add readme for training

README.md

@@ -11,7 +11,7 @@
 Real-ESRGAN aims at developing **Practical Algorithms for General Image Restoration**.<br>
 We extend the powerful ESRGAN to a practical restoration application (namely, Real-ESRGAN), which is trained with pure synthetic data.
 
-:triangular_flag_on_post: The training codes have been released. A detailed guide will be provided later (on July 25th). Note that the codes have a lot of refactoring. So there may be some bugs/performance drops. Welcome to report issues and I wil also retrain the models.
+:triangular_flag_on_post: The training codes have been released. A detailed guide will be provided later (on July 25th).
 
 ### :book: Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
 
@@ -54,6 +54,7 @@ You can download **Windows executable files** from https://github.com/xinntao/Re
 This executable file is **portable** and includes all the binaries and models required. No CUDA or PyTorch environment is needed.<br>
 
 You can simply run the following command:
+
 ```bash
 ./realesrgan-ncnn-vulkan.exe -i input.jpg -o output.png
 ```

Training.md

@@ -0,0 +1,97 @@
+# :computer: How to Train Real-ESRGAN
+
+The training codes have been released. <br>
+Note that the codes have a lot of refactoring. So there may be some bugs/performance drops. Welcome to report issues and I will also retrain the models.
+
+## Overview
+
+The training has been divided into two stages. These two stages have the same data synthesis process and training pipeline, except for the loss functions. Specifically,
+
+1. We first train Real-ESRNet with L1 loss from the pre-trained model ESRGAN.
+1. We then use the trained Real-ESRNet model as an initialization of the generator, and train the Real-ESRGAN with a combination ofL1 loss, perceptual loss and GAN loss.
+
+## Dataset Preparation
+
+We use DF2K (DIV2K and Flickr2K) + OST datasets for our training. Only HR images are required. <br>
+You can download from :
+
+1. DIV2K: http://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_train_HR.zip
+2. Flickr2K: https://cv.snu.ac.kr/research/EDSR/Flickr2K.tar
+3. OST: https://openmmlab.oss-cn-hangzhou.aliyuncs.com/datasets/OST_dataset.zip
+
+For the DF2K dataset, we use a multi-scale strategy, *i.e.*, we downsample HR images to obtain several Ground-Truth images with different scales.
+
+We then crop DF2K images into sub-images for faster IO and processing.
+
+You need to prepare a txt file containing the image paths. Examples in `meta_info_DF2Kmultiscale+OST_sub.txt` (As different users may have different sub-images partition, this file is not suitable for your purpose and you need to prepare your own txt file):
+
+```txt
+DF2K_HR_sub/000001_s001.png
+DF2K_HR_sub/000001_s002.png
+DF2K_HR_sub/000001_s003.png
+...
+```
+
+## Train Real-ESRNet
+
+1. Download pre-trained model [ESRGAN](https://drive.google.com/file/d/1b3_bWZTjNO3iL2js1yWkJfjZykcQgvzT/view?usp=sharing) into `experiments/pretrained_models`.
+1. Modify the content in the option file `options/train_realesrnet_x4plus.yml` accordingly:
+    ```yml
+    train:
+        name: DF2K+OST
+        type: RealESRGANDataset
+        dataroot_gt: datasets/DF2K  # modify to the root path of your folder
+        meta_info: data/meta_info/meta_info_DF2Kmultiscale+OST_sub.txt # modify to your own generate meta info
+        io_backend:
+            type: disk
+    ```
+1. If you want to perform validation during training, uncomment those lines and modify accordingly:
+    ```yml
+      # Uncomment these for validation
+      # val:
+      #   name: validation
+      #   type: PairedImageDataset
+      #   dataroot_gt: path_to_gt
+      #   dataroot_lq: path_to_lq
+      #   io_backend:
+      #     type: disk
+
+    ...
+
+      # Uncomment these for validation
+      # validation settings
+      # val:
+      #   val_freq: !!float 5e3
+      #   save_img: True
+
+      #   metrics:
+      #     psnr: # metric name, can be arbitrary
+      #       type: calculate_psnr
+      #       crop_border: 4
+      #       test_y_channel: false
+    ```
+1. Before the formal training, you may run in the `--debug` mode to see whether everything is OK. We use four GPUs for training:
+    ```bash
+    CUDA_VISIBLE_DEVICES=0,1,2,3 \
+    python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 train.py -opt options/train_realesrnet_x4plus.yml --launcher pytorch --debug
+    ```
+1. The formal training. We use four GPUs for training. We pass `--auto_resume` to resume the training if necessary automatically.
+    ```bash
+    CUDA_VISIBLE_DEVICES=0,1,2,3 \
+    python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 train.py -opt options/train_realesrnet_x4plus.yml --launcher pytorch --auto_resume
+    ```
+
+## Train Real-ESRGAN
+
+1. After you train Real-ESRNet, you now have the file `experiments/train_RealESRNetx4plus_1000k_B12G4_fromESRGAN/model/net_g_1000000.pth`. If you need to specify the pre-trained path of other files. Modify the `pretrain_network_g` value in the option file `train_realesrgan_x4plus.yml`.
+1. Modify the option file `train_realesrgan_x4plus.yml` accordingly. Most modifications are similar to those listed above.
+1. Before the formal training, you may run in the `--debug` mode to see whether everything is OK. We use four GPUs for training:
+    ```bash
+    CUDA_VISIBLE_DEVICES=0,1,2,3 \
+    python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 train.py -opt options/train_realesrgan_x4plus.yml --launcher pytorch --debug
+    ```
+1. The formal training. We use four GPUs for training. We pass `--auto_resume` to resume the training if necessary automatically.
+    ```bash
+    CUDA_VISIBLE_DEVICES=0,1,2,3 \
+    python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 train.py -opt options/train_realesrgan_x4plus.yml --launcher pytorch --auto_resume
+    ```

experiments/pretrained_models/README.md

@@ -0,0 +1 @@
+# Put downloaded pre-trained models here