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update trainining

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Xintao 2021-06-18 10:07:45 +08:00
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11
README.md
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@ -81,6 +81,17 @@ python inference_gfpgan_full.py --model_path experiments/pretrained_models/GFPGA
We provide complete training codes for GFPGAN. <br>
You could improve it according to your own needs.
1. Dataset preparation: [FFHQ](https://github.com/NVlabs/ffhq-dataset)
1. Download pre-trained models and other data. Put them in the `experiments/pretrained_models` folder.
1. [Pretrained StyleGAN2 model: StyleGAN2_512_Cmul1_FFHQ_B12G4_scratch_800k.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/StyleGAN2_512_Cmul1_FFHQ_B12G4_scratch_800k.pth)
1. [Component locations of FFHQ: FFHQ_eye_mouth_landmarks_512.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/FFHQ_eye_mouth_landmarks_512.pth)
1. [A simple ArcFace model: arcface_resnet18.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/arcface_resnet18.pth)
1. Modify the configuration file `train_gfpgan_v1.yml` accordingly.
1. Training
> python -m torch.distributed.launch --nproc_per_node=4 --master_port=22021 train.py -opt train_gfpgan_v1.yml --launcher pytorch
## :scroll: License and Acknowledgement

11
README_CN.md
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@ -81,6 +81,17 @@ python inference_gfpgan_full.py --model_path experiments/pretrained_models/GFPGA
We provide complete training codes for GFPGAN. <br>
You could improve it according to your own needs.
1. Dataset preparation: [FFHQ](https://github.com/NVlabs/ffhq-dataset)
1. Download pre-trained models and other data. Put them in the `experiments/pretrained_models` folder.
1. [Pretrained StyleGAN2 model: StyleGAN2_512_Cmul1_FFHQ_B12G4_scratch_800k.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/StyleGAN2_512_Cmul1_FFHQ_B12G4_scratch_800k.pth)
1. [Component locations of FFHQ: FFHQ_eye_mouth_landmarks_512.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/FFHQ_eye_mouth_landmarks_512.pth)
1. [A simple ArcFace model: arcface_resnet18.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/arcface_resnet18.pth)
1. Modify the configuration file `train_gfpgan_v1.yml` accordingly.
1. Training
> python -m torch.distributed.launch --nproc_per_node=4 --master_port=22021 train.py -opt train_gfpgan_v1.yml --launcher pytorch
## :scroll: License and Acknowledgement

7
experiments/pretrained_models/README.md Normal file
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@ -0,0 +1,7 @@
# Pre-trained Models and Other Data
Download pre-trained models and other data. Put them in this folder.
1. [Pretrained StyleGAN2 model: StyleGAN2_512_Cmul1_FFHQ_B12G4_scratch_800k.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/StyleGAN2_512_Cmul1_FFHQ_B12G4_scratch_800k.pth)
1. [Component locations of FFHQ: FFHQ_eye_mouth_landmarks_512.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/FFHQ_eye_mouth_landmarks_512.pth)
1. [A simple ArcFace model: arcface_resnet18.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/arcface_resnet18.pth)

14
train_gfpgan_v1.yml
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@ -9,9 +9,11 @@ datasets:
train:
name: FFHQ
type: FFHQDegradationDataset
dataroot_gt: datasets/ffhq/ffhq_512.lmdb
# dataroot_gt: datasets/ffhq/ffhq_512.lmdb
dataroot_gt: datasets/ffhq/ffhq_512
io_backend:
type: lmdb
# type: lmdb
type: disk
use_hflip: true
mean: [0.5, 0.5, 0.5]
@ -44,10 +46,12 @@ datasets:
prefetch_mode: ~
val:
name: validation1020_512
# Please modify accordingly to use your own validation
# Or comment the val block if do not need validation during training
name: validation
type: PairedImageDataset
dataroot_lq: datasets/faces/validation1020_512/input # TODO: modify before release
dataroot_gt: datasets/faces/validation1020_512/input
dataroot_lq: datasets/faces/validation/input
dataroot_gt: datasets/faces/validation/reference
io_backend:
type: disk
mean: [0.5, 0.5, 0.5]