arm_compute v18.02
Change-Id: I7207aa488e5470f235f39b6c188b4678dc38d1a6
diff --git a/src/runtime/NEON/functions/NEConvolutionLayer.cpp b/src/runtime/NEON/functions/NEConvolutionLayer.cpp
index 8f7d940..0a49158 100644
--- a/src/runtime/NEON/functions/NEConvolutionLayer.cpp
+++ b/src/runtime/NEON/functions/NEConvolutionLayer.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -23,518 +23,96 @@
*/
#include "arm_compute/runtime/NEON/functions/NEConvolutionLayer.h"
-#include "arm_compute/core/NEON/kernels/arm32/NEGEMMAArch32Kernel.h"
-#include "arm_compute/core/NEON/kernels/arm64/NEGEMMAArch64Kernel.h"
#include "arm_compute/core/PixelValue.h"
-#include "arm_compute/core/Size2D.h"
#include "arm_compute/core/Utils.h"
#include "arm_compute/core/Validate.h"
-#include "arm_compute/runtime/NEON/NEScheduler.h"
#include "support/ToolchainSupport.h"
-namespace arm_compute
-{
-#include "arm_compute/core/NEON/kernels/assembly/gemm_interleaved.hpp"
-#include "arm_compute/core/NEON/kernels/assembly/kernels/a32_sgemm_8x6.hpp"
-#include "arm_compute/core/NEON/kernels/assembly/kernels/a64_sgemm_12x8.hpp"
-} // namespace arm_compute
-
#include <cmath>
#include <tuple>
namespace arm_compute
{
-namespace
-{
-TensorShape get_reshaped_weights_shape(const ITensorInfo *weights, bool has_bias)
-{
- const unsigned int mat_weights_cols = weights->dimension(3);
- const unsigned int mat_weights_rows = weights->dimension(0) * weights->dimension(1) * weights->dimension(2) + (has_bias ? 1 : 0);
- return TensorShape(mat_weights_cols, mat_weights_rows);
-}
-} // namespace
-
-NEConvolutionLayerReshapeWeights::NEConvolutionLayerReshapeWeights(std::shared_ptr<IMemoryManager> memory_manager)
- : _memory_group(std::move(memory_manager)), _weights_reshape_kernel(), _weights_transposed_kernel(), _weights_reshaped(), _transpose1xW(false)
-{
-}
-
-void NEConvolutionLayerReshapeWeights::configure(const ITensor *weights, const ITensor *biases, ITensor *output, bool transpose1xW)
-{
- // Perform validation step
- ARM_COMPUTE_ERROR_ON_NULLPTR(weights, output);
- ARM_COMPUTE_ERROR_THROW_ON(NEConvolutionLayerReshapeWeights::validate(weights->info(),
- (biases != nullptr) ? biases->info() : nullptr,
- output->info(),
- transpose1xW));
-
- // Check if bias are present, if yes they will be embedded to the weights matrix
- const bool _has_bias = (biases != nullptr);
-
- _transpose1xW = transpose1xW;
-
- if(transpose1xW)
- {
- // Create tensor to store the reshaped weights
- TensorInfo info_wr = weights->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(get_reshaped_weights_shape(weights->info(), _has_bias));
-
- _weights_reshaped.allocator()->init(info_wr);
- _memory_group.manage(&_weights_reshaped);
-
- _weights_reshape_kernel.configure(weights, biases, &_weights_reshaped);
- _weights_transposed_kernel.configure(&_weights_reshaped, output);
-
- _weights_reshaped.allocator()->allocate();
- }
- else
- {
- _weights_reshape_kernel.configure(weights, biases, output);
- }
-}
-
-Status NEConvolutionLayerReshapeWeights::validate(const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, bool transpose1xW)
-{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, output);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(weights, output);
- ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4);
-
- if(biases != nullptr)
- {
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(weights, biases);
- ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(3));
- ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
- }
-
- // Check if bias are present, if yes they will be embedded to the weights matrix
