Maybe I did something wrong, or the padding in GpuCorrMM is wrong

theano/sandbox/cuda/blas.py

 import copy
 import os
+import logging
+_logger = logging.getLogger(__name__)
 
 import theano
 from theano import Apply
@@ -504,39 +506,61 @@ gpu_ger_inplace = GpuGer(inplace=True)
 
 class BaseGpuCorrMM(GpuOp):
     """Base class for `GpuCorrMM`, `GpuCorrMM_gradWeights` and
-    `GpuCorrMM_gradInputs`. Cannot be used directly."""
+    `GpuCorrMM_gradInputs`. Cannot be used directly.
 
-    def __init__(self, border_mode="valid",
-            subsample=(1, 1),
-            pad=(0, 0)):
+    :param border_mode: one of 'valid', 'full', 'half'; additionally, the
+        padding size could be directly specified by an integer or a pair of
+        integers
+    :param subsample: perform subsampling of the output (default: (1, 1))
+    :param pad: *deprecated*, now you should always use border_mode
+    
+    """
+
+    def __init__(self, border_mode="valid", subsample=(1, 1), pad=(0, 0)):
+        if pad != (0, 0):
+            _logger.warning(
+                'do not use pad for BaseGpuCorrMM; please set padding in'
+                'border_mode, see the docstring for more details')
             if border_mode != "valid":
                 raise ValueError("border_mode must be 'valid'")
+            border_mode = pad
+        if isinstance(border_mode, int):
+            border_mode = (border_mode, border_mode)
+        if isinstance(border_mode, tuple):
+            pad_h, pad_w = map(int, border_mode)
+            border_mode = (pad_h, pad_w)
+        if not ((isinstance(border_mode, tuple) and min(border_mode) >= 0) or
+                border_mode in ('valid', 'full', 'half')):
+            raise ValueError(
+                'invalid border_mode {}, which must be either '
+                '"valid", "full", "half", an integer or a pair of'
+                ' integers'.format(border_mode))
         self.border_mode = border_mode
         if len(subsample) != 2:
             raise ValueError("subsample must have two elements")
         self.subsample = subsample
-        if (pad not in ("half", "full")) and (len(pad) != 2):
-            raise ValueError("pad must be 'half', 'full', or have two elements")
-        self.pad = pad
+
+    @property
+    def pad(self):
+        if self.border_mode != 'valid':
+            return self.border_mode
+        return (0, 0)
 
     def __eq__(self, other):
         return type(self) == type(other) \
             and self.border_mode == other.border_mode \
-            and self.subsample == other.subsample \
-            and self.pad == other.pad
+            and self.subsample == other.subsample
 
     def __hash__(self):
         return hash(type(self)) \
             ^ hash(self.border_mode) \
-            ^ hash(self.subsample) \
-            ^ hash(self.pad)
+            ^ hash(self.subsample)
 
     def __str__(self):
-        return '%s{%s, %s, pad=%r}' % (
+        return '%s{%s, %s}' % (
             self.__class__.__name__,
             self.border_mode,
-            str(self.subsample),
-            self.pad)
+            str(self.subsample))
 
     def flops(self, inp, outp):
         """ Useful with the hack in profilemode to print the MFlops"""
@@ -558,7 +582,7 @@ class BaseGpuCorrMM(GpuOp):
 
     def c_code_cache_version(self):
         # raise this whenever modifying any of the support_code_files
-        return (0, 23)
+        return (0, 24)
 
     def c_support_code_apply(self, node, nodename):
         # REMEMBER TO RAISE c_code_cache_version when changing any of
@@ -591,27 +615,28 @@ class BaseGpuCorrMM(GpuOp):
         :param sub: Dictionary of substitutions useable to help generating the
             C code.
         :param height: If self.subsample[0] != 1, a variable giving the height
-            of the filters for direction="backprop weights" or the height of the
-            input images for direction="backprop inputs".
-            If self.pad == 'half', a variable giving the height of the filters
-            for direction="backprop weights".
-            Ignored otherwise.
+            of the filters for direction="backprop weights" or the height of
+            the input images for direction="backprop inputs".
+
+            If self.border_mode == 'half', a variable giving the height of the
+            filters for direction="backprop weights".  Ignored otherwise.
         :param width: If self.subsample[1] != 1, a variable giving the width
             of the filters for direction="backprop weights" or the width of the
             input images for direction="backprop inputs".
-            If self.pad == 'half', a variable giving the width of the filters
-            for direction="backprop weights".
-            Ignored otherwise.
+
+            If self.border_mode == 'half', a variable giving the width of the
+            filters for direction="backprop weights".  Ignored otherwise.
         """
-        if self.border_mode != "valid":
-            raise ValueError("mode must be 'valid'")
         dH, dW = self.subsample
-        if self.pad == "half":
+        if self.border_mode == "half":
             padH = padW = -1
-        elif self.pad == "full":
+        elif self.border_mode == "full":
             padH = padW = -2
+        elif isinstance(self.border_mode, tuple):
+            padH, padW = self.border_mode
         else:
-            padH, padW = self.pad
+            assert self.border_mode == "valid"
+            padH = padW = 0
         if direction == "forward":
             direction = 0
             out = top
@@ -841,9 +866,9 @@ class GpuCorrMM(BaseGpuCorrMM):
         bottom, weights = inp
         top, = grads
         top = gpu_contiguous(top)
-        d_bottom = GpuCorrMM_gradInputs(self.border_mode, self.subsample, self.pad)(
+        d_bottom = GpuCorrMM_gradInputs(self.border_mode, self.subsample)(
             weights, top, bottom.shape[-2:])
-        d_weights = GpuCorrMM_gradWeights(self.border_mode, self.subsample, self.pad)(
+        d_weights = GpuCorrMM_gradWeights(self.border_mode, self.subsample)(
             bottom, top, weights.shape[-2:])
         return d_bottom, d_weights
 

