mirror of https://github.com/open-mmlab/mmpose
235 lines
6.6 KiB
Python
235 lines
6.6 KiB
Python
_base_ = ['../../../_base_/default_runtime.py']
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# runtime
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max_epochs = 210
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stage2_num_epochs = 30
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base_lr = 5e-4
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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=0.05),
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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=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=(192, 256),
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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=0.67,
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widen_factor=0.75,
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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/cspnext-m_udp-aic-coco_210e-256x192-f2f7d6f6_20230130.pth' # noqa
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)),
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head=dict(
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type='RTMCCHead',
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in_channels=768,
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out_channels=14,
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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 = 'CrowdPoseDataset'
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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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# backend_args = dict(
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# backend='petrel',
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# path_mapping=dict({
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# f'{data_root}': 's3://openmmlab/datasets/',
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# f'{data_root}': 's3://openmmlab/datasets/'
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# }))
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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='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=1.0),
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]),
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dict(type='GenerateTarget', encoder=codec),
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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(type='GenerateTarget', encoder=codec),
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dict(type='PackPoseInputs')
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]
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# data loaders
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train_dataloader = dict(
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batch_size=64,
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num_workers=10,
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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=dataset_type,
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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=train_pipeline,
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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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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='crowdpose/annotations/mmpose_crowdpose_test.json',
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bbox_file='data/crowdpose/annotations/det_for_crowd_test_0.1_0.5.json',
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data_prefix=dict(img='pose/CrowdPose/images/'),
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test_mode=True,
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pipeline=val_pipeline,
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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(
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save_best='crowdpose/AP', 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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val_evaluator = dict(
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type='CocoMetric',
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ann_file=data_root + 'crowdpose/annotations/mmpose_crowdpose_test.json',
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use_area=False,
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iou_type='keypoints_crowd',
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prefix='crowdpose')
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test_evaluator = val_evaluator
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