Including support for 'full' convolutions. It uses the existing pad…

theano/sandbox/cuda/blas.py

@@ -597,7 +597,6 @@ class GpuConvMM(GpuOp):
         border_mode = self.border_mode
         sub = sub.copy()
         pad = self.pad
-
         sub.update(locals())
 
         return """
@@ -607,6 +606,7 @@ class GpuConvMM(GpuOp):
     //Optional args
     int dx = %(dx)s;
     int dy = %(dy)s;
+    int pad = 0;
     CudaNdarray * img = %(img)s;
     CudaNdarray * kern = %(kern)s;
     CudaNdarray * out2 = NULL;
@@ -640,6 +640,7 @@ class GpuConvMM(GpuOp):
     {
         logical_rows = CudaNdarray_HOST_DIMS(img)[2] + CudaNdarray_HOST_DIMS(kern)[2] - 1;
         logical_cols = CudaNdarray_HOST_DIMS(img)[3] + CudaNdarray_HOST_DIMS(kern)[3] - 1;
+        pad = CudaNdarray_HOST_DIMS(kern)[2] - 1;
     }
     out_dim[2] = ceil_intdiv(logical_rows, dx);
     out_dim[3] = ceil_intdiv(logical_cols, dy);
@@ -657,7 +658,7 @@ class GpuConvMM(GpuOp):
 
     }
 
-    out2 = validMM(%(img)s, %(kern)s, %(out)s);
+    out2 = validMM(%(img)s, %(kern)s, %(out)s, pad);
     if (out2==NULL){
        %(fail)s
     }
@@ -669,6 +670,7 @@ class GpuConvMM(GpuOp):
 ##
 # Not really a BLAS operation, but whatever.
 #
+
 class GpuConv(GpuOp):
     """
     Implement the batched and stacked 2d convolution on the gpu.

theano/sandbox/cuda/conv_gemm.cu

@@ -78,7 +78,8 @@ void im2col(const float* data_im, const int channels,
 // Author: Arjun Jain
 CudaNdarray* validMM(const CudaNdarray *input, 
 				      CudaNdarray *weight,
-				      CudaNdarray *output) 
+				      CudaNdarray *output,
+				      int padding = 0) 
 {
 
   	cublasStatus_t status;
@@ -96,7 +97,6 @@ CudaNdarray* validMM(const CudaNdarray *input,
      // TODO: stride(dW, dH) and padding as function parameter
      int dH = 1; 
      int dW = 1;
-     int padding = 0; 
      int kH = CudaNdarray_HOST_DIMS(weight)[2];
      int kW = CudaNdarray_HOST_DIMS(weight)[3];
      int nInputPlane = CudaNdarray_HOST_DIMS(input)[1]; 

theano/sandbox/cuda/tests/test_conv_cuda_ndarray.py

@@ -839,7 +839,7 @@ def test_gemm():
                         kshape = (nf, ch, rFlt, rFlt)
                         print "ishape: ", ishape
                         print "kshape: ", kshape 
-                        mode = 'valid'
+                        mode = 'full'
                         subsample = (1, 1)
                     
                         npy_img = theano._asarray(numpy.random.rand(*ishape), dtype='float32')
@@ -938,4 +938,3 @@ def test_stack_rows_segfault_070312():
             nkern=1, bsize=1)
     f = theano.function([], [], updates=[(out, op(img, kern))], mode=theano_mode)
     f()
-