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"""CC6204-Hackaton-Cub-Dataset: Multimodal""" |
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import os |
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import re |
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import datasets |
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import pandas as pd |
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from requests import get |
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datasets.logging.set_verbosity_debug() |
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logger = datasets.logging.get_logger(__name__) |
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datasets.logging.set_verbosity_debug() |
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_DESCRIPTION = "XYZ" |
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_CITATION = "XYZ" |
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_HOMEPAGE = "https://github.com/ivansipiran/CC6204-Deep-Learning/blob/main/Hackaton/hackaton.md" |
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_REPO = "https://huggingface.co/datasets/alkzar90/CC6204-Hackaton-Cub-Dataset/resolve/main/data" |
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_URLS = { |
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"train_test_split": f"{_REPO}/train_test_split.txt", |
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"classes": f"{_REPO}/classes.txt", |
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"image_class_labels": f"{_REPO}/image_class_labels.txt", |
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"images": f"{_REPO}/images.txt", |
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"image_urls": f"{_REPO}/images.zip", |
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"text_urls": f"{_REPO}/text.zip", |
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} |
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classes = get(_URLS["classes"]).iter_lines() |
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_ID2LABEL = {} |
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for row in classes: |
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row = row.decode("UTF8") |
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if row != "": |
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idx, label = row.split(" ") |
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_ID2LABEL[idx] = re.search("[^\d\.\_+].+", label).group(0).replace("_", " ") |
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_NAMES = list(_ID2LABEL.values()) |
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imgpath_to_ids = get(_URLS["images"]).iter_lines() |
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_IMGNAME2ID = {} |
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for row in imgpath_to_ids: |
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row = row.decode("UTF8") |
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if row != "": |
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idx, img_name = row.split(" ") |
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_IMGNAME2ID[os.path.basename(img_name)] = idx |
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train_test_split = get(_URLS["train_test_split"]).iter_lines() |
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_TRAIN_IDX_SET = [] |
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for row in train_test_split: |
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row = row.decode("UTF8") |
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if row != "": |
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idx, train_bool = row.split(" ") |
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if train_bool == "1": |
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_TRAIN_IDX_SET.append(idx) |
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_TRAIN_IDX_SET = set(_TRAIN_IDX_SET) |
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class CubDataset(datasets.GeneratorBasedBuilder): |
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"""Cub Dataset""" |
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def _info(self): |
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features = datasets.Features({ |
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"image": datasets.Image(), |
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"labels": datasets.features.ClassLabel(names=_NAMES), |
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}) |
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keys = ("image", "labels") |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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supervised_keys=keys, |
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homepage=_HOMEPAGE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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train_files = [] |
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test_files = [] |
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data_files = dl_manager.download_and_extract(_URLS["image_urls"]) |
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for batch in data_files: |
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path_files = dl_manager.iter_files(batch) |
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for img in path_files: |
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if img.endswith("\d+.jpg"): |
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img_idx = _IMGNAME2ID[img] |
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if img_idx in _TRAIN_IDX_SET: |
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train_files.append(img) |
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else: |
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test_files.append(img) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"files": train_files |
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} |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"files": test_files |
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} |
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) |
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] |
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def _generate_examples(self, files): |
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for i, path in enumerate(files): |
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file_name = os.path.basename(path) |
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if file_name.endswith(".jpg"): |
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yield i, { |
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"image": path, |
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"labels": os.path.basename(os.path.dirname(path)).lower(), |
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} |
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