some_notes_on_testing_model.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Nov 19 17:13:41 2022

@author: wendytsai
"""
#from https://github.com/musikalkemist/pytorchforaudio/tree/main/03%20Making%20predictions


import torch
from train import model, download_mnist_datasets


class_mapping = [
    "0",
    "1",
    "2",   
]


def predict(model, input, target, class_mapping):
    model.eval()
    with torch.no_grad():
        predictions = model(input)
        # Tensor (1, 10) -> [ [0.1, 0.01, ..., 0.6] ]
        predicted_index = predictions[0].argmax(0)
        predicted = class_mapping[predicted_index]
        expected = class_mapping[target]
    return predicted, expected


if __name__ == "__main__":
    # load back the model
    model = model()
    state_dict = torch.load("model.pth")
    model.load_state_dict(state_dict)

    # load MNIST validation dataset
    _, validation_data = download_mnist_datasets()

    # get a sample from the validation dataset for inference
    input, target = validation_data[0][0], validation_data[0][1]

    # make an inference
    predicted, expected = predict(model, input, target,
                                  class_mapping)
    print(f"Predicted: '{predicted}', expected: '{expected}'")



# from https://learn.microsoft.com/en-us/windows/ai/windows-ml/tutorials/pytorch-train-model

import matplotlib.pyplot as plt
import numpy as np

# Function to show the images
def imageshow(img):
    img = img / 2 + 0.5     # unnormalize
    npimg = img.numpy()
    plt.imshow(np.transpose(npimg, (1, 2, 0)))
    plt.show()


# Function to test the model with a batch of images and show the labels predictions
def testBatch():
    # get batch of images from the test DataLoader  
    images, labels = next(iter(test_loader))

    # show all images as one image grid
    imageshow(torchvision.utils.make_grid(images)) 
  
    # Let's see what if the model identifiers the  labels of those example
    outputs = model(images)
    
    # We got the probability for every 10 labels. The highest (max) probability should be correct label
    _, predicted = torch.max(outputs, 1)
    
    # Let's show the predicted labels on the screen to compare with the real ones
    print('Predicted: ', ' '.join('%5s' % classes[predicted[j]] 
                              for j in range(batch_size)))