Fix tests and code so that they can be happy forever after.

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/corr.py

@@ -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]), as_tensor_variable(shape[1])]
         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]), as_tensor_variable(shape[1])] 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;
     }

theano/tensor/nnet/tests/test_abstract_conv.py

@@ -4,12 +4,12 @@ import itertools
 
 import theano
 from theano import tensor
-from theano.tests import unittests_tools as utt
-from theano.tensor.nnet.abstract_conv import conv, get_conv_output_shape
-from theano.tensor.nnet import corr
+from theano.tests import unittest_tools as utt
+from theano.tensor.nnet import corr, abstract_conv as conv
+from theano.tensor.nnet.abstract_conv import get_conv_output_shape
+from theano.tensor.nnet.conv import ConvOp
 from theano.tensor.nnet.corr import (CorrMM, CorrMM_gradWeights,
                                      CorrMM_gradInputs)
-from theano.tensor.nnet.conv import ConvOp
 from theano.tensor.nnet.ConvGrad3D import ConvGrad3D
 from theano.tensor.nnet.ConvTransp3D import ConvTransp3D
 
@@ -21,18 +21,21 @@ def conv_corr(inputs, filters, border_mode="valid", subsample=(1, 1),
     return corr.CorrMM(border_mode, subsample)(inputs, filters)
 
 
-def conv_corr_gw(inputs, filters, border_mode="valid", subsample=(1, 1),
-                 conv_mode='conv'):
+def conv_corr_gw(inputs, topgrad, filters_shape, border_mode="valid",
+                 subsample=(1, 1), conv_mode='conv'):
+    rval = corr.CorrMM_gradWeights(border_mode, subsample)(inputs, topgrad,
+                                                           filters_shape[2:])
     if conv_mode == 'conv':
-        filters = filters[:, :, ::-1, ::-1]
-    return corr.CorrMM(border_mode, subsample)(inputs, filters)
+        rval = rval[:, :, ::-1, ::-1]
+    return rval
 
 
-def conv_corr_gi(inputs, filters, border_mode="valid", subsample=(1, 1),
-                 conv_mode='conv'):
+def conv_corr_gi(filters, topgrad, inputs_shape, border_mode="valid",
+                 subsample=(1, 1), conv_mode='conv'):
     if conv_mode == 'conv':
         filters = filters[:, :, ::-1, ::-1]
-    return corr.CorrMM(border_mode, subsample)(inputs, filters)
+    return corr.CorrMM_gradInputs(border_mode, subsample)(filters, topgrad,
+                                                          inputs_shape[2:])
 
 
 class TestGetConvOutShape(unittest.TestCase):
@@ -279,7 +282,7 @@ class TestCpuConv2d(TestConv2d):
                 gradweight_OK = False
                 gradinput_OK = False
 
-            if b not in ('valid', 'full'):
+            if b not in ((0, 0), 'valid', 'full'):
                 fwd_OK = False
                 gradweight_OK = False
                 gradinput_OK = False
@@ -294,7 +297,7 @@ class TestCpuConv2d(TestConv2d):
 
             if fwd_OK:
                 self.run_fwd(inputs_shape=i, filters_shape=f, subsample=s,
-                             verify_grad=True, mode=mode, device='cpu',
+                             verify_grad=True, mode=mode,
                              provide_shape=provide_shape, border_mode=b,
                              filter_flip=flip, target_op=ConvOp)
             else:
@@ -312,7 +315,7 @@ class TestCpuConv2d(TestConv2d):
             if gradweight_OK:
                 self.run_gradweight(inputs_shape=i, filters_shape=f,
                                     output_shape=o, subsample=s,
-                                    verify_grad=False, mode=mode, device='cpu',
+                                    verify_grad=False, mode=mode,
                                     provide_shape=provide_shape, border_mode=b,
                                     filter_flip=flip,
                                     target_op=(ConvOp, ConvGrad3D))
@@ -332,7 +335,7 @@ class TestCpuConv2d(TestConv2d):
             if gradinput_OK:
                 self.run_gradinput(inputs_shape=i, filters_shape=f,
                                    output_shape=o, subsample=s,
-                                   verify_grad=False, mode=mode, device='cpu',
+                                   verify_grad=False, mode=mode,
                                    provide_shape=provide_shape, border_mode=b,
                                    filter_flip=flip,
                                    target_op=(ConvOp, ConvTransp3D))