diff options
Diffstat (limited to 'arm_compute/core/NEON/kernels/convolution/common/shims.hpp')
| -rw-r--r-- | arm_compute/core/NEON/kernels/convolution/common/shims.hpp | 749 |
1 files changed, 0 insertions, 749 deletions
diff --git a/arm_compute/core/NEON/kernels/convolution/common/shims.hpp b/arm_compute/core/NEON/kernels/convolution/common/shims.hpp deleted file mode 100644 index 243d305e19..0000000000 --- a/arm_compute/core/NEON/kernels/convolution/common/shims.hpp +++ /dev/null @@ -1,749 +0,0 @@ -/* - * Copyright (c) 2017 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. - */ - -#pragma once -#ifndef DOXYGEN_SKIP_THIS -#include <cstdint> -#endif /* DOXYGEN_SKIP_THIS */ -#include "arm.hpp" - -namespace reorder { -/** Re-order a tensor from NCHW format to NHWC. - * - * @note The stride parameters are optional and are provided to allow padding in either input or output tensors. - * - * @param[in] in Input tensor in NCHW format. - * @param[out] out Output tensor, to be written in NHWC format. - * @param n_batches Number of batches in the tensors. - * @param n_channels Number of channels in the tensors - * @param n_rows Height of the tensor - * @param n_cols Width of the tensor - * @param in_batch_stride Stride over batches in the input tensor. If `0` defaults to `n_channels * in_channel_stride`. - * @param in_channel_stride Stride over channels in the input tensor. If `0` defaults to `n_rows * in_row_stride`. - * @param in_row_stride Stride over rows in the input tensor. If `0` defaults to `n_cols`. - * @param out_batch_stride Stride over batches in the output tensor. If `0` defaults to `n_rows * out_row_stride`. - * @param out_row_stride Stride over rows in the output tensor. If `0` defaults to `n_cols * out_col_stride`. - * @param out_col_stride Stride over columns in the output tensor. If `0` defaults to `n_channels`. - */ -template <typename T> -inline void nchw_to_nhwc( - const T* const in, - T* const out, - const int n_batches, - const int n_channels, - const int n_rows, - const int n_cols, - int in_batch_stride=0, - int in_channel_stride=0, - int in_row_stride=0, - int out_batch_stride=0, - int out_row_stride=0, - int out_col_stride=0 -); - -/** Re-order a tensor from NHWC format to NCHW. - * - * @note The stride parameters are optional and are provided to allow padding in either input or output tensors. - * - * @param[in] in Input tensor in NHWC format. - * @param[out] out Output tensor, to be written in NCHW format. - * @param n_batches Number of batches in the tensors. - * @param n_rows Height of the tensor - * @param n_cols Width of the tensor - * @param n_channels Number of channels in the tensors - * @param in_batch_stride Stride over batches in the input tensor. If `0` defaults to `n_rows * in_row_stride`. - * @param in_row_stride Stride over rows in the input tensor. If `0` defaults to `n_cols * in_col_stride`. - * @param in_col_stride Stride over columns in the input tensor. If `0` defaults to `n_channels`. - * @param out_batch_stride Stride over batches in the output tensor. If `0` defaults to `n_channels * out_channel_stride`. - * @param out_channel_stride Stride over channels in the output tensor. If `0` defaults to `n_rows * out_row_stride`. - * @param out_row_stride Stride over rows in the output tensor. If `0` defaults to `n_cols`. - */ -template <typename T> -inline void nhwc_to_nchw( - const T* const in, // Input data in NHWC form - T* const out, // Output data in NCHW form - const int n_batches, - const int n_rows, - const int n_cols, - const int n_channels, - int in_batch_stride=0, - int in_row_stride=0, - int in_col_stride=0, - int out_batch_stride=0, - int out_channel_stride=0, - int out_row_stride=0 -); - -/** Re-order a weight tensor from [Output feature map x Input feature map x - * Height x Width] format to [Height x Width x Input feature