box_coder_op.cc

/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
    http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */

#include "paddle/fluid/operators/detection/box_coder_op.h"

namespace paddle {
namespace operators {

class BoxCoderOp : public framework::OperatorWithKernel {
 public:
  using framework::OperatorWithKernel::OperatorWithKernel;

 protected:
  void InferShape(framework::InferShapeContext *ctx) const override {
    PADDLE_ENFORCE(ctx->HasInput("PriorBox"),
                   "Input(PriorBox) of BoxCoderOp should not be null.");
    PADDLE_ENFORCE(ctx->HasInput("TargetBox"),
                   "Input(TargetBox) of BoxCoderOp should not be null.");
    PADDLE_ENFORCE(ctx->HasOutput("OutputBox"),
                   "Output(OutputBox) of BoxCoderOp should not be null.");

    auto prior_box_dims = ctx->GetInputDim("PriorBox");
    auto target_box_dims = ctx->GetInputDim("TargetBox");

    if (ctx->IsRuntime()) {
      PADDLE_ENFORCE_EQ(prior_box_dims.size(), 2,
                        "The rank of Input of PriorBox must be 2");
      PADDLE_ENFORCE_EQ(prior_box_dims[1], 4,
                        "The shape of PriorBox is [N, 4]");
      if (ctx->HasInput("PriorBoxVar")) {
        auto prior_box_var_dims = ctx->GetInputDim("PriorBoxVar");
        PADDLE_ENFORCE(
            prior_box_var_dims.size() == 1 || prior_box_var_dims.size() == 2,
            "Input(PriorBoxVar) of BoxCoderOp should be 1 or 2.");
        if (prior_box_var_dims.size() == 1) {
          PADDLE_ENFORCE_EQ(
              prior_box_var_dims[0], 4,
              "The 1st dimension of Input(PriorBoxVar) should be 4"
              "when the rank is 1.");
        } else {
          PADDLE_ENFORCE_EQ(
              prior_box_dims, prior_box_var_dims,
              "The dimension of Input(PriorBoxVar) should be equal to"
              "the dimension of Input(PriorBox when the rank is 2.)");
        }
      }
    }

    auto code_type = GetBoxCodeType(ctx->Attrs().Get<std::string>("code_type"));
    int axis = ctx->Attrs().Get<int>("axis");
    if (code_type == BoxCodeType::kEncodeCenterSize) {
      PADDLE_ENFORCE_EQ(target_box_dims.size(), 2,
                        "The rank of Input of TargetBox must be 2");
      PADDLE_ENFORCE_EQ(target_box_dims[1], 4,
                        "The shape of TargetBox is [M, 4]");
      ctx->SetOutputDim(
          "OutputBox",
          framework::make_ddim({target_box_dims[0], prior_box_dims[0], 4}));
    } else if (code_type == BoxCodeType::kDecodeCenterSize) {
      PADDLE_ENFORCE_EQ(target_box_dims.size(), 3,
                        "The rank of Input of TargetBox must be 3");
      if (axis == 0) {
        PADDLE_ENFORCE_EQ(target_box_dims[1], prior_box_dims[0]);
      } else if (axis == 1) {
        PADDLE_ENFORCE_EQ(target_box_dims[0], prior_box_dims[0]);
      } else {
        PADDLE_THROW("axis must be 0 or 1.");
      }
      PADDLE_ENFORCE_EQ(target_box_dims[2], prior_box_dims[1]);
      ctx->ShareDim("TargetBox", /*->*/ "OutputBox");
    }

    if (code_type == BoxCodeType::kDecodeCenterSize && axis == 1) {
      ctx->ShareLoD("PriorBox", /*->*/ "OutputBox");
    } else {
      ctx->ShareLoD("TargetBox", /*->*/ "OutputBox");
    }
  }
};

class BoxCoderOpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  void Make() override {
    AddInput(
        "PriorBox",
        "(Tensor, default Tensor<float>) "
        "Box list PriorBox is a 2-D Tensor with shape [M, 4] holds M boxes, "
        "each box is represented as [xmin, ymin, xmax, ymax], "
        "[xmin, ymin] is the left top coordinate of the anchor box, "
        "if the input is image feature map, they are close to the origin "
        "of the coordinate system. [xmax, ymax] is the right bottom "
        "coordinate of the anchor box.");
    AddInput("PriorBoxVar",
             "(Tensor, default Tensor<float>, optional) "
             "PriorBoxVar is a 2-D Tensor with shape [M, 4] holds M group "
             "of variance. PriorBoxVar will set all elements to 1 by "
             "default.")
        .AsDispensable();
    AddInput(
        "TargetBox",
        "(LoDTensor or Tensor) This input can be a 2-D LoDTensor with shape "
        "[N, 4] when code_type is 'encode_center_size'. This input also can "
        "be a 3-D Tensor with shape [N, M, 4] when code_type is "
        "'decode_center_size'. [N, 4], each box is represented as "
        "[xmin, ymin, xmax, ymax], [xmin, ymin] is the left top coordinate "
        "of the box if the input is image feature map, they are close to "
        "the origin of the coordinate system. [xmax, ymax] is the right "
        "bottom coordinate of the box. This tensor can contain LoD "
        "information to represent a batch of inputs. One instance of this "
        "batch can contain different numbers of entities.");
    AddAttr<std::string>("code_type",
                         "(string, default encode_center_size) "
                         "the code type used with the target box")
        .SetDefault("encode_center_size")
        .InEnum({"encode_center_size", "decode_center_size"});
    AddAttr<bool>("box_normalized",
                  "(bool, default true) "
                  "whether treat the priorbox as a noramlized box")
        .SetDefault(true);
    AddAttr<int>("axis",
                 "(int, default 1)"
                 "which axis to broadcast for box decode, it is only valid"
                 "when code type is decode_center_size")
        .SetDefault(0)
        .InEnum({0, 1});
    AddOutput("OutputBox",
              "(LoDTensor or Tensor) "
              "When code_type is 'encode_center_size', the output tensor of "
              "box_coder_op with shape [N, M, 4] representing the result of N "
              "target boxes encoded with M Prior boxes and variances. When "
              "code_type is 'decode_center_size', N represents the batch size "
              "and M represents the number of deocded boxes.");

    AddComment(R"DOC(

Bounding Box Coder.

Encode/Decode the target bounding box with the priorbox information.

The Encoding schema described below:

    ox = (tx - px) / pw / pxv

    oy = (ty - py) / ph / pyv

    ow = log(abs(tw / pw)) / pwv 

    oh = log(abs(th / ph)) / phv 

The Decoding schema described below:

    ox = (pw * pxv * tx * + px) - tw / 2

    oy = (ph * pyv * ty * + py) - th / 2

    ow = exp(pwv * tw) * pw + tw / 2

    oh = exp(phv * th) * ph + th / 2

where `tx`, `ty`, `tw`, `th` denote the target box's center coordinates, width
and height respectively. Similarly, `px`, `py`, `pw`, `ph` denote the
priorbox's (anchor) center coordinates, width and height. `pxv`, `pyv`, `pwv`,
`phv` denote the variance of the priorbox and `ox`, `oy`, `ow`, `oh` denote the
encoded/decoded coordinates, width and height. 

During Box Decoding, two modes for broadcast are supported. Say target box has 
shape [N, M, 4], and the shape of prior box can be [N, 4] or [M, 4]. Then prior
box will broadcast to target box along the assigned axis. 
)DOC");
  }
};

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;
REGISTER_OPERATOR(box_coder, ops::BoxCoderOp, ops::BoxCoderOpMaker,
                  paddle::framework::EmptyGradOpMaker);
REGISTER_OP_CPU_KERNEL(
    box_coder, ops::BoxCoderKernel<paddle::platform::CPUDeviceContext, float>,
    ops::BoxCoderKernel<paddle::platform::CPUDeviceContext, double>);