add EDSR to modelzoo
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目录
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<!-- TOC -->
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- [目录](#目录)
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- [EDSR描述](#EDSR描述)
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- [模型架构](#模型架构)
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- [数据集](#数据集)
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- [环境要求](#环境要求)
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- [快速入门](#快速入门)
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- [脚本说明](#脚本说明)
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- [脚本及样例代码](#脚本及样例代码)
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- [脚本参数](#脚本参数)
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- [训练过程](#训练过程)
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- [训练](#训练)
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- [分布式训练](#分布式训练)
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- [评估过程](#评估过程)
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- [评估](#评估)
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- [模型描述](#模型描述)
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- [性能](#性能)
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- [训练性能](#训练性能)
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- [DIV2K上的EDSR](#DIV2K上的EDSR)
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- [评估性能](#评估性能)
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- [Set5,Set14,B100,Urban100上的EDSR](#Set5,Set14,B100,Urban100上的EDSR)
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- [随机情况说明](#随机情况说明)
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- [ModelZoo主页](#modelzoo主页)
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<!-- /TOC -->
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# EDSR描述
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EDSR是2017年提出的32层深度网络，在2017年图像恢复和增强的新趋势研讨会上的超分挑战（NTIRE2017 Super-Resolution Challenge）中获得第一名。 EDSR，相比于SRResNet减少了每个残差块中的batch normalization层,SRResNet相对于原本的ResNet则在每个残差块的出口减去了ReLU层.
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[论文](https://arxiv.org/abs/1707.02921)：Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, and Kyoung Mu Lee, **"Enhanced Deep Residual Networks for Single Image Super-Resolution,"** *2nd NTIRE: New Trends in Image Restoration and Enhancement workshop and challenge on image super-resolution in conjunction with **CVPR 2017**.
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# 模型架构
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EDSR先经过1次卷积层,再串联32个残差模块,再经过1次卷积层,最后上采样并卷积。
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# 数据集
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使用的数据集：[DIV2K](<http://www.vision.ee.ethz.ch/~timofter/publications/Agustsson-CVPRW-2017.pdf>)
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- 数据集大小：7.11G，
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- 训练集：共800张图像（采用了前800张进行训练）
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- 测试集：共100张图像
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- 数据格式：png文件
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- 注：数据将在src/data/DIV2K.py中处理。
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```shell
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DIV2K
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├── DIV2K_test_LR_bicubic
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│ ├── X2
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│ │ ├── 0901x2.png
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│ │ ├─ ...
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│ │ └── 1000x2.png
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│ ├── X3
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│ │ ├── 0901x3.png
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│ │ ├─ ...
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│ │ └── 1000x3.png
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│ └── X4
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│ ├── 0901x4.png
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│ ├─ ...
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│ └── 1000x4.png
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├── DIV2K_test_LR_unknown
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│ ├── X2
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│ │ ├── 0901x2.png
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│ │ ├─ ...
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│ │ └── 1000x2.png
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│ ├── X3
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│ │ ├── 0901x3.png
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│ │ ├─ ...
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│ │ └── 1000x3.png
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│ └── X4
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│ ├── 0901x4.png
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│ ├─ ...
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│ └── 1000x4.png
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├── DIV2K_train_HR
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│ ├── 0001.png
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│ ├─ ...
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│ └── 0900.png
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├── DIV2K_train_LR_bicubic
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│ ├── X2
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│ │ ├── 0001x2.png
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│ │ ├─ ...
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│ │ └── 0900x2.png
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│ ├── X3
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│ │ ├── 0001x3.png
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│ │ ├─ ...
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│ │ └── 0900x3.png
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│ └── X4
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│ ├── 0001x4.png
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│ ├─ ...
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│ └── 0900x4.png
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└── DIV2K_train_LR_unknown
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├── X2
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│ ├── 0001x2.png
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│ ├─ ...
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│ └── 0900x2.png
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├── X3
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│ ├── 0001x3.png
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│ ├─ ...
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│ └── 0900x3.png
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└── X4
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├── 0001x4.png
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├─ ...
