EDSR 소스 추가

- https://github.com/sanghyun-son/EDSR-PyTorch/tree/9d3bb0ec620ea2ac1b5e5e7a32b0133fbba66fd2

+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*,cover
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# PyTorch
+*.pt
+*.pdf
+*.png
+*.txt
+*.swp
+.vscode

+MIT License
+
+Copyright (c) 2018 Sanghyun Son
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

+**About PyTorch 1.2.0**
+  * Now the master branch supports PyTorch 1.2.0 by default.
+  * Due to the serious version problem (especially torch.utils.data.dataloader), MDSR functions are temporarily disabled. If you have to train/evaluate the MDSR model, please use legacy branches.
+
+# EDSR-PyTorch
+
+**About PyTorch 1.1.0**
+  * There have been minor changes with the 1.1.0 update. Now we support PyTorch 1.1.0 by default, and please use the legacy branch if you prefer older version.
+
+![](/figs/main.png)
+
+This repository is an official PyTorch implementation of the paper **"Enhanced Deep Residual Networks for Single Image Super-Resolution"** from **CVPRW 2017, 2nd NTIRE**.
+You can find the original code and more information from [here](https://github.com/LimBee/NTIRE2017).
+
+If you find our work useful in your research or publication, please cite our work:
+
+[1] Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, and Kyoung Mu Lee, **"Enhanced Deep Residual Networks for Single Image Super-Resolution,"** <i>2nd NTIRE: New Trends in Image Restoration and Enhancement workshop and challenge on image super-resolution in conjunction with **CVPR 2017**. </i> [[PDF](http://openaccess.thecvf.com/content_cvpr_2017_workshops/w12/papers/Lim_Enhanced_Deep_Residual_CVPR_2017_paper.pdf)] [[arXiv](https://arxiv.org/abs/1707.02921)] [[Slide](https://cv.snu.ac.kr/research/EDSR/Presentation_v3(release).pptx)]
+```
+@InProceedings{Lim_2017_CVPR_Workshops,
+  author = {Lim, Bee and Son, Sanghyun and Kim, Heewon and Nah, Seungjun and Lee, Kyoung Mu},
+  title = {Enhanced Deep Residual Networks for Single Image Super-Resolution},
+  booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
+  month = {July},
+  year = {2017}
+}
+```
+We provide scripts for reproducing all the results from our paper. You can train your model from scratch, or use a pre-trained model to enlarge your images.
+
+**Differences between Torch version**
+* Codes are much more compact. (Removed all unnecessary parts.)
+* Models are smaller. (About half.)
+* Slightly better performances.
+* Training and evaluation requires less memory.
+* Python-based.
+
+## Dependencies
+* Python 3.6
+* PyTorch >= 1.0.0
+* numpy
+* skimage
+* **imageio**
+* matplotlib
+* tqdm
+* cv2 >= 3.xx (Only if you want to use video input/output)
+
+## Code
+Clone this repository into any place you want.
+```bash
+git clone https://github.com/thstkdgus35/EDSR-PyTorch
+cd EDSR-PyTorch
+```
+
+## Quickstart (Demo)
+You can test our super-resolution algorithm with your images. Place your images in ``test`` folder. (like ``test/<your_image>``) We support **png** and **jpeg** files.
+
+Run the script in ``src`` folder. Before you run the demo, please uncomment the appropriate line in ```demo.sh``` that you want to execute.
+```bash
+cd src       # You are now in */EDSR-PyTorch/src
+sh demo.sh
+```
+
+You can find the result images from ```experiment/test/results``` folder.
+
+| Model | Scale | File name (.pt) | Parameters | ****PSNR** |
+|  ---  |  ---  | ---       | ---        | ---  |
+| **EDSR** | 2 | EDSR_baseline_x2 | 1.37 M | 34.61 dB |
+| | | *EDSR_x2 | 40.7 M | 35.03 dB |
+| | 3 | EDSR_baseline_x3 | 1.55 M | 30.92 dB |
+| | | *EDSR_x3 | 43.7 M | 31.26 dB |
+| | 4 | EDSR_baseline_x4 | 1.52 M | 28.95 dB |
+| | | *EDSR_x4 | 43.1 M | 29.25 dB |
+| **MDSR** | 2 | MDSR_baseline | 3.23 M | 34.63 dB |
+| | | *MDSR | 7.95 M| 34.92 dB |
+| | 3 | MDSR_baseline | | 30.94 dB |
+| | | *MDSR | | 31.22 dB |
+| | 4 | MDSR_baseline | | 28.97 dB |
+| | | *MDSR | | 29.24 dB |
+
+*Baseline models are in ``experiment/model``. Please download our final models from [here](https://cv.snu.ac.kr/research/EDSR/model_pytorch.tar) (542MB)
+**We measured PSNR using DIV2K 0801 ~ 0900, RGB channels, without self-ensemble. (scale + 2) pixels from the image boundary are ignored.
+
+You can evaluate your models with widely-used benchmark datasets:
+
+[Set5 - Bevilacqua et al. BMVC 2012](http://people.rennes.inria.fr/Aline.Roumy/results/SR_BMVC12.html),
+
+[Set14 - Zeyde et al. LNCS 2010](https://sites.google.com/site/romanzeyde/research-interests),
+
+[B100 - Martin et al. ICCV 2001](https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/),
+
+[Urban100 - Huang et al. CVPR 2015](https://sites.google.com/site/jbhuang0604/publications/struct_sr).
+
+For these datasets, we first convert the result images to YCbCr color space and evaluate PSNR on the Y channel only. You can download [benchmark datasets](https://cv.snu.ac.kr/research/EDSR/benchmark.tar) (250MB). Set ``--dir_data <where_benchmark_folder_located>`` to evaluate the EDSR and MDSR with the benchmarks.
+
+You can download some results from [here](https://cv.snu.ac.kr/research/EDSR/result_image/edsr-results.tar).
+The link contains **EDSR+_baseline_x4** and **EDSR+_x4**.
+Otherwise, you can easily generate result images with ``demo.sh`` scripts.
+
+## How to train EDSR and MDSR
+We used [DIV2K](http://www.vision.ee.ethz.ch/%7Etimofter/publications/Agustsson-CVPRW-2017.pdf) dataset to train our model. Please download it from [here](https://cv.snu.ac.kr/research/EDSR/DIV2K.tar) (7.1GB).
+
+Unpack the tar file to any place you want. Then, change the ```dir_data``` argument in ```src/option.py``` to the place where DIV2K images are located.
+
+We recommend you to pre-process the images before training. This step will decode all **png** files and save them as binaries. Use ``--ext sep_reset`` argument on your first run. You can skip the decoding part and use saved binaries with ``--ext sep`` argument.
+
+If you have enough RAM (>= 32GB), you can use ``--ext bin`` argument to pack all DIV2K images in one binary file.
+
+You can train EDSR and MDSR by yourself. All scripts are provided in the ``src/demo.sh``. Note that EDSR (x3, x4) requires pre-trained EDSR (x2). You can ignore this constraint by removing ```--pre_train <x2 model>``` argument.
+
+```bash
+cd src       # You are now in */EDSR-PyTorch/src
+sh demo.sh
+```
+
+**Update log**
+* Jan 04, 2018
+  * Many parts are re-written. You cannot use previous scripts and models directly.
+  * Pre-trained MDSR is temporarily disabled.
+  * Training details are included.
+
+* Jan 09, 2018
+  * Missing files are included (```src/data/MyImage.py```).
+  * Some links are fixed.
+
+* Jan 16, 2018
+  * Memory efficient forward function is implemented.
+  * Add --chop_forward argument to your script to enable it.
+  * Basically, this function first split a large image to small patches. Those images are merged after super-resolution. I checked this function with 12GB memory, 4000 x 2000 input image in scale 4. (Therefore, the output will be 16000 x 8000.)
+
+* Feb 21, 2018
+  * Fixed the problem when loading pre-trained multi-GPU model.
+  * Added pre-trained scale 2 baseline model.
+  * This code now only saves the best-performing model by default. For MDSR, 'the best' can be ambiguous. Use --save_models argument to keep all the intermediate models.
+  * PyTorch 0.3.1 changed their implementation of DataLoader function. Therefore, I also changed my implementation of MSDataLoader. You can find it on feature/dataloader branch.
+
+* Feb 23, 2018
+  * Now PyTorch 0.3.1 is a default. Use legacy/0.3.0 branch if you use the old version.
+  * With a new ``src/data/DIV2K.py`` code, one can easily create new data class for super-resolution.
+  * New binary data pack. (Please remove the ``DIV2K_decoded`` folder from your dataset if you have.)
+  * With ``--ext bin``, this code will automatically generate and saves the binary data pack that corresponds to previous ``DIV2K_decoded``. (This requires huge RAM (~45GB, Swap can be used.), so please be careful.)
+  * If you cannot make the binary pack, use the default setting (``--ext img``).
+
+  * Fixed a bug that PSNR in the log and PSNR calculated from the saved images does not match.
+  * Now saved images have better quality! (PSNR is ~0.1dB higher than the original code.)
+  * Added performance comparison between Torch7 model and PyTorch models.
+
+* Mar 5, 2018
+  * All baseline models are uploaded.
+  * Now supports half-precision at test time. Use ``--precision half``  to enable it. This does not degrade the output images.
+
+* Mar 11, 2018
+  * Fixed some typos in the code and script.
+  * Now --ext img is default setting. Although we recommend you to use --ext bin when training, please use --ext img when you use --test_only.
+  * Skip_batch operation is implemented. Use --skip_threshold argument to skip the batch that you want to ignore. Although this function is not exactly the same with that of Torch7 version, it will work as you expected.
+
+* Mar 20, 2018
+  * Use ``--ext sep-reset`` to pre-decode large png files. Those decoded files will be saved to the same directory with DIV2K png files. After the first run, you can use ``--ext sep`` to save time.
+  * Now supports various benchmark datasets. For example, try ``--data_test Set5`` to test your model on the Set5 images.
+  * Changed the behavior of skip_batch.
+
+* Mar 29, 2018
+  * We now provide all models from our paper.
+  * We also provide ``MDSR_baseline_jpeg`` model that suppresses JPEG artifacts in the original low-resolution image. Please use it if you have any trouble.
+  * ``MyImage`` dataset is changed to ``Demo`` dataset. Also, it works more efficient than before.
+  * Some codes and script are re-written.
+
+* Apr 9, 2018
+  * VGG and Adversarial loss is implemented based on [SRGAN](http://openaccess.thecvf.com/content_cvpr_2017/papers/Ledig_Photo-Realistic_Single_Image_CVPR_2017_paper.pdf). [WGAN](https://arxiv.org/abs/1701.07875) and [gradient penalty](https://arxiv.org/abs/1704.00028) are also implemented, but they are not tested yet.
+  * Many codes are refactored. If there exists a bug, please report it.
+  * [D-DBPN](https://arxiv.org/abs/1803.02735) is implemented. The default setting is D-DBPN-L.
+
+* Apr 26, 2018
+  * Compatible with PyTorch 0.4.0
+  * Please use the legacy/0.3.1 branch if you are using the old version of PyTorch.
+  * Minor bug fixes
+
+* July 22, 2018
+  * Thanks for recent commits that contains RDN and RCAN. Please see ``code/demo.sh`` to train/test those models.
+  * Now the dataloader is much stable than the previous version. Please erase ``DIV2K/bin`` folder that is created before this commit. Also, please avoid using ``--ext bin`` argument. Our code will automatically pre-decode png images before training. If you do not have enough spaces(~10GB) in your disk, we recommend ``--ext img``(But SLOW!).
+
+* Oct 18, 2018
+  * with ``--pre_train download``, pretrained models will be automatically downloaded from the server.
+  * Supports video input/output (inference only). Try with ``--data_test video --dir_demo [video file directory]``.
+
+* About PyTorch 1.0.0
+  * We support PyTorch 1.0.0. If you prefer the previous versions of PyTorch, use legacy branches.
+  * ``--ext bin`` is not supported. Also, please erase your bin files with ``--ext sep-reset``. Once you successfully build those bin files, you can remove ``-reset`` from the argument.

