arm_compute v18.08
diff --git a/src/graph/backends/CL/CLDeviceBackend.cpp b/src/graph/backends/CL/CLDeviceBackend.cpp
index bf17f80..1dbeae9 100644
--- a/src/graph/backends/CL/CLDeviceBackend.cpp
+++ b/src/graph/backends/CL/CLDeviceBackend.cpp
@@ -62,19 +62,16 @@
/** Register CL backend */
static detail::BackendRegistrar<CLDeviceBackend> CLDeviceBackend_registrar(Target::CL);
-/** Tuner export file */
-static const std::string tuner_data_filename = "acl_tuner.csv";
-
CLDeviceBackend::CLDeviceBackend()
- : _tuner(), _allocator(cl::Context::getDefault())
+ : _context_count(0), _tuner(), _allocator(nullptr), _tuner_file()
{
}
CLDeviceBackend::~CLDeviceBackend()
{
- if(_tuner.tune_new_kernels() && !_tuner.lws_table().empty())
+ if(_tuner.tune_new_kernels() && !_tuner.lws_table().empty() && !_tuner_file.empty())
{
- _tuner.save_to_file(tuner_data_filename);
+ _tuner.save_to_file(_tuner_file);
}
}
@@ -85,22 +82,40 @@
void CLDeviceBackend::initialize_backend()
{
- // Load tuner data if available
- if(_tuner.lws_table().empty() && file_exists(tuner_data_filename))
- {
- _tuner.load_from_file(tuner_data_filename);
- }
-
// Setup Scheduler
CLScheduler::get().default_init(&_tuner);
// Create allocator with new context
- _allocator = CLBufferAllocator();
+ _allocator = support::cpp14::make_unique<CLBufferAllocator>();
+}
+
+void CLDeviceBackend::release_backend_context(GraphContext &ctx)
+{
+ ARM_COMPUTE_UNUSED(ctx);
+ _context_count--;
+ if(_context_count == 0) // No more context using the backend: free resources
+ {
+ _allocator = nullptr;
+ }
}
void CLDeviceBackend::setup_backend_context(GraphContext &ctx)
{
+ // Force backend initialization
+ _context_count++;
+ if(_context_count == 1)
+ {
+ initialize_backend();
+ }
+
// Setup tuner
+ _tuner_file = ctx.config().tuner_file;
+ // Load tuner data if available
+ if(file_exists(_tuner_file))
+ {
+ _tuner.load_from_file(_tuner_file);
+ }
+
set_kernel_tuning(ctx.config().use_tuner);
// Setup a management backend
@@ -123,7 +138,7 @@
IAllocator *CLDeviceBackend::backend_allocator()
{
- return &_allocator;
+ return _allocator.get();
}
std::unique_ptr<ITensorHandle> CLDeviceBackend::create_tensor(const Tensor &tensor)
@@ -179,7 +194,7 @@
auto pool_mgr = std::make_shared<PoolManager>();
auto mm = std::make_shared<MemoryManagerOnDemand>(lifetime_mgr, pool_mgr);
- mm->set_allocator(&_allocator);
+ mm->set_allocator(_allocator.get());
return mm;
}
diff --git a/src/graph/backends/CL/CLFunctionsFactory.cpp b/src/graph/backends/CL/CLFunctionsFactory.cpp
index db8a7a0..bf3dcba 100644
--- a/src/graph/backends/CL/CLFunctionsFactory.cpp
+++ b/src/graph/backends/CL/CLFunctionsFactory.cpp
@@ -25,16 +25,9 @@
#include "arm_compute/core/utils/misc/Cast.h"
#include "arm_compute/graph/Graph.h"
-#include "arm_compute/graph/GraphContext.h"
-#include "arm_compute/graph/Logger.h"
-#include "arm_compute/graph/TypePrinter.h"
-#include "arm_compute/graph/Types.h"
-#include "arm_compute/graph/backends/Utils.h"
-#include "arm_compute/graph/nodes/Nodes.h"
+#include "arm_compute/graph/backends/FunctionHelpers.h"
#include "arm_compute/runtime/CL/CLFunctions.h"
-#include "support/ToolchainSupport.h"
-
using namespace arm_compute::utils::cast;
namespace arm_compute
@@ -43,526 +36,38 @@
{
namespace backends
{
-namespace
+/** Target specific information structure used to pass information to the layer templates */
+struct CLTargetInfo
{
-/** 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::ICLTensor *get_backing_tensor(arm_compute::graph::Tensor *tensor)
+ using TensorType = arm_compute::ICLTensor;
+ static Target TargetType;
+};
+
+Target CLTargetInfo::TargetType = Target::CL;
+
+/** Collection of CL convolution functions */
+struct CLConvolutionLayerFunctions
{
- arm_compute::ICLTensor *backing_tensor = nullptr;
- if(tensor != nullptr)
- {
- ARM_COMPUTE_ERROR_ON(tensor->desc().target != arm_compute::graph::Target::CL);
