| /* |
| * Copyright (c) 2017-2018 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/runtime/NEON/functions/NEWinogradConvolutionLayer.h" |
| |
| #include "arm_compute/core/Error.h" |
| #include "arm_compute/core/Utils.h" |
| #include "arm_compute/core/Validate.h" |
| #include "arm_compute/core/Validate.h" |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| #include "arm_compute/runtime/NEON/AssemblyHelper.h" |
| #include "arm_compute/runtime/NEON/NEScheduler.h" |
| #include "support/ToolchainSupport.h" |
| |
| #include "arm_compute/core/NEON/kernels/NEWinogradConvolutionLayerKernel.h" |
| |
| #include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp" |
| |
| namespace arm_compute |
| { |
| namespace |
| { |
| inline Tensor4DShape internal_get_input_shape(const arm_compute::ITensor *input) |
| { |
| const DataLayout data_layout = input->info()->data_layout(); |
| const int in_width = input->info()->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH)); |
| const int in_height = input->info()->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT)); |
| const int in_channels = input->info()->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL)); |
| const int in_batches = input->info()->dimension(3); |
| |
| return Tensor4DShape({ in_batches, in_height, in_width, in_channels }); |
| } |
| |
| Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info) |
| { |
| const DataLayout data_layout = input->data_layout(); |
| const unsigned int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); |
| const unsigned int height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); |
| |
| ARM_COMPUTE_UNUSED(output); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->dimension(width_idx) != 3 && weights->dimension(height_idx) != 5, "Only 3 and 5 kernels are supported"); |
| ARM_COMPUTE_RETURN_ERROR_ON(data_layout != DataLayout::NCHW); // COMPMID-1287 |
| ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4); |
| |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.stride().first != 1 || conv_info.stride().second != 1, "Winograd layer only supports unit strides."); |
| |
| if(biases != nullptr) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases); |
| ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1); |
| } |
| |
| return Status{}; |
| } |
| |
| Size2D winograd_output_tile(const Size2D &input_dims, const Size2D &kernel_dims) |
| { |
| Size2D output_tile = Size2D{}; |
| |
| if(kernel_dims == Size2D(3U, 3U)) |
| { |
| output_tile = (input_dims.width <= 4 && input_dims.height <= 4) ? Size2D(2U, 2U) : Size2D(4U, 4U); |
| } |
| else if(kernel_dims == Size2D(5U, 5U)) |
| { |
| output_tile = Size2D(2U, 2U); |
| } |
| |
| return output_tile; |
| } |
| |
| bool check_support_fast_math(const Size2D &output_tile, const Size2D &kernel_size) |
| { |
| // Check if we want to configure a Winograd configuration which requires fast math |
| using WinogradConfiguration = std::pair<std::pair<int, int>, std::pair<int, int>>; |
| |
| std::vector<WinogradConfiguration> fast_math_winograd = |
| { |
| WinogradConfiguration(std::pair<int, int>(2, 2), std::pair<int, int>(5, 5)), |
| WinogradConfiguration(std::pair<int, int>(4, 4), std::pair<int, int>(5, 5)) |
| }; |
| |
| auto p = std::make_pair(std::pair<int, int>(output_tile.width, output_tile.height), |
| std::pair<int, int>(kernel_size.width, kernel_size.height)); |
| |
| return std::find(fast_math_winograd.begin(), fast_math_winograd.end(), p) != fast_math_winograd.end(); |
| } |
| |
| } //namespace |
| |
| NEWinogradConvolutionLayer::NEWinogradConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager) |
| : _memory_group(std::move(memory_manager)), _arm_gemm(nullptr), _gemm_kernel(nullptr), _transform_input_kernel(nullptr), _transform_output_kernel(nullptr), _transform_weights_kernel(nullptr), |
| _activationlayer_function(), _permute_input(), _permute_weights(), _permute_output(), _input_workspace(), _output_workspace(), _kernel_storage(), _input_nhwc(), _output_nhwc(), _weights_hwio(), |
| _workspace(), _input(), _weights(), _output(), _reshaped_kernel(false), _is_activationlayer_enabled(false) |
