fne 1.6.5

Full Network Embedding implementation in pytorch.

Full Network Embedding

fne is a Python package for computing the Full-Network Embedding (FNE) of a dataset using a pre-trained convolutional neural network (CNN) model, as defined in [1].

Essentially, the FNE consists on three steps:

• Extracting the neural activations of the input using some or all of the network's layers.
• Standarizing and discretizing (feature-wise) the activations.
• Training a simpler classifier (e.g. SVM) with the extracted embeddings.

As of now, this package is implemented using PyTorch. A TensorFlow 2 implementation is planned.

User installation

$ pip install fne

Dependencies

• Python>=3.8
• scikit-learn (>=0.24.2)
• torch (>=1.12.1)
• torchvision (>=0.13.1)
• typing-extensions (>=4.1.1)

Usage example

The FNE functionalities are encapsulated in the fne.fne_torch.FullNetworkEmbedding class, which has fit and predict functions like a scikit-learn classifier.

Additionally, the fne package contains an fne.fne_torch.L2Norm class, which contains the same functions but performs instance-wise L2-regularization instead of the full FNE postprocessing.

Creating a new FNE object

fne = FullNetworkEmbedding(
    torchvision.models.vgg.vgg16(),
    [
        'features.0', 'features.2', 'features.5', 'features.7', 'features.10', 'features.12', 'features.14',
        'features.17', 'features.19', 'features.21', 'features.24', 'features.26', 'features.28',
        'classifier.0', 'classifier.3'
    ],
    sklearn.svm.LinearSVC()
    )

The FullNetworkEmbedding is initialized with the following parameters:

• model: A PyTorch neural network the activations of which will be extracted.
• layers: A list of layer names that will be extracted from model.
• classifier: The classifier that will be fed the activations from model.

Using fit

fne.fit(
    train_data,
    with_crops=False,
    crop_size=None
)

The fit function fits fne.classifier with the activations extracted from train_data. It has the following parameters:

• train_data: A PyTorch DataLoader with the training images to extract activations of.
• with_crops: If True, computes 10 crops (i.e. four corners and center crops, plus horizontal mirroring) for each image in train_data instead of using the original one.
• crop_size: A tuple (w, h) with the final width and height that the 10 crops will have. Only needed if with_crops==True.

Using predict

predictions = fne.predict(
    test_data,
    with_crops=False,
    crop_size=None
)

The predict function feeds the previously fitted fne.classifier with the extracted activations of test_data. It has the same parameters as fit.

If with_crops==True, it returns an array of arrays of shape (n, 10), in which each element contains the predictions for each of the 10 generated crops per image. Otherwise, it returns an array of predictions.

Citation

If you use the FNE method in a scientific publication, please cite:

@INPROCEEDINGS{8588789,
author={D. Garcia-Gasulla and A. Vilalta and F. Parés and E. Ayguadé and J. Labarta and U. Cortés and T. Suzumura},
booktitle={2018 IEEE International Conference on Big Knowledge (ICBK)},
title={An Out-of-the-box Full-Network Embedding for Convolutional Neural Networks},
year={2018},
pages={168-175},
keywords={Feature extraction;Training;Computational modeling;Task analysis;Space exploration;Tuning;Transfer Learning, Feature Extraction, Embedding Spaces},
doi={10.1109/ICBK.2018.00030},
month={Nov},}

How to release a new version

Change the version in pyproject.toml following the next standard:

version = "MAJOR.MINOR.PATCH"

• MAJOR version when you make incompatible API changes
• MINOR version when you add functionality in a backwards-compatible manner
• PATCH version when you make backwards-compatible bug fixes.

git tag MAJOR.MINOR.PATCH
git push origin MAJOR.MINOR.PATCH

License

GNU General Public License v3.0

References

[1] D. Garcia-Gasulla et al., "An Out-of-the-box Full-Network Embedding for Convolutional Neural Networks," 2018 IEEE International Conference on Big Knowledge (ICBK), Singapore, 2018, pp. 168-175. doi: 10.1109/ICBK.2018.00030

[2] Vilalta, Armand, et al. "Studying the impact of the Full-Network embedding on multimodal pipelines." Semantic Web Preprint: 1-15.