| /* |
| * 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/ConvolutionLayer.h" |
| |
| #include "arm_compute/graph/Error.h" |
| #include "arm_compute/runtime/CL/functions/CLConvolutionLayer.h" |
| #include "arm_compute/runtime/CL/functions/CLDirectConvolutionLayer.h" |
| #include "arm_compute/runtime/IFunction.h" |
| #include "arm_compute/runtime/NEON/functions/NEConvolutionLayer.h" |
| #include "arm_compute/runtime/NEON/functions/NEDirectConvolutionLayer.h" |
| #include "support/ToolchainSupport.h" |
| #include "utils/GraphTypePrinter.h" |
| #include "utils/TypePrinter.h" |
| |
| #include <tuple> |
| #include <vector> |
| |
| using namespace arm_compute::graph; |
| |
| namespace |
| { |
| /** Calculates the output shaped of the convolution layer |
| * |
| * @param[in] input_shape Input tensor shape |
| * @param[in] weights_shape Weights shape |
| * @param[in] conv_info Convolution information (padding, stride, etc.) |
| * |
| * @return The expected output tensor shape |
| */ |
| TensorShape calculate_convolution_layer_output_shape(const TensorShape &input_shape, const TensorShape &weights_shape, const PadStrideInfo &conv_info) |
| { |
| unsigned int output_width = 0; |
| unsigned int output_height = 0; |
| |
| // Get output width and height |
| std::tie(output_width, output_height) = arm_compute::scaled_dimensions(input_shape.x(), input_shape.y(), weights_shape.x(), weights_shape.y(), conv_info); |
| |
| // Create output shape |
| TensorShape output_shape = input_shape; |
| output_shape.set(0, output_width); |
| output_shape.set(1, output_height); |
| output_shape.set(2, weights_shape[3]); |
| |
| return output_shape; |
| } |
| |
| // Instantiate GEMM based convolution layer |
| template <typename ConvolutionType, typename TensorType, TargetHint target_hint> |
| std::unique_ptr<arm_compute::IFunction> instantiate_function(arm_compute::ITensor *input, arm_compute::ITensor *weights, arm_compute::ITensor *biases, arm_compute::ITensor *output, |
| const PadStrideInfo &conv_info, const WeightsInfo &weights_info) |
| { |
| auto conv = arm_compute::support::cpp14::make_unique<ConvolutionType>(); |
| conv->configure( |
| dynamic_cast<TensorType *>(input), |
| dynamic_cast<TensorType *>(weights), |
| dynamic_cast<TensorType *>(biases), |
| dynamic_cast<TensorType *>(output), |
| conv_info, weights_info); |
| return std::move(conv); |
| } |
| |
| // Instantiate direct convolution layer |
| template <typename ConvolutionType, typename TensorType, TargetHint target_hint> |
| std::unique_ptr<arm_compute::IFunction> instantiate_direct_function(arm_compute::ITensor *input, arm_compute::ITensor *weights, arm_compute::ITensor *biases, arm_compute::ITensor *output, |
| const PadStrideInfo &conv_info) |
| { |
| auto conv = arm_compute::support::cpp14::make_unique<ConvolutionType>(); |
| conv->configure( |
| dynamic_cast<TensorType *>(input), |
| dynamic_cast<TensorType *>(weights), |
| dynamic_cast<TensorType *>(biases), |
| dynamic_cast<TensorType *>(output), |
| conv_info); |
| return std::move(conv); |
| } |
| |
| template <TargetHint target_hint> |
| std::unique_ptr<arm_compute::IFunction> instantiate(arm_compute::ITensor *input, arm_compute::ITensor *weights, arm_compute::ITensor *biases, arm_compute::ITensor *output, |
| const PadStrideInfo &conv_info, const WeightsInfo &weights_info, |
| ConvolutionMethodHint conv_method); |
| |
| template <> |
| std::unique_ptr<arm_compute::IFunction> instantiate<TargetHint::OPENCL>(arm_compute::ITensor *input, arm_compute::ITensor *weights, arm_compute::ITensor *biases, arm_compute::ITensor *output, |
| const PadStrideInfo &conv_info, |
| const WeightsInfo &weights_info, |
| ConvolutionMethodHint conv_method) |
| { |
| if(conv_method == ConvolutionMethodHint::GEMM) |
| { |
| return instantiate_function<arm_compute::CLConvolutionLayer, arm_compute::ICLTensor, TargetHint::OPENCL>(input, weights, biases, output, conv_info, weights_info); |
| } |
| else |
| { |
| return instantiate_direct_function<arm_compute::CLDirectConvolutionLayer, arm_compute::ICLTensor, TargetHint::OPENCL>(input, weights, biases, output, conv_info); |
| } |
| } |
| |
| template <> |
