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@@ -191,3 +191,60 @@ Demosaic/experiment/test/results-Urban100/img097_x1_DM.png filter=lfs diff=lfs m
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DN_RGB/.ipynb_checkpoints/Untitled-checkpoint.ipynb

@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

DN_RGB/.ipynb_checkpoints/Untitled1-checkpoint.ipynb

@@ -0,0 +1,59 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n",
+      "6\n",
+      "7\n"
+     ]
+    }
+   ],
+   "source": [
+    "from time import sleep\n",
+    "for i in range(500):\n",
+    "    print(i)\n",
+    "    sleep(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

DN_RGB/README.md

@@ -0,0 +1,102 @@
+# Pyramid Attention for Image Restoration
+This repository is for PANet and PA-EDSR introduced in the following paper
+
+[Yiqun Mei](http://yiqunm2.web.illinois.edu/), [Yuchen Fan](https://scholar.google.com/citations?user=BlfdYL0AAAAJ&hl=en), [Yulun Zhang](http://yulunzhang.com/), [Jiahui Yu](https://jiahuiyu.com/), [Yuqian Zhou](https://yzhouas.github.io/), [Ding Liu](https://scholar.google.com/citations?user=PGtHUI0AAAAJ&hl=en), [Yun Fu](http://www1.ece.neu.edu/~yunfu/), [Thomas S. Huang](http://ifp-uiuc.github.io/) and [Honghui Shi](https://www.humphreyshi.com/) "Pyramid Attention for Image Restoration", [[Arxiv]](https://arxiv.org/abs/2004.13824) 
+
+The code is built on [EDSR (PyTorch)](https://github.com/thstkdgus35/EDSR-PyTorch) & [RNAN](https://github.com/yulunzhang/RNAN) and tested on Ubuntu 18.04 environment (Python3.6, PyTorch_1.1) with Titan X/1080Ti/V100 GPUs.
+
+## Contents
+1. [Train](#train)
+2. [Test](#test)
+3. [Results](#results)
+4. [Citation](#citation)
+5. [Acknowledgements](#acknowledgements)
+
+## Train
+### Prepare training data 
+
+1. Download DIV2K training data (800 training + 100 validtion images) from [DIV2K dataset](https://data.vision.ee.ethz.ch/cvl/DIV2K/) or [SNU_CVLab](https://cv.snu.ac.kr/research/EDSR/DIV2K.tar).
+
+2. Specify '--dir_data' in optional.py based on the HR and LR images path.
+
+3. Organize training data like:
+```bash
+DIV2K/
+├── DIV2K_train_HR
+├── DIV2K_train_LR_bicubic
+│   └── X10
+│   └── X30
+│   └── X50
+│   └── X70
+├── DIV2K_valid_HR
+└── DIV2K_valid_LR_bicubic
+    └── X10
+    └── X30
+    └── X50
+    └── X70
+```
+
+For more informaiton, please refer to [EDSR(PyTorch)](https://github.com/thstkdgus35/EDSR-PyTorch).
+
+### Begin to train
+
+1. (optional) All the pretrained models and visual results can be downloaded from [Google Drive](https://drive.google.com/open?id=1q9iUzqYX0fVRzDu4J6fvSPRosgOZoJJE).
+
+2. Cd to 'PANet-PyTorch/[Task]/code', run the following scripts to train models.
+
+    **You can use scripts in file 'demo.sb' to train and test models for our paper.**
+
+    ```bash
+    # Example Usage: N=10
+    python main.py --n_GPUs 1 --lr 1e-4  --batch_size 16  --n_resblocks 80 --save_models --epoch 1000 --decay 200-400-600-800 --model PANET --scale 50 --patch_size 48 --reset --save PANET_N50 --n_feats 64 --data_train DIV2K --chop
+    ```
+## Test
+### Quick start
+
+1. Cd to 'PANet-PyTorch/[Task]/code', run the following scripts.
+
+    **You can use scripts in file 'demo.sb' to produce results for our paper.**
+
+    ```bash
+    # No self-ensemble, use different testsets to reproduce the results in the paper.
+    # Example Usage: 
+    python main.py --model PANET --n_resblocks 80 --n_feats 64 --data_test Urban100 --scale 10 --save_results --chop --test_only  --pre_train ../path_to_model
+    ```
+
+### The whole test pipeline
+1. Prepare test data. Organize training data like:
+```bash
+benchmark/
+├── testset1
+│   └── HR 
+│   └── LR_bicubic
+│   	└── X10
+│   	└── ..
+├── testset2
+```
+2. Conduct image denoise. 
+
+    See **Quick start**
+3. Evaluate the results.
+
+    Run 'Evaluate_PSNR_SSIM.m' to obtain PSNR/SSIM values for paper.
+
+## Citation
+If you find the code helpful in your resarch or work, please cite the following papers.
+```
+@article{mei2020pyramid,
+  title={Pyramid Attention Networks for Image Restoration},
+  author={Mei, Yiqun and Fan, Yuchen and Zhang, Yulun and Yu, Jiahui and Zhou, Yuqian and Liu, Ding and Fu, Yun and Huang, Thomas S and Shi, Honghui},
+  journal={arXiv preprint arXiv:2004.13824},
+  year={2020}
+}
+@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}
+}
+```
+## Acknowledgements
+This code is built on [EDSR (PyTorch)](https://github.com/thstkdgus35/EDSR-PyTorch), [RNAN](https://github.com/yulunzhang/RNAN) and [generative-inpainting-pytorch](https://github.com/daa233/generative-inpainting-pytorch). We thank the authors for sharing their codes.