map x Output - * feature map] format. - */ -template <typename T> -inline void ofm_ifm_h_w_to_h_w_ifm_ofm( - const T* const in, // Input in [Output x Input x Height x Width] form - T* const out, // Output in [Height x Width x Input x Output] form - const int n_output_feature_maps, - const int n_input_feature_maps, - const int n_rows, - const int n_cols, - int in_output_feature_map_stride=0, - int in_input_feature_map_stride=0, - int in_row_stride=0, - int out_row_stride=0, - int out_col_stride=0, - int out_input_feature_map_stride=0 -); - -/** Re-order a weight tensor from [Height x Width x Input feature map x Output - * feature map] format to [Output feature map x Input feature map x Height x - * Width] format. - */ -template <typename T> -inline void h_w_ifm_ofm_to_ofm_ifm_h_w( - const T* const in, // Input in [Height x Width x Input x Output] form - T* const out, // Output in [Output x Input x Height x Width] form - const int n_rows, - const int n_cols, - const int n_input_feature_maps, - const int n_output_feature_maps, - int in_row_stride=0, - int in_col_stride=0, - int in_input_feature_map_stride=0, - int out_output_feature_map_stride=0, - int out_input_feature_map_stride=0, - int out_row_stride=0 -); - -/*****************************************************************************/ -/* 32-bit implementation : NCHW -> NHWC - */ -template <> -inline void nchw_to_nhwc( - const int32_t* const in, - int32_t* const out, - const int n_batches, - const int n_channels, - const int n_rows, - const int n_cols, - int in_batch_stride, - int in_channel_stride, - int in_row_stride, - int out_batch_stride, - int out_row_stride, - int out_col_stride -) -{ - typedef int32_t T; - - // Fill in the stride values - in_row_stride = (in_row_stride) ? in_row_stride : n_cols; - in_channel_stride = (in_channel_stride) ? in_channel_stride - : n_rows * in_row_stride; - in_batch_stride = (in_batch_stride) ? in_batch_stride - : n_channels * in_channel_stride; - - out_col_stride = (out_col_stride) ? out_col_stride : n_channels; - out_row_stride = (out_row_stride) ? out_row_stride : n_cols * out_col_stride; - out_batch_stride = (out_batch_stride) ? out_batch_stride - : n_rows * out_row_stride; - - // Perform the re-ordering - for (int n = 0; n < n_batches; n++) - { - const T* const in_batch = in + n*in_batch_stride; - T* const out_batch = out + n*out_batch_stride; - - for (int i = 0; i < n_rows; i++) - { - const T* const in_row = in_batch + i*in_row_stride; - T* const out_row = out_batch + i*out_row_stride; - - int j = 0, j_remaining = n_cols; -#ifdef __arm_any__ - for (; j_remaining >= 4; j += 4, j_remaining -= 4) - { - int c = 0, c_remaining = n_channels; - for (; c_remaining >= 4; c += 4, c_remaining -= 4) - { - // Read 4 channels worth of 4 columns, then zip to produce 4 columns - // worth of 4 channels. - int32x4_t channel_pixels[4]; - channel_pixels[0] = vld1q_s32(in_row + (c + 0)*in_channel_stride + j); - channel_pixels[1] = vld1q_s32(in_row + (c + 1)*in_channel_stride + j); - channel_pixels[2] = vld1q_s32(in_row + (c + 2)*in_channel_stride + j); - channel_pixels[3] = vld1q_s32(in_row + (c + 3)*in_channel_stride + j); - - const auto zip1 = vzipq_s32(channel_pixels[0], channel_pixels[2]); - const auto zip2 = vzipq_s32(channel_pixels[1], channel_pixels[3]); - const auto out_0 = vzipq_s32(zip1.val[0], zip2.val[0]); - const auto out_1 = vzipq_s32(zip1.val[1], zip2.val[1]); - - vst1q_s32(out_row + (j + 0)*out_col_stride + c, out_0.val[0]); - vst1q_s32(out_row + (j + 1)*out_col_stride + c, out_0.val[1]); - vst1q_s32(out_row + (j + 2)*out_col_stride + c, out_1.val[0]); - vst1q_s32(out_row + (j + 3)*out_col_stride + c, out_1.val[1]); - } - for (; c_remaining; c++, c_remaining--) - { - for (int _j = 0; _j < 4; _j++) - { - const T* const in_col = in_row + j + _j; - T* const out_col = out_row + (j + _j)*out_col_stride; - const T* const in_channel = in_col + c*in_channel_stride; - out_col[c] = *(in_channel); - } - } - } - for (; j_remaining >= 2; j += 2, j_remaining -= 2) - { - int c = 0, c_remaining = n_channels; - for (; c_remaining >= 2; c += 2, c_remaining -= 2) - { - // Read 2 channels worth of 2 columns, then zip to produce 2 columns - // worth of 2 channels. - int32x2_t channel_pixels[2]; - channel_pixels[0] = vld1_s32(in_row + (c + 0)*in_channel_stride + j); - channel_pixels[1] = vld1_s32(in_row + (c + 1)*in_channel_stride + j); - - const auto output = vzip_s32(channel_pixels[0], channel_pixels[1]); - - vst1_s32(out_row + (j + 0)*out_col_stride + c, output.val[0]); - vst1_s32(out_row + (j + 1)*out_col_stride + c, output.val[1]); - } - for (; c_remaining; c++, c_remaining--) - { - for (int _j = 0; _j < 2; _j++) - { - const T* const in_col = in_row + j + _j; - T* const out_col = out_row + (j + _j)*out_col_stride; - const T* const in_channel = in_col + c*in_channel_stride; - out_col[c] = *(in_channel); - } - } - } -#endif // __arm_any__ - for (; j_remaining; j++, j_remaining--) - { - const T* const in_col = in_row + j; - T* const out_col = out_row + j*out_col_stride; - - for (int c = 0; c < n_channels; c++) - { - const T* const in_channel = in_col + c*in_channel_stride; - out_col[c] = *(in_channel); - } - } - } - } -} - -template <> -inline void nchw_to_nhwc( - const uint32_t* const in, - uint32_t* const out, - const int n_batches, - const int n_channels, - const int n_rows, - const int n_cols, - int in_batch_stride, - int in_channel_stride, - int in_row_stride, - int out_batch_stride, - int out_row_stride, - int out_col_stride -) -{ - nchw_to_nhwc( - reinterpret_cast<const int32_t*>(in), - reinterpret_cast<int32_t*>(out), - n_batches, n_channels, n_rows, n_cols, - in_batch_stride, in_channel_stride, in_row_stride, - out_batch_stride, out_row_stride, out_col_stride - ); -} - -template <> -inline void nchw_to_nhwc( - const float* const in, - float* const out, - const int n_batches, - const int n_channels, - const int n_rows, - const int n_cols, - int in_batch_stride, - int in_channel_stride, - int in_row_stride, - int out_batch_stride, - int out_row_stride, - int out_col_stride -) -{ - nchw_to_nhwc( - reinterpret_cast<const int32_t*>(in), - reinterpret_cast<int32_t*>(out), - n_batches, n_channels, n_rows, n_cols, - in_batch_stride, in_channel_stride, in_row_stride, - out_batch_stride, out_row_stride, out_col_stride - ); -} - -/*****************************************************************************/ -/* Generic implementation : NCHW -> NHWC - */ -template <typename T> -inline void nchw_to_nhwc( - const T* const in, - T* const out, - const int n_batches, - const int n_channels, - const int n_rows, - const int n_cols, - int in_batch_stride, - int in_channel_stride, - int in_row_stride, - int out_batch_stride, - int out_row_stride, - int out_col_stride -) -{ - // Fill in the stride values - in_row_stride = (in_row_stride) ? in_row_stride : n_cols; - in_channel_stride = (in_channel_stride) ? in_channel_stride - : n_rows * in_row_stride; - in_batch_stride = (in_batch_stride) ? in_batch_stride - : n_channels * in_channel_stride; - - out_col_stride = (out_col_stride) ? out_col_stride : n_channels; - out_row_stride = (out_row_stride) ? out_row_stride : n_cols * out_col_stride; - out_batch_stride = (out_batch_stride) ? out_batch_stride - : n_rows * out_row_stride; - - // Perform the re-ordering - for (int n = 0; n < n_batches; n++) - { - const T* const in_batch = in + n*in_batch_stride; - T* const out_batch = out + n*out_batch_stride; - - for (int i = 0; i < n_rows; i++) - { - const T* const in_row = in_batch + i*in_row_stride; - T* const out_row = out_batch + i*out_row_stride; - - for (int j = 0; j < n_cols; j++) - { - const T* const in_col = in_row + j; - T* const out_col = out_row + j*out_col_stride; - - for (int c = 0; c < n_channels; c++) - { - const T* const in_channel = in_col + c*in_channel_stride; - out_col[c] = *(in_channel); - } - } - } - } -} - -/*****************************************************************************/ -/* 32-bit implementation : NHWC -> NCHW - */ -template <> -inline void nhwc_to_nchw( - const int32_t* const in, // Input data in NHWC form - int32_t* const out, // Output data in NCHW form - const int n_batches, - const int n_rows, - const int n_cols, - const int n_channels, - int in_batch_stride, - int in_row_stride, - int in_col_stride, - int out_batch_stride, - int out_channel_stride, - int out_row_stride -) -{ - typedef int32_t T; - - // Fill in stride values - in_col_stride = (in_col_stride) ? in_col_stride : n_channels; - in_row_stride = (in_row_stride) ? in_row_stride : n_cols * in_col_stride; - in_batch_stride = (in_batch_stride) ? in_batch_stride - : n_rows * in_row_stride; - - out_row_stride = (out_row_stride) ? out_row_stride : n_cols; - out_channel_stride = (out_channel_stride) ? out_channel_stride - : n_rows * out_row_stride; - out_batch_stride = (out_batch_stride) ? out_batch_stride - : n_channels * out_channel_stride; - - // Perform the re-ordering - // For every batch - for (int n = 0; n < n_batches; n++) - { - const T* const in_batch = in + n*in_batch_stride; - T* const out_batch = out + n*out_batch_stride; - - // For every row - for (int i = 0; i < n_rows; i++) - { - const T* const in_i = in_batch + i*in_row_stride; - T* const out_i = out_batch + i*out_row_stride; - - // For every column, beginning with chunks of 4 - int j = 0, j_remaining = n_cols; -#ifdef __arm_any__ - for (; j_remaining >= 4; j += 4, j_remaining -=4) - { - // For every channel, beginning with chunks of 4 - int c = 0, c_remaining = n_channels; - for (; c_remaining >= 4; c += 4, c_remaining -= 4) - { - // Read 4 columns worth of 4 channels then zip to produce 4 channels - // worth of 4 columns. - int32x4_t pixel_channels[4]; - pixel_channels[0] = vld1q_s32(in_i + (j + 0)*in_col_stride + c); - pixel_channels[1] = vld1q_s32(in_i + (j + 1)*in_col_stride + c); - pixel_channels[2] = vld1q_s32(in_i + (j + 2)*in_col_stride + c); - pixel_channels[3] = vld1q_s32(in_i + (j + 3)*in_col_stride + c); - - const auto zip1 = vzipq_s32(pixel_channels[0], pixel_channels[2]); - const auto zip2 = vzipq_s32(pixel_channels[1], pixel_channels[3]); - const auto out_0 = vzipq_s32(zip1.val[0], zip2.val[0]); - const auto out_1 = vzipq_s32(zip1.val[1], zip2.val[1]); - - vst1q_s32(out_i + j + (c + 0)*out_channel_stride, out_0.val[0]); - vst1q_s32(out_i + j + (c + 1)*out_channel_stride, out_0.val[1]); - vst1q_s32(out_i + j + (c + 2)*out_channel_stride, out_1.val[0]); - vst1q_s32(out_i + j + (c + 3)*out_channel_stride, out_1.val[1]); - } - for (; c_remaining; c++, c_remaining--) - { - for (int _j = 0; _j < 4; _j++) - { - const T* const in_j = in_i + (j + _j)*in_col_stride; - T* const out_j = out_i + (j + _j); - - const T* const in_channel = in_j + c; - T* const out_channel = out_j + c*out_channel_stride; - *(out_channel) = *(in_channel); - } - } - } - for (; j_remaining >= 2; j += 2, j_remaining -=2) - { - int c = 0, c_remaining = n_channels; - for (; c_remaining >= 2; c += 2, c_remaining -= 2) - { - // Read 2 columns worth of 2 channels then zip to produce 2 channels - // worth of 2 columns. - int32x2_t pixel_channels[2]; - pixel_channels[0] = vld1_s32(in_i + (j + 0)*in_col_stride + c); - pixel_channels[1] = vld1_s32(in_i + (j + 1)*in_col_stride + c); - - const auto output = vzip_s32(pixel_channels[0], pixel_channels[1]); - - vst1_s32(out_i + j + (c + 0)*out_channel_stride, output.val[0]); - vst1_s32(out_i + j + (c + 1)*out_channel_stride, output.val[1]); - } - for (; c_remaining; c++, c_remaining--) - { - for (int _j = 0; _j < 2; _j++) - { - const T* const