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提交 505bbf36 编写于 作者: E eclipsess
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add box_coder and test

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src/operators/box_coder_op.cpp 0 → 100644
浏览文件 @ 505bbf36
/* Copyright (c) 2016 Baidu, Inc. All Rights Reserved.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
==============================================================================*/
#include "operators/box_coder_op.h"
#include <vector>
namespace paddle_mobile {
namespace operators {
template <typename Dtype, typename T>
void BoxCoderOp<Dtype, T>::InferShape() const {
auto input_priorbox_dims = param_.InputPriorBox()->dims();
auto input_priorboxvar_dims = param_.InputPriorBoxVar()->dims();
auto input_targetbox_dims = param_.InputTargetBox()->dims();
auto code_type = param_.CodeType();
if (code_type == "encode_center_size") {
if (input_targetbox_dims.size() != 2) {
LOG(kLOG_ERROR) << " The rank of Input of TargetBox must be 2";
}
if (input_targetbox_dims[1] != 4) {
LOG(kLOG_ERROR) << " The shape of TargetBox is [M, 4]";
}
}
if (code_type == "decode_center_size") {
if (input_targetbox_dims.size() != 3) {
LOG(kLOG_ERROR) << "The rank of Input of TargetBox must be 3";
}
if (input_targetbox_dims[1] != input_priorbox_dims[0] ||
input_targetbox_dims[2] != input_priorbox_dims[1]) {
LOG(kLOG_ERROR) << " dimension not match";
}
}
param_.OutputBox()->Resize(framework::make_ddim(
{input_targetbox_dims[0], input_priorbox_dims[0], 4}));
}
template class BoxCoderOp<CPU, float>;
} // namespace operators
} // namespace paddle_mobile
src/operators/box_coder_op.h 0 → 100644
浏览文件 @ 505bbf36
/* Copyright (c) 2016 Baidu, Inc. All Rights Reserved.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
==============================================================================*/
#pragma once
#include <string>
#include "framework/operator.h"
#include "operators/kernel/box_coder_kernel.h"
#include "operators/op_param.h"
namespace paddle_mobile {
namespace operators {
using paddle_mobile::framework::Tensor;
template <typename DeviceType, typename T>
class BoxCoderOp : public framework::OperatorWithKernel<DeviceType> {
public:
BoxCoderOp(const std::string &type, const VariableNameMap &inputs,
const VariableNameMap &outputs,
const framework::AttributeMap attrs,
std::shared_ptr<framework::Scope> scope)
: framework::OperatorWithKernel<DeviceType>(type, inputs, outputs, attrs,
scope),
param_(inputs, outputs, attrs, *scope) {}
void Run() const {
operators::BoxCoderKernel<DeviceType, T> kernel;
kernel.Compute(param_);
}
using framework::OperatorWithKernel<DeviceType>::OperatorWithKernel;
void InferShape() const override;
protected:
BoxCoderParam param_;
};
} // namespace operators
} // namespace paddle_mobile
src/operators/kernel/arm/box_coder_kernel.cpp 0 → 100644
浏览文件 @ 505bbf36
/* Copyright (c) 2016 Baidu, Inc. All Rights Reserved.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
==============================================================================*/
#pragma once
#include "operators/kernel/box_coder_kernel.h"
namespace paddle_mobile {
namespace operators {
template <typename T>
void EncodeCenterSize(const framework::Tensor& target_box,
const framework::Tensor& prior_box,
const framework::Tensor& prior_box_var, T* output) {
int64_t row = target_box.dims()[0];
int64_t col = prior_box.dims()[0];
int64_t len = prior_box.dims()[1];
auto* target_box_data = target_box.data<T>();
auto* prior_box_data = prior_box.data<T>();
auto* prior_box_var_data = prior_box_var.data<T>();
for (int64_t i = 0; i < row; ++i) {
for (int64_t j = 0; j < col; ++j) {
T prior_box_width = prior_box_data[j * len + 2] - prior_box_data[j * len];
T prior_box_height =