| std::unique_ptr<arm_compute::IFunction> instantiate<TargetHint::NEON>(arm_compute::ITensor *input, arm_compute::ITensor *weights, arm_compute::ITensor *biases, arm_compute::ITensor *output, |
| const PadStrideInfo &conv_info, |
| const WeightsInfo &weights_info, |
| ConvolutionMethodHint conv_method) |
| { |
| if(conv_method == ConvolutionMethodHint::GEMM) |
| { |
| return instantiate_function<arm_compute::NEConvolutionLayer, arm_compute::ITensor, TargetHint::NEON>(input, weights, biases, output, conv_info, weights_info); |
| } |
| else |
| { |
| return instantiate_direct_function<arm_compute::NEDirectConvolutionLayer, arm_compute::ITensor, TargetHint::NEON>(input, weights, biases, output, conv_info); |
| } |
| } |
| } // namespace |
| |
| /** Grouped Convolution function */ |
| class GroupedConvolutionFunction final : public arm_compute::IFunction |
| { |
| public: |
| /** Default Constructor */ |
| GroupedConvolutionFunction() |
| : _convolutions() |
| { |
| } |
| /** Default Destructor */ |
| ~GroupedConvolutionFunction() final = default; |
| /** Prevent instances from being copy constructed */ |
| GroupedConvolutionFunction(const GroupedConvolutionFunction &) = delete; |
| /** Prevent instances from being copy assigned */ |
| GroupedConvolutionFunction &operator=(const GroupedConvolutionFunction &) = delete; |
| /** Allow instances to be move constructed */ |
| GroupedConvolutionFunction(GroupedConvolutionFunction &&) noexcept = default; |
| /** Allow instances to be move assigned */ |
| GroupedConvolutionFunction &operator=(GroupedConvolutionFunction &&) noexcept = default; |
| /** Adds a convolution |
| * |
| * @param convolution Convolution function to add |
| */ |
| void add_convolution_function(std::unique_ptr<IFunction> convolution) |
| { |
| _convolutions.emplace_back(std::move(convolution)); |
| } |
| |
| // Inherited methods overriden: |
| void run() override |
| { |
| for(auto &c : _convolutions) |
| { |
| c->run(); |
| } |
| } |
| |
| private: |
| std::vector<std::unique_ptr<IFunction>> _convolutions; |
| }; |
| |
| std::unique_ptr<arm_compute::IFunction> ConvolutionLayer::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(); |
| |
| // Set weights and biases info |
| if(_weights.tensor() == nullptr) |
| { |
| _weights.set_info(TensorInfo(TensorShape(_conv_width, _conv_height, in->info()->dimension(2) / _num_groups, _ofm), |
| 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(_ofm), in->info()->num_channels(), in->info()->data_type(), in->info()->fixed_point_position())); |
| } |
| |
| std::unique_ptr<arm_compute::IFunction> func; |
| _target_hint = ctx.hints().target_hint(); |
| const ConvolutionMethodHint conv_method_hint = ctx.hints().convolution_method_hint(); |
| |
| // Check if the weights and biases are loaded |
| bool weights_are_loaded = _weights.tensor() != nullptr; |
| bool biases_are_loaded = _biases.has_accessor() ? _biases.tensor() != nullptr : true; |
| |
| // Set bias and weights target |
| _weights.set_target(_target_hint); |
| if(_biases.has_accessor()) |
| { |
| _biases.set_target(_target_hint); |
| } |
| |
| // Calculate output shape |
| TensorShape output_shape = calculate_convolution_layer_output_shape(in->info()->tensor_shape(), _weights.info().tensor_shape(), _conv_info); |
| |
| // Output auto inizialitation if not yet initialized |
| arm_compute::auto_init_if_empty(*out->info(), output_shape, 1, in->info()->data_type(), in->info()->fixed_point_position()); |
| |
| // Create appropriate convolution function |
| if(_num_groups == 1) |
| { |
| func = instantiate_convolution(in, out, conv_method_hint); |
| } |
| else |
| { |
| func = instantiate_grouped_convolution(in, out, conv_method_hint); |
| } |
| |
| // Fill weights |
| if(!weights_are_loaded) |
| { |
| _weights.allocate_and_fill_if_needed(); |
| } |
| // Fill biases |
| if(!biases_are_loaded) |
| { |
| _biases.allocate_and_fill_if_needed(); |
| } |
| |
| ARM_COMPUTE_LOG_GRAPH_INFO(" 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() |
| << " PadStrideInfo: " << _conv_info |
| << " Groups: " << _num_groups |
| << " WeightsInfo: " << _weights_info |
| << std::endl); |
| |
| return func; |
| } |
| |
| std::unique_ptr<arm_compute::IFunction> ConvolutionLayer::instantiate_convolution(ITensor *input, ITensor *output, ConvolutionMethodHint conv_method_hint) |
