UGATIT

The UGATIT project is a PyTorch implementation of the Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization (UGATIT) model, which is a state-of-the-art image-to-image translation framework. It allows for high-quality and diverse image-to-image translation tasks, such as converting sketches to realistic images or transforming day-to-night scenes.

Pros

• High-Quality Translations: The UGATIT model produces high-quality and realistic image translations, outperforming many existing image-to-image translation methods.
• Diverse Outputs: The model can generate diverse and varied output images, capturing the multi-modal nature of the translation task.
• Unsupervised Learning: The UGATIT framework is an unsupervised learning approach, which means it can be trained without the need for paired training data.
• Adaptive Layer-Instance Normalization: The model's use of Adaptive Layer-Instance Normalization (AdaLIN) allows for better preservation of local and global features during the translation process.

Cons

• Computational Complexity: The UGATIT model can be computationally expensive, especially for high-resolution image translations, which may limit its real-time applications.
• Hyperparameter Tuning: The model's performance can be sensitive to the choice of hyperparameters, which may require extensive experimentation and tuning.
• Limited Datasets: The model's performance may be limited by the availability of suitable datasets for specific image-to-image translation tasks.
• Potential Bias: Like many deep learning models, the UGATIT framework may learn and perpetuate biases present in the training data.

Code Examples

Since this is a code library, here are a few short code examples to demonstrate its usage:

import torch
from ugatit import UGATIT

# Initialize the UGATIT model
model = UGATIT(lr=0.0001, ch=64, n_res=4, n_dis=6, img_size=256)

# Load pre-trained weights
model.load_state_dict(torch.load('pretrained_model.pth'))

# Perform image-to-image translation
source_image = torch.randn(1, 3, 256, 256)
translated_image = model.test(source_image)

This code snippet demonstrates how to initialize the UGATIT model, load pre-trained weights, and perform image-to-image translation on a random input image.

# Train the UGATIT model
train_loader = # Load training data
model.train(train_loader, num_epochs=100)

This code snippet shows how to train the UGATIT model using a PyTorch data loader and training for 100 epochs.

# Evaluate the UGATIT model
val_loader = # Load validation data
metrics = model.evaluate(val_loader)
print(f'Validation Metrics: {metrics}')

This code snippet demonstrates how to evaluate the UGATIT model on a validation dataset and print the resulting metrics.

Getting Started

To get started with the UGATIT project, follow these steps:

1. Clone the repository:

   git clone https://github.com/taki0112/UGATIT.git
   

2. Install the required dependencies:

   cd UGATIT
   pip install -r requirements.txt
   

3. Download the pre-trained model weights:

   wget https://drive.google.com/uc?id=1rSoeq6RwMj8C4FqmDXvmh_4RvuL1OSXE -O pretrained_model.pth
   

4. Run the inference script to translate an image:

   python inference.py --img_path path/to/your/image.jpg --result_dir output_dir
   

5. (Optional) Train the UGATIT model on your own dataset:

   python main.py --dataset_name your_dataset --img_size 256 --ch 64 --n_res 4 --n_dis 6 --iteration 200