| /* |
| * Copyright (c) 2018-2019 ARM Limited. |
| * |
| * SPDX-License-Identifier: MIT |
| * |
| * 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. |
| */ |
| #ifndef __ARM_COMPUTE_GRAPH_BACKENDS_DETAIL_FUNCTION_HELPERS_H__ |
| #define __ARM_COMPUTE_GRAPH_BACKENDS_DETAIL_FUNCTION_HELPERS_H__ |
| |
| #include "arm_compute/graph/Logger.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 "arm_compute/core/utils/misc/Cast.h" |
| |
| namespace arm_compute |
| { |
| namespace graph |
| { |
| namespace backends |
| { |
| namespace detail |
| { |
| /** Returns backing tensor of a given tensor |
| * |
| * @tparam TargetInfo Target information |
| * |
| * @param[in] tensor Tensor to extract the backing tensor from |
| * |
| * @return Backing tensor if present else nullptr |
| */ |
| template <typename TargetInfo> |
| typename TargetInfo::TensorType *get_backing_tensor(arm_compute::graph::Tensor *tensor) |
| { |
| typename TargetInfo::TensorType *backing_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; |
| } |
| |
| return backing_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_ERROR_ON(TargetInfo::TargetType != node.assigned_target()); |
| ARM_COMPUTE_ERROR_ON(node.num_inputs() != num_expected_inputs); |
| ARM_COMPUTE_ERROR_ON(node.num_outputs() != num_expected_outputs); |
| } |
| |
| /** Creates a backend activation layer function |
| * |
| * @tparam ActivationLayerFunction Backend activation function |
| * @tparam TargetInfo Target-specific information |
| * |
| * @param[in] node Node to create the backend function for |
| * |
| * @return Backend activation layer function |
| */ |
| template <typename ActivationLayerFunction, typename TargetInfo> |
| std::unique_ptr<IFunction> create_activation_layer(ActivationLayerNode &node) |
| { |
| validate_node<TargetInfo>(node, 1 /* expected inputs */, 1 /* expected outputs */); |
| |
| // Extract IO and info |
| typename TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0)); |
| typename TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0)); |
| const ActivationLayerInfo act_info = node.activation_info(); |
| |
| // Create function |
| auto func = support::cpp14::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); |
| |
| return std::move(func); |
| } |
| |
| /** Create a backend batch normalization layer function |
| * |
| * @tparam BatchNormalizationLayerFunction Backend batch normalization function |
| * @tparam TargetInfo Target-specific information |
| * |
| * @param[in] node Node to create the backend function for |
| * |
| * @return Backend batch normalization layer function |
| */ |
| template <typename BatchNormalizationLayerFunction, typename TargetInfo> |
| std::unique_ptr<IFunction> create_batch_normalization_layer(BatchNormalizationLayerNode &node) |
| { |
| validate_node<TargetInfo>(node, 5 /* expected inputs */, 1 /* expected outputs */); |
| |
| // Extract IO and info |
| typename TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0)); |
| typename TargetInfo::TensorType *mean = get_backing_tensor<TargetInfo>(node.input(1)); |
| typename TargetInfo::TensorType *var = get_backing_tensor<TargetInfo>(node.input(2)); |
| typename TargetInfo::TensorType *beta = get_backing_tensor<TargetInfo>(node.input(3)); |
| typename TargetInfo::TensorType *gamma = get_backing_tensor<TargetInfo>(node.input(4)); |
| |
| typename TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0)); |
| const float epsilon = node.epsilon(); |
| const ActivationLayerInfo fused_act = node.fused_activation(); |
| |
| // Create and configure function |
| auto func = support::cpp14::make_unique<BatchNormalizationLayerFunction>(); |
| 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); |
| |
| return std::move(func); |
| } |
| |
| /** Create a backend batch normalization layer function |
| * |
| * @tparam BatchNormalizationLayerFunction Backend batch normalization function |
| * @tparam TargetInfo Target-specific information |
| * |
| * @param[in] node Node to create the backend function for |
| * |
| * @return Backend batch normalization layer function |
| */ |
| template <typename FusedLayerTypes, typename TargetInfo> |
| std::unique_ptr<IFunction> create_fused_convolution_batch_normalization_layer(FusedConvolutionBatchNormalizationNode &node) |
| { |
| validate_node<TargetInfo>(node, 7 /* expected inputs */, 1 /* expected outputs */); |
| |
| // Extract IO and info |
| typename TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0)); |
| 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 *mean = get_backing_tensor<TargetInfo>(node.input(3)); |
| typename TargetInfo::TensorType *var = get_backing_tensor<TargetInfo>(node.input(4)); |
| typename TargetInfo::TensorType *beta = get_backing_tensor<TargetInfo>(node.input(5)); |
| typename TargetInfo::TensorType *gamma = get_backing_tensor<TargetInfo>(node.input(6)); |
| |
| typename TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0)); |
| |
| const PadStrideInfo conv_info = node.convolution_info(); |
| const unsigned int num_groups = node.num_groups(); |
| const bool fast_math = node.fast_math_hint() == FastMathHint::Enabled; |
| const ActivationLayerInfo fused_act = node.fused_activation(); |
| const float epsilon = node.epsilon(); |
| |
| // Create and configure function |
| auto func = support::cpp14::make_unique<FusedConvolutionBatchNormalizationFunction<TargetInfo, FusedLayerTypes>>(); |
| func->configure(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() |
| << " Output shape: " << output->info()->tensor_shape() |
