torch inference mode and html changes

solution.py

@@ -849,20 +849,20 @@ def copy_parameters(source_model, target_model):
 predictions = []
 source_labels = []
 target_labels = []
-
-for i, (x, y) in tqdm(enumerate(random_test_mnist), total=num_images):
-    for lbl in range(4):
-        # TODO Create the counterfactual
-        x_fake = generator(x.unsqueeze(0).to(device), ...)
-        # TODO Predict the class of the counterfactual image
-        pred = model(...)
-
-        # TODO Store the source and target labels
-        source_labels.append(...)  # The original label of the image
-        target_labels.append(...)  # The desired label of the counterfactual image
-        # Store the counterfactual image and prediction
-        counterfactuals[lbl][i] = x_fake.cpu().detach().numpy()
-        predictions.append(pred.argmax().item())
+with torch.inference_mode():
+    for i, (x, y) in tqdm(enumerate(random_test_mnist), total=num_images):
+        for lbl in range(4):
+            # TODO Create the counterfactual
+            x_fake = generator(x.unsqueeze(0).to(device), ...)
+            # TODO Predict the class of the counterfactual image
+            pred = model(...)
+
+            # TODO Store the source and target labels
+            source_labels.append(...)  # The original label of the image
+            target_labels.append(...)  # The desired label of the counterfactual image
+            # Store the counterfactual image and prediction
+            counterfactuals[lbl][i] = x_fake.cpu().detach().numpy()
+            predictions.append(pred.argmax().item())
 # %% tags=["solution"]
 num_images = 1000
 random_test_mnist = torch.utils.data.Subset(
@@ -873,22 +873,22 @@ def copy_parameters(source_model, target_model):
 predictions = []
 source_labels = []
 target_labels = []
-
-for i, (x, y) in tqdm(enumerate(random_test_mnist), total=num_images):
-    for lbl in range(4):
-        # Create the counterfactual
-        x_fake = generator(
-            x.unsqueeze(0).to(device), prototypes[lbl].unsqueeze(0).to(device)
-        )
-        # Predict the class of the counterfactual image
-        pred = model(x_fake)
-
-        # Store the source and target labels
-        source_labels.append(y)  # The original label of the image
-        target_labels.append(lbl)  # The desired label of the counterfactual image
-        # Store the counterfactual image and prediction
-        counterfactuals[lbl][i] = x_fake.cpu().detach().numpy()
-        predictions.append(pred.argmax().item())
+with torch.inference_mode():
+    for i, (x, y) in tqdm(enumerate(random_test_mnist), total=num_images):
+        for lbl in range(4):
+            # Create the counterfactual
+            x_fake = generator(
+                x.unsqueeze(0).to(device), prototypes[lbl].unsqueeze(0).to(device)
+            )
+            # Predict the class of the counterfactual image
+            pred = model(x_fake)
+
+            # Store the source and target labels
+            source_labels.append(y)  # The original label of the image
+            target_labels.append(lbl)  # The desired label of the counterfactual image
+            # Store the counterfactual image and prediction
+            counterfactuals[lbl][i] = x_fake.cpu().detach().numpy()
+            predictions.append(pred.argmax().item())
 
 # %% [markdown] tags=[]
 # Let's plot the confusion matrix for the counterfactual images.
@@ -1161,7 +1161,6 @@ def visualize_color_attribution_and_counterfactual(
 plt.legend()
 plt.show()
 
-# %%
 # %% [markdown]
 # <div class="alert alert-block alert-warning"><h3>Questions</h3>
 # <ul>
@@ -1187,15 +1186,16 @@ def visualize_color_attribution_and_counterfactual(
 # If you have extra time, you can try to break the StarGAN!
 # There are a lot of little things that we did to make sure that it runs correctly - but what if we didn't?
 # Some things you might want to try:
-# - What happens if you don't use the EMA model?
-# - What happens if you change the learning rates?
-# - What happens if you add a Sigmoid activation to the output of the style encoder?
+# <li> What happens if you don't use the EMA model? </li>
+# <li> What happens if you change the learning rates? </li>
+# <li> What happens if you add a Sigmoid activation to the output of the style encoder? </li>
 # See what else you can think of, and see how finnicky training a GAN can be!
 
 ## %% [markdown] tags=["solution"]
 # The colors for the classes are sampled from matplotlib colormaps! They are the four seasons: spring, summer, autumn, and winter.
 # Check your style space again to see if you can see the patterns now!
-# %% tags=["solution"]
+
+## %% tags=["solution"]
 # Let's plot the colormaps
 import matplotlib as mpl
 import numpy as np