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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_debug()
logger = datasets.logging.get_logger(__name__)
#datasets.logging.set_verbosity_info()
datasets.logging.set_verbosity_debug()
_DESCRIPTION = "XYZ"
_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",
"mini_images_urls": f"{_REPO}/dummy/mini_images.zip"
}
# Create id-to-label dictionary using the classes file
classes = get(_URLS["classes"]).iter_lines()
_ID2LABEL = {}
for row in classes:
row = row.decode("UTF8")
if row != "":
idx, label = row.split(" ")
_ID2LABEL[idx] = re.search("[^\d\.\_+].+", label).group(0).replace("_", " ")
_NAMES = list(_ID2LABEL.values())
# build from images.txt: a mapping from image_file_name -> id
imgpath_to_ids = get(_URLS["images"]).iter_lines()
_IMGNAME2ID = {}
for row in imgpath_to_ids:
row = row.decode("UTF8")
if row != "":
idx, img_name = row.split(" ")
_IMGNAME2ID[os.path.basename(img_name)] = idx
# Create TRAIN_IDX_SET
train_test_split = get(_URLS["train_test_split"]).iter_lines()
_TRAIN_IDX_SET = []
for row in train_test_split:
row = row.decode("UTF8")
if row != "":
idx, train_bool = row.split(" ")
# 1: train, 0: test
if train_bool == "1":
_TRAIN_IDX_SET.append(idx)
_TRAIN_IDX_SET = set(_TRAIN_IDX_SET)
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):
train_files = []
test_files = []
# Download images
data_files = dl_manager.download_and_extract(_URLS["mini_images_urls"])
path_files = dl_manager.iter_files(data_files)
for img in path_files:
img_idx = _IMGNAME2ID[os.path.basename(img)]
if img_idx in _TRAIN_IDX_SET:
train_files.append(img)
else:
test_files.append(img)
#for batch in data_files:
#path_files = dl_manager.iter_files(batch)
#for img in path_files:
#if img.endswith("\d+.jpg"):
#img_idx = _IMGNAME2ID[img]
#if img_idx in _TRAIN_IDX_SET:
#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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