mmkit-features 0.0.1a0

A Python library to extract, store and fuse multimodal features for deep learning

MMKit-Features: Multimodal Features Extraction Toolkit

A light-weight Python library to extract, fuse and store multimodal features for deep learning.

Objectives

• To extract, store and fuse various features from multimodal datasets rapidly and efficiently;
• To provide a common multimodal information processing framework for multimodal features;
• To achieve generative adversarial network (GAN)-based multimodal knowledge representation dynamically.

Framework

Modalities

1. Text/Language modality
2. Image modality
3. Video modality
4. Speech/sound modality
5. Cross-modality between above

Usage

A toy example showing how to build a multimodal feature (MMF) library is here:

from mmkfeatures.fusion.mm_features_lib import MMFeaturesLib
from mmkfeatures.fusion.mm_features_node import MMFeaturesNode
import numpy as np
if __name__ == "__main__":
    # 1. create an empty multimodal features library with root and dataset names
    feature_lib=MMFeaturesLib(root_name="test features",dataset_name="test_features")
    # 2. set short names for each dimension for convenience
    feature_lib.set_features_name(["feature1","feature2","feature3"])
    # 3. set a  list of content IDs
    content_ids=["content1","content2","content3"]
    # 4. according to IDs, assign a group of features with interval to corresponding content ID
    features_dict={}
    for id in content_ids:
        mmf_node=MMFeaturesNode(id)
        mmf_node.set_item("name",str(id))
        mmf_node.set_item("features",np.array([[1,2,3]]))
        mmf_node.set_item("intervals",np.array([[0,1]]))
        features_dict[id]=mmf_node
    # 5. set the library's data
    feature_lib.set_data(features_dict)
    # 6. save the features to disk for future use
    feature_lib.save_data("test6_feature.csd")
    # 7. check structure of lib file with the format of h5py
    feature_lib.show_structure("test6_feature.csd")
    # 8. have a glance of features content within the dataset
    feature_lib.show_sample_data("test6_feature.csd")

Further instructions on the toolkit refers to here.

Credits

The project includes some source codes from various open-source contributors. Here is a list of their contributions.

1. A2Zadeh/CMU-MultimodalSDK
2. aishoot/Speech_Feature_Extraction
3. antoine77340/video_feature_extractor
4. jgoodman8/py-image-features-extractor
5. v-iashin/Video Features

License

Please cite our project if the project is used in your research.

Chen, D. (2022). MMKit-Features: Multimodal Features Extraction Toolkit (Version 0.0.1) [Computer software]