- // Get backing tensor handle
- ITensorHandle *tensor_handle = tensor->handle();
- // Get backing tensor
- backing_tensor = (tensor_handle != nullptr) ? polymorphic_cast<ICLTensor *>(&tensor_handle->tensor()) : nullptr;
- }
+ using GenericConvolutionLayer = CLConvolutionLayer;
+ using GEMMConvolutionLayer = CLGEMMConvolutionLayer;
+ using DirectConvolutionLayer = CLDirectConvolutionLayer;
+ using WinogradConvolutionLayer = CLWinogradConvolutionLayer;
+};
- 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)
+/** Collection of CL depthwise convolution functions */
+struct CLDepthwiseConvolutionLayerFunctions
{
- ARM_COMPUTE_LOG_GRAPH_VERBOSE(
- "Creating CL 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);
+ using GenericDepthwiseConvolutionLayer = CLDepthwiseConvolutionLayer;
+ using DepthwiseConvolutionLayer3x3 = CLDepthwiseConvolutionLayer3x3;
+};
- // Extract IO and info
- ICLTensor *input = get_backing_tensor(node.input(0));
- ICLTensor *output = get_backing_tensor(node.output(0));
- const ActivationLayerInfo act_info = node.activation_info();
-
- // Create function
- auto func = support::cpp14::make_unique<CLActivationLayer>();
- func->configure(input, output, act_info);
-
- ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated CLActivationLayer"
- << " 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)
+/** Collection of CL element-wise functions */
+struct CLEltwiseFunctions
{
- ARM_COMPUTE_LOG_GRAPH_VERBOSE("Creating CL BatchNormalization node with ID : " << node.id() << " and Name: " << node.name() << std::endl);
-
- ARM_COMPUTE_ERROR_ON(node.num_inputs() != 5);
- ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1);
-
- // Extract IO and info
- ICLTensor *input = get_backing_tensor(node.input(0));
- ICLTensor *mean = get_backing_tensor(node.input(1));
- ICLTensor *var = get_backing_tensor(node.input(2));
- ICLTensor *beta = get_backing_tensor(node.input(3));
- ICLTensor *gamma = get_backing_tensor(node.input(4));
- ICLTensor *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<CLBatchNormalizationLayer>();
- func->configure(input, output, mean, var, beta, gamma, epsilon, fused_act);
-
- // Log info
- ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated CLBatchNormalizationLayer"
- << " 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 CL 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
- ICLTensor *input = get_backing_tensor(node.input(0));
- ICLTensor *weights = get_backing_tensor(node.input(1));
- ICLTensor *biases = get_backing_tensor(node.input(2));
- ICLTensor *output = get_backing_tensor(node.output(0));
-
- if(is_data_type_quantized_asymmetric(input->info()->data_type()))
- {
- biases->info()->set_data_type(DataType::S32);
- }
-
- const PadStrideInfo conv_info = node.convolution_info();
- const ConvolutionMethod conv_algorithm = node.convolution_method();
- const bool fast_math = node.fast_math_hint() == FastMathHint::ENABLED;
-
- // Create and configure function (we assume that functions have been validated before creation)
- std::shared_ptr<IMemoryManager> mm = get_memory_manager(ctx, Target::CL);
- std::unique_ptr<IFunction> func;
- std::string func_name;
-
- if(conv_algorithm == ConvolutionMethod::WINOGRAD)
- {
- std::tie(func, func_name) = create_named_memory_managed_function<CLWinogradConvolutionLayer>(
- std::string("CLWinogradConvolutionLayer"), mm, input, weights, biases, output, conv_info, ActivationLayerInfo(), fast_math);
- }
- else if(conv_algorithm == ConvolutionMethod::DIRECT)
- {
- std::tie(func, func_name) = create_named_function<CLDirectConvolutionLayer>(
- std::string("CLDirectConvolutionLayer"), input, weights, biases, output, conv_info);
- }
- else if(conv_algorithm == ConvolutionMethod::GEMM)
- {
- std::tie(func, func_name) = create_named_memory_managed_function<CLGEMMConvolutionLayer>(std::string("CLGEMMConvolutionLayer"), mm,
