'Interhand3DHead',
- 'HMRMeshHead', 'DeconvHead', 'ViPNASHeatmapSimpleHead'
+ 'HMRMeshHead', 'DeconvHead', 'ViPNASHeatmapSimpleHead', 'CuboidCenterHead',
+ 'CuboidPoseHead'
]
diff --git a/mmpose/models/heads/ae_higher_resolution_head.py b/mmpose/models/heads/ae_higher_resolution_head.py
index 8e8a734bcb..9bf3399cb6 100644
--- a/mmpose/models/heads/ae_higher_resolution_head.py
+++ b/mmpose/models/heads/ae_higher_resolution_head.py
@@ -21,7 +21,7 @@ class AEHigherResolutionHead(nn.Module):
tag_per_joint (bool): If tag_per_joint is True,
the dimension of tags equals to num_joints,
else the dimension of tags is 1. Default: True
- extra:
+ extra (dict): Configs for extra conv layers. Default: None
num_deconv_layers (int): Number of deconv layers.
num_deconv_layers should >= 0. Note that 0 means
no deconv layers.
@@ -176,18 +176,18 @@ def get_loss(self, outputs, targets, masks, joints):
"""Calculate bottom-up keypoint loss.
Note:
- batch_size: N
- num_keypoints: K
- num_outputs: O
- heatmaps height: H
- heatmaps weight: W
+ - batch_size: N
+ - num_keypoints: K
+ - num_outputs: O
+ - heatmaps height: H
+ - heatmaps weight: W
Args:
- outputs (List(torch.Tensor[NxKxHxW])): Multi-scale output heatmaps.
- targets (List(torch.Tensor[NxKxHxW])): Multi-scale target heatmaps.
- masks (List(torch.Tensor[NxHxW])): Masks of multi-scale target
+ outputs (list(torch.Tensor[N,K,H,W])): Multi-scale output heatmaps.
+ targets (List(torch.Tensor[N,K,H,W])): Multi-scale target heatmaps.
+ masks (List(torch.Tensor[N,H,W])): Masks of multi-scale target
heatmaps
- joints (List(torch.Tensor[NxMxKx2])): Joints of multi-scale target
+ joints (List(torch.Tensor[N,M,K,2])): Joints of multi-scale target
heatmaps for ae loss
"""
diff --git a/mmpose/models/heads/ae_multi_stage_head.py b/mmpose/models/heads/ae_multi_stage_head.py
index e3b9358822..195666b27e 100644
--- a/mmpose/models/heads/ae_multi_stage_head.py
+++ b/mmpose/models/heads/ae_multi_stage_head.py
@@ -96,10 +96,10 @@ def get_loss(self, output, targets, masks, joints):
"""Calculate bottom-up keypoint loss.
Note:
- batch_size: N
- num_keypoints: K
- heatmaps height: H
- heatmaps weight: W
+ - batch_size: N
+ - num_keypoints: K
+ - heatmaps height: H
+ - heatmaps weight: W
Args:
output (List(torch.Tensor[NxKxHxW])): Output heatmaps.
diff --git a/mmpose/models/heads/ae_simple_head.py b/mmpose/models/heads/ae_simple_head.py
index 39654fa093..9297f71fd3 100644
--- a/mmpose/models/heads/ae_simple_head.py
+++ b/mmpose/models/heads/ae_simple_head.py
@@ -56,18 +56,18 @@ def get_loss(self, outputs, targets, masks, joints):
"""Calculate bottom-up keypoint loss.
Note:
- batch_size: N
- num_keypoints: K
- num_outputs: O
- heatmaps height: H
- heatmaps weight: W
+ - batch_size: N
+ - num_keypoints: K
+ - num_outputs: O
+ - heatmaps height: H
+ - heatmaps weight: W
Args:
- outputs (list(torch.Tensor[NxKxHxW])): Multi-scale output heatmaps.
- targets (List(torch.Tensor[NxKxHxW])): Multi-scale target heatmaps.
- masks (List(torch.Tensor[NxHxW])): Masks of multi-scale target
+ outputs (list(torch.Tensor[N,K,H,W])): Multi-scale output heatmaps.
+ targets (List(torch.Tensor[N,K,H,W])): Multi-scale target heatmaps.
+ masks (List(torch.Tensor[N,H,W])): Masks of multi-scale target
heatmaps
- joints(List(torch.Tensor[NxMxKx2])): Joints of multi-scale target
+ joints(List(torch.Tensor[N,M,K,2])): Joints of multi-scale target
heatmaps for ae loss
"""
diff --git a/mmpose/models/heads/deconv_head.py b/mmpose/models/heads/deconv_head.py
index a8b8fbecba..90846d27af 100644
--- a/mmpose/models/heads/deconv_head.py
+++ b/mmpose/models/heads/deconv_head.py
@@ -24,13 +24,14 @@ class DeconvHead(nn.Module):
in_index (int|Sequence[int]): Input feature index. Default: 0
input_transform (str|None): Transformation type of input features.
Options: 'resize_concat', 'multiple_select', None.
- 'resize_concat': Multiple feature maps will be resized to the
+ Default: None.
+
+ - 'resize_concat': Multiple feature maps will be resized to the
same size as the first one and then concat together.
Usually used in FCN head of HRNet.
- 'multiple_select': Multiple feature maps will be bundle into
+ - 'multiple_select': Multiple feature maps will be bundle into
a list and passed into decode head.
- None: Only one select feature map is allowed.
- Default: None.
+ - None: Only one select feature map is allowed.
align_corners (bool): align_corners argument of F.interpolate.
Default: False.
loss_keypoint (dict): Config for loss. Default: None.
@@ -139,12 +140,13 @@ def _init_inputs(self, in_channels, in_index, input_transform):
in_index (int|Sequence[int]): Input feature index.
input_transform (str|None): Transformation type of input features.
Options: 'resize_concat', 'multiple_select', None.
- 'resize_concat': Multiple feature maps will be resize to the
+
+ - 'resize_concat': Multiple feature maps will be resize to the
same size as first one and than concat together.
Usually used in FCN head of HRNet.
- 'multiple_select': Multiple feature maps will be bundle into
+ - 'multiple_select': Multiple feature maps will be bundle into
a list and passed into decode head.
- None: Only one select feature map is allowed.
+ - None: Only one select feature map is allowed.
"""
if input_transform is not None:
@@ -247,15 +249,15 @@ def get_loss(self, outputs, targets, masks):
"""Calculate bottom-up masked mse loss.
Note:
- batch_size: N
- num_channels: C
- heatmaps height: H
- heatmaps weight: W
+ - batch_size: N
+ - num_channels: C
+ - heatmaps height: H
+ - heatmaps weight: W
Args:
- outputs (List(torch.Tensor[NxCxHxW])): Multi-scale outputs.
- targets (List(torch.Tensor[NxCxHxW])): Multi-scale targets.
- masks (List(torch.Tensor[NxHxW])): Masks of multi-scale targets.
+ outputs (List(torch.Tensor[N,C,H,W])): Multi-scale outputs.
+ targets (List(torch.Tensor[N,C,H,W])): Multi-scale targets.
+ masks (List(torch.Tensor[N,H,W])): Masks of multi-scale targets.
"""
losses = dict()
diff --git a/mmpose/models/heads/deeppose_regression_head.py b/mmpose/models/heads/deeppose_regression_head.py
index f1b8e5d092..f326e26fa6 100644
--- a/mmpose/models/heads/deeppose_regression_head.py
+++ b/mmpose/models/heads/deeppose_regression_head.py
@@ -13,8 +13,7 @@
class DeepposeRegressionHead(nn.Module):
"""Deeppose regression head with fully connected layers.
- paper ref: Alexander Toshev and Christian Szegedy,
- ``DeepPose: Human Pose Estimation via Deep Neural Networks.''.
+ "DeepPose: Human Pose Estimation via Deep Neural Networks".
Args:
in_channels (int): Number of input channels
@@ -50,8 +49,8 @@ def get_loss(self, output, target, target_weight):
"""Calculate top-down keypoint loss.
Note:
- batch_size: N
- num_keypoints: K
+ - batch_size: N
+ - num_keypoints: K
Args:
output (torch.Tensor[N, K, 2]): Output keypoints.
@@ -71,8 +70,8 @@ def get_accuracy(self, output, target, target_weight):
"""Calculate accuracy for top-down keypoint loss.
Note:
- batch_size: N
- num_keypoints: K
+ - batch_size: N
+ - num_keypoints: K
Args:
output (torch.Tensor[N, K, 2]): Output keypoints.
@@ -121,6 +120,7 @@ def decode(self, img_metas, output, **kwargs):
Args:
img_metas (list(dict)): Information about data augmentation
By default this includes:
+
- "image_file: path to the image file
- "center": center of the bbox
- "scale": scale of the bbox
diff --git a/mmpose/models/heads/hmr_head.py b/mmpose/models/heads/hmr_head.py
index c74cd8e5b4..015a3076bc 100644
--- a/mmpose/models/heads/hmr_head.py
+++ b/mmpose/models/heads/hmr_head.py
@@ -10,8 +10,8 @@
@HEADS.register_module()
class HMRMeshHead(nn.Module):
- """SMPL parameters regressor head of simple baseline paper
- ref: Angjoo Kanazawa. ``End-to-end Recovery of Human Shape and Pose''.
+ """SMPL parameters regressor head of simple baseline. "End-to-end Recovery
+ of Human Shape and Pose", CVPR'2018.
Args:
in_channels (int): Number of input channels
diff --git a/mmpose/models/heads/interhand_3d_head.py b/mmpose/models/heads/interhand_3d_head.py
index 3615c60931..aebe4a5f61 100644
--- a/mmpose/models/heads/interhand_3d_head.py
+++ b/mmpose/models/heads/interhand_3d_head.py
@@ -285,16 +285,16 @@ class Interhand3DHead(nn.Module):
Args:
keypoint_head_cfg (dict): Configs of Heatmap3DHead for hand
- keypoint estimation.
+ keypoint estimation.
root_head_cfg (dict): Configs of Heatmap1DHead for relative
- hand root depth estimation.
+ hand root depth estimation.
hand_type_head_cfg (dict): Configs of MultilabelClassificationHead
- for hand type classification.
+ for hand type classification.
loss_keypoint (dict): Config for keypoint loss. Default: None.
loss_root_depth (dict): Config for relative root depth loss.
- Default: None.
+ Default: None.
loss_hand_type (dict): Config for hand type classification
- loss. Default: None.
+ loss. Default: None.
"""
def __init__(self,
@@ -338,7 +338,7 @@ def get_loss(self, output, target, target_weight):
output (list[Tensor]): a list of outputs from multiple heads.
target (list[Tensor]): a list of targets for multiple heads.
target_weight (list[Tensor]): a list of targets weight for
- multiple heads.
+ multiple heads.
"""
losses = dict()
@@ -369,7 +369,7 @@ def get_accuracy(self, output, target, target_weight):
output (list[Tensor]): a list of outputs from multiple heads.
target (list[Tensor]): a list of targets for multiple heads.
target_weight (list[Tensor]): a list of targets weight for
- multiple heads.
+ multiple heads.
"""
accuracy = dict()
avg_acc = multilabel_classification_accuracy(
@@ -399,7 +399,7 @@ def inference_model(self, x, flip_pairs=None):
heatmaps, relative root depth and hand type.
Args:
- x (torch.Tensor[NxKxHxW]): Input features.
