Semantic Image Synthesis with PG-SPADE (Jittor implementation)

Installation

Clone this repo.

git clone https://github.com/zcablii/jittor-Torile-GanVit.git
cd jittor-Torile-GanVit-main/

This code requires python 3+ and PyTorch 1.8(for pytorch version implementation) or Jittor 1.3 (for Jittor version implementaion). Please install dependencies by

sudo apt install python3.7-dev libomp-dev  
python3.7 -m pip install jittor  
pip install -r requirements.txt

Dataset Preparation

For COCO-Stuff, Cityscapes or ADE20K, the datasets must be downloaded beforehand. Please download them on the respective webpages. In the case of COCO-stuff, we put a few sample images in this code repo.

Preparing COCO-Stuff Dataset. The dataset can be downloaded here. In particular, you will need to download train2017.zip, val2017.zip, stuffthingmaps_trainval2017.zip, and annotations_trainval2017.zip. The images, labels, and instance maps should be arranged in the same directory structure as in datasets/coco_stuff/. In particular, we used an instance map that combines both the boundaries of "things instance map" and "stuff label map". To do this, we used a simple script datasets/coco_generate_instance_map.py. Please install pycocotools using pip install pycocotools and refer to the script to generate instance maps.

Preparing ADE20K Dataset. The dataset can be downloaded here, which is from MIT Scene Parsing BenchMark. After unzipping the datgaset, put the jpg image files ADEChallengeData2016/images/ and png label files ADEChallengeData2016/annotatoins/ in the same directory.

There are different modes to load images by specifying --preprocess_mode along with --load_size. --crop_size. There are options such as resize_and_crop, which resizes the images into square images of side length load_size and randomly crops to crop_size. scale_shortside_and_crop scales the image to have a short side of length load_size and crops to crop_size x crop_size square. To see all modes, please use python train.py --help and take a look at data/base_dataset.py. By default at the training phase, the images are randomly flipped horizontally. To prevent this use --no_flip.

Generating Images Using Pretrained Model

Once the dataset is ready, the result images can be generated using pretrained models.

Download the tar of the pretrained models from the Google Drive Folder, save it in 'checkpoints/', and run
```
cd checkpoints
tar xvf checkpoints.tar.gz
cd ../
```
Generate images using the pretrained model.
```
python test.py --name [type]_pretrained --dataset_mode [dataset] --dataroot [path_to_dataset]
```
[type]_pretrained is the directory name of the checkpoint file downloaded in Step 1, which should be one of coco_pretrained, ade20k_pretrained, and cityscapes_pretrained. [dataset] can be one of coco, ade20k, and cityscapes, and [path_to_dataset], is the path to the dataset. If you are running on CPU mode, append --gpu_ids -1.
The outputs images are stored at ./results/[type]_pretrained/ by default. You can view them using the autogenerated HTML file in the directory.

Generating Landscape Image using GauGAN

In the paper and the demo video, we showed GauGAN, our interactive app that generates realistic landscape images from the layout users draw. The model was trained on landscape images scraped from Flickr.com. We released an online demo that has the same features. Please visit https://www.nvidia.com/en-us/research/ai-playground/. The model weights are not released.

Training New Models

New models can be trained with the following commands.

Prepare dataset. To train on the datasets shown in the paper, you can download the datasets and use --dataset_mode option, which will choose which subclass of BaseDataset is loaded. For custom datasets, the easiest way is to use ./data/custom_dataset.py by specifying the option --dataset_mode custom, along with --label_dir [path_to_labels] --image_dir [path_to_images]. You also need to specify options such as --label_nc for the number of label classes in the dataset, --contain_dontcare_label to specify whether it has an unknown label, or --no_instance to denote the dataset doesn't have instance maps.
Train.

# To train on the Facades or COCO dataset, for example.
python train.py --name [experiment_name] --dataset_mode facades --dataroot [path_to_facades_dataset]
python train.py --name [experiment_name] --dataset_mode coco --dataroot [path_to_coco_dataset]

# To train on your own custom dataset
python train.py --name [experiment_name] --dataset_mode custom --label_dir [path_to_labels] -- image_dir [path_to_images] --label_nc [num_labels]

There are many options you can specify. Please use python train.py --help. The specified options are printed to the console. To specify the number of GPUs to utilize, use --gpu_ids. If you want to use the second and third GPUs for example, use --gpu_ids 1,2.

To log training, use --tf_log for Tensorboard. The logs are stored at [checkpoints_dir]/[name]/logs.

Testing

Testing is similar to testing pretrained models.

python test.py --name [name_of_experiment] --dataset_mode [dataset_mode] --dataroot [path_to_dataset]

Use --results_dir to specify the output directory. --how_many will specify the maximum number of images to generate. By default, it loads the latest checkpoint. It can be changed using --which_epoch.