| << (fused_act.enabled() ? " " + to_string(fused_act.activation()) : "") |
| << std::endl); |
| return std::move(func); |
| } |
| |
| /** Create a backend fused depthwise convolution batch normalization layer function |
| * |
| * @tparam FusedLayerTypes Fused layer types |
| * @tparam TargetInfo Target-specific information |
| * |
| * @param[in] node Node to create the backend function for |
| * |
| * @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) |
| { |
| validate_node<TargetInfo>(node, 7 /* expected inputs */, 1 /* expected outputs */); |
| |
| // Extract IO and info |
| typename TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0)); |
| 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 *mean = get_backing_tensor<TargetInfo>(node.input(3)); |
| typename TargetInfo::TensorType *var = get_backing_tensor<TargetInfo>(node.input(4)); |
| typename TargetInfo::TensorType *beta = get_backing_tensor<TargetInfo>(node.input(5)); |
| typename TargetInfo::TensorType *gamma = get_backing_tensor<TargetInfo>(node.input(6)); |
| |
| typename TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0)); |
| |
| const PadStrideInfo conv_info = node.convolution_info(); |
| const unsigned int depth_multiplier = node.depth_multiplier(); |
| const ActivationLayerInfo fused_act = node.fused_activation(); |
| const float epsilon = node.epsilon(); |
| |
| // Create and configure function |
| auto func = support::cpp14::make_unique<FusedDepthwiseConvolutionBatchNormalizationFunction<TargetInfo, FusedLayerTypes>>(); |
| func->configure(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() |
| << " Output shape: " << output->info()->tensor_shape() |
| << (fused_act.enabled() ? " " + to_string(fused_act.activation()) : "") |
| << std::endl); |
| return std::move(func); |
| } |
| |
| /** Create a backend bounding box transform layer function |
| * |
| * @tparam BoundingBoxTransformLayerFunction Backend bounding box transform function |
| * @tparam TargetInfo Target-specific information |
| * |
| * @param[in] node Node to create the backend function for |
| * |
| * @return Backend bounding box transform layer function |
| */ |
| template <typename BoundingBoxTransformLayerFunction, typename TargetInfo> |
| std::unique_ptr<IFunction> create_bounding_box_transform_layer(BoundingBoxTransformLayerNode &node) |
| { |
| validate_node<TargetInfo>(node, 2 /* expected inputs */, 1 /* expected outputs */); |
| |
| // Extract IO and info |
| typename TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0)); |
| typename TargetInfo::TensorType *deltas = get_backing_tensor<TargetInfo>(node.input(1)); |
| typename TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0)); |
| const BoundingBoxTransformInfo bbox_info = node.info(); |
| |
| // Create and configure function |
| auto func = support::cpp14::make_unique<BoundingBoxTransformLayerFunction>(); |
| 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); |
| |
| return std::move(func); |
| } |
| |
| /** Create a backend channel shuffle layer function |
| * |
| * @tparam ChannelShuffleLayerFunction Backend channel shuffle function |
| * @tparam TargetInfo Target-specific information |
| * |
| * @param[in] node Node to create the backend function for |
| * |
| * @return Backend channel shuffle layer function |
| */ |
| template <typename ChannelShuffleLayerFunction, typename TargetInfo> |
| std::unique_ptr<IFunction> create_channel_shuffle_layer(ChannelShuffleLayerNode &node) |
| { |
| validate_node<TargetInfo>(node, 1 /* expected inputs */, 1 /* expected outputs */); |
| |
| // Extract IO and info |
| typename TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0)); |
| typename TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0)); |
| const unsigned int num_groups = node.num_groups(); |
| |
| // Create function |
| auto func = support::cpp14::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); |
| |
| return std::move(func); |
| } |
| |
| /** Create a backend layer concatenate function |
| * |
| * @tparam ConcatenateLayerFunction Backend concatenate function |
| * @tparam TargetInfo Target-specific information |
| * |
| * @param[in] node Node to create the backend function for |
| * |
| * @return Backend concatenate layer function |
| */ |
| 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_ERROR_ON(node.num_outputs() != 1); |
| |
| // Return nullptr if depth concatenate is switched off |
| if(!node.is_enabled()) |
| { |
| return nullptr; |
| } |
| |
| // Extract IO and info |
| std::vector<typename TargetInfo::TensorType *> inputs; |
| 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()); |
| |
| // Create and configure function |
| auto func = support::cpp14::make_unique<ConcatenateLayerFunction>(); |
| func->configure(inputs, output, concat_axis); |
| |
| // Log info |
| const bool is_quantized = is_data_type_quantized_asymmetric(output->info()->data_type()); |
| std::ostringstream qss; |
| 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); |
| |
| return std::move(func); |
| } |
| |
| /** Create a backend convolution layer function |
| * |
| * @tparam ConvolutionLayerFunctions Backend convolution functions |
| * @tparam TargetInfo Target-specific information |
| * |
| * @param[in] node Node to create the backend function for |
| * @param[in] ctx Graph context |
| * |
| * @return Backend convolution layer function |
| */ |
| template <typename ConvolutionLayerFunctions, typename TargetInfo> |
| std::unique_ptr<IFunction> create_convolution_layer(ConvolutionLayerNode &node, GraphContext &ctx) |
| { |
