Create some_notes_on_testing_model.py

some_notes_on_testing_model.py

@@ -0,0 +1,81 @@
+#!/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)))