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└── 0900x4.png
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```
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# 环境要求
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- 硬件（Ascend）
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- 使用ascend处理器来搭建硬件环境。
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- 框架
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- [MindSpore](https://www.mindspore.cn/install/en)
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- 如需查看详情，请参见如下资源：
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- [MindSpore教程](https://www.mindspore.cn/tutorial/training/zh-CN/master/index.html)
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- [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html)
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# 快速入门
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通过官方网站安装MindSpore后，您可以按照如下步骤进行训练和评估：
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```shell
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#单卡训练
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sh run_ascend_standalone.sh [TRAIN_DATA_DIR]
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```
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```shell
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#分布式训练
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sh run_ascend_distribute.sh [RANK_TABLE_FILE] [TRAIN_DATA_DIR]
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```
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```python
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#评估
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sh run_eval.sh [TEST_DATA_DIR] [CHECKPOINT_PATH] [DATASET_TYPE]
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```
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# 脚本说明
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## 脚本及样例代码
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```bash
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├── model_zoo
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├── EDSR
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├── README_CN.md //自述文件
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├── eval.py //评估脚本
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├── export.py //导出脚本
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├── script
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│ ├── run_ascend_distribute.sh //Ascend分布式训练shell脚本
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│ ├── run_ascend_standalone.sh //Ascend单卡训练shell脚本
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│ └── run_eval.sh //eval验证shell脚本
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├── src
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│ ├── args.py //超参数
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│ ├── common.py //公共网络模块
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│ ├── data
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│ │ ├── common.py //公共数据集
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│ │ ├── div2k.py //div2k数据集
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│ │ └── srdata.py //所有数据集
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│ ├── metrics.py //PSNR和SSIM计算器
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│ ├── model.py //EDSR网络
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│ └── utils.py //训练脚本
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└── train.py //训练脚本
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```
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## 脚本参数
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主要参数如下:
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```python
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-h, --help show this help message and exit
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--dir_data DIR_DATA dataset directory
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--data_train DATA_TRAIN
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train dataset name
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--data_test DATA_TEST
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test dataset name
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--data_range DATA_RANGE
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train/test data range
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--ext EXT dataset file extension
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--scale SCALE super resolution scale
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--patch_size PATCH_SIZE
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output patch size
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--rgb_range RGB_RANGE
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maximum value of RGB
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--n_colors N_COLORS number of color channels to use
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--no_augment do not use data augmentation
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--model MODEL model name
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--n_resblocks N_RESBLOCKS
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number of residual blocks
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--n_feats N_FEATS number of feature maps
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--res_scale RES_SCALE
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residual scaling
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--test_every TEST_EVERY
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do test per every N batches
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--epochs EPOCHS number of epochs to train
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--batch_size BATCH_SIZE
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input batch size for training
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--test_only set this option to test the model
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--lr LR learning rate
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--ckpt_save_path CKPT_SAVE_PATH
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path to save ckpt
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--ckpt_save_interval CKPT_SAVE_INTERVAL
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save ckpt frequency, unit is epoch
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--ckpt_save_max CKPT_SAVE_MAX
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max number of saved ckpt
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--ckpt_path CKPT_PATH
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path of saved ckpt
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--task_id TASK_ID
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```
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## 训练过程
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### 训练
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- Ascend处理器环境运行
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```bash
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sh run_ascend_standalone.sh [TRAIN_DATA_DIR]
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```
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如果数据集保存路径为G:\DIV2K，`TRAIN_DATA_DIR`应传入G:\。
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上述python命令将在后台运行，您可以通过train.log文件查看结果。
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### 分布式训练
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- Ascend处理器环境运行
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```bash
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sh run_ascend_distribute.sh [RANK_TABLE_FILE] [TRAIN_DATA_DIR]
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```
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如果数据集保存路径为G:\DIV2K，`TRAIN_DATA_DIR`应传入G:\。
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## 评估过程
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### 评估
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在运行以下命令之前，请检查用于评估的检查点路径。
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```bash
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sh run_eval.sh [TEST_DATA_DIR] [CHECKPOINT_PATH] [DATASET_TYPE]
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```
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`DATASET_TYPE`可选 ["Set5", "Set14", "B100", "Urban100", "DIV2K"]
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如果数据集保存路径为G:\DIV2K或者G:\Set5或者G:\Set14或者G:\B100或者G:\Urban100，`TRAIN_DATA_DIR`应传入G:\。
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您可以通过log.txt文件查看结果。
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# 模型描述
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## 性能
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### 训练性能
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| 参数 | Ascend |
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| ------------- | ------------------------------------------------------------ |
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| 资源 | Ascend 910 |
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| 上传日期 | 2021-7-4 |
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| MindSpore版本 | 1.2.0 |
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| 数据集 | DIV2K |
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| 训练参数 | epoch=1000, steps=1000, batch_size =16, lr=0.0001 |
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| 优化器 | Adam |
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| 损失函数 | L1 |
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| 输出 | 超分辨率图片 |
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| 损失 | 3.1 |
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| 速度 | 8卡：50.75毫秒/步 |
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| 总时长 | 8卡：12.865小时 |
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| 微调检查点 | 466.13 MB (.ckpt文件) |
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| 脚本 | [EDSR](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/cv/EDSR) |
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### 评估性能
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| 参数 | Ascend |
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| ------------- | ----------------------------------------------------------- |
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| 资源 | Ascend 910 |
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| 上传日期 | 2021-7-4 |
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| MindSpore版本 | 1.2.0 |
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| 数据集 | Set5,Set14,B100,Urban100 |
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| batch_size | 1 |
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| 输出 | 超分辨率图片 |
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| PSNR | Set5:38.2136, Set14:34.0081, B100:32.3590, Urban100:33.0162 |
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# 随机情况说明
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在train.py中，我们设置了“train_net”函数内的种子。
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# ModelZoo主页
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请浏览官网[主页](https://gitee.com/mindspore/mindspore/tree/master/model_zoo)。
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# Copyright 2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""edsr eval script"""
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import os
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import numpy as np
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import mindspore.dataset as ds
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from mindspore import Tensor, context
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from mindspore.common import dtype as mstype
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from src.args import args
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import src.model as edsr
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from src.data.srdata import SRData
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from src.data.div2k import DIV2K
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from src.metrics import calc_psnr, quantize, calc_ssim
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device_id = int(os.getenv('DEVICE_ID', '0'))
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=device_id, save_graphs=False)
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context.set_context(max_call_depth=10000)
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def eval_net():
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"""eval"""
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if args.epochs == 0:
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args.epochs = 1e8
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for arg in vars(args):
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if vars(args)[arg] == 'True':
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vars(args)[arg] = True
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elif vars(args)[arg] == 'False':
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vars(args)[arg] = False
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if args.data_test[0] == 'DIV2K':
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train_dataset = DIV2K(args, name=args.data_test, train=False, benchmark=False)
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else:
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train_dataset = SRData(args, name=args.data_test, train=False, benchmark=False)
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train_de_dataset = ds.GeneratorDataset(train_dataset, ['LR', 'HR'], shuffle=False)
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train_de_dataset = train_de_dataset.batch(1, drop_remainder=True)
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train_loader = train_de_dataset.create_dict_iterator(output_numpy=True)
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net_m = edsr.EDSR(args)
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if args.ckpt_path:
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param_dict = load_checkpoint(args.ckpt_path)
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load_param_into_net(net_m, param_dict)
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net_m.set_train(False)
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print('load mindspore net successfully.')
