inference.py

from cnn import CNNNetwork
import torch
from sounds import sounds
from sklearn.metrics import accuracy_score, f1_score, confusion_matrix

def predict(model, input, target, classmap):
    model.eval()
    with torch.no_grad():
        predictions = model(input)
        predictedInd = predictions[0].argmax(0)
        predicted = classmap[predictedInd]
        expected = classmap[target]
    return predicted, expected

def _predict(model, input, classmap):
    model.eval()
    with torch.no_grad():
        predictions = model(input)
        predictedInd = predictions[0].argmax(0)
        predicted = classmap[predictedInd]
        #expected = classmap[target]
    return predicted