1 // Tencent is pleased to support the open source community by making ncnn available.
2 //
3 // Copyright (C) 2017 THL A29 Limited, a Tencent company. All rights reserved.
4 //
5 // Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
6 // in compliance with the License. You may obtain a copy of the License at
7 //
8 // https://opensource.org/licenses/BSD-3-Clause
9 //
10 // Unless required by applicable law or agreed to in writing, software distributed
11 // under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
12 // CONDITIONS OF ANY KIND, either express or implied. See the License for the
13 // specific language governing permissions and limitations under the License.
14 
15 #include "spp.h"
16 
17 #include <math.h>
18 
19 namespace ncnn {
20 
SPP()21 SPP::SPP()
22 {
23     one_blob_only = true;
24     support_inplace = false;
25 }
26 
load_param(const ParamDict & pd)27 int SPP::load_param(const ParamDict& pd)
28 {
29     pooling_type = pd.get(0, 0);
30     pyramid_height = pd.get(1, 1);
31 
32     return 0;
33 }
34 
forward(const Mat & bottom_blob,Mat & top_blob,const Option & opt) const35 int SPP::forward(const Mat& bottom_blob, Mat& top_blob, const Option& opt) const
36 {
37     size_t elemsize = bottom_blob.elemsize;
38 
39     // 1 + 4 + 16 + 64 + ... + (2*pyramid_height)^2
40     int pyramid_num_bins = ((1 << (pyramid_height * 2)) - 1) / 3;
41     top_blob.create(pyramid_num_bins, 1, 2, elemsize, opt.blob_allocator);
42     if (top_blob.empty())
43         return -100;
44 
45     float* pyramid_ptr = top_blob;
46 
47     // all spatial pyramids
48     for (int p = 0; p < pyramid_height; p++)
49     {
50         int w = bottom_blob.w;
51         int h = bottom_blob.h;
52         int channels = bottom_blob.c;
53 
54         int num_bins = 1 << p;
55 
56         int kernel_h = ceil(h / (float)num_bins);
57         int stride_h = kernel_h;
58         int remainder_h = stride_h * num_bins - h;
59         int pad_h = (remainder_h + 1) / 2;
60 
61         int kernel_w = ceil(w / (float)num_bins);
62         int stride_w = kernel_w;
63         int remainder_w = stride_w * num_bins - w;
64         int pad_w = (remainder_w + 1) / 2;
65 
66         // max value in NxN window
67         // avg value in NxN window
68 
69         int outw = num_bins;
70         int outh = num_bins;
71 
72         Mat bottom_blob_bordered = bottom_blob;
73         if (pad_h > 0 || pad_w > 0)
74         {
75             copy_make_border(bottom_blob, bottom_blob_bordered, pad_h, pad_h, pad_w, pad_w, BORDER_CONSTANT, 0.f, opt);
76             if (bottom_blob_bordered.empty())
77                 return -100;
78 
79             w = bottom_blob_bordered.w;
80             h = bottom_blob_bordered.h;
81         }
82 
83         const int maxk = kernel_h * kernel_w;
84 
85         // kernel offsets
86         std::vector<int> _space_ofs(maxk);
87         int* space_ofs = &_space_ofs[0];
88         {
89             int p1 = 0;
90             int p2 = 0;
91             int gap = w - kernel_w;
92             for (int i = 0; i < kernel_h; i++)
93             {
94                 for (int j = 0; j < kernel_w; j++)
95                 {
96                     space_ofs[p1] = p2;
97                     p1++;
98                     p2++;
99                 }
100                 p2 += gap;
101             }
102         }
103 
104         if (pooling_type == PoolMethod_MAX)
105         {
106             #pragma omp parallel for num_threads(opt.num_threads)
107             for (int q = 0; q < channels; q++)
108             {
109                 const Mat m(w, h, bottom_blob_bordered.channel(q));
110                 float* outptr = pyramid_ptr + outh * outw * q;
111 
112                 for (int i = 0; i < outh; i++)
113                 {
114                     for (int j = 0; j < outw; j++)
115                     {
116                         const float* sptr = m.row(i * stride_h) + j * stride_w;
117 
118                         float max = sptr[0];
119 
120                         for (int k = 0; k < maxk; k++)
121                         {
122                             float val = sptr[space_ofs[k]];
123                             max = std::max(max, val);
124                         }
125 
126                         outptr[j] = max;
127                     }
128 
129                     outptr += outw;
130                 }
131             }
132         }
133         else if (pooling_type == PoolMethod_AVE)
134         {
135             #pragma omp parallel for num_threads(opt.num_threads)
136             for (int q = 0; q < channels; q++)
137             {
138                 const Mat m(w, h, bottom_blob_bordered.channel(q));
139                 float* outptr = pyramid_ptr + outh * outw * q;
140 
141                 for (int i = 0; i < outh; i++)
142                 {
143                     for (int j = 0; j < outw; j++)
144                     {
145                         const float* sptr = m.row(i * stride_h) + j * stride_w;
146 
147                         float sum = 0;
148 
149                         for (int k = 0; k < maxk; k++)
150                         {
151                             float val = sptr[space_ofs[k]];
152                             sum += val;
153                         }
154 
155                         outptr[j] = sum / maxk;
156                     }
157 
158                     outptr += outw;
159                 }
160             }
161         }
162 
163         pyramid_ptr += channels * outh * outw;
164     }
165 
166     return 0;
167 }
168 
169 } // namespace ncnn
170