file theano/gpuarray/subtensor.py

theano/gpuarray/subtensor.py

@@ -2,7 +2,7 @@ from __future__ import absolute_import, print_function, division
 
 import os
 
-import numpy
+import numpy as np
 from six import integer_types
 from six.moves import StringIO
 
@@ -128,7 +128,7 @@ class GpuSubtensor(HideC, Subtensor):
         def fix_idx(idx):
             if idx is None:
                 return "0", 1
-            elif isinstance(idx, (numpy.integer, integer_types)):
+            elif isinstance(idx, (np.integer, integer_types)):
                 return str(idx), 0
             elif isinstance(idx, gof.Type):
                 return indices.pop(0), 0
@@ -155,7 +155,7 @@ class GpuSubtensor(HideC, Subtensor):
             else:
                 if isinstance(idx, gof.Type):
                     start = indices.pop(0)
-                elif isinstance(idx, (numpy.integer, integer_types)):
+                elif isinstance(idx, (np.integer, integer_types)):
                     start = idx
                 else:
                     assert 0, idx
@@ -511,7 +511,7 @@ class GpuAdvancedSubtensor(HideC, tensor.AdvancedSubtensor):
         # if there are more than one (narray > 1) it should be ignored.
         ap = 0
         for k, i in enumerate(list(nidx)):
-            if (isinstance(i, numpy.ndarray) and
+            if (isinstance(i, np.ndarray) and
                     i.ndim != 0):
                 transp.remove(k)
                 transp.insert(p, k)
@@ -545,7 +545,7 @@ class GpuAdvancedSubtensor(HideC, tensor.AdvancedSubtensor):
         x = x.__getitem__(idx_)
 
         # flatten the array-indexed dimensions
-        shape = ((numpy.prod(x.shape[0: p]),) +
+        shape = ((np.prod(x.shape[0: p]),) +
                  x.shape[p:])
         input_flat = x.reshape(shape)
 
@@ -644,7 +644,7 @@ class GpuAdvancedIncSubtensor1(Op):
         # content to index x and y (This is because we serve as
         # fallback for _dev20).
         if isinstance(idx, gpuarray.GpuArray):
-            idx = numpy.asarray(idx)
+            idx = np.asarray(idx)
 
         # If `y` has as many dimensions as `x`, then we want to iterate
         # jointly on `x` and `y`. Otherwise, it means `y` should be
@@ -877,10 +877,10 @@ if (GpuArray_vector_add_fast(%(out)s, %(y)s, %(ind)s, %(set_instead_of_inc)s)) {
         dtype_y = node.inputs[1].dtype
         dtype_ind = node.inputs[2].dtype
         dtype_out = node.outputs[0].dtype
-        itemsize_x = numpy.dtype(dtype_x).itemsize
-        itemsize_y = numpy.dtype(dtype_y).itemsize
-        itemsize_ind = numpy.dtype(dtype_ind).itemsize
-        itemsize_out = numpy.dtype(dtype_out).itemsize
+        itemsize_x = np.dtype(dtype_x).itemsize
+        itemsize_y = np.dtype(dtype_y).itemsize
+        itemsize_ind = np.dtype(dtype_ind).itemsize
+        itemsize_out = np.dtype(dtype_out).itemsize
         flags = Kernel.get_flags(dtype_x, dtype_y, dtype_ind)
         type_x = gpuarray.dtype_to_ctype(dtype_x)
         type_y = gpuarray.dtype_to_ctype(dtype_y)
@@ -1007,10 +1007,10 @@ __device__ ga_half atomicExch(ga_half *addr, ga_half val) {
         dtype_y = node.inputs[1].dtype
         dtype_ind = node.inputs[2].dtype
         dtype_out = node.outputs[0].dtype
-        itemsize_x = numpy.dtype(dtype_x).itemsize
-        itemsize_y = numpy.dtype(dtype_y).itemsize
-        itemsize_ind = numpy.dtype(dtype_ind).itemsize
-        itemsize_out = numpy.dtype(dtype_out).itemsize
+        itemsize_x = np.dtype(dtype_x).itemsize
+        itemsize_y = np.dtype(dtype_y).itemsize
+        itemsize_ind = np.dtype(dtype_ind).itemsize
+        itemsize_out = np.dtype(dtype_out).itemsize
         k_var = "k_vector_add_fast_" + nodename
 
         return super(GpuAdvancedIncSubtensor1_dev20, self).c_support_code_struct(node, nodename) + """
@@ -1112,7 +1112,7 @@ class GpuDiagonal(Subtensor):
         if x.size == 0:
             out_shape = [d for i, d in enumerate(x.shape)
                          if i not in (self.axis1, self.axis2)]
-            diag_size = numpy.min((x.shape[self.axis1], x.shape[self.axis2]))
+            diag_size = np.min((x.shape[self.axis1], x.shape[self.axis2]))
             out_shape.append(diag_size)
             z[0] = node.outputs[0].type.value_zeros(tuple(out_shape))
             return
@@ -1128,15 +1128,15 @@ class GpuDiagonal(Subtensor):
 
         if x.shape[stride_axis] < x.shape[slice_axis]:
             # in the bigger triangle
-            numstride = small_axis - numpy.max((
-                0, small_axis + numpy.abs(self.offset) - large_axis))
+            numstride = small_axis - np.max((
+                0, small_axis + np.abs(self.offset) - large_axis))
         else:
             # in the smaller triangle
-            numstride = small_axis - numpy.abs(self.offset)
+            numstride = small_axis - np.abs(self.offset)
 
-        slicer = [numpy.s_[:], ] * x.ndim
-        slicer[stride_axis] = numpy.s_[:numstride]
-        slicer[slice_axis] = numpy.abs(self.offset)
+        slicer = [np.s_[:], ] * x.ndim
+        slicer[stride_axis] = np.s_[:numstride]
+        slicer[slice_axis] = np.abs(self.offset)
         slicer = tuple(slicer)
 
         # step 2) Swap stride_axis to the last dim because we want the dim on