PyTorchLayerViz 1.2.5

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
  • Advanced Usage
  • 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, print_image=True) # 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.
• print_image (bool, optional) – If True the Images are getting printed with matplotlib. Default is False. 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.

Advanced Usage

### Extracting Feature Maps with Layer Names

The get_feature_maps function now returns a list of tuples, each containing the feature map and its corresponding layer name. This enhancement allows you to identify which layer each feature map comes from.

feature_maps = get_feature_maps(
    model=model,
    layers_to_check=[nn.Conv2d, nn.MaxPool2d],
    input_image_path='path_to_image.jpg',
    print_image=False
)

for image_array, layer_name in feature_maps:
    print(f"Layer: {layer_name}")
    # Process or display the image as needed

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, print_image = True)

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