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Diffstat (limited to 'src/graph/operations/NESimpleOperations.cpp')
| -rw-r--r-- | src/graph/operations/NESimpleOperations.cpp | 495 |
1 files changed, 0 insertions, 495 deletions
diff --git a/src/graph/operations/NESimpleOperations.cpp b/src/graph/operations/NESimpleOperations.cpp deleted file mode 100644 index 4154b9a59c..0000000000 --- a/src/graph/operations/NESimpleOperations.cpp +++ /dev/null @@ -1,495 +0,0 @@ -/* - * Copyright (c) 2017-2018 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. - */ -#include "arm_compute/core/Error.h" -#include "arm_compute/core/ITensor.h" -#include "arm_compute/graph/IOperation.h" -#include "arm_compute/graph/NodeContext.h" -#include "arm_compute/graph/OperationRegistrar.h" -#include "arm_compute/graph/Types.h" -#include "arm_compute/runtime/NEON/NEFunctions.h" -#include "support/ToolchainSupport.h" -#include "utils/GraphTypePrinter.h" -#include "utils/TypePrinter.h" - -#include <memory> - -using namespace arm_compute::graph; - -/* Activation Layer */ -REGISTER_SIMPLE_OPERATION(NEActivationLayerOperation, NEON, OperationType::ActivationLayer) -{ - ARM_COMPUTE_ERROR_ON(ctx.num_inputs() != 1); - ARM_COMPUTE_ERROR_ON(ctx.num_outputs() != 1); - ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.input(0)) == nullptr); - ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.output(0)) == nullptr); - - // Extract IO and info - auto *in = dynamic_cast<arm_compute::ITensor *>(ctx.input(0)); - auto *out = dynamic_cast<arm_compute::ITensor *>(ctx.output(0)); - const auto act_info = ctx.parameter<ActivationLayerInfo>("ActivationLayerInfo"); - - // Create and configure function - auto activation = arm_compute::support::cpp14::make_unique<arm_compute::NEActivationLayer>(); - activation->configure(in, out, act_info); - - // Log info - ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NEActivationLayer" - << " Data Type: " << in->info()->data_type() - << " Input shape: " << in->info()->tensor_shape() - << " Output shape: " << out->info()->tensor_shape() - << " Activation function: " << act_info.activation() - << " a: " << act_info.a() - << " b: " << act_info.b() - << std::endl); - - return std::move(activation); -} - -/* Arithmetic addition */ -REGISTER_SIMPLE_OPERATION(NEArithmeticAdditionOperation, NEON, OperationType::ArithmeticAddition) -{ - ARM_COMPUTE_ERROR_ON(ctx.num_inputs() != 2); - ARM_COMPUTE_ERROR_ON(ctx.num_outputs() != 1); - ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.input(0)) == nullptr); - ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.input(1)) == nullptr); - ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.output(0)) == nullptr); - - // Extract IO and info - auto *in1 = dynamic_cast<arm_compute::ITensor *>(ctx.input(0)); - auto *in2 = dynamic_cast<arm_compute::ITensor *>(ctx.input(1)); - auto *out = dynamic_cast<arm_compute::ITensor *>(ctx.output(0)); - - auto addition = arm_compute::support::cpp14::make_unique<arm_compute::NEArithmeticAddition>(); - addition->configure(in1, in2, out, ConvertPolicy::SATURATE); - - // Log info - ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NEArithmeticAddition" - << " Data Type: " << in1->info()->data_type() - << " Input 1 shape: " << in1->info()->tensor_shape() - << " Input 2 shape: " << in2->info()->tensor_shape() - << " Output shape: " << out->info()->tensor_shape() - << std::endl); - - return std::move(addition); -} - -/* Batch Normalization Layer */ -REGISTER_SIMPLE_OPERATION(NEBatchNormalizationLayerOperation, NEON, OperationType::BatchNormalizationLayer) -{ - ARM_COMPUTE_ERROR_ON(ctx.num_inputs() != 5); - ARM_COMPUTE_ERROR_ON(ctx.num_outputs() != 1); - ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.input(0)) == nullptr); - ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.input(1)) == nullptr); - ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.input(2)) == nullptr); - ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.input(3)) == nullptr); - ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.input(4)) == nullptr); - ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.output(0)) == nullptr); - - // Extract IO and info - auto *in = dynamic_cast<arm_compute::ITensor *>(ctx.input(0)); - auto *mean = dynamic_cast<arm_compute::ITensor *>(ctx.input(1)); - auto *var = dynamic_cast<arm_compute::ITensor *>(ctx.input(2)); - auto *beta = dynamic_cast<arm_compute::ITensor *>(ctx.input(3)); - auto *gamma = dynamic_cast<arm_compute::ITensor *>(ctx.input(4)); - auto *out = dynamic_cast<arm_compute::ITensor *>(ctx.output(0)); - const auto epsilon = ctx.parameter<float>("epsilon"); - const auto act_info = ctx.parameter<ActivationLayerInfo>("act_info"); - - // Create and configure function - auto batch_norm = arm_compute::support::cpp14::make_unique<arm_compute::NEBatchNormalizationLayer>(); - batch_norm->configure(in, out, mean, var, beta, gamma, epsilon, act_info); - - // Log info - ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NEBatchNormalizationLayer" - << " Data Type: " << in->info()->data_type() - << " Input shape: " << in->info()->tensor_shape() - << " Output shape: " << out->info()->tensor_shape() - << " Mean shape: " << mean->info()->tensor_shape() - << " Var shape: " << var->info()->tensor_shape() - << " Beta shape: " << beta->info()->tensor_shape() - << " Gamma shape: " << gamma->info()->tensor_shape() - << " Epsilon: " << epsilon - << " Activation function: " << act_info.activation() - << " a: " << act_info.a() - << " b: " << act_info.b() - << std::endl); - - return std::move(batch_norm); -} - -/* DepthConvertLayer Layer */ -REGISTER_SIMPLE_OPERATION(NEDepthConvertLayerOperation, NEON, OperationType::DepthConvertLayer) -{ - ARM_COMPUTE_ERROR_ON(ctx.num_inputs() != 1); - ARM_COMPUTE_ERROR_ON(ctx.num_outputs() != 1); - ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.input(0)) == nullptr); - ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.output(0)) == nullptr); - - // Extract IO and info - auto *in = dynamic_cast<arm_compute::ITensor *>(ctx.input(0)); - auto *out = dynamic_cast<arm_compute::ITensor *>(ctx.output(0)); - const auto conv_policy = ctx.parameter<ConvertPolicy>("ConvertPolicy"); - const auto shift = ctx.parameter<uint32_t>("shift"); - - // Create and configure function - auto depthconvert = arm_compute::support::cpp14::make_unique<arm_compute::NEDepthConvertLayer>(); - depthconvert->configure(in, out, conv_policy, shift); - - // Log info - ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NEDepthConvertLayer" - << " Data Type: " << in->info()->data_type() - << " Input shape: " << in->info()->tensor_shape() - << " Output shape: " << out->info()->tensor_shape() - << " shift: " << shift - << std::endl); - - return std::move(depthconvert); -} - -/* DepthwiseConvolutionLayer Layer */ -REGISTER_SIMPLE_OPERATION(NEDepthwiseConvolutionOperation, NEON, OperationType::DepthwiseConvolutionLayer) -{ - ARM_COMPUTE_ERROR_ON(ctx.num_inputs() != 2 && ctx.num_inputs() != 3); - ARM_COMPUTE_ERROR_ON(ctx.num_outputs() != 1); - ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.input(0)) == nullptr); - ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.output(0)) == nullptr); - - // Extract IO and info - auto *in = dynamic_cast<arm_compute::ITensor *>(ctx.input(0)); - auto *weights = dynamic_cast<arm_compute::ITensor *>(ctx.input(1)); - auto *biases = ctx.num_inputs() == 3 ? dynamic_cast<arm_compute::ITensor *>(ctx.input(2)) : nullptr; - auto *out = dynamic_cast<arm_compute::ITensor *>(ctx.output(0)); - const auto conv_info = ctx.parameter<PadStrideInfo>("ConvolutionInfo"); - const auto opt3x3 = ctx.parameter<bool>("Optimized3x3"); - - // Create and configure function - std::unique_ptr<arm_compute::IFunction> func; - bool run_3x3_opt = opt3x3 && weights->info()->dimension(0) == 3; - if(run_3x3_opt) - { - auto depwthwise_conv = arm_compute::support::cpp14::make_unique<arm_compute::NEDepthwiseConvolutionLayer3x3>(); - depwthwise_conv->configure(in, weights, biases, out, conv_info); - func = std::move(depwthwise_conv); - } - else - { - auto depwthwise_conv = arm_compute::support::cpp14::make_unique<arm_compute::NEDepthwiseConvolutionLayer>(); - depwthwise_conv->configure(in, weights, biases, out, conv_info); - func = std::move(depwthwise_conv); - } - - // Log info - ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NEDepthwiseConvolutionLayer" - << " Data Type: " << in->info()->data_type() - << " Input shape: " << in->info()->tensor_shape() - << " Weights shape: " << weights->info()->tensor_shape() - << " Output shape: " << out->info()->tensor_shape()); - if(biases == nullptr) - { - ARM_COMPUTE_LOG_GRAPH_INFO(" Biases shape: No biases provided" << std::endl); - } - else - { - ARM_COMPUTE_LOG_GRAPH_INFO(" Biases shape: " << biases->info()->tensor_shape() << std::endl); - } - - return func; -} - -/* DeQuantizationLayer Layer */ -REGISTER_SIMPLE_OPERATION(NEDequantizationLayerOperation, NEON, OperationType::DequantizationLayer) -{ - ARM_COMPUTE_ERROR_ON(ctx.num_inputs() != 1); - ARM_COMPUTE_ERROR_ON(ctx.num_outputs() != 2); - ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.input(0)) == nullptr); - ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.output(0)) == nullptr); - ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.output(1)) == nullptr); - - // Extract IO and info - auto *in = dynamic_cast<arm_compute::ITensor *>(ctx.input(0)); - auto *out = dynamic_cast<arm_compute::ITensor *>(ctx.output(0)); - auto *min_max = dynamic_cast<arm_compute::ITensor *>(ctx.output(1)); - - // Create and configure function - auto dequantization = arm_compute::support::cpp14::make_unique<arm_compute::NEDequantizationLayer>(); - dequantization->configure(in, out, min_max); - - // Log info - ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NEDequantizationLayer" - << " Data Type: " << in->info()->data_type() - << " Input shape: " << in->info()->tensor_shape() - << " Output shape: " << out->info()->tensor_shape() - << " Min max shape: " << min_max->info()->tensor_shape() - << std::endl); - - return std::move(dequantization); -} - -/* Flatten Layer */ -REGISTER_SIMPLE_OPERATION(NEFlattenLayerOperation, NEON, OperationType::FlattenLayer) -{ - ARM_COMPUTE_ERROR_ON(ctx.num_inputs() != 1); - ARM_COMPUTE_ERROR_ON(ctx.num_outputs() != 1); - ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.input(0)) == nullptr); - ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.output(0)) == nullptr); - - // Extract IO and info - auto *in = dynamic_cast<arm_compute::ITensor *>(ctx.input(0)); - auto *out = dynamic_cast<arm_compute::ITensor *>(ctx.output(0)); - - // Create and configure function - auto flatten = arm_compute::support::cpp14::make_unique<arm_compute::NEFlattenLayer>(); - flatten->configure(in, out); - - // Log info - ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NEFlattenLayer" - << " Data Type: " << in->info()->data_type() - << " Input shape: " << in->info()->tensor_shape() - << " Output shape: " << out->info()->tensor_shape() - << std::endl); - - return std::move(flatten); -} - -/* Floor Layer */ -REGISTER_SIMPLE_OPERATION(NEFloorLayerOperation, NEON, OperationType::FloorLayer) -{ - ARM_COMPUTE_ERROR_ON(ctx.num_inputs() != 1); - ARM_COMPUTE_ERROR_ON(ctx.num_outputs() != 1); - ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.input(0)) == nullptr); - ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.output(0)) == nullptr); - - // Extract IO and info - auto *in = dynamic_cast<arm_compute::ITensor *>(ctx.input(0)); - auto *out = dynamic_cast<arm_compute::ITensor *>(ctx.output(0)); - - // Create and configure function - auto floor = arm_compute::support::cpp14::make_unique<arm_compute::NEFloor>(); - floor->configure(in, out); - - // Log info - ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NEFloorLayer" - << " Data Type: " << in->info()->data_type() - << " Input