Add a test for the gradients of convolution and fix them.

theano/sandbox/cuda/dnn.py

@@ -4,7 +4,7 @@ import numpy
 import theano
 from theano import Apply, gof, tensor, config, Variable
 from theano.scalar import as_scalar, constant
-from theano.gradient import DisconnectedType
+from theano.gradient import DisconnectedType, grad_not_implemented
 from theano.gof import Optimizer, local_optimizer, COp
 from theano.gof.type import CDataType, Generic
 from theano.compat import PY3
@@ -434,13 +434,13 @@ class GpuDnnConv(DnnBase, COp):
 
         d_img = GpuDnnConvGradI()(kerns, top, img.zeros_like(), desc)
         d_kerns = GpuDnnConvGradW()(img, top, kerns.zeros_like(), desc)
+        d_alpha = grad_not_implemented(self, 4, alpha)
 
-        return [d_img, d_kerns, output.zeros_like(),
-                DisconnectedType()(), DisconnectedType()()]
+        return [d_img, d_kerns, top * alpha, DisconnectedType()(), d_alpha]
 
     def connection_pattern(self, node):
-        # not connected to desc, alpha
-        return [[1], [1], [1], [0], [0]]
+        # not connected to desc
+        return [[1], [1], [1], [0], [1]]
 
     @staticmethod
     def get_out_shape(ishape, kshape, border_mode, subsample):
@@ -509,13 +509,13 @@ class GpuDnnConvGradW(DnnBase, COp):
 
         d_img = GpuDnnConvGradI()(kerns, top, img.zeros_like(), desc)
         d_top = GpuDnnConv()(img, kerns, top.zeros_like(), desc)
+        d_alpha = grad_not_implemented(self, 4, alpha)
 
-        return (d_img, d_top, output.zeros_like(),
-                DisconnectedType()(), DiconnnectedType()())
+        return (d_img, d_top, kerns * alpha, DisconnectedType()(), d_alpha)
 
     def connection_pattern(self, node):
-        # not connected to desc, alpha
-        return [[1], [1], [1], [0], [0]]
+        # not connected to desc
+        return [[1], [1], [1], [0], [1]]
 
     def get_op_params(self):
         if self.inplace:
@@ -573,12 +573,13 @@ class GpuDnnConvGradI(DnnBase, COp):
 
         d_kerns = GpuDnnConvGradW()(img, top, kerns.zeros_like(), desc)
         d_top = GpuDnnConv()(img, kerns, top.zeros_like(), desc)
-        return (d_kerns, d_top, output.zeros_like(),
-                DisconnectedType()(), DisconnectedType()())
+        d_alpha = grad_not_implemented(self, 4, alpha)
+
+        return (d_kerns, d_top, img * alpha, DisconnectedType()(), d_alpha)
 
     def connection_pattern(self, node):
-        # not connected to desc, alpha
-        return [[1], [1], [1], [0], [0]]
+        # not connected to desc
+        return [[1], [1], [1], [0], [1]]
 
     def get_op_params(self):
         if self.inplace:

theano/sandbox/cuda/tests/test_dnn.py

@@ -492,6 +492,41 @@ def test_dnn_conv_merge():
         utt.assert_allclose(v1, v2)
 
 
+def test_dnn_conv_grad():
+    if dnn.version() == -1:
+        raise SkipTest('alpha != 1.0 not supported in cudnn v1')
+
+    b = 1
+    c = 4
+    f = 3
+    ih = 2
+    iw = 8
+    kh = 2
+    kw = 2
+    img_val = numpy.random.random((b, c, ih, iw)).astype('float32')
+    kern_val = numpy.random.random((f, c, kh, kw)).astype('float32')
+    out_val = numpy.random.random((b, f, ih-kw+1, iw-kw+1)).astype('float32')
+
+    def dconv(img, kern, out):
+        desc = dnn.GpuDnnConvDesc(border_mode='valid', subsample=(1, 1),
+                                  conv_mode='conv')(img.shape, kern.shape)
+        return dnn.GpuDnnConv()(img, kern, out, desc)
+
+    def dconvi(img, kern, out):
+        desc = dnn.GpuDnnConvDesc(border_mode='valid', subsample=(1, 1),
+                                  conv_mode='conv')(img.shape, kern.shape)
+        return dnn.GpuDnnConvGradI()(kern, out, img, desc)
+
+    def dconvw(img, kern, out):
+        desc = dnn.GpuDnnConvDesc(border_mode='valid', subsample=(1, 1),
+                                  conv_mode='conv')(img.shape, kern.shape)
+        return dnn.GpuDnnConvGradW()(img, out, kern, desc)
+
+    utt.verify_grad(dconv, [img_val, kern_val, out_val])
+    utt.verify_grad(dconvi, [img_val, kern_val, out_val])
+    utt.verify_grad(dconvw, [img_val, kern_val, out_val])
+
+
 def test_version():
     if not cuda.dnn.dnn_available():
         raise SkipTest(cuda.dnn.dnn_available.msg)