prior_box_data[j * len + 3] - prior_box_data[j * len + 1];
T prior_box_center_x =
(prior_box_data[j * len + 2] + prior_box_data[j * len]) / 2;
T prior_box_center_y =
(prior_box_data[j * len + 3] + prior_box_data[j * len + 1]) / 2;
T target_box_center_x =
(target_box_data[i * len + 2] + target_box_data[i * len]) / 2;
T target_box_center_y =
(target_box_data[i * len + 3] + target_box_data[i * len + 1]) / 2;
T target_box_width =
target_box_data[i * len + 2] - target_box_data[i * len];
T target_box_height =
target_box_data[i * len + 3] - target_box_data[i * len + 1];
size_t offset = i * col * len + j * len;
output[offset] = (target_box_center_x - prior_box_center_x) /
prior_box_width / prior_box_var_data[j * len];
output[offset + 1] = (target_box_center_y - prior_box_center_y) /
prior_box_height / prior_box_var_data[j * len + 1];
output[offset + 2] =
std::log(std::fabs(target_box_width / prior_box_width)) /
prior_box_var_data[j * len + 2];
output[offset + 3] =
std::log(std::fabs(target_box_height / prior_box_height)) /
prior_box_var_data[j * len + 3];
}
}
}
template <typename T>
void DecodeCenterSize(const framework::Tensor& target_box,
const framework::Tensor& prior_box,
const framework::Tensor& prior_box_var, T* output) {
int64_t row = target_box.dims()[0];
int64_t col = prior_box.dims()[0];
int64_t len = prior_box.dims()[1];
auto* target_box_data = target_box.data<T>();
auto* prior_box_data = prior_box.data<T>();
auto* prior_box_var_data = prior_box_var.data<T>();
for (int64_t i = 0; i < row; ++i) {
for (int64_t j = 0; j < col; ++j) {
size_t offset = i * col * len + j * len;
T prior_box_width = prior_box_data[j * len + 2] - prior_box_data[j * len];
T prior_box_height =
prior_box_data[j * len + 3] - prior_box_data[j * len + 1];
T prior_box_center_x =
(prior_box_data[j * len + 2] + prior_box_data[j * len]) / 2;
T prior_box_center_y =
(prior_box_data[j * len + 3] + prior_box_data[j * len + 1]) / 2;
T target_box_center_x = prior_box_var_data[j * len] *
target_box_data[offset] * prior_box_width +
prior_box_center_x;
T target_box_center_y = prior_box_var_data[j * len + 1] *
target_box_data[offset + 1] *
prior_box_height +
prior_box_center_y;
T target_box_width = std::exp(prior_box_var_data[j * len + 2] *
target_box_data[offset + 2]) *
prior_box_width;
T target_box_height = std::exp(prior_box_var_data[j * len + 3] *
target_box_data[offset + 3]) *
prior_box_height;
output[offset] = target_box_center_x - target_box_width / 2;
output[offset + 1] = target_box_center_y - target_box_height / 2;
output[offset + 2] = target_box_center_x + target_box_width / 2;
output[offset + 3] = target_box_center_y + target_box_height / 2;
}
}
}
template <>
void BoxCoderKernel<CPU, float>::Compute(const BoxCoderParam& param) const {
const auto* input_priorbox = param.InputPriorBox();
const auto* input_priorboxvar = param.InputPriorBoxVar();
const auto* input_targetbox = param.InputTargetBox();
const auto& code_type = param.CodeType();
auto row = input_targetbox->dims()[0];
auto col = input_priorbox->dims()[0];
auto len = input_priorbox->dims()[1];
Tensor* output_box = param.OutputBox();
auto* output_box_dataptr = output_box->mutable_data<float>({row, col, len});
if (code_type == "encode_center_size") {
EncodeCenterSize<float>(*input_targetbox, *input_priorbox,
*input_priorboxvar, output_box_dataptr);
}
if (code_type == "decode_center_size") {
DecodeCenterSize<float>(*input_targetbox, *input_priorbox,
*input_priorboxvar, output_box_dataptr);
}
}
} // namespace operators
} // namespace paddle_mobile
src/operators/kernel/arm/concat_kernel.cpp
浏览文件 @ 505bbf36
......@@ -61,7 +61,7 @@ void StridedNumelCopyWithAxis(int64_t axis, T *dst,
int64_t src_after = src_stride_numel[axis];
int64_t dst_after = dst_stride_numel[axis];
///"src and dst tensor should have the same dims size."
