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s2anet-le90_r50_fpn_1x_rsar.py
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_base_ = [
'../_base_/datasets/rsar.py', '../_base_/schedules/schedule_1x.py',
'../_base_/default_runtime.py'
]
angle_version = 'le90'
model = dict(
type='RefineSingleStageDetector',
data_preprocessor=dict(
type='mmdet.DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_size_divisor=32,
boxtype2tensor=False),
backbone=dict(
type='mmdet.ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
zero_init_residual=False,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
neck=dict(
type='mmdet.FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
start_level=1,
add_extra_convs='on_input',
num_outs=5),
bbox_head_init=dict(
type='S2AHead',
num_classes=6, #
in_channels=256,
stacked_convs=2,
feat_channels=256,
anchor_generator=dict(
type='FakeRotatedAnchorGenerator',
angle_version=angle_version,
scales=[4],
ratios=[1.0],
strides=[8, 16, 32, 64, 128]),
bbox_coder=dict(
type='DeltaXYWHTRBBoxCoder',
angle_version=angle_version,
norm_factor=None,
edge_swap=True,
proj_xy=True,
target_means=(.0, .0, .0, .0, .0),
target_stds=(1.0, 1.0, 1.0, 1.0, 1.0),
use_box_type=False),
loss_cls=dict(
type='mmdet.FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox=dict(type='mmdet.SmoothL1Loss', beta=0.11, loss_weight=1.0)),
bbox_head_refine=[
dict(
type='S2ARefineHead',
num_classes=6, #
in_channels=256,
stacked_convs=2,
feat_channels=256,
frm_cfg=dict(
type='AlignConv',
feat_channels=256,
kernel_size=3,
strides=[8, 16, 32, 64, 128]),
anchor_generator=dict(
type='PseudoRotatedAnchorGenerator',
strides=[8, 16, 32, 64, 128]),
bbox_coder=dict(
type='DeltaXYWHTRBBoxCoder',
angle_version=angle_version,
norm_factor=None,
edge_swap=True,
proj_xy=True,
target_means=(0.0, 0.0, 0.0, 0.0, 0.0),
target_stds=(1.0, 1.0, 1.0, 1.0, 1.0)),
loss_cls=dict(
type='mmdet.FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox=dict(
type='mmdet.SmoothL1Loss', beta=0.11, loss_weight=1.0))
],
train_cfg=dict(
init=dict(
assigner=dict(
type='mmdet.MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.4,
min_pos_iou=0,
ignore_iof_thr=-1,
iou_calculator=dict(type='RBboxOverlaps2D')),
allowed_border=-1,
pos_weight=-1,
debug=False),
refine=[
dict(
assigner=dict(
type='mmdet.MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.4,
min_pos_iou=0,
ignore_iof_thr=-1,
iou_calculator=dict(type='RBboxOverlaps2D')),
allowed_border=-1,
pos_weight=-1,
debug=False)
],
stage_loss_weights=[1.0]),
test_cfg=dict(
nms_pre=2000,
min_bbox_size=0,
score_thr=0.05,
nms=dict(type='nms_rotated', iou_threshold=0.1),
max_per_img=2000))
test_evaluator = dict(outfile_prefix='./work_dirs/dota/s2anet_le90_Task1')