fix doc and unit test

paddle/operators/conv_transpose_op.cc

@@ -65,16 +65,17 @@ Conv2DTransposeOpMaker::Conv2DTransposeOpMaker(
       "Input",
       "(Tensor) The input tensor of convolution transpose operator. "
       "The format of input tensor is NCHW. Where N is batch size, C is the "
-      "number of input channels, H and W is the height and width of image.");
+      "number of input channels, H is the height of the feature, and "
+      "W is the width of the feature.");
   AddInput("Filter",
-           "(Tensor) The filter tensor of convolution transpose operator."
+           "(Tensor) The filter tensor of convolution transpose operator. "
            "The format of the filter tensor is CMHW, where C is the number of "
            "output image channels, M is the number of input image channels, "
-           "H and W is height and width of filter. "
+           "H is the height of the filter, and W is the width of the filter. "
            "We enforce groups number == 1 and padding == 0 in "
-           "convolution transpose Scenario.");
+           "the convolution transpose scenario.");
   AddOutput("Output",
-            "(Tensor) The output tensor of convolution transpose operator."
+            "(Tensor) The output tensor of convolution transpose operator. "
             "The format of output tensor is also NCHW.");
   AddAttr<std::vector<int>>(
       "strides",
@@ -85,13 +86,15 @@ Conv2DTransposeOpMaker::Conv2DTransposeOpMaker(
       "(vector defalut:{0, 0}), paddings of convolution transpose operator.")
       .SetDefault({0, 0});
   AddComment(R"DOC(
+Convolution2D Transpose Operator.
+
 The convolution transpose operation calculates the output based on the input, filter
 and strides, paddings, groups parameters. The size of each dimension of the
 parameters is checked in the infer-shape.
 
 Input(Input, Filter) and output(Output) are in NCHW format. Where N is batch
-size, C is the number of channels, H and W is the height and
-width of feature. Parameters(ksize, strides, paddings) are two elements.
+size, C is the number of channels, H is the height of the feature, and 
+W is the width of the feature. Parameters(ksize, strides, paddings) are two elements.
 These two elements represent height and width, respectively.
 The input(X) size and output(Out) size may be different.
 Example:
@@ -109,25 +112,26 @@ Example:
 Conv3DTransposeOpMaker::Conv3DTransposeOpMaker(
     framework::OpProto* proto, framework::OpAttrChecker* op_checker)
     : OpProtoAndCheckerMaker(proto, op_checker) {
-  AddInput(
-      "Input",
+  AddInput("Input",
            "(Tensor) The input tensor of convolution transpose operator."
            "The format of input tensor is NCDHW. Where N is batch size, C is "
-      "the number of channels, D, H and W is the depth, height and width of "
-      "feature.");
+           "the number of channels, D is the depth of the feature, H is the "
+           "height of the feature, and "
+           "W is the width of the feature.");
   AddInput("Filter",
            "(Tensor) The filter tensor of convolution transpose operator."
            "The format of the filter tensor is CMDHW, where C is the number of "
-           "output image channels, M is the number of input image channels, "
-           "D, H and W is depth, height and width of filter. "
+           "output image channels, M is the number of input image channels, D "
+           "is the depth of the filter, H is the height of the filter, and "
+           "W is the width of the filter."
            "We enforce groups number == 1 and padding == 0 in "
-           "convolution transpose Scenario.");
+           "the convolution3d transpose scenario.");
   AddOutput("Output",
             "(Tensor) The output tensor of convolution transpose operator."
             "The format of output tensor is also NCDHW."
             "Where N is batch size, C is "
-            "the number of channels, D, H and W is the depth, height and "
-            "width of feature.");
+            "the number of channels, D is the depth of the feature, H is the "
+            "height of the feature, and W is the width of the feature.");
   AddAttr<std::vector<int>>(
       "strides",
       "(vector defalut:{1, 1, 1}), strides of convolution transpose operator.")
@@ -137,13 +141,16 @@ Conv3DTransposeOpMaker::Conv3DTransposeOpMaker(
       "(vector defalut:{0, 0, 0}), paddings of convolution transpose operator.")
       .SetDefault({0, 0, 0});
   AddComment(R"DOC(
+Convolution3D Transpose Operator.
+
 The convolution transpose operation calculates the output based on the input, filter
 and strides, paddings, groups parameters. The size of each dimension of the
 parameters is checked in the infer-shape.
 
