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@ -35,8 +35,8 @@ class BoxDecoderAndAssignOp : public framework::OperatorWithKernel {
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ctx->HasInput("BoxScore"),
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"Input(BoxScore) of BoxDecoderAndAssignOp should not be null.");
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PADDLE_ENFORCE(
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ctx->HasOutput("OutputBox"),
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"Output(OutputBox) of BoxDecoderAndAssignOp should not be null.");
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ctx->HasOutput("DecodeBox"),
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"Output(DecodeBox) of BoxDecoderAndAssignOp should not be null.");
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PADDLE_ENFORCE(
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ctx->HasOutput("OutputAssignBox"),
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"Output(OutputAssignBox) of BoxDecoderAndAssignOp should not be null.");
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@ -68,9 +68,9 @@ class BoxDecoderAndAssignOp : public framework::OperatorWithKernel {
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"of box_score is [N, classnum], The shape of prior_box "
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"is [N, 4]");
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ctx->SetOutputDim("OutputBox", framework::make_ddim({target_box_dims[0],
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ctx->SetOutputDim("DecodeBox", framework::make_ddim({target_box_dims[0],
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target_box_dims[1]}));
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ctx->ShareLoD("TargetBox", /*->*/ "OutputBox");
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ctx->ShareLoD("TargetBox", /*->*/ "DecodeBox");
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ctx->SetOutputDim(
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"OutputAssignBox",
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framework::make_ddim({prior_box_dims[0], prior_box_dims[1]}));
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@ -84,38 +84,32 @@ class BoxDecoderAndAssignOpMaker : public framework::OpProtoAndCheckerMaker {
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AddInput(
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"PriorBox",
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"(Tensor, default Tensor<float>) "
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"Box list PriorBox is a 2-D Tensor with shape [M, 4] holds M boxes, "
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"each box is represented as [xmin, ymin, xmax, ymax], "
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"Box list PriorBox is a 2-D Tensor with shape [N, 4] which holds N "
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"boxes and each box is represented as [xmin, ymin, xmax, ymax], "
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"[xmin, ymin] is the left top coordinate of the anchor box, "
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"if the input is image feature map, they are close to the origin "
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"of the coordinate system. [xmax, ymax] is the right bottom "
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"coordinate of the anchor box.");
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AddInput("PriorBoxVar",
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"(Tensor, default Tensor<float>, optional) "
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"PriorBoxVar is a 2-D Tensor with shape [M, 4] holds M group "
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"of variance. PriorBoxVar will set all elements to 1 by "
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"PriorBoxVar is a 2-D Tensor with shape [N, 4] which holds N "
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"group of variance. PriorBoxVar will set all elements to 1 by "
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"default.")
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.AsDispensable();
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AddInput(
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"TargetBox",
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"(LoDTensor or Tensor) This input can be a 2-D LoDTensor with shape "
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"[N, classnum*4]. [N, classnum*4], each box is represented as "
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"[xmin, ymin, xmax, ymax], [xmin, ymin] is the left top coordinate "
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"of the box if the input is image feature map, they are close to "
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"the origin of the coordinate system. [xmax, ymax] is the right "
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"bottom coordinate of the box. This tensor can contain LoD "
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"information to represent a batch of inputs. One instance of this "
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"batch can contain different numbers of entities.");
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AddInput(
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"BoxScore",
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"(LoDTensor or Tensor) This input can be a 2-D LoDTensor with shape "
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AddInput("TargetBox",
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"(LoDTensor or Tensor) "
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"This input can be a 2-D LoDTensor with shape "
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"[N, classnum*4]. It holds N targets for N boxes.");
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AddInput("BoxScore",
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"(LoDTensor or Tensor) "
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"This input can be a 2-D LoDTensor with shape "
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"[N, classnum], each box is represented as [classnum] which is "
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"the classification probabilities.");
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AddAttr<float>("box_clip",
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"(float, default 4.135, np.log(1000. / 16.)) "
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"clip box to prevent overflowing")
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.SetDefault(4.135f);
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AddOutput("OutputBox",
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AddOutput("DecodeBox",
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"(LoDTensor or Tensor) "
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"the output tensor of op with shape [N, classnum * 4] "
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"representing the result of N target boxes decoded with "
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@ -130,12 +124,12 @@ class BoxDecoderAndAssignOpMaker : public framework::OpProtoAndCheckerMaker {
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Bounding Box Coder.
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Decode the target bounding box with the priorbox information.
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Decode the target bounding box with the prior_box information.
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The Decoding schema described below:
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The Decoding schema is described below:
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$$
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oy = (ph \\times pyv \\times ty + py) - \\frac{th}{2}
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ox = (pw \\times pxv \\times tx + px) - \\frac{tw}{2}
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$$
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$$
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oy = (ph \\times pyv \\times ty + py) - \\frac{th}{2}
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@ -149,15 +143,15 @@ The Decoding schema described below:
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where `tx`, `ty`, `tw`, `th` denote the target box's center coordinates, width
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and height respectively. Similarly, `px`, `py`, `pw`, `ph` denote the
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priorbox's (anchor) center coordinates, width and height. `pxv`, `pyv`, `pwv`,
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`phv` denote the variance of the priorbox and `ox`, `oy`, `ow`, `oh` denote the
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encoded/decoded coordinates, width and height.
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prior_box's (anchor) center coordinates, width and height. `pxv`, `pyv`, `pwv`,
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`phv` denote the variance of the prior_box and `ox`, `oy`, `ow`, `oh` denote the
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decoded coordinates, width and height in decode_box.
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After box decode, the Assigning schema described below:
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decode_box is obtained after box decode, then assigning schema is described below:
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For each priorbox, use the best non-background class's decoded values to
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updata the priorbox locations and get outputassignbox. So, the shape of
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output_assign_box is the same as priorbox.
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For each prior_box, use the best non-background class's decoded values to
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update the prior_box locations and get output_assign_box. So, the shape of
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output_assign_box is the same as PriorBox.
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)DOC");
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}
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};
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jerrywgz
6 years ago
committed by
ceci3