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-rw-r--r--arm_compute/graph/backends/FunctionHelpers.h732
1 files changed, 285 insertions, 447 deletions
diff --git a/arm_compute/graph/backends/FunctionHelpers.h b/arm_compute/graph/backends/FunctionHelpers.h
index 9830290d0f..fd8b6b5a69 100644
--- a/arm_compute/graph/backends/FunctionHelpers.h
+++ b/arm_compute/graph/backends/FunctionHelpers.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2018-2021 Arm Limited.
+ * Copyright (c) 2018-2021, 2023 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -21,22 +21,22 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
-#ifndef ARM_COMPUTE_GRAPH_BACKENDS_DETAIL_FUNCTION_HELPERS_H
-#define ARM_COMPUTE_GRAPH_BACKENDS_DETAIL_FUNCTION_HELPERS_H
+#ifndef ACL_ARM_COMPUTE_GRAPH_BACKENDS_FUNCTIONHELPERS_H
+#define ACL_ARM_COMPUTE_GRAPH_BACKENDS_FUNCTIONHELPERS_H
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensorInfo.h"
+#include "arm_compute/graph/backends/FusedConvolutionBatchNormalizationFunction.h"
+#include "arm_compute/graph/backends/FusedDepthwiseConvolutionBatchNormalizationFunction.h"
+#include "arm_compute/graph/backends/Utils.h"
#include "arm_compute/graph/Logger.h"
+#include "arm_compute/graph/nodes/Nodes.h"
#include "arm_compute/graph/Tensor.h"
#include "arm_compute/graph/TypePrinter.h"
#include "arm_compute/graph/Types.h"
#include "arm_compute/graph/Utils.h"
-#include "arm_compute/graph/backends/FusedConvolutionBatchNormalizationFunction.h"
-#include "arm_compute/graph/backends/FusedDepthwiseConvolutionBatchNormalizationFunction.h"
-#include "arm_compute/graph/backends/Utils.h"
-#include "arm_compute/graph/nodes/Nodes.h"
-#include "arm_compute/core/Error.h"
-#include "arm_compute/core/Helpers.h"
-#include "arm_compute/core/ITensorInfo.h"
#include "support/Cast.h"
namespace arm_compute
@@ -59,13 +59,16 @@ template <typename TargetInfo>
typename TargetInfo::TensorType *get_backing_tensor(arm_compute::graph::Tensor *tensor)
{
typename TargetInfo::TensorType *backing_tensor = nullptr;
- if(tensor != nullptr)
+ if (tensor != nullptr)
{
ARM_COMPUTE_ERROR_ON(tensor->desc().target != TargetInfo::TargetType);
// Get backing tensor handle
ITensorHandle *tensor_handle = tensor->handle();
// Get backing tensor
- backing_tensor = (tensor_handle != nullptr) ? arm_compute::utils::cast::polymorphic_cast<typename TargetInfo::TensorType *>(&tensor_handle->tensor()) : nullptr;
+ backing_tensor = (tensor_handle != nullptr)
+ ? arm_compute::utils::cast::polymorphic_cast<typename TargetInfo::TensorType *>(
+ &tensor_handle->tensor())
+ : nullptr;
}
return backing_tensor;
@@ -74,11 +77,8 @@ typename TargetInfo::TensorType *get_backing_tensor(arm_compute::graph::Tensor *
template <typename TargetInfo>
void validate_node(const INode &node, size_t num_expected_inputs, size_t num_expected_outputs)
{
- ARM_COMPUTE_LOG_GRAPH_VERBOSE("Creating " << node.type()
- << " Target: " << TargetInfo::TargetType
- << " ID: " << node.id()
- << node.name()
- << std::endl);
+ ARM_COMPUTE_LOG_GRAPH_VERBOSE("Creating " << node.type() << " Target: " << TargetInfo::TargetType
+ << " ID: " << node.id() << node.name() << std::endl);
ARM_COMPUTE_ERROR_ON(TargetInfo::TargetType != node.assigned_target());
ARM_COMPUTE_ERROR_ON(node.num_inputs() != num_expected_inputs);
@@ -109,19 +109,13 @@ std::unique_ptr<IFunction> create_activation_layer(ActivationLayerNode &node)
auto func = std::make_unique<ActivationLayerFunction>();
func->configure(input, output, act_info);
- ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated "
- << node.name()
- << " Type: " << node.type()
- << " Target: " << TargetInfo::TargetType
- << " Data Type: " << input->info()->data_type()
- << " Shape: " << input->info()->tensor_shape()
- << " Activation function: " << act_info.activation()
- << " a: " << act_info.a()
- << " b: " << act_info.b()
- << " InPlace : " << is_in_place_operation(input, output)
- << std::endl);
+ ARM_COMPUTE_LOG_GRAPH_INFO(
+ "Instantiated " << node.name() << " Type: " << node.type() << " Target: " << TargetInfo::TargetType
+ << " Data Type: " << input->info()->data_type() << " Shape: " << input->info()->tensor_shape()
+ << " Activation function: " << act_info.activation() << " a: " << act_info.a() << " b: "
+ << act_info.b() << " InPlace : " << is_in_place_operation(input, output) << std::endl);
- return std::move(func);
+ return func;
}
/** Creates a backend argminmax layer function
@@ -148,17 +142,12 @@ std::unique_ptr<IFunction> create_arg_min_max_layer(ArgMinMaxLayerNode &node)
auto func = std::make_unique<ArgMinMaxLayerFunction>();
func->configure(input, axis, output, op);
- ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated "
- << node.name()
- << " Type: " << node.type()
- << " Target: " << TargetInfo::TargetType
- << " Data Type: " << input->info()->data_type()
- << " Shape: " << input->info()->tensor_shape()
- << " Reduction Operation: " << op
- << " axis: " << axis
- << std::endl);
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << node.name() << " Type: " << node.type() << " Target: "
+ << TargetInfo::TargetType << " Data Type: " << input->info()->data_type()
+ << " Shape: " << input->info()->tensor_shape()
+ << " Reduction Operation: " << op << " axis: " << axis << std::endl);
- return std::move(func);
+ return func;
}
/** Create a backend batch normalization layer function
@@ -191,18 +180,13 @@ std::unique_ptr<IFunction> create_batch_normalization_layer(BatchNormalizationLa
func->configure(input, output, mean, var, beta, gamma, epsilon, fused_act);
// Log info
- ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated "
- << node.name()
- << " Type: " << node.type()
- << " Target: " << TargetInfo::TargetType
- << " Data Type: " << input->info()->data_type()
- << " Shape: " << input->info()->tensor_shape()
- << " Epsilon: " << epsilon << " "
- << (fused_act.enabled() ? to_string(fused_act.activation()) : "")
- << " InPlace: " << is_in_place_operation(input, output)
- << std::endl);
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << node.name() << " Type: " << node.type() << " Target: "
+ << TargetInfo::TargetType << " Data Type: " << input->info()->data_type()
+ << " Shape: " << input->info()->tensor_shape() << " Epsilon: " << epsilon
+ << " " << (fused_act.enabled() ? to_string(fused_act.activation()) : "")
+ << " InPlace: " << is_in_place_operation(input, output) << std::endl);
- return std::move(func);
+ return func;
}
/** Create a backend batch normalization layer function
@@ -216,7 +200,8 @@ std::unique_ptr<IFunction> create_batch_normalization_layer(BatchNormalizationLa
* @return Backend batch normalization layer function
*/
template <typename FusedLayerTypes, typename TargetInfo>
