realesrgan_c64b23g32_4xb12-lr1e-4-400k_df2k-ost.py

_base_ = './realesrnet_c64b23g32_4xb12-lr2e-4-1000k_df2k-ost.py'

experiment_name = 'realesrgan_c64b23g32_4xb12-lr1e-4-400k_df2k-ost'
work_dir = f'./work_dirs/{experiment_name}'
save_dir = './work_dirs/'

# load_from =  # path of pre-trained real-esrnet

scale = 4

# model settings
model = dict(
    type='RealESRGAN',
    generator=dict(
        type='RRDBNet',
        in_channels=3,
        out_channels=3,
        mid_channels=64,
        num_blocks=23,
        growth_channels=32,
        upscale_factor=scale),
    discriminator=dict(
        type='UNetDiscriminatorWithSpectralNorm',
        in_channels=3,
        mid_channels=64,
        skip_connection=True),
    pixel_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=1e-1,
        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,
    train_cfg=dict(start_iter=1000000),
    test_cfg=dict(),
    data_preprocessor=dict(
        type='DataPreprocessor',
        mean=[0., 0., 0.],
        std=[255., 255., 255.],
    ))

train_cfg = dict(
    _delete_=True,
    type='IterBasedTrainLoop',
    max_iters=400_000,
    val_interval=5000)

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

# learning policy
param_scheduler = None