| { |
| } /* arm_compute */ |
| |
| void NEWinogradConvolutionLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info, |
| bool enable_fast_math) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), weights->info(), (biases != nullptr) ? biases->info() : nullptr, output->info(), conv_info)); |
| |
| // Get indices for the width and height |
| const DataLayout data_layout = input->info()->data_layout(); |
| const unsigned int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); |
| const unsigned int height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); |
| const unsigned int channel_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL); |
| |
| const Size2D input_dims = Size2D(input->info()->dimension(width_idx), input->info()->dimension(height_idx)); |
| const Size2D kernel_size = Size2D(weights->info()->dimension(width_idx), weights->info()->dimension(height_idx)); |
| const Size2D output_tile = winograd_output_tile(input_dims, kernel_size); |
| |
| // Check if the Winograd configuration requires fast math |
| if(!enable_fast_math) |
| { |
| ARM_COMPUTE_ERROR_ON_MSG(check_support_fast_math(output_tile, kernel_size), "This Winograd configuration requires enable_fast_math=true"); |
| } |
| |
| _weights = weights; |
| _input = input; |
| _output = output; |
| |
| std::unique_ptr<INEWinogradLayerTransformInputKernel<float>> transform_input_kernel; |
| std::unique_ptr<INEWinogradLayerTransformWeightsKernel<float>> transform_weights_kernel; |
| std::unique_ptr<INEWinogradLayerTransformOutputKernel<float>> transform_output_kernel; |
| |
| int n_gemms = 0; |
| int N_BLOCK = 0; // Size of block used by GEMM. |
| |
| switch(kernel_size.width) |
| { |
| case 3: |
| { |
| if(input->info()->dimension(width_idx) > 4 && input->info()->dimension(height_idx) > 4) |
| { |
| transform_input_kernel = support::cpp14::make_unique<NEWinogradLayerTransformInputKernel<float, 4, 4, 3, 3>>(); |
| transform_weights_kernel = support::cpp14::make_unique<NEWinogradLayerTransformWeightsKernel<float, 4, 4, 3, 3>>(); |
| transform_output_kernel = support::cpp14::make_unique<NEWinogradLayerTransformOutputKernel<float, 4, 4, 3, 3>>(); |
| n_gemms = NEWinogradLayerBatchedGEMMKernel<float, float, 4, 4, 3, 3>::WinogradBase::N_GEMMS; |
| N_BLOCK = NEWinogradLayerBatchedGEMMKernel<float, float, 4, 4, 3, 3>::WinogradConv::N_BLOCK; |
| } |
| else |
| { |
| transform_input_kernel = support::cpp14::make_unique<NEWinogradLayerTransformInputKernel<float, 2, 2, 3, 3>>(); |
| transform_weights_kernel = support::cpp14::make_unique<NEWinogradLayerTransformWeightsKernel<float, 2, 2, 3, 3>>(); |
| transform_output_kernel = support::cpp14::make_unique<NEWinogradLayerTransformOutputKernel<float, 2, 2, 3, 3>>(); |
| n_gemms = NEWinogradLayerBatchedGEMMKernel<float, float, 2, 2, 3, 3>::WinogradBase::N_GEMMS; |
| N_BLOCK = NEWinogradLayerBatchedGEMMKernel<float, float, 2, 2, 3, 3>::WinogradConv::N_BLOCK; |
| } |
| break; |
| } |
| case 5: |
| { |
| transform_input_kernel = support::cpp14::make_unique<NEWinogradLayerTransformInputKernel<float, 2, 2, 5, 5>>(); |
| transform_weights_kernel = support::cpp14::make_unique<NEWinogradLayerTransformWeightsKernel<float, 2, 2, 5, 5>>(); |
| transform_output_kernel = support::cpp14::make_unique<NEWinogradLayerTransformOutputKernel<float, 2, 2, 5, 5>>(); |
| n_gemms = NEWinogradLayerBatchedGEMMKernel<float, float, 2, 2, 5, 5>::WinogradBase::N_GEMMS; |
| N_BLOCK = NEWinogradLayerBatchedGEMMKernel<float, float, 2, 2, 5, 5>::WinogradConv::N_BLOCK; |
| break; |
| } |
| default: |
| { |
| ARM_COMPUTE_ERROR("Not supported."); |
| break; |
| } |
| } |
| |
| const PaddingType use_padding_type = (conv_info.pad_left() != 0u) ? PADDING_SAME : PADDING_VALID; |
| const bool use_same_padding = use_padding_type == PADDING_SAME; |
| |
| // Get convolved dimensions |
| const int in_channels = input->info()->dimension(channel_idx); |
| const int out_channels = output->info()->dimension(channel_idx); |
| |
| const Tensor4DShape in_shape(internal_get_input_shape(input)); |
| const size_t data_type_size = input->info()->element_size(); |
