mirror of https://github.com/open-mmlab/mmpose
536 lines
13 KiB
Python
536 lines
13 KiB
Python
_base_ = ['../../../_base_/default_runtime.py']
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# common setting
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num_keypoints = 26
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input_size = (192, 256)
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# runtime
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max_epochs = 700
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stage2_num_epochs = 30
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base_lr = 4e-3
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train_batch_size = 1024
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val_batch_size = 64
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train_cfg = dict(max_epochs=max_epochs, val_interval=10)
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randomness = dict(seed=21)
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# optimizer
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optim_wrapper = dict(
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type='OptimWrapper',
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optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.0),
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clip_grad=dict(max_norm=35, norm_type=2),
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paramwise_cfg=dict(
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norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True))
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# learning rate
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param_scheduler = [
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dict(
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type='LinearLR',
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start_factor=1.0e-5,
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by_epoch=False,
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begin=0,
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end=1000),
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dict(
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type='CosineAnnealingLR',
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eta_min=base_lr * 0.05,
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begin=max_epochs // 2,
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end=max_epochs,
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T_max=max_epochs // 2,
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by_epoch=True,
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convert_to_iter_based=True),
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]
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# automatically scaling LR based on the actual training batch size
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auto_scale_lr = dict(base_batch_size=1024)
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# codec settings
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codec = dict(
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type='SimCCLabel',
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input_size=input_size,
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sigma=(4.9, 5.66),
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simcc_split_ratio=2.0,
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normalize=False,
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use_dark=False)
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# model settings
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model = dict(
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type='TopdownPoseEstimator',
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data_preprocessor=dict(
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type='PoseDataPreprocessor',
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mean=[123.675, 116.28, 103.53],
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std=[58.395, 57.12, 57.375],
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bgr_to_rgb=True),
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backbone=dict(
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_scope_='mmdet',
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type='CSPNeXt',
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arch='P5',
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expand_ratio=0.5,
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deepen_factor=0.33,
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widen_factor=0.5,
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out_indices=(4, ),
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channel_attention=True,
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norm_cfg=dict(type='SyncBN'),
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act_cfg=dict(type='SiLU'),
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init_cfg=dict(
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type='Pretrained',
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prefix='backbone.',
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checkpoint='https://download.openmmlab.com/mmpose/v1/projects/'
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'rtmposev1/rtmpose-s_simcc-body7_pt-body7_420e-256x192-acd4a1ef_20230504.pth' # noqa
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)),
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head=dict(
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type='RTMCCHead',
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in_channels=512,
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out_channels=num_keypoints,
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input_size=input_size,
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in_featuremap_size=tuple([s // 32 for s in input_size]),
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simcc_split_ratio=codec['simcc_split_ratio'],
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final_layer_kernel_size=7,
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gau_cfg=dict(
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hidden_dims=256,
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s=128,
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expansion_factor=2,
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dropout_rate=0.,
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drop_path=0.,
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act_fn='SiLU',
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use_rel_bias=False,
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pos_enc=False),
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loss=dict(
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type='KLDiscretLoss',
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use_target_weight=True,
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beta=10.,
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label_softmax=True),
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decoder=codec),
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test_cfg=dict(flip_test=True))
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# base dataset settings
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dataset_type = 'CocoWholeBodyDataset'
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data_mode = 'topdown'
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data_root = 'data/'
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backend_args = dict(backend='local')
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# pipelines
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train_pipeline = [
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dict(type='LoadImage', backend_args=backend_args),
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dict(type='GetBBoxCenterScale'),
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dict(type='RandomFlip', direction='horizontal'),
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dict(type='RandomHalfBody'),
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dict(
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type='RandomBBoxTransform', scale_factor=[0.6, 1.4], rotate_factor=80),
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dict(type='TopdownAffine', input_size=codec['input_size']),
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dict(type='PhotometricDistortion'),
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dict(
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type='Albumentation',
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transforms=[
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dict(type='Blur', p=0.1),
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dict(type='MedianBlur', p=0.1),
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dict(
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type='CoarseDropout',
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max_holes=1,
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max_height=0.4,
