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[CodeCamp2023-653] Add new configs of Real BasicVSR (#2030)
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* Add new configs of real basic vsr

* fix import location
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RangeKing authored Sep 14, 2023
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# 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 *

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.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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