Merge pull request #4018 from abergeron/fix_buildbot

theano/tensor/nnet/__init__.py

@@ -31,6 +31,7 @@ from .bn import batch_normalization
 import warnings
 from .abstract_conv import conv2d as abstract_conv2d
 
+
 def conv2d(input, filters, input_shape=None, filter_shape=None,
            border_mode='valid', subsample=(1, 1), filter_flip=True,
            image_shape=None, **kwargs):
@@ -139,5 +140,3 @@ def conv2d(input, filters, input_shape=None, filter_shape=None,
 
     return abstract_conv2d(input, filters, input_shape, filter_shape,
                            border_mode, subsample, filter_flip)
-
-

theano/tensor/nnet/abstract_conv.py

@@ -408,7 +408,7 @@ class BaseAbstractConv2d(Op):
 
         if len(subsample) != 2:
             raise ValueError("subsample must have two elements")
-        self.subsample = subsample
+        self.subsample = tuple(subsample)
 
     def flops(self, inp, outp):
         """ Useful with the hack in profilemode to print the MFlops"""

theano/tensor/nnet/corr.py

@@ -52,7 +52,7 @@ class BaseCorrMM(gof.Op):
         self.border_mode = border_mode
         if len(subsample) != 2:
             raise ValueError("subsample must have two elements")
-        self.subsample = subsample
+        self.subsample = tuple(subsample)
 
     @property
     def pad(self):
@@ -177,13 +177,13 @@ class BaseCorrMM(gof.Op):
         if ((direction != 0) and (dH != 1)) or ((direction == 1) and (padH == -1)):
             if not height:
                 raise ValueError("height must be given for backprop with vertical sampling or border_mode='half'")
-            height = '(*(npy_int*)(PyArray_DATA(%s)))' % height
+            height = '(*(npy_int64 *)(PyArray_DATA(%s)))' % height
         else:
             height = '-1'
         if ((direction != 0) and (dW != 1)) or ((direction == 1) and (padW == -1)):
             if not width:
                 raise ValueError("width must be given for backprop with horizontal sampling or border_mode='half'")
-            width = '(*(npy_int*)(PyArray_DATA(%s)))' % width
+            width = '(*(npy_int64 *)(PyArray_DATA(%s)))' % width
         else:
             width = '-1'
         sub = sub.copy()
@@ -314,8 +314,8 @@ class BaseCorrMM(gof.Op):
         if (NULL == %(out)s)
         {
             PyErr_Format(PyExc_RuntimeError,
-                    "BaseCorrMM: Failed to allocate output of %%d x %%d x %%d x %%d",
-                    out_dim[0], out_dim[1], out_dim[2], out_dim[3]);
+                    "BaseCorrMM: Failed to allocate output of %%lld x %%lld x %%lld x %%lld",
+                    (long long)out_dim[0], (long long)out_dim[1], (long long)out_dim[2], (long long)out_dim[3]);
             %(fail)s
         }
     }
@@ -424,7 +424,7 @@ class CorrMM_gradWeights(BaseCorrMM):
             if shape is None:
                 raise ValueError('shape must be given if subsample != (1, 1)'
                                  ' or border_mode == "half"')
-            height_width = [shape[0], shape[1]]
+            height_width = [as_tensor_variable(shape[0]).astype('int64'), as_tensor_variable(shape[1]).astype('int64')]
         else:
             height_width = []
 
@@ -519,7 +519,7 @@ class CorrMM_gradInputs(BaseCorrMM):
             raise TypeError('topgrad must be 4D tensor')
         if self.subsample != (1, 1) and shape is None:
             raise ValueError('shape must be given if subsample != (1, 1)')
-        height_width = [shape[0], shape[1]] if self.subsample != (1, 1) else []
+        height_width = [as_tensor_variable(shape[0]).astype('int64'), as_tensor_variable(shape[1]).astype('int64')] if self.subsample != (1, 1) else []
 
         broadcastable = [topgrad.type.broadcastable[0], kern.type.broadcastable[1],
                          False, False]

theano/tensor/nnet/corr_gemm.c

@@ -162,7 +162,7 @@ PyArrayObject* corrMM(PyArrayObject* bottom,
                 "CorrMM shape inconsistency:\n"
                 "  bottom shape: %%d %%d %%d %%d\n"
                 "  weight shape: %%d %%d %%d %%d\n"
-                "  top shape: %%d %%d %%d %%d (expected %%d %%d %%d %%d)\n",
+                "  top shape: %%ld %%ld %%ld %%ld (expected %%d %%d %%d %%d)\n",
                 batchSize, nChannels, bottomHeight, bottomWidth,
                 nFilters, nChannels, kH, kW,
                 PyArray_DIMS(top)[0], PyArray_DIMS(top)[1],
@@ -182,7 +182,7 @@ PyArrayObject* corrMM(PyArrayObject* bottom,
     if (NULL == col)
     {
         PyErr_Format(PyExc_RuntimeError,
-                "CorrMM failed to allocate working memory of %%d x %%d\n",
+                "CorrMM failed to allocate working memory of %%ld x %%ld\n",
                 col_dim[0], col_dim[1]);
         return NULL;
     }