SemanticGAN
This is the official code for:
Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalization
Daiqing Li, Junlin Yang, Karsten Kreis, Antonio Torralba, Sanja Fidler
CVPR 2021 [Paper] [Supp] [Page]
Requirements
- Python 3.6 or 3.7 are supported.
- Pytorch 1.4.0 + is recommended.
- This code is tested with CUDA 10.2 toolkit and CuDNN 7.5.
- Please check the python package requirement from
requirements.txt, and install using
pip install -r requirements.txt
Dataset
We recently release MetFaces40 annotation we use as out-of-domain testing. Please notice this dataset is under the Creative Commons BY-NC 4.0 license by NVIDIA Corporation. To view a copy of this license, visit LICENSE. Please see GDrive.
Training
To reproduce paper Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalization:
- Run Step1: Semantic GAN training
- Run Step2: Encoder training
- Run Inference & Optimization.
0. Prepare for FID calculation
In order to calculate FID score, you need to prepare inception features for your dataset,
python prepare_inception.py \
--size [resolution of the image] \
--batch [batch size] \
--output [path to save the inception file, in .pkl] \
--dataset_name celeba-mask \
[positional argument 1, path to the image folder]] \
1. GAN Training
For training GAN with both image and its label,
python train_seg_gan.py \
--img_dataset [path-to-img-folder] \
--seg_dataset [path-to-seg-folder] \
--inception [path-to-inception file] \
--seg_name celeba-mask \
--checkpoint_dir [path-to-ckpt-dir] \
To use multi-gpus training in the cloud,
python -m torch.distributed.launch \
--nproc_per_node=N_GPU \
--master_port=PORTtrain_gan.py \
train_gan.py \
--img_dataset [path-to-img-folder] \
--inception [path-to-inception file] \
--dataset_name celeba-mask \
--checkpoint_dir [path-to-ckpt-dir] \
2. Encoder Triaining
python train_enc.py \
--img_dataset [path-to-img-folder] \
--seg_dataset [path-to-seg-folder] \
--ckpt [path-to-pretrained GAN model] \
--seg_name celeba-mask \
--enc_backboend [fpn|res] \
--checkpoint_dir [path-to-ckpt-dir] \
Inference
For Face Parts Segmentation Task
python inference.py \
--ckpt [path-to-ckpt] \
--img_dir [path-to-test-folder] \
--outdir [path-to-output-folder] \
--dataset_name celeba-mask \
--w_plus \
--image_mode RGB \
--seg_dim 8 \
--step 200 [optimization steps] \
Visualization of different optimization steps
Citation
Please cite the following paper if you used the code in this repository.
@inproceedings{semanticGAN,
title={Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalization},
booktitle={Conference on Computer Vision and Pattern Recognition (CVPR)},
author={Li, Daiqing and Yang, Junlin and Kreis, Karsten and Torralba, Antonio and Fidler, Sanja},
year={2021},
}
License
For any code dependency related to Stylegan2, the license is under the Nvidia Source Code License-NC. To view a copy of this license, visit https://nvlabs.github.io/stylegan2/license.html
The work SemanticGAN is released under MIT License.
The MIT License (MIT)
Copyright (c) 2021 NVIDIA Corporation.
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the Software, and to permit persons to whom the Software is furnished to do so,
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The above copyright notice and this permission notice shall be included in all
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