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num_imgs = train_de_dataset.get_dataset_size()
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psnrs = np.zeros((num_imgs, 1))
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ssims = np.zeros((num_imgs, 1))
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for batch_idx, imgs in enumerate(train_loader):
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lr = imgs['LR']
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hr = imgs['HR']
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lr = Tensor(lr, mstype.float32)
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pred = net_m(lr)
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pred_np = pred.asnumpy()
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pred_np = quantize(pred_np, 255)
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psnr = calc_psnr(pred_np, hr, args.scale[0], 255.0)
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pred_np = pred_np.reshape(pred_np.shape[-3:]).transpose(1, 2, 0)
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hr = hr.reshape(hr.shape[-3:]).transpose(1, 2, 0)
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ssim = calc_ssim(pred_np, hr, args.scale[0])
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print("current psnr: ", psnr)
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print("current ssim: ", ssim)
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psnrs[batch_idx, 0] = psnr
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ssims[batch_idx, 0] = ssim
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print('Mean psnr of %s x%s is %.4f' % (args.data_test[0], args.scale[0], psnrs.mean(axis=0)[0]))
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print('Mean ssim of %s x%s is %.4f' % (args.data_test[0], args.scale[0], ssims.mean(axis=0)[0]))
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if __name__ == '__main__':
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print("Start eval function!")
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eval_net()
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# Copyright 2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""export net together with checkpoint into air/mindir/onnx models"""
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import os
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import argparse
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import numpy as np
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from mindspore import Tensor, context, load_checkpoint, load_param_into_net, export
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import src.model as edsr
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parser = argparse.ArgumentParser(description='edsr export')
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parser.add_argument("--ckpt_path", type=str, required=True, help="path of checkpoint file")
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parser.add_argument("--file_name", type=str, default="edsr", help="output file name.")
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parser.add_argument("--file_format", type=str, default="MINDIR", choices=['MINDIR', 'AIR', 'ONNX'], help="file format")
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parser.add_argument('--scale', type=str, default='2', help='super resolution scale')
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parser.add_argument('--rgb_range', type=int, default=255, help='maximum value of RGB')
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parser.add_argument('--n_colors', type=int, default=3, help='number of color channels to use')
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parser.add_argument('--n_resblocks', type=int, default=32, help='number of residual blocks')
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parser.add_argument('--n_feats', type=int, default=256, help='number of feature maps')
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parser.add_argument('--res_scale', type=float, default=0.1, help='residual scaling')
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parser.add_argument('--task_id', type=int, default=0)
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|
||||
parser.add_argument('--batch_size', type=int, default=1)
|
||||
|
||||
args1 = parser.parse_args()
|
||||
args1.scale = [int(x) for x in args1.scale.split("+")]
|
||||
for arg in vars(args1):
|
||||
if vars(args1)[arg] == 'True':
|
||||
vars(args1)[arg] = True
|
||||
elif vars(args1)[arg] == 'False':
|
||||
vars(args1)[arg] = False
|
||||
|
||||
def run_export(args):
|
||||
"""run_export"""
|
||||
device_id = int(os.getenv("DEVICE_ID", '0'))
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=device_id)
|
||||
net = edsr.EDSR(args)
|
||||
param_dict = load_checkpoint(args.ckpt_path)
|
||||
load_param_into_net(net, param_dict)
|
||||
net.set_train(False)
|
||||
print('load mindspore net and checkpoint successfully.')
|
||||
inputs = Tensor(np.zeros([args.batch_size, 3, 678, 1020], np.float32))
|
||||
export(net, inputs, file_name=args.file_name, file_format=args.file_format)
|
||||
print('export successfully!')
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
run_export(args1)
|
||||
|
|
@ -0,0 +1,72 @@
|
|||
#!/bin/bash
|
||||
# Copyright 2021 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
|
||||
if [ $# != 2 ]; then
|
||||
echo "Usage: sh run_distribute_train.sh [RANK_TABLE_FILE] [TRAIN_DATA_DIR]"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
get_real_path() {
|
||||
if [ "${1:0:1}" == "/" ]; then
|
||||
echo "$1"
|
||||
else
|
||||
echo "$(realpath -m $PWD/$1)"
|
||||
fi
|
||||
}
|
||||
|
||||
PATH1=$(get_real_path $1)
|
||||
PATH2=$(get_real_path $2)
|
||||
|
||||
if [ ! -f $PATH1 ]; then
|
||||
echo "error: RANK_TABLE_FILE=$PATH1 is not a file"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
if [ ! -d $PATH2 ]; then
|
||||
echo "error: TRAIN_DATA_DIR=$PATH2 is not a directory"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
export DEVICE_NUM=8
|
||||
export RANK_SIZE=8
|
||||
export RANK_TABLE_FILE=$PATH1
|
||||
|
||||
for ((i = 0; i < ${DEVICE_NUM}; i++)); do
|
||||
export DEVICE_ID=$i
|
||||
export RANK_ID=$i
|
||||
rm -rf ./train_parallel$i
|
||||
mkdir ./train_parallel$i
|
||||
cp ../*.py ./train_parallel$i
|
||||
cp *.sh ./train_parallel$i
|
||||
cp -r ../src ./train_parallel$i
|
||||
cd ./train_parallel$i || exit
|
||||
echo "start training for rank $RANK_ID, device $DEVICE_ID"
|
||||
env >env.log
|
||||
|
||||
python train.py \
|
||||
--batch_size 2 \
|
||||
--lr 1e-4 \
|
||||
--scale 2 \
|
||||
--task_id 0 \
|
||||
--dir_data $PATH2 \
|
||||
--epochs 1000 \
|
||||
--test_every 8000 \
|
||||
--n_resblocks 32 \
|
||||
--n_feats 256 \
|
||||
--res_scale 0.1 \
|
||||
--patch_size 48 > train.log 2>&1 &
|
||||
cd ..
