未验证 提交 ed040a17 编写于 作者: W Wang Xin 提交者: GitHub

add autogen code support for box_coder op (#53309)

上级 652d100d
......@@ -28,7 +28,6 @@ function(detection_library TARGET_NAME)
PARENT_SCOPE)
endfunction()
detection_library(box_coder_op SRCS box_coder_op.cc)
detection_library(density_prior_box_op SRCS density_prior_box_op.cc
density_prior_box_op.cu)
......
/* 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 <vector>
#include "paddle/fluid/framework/infershape_utils.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/phi/core/infermeta_utils.h"
#include "paddle/phi/infermeta/ternary.h"
namespace paddle {
namespace operators {
class BoxCoderOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
};
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",
"(phi::DenseTensor or Tensor) This input can be a 2-D phi::DenseTensor "
"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 normalized box")
.SetDefault(true);
AddAttr<int>("axis",
"(int, default 0)"
"which axis in PriorBox to broadcast for box decode,"
"for example, if axis is 0 and TargetBox has shape"
"[N, M, 4] and PriorBox has shape [M, 4], then PriorBox "
"will broadcast to [N, M, 4] for decoding. It is only valid"
"when code type is decode_center_size")
.SetDefault(0)
.InEnum({0, 1});
AddAttr<std::vector<float>>(
"variance",
"(vector<float>, default {}),"
"variance of prior box with shape [4]. PriorBoxVar and variance can"
"not be provided at the same time.")
.SetDefault(std::vector<float>{});
AddOutput("OutputBox",
"(phi::DenseTensor 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 decoded 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;
DECLARE_INFER_SHAPE_FUNCTOR(box_coder,
BoxCoderInferShapeFunctor,
PD_INFER_META(phi::BoxCoderInferMeta));
REGISTER_OPERATOR(
box_coder,
ops::BoxCoderOp,
ops::BoxCoderOpMaker,
paddle::framework::EmptyGradOpMaker<paddle::framework::OpDesc>,
paddle::framework::EmptyGradOpMaker<paddle::imperative::OpBase>,
BoxCoderInferShapeFunctor);
......@@ -139,15 +139,6 @@
func: bincount
optional: weights
- op : box_coder
args : (Tensor prior_box, Tensor prior_box_var, Tensor target_box, str code_type, bool box_normalized, int axis, float[] variance)
output : Tensor(output_box)
infer_meta :
func : BoxCoderInferMeta
kernel :
func : box_coder
optional : prior_box_var
- op : cast
args : (Tensor x, DataType dtype)
output : Tensor
......
......@@ -343,6 +343,12 @@
outputs :
out : Out
- op : box_coder
inputs :
{prior_box : PriorBox , prior_box_var : PriorBoxVar, target_box: TargetBox}
outputs :
output_box : OutputBox
- op : broadcast_tensors
backward : broadcast_tensors_grad
inputs :
......
......@@ -329,6 +329,15 @@
func : bmm
backward : bmm_grad
- op : box_coder
args : (Tensor prior_box, Tensor prior_box_var, Tensor target_box, str code_type = "encode_center_size", bool box_normalized = true, int axis = 0, float[] variance = {})
output : Tensor(output_box)
infer_meta :
func : BoxCoderInferMeta
kernel :
func : box_coder
optional : prior_box_var
- op : broadcast_tensors
args: (Tensor[] input)
output: Tensor[]{input.size()}
......
// Copyright (c) 2022 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/phi/core/compat/op_utils.h"
namespace phi {
KernelSignature BoxCoderOpArgumentMapping(const ArgumentMappingContext& ctx) {
return KernelSignature("box_coder",
{"PriorBox", "PriorBoxVar", "TargetBox"},
{"code_type", "box_normalized", "axis", "variance"},
{"OutputBox"});
}
} // namespace phi
PD_REGISTER_ARG_MAPPING_FN(box_coder, phi::BoxCoderOpArgumentMapping);
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