PyTorchLayerViz 1.2.1

PyTorchLayerViz is a Python library that allows you to visualize the weights and feature maps of a PyTorch model.

PyTorchLayerViz

PyTorchLayerViz is a Python library designed to assist developers and researchers in visualizing the weights and feature maps of PyTorch models. This tool provides easy-to-use functions to help understand and interpret deep learning models, making it an essential utility for anyone working with PyTorch.

Table of Contents

• PyTorchLayerViz
  • Table of Contents
  • Installation
  • Usage
    • Parameters
  • Features
  • Examples
    • Example Picture
    • Code
    • Output
  • Contributing
  • License
  • Contact

Installation

To install PyTorchLayerViz, you can use pip:

pip install pytorchlayerviz

Usage

Here is a basic example of how to use PyTorchLayerViz:

from PyTorchLayerViz import get_feature_maps
import matplotlib.pyplot as plt
from PIL import Image
import torch
from torch import nn
from torchvision import datasets, transforms, models
from torchvision.transforms import ToTensor

# Define your model
model = torch.nn.Sequential(
    torch.nn.Conv2d(3, 20, 5),
    torch.nn.ReLU(),
    torch.nn.Conv2d(20, 64, 5),
    torch.nn.ReLU()
)

layers_to_check = [nn.Conv2d] # Define all Layers you want to pass your picture

input_image_path = 'pictures/hamburger.jpg' # Path to your example picture

numpyArr = get_feature_maps(model = model, layers_to_check = layers_to_check, input_image_path = input_image_path) # Call function from pytorchlayerviz

Parameters

• model (nn.Module) – The PyTorch model whose layers' feature maps you want to visualize. Required.
• layers_to_check (arr of nn.Module) – List of layer types (e.g., nn.Conv2d) to check for feature maps. Required.
• input_image_path (str) – Path to the input image file. Required.
• transform (transforms.Compose, optional) – A function/transform that takes in an image and returns a transformed version. Default is None. Optional.
• sequential_order (bool, optional) – If True, the layers are visualized in the order they are defined in the model. If false it will first go through the first layer defined in the arrDefault is True. Optional.

Return The function 'get_feature_maps()` returns the pictures as NumPy Arrays

If transform is none, this will be used:

    transform = transforms.Compose([
    transforms.Resize((224, 224)),  # Resize the image to 224x224 pixels
    transforms.ToTensor(),  # Convert the image to a PyTorch tensor
    ])

If you want to pass your own transform, make sure you resize the image and convert it to a tensor with transforms.ToTensor()

Features

• Visualize Weights: Easily visualize the weights of each layer in your PyTorch model.
• Visualize Feature Maps: Generate and visualize feature maps for given inputs.
• Customizable: Flexible options for customizing visualizations.

Examples

Example Picture

Code

pretrained_model = models.vgg16(pretrained=True)
input_image_path = 'hamburger.jpg'
layers_to_check= [nn.MaxPool2d]

numpyArr = get_feature_maps(model = pretrained_model, layers_to_check = layers_to_check, input_image_path = input_image_path, sequential_order = False)

Output

Contributing

I welcome contributions to PyTorchLayerViz! If you'd like to contribute, please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Make your changes.
4. Commit your changes (git commit -m 'Add new feature').
5. Push to the branch (git push origin feature-branch).
6. Open a pull request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact

For any questions, suggestions, or issues, please open an issue on GitHub or contact me.

• Simone Panico: [e-mail]
• Github Issues: https://github.com/simone-panico/PyTorchLayerViz/issues