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Diffstat (limited to 'src/graph2/backends/GLES/GCFunctionsFactory.cpp')
| -rw-r--r-- | src/graph2/backends/GLES/GCFunctionsFactory.cpp | 507 |
1 files changed, 0 insertions, 507 deletions
diff --git a/src/graph2/backends/GLES/GCFunctionsFactory.cpp b/src/graph2/backends/GLES/GCFunctionsFactory.cpp deleted file mode 100644 index 24ab2bce37..0000000000 --- a/src/graph2/backends/GLES/GCFunctionsFactory.cpp +++ /dev/null @@ -1,507 +0,0 @@ -/* - * Copyright (c) 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/graph2/backends/GLES/GCFunctionFactory.h" - -#include "arm_compute/core/utils/misc/Cast.h" -#include "arm_compute/graph2/Graph.h" -#include "arm_compute/graph2/GraphContext.h" -#include "arm_compute/graph2/Logger.h" -#include "arm_compute/graph2/TypePrinter.h" -#include "arm_compute/graph2/Types.h" -#include "arm_compute/graph2/backends/Utils.h" -#include "arm_compute/graph2/nodes/Nodes.h" -#include "arm_compute/runtime/GLES_COMPUTE/GCFunctions.h" - -#include "support/ToolchainSupport.h" - -using namespace arm_compute::utils::cast; - -namespace arm_compute -{ -namespace graph2 -{ -namespace backends -{ -namespace -{ -/** Returns backing tensor of a given tensor - * - * @param[in] tensor Tensor to extract the backing tensor from - * - * @return Backing tensor if present else nullptr - */ -arm_compute::IGCTensor *get_backing_tensor(arm_compute::graph2::Tensor *tensor) -{ - arm_compute::IGCTensor *backing_tensor = nullptr; - if(tensor != nullptr) - { - ARM_COMPUTE_ERROR_ON(tensor->desc().target != arm_compute::graph2::Target::GC); - // Get backing tensor handle - ITensorHandle *tensor_handle = tensor->handle(); - // Get backing tensor - backing_tensor = (tensor_handle != nullptr) ? polymorphic_cast<IGCTensor *>(&tensor_handle->tensor()) : nullptr; - } - - return backing_tensor; -} - -/** Create a backend activation layer function - * - * @param[in] node Node to create the backend function for - * - * @return Backend activation layer function - */ -std::unique_ptr<IFunction> create_activation_layer(ActivationLayerNode &node) -{ - ARM_COMPUTE_LOG_GRAPH_VERBOSE( - "Creating GC ActivationLayerNode node with ID : " << node.id() << " and Name: " << node.name() - << std::endl); - ARM_COMPUTE_ERROR_ON(node.num_inputs() != 1); - ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1); - - // Extract IO and info - IGCTensor *input = get_backing_tensor(node.input(0)); - IGCTensor *output = get_backing_tensor(node.output(0)); - const ActivationLayerInfo act_info = node.activation_info(); - - // Create function - auto func = support::cpp14::make_unique<GCActivationLayer>(); - func->configure(input, output, act_info); - - ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated GCActivationLayer" - << " 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 - * - * @param[in] node Node to create the backend function for - * - * @return Backend batch normalization layer function - */ -std::unique_ptr<IFunction> create_batch_normalization_layer(BatchNormalizationLayerNode &node) -{ - ARM_COMPUTE_LOG_GRAPH_VERBOSE("Creating GC BatchNormalization node with ID : " << node.id() << " and Name: " << node.name() << std::endl); - - // TODO (geopin01) : Var and mean are compulsory, switch function to accept nullptr as beta and/or gamma - ARM_COMPUTE_ERROR_ON(node.num_inputs() != 5); - ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1); - - // Extract IO and info - IGCTensor *input = get_backing_tensor(node.input(0)); - IGCTensor *mean = get_backing_tensor(node.input(1)); - IGCTensor *var = get_backing_tensor(node.input(2)); - IGCTensor *beta = get_backing_tensor(node.input(3)); - IGCTensor *gamma = get_backing_tensor(node.input(4)); - IGCTensor *output = get_backing_tensor(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<GCBatchNormalizationLayer>(); - func->configure(input, output, mean, var, beta, gamma, epsilon, fused_act); - - // Log info - ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated GCBatchNormalizationLayer" - << " 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 convolution layer function - * - * @param[in] node Node to create the backend function for - * - * @return Backend convolution layer function - */ -std::unique_ptr<IFunction> create_convolution_layer(ConvolutionLayerNode &node, GraphContext &ctx) -{ - ARM_COMPUTE_LOG_GRAPH_VERBOSE("Creating GC ConvolutionLayer node with ID : " << node.id() << " and Name: " << node.name() << std::endl); - ARM_COMPUTE_ERROR_ON(node.num_inputs() != 3); - ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1); - - // Extract IO and info - IGCTensor *input = get_backing_tensor(node.input(0)); - IGCTensor *weights = get_backing_tensor(node.input(1)); - IGCTensor *biases = get_backing_tensor(node.input(2)); - IGCTensor *output = get_backing_tensor(node.output(0)); - const PadStrideInfo conv_info = node.convolution_info(); - const ConvolutionMethod conv_algorithm = node.convolution_method(); - - // Create and configure function (we assume that functions have been validated before creation) - std::shared_ptr<IMemoryManager> mm = get_memory_manager(ctx, Target::GC); - std::unique_ptr<IFunction> func; - std::string func_name; - - if(conv_algorithm == ConvolutionMethod::DIRECT) - { - std::tie(func, func_name) = create_named_function<GCDirectConvolutionLayer>( - std::string("GCDirectConvolutionLayer"), input, weights, biases, output, conv_info); - } - else - { - std::tie(func, func_name) = create_named_memory_managed_function<GCConvolutionLayer>(std::string("GCConvolutionLayer"), mm, - input, weights, biases, output, conv_info); - } - - // Log info - ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << func_name - << " 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 concatenate function - * - * @param[in] node Node to create the backend function for - * - * @return Backend depth concatenate layer function - */ -std::unique_ptr<arm_compute::IFunction> create_depth_concatenate_layer(DepthConcatenateLayerNode &node) -{ - ARM_COMPUTE_LOG_GRAPH_VERBOSE("Creating GC DepthConcatenate 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<arm_compute::IGCTensor *> inputs; - for(unsigned int i = 0; i < node.num_inputs(); ++i) - { - inputs.push_back(get_backing_tensor(node.input(i))); - } - IGCTensor *output = get_backing_tensor(node.output(0)); - - // Create and configure function - auto func = support::cpp14::make_unique<GCDepthConcatenateLayer>(); - func->configure(inputs, output); - - // Log info - ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated GCDepthConcatenateLayer" - << " Data Type: " << output->info()->data_type() - << " Shape: " << output->info()->tensor_shape() - << " Num Inputs: " << inputs.size() - << std::endl); - - return std::move(func); -} - -/** Create a backend layer depth-wise convolution function - * - * @param[in] node Node to create the backend function for - * - * @return Backend depth-wise convolution layer function - */ -std::unique_ptr<IFunction> create_depthwise_convolution_layer(DepthwiseConvolutionLayerNode &node) -{ - ARM_COMPUTE_LOG_GRAPH_VERBOSE( - "Creating GC DepthwiseConvolutionLayer node with ID : " << node.id() << " and Name: " << node.name() - << std::endl); - ARM_COMPUTE_ERROR_ON(node.num_inputs() != 3); - ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1); - - // Extract IO and info - IGCTensor *input = get_backing_tensor(node.input(0)); - IGCTensor *weights = get_backing_tensor(node.input(1)); - IGCTensor *biases = get_backing_tensor(node.input(2)); - IGCTensor *output = get_backing_tensor(node.output(0)); - const PadStrideInfo conv_info = node.convolution_info(); - const DepthwiseConvolutionMethod dwc_algorithm = node.depthwise_convolution_method(); - - // 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::OPTIMIZED_3x3) - { - std::tie(func, func_name) = create_named_function<GCDepthwiseConvolutionLayer3x3>( - std::string("GCDepthwiseConvolutionLayer3x3"), input, weights, biases, output, conv_info); - } - else - { - ARM_COMPUTE_ERROR("Generic DepthwiseConvolutionLayer is not supported in GLES backend"); - } - - // Log info - ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << func_name - << " 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 element-wise operation layer function - * - * @param[in] node Node to create the backend function for - * - * @return Backend element-wise operation layer function - */ -std::unique_ptr<IFunction> create_eltwise_layer(EltwiseLayerNode &node) -{ - ARM_COMPUTE_LOG_GRAPH_VERBOSE( - "Creating GC EltwiseLayer node with ID : " << node.id() << " and Name: " << node.name() << std::endl); - ARM_COMPUTE_ERROR_ON(node.num_inputs() != 2); - ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1); - - // Extract IO and info - IGCTensor *input1 = get_backing_tensor(node.input(0)); - IGCTensor *input2 = get_backing_tensor(node.input(1)); - IGCTensor *output = get_backing_tensor(node.output(0)); - const EltwiseOperation eltwise_op = node.eltwise_operation(); - 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<GCArithmeticAddition>(std::string("GCArithmeticAddition"), - input1, input2, output, - ConvertPolicy::SATURATE); - } - else if(eltwise_op == EltwiseOperation::SUB) - { - ARM_COMPUTE_ERROR("Arithmetic subtraction is not supported in GLES backend"); - } - else if(eltwise_op == EltwiseOperation::MUL) - { - std::tie(func, func_name) = create_named_function<GCPixelWiseMultiplication>( - std::string("GCPixelWiseMultiplication"), input1, input2, output, 1.f); - } - else - { - ARM_COMPUTE_ERROR("Unsupported element-wise operation!"); - } - - // Log info - ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << func_name - << " Data Type: " << input1->info()->data_type() - << " Shape : " << input1->info()->tensor_shape() - << std::endl); - - return func; -} - -/** Create a backend fully connected layer function - * - * @param[in] node Node to create the backend function for - * - * @return Backend fully connected layer function - */ -std::unique_ptr<IFunction> create_fully_connected_layer(FullyConnectedLayerNode &node, GraphContext &ctx) -{ - ARM_COMPUTE_LOG_GRAPH_VERBOSE( - "Creating GC FullyConnectedLayer node with ID : " << node.id() << " and Name: " << node.name() - << std::endl); - ARM_COMPUTE_ERROR_ON(node.num_inputs() != 3); - ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1); - - // Extract IO and info - IGCTensor *input = get_backing_tensor(node.input(0)); - IGCTensor *weights = get_backing_tensor(node.input(1)); - IGCTensor *biases = get_backing_tensor(node.input(2)); - IGCTensor *output = get_backing_tensor(node.output(0)); - - // Create and configure function - auto func = support::cpp14::make_unique<GCFullyConnectedLayer>(get_memory_manager(ctx, Target::GC)); - func->configure(input, weights, biases, output); - ARM_COMPUTE_ERROR_ON(input == nullptr); - ARM_COMPUTE_ERROR_ON(weights == nullptr); - ARM_COMPUTE_ERROR_ON(output == nullptr); - - // Log info - ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated GCFullyConnectedLayer" - << " Data Type: " << input->info()->data_type() - << " Input shape: " << input->info()->tensor_shape() - << " Weights shape: " << weights->info()->tensor_shape() - << " Biases Shape: " << biases->info()->tensor_shape() - << " Output shape: " << output->info()->tensor_shape() - << std::endl); - - return std::move(func); -} - -/** Create a backend normalization layer function - * - * @param[in] node Node to create the backend function for - * - * @return Backend normalization layer function - */ -std::unique_ptr<IFunction> create_normalization_layer(NormalizationLayerNode &node) -{ - ARM_COMPUTE_LOG_GRAPH_VERBOSE( - "Creating GC NormalizationLayer node with ID : " << node.id() << " and Name: " << node.name() << std::endl); - ARM_COMPUTE_ERROR_ON(node.num_inputs() != 1); - ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1); - - // Extract