mirror of https://github.com/open-mmlab/mmpose
692 lines
14 KiB
Python
692 lines
14 KiB
Python
_base_ = ['../../../_base_/default_runtime.py']
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# lapa coco wflw 300w cofw halpe
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# runtime
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max_epochs = 120
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stage2_num_epochs = 10
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base_lr = 4e-3
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train_cfg = dict(max_epochs=max_epochs, val_interval=1)
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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.),
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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 * 0.005,
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begin=30,
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end=max_epochs,
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T_max=max_epochs - 30,
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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=512)
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# codec settings
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codec = dict(
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type='SimCCLabel',
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input_size=(256, 256),
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sigma=(5.66, 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/mmdetection/v3.0/'
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'rtmdet/cspnext_rsb_pretrain/cspnext-s_imagenet_600e-ea671761.pth')
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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=106,
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input_size=codec['input_size'],
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in_featuremap_size=tuple([s // 32 for s in codec['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 = 'LapaDataset'
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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=[0.5, 1.5], rotate_factor=80),
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dict(type='TopdownAffine', input_size=codec['input_size']),
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dict(type='mmdet.YOLOXHSVRandomAug'),
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dict(
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type='Albumentation',
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transforms=[
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dict(type='Blur', p=0.2),
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dict(type='MedianBlur', p=0.2),
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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=[0.75, 1.25],
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rotate_factor=60),
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dict(type='TopdownAffine', input_size=codec['input_size']),
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dict(type='mmdet.YOLOXHSVRandomAug'),
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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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# train dataset
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dataset_lapa = 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='LaPa/annotations/lapa_trainval.json',
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data_prefix=dict(img='pose/LaPa/'),
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pipeline=[],
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)
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kpt_68_to_106 = [
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#
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(0, 0),
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(1, 2),
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(2, 4),
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(3, 6),
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(4, 8),
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(5, 10),
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(6, 12),
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(7, 14),
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(8, 16),
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(9, 18),
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(10, 20),
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(11, 22),
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(12, 24),
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(13, 26),
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(14, 28),
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(15, 30),
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(16, 32),
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#
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(17, 33),
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(18, 34),
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(19, 35),
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(20, 36),
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(21, 37),
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#
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(22, 42),
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(23, 43),
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(24, 44),
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(25, 45),
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(26, 46),
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#
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(27, 51),
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(28, 52),
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(29, 53),
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(30, 54),
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#
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(31, 58),
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(32, 59),
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(33, 60),
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(34, 61),
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(35, 62),
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#
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(36, 66),
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(39, 70),
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#
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((37, 38), 68),
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((40, 41), 72),
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#
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(42, 75),
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(45, 79),
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#
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((43, 44), 77),
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((46, 47), 81),
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#
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(48, 84),
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(49, 85),
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(50, 86),
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(51, 87),
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(52, 88),
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(53, 89),
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(54, 90),
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(55, 91),
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(56, 92),
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(57, 93),
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(58, 94),
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(59, 95),
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(60, 96),
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(61, 97),
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(62, 98),
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(63, 99),
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(64, 100),
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(65, 101),
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(66, 102),
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(67, 103)
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]
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mapping_halpe = [
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#
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(26, 0),
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(27, 2),
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(28, 4),
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(29, 6),
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(30, 8),
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(31, 10),
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(32, 12),
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(33, 14),
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(34, 16),
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(35, 18),
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(36, 20),
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(37, 22),
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(38, 24),
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(39, 26),
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(40, 28),
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(41, 30),
