mirror of https://github.com/open-mmlab/mmpose
616 lines
17 KiB
Python
616 lines
17 KiB
Python
_base_ = ['mmpose::_base_/default_runtime.py']
|
|
|
|
# common setting
|
|
num_keypoints = 133
|
|
input_size = (192, 256)
|
|
|
|
# runtime
|
|
max_epochs = 270
|
|
stage2_num_epochs = 10
|
|
base_lr = 5e-4
|
|
train_batch_size = 1024
|
|
val_batch_size = 32
|
|
|
|
train_cfg = dict(max_epochs=max_epochs, val_interval=10)
|
|
randomness = dict(seed=21)
|
|
|
|
# optimizer
|
|
optim_wrapper = dict(
|
|
type='OptimWrapper',
|
|
optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.05),
|
|
clip_grad=dict(max_norm=35, norm_type=2),
|
|
paramwise_cfg=dict(
|
|
norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True))
|
|
|
|
# learning rate
|
|
param_scheduler = [
|
|
dict(
|
|
type='LinearLR',
|
|
start_factor=1.0e-5,
|
|
by_epoch=False,
|
|
begin=0,
|
|
end=1000),
|
|
dict(
|
|
type='CosineAnnealingLR',
|
|
eta_min=base_lr * 0.05,
|
|
begin=max_epochs // 2,
|
|
end=max_epochs,
|
|
T_max=max_epochs // 2,
|
|
by_epoch=True,
|
|
convert_to_iter_based=True),
|
|
]
|
|
|
|
# automatically scaling LR based on the actual training batch size
|
|
auto_scale_lr = dict(base_batch_size=8192)
|
|
|
|
# codec settings
|
|
codec = dict(
|
|
type='SimCCLabel',
|
|
input_size=input_size,
|
|
sigma=(4.9, 5.66),
|
|
simcc_split_ratio=2.0,
|
|
normalize=False,
|
|
use_dark=False)
|
|
|
|
# model settings
|
|
model = dict(
|
|
type='TopdownPoseEstimator',
|
|
data_preprocessor=dict(
|
|
type='PoseDataPreprocessor',
|
|
mean=[123.675, 116.28, 103.53],
|
|
std=[58.395, 57.12, 57.375],
|
|
bgr_to_rgb=True),
|
|
backbone=dict(
|
|
type='CSPNeXt',
|
|
arch='P5',
|
|
expand_ratio=0.5,
|
|
deepen_factor=0.67,
|
|
widen_factor=0.75,
|
|
channel_attention=True,
|
|
norm_cfg=dict(type='BN'),
|
|
act_cfg=dict(type='SiLU'),
|
|
init_cfg=dict(
|
|
type='Pretrained',
|
|
prefix='backbone.',
|
|
checkpoint='https://download.openmmlab.com/mmpose/v1/projects/'
|
|
'rtmposev1/rtmpose-m_simcc-ucoco_dw-ucoco_270e-256x192-c8b76419_20230728.pth' # noqa
|
|
)),
|
|
neck=dict(
|
|
type='CSPNeXtPAFPN',
|
|
in_channels=[192, 384, 768],
|
|
out_channels=None,
|
|
out_indices=(
|
|
1,
|
|
2,
|
|
),
|
|
num_csp_blocks=2,
|
|
expand_ratio=0.5,
|
|
norm_cfg=dict(type='SyncBN'),
|
|
act_cfg=dict(type='SiLU', inplace=True)),
|
|
head=dict(
|
|
type='RTMWHead',
|
|
in_channels=768,
|
|
out_channels=num_keypoints,
|
|
input_size=input_size,
|
|
in_featuremap_size=tuple([s // 32 for s in input_size]),
|
|
simcc_split_ratio=codec['simcc_split_ratio'],
|
|
final_layer_kernel_size=7,
|
|
gau_cfg=dict(
|
|
hidden_dims=256,
|
|
s=128,
|
|
expansion_factor=2,
|
|
dropout_rate=0.,
|
|
drop_path=0.,
|
|
act_fn='SiLU',
|
|