+*
+!.gitignore
+!/model/*.pt

+from importlib import import_module
+#from dataloader import MSDataLoader
+from torch.utils.data import dataloader
+from torch.utils.data import ConcatDataset
+
+# This is a simple wrapper function for ConcatDataset
+class MyConcatDataset(ConcatDataset):
+    def __init__(self, datasets):
+        super(MyConcatDataset, self).__init__(datasets)
+        self.train = datasets[0].train
+
+    def set_scale(self, idx_scale):
+        for d in self.datasets:
+            if hasattr(d, 'set_scale'): d.set_scale(idx_scale)
+
+class Data:
+    def __init__(self, args):
+        self.loader_train = None
+        if not args.test_only:
+            datasets = []
+            for d in args.data_train:
+                module_name = d if d.find('DIV2K-Q') < 0 else 'DIV2KJPEG'
+                m = import_module('data.' + module_name.lower())
+                datasets.append(getattr(m, module_name)(args, name=d))
+
+            self.loader_train = dataloader.DataLoader(
+                MyConcatDataset(datasets),
+                batch_size=args.batch_size,
+                shuffle=True,
+                pin_memory=not args.cpu,
+                num_workers=args.n_threads,
+            )
+
+        self.loader_test = []
+        for d in args.data_test:
+            if d in ['Set5', 'Set14', 'B100', 'Urban100']:
+                m = import_module('data.benchmark')
+                testset = getattr(m, 'Benchmark')(args, train=False, name=d)
+            else:
+                module_name = d if d.find('DIV2K-Q') < 0 else 'DIV2KJPEG'
+                m = import_module('data.' + module_name.lower())
+                testset = getattr(m, module_name)(args, train=False, name=d)
+
+            self.loader_test.append(
+                dataloader.DataLoader(
+                    testset,
+                    batch_size=1,
+                    shuffle=False,
+                    pin_memory=not args.cpu,
+                    num_workers=args.n_threads,
+                )
+            )

+import os
+
+from data import common
+from data import srdata
+
+import numpy as np
+
+import torch
+import torch.utils.data as data
+
+class Benchmark(srdata.SRData):
+    def __init__(self, args, name='', train=True, benchmark=True):
+        super(Benchmark, self).__init__(
+            args, name=name, train=train, benchmark=True
+        )
+
+    def _set_filesystem(self, dir_data):
+        self.apath = os.path.join(dir_data, 'benchmark', self.name)
+        self.dir_hr = os.path.join(self.apath, 'HR')
+        if self.input_large:
+            self.dir_lr = os.path.join(self.apath, 'LR_bicubicL')
+        else:
+            self.dir_lr = os.path.join(self.apath, 'LR_bicubic')
+        self.ext = ('', '.png')
+

+import random
+
+import numpy as np
+import skimage.color as sc
+
+import torch
+
+def get_patch(*args, patch_size=96, scale=2, multi=False, input_large=False):
+    ih, iw = args[0].shape[:2]
+
+    if not input_large:
+        p = scale if multi else 1
+        tp = p * patch_size
+        ip = tp // scale
+    else:
+        tp = patch_size
+        ip = patch_size
+
+    ix = random.randrange(0, iw - ip + 1)
+    iy = random.randrange(0, ih - ip + 1)
+
+    if not input_large:
+        tx, ty = scale * ix, scale * iy
+    else:
+        tx, ty = ix, iy
+
+    ret = [
+        args[0][iy:iy + ip, ix:ix + ip, :],
+        *[a[ty:ty + tp, tx:tx + tp, :] for a in args[1:]]
+    ]
+
+    return ret
+
+def set_channel(*args, n_channels=3):
+    def _set_channel(img):
+        if img.ndim == 2:
+            img = np.expand_dims(img, axis=2)
+
+        c = img.shape[2]
+        if n_channels == 1 and c == 3:
+            img = np.expand_dims(sc.rgb2ycbcr(img)[:, :, 0], 2)
+        elif n_channels == 3 and c == 1:
+            img = np.concatenate([img] * n_channels, 2)
+
+        return img
+
+    return [_set_channel(a) for a in args]
+
+def np2Tensor(*args, rgb_range=255):
+    def _np2Tensor(img):
+        np_transpose = np.ascontiguousarray(img.transpose((2, 0, 1)))
+        tensor = torch.from_numpy(np_transpose).float()
+        tensor.mul_(rgb_range / 255)
+
+        return tensor
+
+    return [_np2Tensor(a) for a in args]
+
+def augment(*args, hflip=True, rot=True):
+    hflip = hflip and random.random() < 0.5
+    vflip = rot and random.random() < 0.5
+    rot90 = rot and random.random() < 0.5
+
+    def _augment(img):
+        if hflip: img = img[:, ::-1, :]
+        if vflip: img = img[::-1, :, :]
+        if rot90: img = img.transpose(1, 0, 2)
+        
+        return img
+
+    return [_augment(a) for a in args]
+

+import os
+
+from data import common
+
+import numpy as np
+import imageio
+
+import torch
+import torch.utils.data as data
+
+class Demo(data.Dataset):
+    def __init__(self, args, name='Demo', train=False, benchmark=False):
+        self.args = args
+        self.name = name
+        self.scale = args.scale
+        self.idx_scale = 0
+        self.train = False
+        self.benchmark = benchmark
+
+        self.filelist = []
+        for f in os.listdir(args.dir_demo):
+            if f.find('.png') >= 0 or f.find('.jp') >= 0:
+                self.filelist.append(os.path.join(args.dir_demo, f))
+        self.filelist.sort()
+
+    def __getitem__(self, idx):
+        filename = os.path.splitext(os.path.basename(self.filelist[idx]))[0]
+        lr = imageio.imread(self.filelist[idx])
+        lr, = common.set_channel(lr, n_channels=self.args.n_colors)
+        lr_t, = common.np2Tensor(lr, rgb_range=self.args.rgb_range)
+
+        return lr_t, -1, filename
+
+    def __len__(self):
+        return len(self.filelist)
+
+    def set_scale(self, idx_scale):
+        self.idx_scale = idx_scale
+

+import os
+from data import srdata
+
+class DIV2K(srdata.SRData):
+    def __init__(self, args, name='DIV2K', train=True, benchmark=False):
+        data_range = [r.split('-') for r in args.data_range.split('/')]
+        if train:
+            data_range = data_range[0]
+        else:
+            if args.test_only and len(data_range) == 1:
+                data_range = data_range[0]
+            else:
+                data_range = data_range[1]
+
+        self.begin, self.end = list(map(lambda x: int(x), data_range))
+        super(DIV2K, self).__init__(
+            args, name=name, train=train, benchmark=benchmark
+        )
+
+    def _scan(self):
+        names_hr, names_lr = super(DIV2K, self)._scan()
+        names_hr = names_hr[self.begin - 1:self.end]
+        names_lr = [n[self.begin - 1:self.end] for n in names_lr]
+
+        return names_hr, names_lr
+
+    def _set_filesystem(self, dir_data):
+        super(DIV2K, self)._set_filesystem(dir_data)
+        self.dir_hr = os.path.join(self.apath, 'DIV2K_train_HR')
+        self.dir_lr = os.path.join(self.apath, 'DIV2K_train_LR_bicubic')
+        if self.input_large: self.dir_lr += 'L'
+

+import os
+from data import srdata
+from data import div2k
+
+class DIV2KJPEG(div2k.DIV2K):
+    def __init__(self, args, name='', train=True, benchmark=False):
+        self.q_factor = int(name.replace('DIV2K-Q', ''))
+        super(DIV2KJPEG, self).__init__(
+            args, name=name, train=train, benchmark=benchmark
+        )
+
+    def _set_filesystem(self, dir_data):
+        self.apath = os.path.join(dir_data, 'DIV2K')
+        self.dir_hr = os.path.join(self.apath, 'DIV2K_train_HR')
+        self.dir_lr = os.path.join(
+            self.apath, 'DIV2K_Q{}'.format(self.q_factor)
+        )
+        if self.input_large: self.dir_lr += 'L'
+        self.ext = ('.png', '.jpg')
+

+from data import srdata
+
+class SR291(srdata.SRData):
+    def __init__(self, args, name='SR291', train=True, benchmark=False):
+        super(SR291, self).__init__(args, name=name)
+

+import os
+import glob
+import random
+import pickle
+
+from data import common
+
+import numpy as np
+import imageio
+import torch
+import torch.utils.data as data
+
+class SRData(data.Dataset):
+    def __init__(self, args, name='', train=True, benchmark=False):
+        self.args = args
+        self.name = name
+        self.train = train
+        self.split = 'train' if train else 'test'
+        self.do_eval = True
+        self.benchmark = benchmark
+        self.input_large = (args.model == 'VDSR')
+        self.scale = args.scale
+        self.idx_scale = 0
+        
+        self._set_filesystem(args.dir_data)
+        if args.ext.find('img') < 0:
+            path_bin = os.path.join(self.apath, 'bin')
+            os.makedirs(path_bin, exist_ok=True)
+
+        list_hr, list_lr = self._scan()
+        if args.ext.find('img') >= 0 or benchmark:
+            self.images_hr, self.images_lr = list_hr, list_lr
+        elif args.ext.find('sep') >= 0:
+            os.makedirs(
+                self.dir_hr.replace(self.apath, path_bin),
+                exist_ok=True
+            )
+            for s in self.scale:
+                os.makedirs(
+                    os.path.join(
+                        self.dir_lr.replace(self.apath, path_bin),
+                        'X{}'.format(s)
+                    ),
+                    exist_ok=True
+                )
+            
+            self.images_hr, self.images_lr = [], [[] for _ in self.scale]
+            for h in list_hr:
+                b = h.replace(self.apath, path_bin)
+                b = b.replace(self.ext[0], '.pt')
+                self.images_hr.append(b)
+                self._check_and_load(args.ext, h, b, verbose=True) 
+            for i, ll in enumerate(list_lr):
+                for l in ll:
+                    b = l.replace(self.apath, path_bin)
+                    b = b.replace(self.ext[1], '.pt')
+                    self.images_lr[i].append(b)
+                    self._check_and_load(args.ext, l, b, verbose=True) 
+        if train:
+            n_patches = args.batch_size * args.test_every
+            n_images = len(args.data_train) * len(self.images_hr)
+            if n_images == 0:
+                self.repeat = 0
+            else:
+                self.repeat = max(n_patches // n_images, 1)
+
+    # Below functions as used to prepare images
+    def _scan(self):
+        names_hr = sorted(
+            glob.glob(os.path.join(self.dir_hr, '*' + self.ext[0]))
+        )
+        names_lr = [[] for _ in self.scale]
+        for f in names_hr:
+            filename, _ = os.path.splitext(os.path.basename(f))
+            for si, s in enumerate(self.scale):
+                names_lr[si].append(os.path.join(
+                    self.dir_lr, 'X{}/{}x{}{}'.format(
+                        s, filename, s, self.ext[1]
+                    )
+                ))
+
+        return names_hr, names_lr
+
+    def _set_filesystem(self, dir_data):
+        self.apath = os.path.join(dir_data, self.name)
+        self.dir_hr = os.path.join(self.apath, 'HR')
+        self.dir_lr = os.path.join(self.apath, 'LR_bicubic')
+        if self.input_large: self.dir_lr += 'L'
+        self.ext = ('.png', '.png')
+
+    def _check_and_load(self, ext, img, f, verbose=True):
+        if not os.path.isfile(f) or ext.find('reset') >= 0:
+            if verbose:
+                print('Making a binary: {}'.format(f))
+            with open(f, 'wb') as _f:
+                pickle.dump(imageio.imread(img), _f)
+
+    def __getitem__(self, idx):
+        lr, hr, filename = self._load_file(idx)
+        pair = self.get_patch(lr, hr)
+        pair = common.set_channel(*pair, n_channels=self.args.n_colors)
+        pair_t = common.np2Tensor(*pair, rgb_range=self.args.rgb_range)
+
+        return pair_t[0], pair_t[1], filename
+
+    def __len__(self):
+        if self.train:
+            return len(self.images_hr) * self.repeat
+        else:
+            return len(self.images_hr)
+
+    def _get_index(self, idx):
+        if self.train:
+            return idx % len(self.images_hr)
+        else:
+            return idx
+
+    def _load_file(self, idx):
+        idx = self._get_index(idx)
+        f_hr = self.images_hr[idx]
+        f_lr = self.images_lr[self.idx_scale][idx]
+
+        filename, _ = os.path.splitext(os.path.basename(f_hr))
+        if self.args.ext == 'img' or self.benchmark:
+            hr = imageio.imread(f_hr)
+            lr = imageio.imread(f_lr)
+        elif self.args.ext.find('sep') >= 0:
+            with open(f_hr, 'rb') as _f:
+                hr = pickle.load(_f)
+            with open(f_lr, 'rb') as _f:
+                lr = pickle.load(_f)
+
+        return lr, hr, filename
+
+    def get_patch(self, lr, hr):
+        scale = self.scale[self.idx_scale]
+        if self.train:
+            lr, hr = common.get_patch(
+                lr, hr,
+                patch_size=self.args.patch_size,
+                scale=scale,
+                multi=(len(self.scale) > 1),
+                input_large=self.input_large
+            )
+            if not self.args.no_augment: lr, hr = common.augment(lr, hr)
+        else:
+            ih, iw = lr.shape[:2]
+            hr = hr[0:ih * scale, 0:iw * scale]
+
+        return lr, hr
+
+    def set_scale(self, idx_scale):
+        if not self.input_large:
+            self.idx_scale = idx_scale
+        else:
+            self.idx_scale = random.randint(0, len(self.scale) - 1)
+

