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ddq-lsj_300q_4scale_1x_r50.py
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ddq-lsj_300q_4scale_1x_r50.py
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_base_ = [
'./_base_/datasets/lsj_data_re_aug_coco_detection.py',
]
randomness=dict(seed=681328528)
batch_augments = [
dict(type='BatchFixedSizePad', size=_base_.image_size, pad_mask=True)
]
model = dict(
type='HPRDDQDETR',
num_queries=300, # num_matching_queries
aux_weights=[0.5,0.5],
# ratio of num_dense queries to num_queries
dense_topk_ratio=1.5,
with_box_refine=True,
as_two_stage=True,
data_preprocessor=dict(
type='MultiBranchDataPreprocessor',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_mask=True,
batch_augments=batch_augments)),
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=False),
norm_eval=True,
style='pytorch',
init_cfg=dict(type='Pretrained', checkpoint='backbone_pth/backbone.pth')),
neck=dict(
type='ChannelMapper',
in_channels=[512, 1024, 2048],
kernel_size=1,
out_channels=256,
act_cfg=None,
norm_cfg=dict(type='GN', num_groups=32),
num_outs=4),
encoder=dict(
num_layers=6,
num_cp=0,
layer_cfg=dict(
self_attn_cfg=dict(embed_dims=256, num_levels=4,
dropout=0.0), # 0.1 for DeformDETR
ffn_cfg=dict(
embed_dims=256,
feedforward_channels=2048, # 1024 for DeformDETR
ffn_drop=0.0))), # 0.1 for DeformDETR
decoder=dict(
num_layers=6,
return_intermediate=True,
bbox_roi_extractor = dict(
type='SingleRoIExtractor',
finest_scale=56,
roi_layer=dict(
type='RoIAlign', output_size=7, sampling_ratio=2),
out_channels=256,
featmap_strides=[8, 16, 32, 64]),
layer_cfg=dict(
merge_method='learnable_channel_aware',
initial_weights=[1,1,1],
merge_dropout=0.,# only be used when method='cross_attn'
dy_conv_cfg=dict(
in_channels=256,
feat_channels=64,
out_channels=256,
input_feat_shape=7,
act_cfg=dict(type='ReLU', inplace=True),
norm_cfg=dict(type='LN')), # 0.1 for DeformDETR
regional_ca_cfg=dict(
sample_num=5,
embed_dims=256,
num_heads=8,
use_key_pos=True,
positional_encoding=dict(
num_feats=128,
normalize=True,
offset=0.0, # -0.5 for DeformDETR
temperature=20),
attn_drop=0.,
proj_drop=0.,
dropout_layer=dict(type='Dropout', drop_prob=0.),
init_cfg=None,
batch_first=True,
norm_cfg=dict(type='LN'),
act_cfg = dict(type='ReLU', inplace=True),), # 0.1 for DeformDETR
self_attn_cfg=dict(embed_dims=256, num_heads=8,
dropout=0.0), # 0.1 for DeformDETR
cross_attn_cfg=dict(embed_dims=256, num_levels=4,
dropout=0.0), # 0.1 for DeformDETR
ffn_cfg=dict(
embed_dims=256,
feedforward_channels=2048, # 1024 for DeformDETR
ffn_drop=0.0)), # 0.1 for DeformDETR
post_norm_cfg=None),
positional_encoding=dict(
num_feats=128,
normalize=True,
offset=0.0, # -0.5 for DeformDETR
temperature=20), # 10000 for DeformDETR
bbox_head=dict(
type='HPRDDQDETRHead',
num_classes=80,
sync_cls_avg_factor=True,
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox=dict(type='L1Loss', loss_weight=5.0),
loss_iou=dict(type='GIoULoss', loss_weight=2.0)),
dn_cfg=dict(
label_noise_scale=0.5,
box_noise_scale=1.0,
group_cfg=dict(dynamic=True, num_groups=None, num_dn_queries=100)),
dqs_cfg=dict(type='nms', iou_threshold=0.8),
# training and testing settings
train_cfg=dict(
assigner=dict(
type='HungarianAssigner',
match_costs=[
dict(type='FocalLossCost', weight=2.0),
dict(type='BBoxL1Cost', weight=5.0, box_format='xywh'),
dict(type='IoUCost', iou_mode='giou', weight=2.0)
])),
test_cfg=dict(max_per_img=300))
train_dataloader = dict(
batch_size=1) # (16 GPUs) x (1 samples per GPU)
# optimizer
optim_wrapper = dict(
_delete_=True,
type='OptimWrapper',
optimizer=dict(type='AdamW', lr=0.0002, weight_decay=0.05),
clip_grad=dict(max_norm=0.1, norm_type=2),
paramwise_cfg=dict(custom_keys={'backbone': dict(lr_mult=0.1)}))
# learning policy
max_iters = 88000 # 88000/(118287-coco train set/(16-bs))≈12ep
train_cfg = dict(
_delete_=True,
type='IterBasedTrainLoop',
max_iters=max_iters,
val_interval=8000)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
log_processor = dict(by_epoch=False)
param_scheduler = [
dict(
type='LinearLR',
start_factor=0.0001,
by_epoch=False,
begin=0,
end=2000),
dict(
type='MultiStepLR',
begin=0,
end=max_iters,
by_epoch=False,
milestones=[80000],
gamma=0.1)
]
default_hooks = dict(checkpoint=dict(by_epoch=False, interval=8000))
vis_backends = [dict(type='LocalVisBackend'),
dict(type='TensorboardVisBackend')]
visualizer = dict(
type='DetLocalVisualizer',
vis_backends=vis_backends,
name='visualizer')
# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (8 GPUs) x (2 samples per GPU)
auto_scale_lr = dict(base_batch_size=16)