mmdetection-2.26.0/configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py

# We follow the original implementation which
# adopts the Caffe pre-trained backbone.
_base_ = [
 '../_base_/datasets/coco_detection.py',
 '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
 type='AutoAssign',
 backbone=dict(
 type='ResNet',
 depth=50,
 num_stages=4,
 out_indices=(0, 1, 2, 3),
 frozen_stages=1,
 norm_cfg=dict(type='BN', requires_grad=False),
 norm_eval=True,
 style='caffe',
 init_cfg=dict(
 type='Pretrained',
 checkpoint='open-mmlab://detectron2/resnet50_caffe')),
 neck=dict(
 type='FPN',
 in_channels=[256, 512, 1024, 2048],
 out_channels=256,
 start_level=1,
 add_extra_convs=True,
 num_outs=5,
 relu_before_extra_convs=True,
 init_cfg=dict(type='Caffe2Xavier', layer='Conv2d')),
 bbox_head=dict(
 type='AutoAssignHead',
 num_classes=80,
 in_channels=256,
 stacked_convs=4,
 feat_channels=256,
 strides=[8, 16, 32, 64, 128],
 loss_bbox=dict(type='GIoULoss', loss_weight=5.0)),
 train_cfg=None,
 test_cfg=dict(
 nms_pre=1000,
 min_bbox_size=0,
 score_thr=0.05,
 nms=dict(type='nms', iou_threshold=0.6),
 max_per_img=100))
img_norm_cfg = dict(
 mean=[102.9801, 115.9465, 122.7717], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
 dict(type='LoadImageFromFile'),
 dict(type='LoadAnnotations', with_bbox=True),
 dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
 dict(type='RandomFlip', flip_ratio=0.5),
 dict(type='Normalize', **img_norm_cfg),
 dict(type='Pad', size_divisor=32),
 dict(type='DefaultFormatBundle'),
 dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]
test_pipeline = [
 dict(type='LoadImageFromFile'),
 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=32),
 dict(type='ImageToTensor', keys=['img']),
 dict(type='Collect', keys=['img'])
 ])
]
data = dict(
 train=dict(pipeline=train_pipeline),
 val=dict(pipeline=test_pipeline),
 test=dict(pipeline=test_pipeline))
# optimizer
optimizer = dict(lr=0.01, paramwise_cfg=dict(norm_decay_mult=0.))
# learning policy
lr_config = dict(
 policy='step',
 warmup='linear',
 warmup_iters=1000,
 warmup_ratio=1.0 / 1000,
 step=[8, 11])
total_epochs = 12