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pct_base_classifier.py
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_base_ = ['./coco.py']
log_level = 'INFO'
load_from = None
resume_from = None
dist_params = dict(backend='nccl')
workflow = [('train', 1)]
find_unused_parameters=False
checkpoint_config = dict(interval=5, create_symlink=False)
evaluation = dict(interval=5, metric='mAP', save_best='AP')
optimizer = dict(type='AdamW', lr=8e-4, betas=(0.9, 0.999), weight_decay=0.05,
constructor='SwinLayerDecayOptimizerConstructor',
paramwise_cfg=dict(num_layers=[2, 2, 18, 2], layer_decay_rate=0.9,
no_decay_names=['relative_position_bias_table',
'rpe_mlp',
'logit_scale']))
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='CosineAnnealing',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
min_lr_ratio=1e-5)
total_epochs = 210
log_config = dict(
interval=100,
hooks=[
dict(type='TextLoggerHook'),
])
channel_cfg = dict(
num_output_channels=17,
dataset_joints=17,
dataset_channel=[
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16
])
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'],
soft_nms=False,
nms_thr=1.0,
oks_thr=0.9,
vis_thr=0.2,
use_gt_bbox=True,
det_bbox_thr=0.0,
bbox_file='data/coco/person_detection_results/'
'COCO_val2017_detections_AP_H_56_person.json',
)
# model settings
model = dict(
type='PCT',
pretrained='weights/heatmap/swin_base.pth',
backbone=dict(
type='SwinV2TransformerRPE2FC',
embed_dim=128,
depths=[2, 2, 18, 2],
num_heads=[4, 8, 16, 32],
window_size=[16, 16, 16, 8],
pretrain_window_size=[12, 12, 12, 6],
ape=False,
drop_path_rate=0.3,
patch_norm=True,
use_checkpoint=True,
rpe_interpolation='geo',
use_shift=[True, True, False, False],
relative_coords_table_type='norm8_log_bylayer',
attn_type='cosine_mh',
rpe_output_type='sigmoid',
postnorm=True,
mlp_type='normal',
out_indices=(3,),
patch_embed_type='normal',
patch_merge_type='normal',
strid16=False,
frozen_stages=5,
),
keypoint_head=dict(
type='PCT_Head',
stage_pct='classifier',
in_channels=1024,
image_size=data_cfg['image_size'],
num_joints=channel_cfg['num_output_channels'],
loss_keypoint=dict(
type='Classifer_loss',
token_loss=1.0,
joint_loss=1.0),
cls_head=dict(
conv_num_blocks=2,
conv_channels=256,
dilation=1,
num_blocks=4,
hidden_dim=64,
token_inter_dim=64,
hidden_inter_dim=256,
dropout=0.0),
tokenizer=dict(
guide_ratio=0.5,
ckpt="weights/tokenizer/swin_base.pth",
encoder=dict(
drop_rate=0.2,
num_blocks=4,
hidden_dim=512,
token_inter_dim=64,
hidden_inter_dim=512,
dropout=0.0,
),
decoder=dict(
num_blocks=1,
hidden_dim=32,
token_inter_dim=64,
hidden_inter_dim=64,
dropout=0.0,
),
codebook=dict(
token_num=34,
token_dim=512,
token_class_num=2048,
ema_decay=0.9,
),
loss_keypoint=dict(
type='Tokenizer_loss',
joint_loss_w=1.0,
e_loss_w=15.0,
beta=0.05,)
)),
test_cfg=dict(
flip_test=True,
dataset_name='COCO'))
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownGetBboxCenterScale', padding=1.25),
dict(type='TopDownRandomShiftBboxCenter', shift_factor=0.16, prob=0.3),
dict(type='TopDownRandomFlip', flip_prob=0.5),
dict(
type='TopDownHalfBodyTransform',
num_joints_half_body=8,
prob_half_body=0.3),
dict(
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5),
dict(type='TopDownAffine'),
dict(
type='Albumentation',
transforms=[
dict(
type='ColorJitter',
brightness=0.4,
contrast=0.4,
saturation=0.4,
hue=0.2,
p=1.0),
dict(
type='GridDropout',
unit_size_min=10,
unit_size_max=40,
random_offset=True,
p=0.5),
]),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(
type='Collect',
keys=['img', 'joints_3d', 'joints_3d_visible'],
meta_keys=[
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
'rotation', 'bbox_score', 'flip_pairs'
]),
]
val_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownGetBboxCenterScale', padding=1.12),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(
type='Collect',
keys=['img'],
meta_keys=[
'image_file', 'center', 'scale', 'rotation', 'bbox_score',
'flip_pairs'
]),
]
test_pipeline = val_pipeline
data_root = 'data/coco'
data = dict(
samples_per_gpu=32,
workers_per_gpu=2,
val_dataloader=dict(samples_per_gpu=32),
test_dataloader=dict(samples_per_gpu=32),
train=dict(
type='TopDownCocoDataset',
ann_file=f'{data_root}/annotations/person_keypoints_train2017.json',
img_prefix=f'{data_root}/images/train2017/',
data_cfg=data_cfg,
pipeline=train_pipeline,
dataset_info={{_base_.dataset_info}}),
val=dict(
type='TopDownCocoDataset',
ann_file=f'{data_root}/annotations/person_keypoints_val2017.json',
img_prefix=f'{data_root}/images/val2017/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info={{_base_.dataset_info}}),
test=dict(
type='TopDownCocoDataset',
ann_file=f'{data_root}/annotations/person_keypoints_val2017.json',
img_prefix=f'{data_root}/images/val2017/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info={{_base_.dataset_info}})
)
# fp16 settings
fp16 = dict(loss_scale='dynamic')