/// "src and dst tensor should have the same dims size."
assert(src_stride_numel.size() == dst_stride_numel.size());
for (int64_t i = 0; i < axis; ++i) {
......@@ -79,7 +79,6 @@ void StridedNumelCopyWithAxis(int64_t axis, T *dst,
assert(src_stride_numel[i] == dst_stride_numel[i]);
}
}
for (int64_t i = 0; i < before; ++i) {
memory::Copy(dst + i * dst_after, src + i * src_after, sizeof(T) * size);
}
......
src/operators/kernel/arm/prior_box_kernel.cpp
浏览文件 @ 505bbf36
......@@ -131,12 +131,11 @@ void PriorBoxKernel<CPU, float>::Compute(const PriorBoxParam &param) const {
output_boxes_dataptr, clip_func);
}
Tensor var_t;
var_t.mutable_data<float>(make_ddim({1, static_cast<int>(variances.size())}));
if ((variances.size() != 4)) {
LOG(kLOG_ERROR) << " variances.size() must be 4.";
}
int box_num = feature_height * feature_width * num_priors;
// auto var_dim = output_variances->dims();
// output_variances->Resize({box_num, static_cast<int>(variances.size())});
int64_t box_num = feature_height * feature_width * num_priors;
for (int i = 0; i < box_num; i++) {
output_variances_dataptr[4 * i] = variances[0];
......@@ -144,8 +143,6 @@ void PriorBoxKernel<CPU, float>::Compute(const PriorBoxParam &param) const {
output_variances_dataptr[4 * i + 2] = variances[2];
output_variances_dataptr[4 * i + 3] = variances[3];
}
// output_variances->Resize(var_dim);
}
} // namespace operators
......
src/operators/kernel/box_coder_kernel.h 0 → 100644
浏览文件 @ 505bbf36
/* Copyright (c) 2016 Baidu, Inc. All Rights Reserved.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
==============================================================================*/
#include <vector>
#include "framework/operator.h"
#include "operators/math/transform.h"
#include "operators/op_param.h"
#pragma once;
namespace paddle_mobile {
namespace operators {
template <typename DeviceType, typename T>
class BoxCoderKernel
: public framework::OpKernelBase<DeviceType, BoxCoderParam> {
public:
void Compute(const BoxCoderParam& param) const;
};
} // namespace operators
} // namespace paddle_mobile
src/operators/op_param.h
浏览文件 @ 505bbf36
......@@ -18,6 +18,7 @@ SOFTWARE.
#pragma once
#include <string>
#include "common/log.h"
#include "common/type_define.h"
#include "framework/lod_tensor.h"
......@@ -69,6 +70,22 @@ class OpParam : PaddleMobileObject {
static T *InputImageFrom(const VariableNameMap &inputs, const Scope &scope) {
return GetVarValue<T>("Image", inputs, scope);
}
template <typename T>
static T *InputPriorBoxFrom(const VariableNameMap &inputs,
const Scope &scope) {
return GetVarValue<T>("PriorBox", inputs, scope);
}
template <typename T>
static T *InputPriorBoxVarFrom(const VariableNameMap &inputs,
const Scope &scope) {
return GetVarValue<T>("PriorBoxVar", inputs, scope);
}
// LoDTensor but now use Tensor
template <typename T>
static T *InputTargetBoxFrom(const VariableNameMap &inputs,
const Scope &scope) {
return GetVarValue<T>("TargetBox", inputs, scope);
}
template <typename T>
static std::vector<T *> InputMultiFrom(const VariableNameMap &inputs,
......@@ -97,6 +114,11 @@ class OpParam : PaddleMobileObject {
return GetVarValue<T>("Boxes", outputs, scope);
}
template <typename T>
static T *OutputBoxFrom(const VariableNameMap &outputs, const Scope &scope) {
return GetVarValue<T>("OutputBox", outputs, scope);
}
template <typename T>
static T *OutputVariancesFrom(const VariableNameMap &outputs,
const Scope &scope) {
......@@ -458,5 +480,34 @@ class PriorBoxParam : public OpParam {
float step_h_;
float offset_;
};
class BoxCoderParam : public OpParam {
public:
BoxCoderParam(const VariableNameMap &inputs, const VariableNameMap &outputs,
const framework::AttributeMap &attrs,
const framework::Scope &scope) {
input_priorbox_ = InputPriorBoxFrom<framework::Tensor>(inputs, scope);
input_priorboxvar_ = InputPriorBoxVarFrom<framework::Tensor>(inputs, scope);
input_targetbox_ = InputTargetBoxFrom<framework::Tensor>(inputs, scope);
output_box_ = OutputBoxFrom<framework::Tensor>(outputs, scope);
code_type_ = GetAttr<std::string>("code_type", attrs);
}
const Tensor *InputPriorBox() const { return input_priorbox_; }
const Tensor *InputPriorBoxVar() const { return input_priorboxvar_; }
const Tensor *InputTargetBox() const { return input_targetbox_; }
Tensor *OutputBox() const { return output_box_; }
const std::string &CodeType() const { return code_type_; }
private:
Tensor *input_priorbox_;
Tensor *input_priorboxvar_;
Tensor *input_targetbox_;
Tensor *output_box_;
std::string code_type_;
};
} // namespace operators
} // namespace paddle_mobile
test/CMakeLists.txt
浏览文件 @ 505bbf36
......@@ -27,6 +27,10 @@ target_link_libraries(test-batchnorm-op paddle-mobile)
ADD_EXECUTABLE(test-priorbox-op operators/test_prior_box_op.cpp test_helper.h test_include.h)
target_link_libraries(test-priorbox-op paddle-mobile)
# gen test
ADD_EXECUTABLE(test-boxcoder-op operators/test_box_coder_op.cpp test_helper.h test_include.h)
target_link_libraries(test-boxcoder-op paddle-mobile)
# gen test log
ADD_EXECUTABLE(test-log common/test_log.cpp)
target_link_libraries(test-log paddle-mobile)
......
