Upload 2 files

README.md

@@ -1,3 +1,14 @@
 ---
 license: cc-by-sa-4.0
----
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path:
+    - "train.csv"
+    - "images/train"
+  - split: test
+    path:
+    - "test.csv"
+    - "images/test"
+---

osv5m.py

@@ -0,0 +1,127 @@
+import os
+import pandas as pd
+import datasets
+from os.path import join
+
+# convert these to features
+#id,latitude,longitude,thumb_original_url,country,sequence,captured_at,lon_bin,lat_bin,cell,region,sub-region,city,land_cover,road_index,drive_side,climate,soil,dist_sea,quadtree_10_5000,quadtree_10_25000,quadtree_10_1000,quadtree_10_50000,quadtree_10_12500,quadtree_10_500,quadtree_10_2500,unique_region,unique_sub-region,unique_city,unique_country,creator_username,creator_id
+#3859149887465501,-43.804769384023,-176.61409250805,,8,"(0, 8)",Chatham Islands,,Waitangi,4,4.661764145,1,15,3,0.0068841379890803,0,0,0,0,0,0,0,Chatham Islands_NZ,,Waitangi_NaN_Chatham Islands_NZ,NZ,roadroid,111336221091714.0
+
+class OSV5M(datasets.GeneratorBasedBuilder):
+    def __init__(self, *args, **kwargs):
+        self.full = kwargs.pop('full', False)
+        super().__init__(*args, **kwargs)
+        print('OSV5M', self.__dict__)
+
+    def _info(self):
+        if self.full:
+            return datasets.DatasetInfo(
+                features=datasets.Features(
+                    {
+                        "image": datasets.Image(),
+                        "latitude": datasets.Value("float32"),
+                        "longitude": datasets.Value("float32"),
+                        "thumb_original_url": datasets.Value("string"),
+                        "country": datasets.Value("string"),
+                        "sequence": datasets.Value("string"),
+                        "captured_at": datasets.Value("string"),
+                        "lon_bin": datasets.Value("float32"),
+                        "lat_bin": datasets.Value("float32"),
+                        "cell": datasets.Value("string"),
+                        "region": datasets.Value("string"),
+                        "sub-region": datasets.Value("string"),
+                        "city": datasets.Value("string"),
+                        "land_cover": datasets.Value("float32"),
+                        "road_index": datasets.Value("float32"),
+                        "drive_side": datasets.Value("float32"),
+                        "climate": datasets.Value("float32"),
+                        "soil": datasets.Value("float32"),
+                        "dist_sea": datasets.Value("float32"),
+                        "quadtree_10_5000": datasets.Value("int32"),
+                        "quadtree_10_25000": datasets.Value("int32"),
+                        "quadtree_10_1000": datasets.Value("int32"),
+                        "quadtree_10_50000": datasets.Value("int32"),
+                        "quadtree_10_12500": datasets.Value("int32"),
+                        "quadtree_10_500": datasets.Value("int32"),
+                        "quadtree_10_2500": datasets.Value("int32"),
+                        "unique_region": datasets.Value("string"),
+                        "unique_sub-region": datasets.Value("string"),
+                        "unique_city": datasets.Value("string"),
+                        "unique_country": datasets.Value("string"),
+                        "creator_username": datasets.Value("string"),
+                        "creator_id": datasets.Value("string"),
+                    }
+                )
+            )
+        else:
+            return datasets.DatasetInfo(
+                features=datasets.Features(
+                    {
+                        "image": datasets.Image(),
+                        "latitude": datasets.Value("float32"),
+                        "longitude": datasets.Value("float32"),
+                        "country": datasets.Value("string"),
+                        "region": datasets.Value("string"),
+                        "sub-region": datasets.Value("string"),
+                        "city": datasets.Value("string"),
+                    }
+                )
+            )
+
+    def df(self, annotation_path):
+        if not hasattr(self, 'df_'):
+            self.df_ = {}
+        if annotation_path not in self.df_:
+            df = pd.read_csv(annotation_path, dtype={
+                'id': str, 'creator_id': str, 'creator_username': str, 
+                'unique_country': str, 'unique_city': str, 'unique_sub-region': str, 'unique_region': str,
+                'quadtree_10_2500': int, 'quadtree_10_500': int, 'quadtree_10_12500': int, 'quadtree_10_50000': int, 'quadtree_10_1000': int, 'quadtree_10_25000': int, 'quadtree_10_5000': int,
+                'dist_sea': float, 'soil': float, 'climate': float, 'drive_side': float, 'road_index': float, 'land_cover': float, 'city': str, 'sub-region': str, 'region': str, 'cell': str, 'lat_bin': float, 'lon_bin': float, 'captured_at': str, 'sequence': str, 'country': str, 'thumb_original_url': str, 'longitude': float, 'latitude': float
+            })
+            if not self.full:
+                df = df[['id', 'latitude', 'longitude', 'country', 'region', 'sub-region', 'city']]
+
+            df = df.set_index('id')
+            self.df_[annotation_path] = df.to_dict('index')
+        return self.df_[annotation_path]
+
+    def _split_generators(self, dl_manager):
+        _URLS = {
+            "train": [join('images', 'train', str(i).zfill(2) + '.zip') for i in range(98)],
+            "test": [join('images', 'test', str(i).zfill(2) + '.zip') for i in range(5)],
+            "train_meta": "train.csv",
+            "test_meta": "test.csv",
+        }
+
+        data_files = dl_manager.download_and_extract(_URLS)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "image_paths": dl_manager.iter_files(data_files["train"]),
+                    "annotation_path": data_files["train_meta"],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "image_paths": dl_manager.iter_files(data_files["test"]),
+                    "annotation_path": data_files["test_meta"],
+                },
+            ),
+        ]
+
+    def _generate_examples(self, image_paths, annotation_path):
+        """Generate examples."""
+        df = self.df(annotation_path)
+        for idx, image_path in enumerate(image_paths):
+            info_id = os.path.splitext(os.path.split(image_path)[-1])[0]
+            try:
+                example = {
+                    "image": image_path,
+                } | df[info_id]
+            except Exception as e:
+                print('Exception ' + str(e), info_id, idx, image_path, sep='\n')
+                continue
+
+            yield idx, example