From 4f5e2ced93ebb21b42c8568b044c143a4be4a651 Mon Sep 17 00:00:00 2001
From: Sanghyun Son <thstkdgus35@snu.ac.kr>
Date: Mon, 20 Aug 2018 11:09:15 +0900
Subject: [PATCH] fix dependency, directory structure
---
README.md | 17 +++++-----
code/tools/downsampling_jpeg.m | 44 --------------------------
code/tools/jpeg2binary.py | 49 -----------------------------
{code => src}/__init__.py | 0
{code => src}/data/__init__.py | 0
{code => src}/data/benchmark.py | 0
{code => src}/data/common.py | 2 --
{code => src}/data/demo.py | 0
{code => src}/data/div2k.py | 0
{code => src}/data/srdata.py | 0
{code => src}/dataloader.py | 0
{code => src}/demo.sh | 5 ++-
{code => src}/loss/__init__.py | 0
{code => src}/loss/adversarial.py | 0
{code => src}/loss/discriminator.py | 0
{code => src}/loss/vgg.py | 0
{code => src}/main.py | 0
{code => src}/model/__init__.py | 0
{code => src}/model/common.py | 0
{code => src}/model/ddbpn.py | 0
{code => src}/model/edsr.py | 0
{code => src}/model/mdsr.py | 0
{code => src}/model/rdn.py | 0
{code => src}/option.py | 0
{code => src}/template.py | 0
{code => src}/trainer.py | 0
{code => src}/utility.py | 0
27 files changed, 13 insertions(+), 104 deletions(-)
delete mode 100644 code/tools/downsampling_jpeg.m
delete mode 100644 code/tools/jpeg2binary.py
rename {code => src}/__init__.py (100%)
rename {code => src}/data/__init__.py (100%)
rename {code => src}/data/benchmark.py (100%)
rename {code => src}/data/common.py (97%)
rename {code => src}/data/demo.py (100%)
rename {code => src}/data/div2k.py (100%)
rename {code => src}/data/srdata.py (100%)
rename {code => src}/dataloader.py (100%)
rename {code => src}/demo.sh (88%)
rename {code => src}/loss/__init__.py (100%)
rename {code => src}/loss/adversarial.py (100%)
rename {code => src}/loss/discriminator.py (100%)
rename {code => src}/loss/vgg.py (100%)
rename {code => src}/main.py (100%)
rename {code => src}/model/__init__.py (100%)
rename {code => src}/model/common.py (100%)
rename {code => src}/model/ddbpn.py (100%)
rename {code => src}/model/edsr.py (100%)
rename {code => src}/model/mdsr.py (100%)
rename {code => src}/model/rdn.py (100%)
rename {code => src}/option.py (100%)
rename {code => src}/template.py (100%)
rename {code => src}/trainer.py (100%)
rename {code => src}/utility.py (100%)
diff --git a/README.md b/README.md
index bd61eee..8554980 100755
--- a/README.md
+++ b/README.md
@@ -26,10 +26,11 @@ We provide scripts for reproducing all the results from our paper. You can train
* Python-based.
## Dependencies
-* Python (Tested with 3.6)
+* Python 3.6
* PyTorch >= 0.4.0
* numpy
* scipy
+* skimage
* matplotlib
* tqdm
@@ -50,9 +51,9 @@ cd EDSR-PyTorch
## Quick start (Demo)
You can test our super-resolution algorithm with your own images. Place your images in ``test`` folder. (like ``test/<your_image>``) We support **png** and **jpeg** files.
-Run the script in ``code`` folder. Before you run the demo, please uncomment the appropriate line in ```demo.sh``` that you want to execute.
+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 code # You are now in */EDSR-PyTorch/code
+cd src # You are now in */EDSR-PyTorch/src
sh demo.sh
```
@@ -91,16 +92,16 @@ For these datasets, we first convert the result images to YCbCr color space and
## 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 ```code/option.py``` to the place where DIV2K images are located.
+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 ``code/demo.sh``. Note that EDSR (x3, x4) requires pre-trained EDSR (x2). You can ignore this constraint by removing ```--pre_train <x2 model>``` argument.
+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 code # You are now in */EDSR-PyTorch/code
+cd src # You are now in */EDSR-PyTorch/src
sh demo.sh
```
@@ -111,7 +112,7 @@ sh demo.sh
* Training details are included.
* Jan 09, 2018
- * Missing files are included (```code/data/MyImage.py```).
+ * Missing files are included (```src/data/MyImage.py```).
* Some links are fixed.
* Jan 16, 2018
@@ -128,7 +129,7 @@ sh demo.sh
* Feb 23, 2018
* Now PyTorch 0.3.1 is default. Use legacy/0.3.0 branch if you use the old version.
