Merge branch 'finetune' into mopts

extras/mmpose/README.md

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+# Finetuning MMPose models
+
+See: <https://mmpose.readthedocs.io/en/latest/user_guides/train_and_test.html>
+
+<br>
+
+```bash
+docker build --progress=plain -f extras/mmpose/dockerfile -t rpt_mmpose .
+
+./extras/mmpose/run_container.sh
+```
+
+```bash
+cd /mmpose/
+export CUDA_VISIBLE_DEVICES=0
+
+python3 ./tools/train.py \
+    /RapidPoseTriangulation/extras/mmpose/configs/rtmpose-l_8xb32-270e_coco-wholebody-384x288.py \
+    --amp \
+    --cfg-options \
+      load_from=https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_simcc-coco-wholebody_pt-aic-coco_270e-384x288-eaeb96c8_20230125.pth \
+      base_lr=0.00004
+```

extras/mmpose/configs/rtmpose-l_8xb32-270e_coco-wholebody-384x288.py

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+_base_ = ['mmpose::_base_/default_runtime.py']
+
+val_interval=1
+max_epochs = 3
+
+# common setting
+num_keypoints = 133
+input_size = (288, 384)
+
+# runtime
+stage2_num_epochs = 30
+base_lr = 4e-3
+train_batch_size = 32
+val_batch_size = 32
+
+train_cfg = dict(max_epochs=max_epochs, val_interval=val_interval)
+randomness = dict(seed=21)
+
+# optimizer
+optim_wrapper = dict(
+    type='OptimWrapper',
+    optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.05),
+    clip_grad=dict(max_norm=35, norm_type=2),
+    paramwise_cfg=dict(
+        norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True))
+
+# learning rate
+param_scheduler = [
+    dict(
+        type='LinearLR',
+        start_factor=1.0e-5,
+        by_epoch=False,
+        begin=0,
+        end=1000),
+    dict(
+        type='CosineAnnealingLR',
+        eta_min=base_lr * 0.05,
+        begin=max_epochs // 2,
+        end=max_epochs,
+        T_max=max_epochs // 2,
+        by_epoch=True,
+        convert_to_iter_based=True),
+]
+
+# automatically scaling LR based on the actual training batch size
+auto_scale_lr = dict(base_batch_size=512)
+
+# codec settings
+codec = dict(
+    type='SimCCLabel',
+    input_size=input_size,
+    sigma=(6., 6.93),
+    simcc_split_ratio=2.0,
+    normalize=False,
+    use_dark=False)
+
+# model settings
+model = dict(
+    type='TopdownPoseEstimator',
+    data_preprocessor=dict(
+        type='PoseDataPreprocessor',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        bgr_to_rgb=True),
+    backbone=dict(
+        _scope_='mmdet',
+        type='CSPNeXt',
+        arch='P5',
+        expand_ratio=0.5,
+        deepen_factor=1.,
+        widen_factor=1.,
+        out_indices=(4, ),
+        channel_attention=True,
+        norm_cfg=dict(type='SyncBN'),
+        act_cfg=dict(type='SiLU'),
+        init_cfg=dict(
+            type='Pretrained',
+            prefix='backbone.',
+            checkpoint='https://download.openmmlab.com/mmpose/v1/projects/'
+            'rtmposev1/cspnext-l_udp-aic-coco_210e-256x192-273b7631_20230130.pth'  # noqa
+        )),
+    head=dict(
+        type='RTMCCHead',
+        in_channels=1024,
+        out_channels=num_keypoints,
+        input_size=codec['input_size'],
+        in_featuremap_size=tuple([s // 32 for s in codec['input_size']]),
+        simcc_split_ratio=codec['simcc_split_ratio'],
+        final_layer_kernel_size=7,
+        gau_cfg=dict(
+            hidden_dims=256,
+            s=128,
+            expansion_factor=2,
+            dropout_rate=0.,
+            drop_path=0.,
+            act_fn='SiLU',
+            use_rel_bias=False,
+            pos_enc=False),
+        loss=dict(
+            type='KLDiscretLoss',
+            use_target_weight=True,
+            beta=10.,
+            label_softmax=True),
+        decoder=codec),
+    test_cfg=dict(flip_test=True, ))
+
+# base dataset settings
+dataset_type = 'CocoWholeBodyDataset'
+data_mode = 'topdown'
+data_root = 'data/coco/'
+
+backend_args = dict(backend='local')
+
+# pipelines
+train_pipeline = [
+    dict(type='LoadImage', backend_args=backend_args),
+    dict(type='GetBBoxCenterScale'),
+    dict(type='RandomFlip', direction='horizontal'),
+    dict(type='RandomHalfBody'),
+    dict(
+        type='RandomBBoxTransform', scale_factor=[0.6, 1.4], rotate_factor=80),
