https://github.com/zetane/torch-xai

ZetaForge XAI Torch Pipeline
https://github.com/zetane/torch-xai

Last synced: 41 minutes ago
JSON representation

ZetaForge XAI Torch Pipeline

• Host: GitHub
• URL: https://github.com/zetane/torch-xai
• Owner: zetane
• License: agpl-3.0
• Created: 2024-07-11T22:04:21.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2024-07-12T14:42:00.000Z (6 months ago)
• Last Synced: 2024-11-10T14:08:26.503Z (about 2 months ago)
• Language: Python
• Size: 58.6 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# torch-xai

## ZetaForge XAI Torch Pipeline

![image (32)](https://github.com/user-attachments/assets/34127c39-5e85-4d90-8c0f-99a34e119345)

Block input details:

- test_dataset_file : zip file contaning all the images and xlsx dataframe, where image column name must be "filename".

- model_process_file: pickle file containing model and the pre-process code, see the code below for how to save the model and processor.

- model_architecture_type: three options we have "cnn", "ViT", "SwiT"

- target_layer: name of the target layer on which xai will be performed.

- saving_dir: name of the save dir.

Here is the demo script for how to save model and processor.

```

import torch

import torchvision.models as models

import torchvision.transforms as transforms

import pickle

# Load the ResNet18 model

model = models.resnet50(pretrained=True)

model.eval()

# Create the transform

transform = transforms.Compose([

    transforms.Resize(256),

    transforms.CenterCrop(224),

    transforms.ToTensor(),

    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),

])

# Create a dictionary with the model and transform

data = {

    'model': model,

    'processor': transform

}

# Save the dictionary to a pickle file

with open('resnet18_model_and_transform.pkl', 'wb') as f:

    pickle.dump(data, f)

```