| validate_node<TargetInfo>(node, 3 /* expected inputs */, 1 /* expected outputs */); |
| |
| // Extract IO and info |
| typename TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0)); |
| 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 bool is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type()); |
| |
| if(is_quantized) |
| { |
| biases->info()->set_data_type(DataType::S32); |
| } |
| |
| const PadStrideInfo conv_info = node.convolution_info(); |
| const unsigned int num_groups = node.num_groups(); |
| const ConvolutionMethod conv_algorithm = node.convolution_method(); |
| const bool fast_math = node.fast_math_hint() == FastMathHint::Enabled; |
| const ActivationLayerInfo fused_act = node.fused_activation(); |
| |
| // Create and configure function (we assume that functions have been validated before creation) |
| std::shared_ptr<IMemoryManager> mm = get_memory_manager(ctx, TargetInfo::TargetType); |
| std::unique_ptr<IFunction> func; |
| std::string func_name; |
| |
| 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); |
| } |
| 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); |
| } |
| 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); |
| } |
| 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); |
| } |
| |
| // Log info |
| std::ostringstream qss; |
| 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 |
| << " 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 func; |
| } |
| |
| /** Create a backend deconvolution layer function |
| * |
| * @tparam DeconvolutionLayerFunction Backend deconvolution function |
| * @tparam TargetInfo Target-specific information |
| * |
| * @param[in] node Node to create the backend function for |
| * @param[in] ctx Graph context |
| * |
| * @return Backend deconvolution layer function |
| */ |
| template <typename DeconvolutionLayerFunction, typename TargetInfo> |
| std::unique_ptr<IFunction> create_deconvolution_layer(DeconvolutionLayerNode &node, GraphContext &ctx) |
| { |
| validate_node<TargetInfo>(node, 3 /* expected inputs */, 1 /* expected outputs */); |
| |
| // Extract IO and info |
| typename TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0)); |
| 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 PadStrideInfo deconv_info = node.deconvolution_info(); |
| |
| // Create and configure function (we assume that functions have been validated before creation) |
| std::shared_ptr<IMemoryManager> mm = get_memory_manager(ctx, TargetInfo::TargetType); |
| 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); |
| |
| // 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); |
| return func; |
| } |
| |
| /** Create a backend layer depth-wise convolution function |
| * |
| * @tparam DepthwiseConvolutionLayerFunctions Backend depthwise convolution function |
| * @tparam TargetInfo Target-specific information |
| * |
| * @param[in] node Node to create the backend function for |
| * |
| * @return Backend depth-wise convolution layer function |
| */ |
| template <typename DepthwiseConvolutionLayerFunctions, typename TargetInfo> |
| std::unique_ptr<IFunction> create_depthwise_convolution_layer(DepthwiseConvolutionLayerNode &node) |
| { |
| validate_node<TargetInfo>(node, 3 /* expected inputs */, 1 /* expected outputs */); |
| |
| // Extract IO and info |
| typename TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0)); |
| 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 bool is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type()); |
| |
| if(is_quantized) |
| { |
| biases->info()->set_data_type(DataType::S32); |
| } |
| |
| const PadStrideInfo conv_info = node.convolution_info(); |
| const DepthwiseConvolutionMethod dwc_algorithm = node.depthwise_convolution_method(); |
| const unsigned int depth_multiplier = node.depth_multiplier(); |
| const ActivationLayerInfo fused_act = node.fused_activation(); |
| |
| // Create and configure function (we assume that functions have been validated before creation) |
| std::unique_ptr<IFunction> func; |
| std::string func_name; |
| if(dwc_algorithm == DepthwiseConvolutionMethod::Optimized3x3) |
| { |
| std::tie(func, func_name) = create_named_function<typename DepthwiseConvolutionLayerFunctions::OptimizedDepthwiseConvolutionLayer>( |
| std::string("DepthwiseConvolutionLayer3x3"), |
| input, weights, biases, output, conv_info, depth_multiplier, fused_act); |
| } |
| else |
| { |
| std::tie(func, func_name) = create_named_function<typename DepthwiseConvolutionLayerFunctions::GenericDepthwiseConvolutionLayer>( |
| std::string("DepthwiseConvolutionLayer"), |
| input, weights, biases, output, conv_info, depth_multiplier, fused_act); |
| } |
| |
| // Log info |
| std::ostringstream qss; |
| 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() |
| << " Output shape: " << output->info()->tensor_shape() |
| << " Depth multiplier: " << depth_multiplier |
| << qss.str() |
| << (fused_act.enabled() ? " " + to_string(fused_act.activation()) : "") |
| << std::endl); |
| return func; |
| } |
| |
| /** Create a backend detection output layer function |
| * |
| * @tparam DetectionOutputLayer Function Backend detection output function |
| * @tparam TargetInfo Target-specific information |
| * |
| * @param[in] node Node to create the backend function for |
| * |
| * @return Backend detection output layer function |
| */ |
| template <typename DetectionOutputLayerFunction, typename TargetInfo> |
| std::unique_ptr<IFunction> create_detection_output_layer(DetectionOutputLayerNode &node) |
| { |
| validate_node<TargetInfo>(node, 3 /* expected inputs */, 1 /* expected outputs */); |
| |