use_rel_bias=False,
|
|
pos_enc=False),
|
|
loss=dict(
|
|
type='KLDiscretLoss',
|
|
use_target_weight=True,
|
|
beta=1.,
|
|
label_softmax=True,
|
|
label_beta=10.,
|
|
mask=list(range(23, 91)),
|
|
mask_weight=0.5,
|
|
),
|
|
decoder=codec),
|
|
test_cfg=dict(flip_test=True))
|
|
|
|
# base dataset settings
|
|
dataset_type = 'CocoWholeBodyDataset'
|
|
data_mode = 'topdown'
|
|
data_root = 'data/'
|
|
|
|
backend_args = dict(backend='local')
|
|
|
|
# pipelines
|
|
train_pipeline = [
|
|
dict(type='LoadImage', backend_args=backend_args),
|
|
dict(type='GetBBoxCenterScale'),
|
|
dict(type='RandomFlip', direction='horizontal'),
|
|
dict(type='RandomHalfBody'),
|
|
dict(
|
|
type='RandomBBoxTransform', scale_factor=[0.5, 1.5], rotate_factor=90),
|
|
dict(type='TopdownAffine', input_size=codec['input_size']),
|
|
dict(type='PhotometricDistortion'),
|
|
dict(
|
|
type='Albumentation',
|
|
transforms=[
|
|
dict(type='Blur', p=0.1),
|
|
dict(type='MedianBlur', p=0.1),
|
|
dict(
|
|
type='CoarseDropout',
|
|
max_holes=1,
|
|
max_height=0.4,
|
|
max_width=0.4,
|
|
min_holes=1,
|
|
min_height=0.2,
|
|
min_width=0.2,
|
|
p=0.5),
|
|
]),
|
|
dict(
|
|
type='GenerateTarget',
|
|
encoder=codec,
|
|
use_dataset_keypoint_weights=True),
|
|
dict(type='PackPoseInputs')
|
|
]
|
|
val_pipeline = [
|
|
dict(type='LoadImage', backend_args=backend_args),
|
|
dict(type='GetBBoxCenterScale'),
|
|
dict(type='TopdownAffine', input_size=codec['input_size']),
|
|
dict(type='PackPoseInputs')
|
|
]
|
|
train_pipeline_stage2 = [
|
|
dict(type='LoadImage', backend_args=backend_args),
|
|
dict(type='GetBBoxCenterScale'),
|
|
dict(type='RandomFlip', direction='horizontal'),
|
|
dict(type='RandomHalfBody'),
|
|
dict(
|
|
type='RandomBBoxTransform',
|
|
shift_factor=0.,
|
|
scale_factor=[0.5, 1.5],
|
|
rotate_factor=90),
|
|
dict(type='TopdownAffine', input_size=codec['input_size']),
|
|
dict(
|
|
type='Albumentation',
|
|
transforms=[
|
|
dict(type='Blur', p=0.1),
|
|
dict(type='MedianBlur', p=0.1),
|
|
]),
|
|
dict(
|
|
type='GenerateTarget',
|
|
encoder=codec,
|
|
use_dataset_keypoint_weights=True),
|
|
dict(type='PackPoseInputs')
|
|
]
|
|
|
|
# mapping
|
|
|
|
aic_coco133 = [(0, 6), (1, 8), (2, 10), (3, 5), (4, 7), (5, 9), (6, 12),
|
|
(7, 14), (8, 16), (9, 11), (10, 13), (11, 15)]
|
|
|
|
crowdpose_coco133 = [(0, 5), (1, 6), (2, 7), (3, 8), (4, 9), (5, 10), (6, 11),
|
|
(7, 12), (8, 13), (9, 14), (10, 15), (11, 16)]
|
|
|
|
mpii_coco133 = [
|
|
(0, 16),
|
|
(1, 14),
|
|
(2, 12),
|
|
(3, 11),
|
|
(4, 13),
|
|
(5, 15),
|
|
(10, 10),
|
|
(11, 8),
|
|
(12, 6),
|
|
(13, 5),
|
|
(14, 7),
|
|
(15, 9),
|
|
]
|
|
|
|
jhmdb_coco133 = [
|
|
(3, 6),
|
|
(4, 5),
|
|
(5, 12),
|
|
(6, 11),
|
|
(7, 8),
|
|
