CocoCaptions in PyTorch (3)

This post demonstrates using the CocoCaptions class from the torchvision.datasets library with various MS COCO datasets. The examples illustrate loading and displaying images from the unlabeled2017 subset. However, attempts to access data from stuff_train2017, stuff_val2017, stuff_train2017_pixelmaps, and stuff_val2017_pixelmaps resulted in errors, indicating incompatibility with CocoCaptions. The code snippets and their outputs are provided below.

Code and Output:

The code attempts to load and utilize different subsets of the MS COCO dataset using CocoCaptions. The unlabeled2017 subset loads successfully and allows for image display. Other subsets (containing "stuff" and "panoptic" data) cause errors, highlighting limitations of using CocoCaptions with these specific data structures.

<code class="language-python">from torchvision.datasets import CocoCaptions
import matplotlib.pyplot as plt

# ... (CocoCaptions instantiation code as provided in the input) ...

# ... (len() calls and error handling code as provided in the input) ...

unlabeled2017_data[2]  # Displays image and empty caption list
unlabeled2017_data[47] # Displays image and empty caption list
unlabeled2017_data[64] # Displays image and empty caption list

def show_images(data, ims, main_title=None):
    file = data.root.split('/')[-1]
    fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(14, 8))
    fig.suptitle(t=main_title, y=0.9, fontsize=14)
    for i, axis in zip(ims, axes.ravel()):
        if not data[i][1]:
            im, _ = data[i]
            axis.imshow(X=im)
    fig.tight_layout()
    plt.show()

ims = (2, 47, 64)
show_images(data=unlabeled2017_data, ims=ims, main_title="unlabeled2017_data")</code>

The show_images function displays three images from the unlabeled2017_data subset.

Conclusion:

This experiment shows that while CocoCaptions works with certain MS COCO data subsets (like unlabeled2017), it's not directly compatible with all annotations. The errors encountered suggest that the "stuff" and "panoptic" annotations require a different approach or a different dataset class for proper loading and usage. Further investigation into the structure of these annotations and the available torchvision dataset classes is necessary for successful data access.

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