This repository contains an implementation of the TracIn method using PyTorch, as described in the paper "TracIn: A Simple Method for Assessing the Influence of Training Data on Models".
TracIn (Tracking Influential Examples) is a method that quantifies the influence of training data points on a model's predictions. This implementation extends the original code to support batch processing, allowing you to calculate the influence of several samples simultaneously, which improves efficiency and scalability.
- Batch Processing: Calculate influences for multiple samples in parallel.
- Easy Integration: Simple to integrate with existing PyTorch models and datasets.
- Example Included: An example using the MNIST dataset is provided for quick setup and testing.
To install the necessary dependencies, run:
pip install -r requirements.txt
An example of how to use this implementation can be found in main.py. The example demonstrates how to calculate the influence of training samples on the predictions of a model trained on the MNIST dataset.
Here's a brief overview of how you can use TracIn in your project:
from src.tracin import vectorized_calculate_tracin_score
model = YourModel()
criterion = YourCriterion()
weights = YourListOfWeights # currently glob.glob(os.path.join("supplementary", "weights", "*"))
train_loader = YourTrainLoader()
test_loader = YourTestLoader()
lr = YourLearningRateScale
device = YourDevice
use_nested_loop_for_dot_product = False # via einsum
float_labels = False # depends on your loss function
matrix = vectorized_calculate_tracin_score(
model=model,
criterion=criterion,
weights_paths=weights,
train_dataloader=train_loader,
test_dataloader=test_loader,
lr=lr,
device=device,
use_nested_loop_for_dot_product=use_nested_loop_for_dot_product,
float_labels=float_labels,
)For more detailed information, please refer to the src/tracin.py file.
├── README.md
├── main.py # Example script demonstrating the use of TracIn with the MNIST dataset.
├── requirements.txt # List of dependencies required to run the implementation.
├── src
│ ├── net.py # Your architecture
│ └── tracin.py # Core implementation of the TracIn method.
└── supplementary
└── weights # Your models' weights.
main.py: Example script demonstrating the use of TracIn with the MNIST dataset.src/tracin.py: Core implementation of the TracIn method.requirements.txt: List of dependencies required to run the implementation.
Contributions are welcome! Please feel free to submit a pull request or open an issue if you have any suggestions or bug reports.