diff options
| -rw-r--r-- | arm_compute/graph/Types.h | 1 | ||||
| -rw-r--r-- | arm_compute/graph/nodes/DepthwiseConvolutionLayer.h | 73 | ||||
| -rw-r--r-- | src/graph/nodes/DepthwiseConvolutionLayer.cpp | 84 | ||||
| -rw-r--r-- | src/graph/operations/CLSimpleOperations.cpp | 50 | ||||
| -rw-r--r-- | src/graph/operations/NESimpleOperations.cpp | 50 |
5 files changed, 258 insertions, 0 deletions
diff --git a/arm_compute/graph/Types.h b/arm_compute/graph/Types.h index 5b6c9bde0d..f8d20615d6 100644 --- a/arm_compute/graph/Types.h +++ b/arm_compute/graph/Types.h @@ -94,6 +94,7 @@ enum class OperationType BatchNormalizationLayer, ConvolutionLayer, DepthConvertLayer, + DepthwiseConvolutionLayer, DequantizationLayer, FlattenLayer, FloorLayer, diff --git a/arm_compute/graph/nodes/DepthwiseConvolutionLayer.h b/arm_compute/graph/nodes/DepthwiseConvolutionLayer.h new file mode 100644 index 0000000000..48b2ef9140 --- /dev/null +++ b/arm_compute/graph/nodes/DepthwiseConvolutionLayer.h @@ -0,0 +1,73 @@ +/* + * 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. + */ +#ifndef __ARM_COMPUTE_GRAPH_DEPTHWISE_CONVOLUTION_LAYER_H__ +#define __ARM_COMPUTE_GRAPH_DEPTHWISE_CONVOLUTION_LAYER_H__ + +#include "arm_compute/graph/GraphContext.h" +#include "arm_compute/graph/INode.h" +#include "arm_compute/graph/ITensorObject.h" +#include "arm_compute/graph/SubTensor.h" +#include "arm_compute/graph/Tensor.h" +#include "arm_compute/graph/Types.h" +#include "arm_compute/runtime/IFunction.h" + +#include <memory> + +namespace arm_compute +{ +namespace graph +{ +/** Convolution layer node */ +class DepthwiseConvolutionLayer final : public INode +{ +public: + /** Default constructor + * + * @param[in] conv_width Convolution width + * @param[in] conv_height Convolution height + * @param[in] weights Weights values tensor + * @param[in] conv_info Convolution info + * @param[in] biases (Optional) Biases values tensor + * @param[in] opt3x3 (Optional) If true executes DepthwiseConvolutionLayer3x3 + */ + template <typename AccessorType> + DepthwiseConvolutionLayer(unsigned int conv_width, unsigned int conv_height, AccessorType &&weights, const PadStrideInfo conv_info, AccessorType &&biases = nullptr, bool opt3x3 = true) + : _conv_width(conv_width), _conv_height(conv_height), _weights(std::move(weights)), _conv_info(conv_info), _biases(std::move(biases)), _opt3x3(opt3x3) + { + } + + // Inherited methods overriden: + std::unique_ptr<arm_compute::IFunction> instantiate_node(GraphContext &ctx, ITensorObject *input, ITensorObject *output) override; + +private: + unsigned int _conv_width; + unsigned int _conv_height; + Tensor _weights; + const PadStrideInfo _conv_info; + Tensor _biases; + bool _opt3x3; +}; +} // namespace graph +} // namespace arm_compute +#endif /* __ARM_COMPUTE_GRAPH_DEPTHWISE_CONVOLUTION_LAYER_H__ */ diff --git a/src/graph/nodes/DepthwiseConvolutionLayer.cpp b/src/graph/nodes/DepthwiseConvolutionLayer.cpp new file mode 100644 index 0000000000..1c006d61db --- /dev/null +++ b/src/graph/nodes/DepthwiseConvolutionLayer.cpp @@ -0,0 +1,84 @@ +/* + * 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/graph/nodes/DepthwiseConvolutionLayer.h" + +#include "arm_compute/graph/Error.h" +#include "arm_compute/graph/NodeContext.h" +#include "arm_compute/graph/OperationRegistry.h" +#include "support/ToolchainSupport.h" + +using namespace arm_compute::graph; + +std::unique_ptr<arm_compute::IFunction> DepthwiseConvolutionLayer::instantiate_node(GraphContext &ctx, ITensorObject *input, ITensorObject *output) +{ + ARM_COMPUTE_ERROR_ON_UNALLOCATED_TENSOR_OBJECT(input, output); + + arm_compute::ITensor *in = input->tensor(); + arm_compute::ITensor *out = output->tensor(); + _target_hint = ctx.hints().target_hint(); + + if(_weights.tensor() == nullptr) + { + TensorShape shape = in->info()->tensor_shape(); + shape.set(Window::DimX, _conv_width); + shape.set(Window::DimY, _conv_height); + _weights.set_info(TensorInfo(TensorShape(shape), in->info()->num_channels(), in->info()->data_type(), in->info()->fixed_point_position())); + } + if(_biases.has_accessor() && _biases.tensor() == nullptr) + { + _biases.set_info(TensorInfo(TensorShape(in->info()->dimension(2)), in->info()->num_channels(), in->info()->data_type(), in->info()->fixed_point_position())); + } + + bool weights_is_loaded = _weights.tensor() != nullptr; + bool biases_is_loaded = _biases.has_accessor() ? _biases.tensor() != nullptr : false; + + _weights.set_target(_target_hint); + _biases.set_target(_target_hint); + + // Create node context + NodeContext node_ctx(OperationType::DepthwiseConvolutionLayer); + node_ctx.set_target(_target_hint); + node_ctx.add_input(in); + node_ctx.add_input(_weights.tensor()); + if(_biases.has_accessor()) + { + node_ctx.add_input(_biases.tensor()); + } + node_ctx.add_output(out); + node_ctx.add_parameter<PadStrideInfo>("ConvolutionInfo", _conv_info); + node_ctx.add_parameter<bool>("Optimized3x3", _opt3x3); + + // Fill tensors + if(!weights_is_loaded) + { + _weights.allocate_and_fill_if_needed(); + } + if(!biases_is_loaded) + { + _biases.allocate_and_fill_if_needed(); + } + + // Get function + return OperationRegistry::get().find_operation(OperationType::DepthwiseConvolutionLayer, _target_hint)->configure(node_ctx); +} diff --git a/src/graph/operations/CLSimpleOperations.cpp b/src/graph/operations/CLSimpleOperations.cpp index 4ec3a22f37..881f4910ad 100644 --- a/src/graph/operations/CLSimpleOperations.cpp +++ b/src/graph/operations/CLSimpleOperations.cpp @@ -135,6 +135,56 @@ REGISTER_SIMPLE_OPERATION(CLDepthConvertLayerOperation, OPENCL, OperationType::D 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::CLDepthwiseConvolution>(); + 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::CLDepthwiseConvolution3x3>(); + 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) { diff --git a/src/graph/operations/NESimpleOperations.cpp b/src/graph/operations/NESimpleOperations.cpp index 12f8c6c76b..c77aeeca11 100644 --- a/src/graph/operations/NESimpleOperations.cpp +++ b/src/graph/operations/NESimpleOperations.cpp @@ -135,6 +135,56 @@ REGISTER_SIMPLE_OPERATION(NEDepthConvertLayerOperation, NEON, OperationType::Dep 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::NEDepthwiseConvolution>(); + 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::NEDepthwiseConvolution3x3>(); + 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) { |