|
||||
done
|
||||
|
|
@ -0,0 +1,59 @@
|
|||
#!/bin/bash
|
||||
# Copyright 2021 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
|
||||
if [ $# != 1 ]; then
|
||||
echo "Usage: sh run_standalone_train.sh [TRAIN_DATA_DIR]"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
get_real_path() {
|
||||
if [ "${1:0:1}" == "/" ]; then
|
||||
echo "$1"
|
||||
else
|
||||
echo "$(realpath -m $PWD/$1)"
|
||||
fi
|
||||
}
|
||||
|
||||
PATH1=$(get_real_path $1)
|
||||
|
||||
if [ ! -d $PATH1 ]; then
|
||||
echo "error: TRAIN_DATA_DIR=$PATH1 is not a directory"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
|
||||
if [ -d "train" ]; then
|
||||
rm -rf ./train
|
||||
fi
|
||||
mkdir ./train
|
||||
cp ../*.py ./train
|
||||
cp -r ../src ./train
|
||||
cd ./train || exit
|
||||
|
||||
env >env.log
|
||||
|
||||
python train.py \
|
||||
--batch_size 16 \
|
||||
--lr 1e-4 \
|
||||
--scale 2 \
|
||||
--task_id 0 \
|
||||
--dir_data $PATH1 \
|
||||
--epochs 1000 \
|
||||
--test_every 1000 \
|
||||
--n_resblocks 32 \
|
||||
--n_feats 256 \
|
||||
--res_scale 0.1 \
|
||||
--patch_size 48 > train.log 2>&1 &
|
||||
|
|
@ -0,0 +1,65 @@
|
|||
#!/bin/bash
|
||||
# Copyright 2021 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
|
||||
if [ $# != 3 ]; then
|
||||
echo "Usage: sh run_eval.sh [TEST_DATA_DIR] [CHECKPOINT_PATH] [DATASET_TYPE]"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
get_real_path() {
|
||||
if [ "${1:0:1}" == "/" ]; then
|
||||
echo "$1"
|
||||
else
|
||||
echo "$(realpath -m $PWD/$1)"
|
||||
fi
|
||||
}
|
||||
|
||||
PATH1=$(get_real_path $1)
|
||||
PATH2=$(get_real_path $2)
|
||||
DATASET_TYPE=$3
|
||||
|
||||
if [ ! -d $PATH1 ]; then
|
||||
echo "error: TEST_DATA_DIR=$PATH1 is not a directory"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
if [ ! -f $PATH2 ]; then
|
||||
echo "error: CHECKPOINT_PATH=$PATH2 is not a file"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
if [ -d "eval" ]; then
|
||||
rm -rf ./eval
|
||||
fi
|
||||
mkdir ./eval
|
||||
cp ../*.py ./eval
|
||||
cp -r ../src ./eval
|
||||
cd ./eval || exit
|
||||
env >env.log
|
||||
echo "start evaluation ..."
|
||||
|
||||
python eval.py \
|
||||
--dir_data=${PATH1} \
|
||||
--test_only \
|
||||
--ext img \
|
||||
--ckpt_path=${PATH2} \
|
||||
--task_id 0 \
|
||||
--scale 2 \
|
||||
--data_test=${DATASET_TYPE} \
|
||||
--device_id 0 \
|
||||
--n_resblocks 32 \
|
||||
--n_feats 256 \
|
||||
--res_scale 0.1 > log.txt 2>&1 &
|
||||
|
|
@ -0,0 +1,84 @@
|
|||
# Copyright 2021 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
"""args parser"""
|
||||
import argparse
|
||||
parser = argparse.ArgumentParser(description='EDSR')
|
||||
# Data specifications
|
||||
parser.add_argument('--dir_data', type=str, default='/cache/data/',
|
||||
help='dataset 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='1-800/801-900',
|
||||
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=48,
|
||||
help='input 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('--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('--n_resblocks', type=int, default=32,
|
||||
help='number of residual blocks')
|
||||
parser.add_argument('--n_feats', type=int, default=256,
|
||||
help='number of feature maps')
|
||||
parser.add_argument('--res_scale', type=float, default=0.1,
|
||||
help='residual scaling')
|
||||
# Training specifications
|
||||
parser.add_argument('--test_every', type=int, default=8000,
|
||||
help='do test per every N batches')
|
||||
parser.add_argument('--epochs', type=int, default=1000,
|
||||
help='number of epochs to train')
|
||||
parser.add_argument('--batch_size', type=int, default=2,
|
||||
help='input batch size for training')
|
||||
parser.add_argument('--test_only', action='store_true',
|
||||
help='set this option to test the model')
|
||||
# Optimization specifications
|
||||
parser.add_argument('--lr', type=float, default=1e-4,
|
||||
help='learning rate')
|
||||
parser.add_argument('--loss_scale', type=float, default=1024.0,
|
||||
help='init loss scale')
|
||||
# ckpt specifications
|
||||
parser.add_argument('--ckpt_save_path', type=str, default='./ckpt/',
|
||||
help='path to save ckpt')
|
||||
parser.add_argument('--ckpt_save_interval', type=int, default=10,
|
||||
help='save ckpt frequency, unit is epoch')
|
||||
parser.add_argument('--ckpt_save_max', type=int, default=5,
|
||||
help='max number of saved ckpt')
|
||||
parser.add_argument('--ckpt_path', type=str, default='',
|
||||
help='path of saved ckpt')
|
||||
# alltask
|
||||
parser.add_argument('--task_id', type=int, default=0)
|
||||
|
||||
args, unparsed = parser.parse_known_args()
|
||||
args.scale = [int(x) for x in args.scale.split("+")]
|
||||
args.data_train = args.data_train.split('+')
|
||||
args.data_test = args.data_test.split('+')
|
||||
if args.epochs == 0:
|
||||
args.epochs = 1e4
|
||||
for arg in vars(args):
|