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(42, 32),
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#
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(43, 33),
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(44, 34),
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(45, 35),
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(46, 36),
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(47, 37),
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#
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(48, 42),
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(49, 43),
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(50, 44),
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(51, 45),
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(52, 46),
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#
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(53, 51),
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(54, 52),
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(55, 53),
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(56, 54),
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#
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(57, 58),
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(58, 59),
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(59, 60),
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(60, 61),
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(61, 62),
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#
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(62, 66),
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(65, 70),
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#
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((63, 64), 68),
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((66, 67), 72),
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#
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(68, 75),
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(71, 79),
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#
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((69, 70), 77),
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((72, 73), 81),
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#
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(74, 84),
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(75, 85),
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(76, 86),
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(77, 87),
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(78, 88),
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(79, 89),
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(80, 90),
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(81, 91),
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(82, 92),
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(83, 93),
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(84, 94),
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(85, 95),
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(86, 96),
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(87, 97),
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(88, 98),
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(89, 99),
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(90, 100),
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(91, 101),
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(92, 102),
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(93, 103)
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]
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mapping_wflw = [
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#
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(0, 0),
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(1, 1),
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(2, 2),
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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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(17, 17),
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(18, 18),
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(19, 19),
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(20, 20),
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(21, 21),
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(22, 22),
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(23, 23),
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(24, 24),
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(25, 25),
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(26, 26),
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(27, 27),
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(28, 28),
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(29, 29),
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(30, 30),
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(31, 31),
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(32, 32),
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#
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(33, 33),
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(34, 34),
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(35, 35),
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(36, 36),
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(37, 37),
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(38, 38),
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(39, 39),
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(40, 40),
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(41, 41),
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#
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(42, 42),
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(43, 43),
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(44, 44),
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(45, 45),
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(46, 46),
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(47, 47),
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(48, 48),
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(49, 49),
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(50, 50),
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#
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(51, 51),
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(52, 52),
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(53, 53),
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(54, 54),
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#
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(55, 58),
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(56, 59),
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(57, 60),
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(58, 61),
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(59, 62),
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#
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(60, 66),
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(61, 67),
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(62, 68),
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(63, 69),
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(64, 70),
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(65, 71),
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(66, 72),
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(67, 73),
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#
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(68, 75),
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(69, 76),
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(70, 77),
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(71, 78),
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(72, 79),
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(73, 80),
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(74, 81),
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(75, 82),
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#
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(76, 84),
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(77, 85),
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(78, 86),
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(79, 87),
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(80, 88),
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(81, 89),
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(82, 90),
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(83, 91),
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(84, 92),
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(85, 93),
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(86, 94),
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(87, 95),
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(88, 96),
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(89, 97),
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(90, 98),
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(91, 99),
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(92, 100),
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(93, 101),
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(94, 102),
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(95, 103),
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#
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(96, 104),