- const bool has_bias = (biases != nullptr);
-
- // Checks performed when biases are present
- if(has_bias)
- {
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases);
- ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(3));
- ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
- }
-
- if(transpose1xW)
- {
- TensorInfo weights_reshaped = weights->clone()->set_tensor_shape(get_reshaped_weights_shape(weights, has_bias));
- ARM_COMPUTE_RETURN_ON_ERROR(NEWeightsReshapeKernel::validate(weights, biases, &weights_reshaped));
- ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMTranspose1xWKernel::validate(&weights_reshaped, output));
- }
- else
- {
- ARM_COMPUTE_RETURN_ON_ERROR(NEWeightsReshapeKernel::validate(weights, biases, output));
- }
-
- return Status{};
-}
-
-void NEConvolutionLayerReshapeWeights::run()
-{
- _memory_group.acquire();
-
- NEScheduler::get().schedule(&_weights_reshape_kernel, 3);
-
- if(_transpose1xW)
- {
- NEScheduler::get().schedule(&_weights_transposed_kernel, Window::DimY);
- }
-
- _memory_group.release();
-}
-
-namespace
-{
-TensorShape get_reshaped_weights_shape_conv(const ITensorInfo *weights, bool has_bias, bool is_fully_connected_convolution)
-{
- unsigned int mat_weights_cols = weights->dimension(3);
- unsigned int mat_weights_rows = weights->dimension(0) * weights->dimension(1) * weights->dimension(2) + (has_bias ? 1 : 0);
-
- if(is_fully_connected_convolution)
- {
- // Create tensor to store the reshaped weights
- return TensorShape(mat_weights_cols, mat_weights_rows);
- }
- else
- {
- // Create tensor to store transposed weights
- const float transpose_width = 16.0f / weights->element_size();
- return TensorShape(mat_weights_rows * static_cast<unsigned int>(transpose_width), static_cast<unsigned int>(std::ceil(mat_weights_cols / transpose_width)));
- }
-}
-
-Status validate_and_initialize_values(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const PadStrideInfo &conv_info, const WeightsInfo &weights_info, DataType &dt,
- bool &has_bias,
- bool &are_weights_reshaped, unsigned int &kernel_width, unsigned int &kernel_height, bool &is_fully_connected_convolution, unsigned int &mat_weights_cols, unsigned int &mat_weights_rows,
- unsigned int &conv_w, unsigned int &conv_h)
-{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, weights);
- ARM_COMPUTE_RETURN_ERROR_ON(!weights_info.are_reshaped() && weights->dimension(2) != input->dimension(2));
- ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4);
-
- if(biases != nullptr)
- {
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, biases);
- ARM_COMPUTE_RETURN_ERROR_ON(!weights_info.are_reshaped() && biases->dimension(0) != weights->dimension(3));
- ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
- }
-
- dt = input->data_type();
- has_bias = (biases != nullptr);
- are_weights_reshaped = weights_info.are_reshaped();
- kernel_width = (are_weights_reshaped) ? weights_info.kernel_size().first : weights->dimension(0);
- kernel_height = (are_weights_reshaped) ? weights_info.kernel_size().second : weights->dimension(1);
- mat_weights_cols = weights->dimension(3);
- mat_weights_rows = weights->dimension(0) * weights->dimension(1) * weights->dimension(2) + (has_bias ? 1 : 0);
-
- std::tie(conv_w, conv_h) = scaled_dimensions(input->dimension(0), input->dimension(1), kernel_width, kernel_height,
- conv_info);
-
- is_fully_connected_convolution = ((conv_w == 1) && (conv_h == 1));
-
- return Status{};
-}
-} // namespace
-
NEConvolutionLayer::NEConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager)
