pool_2x2.cpp 10.3 KB
Newer Older
W
wangliu 已提交
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
/* Copyright (c) 2018 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. */

#ifdef POOL_OP
16 17 18
#include "operators/math/pool_2x2.h"
#include <algorithm>
#include <vector>
W
wangliu 已提交
19 20 21 22

namespace paddle_mobile {
namespace operators {
namespace math {
23
#define FLT_MAX __FLT_MAX__
W
wangliu 已提交
24

25 26
void Pool2x2Maxs2p0(vector<int> strides, vector<int> paddings,
                    const Tensor *input, Tensor *output) {
W
wangliu 已提交
27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43
  const int batch_size = input->dims()[0];
  const int input_height = input->dims()[2];
  const int input_width = input->dims()[3];

  const int output_channels = output->dims()[1];
  int output_height = output->dims()[2];
  const int output_width = output->dims()[3];
  const int ksize_height = 2;
  const int ksize_width = 2;
  const int stride_height = strides[0];
  const int stride_width = strides[1];
  const int padding_height = paddings[0];
  const int padding_width = paddings[1];

  const int input_channel_stride = input_height * input_width;
  const int output_channel_stride = output_height * output_width;

44 45 46
  const int input_batch_stride = output_channels * input_channel_stride;
  const int output_batch_stride = output_channels * output_channel_stride;

W
wangliu 已提交
47 48 49
  const float *input_data = input->data<float>();
  float *output_data = output->mutable_data<float>();

50 51 52 53 54
  int w1 = input_width / 16;
  int _w1 = input_width % 16;
  int w2 = _w1 / 4;
  int _w2 = _w1 % 4;

W
wangliu 已提交
55 56
  for (int i = 0; i < batch_size; ++i) {
    for (int c = 0; c < output_channels; ++c) {
57 58 59 60 61 62 63 64 65
      for (int ph = 0; ph < input_height; ph += 2) {
        const float *in_ptr1 = input_data + i * input_batch_stride +
                               c * input_channel_stride + ph * input_width;
        const float *in_ptr2 = in_ptr1 + input_width;
        if (ph + 1 >= input_height) {
          in_ptr2 = static_cast<float *>(
              paddle_mobile::memory::Alloc(sizeof(float) * input_width));
          memset(static_cast<void *>(const_cast<float *>(in_ptr2)), -FLT_MAX,
                 sizeof(float) * input_width);
W
wangliu 已提交
66
        }
67 68 69 70 71 72 73 74 75 76 77 78 79 80
        float *out_ptr = output_data + i * output_batch_stride +
                         c * output_channel_stride + ph / 2 * output_width;
        asm volatile(
            "subs       %[w1], %[w1], #1        \n\t"
            "blt        end_w1_%=               \n\t"
            "loop_w1_%=:                        \n\t"

            "pld        [%[in_ptr1], #64]       \n\t"
            "pld        [%[in_ptr2], #64]       \n\t"

            "vld1.f32   {q0, q1},   [%[in_ptr1]]!   \n\t"
            "vld1.f32   {q2, q3},   [%[in_ptr2]]!   \n\t"
            "vld1.f32   {q6, q7},   [%[in_ptr1]]!   \n\t"
            "vld1.f32   {q8, q9},   [%[in_ptr2]]!   \n\t"
W
wangliu 已提交
81

82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134
            "vmax.f32   q0,     q0,   q2        \n\t"
            "vmax.f32   q1,     q1,   q3        \n\t"

            "vmax.f32   q6,     q6,   q8        \n\t"
            "vmax.f32   q7,     q7,   q9        \n\t"

            "vpmax.f32  d8,     d0,   d1        \n\t"
            "vpmax.f32  d9,     d2,   d3        \n\t"

            "vpmax.f32  d10,    d12,  d13       \n\t"
            "vpmax.f32  d11,    d14,  d15       \n\t"

            "vst1.32  {q4, q5},  [%[out_ptr]]!  \n\t"

            "subs       %[w1], %[w1], #1        \n\t"
            "bge        loop_w1_%=              \n\t"
            "end_w1_%=:                         \n\t"