DN_RGB/Untitled.ipynb

@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

DN_RGB/Untitled1.ipynb

@@ -0,0 +1,59 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n",
+      "6\n",
+      "7\n"
+     ]
+    }
+   ],
+   "source": [
+    "from time import sleep\n",
+    "for i in range(500):\n",
+    "    print(i)\n",
+    "    sleep(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

DN_RGB/code/.ipynb_checkpoints/train-1rec-raft-s-gpu1-checkpoint.sh

@@ -0,0 +1,18 @@
+#!/bin/bash
+
+bash ./prepare.sh
+lst=0
+for i in {0..3};
+do
+    if nvidia-smi -i $i | grep -q "python"; then 
+        :
+    else
+        lst=$i
+        break
+    fi
+done
+
+CUDA_VISIBLE_DEVICES=$lst python main.py --n_GPUs 1 --lr 1e-4  --batch_size 16  --n_resblocks 20 --save_models \
+--epoch 1000 --decay 200-400-600-800 --model RAFTNETSINGLE --scale 50 --patch_size 48 \
+--save RAFTSINGLE_N50_R1 --n_feats 64 --data_train DIV2K --recurrence 1 \
+--load RAFTSINGLE_N50_R1 --resume -1

DN_RGB/code/.ipynb_checkpoints/train-1rec-raft-s-gpu1-nobn-checkpoint.sh

@@ -0,0 +1,18 @@
+#!/bin/bash
+
+bash ./prepare.sh
+lst=0
+for i in {0..3};
+do
+    if nvidia-smi -i $i | grep -q "python"; then 
+        :
+    else
+        lst=$i
+        break
+    fi
+done
+
+CUDA_VISIBLE_DEVICES=$lst python main.py --n_GPUs 1 --lr 1e-4  --batch_size 16  --n_resblocks 20 --save_models \
+--epoch 1000 --decay 200-400-600-800 --model RAFTNETSINGLE --scale 50 --patch_size 48 \
+--save RAFTSINGLE_N50_R1_nobn --n_feats 64 --data_train DIV2K --recurrence 1 --normalization none \
+--load RAFTSINGLE_N50_R1_nobn --resume -1