 Input(Input, Filter) and output(Output) are in NCDHW format. Where N is batch
-size, C is the number of channels, d, H and W is the depth, height and
-width of feature. Parameters(ksize, strides, paddings) are three elements.
+size, C is the number of channels, D is the depth of the feature, 
+H is the height of the feature, and W is the width of the feature. 
+Parameters(ksize, strides, paddings) are three elements.
 These three elements represent depth, height and width, respectively.
 The input(X) size and output(Out) size may be different.
 Example:

paddle/operators/conv_transpose_op.h

@@ -175,6 +175,10 @@ class GemmConv2DTransposeGradKernel : public framework::OpKernel<T> {
     DDim filter_matrix_shape = {m, c * k_h * k_w};
     filter.Resize(filter_matrix_shape);
 
+    if ((!input_grad) && (!filter_grad)) {
+      return;
+    }
+
     // convolution transpose grad on input:
     // im2col + gemm (similar to conv-forward)
     // input need to compute gradient
@@ -265,7 +269,7 @@ class GemmConv3DTransposeKernel : public framework::OpKernel<T> {
     const int64_t o_h = output->dims()[3];
     const int64_t o_w = output->dims()[4];
 
-    paddle::operators::math::Col2VolFunctor<Place, T> col2vol;
+    math::Col2VolFunctor<Place, T> col2vol;
 
     // use col_shape in the vol2col and col2vol calculation
     DDim col_shape = {c, k_d, k_h, k_w, d, h, w};
@@ -349,7 +353,7 @@ class GemmConv3DTransposeGradKernel : public framework::OpKernel<T> {
     const int64_t o_w = output_grad->dims()[4];
 
     // Only vol2col functor required for bp to get to the right shape
-    paddle::operators::math::Vol2ColFunctor<Place, T> vol2col;
+    math::Vol2ColFunctor<Place, T> vol2col;
 
     // use col_shape in the vol2col and col2vol calculation
     DDim col_shape = {c, k_d, k_h, k_w, d, h, w};
@@ -363,6 +367,10 @@ class GemmConv3DTransposeGradKernel : public framework::OpKernel<T> {
     DDim filter_matrix_shape = {m, c * k_d * k_h * k_w};
     filter.Resize(filter_matrix_shape);
 
+    if ((!input_grad) && (!filter_grad)) {
+      return;
+    }
+
     // convolution transpose grad on input:
     // vol2col + gemm (similar to conv-forward)
     // input need to compute gradient

python/paddle/v2/framework/tests/test_conv2d_transpose_op.py

@@ -58,36 +58,37 @@ class TestConv2dTransposeOp(OpTest):
         print 'check output here for', self.op_type
         self.check_output()
 
-    def init_test_case(self):
-        self.pad = [0, 0]
-        self.stride = [1, 1]
-        self.dilations = [1, 1]
-        self.input_size = [2, 3, 5, 5]  # NCHW
-        f_c = self.input_size[1]
-        self.filter_size = [f_c, 6, 3, 3]
-
-    def init_op_type(self):
-        self.op_type = "conv2d_transpose"
-
     def test_check_grad_no_input(self):
         self.check_grad(
             ['Filter'],
             'Output',
-            max_relative_error=0.05,
+            max_relative_error=0.02,
             no_grad_set=set(['Input']))
 
     def test_check_grad_no_filter(self):
         self.check_grad(
             ['Input'],
             'Output',
-            max_relative_error=0.05,
+            max_relative_error=0.02,
             no_grad_set=set(['Filter']))
 
     def test_check_grad(self):
         self.check_grad(
-            set(['Input', 'Filter']), 'Output', max_relative_error=0.05)
+            set(['Input', 'Filter']), 'Output', max_relative_error=0.02)
+
+    def init_test_case(self):
+        self.pad = [0, 0]
+        self.stride = [1, 1]
+        self.dilations = [1, 1]
+        self.input_size = [2, 3, 5, 5]  # NCHW
+        f_c = self.input_size[1]
+        self.filter_size = [f_c, 6, 3, 3]
+
+    def init_op_type(self):
+        self.op_type = "conv2d_transpose"
 
 
+# ------------ test_cudnn ------------
 class TestCudnn(TestConv2dTransposeOp):
     def init_op_type(self):
         self.op_type = "conv2d_transpose_cudnn"

python/paddle/v2/framework/tests/test_conv3d_transpose_op.py

@@ -65,20 +65,20 @@ class TestConv3dTransposeOp(OpTest):
 
     def test_check_grad(self):
         self.check_grad(
-            set(['Input', 'Filter']), 'Output', max_relative_error=0.05)
+            set(['Input', 'Filter']), 'Output', max_relative_error=0.02)
 
     def test_check_grad_no_filter(self):
         self.check_grad(
             ['Input'],
             'Output',
-            max_relative_error=0.05,
+            max_relative_error=0.02,
             no_grad_set=set(['Filter']))
 
     def test_check_grad_no_input(self):
         self.check_grad(
             ['Filter'],
             'Output',
-            max_relative_error=0.05,
+            max_relative_error=0.02,
             no_grad_set=set(['Input']))
 
     def init_test_case(self):