|
|
|
model = dict( |
|
type='RPN', |
|
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=True), |
|
norm_eval=True, |
|
style='pytorch', |
|
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), |
|
neck=dict( |
|
type='FPN', |
|
in_channels=[256, 512, 1024, 2048], |
|
out_channels=256, |
|
num_outs=5), |
|
rpn_head=dict( |
|
type='RPNHead', |
|
in_channels=256, |
|
feat_channels=256, |
|
anchor_generator=dict( |
|
type='AnchorGenerator', |
|
scales=[8], |
|
ratios=[0.5, 1.0, 2.0], |
|
strides=[4, 8, 16, 32, 64]), |
|
bbox_coder=dict( |
|
type='DeltaXYWHBBoxCoder', |
|
target_means=[.0, .0, .0, .0], |
|
target_stds=[1.0, 1.0, 1.0, 1.0]), |
|
loss_cls=dict( |
|
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), |
|
loss_bbox=dict(type='L1Loss', loss_weight=1.0)), |
|
|
|
train_cfg=dict( |
|
rpn=dict( |
|
assigner=dict( |
|
type='MaxIoUAssigner', |
|
pos_iou_thr=0.7, |
|
neg_iou_thr=0.3, |
|
min_pos_iou=0.3, |
|
ignore_iof_thr=-1), |
|
sampler=dict( |
|
type='RandomSampler', |
|
num=256, |
|
pos_fraction=0.5, |
|
neg_pos_ub=-1, |
|
add_gt_as_proposals=False), |
|
allowed_border=0, |
|
pos_weight=-1, |
|
debug=False)), |
|
test_cfg=dict( |
|
rpn=dict( |
|
nms_pre=2000, |
|
max_per_img=1000, |
|
nms=dict(type='nms', iou_threshold=0.7), |
|
min_bbox_size=0))) |
|
|
