| /* |
| * Copyright (c) 2017-2022 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/cpu/kernels/CpuDirectConv2dKernel.h" |
| |
| #include "src/core/NEON/kernels/detail/NEDirectConvolutionDetail.h" |
| #include "src/core/NEON/wrapper/wrapper.h" |
| |
| #include "arm_compute/core/Error.h" |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/IAccessWindow.h" |
| #include "arm_compute/core/ITensor.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/core/Utils.h" |
| #include "arm_compute/core/Validate.h" |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| #include "src/core/AccessWindowStatic.h" |
| #include "src/core/CPP/Validate.h" |
| #include "src/core/NEON/NEFixedPoint.h" |
| #include "src/core/helpers/AutoConfiguration.h" |
| #include "src/core/helpers/WindowHelpers.h" |
| |
| #include <algorithm> |
| |
| using namespace arm_compute::detail; |
| |
| namespace arm_compute |
| { |
| namespace cpu |
| { |
| namespace kernels |
| { |
| namespace |
| { |
| Status validate_arguments(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *dst, const PadStrideInfo &conv_info) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, weights, dst); |
| ARM_COMPUTE_RETURN_ERROR_ON(src->data_layout() == DataLayout::UNKNOWN); |
| ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(src); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F16, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, weights); |
| |
| const DataLayout data_layout = src->data_layout(); |
| const int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); |
| const int height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); |
| const int channel_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL); |
| |
| ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(channel_idx) != src->dimension(channel_idx)); |
| ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(width_idx) != weights->dimension(height_idx)); |
| ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4); |
| ARM_COMPUTE_RETURN_ERROR_ON(data_layout == DataLayout::NHWC && src->data_type() != DataType::F32); |
| ARM_COMPUTE_UNUSED(width_idx); |
| // Checks performed when output is configured |
| if(dst->total_size() != 0) |
| { |
| TensorShape output_shape = misc::shape_calculator::compute_deep_convolution_shape(*src, *weights, conv_info); |
| |
| DataType data_type = src->data_type(); |
| |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(dst->tensor_shape(), output_shape); |
| ARM_COMPUTE_RETURN_ERROR_ON(dst->data_type() != data_type); |
| } |
| |
| return Status{}; |
| } |
| |
| std::pair<Status, Window> validate_and_configure_window(ITensorInfo *src, ITensorInfo *dst) |
| { |
| ARM_COMPUTE_ERROR_ON(src->data_layout() == DataLayout::UNKNOWN); |
| ARM_COMPUTE_UNUSED(src); |
| |
| Window win{}; |
| bool window_changed = false; |
| |
| // Configure window without any padding |
| win = calculate_max_window(*dst, Steps()); |
| |
| Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; |
| return std::make_pair(err, win); |
| } |
| |
| bool have_zero_x_internal_padding(ITensorInfo *src, const ITensorInfo *weights) |
| { |
| return (src->padding().left == 0 && weights->padding().left == 0 && src->padding().right == 0 && weights->padding().right == 0); |
| } |
| |
| } // namespace |
| |
| template <typename T> |
| void CpuDirectConv2dKernel::convolve_nhwc_optimized(const Window &window, const ITensor *src, const ITensor *weights, ITensor *dst) |
| { |
| // This function assumes that input and weights have not padding in channel |
| |
| // Declare useful types |
| using vtype = wrapper::traits::neon_bitvector<T, wrapper::traits::BitWidth::W128>; |