- * With a new ``code/data/DIV2K.py`` code, one can easily create new data class for super-resolution.
+ * 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 generates 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, just use the default setting (``--ext img``).
diff --git a/code/tools/downsampling_jpeg.m b/code/tools/downsampling_jpeg.m
deleted file mode 100644
index 16eb8fa..0000000
--- a/code/tools/downsampling_jpeg.m
+++ /dev/null
@@ -1,44 +0,0 @@
-scale = [2, 3, 4];
-dataset = 'DIV2K';
-apath = '../../../../dataset';
-quality = 87;
-hrDir = fullfile(apath, dataset, 'DIV2K_train_HR');
-lrDir = fullfile(apath, dataset, ['DIV2K_train_LR_bicubic', num2str(quality)]);
-
-if ~exist(lrDir, 'dir')
- mkdir(lrDir)
-end
-
-for sc = 1:length(scale)
- lrSubDir = fullfile(lrDir, sprintf('X%d', scale(sc)));
- if ~exist(lrSubDir, 'dir')
- mkdir(lrSubDir);
- end
-end
-
-hrImgs = dir(fullfile(hrDir, '*.png'));
-for idx = 1:length(hrImgs)
- imgName = hrImgs(idx).name;
- try
- hrImg = imread(fullfile(hrDir, imgName));
- catch
- disp(imgName);
- continue;
- end
- [h, w, ~] = size(hrImg);
- for sc = 1:length(scale)
- ch = floor(h / scale(sc)) * scale(sc);
- cw = floor(w / scale(sc)) * scale(sc);
- cropped = hrImg(1:ch, 1:cw, :);
- lrImg = imresize(cropped, 1 / scale(sc), 'bicubic');
- [~, woExt, ext] = fileparts(imgName);
- lrName = sprintf('%sx%d%s', woExt, scale(sc), '.jpeg');
- imwrite( ...
- lrImg, ...
- fullfile(lrDir, sprintf('X%d', scale(sc)), lrName), ...
- 'quality', quality);
- end
- if mod(idx, 100) == 0
- fprintf('Processed %d / %d images\n', idx, length(hrImgs));
- end
-end
diff --git a/code/tools/jpeg2binary.py b/code/tools/jpeg2binary.py
deleted file mode 100644
index 7373f14..0000000
--- a/code/tools/jpeg2binary.py
+++ /dev/null
@@ -1,49 +0,0 @@
-import os
-import argparse
-
-import skimage
-import skimage.io as sio
-
-import torch
-
-parser = argparse.ArgumentParser(description='Pre-processing DIV2K .jpeg images')
-
-parser.add_argument('--pathFrom', default='../../../../dataset/DIV2K',
- help='directory of images to convert')
-parser.add_argument('--pathTo', default='../../../../dataset/DIV2K_decoded',
- help='directory of images to save')
-parser.add_argument('--split', default=False,
- help='save individual images')
-parser.add_argument('--select', default='',
- help='select certain path')
-
-args = parser.parse_args()
-
-for (path, dirs, files) in os.walk(args.pathFrom):
- print(path)
- targetDir = path.replace(args.pathFrom, args.pathTo)
- if len(args.select) > 0 and path.find(args.select) == -1:
- continue
-
- if not os.path.exists(targetDir):
- os.mkdir(targetDir)
-
- if len(dirs) == 0:
- pack = {}
- n = 0
- for fileName in files:
- (idx, ext) = os.path.splitext(fileName)
- if ext == '.jpeg':
- png = sio.imread(os.path.join(path, fileName))
- tensor = torch.Tensor(png.astype(float)).byte()
- if args.split:
- torch.save(tensor, os.path.join(targetDir, idx + '.pt'))
- else:
- pack[int(idx.split('x')[0])] = tensor
- n += 1
- if n % 100 == 0:
- print('Converted ' + str(n) + ' images.')
- if len(pack) > 0:
- torch.save(pack, targetDir + '/pack.pt')
- print('Saved pt binary.')
