fast-torch 1.2.2

Library that implements boiler plate code in PyTorch for training, testing and plotting your model

FastTorch

Library that implements the training and test loops for your Deep Learning model. After your traning, you can also plot the training results, plot some predictions that your model has made on your test set and also a confusion matrix on the predictions made on your test set.

This Library also allow you to plot some usefull informations about your dataset.

/!\ Note: Currently only supports classification tasks, it'll support other types of task in the future...

Installation

run the following command then you are ready to go !

pip install fast_torch

Usage

Take a look at the complete Documentation of this framework.

A complete example is available on the Notebook.

Plot stats about your datasets

# Import the plotting module of the library
import fast_torch.plotter as ftplot

# Will plot some random images of the dataloader that you've passed in the parameter
ftplot.plot_images(train_dataloader)

# Will plot the class distributions of your train, val, test datasets
ftplot.plot_classes_distributions(train_dataloader, test_dataloader, val_dataloader)

Train and test your model

# Import the model_wrapper module
import fast_torch.model_wrapper as mw


# Initialize your model
model = Model()

# Initialize the options
training_opts = {
    "epochs": 5,
    "criterion": nn.CrossEntropyLoss(),
    "optimizer": optim.SGD(model.parameters(), lr=0.01),
    "early_stopping_patience": 2
}

# Instantiate the classifier wrapper 
clf = mw.Classifier(model, training_opts, train_loader, test_loader, val_loader, device="cuda")

# Train your model
clf.train()
# Test your model
clf.test()

# Plot the training stats (Training loss, Vaildation loss + accuracy)
clf.plot_training_stats()
# Plot random prediction made by your trained model
clf.plot_random_predictions()
# Plot the confusion matrix
clf.plot_confusion_matrix()

TODOS

• Add a dataset creator for images (with the splits etc..)
• Clean the code
• Make the confusion matrix figsize flexible
• Add Learning rate decay to the 'Classifier'
• Add other model wrappers
• Make the plot functions more flexible (working with other type of dataset, not only dataloader)