+ x (torch.Tensor[N,K,H,W]): Input features.
flip_pairs (None | list[tuple()):
Pairs of keypoints which are mirrored.
"""
@@ -446,18 +446,18 @@ def decode(self, img_metas, output, **kwargs):
Args:
img_metas (list(dict)): Information about data augmentation
By default this includes:
+
- "image_file: path to the image file
- "center": center of the bbox
- "scale": scale of the bbox
- "rotation": rotation of the bbox
- "bbox_score": score of bbox
- "heatmap3d_depth_bound": depth bound of hand keypoint
- 3D heatmap
+ 3D heatmap
- "root_depth_bound": depth bound of relative root depth
- 1D heatmap
-
+ 1D heatmap
output (list[np.ndarray]): model predicted 3D heatmaps, relative
- root depth and hand type.
+ root depth and hand type.
"""
batch_size = len(img_metas)
diff --git a/mmpose/models/heads/temporal_regression_head.py b/mmpose/models/heads/temporal_regression_head.py
index 6e24778c27..97a07f9cf2 100644
--- a/mmpose/models/heads/temporal_regression_head.py
+++ b/mmpose/models/heads/temporal_regression_head.py
@@ -13,15 +13,14 @@
class TemporalRegressionHead(nn.Module):
"""Regression head of VideoPose3D.
- Paper ref: Dario Pavllo.
- ``3D human pose estimation in video with temporal convolutions and
- semi-supervised training``
-
- Args:
- in_channels (int): Number of input channels
- num_joints (int): Number of joints
- loss_keypoint (dict): Config for keypoint loss. Default: None.
- max_norm (float|None): if not None, the weight of convolution layers
+ "3D human pose estimation in video with temporal convolutions and
+ semi-supervised training", CVPR'2019.
+
+ Args:
+ in_channels (int): Number of input channels
+ num_joints (int): Number of joints
+ loss_keypoint (dict): Config for keypoint loss. Default: None.
+ max_norm (float|None): if not None, the weight of convolution layers
will be clipped to have a maximum norm of max_norm.
is_trajectory (bool): If the model only predicts root joint
position, then this arg should be set to True. In this case,
@@ -65,7 +64,7 @@ def _transform_inputs(x):
"""Transform inputs for decoder.
Args:
- inputs (tuple/list of Tensor | Tensor): multi-level features.
+ inputs (tuple or list of Tensor | Tensor): multi-level features.
Returns:
Tensor: The transformed inputs
@@ -91,8 +90,8 @@ def get_loss(self, output, target, target_weight):
"""Calculate keypoint loss.
Note:
- batch_size: N
- num_keypoints: K
+ - batch_size: N
+ - num_keypoints: K
Args:
output (torch.Tensor[N, K, 3]): Output keypoints.
@@ -130,8 +129,8 @@ def get_accuracy(self, output, target, target_weight, metas):
"""Calculate accuracy for keypoint loss.
Note:
- batch_size: N
- num_keypoints: K
+ - batch_size: N
+ - num_keypoints: K
Args:
output (torch.Tensor[N, K, 3]): Output keypoints.
@@ -139,6 +138,7 @@ def get_accuracy(self, output, target, target_weight, metas):
target_weight (torch.Tensor[N, K, 3]):
Weights across different joint types.
metas (list(dict)): Information about data augmentation including:
+
- target_image_path (str): Optional, path to the image file
- target_mean (float): Optional, normalization parameter of
the target pose.
@@ -228,9 +228,11 @@ def decode(self, metas, output):
Args:
metas (list(dict)): Information about data augmentation.
By default this includes:
+
- "target_image_path": path to the image file
output (np.ndarray[N, K, 3]): predicted regression vector.
metas (list(dict)): Information about data augmentation including:
+
- target_image_path (str): Optional, path to the image file
- target_mean (float): Optional, normalization parameter of
the target pose.
diff --git a/mmpose/models/heads/topdown_heatmap_base_head.py b/mmpose/models/heads/topdown_heatmap_base_head.py
index 75e591e19c..09646ead35 100644
--- a/mmpose/models/heads/topdown_heatmap_base_head.py
+++ b/mmpose/models/heads/topdown_heatmap_base_head.py
@@ -43,6 +43,7 @@ def decode(self, img_metas, output, **kwargs):
Args:
img_metas (list(dict)): Information about data augmentation
By default this includes:
+
- "image_file: path to the image file
- "center": center of the bbox
- "scale": scale of the bbox
diff --git a/mmpose/models/heads/topdown_heatmap_multi_stage_head.py b/mmpose/models/heads/topdown_heatmap_multi_stage_head.py
index f9410abb77..c439f5b633 100644
--- a/mmpose/models/heads/topdown_heatmap_multi_stage_head.py
+++ b/mmpose/models/heads/topdown_heatmap_multi_stage_head.py
@@ -110,18 +110,18 @@ def get_loss(self, output, target, target_weight):
"""Calculate top-down keypoint loss.
Note:
- batch_size: N
- num_keypoints: K
- num_outputs: O
- heatmaps height: H
- heatmaps weight: W
+ - batch_size: N
+ - num_keypoints: K
+ - num_outputs: O
+ - heatmaps height: H
+ - heatmaps weight: W
Args:
- output (torch.Tensor[NxKxHxW]):
+ output (torch.Tensor[N,K,H,W]):
Output heatmaps.
- target (torch.Tensor[NxKxHxW]):
+ target (torch.Tensor[N,K,H,W]):
Target heatmaps.
- target_weight (torch.Tensor[NxKx1]):
+ target_weight (torch.Tensor[N,K,1]):
Weights across different joint types.
"""
@@ -140,10 +140,10 @@ def get_loss(self, output, target, target_weight):
else:
loss_func = self.loss
loss_i = loss_func(output[i], target_i, target_weight_i)
- if 'mse_loss' not in losses:
- losses['mse_loss'] = loss_i
+ if 'heatmap_loss' not in losses:
+ losses['heatmap_loss'] = loss_i
else:
- losses['mse_loss'] += loss_i
+ losses['heatmap_loss'] += loss_i
return losses
@@ -151,15 +151,15 @@ def get_accuracy(self, output, target, target_weight):
"""Calculate accuracy for top-down keypoint loss.
Note:
- batch_size: N
- num_keypoints: K
- heatmaps height: H
- heatmaps weight: W
+ - batch_size: N
+ - num_keypoints: K
+ - heatmaps height: H
+ - heatmaps weight: W
Args:
- output (torch.Tensor[NxKxHxW]): Output heatmaps.
- target (torch.Tensor[NxKxHxW]): Target heatmaps.
- target_weight (torch.Tensor[NxKx1]):
+ output (torch.Tensor[N,K,H,W]): Output heatmaps.
+ target (torch.Tensor[N,K,H,W]): Target heatmaps.
+ target_weight (torch.Tensor[N,K,1]):
Weights across different joint types.
"""
@@ -320,8 +320,9 @@ def forward(self, feature):
class PRM(nn.Module):
"""Pose Refine Machine.
- For more details about PRM, refer to Learning Delicate
- Local Representations for Multi-Person Pose Estimation (ECCV 2020).
+ Please refer to "Learning Delicate Local Representations
+ for Multi-Person Pose Estimation" (ECCV 2020).
+
Args:
out_channels (int): Channel number of the output. Equals to
the number of key points.
@@ -446,16 +447,16 @@ def get_loss(self, output, target, target_weight):
"""Calculate top-down keypoint loss.
Note:
- batch_size: N
- num_keypoints: K
- num_outputs: O
- heatmaps height: H
- heatmaps weight: W
+ - batch_size: N
+ - num_keypoints: K
+ - num_outputs: O
+ - heatmaps height: H
+ - heatmaps weight: W
Args:
- output (torch.Tensor[NxOxKxHxW]): Output heatmaps.
- target (torch.Tensor[NxOxKxHxW]): Target heatmaps.
- target_weight (torch.Tensor[NxOxKx1]):
+ output (torch.Tensor[N,O,K,H,W]): Output heatmaps.
+ target (torch.Tensor[N,O,K,H,W]): Target heatmaps.
+ target_weight (torch.Tensor[N,O,K,1]):
Weights across different joint types.
"""
@@ -477,10 +478,10 @@ def get_loss(self, output, target, target_weight):
loss_func = self.loss
loss_i = loss_func(output[i], target_i, target_weight_i)
- if 'mse_loss' not in losses:
- losses['mse_loss'] = loss_i
+ if 'heatmap_loss' not in losses:
+ losses['heatmap_loss'] = loss_i
else:
- losses['mse_loss'] += loss_i
+ losses['heatmap_loss'] += loss_i
return losses
@@ -488,15 +489,15 @@ def get_accuracy(self, output, target, target_weight):
"""Calculate accuracy for top-down keypoint loss.
Note:
- batch_size: N
- num_keypoints: K
- heatmaps height: H
- heatmaps weight: W
+ - batch_size: N
+ - num_keypoints: K
+ - heatmaps height: H
+ - heatmaps weight: W
Args:
- output (torch.Tensor[NxKxHxW]): Output heatmaps.
- target (torch.Tensor[NxKxHxW]): Target heatmaps.
- target_weight (torch.Tensor[NxKx1]):
+ output (torch.Tensor[N,K,H,W]): Output heatmaps.
+ target (torch.Tensor[N,K,H,W]): Target heatmaps.
+ target_weight (torch.Tensor[N,K,1]):
Weights across different joint types.
"""
@@ -541,8 +542,8 @@ def inference_model(self, x, flip_pairs=None):
output_heatmap (np.ndarray): Output heatmaps.
Args:
- x (List[torch.Tensor[NxKxHxW]]): Input features.
- flip_pairs (None | list[tuple()):
+ x (list[torch.Tensor[N,K,H,W]]): Input features.
+ flip_pairs (None | list[tuple]):
Pairs of keypoints which are mirrored.
"""
output = self.forward(x)
@@ -562,7 +563,6 @@ def inference_model(self, x, flip_pairs=None):
def init_weights(self):
"""Initialize model weights."""
-
for m in self.predict_layers.modules():
if isinstance(m, nn.Conv2d):
kaiming_init(m)
diff --git a/mmpose/models/heads/topdown_heatmap_simple_head.py b/mmpose/models/heads/topdown_heatmap_simple_head.py
index 6ec075cf6c..5ddc058d56 100644
--- a/mmpose/models/heads/topdown_heatmap_simple_head.py
+++ b/mmpose/models/heads/topdown_heatmap_simple_head.py
@@ -32,13 +32,14 @@ class TopdownHeatmapSimpleHead(TopdownHeatmapBaseHead):
in_index (int|Sequence[int]): Input feature index. Default: 0
input_transform (str|None): Transformation type of input features.
Options: 'resize_concat', 'multiple_select', None.
- 'resize_concat': Multiple feature maps will be resized to the
+ Default: None.
+
+ - 'resize_concat': Multiple feature maps will be resized to the
same size as the first one and then concat together.
Usually used in FCN head of HRNet.
- 'multiple_select': Multiple feature maps will be bundle into
+ - 'multiple_select': Multiple feature maps will be bundle into
a list and passed into decode head.
- None: Only one select feature map is allowed.
- Default: None.
+ - None: Only one select feature map is allowed.
align_corners (bool): align_corners argument of F.interpolate.
Default: False.
loss_keypoint (dict): Config for keypoint loss. Default: None.
@@ -143,15 +144,15 @@ def get_loss(self, output, target, target_weight):
"""Calculate top-down keypoint loss.