shape: " << in->info()->tensor_shape() - << " Output shape: " << out->info()->tensor_shape() - << std::endl); - - return std::move(floor); -} - -/* Fully Connected Layer */ -REGISTER_SIMPLE_OPERATION(NEFullyConnectedLayer, NEON, OperationType::FullyConnectedLayer) -{ - ARM_COMPUTE_ERROR_ON(ctx.num_inputs() != 3); - ARM_COMPUTE_ERROR_ON(ctx.num_outputs() != 1); - ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.input(0)) == nullptr); - ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.input(1)) == nullptr); - ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.input(2)) == nullptr); - ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.output(0)) == nullptr); - - // Extract IO and info - auto *in = dynamic_cast<arm_compute::ITensor *>(ctx.input(0)); - auto *weights = dynamic_cast<arm_compute::ITensor *>(ctx.input(1)); - auto *biases = dynamic_cast<arm_compute::ITensor *>(ctx.input(2)); - auto *out = dynamic_cast<arm_compute::ITensor *>(ctx.output(0)); - - // Create and configure function - auto fc = arm_compute::support::cpp14::make_unique<arm_compute::NEFullyConnectedLayer>(); - fc->configure(in, weights, biases, out); - - // Log info - ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NEFullyConnectedLayer" - << " Data Type: " << in->info()->data_type() - << " Input shape: " << in->info()->tensor_shape() - << " Weights shape: " << weights->info()->tensor_shape() - << " Biases Shape: " << biases->info()->tensor_shape() - << " Output shape: " << out->info()->tensor_shape() - << std::endl); - - return std::move(fc); -} - -/* L2 Normalize Layer */ -REGISTER_SIMPLE_OPERATION(NEL2NormalizeLayerOperation, NEON, OperationType::L2NormalizeLayer) -{ - ARM_COMPUTE_ERROR_ON(ctx.num_inputs() != 1); - ARM_COMPUTE_ERROR_ON(ctx.num_outputs() != 1); - ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.input(0)) == nullptr); - ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.output(0)) == nullptr); - - // Extract IO and info - auto *in = dynamic_cast<arm_compute::ITensor *>(ctx.input(0)); - auto *out = dynamic_cast<arm_compute::ITensor *>(ctx.output(0)); - const auto axis = ctx.parameter<unsigned int>("axis"); - const auto epsilon = ctx.parameter<float>("epsilon"); - - // Create and configure function - auto l2_norm = arm_compute::support::cpp14::make_unique<arm_compute::NEL2NormalizeLayer>(); - l2_norm->configure(in, out, axis, epsilon); - - // Log info - ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NEL2NormalizeLayer" - << " Data Type: " << in->info()->data_type() - << " Input shape: " << in->info()->tensor_shape() - << " Output shape: " << out->info()->tensor_shape() - << " Axis: " << axis - << " Epsilon: " << epsilon - << std::endl); - - return std::move(l2_norm); -} - -/* Normalization Layer */ -REGISTER_SIMPLE_OPERATION(NENormalizationLayerOperation, NEON, OperationType::NormalizationLayer) -{ - ARM_COMPUTE_ERROR_ON(ctx.num_inputs() != 1); - ARM_COMPUTE_ERROR_ON(ctx.num_outputs() != 1); - ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.input(0)) == nullptr); - ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.output(0)) == nullptr); - - // Extract IO and info - auto *in = dynamic_cast<arm_compute::ITensor *>(ctx.input(0)); - auto *out = dynamic_cast<arm_compute::ITensor *>(ctx.output(0)); - const auto norm_info = ctx.parameter<NormalizationLayerInfo>("NormalizationLayerInfo"); - - // Create and configure function - auto norm = arm_compute::support::cpp14::make_unique<arm_compute::NENormalizationLayer>(); - norm->configure(in, out, norm_info); - - // Log info - ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NENormalizationLayer" - << " Data Type: " << in->info()->data_type() - << " Input shape: " << in->info()->tensor_shape() - << " Output shape: " << out->info()->tensor_shape() - << " Normalization info: " << norm_info - << std::endl); - - return std::move(norm); -} - -/* Pooling Layer */ -REGISTER_SIMPLE_OPERATION(NEPoolingLayerOperation, NEON, OperationType::PoolingLayer) -{ - ARM_COMPUTE_ERROR_ON(ctx.num_inputs() != 1); - ARM_COMPUTE_ERROR_ON(ctx.num_outputs() != 1); - ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.input(0)) == nullptr); - ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.output(0)) == nullptr); - - // Extract IO and info - auto *in = dynamic_cast<arm_compute::ITensor *>(ctx.input(0)); - auto *out = dynamic_cast<arm_compute::ITensor *>(ctx.output(0)); - const auto pool_info = ctx.parameter<PoolingLayerInfo>("PoolingLayerInfo"); - - // Create and configure function - auto pool = arm_compute::support::cpp14::make_unique<arm_compute::NEPoolingLayer>(); - pool->configure(in, out, pool_info); - - // Log info - ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NEPoolingLayer" - << " Data Type: " << in->info()->data_type() - << " Input shape: " << in->info()->tensor_shape() - << " Output shape: " << out->info()->tensor_shape() - << " Pooling info: " << pool_info - << std::endl); - - return std::move(pool); -} - -/* Quantization Layer */ -REGISTER_SIMPLE_OPERATION(NEQuantizationLayerOperation, NEON, OperationType::QuantizationLayer) -{ - ARM_COMPUTE_ERROR_ON(ctx.num_inputs() != 1); - ARM_COMPUTE_ERROR_ON(ctx.num_outputs() != 1); - ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.input(0)) == nullptr); - ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.output(0)) == nullptr); - - // Extract IO and info - auto *in = dynamic_cast<arm_compute::ITensor *>(ctx.input(0)); - auto *out = dynamic_cast<arm_compute::ITensor *>(ctx.output(0)); - - // Create and configure function - auto quantization = arm_compute::support::cpp14::make_unique<arm_compute::NEQuantizationLayer>(); - quantization->configure(in, out); - - // Log info - ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NEQuantizationLayer" - << " Data Type: " << in->info()->data_type() - << " Input shape: " << in->info()->tensor_shape() - << " Output shape: " << out->info()->tensor_shape() - << std::endl); - - return std::move(quantization); -} - -/* Reshape Layer */ -REGISTER_SIMPLE_OPERATION(NEReshapeLayerOperation, NEON, OperationType::ReshapeLayer) -{ - ARM_COMPUTE_ERROR_ON(ctx.num_inputs() != 1); - ARM_COMPUTE_ERROR_ON(ctx.num_outputs() != 1); - ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.input(0)) == nullptr); - ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.output(0)) == nullptr); - - // Extract IO and info - auto *in = dynamic_cast<arm_compute::ITensor *>(ctx.input(0)); - auto *out = dynamic_cast<arm_compute::ITensor *>(ctx.output(0)); - - // Create and configure function - auto reshape = arm_compute::support::cpp14::make_unique<arm_compute::NEReshapeLayer>(); - reshape->configure(in, out); - - // Log info - ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NEReshapeLayer" - << " Data Type: " << in->info()->data_type() - << " Input shape: " << in->info()->tensor_shape() - << " Output shape: " << out->info()->tensor_shape() - << std::endl); - - return std::move(reshape); -} - -/* Softmax Layer */ -REGISTER_SIMPLE_OPERATION(NESoftmaxLayerOperation, NEON, OperationType::SoftmaxLayer) -{ - ARM_COMPUTE_ERROR_ON(ctx.num_inputs() != 1); - ARM_COMPUTE_ERROR_ON(ctx.num_outputs() != 1); - ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.input(0)) == nullptr); - ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.output(0)) == nullptr); - - // Extract IO and info - auto *in = dynamic_cast<arm_compute::ITensor *>(ctx.input(0)); - auto *out = dynamic_cast<arm_compute::ITensor *>(ctx.output(0)); - - // Create and configure function - auto smx = arm_compute::support::cpp14::make_unique<arm_compute::NESoftmaxLayer>(); - smx->configure(in, out); - - // Log info - ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NESoftmaxLayer" - << " Data Type: " << in->info()->data_type() - << " Input shape: " << in->info()->tensor_shape() - << " Output shape: " << out->info()->tensor_shape() - << std::endl); - - return std::move(smx); -} |