in_j = in_i + (j + _j)*in_col_stride; - T* const out_j = out_i + (j + _j); - - const T* const in_channel = in_j + c; - T* const out_channel = out_j + c*out_channel_stride; - *(out_channel) = *(in_channel); - } - } - } -#endif // __arm_any__ - for (; j_remaining; j++, j_remaining--) - { - const T* const in_j = in_i + j*in_col_stride; - T* const out_j = out_i + j; - - // For every channel - for (int c = 0; c < n_channels; c++) - { - const T* const in_channel = in_j + c; - T* const out_channel = out_j + c*out_channel_stride; - *(out_channel) = *(in_channel); - } - } - } - } -} - -template <> -inline void nhwc_to_nchw( - const uint32_t* const in, // Input data in NHWC form - uint32_t* const out, // Output data in NCHW form - const int n_batches, - const int n_rows, - const int n_cols, - const int n_channels, - int in_batch_stride, - int in_row_stride, - int in_col_stride, - int out_batch_stride, - int out_channel_stride, - int out_row_stride -) -{ - // Redirect to generic 32-bit implementation - nhwc_to_nchw( - reinterpret_cast<const int32_t*>(in), - reinterpret_cast<int32_t*>(out), - n_batches, n_rows, n_cols, n_channels, - in_batch_stride, in_row_stride, in_col_stride, - out_batch_stride, out_channel_stride, out_row_stride - ); -} - -template <> -inline void nhwc_to_nchw( - const float* const in, // Input data in NHWC form - float* const out, // Output data in NCHW form - const int n_batches, - const int n_rows, - const int n_cols, - const int n_channels, - int in_batch_stride, - int in_row_stride, - int in_col_stride, - int out_batch_stride, - int out_channel_stride, - int out_row_stride -) -{ - // Redirect to generic 32-bit implementation - nhwc_to_nchw( - reinterpret_cast<const int32_t*>(in), - reinterpret_cast<int32_t*>(out), - n_batches, n_rows, n_cols, n_channels, - in_batch_stride, in_row_stride, in_col_stride, - out_batch_stride, out_channel_stride, out_row_stride - ); -} - -/*****************************************************************************/ -/* Generic implementation : NHWC -> NCHW - */ -template <typename T> -inline void nhwc_to_nchw( - const T* const in, // Input data in NHWC form - T* const out, // Output data in NCHW form - const int n_batches, - const int n_rows, - const int n_cols, - const int n_channels, - int in_batch_stride, - int in_row_stride, - int in_col_stride, - int out_batch_stride, - int out_channel_stride, - int out_row_stride -) -{ - // Fill in stride values - in_col_stride = (in_col_stride) ? in_col_stride : n_channels; - in_row_stride = (in_row_stride) ? in_row_stride : n_cols * in_col_stride; - in_batch_stride = (in_batch_stride) ? in_batch_stride - : n_rows * in_row_stride; - - out_row_stride = (out_row_stride) ? out_row_stride : n_cols; - out_channel_stride = (out_channel_stride) ? out_channel_stride - : n_rows * out_row_stride; - out_batch_stride = (out_batch_stride) ? out_batch_stride - : n_channels * out_channel_stride; - - // Perform the re-ordering - // For every batch - for (int n = 0; n < n_batches; n++) - { - const T* const in_batch = in + n*in_batch_stride; - T* const out_batch = out + n*out_batch_stride; - - // For every row - for (int i = 0; i < n_rows; i++) - { - const T* const in_i = in_batch + i*in_row_stride; - T* const out_i = out_batch + i*out_row_stride; - - // For every column - for (int j = 0; j < n_cols; j++) - { - const T* const in_j = in_i + j*in_col_stride; - T* const out_j = out_i + j; - - // For every channel - for (int c = 0; c < n_channels; c++) - { - const T* const in_channel = in_j + c; - T* const out_channel = out_j + c*out_channel_stride; - *(out_channel) = *(in_channel); - } - } - } - } -} - -/*****************************************************************************/ -/* Generic weight re-order implementation. - */ -template <typename T> -inline void ofm_ifm_h_w_to_h_w_ifm_ofm( - const T* const