| using vector_type = typename vtype::type; |
| using tag_type = typename vtype::tag_type; |
| |
| // Scalar quantities |
| const int element_size = src->info()->element_size(); |
| const int input_stride_w = src->info()->strides_in_bytes().y() / element_size; |
| const int input_stride_h = src->info()->strides_in_bytes().z() / element_size; |
| const int input_stride_n = src->info()->strides_in_bytes()[3] / element_size; |
| const int input_dim_w = src->info()->dimension(1); |
| const int input_dim_h = src->info()->dimension(2); |
| |
| const int output_stride_c = dst->info()->strides_in_bytes().x(); |
| |
| const unsigned int kernel_stride_w = weights->info()->strides_in_bytes().y() / element_size; |
| const unsigned int kernel_stride_h = weights->info()->strides_in_bytes().z() / element_size; |
| const int kernel_dim_w = weights->info()->dimension(1); |
| const int kernel_dim_h = weights->info()->dimension(2); |
| |
| const int conv_pad_top = _conv_info.pad_top(); |
| const int conv_pad_left = _conv_info.pad_left(); |
| const int conv_stride_w = std::get<0>(_conv_info.stride()); |
| const int conv_stride_h = std::get<1>(_conv_info.stride()); |
| |
| // Setup input window for the output iterator |
| Window window_out = window; |
| window_out.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| |
| // Setup input window for the weights iterator |
| Window window_w = calculate_max_window(*weights->info(), Steps()); |
| window_w.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| window_w.set(Window::DimY, Window::Dimension(0, 1, 1)); |
| window_w.set(Window::DimZ, Window::Dimension(0, 1, 1)); |
| |
| Iterator out(dst, window_out); |
| Iterator wei(weights, window_w); |
| |
| constexpr int num_elems_read_per_iteration = 16 / sizeof(T); |
| /* |
| * This implementation parallelize the full WC plane of input and weights by |
| * treating them as series of elements. So for example, a 3x3 weights and |
| * floating point vector operations of 4 elements per time, the first 3 |
| * channel elements of the first row would be taken and additionally the first |
| * element of the second row. The 9 elements in each single WC weight plane |
| * would require 2 4-element vector operations and a last single element operation. |
| * |
| * This works since when we create the input vector to multiply with the weights, |
| * the exact required elements are loaded in the same order. Therefore the |
| * multiplication works on the correct input/weight elements. |
| */ |
| execute_window_loop(window_out, [&](const Coordinates & id) |
| { |
| /* |
| * In here we create theoretical indexes which then we validate for both |
| * inputs and weights. |
| * As a reminder, this loop take each output point in NHW, C is treated |
| * in the weights loop. |
| */ |
| // We are computing the theoretical starting input starting points |
| const int in_w_start_t = static_cast<int>(id.y()) * conv_stride_w - conv_pad_left; |
| const int in_h_start_t = static_cast<int>(id.z()) * conv_stride_h - conv_pad_top; |
| const int in_w_end_t = in_w_start_t + kernel_dim_w; |
| const int in_h_end_t = in_h_start_t + kernel_dim_h; |
| |
| // We are computing the valid initial and ending input points by checking the borders |
| const int in_w_start = std::max(in_w_start_t, 0); |
| const int in_h_start = std::max(in_h_start_t, 0); |
| const int in_w_end = std::min(in_w_end_t, input_dim_w); |
| const int in_h_end = std::min(in_h_end_t, input_dim_h); |
| |
| // We use the input points to select the valid weight points to use |
| const int index_wc_start = (in_w_start - in_w_start_t) * kernel_stride_w; |
| const int index_h_start = in_h_start - in_h_start_t; |
