_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)))