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@ -20,7 +20,7 @@ namespace operators {
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enum class BoxCodeType { kEncodeCenterSize = 0, kDecodeCenterSize = 1 };
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inline BoxCodeType GetBoxCodeType(const std::string& type) {
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inline BoxCodeType GetBoxCodeType(const std::string &type) {
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if (type == "encode_center_size") {
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return BoxCodeType::kEncodeCenterSize;
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} else if (type == "decode_center_size") {
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@ -32,24 +32,23 @@ inline BoxCodeType GetBoxCodeType(const std::string& type) {
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template <typename DeviceContext, typename T>
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class BoxCoderKernel : public framework::OpKernel<T> {
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public:
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void EncodeCenterSize(const framework::Tensor* target_box,
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const framework::Tensor* prior_box,
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const framework::Tensor* prior_box_var,
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void EncodeCenterSize(const framework::Tensor *target_box,
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const framework::Tensor *prior_box,
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const framework::Tensor *prior_box_var,
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const bool normalized,
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const std::vector<float> variance, T* output) const {
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const std::vector<float> variance, T *output) const {
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int64_t row = target_box->dims()[0];
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int64_t col = prior_box->dims()[0];
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int64_t len = prior_box->dims()[1];
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auto* target_box_data = target_box->data<T>();
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auto* prior_box_data = prior_box->data<T>();
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const T* prior_box_var_data = nullptr;
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if (prior_box_var) prior_box_var_data = prior_box_var->data<T>();
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#ifdef PADDLE_WITH_MKLML
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#pragma omp parallel for collapse(2)
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#endif
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for (int64_t i = 0; i < row; ++i) {
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for (int64_t j = 0; j < col; ++j) {
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auto *target_box_data = target_box->data<T>();
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auto *prior_box_data = prior_box->data<T>();
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size_t offset = i * col * len + j * len;
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T prior_box_width = prior_box_data[j * len + 2] -
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prior_box_data[j * len] + (normalized == false);
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T prior_box_height = prior_box_data[j * len + 3] -
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@ -69,7 +68,6 @@ class BoxCoderKernel : public framework::OpKernel<T> {
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target_box_data[i * len + 1] +
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(normalized == false);
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size_t offset = i * col * len + j * len;
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output[offset] =
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(target_box_center_x - prior_box_center_x) / prior_box_width;
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output[offset + 1] =
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@ -78,44 +76,61 @@ class BoxCoderKernel : public framework::OpKernel<T> {
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std::log(std::fabs(target_box_width / prior_box_width));
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output[offset + 3] =
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std::log(std::fabs(target_box_height / prior_box_height));
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if (prior_box_var) {
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int prior_var_offset = j * len;
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output[offset] /= prior_box_var_data[prior_var_offset];
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output[offset + 1] /= prior_box_var_data[prior_var_offset + 1];
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output[offset + 2] /= prior_box_var_data[prior_var_offset + 2];
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output[offset + 3] /= prior_box_var_data[prior_var_offset + 3];
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} else if (!(variance.empty())) {
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}
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}
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if (prior_box_var) {
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const T *prior_box_var_data = prior_box_var->data<T>();
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#ifdef PADDLE_WITH_MKLML
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#pragma omp parallel for collapse(3)
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#endif
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for (int64_t i = 0; i < row; ++i) {
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for (int64_t j = 0; j < col; ++j) {
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for (int k = 0; k < 4; ++k) {
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size_t offset = i * col * len + j * len;
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int prior_var_offset = j * len;
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output[offset + k] /= prior_box_var_data[prior_var_offset + k];
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}
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}
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}
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} else if (!(variance.empty())) {
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#ifdef PADDLE_WITH_MKLML
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#pragma omp parallel for collapse(3)
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#endif
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for (int64_t i = 0; i < row; ++i) {
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for (int64_t j = 0; j < col; ++j) {
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for (int k = 0; k < 4; ++k) {
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size_t offset = i * col * len + j * len;
