diff options
Diffstat (limited to 'compiler/locomotiv/src')
25 files changed, 45 insertions, 47 deletions
diff --git a/compiler/locomotiv/src/Node/AvgPool2D.cpp b/compiler/locomotiv/src/Node/AvgPool2D.cpp index 5fdf1e725..0adabd49a 100644 --- a/compiler/locomotiv/src/Node/AvgPool2D.cpp +++ b/compiler/locomotiv/src/Node/AvgPool2D.cpp @@ -78,9 +78,9 @@ nncc::core::ADT::tensor::Buffer<T> avgPool2D(const loco::AvgPool2D *avgpool2d, const uint32_t pad_right = avgpool2d->pad()->right(); const uint32_t output_height = - compute_out_size(ifm_height, pad_top + pad_bottom, window_height, stride_height); + compute_out_size(ifm_height, pad_top + pad_bottom, window_height, stride_height); const uint32_t output_width = - compute_out_size(ifm_width, pad_left + pad_right, window_width, stride_width); + compute_out_size(ifm_width, pad_left + pad_right, window_width, stride_width); // prepare output buffer Shape output_shape{batches, output_height, output_width, depth}; diff --git a/compiler/locomotiv/src/Node/AvgPool2D.test.cpp b/compiler/locomotiv/src/Node/AvgPool2D.test.cpp index f9863b47d..ec5f3cd82 100644 --- a/compiler/locomotiv/src/Node/AvgPool2D.test.cpp +++ b/compiler/locomotiv/src/Node/AvgPool2D.test.cpp @@ -84,7 +84,7 @@ void run_test(const float *ifm, const float *expected_ofm, const Shape &ifm_shap ASSERT_TRUE(*(avgpool2d_data->shape()) == ofm_shape); auto ofm_overlay = - make_overlay<float, LexicalLayout>(ofm_shape, const_cast<float *>(expected_ofm)); + make_overlay<float, LexicalLayout>(ofm_shape, const_cast<float *>(expected_ofm)); for (nncc::core::ADT::tensor::IndexEnumerator e{ofm_shape}; e.valid(); e.advance()) { const auto &ind = e.current(); diff --git a/compiler/locomotiv/src/Node/BiasAdd.cpp b/compiler/locomotiv/src/Node/BiasAdd.cpp index b84fa7e3c..0c45cc12f 100644 --- a/compiler/locomotiv/src/Node/BiasAdd.cpp +++ b/compiler/locomotiv/src/Node/BiasAdd.cpp @@ -55,7 +55,7 @@ void execute_node(loco::BiasAdd<loco::Domain::Tensor> *bias_add) validate(input_data && bias_data, "Input not ready"); validate(locomotiv::annot_domain(bias_add->value()) == loco::Domain::Tensor && - locomotiv::annot_domain(bias_add->bias()) == loco::Domain::Bias, + locomotiv::annot_domain(bias_add->bias()) == loco::Domain::Bias, "Wrong input domain"); std::unique_ptr<NodeData> bias_add_data = calc(input_data, bias_data, bias_add->axis()); @@ -74,7 +74,7 @@ void execute_node(loco::BiasAdd<loco::Domain::Feature> *bias_add) validate(input_data && bias_data, "Input not ready"); validate(locomotiv::annot_domain(bias_add->value()) == loco::Domain::Feature && - locomotiv::annot_domain(bias_add->bias()) == loco::Domain::Bias, + locomotiv::annot_domain(bias_add->bias()) == loco::Domain::Bias, "Wrong input domain"); std::unique_ptr<NodeData> bias_add_data = calc(input_data, bias_data, 3); diff --git a/compiler/locomotiv/src/Node/Conv2D.cpp b/compiler/locomotiv/src/Node/Conv2D.cpp index cdf0dfd56..2f9ca5a7e 100644 --- a/compiler/locomotiv/src/Node/Conv2D.cpp +++ b/compiler/locomotiv/src/Node/Conv2D.cpp @@ -82,9 +82,9 @@ Buffer<RET_T> calc_conv2D(const loco::Conv2D *conv2d, const Buffer<IFM_T> *input const uint32_t pad_right = conv2d->pad()->right(); const uint32_t output_height = - compute_out_size(input_height + pad_top + pad_bottom, filter_height, stride_height); + compute_out_size(input_height + pad_top + pad_bottom, filter_height, stride_height); const uint32_t output_width = - compute_out_size(input_width + pad_left + pad_right, filter_width, stride_width); + compute_out_size(input_width + pad_left + pad_right, filter_width, stride_width); const uint32_t batches = input_shape.dim(0); const uint32_t input_depth = input_shape.dim(3); @@ -121,9 +121,9 @@ Buffer<RET_T> calc_conv2D(const loco::Conv2D *conv2d, const Buffer<IFM_T> *input ((unsigned)in_y < input_height)) { auto input_value = - input_buf->at(Index({batch, (unsigned)in_y, (unsigned)in_x, in_channel})); + input_buf->at(Index({batch, (unsigned)in_y, (unsigned)in_x, in_channel})); auto filter_value = - filter_buf->at(Index({out_channel, filter_y, filter_x, in_channel})); + filter_buf->at(Index({out_channel, filter_y, filter_x, in_channel})); total += (input_value * filter_value); } } diff --git a/compiler/locomotiv/src/Node/Conv2D.test.cpp b/compiler/locomotiv/src/Node/Conv2D.test.cpp index 66e947acc..93afa79b7 100644 --- a/compiler/locomotiv/src/Node/Conv2D.test.cpp +++ b/compiler/locomotiv/src/Node/Conv2D.test.cpp @@ -97,7 +97,7 @@ void run_test(const float *ifm, const float *ker, const float *expected_ofm, con ASSERT_TRUE(*(conv2d_result->shape()) == ofm_shape); auto ofm_overlay = - make_overlay<float, LexicalLayout>(ofm_shape, const_cast<float *>(expected_ofm)); + make_overlay<float, LexicalLayout>(ofm_shape, const_cast<float *>(expected_ofm)); for (nncc::core::ADT::tensor::IndexEnumerator e{ofm_shape}; e.valid(); e.advance()) { const auto &ind = e.current(); diff --git a/compiler/locomotiv/src/Node/DepthwiseConv2D.cpp b/compiler/locomotiv/src/Node/DepthwiseConv2D.cpp index f39cd177e..a1a8e506f 100644 --- a/compiler/locomotiv/src/Node/DepthwiseConv2D.cpp +++ b/compiler/locomotiv/src/Node/DepthwiseConv2D.cpp @@ -89,9 +89,9 @@ Buffer<RET_T> calc_dw_conv2d(const loco::DepthwiseConv2D *dw_conv2d, const Buffe const uint32_t pad_right = dw_conv2d->pad()->right(); const uint32_t ofm_height = - compute_out_size(ifm_height, pad_top + pad_bottom, ker_height, stride_height); + compute_out_size(ifm_height, pad_top + pad_bottom, ker_height, stride_height); const uint32_t ofm_width = - compute_out_size(ifm_width, pad_left + pad_right, ker_width, stride_width); + compute_out_size(ifm_width, pad_left + pad_right, ker_width, stride_width); const uint32_t batches = ifm_shape.dim(0); const uint32_t ifm_depth = ifm_shape.dim(3); diff --git a/compiler/locomotiv/src/Node/DepthwiseConv2D.test.cpp b/compiler/locomotiv/src/Node/DepthwiseConv2D.test.cpp index 1ff333be0..8a435b6ab 100644 --- a/compiler/locomotiv/src/Node/DepthwiseConv2D.test.cpp +++ b/compiler/locomotiv/src/Node/DepthwiseConv2D.test.cpp @@ -97,7 +97,7 @@ void run_test(const float *ifm, const float *ker, const float *expected_ofm, con ASSERT_TRUE(*(dw_conv2d_result->shape()) == ofm_shape); auto