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max_width=0.4,
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min_holes=1,
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min_height=0.2,
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min_width=0.2,
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p=1.0),
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]),
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dict(
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type='GenerateTarget',
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encoder=codec,
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use_dataset_keypoint_weights=True),
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dict(type='PackPoseInputs')
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]
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val_pipeline = [
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dict(type='LoadImage', backend_args=backend_args),
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dict(type='GetBBoxCenterScale'),
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dict(type='TopdownAffine', input_size=codec['input_size']),
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dict(type='PackPoseInputs')
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]
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train_pipeline_stage2 = [
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dict(type='LoadImage', backend_args=backend_args),
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dict(type='GetBBoxCenterScale'),
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dict(type='RandomFlip', direction='horizontal'),
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dict(type='RandomHalfBody'),
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dict(
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type='RandomBBoxTransform',
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shift_factor=0.,
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scale_factor=[0.6, 1.4],
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rotate_factor=80),
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dict(type='TopdownAffine', input_size=codec['input_size']),
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dict(
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type='Albumentation',
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transforms=[
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dict(type='Blur', p=0.1),
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dict(type='MedianBlur', p=0.1),
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dict(
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type='CoarseDropout',
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max_holes=1,
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max_height=0.4,
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max_width=0.4,
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min_holes=1,
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min_height=0.2,
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min_width=0.2,
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p=0.5),
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]),
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dict(
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type='GenerateTarget',
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encoder=codec,
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use_dataset_keypoint_weights=True),
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dict(type='PackPoseInputs')
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]
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# mapping
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coco_halpe26 = [(i, i) for i in range(17)] + [(17, 20), (18, 22), (19, 24),
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(20, 21), (21, 23), (22, 25)]
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aic_halpe26 = [(0, 6), (1, 8), (2, 10), (3, 5), (4, 7),
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(5, 9), (6, 12), (7, 14), (8, 16), (9, 11), (10, 13), (11, 15),
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(12, 17), (13, 18)]
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crowdpose_halpe26 = [(0, 5), (1, 6), (2, 7), (3, 8), (4, 9), (5, 10), (6, 11),
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(7, 12), (8, 13), (9, 14), (10, 15), (11, 16), (12, 17),
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(13, 18)]
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mpii_halpe26 = [
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(0, 16),
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(1, 14),
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(2, 12),
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(3, 11),
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(4, 13),
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(5, 15),
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(8, 18),
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(9, 17),
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(10, 10),
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(11, 8),
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(12, 6),
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(13, 5),
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(14, 7),
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(15, 9),
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]
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jhmdb_halpe26 = [
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(0, 18),
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(2, 17),
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(3, 6),
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(4, 5),
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(5, 12),
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(6, 11),
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(7, 8),
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(8, 7),
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(9, 14),
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(10, 13),
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(11, 10),
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(12, 9),
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(13, 16),
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(14, 15),
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]
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halpe_halpe26 = [(i, i) for i in range(26)]
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ochuman_halpe26 = [(i, i) for i in range(17)]
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posetrack_halpe26 = [
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(0, 0),
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(2, 17),
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(3, 3),
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(4, 4),
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(5, 5),
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(6, 6),
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(7, 7),
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(8, 8),
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(9, 9),
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(10, 10),
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(11, 11),
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(12, 12),
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(13, 13),
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(14, 14),
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(15, 15),
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(16, 16),
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]
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# train datasets
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dataset_coco = dict(
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type=dataset_type,
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data_root=data_root,
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data_mode=data_mode,
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ann_file='coco/annotations/coco_wholebody_train_v1.0.json',
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data_prefix=dict(img='detection/coco/train2017/'),
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pipeline=[
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dict(
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type='KeypointConverter',
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num_keypoints=num_keypoints,
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mapping=coco_halpe26)
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],
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)
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dataset_aic = dict(
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type='AicDataset',
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data_root=data_root,
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data_mode=data_mode,
