update slice and tfield

Author: chrischoy

MinkowskiEngine/MinkowskiSparseTensor.py

@@ -512,15 +512,13 @@ def dense(self, shape=None, min_coordinate=None, contract_stride=True):
         tensor_stride = torch.IntTensor(self.tensor_stride)
         return dense_F, min_coordinate, tensor_stride
 
-    def slice(self, X, slicing_mode=0):
+    def slice(self, X):
         r"""
 
         Args:
            :attr:`X` (:attr:`MinkowskiEngine.SparseTensor`): a sparse tensor
            that discretized the original input.
 
-           :attr:`slicing_mode`: For future updates.
-
         Returns:
            :attr:`tensor_field` (:attr:`MinkowskiEngine.TensorField`): the
            resulting tensor field contains features on the continuous
@@ -530,7 +528,7 @@ def slice(self, X, slicing_mode=0):
 
            >>> # coords, feats from a data loader
            >>> print(len(coords))  # 227742
-           >>> tfield = ME.TensorField(coords=coords, feats=feats, quantization_mode=SparseTensorQuantizationMode.UNWEIGHTED_AVERAGE)
+           >>> tfield = ME.TensorField(coordinates=coords, features=feats, quantization_mode=SparseTensorQuantizationMode.UNWEIGHTED_AVERAGE)
            >>> print(len(tfield))  # 227742
            >>> sinput = tfield.sparse() # 161890 quantization results in fewer voxels
            >>> soutput = MinkUNet(sinput)
@@ -545,9 +543,7 @@ def slice(self, X, slicing_mode=0):
             SparseTensorQuantizationMode.RANDOM_SUBSAMPLE,
             SparseTensorQuantizationMode.UNWEIGHTED_AVERAGE,
         ], "slice only available for sparse tensors with quantization RANDOM_SUBSAMPLE or UNWEIGHTED_AVERAGE"
-        assert (
-            X.coordinate_map_key == self.coordinate_map_key
-        ), "Slice can only be applied on the same coordinates (coordinate_map_key)"
+
         from MinkowskiTensorField import TensorField
 
         if isinstance(X, TensorField):
@@ -557,23 +553,28 @@ def slice(self, X, slicing_mode=0):
                 coordinate_manager=X.coordinate_manager,
                 quantization_mode=X.quantization_mode,
             )
-        else:
+        elif isinstance(X, SparseTensor):
+            assert (
+                X.coordinate_map_key == self.coordinate_map_key
+            ), "Slice can only be applied on the same coordinates (coordinate_map_key)"
             return TensorField(
                 self.F[X.inverse_mapping],
                 coordinates=self.C[X.inverse_mapping],
-                coordinate_manager=X.coordinate_manager,
-                quantization_mode=X.quantization_mode,
+                coordinate_manager=self.coordinate_manager,
+                quantization_mode=self.quantization_mode,
+            )
+        else:
+            raise ValueError(
+                "Invalid input. The input must be an instance of TensorField or SparseTensor."
             )
 
-    def cat_slice(self, X, slicing_mode=0):
+    def cat_slice(self, X):
         r"""
 
         Args:
            :attr:`X` (:attr:`MinkowskiEngine.SparseTensor`): a sparse tensor
            that discretized the original input.
 
-           :attr:`slicing_mode`: For future updates.
-
         Returns:
            :attr:`tensor_field` (:attr:`MinkowskiEngine.TensorField`): the
            resulting tensor field contains the concatenation of features on the
@@ -584,7 +585,7 @@ def cat_slice(self, X, slicing_mode=0):
 
            >>> # coords, feats from a data loader
            >>> print(len(coords))  # 227742
-           >>> sinput = ME.SparseTensor(coords=coords, feats=feats, quantization_mode=SparseTensorQuantizationMode.UNWEIGHTED_AVERAGE)
+           >>> sinput = ME.SparseTensor(coordinates=coords, features=feats, quantization_mode=SparseTensorQuantizationMode.UNWEIGHTED_AVERAGE)
            >>> print(len(sinput))  # 161890 quantization results in fewer voxels
            >>> soutput = network(sinput)
            >>> print(len(soutput))  # 161890 Output with the same resolution
@@ -596,29 +597,30 @@ def cat_slice(self, X, slicing_mode=0):
             SparseTensorQuantizationMode.RANDOM_SUBSAMPLE,
             SparseTensorQuantizationMode.UNWEIGHTED_AVERAGE,
         ], "slice only available for sparse tensors with quantization RANDOM_SUBSAMPLE or UNWEIGHTED_AVERAGE"
-        assert (
-            X.coordinate_map_key == self.coordinate_map_key
-        ), "Slice can only be applied on the same coordinates (coordinate_map_key)"
+
         from MinkowskiTensorField import TensorField
 
         features = torch.cat((self.F[X.inverse_mapping], X.F), dim=1)
         if isinstance(X, TensorField):
             return TensorField(
                 features,
-                coordinate_map_key=X.coordinate_map_key,
                 coordinate_field_map_key=X.coordinate_field_map_key,
                 coordinate_manager=X.coordinate_manager,
-                inverse_mapping=X.inverse_mapping,
                 quantization_mode=X.quantization_mode,
             )
-        else:
+        elif isinstance(X, SparseTensor):
+            assert (
+                X.coordinate_map_key == self.coordinate_map_key
+            ), "Slice can only be applied on the same coordinates (coordinate_map_key)"
             return TensorField(
                 features,
                 coordinates=self.C[X.inverse_mapping],
-                coordinate_map_key=X.coordinate_map_key,
-                coordinate_manager=X.coordinate_manager,
-                inverse_mapping=X.inverse_mapping,
-                quantization_mode=X.quantization_mode,
+                coordinate_manager=self.coordinate_manager,
+                quantization_mode=self.quantization_mode,
+            )
+        else:
+            raise ValueError(
+                "Invalid input. The input must be an instance of TensorField or SparseTensor."
             )
 
     def features_at_coordinates(self, query_coordinates: torch.Tensor):

MinkowskiEngine/MinkowskiTensorField.py

@@ -234,13 +234,25 @@ def sparse(self, quantization_mode=None):
         if quantization_mode is None:
             quantization_mode = self.quantization_mode
 
-        return SparseTensor(
+        sparse_tensor = SparseTensor(
             self._F,
             coordinates=self.coordinates,
             quantization_mode=quantization_mode,
             coordinate_manager=self.coordinate_manager,
         )
 
+        # Save the inverse mapping
+        self._inverse_mapping = sparse_tensor.inverse_mapping
+        return sparse_tensor
+
+    @property
+    def inverse_mapping(self):
+        if not hasattr(self, "_inverse_mapping"):
+            raise ValueError(
+                "Did you run SparseTensor.slice? The slice must take a tensor field that returned TensorField.space."
+            )
+        return self._inverse_mapping
+
     def __repr__(self):
         return (
             self.__class__.__name__
@@ -269,5 +281,6 @@ def __repr__(self):
         "coordinate_field_map_key",
         "_manager",
         "quantization_mode",
+        "_inverse_mapping",
         "_batch_rows",
     )