-std::unique_ptr<IFunction> create_fused_convolution_batch_normalization_layer(FusedConvolutionBatchNormalizationNode &node, GraphContext &ctx)
+std::unique_ptr<IFunction>
+create_fused_convolution_batch_normalization_layer(FusedConvolutionBatchNormalizationNode &node, GraphContext &ctx)
{
validate_node<TargetInfo>(node, 7 /* expected inputs */, 1 /* expected outputs */);
@@ -246,20 +231,17 @@ std::unique_ptr<IFunction> create_fused_convolution_batch_normalization_layer(Fu
// Create and configure function
std::tie(func, func_name) = create_named_memory_managed_function<FType>(
- std::string("FusedConvolutionBatchNormalizationLayer"), mm, input, weights, biases, output, mean, var, beta, gamma, epsilon, conv_info, num_groups, fast_math, fused_act);
+ std::string("FusedConvolutionBatchNormalizationLayer"), mm, input, weights, biases, output, mean, var, beta,
+ gamma, epsilon, conv_info, num_groups, fast_math, fused_act);
// Log info
ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated "
- << node.name()
- << " Type: " << node.type()
- << " Target: " << TargetInfo::TargetType
- << " Data Type: " << input->info()->data_type()
- << " Input shape: " << input->info()->tensor_shape()
- << " Weights shape: " << weights->info()->tensor_shape()
+ << node.name() << " Type: " << node.type() << " Target: " << TargetInfo::TargetType
+ << " Data Type: " << input->info()->data_type() << " Input shape: "
+ << input->info()->tensor_shape() << " Weights shape: " << weights->info()->tensor_shape()
<< " Output shape: " << output->info()->tensor_shape()
- << (fused_act.enabled() ? " " + to_string(fused_act.activation()) : "")
- << std::endl);
- return std::move(func);
+ << (fused_act.enabled() ? " " + to_string(fused_act.activation()) : "") << std::endl);
+ return func;
}
/** Create a backend fused depthwise convolution batch normalization layer function
@@ -273,7 +255,9 @@ std::unique_ptr<IFunction> create_fused_convolution_batch_normalization_layer(Fu
* @return Backend fused depthwise convolution batch normalization layer function
*/
template <typename FusedLayerTypes, typename TargetInfo>
-std::unique_ptr<IFunction> create_fused_depthwise_convolution_batch_normalization_layer(FusedDepthwiseConvolutionBatchNormalizationNode &node, GraphContext &ctx)
+std::unique_ptr<IFunction>
+create_fused_depthwise_convolution_batch_normalization_layer(FusedDepthwiseConvolutionBatchNormalizationNode &node,
+ GraphContext &ctx)
{
validate_node<TargetInfo>(node, 7 /* expected inputs */, 1 /* expected outputs */);
@@ -302,20 +286,17 @@ std::unique_ptr<IFunction> create_fused_depthwise_convolution_batch_normalizatio
// Create and configure function
std::tie(func, func_name) = create_named_memory_managed_function<FType>(
- std::string("FusedDepthwiseConvolutionBatchNormalizationLayer"), mm, input, weights, biases, output, mean, var, beta, gamma, epsilon, conv_info, depth_multiplier, fused_act);
+ std::string("FusedDepthwiseConvolutionBatchNormalizationLayer"), mm, input, weights, biases, output, mean, var,
+ beta, gamma, epsilon, conv_info, depth_multiplier, fused_act);
// Log info
ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated "
- << node.name()
- << " Type: " << node.type()
- << " Target: " << TargetInfo::TargetType
- << " Data Type: " << input->info()->data_type()
- << " Input shape: " << input->info()->tensor_shape()
- << " Weights shape: " << weights->info()->tensor_shape()
+ << node.name() << " Type: " << node.type() << " Target: " << TargetInfo::TargetType
+ << " Data Type: " << input->info()->data_type() << " Input shape: "
+ << input->info()->tensor_shape() << " Weights shape: " << weights->info()->tensor_shape()
<< " Output shape: " << output->info()->tensor_shape()
- << (fused_act.enabled() ? " " + to_string(fused_act.activation()) : "")
- << std::endl);
- return std::move(func);
+ << (fused_act.enabled() ? " " + to_string(fused_act.activation()) : "") << std::endl);
+ return func;
}
/** Create a backend bounding box transform layer function
@@ -343,15 +324,11 @@ std::unique_ptr<IFunction> create_bounding_box_transform_layer(BoundingBoxTransf
func->configure(input, output, deltas, bbox_info);
// Log info
- ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated "
- << node.name()
- << " Type: " << node.type()
- << " Target: " << TargetInfo::TargetType
- << " Data Type: " << input->info()->data_type()
- << " Shape: " << input->info()->tensor_shape()
- << " BoundingBox Info img W: " << bbox_info.img_width() << " "
- << " BoundingBox Info img H: " << bbox_info.img_height() << " "
- << std::endl);
+ ARM_COMPUTE_LOG_GRAPH_INFO(
+ "Instantiated " << node.name() << " Type: " << node.type() << " Target: " << TargetInfo::TargetType
+ << " Data Type: " << input->info()->data_type() << " Shape: " << input->info()->tensor_shape()
+ << " BoundingBox Info img W: " << bbox_info.img_width() << " "
+ << " BoundingBox Info img H: " << bbox_info.img_height() << " " << std::endl);
return std::move(func);
}
@@ -379,16 +356,12 @@ std::unique_ptr<IFunction> create_channel_shuffle_layer(ChannelShuffleLayerNode
auto func = std::make_unique<ChannelShuffleLayerFunction>();
func->configure(input, output, num_groups);
- ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated "
- << node.name()
- << " Type: " << node.type()
- << " Target: " << TargetInfo::TargetType
- << " Data Type: " << input->info()->data_type()
- << " Shape: " << input->info()->tensor_shape()
- << " Num groups: " << num_groups
- << std::endl);
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << node.name() << " Type: " << node.type() << " Target: "
+ << TargetInfo::TargetType << " Data Type: " << input->info()->data_type()
+ << " Shape: " << input->info()->tensor_shape()
+ << " Num groups: " << num_groups << std::endl);
- return std::move(func);
+ return func;
}
/** Create a backend layer concatenate function
@@ -403,24 +376,25 @@ std::unique_ptr<IFunction> create_channel_shuffle_layer(ChannelShuffleLayerNode
template <typename ConcatenateLayerFunction, typename TargetInfo>
std::unique_ptr<arm_compute::IFunction> create_concatenate_layer(ConcatenateLayerNode &node)
{
- ARM_COMPUTE_LOG_GRAPH_VERBOSE("Creating Concatenate node with ID : " << node.id() << " and Name: " << node.name() << std::endl);
+ ARM_COMPUTE_LOG_GRAPH_VERBOSE("Creating Concatenate node with ID : " << node.id() << " and Name: " << node.name()
+ << std::endl);
ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1);
// Return nullptr if depth concatenate is switched off
- if(!node.is_enabled())
+ if (!node.is_enabled())
{
return nullptr;
}
// Extract IO and info
std::vector<typename TargetInfo::SrcTensorType *> inputs;
- for(unsigned int i = 0; i < node.num_inputs(); ++i)
+ for (unsigned int i = 0; i < node.num_inputs(); ++i)