| const int index_wc_end = (kernel_dim_w - (in_w_end_t - in_w_end)) * kernel_stride_w; |
| const int index_h_end = kernel_dim_h - (in_h_end_t - in_h_end); |
| |
| execute_window_loop(window_w, [&](const Coordinates & id_w) |
| { |
| /* |
| * This is the loop in the weights, and it goes along N (the batches) |
| * As a reminder, the batches of the weights are translated into the |
| * channels of the output |
| */ |
| const T *in_ptr_row = reinterpret_cast<const T *>(src->buffer() + src->info()->offset_first_element_in_bytes()) |
| + id[3] * input_stride_n + in_w_start * input_stride_w + in_h_start * input_stride_h; |
| const T *weights_ptr_row = reinterpret_cast<const T *>(wei.ptr()) + index_h_start * kernel_stride_h; |
| uint8_t *out_ptr = out.ptr() + id_w[3] * output_stride_c; |
| |
| T out_temp = static_cast<T>(0); |
| for(int index_h = index_h_start; index_h < index_h_end; ++index_h, in_ptr_row += input_stride_h, weights_ptr_row += kernel_stride_h) |
| { |
| const T *in_ptr_mover = in_ptr_row; |
| int index_wc = index_wc_start; |
| vector_type out_temp_vec = wrapper::vdup_n(static_cast<T>(0), tag_type()); |
| for(; index_wc <= index_wc_end - num_elems_read_per_iteration; index_wc += num_elems_read_per_iteration, in_ptr_mover += num_elems_read_per_iteration) |
| { |
| const auto src_vec = wrapper::vloadq(in_ptr_mover); |
| const auto w_vec = wrapper::vloadq(weights_ptr_row + index_wc); |
| out_temp_vec = wrapper::vmla(out_temp_vec, w_vec, src_vec); |
| } |
| out_temp += vreduce(out_temp_vec); |
| for(; index_wc < index_wc_end; ++index_wc, ++in_ptr_mover) |
| { |
| const auto src_val = *(in_ptr_mover); |
| const auto w_val = *(weights_ptr_row + index_wc); |
| out_temp += src_val * w_val; |
| } |
| } |
| *(reinterpret_cast<T *>(out_ptr)) = out_temp; |
| }, |
| wei); |
| }, |
| out); |
| } |
| |
| template <typename T> |
| void CpuDirectConv2dKernel::convolve_nhwc(const Window &window, const ITensor *src, const ITensor *weights, ITensor *dst) |
| { |
| // Declare useful types |
| using vtype = wrapper::traits::neon_bitvector<T, wrapper::traits::BitWidth::W128>; |
| using vector_type = typename vtype::type; |
| using tag_type = typename vtype::tag_type; |
| |
| // Scalar quantities |
| const int element_size = src->info()->element_size(); |
| const int input_stride_w = src->info()->strides_in_bytes().y() / element_size; |
| const int input_stride_h = src->info()->strides_in_bytes().z() / element_size; |
| const int input_stride_n = src->info()->strides_in_bytes()[3] / element_size; |
| const int input_dim_w = src->info()->dimension(1); |
| const int input_dim_h = src->info()->dimension(2); |
| |
| const int output_stride_c = dst->info()->strides_in_bytes().x(); |
| |
| const unsigned int kernel_stride_w = weights->info()->strides_in_bytes().y() / element_size; |
| const unsigned int kernel_stride_h = weights->info()->strides_in_bytes().z() / element_size; |
| const int kernel_dim_w = weights->info()->dimension(1); |
| const int kernel_dim_h = weights->info()->dimension(2); |
| |
| const int conv_pad_top = _conv_info.pad_top(); |
| const int conv_pad_left = _conv_info.pad_left(); |
| const int conv_stride_w = std::get<0>(_conv_info.stride()); |
| const int conv_stride_h = std::get<1>(_conv_info.stride()); |
| |
| // Setup input window for the output iterator |
| Window window_out = window; |
| window_out.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| |
| // Setup input window for the weights iterator |
| Window window_w = calculate_max_window(*weights->info(), Steps()); |
| window_w.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| window_w.set(Window::DimY, Window::Dimension(0, 1, 1)); |
