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Boat_dataset.py

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+# 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
+
+
+import csv
+import json
+import os
+
+import datasets
+
+
+# Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {Boat dataset},
+author={huggingface, Inc.
+},
+year={2024}
+}
+"""
+
+# Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+This new dataset is designed to solve this great object detection task.
+"""
+
+# Add a link to an official homepage for the dataset here
+_HOMEPAGE = ""
+
+# Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+# Add link to the official dataset URLs here
+# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_URLS = {
+    "Boat_dataset": "ftp://arg.lab.nycu.edu.tw/arg-projectfile-download/detr/dataset/Boat_dataset.zip",
+}
+
+
+
+# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
+class BoatDataset(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    # This is an example of a dataset with multiple configurations.
+    # If you don't want/need to define several sub-sets in your dataset,
+    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
+
+    # If you need to make complex sub-parts in the datasets with configurable options
+    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
+    # BUILDER_CONFIG_CLASS = MyBuilderConfig
+
+    # You will be able to load one or the other configurations in the following list with
+    # data = datasets.load_dataset('my_dataset', 'first_domain')
+    # data = datasets.load_dataset('my_dataset', 'second_domain')
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="Boat_dataset", version=VERSION, description="Images of real and virtual boats."),
+    ]
+
+    DEFAULT_CONFIG_NAME = "Boat_dataset"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    def _info(self):
+        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
+
+        features=datasets.Features({
+            'image_id': datasets.Value('int32'),
+            # 'image': datasets.Image(), # This is commented out because you can't directly store PIL images in the dataset.
+            'file_name': datasets.Value('string'),  # Store the path to the image file instead.
+            'width': datasets.Value('int32'),
+            'height': datasets.Value('int32'),
+            'objects': datasets.Sequence({
+                'id': datasets.Value('int32'),
+                'area': datasets.Value('float32'),
+                'bbox': datasets.Sequence(datasets.Value('float32'), length=4),  # [x, y, width, height]
+                'category': datasets.Value('int32')
+            }),
+        })
+        
+        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):
+        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+        urls = _URLS[self.config.name]
+        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={
+                    "filepath": os.path.join(data_dir, "instances_train2023.jsonl"),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "instances_val2023.jsonl"),
+                    "split": "val",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "instances_val2023r.jsonl"),
+                    "split": "val_real",
+                },
+            ),
+            # datasets.SplitGenerator(
+            #     name=datasets.Split.TEST,
+            #     # These kwargs will be passed to _generate_examples
+            #     gen_kwargs={
+            #         "filepath": os.path.join(data_dir, "test.jsonl"),
+            #         "split": "test"
+            #     },
+            # ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath, split):
+        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
+        with open(filepath, encoding="utf-8") as f:
+            for key, row in enumerate(f):
+                data = json.loads(row)
+                yield key, {
+                        "image_id": data["image_id"],
+                        "file_name": data["file_name"],
+                        "width": data["width"],
+                        "height": data["height"],
+                        "objects": {
+                            "id": data["objects"]["id"],
+                            "area": data["objects"]["area"],
+                            "bbox": data["objects"]["bbox"],
+                            "category": data["objects"]["category"],
+                        },
+                    }