(8, 7),
|
|
(9, 14),
|
|
(10, 13),
|
|
(11, 10),
|
|
(12, 9),
|
|
(13, 16),
|
|
(14, 15),
|
|
]
|
|
|
|
halpe_coco133 = [(i, i)
|
|
for i in range(17)] + [(20, 17), (21, 20), (22, 18), (23, 21),
|
|
(24, 19),
|
|
(25, 22)] + [(i, i - 3)
|
|
for i in range(26, 136)]
|
|
|
|
posetrack_coco133 = [
|
|
(0, 0),
|
|
(3, 3),
|
|
(4, 4),
|
|
(5, 5),
|
|
(6, 6),
|
|
(7, 7),
|
|
(8, 8),
|
|
(9, 9),
|
|
(10, 10),
|
|
(11, 11),
|
|
(12, 12),
|
|
(13, 13),
|
|
(14, 14),
|
|
(15, 15),
|
|
(16, 16),
|
|
]
|
|
|
|
humanart_coco133 = [(i, i) for i in range(17)] + [(17, 99), (18, 120),
|
|
(19, 17), (20, 20)]
|
|
|
|
# train datasets
|
|
dataset_coco = dict(
|
|
type=dataset_type,
|
|
data_root=data_root,
|
|
data_mode=data_mode,
|
|
ann_file='coco/annotations/coco_wholebody_train_v1.0.json',
|
|
data_prefix=dict(img='detection/coco/train2017/'),
|
|
pipeline=[],
|
|
)
|
|
|
|
dataset_aic = dict(
|
|
type='AicDataset',
|
|
data_root=data_root,
|
|
data_mode=data_mode,
|
|
ann_file='aic/annotations/aic_train.json',
|
|
data_prefix=dict(img='pose/ai_challenge/ai_challenger_keypoint'
|
|
'_train_20170902/keypoint_train_images_20170902/'),
|
|
pipeline=[
|
|
dict(
|
|
type='KeypointConverter',
|
|
num_keypoints=num_keypoints,
|
|
mapping=aic_coco133)
|
|
],
|
|
)
|
|
|
|
dataset_crowdpose = dict(
|
|
type='CrowdPoseDataset',
|
|
data_root=data_root,
|
|
data_mode=data_mode,
|
|
ann_file='crowdpose/annotations/mmpose_crowdpose_trainval.json',
|
|
data_prefix=dict(img='pose/CrowdPose/images/'),
|
|
pipeline=[
|
|
dict(
|
|
type='KeypointConverter',
|
|
num_keypoints=num_keypoints,
|
|
mapping=crowdpose_coco133)
|
|
],
|
|
)
|
|
|
|
dataset_mpii = dict(
|
|
type='MpiiDataset',
|
|
data_root=data_root,
|
|
data_mode=data_mode,
|
|
ann_file='mpii/annotations/mpii_train.json',
|
|
data_prefix=dict(img='pose/MPI/images/'),
|
|
pipeline=[
|
|
dict(
|
|
type='KeypointConverter',
|
|
num_keypoints=num_keypoints,
|
|
mapping=mpii_coco133)
|
|
],
|
|
)
|
|
|
|
dataset_jhmdb = dict(
|
|
type='JhmdbDataset',
|
|
data_root=data_root,
|
|
data_mode=data_mode,
|
|
ann_file='jhmdb/annotations/Sub1_train.json',
|
|
data_prefix=dict(img='pose/JHMDB/'),
|
|
pipeline=[
|
|
dict(
|
|
type='KeypointConverter',
|
|
num_keypoints=num_keypoints,
|
|
mapping=jhmdb_coco133)
|
|
],
|
|
)
|
|
|
|
dataset_halpe = dict(
|
|
type='HalpeDataset',
|
|
data_root=data_root,
|
|
data_mode=data_mode,
|
|
ann_file='halpe/annotations/halpe_train_v1.json',
|
|
data_prefix=dict(img='pose/Halpe/hico_20160224_det/images/train2015'),
|
|
pipeline=[
|
|
dict(
|
|
type='KeypointConverter',
|
|
num_keypoints=num_keypoints,
|
|
mapping=halpe_coco133)
|
|
],
|
|
)
|
|
|
|
dataset_posetrack = dict(
|
|
type='PoseTrack18Dataset',
|
|
