text: /* Copyright (c) 2019 PaddlePaddle 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. */
  
  #include "lite/backends/arm/math/reduce_min.h"
  #include "lite/backends/arm/math/funcs.h"
  #include "lite/core/tensor.h"
  
  namespace paddle {
  namespace lite {
  namespace arm {
  namespace math {
  
  template <>
  void reduce_min_n<float>(const float* src,
                           float* dst,
                           int num_in,
                           int channel_in,
                           int height_in,
                           int width_in) {
    int hw_size = height_in * width_in;
    int chw_size = channel_in * hw_size;
    int data_index, src_index, src_index0;
    for (int c = 0; c < channel_in; ++c) {
      for (int h = 0; h < height_in; ++h) {
        for (int w = 0; w < width_in; ++w) {
          data_index = c * hw_size + h * width_in + w;
          dst[data_index] = src[data_index];
          for (int n = 1; n < num_in; ++n) {
            src_index = n * chw_size + data_index;
            dst[data_index] = dst[data_index] < src[src_index] ? dst[data_index]
                                                               : src[src_index];
          }
        }
      }
    }
  }
  
  template <>
  void reduce_min_first_of_three<float>(
      const float* src, float* dst, int first_in, int second_in, int third_in) {
    for (int i = 0; i < second_in; i++) {
      for (int j = 0; j < third_in; j++) {
        dst[i * third_in + j] = src[i * third_in + j];
        for (int k = 1; k < first_in; k++) {
          dst[i * third_in + j] =
              src[k * second_in * third_in + i * third_in + j] <
                      dst[i * third_in + j]
                  ? src[k * second_in * third_in + i * third_in + j]
                  : dst[i * third_in + j];
        }
      }
    }
  }
  
  template <>
  void reduce_min_second_of_three<float>(
      const float* src, float* dst, int first_in, int second_in, int third_in) {
    for (int i = 0; i < first_in; i++) {
      for (int j = 0; j < third_in; j++) {
        dst[i * third_in + j] = src[i * second_in * third_in + j];
        for (int k = 1; k < second_in; k++) {
          dst[i * third_in + j] =
              src[i * second_in * third_in + third_in * k + j] <
                      dst[i * third_in + j]
                  ? src[i * second_in * third_in + third_in * k + j]
                  : dst[i * third_in + j];
        }
      }
    }
  }
  
  template <>
  void reduce_min_third_of_three<float>(
      const float* src, float* dst, int first_in, int second_in, int third_in) {
    for (int i = 0; i < first_in; i++) {
      for (int j = 0; j < second_in; j++) {
        dst[i * second_in + j] = src[i * second_in * third_in + j * second_in];
        for (int k = 0; k < third_in; k++) {
          dst[i * second_in + j] =
              src[i * second_in * third_in + j * second_in + k] <
                      dst[i * second_in + j]
                  ? src[i * second_in * third_in + j * second_in + k]
                  : dst[i * second_in + j];
        }
      }
    }
  }
  
  template <>
  void reduce_min_all_of_three<float>(
      const float* src, float* dst, int first_in, int second_in, int third_in) {
    float min = src[0];
    int total_element = first_in * second_in * third_in;
    for (int i = 0; i < total_element; i++) {
      min = src[i] < min ? src[i] : min;
    }
    dst[0] = min;
  }
  
  template <>
  void reduce_min_c<float>(const float* src,
                           float* dst,
                           int num_in,
                           int channel_in,
                           int height_in,
                           int width_in) {
    int hw_size = height_in * width_in;
    int chw_size = hw_size * channel_in;
    int data_index, src_index0, src_index;
    for (int n = 0; n < num_in; ++n) {
      for (int h = 0; h < height_in; ++h) {
        for (int w = 0; w < width_in; ++w) {
          data_index = n * hw_size + h * width_in + w;
          src_index0 = n * chw_size + h * width_in + w;
          dst[data_index] = src[src_index0];
          for (int c = 1; c < channel_in; ++c) {
            src_index = src_index0 + c * hw_size;
            dst[data_index] = dst[data_index] < src[src_index] ? dst[data_index]
                                                               : src[src_index];
          }
        }
      }
    }
  }
  