Note:
- batch_size: N
- num_keypoints: K
- heatmaps height: H
- heatmaps weight: W
+ - batch_size: N
+ - num_keypoints: K
+ - heatmaps height: H
+ - heatmaps weight: W
Args:
- output (torch.Tensor[NxKxHxW]): Output heatmaps.
- target (torch.Tensor[NxKxHxW]): Target heatmaps.
- target_weight (torch.Tensor[NxKx1]):
+ output (torch.Tensor[N,K,H,W]): Output heatmaps.
+ target (torch.Tensor[N,K,H,W]): Target heatmaps.
+ target_weight (torch.Tensor[N,K,1]):
Weights across different joint types.
"""
@@ -159,7 +160,7 @@ def get_loss(self, output, target, target_weight):
assert not isinstance(self.loss, nn.Sequential)
assert target.dim() == 4 and target_weight.dim() == 3
- losses['mse_loss'] = self.loss(output, target, target_weight)
+ losses['heatmap_loss'] = self.loss(output, target, target_weight)
return losses
@@ -167,15 +168,15 @@ def get_accuracy(self, output, target, target_weight):
"""Calculate accuracy for top-down keypoint loss.
Note:
- batch_size: N
- num_keypoints: K
- heatmaps height: H
- heatmaps weight: W
+ - batch_size: N
+ - num_keypoints: K
+ - heatmaps height: H
+ - heatmaps weight: W
Args:
- output (torch.Tensor[NxKxHxW]): Output heatmaps.
- target (torch.Tensor[NxKxHxW]): Target heatmaps.
- target_weight (torch.Tensor[NxKx1]):
+ output (torch.Tensor[N,K,H,W]): Output heatmaps.
+ target (torch.Tensor[N,K,H,W]): Target heatmaps.
+ target_weight (torch.Tensor[N,K,1]):
Weights across different joint types.
"""
@@ -204,8 +205,8 @@ def inference_model(self, x, flip_pairs=None):
output_heatmap (np.ndarray): Output heatmaps.
Args:
- x (torch.Tensor[NxKxHxW]): Input features.
- flip_pairs (None | list[tuple()):
+ x (torch.Tensor[N,K,H,W]): Input features.
+ flip_pairs (None | list[tuple]):
Pairs of keypoints which are mirrored.
"""
output = self.forward(x)
@@ -236,12 +237,13 @@ def _init_inputs(self, in_channels, in_index, input_transform):
in_index (int|Sequence[int]): Input feature index.
input_transform (str|None): Transformation type of input features.
Options: 'resize_concat', 'multiple_select', None.
- 'resize_concat': Multiple feature maps will be resize to the
+
+ - 'resize_concat': Multiple feature maps will be resize to the
same size as first one and than concat together.
Usually used in FCN head of HRNet.
- 'multiple_select': Multiple feature maps will be bundle into
+ - 'multiple_select': Multiple feature maps will be bundle into
a list and passed into decode head.
- None: Only one select feature map is allowed.
+ - None: Only one select feature map is allowed.
"""
if input_transform is not None:
diff --git a/mmpose/models/heads/vipnas_heatmap_simple_head.py b/mmpose/models/heads/vipnas_heatmap_simple_head.py
index 6062cade4f..41703128c4 100644
--- a/mmpose/models/heads/vipnas_heatmap_simple_head.py
+++ b/mmpose/models/heads/vipnas_heatmap_simple_head.py
@@ -36,13 +36,14 @@ class ViPNASHeatmapSimpleHead(TopdownHeatmapBaseHead):
in_index (int|Sequence[int]): Input feature index. Default: -1
input_transform (str|None): Transformation type of input features.
Options: 'resize_concat', 'multiple_select', None.
- 'resize_concat': Multiple feature maps will be resize to the
+ Default: None.
+
+ - 'resize_concat': Multiple feature maps will be resize to the
same size as first one and than concat together.
Usually used in FCN head of HRNet.
- 'multiple_select': Multiple feature maps will be bundle into
+ - 'multiple_select': Multiple feature maps will be bundle into
a list and passed into decode head.
- None: Only one select feature map is allowed.
- Default: None.
+ - None: Only one select feature map is allowed.
align_corners (bool): align_corners argument of F.interpolate.
Default: False.
loss_keypoint (dict): Config for keypoint loss. Default: None.
@@ -146,15 +147,15 @@ def get_loss(self, output, target, target_weight):
"""Calculate top-down keypoint loss.
Note:
- batch_size: N
- num_keypoints: K
- heatmaps height: H
- heatmaps weight: W
+ - batch_size: N
+ - num_keypoints: K
+ - heatmaps height: H
+ - heatmaps weight: W
Args:
- output (torch.Tensor[NxKxHxW]): Output heatmaps.
- target (torch.Tensor[NxKxHxW]): Target heatmaps.
- target_weight (torch.Tensor[NxKx1]):
+ output (torch.Tensor[N,K,H,W]): Output heatmaps.
+ target (torch.Tensor[N,K,H,W]): Target heatmaps.
+ target_weight (torch.Tensor[N,K,1]):
Weights across different joint types.
"""
@@ -162,7 +163,7 @@ def get_loss(self, output, target, target_weight):
assert not isinstance(self.loss, nn.Sequential)
assert target.dim() == 4 and target_weight.dim() == 3
- losses['mse_loss'] = self.loss(output, target, target_weight)
+ losses['heatmap_loss'] = self.loss(output, target, target_weight)
return losses
@@ -170,15 +171,15 @@ def get_accuracy(self, output, target, target_weight):
"""Calculate accuracy for top-down keypoint loss.
Note:
- batch_size: N
- num_keypoints: K
- heatmaps height: H
- heatmaps weight: W
+ - batch_size: N
+ - num_keypoints: K
+ - heatmaps height: H
+ - heatmaps weight: W
Args:
- output (torch.Tensor[NxKxHxW]): Output heatmaps.
- target (torch.Tensor[NxKxHxW]): Target heatmaps.
- target_weight (torch.Tensor[NxKx1]):
+ output (torch.Tensor[N,K,H,W]): Output heatmaps.
+ target (torch.Tensor[N,K,H,W]): Target heatmaps.
+ target_weight (torch.Tensor[N,K,1]):
Weights across different joint types.
"""
@@ -207,8 +208,8 @@ def inference_model(self, x, flip_pairs=None):
output_heatmap (np.ndarray): Output heatmaps.
Args:
- x (torch.Tensor[NxKxHxW]): Input features.
- flip_pairs (None | list[tuple()):
+ x (torch.Tensor[N,K,H,W]): Input features.
+ flip_pairs (None | list[tuple]):
Pairs of keypoints which are mirrored.
"""
output = self.forward(x)
@@ -239,12 +240,13 @@ def _init_inputs(self, in_channels, in_index, input_transform):
in_index (int|Sequence[int]): Input feature index.
input_transform (str|None): Transformation type of input features.
Options: 'resize_concat', 'multiple_select', None.
- 'resize_concat': Multiple feature maps will be resize to the
+
+ - 'resize_concat': Multiple feature maps will be resize to the
same size as first one and than concat together.
Usually used in FCN head of HRNet.
- 'multiple_select': Multiple feature maps will be bundle into
+ - 'multiple_select': Multiple feature maps will be bundle into
a list and passed into decode head.
- None: Only one select feature map is allowed.
+ - None: Only one select feature map is allowed.
"""
if input_transform is not None:
diff --git a/mmpose/models/heads/voxelpose_head.py b/mmpose/models/heads/voxelpose_head.py
new file mode 100644
index 0000000000..8cd849fabb
--- /dev/null
+++ b/mmpose/models/heads/voxelpose_head.py
@@ -0,0 +1,163 @@
+# ------------------------------------------------------------------------------
+# Adapted from
+# https://github.com/microsoft/voxelpose-pytorch/blob/main/lib/models
+# Original licence: MIT License.
+# ------------------------------------------------------------------------------
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ..builder import HEADS
+
+
+@HEADS.register_module()
+class CuboidCenterHead(nn.Module):
+ """Get results from the 3D human center heatmap. In this module, human 3D
+ centers are local maximums obtained from the 3D heatmap via NMS (max-
+ pooling).
+
+ Args:
+ cfg (dict):
+ space_size (list[3]): The size of the 3D space.
+ cube_size (list[3]): The size of the heatmap volume.
+ space_center (list[3]): The coordinate of space center.
+ max_num (int): Maximum of human center detections.
+ max_pool_kernel (int): Kernel size of the max-pool kernel in nms.
+ """
+
+ def __init__(self, cfg):
+ super(CuboidCenterHead, self).__init__()
+ # use register_buffer
+ self.register_buffer('grid_size', torch.tensor(cfg['space_size']))
+ self.register_buffer('cube_size', torch.tensor(cfg['cube_size']))
+ self.register_buffer('grid_center', torch.tensor(cfg['space_center']))
+
+ self.num_candidates = cfg['max_num']
+ self.max_pool_kernel = cfg['max_pool_kernel']
+ self.loss = nn.MSELoss()
+
+ def _get_real_locations(self, indices):
+ """
+ Args:
+ indices (torch.Tensor(NXP)): Indices of points in the 3D tensor
+
+ Returns:
+ real_locations (torch.Tensor(NXPx3)): Locations of points
+ in the world coordinate system
+ """
+ real_locations = indices.float() / (
+ self.cube_size - 1) * self.grid_size + \
+ self.grid_center - self.grid_size / 2.0
+ return real_locations
+
+ def _nms_by_max_pool(self, heatmap_volumes):
+ max_num = self.num_candidates
+ batch_size = heatmap_volumes.shape[0]
+ root_cubes_nms = self._max_pool(heatmap_volumes)
+ root_cubes_nms_reshape = root_cubes_nms.reshape(batch_size, -1)
+ topk_values, topk_index = root_cubes_nms_reshape.topk(max_num)
+ topk_unravel_index = self._get_3d_indices(topk_index,
+ heatmap_volumes[0].shape)
+
+ return topk_values, topk_unravel_index
+
+ def _max_pool(self, inputs):
+ kernel = self.max_pool_kernel
+ padding = (kernel - 1) // 2
+ max = F.max_pool3d(
+ inputs, kernel_size=kernel, stride=1, padding=padding)
+ keep = (inputs == max).float()
+ return keep * inputs
+
+ @staticmethod
+ def _get_3d_indices(indices, shape):
+ """Get indices in the 3-D tensor.
+
+ Args:
+ indices (torch.Tensor(NXp)): Indices of points in the 1D tensor
+ shape (torch.Size(3)): The shape of the original 3D tensor
+
+ Returns:
+ indices: Indices of points in the original 3D tensor
+ """
+ batch_size = indices.shape[0]
+ num_people = indices.shape[1]
+ indices_x = (indices //
+ (shape[1] * shape[2])).reshape(batch_size, num_people, -1)
+ indices_y = ((indices % (shape[1] * shape[2])) //
+ shape[2]).reshape(batch_size, num_people, -1)
+ indices_z = (indices % shape[2]).reshape(batch_size, num_people, -1)
+ indices = torch.cat([indices_x, indices_y, indices_z], dim=2)
+ return indices
+
+ def forward(self, heatmap_volumes):
+ """
+
+ Args:
+ heatmap_volumes (torch.Tensor(NXLXWXH)):
+ 3D human center heatmaps predicted by the network.
