src/main/python/tests/scuro/test_unimodal_representations.py

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import os
import shutil
import unittest
import copy
import numpy as np

from systemds.scuro.representations.bow import BoW
from systemds.scuro.representations.covarep_audio_features import (
    Spectral,
    RMSE,
    Pitch,
    ZeroCrossing,
)
from systemds.scuro.representations.wav2vec import Wav2Vec
from systemds.scuro.representations.spectrogram import Spectrogram
from systemds.scuro.representations.word2vec import W2V
from systemds.scuro.representations.tfidf import TfIdf
from systemds.scuro.modality.unimodal_modality import UnimodalModality
from systemds.scuro.representations.bert import Bert
from systemds.scuro.representations.mel_spectrogram import MelSpectrogram
from systemds.scuro.representations.mfcc import MFCC
from systemds.scuro.representations.resnet import ResNet
from systemds.scuro.representations.swin_video_transformer import SwinVideoTransformer
from tests.scuro.data_generator import setup_data
from tests.scuro.data_generator import (
    setup_data,
    TestDataLoader,
    ModalityRandomDataGenerator,
)
from systemds.scuro.dataloader.audio_loader import AudioLoader
from systemds.scuro.dataloader.video_loader import VideoLoader
from systemds.scuro.dataloader.text_loader import TextLoader
from systemds.scuro.modality.type import ModalityType


class TestUnimodalRepresentations(unittest.TestCase):
    test_file_path = None
    mods = None
    text = None
    audio = None
    video = None
    data_generator = None
    num_instances = 0

    @classmethod
    def setUpClass(cls):
        cls.num_instances = 4
        cls.indices = np.array(range(cls.num_instances))

    def test_audio_representations(self):
        audio_representations = [
            MFCC(),
            MelSpectrogram(),
            Spectrogram(),
            Wav2Vec(),
            Spectral(),
            ZeroCrossing(),
            RMSE(),
            Pitch(),
        ]  # TODO: add FFT, TFN, 1DCNN
        audio_data, audio_md = ModalityRandomDataGenerator().create_audio_data(
            self.num_instances, 1000
        )

        audio = UnimodalModality(
            TestDataLoader(
                self.indices, None, ModalityType.AUDIO, audio_data, np.float32, audio_md
            )
        )

        audio.extract_raw_data()
        original_data = copy.deepcopy(audio.data)

        for representation in audio_representations:
            r = audio.apply_representation(representation)
            assert r.data is not None
            assert len(r.data) == self.num_instances
            for i in range(self.num_instances):
                assert (audio.data[i] == original_data[i]).all()
            assert r.data[0].ndim == 2

    def test_video_representations(self):
        video_representations = [
            ResNet(),
            SwinVideoTransformer(),
        ]  # Todo: add other video representations
        video_data, video_md = ModalityRandomDataGenerator().create_visual_modality(
            self.num_instances, 60
        )
        video = UnimodalModality(
            TestDataLoader(
                self.indices, None, ModalityType.VIDEO, video_data, np.float32, video_md
            )
        )
        for representation in video_representations:
            r = video.apply_representation(representation)
            assert r.data is not None
            assert len(r.data) == self.num_instances
            assert r.data[0].ndim == 2

    def test_text_representations(self):
        test_representations = [BoW(2, 2), W2V(5, 2, 2), TfIdf(2), Bert()]
        text_data, text_md = ModalityRandomDataGenerator().create_text_data(
            self.num_instances
        )
        text = UnimodalModality(
            TestDataLoader(
                self.indices, None, ModalityType.TEXT, text_data, str, text_md
            )
        )
        for representation in test_representations:
            r = text.apply_representation(representation)
            assert r.data is not None
            assert len(r.data) == self.num_instances

    def test_chunked_video_representations(self):
        video_representations = [ResNet()]
        video_data, video_md = ModalityRandomDataGenerator().create_visual_modality(
            self.num_instances, 60
        )
        video = UnimodalModality(
            TestDataLoader(
                self.indices, None, ModalityType.VIDEO, video_data, np.float32, video_md
            )
        )
        for representation in video_representations:
            r = video.apply_representation(representation)
            assert r.data is not None
            assert len(r.data) == self.num_instances
            assert len(r.metadata) == self.num_instances


if __name__ == "__main__":
    unittest.main()