| // Extract IO and info |
| typename TargetInfo::TensorType *input0 = get_backing_tensor<TargetInfo>(node.input(0)); |
| typename TargetInfo::TensorType *input1 = get_backing_tensor<TargetInfo>(node.input(1)); |
| typename TargetInfo::TensorType *input2 = get_backing_tensor<TargetInfo>(node.input(2)); |
| typename TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0)); |
| const DetectionOutputLayerInfo detect_info = node.detection_output_info(); |
| |
| ARM_COMPUTE_ERROR_ON(input0 == nullptr); |
| ARM_COMPUTE_ERROR_ON(input1 == nullptr); |
| ARM_COMPUTE_ERROR_ON(input2 == nullptr); |
| ARM_COMPUTE_ERROR_ON(output == nullptr); |
| |
| // Create and configure function |
| auto func = support::cpp14::make_unique<DetectionOutputLayerFunction>(); |
| func->configure(input0, input1, input2, output, detect_info); |
| |
| // 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() |
| << " Input2 shape: " << input2->info()->tensor_shape() |
| << " Output shape: " << output->info()->tensor_shape() |
| << " DetectionOutputLayer info: " << detect_info |
| << std::endl); |
| |
| return std::move(func); |
| } |
| |
| /** Create a backend detection post process layer function |
| * |
| * @tparam DetectionPostProcessLayerFunction Backend detection output function |
| * @tparam TargetInfo Target-specific information |
| * |
| * @param[in] node Node to create the backend function for |
| * |
| * @return Backend detection post process layer function |
| */ |
| template <typename DetectionPostProcessLayerFunction, typename TargetInfo> |
| std::unique_ptr<IFunction> create_detection_post_process_layer(DetectionPostProcessLayerNode &node) |
| { |
| validate_node<TargetInfo>(node, 3 /* expected inputs */, 4 /* expected outputs */); |
| |
| // Extract IO and info |
| typename TargetInfo::TensorType *input0 = get_backing_tensor<TargetInfo>(node.input(0)); |
| typename TargetInfo::TensorType *input1 = get_backing_tensor<TargetInfo>(node.input(1)); |
| typename TargetInfo::TensorType *input2 = get_backing_tensor<TargetInfo>(node.input(2)); |
| typename TargetInfo::TensorType *output0 = get_backing_tensor<TargetInfo>(node.output(0)); |
| typename TargetInfo::TensorType *output1 = get_backing_tensor<TargetInfo>(node.output(1)); |
| typename TargetInfo::TensorType *output2 = get_backing_tensor<TargetInfo>(node.output(2)); |
| typename TargetInfo::TensorType *output3 = get_backing_tensor<TargetInfo>(node.output(3)); |
| const DetectionPostProcessLayerInfo detect_info = node.detection_post_process_info(); |
| |
| ARM_COMPUTE_ERROR_ON(input0 == nullptr); |
| ARM_COMPUTE_ERROR_ON(input1 == nullptr); |
| ARM_COMPUTE_ERROR_ON(input2 == nullptr); |
| ARM_COMPUTE_ERROR_ON(output0 == nullptr); |
| ARM_COMPUTE_ERROR_ON(output1 == nullptr); |
| ARM_COMPUTE_ERROR_ON(output2 == nullptr); |
| ARM_COMPUTE_ERROR_ON(output3 == nullptr); |
| |
| // Create and configure function |
| auto func = support::cpp14::make_unique<DetectionPostProcessLayerFunction>(); |
| func->configure(input0, input1, input2, output0, output1, output2, output3, detect_info); |
| |
| // 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() |
| << " 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); |
| |
| return std::move(func); |
| } |
| |
| /** Create a backend element-wise operation layer function |
| * |
| * @tparam EltwiseFunctions Backend element-wise function |
| * @tparam TargetInfo Target-specific information |
| * |
| * @param[in] node Node to create the backend function for |
| * |
| * @return Backend element-wise operation layer function |
| */ |
| template <typename EltwiseFunctions, typename TargetInfo> |
| std::unique_ptr<IFunction> create_eltwise_layer(EltwiseLayerNode &node) |
| { |
| validate_node<TargetInfo>(node, 2 /* expected inputs */, 1 /* expected outputs */); |
| |
| // Extract IO and info |
| typename TargetInfo::TensorType *input1 = get_backing_tensor<TargetInfo>(node.input(0)); |
| typename TargetInfo::TensorType *input2 = get_backing_tensor<TargetInfo>(node.input(1)); |
| typename TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0)); |
| const EltwiseOperation eltwise_op = node.eltwise_operation(); |
| const ConvertPolicy convert_policy = node.convert_policy(); |
| ARM_COMPUTE_ERROR_ON(input1 == nullptr); |
| ARM_COMPUTE_ERROR_ON(input2 == nullptr); |
| ARM_COMPUTE_ERROR_ON(output == nullptr); |
| |
| std::unique_ptr<IFunction> func = nullptr; |
| std::string func_name; |
| if(eltwise_op == EltwiseOperation::Add) |
| { |
| std::tie(func, func_name) = create_named_function<typename EltwiseFunctions::Addition>( |
| std::string("ArithmeticAddition"), |
| input1, input2, output, convert_policy); |
| } |
| 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); |
| } |
| 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()); |
| } |
| else |
| { |
| ARM_COMPUTE_ERROR("Unsupported element-wise operation!"); |
| } |
| |
| // 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); |
| |
| return func; |
| } |
| |
| /** Create a backend flatten layer function |
| * |
| * @tparam FlattenLayerFunction Backend flatten function |
| * @tparam TargetInfo Target-specific information |
| * |
| * @param[in] node Node to create the backend function for |
| * |
| * @return Backend flatten layer function |
| */ |
| template <typename FlattenLayerFunction, typename TargetInfo> |
| std::unique_ptr<IFunction> create_flatten_layer(FlattenLayerNode &node) |
| { |
| validate_node<TargetInfo>(node, 1 /* expected inputs */, 1 /* expected outputs */); |
| |
| // Extract IO and info |
| typename TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0)); |