- : _memory_group(std::move(memory_manager)), _input_im2col_kernel(), _input_interleave_kernel(), _reshape_weights(), _mm_kernel(), _mm_optimised_kernel(nullptr), _output_col2im_kernel(),
- _input_im2col_reshaped(), _input_interleaved_reshaped(), _weights_reshaped(), _gemm_output(), _workspace(), _has_bias(false), _is_fully_connected_convolution(false), _are_weights_reshaped(false)
+ : _memory_manager(std::move(memory_manager)), _function()
{
}
-void NEConvolutionLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info)
+void NEConvolutionLayer::configure(ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info)
{
// Perform validate step
ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
+ ARM_COMPUTE_ERROR_THROW_ON(NEConvolutionLayer::validate(input->info(), weights->info(), ((biases != nullptr) ? biases->info() : nullptr), output->info(), conv_info, weights_info));
- DataType dt{};
- unsigned int kernel_width = 0;
- unsigned int kernel_height = 0;
- unsigned int mat_weights_cols = 0;
- unsigned int mat_weights_rows = 0;
- unsigned int conv_w = 0;
- unsigned int conv_h = 0;
-
- Status status = validate_and_initialize_values(input->info(), weights->info(), (biases == nullptr) ? nullptr : biases->info(), conv_info, weights_info, dt, _has_bias, _are_weights_reshaped,
- kernel_width, kernel_height,
- _is_fully_connected_convolution,
- mat_weights_cols, mat_weights_rows, conv_w, conv_h);
-
- ARM_COMPUTE_ERROR_THROW_ON(status);
-
- const unsigned int fixed_point_position = input->info()->fixed_point_position();
-
-#if defined(__arm__)
- if(NEScheduler::get().cpu_info().CPU == CPUTarget::ARMV7 && dt == DataType::F32)
+ switch(NEConvolutionLayer::get_convolution_method(input->info(), weights->info(), ((biases != nullptr) ? biases->info() : nullptr), output->info(), conv_info,
+ weights_info))
{
- _mm_optimised_kernel = support::cpp14::make_unique<NEGEMMAArch32Kernel>();
- }
-#elif defined(__aarch64__)
- if(NEScheduler::get().cpu_info().CPU >= CPUTarget::ARMV8 && dt == DataType::F32)
- {
- _mm_optimised_kernel = support::cpp14::make_unique<NEGEMMAArch64Kernel>();
- }
-#endif /* defined(__arm__) || defined(__aarch64__) */
-
- // Reshape weights if needed
- if(_mm_optimised_kernel != nullptr)
- {
- if(_are_weights_reshaped)
+ case ConvolutionMethod::WINOGRAD:
{
- mat_weights_cols = weights_info.num_kernels();
- mat_weights_rows = weights->info()->dimension(1);
+ auto f = arm_compute::support::cpp14::make_unique<NEWinogradLayer>(_memory_manager);
+ f->configure(input, weights, biases, output, conv_info);
+ _function = std::move(f);
+ break;
}
- else
+ case ConvolutionMethod::GEMM:
{
- TensorShape reshaped_weights_shape{ mat_weights_cols, mat_weights_rows };
-
- // Create tensor to store the reshaped weights
- _weights_reshaped.allocator()->init(TensorInfo(reshaped_weights_shape, 1, dt, fixed_point_position));
- _reshape_weights.configure(weights, biases, &_weights_reshaped, false /* 1xW transpose */);
- weights = &_weights_reshaped;
+ auto f = arm_compute::support::cpp14::make_unique<NEGEMMConvolutionLayer>(_memory_manager);
+ f->configure(input, weights, biases, output, conv_info, weights_info);
+ _function = std::move(f);
+ break;
}
- }
- else
- {
- if(_are_weights_reshaped)
+ case ConvolutionMethod::DIRECT:
{
- if(_is_fully_connected_convolution)
- {
- mat_weights_cols = weights_info.num_kernels();
- mat_weights_rows = weights->info()->dimension(1);
- }
- else
- {
- const unsigned int transpose_width = 16 / input->info()->element_size();
- mat_weights_cols = weights_info.num_kernels();
- mat_weights_rows = weights->info()->dimension(0) / transpose_width + (_has_bias ? 1 : 0);
- }
+ auto f = arm_compute::support::cpp14::make_unique<NEDirectConvolutionLayer>(_memory_manager);