DN_RGB/code/.ipynb_checkpoints/train-1rec-raft-s-gpu1-res-checkpoint.sh

@@ -0,0 +1,24 @@
+#!/bin/bash
+
+bash ./prepare.sh
+lst=0
+for i in {0..3};
+do
+    if nvidia-smi -i $i | grep -q "python"; then 
+        :
+    else
+        lst=$i
+        break
+    fi
+done
+
+# CUDA_VISIBLE_DEVICES=$lst python main.py --n_GPUs 1 --lr 1e-4 --decay 200-400-600-800 --epoch 1000 --batch_size 16 --n_resblocks 10 --save_models \
+# --model RAFTNETS --scale 1 --patch_size 48 --save RAFTS_DEMOSAIC20_R4 --n_feats 64 --data_train DIV2K --recurrence 4 --data_range "1-800/901-942"
+
+# python main.py --model LAMBDANET --n_resblocks 20 --recurrence 1 --save_results --n_GPUs 1 --chop --data_test McM+Kodak24+CBSD68+Urban100 --scale 1 \
+# --pre_train ../experiment/LAMBDA_DEMOSAIC20_R1/model/model_best.pt --test_only
+
+CUDA_VISIBLE_DEVICES=$lst python main.py --n_GPUs 1 --lr 1e-4  --batch_size 16  --n_resblocks 20 --save_models \
+--epoch 1000 --decay 200-400-600-800 --model RESNET --scale 50 --patch_size 48 \
+--save RAFTS_RES --n_feats 64 --data_train DIV2K --recurrence 1 \
+--load RAFTS_RES --resume -1

DN_RGB/code/.ipynb_checkpoints/train-4rec-raft-s-gpu1-checkpoint.sh

@@ -0,0 +1,18 @@
+#!/bin/bash
+
+bash ./prepare.sh
+lst=0
+for i in {0..3};
+do
+    if nvidia-smi -i $i | grep -q "python"; then 
+        :
+    else
+        lst=$i
+        break
+    fi
+done
+
+CUDA_VISIBLE_DEVICES=$lst python main.py --n_GPUs 1 --lr 1e-4  --batch_size 16  --n_resblocks 20 --save_models \
+--epoch 1000 --decay 200-400-600-800 --model RAFTNETS --scale 50 --patch_size 48 \
+--save RAFTS_N50_R4_bn --n_feats 64 --data_train DIV2K --recurrence 4 \
+--load RAFTS_N50_R4_bn --resume -1

DN_RGB/code/.ipynb_checkpoints/train-4rec-raft-s-gpu1-in-checkpoint.sh

@@ -0,0 +1,18 @@
+#!/bin/bash
+
+bash ./prepare.sh
+lst=0
+for i in {0..3};
+do
+    if nvidia-smi -i $i | grep -q "python"; then 
+        :
+    else
+        lst=$i
+        break
+    fi
+done
+
+CUDA_VISIBLE_DEVICES=$lst python main.py --n_GPUs 1 --lr 1e-4  --batch_size 16  --n_resblocks 20 --save_models \
+--epoch 1000 --decay 200-400-600-800 --model RAFTNETS --scale 50 --patch_size 48 \
+--save RAFTS_N50_R4_in --n_feats 64 --data_train DIV2K --recurrence 4  --normalization instance \
+--resume -1 --load RAFTS_N50_R4_in

DN_RGB/code/.ipynb_checkpoints/train-4rec-raft-s-gpu1-large-checkpoint.sh

@@ -0,0 +1,18 @@
+#!/bin/bash
+
+bash ./prepare.sh
+lst=0
+for i in {0..3};
+do
+    if nvidia-smi -i $i | grep -q "python"; then 
+        :
+    else
+        lst=$i
+        break
+    fi
+done
+
+CUDA_VISIBLE_DEVICES=$lst python main.py --n_GPUs 1 --lr 1e-4  --batch_size 16  --n_resblocks 20 --save_models \
+--epoch 1000 --decay 200-400-600-800 --model RAFTNETS --scale 50 --patch_size 96 \
+--save RAFTS_N50_R4_bn_large --n_feats 64 --data_train DIV2K --recurrence 4 \
+--load RAFTS_N50_R4_bn_large --resume -1

