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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
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