Support PyTorch 2.5.1

.vscode/settings.json

@@ -0,0 +1,6 @@
+{
+    "cSpell.words": [
+        "Depthwise",
+        "posenet"
+    ]
+}

configs/wholebody/2d_kpt_sview_rgb_img/topdown_heatmap/coco-wholebody/ViTPose_huge_wholebody_256x192.py

@@ -1,36 +1,29 @@
-_base_ = [
-    '../../../../_base_/default_runtime.py',
-    '../../../../_base_/datasets/coco_wholebody.py'
-]
-evaluation = dict(interval=10, metric='mAP', save_best='AP')
+_base_ = ["../../../../_base_/default_runtime.py", "../../../../_base_/datasets/coco_wholebody.py"]
+evaluation = dict(interval=10, metric="mAP", save_best="AP")
 
 optimizer = dict(
-    type='Adam',
+    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])
+lr_config = dict(policy="step", warmup="linear", warmup_iters=500, warmup_ratio=0.001, step=[170, 200])
 total_epochs = 210
 channel_cfg = dict(
     num_output_channels=133,
     dataset_joints=133,
     dataset_channel=[
         list(range(133)),
     ],
-    inference_channel=list(range(133)))
+    inference_channel=list(range(133)),
+)
 
 # model settings
 model = dict(
-    type='TopDown',
-    pretrained=None,
+    type="TopdownPoseEstimator",
+    # pretrained=None,
     backbone=dict(
-        type='ViT',
+        type="ViT",
         img_size=(256, 192),
         patch_size=16,
         embed_dim=1280,
@@ -42,108 +35,143 @@
         qkv_bias=True,
         drop_path_rate=0.3,
     ),
-    keypoint_head=dict(
-        type='TopdownHeatmapSimpleHead',
+    head=dict(
+        type="TopdownHeatmapSimpleHead",
         in_channels=1280,
         num_deconv_layers=2,
         num_deconv_filters=(256, 256),
         num_deconv_kernels=(4, 4),
-        extra=dict(final_conv_kernel=1, ),
-        out_channels=channel_cfg['num_output_channels'],
-        loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
+        extra=dict(
+            final_conv_kernel=1,
+        ),
+        out_channels=channel_cfg["num_output_channels"],
+        loss_keypoint=dict(type="JointsMSELoss", use_target_weight=True),
+    ),
     train_cfg=dict(),
-    test_cfg=dict(
-        flip_test=True,
-        post_process='default',
-        shift_heatmap=True,
-        modulate_kernel=11))
+    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'],
+    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',
+    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="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'],
+        type="Collect",
+        keys=["img", "target", "target_weight"],
         meta_keys=[
-            'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
-            'rotation', 'bbox_score', 'flip_pairs'
-        ]),
+            "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="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'
-        ]),
+        type="Collect",
+        keys=["img"],
+        meta_keys=["image_file", "center", "scale", "rotation", "bbox_score", "flip_pairs"],
+    ),
 ]
 
