From 900289936c458eff95499e0a0eaba989a27aaa4d Mon Sep 17 00:00:00 2001 From: Manuel Bottini Date: Wed, 30 Jun 2021 18:29:18 +0100 Subject: Port NEIm2ColKernel Resolves: COMPMID-4510 Change-Id: Ia3e588f599449d975dabad4afafb2974dd44d0ad Signed-off-by: Manuel Bottini Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5899 Tested-by: Arm Jenkins Reviewed-by: Michele Di Giorgio Comments-Addressed: Arm Jenkins --- Android.bp | 2 +- .../runtime/NEON/functions/NEFullyConnectedLayer.h | 2 +- .../NEON/functions/NEGEMMConvolutionLayer.h | 13 +- docs/user_guide/release_version_and_change_log.dox | 4 +- filelist.json | 2 +- src/core/NEON/NEKernels.h | 1 - src/core/NEON/kernels/NECol2ImKernel.h | 2 +- src/core/NEON/kernels/NEIm2ColKernel.cpp | 460 --------------------- src/core/NEON/kernels/NEIm2ColKernel.h | 139 ------- src/core/NEON/kernels/NEWeightsReshapeKernel.h | 2 +- src/core/cpu/kernels/CpuIm2ColKernel.cpp | 448 ++++++++++++++++++++ src/core/cpu/kernels/CpuIm2ColKernel.h | 123 ++++++ .../NEON/functions/NEGEMMConvolutionLayer.cpp | 18 +- tests/validation/NEON/Im2Col.cpp | 49 ++- tests/validation/fixtures/Im2ColFixture.h | 91 ++++ 15 files changed, 718 insertions(+), 638 deletions(-) delete mode 100644 src/core/NEON/kernels/NEIm2ColKernel.cpp delete mode 100644 src/core/NEON/kernels/NEIm2ColKernel.h create mode 100644 src/core/cpu/kernels/CpuIm2ColKernel.cpp create mode 100644 src/core/cpu/kernels/CpuIm2ColKernel.h diff --git a/Android.bp b/Android.bp index 670f0697d7..621d013e8b 100644 --- a/Android.bp +++ b/Android.bp @@ -159,7 +159,6 @@ cc_library_static { "src/core/NEON/kernels/NEGEMMLowpReductionKernel.cpp", "src/core/NEON/kernels/NEGatherKernel.cpp", "src/core/NEON/kernels/NEGenerateProposalsLayerKernel.cpp", - "src/core/NEON/kernels/NEIm2ColKernel.cpp", "src/core/NEON/kernels/NEInstanceNormalizationLayerKernel.cpp", "src/core/NEON/kernels/NEL2NormalizeLayerKernel.cpp", "src/core/NEON/kernels/NELogicalKernel.cpp", @@ -275,6 +274,7 @@ cc_library_static { "src/core/cpu/kernels/CpuGemmMatrixAdditionKernel.cpp", "src/core/cpu/kernels/CpuGemmMatrixMultiplyKernel.cpp", "src/core/cpu/kernels/CpuGemmTranspose1xWKernel.cpp", + "src/core/cpu/kernels/CpuIm2ColKernel.cpp", "src/core/cpu/kernels/CpuMulKernel.cpp", "src/core/cpu/kernels/CpuPermuteKernel.cpp", "src/core/cpu/kernels/CpuPool2dKernel.cpp", diff --git a/arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h b/arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h index e409a61ba1..43f1d4cc05 100644 --- a/arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h +++ b/arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h @@ -77,7 +77,7 @@ private: } // namespace weights_transformations /** Basic function to compute a Fully Connected layer. This function calls the following kernels: - * -# @ref NEIm2ColKernel (called when the input comes from a convolutional layer) + * -# @ref cpu::kernels::CpuIm2ColKernel (called when the input comes from a convolutional layer) * -# @ref NETranspose (if @p are_weights_reshaped is set to false and transpose_weights is set to true ) (called once) * -# @ref NEGEMM or @ref NEGEMMLowpMatrixMultiplyCore (if quantized asymmetric) * -# @ref cpu::kernels::CpuGemmMatrixAdditionKernel or @ref NEGEMMLowpOutputStage (if quantized asymmetric) (if @p biases is not equal to nullptr) diff --git a/arm_compute/runtime/NEON/functions/NEGEMMConvolutionLayer.h b/arm_compute/runtime/NEON/functions/NEGEMMConvolutionLayer.h index d334d518e2..655d733bd1 100644 --- a/arm_compute/runtime/NEON/functions/NEGEMMConvolutionLayer.h +++ b/arm_compute/runtime/NEON/functions/NEGEMMConvolutionLayer.h @@ -41,8 +41,14 @@ namespace arm_compute { class ITensor; class NECol2ImKernel; -class NEIm2ColKernel; class NEWeightsReshapeKernel; +namespace cpu +{ +namespace kernels +{ +class CpuIm2ColKernel; +} // namespace kernels +} // namespace cpu /** Function to reshape the weights. This function calls the following kernel: * -# @ref NEWeightsReshapeKernel @@ -152,7 +158,7 @@ private: /** Basic function to compute the convolution layer. This function calls the following kernels/functions: * - * -# @ref NEIm2ColKernel + * -# @ref cpu::kernels::CpuIm2ColKernel * -# @ref NEGEMM (if the data type is BFLOAT16/FP16/FP32) * -# @ref NEGEMMLowpMatrixMultiplyCore (if the data type is QASYMM8/QASYMM8_SIGNED) * -# @ref NEGEMMLowpOutputStage (if the data type is QASYMM8/QASYMM8_SIGNED) @@ -283,12 +289,13 @@ private: IWeightsManager *_weights_manager; NEConvolutionLayerReshapeWeights _reshape_weights; weights_transformations::NEConvolutionLayerReshapeWeightsTransform _reshape_weights_managed; - std::unique_ptr _im2col_kernel; + std::unique_ptr _im2col_kernel; NEGEMM _mm_gemm; NEGEMMLowpMatrixMultiplyCore _mm_gemmlowp; std::unique_ptr _col2im_kernel; NEReshapeLayer _reshape_layer; + const ITensor *_input; const ITensor *_original_weights; const ITensor *_original_output; diff --git a/docs/user_guide/release_version_and_change_log.dox b/docs/user_guide/release_version_and_change_log.dox index 0c8b57ff9f..78c13041ee 100644 --- a/docs/user_guide/release_version_and_change_log.dox +++ b/docs/user_guide/release_version_and_change_log.dox @@ -585,7 +585,7 @@ v20.05 Public major release - Added Bfloat16 support in: - @ref NEWeightsReshapeKernel - @ref NEConvolutionLayerReshapeWeights - - @ref NEIm2ColKernel + - NEIm2ColKernel - NEIm2Col - NEDepthConvertLayerKernel - @ref NEDepthConvertLayer @@ -1362,7 +1362,7 @@ v17.03.1 First Major public release of the sources - @ref NENormalizationLayerKernel / @ref NENormalizationLayer - NETransposeKernel / @ref NETranspose - NELogits1DMaxKernel, NELogits1DShiftExpSumKernel, NELogits1DNormKernel / @ref NESoftmaxLayer - - @ref NEIm2ColKernel, @ref NECol2ImKernel, NEConvolutionLayerWeightsReshapeKernel / @ref NEConvolutionLayer + - NEIm2ColKernel, @ref NECol2ImKernel, NEConvolutionLayerWeightsReshapeKernel / @ref NEConvolutionLayer - NEGEMMMatrixAccumulateBiasesKernel / @ref NEFullyConnectedLayer - @ref NEGEMMLowpMatrixMultiplyKernel / NEGEMMLowp diff --git a/filelist.json b/filelist.json index 7512ac12bd..9562cc7115 100644 --- a/filelist.json +++ b/filelist.json @@ -1330,7 +1330,7 @@ "Im2Col": { "files": { "kernel": [ - "src/core/NEON/kernels/NEIm2ColKernel.cpp" + "src/core/cpu/kernels/CpuIm2ColKernel.cpp" ] } }, diff --git a/src/core/NEON/NEKernels.h b/src/core/NEON/NEKernels.h index 665c8c7fba..69c8d7bebc 100644 --- a/src/core/NEON/NEKernels.h +++ b/src/core/NEON/NEKernels.h @@ -47,7 +47,6 @@ #include "src/core/NEON/kernels/NEGEMMLowpReductionKernel.h" #include "src/core/NEON/kernels/NEGatherKernel.h" #include "src/core/NEON/kernels/NEGenerateProposalsLayerKernel.h" -#include "src/core/NEON/kernels/NEIm2ColKernel.h" #include "src/core/NEON/kernels/NEInstanceNormalizationLayerKernel.h" #include "src/core/NEON/kernels/NEL2NormalizeLayerKernel.h" #include "src/core/NEON/kernels/NELogicalKernel.h" diff --git a/src/core/NEON/kernels/NECol2ImKernel.h b/src/core/NEON/kernels/NECol2ImKernel.h index 397bf5ab17..1976302036 100644 --- a/src/core/NEON/kernels/NECol2ImKernel.h +++ b/src/core/NEON/kernels/NECol2ImKernel.h @@ -34,7 +34,7 @@ class ITensor; /** Kernel to perform col2im reshaping. * - * Rearranges each matrix column into image blocks. It's the inverse operation of @ref