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#
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(97, 105)
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]
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mapping_cofw = [
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#
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(0, 33),
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(2, 38),
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(4, 35),
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(5, 40),
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#
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(1, 46),
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(3, 50),
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(6, 44),
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(7, 48),
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#
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(8, 60),
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(10, 64),
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(12, 62),
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(13, 66),
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#
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(9, 72),
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(11, 68),
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(14, 70),
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(15, 74),
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#
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(18, 57),
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(19, 63),
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(20, 54),
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(21, 60),
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#
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(22, 84),
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(23, 90),
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(24, 87),
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(25, 98),
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(26, 102),
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(27, 93),
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#
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(28, 16)
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]
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dataset_coco = dict(
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type='CocoWholeBodyFaceDataset',
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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', num_keypoints=106, mapping=kpt_68_to_106)
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],
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)
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dataset_wflw = dict(
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type='WFLWDataset',
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data_root=data_root,
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data_mode=data_mode,
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ann_file='wflw/annotations/face_landmarks_wflw_train.json',
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data_prefix=dict(img='pose/WFLW/images/'),
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pipeline=[
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dict(
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type='KeypointConverter', num_keypoints=106, mapping=mapping_wflw)
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],
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)
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dataset_300w = dict(
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type='Face300WDataset',
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data_root=data_root,
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data_mode=data_mode,
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ann_file='300w/annotations/face_landmarks_300w_train.json',
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data_prefix=dict(img='pose/300w/images/'),
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pipeline=[
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dict(
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type='KeypointConverter', num_keypoints=106, mapping=kpt_68_to_106)
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],
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)
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dataset_cofw = dict(
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type='COFWDataset',
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data_root=data_root,
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data_mode=data_mode,
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ann_file='cofw/annotations/cofw_train.json',
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data_prefix=dict(img='pose/COFW/images/'),
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pipeline=[
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dict(
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type='KeypointConverter', num_keypoints=106, mapping=mapping_cofw)
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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_133kpt.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', num_keypoints=106, mapping=mapping_halpe)
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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=256,
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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/lapa.py'),
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datasets=[
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dataset_lapa, dataset_coco, dataset_wflw, dataset_300w,
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dataset_cofw, dataset_halpe
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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_dataloader = dict(
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batch_size=32,
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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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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=dataset_type,
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data_root=data_root,
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data_mode=data_mode,
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ann_file='LaPa/annotations/lapa_test.json',
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data_prefix=dict(img='pose/LaPa/'),
|
|
test_mode=True,
|
|
pipeline=val_pipeline,
|
|
))
|
|
|
|
# test dataset
|
|
val_lapa = dict(
|
|
type=dataset_type,
|
|
data_root=data_root,
|
|
data_mode=data_mode,
|
|
ann_file='LaPa/annotations/lapa_test.json',
|
|
data_prefix=dict(img='pose/LaPa/'),
|
|
pipeline=[],
|
|
)
|
|
|
|
val_coco = dict(
|
|
type='CocoWholeBodyFaceDataset',
|
|
data_root=data_root,
|
|
data_mode=data_mode,
|
|
ann_file='coco/annotations/coco_wholebody_val_v1.0.json',
|
|
data_prefix=dict(img='detection/coco/val2017/'),
|
|
pipeline=[
|
|
dict(
|
|
type='KeypointConverter', num_keypoints=106, mapping=kpt_68_to_106)
|
|
],
|
|
)
|
|
|
|
val_wflw = dict(
|
|
type='WFLWDataset',
|
|
data_root=data_root,
|
|
data_mode=data_mode,
|
|
ann_file='wflw/annotations/face_landmarks_wflw_test.json',
|
|
data_prefix=dict(img='pose/WFLW/images/'),
|
|
pipeline=[
|
|
dict(
|
|
type='KeypointConverter', num_keypoints=106, mapping=mapping_wflw)
|
|
],
|
|
)
|
|
|
|
val_300w = dict(
|
|
type='Face300WDataset',
|
|
data_root=data_root,
|
|
data_mode=data_mode,
|
|
ann_file='300w/annotations/face_landmarks_300w_test.json',
|
|
data_prefix=dict(img='pose/300w/images/'),
|
|
pipeline=[
|
|
dict(
|
|
type='KeypointConverter', num_keypoints=106, mapping=kpt_68_to_106)
|
|
],
|
|
)
|
|
|
|
val_cofw = dict(
|
|
type='COFWDataset',
|
|
data_root=data_root,
|
|
data_mode=data_mode,
|
|
ann_file='cofw/annotations/cofw_test.json',
|
|
data_prefix=dict(img='pose/COFW/images/'),
|
|
pipeline=[
|
|
dict(
|
|
type='KeypointConverter', num_keypoints=106, mapping=mapping_cofw)
|
|
],
|
|
)
|
|
|
|
val_halpe = dict(
|
|
type='HalpeDataset',
|
|
data_root=data_root,
|
|
data_mode=data_mode,
|
|
ann_file='halpe/annotations/halpe_val_v1.json',
|
|
data_prefix=dict(img='detection/coco/val2017/'),
|
|
pipeline=[
|
|
dict(
|
|
type='KeypointConverter', num_keypoints=106, mapping=mapping_halpe)
|
|
],
|
|
)
|
|
|
|
test_dataloader = dict(
|
|
batch_size=32,
|
|
num_workers=10,
|
|
persistent_workers=True,
|
|
drop_last=False,
|
|
sampler=dict(type='DefaultSampler', shuffle=False, round_up=False),
|
|
dataset=dict(
|
|
type='CombinedDataset',
|
|
metainfo=dict(from_file='configs/_base_/datasets/lapa.py'),
|
|
datasets=[val_lapa, val_coco, val_wflw, val_300w, val_cofw, val_halpe],
|
|
pipeline=val_pipeline,
|
|
test_mode=True,
|
|
))
|
|
|
|
# hooks
|
|
default_hooks = dict(
|
|
checkpoint=dict(
|
|
save_best='NME', rule='less', max_keep_ckpts=1, interval=1))
|
|
|
|
custom_hooks = [
|
|
dict(
|
|
type='EMAHook',
|
|
ema_type='ExpMomentumEMA',
|
|
momentum=0.0002,
|
|
update_buffers=True,
|
|
priority=49),
|
|
dict(
|
|
type='mmdet.PipelineSwitchHook',
|
|
switch_epoch=max_epochs - stage2_num_epochs,
|
|
switch_pipeline=train_pipeline_stage2)
|
|
]
|
|
|
|
# evaluators
|
|
val_evaluator = dict(
|
|
type='NME',
|
|
norm_mode='keypoint_distance',
|
|
)
|
|
test_evaluator = val_evaluator
|