Update BioMap.py

BioMap.py

@@ -14,14 +14,12 @@
 # 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 = """\
@@ -45,123 +43,104 @@ _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 = {
-    "default": "https://huggingface.co/great-new-dataset-first_domain.zip",
-}
-
-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}}
+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')}
 
-# 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."""
+    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")
 
-    # 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"),
-        datasets.BuilderConfig(name="unnormalized", version=VERSION, description="This part of my dataset covers a first domain"),
+        datasets.BuilderConfig(name="default", version=VERSION, description="Normalized data"),
+        datasets.BuilderConfig(name="unnormalized", version=VERSION, description="Unnormalized data"),
     ]
 
-    DEFAULT_CONFIG_NAME = "default"  # It's not mandatory to have a default configuration. Just use one if it make sense.
-   
+    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):
-        self.ds = datasets.load_dataset("prs-eth/AGBD",streaming=True)
-        features = datasets.Features({
+        all_features = {
             'input': datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Value('float32')))),
-            'label': 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(
-            # 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
+            features=features,
             homepage=_HOMEPAGE,
-            # License for the dataset if available
             license=_LICENSE,
-            # Citation for the dataset
             citation=_CITATION,
         )
 
-    def denormalize_s2(self,patch):
-        r,g,b = patch[3],patch[2],patch[1]
+    def denormalize_s2(self, patch):
         res = []
-        for band, band_value in zip(['B04','B03','B02'],[r,g,b]) :
-                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
+        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):
-        # 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
 
+    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,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                  
-                    "split": "train",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-            
-                    "split": "val",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-
-                    "split": "test"
-                },
-            ),
+            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"}),
         ]
 
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
     def _generate_examples(self, split):
-        for i,d in enumerate(self.ds[split]):
+        for i, d in enumerate(self.original_dataset[split]):
             if self.config.name == "default":
-                yield i, {
-                    'input': d["input"],
-                    'label': d["label"]
-                }
-                
+                data = {'input': np.asarray(d["input"]), 'label': d["label"]}
             elif self.config.name == "unnormalized":
-                yield i, {
-                    'input': self.denormalize_s2(np.array(d["input"])),
-                    'label': d["label"]
-                }
-    
-    
+                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
+