+import os
+
+from data import common
+
+import cv2
+import numpy as np
+import imageio
+
+import torch
+import torch.utils.data as data
+
+class Video(data.Dataset):
+    def __init__(self, args, name='Video', train=False, benchmark=False):
+        self.args = args
+        self.name = name
+        self.scale = args.scale
+        self.idx_scale = 0
+        self.train = False
+        self.do_eval = False
+        self.benchmark = benchmark
+
+        self.filename, _ = os.path.splitext(os.path.basename(args.dir_demo))
+        self.vidcap = cv2.VideoCapture(args.dir_demo)
+        self.n_frames = 0
+        self.total_frames = int(self.vidcap.get(cv2.CAP_PROP_FRAME_COUNT))
+
+    def __getitem__(self, idx):
+        success, lr = self.vidcap.read()
+        if success:
+            self.n_frames += 1
+            lr, = common.set_channel(lr, n_channels=self.args.n_colors)
+            lr_t, = common.np2Tensor(lr, rgb_range=self.args.rgb_range)
+
+            return lr_t, -1, '{}_{:0>5}'.format(self.filename, self.n_frames)
+        else:
+            vidcap.release()
+            return None
+
+    def __len__(self):
+        return self.total_frames
+
+    def set_scale(self, idx_scale):
+        self.idx_scale = idx_scale
+

+import threading
+import random
+
+import torch
+import torch.multiprocessing as multiprocessing
+from torch.utils.data import DataLoader
+from torch.utils.data import SequentialSampler
+from torch.utils.data import RandomSampler
+from torch.utils.data import BatchSampler
+from torch.utils.data import _utils
+from torch.utils.data.dataloader import _DataLoaderIter
+
+from torch.utils.data._utils import collate
+from torch.utils.data._utils import signal_handling
+from torch.utils.data._utils import MP_STATUS_CHECK_INTERVAL
+from torch.utils.data._utils import ExceptionWrapper
+from torch.utils.data._utils import IS_WINDOWS
+from torch.utils.data._utils.worker import ManagerWatchdog
+
+from torch._six import queue
+
+def _ms_loop(dataset, index_queue, data_queue, done_event, collate_fn, scale, seed, init_fn, worker_id):
+    try:
+        collate._use_shared_memory = True
+        signal_handling._set_worker_signal_handlers()
+
+        torch.set_num_threads(1)
+        random.seed(seed)
+        torch.manual_seed(seed)
+
+        data_queue.cancel_join_thread()
+
+        if init_fn is not None:
+            init_fn(worker_id)
+
+        watchdog = ManagerWatchdog()
+
+        while watchdog.is_alive():
+            try:
+                r = index_queue.get(timeout=MP_STATUS_CHECK_INTERVAL)
+            except queue.Empty:
+                continue
+
+            if r is None:
+                assert done_event.is_set()
+                return
+            elif done_event.is_set():
+                continue
+
+            idx, batch_indices = r
+            try:
+                idx_scale = 0
+                if len(scale) > 1 and dataset.train:
+                    idx_scale = random.randrange(0, len(scale))
+                    dataset.set_scale(idx_scale)
+
+                samples = collate_fn([dataset[i] for i in batch_indices])
+                samples.append(idx_scale)
+            except Exception:
+                data_queue.put((idx, ExceptionWrapper(sys.exc_info())))
+            else:
+                data_queue.put((idx, samples))
+                del samples
+
+    except KeyboardInterrupt:
+        pass
+
+class _MSDataLoaderIter(_DataLoaderIter):
+
+    def __init__(self, loader):
+        self.dataset = loader.dataset
+        self.scale = loader.scale
+        self.collate_fn = loader.collate_fn
+        self.batch_sampler = loader.batch_sampler
+        self.num_workers = loader.num_workers
+        self.pin_memory = loader.pin_memory and torch.cuda.is_available()
+        self.timeout = loader.timeout
+
+        self.sample_iter = iter(self.batch_sampler)
+
+        base_seed = torch.LongTensor(1).random_().item()
+
+        if self.num_workers > 0:
+            self.worker_init_fn = loader.worker_init_fn
+            self.worker_queue_idx = 0
+            self.worker_result_queue = multiprocessing.Queue()
+            self.batches_outstanding = 0
+            self.worker_pids_set = False
+            self.shutdown = False
+            self.send_idx = 0
+            self.rcvd_idx = 0
+            self.reorder_dict = {}
+            self.done_event = multiprocessing.Event()
+
+            base_seed = torch.LongTensor(1).random_()[0]
+
+            self.index_queues = []
+            self.workers = []
+            for i in range(self.num_workers):
+                index_queue = multiprocessing.Queue()
+                index_queue.cancel_join_thread()
+                w = multiprocessing.Process(
+                    target=_ms_loop,
+                    args=(
+                        self.dataset,
+                        index_queue,
+                        self.worker_result_queue,
+                        self.done_event,
+                        self.collate_fn,
+                        self.scale,
+                        base_seed + i,
+                        self.worker_init_fn,
+                        i
+                    )
+                )
+                w.daemon = True
+                w.start()
+                self.index_queues.append(index_queue)
+                self.workers.append(w)
+
+            if self.pin_memory:
+                self.data_queue = queue.Queue()
+                pin_memory_thread = threading.Thread(
+                    target=_utils.pin_memory._pin_memory_loop,
+                    args=(
+                        self.worker_result_queue,
+                        self.data_queue,
+                        torch.cuda.current_device(),
+                        self.done_event
+                    )
+                )
+                pin_memory_thread.daemon = True
+                pin_memory_thread.start()
+                self.pin_memory_thread = pin_memory_thread
+            else:
+                self.data_queue = self.worker_result_queue
+
+            _utils.signal_handling._set_worker_pids(
+                id(self), tuple(w.pid for w in self.workers)
+            )
+            _utils.signal_handling._set_SIGCHLD_handler()
+            self.worker_pids_set = True
+
+            for _ in range(2 * self.num_workers):
+                self._put_indices()
+
+
+class MSDataLoader(DataLoader):
+
+    def __init__(self, cfg, *args, **kwargs):
+        super(MSDataLoader, self).__init__(
+            *args, **kwargs, num_workers=cfg.n_threads
+        )
+        self.scale = cfg.scale
+
+    def __iter__(self):
+        return _MSDataLoaderIter(self)
+

+# EDSR baseline model (x2) + JPEG augmentation
+python main.py --model EDSR --scale 2 --patch_size 96 --save edsr_baseline_x2 --reset
+#python main.py --model EDSR --scale 2 --patch_size 96 --save edsr_baseline_x2 --reset --data_train DIV2K+DIV2K-Q75 --data_test DIV2K+DIV2K-Q75
+
+# EDSR baseline model (x3) - from EDSR baseline model (x2)
+#python main.py --model EDSR --scale 3 --patch_size 144 --save edsr_baseline_x3 --reset --pre_train [pre-trained EDSR_baseline_x2 model dir]
+
+# EDSR baseline model (x4) - from EDSR baseline model (x2)
+#python main.py --model EDSR --scale 4 --save edsr_baseline_x4 --reset --pre_train [pre-trained EDSR_baseline_x2 model dir]
+
+# EDSR in the paper (x2)
+#python main.py --model EDSR --scale 2 --save edsr_x2 --n_resblocks 32 --n_feats 256 --res_scale 0.1 --reset
+
+# EDSR in the paper (x3) - from EDSR (x2)
+#python main.py --model EDSR --scale 3 --save edsr_x3 --n_resblocks 32 --n_feats 256 --res_scale 0.1 --reset --pre_train [pre-trained EDSR model dir]
+
+# EDSR in the paper (x4) - from EDSR (x2)
+#python main.py --model EDSR --scale 4 --save edsr_x4 --n_resblocks 32 --n_feats 256 --res_scale 0.1 --reset --pre_train [pre-trained EDSR_x2 model dir]
+
+# MDSR baseline model
+#python main.py --template MDSR --model MDSR --scale 2+3+4 --save MDSR_baseline --reset --save_models
+
+# MDSR in the paper
+#python main.py --template MDSR --model MDSR --scale 2+3+4 --n_resblocks 80 --save MDSR --reset --save_models
+
+# Standard benchmarks (Ex. EDSR_baseline_x4)
+#python main.py --data_test Set5+Set14+B100+Urban100+DIV2K --data_range 801-900 --scale 4 --pre_train download --test_only --self_ensemble
+
+#python main.py --data_test Set5+Set14+B100+Urban100+DIV2K --data_range 801-900 --scale 4 --n_resblocks 32 --n_feats 256 --res_scale 0.1 --pre_train download --test_only --self_ensemble
+
+# Test your own images
+#python main.py --data_test Demo --scale 4 --pre_train download --test_only --save_results
+
+# Advanced - Test with JPEG images 
+#python main.py --model MDSR --data_test Demo --scale 2+3+4 --pre_train download --test_only --save_results
+
+# Advanced - Training with adversarial loss
+#python main.py --template GAN --scale 4 --save edsr_gan --reset --patch_size 96 --loss 5*VGG54+0.15*GAN --pre_train download
+
+# RDN BI model (x2)
+#python3.6 main.py --scale 2 --save RDN_D16C8G64_BIx2 --model RDN --epochs 200 --batch_size 16 --data_range 801-805 --patch_size 64 --reset
+# RDN BI model (x3)
+#python3.6 main.py --scale 3 --save RDN_D16C8G64_BIx3 --model RDN --epochs 200 --batch_size 16 --data_range 801-805 --patch_size 96 --reset
+# RDN BI model (x4)
+#python3.6 main.py --scale 4 --save RDN_D16C8G64_BIx4 --model RDN --epochs 200 --batch_size 16 --data_range 801-805 --patch_size 128 --reset
+
+# RCAN_BIX2_G10R20P48, input=48x48, output=96x96
+# pretrained model can be downloaded from https://www.dropbox.com/s/mjbcqkd4nwhr6nu/models_ECCV2018RCAN.zip?dl=0
+#python main.py --template RCAN --save RCAN_BIX2_G10R20P48 --scale 2 --reset --save_results --patch_size 96
+# RCAN_BIX3_G10R20P48, input=48x48, output=144x144
+#python main.py --template RCAN --save RCAN_BIX3_G10R20P48 --scale 3 --reset --save_results --patch_size 144 --pre_train ../experiment/model/RCAN_BIX2.pt
+# RCAN_BIX4_G10R20P48, input=48x48, output=192x192
+#python main.py --template RCAN --save RCAN_BIX4_G10R20P48 --scale 4 --reset --save_results --patch_size 192 --pre_train ../experiment/model/RCAN_BIX2.pt
+# RCAN_BIX8_G10R20P48, input=48x48, output=384x384
+#python main.py --template RCAN --save RCAN_BIX8_G10R20P48 --scale 8 --reset --save_results --patch_size 384 --pre_train ../experiment/model/RCAN_BIX2.pt
+

