This code performs the following tasks: 1. **Initialize a dictionary to...

This code performs the following tasks:

1. Initialize a dictionary to store one example per class:

   class_examples = {}
   

   This dictionary (class_examples) will store one example (image) for each class in the train_dataset. The keys represent class labels, and the values represent the corresponding images from the dataset.

2. Iterate through the dataset to retrieve one example per class:

   for img, label in train_dataset:
       if label not in class_examples:
           class_examples[label] = img
       if len(class_examples) == len(train_dataset.classes):
           break
   

   The loop iterates over the train_dataset, which contains image-label pairs. For each image (img) and its associated label (label):

   • If the current label is not already in class_examples, it adds the image (img) to the dictionary under its label (label).
   • The loop stops early if the dictionary class_examples contains examples for all classes (the number of keys in the dictionary is equal to the total number of classes in train_dataset).

3. Plot one example image for each class:

   fig, axes = plt.subplots(1, len(class_examples), figsize=(15, 5))
   for idx, (label, img) in enumerate(class_examples.items()):
       axes[idx].imshow(img.permute(1, 2, 0))
       axes[idx].set_title(train_dataset.classes[label])
       axes[idx].axis('off')
   

   • A matplotlib figure is created with subplots arranged in a single row (1) and as many columns as there are classes (len(class_examples)).
   • The loop iterates over each label and its corresponding image in class_examples:
     • The image (img) is plotted on the respective subplot using imshow. The .permute(1, 2, 0) rearranges the image tensor dimensions from PyTorch's default format ([C, H, W], where C is channels, H is height, and W is width) to the format expected by matplotlib ([H, W, C]).
     • The title of each subplot is set to the name of the class using train_dataset.classes[label].
     • The axes around the image are turned off for cleaner visualization.

In summary:

The code extracts one example image for each class from a training dataset (train_dataset) and visualizes these examples in a single row of subplots.