use plt.imshow rather than cv2_imshow (better compat. with jupyter)

episodes/5d-gradcam.md

@@ -24,10 +24,10 @@ exercises: 0
 import requests
 
 # Packages to view and process images
+import matplotlib.pyplot as plt
 import cv2
 import numpy as np
 from PIL import Image
-from google.colab.patches import cv2_imshow
 
 # Packages to load the model
 import torch
@@ -39,7 +39,7 @@ from pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget
 from pytorch_grad_cam.utils.image import show_cam_on_image, preprocess_image
 ```
 ```python
-device = 'gpu' if torch.cuda.is_available() else 'cpu'
+device = 'cpu' # we're using the CPU only version of this workshop 
 ```
 ##### Load Model
 
@@ -112,22 +112,23 @@ visualized_class_id = 245
 ```python
 def viz_gradcam(model, target_layers, class_id):
 
-  if class_id is None:
-    targets = None
-  else:
-    targets = [ClassifierOutputTarget(class_id)]
+    if class_id is None:
+        targets = None
+    else:
+        targets = [ClassifierOutputTarget(class_id)]
 
-  cam_algorithm = GradCAM
-  with cam_algorithm(model=model, target_layers=target_layers) as cam:
-      grayscale_cam = cam(input_tensor=input_tensor,
-                          targets=targets)
+    cam_algorithm = GradCAM
+    with cam_algorithm(model=model, target_layers=target_layers) as cam:
+        grayscale_cam = cam(input_tensor=input_tensor, targets=targets)
 
-      grayscale_cam = grayscale_cam[0, :]
+        grayscale_cam = grayscale_cam[0, :]
 
-      cam_image = show_cam_on_image(rgb_image, grayscale_cam, use_rgb=True)
-      cam_image = cv2.cvtColor(cam_image, cv2.COLOR_RGB2BGR)
+        cam_image = show_cam_on_image(rgb_image, grayscale_cam, use_rgb=True)
+        cam_image = cv2.cvtColor(cam_image, cv2.COLOR_RGB2BGR)
 
-  cv2_imshow(cam_image)
+    plt.imshow(cam_image)
+    plt.axis("off")
+    plt.show()
 ```
 Finally, we can start visualizing! Let's begin by seeing what parts of the image the model looks at to make its most confident prediction.
 ```python
@@ -162,4 +163,4 @@ viz_gradcam(model=model, target_layers=target_layers, class_id=537)
 ```
 Explaining model predictions though visualization techniques like this can be very subjective and prone to error. However, this still provides some degree of insight a completely black box model would not provide.
 
-Spend some time playing around with different classes and seeing which part of the image the model looks at. Feel free to play around with other base images as well. Have fun!
+Spend some time playing around with different classes and seeing which part of the image the model looks at. Feel free to play around with other base images as well. Have fun!