Upload risk_biased_dataset.py

risk_biased_dataset.py

@@ -20,70 +20,78 @@ _CITATION = """\
 }
 """
 
+_URL = "/home/jeanmercat/Codes/risk_biased_dataset/data.json"
 
-class RAPConfig(datasets.BuilderConfig):
-    """BuilderConfig for RiskBiasedDataset."""
-
-    def __init__(self, **kwargs):
-        """BuilderConfig for RiskBiasedDataset.
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(RAPConfig, self).__init__(version=datasets.Version("0.0.0", ""), **kwargs)
-
-
-
-class RiskBiasedDataset(datasets.Dataset):
+class RiskBiasedDataset(datasets.GeneratorBasedBuilder):
     """Dataset of pre-processed samples from a portion of the 
     Waymo Open Motion Data for the risk-biased prediction task."""
     
+    VERSION = datasets.Version("0.0.0")
+    
     BUILDER_CONFIGS = [
-        RAPConfig(
-            name="json_lists",
-            description="JSON lists sample format"
-        )
+        datasets.BuilderConfig(name="risk_biased_dataset", version=VERSION, description="Dataset of pre-processed samples from a portion of the Waymo Open Motion Data for the risk-biased prediction task."),
     ]
     
+    DEFAULT_CONFIG_NAME = "risk_biased_dataset"
+    
     def _info(self):
         return datasets.DatasetInfo(
             description= _DESCRIPTION,
             features=datasets.Features(
-                {"x": datasets.Sequence(datasets.Value("float32")),
-                 "mask_x": datasets.Sequence(datasets.Value("bool")),
-                 "y": datasets.Sequence(datasets.Value("float32")),
-                 "mask_y": datasets.Sequence(datasets.Value("bool")),
-                 "mask_loss": datasets.Sequence(datasets.Value("bool")),
-                 "map_data": datasets.Sequence(datasets.Value("float32")),
-                 "mask_map": datasets.Sequence(datasets.Value("bool")),
-                 "offset": datasets.Sequence(datasets.Value("float32")),
-                 "x_ego": datasets.Sequence(datasets.Value("float32")),
-                 "y_ego": datasets.Sequence(datasets.Value("float32")),
+                {"x": datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Value("float32"))))),
+                 "mask_x": datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Value("bool")))),
+                 "y": datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Value("float32"))))),
+                 "mask_y": datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Value("bool")))),
+                 "mask_loss": datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Value("bool")))),
+                 "map_data": datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Value("float32"))))),
+                 "mask_map": datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Value("bool")))),
+                 "offset": datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Value("float32")))),
+                 "x_ego": datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Value("float32"))))),
+                 "y_ego": datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Value("float32"))))),
                  }
             ),
             supervised_keys=None,
-            homepage="https://sites.google.com/d/1cwohIm9fzTZEPAo7b_Di4h9iNgMiKHRo/p/1nPdTBSee6E40dmUXNyqxzUtb4_NnkI_6/edit",
+            homepage="https://sites.google.com/view/corl-risk/home",
             citation=_CITATION,
         )
         
-    def _generate_examples(self, filepath):
+    def _split_generators(self, dl_manager):
+        urls_to_download = _URL
+        downloaded_file = dl_manager.download(urls_to_download)
+        
+        return [datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_file, "split": "test"}),]
+        
+    def _generate_examples(self, filepath, split):
         """Yields examples."""
+        assert split == "test"
         with open(filepath, "r") as f:
             data = json.load(f)
             
-        x = torch.from_numpy(numpy.array(data["x"]).astype(numpy.float32))
-        mask_x = torch.from_numpy(numpy.array(data["mask_x"]).astype(numpy.bool8))
-        y = torch.from_numpy(numpy.array(data["y"]).astype(numpy.float32))
-        mask_y = torch.from_numpy(numpy.array(data["mask_y"]).astype(numpy.bool8))
-        mask_loss = torch.from_numpy( numpy.array(data["mask_loss"]).astype(numpy.bool8))
-        map_data = torch.from_numpy(numpy.array(data["map_data"]).astype(numpy.float32))
-        mask_map = torch.from_numpy(numpy.array(data["mask_map"]).astype(numpy.bool8))
-        offset = torch.from_numpy(numpy.array(data["offset"]).astype(numpy.float32))
-        x_ego = torch.from_numpy(numpy.array(data["x_ego"]).astype(numpy.float32))
-        y_ego = torch.from_numpy(numpy.array(data["y_ego"]).astype(numpy.float32))
-        
-        batch_size = x.shape[0]
-        
-        for i in range(batch_size):
-            yield x, mask_x, y, mask_y, mask_loss, map_data, mask_map, offset, x_ego, y_ego
-        
-        
+            x = torch.from_numpy(numpy.array(data["x"]).astype(numpy.float32))
+            mask_x = torch.from_numpy(numpy.array(data["mask_x"]).astype(numpy.bool8))
+            y = torch.from_numpy(numpy.array(data["y"]).astype(numpy.float32))
+            mask_y = torch.from_numpy(numpy.array(data["mask_y"]).astype(numpy.bool8))
+            mask_loss = torch.from_numpy( numpy.array(data["mask_loss"]).astype(numpy.bool8))
+            map_data = torch.from_numpy(numpy.array(data["map_data"]).astype(numpy.float32))
+            mask_map = torch.from_numpy(numpy.array(data["mask_map"]).astype(numpy.bool8))
+            offset = torch.from_numpy(numpy.array(data["offset"]).astype(numpy.float32))
+            x_ego = torch.from_numpy(numpy.array(data["x_ego"]).astype(numpy.float32))
+            y_ego = torch.from_numpy(numpy.array(data["y_ego"]).astype(numpy.float32))
+            
+            batch_size = x.shape[0]
+            
+            for i in range(batch_size):
+                # yield i, {"x": x[i], "mask_x": mask_x[i],
+                #           "y": y[i], "mask_y": mask_y[i], "mask_loss": mask_loss[i],
+                #           "map_data": map_data[i], "mask_map": mask_map[i],
+                #           "offset": offset[i], 
+                #           "x_ego": x_ego[i],
+                #           "y_ego": y_ego[i]}
+                yield i, {"x": x[i:i+1], "mask_x": mask_x[i:i+1],
+                          "y": y[i:i+1], "mask_y": mask_y[i:i+1], "mask_loss": mask_loss[i:i+1],
+                          "map_data": map_data[i:i+1], "mask_map": mask_map[i:i+1],
+                          "offset": offset[i:i+1], 
+                          "x_ego": x_ego[i:i+1],
+                          "y_ego": y_ego[i:i+1]}
+            
+