+ Returns:
+ human_centers (torch.Tensor(NXPX5)):
+ Coordinates of human centers.
+ """
+ batch_size = heatmap_volumes.shape[0]
+
+ topk_values, topk_unravel_index = self._nms_by_max_pool(
+ heatmap_volumes.detach())
+
+ topk_unravel_index = self._get_real_locations(topk_unravel_index)
+
+ human_centers = torch.zeros(
+ batch_size, self.num_candidates, 5, device=heatmap_volumes.device)
+ human_centers[:, :, 0:3] = topk_unravel_index
+ human_centers[:, :, 4] = topk_values
+
+ return human_centers
+
+ def get_loss(self, pred_cubes, gt):
+
+ return dict(loss_center=self.loss(pred_cubes, gt))
+
+
+@HEADS.register_module()
+class CuboidPoseHead(nn.Module):
+
+ def __init__(self, beta):
+ """Get results from the 3D human pose heatmap. Instead of obtaining
+ maximums on the heatmap, this module regresses the coordinates of
+ keypoints via integral pose regression. Refer to `paper.
+
+ ` for more details.
+
+ Args:
+ beta: Constant to adjust the magnification of soft-maxed heatmap.
+ """
+ super(CuboidPoseHead, self).__init__()
+ self.beta = beta
+ self.loss = nn.L1Loss()
+
+ def forward(self, heatmap_volumes, grid_coordinates):
+ """
+
+ Args:
+ heatmap_volumes (torch.Tensor(NxKxLxWxH)):
+ 3D human pose heatmaps predicted by the network.
+ grid_coordinates (torch.Tensor(Nx(LxWxH)x3)):
+ Coordinates of the grids in the heatmap volumes.
+ Returns:
+ human_poses (torch.Tensor(NxKx3)): Coordinates of human poses.
+ """
+ batch_size = heatmap_volumes.size(0)
+ channel = heatmap_volumes.size(1)
+ x = heatmap_volumes.reshape(batch_size, channel, -1, 1)
+ x = F.softmax(self.beta * x, dim=2)
+ grid_coordinates = grid_coordinates.unsqueeze(1)
+ x = torch.mul(x, grid_coordinates)
+ human_poses = torch.sum(x, dim=2)
+
+ return human_poses
+
+ def get_loss(self, preds, targets, weights):
+
+ return dict(loss_pose=self.loss(preds * weights, targets * weights))
diff --git a/mmpose/models/losses/__init__.py b/mmpose/models/losses/__init__.py
index d284eb420a..d67973fc5c 100644
--- a/mmpose/models/losses/__init__.py
+++ b/mmpose/models/losses/__init__.py
@@ -1,14 +1,16 @@
# Copyright (c) OpenMMLab. All rights reserved.
from .classfication_loss import BCELoss
+from .heatmap_loss import AdaptiveWingLoss
from .mesh_loss import GANLoss, MeshLoss
from .mse_loss import JointsMSELoss, JointsOHKMMSELoss
from .multi_loss_factory import AELoss, HeatmapLoss, MultiLossFactory
from .regression_loss import (BoneLoss, L1Loss, MPJPELoss, MSELoss,
- SemiSupervisionLoss, SmoothL1Loss, WingLoss)
+ SemiSupervisionLoss, SmoothL1Loss, SoftWingLoss,
+ WingLoss)
__all__ = [
'JointsMSELoss', 'JointsOHKMMSELoss', 'HeatmapLoss', 'AELoss',
'MultiLossFactory', 'MeshLoss', 'GANLoss', 'SmoothL1Loss', 'WingLoss',
'MPJPELoss', 'MSELoss', 'L1Loss', 'BCELoss', 'BoneLoss',
- 'SemiSupervisionLoss'
+ 'SemiSupervisionLoss', 'SoftWingLoss', 'AdaptiveWingLoss'
]
diff --git a/mmpose/models/losses/classfication_loss.py b/mmpose/models/losses/classfication_loss.py
index 868d98c645..b79b69d035 100644
--- a/mmpose/models/losses/classfication_loss.py
+++ b/mmpose/models/losses/classfication_loss.py
@@ -19,8 +19,8 @@ def forward(self, output, target, target_weight=None):
"""Forward function.
Note:
- batch_size: N
- num_labels: K
+ - batch_size: N
+ - num_labels: K
Args:
output (torch.Tensor[N, K]): Output classification.
diff --git a/mmpose/models/losses/heatmap_loss.py b/mmpose/models/losses/heatmap_loss.py
new file mode 100644
index 0000000000..9471457ca0
--- /dev/null
+++ b/mmpose/models/losses/heatmap_loss.py
@@ -0,0 +1,86 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+import torch.nn as nn
+
+from ..builder import LOSSES
+
+
+@LOSSES.register_module()
+class AdaptiveWingLoss(nn.Module):
+ """Adaptive wing loss. paper ref: 'Adaptive Wing Loss for Robust Face
+ Alignment via Heatmap Regression' Wang et al. ICCV'2019.
+
+ Args:
+ alpha (float), omega (float), epsilon (float), theta (float)
+ are hyper-parameters.
+ use_target_weight (bool): Option to use weighted MSE loss.
+ Different joint types may have different target weights.
+ loss_weight (float): Weight of the loss. Default: 1.0.
+ """
+
+ def __init__(self,
+ alpha=2.1,
+ omega=14,
+ epsilon=1,
+ theta=0.5,
+ use_target_weight=False,
+ loss_weight=1.):
+ super().__init__()
+ self.alpha = float(alpha)
+ self.omega = float(omega)
+ self.epsilon = float(epsilon)
+ self.theta = float(theta)
+ self.use_target_weight = use_target_weight
+ self.loss_weight = loss_weight
+
+ def criterion(self, pred, target):
+ """Criterion of wingloss.
+
+ Note:
+ batch_size: N
+ num_keypoints: K
+
+ Args:
+ pred (torch.Tensor[NxKxHxW]): Predicted heatmaps.
+ target (torch.Tensor[NxKxHxW]): Target heatmaps.
+ """
+ H, W = pred.shape[2:4]
+ delta = (target - pred).abs()
+
+ A = self.omega * (
+ 1 / (1 + torch.pow(self.theta / self.epsilon, self.alpha - target))
+ ) * (self.alpha - target) * (torch.pow(
+ self.theta / self.epsilon,
+ self.alpha - target - 1)) * (1 / self.epsilon)
+ C = self.theta * A - self.omega * torch.log(
+ 1 + torch.pow(self.theta / self.epsilon, self.alpha - target))
+
+ losses = torch.where(
+ delta < self.theta,
+ self.omega *
+ torch.log(1 +
+ torch.pow(delta / self.epsilon, self.alpha - target)),
+ A * delta - C)
+
+ return torch.mean(losses)
+
+ def forward(self, output, target, target_weight):
+ """Forward function.
+
+ Note:
+ batch_size: N
+ num_keypoints: K
+
+ Args:
+ output (torch.Tensor[NxKxHxW]): Output heatmaps.
+ target (torch.Tensor[NxKxHxW]): Target heatmaps.
+ target_weight (torch.Tensor[NxKx1]):
+ Weights across different joint types.
+ """
+ if self.use_target_weight:
+ loss = self.criterion(output * target_weight.unsqueeze(-1),
+ target * target_weight.unsqueeze(-1))
+ else:
+ loss = self.criterion(output, target)
+
+ return loss * self.loss_weight
diff --git a/mmpose/models/losses/mesh_loss.py b/mmpose/models/losses/mesh_loss.py
index a06394bf30..f9d18bd729 100644
--- a/mmpose/models/losses/mesh_loss.py
+++ b/mmpose/models/losses/mesh_loss.py
@@ -10,9 +10,9 @@ def perspective_projection(points, rotation, translation, focal_length,
camera_center):
"""This function computes the perspective projection of a set of 3D points.
- Notes:
- batch size: B
- point number: N
+ Note:
+ - batch size: B
+ - point number: N
Args:
points (Tensor([B, N, 3])): A set of 3D points
@@ -149,9 +149,9 @@ def project_points(self, points_3d, camera):
"""Perform orthographic projection of 3D points using the camera
parameters, return projected 2D points in image plane.
- Notes:
- batch size: B
- point number: N
+ Note:
+ - batch size: B
+ - point number: N
Args:
points_3d (Tensor([B, N, 3])): 3D points.
@@ -159,7 +159,7 @@ def project_points(self, points_3d, camera):
3 channel as (scale, translation_x, translation_y)
Returns:
- points_2d (Tensor([B, N, 2])): projected 2D points
+ Tensor([B, N, 2]): projected 2D points \
in image space.
"""
batch_size = points_3d.shape[0]
@@ -194,7 +194,7 @@ def forward(self, output, target):
'has_smpl'
Returns:
- losses (dict): dict of losses.
+ dict: dict of losses.
"""
losses = {}
@@ -304,8 +304,8 @@ def get_target_label(self, input, target_is_real):
target_is_real (bool): Whether the target is real or fake.
Returns:
- (bool | Tensor): Target tensor. Return bool for wgan, otherwise,
- return Tensor.
+ (bool | Tensor): Target tensor. Return bool for wgan, \
+ otherwise, return Tensor.
"""
if self.gan_type == 'wgan':
diff --git a/mmpose/models/losses/multi_loss_factory.py b/mmpose/models/losses/multi_loss_factory.py
index 363fbfa028..65f90a761d 100644
--- a/mmpose/models/losses/multi_loss_factory.py
+++ b/mmpose/models/losses/multi_loss_factory.py
@@ -39,17 +39,19 @@ def __init__(self, supervise_empty=True):
self.supervise_empty = supervise_empty
def forward(self, pred, gt, mask):
- """
+ """Forward function.
+
Note:
- batch_size: N
- heatmaps weight: W
- heatmaps height: H
- max_num_people: M
- num_keypoints: K
+ - batch_size: N
+ - heatmaps weight: W
+ - heatmaps height: H
+ - max_num_people: M
+ - num_keypoints: K
+
Args:
- pred (torch.Tensor[NxKxHxW]):heatmap of output.
- gt (torch.Tensor[NxKxHxW]): target heatmap.
- mask (torch.Tensor[NxHxW]): mask of target.
+ pred (torch.Tensor[N,K,H,W]):heatmap of output.
+ gt (torch.Tensor[N,K,H,W]): target heatmap.
+ mask (torch.Tensor[N,H,W]): mask of target.
"""
assert pred.size() == gt.size(
), f'pred.size() is {pred.size()}, gt.size() is {gt.size()}'
@@ -69,7 +71,7 @@ class AELoss(nn.Module):
"""Associative Embedding loss.
`Associative Embedding: End-to-End Learning for Joint Detection and
- Grouping `
+ Grouping `_.
"""
def __init__(self, loss_type):
@@ -80,14 +82,14 @@ def singleTagLoss(self, pred_tag, joints):
"""Associative embedding loss for one image.
Note:
- heatmaps weight: W
- heatmaps height: H
- max_num_people: M
- num_keypoints: K
+ - heatmaps weight: W
+ - heatmaps height: H
+ - max_num_people: M
+ - num_keypoints: K
Args:
- pred_tag (torch.Tensor[(KxHxW)x1]): tag of output for one image.
- joints (torch.Tensor[MxKx2]): joints information for one image.
+ pred_tag (torch.Tensor[KxHxW,1]): tag of output for one image.
+ joints (torch.Tensor[M,K,2]): joints information for one image.
