Raise exception for Batch __getitem__. (#119)

* Raise exception for Batch __getitem__.

* Try fixing access to reserved key.

* Simpler patch.

* Add unit test to check indexing empty Batch raises an exception.

Co-authored-by: Alexis Duburcq <alexis.duburcq@wandercraft.eu>

test/base/test_batch.py

@@ -277,7 +277,7 @@ def test_batch_empty():
     assert b0.shape == []
 
 
-def test_batch_numpy_compatibility():
+def test_batch_standard_compatibility():
     batch = Batch(a=np.array([[1.0, 2.0], [3.0, 4.0]]),
                   b=Batch(),
                   c=np.array([5.0, 6.0]))
@@ -288,6 +288,8 @@ def test_batch_numpy_compatibility():
         len(batch_mean)
     assert np.all(batch_mean.a == np.mean(batch.a, axis=0))
     assert batch_mean.c == np.mean(batch.c, axis=0)
+    with pytest.raises(IndexError):
+        Batch()[0]
 
 
 if __name__ == '__main__':
@@ -297,7 +299,7 @@ if __name__ == '__main__':
     test_utils_to_torch()
     test_batch_pickle()
     test_batch_from_to_numpy_without_copy()
-    test_batch_numpy_compatibility()
+    test_batch_standard_compatibility()
     test_batch_cat_and_stack()
     test_batch_copy()
     test_batch_empty()

tianshou/data/batch.py

@@ -24,25 +24,6 @@ def _is_batch_set(data: Any) -> bool:
     return False
 
 
-def _valid_bounds(length: int, index: Union[
-        slice, int, np.integer, np.ndarray, List[int]]) -> bool:
-    if isinstance(index, (int, np.integer)):
-        return -length <= index and index < length
-    elif isinstance(index, (list, np.ndarray)):
-        return _valid_bounds(length, np.min(index)) and \
-            _valid_bounds(length, np.max(index))
-    elif isinstance(index, slice):
-        if index.start is not None:
-            start_valid = _valid_bounds(length, index.start)
-        else:
-            start_valid = True
-        if index.stop is not None:
-            stop_valid = _valid_bounds(length, index.stop - 1)
-        else:
-            stop_valid = True
-        return start_valid and stop_valid
-
-
 def _create_value(inst: Any, size: int) -> Union[
         'Batch', np.ndarray, torch.Tensor]:
     if isinstance(inst, np.ndarray):
@@ -333,13 +314,17 @@ class Batch:
         """Return self[index]."""
         if isinstance(index, str):
             return self.__dict__[index]
-        b = Batch()
+        batch_items = self.items()
-        for k, v in self.items():
+        if len(batch_items) > 0:
-            if isinstance(v, Batch) and len(v.__dict__) == 0:
+            b = Batch()
-                b.__dict__[k] = Batch()
+            for k, v in batch_items:
-            else:
+                if isinstance(v, Batch) and len(v.__dict__) == 0:
-                b.__dict__[k] = v[index]
+                    b.__dict__[k] = Batch()
-        return b
+                else:
+                    b.__dict__[k] = v[index]
+            return b
+        else:
+            raise IndexError("Cannot access item from empty Batch object.")
 
     def __setitem__(
             self,
@@ -452,14 +437,6 @@ class Batch:
         """Return self[k] if k in self else d. d defaults to None."""
         return self.__dict__.get(k, d)
 
-    def __iter__(self):
-        try:
-            length = len(self)
-        except Exception:
-            length = 0
-        for i in range(length):
-            yield self[i]
-
     def to_numpy(self) -> None:
         """Change all torch.Tensor to numpy.ndarray. This is an in-place
         operation.

tianshou/data/buffer.py

@@ -272,8 +272,10 @@ class ReplayBuffer:
                     stack = np.stack(stack, axis=indice.ndim)
             else:
                 stack = val[indice]
-        except TypeError:
+        except IndexError as e:
             stack = Batch()
+            if not isinstance(val, Batch) or len(val.__dict__) > 0:
+                raise e
         self.done[last_index] = last_done
         return stack