||||
if vars(args)[arg] == 'True':
|
||||
vars(args)[arg] = True
|
||||
elif vars(args)[arg] == 'False':
|
||||
vars(args)[arg] = False
|
||||
|
|
@ -0,0 +1,96 @@
|
|||
# Copyright 2021 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
"""common"""
|
||||
import math
|
||||
import numpy as np
|
||||
import mindspore
|
||||
import mindspore.nn as nn
|
||||
|
||||
|
||||
def default_conv(in_channels, out_channels, kernel_size, bias=True):
|
||||
return nn.Conv2d(
|
||||
in_channels, out_channels, kernel_size,
|
||||
pad_mode='pad',
|
||||
padding=(kernel_size//2), has_bias=bias)
|
||||
|
||||
|
||||
class MeanShift(mindspore.nn.Conv2d):
|
||||
"""MeanShift"""
|
||||
def __init__(
|
||||
self, rgb_range,
|
||||
rgb_mean=(0.4488, 0.4371, 0.4040), rgb_std=(1.0, 1.0, 1.0), sign=-1, dtype=mindspore.float32):
|
||||
|
||||
std = mindspore.Tensor(rgb_std, dtype)
|
||||
weight = mindspore.Tensor(np.eye(3), dtype).reshape(
|
||||
3, 3, 1, 1) / std.reshape(3, 1, 1, 1)
|
||||
bias = sign * rgb_range * mindspore.Tensor(rgb_mean, dtype) / std
|
||||
|
||||
super(MeanShift, self).__init__(3, 3, kernel_size=1,
|
||||
has_bias=True, weight_init=weight, bias_init=bias)
|
||||
|
||||
for p in self.get_parameters():
|
||||
p.requires_grad = False
|
||||
|
||||
|
||||
class ResBlock(nn.Cell):
|
||||
"""ResBlock"""
|
||||
def __init__(
|
||||
self, conv, n_feats, kernel_size,
|
||||
bias=True, act=nn.ReLU(), 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 i == 0:
|
||||
m.append(act)
|
||||
|
||||
self.body = nn.SequentialCell(m)
|
||||
self.res_scale = res_scale
|
||||
self.mul = mindspore.ops.Mul()
|
||||
|
||||
def construct(self, x):
|
||||
res = self.body(x)
|
||||
res = self.mul(res, self.res_scale)
|
||||
res += x
|
||||
|
||||
return res
|
||||
|
||||
|
||||
class PixelShuffle(nn.Cell):
|
||||
"""PixelShuffle"""
|
||||
def __init__(self, upscale_factor):
|
||||
super(PixelShuffle, self).__init__()
|
||||
self.DepthToSpace = mindspore.ops.DepthToSpace(upscale_factor)
|
||||
|
||||
def construct(self, x):
|
||||
return self.DepthToSpace(x)
|
||||
|
||||
|
||||
def Upsampler(conv, scale, n_feats, bias=True):
|
||||
"""Upsampler"""
|
||||
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(PixelShuffle(2))
|
||||
elif scale == 3:
|
||||
m.append(conv(n_feats, 9 * n_feats, 3, bias))
|
||||
m.append(PixelShuffle(3))
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
return m
|
||||
|
|
@ -0,0 +1,75 @@
|
|||
# Copyright 2021 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
"""common"""
|
||||
import random
|
||||
import numpy as np
|
||||
def get_patch(*args, patch_size=96, scale=2, input_large=False):
|
||||
"""common"""
|
||||
ih, iw = args[0].shape[:2]
|
||||
tp = patch_size
|
||||
ip = tp // scale
|
||||
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):
|
||||
"""common"""
|
||||
def _set_channel(img):
|
||||
if img.ndim == 2:
|
||||
img = np.expand_dims(img, axis=2)
|
||||
c = img.shape[2]
|
||||
if n_channels == 3 and c == 1:
|
||||
img = np.concatenate([img] * n_channels, 2)
|
||||
return img[:, :, :n_channels]
|
||||
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)))
|
||||
input_data = np_transpose.astype(np.float32)
|
||||
output = input_data * (rgb_range / 255)
|
||||
return output
|
||||
return [_np2Tensor(a) for a in args]
|
||||
def augment(*args, hflip=True, rot=True):
|
||||
"""common"""
|
||||
hflip = hflip and random.random() < 0.5
|
||||
vflip = rot and random.random() < 0.5
|
||||
rot90 = rot and random.random() < 0.5
|
||||
def _augment(img):
|
||||
"""common"""
|
||||
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]
|
||||
def search(root, target="JPEG"):
|
||||
"""srdata"""
|
||||
item_list = []
|
||||
items = os.listdir(root)
|
||||
for item in items:
|
||||
path = os.path.join(root, item)
|
||||
if os.path.isdir(path):
|
||||
item_list.extend(search(path, target))
|
||||
elif path.split('/')[-1].startswith(target):
|
||||
item_list.append(path)
|
||||
elif target in (path.split('/')[-2], path.split('/')[-3], path.split('/')[-4]):
|
||||
item_list.append(path)
|
||||
return item_list
|
||||
|
|
@ -0,0 +1,43 @@
|
|||
# Copyright 2021 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
"""div2k"""
|
||||
import os
|
||||
from src.data.srdata import SRData
|
||||
class DIV2K(SRData):
|
||||
"""DIV2K"""
|
||||
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(int, data_range))
|
||||
super(DIV2K, self).__init__(args, name=name, train=train, benchmark=benchmark)
|
||||
self.dir_hr = None
|
||||
self.dir_lr = None
|
||||
|
||||
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')
|
||||
|
|
@ -0,0 +1,212 @@
|
|||
# Copyright 2021 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
""""srdata"""
|
||||
import os
|
||||
import glob
|
||||
import random
|
||||
import pickle
|
||||
import imageio
|
||||
from src.data import common
|
||||