{
inputs.push_back(get_backing_tensor<TargetInfo>(node.input(i)));
}
- typename TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0));
- const DataLayout data_layout = node.output(0) != nullptr ? node.output(0)->desc().layout : DataLayout::UNKNOWN;
- const size_t concat_axis = get_dimension_idx(data_layout, node.concatenation_axis());
+ typename TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0));
+ const DataLayout data_layout = node.output(0) != nullptr ? node.output(0)->desc().layout : DataLayout::UNKNOWN;
+ const size_t concat_axis = get_dimension_idx(data_layout, node.concatenation_axis());
// Create and configure function
auto func = std::make_unique<ConcatenateLayerFunction>();
@@ -429,28 +403,22 @@ std::unique_ptr<arm_compute::IFunction> create_concatenate_layer(ConcatenateLaye
// Log info
const bool is_quantized = is_data_type_quantized_asymmetric(output->info()->data_type());
std::ostringstream qss;
- if(is_quantized)
+ if (is_quantized)
{
qss << " Output QuantInfo: " << output->info()->quantization_info();
}
- ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated "
- << node.name()
- << " Type: " << node.type()
- << " Target: " << TargetInfo::TargetType
- << " Data Type: " << output->info()->data_type()
- << " Shape: " << output->info()->tensor_shape()
- << " Num Inputs: " << inputs.size()
- << " Axis: " << concat_axis
- << qss.str()
- << std::endl);
+ ARM_COMPUTE_LOG_GRAPH_INFO(
+ "Instantiated " << node.name() << " Type: " << node.type() << " Target: " << TargetInfo::TargetType
+ << " Data Type: " << output->info()->data_type() << " Shape: " << output->info()->tensor_shape()
+ << " Num Inputs: " << inputs.size() << " Axis: " << concat_axis << qss.str() << std::endl);
- return std::move(func);
+ return func;
}
/** Create a backend convolution layer function
*
* @tparam ConvolutionLayerFunctions Backend convolution functions
- * @tparam TargetInfo Target-specific information
+ * @tparam TargetInfo Target-specific information
*
* @param[in] node Node to create the backend function for
* @param[in] ctx Graph context
@@ -470,7 +438,7 @@ std::unique_ptr<IFunction> create_convolution_layer(ConvolutionLayerNode &node,
const bool is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type());
- if(is_quantized)
+ if (is_quantized)
{
biases->info()->set_data_type(DataType::S32);
}
@@ -486,56 +454,51 @@ std::unique_ptr<IFunction> create_convolution_layer(ConvolutionLayerNode &node,
std::unique_ptr<IFunction> func;
std::string func_name;
- if(conv_algorithm == ConvolutionMethod::Winograd)
+ if (conv_algorithm == ConvolutionMethod::Winograd)
{
ARM_COMPUTE_ERROR_ON_MSG(num_groups != 1, "WinogradConvolutionLayer does not support grouping!");
- std::tie(func, func_name) = create_named_memory_managed_function<typename ConvolutionLayerFunctions::WinogradConvolutionLayer>(
- std::string("WinogradConvolutionLayer"), mm,
- input, weights, biases, output, conv_info, fused_act, fast_math);
+ std::tie(func, func_name) =
+ create_named_memory_managed_function<typename ConvolutionLayerFunctions::WinogradConvolutionLayer>(
+ std::string("WinogradConvolutionLayer"), mm, input, weights, biases, output, conv_info, fused_act,
+ fast_math);
}
- else if(conv_algorithm == ConvolutionMethod::Direct)
+ else if (conv_algorithm == ConvolutionMethod::Direct)
{
ARM_COMPUTE_ERROR_ON_MSG(num_groups != 1, "DirectConvolutionLayer does not support grouping!");
std::tie(func, func_name) = create_named_function<typename ConvolutionLayerFunctions::DirectConvolutionLayer>(
- std::string("DirectConvolutionLayer"),
- input, weights, biases, output, conv_info, fused_act);
+ std::string("DirectConvolutionLayer"), input, weights, biases, output, conv_info, fused_act);
}
- else if(conv_algorithm == ConvolutionMethod::GEMM)
+ else if (conv_algorithm == ConvolutionMethod::GEMM)
{
- std::tie(func, func_name) = create_named_memory_managed_function<typename ConvolutionLayerFunctions::GEMMConvolutionLayer>(
- std::string("GEMMConvolutionLayer"), mm,
- input, weights, biases, output, conv_info,
- WeightsInfo(), Size2D(1U, 1U), fused_act, num_groups);
+ std::tie(func, func_name) =
+ create_named_memory_managed_function<typename ConvolutionLayerFunctions::GEMMConvolutionLayer>(
+ std::string("GEMMConvolutionLayer"), mm, input, weights, biases, output, conv_info, WeightsInfo(),
+ Size2D(1U, 1U), fused_act, num_groups);
}
else
{
- std::tie(func, func_name) = create_named_memory_managed_function<typename ConvolutionLayerFunctions::GenericConvolutionLayer>(
- std::string("GenericConvolutionLayer"), mm,
- input, weights, biases, output, conv_info,
- WeightsInfo(), Size2D(1U, 1U), fused_act, fast_math, num_groups);
+ std::tie(func, func_name) =
+ create_named_memory_managed_function<typename ConvolutionLayerFunctions::GenericConvolutionLayer>(
+ std::string("GenericConvolutionLayer"), mm, input, weights, biases, output, conv_info, WeightsInfo(),
+ Size2D(1U, 1U), fused_act, fast_math, num_groups);
}
// Log info
std::ostringstream qss;
- if(is_quantized)
+ if (is_quantized)
{
qss << " Input QuantInfo: " << input->info()->quantization_info()
<< " Weights QuantInfo: " << weights->info()->quantization_info()
<< " Output QuantInfo: " << output->info()->quantization_info();
}
ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated "
- << node.name()
- << " Type: " << func_name
- << " Target: " << TargetInfo::TargetType
- << " Data Type: " << input->info()->data_type()
- << " Groups: " << num_groups
+ << node.name() << " Type: " << func_name << " Target: " << TargetInfo::TargetType
+ << " Data Type: " << input->info()->data_type() << " Groups: " << num_groups
<< " Input shape: " << input->info()->tensor_shape()
<< " Weights shape: " << weights->info()->tensor_shape()
- << " Output shape: " << output->info()->tensor_shape()
- << qss.str()
- << (fused_act.enabled() ? " " + to_string(fused_act.activation()) : "")
- << std::endl);
- return std::move(func);
+ << " Output shape: " << output->info()->tensor_shape() << qss.str()
+ << (fused_act.enabled() ? " " + to_string(fused_act.activation()) : "") << std::endl);
+ return func;
}
/** Create a backend deconvolution layer function
@@ -566,19 +529,14 @@ std::unique_ptr<IFunction> create_deconvolution_layer(DeconvolutionLayerNode &no
std::unique_ptr<IFunction> func;
std::tie(func, std::ignore) = create_named_memory_managed_function<DeconvolutionLayerFunction>(
- std::string(), mm,
- input, weights, biases, output, deconv_info);
+ std::string(), mm, input, weights, biases, output, deconv_info);
// Log info
- ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated "
- << node.name()
- << " Type: " << node.type()
- << " Target: " << TargetInfo::TargetType
- << " Data Type: " << input->info()->data_type()