            "subs       %[w2], %[w2], #1        \n\t"
            "blt        end_w2_%=               \n\t"
            "loop_w2_%=:                        \n\t"

            "vld1.f32   {q0},   [%[in_ptr1]]!   \n\t"
            "vld1.f32   {q1},   [%[in_ptr2]]!   \n\t"
            "vmax.f32   q0,     q0,   q1        \n\t"
            "vpmax.f32  d4,     d0,   d1        \n\t"
            "vst1.32    {d4},   [%[out_ptr]]!   \n\t"

            "subs       %[w2], %[w2], #1        \n\t"
            "bge        loop_w2_%=              \n\t"
            "end_w2_%=:                         \n\t"
            :
            : [w1] "r"(w1), [w2] "r"(w2), [in_ptr1] "r"(in_ptr1),
              [in_ptr2] "r"(in_ptr2), [out_ptr] "r"(out_ptr)
            : "memory", "q0", "q1", "q2", "q3", "q4", "q5", "q6", "q7", "q8",
              "q9");

        if (_w2 != 0) {
          in_ptr1 += 16 * w1 + 4 * w2;
          in_ptr2 += 16 * w1 + 4 * w2;
          out_ptr += 8 * w1 + 2 * w2;
          if (_w2 == 1) {
            *out_ptr = (*in_ptr1 > *in_ptr2) ? *in_ptr1 : *in_ptr2;
          } else if (_w2 == 2) {
            float temp = (*in_ptr1++ > *in_ptr2++) ? *in_ptr1++ : *in_ptr2++;
            float temp1 = (*in_ptr1 > *in_ptr2) ? *in_ptr1 : *in_ptr2;
            *out_ptr = (temp > temp1) ? temp : temp1;
          } else if (_w2 == 3) {
            float temp = (*in_ptr1++ > *in_ptr2++) ? *in_ptr1++ : *in_ptr2++;
            float temp1 = (*in_ptr1++ > *in_ptr2++) ? *in_ptr1++ : *in_ptr2++;
            *out_ptr++ = (temp > temp1) ? temp : temp1;
            *out_ptr = (*in_ptr1 > *in_ptr2) ? *in_ptr1 : *in_ptr2;
          }
W
wangliu 已提交
135 136 137 138 139 140
        }
      }
    }
  }
}

141 142
void Pool2x2Avgs2p0(vector<int> strides, vector<int> paddings,
                    const Tensor *input, Tensor *output) {
L
liuruilong 已提交
143
  const int batch_size = input->dims()[0];
W
wangliu 已提交
144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159
  const int input_height = input->dims()[2];
  const int input_width = input->dims()[3];

  const int output_channels = output->dims()[1];
  int output_height = output->dims()[2];
  const int output_width = output->dims()[3];
  const int ksize_height = 2;
  const int ksize_width = 2;
  const int stride_height = strides[0];
  const int stride_width = strides[1];
  const int padding_height = paddings[0];
  const int padding_width = paddings[1];

  const int input_channel_stride = input_height * input_width;
  const int output_channel_stride = output_height * output_width;

160 161 162
  const int input_batch_stride = output_channels * input_channel_stride;
  const int output_batch_stride = output_channels * output_channel_stride;

W
wangliu 已提交
163 164 165
  const float *input_data = input->data<float>();
  float *output_data = output->mutable_data<float>();

166 167 168 169 170 171
  int w1 = input_width / 16;
  int _w1 = input_width % 16;
  int w2 = _w1 / 4;
  int _w2 = _w1 % 4;

  float quarter = 1 / 4;
W
wangliu 已提交
172 173
  for (int i = 0; i < batch_size; ++i) {
    for (int c = 0; c < output_channels; ++c) {
174 175 176 177 178 179 180 181 182
      for (int ph = 0; ph < input_height; ph += 2) {
        const float *in_ptr1 = input_data + i * input_batch_stride +
                               c * input_channel_stride + ph * input_width;
        const float *in_ptr2 = in_ptr1 + input_width;
        if (ph + 1 >= input_height) {
          in_ptr2 = static_cast<float *>(
              paddle_mobile::memory::Alloc(sizeof(float) * input_width));
          memset(static_cast<void *>(const_cast<float *>(in_ptr2)), 0,
                 sizeof(float) * input_width);
W
wangliu 已提交
183
        }
184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201
        float *out_ptr = output_data + i * output_batch_stride +
                         c * output_channel_stride + ph / 2 * output_width;
        asm volatile(
            "subs       %[w1], %[w1], #1        \n\t"
            "blt        end_w1_%=               \n\t"
            "loop_w1_%=:                        \n\t"