ofm_overlay = - make_overlay<float, LexicalLayout>(ofm_shape, const_cast<float *>(expected_ofm)); + make_overlay<float, LexicalLayout>(ofm_shape, const_cast<float *>(expected_ofm)); for (nncc::core::ADT::tensor::IndexEnumerator e{ofm_shape}; e.valid(); e.advance()) { const auto &ind = e.current(); diff --git a/compiler/locomotiv/src/Node/DepthwiseFilterEncode.cpp b/compiler/locomotiv/src/Node/DepthwiseFilterEncode.cpp index 03f5bf833..e161287ea 100644 --- a/compiler/locomotiv/src/Node/DepthwiseFilterEncode.cpp +++ b/compiler/locomotiv/src/Node/DepthwiseFilterEncode.cpp @@ -59,8 +59,8 @@ std::unique_ptr<locomotiv::NodeData> dw_filter_encode(const loco::DepthwiseFilte // Make HWCM (i.e. height, width, depth, multiplier) buffer from DepthwiseFilterShape Buffer<T> node_buf = make_buffer<T, LexicalLayout>( - Shape{node_shape.height().value(), node_shape.width().value(), node_shape.depth().value(), - node_shape.multiplier().value()}); + Shape{node_shape.height().value(), node_shape.width().value(), node_shape.depth().value(), + node_shape.multiplier().value()}); // Copy buffer in an order arranged by encoder for (IndexEnumerator e{node_buf.shape()}; e.valid(); e.advance()) diff --git a/compiler/locomotiv/src/Node/DepthwiseFilterEncode.test.cpp b/compiler/locomotiv/src/Node/DepthwiseFilterEncode.test.cpp index 5b2ec9326..44364723c 100644 --- a/compiler/locomotiv/src/Node/DepthwiseFilterEncode.test.cpp +++ b/compiler/locomotiv/src/Node/DepthwiseFilterEncode.test.cpp @@ -62,7 +62,7 @@ TEST(NodeExecution_DepthwiseFilterEncode, f32) // Encoder to correctly read input tensor as MHWC auto encoder = std::unique_ptr<loco::PermutingEncoder<loco::Domain::DepthwiseFilter>>( - new loco::PermutingEncoder<loco::Domain::DepthwiseFilter>); + new loco::PermutingEncoder<loco::Domain::DepthwiseFilter>); encoder->perm()->axis(loco::DepthwiseFilterAxis::Multiplier) = 0; encoder->perm()->axis(loco::DepthwiseFilterAxis::Height) = 1; encoder->perm()->axis(loco::DepthwiseFilterAxis::Width) = 2; diff --git a/compiler/locomotiv/src/Node/FeatureCodec.test.cpp b/compiler/locomotiv/src/Node/FeatureCodec.test.cpp index 1b6b06c13..dacd0170c 100644 --- a/compiler/locomotiv/src/Node/FeatureCodec.test.cpp +++ b/compiler/locomotiv/src/Node/FeatureCodec.test.cpp @@ -64,7 +64,7 @@ protected: const loco::Permutation<loco::Domain::Feature> &perm) { auto encoder = std::unique_ptr<loco::PermutingEncoder<loco::Domain::Feature>>( - new loco::PermutingEncoder<loco::Domain::Feature>); + new loco::PermutingEncoder<loco::Domain::Feature>); encoder->perm(perm); @@ -80,7 +80,7 @@ protected: const loco::Permutation<loco::Domain::Feature> &perm) { auto decoder = std::unique_ptr<loco::PermutingDecoder<loco::Domain::Feature>>( - new loco::PermutingDecoder<loco::Domain::Feature>); + new loco::PermutingDecoder<loco::Domain::Feature>); decoder->perm(perm); diff --git a/compiler/locomotiv/src/Node/FeatureDecode.cpp b/compiler/locomotiv/src/Node/FeatureDecode.cpp index 8776e1b42..2877906f9 100644 --- a/compiler/locomotiv/src/Node/FeatureDecode.cpp +++ b/compiler/locomotiv/src/Node/FeatureDecode.cpp @@ -54,8 +54,8 @@ std::unique_ptr<locomotiv::NodeData> feature_decode(const loco::FeatureDecode *n // Make tensor buffer from TensorShape Buffer<T> node_buf = - make_buffer<T, LexicalLayout>(Shape{node_shape.dim(0).value(), node_shape.dim(1).value(), - node_shape.dim(2).value(), node_shape.dim(3).value()}); + make_buffer<T, LexicalLayout>(Shape{node_shape.dim(0).value(), node_shape.dim(1).value(), + node_shape.dim(2).value(), node_shape.dim(3).value()}); // Copy buffer in an order arranged by decoder for (IndexEnumerator e{node_buf.shape()}; e.valid(); e.advance()) diff --git a/compiler/locomotiv/src/Node/FeatureEncode.cpp b/compiler/locomotiv/src/Node/FeatureEncode.cpp index 406de76ff..c3570b981 100644 --- a/compiler/locomotiv/src/Node/FeatureEncode.cpp +++ b/compiler/locomotiv/src/Node/FeatureEncode.cpp @@ -54,8 +54,8 @@ std::unique_ptr<locomotiv::NodeData> feature_encode(const loco::FeatureEncode *n // Make NHWC buffer from FeatureShape Buffer<T> node_buf = - make_buffer<T, LexicalLayout>(Shape{node_shape.count().value(), node_shape.height().value(), - node_shape.width().value(), node_shape.depth().value()}); + make_buffer<T, LexicalLayout>(Shape{node_shape.count().value(), node_shape.height().value(), + node_shape.width().value(), node_shape.depth().value()}); // Copy buffer in an order arranged by encoder for (IndexEnumerator e{node_buf.shape()}; e.valid(); e.advance()) diff --git a/compiler/locomotiv/src/Node/FilterEncode.cpp b/compiler/locomotiv/src/Node/FilterEncode.cpp index 0e2ac918f..84ba681ba 100644 --- a/compiler/locomotiv/src/Node/FilterEncode.cpp +++ b/compiler/locomotiv/src/Node/FilterEncode.cpp @@ -54,8 +54,8 @@ std::unique_ptr<locomotiv::NodeData> filter_encode(const loco::FilterEncode *nod // Make NHWC buffer from FilterShape Buffer<T> node_buf = - make_buffer<T, LexicalLayout>(Shape{node_shape.count().value(), node_shape.height().value(), - node_shape.width().value(), node_shape.depth().value()}); + make_buffer<T, LexicalLayout>(Shape{node_shape.count().value(), node_shape.height().value(), + node_shape.width().value(), node_shape.depth().value()}); // Copy buffer in an order arranged by encoder for (IndexEnumerator e{node_buf.shape()}; e.valid(); e.advance()) diff --git a/compiler/locomotiv/src/Node/FilterEncode.test.cpp b/compiler/locomotiv/src/Node/FilterEncode.test.cpp index dcca94993..80d108ece 100644 --- a/compiler/locomotiv/src/Node/FilterEncode.test.cpp +++ b/compiler/locomotiv/src/Node/FilterEncode.test.cpp @@ -62,7 +62,7 @@ TEST(NodeExecution_FilterEncode, s32) // Encoder to correctly read input tensor as NCHW auto encoder = std::unique_ptr<loco::PermutingEncoder<loco::Domain::Filter>>( - new loco::PermutingEncoder<loco::Domain::Filter>); + new loco::PermutingEncoder<loco::Domain::Filter>); encoder->perm()->axis(loco::FilterAxis::Count) = 0; encoder->perm()->axis(loco::FilterAxis::Depth) = 1; encoder->perm()->axis(loco::FilterAxis::Height) = 2; @@ -116,7 +116,7 @@ TEST(NodeExecution_FilterEncode, f32) // Encoder to correctly read input tensor as CHNW auto encoder = std::unique_ptr<loco::PermutingEncoder<loco::Domain::Filter>>( - new loco::PermutingEncoder<loco::Domain::Filter>); + new loco::PermutingEncoder<loco::Domain::Filter>); encoder->perm()->axis(loco::FilterAxis::Depth) = 0; encoder->perm()->axis(loco::FilterAxis::Height) = 1; encoder->perm()->axis(loco::FilterAxis::Count) = 2; diff --git a/compiler/locomotiv/src/Node/MatrixCodec.test.cpp b/compiler/locomotiv/src/Node/MatrixCodec.test.cpp index da4afeded..7f684e41f 100644 --- a/compiler/locomotiv/src/Node/MatrixCodec.test.cpp +++ b/compiler/locomotiv/src/Node/MatrixCodec.test.cpp @@ -64,7 +64,7 @@ protected: const loco::Permutation<loco::Domain::Matrix> &perm) { auto encoder = std::unique_ptr<loco::PermutingEncoder<loco::Domain::Matrix>>( - new loco::PermutingEncoder<loco::Domain::Matrix>); + new loco::PermutingEncoder<loco::Domain::Matrix>); encoder->perm(perm); @@ -80,7 +80,7 @@ protected: const loco::Permutation<loco::Domain::Matrix> &perm) { auto decoder = std::unique_ptr<loco::PermutingDecoder<loco::Domain::Matrix>>( - new loco::PermutingDecoder<loco::Domain::Matrix>); + new loco::PermutingDecoder<loco::Domain::Matrix>); decoder->perm(perm); diff --git a/compiler/locomotiv/src/Node/MatrixDecode.cpp b/compiler/locomotiv/src/Node/MatrixDecode.cpp index 0310015f1..2a65a7b74 100644 --- a/compiler/locomotiv/src/Node/MatrixDecode.cpp +++ b/compiler/locomotiv/src/Node/MatrixDecode.cpp @@ -52,7 +52,7 @@ std::unique_ptr<locomotiv::NodeData> matrix_decode(const loco::MatrixDecode *nod // Make tensor buffer from TensorShape Buffer<T> node_buf = - make_buffer<T, LexicalLayout>(Shape{node_shape.dim(0).value(), node_shape.dim(1).value()}); + make_buffer<T, LexicalLayout>(Shape{node_shape.dim(0).value(), node_shape.dim(1).value()}); // Copy buffer in an order arranged by decoder for (IndexEnumerator e{node_buf.shape()}; e.valid(); e.advance()) diff --git a/compiler/locomotiv/src/Node/MatrixEncode.cpp b/compiler/locomotiv/src/Node/MatrixEncode.cpp index e3554e15a..ac51e4256 100644 --- a/compiler/locomotiv/src/Node/MatrixEncode.cpp +++ b/compiler/locomotiv/src/Node/MatrixEncode.cpp @@ -54,7 +54,7 @@ std::unique_ptr<locomotiv::NodeData> matrix_encode(const loco::MatrixEncode *nod // Make HW buffer from MatrixShape Buffer<T> node_buf = - make_buffer<T, LexicalLayout>(Shape{node_shape.height().value(), node_shape.width().value()}); + make_buffer<T, LexicalLayout>(Shape{node_shape.height().value(), node_shape.width().value()}); // Copy buffer in an order arranged by encoder for (IndexEnumerator e{node_buf.shape()}; e.valid(); e.advance()) diff --git a/compiler/locomotiv/src/Node/MaxPool2D.cpp b/compiler/locomotiv/src/Node/MaxPool2D.cpp index 8dce1cb1e..dc626387b 100644 --- a/compiler/locomotiv/src/Node/MaxPool2D.cpp +++ b/compiler/locomotiv/src/Node/MaxPool2D.cpp @@ -79,9 +79,9 @@ nncc::core::ADT::tensor::Buffer<T> maxPool2D(const loco::MaxPool2D *maxpool2d, const uint32_t pad_right = maxpool2d->pad()->right(); const uint32_t output_height = - compute_out_size(ifm_height, pad_top + pad_bottom, window_height, stride_height); + compute_out_size(ifm_height, pad_top + pad_bottom, window_height, stride_height); const uint32_t output_width = - compute_out_size(ifm_width, pad_left + pad_right, window_width, stride_width); + compute_out_size(ifm_width, pad_left + pad_right, window_width, stride_width); // prepare output buffer Shape output_shape{batches, output_height, output_width, depth}; diff --git a/compiler/locomotiv/src/Node/MaxPool2D.test.cpp b/compiler/locomotiv/src/Node/MaxPool2D.test.cpp index 5046d4a6e..d00282dd7 100644 --- a/compiler/locomotiv/src/Node/MaxPool2D.test.cpp +++ b/compiler/locomotiv/src/Node/MaxPool2D.test.cpp @@ -82,7 +82,7 @@ void run_test(const float *ifm, const float *expected_ofm, const Shape &ifm_shap ASSERT_TRUE(*(maxpool2d_data->shape()) == ofm_shape); auto ofm_overlay = - make_overlay<float, LexicalLayout>(ofm_shape, const_cast<float *>(expected_ofm)); + make_overlay<float, LexicalLayout>(ofm_shape, const_cast<float *>(expected_ofm)); for (nncc::core::ADT::tensor::IndexEnumerator e{ofm_shape}; e.valid(); e.advance()) { const auto &ind = e.current(); diff --git a/compiler/locomotiv/src/Node/TensorConcat.cpp b/compiler/locomotiv/src/Node/TensorConcat.cpp index 188bb635b..84da3a3e5 100644 --- a/compiler/locomotiv/src/Node/TensorConcat.cpp +++ b/compiler/locomotiv/src/Node/TensorConcat.cpp @@ -52,7 +52,7 @@ void execute_node(loco::TensorConcat *tensor_concat) validate(lhs_data->dtype() == rhs_data->dtype(), "lhs and rhs of Concat should have same dtype"); validate(annot_domain(tensor_concat->lhs()) == loco::Domain::Tensor && - annot_domain(tensor_concat->rhs()) == loco::Domain::Tensor, + annot_domain(tensor_concat->rhs()) == loco::Domain::Tensor, "Some ingredients of TensorConcat is not Tensor"); // Calculate output shape diff --git a/compiler/locomotiv/src/Node/TransposedConv2D.cpp b/compiler/locomotiv/src/Node/TransposedConv2D.cpp index bec15a5df..2f3c3d089 100644 --- a/compiler/locomotiv/src/Node/TransposedConv2D.cpp +++ b/compiler/locomotiv/src/Node/TransposedConv2D.cpp @@ -65,7 +65,7 @@ Buffer<RET_T> calc_tr_conv2D(const loco::TransposedConv2D *tr_conv2d, locomotiv::validate(input_shape.rank() == 4, "ifm rank must be 4"); locomotiv::validate(filter_shape.rank() == 4, "filter rank must be 4"); locomotiv::validate(input_shape.dim(3) /* depth of input */ == - filter_shape.dim(3) /* depth of filter */, + filter_shape.dim(3) /* depth of filter */, "channel value mismatch"); const uint32_t input_height = input_shape.dim(1); @@ -86,9 +86,9 @@ Buffer<RET_T> calc_tr_conv2D(const loco::TransposedConv2D *tr_conv2d, // TODO Support dilations const uint32_t output_height = - compute_transposed_out_size(input_height, pad_top + pad_bottom, filter_height, stride_height); + compute_transposed_out_size(input_height, pad_top + pad_bottom, filter_height, stride_height); const uint32_t output_width = - compute_transposed_out_size(input_width, pad_left + pad_right, filter_width, stride_width); + compute_transposed_out_size(input_width, pad_left + pad_right, filter_width, stride_width); const uint32_t batches = input_shape.dim(0); const uint32_t input_depth = input_shape.dim(3); @@ -131,9 +131,9 @@ Buffer<RET_T> calc_tr_conv2D(const loco::TransposedConv2D *tr_conv2d, { auto input_value = input_buf->at(Index({batch, in_y, in_x, in_channel})); auto filter_value = - filter_buf->at(Index({out_channel, filter_y, filter_x, in_channel})); + filter_buf->at(Index({out_channel, filter_y, filter_x, in_channel})); output_buf.at(Index({batch, (unsigned)out_y, (unsigned)out_x, out_channel})) += - input_value * filter_value; + input_value * filter_value; } } } diff --git a/compiler/locomotiv/src/Node/TransposedConv2D.test.cpp b/compiler/locomotiv/src/Node/TransposedConv2D.test.cpp index ef759f51b..a516ef9f2 100644 --- a/compiler/locomotiv/src/Node/TransposedConv2D.test.cpp +++ b/compiler/locomotiv/src/Node/TransposedConv2D.test.cpp @@ -97,7 +97,7 @@ void run_test(const float *ifm, const float *ker, const float *expected_ofm, con ASSERT_TRUE(*(conv2d_result->shape()) == ofm_shape); auto ofm_overlay = - make_overlay<float, LexicalLayout>(ofm_shape, const_cast<float *>(expected_ofm)); + make_overlay<float, LexicalLayout>(ofm_shape, const_cast<float *>(expected_ofm)); for (nncc::core::ADT::tensor::IndexEnumerator e{ofm_shape}; e.valid(); e.advance()) { const auto &ind = e.current(); diff --git a/compiler/locomotiv/src/NodeDataImpl.cpp b/compiler/locomotiv/src/NodeDataImpl.cpp index 2efebe5a9..9373b8dd2 100644 --- a/compiler/locomotiv/src/NodeDataImpl.cpp +++ b/compiler/locomotiv/src/NodeDataImpl.cpp @@ -16,8 +16,7 @@ #include "NodeDataImpl.h" -#include <stdex/Memory.h> - +#include <memory> #include <cassert> namespace @@ -59,7 +58,7 @@ template <> NodeDataImpl::NodeDataImpl(const Buffer<float> &buf) void annot_data(loco::Node *node, std::unique_ptr<NodeData> &&data) { - node->annot(stdex::make_unique<NodeDataAnnotation>(std::move(data))); + node->annot(std::make_unique<NodeDataAnnotation>(std::move(data))); } const NodeData *annot_data(const loco::Node *node) diff --git a/compiler/locomotiv/src/NodeExecution.h b/compiler/locomotiv/src/NodeExecution.h index 363188d38..eb0608d2b 100644 --- a/compiler/locomotiv/src/NodeExecution.h +++ b/compiler/locomotiv/src/NodeExecution.h @@ -62,7 +62,7 @@ private: return dynamic_cast<Derived *>(node); } -// clang-format off + // clang-format off /** * @brief Calculate for one specified node and update its result as NodeData. * Abort program when its ingredients are not ready or not supported. diff --git a/compiler/locomotiv/src/UserData.cpp b/compiler/locomotiv/src/UserData.cpp index b658ada9b..98f761efd 100644 --- a/compiler/locomotiv/src/UserData.cpp +++ b/compiler/locomotiv/src/UserData.cpp @@ -16,8 +16,7 @@ #include "UserData.h" -#include <stdex/Memory.h> - +#include <memory> #include <cassert> namespace @@ -55,7 +54,7 @@ const NodeData *user_data(const loco::Node *node) void user_data(loco::Node *node, std::unique_ptr<NodeData> &&data) { - node->annot(stdex::make_unique<UserDataAnnotation>(std::move(data))); + node->annot(std::make_unique<UserDataAnnotation>(std::move(data))); } void erase_user_data(loco::Node *node) { node->annot<UserDataAnnotation>(nullptr); } |