+ f->configure(input, weights, biases, output, conv_info);
+ _function = std::move(f);
+ break;
}
- else
- {
- TensorShape reshaped_weights_shape;
-
- if(_is_fully_connected_convolution)
- {
- reshaped_weights_shape = TensorShape{ mat_weights_cols, mat_weights_rows };
- }
- else
- {
- // Create tensor to store transposed weights
- const float transpose_width = 16.0f / input->info()->element_size();
- reshaped_weights_shape = TensorShape{ mat_weights_rows *static_cast<unsigned int>(transpose_width),
- static_cast<unsigned int>(std::ceil(mat_weights_cols / transpose_width)) };
- }
-
- // Create tensor to store the reshaped weights
- _weights_reshaped.allocator()->init(TensorInfo(reshaped_weights_shape, 1, dt, fixed_point_position));
- _reshape_weights.configure(weights, biases, &_weights_reshaped, !_is_fully_connected_convolution /* 1xW transpose */);
- weights = &_weights_reshaped;
- }
- }
-
- // Create tensor to store im2col reshaped inputs
- const unsigned int mat_input_cols = mat_weights_rows;
- const unsigned int mat_input_rows = conv_w * conv_h;
-
- TensorShape shape_im2col(input->info()->tensor_shape());
- shape_im2col.set(0, mat_input_cols);
- shape_im2col.set(1, mat_input_rows);
- shape_im2col.set(2, 1);
- _input_im2col_reshaped.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col));
- _memory_group.manage(&_input_im2col_reshaped);
-
- // Create tensor (interleave) to prepare input tensor for GEMM
- if(!_is_fully_connected_convolution && _mm_optimised_kernel == nullptr)
- {
- TensorShape shape_interleaved(shape_im2col);
- shape_interleaved.set(0, shape_interleaved.x() * 4);
- shape_interleaved.set(1, std::ceil(shape_interleaved.y() / 4.f));
- _input_interleaved_reshaped.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_interleaved));
- _memory_group.manage(&_input_interleaved_reshaped);
- }
-
- // Create GEMM output tensor
- TensorShape shape_gemm(_input_im2col_reshaped.info()->tensor_shape());
- shape_gemm.set(0, mat_weights_cols);
- shape_gemm.set(1, mat_input_rows);
- _gemm_output.allocator()->init(_input_im2col_reshaped.info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_gemm));
- _memory_group.manage(&_gemm_output);
-
- // Configure kernels
- _input_im2col_kernel.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, _has_bias);
-
-#if defined(__arm__) || defined(__aarch64__)
- if(_mm_optimised_kernel != nullptr)
- {
- struct CPUInfo ci = NEScheduler::get().cpu_info();
-
- const int M = _gemm_output.info()->tensor_shape().y();
- const int N = _gemm_output.info()->tensor_shape().x();
- const int K = _input_im2col_reshaped.info()->tensor_shape().x();
-
-#if defined(__arm__)
- GemmInterleaved<sgemm_8x6, float, float> gemm(&ci, M, N, K, false, false);
-#elif defined(__aarch64__)
- GemmInterleaved<sgemm_12x8, float, float> gemm(&ci, M, N, K, false, false);
-#endif /* defined(__arm__) || defined(__aarch64__) */
-
- constexpr size_t alignment = 4096;
- _workspace.allocator()->init(TensorInfo(TensorShape{ (gemm.get_working_size() + alignment - 1) * NEScheduler::get().num_threads() }, 1, DataType::U8));
- _memory_group.manage(&_workspace);
-
- // Configure matrix multiplication kernel
- _mm_optimised_kernel->configure(&_input_im2col_reshaped, weights, &_gemm_output, &_workspace);
-
- _workspace.allocator()->allocate();
- }
- else
-#endif /* defined(__arm__) || defined(__aarch64__) */
- {
- if(_is_fully_connected_convolution)
- {
- _mm_kernel.configure(&_input_im2col_reshaped, weights, &_gemm_output, 1.0f);
- }
- else
- {
- _input_interleave_kernel.configure(&_input_im2col_reshaped, &_input_interleaved_reshaped);
