Anomaly Detection for Multimodality Dataset

test.py

if __name__ == '__main__':
    aligned_cmumosi_highlevel = mmdatasdk.mmdataset('./data/MOSI/deployed')
    data_keys = mmdatasdk.dataset.standard_datasets.CMU_MOSI.cmu_mosi_std_folds.standard_train_fold + \
                mmdatasdk.dataset.standard_datasets.CMU_MOSI.cmu_mosi_std_folds.standard_valid_fold + \
                mmdatasdk.dataset.standard_datasets.CMU_MOSI.cmu_mosi_std_folds.standard_test_fold
    supported_feature_names = {'covarep':'COVAREP','opensmile':'OpenSmile-emobase2010','facet':'FACET 4.1','glove':'glove_vectors','bert':'BERT embeddings','label':'Opinion Segment Labels'}
    data = {k:{} for k in supported_feature_names}
    id = {k:[] for k in supported_feature_names}
    print('multiple labels keys')
    for k in supported_feature_names:
        exec('{} = aligned_cmumosi_highlevel.computational_sequences[\'{}\'].data'.format(k,supported_feature_names[k]))
        exec('for m in {0}:\n'.format(k) +
            '   tmp_data = {0}[m][\'features\'][:]\n'.format(k) +
            '   tmp_data[tmp_data == -np.inf] = 0\n' +
            '   if(k==\'label\'):\n' +
            '       if(tmp_data.shape[0]>1):\n' +
            '           print(m,k,tmp_data.shape)\n' +
            '       tmp_data = tmp_data.item(0)\n' +
            '   else:\n' +
            '       tmp_data = tmp_data.astype(np.float32)\n' +
            '   if(m[:m.index(\'[\')] in data_keys):\n' +
            '       data[\'{0}\'][m] = tmp_data\n'.format(k) +
            '       id[\'{0}\'].append(m)\n'.format(k))
    print('non-aligned keys')
    for idx in sorted(id['label']):
        pivot = data['covarep'][idx]
        for k in supported_feature_names:
            if(k != 'label' and k != 'bert'):
                if(pivot.shape[0] != data[k][idx].shape[0]):
                    print(idx,'covarep',pivot.shape,k,data[k][idx].shape)