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@ -38,7 +38,7 @@
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增强的深度超分辨率网络(EDSR)是2017年提出的单图超分辨重建网络，在NTIRE2017超分辨重建比赛中获取第一名。它通过删除传统剩余网络中不必要的模块（BatchNorm），扩大模型的大小，同时应用了稳定训练的方法进行优化，显著提升了性能。
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[论文](https://arxiv.org/pdf/1707.02921.pdf)：Lim B , Son S , Kim H , et al. Enhanced Deep Residual Networks for Single Image Super-Resolution[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2017.
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论文: [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://arxiv.org/pdf/1707.02921.pdf): Lim B , Son S , Kim H , et al. Enhanced Deep Residual Networks for Single Image Super-Resolution[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2017.
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# 模型架构
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@ -54,6 +54,44 @@ EDSR是由多个优化后的residual blocks串联而成，相比原始版本的r
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- 测试集：349.53M，共100组图像(无HR图)
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- 数据格式：PNG图片文件文件
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- 注：数据将在src/dataset.py中处理。
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- 数据目录树：官网下载数据后，解压压缩包，训练和验证所需的数据目录结构如下：
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```shell
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├─DIV2K_train_HR
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│ ├─0001.png
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│ ├─...
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│ └─0800.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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│ │ └─0800x2.png
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│ ├─X3
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│ │ ├─0001x3.png
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│ │ ├─...
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│ │ └─0800x3.png
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│ └─X4
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│ ├─0001x4.png
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│ ├─...
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│ └─0800x4.png
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├─DIV2K_valid_LR_bicubic
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│ ├─0801.png
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│ ├─...
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│ └─0900.png
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└─DIV2K_valid_LR_bicubic
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├─X2
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│ ├─0801x2.png
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│ ├─...
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│ └─0900x2.png
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├─X3
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│ ├─0801x3.png
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│ ├─...
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│ └─0900x3.png
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└─X4
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├─0801x4.png
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├─...
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└─0900x4.png
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```
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# 特性
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@ -81,66 +119,66 @@ EDSR是由多个优化后的residual blocks串联而成，相比原始版本的r
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```python
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# 运行训练示例(EDSR(x2) in the paper)
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python train.py --batch_size 16 --scale 2 --config_path DIV2K_config.yaml > train.log 2>&1 &
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python train.py --batch_size 16 --config_path DIV2K_config.yaml --scale 2 --data_path [DIV2K path] --output_path [path to save .ckpt] > train.log 2>&1 &
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# 运行训练示例(EDSR(x3) in the paper - from EDSR(x2))
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python train.py --batch_size 16 --scale 3 --config_path DIV2K_config.yaml --pre_trained [pre-trained EDSR_x2 model path] train.log 2>&1 &
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python train.py --batch_size 16 --config_path DIV2K_config.yaml --scale 3 --data_path [DIV2K path] --output_path [path to save .ckpt] --pre_trained [pre-trained EDSR_x2 model path] train.log 2>&1 &
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# 运行训练示例(EDSR(x4) in the paper - from EDSR(x2))
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python train.py --batch_size 16 --scale 4 --config_path DIV2K_config.yaml --pre_trained [pre-trained EDSR_x2 model path] train.log 2>&1 &
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python train.py --batch_size 16 --config_path DIV2K_config.yaml --scale 4 --data_path [DIV2K path] --output_path [path to save .ckpt] --pre_trained [pre-trained EDSR_x2 model path] train.log 2>&1 &
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```
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- Ascend-910处理器环境运行8卡训练DIV2K
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```python
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# 运行分布式训练示例(EDSR(x2) in the paper)
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bash scripts/run_train.sh rank_table.json --scale 2 --config_path DIV2K_config.yaml
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bash scripts/run_train.sh rank_table.json --config_path DIV2K_config.yaml --scale 2 --data_path [DIV2K path] --output_path [path to save .ckpt]
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# 运行分布式训练示例(EDSR(x3) in the paper)
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bash scripts/run_train.sh rank_table.json --scale 3 --config_path DIV2K_config.yaml --pre_trained [pre-trained EDSR_x2 model path]
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bash scripts/run_train.sh rank_table.json --config_path DIV2K_config.yaml --scale 3 --data_path [DIV2K path] --output_path [path to save .ckpt] --pre_trained [pre-trained EDSR_x2 model path]
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# 运行分布式训练示例(EDSR(x4) in the paper)
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bash scripts/run_train.sh rank_table.json --scale 4 --config_path DIV2K_config.yaml --pre_trained [pre-trained EDSR_x2 model path]
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bash scripts/run_train.sh rank_table.json --config_path DIV2K_config.yaml --scale 4 --data_path [DIV2K path] --output_path [path to save .ckpt] --pre_trained [pre-trained EDSR_x2 model path]
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```
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- Ascend-910处理器环境运行单卡评估DIV2K
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```python
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# 运行评估示例(EDSR(x2) in the paper)
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python eval.py --scale 2 --config_path DIV2K_config.yaml --pre_trained [pre-trained EDSR_x2 model path] > train.log 2>&1 &
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python eval.py --config_path DIV2K_config.yaml --scale 2 --data_path [DIV2K path] --output_path [path to save sr] --pre_trained [pre-trained EDSR_x2 model path] > train.log 2>&1 &
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# 运行评估示例(EDSR(x3) in the paper)