| // Get the memory required to instantiate a new Winograd operator. |
| constexpr size_t storage_alignment = 64; |
| const size_t kernel_storage_size = transform_weights_kernel->get_weight_storage_size(out_channels, in_channels) * data_type_size; |
| _kernel_storage.allocator()->init(TensorInfo(TensorShape{ (kernel_storage_size + storage_alignment - 1) }, 1, DataType::U8)); |
| _kernel_storage.allocator()->allocate(); |
| // Input storage |
| const size_t input_storage_size = transform_input_kernel->get_input_storage_size(in_shape.n_batches, in_shape.n_channels, in_shape.n_rows, in_shape.n_cols, use_same_padding) * data_type_size; |
| _input_workspace.allocator()->init(TensorInfo(TensorShape{ (input_storage_size + storage_alignment - 1) }, 1, DataType::U8)); |
| _input_workspace.allocator()->allocate(); |
| |
| // Output storage |
| const size_t output_storage_size = transform_output_kernel->get_output_storage_size(in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, out_channels, use_same_padding) * data_type_size; |
| _output_workspace.allocator()->init(TensorInfo(TensorShape{ (output_storage_size + storage_alignment - 1) }, 1, DataType::U8)); |
| _output_workspace.allocator()->allocate(); |
| |
| // 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(); |
| |
| const KernelShape kernel_shape({ out_channels, static_cast<int>(kernel_size.height), static_cast<int>(kernel_size.width), in_channels }); |
| |
| // Configure the InputTransform |
| const int input_matrix_stride = transform_input_kernel->get_matrix_stride(kernel_shape, in_shape, use_padding_type); |
| |
| if(data_layout == DataLayout::NCHW) |
| { |
| // configure the kernel to transform the input tensor from NCHW -> NHWC |
| _permute_input.configure(input, &_input_nhwc, PermutationVector(2U, 0U, 1U)); |
| _input_nhwc.allocator()->allocate(); |
| transform_input_kernel->configure(&_input_nhwc, in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, in_shape.n_channels, use_padding_type, |
| reinterpret_cast<float *>(_input_workspace.buffer()), input_matrix_stride); |
| } |
| else |
| { |
| transform_input_kernel->configure(_input, in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, in_shape.n_channels, use_padding_type, |
| reinterpret_cast<float *>(_input_workspace.buffer()), input_matrix_stride); |
| } |
| |
| // Configure WeightsTransform |
| const int kernel_matrix_stride = transform_weights_kernel->get_matrix_stride(kernel_shape); |
| if(data_layout == DataLayout::NCHW) |
| { |
| // Re-order a weight tensor from [Output feature map x Input feature map x Height x Width] to [Height x Width x Input feature map x Output feature map] |
| _permute_weights.configure(weights, &_weights_hwio, PermutationVector(3U, 2U, 0U, 1U)); |
| |
| transform_weights_kernel->configure(&_weights_hwio, reinterpret_cast<float *>(_kernel_storage.buffer()), kernel_matrix_stride, out_channels, in_channels); |
| } |
| else |
| { |
| // Re-order a weight tensor from [Output feature map x Input feature map x Height x Width] to [Height x Width x Input feature map x Output feature map] |
| _permute_weights.configure(weights, &_weights_hwio, PermutationVector(3U, 0U, 1U, 2U)); |
| |
| transform_weights_kernel->configure(&_weights_hwio, reinterpret_cast<float *>(_kernel_storage.buffer()), kernel_matrix_stride, out_channels, in_channels); |
| } |
| _weights_hwio.allocator()->allocate(); |
| |
| // Configure OutputTransform |
| //The biases tensor has not been allocated at this point in time, the output transform will add the biases to the final result in the run() method |
| const int output_matrix_stride = transform_output_kernel->get_matrix_stride(kernel_shape, in_shape, use_padding_type); |
| const auto output_shape(transform_output_kernel->get_output_shape(kernel_shape, in_shape, use_padding_type)); |
| |
| if(data_layout == DataLayout::NCHW) |
| { |
| transform_output_kernel->configure(biases, reinterpret_cast<float *>(_output_workspace.buffer()), |
| output_matrix_stride, &_output_nhwc, |
| in_shape.n_batches, output_shape.n_rows, output_shape.n_cols, out_channels); |
| } |
| else |
| { |