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ann_file='aic/annotations/aic_train.json',
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data_prefix=dict(img='pose/ai_challenge/ai_challenger_keypoint'
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'_train_20170902/keypoint_train_images_20170902/'),
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pipeline=[
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dict(
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type='KeypointConverter',
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num_keypoints=num_keypoints,
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mapping=aic_halpe26)
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],
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)
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dataset_crowdpose = dict(
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type='CrowdPoseDataset',
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data_root=data_root,
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data_mode=data_mode,
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ann_file='crowdpose/annotations/mmpose_crowdpose_trainval.json',
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data_prefix=dict(img='pose/CrowdPose/images/'),
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pipeline=[
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dict(
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type='KeypointConverter',
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num_keypoints=num_keypoints,
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mapping=crowdpose_halpe26)
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],
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)
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dataset_mpii = dict(
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type='MpiiDataset',
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data_root=data_root,
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data_mode=data_mode,
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ann_file='mpii/annotations/mpii_train.json',
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data_prefix=dict(img='pose/MPI/images/'),
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pipeline=[
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dict(
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type='KeypointConverter',
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num_keypoints=num_keypoints,
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mapping=mpii_halpe26)
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],
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)
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dataset_jhmdb = dict(
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type='JhmdbDataset',
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data_root=data_root,
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data_mode=data_mode,
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ann_file='jhmdb/annotations/Sub1_train.json',
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data_prefix=dict(img='pose/JHMDB/'),
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pipeline=[
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dict(
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type='KeypointConverter',
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num_keypoints=num_keypoints,
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mapping=jhmdb_halpe26)
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],
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)
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dataset_halpe = dict(
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type='HalpeDataset',
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data_root=data_root,
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data_mode=data_mode,
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ann_file='halpe/annotations/halpe_train_v1.json',
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data_prefix=dict(img='pose/Halpe/hico_20160224_det/images/train2015'),
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pipeline=[
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dict(
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type='KeypointConverter',
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num_keypoints=num_keypoints,
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mapping=halpe_halpe26)
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],
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)
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dataset_posetrack = dict(
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type='PoseTrack18Dataset',
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data_root=data_root,
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data_mode=data_mode,
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ann_file='posetrack18/annotations/posetrack18_train.json',
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data_prefix=dict(img='pose/PoseChallenge2018/'),
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pipeline=[
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dict(
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type='KeypointConverter',
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num_keypoints=num_keypoints,
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mapping=posetrack_halpe26)
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],
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)
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# data loaders
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train_dataloader = dict(
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batch_size=train_batch_size,
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num_workers=10,
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pin_memory=True,
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persistent_workers=True,
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sampler=dict(type='DefaultSampler', shuffle=True),
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dataset=dict(
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type='CombinedDataset',
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metainfo=dict(from_file='configs/_base_/datasets/halpe26.py'),
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datasets=[
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dataset_coco,
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dataset_aic,
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dataset_crowdpose,
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dataset_mpii,
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dataset_jhmdb,
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dataset_halpe,
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dataset_posetrack,
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],
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pipeline=train_pipeline,
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test_mode=False,
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))
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# val datasets
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val_coco = dict(
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type=dataset_type,
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data_root=data_root,
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data_mode=data_mode,
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ann_file='coco/annotations/coco_wholebody_val_v1.0.json',
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data_prefix=dict(img='detection/coco/val2017/'),
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pipeline=[
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dict(
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type='KeypointConverter',
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num_keypoints=num_keypoints,
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mapping=coco_halpe26)
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],
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)
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val_aic = dict(
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type='AicDataset',
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data_root=data_root,
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data_mode=data_mode,
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ann_file='aic/annotations/aic_val.json',
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data_prefix=dict(
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img='pose/ai_challenge/ai_challenger_keypoint'
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'_validation_20170911/keypoint_validation_images_20170911/'),
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pipeline=[
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dict(
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type='KeypointConverter',
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num_keypoints=num_keypoints,
