| /* |
| * Copyright (c) 2017 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/CL/ICLTensor.h" |
| #include "arm_compute/core/Error.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/CL/CLFunctions.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(CLActivationLayerOperation, OPENCL, 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::ICLTensor *>(ctx.input(0)) == nullptr); |
| ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ICLTensor *>(ctx.output(0)) == nullptr); |
| |
| // Extract IO and info |
| auto *in = dynamic_cast<arm_compute::ICLTensor *>(ctx.input(0)); |
| auto *out = dynamic_cast<arm_compute::ICLTensor *>(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::CLActivationLayer>(); |
| activation->configure(in, out, act_info); |
| |
| // Log info |
| ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating CLActivationLayer" |
| << " 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); |
| } |
| |
| /* Batch Normalization Layer */ |
| REGISTER_SIMPLE_OPERATION(CLBatchNormalizationLayerOperation, OPENCL, 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::ICLTensor *>(ctx.input(0)) == nullptr); |
| ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ICLTensor *>(ctx.input(1)) == nullptr); |
| ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ICLTensor *>(ctx.input(2)) == nullptr); |
| ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ICLTensor *>(ctx.input(3)) == nullptr); |
| ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ICLTensor *>(ctx.input(4)) == nullptr); |
| ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ICLTensor *>(ctx.output(0)) == nullptr); |
| |
| // Extract IO and info |
| auto *in = dynamic_cast<arm_compute::ICLTensor *>(ctx.input(0)); |
| auto *mean = dynamic_cast<arm_compute::ICLTensor *>(ctx.input(1)); |
| auto *var = dynamic_cast<arm_compute::ICLTensor *>(ctx.input(2)); |
| auto *beta = dynamic_cast<arm_compute::ICLTensor *>(ctx.input(3)); |
| auto *gamma = dynamic_cast<arm_compute::ICLTensor *>(ctx.input(4)); |
| auto *out = dynamic_cast<arm_compute::ICLTensor *>(ctx.output(0)); |
| const auto epsilon = ctx.parameter<float>("epsilon"); |
| |
| // Create and configure function |
| auto batch_norm = arm_compute::support::cpp14::make_unique<arm_compute::CLBatchNormalizationLayer>(); |
| batch_norm->configure(in, out, mean, var, beta, gamma, epsilon); |
| |
| // Log info |
| ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating CLBatchNormalizationLayer" |
| << " 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 |
| << std::endl); |
| |
| return std::move(batch_norm); |
| } |
| |
| /* DepthConvertLayer Layer */ |
| REGISTER_SIMPLE_OPERATION(CLDepthConvertLayerOperation, OPENCL, 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::ICLTensor *>(ctx.input(0)) == nullptr); |
| ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ICLTensor *>(ctx.output(0)) == nullptr); |
| |
| // Extract IO and info |
| auto *in = dynamic_cast<arm_compute::ICLTensor *>(ctx.input(0)); |
| auto *out = dynamic_cast<arm_compute::ICLTensor *>(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::CLDepthConvertLayer>(); |
| depthconvert->configure(in, out, conv_policy, shift); |
| |
| // Log info |
| ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating CLDepthConvertLayer" |
| << " 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(CLDepthwiseConvolutionOperation, OPENCL, 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::ICLTensor *>(ctx.input(0)) == nullptr); |
| ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ICLTensor *>(ctx.output(0)) == nullptr); |
| |
| // Extract IO and info |
| auto *in = dynamic_cast<arm_compute::ICLTensor *>(ctx.input(0)); |
| auto *weights = dynamic_cast<arm_compute::ICLTensor *>(ctx.input(1)); |
| auto *biases = ctx.num_inputs() == 3 ? dynamic_cast<arm_compute::ICLTensor *>(ctx.input(2)) : nullptr; |
| auto *out = dynamic_cast<arm_compute::ICLTensor *>(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::CLDepthwiseConvolutionLayer>(); |
| 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::CLDepthwiseConvolutionLayer3x3>(); |