| typename TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0)); |
| |
| ARM_COMPUTE_ERROR_ON(input == nullptr); |
| ARM_COMPUTE_ERROR_ON(output == nullptr); |
| |
| // Create and configure function |
| auto func = support::cpp14::make_unique<FlattenLayerFunction>(); |
| 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); |
| |
| return std::move(func); |
| } |
| |
| /** Create a backend fully connected layer function |
| * |
| * @tparam FullyConnectedLayerFunction Backend fully-connected function |
| * @tparam TargetInfo Target-specific information |
| * |
| * @param[in] node Node to create the backend function for |
| * @param[in] ctx Graph context |
| * |
| * @return Backend fully connected layer function |
| */ |
| template <typename FullyConnectedLayerFunction, typename TargetInfo> |
| std::unique_ptr<IFunction> create_fully_connected_layer(FullyConnectedLayerNode &node, GraphContext &ctx) |
| { |
| validate_node<TargetInfo>(node, 3 /* expected inputs */, 1 /* expected outputs */); |
| |
| // Extract IO and info |
| typename TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0)); |
| 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(); |
| |
| ARM_COMPUTE_ERROR_ON(input == nullptr); |
| ARM_COMPUTE_ERROR_ON(weights == nullptr); |
| ARM_COMPUTE_ERROR_ON(output == nullptr); |
| |
| // Create and configure function |
| auto func = support::cpp14::make_unique<FullyConnectedLayerFunction>(get_memory_manager(ctx, TargetInfo::TargetType)); |
| func->configure(input, weights, biases, output, fc_info); |
| |
| const bool is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type()); |
| |
| // Log info |
| std::ostringstream qss; |
| 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); |
| |
| return std::move(func); |
| } |
| |
| /** Create a backend generate proposals layer function |
| * |
| * @tparam GenerateProposalsLayerFunction Backend generate proposals function |
| * @tparam TargetInfo Target-specific information |
| * |
| * @param[in] node Node to create the backend function for |
| * @param[in] ctx Graph context |
| * |
| * @return Backend generate proposals layer function |
| */ |
| template <typename GenerateProposalsLayerFunction, typename TargetInfo> |
| std::unique_ptr<IFunction> create_generate_proposals_layer(GenerateProposalsLayerNode &node, GraphContext &ctx) |
| { |
| validate_node<TargetInfo>(node, 3 /* expected inputs */, 3 /* expected outputs */); |
| |
| // Extract IO and info |
| typename TargetInfo::TensorType *scores = get_backing_tensor<TargetInfo>(node.input(0)); |
| typename TargetInfo::TensorType *deltas = get_backing_tensor<TargetInfo>(node.input(1)); |
| typename TargetInfo::TensorType *anchors = get_backing_tensor<TargetInfo>(node.input(2)); |
| typename TargetInfo::TensorType *proposals = get_backing_tensor<TargetInfo>(node.output(0)); |
| typename TargetInfo::TensorType *scores_out = get_backing_tensor<TargetInfo>(node.output(1)); |
| typename TargetInfo::TensorType *num_valid_proposals = get_backing_tensor<TargetInfo>(node.output(2)); |
| const GenerateProposalsInfo info = node.info(); |
| |
| ARM_COMPUTE_ERROR_ON(scores == nullptr); |
| ARM_COMPUTE_ERROR_ON(deltas == nullptr); |
| ARM_COMPUTE_ERROR_ON(anchors == nullptr); |
| ARM_COMPUTE_ERROR_ON(proposals == nullptr); |
| ARM_COMPUTE_ERROR_ON(scores_out == nullptr); |
| |
| // Create and configure function |
| auto func = support::cpp14::make_unique<GenerateProposalsLayerFunction>(get_memory_manager(ctx, TargetInfo::TargetType)); |
| 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() |
| << " 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); |
| |
| return std::move(func); |
| } |
| |
| /** Create a backend normalization layer function |
| * |
| * @tparam NormalizationLayerFunction Backend normalization function |
| * @tparam TargetInfo Target-specific information |
| * |
| * @param[in] node Node to create the backend function for |
| * @param[in] ctx Graph context |
| * |
| * @return Backend normalization layer function |
| */ |
| template <typename NormalizationLayerFunction, typename TargetInfo> |
| std::unique_ptr<IFunction> create_normalization_layer(NormalizationLayerNode &node, GraphContext &ctx) |
| { |
| ARM_COMPUTE_UNUSED(ctx); |
| |
| validate_node<TargetInfo>(node, 1 /* expected inputs */, 1 /* expected outputs */); |
| |
| // Extract IO and info |
| typename TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0)); |
| typename TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0)); |
| const NormalizationLayerInfo norm_info = node.normalization_info(); |
| ARM_COMPUTE_ERROR_ON(input == nullptr); |
| ARM_COMPUTE_ERROR_ON(output == nullptr); |
| |
| // Create and configure function |
| auto func = support::cpp14::make_unique<NormalizationLayerFunction>(); |
| 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); |
| |
| return std::move(func); |
| } |
| |
| /** Create a backend normalize planar YUV layer function |
| * |
| * @tparam NormalizePlanarYUVLayerFunction Backend normalize planar YUV function |
| * @tparam TargetInfo Target-specific information |
| * |
| * @param[in] node Node to create the backend function for |
| * |
| * @return Backend normalize plnar YUV layer function |
| */ |
| template <typename NormalizePlanarYUVLayerFunction, typename TargetInfo> |
| std::unique_ptr<IFunction> create_normalize_planar_yuv_layer(NormalizePlanarYUVLayerNode &node) |
| { |
| validate_node<TargetInfo>(node, 3 /* expected inputs */, 1 /* expected outputs */); |
| |
| // Extract IO and info |
