From c1a6e28b5ebd13e9169f02bfdd6106391107efc2 Mon Sep 17 00:00:00 2001
From: Diane Adjavon <adjavond@hhmi.org>
Date: Thu, 15 Aug 2024 10:47:03 -0400
Subject: [PATCH] Fix numbering, missing todos, and plotting bug

---
 solution.py | 36 +++++++++++++++---------------------
 1 file changed, 15 insertions(+), 21 deletions(-)

diff --git a/solution.py b/solution.py
index 1e3bc59..318c44e 100644
--- a/solution.py
+++ b/solution.py
@@ -469,7 +469,7 @@ def forward(self, x, y):
 # We will have two different optimizers, one for the Generator and one for the Discriminator.
 #
 # %%
-optimizer_d = torch.optim.Adam(discriminator.parameters(), lr=1e-4)
+optimizer_d = torch.optim.Adam(discriminator.parameters(), lr=1e-5)
 optimizer_g = torch.optim.Adam(generator.parameters(), lr=1e-4)
 # %% [markdown] tags=[]
 #
@@ -545,7 +545,7 @@ def copy_parameters(source_model, target_model):
 generator_ema = generator_ema.to(device)
 
 # %% [markdown] tags=[]
-# <div class="alert alert-banner alert-info"><h4>Task 3.2: Training!</h4>
+# <div class="alert alert-banner alert-info"><h4>Task 3.3: Training!</h4>
 # You were given several different options in the training code below. In each case, one of the options will work, and the other will not.
 # Comment out the option that you think will not work.
 # <ul>
@@ -760,7 +760,7 @@ def copy_parameters(source_model, target_model):
 # </div>
 
 # %% [markdown] tags=[]
-# # Part 4: Evaluating the GAN
+# # Part 4: Evaluating the GAN and creating Counterfactuals
 
 # %% [markdown] tags=[]
 # ## Creating counterfactuals
@@ -777,7 +777,7 @@ def copy_parameters(source_model, target_model):
 
 
 for i in range(4):
-    options = np.where(test_mnist.targets == i)[0]
+    options = np.where(test_mnist.conditions == i)[0]
     # Note that you can change the image index if you want to use a different prototype.
     image_index = 0
     x, y = test_mnist[options[image_index]]
@@ -795,7 +795,7 @@ def copy_parameters(source_model, target_model):
 # %% [markdown]
 # Now we need to use these prototypes to create counterfactual images!
 # %% [markdown]
-# <div class="alert alert-block alert-info"><h3>Task 4.1: Create counterfactuals</h3>
+# <div class="alert alert-block alert-info"><h3>Task 4: Create counterfactuals</h3>
 # In the below, we will store the counterfactual images in the `counterfactuals` array.
 #
 # <ul>
@@ -887,9 +887,6 @@ def copy_parameters(source_model, target_model):
 # </ul>
 # </div>
 
-# %% [markdown]
-# # Part 5: Highlighting Class-Relevant Differences
-
 # %% [markdown]
 # At this point we have:
 # - A classifier that can differentiate between image of different classes
@@ -954,7 +951,7 @@ def visualize_color_attribution_and_counterfactual(
 # - Used the counterfactual images to highlight the differences between classes
 #
 # %% [markdown]
-# # Part 6: Exploring the Style Space, finding the answer
+# # Part 5: Exploring the Style Space, finding the answer
 # By now you will have hopefully noticed that it isn't the exact color of the image that determines its class, but that two images with a very similar color can be of different classes!
 #
 # Here is an example of two images that are very similar in color, but are of different classes.
@@ -1002,15 +999,17 @@ def visualize_color_attribution_and_counterfactual(
 plt.show()
 
 # %% [markdown]
-# <div class="alert alert-block alert-info"><h3>Task 6.2: Adding color to the style space</h3>
+# <div class="alert alert-block alert-info"><h3>Task 5.1: Adding color to the style space</h3>
 # We know that color is important. Does interpreting the style space as colors help us understand better?
 #
 # Let's use the style space to color the PCA plot.
 # (Note: there is no code to write here, just run the cell and answer the questions below)
 # </div>
-# TODO WIP HERE
 # %%
-normalized_styles = (styles - np.min(styles, axis=1)) / styles.ptp(axis=1)
+styles = np.array(styles)
+normalized_styles = (styles - np.min(styles, axis=1, keepdims=True)) / np.ptp(
+    styles, axis=1, keepdims=True
+)
 
 # Plot the PCA again!
 plt.figure(figsize=(10, 10))
@@ -1027,27 +1026,22 @@ def visualize_color_attribution_and_counterfactual(
 # <li> Can you see any patterns in the colors? Is the space smooth, for example?</li>
 # </ul>
 # %% [markdown]
-# <div class="alert alert-block alert-info"><h3>Using the images to color the style space</h3>
+# <div class="alert alert-block alert-info"><h3>Task 5.2: Using the images to color the style space</h3>
 # Finally, let's just use the colors from the images themselves!
-# All of the non-zero values in the image can be averaged to get a color.
+# The maximum value in the image (since they are "black-and-color") can be used as a color!
 #
 # Let's get that color, then plot the style space again.
 # (Note: once again, no coding needed here, just run the cell and think about the results with the questions below)
 # </div>
 # %% tags=["solution"]
-tol = 1e-6
-
-colors = []
-for x, y in random_test_mnist:
-    non_zero = x[x > tol]
-    colors.append(non_zero.mean(dim=(1, 2)).cpu().numpy().squeeze())
+colors = [np.max(x.numpy(), axis=(1, 2)) for x, _ in random_test_mnist]
 
 # Plot the PCA again!
 plt.figure(figsize=(10, 10))
 plt.scatter(
     styles_pca[:, 0],
     styles_pca[:, 1],
-    c=normalized_styles,
+    c=colors,
 )
 plt.show()