| depwthwise_conv->configure(in, weights, biases, out, conv_info); |
| func = std::move(depwthwise_conv); |
| } |
| |
| // Log info |
| ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating CLDepthwiseConvolutionLayer" |
| << " 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(CLDequantizationLayerOperation, OPENCL, 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::ICLTensor *>(ctx.input(0)) == nullptr); |
| ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ICLTensor *>(ctx.output(0)) == nullptr); |
| ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ICLTensor *>(ctx.output(1)) == nullptr); |
| |
| // Extract IO and info |
| auto *in = dynamic_cast<arm_compute::ICLTensor *>(ctx.input(0)); |
| auto *out = dynamic_cast<arm_compute::ICLTensor *>(ctx.output(0)); |
| auto *min_max = dynamic_cast<arm_compute::ICLTensor *>(ctx.output(1)); |
| |
| // Create and configure function |
| auto dequantization = arm_compute::support::cpp14::make_unique<arm_compute::CLDequantizationLayer>(); |
| dequantization->configure(in, out, min_max); |
| |
| // Log info |
| ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating CLDequantizationLayer" |
| << " 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(CLFlattenLayerOperation, OPENCL, 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::ICLTensor *>(ctx.input(0)) == nullptr); |
| ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ICLTensor *>(ctx.output(0)) == nullptr); |
| |
| // Extract IO and info |
| auto *in = dynamic_cast<arm_compute::ICLTensor *>(ctx.input(0)); |
| auto *out = dynamic_cast<arm_compute::ICLTensor *>(ctx.output(0)); |
| |
| // Create and configure function |
| auto flatten = arm_compute::support::cpp14::make_unique<arm_compute::CLFlattenLayer>(); |
| flatten->configure(in, out); |
| |
| // Log info |
| ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating CLFlattenLayer" |
| << " 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(CLFloorLayerOperation, OPENCL, 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::ICLTensor *>(ctx.input(0)) == nullptr); |
| ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ICLTensor *>(ctx.output(0)) == nullptr); |
| |
| // Extract IO and info |
| auto *in = dynamic_cast<arm_compute::ICLTensor *>(ctx.input(0)); |
| auto *out = dynamic_cast<arm_compute::ICLTensor *>(ctx.output(0)); |
| |
| // Create and configure function |
| auto floor = arm_compute::support::cpp14::make_unique<arm_compute::CLFloor>(); |
| floor->configure(in, out); |
| |
| // Log info |
| ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating CLFloorLayer" |
| << " 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(CLFullyConnectedLayer, OPENCL, 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::ICLTensor *>(ctx.input(0)) == nullptr); |
| ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ICLTensor *>(ctx.input(1)) == nullptr); |
| ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ICLTensor *>(ctx.input(2)) == nullptr); |
| ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ICLTensor *>(ctx.output(0)) == nullptr); |
| |
| // Extract IO and info |
| auto *in = dynamic_cast<arm_compute::ICLTensor *>(ctx.input(0)); |
| auto *weights = dynamic_cast<arm_compute::ICLTensor *>(ctx.input(1)); |
| auto *biases = dynamic_cast<arm_compute::ICLTensor *>(ctx.input(2)); |
| auto *out = dynamic_cast<arm_compute::ICLTensor *>(ctx.output(0)); |
| |
| // Create and configure function |
| auto fc = arm_compute::support::cpp14::make_unique<arm_compute::CLFullyConnectedLayer>(); |
| fc->configure(in, weights, biases, out); |
| |
| // Log info |
| ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating CLFullyConnectedLayer" |
| << " 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(CLL2NormalizeLayerOperation, OPENCL, 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::ICLTensor *>(ctx.input(0)) == nullptr); |
| ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ICLTensor *>(ctx.output(0)) == nullptr); |
| |
| // Extract IO and info |
