提交 b34df5f1 编写于 作者: S sweetsky0901

add some doc

上级 5fe4d7fb
...@@ -22,36 +22,39 @@ class Detection_output_OpMaker : public framework::OpProtoAndCheckerMaker { ...@@ -22,36 +22,39 @@ class Detection_output_OpMaker : public framework::OpProtoAndCheckerMaker {
framework::OpAttrChecker* op_checker) framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) { : OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Loc", AddInput("Loc",
"(Tensor) The input tensor of detection_output operator. " "(Tensor) The input tensor of detection_output operator."
"The input predict locations"
"The format of input tensor is kNCHW. Where K is priorbox point " "The format of input tensor is kNCHW. Where K is priorbox point "
"numbers," "numbers,"
"N is How many boxes are there on each point, " "N is How many boxes are there on each point, "
"C is 4, H and W both are 1."); "C is 4, H and W both are 1.");
AddInput("Conf", AddInput("Conf",
"(Tensor) The input tensor of detection_output operator. " "(Tensor) The input tensor of detection_output operator."
"The input priorbox confidence."
"The format of input tensor is kNCHW. Where K is priorbox point " "The format of input tensor is kNCHW. Where K is priorbox point "
"numbers," "numbers,"
"N is How many boxes are there on each point, " "N is How many boxes are there on each point, "
"C is the number of classes, H and W both are 1."); "C is the number of classes, H and W both are 1.");
AddInput("PriorBox", AddInput("PriorBox",
"(Tensor) The input tensor of detection_output operator. " "(Tensor) The input tensor of detection_output operator."
"The format of input tensor is the position and variance " "The format of input tensor is the position and variance "
"of the boxes"); "of the boxes");
AddOutput("Out", AddOutput("Out",
"(Tensor) The output tensor of detection_output operator."); "(Tensor) The output tensor of detection_output operator.");
AddAttr<int>("background_label_id", AddAttr<int>("background_label_id", "(int), The background class index.");
"(int), the attr of detection_output operator"); AddAttr<int>("num_classes", "(int), The number of the classification.");
AddAttr<int>("num_classes",
"(int), the attr of detection_output operator");
AddAttr<float>("nms_threshold", AddAttr<float>("nms_threshold",
"(float), the attr of detection_output operator"); "(float), The Non-maximum suppression threshold.");
AddAttr<float>("confidence_threshold", AddAttr<float>("confidence_threshold",
"(float), the attr of detection_output operator"); "(float), The classification confidence threshold.");
AddAttr<int>("top_k", "(int), the attr of detection_output operator"); AddAttr<int>("top_k", "(int), The bbox number kept of the layer’s output.");
AddAttr<int>("nms_top_k", "(int), the attr of detection_output operator"); AddAttr<int>("nms_top_k",
"(int), The bbox number kept of the NMS’s output.");
AddComment(R"DOC( AddComment(R"DOC(
detection output for SSD(single shot multibox detector) detection output for SSD(single shot multibox detector)
Apply the NMS to the output of network and compute the predict
bounding box location. The output’s shape of this layer could
be zero if there is no valid bounding box.
)DOC"); )DOC");
} }
}; };
......
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