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mapping=aic_halpe26)
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],
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)
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val_crowdpose = dict(
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type='CrowdPoseDataset',
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data_root=data_root,
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data_mode=data_mode,
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ann_file='crowdpose/annotations/mmpose_crowdpose_test.json',
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data_prefix=dict(img='pose/CrowdPose/images/'),
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pipeline=[
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dict(
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type='KeypointConverter',
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num_keypoints=num_keypoints,
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mapping=crowdpose_halpe26)
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],
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)
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val_mpii = dict(
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type='MpiiDataset',
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data_root=data_root,
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data_mode=data_mode,
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ann_file='mpii/annotations/mpii_val.json',
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data_prefix=dict(img='pose/MPI/images/'),
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pipeline=[
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dict(
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type='KeypointConverter',
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num_keypoints=num_keypoints,
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mapping=mpii_halpe26)
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],
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)
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val_jhmdb = dict(
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type='JhmdbDataset',
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data_root=data_root,
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data_mode=data_mode,
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ann_file='jhmdb/annotations/Sub1_test.json',
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data_prefix=dict(img='pose/JHMDB/'),
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pipeline=[
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dict(
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type='KeypointConverter',
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num_keypoints=num_keypoints,
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mapping=jhmdb_halpe26)
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],
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)
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val_halpe = dict(
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type='HalpeDataset',
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data_root=data_root,
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data_mode=data_mode,
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ann_file='halpe/annotations/halpe_val_v1.json',
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data_prefix=dict(img='detection/coco/val2017/'),
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pipeline=[
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dict(
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type='KeypointConverter',
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num_keypoints=num_keypoints,
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mapping=halpe_halpe26)
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],
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)
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val_ochuman = dict(
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type='OCHumanDataset',
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data_root=data_root,
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data_mode=data_mode,
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ann_file='ochuman/annotations/'
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'ochuman_coco_format_val_range_0.00_1.00.json',
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data_prefix=dict(img='pose/OCHuman/images/'),
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pipeline=[
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dict(
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type='KeypointConverter',
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num_keypoints=num_keypoints,
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mapping=ochuman_halpe26)
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],
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)
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val_posetrack = dict(
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type='PoseTrack18Dataset',
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data_root=data_root,
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data_mode=data_mode,
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ann_file='posetrack18/annotations/posetrack18_val.json',
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data_prefix=dict(img='pose/PoseChallenge2018/'),
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pipeline=[
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dict(
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type='KeypointConverter',
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num_keypoints=num_keypoints,
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mapping=posetrack_halpe26)
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],
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)
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val_dataloader = dict(
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batch_size=val_batch_size,
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num_workers=10,
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persistent_workers=True,
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drop_last=False,
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sampler=dict(type='DefaultSampler', shuffle=False, round_up=False),
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dataset=dict(
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type='CombinedDataset',
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metainfo=dict(from_file='configs/_base_/datasets/halpe26.py'),
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datasets=[
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val_coco,
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val_aic,
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val_crowdpose,
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val_mpii,
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val_jhmdb,
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val_halpe,
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val_ochuman,
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val_posetrack,
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],
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pipeline=val_pipeline,
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test_mode=True,
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))
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test_dataloader = val_dataloader
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# hooks
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default_hooks = dict(
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checkpoint=dict(save_best='AUC', rule='greater', max_keep_ckpts=1))
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custom_hooks = [
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dict(
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type='EMAHook',
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ema_type='ExpMomentumEMA',
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momentum=0.0002,
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update_buffers=True,
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priority=49),
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dict(
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type='mmdet.PipelineSwitchHook',
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switch_epoch=max_epochs - stage2_num_epochs,
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switch_pipeline=train_pipeline_stage2)
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]
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# evaluators
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test_evaluator = [dict(type='PCKAccuracy', thr=0.1), dict(type='AUC')]
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val_evaluator = test_evaluator
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