tfcol.py

# 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"]),
                }