realbasicvsr_c64b20-1x30x8_8xb1-lr5e-5-150k_reds.py

_base_ = './realbasicvsr_wogan-c64b20-2x30x8_8xb2-lr1e-4-300k_reds.py'

experiment_name = 'realbasicvsr_c64b20-1x30x8_8xb1-lr5e-5-150k_reds'
work_dir = f'./work_dirs/{experiment_name}'
save_dir = './work_dirs/'

# load_from = 'https://download.openmmlab.com/mmediting/restorers/real_basicvsr/realbasicvsr_wogan_c64b20_2x30x8_lr1e-4_300k_reds_20211027-0e2ff207.pth'  # noqa

scale = 4

# model settings
model = dict(
    type='RealBasicVSR',
    generator=dict(
        type='RealBasicVSRNet',
        mid_channels=64,
        num_propagation_blocks=20,
        num_cleaning_blocks=20,
        dynamic_refine_thres=255,  # change to 5 for test
        spynet_pretrained='https://download.openmmlab.com/mmediting/restorers/'
        'basicvsr/spynet_20210409-c6c1bd09.pth',
        is_fix_cleaning=False,
        is_sequential_cleaning=False),
    discriminator=dict(
        type='UNetDiscriminatorWithSpectralNorm',
        in_channels=3,
        mid_channels=64,
        skip_connection=True),
    pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),
    cleaning_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'),
    perceptual_loss=dict(
        type='PerceptualLoss',
        layer_weights={
            '2': 0.1,
            '7': 0.1,
            '16': 1.0,
            '25': 1.0,
            '34': 1.0,
        },
        vgg_type='vgg19',
        perceptual_weight=1.0,
        style_weight=0,
        norm_img=False),
    gan_loss=dict(
        type='GANLoss',
        gan_type='vanilla',
        loss_weight=5e-2,
        real_label_val=1.0,
        fake_label_val=0),
    is_use_sharpened_gt_in_pixel=True,
    is_use_sharpened_gt_in_percep=True,
    is_use_sharpened_gt_in_gan=False,
    is_use_ema=True,
    data_preprocessor=dict(
        type='DataPreprocessor',
        mean=[0., 0., 0.],
        std=[255., 255., 255.],
    ))

# optimizer
optim_wrapper = dict(
    _delete_=True,
    constructor='MultiOptimWrapperConstructor',
    generator=dict(
        type='OptimWrapper',
        optimizer=dict(type='Adam', lr=5e-5, betas=(0.9, 0.99))),
    discriminator=dict(
        type='OptimWrapper',
        optimizer=dict(type='Adam', lr=1e-4, betas=(0.9, 0.99))),
)

train_cfg = dict(
    type='IterBasedTrainLoop', max_iters=150_000, val_interval=5000)