data_root=data_root,
|
|
data_mode=data_mode,
|
|
ann_file='posetrack18/annotations/posetrack18_train.json',
|
|
data_prefix=dict(img='pose/PoseChallenge2018/'),
|
|
pipeline=[
|
|
dict(
|
|
type='KeypointConverter',
|
|
num_keypoints=num_keypoints,
|
|
mapping=posetrack_coco133)
|
|
],
|
|
)
|
|
|
|
dataset_humanart = dict(
|
|
type='HumanArt21Dataset',
|
|
data_root=data_root,
|
|
data_mode=data_mode,
|
|
ann_file='HumanArt/annotations/training_humanart.json',
|
|
filter_cfg=dict(scenes=['real_human']),
|
|
data_prefix=dict(img='pose/'),
|
|
pipeline=[
|
|
dict(
|
|
type='KeypointConverter',
|
|
num_keypoints=num_keypoints,
|
|
mapping=humanart_coco133)
|
|
])
|
|
|
|
ubody_scenes = [
|
|
'Magic_show', 'Entertainment', 'ConductMusic', 'Online_class', 'TalkShow',
|
|
'Speech', 'Fitness', 'Interview', 'Olympic', 'TVShow', 'Singing',
|
|
'SignLanguage', 'Movie', 'LiveVlog', 'VideoConference'
|
|
]
|
|
|
|
ubody_datasets = []
|
|
for scene in ubody_scenes:
|
|
each = dict(
|
|
type='UBody2dDataset',
|
|
data_root=data_root,
|
|
data_mode=data_mode,
|
|
ann_file=f'Ubody/annotations/{scene}/train_annotations.json',
|
|
data_prefix=dict(img='pose/UBody/images/'),
|
|
pipeline=[],
|
|
sample_interval=10)
|
|
ubody_datasets.append(each)
|
|
|
|
dataset_ubody = dict(
|
|
type='CombinedDataset',
|
|
metainfo=dict(from_file='configs/_base_/datasets/ubody2d.py'),
|
|
datasets=ubody_datasets,
|
|
pipeline=[],
|
|
test_mode=False,
|
|
)
|
|
|
|
face_pipeline = [
|
|
dict(type='LoadImage', backend_args=backend_args),
|
|
dict(type='GetBBoxCenterScale', padding=1.25),
|
|
dict(
|
|
type='RandomBBoxTransform',
|
|
shift_factor=0.,
|
|
scale_factor=[1.5, 2.0],
|
|
rotate_factor=0),
|
|
]
|
|
|
|
wflw_coco133 = [(i * 2, 23 + i)
|
|
for i in range(17)] + [(33 + i, 40 + i) for i in range(5)] + [
|
|
(42 + i, 45 + i) for i in range(5)
|
|
] + [(51 + i, 50 + i)
|
|
for i in range(9)] + [(60, 59), (61, 60), (63, 61),
|
|
(64, 62), (65, 63), (67, 64),
|
|
(68, 65), (69, 66), (71, 67),
|
|
(72, 68), (73, 69),
|
|
(75, 70)] + [(76 + i, 71 + i)
|
|
for i in range(20)]
|
|
dataset_wflw = dict(
|
|
type='WFLWDataset',
|
|
data_root=data_root,
|
|
data_mode=data_mode,
|
|
ann_file='wflw/annotations/face_landmarks_wflw_train.json',
|
|
data_prefix=dict(img='pose/WFLW/images/'),
|
|
pipeline=[
|
|
dict(
|
|
type='KeypointConverter',
|
|
num_keypoints=num_keypoints,
|
|
mapping=wflw_coco133), *face_pipeline
|
|
],
|
|
)
|
|
|
|
mapping_300w_coco133 = [(i, 23 + i) for i in range(68)]
|
|
dataset_300w = dict(
|
|
type='Face300WDataset',
|
|
data_root=data_root,
|
|
data_mode=data_mode,
|
|
ann_file='300w/annotations/face_landmarks_300w_train.json',
|
|
data_prefix=dict(img='pose/300w/images/'),
|
|
