mirror of https://github.com/open-mmlab/mmpose
530 lines
13 KiB
Python
530 lines
13 KiB
Python
_base_ = ['../../../_base_/default_runtime.py']
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# runtime
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train_cfg = dict(max_epochs=600, val_interval=20, dynamic_intervals=[(580, 1)])
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auto_scale_lr = dict(base_batch_size=256)
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default_hooks = dict(
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checkpoint=dict(type='CheckpointHook', interval=40, max_keep_ckpts=3))
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optim_wrapper = dict(
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type='OptimWrapper',
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constructor='ForceDefaultOptimWrapperConstructor',
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optimizer=dict(type='AdamW', lr=0.004, weight_decay=0.05),
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paramwise_cfg=dict(
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norm_decay_mult=0,
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bias_decay_mult=0,
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bypass_duplicate=True,
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force_default_settings=True,
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custom_keys=dict({'neck.encoder': dict(lr_mult=0.05)})),
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clip_grad=dict(max_norm=0.1, norm_type=2))
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param_scheduler = [
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dict(
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type='QuadraticWarmupLR',
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by_epoch=True,
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begin=0,
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end=5,
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convert_to_iter_based=True),
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dict(
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type='CosineAnnealingLR',
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eta_min=0.0002,
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begin=5,
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T_max=280,
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end=280,
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by_epoch=True,
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convert_to_iter_based=True),
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# this scheduler is used to increase the lr from 2e-4 to 5e-4
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dict(type='ConstantLR', by_epoch=True, factor=2.5, begin=280, end=281),
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dict(
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type='CosineAnnealingLR',
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eta_min=0.0002,
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begin=281,
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T_max=300,
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end=580,
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by_epoch=True,
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convert_to_iter_based=True),
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dict(type='ConstantLR', by_epoch=True, factor=1, begin=580, end=600),
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]
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# data
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input_size = (416, 416)
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metafile = 'configs/_base_/datasets/coco.py'
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codec = dict(type='YOLOXPoseAnnotationProcessor', input_size=input_size)
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train_pipeline_stage1 = [
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dict(type='LoadImage', backend_args=None),
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dict(
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type='Mosaic',
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img_scale=(416, 416),
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pad_val=114.0,
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pre_transform=[dict(type='LoadImage', backend_args=None)]),
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dict(
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type='BottomupRandomAffine',
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input_size=(416, 416),
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shift_factor=0.1,
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rotate_factor=10,
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scale_factor=(0.75, 1.0),
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pad_val=114,
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distribution='uniform',
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transform_mode='perspective',
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bbox_keep_corner=False,
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clip_border=True,
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),
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dict(type='YOLOXHSVRandomAug'),
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dict(type='RandomFlip'),
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dict(type='FilterAnnotations', by_kpt=True, by_box=True, keep_empty=False),
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dict(type='GenerateTarget', encoder=codec),
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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'),
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dict(
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type='BottomupRandomAffine',
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input_size=(416, 416),
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shift_prob=0,
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rotate_prob=0,
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scale_prob=0,
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scale_type='long',
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pad_val=(114, 114, 114),
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bbox_keep_corner=False,
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clip_border=True,
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),
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dict(type='YOLOXHSVRandomAug'),
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dict(type='RandomFlip'),
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dict(type='BottomupGetHeatmapMask', get_invalid=True),
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dict(type='FilterAnnotations', by_kpt=True, by_box=True, keep_empty=False),
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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 settings
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data_mode = 'bottomup'
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data_root = 'data/'
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# mapping
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aic_coco = [
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(0, 6),
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(1, 8),
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(2, 10),
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(3, 5),
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(4, 7),
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(5, 9),
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(6, 12),
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(7, 14),
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(8, 16),
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(9, 11),
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(10, 13),
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(11, 15),
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]
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crowdpose_coco = [
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(0, 5),
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(1, 6),
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(2, 7),
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(3, 8),
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(4, 9),
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(5, 10),
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(6, 11),
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(7, 12),
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(8, 13),
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(9, 14),
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(10, 15),
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(11, 16),
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]
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mpii_coco = [
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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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(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_coco = [
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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_coco = [
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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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]
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ochuman_coco = [
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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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]
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posetrack_coco = [
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(0, 0),
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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='CocoDataset',
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data_root=data_root,
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data_mode=data_mode,
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ann_file='coco/annotations/person_keypoints_train2017.json',
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data_prefix=dict(img='coco/train2017/'),
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pipeline=[
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dict(
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type='KeypointConverter',
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num_keypoints=17,
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mapping=[(i, i) for i in range(17)])
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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(type='KeypointConverter', num_keypoints=17, mapping=aic_coco)
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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', num_keypoints=17, mapping=crowdpose_coco)
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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(type='KeypointConverter', num_keypoints=17, mapping=mpii_coco)
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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(type='KeypointConverter', num_keypoints=17, mapping=jhmdb_coco)
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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(type='KeypointConverter', num_keypoints=17, mapping=halpe_coco)
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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', num_keypoints=17, mapping=posetrack_coco)
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],
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)
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train_dataset = dict(
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type='CombinedDataset',
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metainfo=dict(from_file=metafile),
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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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sample_ratio_factor=[1, 0.3, 0.5, 0.3, 0.3, 0.4, 0.3],
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test_mode=False,
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pipeline=train_pipeline_stage1)
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train_dataloader = dict(
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batch_size=32,
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num_workers=8,
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persistent_workers=True,
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pin_memory=True,
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sampler=dict(type='DefaultSampler', shuffle=True),
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dataset=train_dataset)
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# val datasets
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val_pipeline = [
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dict(type='LoadImage'),
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dict(