| transform_output_kernel->configure(biases, reinterpret_cast<float *>(_output_workspace.buffer()), |
| output_matrix_stride, _output, |
| in_shape.n_batches, output_shape.n_rows, output_shape.n_cols, out_channels); |
| } |
| |
| // Configure GEMM |
| const int tile_rows = iceildiv(output_shape.n_rows, output_tile.height); |
| const int tile_cols = iceildiv(output_shape.n_cols, output_tile.width); |
| const int m = in_shape.n_batches * tile_rows * tile_cols; |
| const int k = in_shape.n_channels; |
| const int n = out_channels; |
| const int input_matrix_row_stride = in_shape.n_channels; |
| const int kernel_matrix_row_stride = roundup(out_channels, N_BLOCK); |
| const int output_matrix_row_stride = kernel_matrix_row_stride; |
| unsigned int num_threads = NEScheduler::get().num_threads(); |
| |
| _arm_gemm = arm_gemm::gemm<float, float>(NEScheduler::get().cpu_info(), m, n, k, 1, n_gemms, false, false, 1.f, 0.f, num_threads, false); |
| _arm_gemm->set_arrays(reinterpret_cast<float *>(_input_workspace.buffer()), input_matrix_row_stride, 0, input_matrix_stride, reinterpret_cast<float *>(_kernel_storage.buffer()), |
| kernel_matrix_row_stride, kernel_matrix_stride, reinterpret_cast<float *>(_output_workspace.buffer()), output_matrix_row_stride, 0, output_matrix_stride); |
| |
| auto acl_gemm_wrapper = support::cpp14::make_unique<NEGEMMAssemblyWrapper<arm_gemm::GemmCommon<float, float>>>(); |
| acl_gemm_wrapper->configure(_arm_gemm.get()); |
| const size_t workspace_size = _arm_gemm->get_working_size(); |
| |
| // Allocate workspace |
| if(workspace_size > 0) |
| { |
| const unsigned int alignment = 4096; |
| // TODO (COMPMID-1248) : Add support for memory manager in NEWinogradConvolutionLayer |
| // Warning : Do not set a memory group in allocate_workspace, should be done under COMPMID-1248 |
| allocate_workspace(workspace_size, _workspace, nullptr, alignment, 1); |
| _arm_gemm->set_working_space(reinterpret_cast<float *>(_workspace.buffer())); |
| } |
| |
| const unsigned int window_size = _arm_gemm->get_window_size(); |
| if(window_size < num_threads) |
| { |
| num_threads = window_size; |
| _arm_gemm->set_nthreads(num_threads); |
| } |
| |
| _gemm_kernel = std::move(acl_gemm_wrapper); |
| |
| // Reorder the convoluted output to ACL's ordering NCHW |
| _permute_output.configure(&_output_nhwc, _output, PermutationVector(1U, 2U, 0U)); |
| |
| _transform_input_kernel = std::move(transform_input_kernel); |
| _transform_weights_kernel = std::move(transform_weights_kernel); |
| _transform_output_kernel = std::move(transform_output_kernel); |
| |
| //Configure Activation Layer |
| _is_activationlayer_enabled = act_info.enabled(); |
| if(data_layout == DataLayout::NCHW && _is_activationlayer_enabled) |
| { |
| _activationlayer_function.configure(_output, nullptr, act_info); |
| } |
| } |
| |
| void NEWinogradConvolutionLayer::run() |
| { |
| const DataLayout data_layout = _input->info()->data_layout(); |
| |
| _memory_group.acquire(); |
| if(!_reshaped_kernel) |
| { |
| _reshaped_kernel = true; |
| _permute_weights.run(); |
| NEScheduler::get().schedule(_transform_weights_kernel.get(), Window::DimX); |
| } |
| |
| if(data_layout == DataLayout::NCHW) |
| { |
| //Bring channels to the front as Winograd code expects the tensor to be in the format NHWC |
| _permute_input.run(); |
| } |
| // Transform input tensor to the winograd domain |
| NEScheduler::get().schedule(_transform_input_kernel.get(), Window::DimX); |
| |
| //Run 16 GEMMs in multiple threads, each kernel runs one or more GEMMs |
| NEScheduler::get().schedule(_gemm_kernel.get(), Window::DimX); |
| |
| // Transform output tensor to the spatial domain |
| NEScheduler::get().schedule(_transform_output_kernel.get(), Window::DimX); |
| |
| if(data_layout == DataLayout::NCHW) |
| { |
| // Reorder the convoluted output to ACL's ordering NCHW |
| _permute_output.run(); |
| } |
| |
| if(_is_activationlayer_enabled) |
| { |
| _activationlayer_function.run(); |
| } |
| _memory_group.release(); |
| } |
| |
| Status NEWinogradConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, |
