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s2anet_r50_fpn_1x_dota.py
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s2anet_r50_fpn_1x_dota.py
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# model settings
model = dict(
type='S2ANet',
backbone=dict(
type='Resnet50',
frozen_stages=1,
return_stages=["layer1","layer2","layer3","layer4"],
pretrained= True),
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='S2ANetHead',
num_classes=16,
in_channels=256,
feat_channels=256,
stacked_convs=2,
with_orconv=True,
anchor_ratios=[1.0],
anchor_strides=[8, 16, 32, 64, 128],
anchor_scales=[4],
target_means=[.0, .0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0, 1.0],
loss_fam_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_fam_bbox=dict(
type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0),
loss_odm_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_odm_bbox=dict(
type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0),
test_cfg=dict(
nms_pre=2000,
min_bbox_size=0,
score_thr=0.05,
nms=dict(type='nms_rotated', iou_thr=0.1),
max_per_img=2000),
train_cfg=dict(
fam_cfg=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.4,
min_pos_iou=0,
ignore_iof_thr=-1,
iou_calculator=dict(type='BboxOverlaps2D_rotated')),
bbox_coder=dict(type='DeltaXYWHABBoxCoder',
target_means=(0., 0., 0., 0., 0.),
target_stds=(1., 1., 1., 1., 1.),
clip_border=True),
allowed_border=-1,
pos_weight=-1,
debug=False),
odm_cfg=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.4,
min_pos_iou=0,
ignore_iof_thr=-1,
iou_calculator=dict(type='BboxOverlaps2D_rotated')),
bbox_coder=dict(type='DeltaXYWHABBoxCoder',
target_means=(0., 0., 0., 0., 0.),
target_stds=(1., 1., 1., 1., 1.),
clip_border=True),
allowed_border=-1,
pos_weight=-1,
debug=False))
)
)
dataset = dict(
train=dict(
type="DOTADataset",
dataset_dir='/home/cxjyxx_me/workspace/JAD/datasets/processed_DOTA/trainval_1024_200_1.0',
transforms=[
dict(
type="RotatedResize",
min_size=1024,
max_size=1024
),
dict(type='RotatedRandomFlip', prob=0.5),
dict(
type = "Pad",
size_divisor=32),
dict(
type = "Normalize",
mean = [123.675, 116.28, 103.53],
std = [58.395, 57.12, 57.375],
to_bgr=False,)
],
batch_size=2,
num_workers=4,
shuffle=True,
filter_empty_gt=False
),
val=dict(
type="DOTADataset",
dataset_dir='/home/cxjyxx_me/workspace/JAD/datasets/processed_DOTA/trainval_1024_200_1.0',
transforms=[
dict(
type="RotatedResize",
min_size=1024,
max_size=1024
),
dict(
type = "Pad",
size_divisor=32),
dict(
type = "Normalize",
mean = [123.675, 116.28, 103.53],
std = [58.395, 57.12, 57.375],
to_bgr=False),
],
batch_size=2,
num_workers=4,
shuffle=False
),
test=dict(
type="ImageDataset",
images_dir='/home/cxjyxx_me/workspace/JAD/datasets/processed_DOTA/test_1024_200_1.0/images',
transforms=[
dict(
type="RotatedResize",
min_size=1024,
max_size=1024
),
dict(
type = "Pad",
size_divisor=32),
dict(
type = "Normalize",
mean = [123.675, 116.28, 103.53],
std = [58.395, 57.12, 57.375],
to_bgr=False,),
],
num_workers=4,
batch_size=1,
)
)
optimizer = dict(
type='SGD',
lr=0.01/4., #0.0,#0.01*(1/8.),
momentum=0.9,
weight_decay=0.0001,
grad_clip=dict(
max_norm=35,
norm_type=2))
scheduler = dict(
type='StepLR',
warmup='linear',
warmup_iters=500,
warmup_ratio=1.0 / 3,
milestones=[7, 10])
logger = dict(
type="RunLogger")
# when we the trained model from cshuan, image is rgb
max_epoch = 12
eval_interval = 1
checkpoint_interval = 1
log_interval = 50