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type='BottomupResize', input_size=input_size, pad_val=(114, 114, 114)),
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dict(
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type='PackPoseInputs',
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meta_keys=('id', 'img_id', 'img_path', 'ori_shape', 'img_shape',
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'input_size', 'input_center', 'input_scale'))
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]
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val_dataloader = dict(
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batch_size=1,
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num_workers=2,
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persistent_workers=True,
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pin_memory=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='CocoDataset',
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data_root=data_root,
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data_mode=data_mode,
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ann_file='coco/annotations/person_keypoints_val2017.json',
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data_prefix=dict(img='coco/val2017/'),
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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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# evaluators
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val_evaluator = dict(
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type='CocoMetric',
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ann_file=data_root + 'coco/annotations/person_keypoints_val2017.json',
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score_mode='bbox',
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nms_mode='none',
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)
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test_evaluator = val_evaluator
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# hooks
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custom_hooks = [
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dict(
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type='YOLOXPoseModeSwitchHook',
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num_last_epochs=20,
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new_train_dataset=dataset_coco,
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new_train_pipeline=train_pipeline_stage2,
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priority=48),
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dict(
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type='RTMOModeSwitchHook',
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epoch_attributes={
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280: {
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'proxy_target_cc': True,
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'loss_mle.loss_weight': 5.0,
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'loss_oks.loss_weight': 10.0
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},
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},
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priority=48),
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dict(type='SyncNormHook', priority=48),
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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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strict_load=False,
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priority=49),
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]
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# model
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widen_factor = 0.375
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deepen_factor = 0.33
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model = dict(
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type='BottomupPoseEstimator',
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init_cfg=dict(
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type='Kaiming',
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layer='Conv2d',
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a=2.23606797749979,
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distribution='uniform',
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mode='fan_in',
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nonlinearity='leaky_relu'),
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data_preprocessor=dict(
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type='PoseDataPreprocessor',
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pad_size_divisor=32,
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mean=[0, 0, 0],
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std=[1, 1, 1],
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batch_augments=[
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dict(
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type='BatchSyncRandomResize',
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random_size_range=(320, 640),
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size_divisor=32,
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interval=1),
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]),
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backbone=dict(
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type='CSPDarknet',
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deepen_factor=deepen_factor,
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widen_factor=widen_factor,
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out_indices=(2, 3, 4),
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spp_kernal_sizes=(5, 9, 13),
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norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
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act_cfg=dict(type='Swish'),
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init_cfg=dict(
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type='Pretrained',
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checkpoint='https://download.openmmlab.com/mmdetection/v2.0/'
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'yolox/yolox_tiny_8x8_300e_coco/yolox_tiny_8x8_300e_coco_'
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'20211124_171234-b4047906.pth',
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prefix='backbone.',
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)),
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neck=dict(
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type='HybridEncoder',
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in_channels=[96, 192, 384],
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deepen_factor=deepen_factor,
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widen_factor=widen_factor,
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hidden_dim=256,
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output_indices=[1, 2],
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encoder_cfg=dict(
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self_attn_cfg=dict(embed_dims=256, num_heads=8, dropout=0.0),
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ffn_cfg=dict(
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embed_dims=256,
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feedforward_channels=1024,
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ffn_drop=0.0,
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act_cfg=dict(type='GELU'))),
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projector=dict(
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type='ChannelMapper',
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in_channels=[256, 256],
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kernel_size=1,
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out_channels=192,
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act_cfg=None,
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norm_cfg=dict(type='BN'),
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num_outs=2)),
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head=dict(
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type='RTMOHead',
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num_keypoints=17,
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featmap_strides=(16, 32),
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head_module_cfg=dict(
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num_classes=1,
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in_channels=256,
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cls_feat_channels=256,
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channels_per_group=36,
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pose_vec_channels=192,
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widen_factor=widen_factor,
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stacked_convs=2,
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norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
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act_cfg=dict(type='Swish')),
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assigner=dict(
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type='SimOTAAssigner',
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dynamic_k_indicator='oks',
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oks_calculator=dict(type='PoseOKS', metainfo=metafile),
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use_keypoints_for_center=True),
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prior_generator=dict(
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type='MlvlPointGenerator',
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centralize_points=True,
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strides=[16, 32]),
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dcc_cfg=dict(
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in_channels=192,
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feat_channels=128,
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num_bins=(192, 256),
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spe_channels=128,
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gau_cfg=dict(
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s=128,
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expansion_factor=2,
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dropout_rate=0.0,
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drop_path=0.0,
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act_fn='SiLU',
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pos_enc='add')),
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overlaps_power=0.5,
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loss_cls=dict(
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type='VariFocalLoss',
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reduction='sum',
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use_target_weight=True,
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loss_weight=1.0),
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loss_bbox=dict(
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type='IoULoss',
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mode='square',
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eps=1e-16,
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reduction='sum',
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loss_weight=5.0),
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loss_oks=dict(
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type='OKSLoss',
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reduction='none',
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metainfo=metafile,
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loss_weight=30.0),
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loss_vis=dict(
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type='BCELoss',
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use_target_weight=True,
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reduction='mean',
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loss_weight=1.0),
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loss_mle=dict(
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type='MLECCLoss',
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use_target_weight=True,
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loss_weight=1.0,
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),
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loss_bbox_aux=dict(type='L1Loss', reduction='sum', loss_weight=1.0),
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),
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test_cfg=dict(
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input_size=input_size,
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score_thr=0.1,
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nms_thr=0.65,
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))
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