- input, weights, biases, output, conv_info);
- }
- else
- {
- std::tie(func, func_name) = create_named_memory_managed_function<CLConvolutionLayer>(std::string("CLConvolutionLayer"), mm,
- input, weights, biases, output, conv_info, WeightsInfo(), Size2D(1U, 1U), ActivationLayerInfo(), fast_math);
- }
-
- // Log info
- ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << func_name
- << " Data Type: " << input->info()->data_type()
- << " Input QuantInfo: " << input->info()->quantization_info()
- << " Weights QuantInfo: " << weights->info()->quantization_info()
- << " 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 CL 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::ICLTensor *> inputs;
- for(unsigned int i = 0; i < node.num_inputs(); ++i)
- {
- inputs.push_back(get_backing_tensor(node.input(i)));
- }
- ICLTensor *output = get_backing_tensor(node.output(0));
-
- // Create and configure function
- auto func = support::cpp14::make_unique<CLDepthConcatenateLayer>();
- func->configure(inputs, output);
-
- // Log info
- ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated CLDepthConcatenateLayer"
- << " 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 CL 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
- ICLTensor *input = get_backing_tensor(node.input(0));
- ICLTensor *weights = get_backing_tensor(node.input(1));
- ICLTensor *biases = get_backing_tensor(node.input(2));
- ICLTensor *output = get_backing_tensor(node.output(0));
-
- if(is_data_type_quantized_asymmetric(input->info()->data_type()))
- {
- biases->info()->set_data_type(DataType::S32);
- }
-
- 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<CLDepthwiseConvolutionLayer3x3>(
- std::string("CLDepthwiseConvolutionLayer3x3"), input, weights, biases, output, conv_info);
- }
- else
- {
- std::tie(func, func_name) = create_named_function<CLDepthwiseConvolutionLayer>(
- std::string("CLDepthwiseConvolutionLayer"), input, weights, biases, output, conv_info);
- }
-
- // Log info
- ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << func_name
- << " Data Type: " << input->info()->data_type()
- << " Input QuantInfo: " << input->info()->quantization_info()
- << " Weights QuantInfo: " << weights->info()->quantization_info()
- << " 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 CL 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
- ICLTensor *input1 = get_backing_tensor(node.input(0));
- ICLTensor *input2 = get_backing_tensor(node.input(1));
- ICLTensor *output = get_backing_tensor(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<CLArithmeticAddition>(std::string("CLArithmeticAddition"),
- input1, input2, output,
- convert_policy);
- }
- else if(eltwise_op == EltwiseOperation::SUB)
- {
- std::tie(func, func_name) = create_named_function<CLArithmeticSubtraction>(
- std::string("CLArithmeticSubtraction"), input1, input2, output, convert_policy);
- }
- else if(eltwise_op == EltwiseOperation::MUL)
- {
- std::tie(func, func_name) = create_named_function<CLPixelWiseMultiplication>(
- std::string("CLPixelWiseMultiplication"), 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 " << func_name
- << " Data Type: " << input1->info()->data_type()
- << " Shape : " << input1->info()->tensor_shape()
- << std::endl);
-
- return func;
-}
-
-/** Create a backend flatten layer function
- *
- * @param[in] node Node to create the backend function for
- *
- * @return Backend flatten layer function
- */
-std::unique_ptr<IFunction> create_flatten_layer(FlattenLayerNode &node)
-{
- ARM_COMPUTE_LOG_GRAPH_VERBOSE(
- "Creating CL FlattenLayer 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
- ICLTensor *input = get_backing_tensor(node.input(0));
- ICLTensor *output = get_backing_tensor(node.output(0));
-
- // Create and configure function
- auto func = support::cpp14::make_unique<CLFlattenLayer>();
- func->configure(input, output);
