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.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# 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/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# custom
+/data
+src/samples/
+src/models
+.vscode

Makefile

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+SHELL := /bin/bash
+CONDA_ACTIVATE=source $$(conda info --base)/etc/profile.d/conda.sh ; conda activate ; conda activate
+
+.PHONY: help lint local build pull run
+
+.DEFAULT: help
+
+help:
+	@echo "make run"
+	@echo "	run python main.py"
+
+ENV_NAME := super-res
+visdom:
+	@cd src;\
+	$(CONDA_ACTIVATE) $(ENV_NAME);\
+	python -m visdom.server;\
+
+run:
+	@cd src;\
+	$(CONDA_ACTIVATE) $(ENV_NAME);\
+ 	CUDA_VISIBLE_DEVICES=2,3 nohup python main.py > main.log &
+	cd ..;

README.md

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+# Project 7
+- Topic - Image Super Resolution
+- Paper - Learning a Single Convolutional Super-Resolution Network for
+Multiple Degradations
+- Paper Link - https://arxiv.org/pdf/1712.06116v2.pdf
+
+
+# Run Instructions
+1. `make run`
+
+# Usage
+## Requirements
+- `python=3.8`
+- `pytorch=1.7.1`
+- `cudatoolkit=10.2`
+
+## Code structure
+```
+.
+├── docs
+|   ├── TeamKota-MidEvalReport.pdf
+|   ├── Project Proposal
+│       ├── Method.pdf
+│       └── TeamKota-ProjectProposal.pdf
+├── README.md
+├── Makefile
+├── data
+    ├── BSDS300
+    ├── DIV2K_train_HR
+    ├── waterloo
+├── environment.yml
+└── src
+    ├── dataset.py
+    ├── kernels.py
+    ├── logger.py
+    ├── main.py
+    ├── model.py
+    ├── train.py
+    └── utils.py
+```
+## Setup env
+- `conda env create --file environment.yaml`
+
+## Data
+
+- use `Name` in main.py
+
+| Id | Name |Info      | Link |
+| --- | --- | ----------- | ----------- |
+| 1 | DIV2K_train_HR | HR images  | http://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_train_HR.zip       |
+| 2 | DIV2K_train_LR_bicubic_X2 | Bicubic x2 downscaling  | http://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_train_LR_bicubic_X2.zip       |
+| 3 | Waterloo Exploration Database | Pristine Natural Images | http://ivc.uwaterloo.ca/database/WaterlooExploration/exploration_database_and_code.rar |
+| 4 | Berkeley Segementation Dataset and Benchmark (BSDS300) | Natural Images with greyscale and color segementation | https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/BSDS300-images.tgz |

environment.yml

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+name: super-res
+channels:
+  - defaults
+dependencies:
+  - python=3.8
+  - cudatoolkit=10.2
+  - torchaudio
+  - pytorch
+  - torchvision
+  - scipy
+  - visdom
+  - scikit-learn
+  - jsonpatch

src/dataset.py

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+import torch
+from torch.utils import data
+from PIL import Image
+import numpy as np
+from kernels import Kernels
+from torchvision import transforms
+import math
+import random
+from torchvision.utils import save_image
+import glob
+
+def Scaling(image):
+    return np.array(image) / 255.0
+
+class AddGaussianNoise(object):
+    def __init__(self, mean=0.):
+        self.mean = mean
+
+    def __call__(self, vec):
+        vec = np.asarray(vec)
+        h, w, n = vec.shape
+        self.std = float(random.randint(0, 76))
+        return vec + np.random.rand(h,w,n) * self.std + self.mean
+
+    def __repr__(self):
+        return self.__class__.__name__ + '(mean={0}, std={1})'.format(self.mean, self.std)
+
+class DIV2K_train(data.Dataset):
+    def __init__(self, config=None):
+
+        self.image_paths = []
+        num_images = 800
+        for i in range(1, num_images+1):
+            name = '0000' + str(i)
+            name = name[-4:]
+            Y_path = config.y_path + name + '.png'
+            self.image_paths.append(Y_path)
+        self.image_paths += glob.glob(config.y_path2)
+        self.image_paths += glob.glob(config.y_path3)
+        self.scale_factor = config.scale_factor
+        self.image_size = config.image_size
+
+        self.kernels = Kernels(self.scale_factor)
+
+
+    def __getitem__(self, index):
+        Y_path = self.image_paths[index]
+
+        Y_image = Image.open(Y_path).convert('RGB') # hr image
+        # Y_image.save("ogyimage"+str(index)+".jpg")
+        X_imageact,X_image, Y_image = self.transformlr(Y_image,index)
+
+        return X_imageact.to(torch.float64), X_image.to(torch.float64), Y_image.to(torch.float64)
+
+    def __len__(self):
+        return len(self.image_paths)
+
+    def transformlr(self, Y_image,index):
+        transform = transforms.RandomCrop(self.image_size * self.scale_factor)
+        hr_image = transform(Y_image) #image
+        # print(hr_image)
+        # hr_image.save("transyimage"+str(index)+".jpg")
+
+        kernel, degradinfo = random.choice(self.kernels.allkernels)
+
+        transform = transforms.Compose([
+                            transforms.Lambda(lambda x: self.kernels.Blur(x,kernel)),
+                            transforms.Resize((self.image_size, self.image_size), interpolation=3),
+                            AddGaussianNoise()
+                    ])
+
+        lr_image = np.asarray(transform(hr_image)) #numpy
+        transform = transforms.ToTensor()
+        lr_imageact= transform(lr_image)
+        # print("here",lr_image.shape)
+        # temp = Image.fromarray(lr_image.astype(np.uint8))
+        # temp.save("transximage"+str(index)+".jpg")
+
+        transform = transforms.Compose([transforms.Lambda(lambda x: self.kernels.ConcatDegraInfo(x,degradinfo))])
+        lr_image = transform(lr_image)
+
+        transform = transforms.ToTensor()
+        lr_image, hr_image = transform(lr_image), transform(hr_image)
+        return lr_imageact,lr_image, hr_image