Merged

theano/sparse/basic.py

@@ -343,22 +343,29 @@ class CSM(gof.Op):
 
         """
         data = tensor.as_tensor_variable(data)
-        # Note that we use `view(numpy.int32)` instead of providing the 'int32'
-        # dtype to `numpy.asarray`. This is because on some computers (e.g. a
-        # Windows 32 bits machine), we can have the following assert fail:
+
+        # Note that we use `view(numpy.int32)` in addition to providing the
+        # 'int32' dtype to `numpy.asarray`. This is because on some computers
+        # (e.g. a Windows 32 bits machine), we can have the following assert
+        # fail:
         #   x = numpy.array([0], dtype=numpy.intc)
         #   y = numpy.asarray(x, dtype=numpy.int32)
         #   assert y.dtype.num == numpy.dtype(numpy.int32).num
         # while the assert does *not* fail when replacing the second line by:
-        #   y = numpy.asarray(x).view(numpy.int32)
+        #   y = numpy.asarray(x, dtype='int32').view(numpy.int32)
         # This is a known defect in Numpy. For more information see ticket
         # http://projects.scipy.org/numpy/ticket/870
+
+        # Note also that it is important to keep "dtype='int32'" when calling
+        # `numpy.asarray`. This is because `view` is only some kind of cast to
+        # the exact data type we want to use. If a conversion is required (e.g.
+        # from int64 to int32), it must be done in the call to `numpy.asarray`.
         if not isinstance(indices, tensor.TensorVariable):
-            indices = numpy.asarray(indices).view(numpy.int32)
+            indices = numpy.asarray(indices, dtype='int32').view(numpy.int32)
         if not isinstance(indptr, tensor.TensorVariable):
-            indptr = numpy.asarray(indptr).view(numpy.int32)
+            indptr = numpy.asarray(indptr, dtype='int32').view(numpy.int32)
         if not isinstance(shape, tensor.TensorVariable):
-            shape = numpy.asarray(shape).view(numpy.int32)
+            shape = numpy.asarray(shape, dtype='int32').view(numpy.int32)
         indices = tensor.as_tensor_variable(indices)
         indptr = tensor.as_tensor_variable(indptr)
         shape = tensor.as_tensor_variable(shape)