+import os
+from importlib import import_module
+
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+
+import numpy as np
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+class Loss(nn.modules.loss._Loss):
+    def __init__(self, args, ckp):
+        super(Loss, self).__init__()
+        print('Preparing loss function:')
+
+        self.n_GPUs = args.n_GPUs
+        self.loss = []
+        self.loss_module = nn.ModuleList()
+        for loss in args.loss.split('+'):
+            weight, loss_type = loss.split('*')
+            if loss_type == 'MSE':
+                loss_function = nn.MSELoss()
+            elif loss_type == 'L1':
+                loss_function = nn.L1Loss()
+            elif loss_type.find('VGG') >= 0:
+                module = import_module('loss.vgg')
+                loss_function = getattr(module, 'VGG')(
+                    loss_type[3:],
+                    rgb_range=args.rgb_range
+                )
+            elif loss_type.find('GAN') >= 0:
+                module = import_module('loss.adversarial')
+                loss_function = getattr(module, 'Adversarial')(
+                    args,
+                    loss_type
+                )
+
+            self.loss.append({
+                'type': loss_type,
+                'weight': float(weight),
+                'function': loss_function}
+            )
+            if loss_type.find('GAN') >= 0:
+                self.loss.append({'type': 'DIS', 'weight': 1, 'function': None})
+
+        if len(self.loss) > 1:
+            self.loss.append({'type': 'Total', 'weight': 0, 'function': None})
+
+        for l in self.loss:
+            if l['function'] is not None:
+                print('{:.3f} * {}'.format(l['weight'], l['type']))
+                self.loss_module.append(l['function'])
+
+        self.log = torch.Tensor()
+
+        device = torch.device('cpu' if args.cpu else 'cuda')
+        self.loss_module.to(device)
+        if args.precision == 'half': self.loss_module.half()
+        if not args.cpu and args.n_GPUs > 1:
+            self.loss_module = nn.DataParallel(
+                self.loss_module, range(args.n_GPUs)
+            )
+
+        if args.load != '': self.load(ckp.dir, cpu=args.cpu)
+
+    def forward(self, sr, hr):
+        losses = []
+        for i, l in enumerate(self.loss):
+            if l['function'] is not None:
+                loss = l['function'](sr, hr)
+                effective_loss = l['weight'] * loss
+                losses.append(effective_loss)
+                self.log[-1, i] += effective_loss.item()
+            elif l['type'] == 'DIS':
+                self.log[-1, i] += self.loss[i - 1]['function'].loss
+
+        loss_sum = sum(losses)
+        if len(self.loss) > 1:
+            self.log[-1, -1] += loss_sum.item()
+
+        return loss_sum
+
+    def step(self):
+        for l in self.get_loss_module():
+            if hasattr(l, 'scheduler'):
+                l.scheduler.step()
+
+    def start_log(self):
+        self.log = torch.cat((self.log, torch.zeros(1, len(self.loss))))
+
+    def end_log(self, n_batches):
+        self.log[-1].div_(n_batches)
+
+    def display_loss(self, batch):
+        n_samples = batch + 1
+        log = []
+        for l, c in zip(self.loss, self.log[-1]):
+            log.append('[{}: {:.4f}]'.format(l['type'], c / n_samples))
+
+        return ''.join(log)
+
+    def plot_loss(self, apath, epoch):
+        axis = np.linspace(1, epoch, epoch)
+        for i, l in enumerate(self.loss):
+            label = '{} Loss'.format(l['type'])
+            fig = plt.figure()
+            plt.title(label)
+            plt.plot(axis, self.log[:, i].numpy(), label=label)
+            plt.legend()
+            plt.xlabel('Epochs')
+            plt.ylabel('Loss')
+            plt.grid(True)
+            plt.savefig(os.path.join(apath, 'loss_{}.pdf'.format(l['type'])))
+            plt.close(fig)
+
+    def get_loss_module(self):
+        if self.n_GPUs == 1:
+            return self.loss_module
+        else:
+            return self.loss_module.module
+
+    def save(self, apath):
+        torch.save(self.state_dict(), os.path.join(apath, 'loss.pt'))
+        torch.save(self.log, os.path.join(apath, 'loss_log.pt'))
+
+    def load(self, apath, cpu=False):
+        if cpu:
+            kwargs = {'map_location': lambda storage, loc: storage}
+        else:
+            kwargs = {}
+
+        self.load_state_dict(torch.load(
+            os.path.join(apath, 'loss.pt'),
+            **kwargs
+        ))
+        self.log = torch.load(os.path.join(apath, 'loss_log.pt'))
+        for l in self.get_loss_module():
+            if hasattr(l, 'scheduler'):
+                for _ in range(len(self.log)): l.scheduler.step()
+

+import utility
+from types import SimpleNamespace
+
+from model import common
+from loss import discriminator
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+
+class Adversarial(nn.Module):
+    def __init__(self, args, gan_type):
+        super(Adversarial, self).__init__()
+        self.gan_type = gan_type
+        self.gan_k = args.gan_k
+        self.dis = discriminator.Discriminator(args)
+        if gan_type == 'WGAN_GP':
+            # see https://arxiv.org/pdf/1704.00028.pdf pp.4
+            optim_dict = {
+                'optimizer': 'ADAM',
+                'betas': (0, 0.9),
+                'epsilon': 1e-8,
+                'lr': 1e-5,
+                'weight_decay': args.weight_decay,
+                'decay': args.decay,
+                'gamma': args.gamma
+            }
+            optim_args = SimpleNamespace(**optim_dict)
+        else:
+            optim_args = args
+
+        self.optimizer = utility.make_optimizer(optim_args, self.dis)
+
+    def forward(self, fake, real):
+        # updating discriminator...
+        self.loss = 0
+        fake_detach = fake.detach()     # do not backpropagate through G
+        for _ in range(self.gan_k):
+            self.optimizer.zero_grad()
+            # d: B x 1 tensor
+            d_fake = self.dis(fake_detach)
+            d_real = self.dis(real)
+            retain_graph = False
+            if self.gan_type == 'GAN':
+                loss_d = self.bce(d_real, d_fake)
+            elif self.gan_type.find('WGAN') >= 0:
+                loss_d = (d_fake - d_real).mean()
+                if self.gan_type.find('GP') >= 0:
+                    epsilon = torch.rand_like(fake).view(-1, 1, 1, 1)
+                    hat = fake_detach.mul(1 - epsilon) + real.mul(epsilon)
+                    hat.requires_grad = True
+                    d_hat = self.dis(hat)
+                    gradients = torch.autograd.grad(
+                        outputs=d_hat.sum(), inputs=hat,
+                        retain_graph=True, create_graph=True, only_inputs=True
+                    )[0]
+                    gradients = gradients.view(gradients.size(0), -1)
+                    gradient_norm = gradients.norm(2, dim=1)
+                    gradient_penalty = 10 * gradient_norm.sub(1).pow(2).mean()
+                    loss_d += gradient_penalty
+            # from ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
+            elif self.gan_type == 'RGAN':
+                better_real = d_real - d_fake.mean(dim=0, keepdim=True)
+                better_fake = d_fake - d_real.mean(dim=0, keepdim=True)
+                loss_d = self.bce(better_real, better_fake)
+                retain_graph = True
+
+            # Discriminator update
+            self.loss += loss_d.item()
+            loss_d.backward(retain_graph=retain_graph)
+            self.optimizer.step()
+
+            if self.gan_type == 'WGAN':
+                for p in self.dis.parameters():
+                    p.data.clamp_(-1, 1)
+
+        self.loss /= self.gan_k
+
+        # updating generator...
+        d_fake_bp = self.dis(fake)      # for backpropagation, use fake as it is
+        if self.gan_type == 'GAN':
+            label_real = torch.ones_like(d_fake_bp)
+            loss_g = F.binary_cross_entropy_with_logits(d_fake_bp, label_real)
+        elif self.gan_type.find('WGAN') >= 0:
+            loss_g = -d_fake_bp.mean()
+        elif self.gan_type == 'RGAN':
+            better_real = d_real - d_fake_bp.mean(dim=0, keepdim=True)
+            better_fake = d_fake_bp - d_real.mean(dim=0, keepdim=True)
+            loss_g = self.bce(better_fake, better_real)
+
+        # Generator loss
+        return loss_g
+    
+    def state_dict(self, *args, **kwargs):
+        state_discriminator = self.dis.state_dict(*args, **kwargs)
+        state_optimizer = self.optimizer.state_dict()
+
+        return dict(**state_discriminator, **state_optimizer)
+
+    def bce(self, real, fake):
+        label_real = torch.ones_like(real)
+        label_fake = torch.zeros_like(fake)
+        bce_real = F.binary_cross_entropy_with_logits(real, label_real)
+        bce_fake = F.binary_cross_entropy_with_logits(fake, label_fake)
+        bce_loss = bce_real + bce_fake
+        return bce_loss
+               
+# Some references
+# https://github.com/kuc2477/pytorch-wgan-gp/blob/master/model.py
+# OR
+# https://github.com/caogang/wgan-gp/blob/master/gan_cifar10.py

+from model import common
+
+import torch.nn as nn
+
+class Discriminator(nn.Module):
+    '''
+        output is not normalized
+    '''
+    def __init__(self, args):
+        super(Discriminator, self).__init__()
+
+        in_channels = args.n_colors
+        out_channels = 64
+        depth = 7
+
+        def _block(_in_channels, _out_channels, stride=1):
+            return nn.Sequential(
+                nn.Conv2d(
+                    _in_channels,
+                    _out_channels,
+                    3,
+                    padding=1,
+                    stride=stride,
+                    bias=False
+                ),
+                nn.BatchNorm2d(_out_channels),
+                nn.LeakyReLU(negative_slope=0.2, inplace=True)
+            )
+
+        m_features = [_block(in_channels, out_channels)]
+        for i in range(depth):
+            in_channels = out_channels
+            if i % 2 == 1:
+                stride = 1
+                out_channels *= 2
+            else:
+                stride = 2
+            m_features.append(_block(in_channels, out_channels, stride=stride))
+
+        patch_size = args.patch_size // (2**((depth + 1) // 2))
+        m_classifier = [
+            nn.Linear(out_channels * patch_size**2, 1024),
+            nn.LeakyReLU(negative_slope=0.2, inplace=True),
+            nn.Linear(1024, 1)
+        ]
+
+        self.features = nn.Sequential(*m_features)
+        self.classifier = nn.Sequential(*m_classifier)
+
+    def forward(self, x):
+        features = self.features(x)
+        output = self.classifier(features.view(features.size(0), -1))
+
+        return output
+

+from model import common
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision.models as models
+
+class VGG(nn.Module):
+    def __init__(self, conv_index, rgb_range=1):
+        super(VGG, self).__init__()
+        vgg_features = models.vgg19(pretrained=True).features
+        modules = [m for m in vgg_features]
+        if conv_index.find('22') >= 0:
+            self.vgg = nn.Sequential(*modules[:8])
+        elif conv_index.find('54') >= 0:
+            self.vgg = nn.Sequential(*modules[:35])
+
+        vgg_mean = (0.485, 0.456, 0.406)
+        vgg_std = (0.229 * rgb_range, 0.224 * rgb_range, 0.225 * rgb_range)
+        self.sub_mean = common.MeanShift(rgb_range, vgg_mean, vgg_std)
+        for p in self.parameters():
+            p.requires_grad = False
+
+    def forward(self, sr, hr):
+        def _forward(x):
+            x = self.sub_mean(x)
+            x = self.vgg(x)
+            return x
+            
+        vgg_sr = _forward(sr)
+        with torch.no_grad():
+            vgg_hr = _forward(hr.detach())
+
+        loss = F.mse_loss(vgg_sr, vgg_hr)
+
+        return loss

+import torch
+
+import utility
+import data
+import model
+import loss
+from option import args
+from trainer import Trainer
+
+torch.manual_seed(args.seed)
+checkpoint = utility.checkpoint(args)
+
+def main():
+    global model
+    if args.data_test == ['video']:
+        from videotester import VideoTester
+        model = model.Model(args, checkpoint)
+        t = VideoTester(args, model, checkpoint)
+        t.test()
+    else:
+        if checkpoint.ok:
+            loader = data.Data(args)
+            _model = model.Model(args, checkpoint)
+            _loss = loss.Loss(args, checkpoint) if not args.test_only else None
+            t = Trainer(args, loader, _model, _loss, checkpoint)
+            while not t.terminate():
+                t.train()
+                t.test()
+
+            checkpoint.done()
+
+if __name__ == '__main__':
+    main()

