postprocessing.py

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

labels_map = {
    0: 'sinus_rhythm',
    1: 'atrial_fibrillation',
    2: 'av_block',
    3: 'bradycardia',
    4: 'sinus_arrhythmia',
    5: 'sinus_rhythm-sinus_arrhythmia',
    6: 'sinus_rhythm-av_block'
}

PATH = 'model/seven-diseases/' #ResNet-lead-0.pth'
lead_1 = f"{PATH}/ResNet-lead-0-BEST.pth"
lead_2 = f"{PATH}/ResNet-lead-1-BEST.pth"
lead_3 = f"{PATH}/ResNet-lead-2-BEST.pth"

# PATH = 'model/' #ResNet-lead-0.pth'
# lead_1 = f"{PATH}/ResNet-lead-0.pth"
# lead_2 = f"{PATH}/ResNet-lead-1.pth"
# lead_3 = f"{PATH}/ResNet-lead-2.pth"

lead_1_model = NeuralNetwork()
lead_1_model.load_state_dict(torch.load(lead_1, map_location=torch.device('cpu')))

lead_2_model = NeuralNetwork()
lead_2_model.load_state_dict(torch.load(lead_2, map_location=torch.device('cpu')))

lead_3_model = NeuralNetwork()
lead_3_model.load_state_dict(torch.load(lead_3, 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 predict_disease(lead1, lead2, lead3):
    p1 = helper(lead1, lead_1_model)
    p2 = helper(lead2, lead_2_model)
    p3 = helper(lead3, lead_3_model)
    # print(p1.argmax(axis=1))
    # print(p2.argmax(axis=1))
    # print(p3.argmax(axis=1))
    p_avg = (p1 + p2 + p3)/3
    # print(p_avg.argmax(axis=1))
    # print("-----------------")
    return p_avg, p1, p2, p3