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# Common configuration
optimizer = dict(type='AdamW', lr=1e-3, betas=(0.9, 0.999), weight_decay=0.1,
constructor='LayerDecayOptimizerConstructor',
paramwise_cfg=dict(
num_layers=12,
layer_decay_rate=1 - 2e-4,
custom_keys={
'bias': dict(decay_multi=0.),
'pos_embed': dict(decay_mult=0.),
'relative_position_bias_table': dict(decay_mult=0.),
'norm': dict(decay_mult=0.)
}
)
)
optimizer_config = dict(grad_clip=dict(max_norm=1., norm_type=2))
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=300,
warmup_ratio=0.001,
step=[3])
total_epochs = 4
target_type = 'GaussianHeatmap'
data_cfg = dict(
image_size=[192, 256],
heatmap_size=[48, 64],
soft_nms=False,
nms_thr=1.0,
oks_thr=0.9,
vis_thr=0.2,
use_gt_bbox=False,
det_bbox_thr=0.0,
bbox_file='data/coco/person_detection_results/'
'COCO_val2017_detections_AP_H_56_person.json',
)
data_root = '/home/adryw/dataset/COCO17'
data = dict(
samples_per_gpu=64,
workers_per_gpu=6,
val_dataloader=dict(samples_per_gpu=128),
test_dataloader=dict(samples_per_gpu=128),
train=dict(
type='TopDownCocoDataset',
ann_file=f'{data_root}/annotations/person_keypoints_train2017.json',
img_prefix=f'{data_root}/train2017/',
data_cfg=data_cfg),
val=dict(
type='TopDownCocoDataset',
ann_file=f'{data_root}/annotations/person_keypoints_val2017.json',
img_prefix=f'{data_root}/val2017/',
data_cfg=data_cfg),
test=dict(
type='TopDownCocoDataset',
ann_file=f'{data_root}/annotations/person_keypoints_val2017.json',
img_prefix=f'{data_root}/val2017/',
data_cfg=data_cfg)
)
model_small = dict(
type='TopDown',
pretrained=None,
backbone=dict(
type='ViT',
img_size=(256, 192),
patch_size=16,
embed_dim=384,
depth=12,
num_heads=12,
ratio=1,
use_checkpoint=False,
mlp_ratio=4,
qkv_bias=True,
drop_path_rate=0.1,
),
keypoint_head=dict(
type='TopdownHeatmapSimpleHead',
in_channels=384,
num_deconv_layers=2,
num_deconv_filters=(256, 256),
num_deconv_kernels=(4, 4),
extra=dict(final_conv_kernel=1, ),
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
train_cfg=dict(),
test_cfg=dict(
flip_test=True,
post_process='default',
shift_heatmap=False,
target_type=target_type,
modulate_kernel=11,
use_udp=True))
model_base = dict(
type='TopDown',
pretrained=None,
backbone=dict(
type='ViT',
img_size=(256, 192),
patch_size=16,
embed_dim=768,
depth=12,
num_heads=12,
ratio=1,
use_checkpoint=False,
mlp_ratio=4,
qkv_bias=True,
drop_path_rate=0.3,
),
keypoint_head=dict(
type='TopdownHeatmapSimpleHead',
in_channels=768,
num_deconv_layers=2,
num_deconv_filters=(256, 256),
num_deconv_kernels=(4, 4),
extra=dict(final_conv_kernel=1, ),
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
train_cfg=dict(),
test_cfg=dict(
flip_test=True,
post_process='default',
shift_heatmap=False,
target_type=target_type,
modulate_kernel=11,
use_udp=True))
model_large = dict(
type='TopDown',
pretrained=None,
backbone=dict(
type='ViT',
img_size=(256, 192),
patch_size=16,
embed_dim=1024,
depth=24,
num_heads=16,
ratio=1,
use_checkpoint=False,
mlp_ratio=4,
qkv_bias=True,
drop_path_rate=0.5,
),
keypoint_head=dict(
type='TopdownHeatmapSimpleHead',
in_channels=1024,
num_deconv_layers=2,
num_deconv_filters=(256, 256),
num_deconv_kernels=(4, 4),
extra=dict(final_conv_kernel=1, ),
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
train_cfg=dict(),
test_cfg=dict(
flip_test=True,
post_process='default',
shift_heatmap=False,
target_type=target_type,
modulate_kernel=11,
use_udp=True))
model_huge = dict(
type='TopDown',
pretrained=None,
backbone=dict(
type='ViT',
img_size=(256, 192),
patch_size=16,
embed_dim=1280,
depth=32,
num_heads=16,
ratio=1,
use_checkpoint=False,
mlp_ratio=4,
qkv_bias=True,
drop_path_rate=0.55,
),
keypoint_head=dict(
type='TopdownHeatmapSimpleHead',
in_channels=1280,
num_deconv_layers=2,
num_deconv_filters=(256, 256),
num_deconv_kernels=(4, 4),
extra=dict(final_conv_kernel=1, ),
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
train_cfg=dict(),
test_cfg=dict(
flip_test=True,
post_process='default',
shift_heatmap=False,
target_type=target_type,
modulate_kernel=11,
use_udp=True))
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