From 8ac1178707fed50d3061445ee410d6987e3b70de Mon Sep 17 00:00:00 2001 From: chengduoZH Date: Mon, 6 Nov 2017 10:26:01 +0800 Subject: [PATCH] fix doc --- paddle/operators/conv_op.cc | 76 +++++++++++++++++++------------------ 1 file changed, 40 insertions(+), 36 deletions(-) diff --git a/paddle/operators/conv_op.cc b/paddle/operators/conv_op.cc index 1250900d15..54ac4f4111 100644 --- a/paddle/operators/conv_op.cc +++ b/paddle/operators/conv_op.cc @@ -64,42 +64,41 @@ Conv2DOpMaker::Conv2DOpMaker(framework::OpProto* proto, : OpProtoAndCheckerMaker(proto, op_checker) { AddInput( "Input", - "(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."); + "(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 is the height of the feature, " + "and W is the width of the feature."); AddInput("Filter", - "(Tensor), 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 " + "H is the height of the filter, and W is the width of the filter. " + "If the groups attribute is greater than 1, C equals the number of " "input image channels divided by the groups."); AddOutput("Output", - "(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>( - "strides", "(vector default:{1, 1}), strides of convolution operator.") + "(Tensor) The output tensor of convolution operator. " + "The format of output tensor is also NCHW."); + AddAttr>("strides", "strides of convolution operator.") .SetDefault({1, 1}); - AddAttr>( - "paddings", "(vector default:{0, 0}), paddings of convolution operator.") + AddAttr>("paddings", "paddings of convolution operator.") .SetDefault({0, 0}); AddAttr( "groups", - "(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 " - "to the second half.") + "(int default:1), the group size of convolution operator. " + "According to grouped convolution in Alex Krizhevsky's Deep CNN paper: " + "when group=2, the first half of the filters is only connected to the " + "first half of the input channels, while the second half of the filters " + "is only connected to the second half of the input channels.") .SetDefault(1); AddComment(R"DOC( +Convolution Operator. + 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. +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. @@ -120,19 +119,21 @@ Conv3DOpMaker::Conv3DOpMaker(framework::OpProto* proto, : OpProtoAndCheckerMaker(proto, op_checker) { AddInput( "Input", - "(Tensor), the input tensor of convolution operator. " + "(Tensor) The input tensor of convolution 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 " - "image."); + "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 operator." + "(Tensor) The filter tensor of convolution operator. " "The format of the filter tensor is MCDHW, where M is the number of " "output image channels, C is the number of input image channels, " - "D, H and W is depth, height and width of filter. " - "If the groups attribute is greater than 1, C equal the number of " + "D is the depth of the filter, H is the height of the filter, and W " + "is the width of the filter." + "If the groups attribute is greater than 1, C equals the number of " "input image channels divided by the groups."); AddOutput("Output", - "(Tensor), the output tensor of convolution operator." + "(Tensor) The output tensor of convolution operator." "The format of output tensor is also NCDHW."); AddAttr>( "strides", @@ -144,20 +145,23 @@ Conv3DOpMaker::Conv3DOpMaker(framework::OpProto* proto, .SetDefault({0, 0, 0}); AddAttr( "groups", - "(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 " - "to the second half.") + "(int default:1), the group size of convolution operator. " + "According to grouped convolution in Alex Krizhevsky's Deep CNN paper: " + "when group=2, the first half of the filters is only connected to the " + "first half of the input channels, while the second half of the filters " + "is only connected to the second half of the input channels.") .SetDefault(1); + AddComment(R"DOC( +Convolution3D Operator. + 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 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. -These three elements represent depth, height and width, respectively. +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: -- GitLab