funcs/ml_inference.py

import torch
from funcs.dataloader import BaseDataset2, read_json_files

def get_som_mp4(file, reducer10d, cluster_som, slice_select):

    try:
        train_x, train_y  = read_json_files(file)
    except:
        train_x, train_y  = read_json_files(file.name)

    # Convert tensors to numpy arrays if necessary
    if isinstance(train_x, torch.Tensor):
        train_x = train_x.numpy()
    if isinstance(train_y, torch.Tensor):
        train_y = train_y.numpy()

    # load the time series slices of the data 4*3*2*64 (feeds+axis*sensor*samples) + 5 for time diff
    data = BaseDataset2(train_x.reshape(len(train_x), -1) / 32768, train_y)

    #compute the 10 dimensional embeding vector
    embedding10d = reducer10d.transform(data)

    # prediction = cluster_som.predict(embedding10d)
    fig = cluster_som.plot_activation_v2(embedding10d, slice_select)

    return fig