Create lila.py

lila.py

@@ -0,0 +1,187 @@
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# 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.
+# TODO: Address all TODOs and remove all explanatory comments
+"""TODO: Add a description here."""
+
+
+import csv
+import json
+import os
+import math
+import requests
+from io import BytesIO
+from zipfile import ZipFile
+from urllib.request import urlopen
+import pandas as pd
+
+import datasets
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {A great new dataset},
+author={huggingface, Inc.
+},
+year={2020}
+}
+"""
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = ""
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+_LILA_SAS_URLS = pd.read_csv("https://lila.science/wp-content/uploads/2020/03/lila_sas_urls.txt")
+_LILA_SAS_URLS.rename(columns={"# name": "name"}, inplace=True)
+
+# How do I make these point to the particular commit ID?
+_LILA_URLS = {
+    "Caltech Camera Traps": "https://huggingface.co/datasets/NimaBoscarino/LILA/resolve/main/data/Caltech_Camera_Traps.jsonl",
+    "ENA24": "https://huggingface.co/datasets/NimaBoscarino/LILA/resolve/main/data/ENA24.jsonl",
+    "Missouri Camera Traps": "",
+    "NACTI": "",
+    "WCS Camera Traps": "",
+    "Wellington Camera Traps": "",
+    "Island Conservation Camera Traps": "",
+    "Channel Islands Camera Traps": "",
+    "Idaho Camera Traps": "",
+    "Snapshot Serengeti": "",
+    "Snapshot Karoo": "",
+    "Snapshot Kgalagadi": "",
+    "Snapshot Enonkishu": "",
+    "Snapshot Camdeboo": "",
+    "Snapshot Mountain Zebra": "",
+    "Snapshot Kruger": "",
+    "SWG Camera Traps": "",
+    "Orinoquia Camera Traps": "",
+}
+
+class LILAConfig(datasets.BuilderConfig):
+    """Builder Config for LILA"""
+
+    def __init__(self, image_base_url, metadata_url, **kwargs):
+        """BuilderConfig for LILA.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(LILAConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
+        self.image_base_url = image_base_url
+        self.metadata_url = metadata_url
+
+
+class LILA(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    BUILDER_CONFIGS = [
+        LILAConfig(
+            name=row.name,
+            # description="TODO: Description",
+            image_base_url=row.image_base_url,
+            metadata_url=_LILA_URLS[row.name]
+        ) for row in _LILA_SAS_URLS.itertuples()
+    ]
+
+    def _get_features(self) -> datasets.Features:
+        # TODO: Use ClassLabel for categories...
+        # TODO: Deal with 404s -> In my manual preprocessing, or in the datasets library?
+
+        match self.config.name:
+            case 'Caltech Camera Traps':
+                return datasets.Features({
+                    "id": datasets.Value("string"), "file_name": datasets.Value("string"),
+                    "width": datasets.Value("int32"), "height": datasets.Value("int32"),
+                    "seq_num_frames": datasets.Value("int32"),
+                    "date_captured": datasets.Value("date32"),
+                    "seq_id": datasets.Value("string"),
+                    "location": datasets.Value("string"),
+                    "rights_holder": datasets.Value("string"),
+                    "frame_num": datasets.Value("int32"),
+
+
+                    "annotations": datasets.Sequence({
+                        "id": datasets.Value("string"),
+                        "category_id": datasets.Value("int32"),
+                    }),
+
+                    "bboxes": datasets.Sequence({
+                        "id": datasets.Value("string"),
+                        "category_id": datasets.Value("int32"),
+                        "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
+                    }),
+
+                    "image": datasets.Image(decode=False),
+                })
+            case 'ENA24':
+                return datasets.Features({
+                    "id": datasets.Value("string"), "file_name": datasets.Value("string"),
+                    "width": datasets.Value("int32"), "height": datasets.Value("int32"),
+                    "annotations": datasets.Sequence({
+                        "id": datasets.Value("string"),
+                        "category_id": datasets.Value("int32"),
+                        "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
+                    }),
+                    "image": datasets.Image(decode=False),
+                })
+
+    def _info(self):
+        features = self._get_features()
+
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,  # Here we define them above because they are different between the two configurations
+            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
+            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
+            # supervised_keys=("sentence", "label"),
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        archive_path = dl_manager.download_and_extract(self.config.metadata_url)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": archive_path,
+                    "split": "train",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        with open(filepath) as f:
+            for line in f:
+                example = json.loads(line)
+                image_url = f"{self.config.image_base_url}/{example['file_name']}"
+                yield example["id"], {
+                    **example,
+                    "image": image_url
+                }