+    dict(type='TopdownAffine', input_size=codec['input_size']),
+    dict(type='mmdet.YOLOXHSVRandomAug'),
+    dict(
+        type='Albumentation',
+        transforms=[
+            dict(type='Blur', p=0.1),
+            dict(type='MedianBlur', p=0.1),
+            dict(
+                type='CoarseDropout',
+                max_holes=1,
+                max_height=0.4,
+                max_width=0.4,
+                min_holes=1,
+                min_height=0.2,
+                min_width=0.2,
+                p=1.0),
+        ]),
+    dict(type='GenerateTarget', encoder=codec),
+    dict(type='PackPoseInputs')
+]
+val_pipeline = [
+    dict(type='LoadImage', backend_args=backend_args),
+    dict(type='GetBBoxCenterScale'),
+    dict(type='TopdownAffine', input_size=codec['input_size']),
+    dict(type='PackPoseInputs')
+]
+
+train_pipeline_stage2 = [
+    dict(type='LoadImage', backend_args=backend_args),
+    dict(type='GetBBoxCenterScale'),
+    dict(type='RandomFlip', direction='horizontal'),
+    dict(type='RandomHalfBody'),
+    dict(
+        type='RandomBBoxTransform',
+        shift_factor=0.,
+        scale_factor=[0.75, 1.25],
+        rotate_factor=60),
+    dict(type='TopdownAffine', input_size=codec['input_size']),
+    dict(type='mmdet.YOLOXHSVRandomAug'),
+    dict(
+        type='Albumentation',
+        transforms=[
+            dict(type='Blur', p=0.1),
+            dict(type='MedianBlur', p=0.1),
+            dict(
+                type='CoarseDropout',
+                max_holes=1,
+                max_height=0.4,
+                max_width=0.4,
+                min_holes=1,
+                min_height=0.2,
+                min_width=0.2,
+                p=0.5),
+        ]),
+    dict(type='GenerateTarget', encoder=codec),
+    dict(type='PackPoseInputs')
+]
+
+# data loaders
+train_dataloader = dict(
+    batch_size=train_batch_size,
+    num_workers=10,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_mode=data_mode,
+        ann_file='annotations/coco_wholebody_train_v1.0.json',
+        data_prefix=dict(img='train2017/'),
+        pipeline=train_pipeline,
+    ))
+val_dataloader = dict(
+    batch_size=val_batch_size,
+    num_workers=10,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False, round_up=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_mode=data_mode,
+        ann_file='annotations/coco_wholebody_val_v1.0.json',
+        data_prefix=dict(img='val2017/'),
+        test_mode=True,
+        # bbox_file='data/coco/person_detection_results/'
+        # 'COCO_val2017_detections_AP_H_56_person.json',
+        pipeline=val_pipeline,
+    ))
+test_dataloader = val_dataloader
+
+# hooks
+default_hooks = dict(
+    checkpoint=dict(
+        save_best='coco-wholebody/AP', rule='greater', max_keep_ckpts=1))
+
+custom_hooks = [
+    dict(
+        type='EMAHook',
+        ema_type='ExpMomentumEMA',
+        momentum=0.0002,
+        update_buffers=True,
+        priority=49),
+    dict(
+        type='mmdet.PipelineSwitchHook',
+        switch_epoch=max_epochs - stage2_num_epochs,
+        switch_pipeline=train_pipeline_stage2)
+]
+
+# evaluators
+val_evaluator = dict(
+    type='CocoWholeBodyMetric',
+    ann_file=data_root + 'annotations/coco_wholebody_val_v1.0.json')
+test_evaluator = val_evaluator

extras/mmpose/dockerfile

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+FROM rpt_mmdeploy
+
+RUN apt-get update && apt-get install -y --no-install-recommends nano
+RUN pip3 install --upgrade --no-cache-dir "albumentations<1.4"
+
+RUN sed -i '94i\        self.runner.val_loop.run()' /usr/local/lib/python3.8/dist-packages/mmengine/runner/loops.py
+
+WORKDIR /mmpose/
+CMD ["/bin/bash"]

extras/mmpose/run_container.sh

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+#! /bin/bash
+
+xhost +
+docker run --privileged --rm --network host -it \
+  --gpus all --shm-size=16g --ulimit memlock=-1 --ulimit stack=67108864 \
+  --volume "$(pwd)"/:/RapidPoseTriangulation/ \
+  --volume "$(pwd)"/../datasets/coco2017/annotations/:/mmpose/data/coco/annotations/ \
+  --volume "$(pwd)"/../datasets/coco2017/images/:/mmpose/data/coco/ \
+  --volume /tmp/.X11-unix:/tmp/.X11-unix \
+  --env DISPLAY --env QT_X11_NO_MITSHM=1 \
+  rpt_mmpose