in, // Input in [Output x Input x Height x Width] form - T* const out, // Output in [Height x Width x Input x Output] form - const int n_output_feature_maps, - const int n_input_feature_maps, - const int n_rows, - const int n_cols, - int in_output_feature_map_stride, - int in_input_feature_map_stride, - int in_row_stride, - int out_row_stride, - int out_col_stride, - int out_input_feature_map_stride -) -{ - // Fill in stride values - in_row_stride = (in_row_stride) - ? in_row_stride - : n_cols; - in_input_feature_map_stride = (in_input_feature_map_stride) - ? in_input_feature_map_stride - : n_rows * in_row_stride; - in_output_feature_map_stride = (in_output_feature_map_stride) - ? in_output_feature_map_stride - : n_input_feature_maps * in_input_feature_map_stride; - - out_input_feature_map_stride = (out_input_feature_map_stride) - ? out_input_feature_map_stride - : n_output_feature_maps; - out_col_stride = (out_col_stride) - ? out_col_stride - : n_input_feature_maps * out_input_feature_map_stride; - out_row_stride = (out_row_stride) - ? out_row_stride - : n_cols * out_col_stride; - - // Perform the re-ordering - for (int i = 0; i < n_rows; i++) - { - const T* const in_row = in + i * in_row_stride; - T* out_row = out + i * out_row_stride; - - for (int j = 0; j < n_cols; j++) - { - const T* const in_col = in_row + j; - T* const out_col = out_row + j * out_col_stride; - - for (int ifm = 0; ifm < n_input_feature_maps; ifm++) - { - const T* const in_ifm = in_col + ifm * in_input_feature_map_stride; - T* const out_ifm = out_col + ifm * out_input_feature_map_stride; - - for (int ofm = 0; ofm < n_output_feature_maps; ofm++) - { - const T* const in_ofm = in_ifm + ofm * in_output_feature_map_stride; - T* const out_ofm = out_ifm + ofm; - *(out_ofm) = *(in_ofm); - } - } - } - } -} - -/*****************************************************************************/ -/* Generic weight re-order implementation. - */ -template <typename T> -inline void h_w_ifm_ofm_to_ofm_ifm_h_w( - const T* const in, // Input in [Height x Width x Input x Output] form - T* const out, // Output in [Output x Input x Height x Width] form - const int n_rows, - const int n_cols, - const int n_input_feature_maps, - const int n_output_feature_maps, - int in_row_stride, - int in_col_stride, - int in_input_feature_map_stride, - int out_output_feature_map_stride, - int out_input_feature_map_stride, - int out_row_stride -) -{ - // Fill in the stride values - in_input_feature_map_stride = (in_input_feature_map_stride) - ? in_input_feature_map_stride - : n_output_feature_maps; - in_col_stride = (in_col_stride) - ? in_col_stride - : n_input_feature_maps * in_input_feature_map_stride; - in_row_stride = (in_row_stride) - ? in_row_stride - : n_cols * in_col_stride; - - out_row_stride = (out_row_stride) - ? out_row_stride - : n_cols; - out_input_feature_map_stride = (out_input_feature_map_stride) - ? out_input_feature_map_stride - : n_rows * out_row_stride; - out_output_feature_map_stride = (out_output_feature_map_stride) - ? out_output_feature_map_stride - : n_input_feature_maps * out_input_feature_map_stride; - - // Perform the re-ordering - for (int i = 0; i < n_rows; i++) - { - const T* const in_row = in + i * in_row_stride; - T* const out_row = out + i * out_row_stride; - - for (int j = 0; j < n_cols; j++) - { - const T* const in_col = in_row + j * in_col_stride; - T* const out_col = out_row + j; - - for (int ifm = 0; ifm < n_input_feature_maps; ifm++) - { - const T* const in_ifm = in_col + ifm * in_input_feature_map_stride; - T* const out_ifm = out_col + ifm * out_input_feature_map_stride; - - for (int ofm = 0; ofm < n_output_feature_maps; ofm++) - { - const T* const in_ofm = in_ifm + ofm; - T* const out_ofm = out_ifm + ofm * out_output_feature_map_stride; - *(out_ofm) = *(in_ofm); - } - } - } - } -} - -} // namespace reorder |