IO and info - IGCTensor *input = get_backing_tensor(node.input(0)); - IGCTensor *output = get_backing_tensor(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<GCNormalizationLayer>(); - func->configure(input, output, norm_info); - - // Log info - ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated GCNormalizationLayer" - << " 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 pooling layer function - * - * @param[in] node Node to create the backend function for - * - * @return Backend pooling layer function - */ -std::unique_ptr<IFunction> create_pooling_layer(PoolingLayerNode &node) -{ - ARM_COMPUTE_LOG_GRAPH_VERBOSE( - "Creating GC PoolingLayer node with ID : " << node.id() << " and Name: " << node.name() << std::endl); - ARM_COMPUTE_ERROR_ON(node.num_inputs() != 1); - ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1); - - // Extract IO and info - IGCTensor *input = get_backing_tensor(node.input(0)); - IGCTensor *output = get_backing_tensor(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<GCPoolingLayer>(); - func->configure(input, output, pool_info); - - // Log info - ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated GCPoolingLayer" - << " 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 softmax layer function - * - * @param[in] node Node to create the backend function for - * - * @return Backend softmax layer function - */ -std::unique_ptr<IFunction> create_softmax_layer(SoftmaxLayerNode &node, GraphContext &ctx) -{ - ARM_COMPUTE_LOG_GRAPH_VERBOSE( - "Creating GC SoftmaxLayer node with ID : " << node.id() << " and Name: " << node.name() << std::endl); - ARM_COMPUTE_ERROR_ON(node.num_inputs() != 1); - ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1); - - // Extract IO and info - IGCTensor *input = get_backing_tensor(node.input(0)); - IGCTensor *output = get_backing_tensor(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<GCSoftmaxLayer>(get_memory_manager(ctx, Target::CL)); - func->configure(input, output, beta); - - // Log info - ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated GCSoftmaxLayer" - << " Data Type: " << input->info()->data_type() - << " Input shape: " << input->info()->tensor_shape() - << " Output shape: " << output->info()->tensor_shape() - << std::endl); - - return std::move(func); -} -} // namespace - -std::unique_ptr<IFunction> GCFunctionFactory::create(INode *node, GraphContext &ctx) -{ - if(node == nullptr) - { - return nullptr; - } - - NodeType type = node->type(); - switch(type) - { - case NodeType::ActivationLayer: - return create_activation_layer(*polymorphic_downcast<ActivationLayerNode *>(node)); - case NodeType::BatchNormalizationLayer: - return create_batch_normalization_layer(*polymorphic_downcast<BatchNormalizationLayerNode *>(node)); - case NodeType::ConvolutionLayer: - return create_convolution_layer(*polymorphic_downcast<ConvolutionLayerNode *>(node), ctx); - case NodeType::DepthConcatenateLayer: - return create_depth_concatenate_layer(*polymorphic_downcast<DepthConcatenateLayerNode *>(node)); - case NodeType::DepthwiseConvolutionLayer: - return create_depthwise_convolution_layer(*polymorphic_downcast<DepthwiseConvolutionLayerNode *>(node)); - case NodeType::EltwiseLayer: - return create_eltwise_layer(*polymorphic_downcast<EltwiseLayerNode *>(node)); - case NodeType::FullyConnectedLayer: - return create_fully_connected_layer(*polymorphic_downcast<FullyConnectedLayerNode *>(node), ctx); - case NodeType::NormalizationLayer: - return create_normalization_layer(*polymorphic_downcast<NormalizationLayerNode *>(node)); - case NodeType::PoolingLayer: - return create_pooling_layer(*polymorphic_downcast<PoolingLayerNode *>(node)); - case NodeType::SoftmaxLayer: - return create_softmax_layer(*polymorphic_downcast<SoftmaxLayerNode *>(node), ctx); - default: - return nullptr; - } -} -} // namespace backends -} // namespace graph2 -} // namespace arm_compute
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