- ARM_COMPUTE_ERROR_ON(input == nullptr);
- ARM_COMPUTE_ERROR_ON(output == nullptr);
-
- // Log info
- ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated CLFlattenLayer"
- << " 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
- *
- * @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 CL 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
- ICLTensor *input = get_backing_tensor(node.input(0));
- ICLTensor *weights = get_backing_tensor(node.input(1));
- ICLTensor *biases = get_backing_tensor(node.input(2));
- ICLTensor *output = get_backing_tensor(node.output(0));
-
- // Create and configure function
- auto func = support::cpp14::make_unique<CLFullyConnectedLayer>(get_memory_manager(ctx, Target::CL));
- 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 CLFullyConnectedLayer"
- << " 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 CL 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
- ICLTensor *input = get_backing_tensor(node.input(0));
- ICLTensor *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<CLNormalizationLayer>();
- func->configure(input, output, norm_info);
-
- // Log info
- ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated CLNormalizationLayer"
- << " 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 CL 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
- ICLTensor *input = get_backing_tensor(node.input(0));
- ICLTensor *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<CLPoolingLayer>();
- func->configure(input, output, pool_info);
-
- // Log info
- ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated CLPoolingLayer"
- << " 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 reshape layer function
- *
- * @param[in] node Node to create the backend function for
- *
- * @return Backend reshape layer function
- */
-std::unique_ptr<IFunction> create_reshape_layer(ReshapeLayerNode &node)
-{
- ARM_COMPUTE_LOG_GRAPH_VERBOSE(
- "Creating CL ReshapeLayer 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
- ICLTensor *input = get_backing_tensor(node.input(0));
- ICLTensor *output = get_backing_tensor(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<CLReshapeLayer>();
- func->configure(input, output);
-
- // Log info
- ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated CLReshapeLayer"
- << " 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
- *
- * @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 CL 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
- ICLTensor *input = get_backing_tensor(node.input(0));
- ICLTensor *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<CLSoftmaxLayer>(get_memory_manager(ctx, Target::CL));
- func->configure(input, output, beta);
-
- // Log info
- ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated CLSoftmaxLayer"
- << " 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
+ using Addition = CLArithmeticAddition;
+ using Subtraction = CLArithmeticSubtraction;
+ using Multiplication = CLPixelWiseMultiplication;
+};
std::unique_ptr<IFunction> CLFunctionFactory::create(INode *node, GraphContext &ctx)
{
@@ -575,33 +80,41 @@
switch(type)
{
case NodeType::ActivationLayer:
- return create_activation_layer(*polymorphic_downcast<ActivationLayerNode *>(node));
+ return detail::create_activation_layer<CLActivationLayer, CLTargetInfo>(*polymorphic_downcast<ActivationLayerNode *>(node));
case NodeType::BatchNormalizationLayer:
- return create_batch_normalization_layer(*polymorphic_downcast<BatchNormalizationLayerNode *>(node));
+ return detail::create_batch_normalization_layer<CLBatchNormalizationLayer, CLTargetInfo>(*polymorphic_downcast<BatchNormalizationLayerNode *>(node));
+ case NodeType::ChannelShuffleLayer:
+ return detail::create_channel_shuffle_layer<CLChannelShuffleLayer, CLTargetInfo>(*polymorphic_downcast<ChannelShuffleLayerNode *>(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));