| { |
| std::unique_ptr<arm_compute::IFunction> func; |
| if(_target_hint == TargetHint::OPENCL) |
| { |
| ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating CLConvolutionLayer"); |
| func = instantiate<TargetHint::OPENCL>(input, _weights.tensor(), _biases.tensor(), output, _conv_info, _weights_info, conv_method_hint); |
| } |
| else |
| { |
| ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NEConvolutionLayer"); |
| func = instantiate<TargetHint::NEON>(input, _weights.tensor(), _biases.tensor(), output, _conv_info, _weights_info, conv_method_hint); |
| } |
| return func; |
| } |
| |
| std::unique_ptr<arm_compute::IFunction> ConvolutionLayer::instantiate_grouped_convolution(ITensor *input, ITensor *output, ConvolutionMethodHint conv_method_hint) |
| { |
| // Get tensor shapes |
| TensorShape input_shape = input->info()->tensor_shape(); |
| TensorShape output_shape = output->info()->tensor_shape(); |
| TensorShape weights_shape = _weights.info().tensor_shape(); |
| TensorShape biases_shape = _biases.info().tensor_shape(); |
| |
| ARM_COMPUTE_ERROR_ON_MSG((input_shape.z() % _num_groups) != 0, "Input depth not multiple of the number of groups!"); |
| ARM_COMPUTE_ERROR_ON_MSG((output_shape.z() % _num_groups) != 0, "Output depth not multiple of the number of groups!"); |
| ARM_COMPUTE_ERROR_ON_MSG((weights_shape[3] % _num_groups) != 0, "Number of kernels not multiple of the number of groups!"); |
| ARM_COMPUTE_ERROR_ON_MSG((biases_shape.x() % _num_groups) != 0, "Biases not multiple of the number of groups!"); |
| |
| // Create a grouped convolution function |
| auto grouped_conv = arm_compute::support::cpp14::make_unique<GroupedConvolutionFunction>(); |
| |
| // Create sub-tensors vectors |
| _is = arm_compute::support::cpp14::make_unique<SubTensor[]>(_num_groups); |
| _os = arm_compute::support::cpp14::make_unique<SubTensor[]>(_num_groups); |
| _ws = arm_compute::support::cpp14::make_unique<SubTensor[]>(_num_groups); |
| _bs = arm_compute::support::cpp14::make_unique<SubTensor[]>(_num_groups); |
| |
| // Calculate sub-tensor splits |
| const int input_split = input_shape.z() / _num_groups; |
| const int output_split = output_shape.z() / _num_groups; |
| const int weights_split = weights_shape[3] / _num_groups; |
| const int biases_split = biases_shape.x() / _num_groups; |
| |
| // Calculate sub-tensor shapes |
| input_shape.set(2, input_split); |
| output_shape.set(2, output_split); |
| weights_shape.set(3, weights_split); |
| biases_shape.set(0, biases_split); |
| |
| // Configure sub-tensors |
| for(int i = 0; i < static_cast<int>(_num_groups); ++i) |
| { |
| // Create convolution function |
| std::unique_ptr<arm_compute::IFunction> func; |
| |
| // Calculate sub-tensors starting coordinates |
| Coordinates input_coord(0, 0, input_split * i); |
| Coordinates output_coord(0, 0, output_split * i); |
| Coordinates weights_coord(0, 0, 0, weights_split * i); |
| Coordinates biases_coord(biases_split * i); |
| |
| // Create sub-tensors for input, output, weights and bias |
| auto hint_to_use = (_target_hint == TargetHint::OPENCL) ? TargetHint::OPENCL : TargetHint::NEON; |
| _is[i] = SubTensor(input, input_shape, input_coord, hint_to_use); |
| _os[i] = SubTensor(output, output_shape, output_coord, hint_to_use); |
| _ws[i] = SubTensor(_weights.tensor(), weights_shape, weights_coord, hint_to_use); |
| _bs[i] = SubTensor(_biases.tensor(), biases_shape, biases_coord, hint_to_use); |
| |
| // Instantiate convolution function |
| if(_target_hint == TargetHint::OPENCL) |
| { |
| ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating CLConvolutionLayer"); |
| func = instantiate<TargetHint::OPENCL>(_is[i].tensor(), _ws[i].tensor(), _bs[i].tensor(), _os[i].tensor(), _conv_info, _weights_info, conv_method_hint); |
| } |
| else |
| { |
| ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NEConvolutionLayer"); |
| func = instantiate<TargetHint::NEON>(_is[i].tensor(), _ws[i].tensor(), _bs[i].tensor(), _os[i].tensor(), _conv_info, _weights_info, conv_method_hint); |
| } |
| |
| // Add convolution function to the list of convolutions for the grouped convolution |
| grouped_conv->add_convolution_function(std::move(func)); |
| } |
| |
| return std::move(grouped_conv); |
| } |