from PIL import ImageFile
|
||||
ImageFile.LOAD_TRUNCATED_IMAGES = True
|
||||
class SRData:
|
||||
"""srdata"""
|
||||
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)
|
||||
self._set_img(args)
|
||||
if train:
|
||||
self._repeat(args)
|
||||
|
||||
def _set_img(self, args):
|
||||
"""srdata"""
|
||||
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 self.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:
|
||||
if s == 1:
|
||||
os.makedirs(os.path.join(self.dir_hr), exist_ok=True)
|
||||
else:
|
||||
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)
|
||||
|
||||
def _repeat(self, args):
|
||||
"""srdata"""
|
||||
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)
|
||||
|
||||
def _scan(self):
|
||||
"""srdata"""
|
||||
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):
|
||||
if s != 1:
|
||||
scale = s
|
||||
names_lr[si].append(os.path.join(self.dir_lr, 'X{}/{}x{}{}' \
|
||||
.format(s, filename, scale, self.ext[1])))
|
||||
for si, s in enumerate(self.scale):
|
||||
if s == 1:
|
||||
names_lr[si] = names_hr
|
||||
return names_hr, names_lr
|
||||
|
||||
def _set_filesystem(self, dir_data):
|
||||
self.apath = os.path.join(dir_data, self.name[0])
|
||||
self.dir_hr = os.path.join(self.apath, 'HR')
|
||||
self.dir_lr = os.path.join(self.apath, 'LR_bicubic')
|
||||
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, _ = 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]
|
||||
|
||||
def __len__(self):
|
||||
if self.train:
|
||||
return len(self.images_hr) * self.repeat
|
||||
return len(self.images_hr)
|
||||
|
||||
def _get_index(self, idx):
|
||||
if self.train:
|
||||
return idx % len(self.images_hr)
|
||||
return idx
|
||||
|
||||
def _load_file_deblur(self, idx, train=True):
|
||||
"""srdata"""
|
||||
idx = self._get_index(idx)
|
||||
if train:
|
||||
f_hr = self.images_hr[idx]
|
||||
f_lr = self.images_lr[idx]
|
||||
else:
|
||||
f_hr = self.deblur_hr_test[idx]
|
||||
f_lr = self.deblur_lr_test[idx]
|
||||
filename, _ = os.path.splitext(os.path.basename(f_hr))
|
||||
filename = f_hr[-27:-17] + filename
|
||||
hr = imageio.imread(f_hr)
|
||||
lr = imageio.imread(f_lr)
|
||||
return lr, hr, filename
|
||||
|
||||
def _load_file_hr(self, idx):
|
||||
"""srdata"""
|
||||
idx = self._get_index(idx)
|
||||
f_hr = self.images_hr[idx]
|
||||
filename, _ = os.path.splitext(os.path.basename(f_hr))
|
||||
if self.args.ext == 'img' or self.benchmark:
|
||||
hr = imageio.imread(f_hr)
|
||||
elif self.args.ext.find('sep') >= 0:
|
||||
with open(f_hr, 'rb') as _f:
|
||||
hr = pickle.load(_f)
|
||||
return hr, filename
|
||||
|
||||
def _load_rain_test(self, idx):
|
||||
f_hr = self.derain_hr_test[idx]
|
||||
f_lr = self.derain_lr_test[idx]
|
||||
filename, _ = os.path.splitext(os.path.basename(f_lr))
|
||||
norain = imageio.imread(f_hr)
|
||||
rain = imageio.imread(f_lr)
|
||||
return norain, rain, filename
|
||||
|
||||
def _load_file(self, idx):
|
||||
"""srdata"""
|
||||
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_hr(self, hr):
|
||||
"""srdata"""
|
||||
if self.train:
|
||||
hr = self.get_patch_img_hr(hr, patch_size=self.args.patch_size, scale=1)
|
||||
return hr
|
||||
|
||||
def get_patch_img_hr(self, img, patch_size=96, scale=2):
|
||||
"""srdata"""
|
||||
ih, iw = img.shape[:2]
|
||||
tp = patch_size
|
||||
ip = tp // scale
|
||||
ix = random.randrange(0, iw - ip + 1)
|
||||
iy = random.randrange(0, ih - ip + 1)
|
||||
ret = img[iy:iy + ip, ix:ix + ip, :]
|
||||
return ret
|
||||
|
||||
def get_patch(self, lr, hr):
|
||||
"""srdata"""
|
||||
scale = self.scale[self.idx_scale]
|
||||
if self.train:
|
||||
lr, hr = common.get_patch(
|
||||
lr, hr,
|
||||
patch_size=self.args.patch_size * scale,
|
||||
scale=scale)
|
||||
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)
|
||||
|
|
@ -0,0 +1,102 @@
|
|||
# Copyright 2021 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
"""metrics"""
|
||||
import math
|
||||
import numpy as np
|
||||
import cv2
|
||||
|
||||
|
||||
def quantize(img, rgb_range):
|
||||
"""quantize image range to 0-255"""
|
||||
pixel_range = 255 / rgb_range
|
||||
img = np.multiply(img, pixel_range)
|
||||
img = np.clip(img, 0, 255)
|
||||
img = np.round(img) / pixel_range
|
||||
return img
|
||||
|
||||
|
||||
def calc_psnr(sr, hr, scale, rgb_range):
|
||||
"""calculate psnr"""
|
||||
hr = np.float32(hr)
|
||||
sr = np.float32(sr)
|
||||
diff = (sr - hr) / rgb_range
|
||||
gray_coeffs = np.array([65.738, 129.057, 25.064]).reshape((1, 3, 1, 1)) / 256
|
||||
diff = np.multiply(diff, gray_coeffs).sum(1)
|
||||
if hr.size == 1:
|
||||
return 0
|
||||
if scale != 1:
|
||||
shave = scale
|
||||
else:
|
||||
shave = scale + 6
|
||||
if scale == 1:
|
||||
valid = diff
|
||||
else:
|
||||
valid = diff[..., shave:-shave, shave:-shave]
|
||||
mse = np.mean(pow(valid, 2))
|
||||
return -10 * math.log10(mse)
|
||||
|
||||
|
||||