NEIm2ColKernel. + * Rearranges each matrix column into image blocks. It's the inverse operation of @ref cpu::kernels::CpuIm2ColKernel. * * For example, a vector of 9 elements can be reshaped to a block(image) of 3x3: * diff --git a/src/core/NEON/kernels/NEIm2ColKernel.cpp b/src/core/NEON/kernels/NEIm2ColKernel.cpp deleted file mode 100644 index a28a77a4fb..0000000000 --- a/src/core/NEON/kernels/NEIm2ColKernel.cpp +++ /dev/null @@ -1,460 +0,0 @@ -/* - * Copyright (c) 2017-2021 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 "src/core/NEON/kernels/NEIm2ColKernel.h" - -#include "arm_compute/core/Error.h" -#include "arm_compute/core/Helpers.h" -#include "arm_compute/core/ITensor.h" -#include "arm_compute/core/Size2D.h" -#include "arm_compute/core/TensorInfo.h" -#include "arm_compute/core/Types.h" -#include "arm_compute/core/Validate.h" -#include "src/core/CPP/Validate.h" -#include "src/core/helpers/AutoConfiguration.h" -#include "src/core/helpers/WindowHelpers.h" - -#include "arm_compute/core/utils/misc/ShapeCalculator.h" - -#include -#include -#include -#include -#include - -using namespace arm_compute; -using namespace misc::shape_calculator; - -namespace -{ -Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, - bool has_bias, const Size2D &dilation, unsigned int num_groups) -{ - ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input); - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::BFLOAT16, DataType::F16, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON(is_data_type_quantized(input->data_type()) && has_bias); - ARM_COMPUTE_RETURN_ERROR_ON((dilation.x() < 1) || (dilation.y() < 1)); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(num_groups > 1, "Number of groups greater than one are not supported on Neon"); - - // Since there's no implicit padding added, check the total input spatial dimensions (with conv paddings) are big enough for the kernel dimensions - const unsigned int width_idx = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH); - const unsigned int height_idx = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT); - const unsigned total_width = input->dimension(width_idx) + conv_info.pad_left() + conv_info.pad_right(); - const unsigned total_height = input->dimension(height_idx) + conv_info.pad_top() + conv_info.pad_bottom(); - ARM_COMPUTE_RETURN_ERROR_ON((total_width < kernel_dims.width) || (total_height < kernel_dims.height)); - - if(output->total_size() > 0) - { - TensorInfo expected_output = output->clone()->set_tensor_shape(compute_im2col_conv_shape(input, kernel_dims, conv_info, has_bias, dilation, false)); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&expected_output, output); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(input, output); - } - - return Status{}; -} - -std::pair validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, - bool has_bias, const Size2D &dilation) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); - - // Output tensor auto initialization if not yet initialized - auto_init_if_empty(*output, input->clone()->set_tensor_shape(compute_im2col_conv_shape(input, kernel_dims, conv_info, has_bias, dilation, false))); - - 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); - const unsigned int channel_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL); - - std::pair convolved_dims = scaled_dimensions(input->dimension(width_idx), input->dimension(height_idx), - kernel_dims.width, kernel_dims.height, - conv_info, dilation); - - Window win = calculate_max_window(*input, Steps()); - win.set(width_idx, Window::Dimension(0, convolved_dims.first, 1)); - win.set(height_idx, Window::Dimension(0, convolved_dims.second, 1)); - win.set(channel_idx, Window::Dimension(0, 1, 1)); - - // The NEIm2ColKernel doesn't need padding so update_window_and_padding() can be skipped - - return std::make_pair(Status{}, win); -} - -template -inline void linearize_volume_nchw(const uint8_t *const in_ptr, - T *out_ptr, - bool has_bias, - int top_left_x, - int top_left_y, - int kernel_width, - int kernel_height, - int kernel_depth, - int input_w, - int input_h, - int input_stride_x, - int input_stride_y, - int input_stride_z, - int pad_value, - int dilation_x, - int dilation_y) -{ - const int kernel_size2 = kernel_width * kernel_height; - const int x_e = top_left_x + kernel_width * dilation_x; - const int y_e = top_left_y + kernel_height * dilation_y; - - // Linearize volume - int d = 0; - // This for loop linearize a volume with 3 slices. This allows: - // 1) to reduce the iterations of the outer for loop "d" - // 2) to have an optimized im2col for the first convolution layer where usually we have 3 IFMs - for(; d <= (kernel_depth - 3); d += 3) - { - for(int y = top_left_y; y < y_e; y += dilation_y) - { - if((y < 0 || y >= input_h) && has_pads) - { - // All the values will be the offset (will be zeros when not quantized) - for(int x = top_left_x; x < x_e; x += dilation_x, ++out_ptr) - { - *(out_ptr + 0 * kernel_size2) = pad_value; - *(out_ptr + 1 * kernel_size2) = pad_value; - *(out_ptr + 2 * kernel_size2) = pad_value; - } - } - else - { - for(int x = top_left_x; x < x_e; x += dilation_x, ++out_ptr) - { - if((x < 0 || x >= input_w) && has_pads) - { - *(out_ptr + 0 * kernel_size2) = pad_value; - *(out_ptr + 1 * kernel_size2) = pad_value; - *(out_ptr + 2 * kernel_size2) = pad_value; - } - else - { - *(out_ptr + 0 * kernel_size2) = *(reinterpret_cast(in_ptr + ((d + 0) * input_stride_z + y * input_stride_y + x * input_stride_x))); - *(out_ptr + 1 * kernel_size2) = *(reinterpret_cast(in_ptr + ((d + 1) * input_stride_z + y * input_stride_y + x * input_stride_x))); - *(out_ptr + 2 * kernel_size2) = *(reinterpret_cast(in_ptr + ((d + 2) * input_stride_z + y * input_stride_y + x * input_stride_x))); - } - } - } - } - out_ptr += 2 * kernel_size2; - } - - // Left over - for(; d < kernel_depth; d++) - { - for(int y = top_left_y; y < y_e; y += dilation_y) - { - if((y < 0 || y >= input_h) && has_pads) - { - // All the values will be the offset (will be zeros when not quantized) - memset(static_cast(out_ptr), pad_value, kernel_width * sizeof(T)); - out_ptr += kernel_width; - } - else - { - for(int x = top_left_x; x < x_e; x += dilation_x, ++out_ptr) - { - if((x < 0 || x >= input_w) && has_pads) - { - *out_ptr = pad_value; - } - else - { - *out_ptr = *(reinterpret_cast(in_ptr + (d * input_stride_z + y * input_stride_y + x * input_stride_x))); - } - } - } - } - } - - // Append 1 if the convolution layer has biases - if(has_bias) - { - *out_ptr = static_cast(1); - } -} - -template -inline void linearize_volume_nhwc(const uint8_t *const in_ptr, - T *out_ptr, - bool has_bias, - int start_x, - int start_y, - int kernel_width, - int kernel_height, - int input_w, - int input_h, - int input_c, - int input_stride_y, - int input_stride_z, - int pad_value, - int dilation_x, - int dilation_y) -{ - const int end_x = start_x + kernel_width * dilation_x; - const int end_y = start_y + kernel_height * dilation_y; - const int pad_quant = kernel_width * input_c; - const int element_size = static_cast(sizeof(T)); - if((start_y >= 0) && (end_y < input_h) && (start_x >= 0) && (end_x < input_w) && (dilation_x == 1) && (input_stride_y == input_c * element_size)) - { - for(int y = start_y; y < end_y; y += dilation_y) - { - //optimized for no dilation and no boundary pixels - memcpy(out_ptr, reinterpret_cast(in_ptr + (y * input_stride_z + start_x * input_stride_y)), input_c * kernel_width * element_size); - out_ptr += input_c * kernel_width; - } - } - else - { - for(int y = start_y; y < end_y; y += dilation_y) - { - if(y < 0 || y >= input_h) - { - memset(static_cast(out_ptr), pad_value, pad_quant * element_size); - out_ptr += pad_quant; - } - else if(dilation_x > 1 || start_x < 0 || end_x >= input_w || input_stride_y != input_c * element_size) - { - for(int x = start_x; x < end_x; x += dilation_x) - { - if(x < 0 || x >= input_w) - { - memset(static_cast(out_ptr), pad_value, input_c * element_size); - out_ptr += input_c; - } - else - { - memcpy(out_ptr, reinterpret_cast(in_ptr + (y * input_stride_z + x * input_stride_y)), input_c * element_size); - out_ptr += input_c; - } - } - } - else - { - //optimized for no dilation and no boundary pixels - memcpy(out_ptr, reinterpret_cast(in_ptr + (y * input_stride_z + start_x * input_stride_y)), input_c * kernel_width * element_size); - out_ptr += input_c * kernel_width; - } - } - } - // Append 1 if the convolution layer has biases - if(has_bias) - { - *out_ptr = static_cast(1); - } -} -} // namespace - -template -void NEIm2ColKernel::run_im2col(const Window &window) -{ - ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); - ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window); - - 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 int input_w = _input->info()->dimension(width_idx); - const int input_h = _input->info()->dimension(height_idx); - const int input_c = _input->info()->dimension(channel_idx); - const int input_stride_x = _input->info()->strides_in_bytes().x(); - const int input_stride_y = _input->info()->strides_in_bytes().y(); - const int input_stride_z = _input->info()->strides_in_bytes().z(); - const int pad_left = _conv_info.pad_left(); - const int pad_top = _conv_info.pad_top(); - const int stride_x = _conv_info.stride().first; - const int stride_y = _conv_info.stride().second; - const int pad_value = is_data_type_quantized(_input->info()->data_type()) ? _input->info()->quantization_info().uniform().offset : 0; - - Window window_in_out(window); - // The first three dimensions of the input and output are increased by the inner loops - window_in_out.set(Window::DimX, Window::Dimension(0, 0, 0)); - window_in_out.set(Window::DimY, Window::Dimension(0, 0, 0)); - window_in_out.set(Window::DimZ, Window::Dimension(0, 0, 0)); - - // Create iterators - Iterator in(_input, window_in_out); - Iterator out(_output, window_in_out); - - execute_window_loop(window, [&](const Coordinates & id) - { - const int start_w = id[width_idx] * stride_x - pad_left; - const int start_h = id[height_idx] * stride_y - pad_top; - - // Get pointers - const uint8_t *const input_ptr = in.ptr(); - auto output_ptr = reinterpret_cast(out.ptr() + (id[width_idx] + id[height_idx] * _convolved_dims.first) * _output->info()->strides_in_bytes().y()); - - // Linearize volume - if(is_nchw) - { - linearize_volume_nchw(input_ptr, - output_ptr, - _has_bias, - start_w, - start_h, - _kernel_width, - _kernel_height, - input_c, - input_w, - input_h, - input_stride_x, - input_stride_y, - input_stride_z, - pad_value, - _dilation.x(), - _dilation.y()); - } - else - { - linearize_volume_nhwc(input_ptr, - output_ptr, - _has_bias, - start_w, - start_h, - _kernel_width, - _kernel_height, - input_w, - input_h, - input_c, - input_stride_y, - input_stride_z, - pad_value, - _dilation.x(), - _dilation.y()); - } - }, - in, out); -} - -NEIm2ColKernel::NEIm2ColKernel() - : _func(), _input(nullptr), _output(nullptr), _convolved_dims(), _conv_info(), _kernel_width(0), _kernel_height(0), _has_bias(false), _dilation(1U, 1U), _data_layout(DataLayout::UNKNOWN) -{ -} - -void NEIm2ColKernel::configure(const ITensor *input, ITensor *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, - bool has_bias, const Size2D &dilation, unsigned int num_groups) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), kernel_dims, conv_info, has_bias, dilation, num_groups)); - ARM_COMPUTE_UNUSED(num_groups); - - _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); - - _input = input; - _output = output; - _conv_info = conv_info; - _kernel_width = kernel_dims.width; - _kernel_height = kernel_dims.height; - _dilation = dilation; - _convolved_dims = scaled_dimensions(input->info()->dimension(width_idx), input->info()->dimension(height_idx), - _kernel_width, _kernel_height, - _conv_info, _dilation); - _has_bias = has_bias; - - if(_data_layout == DataLayout::NCHW) - { - switch(_input->info()->data_type()) - { - case DataType::F32: - _func = (!conv_info.has_padding()) ? &NEIm2ColKernel::run_im2col : &NEIm2ColKernel::run_im2col; - break; -#if defined(__ARM_FEATURE_BF16_VECTOR_ARITHMETIC) || defined(ARM_COMPUTE_FORCE_BF16) - case DataType::BFLOAT16: - _func = (!conv_info.has_padding()) ? &NEIm2ColKernel::run_im2col : &NEIm2ColKernel::run_im2col; - break; -#endif /* defined(__ARM_FEATURE_BF16_VECTOR_ARITHMETIC) || defined(ARM_COMPUTE_FORCE_BF16) */ -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC - case DataType::F16: - _func = (!conv_info.has_padding()) ? &NEIm2ColKernel::run_im2col : &NEIm2ColKernel::run_im2col; - break; -#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ - case DataType::QASYMM8_SIGNED: - case DataType::QASYMM8: - _func = (!conv_info.has_padding()) ? &NEIm2ColKernel::run_im2col : &NEIm2ColKernel::run_im2col; - break; - default: - ARM_COMPUTE_ERROR("Data type not supported"); - break; - } - } - else - { - switch(_input->info()->data_type()) - { - case DataType::F32: - _func = (!conv_info.has_padding()) ? &NEIm2ColKernel::run_im2col : &NEIm2ColKernel::run_im2col; - break; -#if defined(__ARM_FEATURE_BF16_VECTOR_ARITHMETIC) || defined(ARM_COMPUTE_FORCE_BF16) - case DataType::BFLOAT16: - _func = (!conv_info.has_padding()) ? &NEIm2ColKernel::run_im2col : &NEIm2ColKernel::run_im2col; - break; -#endif /* defined(__ARM_FEATURE_BF16_VECTOR_ARITHMETIC) || defined(ARM_COMPUTE_FORCE_BF16) */ -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC - case DataType::F16: - _func = (!conv_info.has_padding()) ? &NEIm2ColKernel::run_im2col : &NEIm2ColKernel::run_im2col; - break; -#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ - case DataType::QASYMM8: - _func = (!conv_info.has_padding()) ? &NEIm2ColKernel::run_im2col : &NEIm2ColKernel::run_im2col; - break; - case DataType::QASYMM8_SIGNED: - _func = (!conv_info.has_padding()) ? &NEIm2ColKernel::run_im2col : &NEIm2ColKernel::run_im2col; - break; - default: - ARM_COMPUTE_ERROR("Data type not supported"); - break; - } - } - - // Configure kernel window - auto win_config = validate_and_configure_window(input->info(), output->info(), kernel_dims, conv_info, has_bias, dilation); - ARM_COMPUTE_ERROR_THROW_ON(win_config.first); - INEKernel::configure(win_config.second); -} - -Status NEIm2ColKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, - bool has_bias, const Size2D &dilation, unsigned int num_groups) -{ - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, kernel_dims, conv_info, has_bias, dilation, num_groups)); - ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output->clone().get(), kernel_dims, conv_info, has_bias, dilation).first); - return Status{}; -} - -void NEIm2ColKernel::run(const Window &window, const ThreadInfo &info) -{ - ARM_COMPUTE_UNUSED(info); - ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); - ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window); - - (this->*_func)(window); -} diff --git a/src/core/NEON/kernels/NEIm2ColKernel.h b/src/core/NEON/kernels/NEIm2ColKernel.h deleted file mode 100644 index 6c1c631d82..0000000000 --- a/src/core/NEON/kernels/NEIm2ColKernel.h +++ /dev/null @@ -1,139 +0,0 @@ -/* - * Copyright (c) 2017-2020 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. - */ -#ifndef ARM_COMPUTE_NEIM2COLKERNEL_H -#define ARM_COMPUTE_NEIM2COLKERNEL_H - -#include "src/core/NEON/INEKernel.h" - -namespace arm_compute -{ -class ITensor; -class Size2D; - -/** Interface for the im2col reshape kernel. - * - * Rearranges image blocks into columns. It is used to strip out each convolution block to a single column. - * It is used to transform a convolution to a plain matrix multiplication. - * - * For example taking into account the image below and assuming 3x3 image blocks with stride of 1 we have: - * - * @f[ - * \left( \begin{array}{cccc} - * a00 & a01 & a02 & a03 \\ - * a10 & a11 & a12 & a13 \\ - * a20 & a21 & a22 & a23 \\ - * a30 & a31 & a32 & a33 \\ - * \end{array} \right) - * \rightarrow - * \left( \begin{array}{ccccccccc} - * a00 & a01 & a02 & a10 & a11 & a12 & a20 & a21 & a22 \\ - * a01 & a02 & a03 & a11 & a12 & a13 & a21 & a22 & a23 \\ - * a10 & a11 & a12 & a20 & a21 & a22 & a30 & a31 & a32 \\ - * a11 & a12 & a13 & a21 & a22 & a23 & a31 & a32 & a33 \\ - * \end{array} \right) - * @f] - */ -class NEIm2ColKernel : public INEKernel -{ -public: - const char *name() const override - { - return "NEIm2ColKernel"; - } - /** Default constructor */ - NEIm2ColKernel(); - /** Prevent instances of this class from being copied (As this class contains pointers) */ - NEIm2ColKernel(const NEIm2ColKernel &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers) */ - NEIm2ColKernel &operator=(const NEIm2ColKernel &) = delete; - /** Allow instances of this class to be moved */ - NEIm2ColKernel(NEIm2ColKernel &&) = default; - /** Allow instances of this class to be moved */ - NEIm2ColKernel &operator=(NEIm2ColKernel &&) = default; - /** Default destructor */ - ~NEIm2ColKernel() = default; - - /** Set the input and output of the kernel. - * - * @param[in] input The input tensor to convert. 3 lower dimensions represent a single input [width, height, IFM], - * while every optional dimension from 4 and above represent a batch of inputs. - * Data types supported: QASYMM8/QASYMM8_SIGNED/BFLOAT16/F16/F32 - * Note: QASYMM8/QASYMM8_SIGNED works only for has_bias = false - * @param[out] output The output tensor. Data types supported: Same as @p input - * @param[in] kernel_dims The kernel dimensions (width and height). - * @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo. - * @param[in] has_bias In case biases are provided expands the matrix with 1. - * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). - * @param[in] num_groups (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is not supported - */ - void configure(const ITensor *input, ITensor *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, - bool has_bias, const Size2D &dilation = Size2D(1U, 1U), unsigned int num_groups = 1); - /** Static function to check if given info will lead to a valid configuration of @ref NEIm2ColKernel - * - * @param[in] input The input tensor to convert. 3 lower dimensions represent a single input [width, height, IFM], - * while every optional dimension from 4 and above represent a batch of inputs. - * Data types supported: QASYMM8/QASYMM8_SIGNED/BFLOAT16/F16/F32 - * Note: QASYMM8/QASYMM8_SIGNED works only for has_bias = false - * @param[in] output The output tensor. Data types supported: Same as @p input - * @param[in] kernel_dims The kernel dimensions (width and height). - * @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo. - * @param[in] has_bias In case biases are provided expands the matrix with 1. - * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). - * @param[in] num_groups (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is not supported - * - * @return a status - */ - static Status validate(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, - bool has_bias, const Size2D &dilation = Size2D(1U, 1U), unsigned int num_groups = 1); - - // Inherited methods overridden: - void run(const Window &window, const ThreadInfo &info) override; - -private: - /** Template function to run im2col - * - * @param[in] window Region on which to execute the kernel. (Must be a valid region of the window returned by window()). - */ - template - void run_im2col(const Window &window); - - /** Common signature for all the specialised im2col functions - * - * @param[in] window Region on which to execute the kernel. - */ - using Im2ColFunctionPtr = void (NEIm2ColKernel::*)(const Window &window); - - Im2ColFunctionPtr _func; - const ITensor *_input; - ITensor *_output; - std::pair _convolved_dims; - PadStrideInfo _conv_info; - unsigned int _kernel_width; - unsigned int _kernel_height; - bool _has_bias; - Size2D _dilation; - DataLayout _data_layout; -}; -} // namespace arm_compute -#endif /*ARM_COMPUTE_NEIM2COLKERNEL_H */ diff --git a/src/core/NEON/kernels/NEWeightsReshapeKernel.h b/src/core/NEON/kernels/NEWeightsReshapeKernel.h index 76eca9fe86..5701c84cac 100644 --- a/src/core/NEON/kernels/NEWeightsReshapeKernel.h +++ b/src/core/NEON/kernels/NEWeightsReshapeKernel.h @@ -33,7 +33,7 @@ class ITensor; /** Kernel to perform reshaping on the weights used by convolution and locally connected layer * * Rearranges each 3-dimensional kernel to a single row leading to a matrix with linearized kernels. - * In combination with the @ref NEIm2ColKernel can transform a convolution to a matrix multiplication. + * In combination with the @ref cpu::kernels::CpuIm2ColKernel can transform a convolution to a matrix multiplication. * * For example assuming a 3D weight kernel of 3x3 dimensions and depth of 2 we have: * @f[ diff --git a/src/core/cpu/kernels/CpuIm2ColKernel.cpp b/src/core/cpu/kernels/CpuIm2ColKernel.cpp new file mode 100644 index 0000000000..a5dbcc29c8 --- /dev/null +++ b/src/core/cpu/kernels/CpuIm2ColKernel.cpp @@ -0,0 +1,448 @@ +/* + * Copyright (c) 2017-2021 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 "src/core/cpu/kernels/CpuIm2ColKernel.h" + +#include "arm_compute/core/Error.h" +#include "arm_compute/core/Helpers.h" +#include "arm_compute/core/ITensor.h" +#include "arm_compute/core/Size2D.h" +#include "arm_compute/core/TensorInfo.h" +#include "arm_compute/core/Types.h" +#include "arm_compute/core/Validate.h" +#include "src/core/CPP/Validate.h" +#include "src/core/helpers/AutoConfiguration.h" +#include "src/core/helpers/WindowHelpers.h" + +#include "arm_compute/core/utils/misc/ShapeCalculator.h" + +#include +#include +#include +#include +#include + +namespace arm_compute +{ +using namespace misc::shape_calculator; +namespace cpu +{ +namespace kernels +{ +namespace +{ +Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, + bool has_bias, const Size2D &dilation, unsigned int