Create AGBD

AGBD

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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 numpy as np
+import datasets
+from datasets import Value
+# 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 = ""
+
+norm_values = {
+    'B01': {'mean': 0.12478869, 'std': 0.024433358, 'min': 1e-04, 'max': 1.8808, 'p1': 0.0787, 'p99': 0.1946},
+    'B02': {'mean': 0.13480005, 'std': 0.02822557, 'min': 1e-04, 'max': 2.1776, 'p1': 0.0925, 'p99': 0.2216},
+    'B03': {'mean': 0.16031432, 'std': 0.032037303, 'min': 1e-04, 'max': 2.12, 'p1': 0.1035, 'p99': 0.2556},
+    'B04': {'mean': 0.1532097, 'std': 0.038628064, 'min': 1e-04, 'max': 2.0032, 'p1': 0.1023, 'p99': 0.2816},
+    'B05': {'mean': 0.20312776, 'std': 0.04205057, 'min': 0.0422, 'max': 1.7502, 'p1': 0.1178, 'p99': 0.319},
+    'B06': {'mean': 0.32636437, 'std': 0.07139242, 'min': 0.0502, 'max': 1.7245, 'p1': 0.1633, 'p99': 0.519},
+    'B07': {'mean': 0.36605212, 'std': 0.08555025, 'min': 0.0616, 'max': 1.7149, 'p1': 0.1776, 'p99': 0.6076},
+    'B08': {'mean': 0.3811653, 'std': 0.092815965, 'min': 1e-04, 'max': 1.7488, 'p1': 0.1691, 'p99': 0.646},
+    'B8A': {'mean': 0.3910436, 'std': 0.0896364, 'min': 0.055, 'max': 1.688, 'p1': 0.1871, 'p99': 0.6386},
+    'B09': {'mean': 0.3910644, 'std': 0.0836445, 'min': 0.0012, 'max': 1.7915, 'p1': 0.2124, 'p99': 0.6241},
+    'B11': {'mean': 0.2917373, 'std': 0.07472579, 'min': 0.0953, 'max': 1.648, 'p1': 0.1334, 'p99': 0.4827},
+    'B12': {'mean': 0.21169408, 'std': 0.05880649, 'min': 0.0975, 'max': 1.6775, 'p1': 0.115, 'p99': 0.3872}}
+
+feature_dtype = {'s2_num_days': Value('int16'),
+                'gedi_num_days': Value('uint16'),
+                'lat': Value('float32'),
+                'lon': Value('float32'),
+                "agbd_se": Value('float32'),
+                "elev_lowes": Value('float32'),
+                "leaf_off_f": Value('uint8'),
+                "pft_class": Value('uint8'),
+                "region_cla": Value('uint8'),
+                "rh98": Value('float32'),
+                "sensitivity": Value('float32'),
+                "solar_elev": Value('float32'),
+                "urban_prop":Value('uint8')}
+
+class NewDataset(datasets.GeneratorBasedBuilder):
+    def __init__(self, *args, additional_features=[], normalize_data=True, patch_size=15, **kwargs):
+        self.inner_dataset_kwargs = kwargs
+        self._is_streaming = False
+        self.patch_size = patch_size
+        self.normalize_data = normalize_data
+        self.additional_features = additional_features
+        super().__init__(*args, **kwargs)
+
+    VERSION = datasets.Version("1.1.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="default", version=VERSION, description="Normalized data"),
+        datasets.BuilderConfig(name="unnormalized", version=VERSION, description="Unnormalized data"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "default"
+
+    def as_streaming_dataset(self, split=None, base_path=None):
+        self._is_streaming = True
+        return super().as_streaming_dataset(split=split, base_path=base_path)
+
+    def _info(self):
+        all_features = {
+            'input': datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Value('float32')))),
+            'label': Value('float32')
+        }
+        for feat in self.additional_features:
+            all_features[feat] = feature_dtype[feat]
+        features = datasets.Features(all_features)
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def denormalize_s2(self, patch):
+        res = []
+        for band, band_value in zip(['B04', 'B03', 'B02'], [patch[3], patch[2], patch[1]]):
+            p1, p99 = norm_values[band]['p1'], norm_values[band]['p99']
+            band_value = (p99 - p1) * band_value + p1
+            res.append(band_value)
+        patch[3], patch[2], patch[1] = res
+        return patch
+
+    def _split_generators(self, dl_manager):
+        self.original_dataset = datasets.load_dataset("prs-eth/AGBD_raw", streaming=self._is_streaming)
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"split": "train"}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"split": "val"}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"split": "test"}),
+        ]
+
+    def _generate_examples(self, split):
+        for i, d in enumerate(self.original_dataset[split]):
+            if self.config.name == "default":
+                data = {'input': np.asarray(d["input"]), 'label': d["label"]}
+            elif self.config.name == "unnormalized":
+                data = {'input': np.asarray(self.denormalize_s2(np.array(d["input"]))), 'label': d["label"]}
+
+            start_x = (data["input"].shape[1] - self.patch_size) // 2
+            start_y = (data["input"].shape[2] - self.patch_size) // 2
+            data["input"] = data["input"][:, start_x:start_x + self.patch_size, start_y:start_y + self.patch_size]
+
+            for feat in self.additional_features:
+                data[feat] = d["metadata"][feat]
+
+            yield i, data
+
+