# 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. import json import os import datasets import h5py import numpy as np import pandas as pd # Find for instance the citation on arxiv or on the dataset repo/website _CITATION = """\ WIP """ # You can copy an official description _DESCRIPTION = """\ QuakeSet is a dataset of earthquake images from the Copernicus Sentinel-1 satellites. It contains images from before, after an earthquake, and a sample before the "before" sample. Ground truth contains magnitudes and locations of earthquakes provided by ISC. """ _HOMEPAGE = "https://huggingface.co/datasets/DarthReca/quakeset" _LICENSE = "OPENRAIL" # 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 = ["earthquakes.h5", "epicenters.parquet"] class QuakeSet(datasets.GeneratorBasedBuilder): """TODO: Short description of my dataset.""" VERSION = datasets.Version("1.0.0") # 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="Default configuration", ), datasets.BuilderConfig( name="epicenter", version=VERSION, description="Epicenter configuration", ), ] DEFAULT_CONFIG_NAME = "default" # It's not mandatory to have a default configuration. Just use one if it make sense. def _info(self): if self.config.name == "default": features = datasets.Features( { "pre_post_image": datasets.Array3D( shape=(4, 512, 512), dtype="float32" ), "affected": datasets.ClassLabel(num_classes=2), "magnitude": datasets.Value("float32"), "hypocenter": datasets.Sequence( datasets.Value("float32"), length=3 ), "epsg": datasets.Value("int32"), "x": datasets.Sequence(datasets.Value("float32"), length=512), "y": datasets.Sequence(datasets.Value("float32"), length=512), } ) elif self.config.name == "epicenter": features = datasets.Features( { "pre_post_image": datasets.Array3D( shape=(4, 512, 512), dtype="float32" ), "contains_epicenter": datasets.ClassLabel(num_classes=2), "epsg": datasets.Value("int32"), "epicenter": datasets.Sequence(datasets.Value("float32"), length=2), "lon": datasets.Sequence(datasets.Value("float32"), length=512), "lat": datasets.Sequence(datasets.Value("float32"), length=512), "affected": datasets.ClassLabel(num_classes=2), } ) 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): # 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 files = dl_manager.download(urls) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": files, "split": "train", }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": files, "split": "validation", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": files, "split": "test", }, ), ] # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` def _generate_examples(self, filepath, split): # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example. df = pd.read_parquet(filepath[1]) sample_ids = [] with h5py.File(filepath[0]) as f: for key, patches in f.items(): attributes = dict(f[key].attrs) if attributes["split"] != split: continue sample_ids += [(f"{key}/{p}", 1, attributes) for p in patches.keys()] sample_ids += [ (f"{key}/{p}", 0, attributes) for p, v in patches.items() if "before" in v ] for sample_id, label, attributes in sample_ids: if "x" in sample_id or "y" in sample_id: continue pre_key = "pre" if label == 1 else "before" post_key = "post" if label == 1 else "pre" pre_sample = f[sample_id][pre_key][...] post_sample = f[sample_id][post_key][...] pre_sample = np.nan_to_num(pre_sample, nan=0).transpose(2, 0, 1) post_sample = np.nan_to_num(post_sample, nan=0).transpose(2, 0, 1) sample = np.concatenate( [pre_sample, post_sample], axis=0, dtype=np.float32 ) sample_key = f"{sample_id}/{post_key}" item = { "pre_post_image": sample, "epsg": attributes["epsg"], } if self.config.name == "default": resource_id, patch_id = sample_id.split("/") x = f[resource_id]["x"][...] y = f[resource_id]["y"][...] x_start = int(patch_id.split("_")[1]) % (x.shape[0] // 512) y_start = int(patch_id.split("_")[1]) // (x.shape[0] // 512) x = x[x_start * 512 : (x_start + 1) * 512] y = y[y_start * 512 : (y_start + 1) * 512] item |= { "affected": label, "magnitude": np.float32(attributes["magnitude"]), "hypocenter": attributes["hypocenter"], "x": x.flatten(), "y": y.flatten(), } elif self.config.name == "epicenter": selected_infos = df[df["sample_id"] == sample_key] if len(selected_infos) > 1: print(selected_infos) item |= { "affected": label, "contains_epicenter": label == 1 and selected_infos["contains_epicenter"].item(), "epicenter": selected_infos["epicenter"].item(), "lon": selected_infos["lon"].item(), "lat": selected_infos["lat"].item(), } yield sample_key, item