vis.py

import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import numpy as np

# Manually define CIFAR-100 fine label names
cifar100_classes = [
    'apple', 'aquarium_fish', 'baby', 'bear', 'beaver', 'bed', 'bee', 'beetle', 'bicycle', 'bottle',
    'bowl', 'boy', 'bridge', 'bus', 'butterfly', 'cactus', 'camel', 'can', 'castle', 'caterpillar',
    'cattle', 'chair', 'chimpanzee', 'clock', 'cloud', 'cockroach', 'couch', 'crab', 'crocodile', 'cup',
    'dinosaur', 'dolphin', 'elephant', 'flatfish', 'forest', 'fox', 'girl', 'hamster', 'house', 'kangaroo',
    'keyboard', 'lamp', 'lawn_mower', 'leopard', 'lion', 'lizard', 'lobster', 'man', 'maple_tree', 'motorcycle',
    'mountain', 'mouse', 'mushroom', 'oak_tree', 'orange', 'orchid', 'otter', 'palm_tree', 'pear', 'pickup_truck',
    'pine_tree', 'plain', 'plate', 'poppy', 'porcupine', 'possum', 'rabbit', 'raccoon', 'ray', 'road',
    'rocket', 'rose', 'sea', 'seal', 'shark', 'shrew', 'skunk', 'skyscraper', 'snail', 'snake',
    'spider', 'squirrel', 'streetcar', 'sunflower', 'sweet_pepper', 'table', 'tank', 'telephone', 'television', 'tiger',
    'tractor', 'train', 'trout', 'tulip', 'turtle', 'wardrobe', 'whale', 'willow_tree', 'wolf', 'woman', 'worm'
]

# Define a basic transform (no normalization for visualization)
transform = transforms.ToTensor()

# Load the CIFAR-100 dataset
dataset = torchvision.datasets.CIFAR100(root='./data', train=True, download=True, transform=transform)

# Function to display an image
def imshow(img, label):
    npimg = img.numpy()
    plt.imshow(np.transpose(npimg, (1, 2, 0)))
    plt.title(f'Label: {label} ({cifar100_classes[label]})')
    plt.axis('off')
    plt.show()

# Show an image (e.g., index 0)
img, label = dataset[0]
imshow(img, label)