text: // Copyright 2018 The MACE Authors. All Rights Reserved.
  //
  // Licensed under the Apache License, Version 2.0 (the "License");
  // you may not use this file except in compliance with the License.
  // You may obtain a copy of the License at
  //
  //     http://www.apache.org/licenses/LICENSE-2.0
  //
  // Unless required by applicable law or agreed to in writing, software
  // distributed under the License is distributed on an "AS IS" BASIS,
  // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  // See the License for the specific language governing permissions and
  // limitations under the License.
  
  #ifndef MACE_OPS_COMMON_TRANSPOSE_H_
  #define MACE_OPS_COMMON_TRANSPOSE_H_
  
  #if defined(MACE_ENABLE_NEON)
  #include <arm_neon.h>
  #endif  // MACE_ENABLE_NEON
  #include <algorithm>
  #include <vector>
  #include "mace/core/op_context.h"
  #include "mace/public/mace.h"
  
  namespace mace {
  namespace ops {
  
  template<typename T>
  void TransposeNHWCToNCHWC3(utils::ThreadPool *thread_pool,
                             const T *input,
                             T *output,
                             const index_t height,
                             const index_t width) {
    index_t image_size = height * width;
  
    thread_pool->Compute1D([=](index_t start, index_t end, index_t step) {
      for (index_t h = start; h < end; h += step) {
        index_t in_offset = h * width * 3;
        index_t out_offset = h * width;
  
        for (index_t w = 0; w < width; ++w) {
          for (index_t c = 0; c < 3; ++c) {
            output[out_offset + c * image_size + w] =
                input[in_offset + w * 3 + c];
          }
        }
      }
    }, 0, height, 1);
  }
  
  template<>
  inline void TransposeNHWCToNCHWC3<float>(utils::ThreadPool *thread_pool,
                                           const float *input,
                                           float *output,
                                           const index_t height,
                                           const index_t width) {
    index_t image_size = height * width;
  
    thread_pool->Compute1D([=](index_t start, index_t end, index_t step) {
      for (index_t h = start; h < end; h += step) {
        index_t in_offset = h * width * 3;
        index_t out_offset = h * width;
  
  #if defined(MACE_ENABLE_NEON)
        index_t w;
        for (w = 0; w + 3 < width; w += 4) {
          float32x4x3_t vi = vld3q_f32(input + in_offset);
          vst1q_f32(output + out_offset, vi.val[0]);
          vst1q_f32(output + out_offset + image_size, vi.val[1]);
          vst1q_f32(output + out_offset + image_size * 2, vi.val[2]);
  
          in_offset += 12;
          out_offset += 4;
        }
        for (; w < width; ++w) {
          for (index_t c = 0; c < 3; ++c) {
            output[h * width + image_size * c + w] =
                input[h * width * 3 + w * 3 + c];
          }
        }
  #else
        for (index_t w = 0; w < width; ++w) {
          for (index_t c = 0; c < 3; ++c) {
            output[out_offset + c * image_size + w] =
                input[in_offset + w * 3 + c];
          }
        }
  #endif
      }
    }, 0, height, 1);
  }
  
  template<typename T>
  void TransposeNCHWToNHWCC2(utils::ThreadPool *thread_pool,
                             const T *input,
                             T *output,
                             const index_t height,
                             const index_t width) {
    index_t image_size = height * width;
  
    thread_pool->Compute1D([=](index_t start, index_t end, index_t step) {
      for (index_t h = start; h < end; h += step) {
        index_t in_offset = h * width;
        index_t out_offset = h * width * 2;
  
        for (index_t w = 0; w < width; ++w) {
          for (index_t c = 0; c < 2; ++c) {
            output[out_offset + w * 2 + c] =
                input[in_offset + c * image_size + w];
          }
        }
      }
    }, 0, height, 1);
  }
  
  template<>
  inline void TransposeNCHWToNHWCC2<float>(utils::ThreadPool *thread_pool,
                                           const float *input,
                                           float *output,
                                           const index_t height,
                                           const index_t width) {
    index_t image_size = height * width;
  
    thread_pool->Compute1D([=](index_t start, index_t end, index_t step) {
      for (index_t h = start; h < end; h += step) {
        index_t in_offset = h * width;
        index_t out_offset = h * width * 2;
  
  #if defined(MACE_ENABLE_NEON)
        index_t w;
        for (w = 0; w + 3 < width; w += 4) {
          float32x4_t vi0 = vld1q_f32(input + in_offset);
          float32x4_t vi1 = vld1q_f32(input + in_offset + image_size);
          float32x4x2_t vi = {{vi0, vi1}};
          vst2q_f32(output + out_offset, vi);
          in_offset += 4;
          out_offset += 8;
        }
        for (; w < width; ++w) {
          for (index_t c = 0; c < 2; ++c) {
            output[h * width * 2 + w * 2 + c] =
                input[h * width + image_size * c + w];
          }
        }
  #else
        for (index_t w = 0; w < width; ++w) {
          for (index_t c = 0; c < 2; ++c) {
            output[out_offset + w * 2 + c] =
                input[in_offset + c * image_size + w];
          }
        }
  #endif
      }
    }, 0, height, 1);
  }
  
