Upload tfcol.py

tfcol.py

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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",
+    },
+}
+
+
+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=[
+                            "ropa",
+                            "licorera",
+                            "belleza/barbería/peluquería",
+                            "electrónica/cómputo",
+                            "parqueadero",
+                            "café/restaurante",
+                            "muebles/tapicería",
+                            "ferretería",
+                            "puesto móvil/toldito",
+                            "electrodomésticos",
+                            "carnicería/fruver",
+                            "bar",
+                            "animales",
+                            "tienda",
+                            "farmacia",
+                            "deporte",
+                            "talleres carros/motos",
+                            "zapatería",
+                            "supermercado",
+                            "hotel",
+                        ],
+                    )
+                ),
+                "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",
+                },
+            ),
+        ]
+
+    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"]),
+                }