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# coding=utf-8
# Copyright 2020 The HuggingFace Datasets, Santiago Hincapie-Potes and the TF Colombia community.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
import datasets
_URLs = {
"train": {
"images": "https://huggingface.co/datasets/shpotes/tfcol/resolve/main/data/train.tar.gz",
"annotations": "https://huggingface.co/datasets/shpotes/tfcol/raw/main/data/train.jsonl",
},
"val": {
"images": "https://huggingface.co/datasets/shpotes/tfcol/resolve/main/data/val.tar.gz",
"annotations": "https://huggingface.co/datasets/shpotes/tfcol/raw/main/data/val.jsonl",
},
"test": {
"images": "https://huggingface.co/datasets/shpotes/tfcol/resolve/main/data/test.tar.gz",
"annotations": "https://huggingface.co/datasets/shpotes/tfcol/raw/main/data/test.jsonl",
}
}
class TFCol(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
def _info(self):
features = datasets.Features(
{
"lat": datasets.Value("float32"),
"lon": datasets.Value("float32"),
"labels": datasets.Sequence(
datasets.ClassLabel(
num_classes=20,
names=[
'animales',
'bar',
'belleza/barbería/peluquería',
'café/restaurante',
'carnicería/fruver',
'deporte',
'electrodomésticos',
'electrónica/cómputo',
'farmacia',
'ferretería',
'hotel',
'licorera',
'muebles/tapicería',
'parqueadero',
'puesto móvil/toldito',
'ropa',
'supermercado',
'talleres carros/motos',
'tienda',
'zapatería'
],
)
),
"image": datasets.Value("string"),
}
)
return datasets.DatasetInfo(features=features)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
data_dir = dl_manager.download_and_extract(_URLs)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"annotations": data_dir["train"]["annotations"],
"images": data_dir["train"]["images"],
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"annotations": data_dir["val"]["annotations"],
"images": data_dir["val"]["images"],
"split": "val",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"annotations": data_dir["test"]["annotations"],
"images": data_dir["test"]["images"],
"split": "test"
}
)
]
def _generate_examples(self, annotations, images, split):
"""Yields examples as (key, example) tuples."""
with open(annotations, encoding="utf-8") as f:
for id_, row in enumerate(f):
data = json.loads(row)
yield id_, {
"lat": data["lat"],
"lon": data["lon"],
"labels": data["labels"],
"image": os.path.join(images, split, data["fname"]),
}
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