DN_RGB/code/.ipynb_checkpoints/train-4rec-raft-s-gpu1-ln-checkpoint.sh

@@ -0,0 +1,17 @@
+#!/bin/bash
+
+bash ./prepare.sh
+lst=0
+for i in {0..3};
+do
+    if nvidia-smi -i $i | grep -q "python"; then 
+        :
+    else
+        lst=$i
+        break
+    fi
+done
+
+CUDA_VISIBLE_DEVICES=$lst python main.py --n_GPUs 1 --lr 1e-4  --batch_size 16  --n_resblocks 20 --save_models \
+--epoch 1000 --decay 200-400-600-800 --model RAFTNETS --scale 50 --patch_size 48 --reset \
+--save RAFTS_N50_R4_ln --n_feats 64 --data_train DIV2K --recurrence 4  --normalization layer

DN_RGB/code/.ipynb_checkpoints/train-4rec-raft-s-gpu1-ln-gclip-checkpoint.sh

@@ -0,0 +1,18 @@
+#!/bin/bash
+
+bash ./prepare.sh
+lst=0
+for i in {0..3};
+do
+    if nvidia-smi -i $i | grep -q "python"; then 
+        :
+    else
+        lst=$i
+        break
+    fi
+done
+
+CUDA_VISIBLE_DEVICES=$lst python main.py --n_GPUs 1 --lr 1e-4  --batch_size 16  --n_resblocks 20 --save_models \
+--epoch 1000 --decay 200-400-600-800 --model RAFTNETS --scale 50 --patch_size 48 \
+--save RAFTS_N50_R4_ln_clip --n_feats 64 --data_train DIV2K --recurrence 4  --normalization layer --gclip 1.2 \
+--load RAFTS_N50_R4_ln_clip --resume -1

DN_RGB/code/.ipynb_checkpoints/train-4rec-raft-s-gpu1-ln-large-checkpoint.sh

@@ -0,0 +1,17 @@
+#!/bin/bash
+
+bash ./prepare.sh
+lst=0
+for i in {0..3};
+do
+    if nvidia-smi -i $i | grep -q "python"; then 
+        :
+    else
+        lst=$i
+        break
+    fi
+done
+
+CUDA_VISIBLE_DEVICES=$lst python main.py --n_GPUs 1 --lr 1e-4  --batch_size 16  --n_resblocks 20 --save_models \
+--epoch 1000 --decay 200-400-600-800 --model RAFTNETS --scale 50 --patch_size 96 --reset \
+--save RAFTS_N50_R4_ln_large --n_feats 64 --data_train DIV2K --recurrence 4  --normalization layer

DN_RGB/code/.ipynb_checkpoints/train-4rec-raft-s-gpu1-ln-small-checkpoint.sh

@@ -0,0 +1,17 @@
+#!/bin/bash
+
+bash ./prepare.sh
+lst=0
+for i in {0..3};
+do
+    if nvidia-smi -i $i | grep -q "python"; then 
+        :
+    else
+        lst=$i
+        break
+    fi
+done
+
+CUDA_VISIBLE_DEVICES=$lst python main.py --n_GPUs 1 --lr 1e-4  --batch_size 64  --n_resblocks 20 --save_models \
+--epoch 1000 --decay 200-400-600-800 --model RAFTNETS --scale 50 --patch_size 24 --reset \
+--save RAFTS_N50_R4_ln_small --n_feats 64 --data_train DIV2K --recurrence 4  --normalization layer --gclip 1.2

DN_RGB/code/.ipynb_checkpoints/train-4rec-raft-s-gpu1-nobn-checkpoint.sh

@@ -0,0 +1,18 @@
+#!/bin/bash
+
+bash ./prepare.sh
+lst=0
+for i in {0..3};
+do
+    if nvidia-smi -i $i | grep -q "python"; then 
+        :
+    else
+        lst=$i
+        break
+    fi
+done
+
+CUDA_VISIBLE_DEVICES=$lst python main.py --n_GPUs 1 --lr 1e-4  --batch_size 16  --n_resblocks 20 --save_models \
+--epoch 1000 --decay 200-400-600-800 --model RAFTNETS --scale 50 --patch_size 48 \
+--save RAFTS_N50_R4_nobn --n_feats 64 --data_train DIV2K --recurrence 4 --normalization none \
+--load RAFTS_N50_R4_nobn --resume -1

