Delete mapai_dataset.py

mapai_dataset.py

@@ -1,161 +0,0 @@
-# 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
-"""
-Aerial image dataset for building segmentation. 
-The dataset has been used in the MapAI: Precision in Building Segmentation competition with the exact same data split.
-The training and validation data is from Denmark and the test data is from Norway.
-"""
-
-
-import csv
-import json
-import os
-
-import datasets
-
-from pyarrow import parquet as pq
-
-
-# TODO: Add BibTeX citation
-# Find for instance the citation on arxiv or on the dataset repo/website
-_CITATION = """
-@article{Jyhne2022,
-   author = {Sander Jyhne and Morten Goodwin and Per-Arne Andersen and Ivar Oveland and Alexander Salveson Nossum and Karianne Ormseth and Mathilde Orstavik and Andrew C Flatman},
-   doi = {10.5617/NMI.9849},
-   issn = {2703-9196},
-   issue = {3},
-   journal = {Nordic Machine Intelligence},
-   keywords = {Aerial Images,Deep Learning,Image segmentation,machine learning,remote sensing,semantic segmentation},
-   month = {9},
-   pages = {1-3},
-   title = {MapAI: Precision in Building Segmentation},
-   volume = {2},
-   url = {https://journals.uio.no/NMI/article/view/9849},
-   year = {2022},
-}
-"""
-
-_DESCRIPTION = """
-The dataset is released to advance the research on building segmentation using aerial images.
-"""
-
-# 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 = ""
-
-# TODO: 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 = {
-    "train": "https://huggingface.co/datasets/sjyhne/mapai_dataset/resolve/main/train.parquet",
-    "validation": "https://huggingface.co/datasets/sjyhne/mapai_dataset/resolve/main/validation.parquet",
-    "task1_test": "https://huggingface.co/datasets/sjyhne/mapai_dataset/resolve/main/task1_test.parquet",
-    "task2_test": "https://huggingface.co/datasets/sjyhne/mapai_dataset/resolve/main/task2_test.parquet",
-}
-
-# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
-class MapaiDataset(datasets.GeneratorBasedBuilder):
-    """Building segmentation dataset with aerial images and lidar"""
-
-    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')
-    
-    # DEFAULT_CONFIG_NAME = "first_domain"  # 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(
-            {
-                "filename": datasets.Value("string"),
-                "image": datasets.Sequence(datasets.Value("uint8")),
-                "lidar": datasets.Sequence(datasets.Value("uint8")),
-                "mask": datasets.Sequence(datasets.Value("uint8")),
-                "shape": datasets.Sequence(datasets.Value("uint8"))
-            }
-        )
-        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)
-        
-        # Retrieving files from parquet files is slow
-        
-        return [datasets.SplitGenerator(name=self.config.name, gen_kwargs={"filepath": os.path.join(data_dir)})]
-
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, filepath):
-        # 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.
-        
-        id_ = 0
-        
-        meta = pq.read_metadata(filepath)
-
-        pqfile = pq.ParquetFile(filepath)
-
-        print(meta)
-        
-        
-        
-        with open(filepath, encoding="utf-8") as f:
-            for key, row in enumerate(f):
-                data = json.loads(row)
-                if self.config.name == "first_domain":
-                    # Yields examples as (key, example) tuples
-                    yield key, {
-                        "sentence": data["sentence"],
-                        "option1": data["option1"],
-                        "answer": "" if split == "test" else data["answer"],
-                    }
-                else:
-                    yield key, {
-                        "sentence": data["sentence"],
-                        "option2": data["option2"],
-                        "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
-                    }