fix code format and doc

paddle/operators/conv_op.cc

@@ -33,6 +33,8 @@ void ConvOp::InferShape(framework::InferShapeContext* ctx) const {
   int input_channels = in_dims[1];
   int output_channels = filter_dims[0];
 
+  PADDLE_ENFORCE(in_dims.size() == 4 || in_dims.size() == 5,
+                 "Conv intput should be 4-D or 5-D tensor.");
   PADDLE_ENFORCE_EQ(
       in_dims.size(), filter_dims.size(),
       "Conv input dimension and filter dimension should be the same.");
@@ -62,26 +64,30 @@ Conv2DOpMaker::Conv2DOpMaker(framework::OpProto* proto,
     : OpProtoAndCheckerMaker(proto, op_checker) {
   AddInput(
       "Input",
-      "The input tensor of convolution operator. "
+      "(Tensor), the input tensor of convolution operator. "
       "The format of input tensor is NCHW. Where N is batch size, C is the "
       "number of channels, H and W is the height and width of image.");
   AddInput("Filter",
-           "The filter tensor of convolution operator."
+           "(Tensor), the filter tensor of convolution operator."
            "The format of the filter tensor is MCHW, where M is the number of "
            "output image channels, C is the number of input image channels, "
            "H and W is height and width of filter. "
            "If the groups attribute is greater than 1, C equal the number of "
            "input image channels divided by the groups.");
   AddOutput("Output",
-            "The output tensor of convolution operator."
-            "The format of output tensor is also NCHW.");
-  AddAttr<std::vector<int>>("strides", "strides of convolution operator.")
+            "(Tensor), the output tensor of convolution operator."
+            "The format of output tensor is also NCHW. Where N is batch size, "
+            "C is the "
+            "number of channels, H and W is the height and width of image.");
+  AddAttr<std::vector<int>>(
+      "strides", "(vector default:{1, 1}), strides of convolution operator.")
       .SetDefault({1, 1});
-  AddAttr<std::vector<int>>("paddings", "paddings of convolution operator.")
+  AddAttr<std::vector<int>>(
+      "paddings", "(vector default:{0, 0}), paddings of convolution operator.")
       .SetDefault({0, 0});
   AddAttr<int>(
       "groups",
-      "group size of convolution operator. "
+      "(int, default:1), group size of convolution operator. "
       "Refer to grouped convolution in Alex Krizhevsky's paper: "
       "when group=2, the first half of the filters are only connected to the "
       "first half of the input channels, and the second half only connected "
@@ -91,6 +97,21 @@ Conv2DOpMaker::Conv2DOpMaker(framework::OpProto* proto,
 The convolution 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.
+These two elements represent height and width, respectively.
+The input(X) size and output(Out) size may be different.
+
+Example:
+  Input:
+       Input shape: (N, C_in, H_in, W_in)
+       Filter shape: (C_out, C_in, H_f, W_f)
+  Output:
+       Output shape: (N, C_out, H_out, W_out)
+  where
+       H_out = (H_in - filter_size[0] + 2 * paddings[0]) / strides[0] + 1;
+       W_out = (W_in - filter_size[1] + 2 * paddings[1]) / strides[1] + 1;
 )DOC");
 }
 
@@ -115,15 +136,15 @@ Conv3DOpMaker::Conv3DOpMaker(framework::OpProto* proto,
             "The format of output tensor is also NCDHW.");
   AddAttr<std::vector<int>>(
       "strides",
-      "(vector, default {0,0,0}), the strides of convolution operator.")
+      "(vector, default:{0, 0, 0}), the strides of convolution operator.")
       .SetDefault({1, 1, 1});
   AddAttr<std::vector<int>>(
       "paddings",
-      "(vector, default {0,0,0}), the paddings of convolution operator.")
+      "(vector, default:{0, 0, 0}), the paddings of convolution operator.")
       .SetDefault({0, 0, 0});
   AddAttr<int>(
       "groups",
-      "(int, default 1) the group size of convolution operator. "
+      "(int, default:1) the group size of convolution operator. "
       "Refer to grouped convolution in Alex Krizhevsky's paper: "
       "when group=2, the first half of the filters are only connected to the "
       "first half of the input channels, and the second half only connected "

paddle/operators/conv_op.h

@@ -85,9 +85,7 @@ class GemmConv2DKernel : public framework::OpKernel<T> {
     int output_height = output->dims()[2];
     int output_width = output->dims()[3];
 
-    paddle::operators::math::Im2ColFunctor<
-        paddle::operators::math::ColFormat::kCFO, Place, T>
-        im2col;
+    math::Im2ColFunctor<math::ColFormat::kCFO, Place, T> im2col;
     // use col_shape in the im2col calculation
     framework::DDim col_shape = {input_channels / groups, filter_height,
                                  filter_width, output_height, output_width};
@@ -162,12 +160,8 @@ class GemmConvGrad2DKernel : public framework::OpKernel<T> {
     int output_height = output_grad->dims()[2];
     int output_width = output_grad->dims()[3];
 
-    paddle::operators::math::Col2ImFunctor<
-        paddle::operators::math::ColFormat::kCFO, Place, T>
-        col2im;
-    paddle::operators::math::Im2ColFunctor<
-        paddle::operators::math::ColFormat::kCFO, Place, T>
-        im2col;
+    math::Col2ImFunctor<math::ColFormat::kCFO, Place, T> col2im;
+    math::Im2ColFunctor<math::ColFormat::kCFO, Place, T> im2col;
     // use col_shape in the im2col and col2im calculation
     framework::DDim col_shape = {input_channels / groups, filter_height,
                                  filter_width, output_height, output_width};
@@ -283,7 +277,7 @@ class GemmConv3DKernel : public framework::OpKernel<T> {
     int output_height = output->dims()[3];
     int output_width = output->dims()[4];
 
-    paddle::operators::math::Vol2ColFunctor<Place, T> vol2col;
+    math::Vol2ColFunctor<Place, T> vol2col;
     // use col_shape in the vol2col calculation
     framework::DDim col_shape = {input_channels / groups,
                                  filter_depth,
@@ -369,8 +363,8 @@ class GemmConvGrad3DKernel : public framework::OpKernel<T> {
     int output_height = output_grad->dims()[3];
     int output_width = output_grad->dims()[4];
 
-    paddle::operators::math::Col2VolFunctor<Place, T> col2vol;
-    paddle::operators::math::Vol2ColFunctor<Place, T> vol2col;
+    math::Col2VolFunctor<Place, T> col2vol;
+    math::Vol2ColFunctor<Place, T> vol2col;
     // use col_shape in the vol2col and col2vol calculation
     framework::DDim col_shape = {input_channels / groups,
                                  filter_depth,

python/paddle/v2/framework/tests/test_conv2d_op.py

@@ -103,6 +103,9 @@ class TestWithGroup(TestConv2dOp):
         self.op_type = "conv2d"
 
 
+#----------------Conv2dCudnn----------------
+
+
 class TestCudnn(TestConv2dOp):
     def init_group(self):
         self.groups = 1