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tfcol / tfcol.py

shpotes

feat: add test support

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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"]),
	}