num_groups) +{ + ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input); + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::BFLOAT16, DataType::F16, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON(is_data_type_quantized(input->data_type()) && has_bias); + ARM_COMPUTE_RETURN_ERROR_ON((dilation.x() < 1) || (dilation.y() < 1)); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(num_groups > 1, "Number of groups greater than one are not supported on Neon"); + + // Since there's no implicit padding added, check the total input spatial dimensions (with conv paddings) are big enough for the kernel dimensions + const unsigned int width_idx = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH); + const unsigned int height_idx = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT); + const unsigned total_width = input->dimension(width_idx) + conv_info.pad_left() + conv_info.pad_right(); + const unsigned total_height = input->dimension(height_idx) + conv_info.pad_top() + conv_info.pad_bottom(); + ARM_COMPUTE_RETURN_ERROR_ON((total_width < kernel_dims.width) || (total_height < kernel_dims.height)); + + if(output->total_size() > 0) + { + TensorInfo expected_output = output->clone()->set_tensor_shape(compute_im2col_conv_shape(input, kernel_dims, conv_info, has_bias, dilation, false)); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&expected_output, output); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(input, output); + } + + return Status{}; +} + +template +inline void linearize_volume_nchw(const uint8_t *const in_ptr, + T *out_ptr, + bool has_bias, + int top_left_x, + int top_left_y, + int kernel_width, + int kernel_height, + int kernel_depth, + int input_w, + int input_h, + int input_stride_x, + int input_stride_y, + int input_stride_z, + int pad_value, + int dilation_x, + int dilation_y) +{ + const int kernel_size2 = kernel_width * kernel_height; + const int x_e = top_left_x + kernel_width * dilation_x; + const int y_e = top_left_y + kernel_height * dilation_y; + + // Linearize volume + int d = 0; + // This for loop linearize a volume with 3 slices. This allows: + // 1) to reduce the iterations of the outer for loop "d" + // 2) to have an optimized im2col for the first convolution layer where usually we have 3 IFMs + for(; d <= (kernel_depth - 3); d += 3) + { + for(int y = top_left_y; y < y_e; y += dilation_y) + { + if((y < 0 || y >= input_h) && has_pads) + { + // All the values will be the offset (will be zeros when not quantized) + for(int x = top_left_x; x < x_e; x += dilation_x, ++out_ptr) + { + *(out_ptr + 0 * kernel_size2) = pad_value; + *(out_ptr + 1 * kernel_size2) = pad_value; + *(out_ptr + 2 * kernel_size2) = pad_value; + } + } + else + { + for(int x = top_left_x; x < x_e; x += dilation_x, ++out_ptr) + { + if((x < 0 || x >= input_w) && has_pads) + { + *(out_ptr + 0 * kernel_size2) = pad_value; + *(out_ptr + 1 * kernel_size2) = pad_value; + *(out_ptr + 2 * kernel_size2) = pad_value; + } + else + { + *(out_ptr + 0 * kernel_size2) = *(reinterpret_cast(in_ptr + ((d + 0) * input_stride_z + y * input_stride_y + x * input_stride_x))); + *(out_ptr + 1 * kernel_size2) = *(reinterpret_cast(in_ptr + ((d + 1) * input_stride_z + y * input_stride_y + x * input_stride_x))); + *(out_ptr + 2 * kernel_size2) = *(reinterpret_cast(in_ptr + ((d + 2) * input_stride_z + y * input_stride_y + x * input_stride_x))); + } + } + } + } + out_ptr += 2 * kernel_size2; + } + + // Left over + for(; d < kernel_depth; d++) + { + for(int y = top_left_y; y < y_e; y += dilation_y) + { + if((y < 0 || y >= input_h) && has_pads) + { + // All the values will be the offset (will be zeros when not quantized) + memset(static_cast(out_ptr), pad_value, kernel_width * sizeof(T)); + out_ptr += kernel_width; + } + else + { + for(int x = top_left_x; x < x_e; x += dilation_x, ++out_ptr) + { + if((x < 0 || x >= input_w) && has_pads) + { + *out_ptr = pad_value; + } + else + { + *out_ptr = *(reinterpret_cast(in_ptr + (d * input_stride_z + y * input_stride_y + x * input_stride_x))); + } + } + } + } + } + + // Append 1 if the convolution layer has biases + if(has_bias) + { + *out_ptr = static_cast(1); + } +} + +template +inline void linearize_volume_nhwc(const uint8_t *const in_ptr, + T *out_ptr, + bool has_bias, + int start_x, + int start_y, + int kernel_width, + int kernel_height, + int input_w, + int input_h, + int input_c, + int input_stride_y, + int input_stride_z, + int pad_value, + int dilation_x, + int dilation_y) +{ + const int end_x = start_x + kernel_width * dilation_x; + const int end_y = start_y + kernel_height * dilation_y; + const int pad_quant = kernel_width * input_c; + const int element_size = static_cast(sizeof(T)); + if((start_y >= 0) && (end_y < input_h) && (start_x >= 0) && (end_x < input_w) && (dilation_x == 1) && (input_stride_y == input_c * element_size)) + { + for(int y = start_y; y < end_y; y += dilation_y) + { + //optimized for no dilation and no boundary pixels + memcpy(out_ptr, reinterpret_cast(in_ptr + (y * input_stride_z + start_x * input_stride_y)), input_c * kernel_width * element_size); + out_ptr += input_c * kernel_width; + } + } + else + { + for(int y = start_y; y < end_y; y += dilation_y) + { + if(y < 0 || y >= input_h) + { + memset(static_cast(out_ptr), pad_value, pad_quant * element_size); + out_ptr += pad_quant; + } + else if(dilation_x > 1 || start_x < 0 || end_x >= input_w || input_stride_y != input_c * element_size) + { + for(int x = start_x; x < end_x; x += dilation_x) + { + if(x < 0 || x >= input_w) + { + memset(static_cast(out_ptr), pad_value, input_c * element_size); + out_ptr += input_c; + } + else + { + memcpy(out_ptr, reinterpret_cast(in_ptr + (y * input_stride_z + x * input_stride_y)), input_c * element_size); + out_ptr += input_c; + } + } + } + else + { + //optimized for no dilation and no boundary pixels + memcpy(out_ptr, reinterpret_cast(in_ptr + (y * input_stride_z + start_x * input_stride_y)), input_c * kernel_width * element_size); + out_ptr += input_c * kernel_width; + } + } + } + // Append 1 if the convolution layer has biases + if(has_bias) + { + *out_ptr = static_cast(1); + } +} +} // namespace + +template +void CpuIm2ColKernel::run_im2col(const ITensor *src, ITensor *dst, const Window &window) +{ + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICpuKernel::window(), window); + + 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 int input_w = src->info()->dimension(width_idx); + const int input_h = src->info()->dimension(height_idx); + const int input_c = src->info()->dimension(channel_idx); + const int input_stride_x = src->info()->strides_in_bytes().x(); + const int input_stride_y = src->info()->strides_in_bytes().y(); + const int input_stride_z = src->info()->strides_in_bytes().z(); + const int pad_left = _conv_info.pad_left(); + const int pad_top = _conv_info.pad_top(); + const int stride_x = _conv_info.stride().first; + const int stride_y = _conv_info.stride().second; + const int pad_value = is_data_type_quantized(src->info()->data_type()) ? src->info()->quantization_info().uniform().offset : 0; + + Window window_in_out(window); + // The first three dimensions of the input and output are increased by the inner loops + window_in_out.set(Window::DimX, Window::Dimension(0, 0, 