+import os
+from importlib import import_module
+
+import torch
+import torch.nn as nn
+import torch.nn.parallel as P
+import torch.utils.model_zoo
+
+class Model(nn.Module):
+    def __init__(self, args, ckp):
+        super(Model, self).__init__()
+        print('Making model...')
+
+        self.scale = args.scale
+        self.idx_scale = 0
+        self.input_large = (args.model == 'VDSR')
+        self.self_ensemble = args.self_ensemble
+        self.chop = args.chop
+        self.precision = args.precision
+        self.cpu = args.cpu
+        self.device = torch.device('cpu' if args.cpu else 'cuda')
+        self.n_GPUs = args.n_GPUs
+        self.save_models = args.save_models
+
+        module = import_module('model.' + args.model.lower())
+        self.model = module.make_model(args).to(self.device)
+        if args.precision == 'half':
+            self.model.half()
+
+        self.load(
+            ckp.get_path('model'),
+            pre_train=args.pre_train,
+            resume=args.resume,
+            cpu=args.cpu
+        )
+        print(self.model, file=ckp.log_file)
+
+    def forward(self, x, idx_scale):
+        self.idx_scale = idx_scale
+        if hasattr(self.model, 'set_scale'):
+            self.model.set_scale(idx_scale)
+
+        if self.training:
+            if self.n_GPUs > 1:
+                return P.data_parallel(self.model, x, range(self.n_GPUs))
+            else:
+                return self.model(x)
+        else:
+            if self.chop:
+                forward_function = self.forward_chop
+            else:
+                forward_function = self.model.forward
+
+            if self.self_ensemble:
+                return self.forward_x8(x, forward_function=forward_function)
+            else:
+                return forward_function(x)
+
+    def save(self, apath, epoch, is_best=False):
+        save_dirs = [os.path.join(apath, 'model_latest.pt')]
+
+        if is_best:
+            save_dirs.append(os.path.join(apath, 'model_best.pt'))
+        if self.save_models:
+            save_dirs.append(
+                os.path.join(apath, 'model_{}.pt'.format(epoch))
+            )
+
+        for s in save_dirs:
+            torch.save(self.model.state_dict(), s)
+
+    def load(self, apath, pre_train='', resume=-1, cpu=False):
+        load_from = None
+        kwargs = {}
+        if cpu:
+            kwargs = {'map_location': lambda storage, loc: storage}
+
+        if resume == -1:
+            load_from = torch.load(
+                os.path.join(apath, 'model_latest.pt'),
+                **kwargs
+            )
+        elif resume == 0:
+            if pre_train == 'download':
+                print('Download the model')
+                dir_model = os.path.join('..', 'models')
+                os.makedirs(dir_model, exist_ok=True)
+                load_from = torch.utils.model_zoo.load_url(
+                    self.model.url,
+                    model_dir=dir_model,
+                    **kwargs
+                )
+            elif pre_train:
+                print('Load the model from {}'.format(pre_train))
+                load_from = torch.load(pre_train, **kwargs)
+        else:
+            load_from = torch.load(
+                os.path.join(apath, 'model_{}.pt'.format(resume)),
+                **kwargs
+            )
+
+        if load_from:
+            self.model.load_state_dict(load_from, strict=False)
+
+    def forward_chop(self, *args, shave=10, min_size=160000):
+        scale = 1 if self.input_large else self.scale[self.idx_scale]
+        n_GPUs = min(self.n_GPUs, 4)
+        # height, width
+        h, w = args[0].size()[-2:]
+
+        top = slice(0, h//2 + shave)
+        bottom = slice(h - h//2 - shave, h)
+        left = slice(0, w//2 + shave)
+        right = slice(w - w//2 - shave, w)
+        x_chops = [torch.cat([
+            a[..., top, left],
+            a[..., top, right],
+            a[..., bottom, left],
+            a[..., bottom, right]
+        ]) for a in args]
+
+        y_chops = []
+        if h * w < 4 * min_size:
+            for i in range(0, 4, n_GPUs):
+                x = [x_chop[i:(i + n_GPUs)] for x_chop in x_chops]
+                y = P.data_parallel(self.model, *x, range(n_GPUs))
+                if not isinstance(y, list): y = [y]
+                if not y_chops:
+                    y_chops = [[c for c in _y.chunk(n_GPUs, dim=0)] for _y in y]
+                else:
+                    for y_chop, _y in zip(y_chops, y):
+                        y_chop.extend(_y.chunk(n_GPUs, dim=0))
+        else:
+            for p in zip(*x_chops):
+                y = self.forward_chop(*p, shave=shave, min_size=min_size)
+                if not isinstance(y, list): y = [y]
+                if not y_chops:
+                    y_chops = [[_y] for _y in y]
+                else:
+                    for y_chop, _y in zip(y_chops, y): y_chop.append(_y)
+
+        h *= scale
+        w *= scale
+        top = slice(0, h//2)
+        bottom = slice(h - h//2, h)
+        bottom_r = slice(h//2 - h, None)
+        left = slice(0, w//2)
+        right = slice(w - w//2, w)
+        right_r = slice(w//2 - w, None)
+
+        # batch size, number of color channels
+        b, c = y_chops[0][0].size()[:-2]
+        y = [y_chop[0].new(b, c, h, w) for y_chop in y_chops]
+        for y_chop, _y in zip(y_chops, y):
+            _y[..., top, left] = y_chop[0][..., top, left]
+            _y[..., top, right] = y_chop[1][..., top, right_r]
+            _y[..., bottom, left] = y_chop[2][..., bottom_r, left]
+            _y[..., bottom, right] = y_chop[3][..., bottom_r, right_r]
+
+        if len(y) == 1: y = y[0]
+
+        return y
+
+    def forward_x8(self, *args, forward_function=None):
+        def _transform(v, op):
+            if self.precision != 'single': v = v.float()
+
+            v2np = v.data.cpu().numpy()
+            if op == 'v':
+                tfnp = v2np[:, :, :, ::-1].copy()
+            elif op == 'h':
+                tfnp = v2np[:, :, ::-1, :].copy()
+            elif op == 't':
+                tfnp = v2np.transpose((0, 1, 3, 2)).copy()
+
+            ret = torch.Tensor(tfnp).to(self.device)
+            if self.precision == 'half': ret = ret.half()
+
+            return ret
+
+        list_x = []
+        for a in args:
+            x = [a]
+            for tf in 'v', 'h', 't': x.extend([_transform(_x, tf) for _x in x])
+
+            list_x.append(x)
+
+        list_y = []
+        for x in zip(*list_x):
+            y = forward_function(*x)
+            if not isinstance(y, list): y = [y]
+            if not list_y:
+                list_y = [[_y] for _y in y]
+            else:
+                for _list_y, _y in zip(list_y, y): _list_y.append(_y)
+
+        for _list_y in list_y:
+            for i in range(len(_list_y)):
+                if i > 3:
+                    _list_y[i] = _transform(_list_y[i], 't')
+                if i % 4 > 1:
+                    _list_y[i] = _transform(_list_y[i], 'h')
+                if (i % 4) % 2 == 1:
+                    _list_y[i] = _transform(_list_y[i], 'v')
+
+        y = [torch.cat(_y, dim=0).mean(dim=0, keepdim=True) for _y in list_y]
+        if len(y) == 1: y = y[0]
+
+        return y

+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+def default_conv(in_channels, out_channels, kernel_size, bias=True):
+    return nn.Conv2d(
+        in_channels, out_channels, kernel_size,
+        padding=(kernel_size//2), bias=bias)
+
+class MeanShift(nn.Conv2d):
+    def __init__(
+        self, rgb_range,
+        rgb_mean=(0.4488, 0.4371, 0.4040), rgb_std=(1.0, 1.0, 1.0), sign=-1):
+
+        super(MeanShift, self).__init__(3, 3, kernel_size=1)
+        std = torch.Tensor(rgb_std)
+        self.weight.data = torch.eye(3).view(3, 3, 1, 1) / std.view(3, 1, 1, 1)
+        self.bias.data = sign * rgb_range * torch.Tensor(rgb_mean) / std
+        for p in self.parameters():
+            p.requires_grad = False
+
+class BasicBlock(nn.Sequential):
+    def __init__(
+        self, conv, in_channels, out_channels, kernel_size, stride=1, bias=False,
+        bn=True, act=nn.ReLU(True)):
+
+        m = [conv(in_channels, out_channels, kernel_size, bias=bias)]
+        if bn:
+            m.append(nn.BatchNorm2d(out_channels))
+        if act is not None:
+            m.append(act)
+
+        super(BasicBlock, self).__init__(*m)
+
+class ResBlock(nn.Module):
+    def __init__(
+        self, conv, n_feats, kernel_size,
+        bias=True, bn=False, act=nn.ReLU(True), res_scale=1):
+
+        super(ResBlock, self).__init__()
+        m = []
+        for i in range(2):
+            m.append(conv(n_feats, n_feats, kernel_size, bias=bias))
+            if bn:
+                m.append(nn.BatchNorm2d(n_feats))
+            if i == 0:
+                m.append(act)
+
+        self.body = nn.Sequential(*m)
+        self.res_scale = res_scale
+
+    def forward(self, x):
+        res = self.body(x).mul(self.res_scale)
+        res += x
+
+        return res
+
+class Upsampler(nn.Sequential):
+    def __init__(self, conv, scale, n_feats, bn=False, act=False, bias=True):
+
+        m = []
+        if (scale & (scale - 1)) == 0:    # Is scale = 2^n?
+            for _ in range(int(math.log(scale, 2))):
+                m.append(conv(n_feats, 4 * n_feats, 3, bias))
+                m.append(nn.PixelShuffle(2))
+                if bn:
+                    m.append(nn.BatchNorm2d(n_feats))
+                if act == 'relu':
+                    m.append(nn.ReLU(True))
+                elif act == 'prelu':
+                    m.append(nn.PReLU(n_feats))
+
+        elif scale == 3:
+            m.append(conv(n_feats, 9 * n_feats, 3, bias))
+            m.append(nn.PixelShuffle(3))
+            if bn:
+                m.append(nn.BatchNorm2d(n_feats))
+            if act == 'relu':
+                m.append(nn.ReLU(True))
+            elif act == 'prelu':
+                m.append(nn.PReLU(n_feats))
+        else:
+            raise NotImplementedError
+
+        super(Upsampler, self).__init__(*m)
+

+# Deep Back-Projection Networks For Super-Resolution
+# https://arxiv.org/abs/1803.02735
+
+from model import common
+
+import torch
+import torch.nn as nn
+
+
+def make_model(args, parent=False):
+    return DDBPN(args)
+
+def projection_conv(in_channels, out_channels, scale, up=True):
+    kernel_size, stride, padding = {
+        2: (6, 2, 2),
+        4: (8, 4, 2),
+        8: (12, 8, 2)
+    }[scale]
+    if up:
+        conv_f = nn.ConvTranspose2d
+    else:
+        conv_f = nn.Conv2d
+
+    return conv_f(
+        in_channels, out_channels, kernel_size,
+        stride=stride, padding=padding
+    )
+
+class DenseProjection(nn.Module):
+    def __init__(self, in_channels, nr, scale, up=True, bottleneck=True):
+        super(DenseProjection, self).__init__()
+        if bottleneck:
+            self.bottleneck = nn.Sequential(*[
+                nn.Conv2d(in_channels, nr, 1),
+                nn.PReLU(nr)
+            ])
+            inter_channels = nr
+        else:
+            self.bottleneck = None
+            inter_channels = in_channels
+
+        self.conv_1 = nn.Sequential(*[
+            projection_conv(inter_channels, nr, scale, up),
+            nn.PReLU(nr)
+        ])
+        self.conv_2 = nn.Sequential(*[
+            projection_conv(nr, inter_channels, scale, not up),
+            nn.PReLU(inter_channels)
+        ])
+        self.conv_3 = nn.Sequential(*[
+            projection_conv(inter_channels, nr, scale, up),
+            nn.PReLU(nr)
+        ])
+
+    def forward(self, x):
+        if self.bottleneck is not None:
+            x = self.bottleneck(x)
+
+        a_0 = self.conv_1(x)
+        b_0 = self.conv_2(a_0)
+        e = b_0.sub(x)
+        a_1 = self.conv_3(e)
+
+        out = a_0.add(a_1)
+
+        return out
+
+class DDBPN(nn.Module):
+    def __init__(self, args):
+        super(DDBPN, self).__init__()
+        scale = args.scale[0]
+
+        n0 = 128
+        nr = 32
+        self.depth = 6
+
+        rgb_mean = (0.4488, 0.4371, 0.4040)
+        rgb_std = (1.0, 1.0, 1.0)
+        self.sub_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std)
+        initial = [
+            nn.Conv2d(args.n_colors, n0, 3, padding=1),
+            nn.PReLU(n0),
+            nn.Conv2d(n0, nr, 1),
+            nn.PReLU(nr)
+        ]
+        self.initial = nn.Sequential(*initial)
+
+        self.upmodules = nn.ModuleList()
+        self.downmodules = nn.ModuleList()
+        channels = nr
+        for i in range(self.depth):
+            self.upmodules.append(
+                DenseProjection(channels, nr, scale, True, i > 1)
+            )
+            if i != 0:
+                channels += nr
+        
+        channels = nr
+        for i in range(self.depth - 1):
+            self.downmodules.append(
+                DenseProjection(channels, nr, scale, False, i != 0)
+            )
+            channels += nr
+
+        reconstruction = [
+            nn.Conv2d(self.depth * nr, args.n_colors, 3, padding=1) 
+        ]
+        self.reconstruction = nn.Sequential(*reconstruction)
+
+        self.add_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std, 1)
+
+    def forward(self, x):
+        x = self.sub_mean(x)
+        x = self.initial(x)
+
+        h_list = []
+        l_list = []
+        for i in range(self.depth - 1):
+            if i == 0:
+                l = x
+            else:
+                l = torch.cat(l_list, dim=1)
+            h_list.append(self.upmodules[i](l))
+            l_list.append(self.downmodules[i](torch.cat(h_list, dim=1)))
+        
+        h_list.append(self.upmodules[-1](torch.cat(l_list, dim=1)))
+        out = self.reconstruction(torch.cat(h_list, dim=1))
+        out = self.add_mean(out)
+
+        return out
+