- del pack
diff --git a/code/__init__.py b/src/__init__.py
similarity index 100%
rename from code/__init__.py
rename to src/__init__.py
diff --git a/code/data/__init__.py b/src/data/__init__.py
similarity index 100%
rename from code/data/__init__.py
rename to src/data/__init__.py
diff --git a/code/data/benchmark.py b/src/data/benchmark.py
similarity index 100%
rename from code/data/benchmark.py
rename to src/data/benchmark.py
diff --git a/code/data/common.py b/src/data/common.py
similarity index 97%
rename from code/data/common.py
rename to src/data/common.py
index 3a36160..848653f 100644
--- a/code/data/common.py
+++ b/src/data/common.py
@@ -1,9 +1,7 @@
import random
import numpy as np
-import skimage.io as sio
import skimage.color as sc
-import skimage.transform as st
import torch
from torchvision import transforms
diff --git a/code/data/demo.py b/src/data/demo.py
similarity index 100%
rename from code/data/demo.py
rename to src/data/demo.py
diff --git a/code/data/div2k.py b/src/data/div2k.py
similarity index 100%
rename from code/data/div2k.py
rename to src/data/div2k.py
diff --git a/code/data/srdata.py b/src/data/srdata.py
similarity index 100%
rename from code/data/srdata.py
rename to src/data/srdata.py
diff --git a/code/dataloader.py b/src/dataloader.py
similarity index 100%
rename from code/dataloader.py
rename to src/dataloader.py
diff --git a/code/demo.sh b/src/demo.sh
similarity index 88%
rename from code/demo.sh
rename to src/demo.sh
index f4cb947..fc11a01 100644
--- a/code/demo.sh
+++ b/src/demo.sh
@@ -42,8 +42,11 @@
#python main.py --data_test Urban100 --scale 4 --n_resblocks 32 --n_feats 256 --res_scale 0.1 --pre_train ../experiment/model/EDSR_x4.pt --test_only --self_ensemble
#python main.py --data_test DIV2K --ext img --n_val 100 --scale 4 --n_resblocks 32 --n_feats 256 --res_scale 0.1 --pre_train ../experiment/model/EDSR_x4.pt --test_only --self_ensemble
+python main.py --data_test DIV2K --ext img --n_val 10 --scale 2 --n_resblocks 32 --n_feats 256 --res_scale 0.1 --pre_train ../experiment/model/EDSR_x2.pt --test_only
+python main.py --data_test DIV2K --ext img --n_val 10 --scale 2 --n_resblocks 32 --n_feats 256 --res_scale 0.1 --pre_train ../experiment/model/EDSR_x2.pt --test_only --self_ensemble
+
# Test your own images
-python main.py --data_test Demo --scale 4 --pre_train ../experiment/model/EDSR_baseline_x4.pt --test_only --save_results
+#python main.py --data_test Demo --scale 4 --pre_train ../experiment/model/EDSR_baseline_x4.pt --test_only --save_results
# Advanced - Test with JPEG images
#python main.py --model MDSR --data_test Demo --scale 2+3+4 --pre_train ../experiment/model/MDSR_baseline_jpeg.pt --test_only --save_results
diff --git a/code/loss/__init__.py b/src/loss/__init__.py
similarity index 100%
rename from code/loss/__init__.py
rename to src/loss/__init__.py
diff --git a/code/loss/adversarial.py b/src/loss/adversarial.py
similarity index 100%
rename from code/loss/adversarial.py
rename to src/loss/adversarial.py
diff --git a/code/loss/discriminator.py b/src/loss/discriminator.py
similarity index 100%
rename from code/loss/discriminator.py
rename to src/loss/discriminator.py
diff --git a/code/loss/vgg.py b/src/loss/vgg.py
similarity index 100%
rename from code/loss/vgg.py
rename to src/loss/vgg.py
diff --git a/code/main.py b/src/main.py
similarity index 100%
rename from code/main.py
rename to src/main.py
diff --git a/code/model/__init__.py b/src/model/__init__.py
similarity index 100%
rename from code/model/__init__.py
rename to src/model/__init__.py
diff --git a/code/model/common.py b/src/model/common.py
similarity index 100%
rename from code/model/common.py
rename to src/model/common.py
diff --git a/code/model/ddbpn.py b/src/model/ddbpn.py
similarity index 100%
rename from code/model/ddbpn.py
rename to src/model/ddbpn.py
diff --git a/code/model/edsr.py b/src/model/edsr.py
similarity index 100%
rename from code/model/edsr.py
rename to src/model/edsr.py
diff --git a/code/model/mdsr.py b/src/model/mdsr.py
similarity index 100%
rename from code/model/mdsr.py
rename to src/model/mdsr.py
diff --git a/code/model/rdn.py b/src/model/rdn.py
similarity index 100%
rename from code/model/rdn.py
rename to src/model/rdn.py
diff --git a/code/option.py b/src/option.py
similarity index 100%
rename from code/option.py
rename to src/option.py
diff --git a/code/template.py b/src/template.py
similarity index 100%
rename from code/template.py
rename to src/template.py
diff --git a/code/trainer.py b/src/trainer.py
similarity index 100%
rename from code/trainer.py
rename to src/trainer.py
diff --git a/code/utility.py b/src/utility.py
similarity index 100%
rename from code/utility.py
rename to src/utility.py
--
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