LoadTFDataset

AUTOTUNE = tf.data.experimental.AUTOTUNE

def __get__tag__data():
    with open("instances_train2017.json") as f:
        listDick = json.load(f)

    images = listDick["images"]
    annotations = listDick["annotations"]

    fileNames = [] // Name Of Files
    catIDs = [] // Category IDS

    // Try And Load 10 Images For Now [ O(N^2) ]
    for i in images[:10]:
        fileNames.append(i["file_name"])
        for j in annotations:
            if(i["id"] == j["image_id"]):
                catIDs.append(j["category_id"])
                break // Break Once You Have Found

    print(fileNames) // Print And See If The List Looks Good
    print(catIDs) // Prints And See If List Looks Good

    list_ds = tf.data.Dataset.from_tensor_slices((fileNames, catIDs)) // Make A Tuple Of The 2 Arrays And Load Into a TF Dataset

    // Print And See If Tensors Are Being Inputted Correctly
    for f in list_ds.take(5):
        print(f)

    # Set `num_parallel_calls` so multiple images are loaded/processed in parallel.
    labeled_ds = list_ds.map(process_img_label, num_parallel_calls=AUTOTUNE)
    print(labeled_ds)

    thing = labeled_ds.take(1)
    print(thing)

    for image, label in labeled_ds.take(1): // ERROR HAPPENS HERE!!!
        print("Image shape: ", image.numpy().shape)
        print("Label: ", label.numpy())