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‎LICENSE.txt

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Apache License
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8. Limitation of Liability. In no event and under no legal theory,
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unless required by applicable law (such as deliberate and grossly
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9. Accepting Warranty or Additional Liability. While redistributing
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‎README.md

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# [ECCV2022] D2C-SR: A Divergence to Convergence Approach for Real-World Image Super-Resolution
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## [ECCV 2022] D2C-SR: A Divergence to Convergence Approach for Real-World Image Super-Resolution
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This is the official MegEngine implementation of our ECCV2022 [paper](https://arxiv.org/abs/2103.14373) "D2C-SR: A Divergence to Convergence Approach for Real-World Image Super-Resolution".
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Welcome to try megengine to train your model，and our PyTorch version will also be coming soon at [D2C-SR-PyTorch](https://github.com/Well-Lee-pro/D2C-SR).
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### Dependencies
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* MegEngine>=1.3.1
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* tqdm
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### Data Preparation
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#### RealSR and DRealSR
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RealSR and DRealSR has been made public and can be downloaded from their official repo.
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#### D2CRealSR
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Our D2CRealSR dataset can be download from [Google Drive](https://drive.google.com/file/d/1ZTjB6q94ge2h9ixf1osEGXXnfuLTYVzO/view?usp=sharing).
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#### Make Dataset List
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Before training and validation, a data list in ```.txt``` format needs to be prepared, and input the path of your data list during training and evaluation.
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You need to prepare data lists for different scale factors as well as for training and validation datasets.
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The data list has the following format：
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```
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absolute_path_LR absolute_path_HR
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——————————————————————————————————————————————————————
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For example (RealSR)
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List of x4 scale:
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——————————————————————————————————————————————
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/data/Canon_034_LR4.png /data/Canon_034_HR.png
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/data/Canon_035_LR4.png /data/Canon_035_HR.png
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...
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——————————————————————————————————————————————
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List of x2 scale:
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——————————————————————————————————————————————
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/data/Canon_034_LR2.png /data/Canon_034_HR.png
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/data/Canon_035_LR2.png /data/Canon_035_HR.png
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...
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——————————————————————————————————————————————
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```
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### Training
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To train the model, you can run:
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```
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python train.py --train_list_path your_train_list_path --val_list_path your_val_list_path --scale 4 --ex_id your_exp_name
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```
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### Evaluation
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For evaluation, load the pretrained checkpoint and run:
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```
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python test.py --checkpoint checkpoint_path --val_list_path your_val_list_path
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```
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MegEngine checkpoint can be download from [Google Drive](https://drive.google.com/file/d/1itbkFWQ8ZP9F9XcDYpac16J6vspJ2wiV/view?usp=sharing).
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### Citations
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To be updating...
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### Contact
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Contact Email: [liyouwei.wellee@gmail.com](liyouwei.wellee@gmail.com)
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### Acknowledgments
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In this project we use (parts of) the official implementations of the following works:
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* [RCAN](https://github.com/yulunzhang/RCAN)
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* [CDC](https://github.com/xiezw5/Component-Divide-and-Conquer-for-Real-World-Image-Super-Resolution)
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We thank the respective authors for open sourcing their methods.

