Upload risk_biased_dataset.py

risk_biased_dataset.py

@@ -1,4 +1,7 @@
 import datasets
+import json
+import numpy
+import torch
 
 _DESCRIPTION = """\
     Dataset of pre-processed samples from a small portion of the \
@@ -60,4 +63,27 @@ class RiskBiasedDataset(datasets.Dataset):
             supervised_keys=None,
             homepage="https://sites.google.com/d/1cwohIm9fzTZEPAo7b_Di4h9iNgMiKHRo/p/1nPdTBSee6E40dmUXNyqxzUtb4_NnkI_6/edit",
             citation=_CITATION,
-        )
+        )
+        
+    def _generate_examples(self, filepath):
+        """Yields examples."""
+        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
+        
+