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

2
README.md
View File

@@ -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).
:triangular_flag_on_post: The training codes have been released. A detailed guide can be found [here](Training.md)
### :book: Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data

10
Training.md
View File

@@ -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