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• ACGAN-Paddle
  • 1. Introduction
  • 2. Acdcuracy
  • 3. Dataset
  • 4. Dependency
  • 5. Quick Start
    • step1:Colne
    • step2:Training
    • step3:Testing
    • Check Log
    • Prediction with pre-trained models
  • 6. Code Structure and Description
    • 6.1 code structure
    • 6.2 description
  • 7. Results
  • 8. Model Information

This project is based on the PaddlePaddle replication paper "Conditional Image Synthesis with Auxiliary Classifier GANs"(ACGAN), which focuses on adding an auxiliary discriminator to a conditional generative adversarial network (Conditional GAN) to guide the image generation process. The main work of this paper is to add an auxiliary discriminator to conditional generative adversarial networks (CGAs) to guide the image generation process by adding a classification layer to the discriminator of the model to force the generated image classes to be as close as possible to the input labels. Experimentally, ACGAN has been shown to perform well in synthesizing high-resolution images.

Paper

• [[1] Odena, A. , C. Olah , and J. Shlens . "Conditional Image Synthesis With Auxiliary Classifier GANs." (2016).

Projects

Since the authors did not release the source code, the following unofficial implementation was referenced for this project.

• ACGAN-Pytorch
• Pytorch-GAN

Online Operation

• Ai Studio job project：ACGAN-Paddle

This replication did not involve metrics measurement, and the main goal was to generate images that could be close to the real sample in terms of visual assessment, so the following shows the randomly generated sample and the real sample.

fake samples	real samples

The dataset in the paper is ImageNet, and the dataset is organized in the following format.

• Training set: 1279591 images
• Validation set: 50000 images
• Testing set: 10000 images

According to the setup in the paper, 1000 image categories are grouped and one group of 10 categories is used to train a model. Three different sets of experiments were performed for this replication.

• 10,000 images of image category number 10-20 as the training set
• 10,000 images of image category number 100-100 as training set
• Randomly selected 10 categories with 10,000 images as the training set

• Hardware：GPU、CPU
• Framework：PaddlePaddle>=2.0.0

git https://github.com/Callifrey/ACGAN-Paddle.git
cd ACGAN-Paddle

python trian.py --dataroot [imagenet path] # [eg:xxx/ImageNet/train]

python test.py --check_path [checkpoints path] --which_epoch [epoch]

visuldl --logdir ./log

The pre-training model is available at Baidu Drive( Extraction code: ce8r ) where there are three files in each folder, which are the generator model parameters, the discriminator model parameters and the log corresponding to the group of experiments, please place the pre-training model in checkpoints directory, and set the path of the corresponding folder when testing.

├─checkpoints                     # dir for saving models
├─imgs                            # dir for saving images
├─log                             # logging files
├─results                         # dir for generated images
│  README.md                      # English readme
│  README_cn.md                   # Chinese readme
│  dataset.py                     # Class for dataset
│  network.py                     # Architecture for model
│  train.py                       # Training script
│  test.py                        # Testing script
│  utils.py                       # Tool classes

• train.py parameter description

  Parameters	Default	Description
  --dataroot	str: ‘/media/gallifrey/DJW/Dataset/Imagenet/train’	Path for training set
  --workers	int : 4	Number of threads
  --batchSize	int: 100	Start checkpoint
  --imageSize	int: 128	Size of load / generate images
  --nz	int: 110	Dimension for latent
  --ngf	int: 64	Scale for channels of G
  --ndf	int: 5	Scale for channels of D
  --lr	float: 0.0002	Initial learning rate
  --beta1	float: 0.5	Beta param for optimizer
  --check_path	str: './checkpoints'	Path for chekpoints
  --result_path	str：'./result'	Path for results
  --log_path	str: './log'	Path for log file
  --save_freq	int: 5	Save frequency
  --num_classes	int: 10	Number of clasess
  --niter	int: 500	Number of training epoches

• test.py parameter description

  Parameters	Default	Description
  --batchSize	int: 100	Number of samole for testing one batch
  --nz	int: 110	Dimension of latent
  --check_path	str: './checkpoints'	Path for checkpoints
  --imageSize	int: 128	Size of load / generate images
  --result_path	str：'./result'	Path for results
  --num_classes	int: 10	Number of classes
  --which_epoch	int: 499	IModel ID for testing

Accuracy	D Loss	G Loss

• Fake samples VS Real samples

Fake samples(Ours)	Fake samples(ACGAN-Pytorch)	Real samples

• More results

  Class	Fake samples 1	Fake samples 2	Fake samples 3	Real samples
  Classes 100-110
  Random 10 classes

Additional information about the model can be found in the following table: