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dab_detr_r50_8x2_50e_coco.py
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_base_ = [
'../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
]
model = dict(
type='DABDETR',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(3, ),
frozen_stages=1,
norm_cfg=dict(
type='FrozenBN2d',
requires_grad=False), # register torch.ops.FrozenBatchNorm2d
norm_eval=True,
style='pytorch',
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
bbox_head=dict(
type='DABDETRHead',
num_query=300,
num_classes=80, # default 80, 91 to align with official repo
in_channels=2048,
iter_update=True,
random_refpoints_xy=False,
transformer=dict(
type='DabDetrTransformer',
num_patterns=0,
encoder=dict(
type='DabDetrTransformerEncoder',
num_layers=6,
transformerlayers=dict(
type='BaseTransformerLayer',
attn_cfgs=[
dict(
type='MultiheadAttention',
embed_dims=256,
num_heads=8,
dropout=0.) # default 0.
],
feedforward_channels=2048,
ffn_dropout=0., # default 0.
act_cfg=dict(type='PReLU'), # default PReLU
operation_order=('self_attn', 'norm', 'ffn', 'norm'))),
decoder=dict(
type='DabDetrTransformerDecoder',
query_dim=4,
num_layers=6,
query_scale_type='cond_elewise',
modulate_hw_attn=True,
bbox_embed_diff_each_layer=False,
transformerlayers=dict(
type='DabDetrTransformerDecoderLayer',
attn_cfgs=dict(
type='ConditionalAttention',
embed_dims=256,
num_heads=8,
dropout=0.), # default 0.
feedforward_channels=2048,
ffn_dropout=0., # default 0.
sa_dropout=0., # default 0.
ca_dropout=0., # default 0.
keep_query_pos=False,
act_cfg=dict(type='PReLU'), # default PReLU
operation_order=('self_attn', 'norm', 'cross_attn', 'norm',
'ffn', 'norm')),
)),
positional_encoding=dict(
type='SinePositionalEncodingHW',
num_feats=128,
temperatureH=20,
temperatureW=20,
normalize=True),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0
), # weights of cls cost and cls loss can't be different for DETRHead
loss_bbox=dict(type='L1Loss', loss_weight=5.0),
loss_iou=dict(type='GIoULoss', loss_weight=2.0)),
# training and testing settings
train_cfg=dict(
assigner=dict(
type='HungarianAssigner',
cls_cost=dict(type='FocalLossCost', weight=2.,
eps=1e-8), # default eps=1e-8
reg_cost=dict(type='BBoxL1Cost', weight=5.0, box_format='xywh'),
iou_cost=dict(type='IoUCost', iou_mode='giou', weight=2.0))),
test_cfg=dict(max_per_img=300)) # default 300
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375],
to_rgb=True) # default True, False for pillow
# img_norm_cfg = dict(
# mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], to_rgb=True)
# train_pipeline, NOTE the img_scale and the Pad's size_divisor is different
# from the default setting in mmdet.
train_pipeline = [
dict(type='LoadImageFromFile', channel_order='bgr'
), # default 'bgr', 'rgb' to align with official repo
dict(type='LoadAnnotations', with_bbox=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(
type='AutoAugment',
policies=[[
dict(
type='Resize',
img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
(608, 1333), (640, 1333), (672, 1333), (704, 1333),
(736, 1333), (768, 1333), (800, 1333)],
multiscale_mode='value',
keep_ratio=True)
],
[
dict(
type='Resize',
img_scale=[(400, 1333), (500, 1333), (600, 1333)],
multiscale_mode='value',
keep_ratio=True),
dict(
type='RandomCrop',
crop_type='absolute_range',
crop_size=(384, 600),
allow_negative_crop=True),
dict(
type='Resize',
img_scale=[(480, 1333), (512, 1333), (544, 1333),
(576, 1333), (608, 1333), (640, 1333),
(672, 1333), (704, 1333), (736, 1333),
(768, 1333), (800, 1333)],
multiscale_mode='value',
override=True,
keep_ratio=True)
]]),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=1),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]
# test_pipeline, NOTE the Pad's size_divisor is different from the default
# setting (size_divisor=32). While there is little effect on the performance
# whether we use the default setting or use size_divisor=1.
test_pipeline = [
dict(type='LoadImageFromFile',
channel_order='bgr'), # default 'bgr', 'rgb' for pillow
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=1),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]
data = dict(
samples_per_gpu=1, # default 2 * 8, use 4 * 4, used in train dataset only
workers_per_gpu=0,
# default 2, 0 for debug, used in train & val & test datasets
train=dict(
continuous_categories=True,
# default True, False to align with official repo
pipeline=train_pipeline),
val=dict(continuous_categories=True,
pipeline=test_pipeline), # add 'samples_per_gpu' here
test=dict(continuous_categories=True,
pipeline=test_pipeline)) # add 'samples_per_gpu' here
evaluation = dict(interval=1, metric='bbox', save_best='bbox_mAP')
checkpoint_config = dict(interval=10)
# optimizer
optimizer = dict(
type='AdamW',
lr=0.0001,
weight_decay=0.0001,
paramwise_cfg=dict(
custom_keys={'backbone': dict(lr_mult=0.1, decay_mult=1.0)}))
optimizer_config = dict(grad_clip=dict(max_norm=0.1, norm_type=2))
# learning policy
lr_config = dict(
policy='step', step=[1]) # default 40 epochs, 1 epoch for debug train
runner = dict(
type='EpochBasedRunner',
max_epochs=1) # default 50 epochs, 1 epoch for debug train
auto_scale_lr = dict(enable=True, base_batch_size=16)