retinanet_r50_fpg_crop640_50e_coco.py
1.53 KB
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_base_ = '../nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py'
norm_cfg = dict(type='BN', requires_grad=True)
model = dict(
neck=dict(
_delete_=True,
type='FPG',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
inter_channels=256,
num_outs=5,
add_extra_convs=True,
start_level=1,
stack_times=9,
paths=['bu'] * 9,
same_down_trans=None,
same_up_trans=dict(
type='conv',
kernel_size=3,
stride=2,
padding=1,
norm_cfg=norm_cfg,
inplace=False,
order=('act', 'conv', 'norm')),
across_lateral_trans=dict(
type='conv',
kernel_size=1,
norm_cfg=norm_cfg,
inplace=False,
order=('act', 'conv', 'norm')),
across_down_trans=dict(
type='interpolation_conv',
mode='nearest',
kernel_size=3,
norm_cfg=norm_cfg,
order=('act', 'conv', 'norm'),
inplace=False),
across_up_trans=None,
across_skip_trans=dict(
type='conv',
kernel_size=1,
norm_cfg=norm_cfg,
inplace=False,
order=('act', 'conv', 'norm')),
output_trans=dict(
type='last_conv',
kernel_size=3,
order=('act', 'conv', 'norm'),
inplace=False),
norm_cfg=norm_cfg,
skip_inds=[(0, 1, 2, 3), (0, 1, 2), (0, 1), (0, ), ()]))
evaluation = dict(interval=2)