+ return detail::create_convolution_layer<CLConvolutionLayerFunctions, CLTargetInfo>(*polymorphic_downcast<ConvolutionLayerNode *>(node), ctx);
+ case NodeType::DeconvolutionLayer:
+ return detail::create_deconvolution_layer<CLDeconvolutionLayer, CLTargetInfo>(*polymorphic_downcast<DeconvolutionLayerNode *>(node), ctx);
+ case NodeType::ConcatenateLayer:
+ return detail::create_concatenate_layer<CLConcatenateLayer, CLTargetInfo>(*polymorphic_downcast<ConcatenateLayerNode *>(node));
case NodeType::DepthwiseConvolutionLayer:
- return create_depthwise_convolution_layer(*polymorphic_downcast<DepthwiseConvolutionLayerNode *>(node));
+ return detail::create_depthwise_convolution_layer<CLDepthwiseConvolutionLayerFunctions, CLTargetInfo>(*polymorphic_downcast<DepthwiseConvolutionLayerNode *>(node));
case NodeType::EltwiseLayer:
- return create_eltwise_layer(*polymorphic_downcast<EltwiseLayerNode *>(node));
+ return detail::create_eltwise_layer<CLEltwiseFunctions, CLTargetInfo>(*polymorphic_downcast<EltwiseLayerNode *>(node));
case NodeType::FlattenLayer:
- return create_flatten_layer(*polymorphic_downcast<FlattenLayerNode *>(node));
+ return detail::create_flatten_layer<CLFlattenLayer, CLTargetInfo>(*polymorphic_downcast<FlattenLayerNode *>(node));
case NodeType::FullyConnectedLayer:
- return create_fully_connected_layer(*polymorphic_downcast<FullyConnectedLayerNode *>(node), ctx);
+ return detail::create_fully_connected_layer<CLFullyConnectedLayer, CLTargetInfo>(*polymorphic_downcast<FullyConnectedLayerNode *>(node), ctx);
case NodeType::NormalizationLayer:
- return create_normalization_layer(*polymorphic_downcast<NormalizationLayerNode *>(node));
+ return detail::create_normalization_layer<CLNormalizationLayer, CLTargetInfo>(*polymorphic_downcast<NormalizationLayerNode *>(node), ctx);
+ case NodeType::PermuteLayer:
+ return detail::create_permute_layer<CLPermute, CLTargetInfo>(*polymorphic_downcast<PermuteLayerNode *>(node));
case NodeType::PoolingLayer:
- return create_pooling_layer(*polymorphic_downcast<PoolingLayerNode *>(node));
+ return detail::create_pooling_layer<CLPoolingLayer, CLTargetInfo>(*polymorphic_downcast<PoolingLayerNode *>(node));
case NodeType::ReshapeLayer:
- return create_reshape_layer(*polymorphic_downcast<ReshapeLayerNode *>(node));
+ return detail::create_reshape_layer<CLReshapeLayer, CLTargetInfo>(*polymorphic_downcast<ReshapeLayerNode *>(node));
+ case NodeType::ResizeLayer:
+ return detail::create_resize_layer<CLScale, CLTargetInfo>(*polymorphic_downcast<ResizeLayerNode *>(node));
case NodeType::SoftmaxLayer:
- return create_softmax_layer(*polymorphic_downcast<SoftmaxLayerNode *>(node), ctx);
+ return detail::create_softmax_layer<CLSoftmaxLayer, CLTargetInfo>(*polymorphic_downcast<SoftmaxLayerNode *>(node), ctx);
default:
return nullptr;
}
}
} // namespace backends
} // namespace graph
-} // namespace arm_compute
+} // namespace arm_compute
\ No newline at end of file
diff --git a/src/graph/backends/CL/CLNodeValidator.cpp b/src/graph/backends/CL/CLNodeValidator.cpp
index c16b2e6..ba5b59d 100644
--- a/src/graph/backends/CL/CLNodeValidator.cpp
+++ b/src/graph/backends/CL/CLNodeValidator.cpp
@@ -47,6 +47,8 @@
NodeType type = node->type();
switch(type)
{
+ case NodeType::ChannelShuffleLayer:
+ return detail::validate_channel_shuffle_layer<CLChannelShuffleLayer>(*polymorphic_downcast<ChannelShuffleLayerNode *>(node));
case NodeType::ConvolutionLayer:
return detail::validate_convolution_layer<CLConvolutionLayer,
CLDirectConvolutionLayer,
@@ -55,6 +57,8 @@
case NodeType::DepthwiseConvolutionLayer:
return detail::validate_depthwise_convolution_layer<CLDepthwiseConvolutionLayer,
CLDepthwiseConvolutionLayer3x3>(*polymorphic_downcast<DepthwiseConvolutionLayerNode *>(node));
+ case NodeType::PermuteLayer:
+ return detail::validate_permute_layer<CLPermute>(*polymorphic_downcast<PermuteLayerNode *>(node));
default:
return Status{};
}