better interface for dnn_conv

theano/sandbox/cuda/dnn.py

@@ -141,15 +141,18 @@ class GpuDnnConvDesc(GpuOp):
     def c_compiler(self):
         return NVCC_compiler
 
-    def __init__(self, border_mode, subsample=(1, 1), conv_mode='conv',
-                 padding=(0, 0)):
-        assert border_mode in ('valid', 'full', 'padding')
+    def __init__(self, border_mode, subsample=(1, 1), conv_mode='conv'):
+        if isinstance(border_mode, int):
+            border_mode = (border_mode, border_mode)
+        assert isinstance(border_mode, tuple) or \
+            border_mode in ('valid', 'full'), \
+            'invalid border_mode {}, which must be either "valid", "full", '\
+            'an integer or a pair of integers'.format(border_mode)
         self.border_mode = border_mode
         assert len(subsample) == 2
         self.subsample = subsample
         assert conv_mode in ('conv', 'cross')
         self.conv_mode = conv_mode
-        self.padding = padding
 
     def make_node(self, img_shape, kern_shape):
         if img_shape.type.ndim != 1 or img_shape.type.dtype != 'int64':
@@ -164,14 +167,13 @@ class GpuDnnConvDesc(GpuOp):
         img_shape, kern_shape = inputs
         desc, = outputs
 
-        pad_h_spec, pad_w_spec = map(int, self.padding)
+        if isinstance(self.border_mode, tuple):
+            pad_h_spec, pad_w_spec = map(int, self.border_mode)
             assert pad_h_spec >= 0 and pad_w_spec >= 0
-
-        if self.border_mode == 'padding':
             bmode = 2
         else:
-            assert pad_h_spec == 0 and pad_w_spec == 0, \
-                'padding not zero, but border_mode != "padding"'
+            pad_h_spec = pad_w_spec = 0
+
             if self.border_mode == "valid":
                 bmode = 1
             else:
@@ -464,7 +466,7 @@ class GpuDnnConvGradI(GpuDnnConvBase):
 
 
 def dnn_conv(img, kerns, border_mode='valid', subsample=(1, 1),
-             conv_mode='conv', padding=(0, 0)):
+             conv_mode='conv'):
     """
     GPU convolution using cuDNN from NVIDIA.
 
@@ -473,7 +475,8 @@ def dnn_conv(img, kerns, border_mode='valid', subsample=(1, 1),
 
     :param img: images to do the convolution over
     :param kerns: convolution filters
-    :param border_mode: one of 'valid', 'full' or 'padding'(default: 'valid')
+    :param border_mode: one of 'valid', 'full'; 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 conv_mode: perform convolution (kernels flipped) or cross-correlation.  One of 'conv', 'cross'. (default: 'conv')
 
@@ -484,7 +487,7 @@ def dnn_conv(img, kerns, border_mode='valid', subsample=(1, 1),
     img = gpu_contiguous(img)
     kerns = gpu_contiguous(kerns)
     desc = GpuDnnConvDesc(border_mode=border_mode, subsample=subsample,
-                          conv_mode=conv_mode, padding=padding)(img.shape, kerns.shape)
+                          conv_mode=conv_mode)(img.shape, kerns.shape)
     return GpuDnnConv()(img, kerns, desc)