MTKD / LightCDNet /large /config.py
circleLZY's picture
Upload 50 files
b1b30c0 verified
_base_ = [
'/home/liuziyuan/proj/RS-CD/rs-cd-cgwx/configs/_base_/models/lightcdnet.py',
'/home/liuziyuan/proj/RS-CD/rs-cd-cgwx/configs/common/train_large_512x512_100k_cgwx.py']
dataset_type = 'LEVIR_CD_Dataset'
data_root = '/nas/datasets/lzy/RS-ChangeDetection/CGWX'
crop_size = (512, 512)
model = dict(
decode_head=dict(
sampler=dict(type='mmseg.OHEMPixelSampler', thresh=0.7, min_kept=100000)))
# optimizer
optimizer = dict(
type='AdamW',
lr=0.003,
betas=(0.9, 0.999),
weight_decay=0.05)
optim_wrapper = dict(
_delete_=True,
type='OptimWrapper',
optimizer=optimizer)
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=50, 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)))