0)); + window_in_out.set(Window::DimY, Window::Dimension(0, 0, 0)); + window_in_out.set(Window::DimZ, Window::Dimension(0, 0, 0)); + + // Create iterators + Iterator in(src, window_in_out); + Iterator out(dst, window_in_out); + + execute_window_loop(window, [&](const Coordinates & id) + { + const int start_w = id[width_idx] * stride_x - pad_left; + const int start_h = id[height_idx] * stride_y - pad_top; + + // Get pointers + const uint8_t *const input_ptr = in.ptr(); + auto output_ptr = reinterpret_cast(out.ptr() + (id[width_idx] + id[height_idx] * _convolved_dims.first) * dst->info()->strides_in_bytes().y()); + + // Linearize volume + if(is_nchw) + { + linearize_volume_nchw(input_ptr, + output_ptr, + _has_bias, + start_w, + start_h, + _kernel_width, + _kernel_height, + input_c, + input_w, + input_h, + input_stride_x, + input_stride_y, + input_stride_z, + pad_value, + _dilation.x(), + _dilation.y()); + } + else + { + linearize_volume_nhwc(input_ptr, + output_ptr, + _has_bias, + start_w, + start_h, + _kernel_width, + _kernel_height, + input_w, + input_h, + input_c, + input_stride_y, + input_stride_z, + pad_value, + _dilation.x(), + _dilation.y()); + } + }, + in, out); +} + +void CpuIm2ColKernel::configure(ITensorInfo *src, ITensorInfo *dst, const Size2D &kernel_dims, const PadStrideInfo &conv_info, + bool has_bias, const Size2D &dilation, unsigned int num_groups) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, dst, kernel_dims, conv_info, has_bias, dilation, num_groups)); + ARM_COMPUTE_UNUSED(num_groups); + + _data_layout = src->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); + + _conv_info = conv_info; + _kernel_width = kernel_dims.width; + _kernel_height = kernel_dims.height; + _dilation = dilation; + _convolved_dims = scaled_dimensions(src->dimension(width_idx), dst->dimension(height_idx), + _kernel_width, _kernel_height, + _conv_info, _dilation); + _has_bias = has_bias; + + if(_data_layout == DataLayout::NCHW) + { + switch(src->data_type()) + { + case DataType::F32: + _func = (!conv_info.has_padding()) ? &CpuIm2ColKernel::run_im2col : &CpuIm2ColKernel::run_im2col; + break; +#if defined(__ARM_FEATURE_BF16_VECTOR_ARITHMETIC) || defined(ARM_COMPUTE_FORCE_BF16) + case DataType::BFLOAT16: + _func = (!conv_info.has_padding()) ? &CpuIm2ColKernel::run_im2col : &CpuIm2ColKernel::run_im2col; + break; +#endif /* defined(__ARM_FEATURE_BF16_VECTOR_ARITHMETIC) || defined(ARM_COMPUTE_FORCE_BF16) */ +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC + case DataType::F16: + _func = (!conv_info.has_padding()) ? &CpuIm2ColKernel::run_im2col : &CpuIm2ColKernel::run_im2col; + break; +#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ + case DataType::QASYMM8_SIGNED: + case DataType::QASYMM8: + _func = (!conv_info.has_padding()) ? &CpuIm2ColKernel::run_im2col : &CpuIm2ColKernel::run_im2col; + break; + default: + ARM_COMPUTE_ERROR("Data type not supported"); + break; + } + } + else + { + switch(src->data_type()) + { + case DataType::F32: + _func = (!conv_info.has_padding()) ? &CpuIm2ColKernel::run_im2col : &CpuIm2ColKernel::run_im2col; + break; +#if defined(__ARM_FEATURE_BF16_VECTOR_ARITHMETIC) || defined(ARM_COMPUTE_FORCE_BF16) + case DataType::BFLOAT16: + _func = (!conv_info.has_padding()) ? &CpuIm2ColKernel::run_im2col : &CpuIm2ColKernel::run_im2col; + break; +#endif /* defined(__ARM_FEATURE_BF16_VECTOR_ARITHMETIC) || defined(ARM_COMPUTE_FORCE_BF16) */ +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC + case DataType::F16: + _func = (!conv_info.has_padding()) ? &CpuIm2ColKernel::run_im2col : &CpuIm2ColKernel::run_im2col; + break; +#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ + case DataType::QASYMM8: + _func = (!conv_info.has_padding()) ? &CpuIm2ColKernel::run_im2col : &CpuIm2ColKernel::run_im2col; + break; + case DataType::QASYMM8_SIGNED: + _func = (!conv_info.has_padding()) ? &CpuIm2ColKernel::run_im2col : &CpuIm2ColKernel::run_im2col; + break; + default: + ARM_COMPUTE_ERROR("Data type not supported"); + break; + } + } + + // Output tensor auto initialization if not yet initialized + auto_init_if_empty(*dst, src->clone()->set_tensor_shape(compute_im2col_conv_shape(src, kernel_dims, conv_info, has_bias, dilation, false))); + + std::pair convolved_dims = scaled_dimensions(src->dimension(width_idx), src->dimension(height_idx), + kernel_dims.width, kernel_dims.height, + conv_info, dilation); + + Window win = calculate_max_window(*src, Steps()); + win.set(width_idx, Window::Dimension(0, convolved_dims.first, 1)); + win.set(height_idx, Window::Dimension(0, convolved_dims.second, 1)); + win.set(channel_idx, Window::Dimension(0, 1, 1)); + // Configure kernel window + ICpuKernel::configure(win); +} + +Status CpuIm2ColKernel::validate(const ITensorInfo *src, const ITensorInfo *dst, const Size2D &kernel_dims, const PadStrideInfo &conv_info, + bool has_bias, const Size2D &dilation, unsigned int num_groups) +{ + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, dst, kernel_dims, conv_info, has_bias, dilation, num_groups)); + return Status{}; +} + +void CpuIm2ColKernel::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) +{ + ARM_COMPUTE_UNUSED(info); + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICpuKernel::window(), window); + + auto src = tensors.get_const_tensor(TensorType::ACL_SRC); + auto dst = tensors.get_tensor(TensorType::ACL_DST); + (this->*_func)(src, dst, window); +} +const char *CpuIm2ColKernel::name() const +{ + return "CpuIm2ColKernel"; +} +} // namespace kernels +} // namespace cpu +} // namespace arm_compute \ No newline at end of file diff --git a/src/core/cpu/kernels/CpuIm2ColKernel.h b/src/core/cpu/kernels/CpuIm2ColKernel.h new file mode 100644 index 0000000000..4301a237fe --- /dev/null +++ b/src/core/cpu/kernels/CpuIm2ColKernel.h @@ -0,0 +1,123 @@ +/* + * Copyright (c) 2017-2021 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. + */ +#ifndef ARM_COMPUTE_CPU_IM2COL_KERNEL_H +#define ARM_COMPUTE_CPU_IM2COL_KERNEL_H + +#include "arm_compute/core/Size2D.h" +#include "src/core/common/Macros.h" +#include "src/core/cpu/ICpuKernel.h" + +namespace arm_compute +{ +class ITensor; +namespace cpu +{ +namespace kernels +{ +/** Interface for the im2col reshape kernel. + * + * Rearranges image blocks into columns. It is used to strip out each convolution block to a single column. + * It is used to transform a convolution to a plain matrix multiplication. + * + * For example taking into account the image below and assuming 3x3 image blocks with stride of 1 we have: + * + * @f[ + * \left( \begin{array}{cccc} + * a00 & a01 & a02 & a03 \\ + * a10 & a11 & a12 & a13 \\ + * a20 & a21 & a22 & a23 \\ + * a30 & a31 & a32 & a33 \\ + * \end{array} \right) + * \rightarrow + * \left( \begin{array}{ccccccccc} + * a00 & a01 & a02 & a10 & a11 & a12 & a20 & a21 & a22 \\ + * a01 & a02 & a03 & a11 & a12 & a13 & a21 & a22 & a23 \\ + * a10 & a11 & a12 & a20 & a21 & a22 & a30 & a31 & a32 \\ + * a11 & a12 & a13 & a21 & a22 & a23 & a31 & a32 & a33 \\ + * \end{array} \right) + * @f] + */ +class CpuIm2ColKernel : public ICpuKernel +{ +public: + /** Default constructor */ + CpuIm2ColKernel() = default; + ARM_COMPUTE_DISALLOW_COPY_ALLOW_MOVE(CpuIm2ColKernel); + /** Set the input and output of the kernel. + * + * @param[in] src The input tensor info to convert. 