- << " Input shape: " << input->info()->tensor_shape()
- << " Weights shape: " << weights->info()->tensor_shape()
- << " Output shape: " << output->info()->tensor_shape()
- << std::endl);
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << node.name() << " Type: " << node.type() << " Target: "
+ << TargetInfo::TargetType << " Data Type: " << input->info()->data_type()
+ << " Input shape: " << input->info()->tensor_shape()
+ << " Weights shape: " << weights->info()->tensor_shape()
+ << " Output shape: " << output->info()->tensor_shape() << std::endl);
return func;
}
@@ -604,7 +562,7 @@ std::unique_ptr<IFunction> create_depthwise_convolution_layer(DepthwiseConvoluti
const bool is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type());
- if(is_quantized)
+ if (is_quantized)
{
biases->info()->set_data_type(DataType::S32);
}
@@ -617,31 +575,26 @@ std::unique_ptr<IFunction> create_depthwise_convolution_layer(DepthwiseConvoluti
std::unique_ptr<IFunction> func;
std::string func_name;
- std::tie(func, func_name) = create_named_function<DepthwiseConvolutionLayer>(
- std::string("DepthwiseConvolutionLayer"),
- input, weights, biases, output, conv_info, depth_multiplier, fused_act);
+ std::tie(func, func_name) =
+ create_named_function<DepthwiseConvolutionLayer>(std::string("DepthwiseConvolutionLayer"), input, weights,
+ biases, output, conv_info, depth_multiplier, fused_act);
// Log info
std::ostringstream qss;
- if(is_quantized)
+ if (is_quantized)
{
qss << " Input QuantInfo: " << input->info()->quantization_info()
<< " Weights QuantInfo: " << weights->info()->quantization_info()
<< " Output QuantInfo: " << output->info()->quantization_info();
}
ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated "
- << node.name()
- << " Type: " << func_name
- << " Target: " << TargetInfo::TargetType
- << " Data Type: " << input->info()->data_type()
- << " Input shape: " << input->info()->tensor_shape()
- << " Weights shape: " << weights->info()->tensor_shape()
+ << node.name() << " Type: " << func_name << " Target: " << TargetInfo::TargetType
+ << " Data Type: " << input->info()->data_type() << " Input shape: "
+ << input->info()->tensor_shape() << " Weights shape: " << weights->info()->tensor_shape()
<< " Output shape: " << output->info()->tensor_shape()
- << " Depth multiplier: " << depth_multiplier
- << qss.str()
- << (fused_act.enabled() ? " " + to_string(fused_act.activation()) : "")
- << std::endl);
- return std::move(func);
+ << " Depth multiplier: " << depth_multiplier << qss.str()
+ << (fused_act.enabled() ? " " + to_string(fused_act.activation()) : "") << std::endl);
+ return func;
}
/** Create a backend depth to space layer function
@@ -670,17 +623,13 @@ std::unique_ptr<IFunction> create_depth_to_space_layer(DepthToSpaceLayerNode &no
func->configure(input, output, node.block_shape());
// Log info
- ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated "
- << node.name()
- << " Type: " << node.type()
- << " Target: " << TargetInfo::TargetType
- << " Data Type: " << input->info()->data_type()
- << " Input shape: " << input->info()->tensor_shape()
- << " Block Size: " << node.block_shape()
- << " Output shape: " << output->info()->tensor_shape()
- << std::endl);
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << node.name() << " Type: " << node.type() << " Target: "
+ << TargetInfo::TargetType << " Data Type: " << input->info()->data_type()
+ << " Input shape: " << input->info()->tensor_shape()
+ << " Block Size: " << node.block_shape()
+ << " Output shape: " << output->info()->tensor_shape() << std::endl);
- return std::move(func);
+ return func;
}
/** Create a backend dequantize layer function
@@ -709,17 +658,13 @@ std::unique_ptr<IFunction> create_dequantization_layer(DequantizationLayerNode &
func->configure(input, output);
// Log info
- ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated "
- << node.name()
- << " Type: " << node.type()
- << " Target: " << TargetInfo::TargetType
- << " Data Type: " << input->info()->data_type()
- << " Input shape: " << input->info()->tensor_shape()
- << " Input quantization info: " << output->info()->quantization_info()
- << " Output shape: " << output->info()->tensor_shape()
- << std::endl);
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << node.name() << " Type: " << node.type() << " Target: "
+ << TargetInfo::TargetType << " Data Type: " << input->info()->data_type()
+ << " Input shape: " << input->info()->tensor_shape()
+ << " Input quantization info: " << output->info()->quantization_info()
+ << " Output shape: " << output->info()->tensor_shape() << std::endl);
- return std::move(func);
+ return func;
}
/** Create a backend detection output layer function
*
@@ -753,18 +698,14 @@ std::unique_ptr<IFunction> create_detection_output_layer(DetectionOutputLayerNod
// Log info
ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated "
- << node.name()
- << " Type: " << node.type()
- << " Target: " << TargetInfo::TargetType
- << " Data Type: " << input0->info()->data_type()
- << " Input0 shape: " << input0->info()->tensor_shape()
- << " Input1 shape: " << input1->info()->tensor_shape()
+ << node.name() << " Type: " << node.type() << " Target: " << TargetInfo::TargetType
+ << " Data Type: " << input0->info()->data_type() << " Input0 shape: "
+ << input0->info()->tensor_shape() << " Input1 shape: " << input1->info()->tensor_shape()
<< " Input2 shape: " << input2->info()->tensor_shape()
<< " Output shape: " << output->info()->tensor_shape()
- << " DetectionOutputLayer info: " << detect_info
- << std::endl);
+ << " DetectionOutputLayer info: " << detect_info << std::endl);
- return std::move(func);
+ return func;
}
/** Create a backend detection post process layer function
@@ -805,21 +746,17 @@ std::unique_ptr<IFunction> create_detection_post_process_layer(DetectionPostProc
// Log info
ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated "
- << node.name()
- << " Type: " << node.type()
- << " Target: " << TargetInfo::TargetType
- << " Data Type: " << input0->info()->data_type()
- << " Input0 shape: " << input0->info()->tensor_shape()
- << " Input1 shape: " << input1->info()->tensor_shape()
+ << node.name() << " Type: " << node.type() << " Target: " << TargetInfo::TargetType
+ << " Data Type: " << input0->info()->data_type() << " Input0 shape: "
+ << input0->info()->tensor_shape() << " Input1 shape: " << input1->info()->tensor_shape()
<< " Input2 shape: " << input2->info()->tensor_shape()
<< " Output0 shape: " << output0->info()->tensor_shape()
<< " Output1 shape: " << output1->info()->tensor_shape()
<< " Output2 shape: " << output2->info()->tensor_shape()
<< " Output3 shape: " << output3->info()->tensor_shape()