"""
tags = []
pull = 0
@@ -138,15 +140,15 @@ def forward(self, tags, joints):
"""Accumulate the tag loss for each image in the batch.
Note:
- batch_size: N
- heatmaps weight: W
- heatmaps height: H
- max_num_people: M
- num_keypoints: K
+ - batch_size: N
+ - heatmaps weight: W
+ - heatmaps height: H
+ - max_num_people: M
+ - num_keypoints: K
Args:
- tags (torch.Tensor[Nx(KxHxW)x1]): tag channels of output.
- joints (torch.Tensor[NxMxKx2]): joints information.
+ tags (torch.Tensor[N,KxHxW,1]): tag channels of output.
+ joints (torch.Tensor[N,M,K,2]): joints information.
"""
pushes, pulls = [], []
joints = joints.cpu().data.numpy()
@@ -231,18 +233,18 @@ def forward(self, outputs, heatmaps, masks, joints):
"""Forward function to calculate losses.
Note:
- batch_size: N
- heatmaps weight: W
- heatmaps height: H
- max_num_people: M
- num_keypoints: K
- output_channel: C C=2K if use ae loss else K
+ - batch_size: N
+ - heatmaps weight: W
+ - heatmaps height: H
+ - max_num_people: M
+ - num_keypoints: K
+ - output_channel: C C=2K if use ae loss else K
Args:
- outputs (List(torch.Tensor[NxCxHxW])): outputs of stages.
- heatmaps (List(torch.Tensor[NxKxHxW])): target of heatmaps.
- masks (List(torch.Tensor[NxHxW])): masks of heatmaps.
- joints (List(torch.Tensor[NxMxKx2])): joints of ae loss.
+ outputs (list(torch.Tensor[N,C,H,W])): outputs of stages.
+ heatmaps (list(torch.Tensor[N,K,H,W])): target of heatmaps.
+ masks (list(torch.Tensor[N,H,W])): masks of heatmaps.
+ joints (list(torch.Tensor[N,M,K,2])): joints of ae loss.
"""
heatmaps_losses = []
push_losses = []
diff --git a/mmpose/models/losses/regression_loss.py b/mmpose/models/losses/regression_loss.py
index 8359ae88a4..db4178355e 100644
--- a/mmpose/models/losses/regression_loss.py
+++ b/mmpose/models/losses/regression_loss.py
@@ -10,7 +10,7 @@
@LOSSES.register_module()
class SmoothL1Loss(nn.Module):
- """SmoothL1Loss loss .
+ """SmoothL1Loss loss.
Args:
use_target_weight (bool): Option to use weighted MSE loss.
@@ -28,9 +28,9 @@ def forward(self, output, target, target_weight=None):
"""Forward function.
Note:
- batch_size: N
- num_keypoints: K
- dimension of keypoints: D (D=2 or D=3)
+ - batch_size: N
+ - num_keypoints: K
+ - dimension of keypoints: D (D=2 or D=3)
Args:
output (torch.Tensor[N, K, D]): Output regression.
@@ -50,11 +50,12 @@ def forward(self, output, target, target_weight=None):
@LOSSES.register_module()
class WingLoss(nn.Module):
- """Wing Loss 'Wing Loss for Robust Facial Landmark Localisation with
- Convolutional Neural Networks' Feng et al. CVPR'2018.
+ """Wing Loss. paper ref: 'Wing Loss for Robust Facial Landmark Localisation
+ with Convolutional Neural Networks' Feng et al. CVPR'2018.
Args:
- omega (float), epsilon (float) are hyper-parameters.
+ omega (float): Also referred to as width.
+ epsilon (float): Also referred to as curvature.
use_target_weight (bool): Option to use weighted MSE loss.
Different joint types may have different target weights.
loss_weight (float): Weight of the loss. Default: 1.0.
@@ -75,6 +76,84 @@ def __init__(self,
# and nonlinear parts
self.C = self.omega * (1.0 - math.log(1.0 + self.omega / self.epsilon))
+ def criterion(self, pred, target):
+ """Criterion of wingloss.
+
+ Note:
+ - batch_size: N
+ - num_keypoints: K
+ - dimension of keypoints: D (D=2 or D=3)
+
+ Args:
+ pred (torch.Tensor[N, K, D]): Output regression.
+ target (torch.Tensor[N, K, D]): Target regression.
+ """
+ delta = (target - pred).abs()
+ losses = torch.where(
+ delta < self.omega,
+ self.omega * torch.log(1.0 + delta / self.epsilon), delta - self.C)
+ return torch.mean(torch.sum(losses, dim=[1, 2]), dim=0)
+
+ def forward(self, output, target, target_weight=None):
+ """Forward function.
+
+ Note:
+ - batch_size: N
+ - num_keypoints: K
+ - dimension of keypoints: D (D=2 or D=3)
+
+ Args:
+ output (torch.Tensor[N, K, D]): Output regression.
+ target (torch.Tensor[N, K, D]): Target regression.
+ target_weight (torch.Tensor[N,K,D]):
+ Weights across different joint types.
+ """
+ if self.use_target_weight:
+ assert target_weight is not None
+ loss = self.criterion(output * target_weight,
+ target * target_weight)
+ else:
+ loss = self.criterion(output, target)
+
+ return loss * self.loss_weight
+
+
+@LOSSES.register_module()
+class SoftWingLoss(nn.Module):
+ """Soft Wing Loss 'Structure-Coherent Deep Feature Learning for Robust Face
+ Alignment' Lin et al. TIP'2021.
+
+ loss =
+ 1. |x| , if |x| < omega1
+ 2. omega2*ln(1+|x|/epsilon) + B, if |x| >= omega1
+
+ Args:
+ omega1 (float): The first threshold.
+ omega2 (float): The second threshold.
+ epsilon (float): Also referred to as curvature.
+ use_target_weight (bool): Option to use weighted MSE loss.
+ Different joint types may have different target weights.
+ loss_weight (float): Weight of the loss. Default: 1.0.
+ """
+
+ def __init__(self,
+ omega1=2.0,
+ omega2=20.0,
+ epsilon=0.5,
+ use_target_weight=False,
+ loss_weight=1.):
+ super().__init__()
+ self.omega1 = omega1
+ self.omega2 = omega2
+ self.epsilon = epsilon
+ self.use_target_weight = use_target_weight
+ self.loss_weight = loss_weight
+
+ # constant that smoothly links the piecewise-defined linear
+ # and nonlinear parts
+ self.B = self.omega1 - self.omega2 * math.log(1.0 + self.omega1 /
+ self.epsilon)
+
def criterion(self, pred, target):
"""Criterion of wingloss.
@@ -89,8 +168,8 @@ def criterion(self, pred, target):
"""
delta = (target - pred).abs()
losses = torch.where(
- delta < self.omega,
- self.omega * torch.log(1.0 + delta / self.epsilon), delta - self.C)
+ delta < self.omega1, delta,
+ self.omega2 * torch.log(1.0 + delta / self.epsilon) + self.B)
return torch.mean(torch.sum(losses, dim=[1, 2]), dim=0)
def forward(self, output, target, target_weight=None):
@@ -136,14 +215,14 @@ def forward(self, output, target, target_weight=None):
"""Forward function.
Note:
- batch_size: N
- num_keypoints: K
- dimension of keypoints: D (D=2 or D=3)
+ - batch_size: N
+ - num_keypoints: K
+ - dimension of keypoints: D (D=2 or D=3)
Args:
output (torch.Tensor[N, K, D]): Output regression.
target (torch.Tensor[N, K, D]): Target regression.
- target_weight (torch.Tensor[N, K, D]):
+ target_weight (torch.Tensor[N,K,D]):
Weights across different joint types.
"""
@@ -171,8 +250,8 @@ def forward(self, output, target, target_weight=None):
"""Forward function.
Note:
- batch_size: N
- num_keypoints: K
+ - batch_size: N
+ - num_keypoints: K
Args:
output (torch.Tensor[N, K, 2]): Output regression.
@@ -204,8 +283,8 @@ def forward(self, output, target, target_weight=None):
"""Forward function.
Note:
- batch_size: N
- num_keypoints: K
+ - batch_size: N
+ - num_keypoints: K
Args:
output (torch.Tensor[N, K, 2]): Output regression.
@@ -249,9 +328,9 @@ def forward(self, output, target, target_weight=None):
"""Forward function.
Note:
- batch_size: N
- num_keypoints: K
- dimension of keypoints: D (D=2 or D=3)
+ - batch_size: N
+ - num_keypoints: K
+ - dimension of keypoints: D (D=2 or D=3)
Args:
output (torch.Tensor[N, K, D]): Output regression.
@@ -282,8 +361,8 @@ class SemiSupervisionLoss(nn.Module):
"""Semi-supervision loss for unlabeled data. It is composed of projection
loss and bone loss.
- Paper ref: `3D human pose estimation in video with temporal convolutions
- and semi-supervised training` Dario Pavllo et al. CVPR'2019.
+ Paper ref: `3D human pose estimation in video with temporal convolutions
+ and semi-supervised training` Dario Pavllo et al. CVPR'2019.
Args:
joint_parents (list): Indices of each joint's parent joint.
diff --git a/mmpose/models/misc/discriminator.py b/mmpose/models/misc/discriminator.py
index 8b36b25afb..712f0a8b56 100644
--- a/mmpose/models/misc/discriminator.py
+++ b/mmpose/models/misc/discriminator.py
@@ -108,8 +108,7 @@ class ShapeDiscriminator(BaseDiscriminator):
"""Discriminator for SMPL shape parameters, the inputs is (batch_size x 10)
Args:
- fc_layers (Tuple): Tuple of neuron count,
- such as (10, 5, 1)
+ fc_layers (Tuple): Tuple of neuron count, such as (10, 5, 1)
use_dropout (Tuple): Tuple of bool define use dropout or
not for each layer, such as (True, True, False)
drop_prob (Tuple): Tuple of float defined the drop prob,
@@ -170,7 +169,7 @@ def __init__(self, channels, joint_count):
def forward(self, inputs):
"""Forward function.
- The input is (batch_size x joint_count x 9)
+ The input is (batch_size x joint_count x 9).
"""
# shape: batch_size x 9 x 1 x joint_count
inputs = inputs.transpose(1, 2).unsqueeze(2).contiguous()
@@ -197,13 +196,13 @@ class FullPoseDiscriminator(BaseDiscriminator):
Args:
fc_layers (Tuple): Tuple of neuron count,
- such as (736, 1024, 1024, 1)
+ such as (736, 1024, 1024, 1)
use_dropout (Tuple): Tuple of bool define use dropout or not
- for each layer, such as (True, True, False)
+ for each layer, such as (True, True, False)
drop_prob (Tuple): Tuple of float defined the drop prob,
- such as (0.5, 0.5, 0)
+ such as (0.5, 0.5, 0)
use_activation(Tuple): Tuple of bool define use active
- function or not, such as (True, True, False)
+ function or not, such as (True, True, False)
"""
def __init__(self, fc_layers, use_dropout, drop_prob, use_activation):
diff --git a/mmpose/models/necks/posewarper_neck.py b/mmpose/models/necks/posewarper_neck.py
index 8d81065032..dd4ddfbf89 100644
--- a/mmpose/models/necks/posewarper_neck.py
+++ b/mmpose/models/necks/posewarper_neck.py
@@ -20,11 +20,10 @@
@NECKS.register_module()
class PoseWarperNeck(nn.Module):
- """PoseWarper neck. Paper ref: Bertasius, Gedas, et al. "Learning temporal
- pose estimation from sparsely-labeled videos." arXiv preprint
- arXiv:1906.04016 (2019).