DN_RGB/code/.ipynb_checkpoints/train-4rec-raft-s-gpu1-nobn-nores-checkpoint.sh

@@ -0,0 +1,18 @@
+#!/bin/bash
+
+bash ./prepare.sh
+lst=0
+for i in {0..3};
+do
+    if nvidia-smi -i $i | grep -q "python"; then 
+        :
+    else
+        lst=$i
+        break
+    fi
+done
+
+CUDA_VISIBLE_DEVICES=$lst python main.py --n_GPUs 1 --lr 1e-4  --batch_size 16  --n_resblocks 20 --save_models \
+--epoch 1000 --decay 200-400-600-800 --model RAFTNETSNORES --scale 50 --patch_size 48 \
+--save RAFTS_N50_R4_nobn_nores --n_feats 64 --data_train DIV2K --recurrence 4 --normalization none --detach \
+--load RAFTS_N50_R4_nobn_nores --resume -1

DN_RGB/code/.ipynb_checkpoints/train-4rec-raft-s-gpu1-nobn-res10-checkpoint.sh

@@ -0,0 +1,18 @@
+#!/bin/bash
+
+bash ./prepare.sh
+lst=0
+for i in {0..3};
+do
+    if nvidia-smi -i $i | grep -q "python"; then 
+        :
+    else
+        lst=$i
+        break
+    fi
+done
+
+CUDA_VISIBLE_DEVICES=$lst python main.py --n_GPUs 1 --lr 1e-4  --batch_size 16  --n_resblocks 10 --save_models \
+--epoch 1000 --decay 200-400-600-800 --model RAFTNETS --scale 50 --patch_size 48 \
+--save RAFTS_N50_R4_nobn_res10 --n_feats 64 --data_train DIV2K --recurrence 4  --normalization none \
+--load RAFTS_N50_R4_nobn_res10 --resume -1

DN_RGB/code/.ipynb_checkpoints/train-4rec-raft-s-gpu1-small-checkpoint.sh

@@ -0,0 +1,17 @@
+#!/bin/bash
+
+bash ./prepare.sh
+lst=0
+for i in {0..3};
+do
+    if nvidia-smi -i $i | grep -q "python"; then 
+        :
+    else
+        lst=$i
+        break
+    fi
+done
+
+CUDA_VISIBLE_DEVICES=$lst python main.py --n_GPUs 1 --lr 1e-4  --batch_size 16  --n_resblocks 20 --save_models \
+--epoch 1000 --decay 200-400-600-800 --model RAFTNETS --scale 50 --patch_size 24 --reset \
+--save RAFTS_N50_R4_bn_small --n_feats 64 --data_train DIV2K --recurrence 4 

DN_RGB/code/.ipynb_checkpoints/train-4rec-raft-s-gpu2-nobn-checkpoint.sh

@@ -0,0 +1,18 @@
+#!/bin/bash
+
+bash ./prepare.sh
+lst=0
+for i in {0..3};
+do
+    if nvidia-smi -i $i | grep -q "python"; then 
+        :
+    else
+        lst=$i
+        break
+    fi
+done
+
+CUDA_VISIBLE_DEVICES=0,3 python main.py --n_GPUs 2 --lr 1e-4  --batch_size 16  --n_resblocks 20 --save_models \
+--epoch 1000 --decay 200-400-600-800 --model RAFTNETS --scale 50 --patch_size 48 \
+--save RAFTS_N50_R4_nobn --n_feats 64 --data_train DIV2K --recurrence 4 --normalization none \
+--load RAFTS_N50_R4_nobn --resume -1