+from model import common
+
+import torch.nn as nn
+
+url = {
+    'r16f64x2': 'https://cv.snu.ac.kr/research/EDSR/models/edsr_baseline_x2-1bc95232.pt',
+    'r16f64x3': 'https://cv.snu.ac.kr/research/EDSR/models/edsr_baseline_x3-abf2a44e.pt',
+    'r16f64x4': 'https://cv.snu.ac.kr/research/EDSR/models/edsr_baseline_x4-6b446fab.pt',
+    'r32f256x2': 'https://cv.snu.ac.kr/research/EDSR/models/edsr_x2-0edfb8a3.pt',
+    'r32f256x3': 'https://cv.snu.ac.kr/research/EDSR/models/edsr_x3-ea3ef2c6.pt',
+    'r32f256x4': 'https://cv.snu.ac.kr/research/EDSR/models/edsr_x4-4f62e9ef.pt'
+}
+
+def make_model(args, parent=False):
+    return EDSR(args)
+
+class EDSR(nn.Module):
+    def __init__(self, args, conv=common.default_conv):
+        super(EDSR, self).__init__()
+
+        n_resblocks = args.n_resblocks
+        n_feats = args.n_feats
+        kernel_size = 3 
+        scale = args.scale[0]
+        act = nn.ReLU(True)
+        url_name = 'r{}f{}x{}'.format(n_resblocks, n_feats, scale)
+        if url_name in url:
+            self.url = url[url_name]
+        else:
+            self.url = None
+        self.sub_mean = common.MeanShift(args.rgb_range)
+        self.add_mean = common.MeanShift(args.rgb_range, sign=1)
+
+        # define head module
+        m_head = [conv(args.n_colors, n_feats, kernel_size)]
+
+        # define body module
+        m_body = [
+            common.ResBlock(
+                conv, n_feats, kernel_size, act=act, res_scale=args.res_scale
+            ) for _ in range(n_resblocks)
+        ]
+        m_body.append(conv(n_feats, n_feats, kernel_size))
+
+        # define tail module
+        m_tail = [
+            common.Upsampler(conv, scale, n_feats, act=False),
+            conv(n_feats, args.n_colors, kernel_size)
+        ]
+
+        self.head = nn.Sequential(*m_head)
+        self.body = nn.Sequential(*m_body)
+        self.tail = nn.Sequential(*m_tail)
+
+    def forward(self, x):
+        x = self.sub_mean(x)
+        x = self.head(x)
+
+        res = self.body(x)
+        res += x
+
+        x = self.tail(res)
+        x = self.add_mean(x)
+
+        return x 
+
+    def load_state_dict(self, state_dict, strict=True):
+        own_state = self.state_dict()
+        for name, param in state_dict.items():
+            if name in own_state:
+                if isinstance(param, nn.Parameter):
+                    param = param.data
+                try:
+                    own_state[name].copy_(param)
+                except Exception:
+                    if name.find('tail') == -1:
+                        raise RuntimeError('While copying the parameter named {}, '
+                                           'whose dimensions in the model are {} and '
+                                           'whose dimensions in the checkpoint are {}.'
+                                           .format(name, own_state[name].size(), param.size()))
+            elif strict:
+                if name.find('tail') == -1:
+                    raise KeyError('unexpected key "{}" in state_dict'
+                                   .format(name))
+

+from model import common
+
+import torch.nn as nn
+
+url = {
+    'r16f64': 'https://cv.snu.ac.kr/research/EDSR/models/mdsr_baseline-a00cab12.pt',
+    'r80f64': 'https://cv.snu.ac.kr/research/EDSR/models/mdsr-4a78bedf.pt'
+}
+
+def make_model(args, parent=False):
+    return MDSR(args)
+
+class MDSR(nn.Module):
+    def __init__(self, args, conv=common.default_conv):
+        super(MDSR, self).__init__()
+        n_resblocks = args.n_resblocks
+        n_feats = args.n_feats
+        kernel_size = 3
+        act = nn.ReLU(True)
+        self.scale_idx = 0
+        self.url = url['r{}f{}'.format(n_resblocks, n_feats)]
+        self.sub_mean = common.MeanShift(args.rgb_range)
+        self.add_mean = common.MeanShift(args.rgb_range, sign=1)
+
+        m_head = [conv(args.n_colors, n_feats, kernel_size)]
+
+        self.pre_process = nn.ModuleList([
+            nn.Sequential(
+                common.ResBlock(conv, n_feats, 5, act=act),
+                common.ResBlock(conv, n_feats, 5, act=act)
+            ) for _ in args.scale
+        ])
+
+        m_body = [
+            common.ResBlock(
+                conv, n_feats, kernel_size, act=act
+            ) for _ in range(n_resblocks)
+        ]
+        m_body.append(conv(n_feats, n_feats, kernel_size))
+
+        self.upsample = nn.ModuleList([
+            common.Upsampler(conv, s, n_feats, act=False) for s in args.scale
+        ])
+
+        m_tail = [conv(n_feats, args.n_colors, kernel_size)]
+
+        self.head = nn.Sequential(*m_head)
+        self.body = nn.Sequential(*m_body)
+        self.tail = nn.Sequential(*m_tail)
+
+    def forward(self, x):
+        x = self.sub_mean(x)
+        x = self.head(x)
+        x = self.pre_process[self.scale_idx](x)
+
+        res = self.body(x)
+        res += x
+
+        x = self.upsample[self.scale_idx](res)
+        x = self.tail(x)
+        x = self.add_mean(x)
+
+        return x
+
+    def set_scale(self, scale_idx):
+        self.scale_idx = scale_idx
+

+## ECCV-2018-Image Super-Resolution Using Very Deep Residual Channel Attention Networks
+## https://arxiv.org/abs/1807.02758
+from model import common
+
+import torch.nn as nn
+
+def make_model(args, parent=False):
+    return RCAN(args)
+
+## Channel Attention (CA) Layer
+class CALayer(nn.Module):
+    def __init__(self, channel, reduction=16):
+        super(CALayer, self).__init__()
+        # global average pooling: feature --> point
+        self.avg_pool = nn.AdaptiveAvgPool2d(1)
+        # feature channel downscale and upscale --> channel weight
+        self.conv_du = nn.Sequential(
+                nn.Conv2d(channel, channel // reduction, 1, padding=0, bias=True),
+                nn.ReLU(inplace=True),
+                nn.Conv2d(channel // reduction, channel, 1, padding=0, bias=True),
+                nn.Sigmoid()
+        )
+
+    def forward(self, x):
+        y = self.avg_pool(x)
+        y = self.conv_du(y)
+        return x * y
+
+## Residual Channel Attention Block (RCAB)
+class RCAB(nn.Module):
+    def __init__(
+        self, conv, n_feat, kernel_size, reduction,
+        bias=True, bn=False, act=nn.ReLU(True), res_scale=1):
+
+        super(RCAB, self).__init__()
+        modules_body = []
+        for i in range(2):
+            modules_body.append(conv(n_feat, n_feat, kernel_size, bias=bias))
+            if bn: modules_body.append(nn.BatchNorm2d(n_feat))
+            if i == 0: modules_body.append(act)
+        modules_body.append(CALayer(n_feat, reduction))
+        self.body = nn.Sequential(*modules_body)
+        self.res_scale = res_scale
+
+    def forward(self, x):
+        res = self.body(x)
+        #res = self.body(x).mul(self.res_scale)
+        res += x
+        return res
+
+## Residual Group (RG)
+class ResidualGroup(nn.Module):
+    def __init__(self, conv, n_feat, kernel_size, reduction, act, res_scale, n_resblocks):
+        super(ResidualGroup, self).__init__()
+        modules_body = []
+        modules_body = [
+            RCAB(
+                conv, n_feat, kernel_size, reduction, bias=True, bn=False, act=nn.ReLU(True), res_scale=1) \
+            for _ in range(n_resblocks)]
+        modules_body.append(conv(n_feat, n_feat, kernel_size))
+        self.body = nn.Sequential(*modules_body)
+
+    def forward(self, x):
+        res = self.body(x)
+        res += x
+        return res
+
+## Residual Channel Attention Network (RCAN)
+class RCAN(nn.Module):
+    def __init__(self, args, conv=common.default_conv):
+        super(RCAN, self).__init__()
+        
+        n_resgroups = args.n_resgroups
+        n_resblocks = args.n_resblocks
+        n_feats = args.n_feats
+        kernel_size = 3
+        reduction = args.reduction 
+        scale = args.scale[0]
+        act = nn.ReLU(True)
+        
+        # RGB mean for DIV2K
+        self.sub_mean = common.MeanShift(args.rgb_range)
+        
+        # define head module
+        modules_head = [conv(args.n_colors, n_feats, kernel_size)]
+
+        # define body module
+        modules_body = [
+            ResidualGroup(
+                conv, n_feats, kernel_size, reduction, act=act, res_scale=args.res_scale, n_resblocks=n_resblocks) \
+            for _ in range(n_resgroups)]
+
+        modules_body.append(conv(n_feats, n_feats, kernel_size))
+
+        # define tail module
+        modules_tail = [
+            common.Upsampler(conv, scale, n_feats, act=False),
+            conv(n_feats, args.n_colors, kernel_size)]
+
+        self.add_mean = common.MeanShift(args.rgb_range, sign=1)
+
+        self.head = nn.Sequential(*modules_head)
+        self.body = nn.Sequential(*modules_body)
+        self.tail = nn.Sequential(*modules_tail)
+
+    def forward(self, x):
+        x = self.sub_mean(x)
+        x = self.head(x)
+
+        res = self.body(x)
+        res += x
+
+        x = self.tail(res)
+        x = self.add_mean(x)
+
+        return x 
+
+    def load_state_dict(self, state_dict, strict=False):
+        own_state = self.state_dict()
+        for name, param in state_dict.items():
+            if name in own_state:
+                if isinstance(param, nn.Parameter):
+                    param = param.data
+                try:
+                    own_state[name].copy_(param)
+                except Exception:
+                    if name.find('tail') >= 0:
+                        print('Replace pre-trained upsampler to new one...')
+                    else:
+                        raise RuntimeError('While copying the parameter named {}, '
+                                           'whose dimensions in the model are {} and '
+                                           'whose dimensions in the checkpoint are {}.'
+                                           .format(name, own_state[name].size(), param.size()))
+            elif strict:
+                if name.find('tail') == -1:
+                    raise KeyError('unexpected key "{}" in state_dict'
+                                   .format(name))
+
+        if strict:
+            missing = set(own_state.keys()) - set(state_dict.keys())
+            if len(missing) > 0:
+                raise KeyError('missing keys in state_dict: "{}"'.format(missing))

+# Residual Dense Network for Image Super-Resolution
+# https://arxiv.org/abs/1802.08797
+
+from model import common
+
+import torch
+import torch.nn as nn
+
+
+def make_model(args, parent=False):
+    return RDN(args)
+
+class RDB_Conv(nn.Module):
+    def __init__(self, inChannels, growRate, kSize=3):
+        super(RDB_Conv, self).__init__()
+        Cin = inChannels
+        G  = growRate
+        self.conv = nn.Sequential(*[
+            nn.Conv2d(Cin, G, kSize, padding=(kSize-1)//2, stride=1),
+            nn.ReLU()
+        ])
+
+    def forward(self, x):
+        out = self.conv(x)
+        return torch.cat((x, out), 1)
+
+class RDB(nn.Module):
+    def __init__(self, growRate0, growRate, nConvLayers, kSize=3):
+        super(RDB, self).__init__()
+        G0 = growRate0
+        G  = growRate
+        C  = nConvLayers
+        
+        convs = []
+        for c in range(C):
+            convs.append(RDB_Conv(G0 + c*G, G))
+        self.convs = nn.Sequential(*convs)
+        
+        # Local Feature Fusion
+        self.LFF = nn.Conv2d(G0 + C*G, G0, 1, padding=0, stride=1)
+
+    def forward(self, x):
+        return self.LFF(self.convs(x)) + x
+
+class RDN(nn.Module):
+    def __init__(self, args):
+        super(RDN, self).__init__()
+        r = args.scale[0]
+        G0 = args.G0
+        kSize = args.RDNkSize
+
+        # number of RDB blocks, conv layers, out channels
+        self.D, C, G = {
+            'A': (20, 6, 32),
+            'B': (16, 8, 64),
+        }[args.RDNconfig]
+
+        # Shallow feature extraction net
+        self.SFENet1 = nn.Conv2d(args.n_colors, G0, kSize, padding=(kSize-1)//2, stride=1)
+        self.SFENet2 = nn.Conv2d(G0, G0, kSize, padding=(kSize-1)//2, stride=1)
+
+        # Redidual dense blocks and dense feature fusion
+        self.RDBs = nn.ModuleList()
+        for i in range(self.D):
+            self.RDBs.append(
+                RDB(growRate0 = G0, growRate = G, nConvLayers = C)
+            )
+
+        # Global Feature Fusion
+        self.GFF = nn.Sequential(*[
+            nn.Conv2d(self.D * G0, G0, 1, padding=0, stride=1),
+            nn.Conv2d(G0, G0, kSize, padding=(kSize-1)//2, stride=1)
+        ])
+
+        # Up-sampling net
+        if r == 2 or r == 3:
+            self.UPNet = nn.Sequential(*[
+                nn.Conv2d(G0, G * r * r, kSize, padding=(kSize-1)//2, stride=1),
+                nn.PixelShuffle(r),
+                nn.Conv2d(G, args.n_colors, kSize, padding=(kSize-1)//2, stride=1)
+            ])
+        elif r == 4:
+            self.UPNet = nn.Sequential(*[
+                nn.Conv2d(G0, G * 4, kSize, padding=(kSize-1)//2, stride=1),
+                nn.PixelShuffle(2),
+                nn.Conv2d(G, G * 4, kSize, padding=(kSize-1)//2, stride=1),
+                nn.PixelShuffle(2),
+                nn.Conv2d(G, args.n_colors, kSize, padding=(kSize-1)//2, stride=1)
+            ])
+        else:
+            raise ValueError("scale must be 2 or 3 or 4.")
+
+    def forward(self, x):
+        f__1 = self.SFENet1(x)
+        x  = self.SFENet2(f__1)
+
+        RDBs_out = []
+        for i in range(self.D):
+            x = self.RDBs[i](x)
+            RDBs_out.append(x)
+
+        x = self.GFF(torch.cat(RDBs_out,1))
+        x += f__1
+
+        return self.UPNet(x)