pipeline=[
|
|
dict(
|
|
type='KeypointConverter',
|
|
num_keypoints=num_keypoints,
|
|
mapping=mapping_300w_coco133), *face_pipeline
|
|
],
|
|
)
|
|
|
|
cofw_coco133 = [(0, 40), (2, 44), (4, 42), (1, 49), (3, 45), (6, 47), (8, 59),
|
|
(10, 62), (9, 68), (11, 65), (18, 54), (19, 58), (20, 53),
|
|
(21, 56), (22, 71), (23, 77), (24, 74), (25, 85), (26, 89),
|
|
(27, 80), (28, 31)]
|
|
dataset_cofw = dict(
|
|
type='COFWDataset',
|
|
data_root=data_root,
|
|
data_mode=data_mode,
|
|
ann_file='cofw/annotations/cofw_train.json',
|
|
data_prefix=dict(img='pose/COFW/images/'),
|
|
pipeline=[
|
|
dict(
|
|
type='KeypointConverter',
|
|
num_keypoints=num_keypoints,
|
|
mapping=cofw_coco133), *face_pipeline
|
|
],
|
|
)
|
|
|
|
lapa_coco133 = [(i * 2, 23 + i) for i in range(17)] + [
|
|
(33 + i, 40 + i) for i in range(5)
|
|
] + [(42 + i, 45 + i) for i in range(5)] + [
|
|
(51 + i, 50 + i) for i in range(4)
|
|
] + [(58 + i, 54 + i) for i in range(5)] + [(66, 59), (67, 60), (69, 61),
|
|
(70, 62), (71, 63), (73, 64),
|
|
(75, 65), (76, 66), (78, 67),
|
|
(79, 68), (80, 69),
|
|
(82, 70)] + [(84 + i, 71 + i)
|
|
for i in range(20)]
|
|
dataset_lapa = dict(
|
|
type='LapaDataset',
|
|
data_root=data_root,
|
|
data_mode=data_mode,
|
|
ann_file='LaPa/annotations/lapa_trainval.json',
|
|
data_prefix=dict(img='pose/LaPa/'),
|
|
pipeline=[
|
|
dict(
|
|
type='KeypointConverter',
|
|
num_keypoints=num_keypoints,
|
|
mapping=lapa_coco133), *face_pipeline
|
|
],
|
|
)
|
|
|
|
dataset_wb = dict(
|
|
type='CombinedDataset',
|
|
metainfo=dict(from_file='configs/_base_/datasets/coco_wholebody.py'),
|
|
datasets=[dataset_coco, dataset_halpe, dataset_ubody],
|
|
pipeline=[],
|
|
test_mode=False,
|
|
)
|
|
|
|
dataset_body = dict(
|
|
type='CombinedDataset',
|
|
metainfo=dict(from_file='configs/_base_/datasets/coco_wholebody.py'),
|
|
datasets=[
|
|
dataset_aic,
|
|
dataset_crowdpose,
|
|
dataset_mpii,
|
|
dataset_jhmdb,
|
|
dataset_posetrack,
|
|
dataset_humanart,
|
|
],
|
|
pipeline=[],
|
|
test_mode=False,
|
|
)
|
|
|
|
dataset_face = dict(
|
|
type='CombinedDataset',
|
|
metainfo=dict(from_file='configs/_base_/datasets/coco_wholebody.py'),
|
|
datasets=[
|
|
dataset_wflw,
|
|
dataset_300w,
|
|
dataset_cofw,
|
|
dataset_lapa,
|
|
],
|
|
pipeline=[],
|
|
test_mode=False,
|
|
)
|
|
|
|
hand_pipeline = [
|
|
dict(type='LoadImage', backend_args=backend_args),
|
|
dict(type='GetBBoxCenterScale'),
|
|
dict(
|
|
type='RandomBBoxTransform',
|
|
shift_factor=0.,
|
|
scale_factor=[1.5, 2.0],
|
|
rotate_factor=0),
|
|
]
|
|
|
|
interhand_left = [(21, 95), (22, 94), (23, 93), (24, 92), (25, 99), (26, 98),
|
|
(27, 97), (28, 96), (29, 103), (30, 102), (31, 101),
|
|
(32, 100), (33, 107), (34, 106), (35, 105), (36, 104),