  template <>
  void reduce_min_h<float>(const float* src,
                           float* dst,
                           int num_in,
                           int channel_in,
                           int height_in,
                           int width_in) {
    int cw_size = channel_in * width_in;
    int chw_size = cw_size * height_in;
    int hw_size = height_in * width_in;
    int data_index, src_index, src_index0;
    for (int n = 0; n < num_in; ++n) {
      for (int c = 0; c < channel_in; ++c) {
        for (int w = 0; w < width_in; ++w) {
          data_index = n * cw_size + c * width_in + w;
          src_index0 = n * chw_size + c * hw_size + w;
          dst[data_index] = src[src_index0];
          for (int h = 1; h < height_in; ++h) {
            src_index = src_index0 + h * width_in;
            dst[data_index] = dst[data_index] < src[src_index] ? dst[data_index]
                                                               : src[src_index];
          }
        }
      }
    }
  }
  
  template <>
  void reduce_min_w<float>(const float* src,
                           float* dst,
                           int num_in,
                           int channel_in,
                           int height_in,
                           int width_in) {
    int ch_size = channel_in * height_in;
    int hw_size = height_in * width_in;
    int chw_size = ch_size * width_in;
    int data_index = 0;
    int src_index0 = 0;
    int src_index = 0;
    for (int n = 0; n < num_in; ++n) {
      for (int c = 0; c < channel_in; ++c) {
        for (int h = 0; h < height_in; ++h) {
          data_index = n * ch_size + c * height_in + h;
          src_index0 = n * chw_size + c * hw_size + h * width_in;
          dst[data_index] = src[src_index0];
          for (int w = 1; w < width_in; ++w) {
            src_index = src_index0 + w;
            dst[data_index] = dst[data_index] < src[src_index] ? dst[data_index]
                                                               : src[src_index];
          }
        }
      }
    }
  }
  
  template <>
  void reduce_min_all<float>(const float* src,
                             float* dst,
                             int num_in,
                             int channel_in,
                             int height_in,
                             int width_in) {
    float min = src[0];
    int src_index;
    int n_id, c_id;
    for (int n = 0; n < num_in; ++n) {
      n_id = n * channel_in * height_in * width_in;
      for (int c = 0; c < channel_in; ++c) {
        c_id = c * height_in * width_in;
        for (int h = 0; h < height_in; ++h) {
          for (int w = 0; w < width_in; ++w) {
            src_index = n_id + c_id + h * width_in + w;
            min = src[src_index] < min ? src[src_index] : min;
          }
        }
      }
    }
    dst[0] = min;
  }
  
  template <>
  void reduce_min_nc<float>(const float* src,
                            float* dst,
                            int num_in,
                            int channel_in,
                            int height_in,
                            int width_in) {
    // reduce n first.
    DDimLite ddimA({1, channel_in, height_in, width_in});
    lite::Tensor tensor_tmp;
    tensor_tmp.Resize(ddimA);
    float* tmp_out = tensor_tmp.mutable_data<float>();
    reduce_min_n(src, tmp_out, num_in, channel_in, height_in, width_in);
    reduce_min_c(tmp_out, dst, 1, channel_in, height_in, width_in);
  }
  
  template <>
  void reduce_min_ch<float>(const float* src,
                            float* dst,
                            int num_in,
                            int channel_in,
                            int height_in,
                            int width_in) {
    // reduce c first
    DDimLite ddimA({num_in, 1, height_in, width_in});
    lite::Tensor tensor_tmp;
    tensor_tmp.Resize(ddimA);
    float* tmp_out = tensor_tmp.mutable_data<float>();
    reduce_min_c(src, tmp_out, num_in, channel_in, height_in, width_in);
    reduce_min_h(tmp_out, dst, num_in, 1, height_in, width_in);
  }
  
  template <>
  void reduce_min_hw<float>(const float* src,
                            float* dst,
                            int num_in,
                            int channel_in,
                            int height_in,
                            int width_in) {
    // reduce h first
    DDimLite ddimA({num_in, channel_in, 1, width_in});
    lite::Tensor tensor_tmp;
    tensor_tmp.Resize(ddimA);
    float* tmp_out = tensor_tmp.mutable_data<float>();
    reduce_min_h(src, tmp_out, num_in, channel_in, height_in, width_in);
    reduce_min_w(tmp_out, dst, num_in, channel_in, 1, width_in);
  }
  
  }  // namespace math
  }  // namespace arm
  }  // namespace lite
  }  // namespace paddle
  