test/operators/test_box_coder_op.cpp 0 → 100644
浏览文件 @ 505bbf36
/* Copyright (c) 2016 Baidu, Inc. All Rights Reserved.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
==============================================================================*/
#pragma once
#include "../test_include.h"
#include "operators/box_coder_op.h"
namespace paddle_mobile {
namespace framework {
template <typename Dtype>
class TestBoxCoderOp {
public:
explicit TestBoxCoderOp(const Program<Dtype> p) : program_(p) {
if (use_optimize_) {
to_predict_program_ = program_.optimizeProgram;
} else {
to_predict_program_ = program_.originProgram;
}
const std::vector<std::shared_ptr<BlockDesc>> blocks =
to_predict_program_->Blocks();
// DLOG << " **block size " << blocks.size();
for (auto block_desc : blocks) {
std::vector<std::shared_ptr<OpDesc>> ops = block_desc->Ops();
// DLOG << " ops " << ops.size();
for (auto op : ops) {
if (op->Type() == "box_coder" &&
op->Input("PriorBox")[0] == "concat_0.tmp_0") {
DLOG << " mul attr size: " << op->GetAttrMap().size();
DLOG << " inputs size: " << op->GetInputs().size();
DLOG << " outputs size: " << op->GetOutputs().size();
DLOG << " Input PriorBox is : " << op->Input("PriorBox")[0];
DLOG << " Input PriorBoxVar is : " << op->Input("PriorBoxVar")[0];
DLOG << " Input TargetBox is : " << op->Input("TargetBox")[0];
DLOG << " OutputBox is : " << op->Output("OutputBox")[0];
DLOG << " code_type : "
<< op->GetAttrMap().at("code_type").Get<std::string>();
std::shared_ptr<operators::BoxCoderOp<Dtype, float>> boxcoder =
std::make_shared<operators::BoxCoderOp<Dtype, float>>(
op->Type(), op->GetInputs(), op->GetOutputs(),
op->GetAttrMap(), program_.scope);
ops_of_block_[*block_desc.get()].push_back(boxcoder);
}
}
}
}
std::shared_ptr<Tensor> predict_boxcoder(const Tensor &t1, const Tensor &t2,
const Tensor &t3) {
// feed
auto scope = program_.scope;
Variable *prior_box = scope->Var("concat_0.tmp_0");
auto tensor_x1 = prior_box->GetMutable<Tensor>();
tensor_x1->ShareDataWith(t1);
Variable *prior_box_var = scope->Var("concat_1.tmp_0");
auto tensor_x2 = prior_box_var->GetMutable<Tensor>();
tensor_x2->ShareDataWith(t2);
Variable *target_box = scope->Var("concat_2.tmp_0");
auto tensor_x3 = target_box->GetMutable<Tensor>();
tensor_x3->ShareDataWith(t3);
Variable *boxes_output = scope->Var("box_coder_0.tmp_0");
auto *boxes_output_tensor = boxes_output->GetMutable<Tensor>();
boxes_output_tensor->mutable_data<float>({1, 1917, 4});
// DLOG << typeid(output_tensor).name();
// DLOG << "output_tensor dims: " << output_tensor->dims();
std::shared_ptr<Tensor> outbox_tensor = std::make_shared<LoDTensor>();
outbox_tensor.reset(boxes_output_tensor);
predict_boxcoder(t1, t2, t3, 0);
return outbox_tensor;
}
private:
const framework::Program<Dtype> program_;
std::shared_ptr<ProgramDesc> to_predict_program_;
std::map<framework::BlockDesc,
std::vector<std::shared_ptr<OperatorBase<Dtype>>>>
ops_of_block_;
bool use_optimize_ = false;
void predict_boxcoder(const Tensor &t1, const Tensor &t2, const Tensor &t3,
int block_id) {
std::shared_ptr<BlockDesc> to_predict_block =
to_predict_program_->Block(block_id);
for (int j = 0; j < ops_of_block_[*to_predict_block.get()].size(); ++j) {
auto op = ops_of_block_[*to_predict_block.get()][j];
DLOG << "op -> run()";
op->Run();
}
}
};