| const ActivationLayerInfo &act_info, bool enable_fast_math) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output); |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, biases, output, conv_info)); |
| |
| // Get indices for the width and height |
| const size_t idx_width = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH); |
| const size_t idx_height = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT); |
| |
| // Input shape, kernel size and output tile |
| const Size2D input_dims = Size2D(input->dimension(idx_width), input->dimension(idx_height)); |
| const Size2D kernel_size = Size2D(weights->dimension(idx_width), weights->dimension(idx_height)); |
| const Size2D output_tile = winograd_output_tile(input_dims, kernel_size); |
| |
| // Check if the Winograd configuration requires fast math |
| if(!enable_fast_math) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(check_support_fast_math(output_tile, kernel_size), "This Winograd configuration requires enable_fast_math=true"); |
| } |
| |
| const WinogradInfo winograd_info = WinogradInfo(output_tile, |
| kernel_size, |
| input_dims, |
| conv_info, |
| input->data_layout()); |
| |
| // Validate input transform |
| const TensorShape input0_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*input, winograd_info); |
| const TensorInfo input0 = input->clone()->set_tensor_shape(input0_shape); |
| switch(weights->dimension(idx_width)) |
| { |
| case 3: |
| { |
| if(input_dims.width > 4 && input_dims.height > 4) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 4, 4, 3, 3>::validate(input, &input0, winograd_info))); |
| } |
| else |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 2, 2, 3, 3>::validate(input, &input0, winograd_info))); |
| } |
| break; |
| } |
| case 5: |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 2, 2, 5, 5>::validate(input, &input0, winograd_info))); |
| break; |
| } |
| default: |
| { |
| ARM_COMPUTE_RETURN_ERROR_MSG("Only 3x3 and 5x5 kernels supported."); |
| break; |
| } |
| } |
| // Validate filter transform |
| const TensorShape input1_shape = misc::shape_calculator::compute_winograd_filter_transform_shape(*weights, winograd_info); |
| const TensorInfo input1 = weights->clone()->set_tensor_shape(input1_shape); |
| |
| switch(weights->dimension(idx_width)) |
| { |
| case 3: |
| { |
| if(input_dims.width > 4 && input_dims.height > 4) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 4, 4, 3, 3>::validate(weights, &input1, winograd_info))); |
| } |
| else |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 2, 2, 3, 3>::validate(weights, &input1, winograd_info))); |
| } |
| break; |
| } |
| case 5: |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 2, 2, 5, 5>::validate(weights, &input1, winograd_info))); |
| break; |
| } |
| default: |
| { |
| ARM_COMPUTE_RETURN_ERROR_MSG("Only 3x3 and 5x5 kernels supported."); |
| break; |
| } |
| } |
| // Validate batched matrix multiply |
| TensorShape batched_mm_output_shape = input0.tensor_shape(); |
| batched_mm_output_shape[0] = input1.tensor_shape()[0]; |
| const TensorInfo batched_mm_output = input0.clone()->set_tensor_shape(batched_mm_output_shape); |
| switch(weights->dimension(idx_width)) |
| { |
| case 3: |
| { |
| if(input_dims.width > 4 && input_dims.height > 4) |
| { |
| // Validate output transform |
| ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 4, 4, 3, 3>::validate(&batched_mm_output, biases, output, winograd_info))); |
| } |
| else |
| { |
| // Validate output transform |
| ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 2, 2, 3, 3>::validate(&batched_mm_output, biases, output, winograd_info))); |
| } |
| break; |
| } |
| case 5: |
| { |
| // Validate output transform |
| ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 2, 2, 5, 5>::validate(&batched_mm_output, biases, output, winograd_info))); |
| break; |
| } |
| default: |
| { |
| ARM_COMPUTE_RETURN_ERROR_MSG("Only 3x3 and 5x5 kernels supported."); |
| break; |
| } |
| } |
| |
| // Validate Activation Layer |
| if(act_info.enabled()) |
| { |
| NEActivationLayer::validate(output, nullptr, act_info); |
| } |
| return Status{}; |
| } |
| |
| } // namespace arm_compute |