+ """PoseWarper neck.
- <``https://arxiv.org/abs/1906.04016``>
+ `"Learning temporal pose estimation from sparsely-labeled videos"
+ `_.
Args:
in_channels (int): Number of input channels from backbone
@@ -34,23 +33,27 @@ class PoseWarperNeck(nn.Module):
dilations (list|tuple): different dilations of the offset conv layers
trans_conv_kernel (int): the kernel of the trans conv layer, which is
used to get heatmap from the output of backbone. Default: 1
- res_blocks_cfg (dict|None): config of residual blocks. It should
- contain the following keys:
- - block (str): the type of residual block, Default: 'BASIC'
- - num_blocks (int): the number of blocks, Default: 20
- If None, use the default values.
+ res_blocks_cfg (dict|None): config of residual blocks. If None,
+ use the default values. If not None, it should contain the
+ following keys:
+
+ - block (str): the type of residual block, Default: 'BASIC'.
+ - num_blocks (int): the number of blocks, Default: 20.
+
offsets_kernel (int): the kernel of offset conv layer.
deform_conv_kernel (int): the kernel of defomrable conv layer.
in_index (int|Sequence[int]): Input feature index. Default: 0
input_transform (str|None): Transformation type of input features.
Options: 'resize_concat', 'multiple_select', None.
- 'resize_concat': Multiple feature maps will be resize to the
- same size as first one and than concat together.
+ Default: None.
+
+ - 'resize_concat': Multiple feature maps will be resize to \
+ the same size as first one and than concat together. \
Usually used in FCN head of HRNet.
- 'multiple_select': Multiple feature maps will be bundle into
+ - 'multiple_select': Multiple feature maps will be bundle into \
a list and passed into decode head.
- None: Only one select feature map is allowed.
- Default: None.
+ - None: Only one select feature map is allowed.
+
freeze_trans_layer (bool): Whether to freeze the transition layer
(stop grad and set eval mode). Default: True.
norm_eval (bool): Whether to set norm layers to eval mode, namely,
diff --git a/mmpose/utils/timer.py b/mmpose/utils/timer.py
index 31cb217276..fcc0437bc1 100644
--- a/mmpose/utils/timer.py
+++ b/mmpose/utils/timer.py
@@ -13,7 +13,7 @@ class StopWatch:
window (int): The sliding window size to calculate the running average
of the time consuming.
- Example::
+ Example:
>>> from mmpose.utils import StopWatch
>>> import time
>>> stop_watch = StopWatch(window=10)
@@ -61,8 +61,8 @@ def report(self):
"""Report timing information.
Returns:
- dict: The key is the timer name and the value is the corresponding
- average time consuming.
+ dict: The key is the timer name and the value is the \
+ corresponding average time consuming.
"""
result = {
name: np.mean(vals) * 1000.
@@ -74,10 +74,10 @@ def report_strings(self):
"""Report timing information in texture strings.
Returns:
- list(str): Each element is the information string of a timed event,
- in format of '{timer_name}: {time_in_ms}'. Specially, if
- timer_name is '_FPS_', the result will be converted to
- fps.
+ list(str): Each element is the information string of a timed \
+ event, in format of '{timer_name}: {time_in_ms}'. \
+ Specially, if timer_name is '_FPS_', the result will \
+ be converted to fps.
"""
result = self.report()
strings = []
diff --git a/model-index.yml b/model-index.yml
index e928776228..e34438da96 100644
--- a/model-index.yml
+++ b/model-index.yml
@@ -76,11 +76,13 @@ Import:
- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/posetrack18/hrnet_posetrack18.yml
- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/posetrack18/resnet_posetrack18.yml
- configs/body/2d_kpt_sview_rgb_vid/posewarper/posetrack18/hrnet_posetrack18_posewarper.yml
+- configs/body/3d_kpt_mview_rgb_img/voxelpose/panoptic/voxelpose_prn64x64x64_cpn80x80x20_panoptic_cam5.yml
- configs/body/3d_kpt_sview_rgb_img/pose_lift/h36m/simplebaseline3d_h36m.yml
- configs/body/3d_kpt_sview_rgb_img/pose_lift/mpi_inf_3dhp/simplebaseline3d_mpi-inf-3dhp.yml
- configs/body/3d_kpt_sview_rgb_vid/video_pose_lift/h36m/videopose3d_h36m.yml
- configs/body/3d_kpt_sview_rgb_vid/video_pose_lift/mpi_inf_3dhp/videopose3d_mpi-inf-3dhp.yml
- configs/body/3d_mesh_sview_rgb_img/hmr/mixed/resnet_mixed.yml
+- configs/face/2d_kpt_sview_rgb_img/deeppose/wflw/resnet_softwingloss_wflw.yml
- configs/face/2d_kpt_sview_rgb_img/deeppose/wflw/resnet_wflw.yml
- configs/face/2d_kpt_sview_rgb_img/deeppose/wflw/resnet_wingloss_wflw.yml
- configs/face/2d_kpt_sview_rgb_img/topdown_heatmap/300w/hrnetv2_300w.yml
@@ -93,6 +95,7 @@ Import:
- configs/face/2d_kpt_sview_rgb_img/topdown_heatmap/coco_wholebody_face/resnet_coco_wholebody_face.yml
- configs/face/2d_kpt_sview_rgb_img/topdown_heatmap/coco_wholebody_face/scnet_coco_wholebody_face.yml
- configs/face/2d_kpt_sview_rgb_img/topdown_heatmap/cofw/hrnetv2_cofw.yml
+- configs/face/2d_kpt_sview_rgb_img/topdown_heatmap/wflw/hrnetv2_awing_wflw.yml
- configs/face/2d_kpt_sview_rgb_img/topdown_heatmap/wflw/hrnetv2_dark_wflw.yml
- configs/face/2d_kpt_sview_rgb_img/topdown_heatmap/wflw/hrnetv2_wflw.yml
- configs/fashion/2d_kpt_sview_rgb_img/deeppose/deepfashion/resnet_deepfashion.yml
diff --git a/tests/data/panoptic_body3d/160906_band1/calibration_160906_band1.json b/tests/data/panoptic_body3d/160906_band1/calibration_160906_band1.json
new file mode 100755
index 0000000000..31c0429b03
--- /dev/null
+++ b/tests/data/panoptic_body3d/160906_band1/calibration_160906_band1.json
@@ -0,0 +1,11965 @@
+{
+ "calibDataSource": "160906_calib_norm",
+ "cameras": [
+ {
+ "name": "01_01",
+ "type": "vga",
+ "resolution": [640,480],
+ "panel": 1,
+ "node": 1,
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+ [0,0,1]
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+ "t": [
+ [-51.56945892],
+ [143.9587601],
+ [282.5664691]
+ ]
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+ "panel": 1,
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+ ],
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+ [278.6626087]
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+ [-0.07024277085,0.996230894,-0.05089164033],
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+ ],
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+ [281.78809]
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+ {
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+ [-0.06162250151,0.9944158624,-0.08567239854],
+ [0.2721978533,0.09932531892,0.9571012536]
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new file mode 100755
index 0000000000..7c2cd6a277
--- /dev/null
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diff --git a/tests/data/panoptic_body3d/160906_band1/hdPose3d_stage1_coco19/body3DScene_00000169.json b/tests/data/panoptic_body3d/160906_band1/hdPose3d_stage1_coco19/body3DScene_00000169.json
new file mode 100755
index 0000000000..ffc3785993
--- /dev/null
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diff --git a/tests/data/panoptic_body3d/160906_band2/calibration_160906_band2.json b/tests/data/panoptic_body3d/160906_band2/calibration_160906_band2.json
new file mode 100755
index 0000000000..31c0429b03
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+ [-0.07601662453,0.6188478234,0.7818240495]
+ ],
+ "t": [
+ [-23.36095562],
+ [58.01362542],
+ [350.0526212]
+ ]
+ },
+ {
+ "name": "50_07",
+ "type": "kinect-color",
+ "resolution": [1920,1080],
+ "panel": 50,
+ "node": 7,
+ "K": [
+ [1058.37,0,951.456],
+ [0,1058.06,537.752],
+ [0,0,1]
+ ],
+ "distCoef": [0.0510704,-0.0625189,-0.000144014,6.68608e-05,0.016463],
+ "R": [
+ [0.4325769754,-0.03234243573,-0.9010167186],
+ [-0.4868424381,0.832758343,-0.2636247005],
+ [0.7588554545,0.5526911516,0.344486415]
+ ],
+ "t": [
+ [-19.0385587],
+ [87.13576568],
+ [341.2560709]
+ ]
+ },
+ {
+ "name": "50_08",
+ "type": "kinect-color",
+ "resolution": [1920,1080],
