Feature: support for all stride sized. This makes it work for all subsample (x,…

theano/sandbox/cuda/blas.py

@@ -517,9 +517,6 @@ class GpuCorrMM(GpuOp):
         if pad != 0:
             raise NotImplementedError(
                 "GpuCorrMM don't implement the pad parameter")
-        if subsample != (1, 1):
-            raise NotImplementedError(
-                "GpuCorrMM we don't implement the subsample parameter")
 
     def __eq__(self, other):
         return type(self) == type(other) \
@@ -662,7 +659,7 @@ class GpuCorrMM(GpuOp):
 
     }
 
-    out2 = corrMM(%(img)s, %(kern)s, %(out)s, padH, padW);
+    out2 = corrMM(%(img)s, %(kern)s, %(out)s, dx, dy, padH, padW);
     if (out2==NULL){
        %(fail)s
     }

theano/sandbox/cuda/conv_gemm.cu

@@ -92,7 +92,10 @@ void im2col(const float* data_im, const int channels,
 CudaNdarray* corrMM(const CudaNdarray *input, 
 				      CudaNdarray *weight,
 				      CudaNdarray *output,
-				      int padH, int padW = 0) 
+				      int dH = 1, 
+     				  int dW = 1,
+				      int padH = 0, 
+				      int padW = 0) 
 {
 
   	cublasStatus_t status;
@@ -107,9 +110,6 @@ CudaNdarray* corrMM(const CudaNdarray *input,
         PyErr_SetString(PyExc_ValueError, "required weight of 4D");
     }
         
-     // TODO: stride(dW, dH) and padding as function parameter
-     int dH = 1; 
-     int dW = 1;
      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

@@ -644,10 +644,6 @@ def test_valid():
     # Add tests with strided inputs by still square images and filters.
     shapes += get_shapes2(scales_img=(2, 2), img_stride=(2, 2))
     shapes += get_shapes2(scales_kern=(2, 2), kern_stride=(2, 2))
-    # Keep only tests with square images and filters even with inputs strides
-    shapes = [shp for shp in shapes if (
-        shp[0][2]/shp[3][0] == shp[0][3]/shp[3][1] and
-        shp[1][2]/shp[4][0] == shp[1][3]/shp[4][1])]
     exec_conv(version, shapes, verbose, random, 'valid',
               print_=print_, ones=ones, rtol=1.1e-5,
               theano_mode=mode, cls=cuda.blas.GpuCorrMM)
@@ -718,8 +714,6 @@ def test_full():
     # Test the GpuCorrMM version
     mode = theano_mode.including("conv_gemm")
 
-    shapes = [shp for shp in shapes if shp[1][2] == shp[1][3]]
-    shapes = [shp for shp in shapes if shp[0][2] == shp[0][3]]
     shapes = shapes[0:10]
     exec_conv(version, shapes, verbose, random, 'full',
               theano_mode=mode, cls=cuda.blas.GpuCorrMM)
@@ -841,11 +835,14 @@ def test_gemm():
                         for rImg2 in range(5, 9):
                             for rFlt1 in range(2, 4):
                                 for rFlt2 in range(2, 4):
+                                    for subsx in range(1, 3):
+                                        for subsy in range(1, 3):
                                             ishape = (bs, ch, rImg1, rImg2)
                                             kshape = (nf, ch, rFlt1, rFlt2)
                                             print "ishape: ", ishape
                                             print "kshape: ", kshape 
-                                    subsample = (1, 1)
+                                            subsample = (subsx, subsy)
+                                            print "subsample: ", subsample 
                                         
                                             npy_img = theano._asarray(numpy.random.rand(*ishape), dtype='float32')
                                             npy_kern = theano._asarray(numpy.random.rand(*kshape), dtype='float32')
@@ -860,7 +857,8 @@ def test_gemm():
                                         
                                             t1 = time.time()
                                             
-                                    op = theano.sandbox.cuda.blas.GpuCorrMM(border_mode=mode)(i, k)
+                                            op = theano.sandbox.cuda.blas.GpuCorrMM(border_mode=mode, \
+                                                    subsample=subsample)(i, k)
                                             f = theano.function([i, k], op, mode=theano_mode)
                                         
                                             npy_kern = npy_kern[:,:,::-1,::-1]