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log_level = 'INFO'
load_from = None
resume_from = None
dist_params = dict(backend='nccl')
workflow = [('train', 1)]
checkpoint_config = dict(interval=20)
evaluation = dict(
interval=25,
metric=['PCK', 'NME', 'AUC', 'EPE'],
key_indicator='PCK',
gpu_collect=True,
res_folder='')
optimizer = dict(
type='Adam',
lr=1e-5,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=1000,
warmup_ratio=0.001,
step=[160, 180])
total_epochs = 200
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')
])
channel_cfg = dict(
num_output_channels=1,
dataset_joints=1,
dataset_channel=[
[
0,
],
],
inference_channel=[
0,
],
max_kpt_num=100)
# model settings
model = dict(
type='PoseAnythingModel',
pretrained='pretrained/swinv2_small_1k_500k.pth',
text_pretrained='Alibaba-NLP/gte-base-en-v1.5',
finetune_text_pretrained=False,
encoder_config=dict(
type='SwinTransformerV2',
embed_dim=96,
depths=[2, 2, 18, 2],
num_heads=[3, 6, 12, 24],
window_size=16,
drop_path_rate=0.3,
img_size=256,
upsample="bilinear"
),
keypoint_head=dict(
type='PoseHead',
img_in_channels=768,
text_in_channels=768,
transformer=dict(
type='EncoderDecoder',
d_model=256,
nhead=8,
num_encoder_layers=3,
num_decoder_layers=3,
dim_feedforward=768,
dropout=0.1,
similarity_proj_dim=256,
dynamic_proj_dim=128,
activation="relu",
normalize_before=False,
return_intermediate_dec=True),
share_kpt_branch=False,
num_decoder_layer=3,
with_heatmap_loss=True,
heatmap_loss_weight=2.0,
support_order_dropout=-1,
positional_encoding=dict(
type='SinePositionalEncoding', num_feats=128, normalize=True)),
# training and testing settings
train_cfg=dict(),
test_cfg=dict(
flip_test=False,
post_process='default',
shift_heatmap=True,
modulate_kernel=11))
data_cfg = dict(
image_size=[256, 256],
heatmap_size=[64, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'])
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='TopDownGetRandomScaleRotation', rot_factor=15,
scale_factor=0.15),
dict(type='TopDownAffineFewShot'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(type='TopDownGenerateTargetFewShot', sigma=1),
dict(
type='Collect',
keys=['img', 'target', 'target_weight'],
meta_keys=[
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
'rotation', 'bbox_score', 'flip_pairs', 'category_id', 'skeleton',
]),
]
valid_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownAffineFewShot'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(type='TopDownGenerateTargetFewShot', sigma=1),
dict(
type='Collect',
keys=['img', 'target', 'target_weight'],
meta_keys=[
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', 'rotation', 'bbox_score',
'flip_pairs', 'category_id',
'skeleton',
]),
]
test_pipeline = valid_pipeline
data_root = 'data/mp100'
data = dict(
samples_per_gpu=16,
workers_per_gpu=16,
# samples_per_gpu=8,
# workers_per_gpu=8,
train=dict(
type='TransformerPoseDataset',
ann_file=f'{data_root}/annotations/mp100_split1_train.json',
img_prefix=f'{data_root}/images/',
# img_prefix=f'{data_root}',
data_cfg=data_cfg,
valid_class_ids=None,
max_kpt_num=channel_cfg['max_kpt_num'],
num_shots=1,
pipeline=train_pipeline),
val=dict(
type='TransformerPoseDataset',
ann_file=f'{data_root}/annotations/mp100_split1_val.json',
img_prefix=f'{data_root}/images/',
# img_prefix=f'{data_root}',
data_cfg=data_cfg,
valid_class_ids=None,
max_kpt_num=channel_cfg['max_kpt_num'],
num_shots=1,
num_queries=15,
num_episodes=100,
pipeline=valid_pipeline),
test=dict(
type='TestPoseDataset',
ann_file=f'{data_root}/annotations/mp100_split1_test.json',
img_prefix=f'{data_root}/images/',
# img_prefix=f'{data_root}',
data_cfg=data_cfg,
valid_class_ids=None,
max_kpt_num=channel_cfg['max_kpt_num'],
num_shots=1,
num_queries=15,
num_episodes=200,
pck_threshold_list=[0.05, 0.10, 0.15, 0.2, 0.25],
pipeline=test_pipeline),
)
vis_backends = [
dict(type='LocalVisBackend'),
dict(type='TensorboardVisBackend'),
]
visualizer = dict(
type='PoseLocalVisualizer', vis_backends=vis_backends, name='visualizer')
shuffle_cfg = dict(interval=1)
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