| window_w.set(Window::DimZ, Window::Dimension(0, 1, 1)); |
| |
| Iterator out(dst, window_out); |
| Iterator wei(weights, window_w); |
| |
| constexpr int num_elems_read_per_iteration = 16 / sizeof(T); |
| |
| execute_window_loop(window_out, [&](const Coordinates & id) |
| { |
| // We are computing the theoretical starting input starting points |
| const int in_w_start_t = static_cast<int>(id.y()) * conv_stride_w - conv_pad_left; |
| const int in_h_start_t = static_cast<int>(id.z()) * conv_stride_h - conv_pad_top; |
| const int in_w_end_t = in_w_start_t + kernel_dim_w; |
| const int in_h_end_t = in_h_start_t + kernel_dim_h; |
| |
| // We are computing the valid initial and ending input points by checking the borders |
| const int in_w_start = std::max(in_w_start_t, 0); |
| const int in_h_start = std::max(in_h_start_t, 0); |
| const int in_w_end = std::min(in_w_end_t, input_dim_w); |
| const int in_h_end = std::min(in_h_end_t, input_dim_h); |
| |
| // We use the input points to select the valid weight points to use |
| const int wei_w_start = in_w_start - in_w_start_t; |
| const int wei_h_start = in_h_start - in_h_start_t; |
| const int wei_w_end = kernel_dim_w - (in_w_end_t - in_w_end); |
| const int wei_h_end = kernel_dim_h - (in_h_end_t - in_h_end); |
| |
| const int index_c_end = weights->info()->dimension(0); |
| const T *const in_ptr_start = reinterpret_cast<const T *>(src->buffer() + src->info()->offset_first_element_in_bytes()) + id[3] * input_stride_n; |
| |
| execute_window_loop(window_w, [&](const Coordinates & id_w) |
| { |
| const T *const weights_ptr_start = reinterpret_cast<const T *>(wei.ptr()); |
| uint8_t *out_ptr = out.ptr() + id_w[3] * output_stride_c; |
| |
| T out_temp = static_cast<T>(0); |
| for(int index_wei_h = wei_h_start, index_in_h = in_h_start; index_wei_h < wei_h_end; ++index_wei_h, ++index_in_h) |
| { |
| const T *const in_ptr_row = in_ptr_start + index_in_h * input_stride_h; |
| const T *const weights_ptr_row = weights_ptr_start + index_wei_h * kernel_stride_h; |
| for(int index_wei_w = wei_w_start, index_in_w = in_w_start; index_wei_w < wei_w_end; ++index_wei_w, ++index_in_w) |
| { |
| const T *in_ptr_mover = in_ptr_row + index_in_w * input_stride_w; |
| const T *weights_ptr_mover = weights_ptr_row + index_wei_w * kernel_stride_w; |
| int index_c = 0; |
| vector_type out_temp_vec = wrapper::vdup_n(static_cast<T>(0), tag_type()); |
| for(; index_c <= index_c_end - num_elems_read_per_iteration; index_c += num_elems_read_per_iteration, in_ptr_mover += num_elems_read_per_iteration, weights_ptr_mover += num_elems_read_per_iteration) |
| { |
| const auto src_vec = wrapper::vloadq(in_ptr_mover); |
| const auto w_vec = wrapper::vloadq(weights_ptr_mover); |
| out_temp_vec = wrapper::vmla(out_temp_vec, w_vec, src_vec); |
| } |
| out_temp += vreduce(out_temp_vec); |
| for(; index_c < index_c_end; ++index_c, ++in_ptr_mover, ++weights_ptr_mover) |
| { |
| const auto src_val = *(in_ptr_mover); |
| const auto w_val = *(weights_ptr_mover); |
| out_temp += src_val * w_val; |
| } |
| } |
| } |
| *(reinterpret_cast<T *>(out_ptr)) = out_temp; |
| }, |
| wei); |
| }, |
| out); |
| } |
| |
| template <typename T> |
| void CpuDirectConv2dKernel::convolve_nchw(const Window &window, const ITensor *src, const ITensor *weights, ITensor *dst) |
| { |
| // Declare useful types |
| using vtype = wrapper::traits::neon_bitvector<T, wrapper::traits::BitWidth::W128>; |
| using vector_type = typename vtype::type; |
| using tag_type = typename vtype::tag_type; |
| |
| // Scalar quantities |
| const int element_size = src->info()->element_size(); |
| const int input_stride_w = src->info()->strides_in_bytes()[0] / element_size; |