- << " DetectionPostProcessLayer info: " << detect_info
- << std::endl);
+ << " DetectionPostProcessLayer info: " << detect_info << std::endl);
- return std::move(func);
+ return func;
}
/** Create a backend element-wise operation layer function
@@ -849,35 +786,31 @@ std::unique_ptr<IFunction> create_eltwise_layer(EltwiseLayerNode &node)
std::unique_ptr<IFunction> func = nullptr;
std::string func_name;
- if(eltwise_op == EltwiseOperation::Add)
+ if (eltwise_op == EltwiseOperation::Add)
{
std::tie(func, func_name) = create_named_function<typename EltwiseFunctions::Addition>(
- std::string("ArithmeticAddition"),
- input1, input2, output, convert_policy, act_info);
+ std::string("ArithmeticAddition"), input1, input2, output, convert_policy, act_info);
}
- else if(eltwise_op == EltwiseOperation::Sub)
+ else if (eltwise_op == EltwiseOperation::Sub)
{
std::tie(func, func_name) = create_named_function<typename EltwiseFunctions::Subtraction>(
- std::string("ArithmeticSubtraction"),
- input1, input2, output, convert_policy, act_info);
+ std::string("ArithmeticSubtraction"), input1, input2, output, convert_policy, act_info);
}
- else if(eltwise_op == EltwiseOperation::Mul)
+ else if (eltwise_op == EltwiseOperation::Mul)
{
std::tie(func, func_name) = create_named_function<typename EltwiseFunctions::Multiplication>(
- std::string("PixelWiseMultiplication"),
- input1, input2, output, 1.f, convert_policy, node.rounding_policy(), act_info);
+ std::string("PixelWiseMultiplication"), input1, input2, output, 1.f, convert_policy, node.rounding_policy(),
+ act_info);
}
- else if(eltwise_op == EltwiseOperation::Max)
+ else if (eltwise_op == EltwiseOperation::Max)
{
std::tie(func, func_name) = create_named_function<typename EltwiseFunctions::Maximum>(
- std::string("ElementwiseMaximum"),
- input1, input2, output, act_info);
+ std::string("ElementwiseMaximum"), input1, input2, output, act_info);
}
- else if(eltwise_op == EltwiseOperation::Div)
+ else if (eltwise_op == EltwiseOperation::Div)
{
std::tie(func, func_name) = create_named_function<typename EltwiseFunctions::Division>(
- std::string("ArithmeticDivision"),
- input1, input2, output, act_info);
+ std::string("ArithmeticDivision"), input1, input2, output, act_info);
}
else
{
@@ -885,16 +818,12 @@ std::unique_ptr<IFunction> create_eltwise_layer(EltwiseLayerNode &node)
}
// Log info
- ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated "
- << node.name()
- << " Type: " << node.type()
- << " Target: " << TargetInfo::TargetType
- << " Operation: " << func_name
- << " Data Type: " << input1->info()->data_type()
- << " Shape: " << input1->info()->tensor_shape()
- << std::endl);
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << node.name() << " Type: " << node.type()
+ << " Target: " << TargetInfo::TargetType << " Operation: " << func_name
+ << " Data Type: " << input1->info()->data_type()
+ << " Shape: " << input1->info()->tensor_shape() << std::endl);
- return std::move(func);
+ return func;
}
/** Create a backend unary element-wise operation layer function
@@ -921,11 +850,10 @@ std::unique_ptr<IFunction> create_unary_eltwise_layer(UnaryEltwiseLayerNode &nod
std::unique_ptr<IFunction> func = nullptr;
std::string func_name;
- if(eltwise_op == UnaryEltwiseOperation::Exp)
+ if (eltwise_op == UnaryEltwiseOperation::Exp)
{
- std::tie(func, func_name) = create_named_function<typename UnaryEltwiseFunctions::Exp>(
- std::string("Exp"),
- input, output);
+ std::tie(func, func_name) =
+ create_named_function<typename UnaryEltwiseFunctions::Exp>(std::string("Exp"), input, output);
}
else
{
@@ -933,16 +861,12 @@ std::unique_ptr<IFunction> create_unary_eltwise_layer(UnaryEltwiseLayerNode &nod
}
// Log info
- ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated "
- << node.name()
- << " Type: " << node.type()
- << " Target: " << TargetInfo::TargetType
- << " Operation: " << func_name
- << " Data Type: " << input->info()->data_type()
- << " Shape: " << input->info()->tensor_shape()
- << std::endl);
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << node.name() << " Type: " << node.type()
+ << " Target: " << TargetInfo::TargetType << " Operation: " << func_name
+ << " Data Type: " << input->info()->data_type()
+ << " Shape: " << input->info()->tensor_shape() << std::endl);
- return std::move(func);
+ return func;
}
/** Create a backend flatten layer function
@@ -971,16 +895,12 @@ std::unique_ptr<IFunction> create_flatten_layer(FlattenLayerNode &node)
func->configure(input, output);
// Log info
- ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated "
- << node.name()
- << " Type: " << node.type()
- << " Target: " << TargetInfo::TargetType
- << " Data Type: " << input->info()->data_type()
- << " Input shape: " << input->info()->tensor_shape()
- << " Output shape: " << output->info()->tensor_shape()
- << std::endl);
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << node.name() << " Type: " << node.type() << " Target: "
+ << TargetInfo::TargetType << " Data Type: " << input->info()->data_type()
+ << " Input shape: " << input->info()->tensor_shape()
+ << " Output shape: " << output->info()->tensor_shape() << std::endl);
- return std::move(func);
+ return func;
}
/** Create a backend fully connected layer function
@@ -1003,7 +923,8 @@ std::unique_ptr<IFunction> create_fully_connected_layer(FullyConnectedLayerNode
typename TargetInfo::TensorType *weights = get_backing_tensor<TargetInfo>(node.input(1));
typename TargetInfo::TensorType *biases = get_backing_tensor<TargetInfo>(node.input(2));
typename TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0));
- const FullyConnectedLayerInfo fc_info = node.info();
+ FullyConnectedLayerInfo fc_info = node.info();
+ fc_info.enable_fast_math = (node.fast_math_hint() == FastMathHint::Enabled);
ARM_COMPUTE_ERROR_ON(input == nullptr);
ARM_COMPUTE_ERROR_ON(weights == nullptr);
@@ -1019,24 +940,19 @@ std::unique_ptr<IFunction> create_fully_connected_layer(FullyConnectedLayerNode
// Log info
std::ostringstream qss;
- if(is_quantized)
+ if (is_quantized)
{
qss << " Input QuantInfo: " << input->info()->quantization_info()
<< " Weights QuantInfo: " << weights->info()->quantization_info()
<< " Output QuantInfo: " << output->info()->quantization_info();
}
- ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated "
- << node.name()
- << " Type: " << node.type()
- << " Target: " << TargetInfo::TargetType
- << " Data Type: " << input->info()->data_type()
- << qss.str()
- << " Input shape: " << input->info()->tensor_shape()
- << " Weights shape: " << weights->info()->tensor_shape()
- << " Output shape: " << output->info()->tensor_shape()
- << std::endl);
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << node.name() << " Type: " << node.type() << " Target: "