Code Structure

train.py, test.py: the entry point for training and testing.
trainers/pix2pix_trainer.py: harnesses and reports the progress of training.
models/pix2pix_model.py: creates the networks, and compute the losses
models/networks/: defines the architecture of all models
options/: creates option lists using argparse package. More individuals are dynamically added in other files as well. Please see the section below.
data/: defines the class for loading images and label maps.

Options

This code repo contains many options. Some options belong to only one specific model, and some options have different default values depending on other options. To address this, the BaseOption class dynamically loads and sets options depending on what model, network, and datasets are used. This is done by calling the static method modify_commandline_options of various classes. It takes in the parser of argparse package and modifies the list of options. For example, since COCO-stuff dataset contains a special label "unknown", when COCO-stuff dataset is used, it sets --contain_dontcare_label automatically at data/coco_dataset.py. You can take a look at def gather_options() of options/base_options.py, or models/network/__init__.py to get a sense of how this works.

VAE-Style Training with an Encoder For Style Control and Multi-Modal Outputs

To train our model along with an image encoder to enable multi-modal outputs as in Figure 15 of the paper, please use --use_vae. The model will create netE in addition to netG and netD and train with KL-Divergence loss.

Acknowledgments

This code borrows heavily from SPADE.

Run scripts

origin baseline 512pix with fp16 random crop from 640pix

CUDA_VISIBLE_DEVICES=0 python train.py --name='label2img' --batchSize=24 --label_dir='../data/train/gray_label' --image_dir='../data/train/imgs'

+pos emb at sematic label and generator intermediate features

CUDA_VISIBLE_DEVICES=0 python train.py --name='label2img' --batchSize=24 --label_dir='../data/train/gray_label' --image_dir='../data/train/imgs' --use_pos=True --use_pos_proj=True --use_interFeature_pos=True

Progressive growing training

CUDA_VISIBLE_DEVICES=0 python train.py --name='label2img' --label_dir='../data/train/gray_label' --image_dir='../data/train/imgs' --pg_strategy=1 --niter=150 --pg_niter=120 --niter_decay=30 --num_D=3

explain by example: if num_D=3, pg_niter=120, pg start with 128 pix, stabilise 128 for 30 eps, fade into 256 in 30 eps, stablilise 256 for 30 eps, fade into 512 for 30 eps. All other ongoing epochs are only for stabilising 512pix output. Add --lr=0.005 --pg_lr_decay=0.5 to use larger lr at lower reolution phase and lower lr at higher resolution phase.

54.68 script

CUDA_VISIBLE_DEVICES=0 python train.py --name='label2img' --label_dir='../data/train/gray_label' --image_dir='../data/train/imgs' --pg_strategy=1 --niter=200 --pg_niter=180 --niter_decay=20 --num_D=4

PG+inception loss+diffaug+logger training

CUDA_VISIBLE_DEVICES=0 python train.py --name='label2img' --label_dir='../data/train/gray_label' --image_dir='../data/train/imgs' --niter=260 --pg_niter=180 --niter_decay=20 --pg_strategy=1 --num_D=4 --diff_aug='color,crop,translation' --inception_loss

PG Strategy1 pretrain (with 572 crop aug)

CUDA_VISIBLE_DEVICES=0 python train.py --name='label2img' --label_dir='../data/train/gray_label' --image_dir='../data/train/imgs' --niter=240 --pg_niter=240 --pg_strategy=1 --num_D=4

(add --reverse_map_D to reverse the mapping of D in PG stages: large D on small scale)

With above checkpoint, add inception loss, diff aug and spatial noise:

CUDA_VISIBLE_DEVICES=0 python train.py --name='label2img' --label_dir='../data/train/gray_label' --image_dir='../data/train/imgs' --niter=340 --pg_niter=240 --niter_decay=20 --pg_strategy=1 --num_D=4 --diff_aug='color,crop,translation' --inception_loss --use_seg_noise --continue_train --which_epoch=240

Test

CUDA_VISIBLE_DEVICES=0 python test.py --name='label2img' --batchSize=32 --label_dir='../data/eval/gray_label'

Test with pure label replacement

CUDA_VISIBLE_DEVICES=0 python test.py --name='label2imgpretrain_280_finetun' --label_dir='../../CGAN/data/test/gray_label' --batchSize=40 --use_seg_noise --use_pure --train_img_ref_path='../../CGAN/data/train/imgs/' --train_label_ref_path='../../CGAN/data/train/labels/' --which_epoch=340

zcablii / jittor-APF-GAN