| const int input_stride_h = src->info()->strides_in_bytes()[1] / element_size; |
| const int input_stride_c = src->info()->strides_in_bytes()[2] / element_size; |
| const int input_stride_n = src->info()->strides_in_bytes()[3] / element_size; |
| |
| const int input_dim_w = src->info()->dimension(0); |
| const int input_dim_h = src->info()->dimension(1); |
| |
| const int output_stride_c = dst->info()->strides_in_bytes()[2]; |
| |
| const unsigned int kernel_stride_w = weights->info()->strides_in_bytes().x() / element_size; |
| const unsigned int kernel_stride_h = weights->info()->strides_in_bytes().y() / element_size; |
| const unsigned int kernel_stride_c = weights->info()->strides_in_bytes().z() / element_size; |
| |
| const int kernel_dim_w = weights->info()->dimension(0); |
| const int kernel_dim_h = weights->info()->dimension(1); |
| |
| const int conv_pad_top = _conv_info.pad_top(); |
| const int conv_pad_left = _conv_info.pad_left(); |
| const int conv_stride_w = std::get<0>(_conv_info.stride()); |
| const int conv_stride_h = std::get<1>(_conv_info.stride()); |
| |
| // Setup input window for the output iterator |
| Window window_out = window; |
| window_out.set(Window::DimZ, Window::Dimension(0, 1, 1)); |
| |
| // Setup input window for the weights iterator |
| Window window_w = calculate_max_window(*weights->info(), Steps()); |
| window_w.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| window_w.set(Window::DimY, Window::Dimension(0, 1, 1)); |
| window_w.set(Window::DimZ, Window::Dimension(0, 1, 1)); |
| |
| Iterator out(dst, window_out); |
| Iterator wei(weights, window_w); |
| |
| constexpr int num_elems_read_per_iteration = 16 / sizeof(T); |
| |
| execute_window_loop(window_out, [&](const Coordinates & id) |
| { |
| // We are computing the theoretical starting input starting points |
| const int in_w_start_t = static_cast<int>(id.x()) * conv_stride_w - conv_pad_left; |
| const int in_h_start_t = static_cast<int>(id.y()) * conv_stride_h - conv_pad_top; |
| const int in_w_end_t = in_w_start_t + kernel_dim_w; |
| const int in_h_end_t = in_h_start_t + kernel_dim_h; |
| |
| // We are computing the valid initial and ending input points by checking the borders |
| const int in_w_start = std::max(in_w_start_t, 0); |
| const int in_h_start = std::max(in_h_start_t, 0); |
| const int in_w_end = std::min(in_w_end_t, input_dim_w); |
| const int in_h_end = std::min(in_h_end_t, input_dim_h); |
| |
| // We use the input points to select the valid weight points to use |
| const int wei_w_start = in_w_start - in_w_start_t; |
| const int wei_h_start = in_h_start - in_h_start_t; |
| const int wei_h_end = kernel_dim_h - (in_h_end_t - in_h_end); |
| |
| const int index_c_end = weights->info()->dimension(2); |
| const T *const in_ptr_start = reinterpret_cast<const T *>(src->buffer() + src->info()->offset_first_element_in_bytes()) + id[3] * input_stride_n; |
| execute_window_loop(window_w, [&](const Coordinates & id_w) |
| { |
| const T *const weights_ptr_start = reinterpret_cast<const T *>(wei.ptr()); |
| uint8_t *out_ptr = out.ptr() + id_w[3] * output_stride_c; |
| T out_temp = static_cast<T>(0); |
| |
| for(int index_wei_c = 0, index_in_c = 0; index_wei_c < index_c_end; ++index_wei_c, ++index_in_c) |
| { |
| const T *const in_ptr_row_0 = in_ptr_start + index_in_c * input_stride_c; |
| const T *const weights_ptr_row_0 = weights_ptr_start + index_wei_c * kernel_stride_c; |
| for(int index_wei_h = wei_h_start, index_in_h = in_h_start; index_wei_h < wei_h_end; ++index_wei_h, ++index_in_h) |
| { |
| const T *in_ptr_row = in_ptr_row_0 + index_in_h * input_stride_h; |