| typename TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0)); |
| typename TargetInfo::TensorType *mean = get_backing_tensor<TargetInfo>(node.input(1)); |
| typename TargetInfo::TensorType *std = get_backing_tensor<TargetInfo>(node.input(2)); |
| typename TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0)); |
| ARM_COMPUTE_ERROR_ON(input == nullptr); |
| ARM_COMPUTE_ERROR_ON(mean == nullptr); |
| ARM_COMPUTE_ERROR_ON(std == nullptr); |
| ARM_COMPUTE_ERROR_ON(output == nullptr); |
| |
| // Create and configure function |
| auto func = support::cpp14::make_unique<NormalizePlanarYUVLayerFunction>(); |
| 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); |
| |
| return std::move(func); |
| } |
| |
| /** Create a backend pad layer function |
| * |
| * @tparam PadLayerFunction Backend pad function |
| * @tparam TargetInfo Target-specific information |
| * |
| * @param[in] node Node to create the backend function for |
| * |
| * @return Backend pad layer function |
| */ |
| template <typename PadLayerFunction, typename TargetInfo> |
| std::unique_ptr<IFunction> create_pad_layer(PadLayerNode &node) |
| { |
| validate_node<TargetInfo>(node, 1 /* expected inputs */, 1 /* expected outputs */); |
| |
| // Extract IO and info |
| typename TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0)); |
| typename TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0)); |
| const PaddingList &padding = node.padding(); |
| ARM_COMPUTE_ERROR_ON(input == nullptr); |
| ARM_COMPUTE_ERROR_ON(output == nullptr); |
| |
| // Create and configure function |
| auto func = support::cpp14::make_unique<PadLayerFunction>(); |
| func->configure(input, output, padding); |
| |
| // 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); |
| |
| return std::move(func); |
| } |
| |
| /** Create a backend permute layer function |
| * |
| * @tparam PermuteLayerFunction Backend permute function |
| * @tparam TargetInfo Target-specific information |
| * |
| * @param[in] node Node to create the backend function for |
| * |
| * @return Backend permute layer function |
| */ |
| template <typename PermuteLayerFunction, typename TargetInfo> |
| std::unique_ptr<IFunction> create_permute_layer(PermuteLayerNode &node) |
| { |
| validate_node<TargetInfo>(node, 1 /* expected inputs */, 1 /* expected outputs */); |
| |
| // Extract IO and info |
| typename TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0)); |
| typename TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0)); |
| const PermutationVector &perm = node.permutation_vector(); |
| ARM_COMPUTE_ERROR_ON(input == nullptr); |
| ARM_COMPUTE_ERROR_ON(output == nullptr); |
| |
| // Create and configure function |
| auto func = support::cpp14::make_unique<PermuteLayerFunction>(); |
| 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); |
| |
| return std::move(func); |
| } |
| |
| /** Create a backend pooling layer function |
| * |
| * @tparam PoolingLayerFunction Backend pooling function |
| * @tparam TargetInfo Target-specific information |
| * |
| * @param[in] node Node to create the backend function for |
| * |
| * @return Backend pooling layer function |
| */ |
| template <typename PoolingLayerFunction, typename TargetInfo> |
| std::unique_ptr<IFunction> create_pooling_layer(PoolingLayerNode &node) |
| { |
| validate_node<TargetInfo>(node, 1 /* expected inputs */, 1 /* expected outputs */); |
| |
| // Extract IO and info |
| typename TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0)); |
| typename TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0)); |
| const PoolingLayerInfo pool_info = node.pooling_info(); |
| ARM_COMPUTE_ERROR_ON(input == nullptr); |
| ARM_COMPUTE_ERROR_ON(output == nullptr); |
| |
| // Create and configure function |
| auto func = support::cpp14::make_unique<PoolingLayerFunction>(); |
| 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); |
| |
| return std::move(func); |
| } |
| |
| /** Create a backend priorbox layer function |
| * |
| * @tparam PriorBoxLayerFunction Backend priorbox function |
| * @tparam TargetInfo Target-specific information |
| * |
| * @param[in] node Node to create the backend function for |
| * |
| * @return Backend priorbox layer function |
| */ |
| template <typename PriorBoxLayerFunction, typename TargetInfo> |
| std::unique_ptr<IFunction> create_priorbox_layer(PriorBoxLayerNode &node) |
| { |
| validate_node<TargetInfo>(node, 2 /* expected inputs */, 1 /* expected outputs */); |
| |
| // Extract IO and info |
| typename TargetInfo::TensorType *input0 = get_backing_tensor<TargetInfo>(node.input(0)); |
| typename TargetInfo::TensorType *input1 = get_backing_tensor<TargetInfo>(node.input(1)); |
| typename TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0)); |
| const PriorBoxLayerInfo prior_info = node.priorbox_info(); |
| ARM_COMPUTE_ERROR_ON(input0 == nullptr); |
| ARM_COMPUTE_ERROR_ON(input1 == nullptr); |
| ARM_COMPUTE_ERROR_ON(output == nullptr); |
| |
| // Create and configure function |
| auto func = support::cpp14::make_unique<PriorBoxLayerFunction>(); |
| func->configure(input0, input1, output, prior_info); |
| |
| // 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() |
| << " Output shape: " << output->info()->tensor_shape() |
| << " PriorBoxLayer info: " << prior_info |
| << std::endl); |
| |
| return std::move(func); |
| } |
| |
| /** Create a backend quantization layer function |
| * |
| * @tparam QuantizationLayerFunction Backend quantization function |
| * @tparam TargetInfo Target-specific information |
| * |
| * @param[in] node Node to create the backend function for |