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python eval.py --scale 3 --config_path DIV2K_config.yaml --pre_trained [pre-trained EDSR_x3 model path] > train.log 2>&1 &
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python eval.py --config_path DIV2K_config.yaml --scale 3 --data_path [DIV2K path] --output_path [path to save sr] --pre_trained [pre-trained EDSR_x3 model path] > train.log 2>&1 &
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# 运行评估示例(EDSR(x4) in the paper)
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python eval.py --scale 4 --config_path DIV2K_config.yaml --pre_trained [pre-trained EDSR_x4 model path] > train.log 2>&1 &
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python eval.py --config_path DIV2K_config.yaml --scale 4 --data_path [DIV2K path] --output_path [path to save sr] --pre_trained [pre-trained EDSR_x4 model path] > train.log 2>&1 &
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```
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- Ascend-910处理器环境运行8卡评估DIV2K
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```python
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# 运行分布式评估示例(EDSR(x2) in the paper)
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bash scripts/run_eval.sh rank_table.json --scale 2 --config_path DIV2K_config.yaml --pre_trained [pre-trained EDSR_x2 model path]
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bash scripts/run_eval.sh rank_table.json --config_path DIV2K_config.yaml --scale 2 --data_path [DIV2K path] --output_path [path to save sr] --pre_trained [pre-trained EDSR_x2 model path]
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# 运行分布式评估示例(EDSR(x3) in the paper)
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bash scripts/run_eval.sh rank_table.json --scale 3 --config_path DIV2K_config.yaml --pre_trained [pre-trained EDSR_x3 model path]
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bash scripts/run_eval.sh rank_table.json --config_path DIV2K_config.yaml --scale 3 --data_path [DIV2K path] --output_path [path to save sr] --pre_trained [pre-trained EDSR_x3 model path]
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# 运行分布式评估示例(EDSR(x4) in the paper)
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bash scripts/run_eval.sh rank_table.json --scale 4 --config_path DIV2K_config.yaml --pre_trained [pre-trained EDSR_x4 model path]
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bash scripts/run_eval.sh rank_table.json --config_path DIV2K_config.yaml --scale 4 --data_path [DIV2K path] --output_path [path to save sr] --pre_trained [pre-trained EDSR_x4 model path]
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```
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- Ascend-910处理器环境运行单卡评估benchmark
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```python
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# 运行评估示例(EDSR(x2) in the paper)
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python eval.py --scale 2 --config_path benchmark_config.yaml --pre_trained [pre-trained EDSR_x2 model path] > train.log 2>&1 &
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python eval.py --config_path benchmark_config.yaml --scale 2 --data_path [benchmark path] --output_path [path to save sr] --pre_trained [pre-trained EDSR_x2 model path] > train.log 2>&1 &
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# 运行评估示例(EDSR(x3) in the paper)
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python eval.py --scale 3 --config_path benchmark_config.yaml --pre_trained [pre-trained EDSR_x3 model path] > train.log 2>&1 &
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python eval.py --config_path benchmark_config.yaml --scale 3 --data_path [benchmark path] --output_path [path to save sr] --pre_trained [pre-trained EDSR_x3 model path] > train.log 2>&1 &
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# 运行评估示例(EDSR(x4) in the paper)
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python eval.py --scale 4 --config_path benchmark_config.yaml --pre_trained [pre-trained EDSR_x4 model path] > train.log 2>&1 &
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python eval.py --config_path benchmark_config.yaml --scale 4 --data_path [benchmark path] --output_path [path to save sr] --pre_trained [pre-trained EDSR_x4 model path] > train.log 2>&1 &
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```
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- Ascend-910处理器环境运行8卡评估benchmark
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```python
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# 运行分布式评估示例(EDSR(x2) in the paper)
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bash scripts/run_eval.sh rank_table.json --scale 2 --config_path benchmark_config.yaml --pre_trained [pre-trained EDSR_x2 model path]
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bash scripts/run_eval.sh rank_table.json --config_path benchmark_config.yaml --scale 2 --data_path [benchmark path] --output_path [path to save sr] --pre_trained [pre-trained EDSR_x2 model path]
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# 运行分布式评估示例(EDSR(x3) in the paper)
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bash scripts/run_eval.sh rank_table.json --scale 3 --config_path benchmark_config.yaml --pre_trained [pre-trained EDSR_x3 model path]
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bash scripts/run_eval.sh rank_table.json --config_path benchmark_config.yaml --scale 3 --data_path [benchmark path] --output_path [path to save sr] --pre_trained [pre-trained EDSR_x3 model path]
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# 运行分布式评估示例(EDSR(x4) in the paper)
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bash scripts/run_eval.sh rank_table.json --scale 4 --config_path benchmark_config.yaml --pre_trained [pre-trained EDSR_x4 model path]
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bash scripts/run_eval.sh rank_table.json --config_path benchmark_config.yaml --scale 4 --data_path [benchmark path] --output_path [path to save sr] --pre_trained [pre-trained EDSR_x4 model path]
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```
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- Ascend-310处理器环境运行单卡评估DIV2K
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@ -47,8 +47,8 @@ class FolderImagePair:
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self.reader = reader
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self.pair_keys, self.image_pairs = self.scan_pair(self.dir_patterns)
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@classmethod
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def scan_pair(cls, dir_patterns):
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@staticmethod
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def scan_pair(dir_patterns):
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"""
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scan pair
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"""
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@ -105,7 +105,8 @@ class LrHrImages(FolderImagePair):
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def __getitem__(self, idx):
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_, *images = super(LrHrImages, self).__getitem__(idx)
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return images
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return tuple(images)
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class _BasePatchCutter:
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"""
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