<jupyter_start><jupyter_text>1. Test Single<jupyter_code># Index 0
INDEX = 0
x, y = trainDataGenerator.test__getSingleItem__(INDEX)
image_fn = os.path.split(train_img_list[INDEX])[1]
mask_fn = os.path.split(train_mask_list[INDEX])[1]
print("Image Batch nparray shape: ", x.shape)
print("Image  shape: ", x[0].shape)
plot_all( [x[0]/255.0, y[0] ], [image_fn, mask_fn])
# Index 5

INDEX = 5
x, y = trainDataGenerator.test__getSingleItem__(INDEX)

image_fn = os.path.split(train_img_list[INDEX])[1]
mask_fn = os.path.split(train_mask_list[INDEX])[1]
plot_all( [x[0]/255.0, y[0] ], [image_fn, mask_fn])<jupyter_output><empty_output><jupyter_text>2. Test Batch Data<jupyter_code>X, Y, indexes = validDataGenerator.test__getBatch__(0,5)

for (i, x, y) in zip(indexes, X,Y):
    image_fn = os.path.split(val_img_list[i])[1]
    mask_fn = os.path.split(val_mask_list[i])[1]
    plot_all([x/255.0,y], [ image_fn, mask_fn ], figsize=(7.5,6.5))
X, Y, indexes = validDataGenerator.test__getBatch__(1,5)

for (i, x, y) in zip(indexes, X,Y):
    image_fn = os.path.split(val_img_list[i])[1]
    mask_fn = os.path.split(val_mask_list[i])[1]
    plot_all([x/255.0,y], [ image_fn, mask_fn ], figsize=(7.5,6.5))
X, Y, indexes = validDataGenerator.test__getBatch__(2,5)

for (i, x, y) in zip(indexes, X,Y):
    image_fn = os.path.split(val_img_list[i])[1]
    mask_fn = os.path.split(val_mask_list[i])[1]
    plot_all([x/255.0,y], [ image_fn, mask_fn ], figsize=(7.5,6.5))<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.18341200269723534, 'fraction_numerical': 0.03978422117329737, 'mean_word_length': 5.838709677419355, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, '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': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '438741', 'license': 'MIT', 'n_tokens_mistral': 674, 'n_tokens_neox': 656, 'n_words': 102, 'path': '4.b. Augmentation Demo - PIL.ipynb', 'repo_name': 'AvidahRai/kaggle-breast-cancer-cell-segmentation', 'size': '1483'}