‎config/__pycache__/config.cpython-38.pyc

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‎config/config.py

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# coding :utf-8
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import argparse
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import os
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lr_init = 0.0001
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epoch = 20000
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decay_epoch = 2000
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lr_decay_rate = 0.5
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batch_size = 4
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INPUT_SIZE = 96
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SCALE = 4
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N_GPUs = 1
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save_dir = "./experiments/"
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ex_id = "D2CSR"
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ex_name = "%s_%sx" % (ex_id, SCALE)
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parser = argparse.ArgumentParser(description="D2C-SR")
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parser.add_argument('--debug', type=bool, default=False,
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help='if debug or not')
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parser.add_argument('--load_checkpoint', type=bool, default=False,
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help='if load checkpoint or not')
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parser.add_argument("--ex_id", type=str, default=ex_id,
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help="exp id")
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parser.add_argument("--print-freq", type=int, default=10,
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help="print freq")
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parser.add_argument("--val-freq", type=int, default=20,
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help="print freq")
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parser.add_argument('--save', type=str, default=os.path.join(save_dir, ex_name),
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help='file name to save')
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parser.add_argument('--checkpoint', type=str, default=None,
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help='checkpoint load path')
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parser.add_argument("--batch-size", type=int, default=batch_size,
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help="Number of images a batch")
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parser.add_argument('--lr', type=float, default=lr_init,
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help='learning rate')
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parser.add_argument('--decay_epoch', type=int, default=decay_epoch,
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help='learning rate decay')
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parser.add_argument('--gamma', type=float, default=lr_decay_rate,
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help='learning rate decay factor for step decay')
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parser.add_argument('--epochs', type=int, default=epoch,
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help='number of epochs to train')
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parser.add_argument('--res_scale', type=float, default=20,
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help='residual scaling')
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parser.add_argument('--L2_w', type=float, default=1,
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help='L2 loss weight')
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parser.add_argument('--margin_same', type=float, default=2e-5,
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help='margin same father branch')
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parser.add_argument('--margin_diff', type=float, default=5e-5,
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help='margin diff father branch')
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parser.add_argument('--diver_w', type=float, default=2e-5,
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help='weight of alpha')
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parser.add_argument('--vgg_w', type=float, default=0.,
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help='weight of vgg loss')
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parser.add_argument('--diver_start_epoch', type=int, default=200,
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help='diver loss start')
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parser.add_argument('--diver_every', type=int, default=1000,
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help='use triplet loss epoch')
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parser.add_argument('--diver_epochs', type=int, default=200,
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help='number of epochs using triplet')
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parser.add_argument('--diver_decay_epoch', type=int, default=2000,
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help='epoch triplet decay')
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parser.add_argument('--diver_decay_rate', type=int, default=0.5,
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help='diver decay rate')
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parser.add_argument('--train_list_path', type=str, default=None,
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help='train data list path')
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parser.add_argument('--val_list_path', type=str, default=None,
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help='val list path')
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parser.add_argument("--patch_size", type=int, default=INPUT_SIZE,
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help="Size of training patch.")
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parser.add_argument('--optimizer', default='ADAM',
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choices=('SGD', 'ADAM', 'RMSprop'),
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help='optimizer to use (SGD | ADAM | RMSprop)')
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parser.add_argument('--momentum', type=float, default=0.9,
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help='SGD momentum')
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parser.add_argument('--betas', type=tuple, default=(0.9, 0.999),
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help='ADAM beta')
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parser.add_argument('--epsilon', type=float, default=1e-8,
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help='ADAM epsilon for numerical stability')
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parser.add_argument('--weight-decay', type=float, default=1e-8,
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help='weight decay')
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parser.add_argument('--workers', type=int, default=8)
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parser.add_argument('--act', type=str, default='relu',
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help='activation function')
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parser.add_argument('--pre_train', type=str, default='',
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help='pre-trained model directory')
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parser.add_argument('--extend', type=str, default='.',
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help='pre-trained model directory')
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parser.add_argument("--scale", type=int, default=SCALE,
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help="Scale factor")
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parser.add_argument('--rgb_range', type=int, default=1,
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help='maximum value of RGB')
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parser.add_argument('--n_colors', type=int, default=3,
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help='number of color channels to use')
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parser.add_argument('--ngpus', type=int, default=N_GPUs,
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help='number of GPUs')
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parser.add_argument('--ex_name', type=str, default=ex_name,
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help='file name to save')
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parser.add_argument('--load', type=str, default='',
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help='file name to load')
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parser.add_argument('--save_models', action='store_true',
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help='save all intermediate models')
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args = parser.parse_args()

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