+from model import common
+
+import torch.nn as nn
+import torch.nn.init as init
+
+url = {
+    'r20f64': ''
+}
+
+def make_model(args, parent=False):
+    return VDSR(args)
+
+class VDSR(nn.Module):
+    def __init__(self, args, conv=common.default_conv):
+        super(VDSR, self).__init__()
+
+        n_resblocks = args.n_resblocks
+        n_feats = args.n_feats
+        kernel_size = 3 
+        self.url = url['r{}f{}'.format(n_resblocks, n_feats)]
+        self.sub_mean = common.MeanShift(args.rgb_range)
+        self.add_mean = common.MeanShift(args.rgb_range, sign=1)
+
+        def basic_block(in_channels, out_channels, act):
+            return common.BasicBlock(
+                conv, in_channels, out_channels, kernel_size,
+                bias=True, bn=False, act=act
+            )
+
+        # define body module
+        m_body = []
+        m_body.append(basic_block(args.n_colors, n_feats, nn.ReLU(True)))
+        for _ in range(n_resblocks - 2):
+            m_body.append(basic_block(n_feats, n_feats, nn.ReLU(True)))
+        m_body.append(basic_block(n_feats, args.n_colors, None))
+
+        self.body = nn.Sequential(*m_body)
+
+    def forward(self, x):
+        x = self.sub_mean(x)
+        res = self.body(x)
+        res += x
+        x = self.add_mean(res)
+
+        return x 
+

+import argparse
+import template
+
+parser = argparse.ArgumentParser(description='EDSR and MDSR')
+
+parser.add_argument('--debug', action='store_true',
+                    help='Enables debug mode')
+parser.add_argument('--template', default='.',
+                    help='You can set various templates in option.py')
+
+# Hardware specifications
+parser.add_argument('--n_threads', type=int, default=6,
+                    help='number of threads for data loading')
+parser.add_argument('--cpu', action='store_true',
+                    help='use cpu only')
+parser.add_argument('--n_GPUs', type=int, default=1,
+                    help='number of GPUs')
+parser.add_argument('--seed', type=int, default=1,
+                    help='random seed')
+
+# Data specifications
+parser.add_argument('--dir_data', type=str, default='../../../dataset',
+                    help='dataset directory')
+parser.add_argument('--dir_demo', type=str, default='../test',
+                    help='demo image directory')
+parser.add_argument('--data_train', type=str, default='DIV2K',
+                    help='train dataset name')
+parser.add_argument('--data_test', type=str, default='DIV2K',
+                    help='test dataset name')
+parser.add_argument('--data_range', type=str, default='1-800/801-810',
+                    help='train/test data range')
+parser.add_argument('--ext', type=str, default='sep',
+                    help='dataset file extension')
+parser.add_argument('--scale', type=str, default='4',
+                    help='super resolution scale')
+parser.add_argument('--patch_size', type=int, default=192,
+                    help='output patch size')
+parser.add_argument('--rgb_range', type=int, default=255,
+                    help='maximum value of RGB')
+parser.add_argument('--n_colors', type=int, default=3,
+                    help='number of color channels to use')
+parser.add_argument('--chop', action='store_true',
+                    help='enable memory-efficient forward')
+parser.add_argument('--no_augment', action='store_true',
+                    help='do not use data augmentation')
+
+# Model specifications
+parser.add_argument('--model', default='EDSR',
+                    help='model name')
+
+parser.add_argument('--act', type=str, default='relu',
+                    help='activation function')
+parser.add_argument('--pre_train', type=str, default='',
+                    help='pre-trained model directory')
+parser.add_argument('--extend', type=str, default='.',
+                    help='pre-trained model directory')
+parser.add_argument('--n_resblocks', type=int, default=16,
+                    help='number of residual blocks')
+parser.add_argument('--n_feats', type=int, default=64,
+                    help='number of feature maps')
+parser.add_argument('--res_scale', type=float, default=1,
+                    help='residual scaling')
+parser.add_argument('--shift_mean', default=True,
+                    help='subtract pixel mean from the input')
+parser.add_argument('--dilation', action='store_true',
+                    help='use dilated convolution')
+parser.add_argument('--precision', type=str, default='single',
+                    choices=('single', 'half'),
+                    help='FP precision for test (single | half)')
+
+# Option for Residual dense network (RDN)
+parser.add_argument('--G0', type=int, default=64,
+                    help='default number of filters. (Use in RDN)')
+parser.add_argument('--RDNkSize', type=int, default=3,
+                    help='default kernel size. (Use in RDN)')
+parser.add_argument('--RDNconfig', type=str, default='B',
+                    help='parameters config of RDN. (Use in RDN)')
+
+# Option for Residual channel attention network (RCAN)
+parser.add_argument('--n_resgroups', type=int, default=10,
+                    help='number of residual groups')
+parser.add_argument('--reduction', type=int, default=16,
+                    help='number of feature maps reduction')
+
+# Training specifications
+parser.add_argument('--reset', action='store_true',
+                    help='reset the training')
+parser.add_argument('--test_every', type=int, default=1000,
+                    help='do test per every N batches')
+parser.add_argument('--epochs', type=int, default=300,
+                    help='number of epochs to train')
+parser.add_argument('--batch_size', type=int, default=16,
+                    help='input batch size for training')
+parser.add_argument('--split_batch', type=int, default=1,
+                    help='split the batch into smaller chunks')
+parser.add_argument('--self_ensemble', action='store_true',
+                    help='use self-ensemble method for test')
+parser.add_argument('--test_only', action='store_true',
+                    help='set this option to test the model')
+parser.add_argument('--gan_k', type=int, default=1,
+                    help='k value for adversarial loss')
+
+# Optimization specifications
+parser.add_argument('--lr', type=float, default=1e-4,
+                    help='learning rate')
+parser.add_argument('--decay', type=str, default='200',
+                    help='learning rate decay type')
+parser.add_argument('--gamma', type=float, default=0.5,
+                    help='learning rate decay factor for step decay')
+parser.add_argument('--optimizer', default='ADAM',
+                    choices=('SGD', 'ADAM', 'RMSprop'),
+                    help='optimizer to use (SGD | ADAM | RMSprop)')
+parser.add_argument('--momentum', type=float, default=0.9,
+                    help='SGD momentum')
+parser.add_argument('--betas', type=tuple, default=(0.9, 0.999),
+                    help='ADAM beta')
+parser.add_argument('--epsilon', type=float, default=1e-8,
+                    help='ADAM epsilon for numerical stability')
+parser.add_argument('--weight_decay', type=float, default=0,
+                    help='weight decay')
+parser.add_argument('--gclip', type=float, default=0,
+                    help='gradient clipping threshold (0 = no clipping)')
+
+# Loss specifications
+parser.add_argument('--loss', type=str, default='1*L1',
+                    help='loss function configuration')
+parser.add_argument('--skip_threshold', type=float, default='1e8',
+                    help='skipping batch that has large error')
+
+# Log specifications
+parser.add_argument('--save', type=str, default='test',
+                    help='file name to save')
+parser.add_argument('--load', type=str, default='',
+                    help='file name to load')
+parser.add_argument('--resume', type=int, default=0,
+                    help='resume from specific checkpoint')
+parser.add_argument('--save_models', action='store_true',
+                    help='save all intermediate models')
+parser.add_argument('--print_every', type=int, default=100,
+                    help='how many batches to wait before logging training status')
+parser.add_argument('--save_results', action='store_true',
+                    help='save output results')
+parser.add_argument('--save_gt', action='store_true',
+                    help='save low-resolution and high-resolution images together')
+
+args = parser.parse_args()
+template.set_template(args)
+
+args.scale = list(map(lambda x: int(x), args.scale.split('+')))
+args.data_train = args.data_train.split('+')
+args.data_test = args.data_test.split('+')
+
+if args.epochs == 0:
+    args.epochs = 1e8
+
+for arg in vars(args):
+    if vars(args)[arg] == 'True':
+        vars(args)[arg] = True
+    elif vars(args)[arg] == 'False':
+        vars(args)[arg] = False
+

+def set_template(args):
+    # Set the templates here
+    if args.template.find('jpeg') >= 0:
+        args.data_train = 'DIV2K_jpeg'
+        args.data_test = 'DIV2K_jpeg'
+        args.epochs = 200
+        args.decay = '100'
+
+    if args.template.find('EDSR_paper') >= 0:
+        args.model = 'EDSR'
+        args.n_resblocks = 32
+        args.n_feats = 256
+        args.res_scale = 0.1
+
+    if args.template.find('MDSR') >= 0:
+        args.model = 'MDSR'
+        args.patch_size = 48
+        args.epochs = 650
+
+    if args.template.find('DDBPN') >= 0:
+        args.model = 'DDBPN'
+        args.patch_size = 128
+        args.scale = '4'
+
+        args.data_test = 'Set5'
+
+        args.batch_size = 20
+        args.epochs = 1000
+        args.decay = '500'
+        args.gamma = 0.1
+        args.weight_decay = 1e-4
+
+        args.loss = '1*MSE'
+
+    if args.template.find('GAN') >= 0:
+        args.epochs = 200
+        args.lr = 5e-5
+        args.decay = '150'
+
+    if args.template.find('RCAN') >= 0:
+        args.model = 'RCAN'
+        args.n_resgroups = 10
+        args.n_resblocks = 20
+        args.n_feats = 64
+        args.chop = True
+
+    if args.template.find('VDSR') >= 0:
+        args.model = 'VDSR'
+        args.n_resblocks = 20
+        args.n_feats = 64
+        args.patch_size = 41
+        args.lr = 1e-1
+