+ "panel": 50,
+ "node": 8,
+ "K": [
+ [1051.92,0,937.937],
+ [0,1051.86,554.246],
+ [0,0,1]
+ ],
+ "distCoef": [0.0499863,-0.0613843,-4.12419e-05,-0.000155211,0.0174279],
+ "R": [
+ [-0.7043873056,-0.07078753835,-0.7062773168],
+ [-0.4398115151,0.8245196459,0.3559960458],
+ [0.5571394394,0.5613879923,-0.6119143463]
+ ],
+ "t": [
+ [-21.03532832],
+ [82.26745729],
+ [344.5100871]
+ ]
+ },
+ {
+ "name": "50_09",
+ "type": "kinect-color",
+ "resolution": [1920,1080],
+ "panel": 50,
+ "node": 9,
+ "K": [
+ [1054,0,961.563],
+ [0,1054.08,544.179],
+ [0,0,1]
+ ],
+ "distCoef": [0.0446773,-0.0530941,0.000226286,-0.000324258,0.0121913],
+ "R": [
+ [-0.8728623151,-0.0989156561,0.4778358211],
+ [0.2068965126,0.8118396582,0.5459946908],
+ [-0.4419334927,0.5754407548,-0.6881589393]
+ ],
+ "t": [
+ [-36.30074608],
+ [73.0041962],
+ [346.5857858]
+ ]
+ },
+ {
+ "name": "50_10",
+ "type": "kinect-color",
+ "resolution": [1920,1080],
+ "panel": 50,
+ "node": 10,
+ "K": [
+ [1050.04,0,941.59],
+ [0,1050.6,559.398],
+ [0,0,1]
+ ],
+ "distCoef": [0.0506861,-0.0636966,0.000195295,-6.41025e-06,0.0181857],
+ "R": [
+ [0.1849149694,0.002001709126,0.9827524852],
+ [0.5894867579,0.7998990427,-0.1125472514],
+ [-0.786328059,0.6001312479,0.146733326]
+ ],
+ "t": [
+ [-12.26435316],
+ [64.88453925],
+ [349.5293231]
+ ]
+ }
+ ]
+}
diff --git a/tests/data/panoptic_body3d/160906_band2/hdPose3d_stage1_coco19/body3DScene_00000139.json b/tests/data/panoptic_body3d/160906_band2/hdPose3d_stage1_coco19/body3DScene_00000139.json
new file mode 100755
index 0000000000..ffcb417672
--- /dev/null
+++ b/tests/data/panoptic_body3d/160906_band2/hdPose3d_stage1_coco19/body3DScene_00000139.json
@@ -0,0 +1,15 @@
+{ "version": 0.7,
+"univTime" :47884.218,
+"fpsType" :"hd_29_97",
+"bodies" :
+[
+{ "id": 0,
+"joints19": [112.193, -105.597, -63.2943, 0.681274, 98.5895, -126.086, -55.7911, 0.631165, 109.902, -62.3343, -62.7694, 0.434326, 103.926, -106.634, -77.9832, 0.634766, 96.8314, -78.8763, -80.315, 0.630554, 81.2761, -65.6605, -63.3084, 0.521851, 104.519, -62.7352, -72.4198, 0.444824, 65.6156, -49.5546, -67.2074, 0.308655, 54.2865, -10.3131, -52.1117, 0.236267, 120.42, -105.112, -49.3556, 0.60022, 116.189, -76.1983, -41.2718, 0.653992, 92.821, -64.5032, -42.2247, 0.527283, 115.285, -61.9334, -53.119, 0.411194, 85.1507, -47.7375, -27.3165, 0.324036, 68.5293, -8.10239, -40.0008, 0.237915, 99.4248, -129.754, -59.6533, 0.558838, 105.207, -126.2, -67.7812, 0.423035, 102.014, -129.179, -53.6288, 0.597534, 112.005, -124.794, -53.0751, 0.523254]
+},
+{ "id": 1,
+"joints19": [-75.6724, -98.3814, -40.586, 0.645996, -56.0144, -116.916, -44.6227, 0.601746, -74.2896, -55.1045, -36.5893, 0.251404, -83.9112, -98.0817, -25.7277, 0.517944, -88.2289, -74.2988, -14.0067, 0.396973, -68.2309, -74.1222, -1.47647, 0.329041, -79.1849, -55.0298, -28.0693, 0.249634, -51.5633, -47.6808, -7.04466, 0.285828, -74.7285, -17.5206, -29.2095, 0.225037, -67.3391, -99.4683, -53.6127, 0.477478, -47.0624, -92.1391, -77.8037, 0.564758, -44.4238, -79.2347, -57.8838, 0.42804, -69.3944, -55.1793, -45.1093, 0.227051, -34.6453, -49.6836, -25.2735, 0.309937, -40.5958, -7.8462, -31.1836, 0.256836, -56.8233, -120.322, -40.7627, 0.527283, -62.646, -116.933, -32.5876, 0.487427, -59.8079, -119.867, -46.6254, 0.471802, -69.67, -116.407, -47.7538, 0.328979]
+},
+{ "id": 2,
+"joints19": [-2.94539, -95.1867, 36.3111, 0.628723, 2.07299, -104.457, 17.1551, 0.454163, -7.05924, -50.5435, 42.1746, 0.244141, 9.71628, -93.5102, 43.4675, 0.522705, 12.0947, -69.463, 41.455, 0.387512, 19.2916, -69.8677, 21.9048, 0.328552, 1.02674, -50.5267, 46.2205, 0.253113, 26.6747, -43.1457, 17.5558, 0.371948, 27.6632, -7.91068, 13.5889, 0.338867, -15.3606, -97.7204, 30.3576, 0.512207, -37.812, -87.1386, 18.9922, 0.449524, -22.3384, -73.7289, 12.8309, 0.288696, -15.1452, -50.5603, 38.1288, 0.238159, 5.46492, -46.765, 4.57304, 0.31604, 4.54105, -11.5529, 11.0104, 0.225098, 5.10417, -106.784, 19.1646, 0.338989, 8.38426, -105.863, 28.9746, 0.374878, -0.147999, -108.49, 17.901, 0.389282, -6.41806, -109.461, 25.684, 0.374512]
+}
+] }
\ No newline at end of file
diff --git a/tests/data/panoptic_body3d/160906_band2/hdPose3d_stage1_coco19/body3DScene_00000140.json b/tests/data/panoptic_body3d/160906_band2/hdPose3d_stage1_coco19/body3DScene_00000140.json
new file mode 100755
index 0000000000..6ae18e79c6
--- /dev/null
+++ b/tests/data/panoptic_body3d/160906_band2/hdPose3d_stage1_coco19/body3DScene_00000140.json
@@ -0,0 +1,15 @@
+{ "version": 0.7,
+"univTime" :47917.574,
+"fpsType" :"hd_29_97",
+"bodies" :
+[
+{ "id": 0,
+"joints19": [112.162, -105.636, -63.2601, 0.683655, 98.6079, -126.096, -55.7691, 0.633362, 109.895, -61.989, -62.8249, 0.434998, 103.92, -106.612, -77.9614, 0.633362, 96.8156, -78.8938, -80.3215, 0.628052, 81.2649, -65.6074, -63.3025, 0.5224, 104.579, -62.7546, -72.4201, 0.444275, 65.7338, -49.5044, -67.2157, 0.308167, 54.3536, -10.2596, -52.2195, 0.234131, 120.391, -105.108, -49.299, 0.603271, 116.181, -76.2786, -41.2666, 0.655334, 92.8451, -64.4865, -42.2389, 0.527039, 115.21, -61.2235, -53.2298, 0.411194, 85.1888, -47.835, -27.3393, 0.316833, 68.6198, -8.08162, -40.0417, 0.240723, 99.4313, -129.72, -59.6381, 0.558228, 105.205, -126.164, -67.7647, 0.423279, 102.011, -129.182, -53.6361, 0.597229, 111.982, -124.783, -53.0672, 0.522705]
+},
+{ "id": 1,
+"joints19": [-75.6746, -98.3656, -40.5723, 0.641663, -56.0544, -116.939, -44.5928, 0.603577, -74.4415, -55.0317, -36.6536, 0.252808, -83.918, -98.0843, -25.7209, 0.519165, -88.2603, -74.1941, -13.9948, 0.396057, -68.2309, -74.0839, -1.42833, 0.328918, -79.5003, -55.0092, -28.0401, 0.25, -51.5172, -47.7041, -7.04263, 0.294495, -74.7647, -17.5892, -29.1887, 0.228638, -67.361, -99.4319, -53.7077, 0.481934, -47.0466, -92.2037, -77.7492, 0.570923, -44.4639, -79.2762, -57.8438, 0.434448, -69.3827, -55.0541, -45.2672, 0.228271, -34.7812, -49.6926, -25.257, 0.32843, -40.4408, -8.21801, -31.4407, 0.283936, -56.906, -120.336, -40.6846, 0.523743, -62.7131, -116.956, -32.5538, 0.486816, -59.8485, -119.899, -46.5796, 0.469604, -69.74, -116.42, -47.7167, 0.333618]
+},
+{ "id": 2,
+"joints19": [-2.68829, -95.166, 36.4048, 0.630371, 2.45545, -104.642, 17.3385, 0.4422, -7.03441, -50.7646, 42.0224, 0.242737, 9.85479, -93.5391, 43.7496, 0.508667, 12.1837, -69.2865, 41.4611, 0.386414, 19.4591, -69.7157, 22.0246, 0.327209, 0.906971, -50.8216, 45.9673, 0.251282, 26.7278, -43.1852, 17.6063, 0.375671, 27.6219, -7.85445, 13.6172, 0.336487, -15.0965, -97.8212, 30.3863, 0.518982, -37.313, -87.4077, 18.8649, 0.450623, -22.0828, -73.86, 12.723, 0.291931, -14.9758, -50.7076, 38.0776, 0.235413, 5.50862, -46.8061, 4.64948, 0.321838, 4.38506, -11.3184, 11.4198, 0.237366, 5.50586, -106.921, 19.3049, 0.338745, 8.65006, -105.825, 29.1475, 0.375244, 0.254989, -108.642, 18.0692, 0.389526, -5.97449, -109.525, 25.9132, 0.369568]
+}
+] }
\ No newline at end of file
diff --git a/tests/test_backbones/test_v2v_net.py b/tests/test_backbones/test_v2v_net.py
new file mode 100644
index 0000000000..33c467a112
--- /dev/null
+++ b/tests/test_backbones/test_v2v_net.py
@@ -0,0 +1,13 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+
+from mmpose.models import builder
+
+
+def test_v2v_net():
+ """Test V2VNet."""
+ cfg = dict(type='V2VNet', input_channels=17, output_channels=15),
+ model = builder.build_backbone(*cfg)
+ input = torch.randn(2, 17, 32, 32, 32)
+ output = model(input)
+ assert output.shape == (2, 15, 32, 32, 32)
diff --git a/tests/test_datasets/test_body3d_dataset.py b/tests/test_datasets/test_body3d_dataset.py
index 561133695c..022146a004 100644
--- a/tests/test_datasets/test_body3d_dataset.py
+++ b/tests/test_datasets/test_body3d_dataset.py
@@ -273,3 +273,85 @@ def test_body3d_mpi_inf_3dhp_dataset():
np.testing.assert_almost_equal(infos['P-3DPCK'], 100.)
