postprocessing.py

import numpy as np
import torch
from torch import nn
from model_def import NeuralNetwork

labels_map = {
    0 : "atrial fibrillation",
    1 : "sinus arrhythmia",
    2 : "bradycardia",
    3 : "1st degree av block",
    4 : "sinus rhythm",
}

PATH = 'model/' #ResNet-lead-0.pth'

lead_1_model = NeuralNetwork()
lead_1_model.load_state_dict(torch.load(f"{PATH}/ResNet-lead-0.pth", map_location=torch.device('cpu')))

lead_2_model = NeuralNetwork()
lead_2_model.load_state_dict(torch.load(f"{PATH}/ResNet-lead-1.pth", map_location=torch.device('cpu')))

lead_3_model = NeuralNetwork()
lead_3_model.load_state_dict(torch.load(f"{PATH}/ResNet-lead-2.pth", map_location=torch.device('cpu')))

def helper(sig, model):
    inpt = sig[:, np.newaxis, :]
    with torch.no_grad():
        res = model(torch.from_numpy(inpt))
    res = torch.exp(res)
    prediction_scores = res.numpy()
    return prediction_scores

def make_predictions_indi(lead1, lead2, lead3):
    p1 = helper(lead1, lead_1_model)
    p2 = helper(lead2, lead_2_model)
    p3 = helper(lead3, lead_3_model)
    p_avg = (p1 + p2 + p3)/3
    return p_avg