<jupyter_start><jupyter_text><jupyter_code>gen = Torch3DDataset(data_dir=DIR, table=TABLE, batch_size=BATCHSIZE, n_classes=6, shuffle=False)

grid = (4,4)
fig, axes = plt.subplots(*grid, figsize=(grid[1]*5,grid[0]*5))

for i, ax in enumerate(np.ravel(axes)):
    X, y = testgen[i]
    left_img = X[0]
    boxes = y['boxes']
    boxes[:,[0,1]] += boxes[:,[2,3]]*0.5 
    utils.plot_bboxes(left_img[0].permute((1,2,0)), classes=y['labels'], boxes=boxes, classmap=num2str, ax=ax)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.20553359683794467, 'fraction_numerical': 0.039525691699604744, 'mean_word_length': 6.681818181818182, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '165234', 'license': 'MIT', 'n_tokens_mistral': 225, 'n_tokens_neox': 217, 'n_words': 31, 'path': 'volume/DataViz.ipynb', 'repo_name': 'Napam/INF399-Master-DeepLearning', 'size': '506'}