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_base_ = [
'/home/liuziyuan/proj/rmcd-kd/configs/_base_/models/cgnet.py',
'/home/liuziyuan/proj/rmcd-kd/configs/common/train_medium_512x512_100k_cgwx.py']
dataset_type = 'LEVIR_CD_Dataset'
data_root = '/nas/datasets/lzy/RS-ChangeDetection/CGWX'
crop_size = (512, 512)
train_pipeline = [
dict(type='MultiImgLoadImageFromFile'),
dict(type='MultiImgLoadAnnotations'),
dict(type='MultiImgRandomRotate', prob=0.5, degree=180),
dict(type='MultiImgRandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
dict(type='MultiImgRandomFlip', prob=0.5, direction='horizontal'),
dict(type='MultiImgRandomFlip', prob=0.5, direction='vertical'),
# dict(type='MultiImgExchangeTime', prob=0.5),
dict(
type='MultiImgPhotoMetricDistortion',
brightness_delta=10,
contrast_range=(0.8, 1.2),
saturation_range=(0.8, 1.2),
hue_delta=10),
dict(type='MultiImgPackSegInputs')
]
test_pipeline = [
dict(type='MultiImgLoadImageFromFile'),
dict(type='MultiImgResize', scale=(512, 512), keep_ratio=True),
# add loading annotation after ``Resize`` because ground truth
# does not need to do resize data transform
dict(type='MultiImgLoadAnnotations'),
dict(type='MultiImgPackSegInputs')
]
img_ratios = [0.75, 1.0, 1.25]
tta_pipeline = [
dict(type='MultiImgLoadImageFromFile', backend_args=None),
dict(
type='TestTimeAug',
transforms=[
[
dict(type='MultiImgResize', scale_factor=r, keep_ratio=True)
for r in img_ratios
],
[
dict(type='MultiImgRandomFlip', prob=0., direction='horizontal'),
dict(type='MultiImgRandomFlip', prob=1., direction='horizontal')
],
[dict(type='MultiImgLoadAnnotations')],
[dict(type='MultiImgPackSegInputs')]
])
]
# optimizer
optimizer = dict(
type='AdamW',
lr=5e-4,
betas=(0.9, 0.999),
weight_decay=0.0025)
optim_wrapper = dict(
_delete_=True,
type='OptimWrapper',
optimizer=optimizer)
crop_size = (512, 512)
model = dict(
test_cfg=dict(mode='slide', crop_size=crop_size, stride=(crop_size[0]//2, crop_size[1]//2)),
)
param_scheduler = [
dict(
type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=1000),
dict(
type='PolyLR',
power=1.0,
begin=1000,
end=200000,
eta_min=0.0,
by_epoch=False,
)
]
# training schedule for 100k
train_cfg = dict(type='IterBasedTrainLoop', max_iters=200000, val_interval=1000)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
default_hooks = dict(
timer=dict(type='IterTimerHook'),
logger=dict(type='LoggerHook', interval=100, log_metric_by_epoch=False),
param_scheduler=dict(type='ParamSchedulerHook'),
checkpoint=dict(type='CheckpointHook', by_epoch=False, interval=1000,
save_best='mIoU'),
sampler_seed=dict(type='DistSamplerSeedHook'),
visualization=dict(type='CDVisualizationHook', interval=1,
img_shape=(512, 512, 3))) |