- _mm_kernel.configure(&_input_interleaved_reshaped, weights, &_gemm_output, 1.0f);
- _input_interleaved_reshaped.allocator()->allocate();
- }
- }
-
- _input_im2col_reshaped.allocator()->allocate();
- _output_col2im_kernel.configure(&_gemm_output, output, Size2D(conv_w, conv_h));
- _gemm_output.allocator()->allocate();
-
- // Allocate intermediate tensor
- if(!_are_weights_reshaped)
- {
- _weights_reshaped.allocator()->allocate();
+ default:
+ ARM_COMPUTE_ERROR("Not supported.");
+ break;
}
}
Status NEConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
const WeightsInfo &weights_info)
{
- DataType dt{};
- bool has_bias{};
- bool are_weights_reshaped{};
- bool is_fully_connected_convolution{};
- unsigned int kernel_width = 0;
- unsigned int kernel_height = 0;
- unsigned int mat_weights_cols = 0;
- unsigned int mat_weights_rows = 0;
- unsigned int conv_w = 0;
- unsigned int conv_h = 0;
-
- Status status = validate_and_initialize_values(input, weights, biases, conv_info, weights_info, dt, has_bias, are_weights_reshaped, kernel_width, kernel_height,
- is_fully_connected_convolution, mat_weights_cols, mat_weights_rows,
- conv_w, conv_h);
-
- ARM_COMPUTE_RETURN_ON_ERROR(status);
-
- std::unique_ptr<ITensorInfo> reshaped_weights = weights->clone();
- bool optimised_kernel = false;
-
-#if defined(__arm__)
- if(NEScheduler::get().cpu_info().CPU == CPUTarget::ARMV7 && dt == DataType::F32)
+ switch(NEConvolutionLayer::get_convolution_method(input, weights, biases, output, conv_info, weights_info))
{
- optimised_kernel = true;
+ case ConvolutionMethod::WINOGRAD:
+ //Validate Winograd
+ NEWinogradLayer::validate(input, weights, biases, output, conv_info);
+ break;
+ case ConvolutionMethod::GEMM:
+ //Validate Gemm-based Convolution
+ NEGEMMConvolutionLayer::validate(input, weights, biases, output, conv_info, weights_info);
+ break;
+ case ConvolutionMethod::DIRECT:
+ //Validate Gemm-based Convolution
+ NEDirectConvolutionLayer::validate(input, weights, biases, output, conv_info);
+ default:
+ ARM_COMPUTE_ERROR("Not supported.");
+ break;
}
-#elif defined(__aarch64__)
- if(NEScheduler::get().cpu_info().CPU >= CPUTarget::ARMV8 && dt == DataType::F32)
- {
- optimised_kernel = true;
- }
-#endif /* defined(__arm__) || defined(__aarch64__) */
-
- // Reshape weights if needed
- if(optimised_kernel)
- {
- if(are_weights_reshaped)
- {
- mat_weights_cols = weights_info.num_kernels();
- mat_weights_rows = weights->dimension(1);
- }
- else
- {
- TensorShape reshaped_weights_shape{ mat_weights_cols, mat_weights_rows };
-
- // Create tensor to store the reshaped weights
- reshaped_weights->set_tensor_shape(get_reshaped_weights_shape_conv(weights, has_bias, is_fully_connected_convolution));
- ARM_COMPUTE_RETURN_ON_ERROR(NEConvolutionLayerReshapeWeights::validate(weights, biases, reshaped_weights.get(), !is_fully_connected_convolution /* 1xW transpose */));
- weights = reshaped_weights.get();
- }
- }
- else
- {
- if(are_weights_reshaped)
- {
- const unsigned int transpose_width = 16 / input->element_size();
- mat_weights_cols = weights_info.num_kernels();
- mat_weights_rows = weights->dimension(0) / transpose_width + (has_bias ? 1 : 0);
- }
- else
- {
- TensorShape reshaped_weights_shape;
-
- if(is_fully_connected_convolution)
- {
- reshaped_weights_shape = TensorShape{ mat_weights_cols, mat_weights_rows };
- }
- else
- {
- // Create tensor to store transposed weights
- const float transpose_width = 16.0f / input->element_size();
- reshaped_weights_shape = TensorShape{ mat_weights_rows *static_cast<unsigned int>(transpose_width),
- static_cast<unsigned int>(std::ceil(mat_weights_cols / transpose_width)) };
