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"""CC6204-Hackaton-Cub-Dataset: Multimodal"""
import os
import re
import datasets
import pandas as pd
from requests import get
datasets.logging.set_verbosity_info()
logger = datasets.logging.get_logger(__name__)
_CITATION = "XYZ"
_HOMEPAGE = "https://github.com/ivansipiran/CC6204-Deep-Learning/blob/main/Hackaton/hackaton.md"
_REPO = "https://huggingface.co/datasets/alkzar90/CC6204-Hackaton-Cub-Dataset/resolve/main/data"
_URLS = {
"train_test_split": f"{_REPO}/train_test_split.txt",
"classes": f"{_REPO}/classes.txt",
"image_class_labels": f"{_REPO}/image_class_labels.txt",
"images": f"{_REPO}/images.txt",
"image_urls": f"{_REPO}/images.zip",
"text_urls": f"{_REPO}/text.zip",
}
# Create id-to-label dictionary using the classes file
classes = get(_URLS['classes']).iter_lines()
logger.info(f"classes: {classes}")
_ID2LABEL = {}
for row.decode("UTF8") in classes:
if row != '':
idx, label = row.split(" ")
classes[int(idx)] = re.search("[^\d\.\_+].+", label).group(0).replace("_", " ")
logger.info(f"_ID2LABEL: {_ID2LABEL}")
# build from images.txt: a mapping from image_file_name -> id
_IMGPATH2ID = {}
class CubDataset(datasets.GeneratorBasedBuilder):
"""Cub Dataset"""
def _info(self):
features = datasets.Features({
"image": datasets.Image(),
"labels": datasets.features.ClassLabel(names=_NAMES),
})
keys = ("image", "labels")
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=keys,
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
# 1: train, 0: test
train_test_split = get(_URLS["train_test_split"]).iter_lines()
train_images_idx = set([x.decode("UTF8").split(" ")[0] for x in train_test_split if x.decode("UTF8").split(" ")[1] == 1])
logger.info(f"train_images_idx length: {len(train_images_idx)}")
train_files = []
test_files = []
# Download images
data_files = dl_manager.download_and_extract(_URLS["image_urls"])
for batch in data_files:
path_files = dl_manager.iter_files(batch)
for img in path_files:
if _IMGPATH2ID[os.path.basename(img)] in train_images_idx:
train_files.append(img)
else:
test_files.append(img)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"files": train_files
}
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"files": test_files
}
)
]
def _generate_examples(self, files):
for i, path in enumerate(files):
file_name = os.path.basename(path)
if file_name.endswith(".jpg"):
yield i, {
"image": path,
"labels": os.path.basename(os.path.dirname(path)).lower(),
}
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