np.testing.assert_almost_equal(infos['3DAUC'], 30 / 31 * 100)
np.testing.assert_almost_equal(infos['P-3DAUC'], 30 / 31 * 100)
+
+
+def test_body3dmview_direct_panoptic_dataset():
+ # Test Mview-Panoptic dataset
+ dataset = 'Body3DMviewDirectPanopticDataset'
+ dataset_class = DATASETS.get(dataset)
+ dataset_info = Config.fromfile(
+ 'configs/_base_/datasets/panoptic_body3d.py').dataset_info
+ space_size = [8000, 8000, 2000]
+ space_center = [0, -500, 800]
+ cube_size = [80, 80, 20]
+ train_data_cfg = dict(
+ image_size=[960, 512],
+ heatmap_size=[[240, 128]],
+ space_size=space_size,
+ space_center=space_center,
+ cube_size=cube_size,
+ num_joints=15,
+ seq_list=['160906_band1', '160906_band2'],
+ cam_list=[(0, 12), (0, 6)],
+ num_cameras=2,
+ seq_frame_interval=1,
+ subset='train',
+ need_2d_label=True,
+ need_camera_param=True,
+ root_id=2)
+
+ test_data_cfg = dict(
+ image_size=[960, 512],
+ heatmap_size=[[240, 128]],
+ num_joints=15,
+ space_size=space_size,
+ space_center=space_center,
+ cube_size=cube_size,
+ seq_list=['160906_band1', '160906_band2'],
+ cam_list=[(0, 12), (0, 6)],
+ num_cameras=2,
+ seq_frame_interval=1,
+ subset='validation',
+ need_2d_label=True,
+ need_camera_param=True,
+ root_id=2)
+ with tempfile.TemporaryDirectory() as tmpdir:
+ _ = dataset_class(
+ ann_file=tmpdir + '/tmp_train.pkl',
+ img_prefix='tests/data/panoptic_body3d/',
+ data_cfg=train_data_cfg,
+ pipeline=[],
+ dataset_info=dataset_info,
+ test_mode=False)
+
+ with tempfile.TemporaryDirectory() as tmpdir:
+ test_dataset = dataset_class(
+ ann_file=tmpdir + '/tmp_validation.pkl',
+ img_prefix='tests/data/panoptic_body3d',
+ data_cfg=test_data_cfg,
+ pipeline=[],
+ dataset_info=dataset_info,
+ test_mode=False)
+
+ import copy
+ gt_num = test_dataset.db_size // test_dataset.num_cameras
+ preds = []
+ for i in range(gt_num):
+ index = test_dataset.num_cameras * i
+ db_rec = copy.deepcopy(test_dataset.db[index])
+ joints_3d = db_rec['joints_3d']
+ joints_3d_vis = db_rec['joints_3d_visible']
+ num_gts = len(joints_3d)
+ gt_pose = -np.ones((1, 10, test_dataset.num_joints, 5))
+
+ if num_gts > 0:
+ gt_pose[0, :num_gts, :, :3] = np.array(joints_3d)
+ gt_pose[0, :num_gts, :, 3] = np.array(joints_3d_vis)[:, :, 0] - 1.0
+ gt_pose[0, :num_gts, :, 4] = 1.0
+
+ preds.append(dict(pose_3d=gt_pose, sample_id=[i]))
+ print('test evaluate')
+ with tempfile.TemporaryDirectory() as tmpdir:
+ results = test_dataset.evaluate(
+ preds, res_folder=tmpdir, metric=['mAP', 'mpjpe'])
+ print(results)
diff --git a/tests/test_evaluation/test_top_down_eval.py b/tests/test_evaluation/test_top_down_eval.py
index 72f7456aaa..5cda7e141f 100644
--- a/tests/test_evaluation/test_top_down_eval.py
+++ b/tests/test_evaluation/test_top_down_eval.py
@@ -32,6 +32,11 @@ def test_keypoints_from_heatmaps():
center = np.array([[127, 127]])
scale = np.array([[64 / 200.0, 64 / 200.0]])
+ udp_heatmaps = np.ones((32, 17, 64, 64), dtype=np.float32)
+ udp_heatmaps[:, :, 31, 31] = 2
+ udp_center = np.tile([127, 127], (32, 1))
+ udp_scale = np.tile([32, 32], (32, 1))
+
preds, maxvals = keypoints_from_heatmaps(heatmaps, center, scale)
assert_array_almost_equal(preds, np.array([[[126, 126]]]), decimal=4)
@@ -51,10 +56,16 @@ def test_keypoints_from_heatmaps():
assert isinstance(preds, np.ndarray)
assert isinstance(maxvals, np.ndarray)
+ # test for udp dimension problem
preds, maxvals = keypoints_from_heatmaps(
- heatmaps, center, scale, post_process='unbiased')
- assert_array_almost_equal(preds, np.array([[[126, 126]]]), decimal=4)
- assert_array_almost_equal(maxvals, np.array([[[2]]]), decimal=4)
+ udp_heatmaps,
+ udp_center,
+ udp_scale,
+ post_process='default',
+ target_type='GaussianHeatMap',
+ use_udp=True)
+ assert_array_almost_equal(preds, np.tile([76, 76], (32, 17, 1)), decimal=0)
+ assert_array_almost_equal(maxvals, np.tile([2], (32, 17, 1)), decimal=4)
assert isinstance(preds, np.ndarray)
assert isinstance(maxvals, np.ndarray)
diff --git a/tests/test_losses/test_regression_losses.py b/tests/test_losses/test_regression_losses.py
index 6756c3ae97..df710ba9e7 100644
--- a/tests/test_losses/test_regression_losses.py
+++ b/tests/test_losses/test_regression_losses.py
@@ -64,8 +64,37 @@ def test_wing_loss():
torch.tensor(.5))
-def test_mse_regression_loss():
+def test_soft_wing_loss():
+ # test SoftWingLoss without target weight(default None)
+ loss_cfg = dict(type='SoftWingLoss')
+ loss = build_loss(loss_cfg)
+
+ fake_pred = torch.zeros((1, 3, 2))
+ fake_label = torch.zeros((1, 3, 2))
+ assert torch.allclose(loss(fake_pred, fake_label), torch.tensor(0.))
+
+ fake_pred = torch.ones((1, 3, 2))
+ fake_label = torch.zeros((1, 3, 2))
+ assert torch.gt(loss(fake_pred, fake_label), torch.tensor(.5))
+
+ # test SoftWingLoss with target weight
+ loss_cfg = dict(type='SoftWingLoss', use_target_weight=True)
+ loss = build_loss(loss_cfg)
+
+ fake_pred = torch.zeros((1, 3, 2))
+ fake_label = torch.zeros((1, 3, 2))
+ assert torch.allclose(
+ loss(fake_pred, fake_label, torch.ones_like(fake_label)),
+ torch.tensor(0.))
+ fake_pred = torch.ones((1, 3, 2))
+ fake_label = torch.zeros((1, 3, 2))
+ assert torch.gt(
+ loss(fake_pred, fake_label, torch.ones_like(fake_label)),
+ torch.tensor(.5))
+
+
+def test_mse_regression_loss():
# w/o target weight(default None)
loss_cfg = dict(type='MSELoss')
loss = build_loss(loss_cfg)
diff --git a/tests/test_losses/test_top_down_losses.py b/tests/test_losses/test_top_down_losses.py
index 6f6e0b62b1..a02595fa59 100644
--- a/tests/test_losses/test_top_down_losses.py
+++ b/tests/test_losses/test_top_down_losses.py
@@ -2,10 +2,29 @@
import pytest
import torch
+from mmpose.models import build_loss
-def test_mse_loss():
- from mmpose.models import build_loss
+def test_adaptive_wing_loss():
+ # test Adaptive WingLoss without target weight
+ loss_cfg = dict(type='AdaptiveWingLoss')
+ loss = build_loss(loss_cfg)
+
+ fake_pred = torch.zeros((1, 3, 64, 64))
+ fake_label = torch.zeros((1, 3, 64, 64))
+ assert torch.allclose(loss(fake_pred, fake_label, None), torch.tensor(0.))
+
+ # test WingLoss with target weight
+ loss_cfg = dict(type='AdaptiveWingLoss', use_target_weight=True)
+ loss = build_loss(loss_cfg)
+
+ fake_pred = torch.ones((1, 3, 64, 64))
+ fake_label = torch.ones((1, 3, 64, 64))
+ assert torch.allclose(
+ loss(fake_pred, fake_label, torch.ones([1, 3, 1])), torch.tensor(0.))
+
+
+def test_mse_loss():
# test MSE loss without target weight
loss_cfg = dict(type='JointsMSELoss')
loss = build_loss(loss_cfg)
@@ -70,8 +89,6 @@ def test_mse_loss():
def test_smoothl1_loss():
- from mmpose.models import build_loss
-
# test MSE loss without target weight
loss_cfg = dict(type='SmoothL1Loss')
loss = build_loss(loss_cfg)
diff --git a/tests/test_models/test_multitask_forward.py b/tests/test_models/test_multitask_forward.py
index f65e0387a5..97cfd7d0b0 100644
--- a/tests/test_models/test_multitask_forward.py
+++ b/tests/test_models/test_multitask_forward.py
@@ -67,7 +67,7 @@ def test_multitask_forward():
# Test forward train
losses = model(inputs, target, target_weight, return_loss=True)
- assert 'mse_loss' in losses and 'acc_pose' in losses
+ assert 'heatmap_loss' in losses and 'acc_pose' in losses
# Test forward test
outputs = model(inputs, img_metas=img_metas, return_loss=False)
diff --git a/tests/test_models/test_voxelpose.py b/tests/test_models/test_voxelpose.py
new file mode 100644
index 0000000000..a1310ef4e3
--- /dev/null
+++ b/tests/test_models/test_voxelpose.py
@@ -0,0 +1,117 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import tempfile
+
+from mmcv import Config
+
+from mmpose.datasets import DATASETS, build_dataloader
+from mmpose.models import builder
+from mmpose.models.detectors.voxelpose import (CuboidProposalNet,
+ PoseRegressionNet, ProjectLayer)
+
+
+def test_voxelpose_forward():
+ dataset = 'Body3DMviewDirectPanopticDataset'
+ dataset_class = DATASETS.get(dataset)
+ dataset_info = Config.fromfile(
+ 'configs/_base_/datasets/panoptic_body3d.py').dataset_info
+ space_size = [8000, 8000, 2000]
+ space_center = [0, -500, 800]
+ cube_size = [20, 20, 8]
+ train_data_cfg = dict(
+ image_size=[960, 512],
+ heatmap_size=[[240, 128]],
+ space_size=space_size,
+ space_center=space_center,
+ cube_size=cube_size,
+ num_joints=15,
+ seq_list=['160906_band1'],
+ cam_list=[(0, 12), (0, 6)],
+ num_cameras=2,
+ seq_frame_interval=1,
+ subset='train',
+ need_2d_label=True,
+ need_camera_param=True,
+ root_id=2)
+
+ pipeline = [
+ dict(
+ type='MultiItemProcess',
+ pipeline=[
+ dict(
+ type='BottomUpGenerateTarget', sigma=3, max_num_people=30)
+ ]),
+ dict(
+ type='DiscardDuplicatedItems',
+ keys_list=[
+ 'joints_3d', 'joints_3d_visible', 'ann_info', 'roots_3d',
+ 'num_persons', 'sample_id'
+ ]),
+ dict(
+ type='GenerateVoxel3DHeatmapTarget',
+ sigma=200.0,
+ joint_indices=[2]),
+ dict(type='RenameKeys', key_pairs=[('targets', 'input_heatmaps')]),
+ dict(
+ type='Collect',
+ keys=['targets_3d', 'input_heatmaps'],
+ meta_keys=['camera', 'center', 'scale', 'joints_3d']),
+ ]
+
+ project_layer = ProjectLayer(
+ dict(image_size=[960, 512], heatmap_size=[240, 128]))
+ root_net = CuboidProposalNet(
+ dict(type='V2VNet', input_channels=15, output_channels=1))
+ center_head = builder.build_head(
+ dict(
+ type='CuboidCenterHead',
+ cfg=dict(
+ space_size=space_size,
+ space_center=space_center,
+ cube_size=cube_size,
+ max_num=10,
+ max_pool_kernel=3)))
+ pose_net = PoseRegressionNet(
+ dict(type='V2VNet', input_channels=15, output_channels=15))
+ pose_head = builder.build_head(dict(type='CuboidPoseHead', beta=100.0))
+
+ with tempfile.TemporaryDirectory() as tmpdir:
+ dataset = dataset_class(
+ ann_file=tmpdir + '/tmp_train.pkl',
+ img_prefix='tests/data/panoptic_body3d/',
+ data_cfg=train_data_cfg,
+ pipeline=pipeline,
+ dataset_info=dataset_info,
+ test_mode=False)
+
+ data_loader = build_dataloader(
+ dataset,
+ seed=None,
+ dist=False,
+ shuffle=False,
+ drop_last=False,
+ workers_per_gpu=1,
+ samples_per_gpu=1)
+
+ for data in data_loader:
+ initial_cubes, _ = project_layer(
+ [htm[0] for htm in data['input_heatmaps']],
+ data['img_metas'].data[0], space_size, [space_center], cube_size)
+ _ = root_net(initial_cubes)
+ center_candidates = center_head(data['targets_3d'])
+ center_candidates[..., 3] = \
+ (center_candidates[..., 4] > 0.5).float() - 1.0
+
+ batch_size, num_candidates, _ = center_candidates.shape
+
+ for n in range(num_candidates):
+ index = center_candidates[:, n, 3] >= 0
+ num_valid = index.sum()
+ if num_valid > 0:
+ pose_input_cube, coordinates \
+ = project_layer([htm[0] for htm in data['input_heatmaps']],
+ data['img_metas'].data[0],
+ [800, 800, 800],
+ center_candidates[:, n, :3],
+ [8, 8, 8])
+ pose_heatmaps_3d = pose_net(pose_input_cube)
+ _ = pose_head(pose_heatmaps_3d[index], coordinates[index])