| * |
| * @return Backend quantization layer function |
| */ |
| template <typename QuantizationLayerFunction, typename TargetInfo> |
| std::unique_ptr<IFunction> create_quantization_layer(QuantizationLayerNode &node) |
| { |
| validate_node<TargetInfo>(node, 1 /* expected inputs */, 1 /* expected outputs */); |
| |
| // Extract IO and info |
| typename TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0)); |
| typename TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0)); |
| ARM_COMPUTE_ERROR_ON(input == nullptr); |
| ARM_COMPUTE_ERROR_ON(output == nullptr); |
| |
| // Create and configure function |
| auto func = support::cpp14::make_unique<QuantizationLayerFunction>(); |
| 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); |
| |
| return std::move(func); |
| } |
| |
| /** Create a backend reorg layer function |
| * |
| * @tparam ReorgLayerFunction Backend reorg function |
| * @tparam TargetInfo Target-specific information |
| * |
| * @param[in] node Node to create the backend function for |
| * |
| * @return Backend reshape layer function |
| */ |
| template <typename ReorgLayerFunction, typename TargetInfo> |
| std::unique_ptr<IFunction> create_reorg_layer(ReorgLayerNode &node) |
| { |
| validate_node<TargetInfo>(node, 1 /* expected inputs */, 1 /* expected outputs */); |
| |
| // Extract IO and info |
| typename TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0)); |
| typename TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0)); |
| ARM_COMPUTE_ERROR_ON(input == nullptr); |
| ARM_COMPUTE_ERROR_ON(output == nullptr); |
| |
| // Create and configure function |
| auto func = support::cpp14::make_unique<ReorgLayerFunction>(); |
| 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); |
| |
| return std::move(func); |
| } |
| |
| /** Create a backend reshape layer function |
| * |
| * @tparam ReshapeLayerFunction Backend reshape function |
| * @tparam TargetInfo Target-specific information |
| * |
| * @param[in] node Node to create the backend function for |
| * |
| * @return Backend reshape layer function |
| */ |
| template <typename ReshapeLayerFunction, typename TargetInfo> |
| std::unique_ptr<IFunction> create_reshape_layer(ReshapeLayerNode &node) |
| { |
| validate_node<TargetInfo>(node, 1 /* expected inputs */, 1 /* expected outputs */); |
| |
| // Extract IO and info |
| typename TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0)); |
| typename TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0)); |
| ARM_COMPUTE_ERROR_ON(input == nullptr); |
| ARM_COMPUTE_ERROR_ON(output == nullptr); |
| |
| // Create and configure function |
| auto func = support::cpp14::make_unique<ReshapeLayerFunction>(); |
| 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); |
| |
| return std::move(func); |
| } |
| |
| /** Create a backend resize layer function |
| * |
| * @tparam ResizeLayerFunction Backend resize function |
| * @tparam TargetInfo Target-specific information |
| * |
| * @param[in] node Node to create the backend function for |
| * |
| * @return Backend resize layer function |
| */ |
| template <typename ResizeLayerFunction, typename TargetInfo> |
| std::unique_ptr<IFunction> create_resize_layer(ResizeLayerNode &node) |
| { |
| validate_node<TargetInfo>(node, 1 /* expected inputs */, 1 /* expected outputs */); |
| |
| // Extract IO and info |
| typename TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0)); |
| typename TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0)); |
| ARM_COMPUTE_ERROR_ON(input == nullptr); |
| ARM_COMPUTE_ERROR_ON(output == nullptr); |
| const InterpolationPolicy policy = node.policy(); |
| |
| // Create and configure function |
| auto func = support::cpp14::make_unique<ResizeLayerFunction>(); |
| func->configure(input, output, policy, BorderMode::CONSTANT); |
| |
| // 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); |
| |
| return std::move(func); |
| } |
| |
| /** Create a backend ROI align layer function |
| * |
| * @tparam ROIAlignLayerFunction ROI Align function |
| * @tparam TargetInfo Target-specific information |
| * |
| * @param[in] node Node to create the backend function for |
| * |
| * @return ROI Align layer function |
| */ |
| template <typename ROIAlignLayerFunction, typename TargetInfo> |
| std::unique_ptr<IFunction> create_roi_align_layer(ROIAlignLayerNode &node) |
| { |
| validate_node<TargetInfo>(node, 2 /* expected inputs */, 1 /* expected outputs */); |
| |
| // Extract IO and info |
| typename TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0)); |
| typename TargetInfo::TensorType *rois = get_backing_tensor<TargetInfo>(node.input(1)); |
| typename TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0)); |
| ARM_COMPUTE_ERROR_ON(input == nullptr); |
| ARM_COMPUTE_ERROR_ON(output == nullptr); |
| ARM_COMPUTE_ERROR_ON(rois == nullptr); |
| |
| const ROIPoolingLayerInfo pool_info = node.pooling_info(); |
| |
| // Create and configure function |
| auto func = support::cpp14::make_unique<ROIAlignLayerFunction>(); |
| |
| 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); |
| |
| return std::move(func); |
| } |
| |
| /** Create a backend slice layer function |
| * |
| * @tparam SliceLayerFunction Backend slice function |
| * @tparam TargetInfo Target-specific information |
| * |
| * @param[in] node Node to create the backend function for |
| * |
| * @return Backend slice layer function |
| */ |
| template <typename SliceLayerFunction, typename TargetInfo> |
| std::unique_ptr<IFunction> create_slice_layer(SliceLayerNode &node) |
| { |