template class TestBoxCoderOp<CPU>;
} // namespace framework
} // namespace paddle_mobile
int main() {
DLOG << "----------**********----------";
DLOG << "begin to run BoxCoderOp Test";
paddle_mobile::Loader<paddle_mobile::CPU> loader;
auto program = loader.Load(std::string("../../test/models/mobilenet+ssd"));
paddle_mobile::framework::Tensor priorbox;
SetupTensor<float>(&priorbox, {1917, 4}, static_cast<float>(0),
static_cast<float>(1));
auto *priorbox_ptr = priorbox.data<float>();
paddle_mobile::framework::Tensor priorboxvar;
SetupTensor<float>(&priorboxvar, {1917, 4}, static_cast<float>(0.1),
static_cast<float>(0.2));
auto *priorboxvar_ptr = priorboxvar.data<float>();
paddle_mobile::framework::Tensor targetbox;
SetupTensor<float>(&targetbox, {1, 1917, 4}, static_cast<float>(0),
static_cast<float>(1));
auto *targetbox_ptr = targetbox.data<float>();
paddle_mobile::framework::TestBoxCoderOp<paddle_mobile::CPU> testBoxCoderOp(
program);
auto output_boxcoder =
testBoxCoderOp.predict_boxcoder(priorbox, priorboxvar, targetbox);
auto output_boxcoder_ptr = output_boxcoder->data<float>();
for (int i = 0; i < output_boxcoder->numel(); i++) {
DLOG << output_boxcoder_ptr[i];
}
DLOGF("\n");
/// testing 25th bbox.
DLOG << "PriorBox**************";
DLOG << priorbox_ptr[100];
DLOG << priorbox_ptr[101];
DLOG << priorbox_ptr[102];
DLOG << priorbox_ptr[103];
DLOG << "PriorBoxVar**************";
DLOG << priorboxvar_ptr[100];
DLOG << priorboxvar_ptr[101];
DLOG << priorboxvar_ptr[102];
DLOG << priorboxvar_ptr[103];
DLOG << "TargetBox***************";
DLOG << targetbox_ptr[100];
DLOG << targetbox_ptr[101];
DLOG << targetbox_ptr[102];
DLOG << targetbox_ptr[103];
DLOG << "OutputBox**************";
DLOG << output_boxcoder_ptr[100];
DLOG << output_boxcoder_ptr[101];
DLOG << output_boxcoder_ptr[102];
DLOG << output_boxcoder_ptr[103];
DLOG << "***********----------------------**************";
auto priorbox_w = priorbox_ptr[102] - priorbox_ptr[100];
auto priorbox_h = priorbox_ptr[103] - priorbox_ptr[101];
auto priorbox_center_x = (priorbox_ptr[100] + priorbox_ptr[102]) / 2;
auto priorbox_center_y = (priorbox_ptr[101] + priorbox_ptr[103]) / 2;
DLOG << "prior box width : " << priorbox_w;
DLOG << "prior box height : " << priorbox_h;
DLOG << "prior box center x : " << priorbox_center_x;
DLOG << "prior box center y : " << priorbox_center_y;
auto target_box_center_x =
priorboxvar_ptr[100] * targetbox_ptr[100] * priorbox_w +
priorbox_center_x;
DLOG << "target_box_center_x : " << target_box_center_x;
auto target_box_center_y =
priorboxvar_ptr[101] * targetbox_ptr[101] * priorbox_h +
priorbox_center_y;
DLOG << "target_box_center_y : " << target_box_center_y;
auto target_box_width =
std::exp(priorboxvar_ptr[102] * targetbox_ptr[102]) * priorbox_w;
DLOG << "target_box_width : " << target_box_width;
auto target_box_height =
std::exp(priorboxvar_ptr[103] * targetbox_ptr[103]) * priorbox_h;
DLOG << "target_box_height : " << target_box_height;
DLOG << "pre x min : " << target_box_center_x - target_box_width / 2;
DLOG << "pre y min : " << target_box_center_y - target_box_height / 2;
DLOG << "pre x max : " << target_box_center_x + target_box_width / 2;
DLOG << "pre y max : " << target_box_center_y + target_box_height / 2;
return 0;
}
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