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BioMap.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
+"""TODO: Add a description here."""
+
+
+import csv
+import json
+import os
+
+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 = ""
+
+# 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 = {
+    "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
+    "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
+}
+
+
+# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
+class NewDataset(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="default", version=VERSION, description="This part of my dataset covers a first domain"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "default"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+    self.ds = datasets.load_dataset("prs-eth/AGBD")
+
+    def _info(self):
+        features = datasets.Features({
+            'input': datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Value('float32')))),
+            'label': datasets.Value('float32')
+        })
+
+        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, "train.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, "dev.jsonl"),
+                    "split": "dev",
+                },
+            ),
+            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):
+        for d in self.ds[split]:
+            yield i, {
+                'input': d["input"],
+                'label': d["label"]
+            }
+
+
+
+

README.md

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-
----
-license: cc-by-nc-4.0
-dataset_info:
-  features:
-  - name: input
-    sequence:
-      sequence:
-        sequence: float32
-  - name: label
-    dtype: float32
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/test*
-  - split: validation
-    path: data/test*
-  - split: test
-    path: data/test*
----
-
-
-
-# 🌲 AGBD: A Global-scale Biomass Dataset 🌳
-
-Authors: Ghjulia Sialelli ([gsialelli@ethz.ch](mailto:gsialelli@ethz.ch)), Torben Peters, Jan Wegner, Konrad Schindler
-
-## 15x15 Version
-
-This dataset is the smaller 15x15 Version if you want to have access to the bigger 25x25 patches goto  [huggingface.co/datasets/prs-eth/AGBD](https://huggingface.co/datasets/prs-eth/AGBD)
-
-## Dataset Description
-
-Each dataset sample consists of a pair of pre-cropped, pre-normalized images and their corresponding biomass labels. 
-The full project page including links to preprocessed uncropped data can be found on [github.com/ghjuliasialelli/AGBD/](https://github.com/ghjuliasialelli/AGBD/)
-
-### Image Details
-
-Each image contains 24 channels, organized as follows:
-
-- **Spectral Bands**: B01, B02, B03, B04, B05, B06, B07, B08, B8A, B09, B11, B12
-- **Geographical Coordinates**: lat_cos, lat_sin, lon_cos, lon_sin
-- **ALOS PALSAR Bands**: alos_hh, alos_hv
-- **Canopy Heights**: ch, ch_std
-- **Land Cover Information**: lc_cos, lc_sin, lc_prob
-- **Digital Elevation Model**: dem
-  
-### Channel Structure
-
-```plaintext
-(B01 B02 B03 B04 B05 B06 B07 B08 B8A B09 B11 B12) | (lat_cos, lat_sin, lon_cos, lon_sin) | (alos_hh, alos_hv) | (ch, ch_std) | (lc_cos, lc_sin, lc_prob) | dem
-

data/test.parquet

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