def rgb2ycbcr(img, y_only=True):
|
||||
"""from rgb space to ycbcr space"""
|
||||
img.astype(np.float32)
|
||||
if y_only:
|
||||
rlt = np.dot(img, [65.481, 128.553, 24.966]) / 255.0 + 16.0
|
||||
return rlt
|
||||
|
||||
|
||||
def calc_ssim(img1, img2, scale):
|
||||
"""calculate ssim value"""
|
||||
def ssim(img1, img2):
|
||||
C1 = (0.01 * 255) ** 2
|
||||
C2 = (0.03 * 255) ** 2
|
||||
img1 = img1.astype(np.float64)
|
||||
img2 = img2.astype(np.float64)
|
||||
kernel = cv2.getGaussianKernel(11, 1.5)
|
||||
window = np.outer(kernel, kernel.transpose())
|
||||
mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5] # valid
|
||||
mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
|
||||
mu1_sq = mu1 ** 2
|
||||
mu2_sq = mu2 ** 2
|
||||
mu1_mu2 = mu1 * mu2
|
||||
sigma1_sq = cv2.filter2D(img1 ** 2, -1, window)[5:-5, 5:-5] - mu1_sq
|
||||
sigma2_sq = cv2.filter2D(img2 ** 2, -1, window)[5:-5, 5:-5] - mu2_sq
|
||||
sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2
|
||||
ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) *
|
||||
(sigma1_sq + sigma2_sq + C2))
|
||||
return ssim_map.mean()
|
||||
border = 0
|
||||
if scale != 1:
|
||||
border = scale
|
||||
else:
|
||||
border = scale + 6
|
||||
img1_y = np.dot(img1, [65.738, 129.057, 25.064]) / 256.0 + 16.0
|
||||
img2_y = np.dot(img2, [65.738, 129.057, 25.064]) / 256.0 + 16.0
|
||||
if not img1.shape == img2.shape:
|
||||
raise ValueError('Input images must have the same dimensions.')
|
||||
h, w = img1.shape[:2]
|
||||
img1_y = img1_y[border:h - border, border:w - border]
|
||||
img2_y = img2_y[border:h - border, border:w - border]
|
||||
if img1_y.ndim == 2:
|
||||
return ssim(img1_y, img2_y)
|
||||
if img1.ndim == 3:
|
||||
if img1.shape[2] == 3:
|
||||
ssims = []
|
||||
for _ in range(3):
|
||||
ssims.append(ssim(img1, img2))
|
||||
return np.array(ssims).mean()
|
||||
if img1.shape[2] == 1:
|
||||
return ssim(np.squeeze(img1), np.squeeze(img2))
|
||||
else:
|
||||
raise ValueError('Wrong input image dimensions.')
|
||||
|
|
@ -0,0 +1,65 @@
|
|||
# Copyright 2021 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
"""edsr_model"""
|
||||
import mindspore.nn as nn
|
||||
from src import common
|
||||
|
||||
|
||||
class EDSR(nn.Cell):
|
||||
"""EDSR"""
|
||||
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()
|
||||
|
||||
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 = []
|
||||
m_tail += common.Upsampler(conv, scale, n_feats)
|
||||
m_tail.append(conv(n_feats, args.n_colors, kernel_size))
|
||||
|
||||
self.head = nn.SequentialCell(m_head)
|
||||
self.body = nn.SequentialCell(m_body)
|
||||
self.tail = nn.SequentialCell(m_tail)
|
||||
|
||||
def construct(self, x):
|
||||
"""construct"""
|
||||
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
|
||||
|
|
@ -0,0 +1,87 @@
|
|||
# Copyright 2021 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
"""edsr train wrapper"""
|
||||
import os
|
||||
import time
|
||||
import mindspore.nn as nn
|
||||
from mindspore import Tensor
|
||||
from mindspore.common import dtype as mstype
|
||||
from mindspore.train.serialization import save_checkpoint
|
||||
class Trainer():
|
||||
"""Trainer"""
|
||||
def __init__(self, args, loader, my_model):
|
||||
self.args = args
|
||||
self.scale = args.scale
|
||||
self.trainloader = loader
|
||||
self.model = my_model
|
||||
self.model.set_train()
|
||||
self.criterion = nn.L1Loss()
|
||||
self.loss_history = []
|
||||
self.begin_time = time.time()
|
||||
self.optimizer = nn.Adam(self.model.trainable_params(), learning_rate=args.lr, loss_scale=1024.0)
|
||||
self.loss_net = nn.WithLossCell(self.model, self.criterion)
|
||||
self.net = nn.TrainOneStepCell(self.loss_net, self.optimizer)
|
||||
def train(self, epoch):
|
||||
"""Trainer"""
|
||||
losses = 0
|
||||
batch_idx = 0
|
||||
for batch_idx, imgs in enumerate(self.trainloader):
|
||||
lr = imgs["LR"]
|
||||
hr = imgs["HR"]
|
||||
lr = Tensor(lr, mstype.float32)
|
||||
hr = Tensor(hr, mstype.float32)
|
||||
t1 = time.time()
|
||||
loss = self.net(lr, hr)
|
||||
t2 = time.time()
|
||||
losses += loss.asnumpy()
|
||||
print('Epoch: %g, Step: %g , loss: %f, time: %f s ' % \
|
||||
(epoch, batch_idx, loss.asnumpy(), t2 - t1), end='\n', flush=True)
|
||||
print("the epoch loss is", losses / (batch_idx + 1), flush=True)
|
||||
self.loss_history.append(losses / (batch_idx + 1))
|
||||
print(self.loss_history)
|
||||
t = time.time() - self.begin_time
|
||||
t = int(t)
|
||||
print(", running time: %gh%g'%g''"%(t//3600, (t-t//3600*3600)//60, t%60), flush=True)
|
||||
os.makedirs(self.args.save, exist_ok=True)
|
||||
if self.args.rank == 0 and (epoch+1)%10 == 0:
|
||||
save_checkpoint(self.net, self.args.save + "model_" + str(self.epoch) + '.ckpt')
|
||||
def update_learning_rate(self, epoch):
|
||||
"""Update learning rates for all the networks; called at the end of every epoch.