| const T *weights_ptr_row = weights_ptr_row_0 + index_wei_h * kernel_stride_h; |
| int index_w = in_w_start; |
| int index_wei_w = wei_w_start; |
| vector_type out_temp_vec = wrapper::vdup_n(static_cast<T>(0), tag_type()); |
| for(; index_w <= ((in_w_end - num_elems_read_per_iteration)); index_w += num_elems_read_per_iteration, index_wei_w += num_elems_read_per_iteration) |
| { |
| const auto src_vec = wrapper::vloadq(in_ptr_row + index_w * input_stride_w); |
| const auto w_vec = wrapper::vloadq(weights_ptr_row + index_wei_w * kernel_stride_w); |
| out_temp_vec = wrapper::vmla(out_temp_vec, w_vec, src_vec); |
| } |
| out_temp += vreduce(out_temp_vec); |
| for(; index_w < in_w_end; ++index_w, ++index_wei_w) |
| { |
| const auto src_val = *(in_ptr_row + index_w * input_stride_w); |
| const auto w_val = *(weights_ptr_row + index_wei_w * kernel_stride_w); |
| out_temp += src_val * w_val; |
| } |
| } |
| } |
| *(reinterpret_cast<T *>(out_ptr)) = out_temp; |
| |
| }, |
| wei); |
| }, |
| out); |
| } |
| |
| void CpuDirectConv2dKernel::configure(ITensorInfo *src, ITensorInfo *weights, ITensorInfo *dst, const PadStrideInfo &conv_info) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(src, weights, dst); |
| |
| _conv_info = conv_info; |
| _data_layout = src->data_layout(); |
| _kernel_size = weights->dimension(get_data_layout_dimension_index(_data_layout, DataLayoutDimension::WIDTH)); |
| |
| // Get convolved dimensions |
| TensorShape output_shape = misc::shape_calculator::compute_deep_convolution_shape(*src, *weights, conv_info); |
| |
| DataType data_type = src->data_type(); |
| |
| // Output auto inizialitation if not yet initialized |
| auto_init_if_empty(*dst, output_shape, 1, data_type); |
| |
| // Perform validation step |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, weights, dst, conv_info)); |
| |
| // Configure kernel window |
| auto win_config = validate_and_configure_window(src, dst); |
| ARM_COMPUTE_ERROR_THROW_ON(win_config.first); |
| ICpuKernel::configure(win_config.second); |
| } |
| |
| Status CpuDirectConv2dKernel::validate(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *dst, const PadStrideInfo &conv_info) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, weights, dst, conv_info)); |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(src->clone().get(), |
| dst->clone().get()) |
| .first); |
| |
| return Status{}; |
| } |
| |
| void CpuDirectConv2dKernel::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_0); |
| auto weights = tensors.get_const_tensor(TensorType::ACL_SRC_1); |
| auto dst = tensors.get_tensor(TensorType::ACL_DST); |
| |
| if(_data_layout == DataLayout::NCHW) |
| { |
| switch(src->info()->data_type()) |
| { |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F16: |
| { |
| convolve_nchw<float16_t>(window, src, weights, dst); |
| break; |
| } |
| #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| case DataType::F32: |
| { |
| convolve_nchw<float>(window, src, weights, dst); |
| break; |
| } |
| default: |
| ARM_COMPUTE_ERROR("Data type not supported"); |
| break; |
| } |
| } |
| else |
| { |
| switch(src->info()->data_type()) |
| { |
| case DataType::F32: |
| { |
| if(have_zero_x_internal_padding(src->info(), weights->info())) |
| { |
| convolve_nhwc_optimized<float>(window, src, weights, dst); |
| } |
| else |
| { |
| convolve_nhwc<float>(window, src, weights, dst); |
| } |
| break; |
| } |
| default: |
| ARM_COMPUTE_ERROR("Data type not supported"); |
| break; |
| } |
| } |
| } |
| const char *CpuDirectConv2dKernel::name() const |
| { |
| return "CpuDirectConvolutionLayerKernel"; |
| } |
| } // namespace kernels |
| } // namespace cpu |
| } // namespace arm_compute |