|
|
(37, 111), (38, 110), (39, 109), (40, 108), (41, 91)]
|
|
interhand_right = [(i - 21, j + 21) for i, j in interhand_left]
|
|
interhand_coco133 = interhand_right + interhand_left
|
|
|
|
dataset_interhand2d = dict(
|
|
type='InterHand2DDoubleDataset',
|
|
data_root=data_root,
|
|
data_mode=data_mode,
|
|
ann_file='interhand26m/annotations/all/InterHand2.6M_train_data.json',
|
|
camera_param_file='interhand26m/annotations/all/'
|
|
'InterHand2.6M_train_camera.json',
|
|
joint_file='interhand26m/annotations/all/'
|
|
'InterHand2.6M_train_joint_3d.json',
|
|
data_prefix=dict(img='interhand2.6m/images/train/'),
|
|
sample_interval=10,
|
|
pipeline=[
|
|
dict(
|
|
type='KeypointConverter',
|
|
num_keypoints=num_keypoints,
|
|
mapping=interhand_coco133,
|
|
), *hand_pipeline
|
|
],
|
|
)
|
|
|
|
dataset_hand = dict(
|
|
type='CombinedDataset',
|
|
metainfo=dict(from_file='configs/_base_/datasets/coco_wholebody.py'),
|
|
datasets=[dataset_interhand2d],
|
|
pipeline=[],
|
|
test_mode=False,
|
|
)
|
|
|
|
train_datasets = [dataset_wb, dataset_body, dataset_face, dataset_hand]
|
|
|
|
# data loaders
|
|
train_dataloader = dict(
|
|
batch_size=train_batch_size,
|
|
num_workers=4,
|
|
pin_memory=False,
|
|
persistent_workers=True,
|
|
sampler=dict(type='DefaultSampler', shuffle=True),
|
|
dataset=dict(
|
|
type='CombinedDataset',
|
|
metainfo=dict(from_file='configs/_base_/datasets/coco_wholebody.py'),
|
|
datasets=train_datasets,
|
|
pipeline=train_pipeline,
|
|
test_mode=False,
|
|
))
|
|
|
|
val_dataloader = dict(
|
|
batch_size=val_batch_size,
|
|
num_workers=4,
|
|
persistent_workers=True,
|
|
drop_last=False,
|
|
sampler=dict(type='DefaultSampler', shuffle=False, round_up=False),
|
|
dataset=dict(
|
|
type='CocoWholeBodyDataset',
|
|
ann_file='data/coco/annotations/coco_wholebody_val_v1.0.json',
|
|
data_prefix=dict(img='data/detection/coco/val2017/'),
|
|
pipeline=val_pipeline,
|
|
bbox_file='data/coco/person_detection_results/'
|
|
'COCO_val2017_detections_AP_H_56_person.json',
|
|
test_mode=True))
|
|
|
|
test_dataloader = val_dataloader
|
|
|
|
# hooks
|
|
default_hooks = dict(
|
|
checkpoint=dict(
|
|
save_best='coco-wholebody/AP', rule='greater', max_keep_ckpts=1))
|
|
|
|
custom_hooks = [
|
|
dict(
|
|
type='EMAHook',
|
|
ema_type='ExpMomentumEMA',
|
|
momentum=0.0002,
|
|
update_buffers=True,
|
|
priority=49),
|
|
dict(
|
|
type='mmdet.PipelineSwitchHook',
|
|
switch_epoch=max_epochs - stage2_num_epochs,
|
|
switch_pipeline=train_pipeline_stage2)
|
|
]
|
|
|
|
# evaluators
|
|
val_evaluator = dict(
|
|
type='CocoWholeBodyMetric',
|
|
ann_file='data/coco/annotations/coco_wholebody_val_v1.0.json')
|
|
test_evaluator = val_evaluator
|