| auto *in = dynamic_cast<arm_compute::ICLTensor *>(ctx.input(0)); |
| auto *out = dynamic_cast<arm_compute::ICLTensor *>(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::CLL2NormalizeLayer>(); |
| l2_norm->configure(in, out, axis, epsilon); |
| |
| // Log info |
| ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating CLL2NormalizeLayer" |
| << " 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(CLNormalizationLayerOperation, OPENCL, 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::ICLTensor *>(ctx.input(0)) == nullptr); |
| ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ICLTensor *>(ctx.output(0)) == nullptr); |
| |
| // Extract IO and info |
| auto *in = dynamic_cast<arm_compute::ICLTensor *>(ctx.input(0)); |
| auto *out = dynamic_cast<arm_compute::ICLTensor *>(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::CLNormalizationLayer>(); |
| norm->configure(in, out, norm_info); |
| |
| // Log info |
| ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating CLNormalizationLayer" |
| << " 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(CLPoolingLayerOperation, OPENCL, 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::ICLTensor *>(ctx.input(0)) == nullptr); |
| ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ICLTensor *>(ctx.output(0)) == nullptr); |
| |
| // Extract IO and info |
| auto *in = dynamic_cast<arm_compute::ICLTensor *>(ctx.input(0)); |
| auto *out = dynamic_cast<arm_compute::ICLTensor *>(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::CLPoolingLayer>(); |
| pool->configure(in, out, pool_info); |
| |
| // Log info |
| ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating CLPoolingLayer" |
| << " 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(CLQuantizationLayerOperation, OPENCL, 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::ICLTensor *>(ctx.input(0)) == nullptr); |
| ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ICLTensor *>(ctx.output(0)) == nullptr); |
| |
| // Extract IO and info |
| auto *in = dynamic_cast<arm_compute::ICLTensor *>(ctx.input(0)); |
| auto *out = dynamic_cast<arm_compute::ICLTensor *>(ctx.output(0)); |
| |
| // Create and configure function |
| auto quantization = arm_compute::support::cpp14::make_unique<arm_compute::CLQuantizationLayer>(); |
| quantization->configure(in, out); |
| |
| // Log info |
| ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating CLQuantizationLayer" |
| << " 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(CLReshapeLayerOperation, OPENCL, 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::ICLTensor *>(ctx.input(0)) == nullptr); |
| ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ICLTensor *>(ctx.output(0)) == nullptr); |
| |
| // Extract IO and info |
| auto *in = dynamic_cast<arm_compute::ICLTensor *>(ctx.input(0)); |
| auto *out = dynamic_cast<arm_compute::ICLTensor *>(ctx.output(0)); |
| |
| // Create and configure function |
| auto reshape = arm_compute::support::cpp14::make_unique<arm_compute::CLReshapeLayer>(); |
| reshape->configure(in, out); |
| |
| // Log info |
| ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating CLReshapeLayer" |
| << " 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(CLSoftmaxLayerOperation, OPENCL, 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::ICLTensor *>(ctx.input(0)) == nullptr); |
| ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ICLTensor *>(ctx.output(0)) == nullptr); |
| |
| // Extract IO and info |
| auto *in = dynamic_cast<arm_compute::ICLTensor *>(ctx.input(0)); |
| auto *out = dynamic_cast<arm_compute::ICLTensor *>(ctx.output(0)); |
| |
| // Create and configure function |
| auto smx = arm_compute::support::cpp14::make_unique<arm_compute::CLSoftmaxLayer>(); |
| smx->configure(in, out); |
| |
| // Log info |
| ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating CLSoftmaxLayer" |
| << " Data Type: " << in->info()->data_type() |
| << " Input shape: " << in->info()->tensor_shape() |
| << " Output shape: " << out->info()->tensor_shape() |
| << std::endl); |
| |
| return std::move(smx); |
| } |