Semantic Image Synthesis with SPADE

Related tags

Deep LearningSPADE
Overview

License CC BY-NC-SA 4.0 Python 3.6

Semantic Image Synthesis with SPADE

GauGAN demo

New implementation available at imaginaire repository

We have a reimplementation of the SPADE method that is more performant. It is avaiable at Imaginaire

Project page | Paper | Online Interactive Demo of GauGAN | GTC 2019 demo | Youtube Demo of GauGAN

Semantic Image Synthesis with Spatially-Adaptive Normalization.
Taesung Park, Ming-Yu Liu, Ting-Chun Wang, and Jun-Yan Zhu.
In CVPR 2019 (Oral).

License

Copyright (C) 2019 NVIDIA Corporation.

All rights reserved. Licensed under the CC BY-NC-SA 4.0 (Attribution-NonCommercial-ShareAlike 4.0 International)

The code is released for academic research use only. For commercial use or business inquiries, please contact [email protected].

For press and other inquiries, please contact Hector Marinez

Installation

Clone this repo.

git clone https://github.com/NVlabs/SPADE.git
cd SPADE/

This code requires PyTorch 1.0 and python 3+. Please install dependencies by

pip install -r requirements.txt

This code also requires the Synchronized-BatchNorm-PyTorch rep.

cd models/networks/
git clone https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
cp -rf Synchronized-BatchNorm-PyTorch/sync_batchnorm .
cd ../../

To reproduce the results reported in the paper, you would need an NVIDIA DGX1 machine with 8 V100 GPUs.

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.

  1. 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 ../
    
  2. 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.

  3. 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.

  1. 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.

  2. 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 theparser 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.

Citation

If you use this code for your research, please cite our papers.

@inproceedings{park2019SPADE,
  title={Semantic Image Synthesis with Spatially-Adaptive Normalization},
  author={Park, Taesung and Liu, Ming-Yu and Wang, Ting-Chun and Zhu, Jun-Yan},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  year={2019}
}

Acknowledgments

This code borrows heavily from pix2pixHD. We thank Jiayuan Mao for his Synchronized Batch Normalization code.

code for the ICLR'22 paper: On Robust Prefix-Tuning for Text Classification

On Robust Prefix-Tuning for Text Classification Prefix-tuning has drawed much attention as it is a parameter-efficient and modular alternative to adap

Zonghan Yang 12 Nov 30, 2022
Semantic graph parser based on Categorial grammars

Lambekseq "Everyone who failed Greek or Latin hates it." This package is for proving theorems in Categorial grammars (CG) and constructing semantic gr

10 Aug 19, 2022
This repository contain code on Novelty-Driven Binary Particle Swarm Optimisation for Truss Optimisation Problems.

This repository contain code on Novelty-Driven Binary Particle Swarm Optimisation for Truss Optimisation Problems. The main directory include the code

0 Dec 23, 2021
Spatial color quantization in Rust

rscolorq Rust port of Derrick Coetzee's scolorq, based on the 1998 paper "On spatial quantization of color images" by Jan Puzicha, Markus Held, Jens K

Collyn O'Kane 37 Dec 22, 2022
VOS: Learning What You Don’t Know by Virtual Outlier Synthesis

VOS This is the source code accompanying the paper VOS: Learning What You Don’t

248 Dec 25, 2022
Just Randoms Cats with python

Random-Cat Just Randoms Cats with python.

OriCode 2 Dec 21, 2021
Repo for "TableParser: Automatic Table Parsing with Weak Supervision from Spreadsheets" at [email protected]

TableParser Repo for "TableParser: Automatic Table Parsing with Weak Supervision from Spreadsheets" at DS3 Lab 11 Dec 13, 2022

A system for quickly generating training data with weak supervision

Programmatically Build and Manage Training Data Announcement The Snorkel team is now focusing their efforts on Snorkel Flow, an end-to-end AI applicat

Snorkel Team 5.4k Jan 02, 2023
This repository introduces a short project about Transfer Learning for Classification of MRI Images.

Transfer Learning for MRI Images Classification This repository introduces a short project made during my stay at Neuromatch Summer School 2021. This

Oscar Guarnizo 3 Nov 15, 2022
Pytorch Implementation of Value Retrieval with Arbitrary Queries for Form-like Documents.

Value Retrieval with Arbitrary Queries for Form-like Documents Introduction Pytorch Implementation of Value Retrieval with Arbitrary Queries for Form-

Salesforce 13 Sep 15, 2022
ObjectDetNet is an easy, flexible, open-source object detection framework

Getting started with the ObjectDetNet ObjectDetNet is an easy, flexible, open-source object detection framework which allows you to easily train, resu

5 Aug 25, 2020
Code for the paper "Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks"

ON-LSTM This repository contains the code used for word-level language model and unsupervised parsing experiments in Ordered Neurons: Integrating Tree

Yikang Shen 572 Nov 21, 2022
Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk

Annoy Annoy (Approximate Nearest Neighbors Oh Yeah) is a C++ library with Python bindings to search for points in space that are close to a given quer

Spotify 10.6k Jan 04, 2023
Replication package for the manuscript "Using Personality Detection Tools for Software Engineering Research: How Far Can We Go?" submitted to TOSEM

tosem2021-personality-rep-package Replication package for the manuscript "Using Personality Detection Tools for Software Engineering Research: How Far

Collaborative Development Group 1 Dec 13, 2021
Unified unsupervised and semi-supervised domain adaptation network for cross-scenario face anti-spoofing, Pattern Recognition

USDAN The implementation of Unified unsupervised and semi-supervised domain adaptation network for cross-scenario face anti-spoofing, which is accepte

11 Nov 03, 2022
All the essential resources and template code needed to understand and practice data structures and algorithms in python with few small projects to demonstrate their practical application.

Data Structures and Algorithms Python INDEX 1. Resources - Books Data Structures - Reema Thareja competitiveCoding Big-O Cheat Sheet DAA Syllabus Inte

Shushrut Kumar 129 Dec 15, 2022
An implementation of shampoo

shampoo.pytorch An implementation of shampoo, proposed in Shampoo : Preconditioned Stochastic Tensor Optimization by Vineet Gupta, Tomer Koren and Yor

Ryuichiro Hataya 69 Sep 10, 2022
This is the code for Deformable Neural Radiance Fields, a.k.a. Nerfies.

Deformable Neural Radiance Fields This is the code for Deformable Neural Radiance Fields, a.k.a. Nerfies. Project Page Paper Video This codebase conta

Google 1k Jan 09, 2023
Source code for models described in the paper "AudioCLIP: Extending CLIP to Image, Text and Audio" (https://arxiv.org/abs/2106.13043)

AudioCLIP Extending CLIP to Image, Text and Audio This repository contains implementation of the models described in the paper arXiv:2106.13043. This

458 Jan 02, 2023
Sync2Gen Code for ICCV 2021 paper: Scene Synthesis via Uncertainty-Driven Attribute Synchronization

Sync2Gen Code for ICCV 2021 paper: Scene Synthesis via Uncertainty-Driven Attribute Synchronization 0. Environment Environment: python 3.6 and cuda 10

Haitao Yang 62 Dec 30, 2022