+ << TargetInfo::TargetType << " Data Type: " << input->info()->data_type()
+ << qss.str() << " Input shape: " << input->info()->tensor_shape()
+ << " Weights shape: " << weights->info()->tensor_shape()
+ << " Output shape: " << output->info()->tensor_shape() << std::endl);
- return std::move(func);
+ return func;
}
/** Create a backend generate proposals layer function
@@ -1074,16 +990,14 @@ std::unique_ptr<IFunction> create_generate_proposals_layer(GenerateProposalsLaye
func->configure(scores, deltas, anchors, proposals, scores_out, num_valid_proposals, info);
// Log info
- ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << node.type()
- << " Target " << TargetInfo::TargetType
- << " Data Type: " << scores->info()->data_type()
- << " Scores shape: " << scores->info()->tensor_shape()
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated "
+ << node.type() << " Target " << TargetInfo::TargetType << " Data Type: "
+ << scores->info()->data_type() << " Scores shape: " << scores->info()->tensor_shape()
<< " Deltas shape: " << deltas->info()->tensor_shape()
<< " Anchors shape: " << anchors->info()->tensor_shape()
<< " Proposals shape: " << proposals->info()->tensor_shape()
<< " Num valid proposals shape: " << num_valid_proposals->info()->tensor_shape()
- << " Scores Out shape: " << scores_out->info()->tensor_shape()
- << std::endl);
+ << " Scores Out shape: " << scores_out->info()->tensor_shape() << std::endl);
return std::move(func);
}
@@ -1118,18 +1032,13 @@ std::unique_ptr<IFunction> create_l2_normalize_layer(L2NormalizeLayerNode &node,
func->configure(input, output, axis, epsilon);
// Log info
- ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated "
- << node.name()
- << " Type: " << node.type()
- << " Target: " << TargetInfo::TargetType
- << " Data Type: " << input->info()->data_type()
- << " Input shape: " << input->info()->tensor_shape()
- << " Output shape: " << output->info()->tensor_shape()
- << " Axis: " << axis
- << " Epsilon: " << epsilon
- << std::endl);
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << node.name() << " Type: " << node.type() << " Target: "
+ << TargetInfo::TargetType << " Data Type: " << input->info()->data_type()
+ << " Input shape: " << input->info()->tensor_shape()
+ << " Output shape: " << output->info()->tensor_shape()
+ << " Axis: " << axis << " Epsilon: " << epsilon << std::endl);
- return std::move(func);
+ return func;
}
/** Create a backend normalization layer function
@@ -1161,15 +1070,11 @@ std::unique_ptr<IFunction> create_normalization_layer(NormalizationLayerNode &no
func->configure(input, output, norm_info);
// Log info
- ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated "
- << node.name()
- << " Type: " << node.type()
- << " Target: " << TargetInfo::TargetType
- << " Data Type: " << input->info()->data_type()
- << " Input shape: " << input->info()->tensor_shape()
- << " Output shape: " << output->info()->tensor_shape()
- << " Normalization info: " << norm_info.type()
- << std::endl);
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << node.name() << " Type: " << node.type() << " Target: "
+ << TargetInfo::TargetType << " Data Type: " << input->info()->data_type()
+ << " Input shape: " << input->info()->tensor_shape()
+ << " Output shape: " << output->info()->tensor_shape()
+ << " Normalization info: " << norm_info.type() << std::endl);
return std::move(func);
}
@@ -1203,13 +1108,9 @@ std::unique_ptr<IFunction> create_normalize_planar_yuv_layer(NormalizePlanarYUVL
func->configure(input, output, mean, std);
// Log info
- ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated "
- << node.name()
- << " Type: " << node.type()
- << " Target: " << TargetInfo::TargetType
- << " Data Type: " << input->info()->data_type()
- << " Shape: " << input->info()->tensor_shape()
- << std::endl);
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << node.name() << " Type: " << node.type() << " Target: "
+ << TargetInfo::TargetType << " Data Type: " << input->info()->data_type()
+ << " Shape: " << input->info()->tensor_shape() << std::endl);
return std::move(func);
}
@@ -1241,16 +1142,12 @@ std::unique_ptr<IFunction> create_pad_layer(PadLayerNode &node)
func->configure(input, output, padding, pad_value);
// Log info
- ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated "
- << node.name()
- << " Type: " << node.type()
- << " Target: " << TargetInfo::TargetType
- << " Data Type: " << input->info()->data_type()
- << " Input shape: " << input->info()->tensor_shape()
- << " Output shape: " << output->info()->tensor_shape()
- << std::endl);
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << node.name() << " Type: " << node.type() << " Target: "
+ << TargetInfo::TargetType << " Data Type: " << input->info()->data_type()
+ << " Input shape: " << input->info()->tensor_shape()
+ << " Output shape: " << output->info()->tensor_shape() << std::endl);
- return std::move(func);
+ return func;
}
/** Create a backend permute layer function
@@ -1279,17 +1176,13 @@ std::unique_ptr<IFunction> create_permute_layer(PermuteLayerNode &node)
func->configure(input, output, perm);
// Log info
- ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated "
- << node.name()
- << " Type: " << node.type()
- << " Target: " << TargetInfo::TargetType
- << " Data Type: " << input->info()->data_type()
- << " Input shape: " << input->info()->tensor_shape()
- << " Output shape: " << output->info()->tensor_shape()
- << " Permutation vector: " << perm
- << std::endl);
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << node.name() << " Type: " << node.type() << " Target: "
+ << TargetInfo::TargetType << " Data Type: " << input->info()->data_type()
+ << " Input shape: " << input->info()->tensor_shape()
+ << " Output shape: " << output->info()->tensor_shape()
+ << " Permutation vector: " << perm << std::endl);
- return std::move(func);
+ return func;
}
/** Create a backend pooling layer function
@@ -1318,17 +1211,13 @@ std::unique_ptr<IFunction> create_pooling_layer(PoolingLayerNode &node)
func->configure(input, output, pool_info);
// Log info
- ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated "
- << node.name()
- << " Type: " << node.type()
- << " Target: " << TargetInfo::TargetType
- << " Data Type: " << input->info()->data_type()
- << " Input shape: " << input->info()->tensor_shape()
- << " Output shape: " << output->info()->tensor_shape()
- << " Pooling info: " << pool_info.pool_type
- << std::endl);
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << node.name() << " Type: " << node.type() << " Target: "
+ << TargetInfo::TargetType << " Data Type: " << input->info()->data_type()
+ << " Input shape: " << input->info()->tensor_shape()
