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realbasicvsr_c64b20_1x30x8_8xb1_lr5e_5_150k_reds 源代码

# Copyright (c) OpenMMLab. All rights reserved.

# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
# mmcv >= 2.0.1
# mmengine >= 0.8.0

from mmengine.config import read_base
from mmengine.optim.optimizer import OptimWrapper
from mmengine.runner.loops import IterBasedTrainLoop
from torch.optim.adam import Adam

from mmagic.engine import MultiOptimWrapperConstructor
from mmagic.models.data_preprocessors import DataPreprocessor
from mmagic.models.editors import (RealBasicVSR, RealBasicVSRNet,
                                   UNetDiscriminatorWithSpectralNorm)
from mmagic.models.losses import GANLoss, L1Loss, PerceptualLoss

with read_base():
    from .realbasicvsr_wogan_c64b20_2x30x8_8xb2_lr1e_4_300k_reds import *

[文档]experiment_name = 'realbasicvsr_c64b20-1x30x8_8xb1-lr5e-5-150k_reds'
[文档]work_dir = f'./work_dirs/{experiment_name}'
[文档]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
[文档]scale = 4
# model settings model.update( 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.update( 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.update( dict(type=IterBasedTrainLoop, max_iters=150_000, val_interval=5000))
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