DN_RGB/code/.ipynb_checkpoints/train-checkpoint.sh

@@ -0,0 +1,4 @@
+#!/bin/bash
+bash ./prepare.sh
+CUDA_VISIBLE_DEVICES=0 python main.py --n_GPUs 1 --lr 1e-5  --batch_size 16  --save_models --epoch 100 --model PANET \
+--scale 50 --patch_size 48 --reset --save MDSR_att_N50 --n_feats 64 --data_train DIV2K --chop --dir_data="/data/ssd/public/liuhy/sr/dataset"

DN_RGB/code/.ipynb_checkpoints/trainer-checkpoint.py

@@ -0,0 +1,182 @@
+import os
+import math
+from decimal import Decimal
+
+import utility
+
+import torch
+import torch.nn.utils as utils
+from tqdm import tqdm
+
+import torch.cuda.amp as amp
+
+from torch.utils.tensorboard import SummaryWriter
+import torchvision
+
+import numpy as np
+
+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
+        self.scaler=amp.GradScaler(
+            enabled=args.amp
+        )
+        self.writter=None
+        self.recurrence=args.recurrence
+        if args.recurrence>1:
+            self.writter=SummaryWriter(f"runs/{args.save}")
+
+    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)
+        total=len(self.loader_train)
+        buffer=[0.0]*self.recurrence
+        # torch.autograd.set_detect_anomaly(True)
+        for batch, (lr, hr, _,) in enumerate(self.loader_train):
+            lr, hr = self.prepare(lr, hr)
+            # print(lr.min(),lr.max(), hr.min(),hr.max())
+            # exit(0)
+            timer_data.hold()
+            timer_model.tic()
+
+            self.optimizer.zero_grad()
+            with amp.autocast(self.args.amp):
+                sr = self.model(lr, 0)
+                if len(sr)==1:
+                    sr=sr[0]
+                # loss,buffer_lst=sequence_loss(sr,hr)
+                loss = self.loss(sr, hr)
+            self.scaler.scale(loss).backward()
+            if self.args.gclip > 0:
+                self.scaler.unscale_(self.optimizer)
+                utils.clip_grad_value_(
+                    self.model.parameters(),
+                    self.args.gclip
+                )
+            self.scaler.step(self.optimizer)
+            self.scaler.update()
+            for i in range(self.recurrence):
+                buffer[i]+=self.loss.buffer[i]
+            # 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()
+        if self.writter:
+            for i in range(self.recurrence):
+                grid=torchvision.utils.make_grid(sr[i])
+                self.writter.add_image(f"Output{i}",grid,epoch)
+                self.writter.add_scalar(f"Loss{i}",buffer[i]/total,epoch)
+            self.writter.add_image("Input",torchvision.utils.make_grid(lr),epoch)
+            self.writter.add_image("Target",torchvision.utils.make_grid(hr),epoch)
+        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)
+                    with amp.autocast(self.args.amp):
+                        sr = self.model(lr, idx_scale)
+                    if isinstance(sr,list):
+                        sr=sr[-1]
+                    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...')
+        # torch.cuda.empty_cache()
+        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
+

DN_RGB/code/LICENSE

@@ -0,0 +1,21 @@
+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.

DN_RGB/code/data/__init__.py

@@ -0,0 +1,52 @@
+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 ['CBSD68','Kodak24','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,
+                )
+            )

DN_RGB/code/data/benchmark.py

@@ -0,0 +1,25 @@
+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')
+

DN_RGB/code/data/common.py

@@ -0,0 +1,72 @@
+import random
+
+import numpy as np
+import skimage.color as sc
+
+import torch
+
+def get_patch(*args, patch_size=96, scale=1, multi=False, input_large=False):
+    ih, iw = args[0].shape[:2]
+
+    if not input_large:
+        p = 1 if multi else 1
+        tp = p * patch_size
+        ip = tp // 1
+    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 = 1 * ix, 1 * 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]
+

DN_RGB/code/data/demo.py

@@ -0,0 +1,39 @@
+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
+

DN_RGB/code/data/div2k.py

@@ -0,0 +1,32 @@
+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'
+

DN_RGB/code/data/div2kjpeg.py

@@ -0,0 +1,20 @@
+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')
+