arm_compute v18.05
diff --git a/src/runtime/NEON/functions/NEWinogradConvolutionLayer.cpp b/src/runtime/NEON/functions/NEWinogradConvolutionLayer.cpp
new file mode 100644
index 0000000..8f2c4c4
--- /dev/null
+++ b/src/runtime/NEON/functions/NEWinogradConvolutionLayer.cpp
@@ -0,0 +1,456 @@
+/*
+ * 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(data_layout != DataLayout::NCHW); // COMPMID-1162
+ 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(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();
+
+ // 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));
+ _weights_hwio.allocator()->allocate();
+
+ // 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();
+
+ 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);
+ transform_input_kernel->configure(reinterpret_cast<float *>(_input_nhwc.buffer()), 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);
+ transform_weights_kernel->configure(&_weights_hwio, reinterpret_cast<float *>(_kernel_storage.buffer()), kernel_matrix_stride, out_channels, in_channels);
+
+ // 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));
+
+ transform_output_kernel->configure(biases, reinterpret_cast<float *>(_output_workspace.buffer()),
+ output_matrix_stride, reinterpret_cast<float *>(_output_nhwc.buffer()),
+ 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;
+ allocate_workspace(workspace_size, _workspace, &_memory_group, 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(_is_activationlayer_enabled)
+ {
+ _activationlayer_function.configure(output, nullptr, act_info);
+ }
+}
+
+void NEWinogradConvolutionLayer::run()
+{
+ _memory_group.acquire();
+ if(!_reshaped_kernel)
+ {
+ _reshaped_kernel = true;
+ _permute_weights.run();
+ NEScheduler::get().schedule(_transform_weights_kernel.get(), Window::DimX);
+ }
+ //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);
+
+ // 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