theano/sandbox/cuda/dnn.py

@@ -122,9 +122,7 @@ class GpuDnnConvDesc(GpuOp):
     """This Op builds a convolution descriptor for use in the other
     convolution operations.
 
-    :param border_mode: 'valid' or 'full'
-    :param subsample: The subsample, tuple like (dx, dy)
-    :param conv_mode: 'conv' or 'cross'
+    see the doc of :func:`dnn_conv` for a description of the parameters
 
     """
     __props__ = ('border_mode', 'subsample', 'conv_mode')

theano/sandbox/cuda/tests/test_conv_cuda_ndarray.py

@@ -586,7 +586,7 @@ def test_gemm_valid():
     extra_shapes += get_shapes2(scales_kern=(2, 2), kern_stride=(2, 2))
 
     for t in _test_valid(cuda.blas.BaseGpuCorrMM,
-                         mode=theano_mode.including("conv_gemm"),
+                         mode=theano_mode.excluding("cudnn"),
                          extra_shapes=extra_shapes):
         yield t
 
@@ -695,7 +695,7 @@ def test_full():
 
 def test_gemm_full():
     for t in _test_full(cuda.blas.BaseGpuCorrMM,
-                        mode=theano_mode.including("conv_gemm")):
+                        mode=theano_mode.excluding("cudnn")):
         yield t
 
 
@@ -747,7 +747,7 @@ def test_subsample():
 
 def test_gemm_subsample():
     for t in _test_subsample(cuda.blas.BaseGpuCorrMM,
-                             theano_mode.including("conv_gemm")):
+                             theano_mode.excluding("cudnn")):
         yield t
 
 
@@ -837,7 +837,8 @@ class TestConvWithPadding(object):
     note that in order to make the yield work, we can not subclass from 
     unittest.TestCase
     """
-    conv_ops = []
+    conv_ops = [lambda i, k, border_mode:
+                theano.sandbox.cuda.blas.GpuCorrMM(border_mode=border_mode)(i, k)]
 
     @classmethod
     def setup_class(cls):
@@ -855,7 +856,7 @@ class TestConvWithPadding(object):
             assert_raises(ValueError, i, img, kern,
                               border_mode='not border')
 
-    def _run_onecase(self, img_shape, kern_shape, padding):
+    def _run_onecase(self, img_shape, kern_shape, padding, op):
         npy_img = numpy.random.rand(*img_shape).astype('float32')
         npy_kern = numpy.random.rand(*kern_shape).astype('float32')
         img = theano._asarray(npy_img, dtype='float32')
@@ -863,10 +864,9 @@ class TestConvWithPadding(object):
         border_mode = padding
         cpuval = py_conv(npy_img, npy_kern, border_mode, (1, 1))
         X = tensor.ftensor4()
-        for op in self.conv_ops:
         Y = op(X, kern, border_mode=border_mode)
-            func = theano.function([X], Y)
-            gpuval = func(img)
+        func = theano.function([X], Y, mode=theano_mode)
+        gpuval = numpy.asarray(func(img))
         assert_allclose(cpuval, gpuval, rtol=1e-5, atol=1e-5)
 
     def test_numeric_value(self):
@@ -877,7 +877,8 @@ class TestConvWithPadding(object):
             ((5, 10, 9, 6), (12, 10, 9, 4), 'valid')
         ]
         for img_shape, kern_shape, padding in params:
-            yield (self._run_onecase, img_shape, kern_shape, padding)
+            for op in self.conv_ops:
+                yield self._run_onecase, img_shape, kern_shape, padding, op
 
 
 def gemm_directly(bs, ch, nf, rImg1, rImg2, rFlt1, rFlt2, subsx, subsy,
@@ -938,8 +939,7 @@ def test_gemm_directly():
 
 
 def gemm_op(mode, subsample):
-    pad = 'full' if mode == 'full' else (0, 0)
-    return theano.sandbox.cuda.blas.GpuCorrMM('valid', subsample, pad)
+    return theano.sandbox.cuda.blas.GpuCorrMM(mode, subsample)
 
 
 def dnn_op(mode, subsample):