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add SRFlow with srflow.py
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# Copyright (c) 2020 Huawei Technologies Co., Ltd.
# Licensed under CC BY-NC-SA 4.0 (Attribution-NonCommercial-ShareAlike 4.0 International) (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode
#
# The code is released for academic research use only. For commercial use, please contact Huawei Technologies Co., Ltd.
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# This file contains content licensed by https://github.com/xinntao/BasicSR/blob/master/LICENSE/LICENSE
#### general settings
name: train
use_tb_logger: true
model: SRFlow
distortion: sr
scale: 4
gpu_ids: [ 0 ]
#### datasets
datasets:
train:
name: DF2K_256_tr
mode: LRHR_PKL
dataroot_GT: /kaggle/input/srflow0103/SRFlow/datasets/DF2K-tr.pklv4
dataroot_LQ: /kaggle/input/srflow0103/SRFlow/datasets/DF2K-tr_X4.pklv4
quant: 32
use_shuffle: true
n_workers: 3 # per GPU
batch_size: 12
GT_size: 256
use_flip: true
color: RGB
val:
name: DF2K_256_tr
mode: LRHR_PKL
dataroot_GT: ../datasets/DIV2K-va.pklv4
dataroot_LQ: ../datasets/DIV2K-va_X4.pklv4
quant: 32
n_max: 20
#### Test Settings
dataroot: /kaggle/input/test-set/test set
model_path: /models/SRFlow/35000_G
heat: 0.6 # This is the standard deviation of the latent vectors
#### network structures
network_G:
which_model_G: SRFlowNet
in_nc: 3
out_nc: 3
nf: 64
nb: 23
upscale: 4
train_RRDB: false
train_RRDB_delay: 0.5
flow:
K: 16
L: 3
noInitialInj: true
coupling: CondAffineSeparatedAndCond
additionalFlowNoAffine: 2
split:
enable: true
fea_up0: true
stackRRDB:
blocks: [ 1, 8, 15, 22 ]
concat: true
#### path
path:
pretrain_model_G:
strict_load: true
resume_state: auto
#### training settings: learning rate scheme, loss
train:
manual_seed: 10
lr_G: !!float 2.5e-4
weight_decay_G: 0
beta1: 0.9
beta2: 0.99
lr_scheme: MultiStepLR
warmup_iter: -1 # no warm up
lr_steps_rel: [ 0.5, 0.75, 0.9, 0.95 ]
lr_gamma: 0.5
niter: 64185
val_freq: 40000
#### validation settings
val:
heats: [ 0.0, 0.5, 0.75, 1.0 ]
n_sample: 3
#### logger
logger:
print_freq: 100
save_checkpoint_freq: !!float 5e3