 test_pipeline = val_pipeline
 
-data_root = 'data/coco'
+data_root = "data/coco"
+data_mode = "topdown"
+
 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='TopDownCocoWholeBodyDataset',
-        ann_file=f'{data_root}/annotations/coco_wholebody_train_v1.0.json',
-        img_prefix=f'{data_root}/train2017/',
+        type="TopDownCocoWholeBodyDataset",
+        ann_file=f"{data_root}/annotations/coco_wholebody_train_v1.0.json",
+        img_prefix=f"{data_root}/train2017/",
         data_cfg=data_cfg,
         pipeline=train_pipeline,
-        dataset_info={{_base_.dataset_info}}),
+        dataset_info={{_base_.dataset_info}},
+    ),
     val=dict(
-        type='TopDownCocoWholeBodyDataset',
-        ann_file=f'{data_root}/annotations/coco_wholebody_val_v1.0.json',
-        img_prefix=f'{data_root}/val2017/',
+        type="TopDownCocoWholeBodyDataset",
+        ann_file=f"{data_root}/annotations/coco_wholebody_val_v1.0.json",
+        img_prefix=f"{data_root}/val2017/",
         data_cfg=data_cfg,
         pipeline=val_pipeline,
-        dataset_info={{_base_.dataset_info}}),
+        dataset_info={{_base_.dataset_info}},
+    ),
     test=dict(
-        type='TopDownCocoWholeBodyDataset',
-        ann_file=f'{data_root}/annotations/coco_wholebody_val_v1.0.json',
-        img_prefix=f'{data_root}/val2017/',
+        type="TopDownCocoWholeBodyDataset",
+        ann_file=f"{data_root}/annotations/coco_wholebody_val_v1.0.json",
+        img_prefix=f"{data_root}/val2017/",
         data_cfg=data_cfg,
         pipeline=test_pipeline,
-        dataset_info={{_base_.dataset_info}}),
+        dataset_info={{_base_.dataset_info}},
+    ),
+)
+
+train_dataloader = dict(
+    batch_size=64,
+    num_workers=4,
+    persistent_works=True,
+    sampler=dict(type="DefaultSampler", shuffle=True),
+    dataset=dict(
+        type="TopDownCocoWholeBodyDataset",
+        data_root=data_root,
+        data_mode=data_mode,
+        ann_file="annotations/coco_wholebody_train_v1.0.json",
+        data_prefix=dict(img="train2017/"),
+        data_cfg=data_cfg,
+        pipeline=train_pipeline,
+    ),
 )
+
+val_dataloader = dict(
+    batch_size=32,
+    num_workers=4,
+    persistent_works=True,
+    drop_last=False,
+    sampler=dict(type="DefaultSampler", shuffle=False, round_up=False),
+    dataset=dict(
+        type="TopDownCocoWholeBodyDataset",
+        data_root=data_root,
+        data_mode=data_mode,
+        ann_file="annotations/coco_wholebody_val_v1.0.json",
+        data_prefix=dict(img="val2017/"),
+        test_mode=True,
+        data_cfg=data_cfg,
+        pipeline=val_pipeline,
+    ),
+)
+
+test_dataloader = val_dataloader

mmpose/.circleci/config.yml

@@ -0,0 +1,34 @@
+version: 2.1
+
+# this allows you to use CircleCI's dynamic configuration feature
+setup: true
+
+# the path-filtering orb is required to continue a pipeline based on
+# the path of an updated fileset
+orbs:
+  path-filtering: circleci/path-filtering@0.1.2
+
+workflows:
+  # the always-run workflow is always triggered, regardless of the pipeline parameters.
+  always-run:
+    jobs:
+      # the path-filtering/filter job determines which pipeline
+      # parameters to update.
+      - path-filtering/filter:
+          name: check-updated-files
+          # 3-column, whitespace-delimited mapping. One mapping per
+          # line:
+          # <regex path-to-test> <parameter-to-set> <value-of-pipeline-parameter>
+          mapping: |
+            mmpose/.* lint_only false
+            requirements/.* lint_only false
+            tests/.* lint_only false
+            tools/.* lint_only false
+            configs/.* lint_only false
+            .circleci/.* lint_only false
+          base-revision: dev-1.x
+          # this is the path of the configuration we should trigger once
+          # path filtering and pipeline parameter value updates are
+          # complete. In this case, we are using the parent dynamic
+          # configuration itself.
+          config-path: .circleci/test.yml

mmpose/.circleci/docker/Dockerfile

@@ -0,0 +1,11 @@
+ARG PYTORCH="1.7.1"
+ARG CUDA="11.0"
+ARG CUDNN="8"
+
+FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel
+
+# To fix GPG key error when running apt-get update
+RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub
+RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub
+
+RUN apt-get update && apt-get install -y ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6 libgl1-mesa-glx

mmpose/.circleci/scripts/get_mmcv_var.sh

@@ -0,0 +1,19 @@
+#!/bin/bash
+
+TORCH=$1
+CUDA=$2
+
+# 10.2 -> cu102
+MMCV_CUDA="cu`echo ${CUDA} | tr -d '.'`"
+
+# MMCV only provides pre-compiled packages for torch 1.x.0
+# which works for any subversions of torch 1.x.
+# We force the torch version to be 1.x.0 to ease package searching
+# and avoid unnecessary rebuild during MMCV's installation.
+TORCH_VER_ARR=(${TORCH//./ })
+TORCH_VER_ARR[2]=0
+printf -v MMCV_TORCH "%s." "${TORCH_VER_ARR[@]}"
+MMCV_TORCH=${MMCV_TORCH%?}  # Remove the last dot
+
+echo "export MMCV_CUDA=${MMCV_CUDA}" >> $BASH_ENV
+echo "export MMCV_TORCH=${MMCV_TORCH}" >> $BASH_ENV