- }
-
- // Create tensor to store the reshaped weights
- reshaped_weights->set_tensor_shape(get_reshaped_weights_shape_conv(weights, has_bias, is_fully_connected_convolution));
- ARM_COMPUTE_RETURN_ON_ERROR(NEConvolutionLayerReshapeWeights::validate(weights, biases, reshaped_weights.get(), !is_fully_connected_convolution /* 1xW transpose */));
- weights = reshaped_weights.get();
- }
- }
-
- // Validate im2col
- const unsigned int mat_input_cols = mat_weights_rows;
- const unsigned int mat_input_rows = conv_w * conv_h;
- TensorShape shape_im2col = input->tensor_shape();
- shape_im2col.set(0, mat_input_cols);
- shape_im2col.set(1, mat_input_rows);
- shape_im2col.set(2, 1);
- TensorInfo im2_col_info = input->clone()->set_tensor_shape(shape_im2col);
- ARM_COMPUTE_RETURN_ON_ERROR(NEIm2ColKernel::validate(input, &im2_col_info, Size2D(weights->dimension(0), weights->dimension(1)), conv_info, has_bias));
-
- // Create GEMM output tensor
- TensorShape shape_gemm(im2_col_info.tensor_shape());
- shape_gemm.set(0, mat_weights_cols);
- shape_gemm.set(1, mat_input_rows);
- TensorInfo gemm_output_info = input->clone()->set_tensor_shape(shape_gemm);
-
- // Validate GEMM interleave and multiply
- if(!is_fully_connected_convolution)
- {
- TensorShape shape_interleaved = shape_im2col;
- shape_interleaved.set(0, shape_interleaved.x() * 4);
- shape_interleaved.set(1, std::ceil(shape_interleaved.y() / 4.f));
- TensorInfo input_interleaved_info = input->clone()->set_tensor_shape(shape_interleaved);
- ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMInterleave4x4Kernel::validate(&im2_col_info, &input_interleaved_info));
- ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMMatrixMultiplyKernel::validate(&input_interleaved_info, weights, &gemm_output_info));
- }
- else
- {
- ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMMatrixMultiplyKernel::validate(&im2_col_info, weights, &gemm_output_info));
- }
-
- ARM_COMPUTE_RETURN_ON_ERROR(NECol2ImKernel::validate(&gemm_output_info, output, Size2D(conv_w, conv_h)));
-
- ARM_COMPUTE_RETURN_ERROR_ON_MSG((output->dimension(0) != conv_w) || (output->dimension(1) != conv_h), "Output shape does not match the expected one");
return Status{};
}
+ConvolutionMethod NEConvolutionLayer::get_convolution_method(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
+ const WeightsInfo &weights_info)
+{
+ ARM_COMPUTE_UNUSED(output);
+ ARM_COMPUTE_UNUSED(weights_info);
+ if((input->data_type() == DataType::F32) && (weights->dimension(0) == 3) && (weights->dimension(1) == 3) && (weights->num_dimensions() <= 4) && (conv_info.stride().first == 1)
+ && (conv_info.stride().second == 1) && (biases != nullptr))
+ {
+ return ConvolutionMethod::WINOGRAD;
+ }
+ return ConvolutionMethod::GEMM;
+}
+
void NEConvolutionLayer::run()
{
- // Run weights reshaping (Runs once for every configure)
- if(!_are_weights_reshaped)
- {
- _are_weights_reshaped = true;
- _reshape_weights.run();
- }
-
- _memory_group.acquire();
-
- // Run input reshaping
- NEScheduler::get().schedule(&_input_im2col_kernel, Window::DimY);
-
- // Runs matrix multiply on reshaped matrices
- if(_mm_optimised_kernel != nullptr)
- {
- NEScheduler::get().schedule(_mm_optimised_kernel.get(), Window::DimY);
- }
- else
- {
- if(!_is_fully_connected_convolution)
- {
- // Run interleave
- NEScheduler::get().schedule(&_input_interleave_kernel, Window::DimY);
- }
-
- NEScheduler::get().schedule(&_mm_kernel, Window::DimY);
- }
-
- // Reshape output matrix
- NEScheduler::get().schedule(&_output_col2im_kernel, Window::DimY);
-
- _memory_group.release();
+ _function->run();
}
} // namespace arm_compute