lucasrufkahr/Real-ESRGAN
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

update

Browse Source
This commit is contained in:
Xintao
2021-07-25 13:08:09 +08:00
parent 248cbedbce
commit ee820df2e2
2 changed files with 7 additions and 7 deletions
Download Patch File Download Diff File Expand all files Collapse all files

12
Training.md
View File

@@ -8,7 +8,7 @@ Note that the codes have a lot of refactoring. So there may be some bugs/perform
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.
1. We then use the trained Real-ESRNet model as an initialization of the generator, and train the Real-ESRGAN with a combination of L1 loss, perceptual loss and GAN loss.
## Dataset Preparation
@@ -23,7 +23,7 @@ For the DF2K dataset, we use a multi-scale strategy, *i.e.*, we downsample HR im
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):
You need to prepare a txt file containing the image paths. The following are some examples in `meta_info_DF2Kmultiscale+OST_sub.txt` (As different users may have different sub-images partitions, this file is not suitable for your purpose and you need to prepare your own txt file):
```txt
DF2K_HR_sub/000001_s001.png
@@ -41,7 +41,7 @@ DF2K_HR_sub/000001_s003.png
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
meta_info: data/meta_info/meta_info_DF2Kmultiscale+OST_sub.txt # modify to your own generate meta info txt
io_backend:
type: disk
```
@@ -75,7 +75,7 @@ DF2K_HR_sub/000001_s003.png
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.
1. The formal training. We use four GPUs for training. We use the `--auto_resume` argument to automatically resume the training if necessary.
```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
@@ -83,14 +83,14 @@ DF2K_HR_sub/000001_s003.png
## 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. After the training of 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 to 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.
1. The formal training. We use four GPUs for training. We use the `--auto_resume` argument to automatically resume the training if necessary.
```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