+import os
+import math
+from decimal import Decimal
+
+import utility
+
+import torch
+import torch.nn.utils as utils
+from tqdm import tqdm
+
+class Trainer():
+    def __init__(self, args, loader, my_model, my_loss, ckp):
+        self.args = args
+        self.scale = args.scale
+
+        self.ckp = ckp
+        self.loader_train = loader.loader_train
+        self.loader_test = loader.loader_test
+        self.model = my_model
+        self.loss = my_loss
+        self.optimizer = utility.make_optimizer(args, self.model)
+
+        if self.args.load != '':
+            self.optimizer.load(ckp.dir, epoch=len(ckp.log))
+
+        self.error_last = 1e8
+
+    def train(self):
+        self.loss.step()
+        epoch = self.optimizer.get_last_epoch() + 1
+        lr = self.optimizer.get_lr()
+
+        self.ckp.write_log(
+            '[Epoch {}]\tLearning rate: {:.2e}'.format(epoch, Decimal(lr))
+        )
+        self.loss.start_log()
+        self.model.train()
+
+        timer_data, timer_model = utility.timer(), utility.timer()
+        # TEMP
+        self.loader_train.dataset.set_scale(0)
+        for batch, (lr, hr, _,) in enumerate(self.loader_train):
+            lr, hr = self.prepare(lr, hr)
+            timer_data.hold()
+            timer_model.tic()
+
+            self.optimizer.zero_grad()
+            sr = self.model(lr, 0)
+            loss = self.loss(sr, hr)
+            loss.backward()
+            if self.args.gclip > 0:
+                utils.clip_grad_value_(
+                    self.model.parameters(),
+                    self.args.gclip
+                )
+            self.optimizer.step()
+
+            timer_model.hold()
+
+            if (batch + 1) % self.args.print_every == 0:
+                self.ckp.write_log('[{}/{}]\t{}\t{:.1f}+{:.1f}s'.format(
+                    (batch + 1) * self.args.batch_size,
+                    len(self.loader_train.dataset),
+                    self.loss.display_loss(batch),
+                    timer_model.release(),
+                    timer_data.release()))
+
+            timer_data.tic()
+
+        self.loss.end_log(len(self.loader_train))
+        self.error_last = self.loss.log[-1, -1]
+        self.optimizer.schedule()
+
+    def test(self):
+        torch.set_grad_enabled(False)
+
+        epoch = self.optimizer.get_last_epoch()
+        self.ckp.write_log('\nEvaluation:')
+        self.ckp.add_log(
+            torch.zeros(1, len(self.loader_test), len(self.scale))
+        )
+        self.model.eval()
+
+        timer_test = utility.timer()
+        if self.args.save_results: self.ckp.begin_background()
+        for idx_data, d in enumerate(self.loader_test):
+            for idx_scale, scale in enumerate(self.scale):
+                d.dataset.set_scale(idx_scale)
+                for lr, hr, filename in tqdm(d, ncols=80):
+                    lr, hr = self.prepare(lr, hr)
+                    sr = self.model(lr, idx_scale)
+                    sr = utility.quantize(sr, self.args.rgb_range)
+
+                    save_list = [sr]
+                    self.ckp.log[-1, idx_data, idx_scale] += utility.calc_psnr(
+                        sr, hr, scale, self.args.rgb_range, dataset=d
+                    )
+                    if self.args.save_gt:
+                        save_list.extend([lr, hr])
+
+                    if self.args.save_results:
+                        self.ckp.save_results(d, filename[0], save_list, scale)
+
+                self.ckp.log[-1, idx_data, idx_scale] /= len(d)
+                best = self.ckp.log.max(0)
+                self.ckp.write_log(
+                    '[{} x{}]\tPSNR: {:.3f} (Best: {:.3f} @epoch {})'.format(
+                        d.dataset.name,
+                        scale,
+                        self.ckp.log[-1, idx_data, idx_scale],
+                        best[0][idx_data, idx_scale],
+                        best[1][idx_data, idx_scale] + 1
+                    )
+                )
+
+        self.ckp.write_log('Forward: {:.2f}s\n'.format(timer_test.toc()))
+        self.ckp.write_log('Saving...')
+
+        if self.args.save_results:
+            self.ckp.end_background()
+
+        if not self.args.test_only:
+            self.ckp.save(self, epoch, is_best=(best[1][0, 0] + 1 == epoch))
+
+        self.ckp.write_log(
+            'Total: {:.2f}s\n'.format(timer_test.toc()), refresh=True
+        )
+
+        torch.set_grad_enabled(True)
+
+    def prepare(self, *args):
+        device = torch.device('cpu' if self.args.cpu else 'cuda')
+        def _prepare(tensor):
+            if self.args.precision == 'half': tensor = tensor.half()
+            return tensor.to(device)
+
+        return [_prepare(a) for a in args]
+
+    def terminate(self):
+        if self.args.test_only:
+            self.test()
+            return True
+        else:
+            epoch = self.optimizer.get_last_epoch() + 1
+            return epoch >= self.args.epochs
+

+import os
+import math
+import time
+import datetime
+from multiprocessing import Process
+from multiprocessing import Queue
+
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+
+import numpy as np
+import imageio
+
+import torch
+import torch.optim as optim
+import torch.optim.lr_scheduler as lrs
+
+class timer():
+    def __init__(self):
+        self.acc = 0
+        self.tic()
+
+    def tic(self):
+        self.t0 = time.time()
+
+    def toc(self, restart=False):
+        diff = time.time() - self.t0
+        if restart: self.t0 = time.time()
+        return diff
+
+    def hold(self):
+        self.acc += self.toc()
+
+    def release(self):
+        ret = self.acc
+        self.acc = 0
+
+        return ret
+
+    def reset(self):
+        self.acc = 0
+
+class checkpoint():
+    def __init__(self, args):
+        self.args = args
+        self.ok = True
+        self.log = torch.Tensor()
+        now = datetime.datetime.now().strftime('%Y-%m-%d-%H:%M:%S')
+
+        if not args.load:
+            if not args.save:
+                args.save = now
+            self.dir = os.path.join('..', 'experiment', args.save)
+        else:
+            self.dir = os.path.join('..', 'experiment', args.load)
+            if os.path.exists(self.dir):
+                self.log = torch.load(self.get_path('psnr_log.pt'))
+                print('Continue from epoch {}...'.format(len(self.log)))
+            else:
+                args.load = ''
+
+        if args.reset:
+            os.system('rm -rf ' + self.dir)
+            args.load = ''
+
+        os.makedirs(self.dir, exist_ok=True)
+        os.makedirs(self.get_path('model'), exist_ok=True)
+        for d in args.data_test:
+            os.makedirs(self.get_path('results-{}'.format(d)), exist_ok=True)
+
+        open_type = 'a' if os.path.exists(self.get_path('log.txt'))else 'w'
+        self.log_file = open(self.get_path('log.txt'), open_type)
+        with open(self.get_path('config.txt'), open_type) as f:
+            f.write(now + '\n\n')
+            for arg in vars(args):
+                f.write('{}: {}\n'.format(arg, getattr(args, arg)))
+            f.write('\n')
+
+        self.n_processes = 8
+
+    def get_path(self, *subdir):
+        return os.path.join(self.dir, *subdir)
+
+    def save(self, trainer, epoch, is_best=False):
+        trainer.model.save(self.get_path('model'), epoch, is_best=is_best)
+        trainer.loss.save(self.dir)
+        trainer.loss.plot_loss(self.dir, epoch)
+
+        self.plot_psnr(epoch)
+        trainer.optimizer.save(self.dir)
+        torch.save(self.log, self.get_path('psnr_log.pt'))
+
+    def add_log(self, log):
+        self.log = torch.cat([self.log, log])
+
+    def write_log(self, log, refresh=False):
+        print(log)
+        self.log_file.write(log + '\n')
+        if refresh:
+            self.log_file.close()
+            self.log_file = open(self.get_path('log.txt'), 'a')
+
+    def done(self):
+        self.log_file.close()
+
+    def plot_psnr(self, epoch):
+        axis = np.linspace(1, epoch, epoch)
+        for idx_data, d in enumerate(self.args.data_test):
+            label = 'SR on {}'.format(d)
+            fig = plt.figure()
+            plt.title(label)
+            for idx_scale, scale in enumerate(self.args.scale):
+                plt.plot(
+                    axis,
+                    self.log[:, idx_data, idx_scale].numpy(),
+                    label='Scale {}'.format(scale)
+                )
+            plt.legend()
+            plt.xlabel('Epochs')
+            plt.ylabel('PSNR')
+            plt.grid(True)
+            plt.savefig(self.get_path('test_{}.pdf'.format(d)))
+            plt.close(fig)
+
+    def begin_background(self):
+        self.queue = Queue()
+
+        def bg_target(queue):
+            while True:
+                if not queue.empty():
+                    filename, tensor = queue.get()
+                    if filename is None: break
+                    imageio.imwrite(filename, tensor.numpy())
+        
+        self.process = [
+            Process(target=bg_target, args=(self.queue,)) \
+            for _ in range(self.n_processes)
+        ]
+        
+        for p in self.process: p.start()
+
+    def end_background(self):
+        for _ in range(self.n_processes): self.queue.put((None, None))
+        while not self.queue.empty(): time.sleep(1)
+        for p in self.process: p.join()
+
+    def save_results(self, dataset, filename, save_list, scale):
+        if self.args.save_results:
+            filename = self.get_path(
+                'results-{}'.format(dataset.dataset.name),
+                '{}_x{}_'.format(filename, scale)
+            )
+
+            postfix = ('SR', 'LR', 'HR')
+            for v, p in zip(save_list, postfix):
+                normalized = v[0].mul(255 / self.args.rgb_range)
+                tensor_cpu = normalized.byte().permute(1, 2, 0).cpu()
+                self.queue.put(('{}{}.png'.format(filename, p), tensor_cpu))
+
+def quantize(img, rgb_range):
+    pixel_range = 255 / rgb_range
+    return img.mul(pixel_range).clamp(0, 255).round().div(pixel_range)
+
+def calc_psnr(sr, hr, scale, rgb_range, dataset=None):
+    if hr.nelement() == 1: return 0
+
+    diff = (sr - hr) / rgb_range
+    if dataset and dataset.dataset.benchmark:
+        shave = scale
+        if diff.size(1) > 1:
+            gray_coeffs = [65.738, 129.057, 25.064]
+            convert = diff.new_tensor(gray_coeffs).view(1, 3, 1, 1) / 256
+            diff = diff.mul(convert).sum(dim=1)
+    else:
+        shave = scale + 6
+
+    valid = diff[..., shave:-shave, shave:-shave]
+    mse = valid.pow(2).mean()
+
+    return -10 * math.log10(mse)
+
+def make_optimizer(args, target):
+    '''
+        make optimizer and scheduler together
+    '''
+    # optimizer
+    trainable = filter(lambda x: x.requires_grad, target.parameters())
+    kwargs_optimizer = {'lr': args.lr, 'weight_decay': args.weight_decay}
+
+    if args.optimizer == 'SGD':
+        optimizer_class = optim.SGD
+        kwargs_optimizer['momentum'] = args.momentum
+    elif args.optimizer == 'ADAM':
+        optimizer_class = optim.Adam
+        kwargs_optimizer['betas'] = args.betas
+        kwargs_optimizer['eps'] = args.epsilon
+    elif args.optimizer == 'RMSprop':
+        optimizer_class = optim.RMSprop
+        kwargs_optimizer['eps'] = args.epsilon
+
+    # scheduler
+    milestones = list(map(lambda x: int(x), args.decay.split('-')))
+    kwargs_scheduler = {'milestones': milestones, 'gamma': args.gamma}
+    scheduler_class = lrs.MultiStepLR
+
+    class CustomOptimizer(optimizer_class):
+        def __init__(self, *args, **kwargs):
+            super(CustomOptimizer, self).__init__(*args, **kwargs)
+
+        def _register_scheduler(self, scheduler_class, **kwargs):
+            self.scheduler = scheduler_class(self, **kwargs)
+
+        def save(self, save_dir):
+            torch.save(self.state_dict(), self.get_dir(save_dir))
+
+        def load(self, load_dir, epoch=1):
+            self.load_state_dict(torch.load(self.get_dir(load_dir)))
+            if epoch > 1:
+                for _ in range(epoch): self.scheduler.step()
+
+        def get_dir(self, dir_path):
+            return os.path.join(dir_path, 'optimizer.pt')
+
+        def schedule(self):
+            self.scheduler.step()
+
+        def get_lr(self):
+            return self.scheduler.get_lr()[0]
+
+        def get_last_epoch(self):
+            return self.scheduler.last_epoch
+    
+    optimizer = CustomOptimizer(trainable, **kwargs_optimizer)
+    optimizer._register_scheduler(scheduler_class, **kwargs_scheduler)
+    return optimizer
+

+import os
+import math
+
+import utility
+from data import common
+
+import torch
+import cv2
+
+from tqdm import tqdm
+
+class VideoTester():
+    def __init__(self, args, my_model, ckp):
+        self.args = args
+        self.scale = args.scale
+
+        self.ckp = ckp
+        self.model = my_model
+
+        self.filename, _ = os.path.splitext(os.path.basename(args.dir_demo))
+
+    def test(self):
+        torch.set_grad_enabled(False)
+
+        self.ckp.write_log('\nEvaluation on video:')
+        self.model.eval()
+
+        timer_test = utility.timer()
+        for idx_scale, scale in enumerate(self.scale):
+            vidcap = cv2.VideoCapture(self.args.dir_demo)
+            total_frames = int(vidcap.get(cv2.CAP_PROP_FRAME_COUNT))
+            vidwri = cv2.VideoWriter(
+                self.ckp.get_path('{}_x{}.avi'.format(self.filename, scale)),
+                cv2.VideoWriter_fourcc(*'XVID'),
+                vidcap.get(cv2.CAP_PROP_FPS),
+                (
+                    int(scale * vidcap.get(cv2.CAP_PROP_FRAME_WIDTH)),
+                    int(scale * vidcap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+                )
+            )
+
+            tqdm_test = tqdm(range(total_frames), ncols=80)
+            for _ in tqdm_test:
+                success, lr = vidcap.read()
+                if not success: break
+
+                lr, = common.set_channel(lr, n_channels=self.args.n_colors)
+                lr, = common.np2Tensor(lr, rgb_range=self.args.rgb_range)
+                lr, = self.prepare(lr.unsqueeze(0))
+                sr = self.model(lr, idx_scale)
+                sr = utility.quantize(sr, self.args.rgb_range).squeeze(0)
+
+                normalized = sr * 255 / self.args.rgb_range
+                ndarr = normalized.byte().permute(1, 2, 0).cpu().numpy()
+                vidwri.write(ndarr)
+
+            vidcap.release()
+            vidwri.release()
+
+        self.ckp.write_log(
+            'Total: {:.2f}s\n'.format(timer_test.toc()), refresh=True
+        )
+        torch.set_grad_enabled(True)
+
+    def prepare(self, *args):
+        device = torch.device('cpu' if self.args.cpu else 'cuda')
+        def _prepare(tensor):
+            if self.args.precision == 'half': tensor = tensor.half()
+            return tensor.to(device)
+
+        return [_prepare(a) for a in args]
+