|
||||
:param epoch: current epoch
|
||||
:type epoch: int
|
||||
:param lr: learning rate of cyclegan
|
||||
:type lr: float
|
||||
:param niter: number of epochs with the initial learning rate
|
||||
:type niter: int
|
||||
:param niter_decay: number of epochs to linearly decay learning rate to zero
|
||||
:type niter_decay: int
|
||||
"""
|
||||
self.epoch = epoch
|
||||
print("*********** epoch: {} **********".format(epoch))
|
||||
lr = self.args.lr / (2 ** ((epoch+1)//200))
|
||||
self.adjust_lr('model', self.optimizer, lr)
|
||||
print("*********************************")
|
||||
def adjust_lr(self, name, optimizer, lr):
|
||||
"""Adjust learning rate for the corresponding model.
|
||||
:param name: name of model
|
||||
:type name: str
|
||||
:param optimizer: the optimizer of the corresponding model
|
||||
:type optimizer: torch.optim
|
||||
:param lr: learning rate to be adjusted
|
||||
:type lr: float
|
||||
"""
|
||||
lr_param = optimizer.get_lr()
|
||||
lr_param.assign_value(Tensor(lr, mstype.float32))
|
||||
print('==> ' + name + ' learning rate: ', lr_param.asnumpy())
|
||||
|
|
@ -0,0 +1,73 @@
|
|||
# Copyright 2021 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
"""edsr train script"""
|
||||
import os
|
||||
from mindspore import context
|
||||
from mindspore import dataset as ds
|
||||
import mindspore.nn as nn
|
||||
from mindspore.context import ParallelMode
|
||||
from mindspore.train.serialization import load_checkpoint, load_param_into_net
|
||||
from mindspore.communication.management import init
|
||||
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
|
||||
from mindspore.common import set_seed
|
||||
from mindspore.train.model import Model
|
||||
from src.args import args
|
||||
from src.data.div2k import DIV2K
|
||||
from src.model import EDSR
|
||||
|
||||
def train_net():
|
||||
"""train edsr"""
|
||||
set_seed(1)
|
||||
device_id = int(os.getenv('DEVICE_ID', '0'))
|
||||
rank_id = int(os.getenv('RANK_ID', '0'))
|
||||
device_num = int(os.getenv('RANK_SIZE', '1'))
|
||||
# if distribute:
|
||||
if device_num > 1:
|
||||
init()
|
||||
context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL,
|
||||
device_num=device_num, gradients_mean=True)
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False, device_id=device_id)
|
||||
train_dataset = DIV2K(args, name=args.data_train, train=True, benchmark=False)
|
||||
train_dataset.set_scale(args.task_id)
|
||||
train_de_dataset = ds.GeneratorDataset(train_dataset, ["LR", "HR"], num_shards=device_num,
|
||||
shard_id=rank_id, shuffle=True)
|
||||
train_de_dataset = train_de_dataset.batch(args.batch_size, drop_remainder=True)
|
||||
net_m = EDSR(args)
|
||||
print("Init net successfully")
|
||||
if args.ckpt_path:
|
||||
param_dict = load_checkpoint(args.ckpt_path)
|
||||
load_param_into_net(net_m, param_dict)
|
||||
print("Load net weight successfully")
|
||||
step_size = train_de_dataset.get_dataset_size()
|
||||
lr = []
|
||||
for i in range(0, args.epochs):
|
||||
cur_lr = args.lr / (2 ** ((i + 1)//200))
|
||||
lr.extend([cur_lr] * step_size)
|
||||
opt = nn.Adam(net_m.trainable_params(), learning_rate=lr, loss_scale=args.loss_scale)
|
||||
loss = nn.L1Loss()
|
||||
model = Model(net_m, loss_fn=loss, optimizer=opt)
|
||||
time_cb = TimeMonitor(data_size=step_size)
|
||||
loss_cb = LossMonitor()
|
||||
cb = [time_cb, loss_cb]
|
||||
config_ck = CheckpointConfig(save_checkpoint_steps=args.ckpt_save_interval * step_size,
|
||||
keep_checkpoint_max=args.ckpt_save_max)
|
||||
ckpt_cb = ModelCheckpoint(prefix="edsr", directory=args.ckpt_save_path, config=config_ck)
|
||||
if device_id == 0:
|
||||
cb += [ckpt_cb]
|
||||
model.train(args.epochs, train_de_dataset, callbacks=cb, dataset_sink_mode=True)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
train_net()
|
||||
Loading…
Reference in New Issue