diff options
| -rw-r--r-- | arm_compute/core/NEON/kernels/NEWinogradLayerKernel.h | 7 | ||||
| -rw-r--r-- | src/core/NEON/kernels/NEWinogradLayerKernel.cpp | 24 | ||||
| -rw-r--r-- | src/runtime/NEON/functions/NEWinogradLayer.cpp | 27 |
3 files changed, 18 insertions, 40 deletions
diff --git a/arm_compute/core/NEON/kernels/NEWinogradLayerKernel.h b/arm_compute/core/NEON/kernels/NEWinogradLayerKernel.h index 9ab2e4de11..73b7e8d2b7 100644 --- a/arm_compute/core/NEON/kernels/NEWinogradLayerKernel.h +++ b/arm_compute/core/NEON/kernels/NEWinogradLayerKernel.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017 ARM Limited. + * Copyright (c) 2017, 2018 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -32,18 +32,15 @@ namespace arm_compute class ITensor; class NEWinogradLayerKernel; -class Winograd3x3F32 +class Winograd3x3F32 final { public: friend class NEWinogradLayerKernel; Winograd3x3F32(const KernelShape &kernel_shape, const Tensor4DShape input_shape, const PaddingType padding_type, void *kernel_storage); ~Winograd3x3F32(); - std::pair<void *, void *> get_nhwc_ptrs(const Tensor4DShape &input_shape, const PaddingType padding_type, void *working_space); void transform_weights(const void *const kernel, void *transform_working_space); void reshape_input(const Tensor4DShape &input_shape, const PaddingType padding_type, const void *const input, void *working_space); void reshape_output(const Tensor4DShape &input_shape, const PaddingType padding_type, void *const output); - void nchw2nhwc(const Tensor4DShape &input_shape, const PaddingType padding_type, void *working_space, const void *const input); - void nhwc2nchw(const Tensor4DShape &input_shape, const PaddingType padding_type, void *working_space, void *const output); private: class Private; diff --git a/src/core/NEON/kernels/NEWinogradLayerKernel.cpp b/src/core/NEON/kernels/NEWinogradLayerKernel.cpp index eaf77e6253..d17630a92e 100644 --- a/src/core/NEON/kernels/NEWinogradLayerKernel.cpp +++ b/src/core/NEON/kernels/NEWinogradLayerKernel.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017 ARM Limited. + * Copyright (c) 2017, 2018 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -29,9 +29,12 @@ #include "arm_compute/core/TensorInfo.h" #include "support/ToolchainSupport.h" -#include "src/core/NEON/kernels/winograd/winograd_shim_nchw.hpp" +#include "src/core/NEON/kernels/winograd/winograd_gemm.hpp" -using T = winograd_shim_nchw::Winograd2x2_3x3GEMM<float, float>; +namespace +{ +using T = winograd::Winograd2x2_3x3GEMM<float, float>; +} // namespace namespace arm_compute { @@ -50,16 +53,6 @@ Winograd3x3F32::~Winograd3x3F32() { } -void Winograd3x3F32::nchw2nhwc(const Tensor4DShape &input_shape, const PaddingType padding_type, void *working_space, const void *const input) -{ - _pimpl->convolver.nchw2nhwc(input_shape, padding_type, working_space, reinterpret_cast<const float *>(input)); -} - -void Winograd3x3F32::nhwc2nchw(const Tensor4DShape &input_shape, const PaddingType padding_type, void *working_space, void *const output) -{ - _pimpl->convolver.nhwc2nchw(input_shape, padding_type, working_space, reinterpret_cast<float *const>(output)); -} - void Winograd3x3F32::transform_weights(const void *const kernel, void *transform_working_space) { _pimpl->convolver.transform_weights(reinterpret_cast<const float *>(kernel), transform_working_space); @@ -82,11 +75,6 @@ void Winograd3x3F32::reshape_output(const Tensor4DShape &input_shape, const Padd #endif /* __aarch64__ */ } -std::pair<void *, void *> Winograd3x3F32::get_nhwc_ptrs(const Tensor4DShape &input_shape, const PaddingType padding_type, void *working_space) -{ - return _pimpl->convolver.get_nhwc_ptrs(input_shape, padding_type, working_space); -} - Winograd3x3F32::Winograd3x3F32(const KernelShape &kernel_shape, const Tensor4DShape input_shape, const PaddingType padding_type, void *kernel_storage) : _pimpl(support::cpp14::make_unique<Private>(kernel_shape, input_shape, padding_type, kernel_storage)) { diff --git a/src/runtime/NEON/functions/NEWinogradLayer.cpp b/src/runtime/NEON/functions/NEWinogradLayer.cpp index 800153e8b1..21f298ca25 100644 --- a/src/runtime/NEON/functions/NEWinogradLayer.cpp +++ b/src/runtime/NEON/functions/NEWinogradLayer.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017 ARM Limited. + * Copyright (c) 2017, 2018 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -125,6 +125,13 @@ void NEWinogradLayer::configure(const ITensor *input, const ITensor *weights, co // configure the kernel to transform the input tensor from NCHW -> NHWC _permute_input.configure(input, &_input_nhwc, PermutationVector(2U, 0U, 1U)); + // configure and allocate dst tensor to be used to convert from winograd domain to spatial domain when calling to reshape_output() + TensorInfo info(TensorShape(_output->info()->dimension(2), _output->info()->dimension(0), + _output->info()->dimension(1), _output->info()->dimension(3)), + 1, _output->info()->data_type()); + _output_nhwc.allocator()->init(info); + + _output_nhwc.allocator()->allocate(); _weights_hwio.allocator()->allocate(); _input_nhwc.allocator()->allocate(); } @@ -145,9 +152,6 @@ void NEWinogradLayer::run() //Bring channels to the front as Winograd code expects the tensor to be in the format NHWC _permute_input.run(); - //Get ptrs into the workspace - std::pair<void *, void *> nhwc_ptrs = _conv->get_nhwc_ptrs(in_shape, padding, _workspace.buffer()); - //Setup matrices ptrs and transfor the input tensor to the appropriate form before running GEMM. _conv->reshape_input(in_shape, padding, reinterpret_cast<float *>(_input_nhwc.buffer()), _workspace.buffer()); @@ -155,21 +159,10 @@ void NEWinogradLayer::run() NEScheduler::get().schedule(&_winograd_kernel, Window::DimX); //Transform the output to the appropriate form - _conv->reshape_output(in_shape, padding, nhwc_ptrs.first); - - const unsigned int out_width = _output->info()->dimension(0); - const unsigned int out_height = _output->info()->dimension(1); - const unsigned int out_channels = _output->info()->dimension(2); - const unsigned int out_batches = _output->info()->dimension(3); - - // We create a temporary tensor with the results in the workspace so that the we can run a function to reorder from NHWC -> NCHW - Tensor output_nhwc; - TensorInfo info(TensorShape(out_channels, out_width, out_height, out_batches), 1, _output->info()->data_type()); - output_nhwc.allocator()->init(info); - output_nhwc.allocator()->import_memory(Memory(static_cast<uint8_t *>(nhwc_ptrs.first))); + _conv->reshape_output(in_shape, padding, reinterpret_cast<float *>(_output_nhwc.buffer())); // Reorder the convoluted output to ACL's ordering NCHW - _permute_output.configure(&output_nhwc, _output, PermutationVector(1U, 2U, 0U)); + _permute_output.configure(&_output_nhwc, _output, PermutationVector(1U, 2U, 0U)); _permute_output.run(); _memory_group.release(); |