+ << " Output shape: " << output->info()->tensor_shape()
+ << " Pooling info: " << pool_info.pool_type << std::endl);
- return std::move(func);
+ return func;
}
/** Create a backend PRelu layer function
@@ -1357,16 +1246,12 @@ std::unique_ptr<IFunction> create_prelu_layer(PReluLayerNode &node)
func->configure(input, alpha, output);
// Log info
- ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated "
- << node.name()
- << " Type: " << node.type()
- << " Target: " << TargetInfo::TargetType
- << " Data Type: " << input->info()->data_type()
- << " Input shape: " << input->info()->tensor_shape()
- << " Output shape: " << output->info()->tensor_shape()
- << std::endl);
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << node.name() << " Type: " << node.type() << " Target: "
+ << TargetInfo::TargetType << " Data Type: " << input->info()->data_type()
+ << " Input shape: " << input->info()->tensor_shape()
+ << " Output shape: " << output->info()->tensor_shape() << std::endl);
- return std::move(func);
+ return func;
}
/** Create a backend print layer function
@@ -1387,13 +1272,9 @@ std::unique_ptr<IFunction> create_print_layer(PrintLayerNode &node)
ARM_COMPUTE_UNUSED(input);
// Log info
- ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated "
- << node.name()
- << " Type: " << node.type()
- << " Target: " << TargetInfo::TargetType
- << " Data Type: " << input->info()->data_type()
- << " Input shape: " << input->info()->tensor_shape()
- << std::endl);
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << node.name() << " Type: " << node.type() << " Target: "
+ << TargetInfo::TargetType << " Data Type: " << input->info()->data_type()
+ << " Input shape: " << input->info()->tensor_shape() << std::endl);
return nullptr;
}
@@ -1427,17 +1308,13 @@ std::unique_ptr<IFunction> create_priorbox_layer(PriorBoxLayerNode &node)
// Log info
ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated "
- << node.name()
- << " Type: " << node.type()
- << " Target: " << TargetInfo::TargetType
- << " Data Type: " << input0->info()->data_type()
- << " Input0 shape: " << input0->info()->tensor_shape()
- << " Input1 shape: " << input1->info()->tensor_shape()
+ << node.name() << " Type: " << node.type() << " Target: " << TargetInfo::TargetType
+ << " Data Type: " << input0->info()->data_type() << " Input0 shape: "
+ << input0->info()->tensor_shape() << " Input1 shape: " << input1->info()->tensor_shape()
<< " Output shape: " << output->info()->tensor_shape()
- << " PriorBoxLayer info: " << prior_info
- << std::endl);
+ << " PriorBoxLayer info: " << prior_info << std::endl);
- return std::move(func);
+ return func;
}
/** Create a backend quantization layer function
@@ -1465,16 +1342,12 @@ std::unique_ptr<IFunction> create_quantization_layer(QuantizationLayerNode &node
func->configure(input, output);
// Log info
- ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated "
- << node.name()
- << " Type: " << node.type()
- << " Target: " << TargetInfo::TargetType
- << " Data Type: " << input->info()->data_type()
- << " Input shape: " << input->info()->tensor_shape()
- << " Output shape: " << output->info()->tensor_shape()
- << std::endl);
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << node.name() << " Type: " << node.type() << " Target: "
+ << TargetInfo::TargetType << " Data Type: " << input->info()->data_type()
+ << " Input shape: " << input->info()->tensor_shape()
+ << " Output shape: " << output->info()->tensor_shape() << std::endl);
- return std::move(func);
+ return func;
}
/** Create a backend reduction operation layer function
@@ -1507,18 +1380,13 @@ std::unique_ptr<IFunction> create_reduction_operation_layer(ReductionLayerNode &
// Log info
ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated "
- << node.name()
- << " Type: " << node.type()
- << " Target: " << TargetInfo::TargetType
+ << node.name() << " Type: " << node.type() << " Target: " << TargetInfo::TargetType
<< " Data Type: " << input->info()->data_type()
<< " Input shape: " << input->info()->tensor_shape()
- << " Output shape: " << output->info()->tensor_shape()
- << " Operation: " << op
- << " Axis: " << axis
- << " Keep dimensions:" << keep_dims
- << std::endl);
+ << " Output shape: " << output->info()->tensor_shape() << " Operation: " << op
+ << " Axis: " << axis << " Keep dimensions:" << keep_dims << std::endl);
- return std::move(func);
+ return func;
}
/** Create a backend reorg layer function
@@ -1546,16 +1414,12 @@ std::unique_ptr<IFunction> create_reorg_layer(ReorgLayerNode &node)
func->configure(input, output, node.stride());
// Log info
- ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated "
- << node.name()
- << " Type: " << node.type()
- << " Target: " << TargetInfo::TargetType
- << " Data Type: " << input->info()->data_type()
- << " Input shape: " << input->info()->tensor_shape()
- << " Output shape: " << output->info()->tensor_shape()
- << std::endl);
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << node.name() << " Type: " << node.type() << " Target: "
+ << TargetInfo::TargetType << " Data Type: " << input->info()->data_type()
+ << " Input shape: " << input->info()->tensor_shape()
+ << " Output shape: " << output->info()->tensor_shape() << std::endl);
- return std::move(func);
+ return func;
}
/** Create a backend reshape layer function
@@ -1583,16 +1447,12 @@ std::unique_ptr<IFunction> create_reshape_layer(ReshapeLayerNode &node)
func->configure(input, output);
// Log info
- ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated "
- << node.name()
- << " Type: " << node.type()
- << " Target: " << TargetInfo::TargetType
- << " Data Type: " << input->info()->data_type()
- << " Input shape: " << input->info()->tensor_shape()
- << " Output shape: " << output->info()->tensor_shape()
- << std::endl);
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << node.name() << " Type: " << node.type() << " Target: "
+ << TargetInfo::TargetType << " Data Type: " << input->info()->data_type()
+ << " Input shape: " << input->info()->tensor_shape()
+ << " Output shape: " << output->info()->tensor_shape() << std::endl);
- return std::move(func);
+ return func;
}
/** Create a backend resize layer function
@@ -1618,20 +1478,17 @@ std::unique_ptr<IFunction> create_resize_layer(ResizeLayerNode &node)
// Create and configure function
auto func = std::make_unique<ResizeLayerFunction>();
- func->configure(input, output, ScaleKernelInfo{ policy, BorderMode::CONSTANT, PixelValue(), SamplingPolicy::CENTER, false, false });
+ func->configure(input, output,
+ ScaleKernelInfo{policy, BorderMode::CONSTANT, PixelValue(), SamplingPolicy::CENTER, false, false});
// Log info
- ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated "
- << node.name()
- << " Type: " << node.type()
- << " Target: " << TargetInfo::TargetType
- << " Data Type: " << input->info()->data_type()
- << " Input shape: " << input->info()->tensor_shape()
- << " Output shape: " << output->info()->tensor_shape()
- << " Interpolation: " << policy
- << std::endl);
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << node.name() << " Type: " << node.type() << " Target: "
+ << TargetInfo::TargetType << " Data Type: " << input->info()->data_type()
+ << " Input shape: " << input->info()->tensor_shape()
+ << " Output shape: " << output->info()->tensor_shape()
+ << " Interpolation: " << policy << std::endl);
- return std::move(func);
+ return func;
}
/** Create a backend ROI align layer function
@@ -1664,17 +1521,13 @@ std::unique_ptr<IFunction> create_roi_align_layer(ROIAlignLayerNode &node)
func->configure(input, rois, output, pool_info);
// Log info
- ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated "
- << node.name()
- << " Type: " << node.type()
- << " Target: " << TargetInfo::TargetType
- << " Data Type: " << input->info()->data_type()
- << " Input shape: " << input->info()->tensor_shape()
- << " Output shape: " << output->info()->tensor_shape()
- << " ROIs shape: " << rois->info()->tensor_shape()
- << " ROIPooling width: " << pool_info.pooled_width()
- << " ROIPooling height: " << pool_info.pooled_height()
- << std::endl);
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << node.name() << " Type: " << node.type() << " Target: "
+ << TargetInfo::TargetType << " Data Type: " << input->info()->data_type()
+ << " Input shape: " << input->info()->tensor_shape()
+ << " Output shape: " << output->info()->tensor_shape()
+ << " ROIs shape: " << rois->info()->tensor_shape()
+ << " ROIPooling width: " << pool_info.pooled_width()
+ << " ROIPooling height: " << pool_info.pooled_height() << std::endl);
return std::move(func);
}
@@ -1704,16 +1557,12 @@ std::unique_ptr<IFunction> create_slice_layer(SliceLayerNode &node)
func->configure(input, output, node.starts(), node.ends());
// Log info
- ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated "
- << node.name()
- << " Type: " << node.type()
- << " Target: " << TargetInfo::TargetType
- << " Data Type: " << input->info()->data_type()
- << " Input shape: " << input->info()->tensor_shape()
- << " Output shape: " << output->info()->tensor_shape()
- << std::endl);
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << node.name() << " Type: " << node.type() << " Target: "
+ << TargetInfo::TargetType << " Data Type: " << input->info()->data_type()
+ << " Input shape: " << input->info()->tensor_shape()
+ << " Output shape: " << output->info()->tensor_shape() << std::endl);
- return std::move(func);
+ return func;
}
/** Create a backend softmax layer function
@@ -1743,16 +1592,12 @@ std::unique_ptr<IFunction> create_softmax_layer(SoftmaxLayerNode &node, GraphCon
func->configure(input, output, beta);
// Log info
- ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated "
- << node.name()
- << " Type: " << node.type()
- << " Target: " << TargetInfo::TargetType
- << " Data Type: " << input->info()->data_type()
- << " Input shape: " << input->info()->tensor_shape()
- << " Output shape: " << output->info()->tensor_shape()
- << std::endl);
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << node.name() << " Type: " << node.type() << " Target: "
+ << TargetInfo::TargetType << " Data Type: " << input->info()->data_type()
+ << " Input shape: " << input->info()->tensor_shape()
+ << " Output shape: " << output->info()->tensor_shape() << std::endl);
- return std::move(func);
+ return func;
}
/** Create a backend layer stack function
@@ -1767,12 +1612,13 @@ std::unique_ptr<IFunction> create_softmax_layer(SoftmaxLayerNode &node, GraphCon
template <typename StackLayerFunction, typename TargetInfo>
std::unique_ptr<arm_compute::IFunction> create_stack_layer(StackLayerNode &node)
{
- ARM_COMPUTE_LOG_GRAPH_VERBOSE("Creating Stack node with ID : " << node.id() << " and Name: " << node.name() << std::endl);
+ ARM_COMPUTE_LOG_GRAPH_VERBOSE("Creating Stack node with ID : " << node.id() << " and Name: " << node.name()
+ << std::endl);
ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1);
// Extract IO and info
std::vector<typename TargetInfo::TensorType *> inputs;
- for(unsigned int i = 0; i < node.num_inputs(); ++i)
+ for (unsigned int i = 0; i < node.num_inputs(); ++i)
{
inputs.push_back(get_backing_tensor<TargetInfo>(node.input(i)));
}
@@ -1784,18 +1630,14 @@ std::unique_ptr<arm_compute::IFunction> create_stack_layer(StackLayerNode &node)
func->configure(inputs, axis, output);
// Log info
- ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated "
- << node.name()
- << " Type: " << node.type()
- << " Target: " << TargetInfo::TargetType
- << " Data Type: " << output->info()->data_type()
- << " Inputs shape: " << inputs[0]->info()->tensor_shape()
- << " Output shape: " << output->info()->tensor_shape()
- << " Num Inputs: " << inputs.size()
- << " Axis: " << axis
- << std::endl);
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << node.name() << " Type: " << node.type()
+ << " Target: " << TargetInfo::TargetType
+ << " Data Type: " << output->info()->data_type()
+ << " Inputs shape: " << inputs[0]->info()->tensor_shape()
+ << " Output shape: " << output->info()->tensor_shape()
+ << " Num Inputs: " << inputs.size() << " Axis: " << axis << std::endl);
- return std::move(func);
+ return func;
}
/** Create a backend slice layer function
@@ -1828,20 +1670,16 @@ std::unique_ptr<IFunction> create_strided_slice_layer(StridedSliceLayerNode &nod
func->configure(input, output, starts, ends, strides, info.begin_mask(), info.end_mask(), info.shrink_axis_mask());
// Log info
- ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated "
- << node.name()
- << " Type: " << node.type()
- << " Target: " << TargetInfo::TargetType
- << " Data Type: " << input->info()->data_type()
- << " Input shape: " << input->info()->tensor_shape()
- << " Output shape: " << output->info()->tensor_shape()
- << std::endl);
+ ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << node.name() << " Type: " << node.type() << " Target: "
+ << TargetInfo::TargetType << " Data Type: " << input->info()->data_type()
+ << " Input shape: " << input->info()->tensor_shape()
+ << " Output shape: " << output->info()->tensor_shape() << std::endl);
- return std::move(func);
+ return func;
}
} // namespace detail
} // namespace backends
} // namespace graph
} // namespace arm_compute
-#endif /* ARM_COMPUTE_GRAPH_BACKENDS_DETAIL_FUNCTION_HELPERS_H */
+#endif // ACL_ARM_COMPUTE_GRAPH_BACKENDS_FUNCTIONHELPERS_H
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-03 22:34:25 +0000