3 lower dimensions represent a single input [width, height, IFM], + * while every optional dimension from 4 and above represent a batch of inputs. + * Data types supported: QASYMM8/QASYMM8_SIGNED/BFLOAT16/F16/F32 + * Note: QASYMM8/QASYMM8_SIGNED works only for has_bias = false + * @param[out] dst The output tensor info. Data types supported: Same as @p input + * @param[in] kernel_dims The kernel dimensions (width and height). + * @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo. + * @param[in] has_bias In case biases are provided expands the matrix with 1. + * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). + * @param[in] num_groups (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is not supported + */ + void configure(ITensorInfo *src, ITensorInfo *dst, const Size2D &kernel_dims, const PadStrideInfo &conv_info, + bool has_bias, const Size2D &dilation = Size2D(1U, 1U), unsigned int num_groups = 1); + /** Static function to check if given info will lead to a valid configuration + * + * Similar to CpuIm2ColKernel::configure() + * + * @return a status + */ + static Status validate(const ITensorInfo *src, const ITensorInfo *dst, const Size2D &kernel_dims, const PadStrideInfo &conv_info, + bool has_bias, const Size2D &dilation = Size2D(1U, 1U), unsigned int num_groups = 1); + + // Inherited methods overridden: + void run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) override; + const char *name() const override; + +private: + /** Template function to run im2col + * + * @param[in] src The input tensor info + * @param[out] dst The output tensor info + * @param[in] window Region on which to execute the kernel. (Must be a valid region of the window returned by window()). + */ + template + void run_im2col(const ITensor *src, ITensor *dst, const Window &window); + + /** Common signature for all the specialised im2col functions + * + * @param[in] window Region on which to execute the kernel. + */ + using Im2ColFunctionPtr = void (CpuIm2ColKernel::*)(const ITensor *src, ITensor *dst, const Window &window); + + Im2ColFunctionPtr _func{ nullptr }; + std::pair _convolved_dims{}; + PadStrideInfo _conv_info{}; + unsigned int _kernel_width{ 0 }; + unsigned int _kernel_height{ 0 }; + bool _has_bias{ false }; + Size2D _dilation{ 1U, 1U }; + DataLayout _data_layout{ DataLayout::UNKNOWN }; +}; +} // namespace kernels +} // namespace cpu +} // namespace arm_compute +#endif /*ARM_COMPUTE_CPU_IM2COL_KERNEL_H */ diff --git a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp index f40cbda779..f333364289 100644 --- a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp +++ b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp @@ -31,8 +31,8 @@ #include "arm_compute/runtime/NEON/NEScheduler.h" #include "src/core/NEON/kernels/NECol2ImKernel.h" -#include "src/core/NEON/kernels/NEIm2ColKernel.h" #include "src/core/NEON/kernels/NEWeightsReshapeKernel.h" +#include "src/core/cpu/kernels/CpuIm2ColKernel.h" #include #include @@ -99,7 +99,7 @@ NEGEMMConvolutionLayer::~NEGEMMConvolutionLayer() = default; NEGEMMConvolutionLayer::NEGEMMConvolutionLayer(const std::shared_ptr &memory_manager, IWeightsManager *weights_manager) : _memory_group(memory_manager), _weights_manager(weights_manager), _reshape_weights(), _reshape_weights_managed(), _im2col_kernel(), _mm_gemm(memory_manager), _mm_gemmlowp(memory_manager), - _col2im_kernel(), _reshape_layer(), _original_weights(nullptr), _original_output(nullptr), _im2col_output(), _weights_reshaped(), _gemm_output(), _gemm_output_3d(), _tmp_output(), + _col2im_kernel(), _reshape_layer(), _input(nullptr), _original_weights(nullptr), _original_output(nullptr), _im2col_output(), _weights_reshaped(), _gemm_output(), _gemm_output_3d(), _tmp_output(), _data_layout(DataLayout::NCHW), _skip_im2col(false), _skip_col2im(false), _is_quantized(false), _is_prepared(false) { } @@ -269,6 +269,7 @@ void NEGEMMConvolutionLayer::configure(const ITensor *input, const ITensor *weig const unsigned int kernel_width = weights->info()->dimension(idx_width); const unsigned int kernel_height = weights->info()->dimension(idx_height); + _input = input; _is_prepared = weights_info.retain_internal_weights(); _original_weights = weights; _original_output = output; @@ -332,8 +333,8 @@ void NEGEMMConvolutionLayer::configure(const ITensor *input, const ITensor *weig _memory_group.manage(&_im2col_output); // Configure - _im2col_kernel = std::make_unique(); - _im2col_kernel->configure(input, &_im2col_output, Size2D(kernel_width, kernel_height), conv_info, false, dilation); + _im2col_kernel = std::make_unique(); + _im2col_kernel->configure(input->info(), _im2col_output.info(), Size2D(kernel_width, kernel_height), conv_info, false, dilation); // Update GEMM input gemm_input_to_use = &_im2col_output; @@ -521,7 +522,7 @@ Status NEGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI im2col_reshaped_info = TensorInfo(shape_im2col, 1, data_type); im2col_reshaped_info.set_quantization_info(input->quantization_info()); - ARM_COMPUTE_RETURN_ON_ERROR(NEIm2ColKernel::validate(input, &im2col_reshaped_info, Size2D(kernel_width, kernel_height), conv_info, append_bias, dilation)); + ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuIm2ColKernel::validate(input, &im2col_reshaped_info, Size2D(kernel_width, kernel_height), conv_info, append_bias, dilation)); gemm_input_to_use = &im2col_reshaped_info; } @@ -563,7 +564,12 @@ void NEGEMMConvolutionLayer::run() { // Run input reshaping unsigned int y_dim = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT); - NEScheduler::get().schedule(_im2col_kernel.get(), y_dim); + ITensorPack pack = + { + { TensorType::ACL_SRC, _input }, + { TensorType::ACL_DST, &_im2col_output } + }; + NEScheduler::get().schedule_op(_im2col_kernel.get(), y_dim, _im2col_kernel->window(), pack); } // Handle the case where output has top/bottom padding diff --git a/tests/validation/NEON/Im2Col.cpp b/tests/validation/NEON/Im2Col.cpp index 156957a601..f338675346 100644 --- a/tests/validation/NEON/Im2Col.cpp +++ b/tests/validation/NEON/Im2Col.cpp @@ -22,7 +22,7 @@ * SOFTWARE. */ #include "arm_compute/core/Types.h" -#include "src/core/NEON/kernels/NEIm2ColKernel.h" +#include "src/core/cpu/kernels/CpuIm2ColKernel.h" #include "tests/NEON/Accessor.h" #include "tests/NEON/Helper.h" #include "tests/datasets/ShapeDatasets.h" @@ -57,7 +57,7 @@ const auto conv_args_small = combine(combine(combine(combine(conv_filter TEST_SUITE(NEON) TEST_SUITE(Im2Col) -using NEIm2Col = NESynthetizeFunction; +using CpuIm2Col = NESynthetizeFunctionWithZeroConstantKernelBorder; // *INDENT-OFF* // clang-format off @@ -78,26 +78,26 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip( framework::dataset::make("Expected", { false, false, false, false, true })), input_info, output_info, has_bias, expected) { - bool status = bool(NEIm2Col::validate(&input_info, &output_info, Size2D(3U, 3U), PadStrideInfo(), has_bias)); + bool status = bool(cpu::kernels::CpuIm2ColKernel::validate(&input_info, &output_info, Size2D(3U, 3U), PadStrideInfo(), has_bias)); ARM_COMPUTE_EXPECT(status == expected, framework::LogLevel::ERRORS); } // clang-format on // *INDENT-ON* template -using NEIm2ColFixture = Im2ColValidationFixture; +using CpuIm2ColFixture = Im2ColOpValidationFixture; TEST_SUITE(Float) TEST_SUITE(FP32) -FIXTURE_DATA_TEST_CASE(RunSmall, NEIm2ColFixture, framework::DatasetMode::PRECOMMIT, combine(combine(im2col_shapes, framework::dataset::make("DataType", DataType::F32)), - conv_args_small)) +FIXTURE_DATA_TEST_CASE(RunSmall, CpuIm2ColFixture, framework::DatasetMode::PRECOMMIT, combine(combine(im2col_shapes, framework::dataset::make("DataType", DataType::F32)), + conv_args_small)) { // Validate output validate(Accessor(_target), _reference); } -FIXTURE_DATA_TEST_CASE(RunLarge, NEIm2ColFixture, framework::DatasetMode::NIGHTLY, combine(combine(concat(im2col_shapes, datasets::LargeShapes()), framework::dataset::make("DataType", - DataType::F32)), - conv_args)) +FIXTURE_DATA_TEST_CASE(RunLarge, CpuIm2ColFixture, framework::DatasetMode::NIGHTLY, combine(combine(concat(im2col_shapes, datasets::LargeShapes()), framework::dataset::make("DataType", + DataType::F32)), + conv_args)) { // Validate output validate(Accessor(_target), _reference); @@ -107,15 +107,15 @@ TEST_SUITE_END() // FP32 #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC TEST_SUITE(FP16) -FIXTURE_DATA_TEST_CASE(RunSmall, NEIm2ColFixture, framework::DatasetMode::PRECOMMIT, combine(combine(im2col_shapes, framework::dataset::make("DataType", DataType::F16)), - conv_args_small)) +FIXTURE_DATA_TEST_CASE(RunSmall, CpuIm2ColFixture, framework::DatasetMode::PRECOMMIT, combine(combine(im2col_shapes, framework::dataset::make("DataType", DataType::F16)), + conv_args_small)) { // Validate output validate(Accessor(_target), _reference); } -FIXTURE_DATA_TEST_CASE(RunLarge, NEIm2ColFixture, framework::DatasetMode::NIGHTLY, combine(combine(concat(im2col_shapes, datasets::LargeShapes()), framework::dataset::make("DataType", - DataType::F16)), - conv_args)) +FIXTURE_DATA_TEST_CASE(RunLarge, CpuIm2ColFixture, framework::DatasetMode::NIGHTLY, combine(combine(concat(im2col_shapes, datasets::LargeShapes()), framework::dataset::make("DataType", + DataType::F16)), + conv_args)) { // Validate output validate(Accessor(_target), _reference); @@ -127,15 +127,15 @@ TEST_SUITE_END() // FP16 TEST_SUITE_END() // Float TEST_SUITE(QASYMM8) -FIXTURE_DATA_TEST_CASE(RunSmall, NEIm2ColFixture, framework::DatasetMode::PRECOMMIT, combine(combine(im2col_shapes, framework::dataset::make("DataType", DataType::QASYMM8)), - conv_args_small)) +FIXTURE_DATA_TEST_CASE(RunSmall, CpuIm2ColFixture, framework::DatasetMode::PRECOMMIT, combine(combine(im2col_shapes, framework::dataset::make("DataType", DataType::QASYMM8)), + conv_args_small)) { // Validate output validate(Accessor(_target), _reference); } -FIXTURE_DATA_TEST_CASE(RunLarge, NEIm2ColFixture, framework::DatasetMode::NIGHTLY, combine(combine(concat(im2col_shapes, datasets::LargeShapes()), - framework::dataset::make("DataType", DataType::QASYMM8)), - conv_args)) +FIXTURE_DATA_TEST_CASE(RunLarge, CpuIm2ColFixture, framework::DatasetMode::NIGHTLY, combine(combine(concat(im2col_shapes, datasets::LargeShapes()), + framework::dataset::make("DataType", DataType::QASYMM8)), + conv_args)) { // Validate output validate(Accessor(_target), _reference); @@ -165,8 +165,8 @@ TEST_CASE(PaddedChannelNHWC, framework::DatasetMode::PRECOMMIT) Tensor dst_target = create_tensor(dst_shape, data_type, 1, qinfo); // Configure target function - NEIm2Col im2col_func; - im2col_func.configure(&src_target, &dst_target, spatial_kernel, conv_info, has_bias); + CpuIm2Col im2col_func; + im2col_func.configure(src_target.info(), dst_target.info(), spatial_kernel, conv_info, has_bias); // Extend padding src_target.info()->extend_padding(PaddingSize(3, 5, 9, 1)); @@ -185,8 +185,13 @@ TEST_CASE(PaddedChannelNHWC, framework::DatasetMode::PRECOMMIT) // Fill target source library->fill_tensor_uniform(Accessor(src_target), 0); + ITensorPack pack = + { + { TensorType::ACL_SRC, &src_target }, + { TensorType::ACL_DST, &dst_target } + }; // Run target function - im2col_func.run(); + im2col_func.run(pack); // Calculate Reference SimpleTensor src_ref{ src_shape, data_type, 1, qinfo, data_layout }; diff --git a/tests/validation/fixtures/Im2ColFixture.h b/tests/validation/fixtures/Im2ColFixture.h index b1fbd76eb2..38970116f6 100644 --- a/tests/validation/fixtures/Im2ColFixture.h +++ b/tests/validation/fixtures/Im2ColFixture.h @@ -44,6 +44,97 @@ namespace validation { using namespace arm_compute::misc::shape_calculator; +template +class Im2ColOpValidationFixture : public framework::Fixture +{ +public: + template + void setup(TensorShape input_shape, DataType data_type, const Size2D &kernel_dims, const PadStrideInfo &conv_info, const QuantizationInfo &quant_info, const DataLayout &data_layout, + unsigned int num_groups) + { + _kernel_dims = kernel_dims; + _conv_info = conv_info; + _quant_info = quant_info; + _data_layout = data_layout; + _has_bias = data_type != DataType::QASYMM8; + _num_groups = num_groups; + + if(_data_layout == DataLayout::NHWC) + { + permute(input_shape, PermutationVector(2U, 0U, 1U)); + } + + TensorInfo input_info(input_shape, 1, data_type); + input_info.set_data_layout(_data_layout); + + const TensorShape output_shape = compute_im2col_conv_shape(&input_info, _kernel_dims, _conv_info, _has_bias, Size2D(1U, 1U), batch_size_on_z && _num_groups == 1, _num_groups); + _target = compute_target(input_shape, output_shape, data_type); + + compute_reference(input_shape, output_shape, data_type); + } + +protected: + template + void fill(U &&tensor) + { + library->fill_tensor_uniform(tensor, 0); + } + + TensorType compute_target(const TensorShape &input_shape, const TensorShape &output_shape, DataType data_type) + { + // Create tensors + TensorType src = create_tensor(input_shape, data_type, 1, _quant_info, _data_layout); + TensorType dst = create_tensor(output_shape, data_type, 1, _quant_info); + + // Create and configure function + FunctionType im2col_func; + im2col_func.configure(src.info(), dst.info(), _kernel_dims, _conv_info, _has_bias, Size2D(1U, 1U), _num_groups); + + ARM_COMPUTE_ASSERT(src.info()->is_resizable()); + ARM_COMPUTE_ASSERT(dst.info()->is_resizable()); + + // Allocate tensors + src.allocator()->allocate(); + dst.allocator()->allocate(); + + ARM_COMPUTE_ASSERT(!src.info()->is_resizable()); + ARM_COMPUTE_ASSERT(!dst.info()->is_resizable()); + + // Fill tensors + fill(AccessorType(src)); + + arm_compute::ITensorPack pack = + { + { arm_compute::TensorType::ACL_SRC, &src }, + { arm_compute::TensorType::ACL_DST, &dst } + }; + // Compute function + im2col_func.run(pack); + + return dst; + } + + void compute_reference(const TensorShape &input_shape, const TensorShape &output_shape, DataType data_type) + { + // Create reference + SimpleTensor src{ input_shape, data_type, 1, _quant_info, _data_layout }; + _reference = SimpleTensor(output_shape, data_type, 1, _quant_info, DataLayout::NCHW); + + // Fill reference + fill(src); + + reference::im2col(src, _reference, _kernel_dims, _conv_info, _has_bias, _num_groups); + } + TensorType _target{}; + SimpleTensor _reference{}; + Size2D _kernel_dims{}; + PadStrideInfo _conv_info{}; + DataLayout _data_layout{}; + QuantizationInfo _quant_info{}; + bool _has_bias{}; + unsigned int _num_groups{}; +}; + template class Im2ColValidationFixture : public framework::Fixture { -- cgit v1.2.1