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# Copyright (c) OpenMMLab. All rights reserved.
from mmcv.ops import nms
from torch.nn import BatchNorm2d
from mmdet.models import (FPN, DetDataPreprocessor, FocalLoss, L1Loss, ResNet,
RetinaHead, RetinaNet)
from mmdet.models.task_modules import (AnchorGenerator, DeltaXYWHBBoxCoder,
MaxIoUAssigner, PseudoSampler)
# model settings
model = dict(
type=RetinaNet,
data_preprocessor=dict(
type=DetDataPreprocessor,
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_size_divisor=32),
backbone=dict(
type=ResNet,
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type=BatchNorm2d, 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,
start_level=1,
add_extra_convs='on_input',
num_outs=5),
bbox_head=dict(
type=RetinaHead,
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
anchor_generator=dict(
type=AnchorGenerator,
octave_base_scale=4,
scales_per_octave=3,
ratios=[0.5, 1.0, 2.0],
strides=[8, 16, 32, 64, 128]),
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=FocalLoss,
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox=dict(type=L1Loss, loss_weight=1.0)),
# model training and testing settings
train_cfg=dict(
assigner=dict(
type=MaxIoUAssigner,
pos_iou_thr=0.5,
neg_iou_thr=0.4,
min_pos_iou=0,
ignore_iof_thr=-1),
sampler=dict(
type=PseudoSampler), # Focal loss should use PseudoSampler
allowed_border=-1,
pos_weight=-1,
debug=False),
test_cfg=dict(
nms_pre=1000,
min_bbox_size=0,
score_thr=0.05,
nms=dict(type=nms, iou_threshold=0.5),
max_per_img=100))
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