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output[offset + k] /= static_cast<T>(variance[k]);
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}
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}
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}
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}
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}
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template <int axis, int var_size>
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void DecodeCenterSize(const framework::Tensor* target_box,
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const framework::Tensor* prior_box,
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const framework::Tensor* prior_box_var,
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void DecodeCenterSize(const framework::Tensor *target_box,
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const framework::Tensor *prior_box,
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const framework::Tensor *prior_box_var,
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const bool normalized, std::vector<float> variance,
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T* output) const {
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T *output) const {
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int64_t row = target_box->dims()[0];
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int64_t col = target_box->dims()[1];
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int64_t len = target_box->dims()[2];
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auto* target_box_data = target_box->data<T>();
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auto* prior_box_data = prior_box->data<T>();
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const T* prior_box_var_data = nullptr;
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if (var_size == 2) prior_box_var_data = prior_box_var->data<T>();
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int prior_box_offset = 0;
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T var_data[4] = {1., 1., 1., 1.};
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T* var_ptr = var_data;
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#ifdef PADDLE_WITH_MKLML
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#pragma omp parallel for collapse(2)
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#endif
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for (int64_t i = 0; i < row; ++i) {
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for (int64_t j = 0; j < col; ++j) {
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auto *target_box_data = target_box->data<T>();
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auto *prior_box_data = prior_box->data<T>();
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T var_data[4] = {1., 1., 1., 1.};
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T *var_ptr = var_data;
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size_t offset = i * col * len + j * len;
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prior_box_offset = axis == 0 ? j * len : i * len;
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int prior_box_offset = axis == 0 ? j * len : i * len;
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T prior_box_width = prior_box_data[prior_box_offset + 2] -
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prior_box_data[prior_box_offset] +
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(normalized == false);
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@ -131,10 +146,10 @@ class BoxCoderKernel : public framework::OpKernel<T> {
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T target_box_width = 0, target_box_height = 0;
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int prior_var_offset = axis == 0 ? j * len : i * len;
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if (var_size == 2) {
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std::memcpy(var_ptr, prior_box_var_data + prior_var_offset,
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std::memcpy(var_ptr, prior_box_var->data<T>() + prior_var_offset,
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4 * sizeof(T));
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} else if (var_size == 1) {
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var_ptr = reinterpret_cast<T*>(variance.data());
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var_ptr = reinterpret_cast<T *>(variance.data());
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}
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T box_var_x = *var_ptr;
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T box_var_y = *(var_ptr + 1);
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@ -162,11 +177,11 @@ class BoxCoderKernel : public framework::OpKernel<T> {
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}
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}
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void Compute(const framework::ExecutionContext& context) const override {
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auto* prior_box = context.Input<framework::Tensor>("PriorBox");
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auto* prior_box_var = context.Input<framework::Tensor>("PriorBoxVar");
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auto* target_box = context.Input<framework::LoDTensor>("TargetBox");
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auto* output_box = context.Output<framework::Tensor>("OutputBox");
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void Compute(const framework::ExecutionContext &context) const override {
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auto *prior_box = context.Input<framework::Tensor>("PriorBox");
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auto *prior_box_var = context.Input<framework::Tensor>("PriorBoxVar");
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auto *target_box = context.Input<framework::LoDTensor>("TargetBox");
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auto *output_box = context.Output<framework::Tensor>("OutputBox");
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std::vector<float> variance = context.Attr<std::vector<float>>("variance");
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const int axis = context.Attr<int>("axis");
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if (target_box->lod().size()) {
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@ -194,7 +209,7 @@ class BoxCoderKernel : public framework::OpKernel<T> {
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output_box->mutable_data<T>({row, col, len}, context.GetPlace());
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T* output = output_box->data<T>();
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T *output = output_box->data<T>();
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if (code_type == BoxCodeType::kEncodeCenterSize) {
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EncodeCenterSize(target_box, prior_box, prior_box_var, normalized,
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variance, output);
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luotao1
7 years ago