  template<typename T>
  MaceStatus Transpose(utils::ThreadPool *thread_pool,
                       const T *input,
                       const std::vector<int64_t> &input_shape,
                       const std::vector<int> &dst_dims,
                       T *output) {
    MACE_CHECK((input_shape.size() == 2 && dst_dims.size() == 2) ||
        (input_shape.size() == 4 && dst_dims.size() == 4),
               "Only support 2D or 4D transpose");
  
    std::vector<index_t> output_shape;
    for (size_t i = 0; i < dst_dims.size(); ++i) {
      output_shape.push_back(input_shape[dst_dims[i]]);
    }
  
    if (input_shape.size() == 2) {
      MACE_CHECK(dst_dims[0] == 1 && dst_dims[1] == 0, "no need transform");
      index_t height = input_shape[0];
      index_t width = input_shape[1];
      index_t stride_i = height;
      index_t stride_j = width;
      index_t tile_size = height > 512 || width > 512 ? 64 : 32;
  
      thread_pool->Compute2D([=](index_t start0, index_t end0, index_t step0,
                                 index_t start1, index_t end1, index_t step1) {
        for (index_t i = start0; i < end0; i += step0) {
          for (index_t j = start1; j < end1; j += step1) {
            index_t end_i = std::min(i + tile_size, height);
            index_t end_j = std::min(j + tile_size, width);
            for (index_t tile_i = i; tile_i < end_i; ++tile_i) {
              for (index_t tile_j = j; tile_j < end_j; ++tile_j) {
                output[tile_j * stride_i + tile_i] =
                    input[tile_i * stride_j + tile_j];
              }
            }
          }
        }
      }, 0, height, tile_size, 0, width, tile_size);
    } else if (input_shape.size() == 4) {
      std::vector<int> transpose_order_from_NHWC_to_NCHW{0, 3, 1, 2};
      std::vector<int> transpose_order_from_NCHW_to_NHWC{0, 2, 3, 1};
      index_t batch_size = input_shape[1] * input_shape[2] * input_shape[3];
  
      if (dst_dims == transpose_order_from_NHWC_to_NCHW && input_shape[3] == 3) {
        for (index_t b = 0; b < input_shape[0]; ++b) {
          TransposeNHWCToNCHWC3(thread_pool,
                                input + b * batch_size,
                                output + b * batch_size,
                                input_shape[1],
                                input_shape[2]);
        }
      } else if (dst_dims == transpose_order_from_NCHW_to_NHWC
          && input_shape[1] == 2) {
        for (index_t b = 0; b < input_shape[0]; ++b) {
          TransposeNCHWToNHWCC2(thread_pool,
                                input + b * batch_size,
                                output + b * batch_size,
                                input_shape[2],
                                input_shape[3]);
        }
      } else if (dst_dims == std::vector<int>{0, 2, 1, 3}) {
        index_t height = input_shape[1];
        index_t width = input_shape[2];
        index_t channel = input_shape[3];
        index_t channel_raw_size = channel * sizeof(T);
        index_t stride_i = height;
        index_t stride_j = width;
        index_t tile_size = std::max(static_cast<index_t>(1),
                                     static_cast<index_t>(std::sqrt(
                                         8 * 1024 / channel)));
        for (index_t i = 0; i < height; i += tile_size) {
          for (index_t j = 0; j < width; j += tile_size) {
            index_t end_i = std::min(i + tile_size, height);
            index_t end_j = std::min(j + tile_size, width);
            for (index_t tile_i = i; tile_i < end_i; ++tile_i) {
              for (index_t tile_j = j; tile_j < end_j; ++tile_j) {
                memcpy(output + (tile_j * stride_i + tile_i) * channel,
                       input + (tile_i * stride_j + tile_j) * channel,
                       channel_raw_size);
              }
            }
          }
        }
      } else {
        std::vector<index_t>
            in_stride{input_shape[1] * input_shape[2] * input_shape[3],
                      input_shape[2] * input_shape[3], input_shape[3], 1};
        std::vector<index_t>
            out_stride{output_shape[1] * output_shape[2] * output_shape[3],
                       output_shape[2] * output_shape[3], output_shape[3], 1};
  
        std::vector<index_t> idim(4, 0);
        std::vector<index_t> odim(4, 0);
        for (odim[0] = 0; odim[0] < output_shape[0]; ++odim[0]) {
          for (odim[1] = 0; odim[1] < output_shape[1]; ++odim[1]) {
            for (odim[2] = 0; odim[2] < output_shape[2]; ++odim[2]) {
              for (odim[3] = 0; odim[3] < output_shape[3]; ++odim[3]) {
                idim[dst_dims[0]] = odim[0];
                idim[dst_dims[1]] = odim[1];
                idim[dst_dims[2]] = odim[2];
                idim[dst_dims[3]] = odim[3];
  
                output[odim[0] * out_stride[0] + odim[1] * out_stride[1]
                    + odim[2] * out_stride[2] + odim[3]] =
                    input[idim[0] * in_stride[0] + idim[1] * in_stride[1]
                        + idim[2] * in_stride[2] + idim[3]];
              }
            }
          }
        }
      }
    } else {
      MACE_NOT_IMPLEMENTED;
    }
  
    return MaceStatus::MACE_SUCCESS;
  }
  
  }  // namespace ops
  }  // namespace mace
  
  #endif  // MACE_OPS_COMMON_TRANSPOSE_H_
  