            "pld        [%[in_ptr1], #64]       \n\t"
            "pld        [%[in_ptr2], #64]       \n\t"

            "vmov.f32   d0[0],      %[quarter]      \n\t"
            "vld1.f32   {q1, q2},   [%[in_ptr1]]!   \n\t"
            "vld1.f32   {q3, q4},   [%[in_ptr2]]!   \n\t"
            "vld1.f32   {q7, q8},   [%[in_ptr1]]!   \n\t"
            "vld1.f32   {q9, q10},  [%[in_ptr2]]!   \n\t"

            "vadd.f32   q1,     q1,   q3        \n\t"
            "vadd.f32   q2,     q2,   q4        \n\t"
W
wangliu 已提交
202

203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263
            "vadd.f32   q7,     q7,   q9        \n\t"
            "vadd.f32   q8,     q8,   q10       \n\t"

            "vpadd.f32  d10,    d2,   d3        \n\t"
            "vpadd.f32  d11,    d4,   d5        \n\t"

            "vpadd.f32  d12,    d14,  d15       \n\t"
            "vpadd.f32  d13,    d16,  d17       \n\t"

            "vmul.f32   q5,     q5,   d0[0]     \n\t"
            "vmul.f32   q6,     q6,   d0[0]     \n\t"

            "vst1.32  {q5, q6},  [%[out_ptr]]!  \n\t"

            "subs       %[w1], %[w1], #1        \n\t"
            "bge        loop_w1_%=              \n\t"
            "end_w1_%=:                         \n\t"

            "subs       %[w2], %[w2], #1        \n\t"
            "blt        end_w2_%=               \n\t"
            "loop_w2_%=:                        \n\t"

            "vld1.f32   {q1},   [%[in_ptr1]]!   \n\t"
            "vld1.f32   {q2},   [%[in_ptr2]]!   \n\t"
            "vadd.f32   q1,     q1,   q2        \n\t"
            "vpadd.f32  d4,     d2,   d3        \n\t"
            "vmul.f32   d4,     d4,   d0[0]     \n\t"
            "vst1.32    {d4},   [%[out_ptr]]!   \n\t"

            "subs       %[w2], %[w2], #1        \n\t"
            "bge        loop_w2_%=              \n\t"
            "end_w2_%=:                         \n\t"
            :
            : [w1] "r"(w1), [w2] "r"(w2), [in_ptr1] "r"(in_ptr1),
              [in_ptr2] "r"(in_ptr2), [out_ptr] "r"(out_ptr),
              [quarter] "r"(quarter)
            : "memory", "q0", "q1", "q2", "q3", "q4", "q5", "q6", "q7", "q8",
              "q9", "q10");

        if (_w2 != 0) {
          in_ptr1 += 16 * w1 + 4 * w2;
          in_ptr2 += 16 * w1 + 4 * w2;
          out_ptr += 8 * w1 + 2 * w2;
          if (_w2 == 1) {
            *out_ptr = 0.5 * (*in_ptr1 + *in_ptr2);
          } else if (_w2 == 2) {
            float temp = 0;
            temp += *in_ptr1++;
            temp += *in_ptr2++;
            temp += *in_ptr1;
            temp += *in_ptr2;
            *out_ptr = 0.5 * temp;
          } else if (_w2 == 3) {
            float temp = 0;
            temp += *in_ptr1++;
            temp += *in_ptr2++;
            temp += *in_ptr1++;
            temp += *in_ptr2++;
            *out_ptr++ = 0.5 * temp;
            *out_ptr = 0.5 * (*in_ptr1 + *in_ptr2);
          }
W
wangliu 已提交
264 265 266 267 268 269 270 271 272 273 274 275 276
        }
      }
    }
  }
}

//}
}  // namespace math

}  // namespace operators
}  // namespace paddle_mobile

#endif