Move convolution to the GPU when the image shape and logical image shape differ.

theano/sandbox/cuda/opt.py

@@ -1011,11 +1011,7 @@ def local_gpu_conv(node):
     """
     def GpuConvOp_from_ConvOp(op):
         logical_img_hw = None
-        if op.imshp_logical is not None:
-            logical_img_hw = op.imshp_logical[1:3]
-            if logical_img_hw != op.imshp[1:3]:
-                # this case is not implemented
-                return None
+
         if op.kshp_logical is not None and op.kshp_logical != op.kshp:
             return None
         #print op.kshp, op.imshp[1:3]
@@ -1033,6 +1029,23 @@ def local_gpu_conv(node):
         #HACK to print the number of MFlops in the profiler output.
         if hasattr(op, 'flops'):
             ret.flops = op.flops
+        if op.imshp_logical is not None:
+            logical_img_hw = op.imshp_logical[1:3]
+            if logical_img_hw != op.imshp[1:3]:
+                # this case is not implemented
+                #return None
+                rstride = int(numpy.ceil(op.imshp_logical[1] /
+                                         float(op.imshp[1])))
+                cstride = int(numpy.ceil(op.imshp_logical[2] /
+                                         float(op.imshp[2])))
+                def make_graph(img, kern):
+                    buf = tensor.alloc(numpy.asarray(0, dtype=img.dtype),
+                                       img.shape[0], *op.imshp_logical)
+                    img = tensor.set_subtensor(buf[:, :, ::rstride, ::cstride],
+                                               img)
+                    img = gpu_from_host(img)
+                    return ret(img, kern)
+                return make_graph
         return ret
 
     if node.op == gpu_from_host:

theano/sandbox/cuda/tests/test_conv_cuda_ndarray.py

@@ -17,6 +17,7 @@ except ImportError:
     pass
 
 import theano
+from theano import tensor
 
 # Skip test if cuda_ndarray is not available.
 import theano.sandbox.cuda as cuda_ndarray
@@ -707,14 +708,41 @@ def test_subsample():
     exec_conv(version_full, shapes, verbose, random, 'full',
               print_=print_, ones=ones)
 
-## See #616
-#def test_logical_shapes():
-#    # implement when
-#    print >> sys.stderr, ("WARNING TODO: test_logical_shapes not implemented"
-#    " (i.e. imshp_logical, kshp_logical, kshp_logical_top_aligned)")
-
 
 class TestConv2DGPU(unittest.TestCase):
+    def test_logical_shapes(self):
+        for stride in range(1, 4):
+            kshp = (10, 2, 10, 10)
+            featshp = (3, 10, 11, 11)
+
+            a = tensor.ftensor4()
+            A = tensor.ftensor4()
+
+            # Need to transpose first two dimensions of kernel, and reverse
+            # index kernel image dims (for correlation)
+            kernel_rotated = tensor.transpose(A, axes=[1, 0, 2, 3])
+
+            featshp_logical = (featshp[0], featshp[1], featshp[2] * stride,
+                               featshp[3] * stride)
+            kshp_rotated = (kshp[1], kshp[0], kshp[2], kshp[3])
+            print featshp, kshp_rotated, featshp_logical[1:], kshp[2:]
+            image_estimate = tensor.nnet.conv2d(a, kernel_rotated,
+                                                border_mode='full',
+                                                image_shape=featshp,
+                                                filter_shape=kshp_rotated,
+                                                imshp_logical=featshp_logical[1:],
+                                                kshp_logical=kshp[2:])
+
+            func = theano.function([a, A], image_estimate, mode=theano_mode)
+            theano.printing.debugprint(func,)
+            assert any([isinstance(node.op, theano.sandbox.cuda.blas.GpuConv)
+                        for node in func.maker.fgraph.toposort()])
+
+            a_in = numpy.random.randn(*featshp).astype("float32")
+            A_in = numpy.random.randn(*kshp).astype("float32")
+
+            func(a_in, A_in)
+
     def test_invalid_input_shape(self):
         """
         Tests that when the shape gived at build time is not the same as