| validate_node<TargetInfo>(node, 1 /* expected inputs */, 1 /* expected outputs */); |
| |
| // Extract IO and info |
| typename TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0)); |
| typename TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0)); |
| ARM_COMPUTE_ERROR_ON(input == nullptr); |
| ARM_COMPUTE_ERROR_ON(output == nullptr); |
| |
| // Create and configure function |
| auto func = support::cpp14::make_unique<SliceLayerFunction>(); |
| 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); |
| |
| return std::move(func); |
| } |
| |
| /** Create a backend softmax layer function |
| * |
| * @tparam SoftmaxLayerFunction Backend softmax function |
| * @tparam TargetInfo Target-specific information |
| * |
| * @param[in] node Node to create the backend function for |
| * @param[in] ctx Graph context |
| * |
| * @return Backend softmax layer function |
| */ |
| template <typename SoftmaxLayerFunction, typename TargetInfo> |
| std::unique_ptr<IFunction> create_softmax_layer(SoftmaxLayerNode &node, GraphContext &ctx) |
| { |
| validate_node<TargetInfo>(node, 1 /* expected inputs */, 1 /* expected outputs */); |
| |
| // Extract IO and info |
| typename TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0)); |
| typename TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0)); |
| const float beta = node.beta(); |
| ARM_COMPUTE_ERROR_ON(input == nullptr); |
| ARM_COMPUTE_ERROR_ON(output == nullptr); |
| |
| // Create and configure function |
| auto func = support::cpp14::make_unique<SoftmaxLayerFunction>(get_memory_manager(ctx, TargetInfo::TargetType)); |
| 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); |
| |
| return std::move(func); |
| } |
| |
| /** Create a backend layer stack function |
| * |
| * @tparam StackLayerFunction Backend stack function |
| * @tparam TargetInfo Target-specific information |
| * |
| * @param[in] node Node to create the backend function for |
| * |
| * @return Backend stack layer function |
| */ |
| 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_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) |
| { |
| inputs.push_back(get_backing_tensor<TargetInfo>(node.input(i))); |
| } |
| typename TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0)); |
| const int axis = node.axis(); |
| |
| // Create and configure function |
| auto func = support::cpp14::make_unique<StackLayerFunction>(); |
| 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); |
| |
| return std::move(func); |
| } |
| /** Create a backend Upsample layer function |
| * |
| * @tparam UpsampleLayerFunction Backend Upsample function |
| * @tparam TargetInfo Target-specific information |
| * |
| * @param[in] node Node to create the backend function for |
| * @param[in] ctx Graph context |
| * |
| * @return Backend Upsample layer function |
| */ |
| template <typename UpsampleLayerFunction, typename TargetInfo> |
| std::unique_ptr<IFunction> create_upsample_layer(UpsampleLayerNode &node, GraphContext &ctx) |
| { |
| validate_node<TargetInfo>(node, 1 /* expected inputs */, 1 /* expected outputs */); |
| |
| // Extract IO and info |
| typename TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0)); |
| typename TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0)); |
| const Size2D info = node.info(); |
| const InterpolationPolicy upsampling_policy = node.upsampling_policy(); |
| ARM_COMPUTE_ERROR_ON(upsampling_policy != InterpolationPolicy::NEAREST_NEIGHBOR); |
| ARM_COMPUTE_ERROR_ON(info.x() != 2 || info.y() != 2); |
| ARM_COMPUTE_ERROR_ON(input == nullptr); |
| ARM_COMPUTE_ERROR_ON(output == nullptr); |
| |
| // Create and configure function |
| auto func = support::cpp14::make_unique<UpsampleLayerFunction>(); |
| func->configure(input, output, info, upsampling_policy); |
| |
| // 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() |
| << " Strides: " << info |
| << " Upsampling policy: " << upsampling_policy |
| << std::endl); |
| |
| return std::move(func); |
| } |
| /** Create a backend YOLO layer function |
| * |
| * @tparam YoloLayerFunction Backend YOLO function |
| * @tparam TargetInfo Target-specific information |
| * |
| * @param[in] node Node to create the backend function for |
| * @param[in] ctx Graph context |
| * |
| * @return Backend YOLO layer function |
| */ |
| template <typename YOLOlayerFunction, typename TargetInfo> |
| std::unique_ptr<IFunction> create_yolo_layer(YOLOLayerNode &node, GraphContext &ctx) |
| { |
| validate_node<TargetInfo>(node, 1 /* expected inputs */, 1 /* expected outputs */); |
| |
| // Extract IO and info |
| typename TargetInfo::TensorType *input = get_backing_tensor<TargetInfo>(node.input(0)); |
| typename TargetInfo::TensorType *output = get_backing_tensor<TargetInfo>(node.output(0)); |
| const ActivationLayerInfo act_info = node.activation_info(); |
| const int32_t num_classes = node.num_classes(); |
| ARM_COMPUTE_ERROR_ON(num_classes <= 0); |
| ARM_COMPUTE_ERROR_ON(input == nullptr); |
| ARM_COMPUTE_ERROR_ON(output == nullptr); |
| |
| // Create and configure function |
| auto func = support::cpp14::make_unique<YOLOlayerFunction>(); |
| func->configure(input, output, act_info, num_classes); |
| |
| // 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() |
| << " Activation function: " << act_info.activation() |
| << " Num classes: " << num_classes |
| << std::endl); |
| |
| return std::move(func); |
| } |
| } // namespace detail |
| } // namespace backends |
| } // namespace graph |
| } // namespace arm_compute |
| |
| #endif /* __ARM_COMPUTE_GRAPH_BACKENDS_DETAIL_FUNCTION_HELPERS_H__ */ |
