File size: 7,380 Bytes
be903e2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 | // Tencent is pleased to support the open source community by making ncnn available.
//
// Copyright (C) 2021 THL A29 Limited, a Tencent company. All rights reserved.
//
// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
// in compliance with the License. You may obtain a copy of the License at
//
// https://opensource.org/licenses/BSD-3-Clause
//
// 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 "convolution3d.h"
#include "fused_activation.h"
namespace ncnn {
Convolution3D::Convolution3D()
{
one_blob_only = true;
support_inplace = false;
}
int Convolution3D::load_param(const ParamDict& pd)
{
num_output = pd.get(0, 0);
kernel_w = pd.get(1, 0);
kernel_h = pd.get(11, kernel_w);
kernel_d = pd.get(21, kernel_w);
dilation_w = pd.get(2, 1);
dilation_h = pd.get(12, dilation_w);
dilation_d = pd.get(22, dilation_w);
stride_w = pd.get(3, 1);
stride_h = pd.get(13, stride_w);
stride_d = pd.get(23, stride_w);
pad_left = pd.get(4, 0);
pad_right = pd.get(15, pad_left);
pad_top = pd.get(14, pad_left);
pad_bottom = pd.get(16, pad_top);
pad_front = pd.get(24, pad_left);
pad_behind = pd.get(17, pad_front);
pad_value = pd.get(18, 0.f);
bias_term = pd.get(5, 0);
weight_data_size = pd.get(6, 0);
activation_type = pd.get(9, 0);
activation_params = pd.get(10, Mat());
return 0;
}
int Convolution3D::load_model(const ModelBin& mb)
{
weight_data = mb.load(weight_data_size, 0);
if (weight_data.empty())
return -100;
if (bias_term)
{
bias_data = mb.load(num_output, 1);
if (bias_data.empty())
return -100;
}
return 0;
}
int Convolution3D::forward(const Mat& bottom_blob, Mat& top_blob, const Option& opt) const
{
int w = bottom_blob.w;
int h = bottom_blob.h;
int d = bottom_blob.d;
int channels = bottom_blob.c;
size_t elemsize = bottom_blob.elemsize;
const int kernel_extend_w = dilation_w * (kernel_w - 1) + 1;
const int kernel_extend_h = dilation_h * (kernel_h - 1) + 1;
const int kernel_extend_d = dilation_d * (kernel_d - 1) + 1;
Mat bottom_blob_bordered;
Option opt_pad = opt;
opt_pad.use_packing_layout = false;
make_padding(bottom_blob, bottom_blob_bordered, opt_pad);
if (bottom_blob_bordered.empty())
return -100;
w = bottom_blob_bordered.w;
h = bottom_blob_bordered.h;
d = bottom_blob_bordered.d;
int outw = (w - kernel_extend_w) / stride_w + 1;
int outh = (h - kernel_extend_h) / stride_h + 1;
int outd = (d - kernel_extend_d) / stride_d + 1;
const int maxk = kernel_w * kernel_h * kernel_d;
// kernel offsets
std::vector<int> _space_ofs(maxk);
int* space_ofs = &_space_ofs[0];
{
int p1 = 0;
int p2 = 0;
int gap0 = w * dilation_h - kernel_w * dilation_w;
int gap1 = h * w * dilation_d - w * kernel_h * dilation_h;
for (int z = 0; z < kernel_d; z++)
{
for (int i = 0; i < kernel_h; i++)
{
for (int j = 0; j < kernel_w; j++)
{
space_ofs[p1] = p2;
p1++;
p2 += dilation_w;
}
p2 += gap0;
}
p2 += gap1;
}
}
top_blob.create(outw, outh, outd, num_output, elemsize, opt.blob_allocator);
if (top_blob.empty())
return -100;
#pragma omp parallel for num_threads(opt.num_threads)
for (int p = 0; p < num_output; p++)
{
float* outptr = top_blob.channel(p);
for (int z = 0; z < outd; z++)
{
for (int i = 0; i < outh; i++)
{
for (int j = 0; j < outw; j++)
{
float sum = 0.f;
if (bias_term)
sum = bias_data[p];
const float* kptr = (const float*)weight_data + maxk * channels * p;
for (int q = 0; q < channels; q++)
{
const Mat m = bottom_blob_bordered.channel(q);
const float* sptr = m.depth(z * stride_d).row(i * stride_h) + j * stride_w;
for (int l = 0; l < maxk; l++)
{
float val = sptr[space_ofs[l]];
float wt = kptr[l];
sum += val * wt;
}
kptr += maxk;
}
outptr[j] = activation_ss(sum, activation_type, activation_params);
}
outptr += outw;
}
}
}
return 0;
}
void Convolution3D::make_padding(const Mat& bottom_blob, Mat& bottom_blob_bordered, const Option& opt) const
{
int w = bottom_blob.w;
int h = bottom_blob.h;
int d = bottom_blob.d;
const int kernel_extent_w = dilation_w * (kernel_w - 1) + 1;
const int kernel_extent_h = dilation_h * (kernel_h - 1) + 1;
const int kernel_extent_d = dilation_d * (kernel_d - 1) + 1;
bottom_blob_bordered = bottom_blob;
if (pad_left > 0 || pad_right > 0 || pad_top > 0 || pad_bottom > 0 || pad_front > 0 || pad_behind > 0)
{
Option opt_b = opt;
opt_b.blob_allocator = opt.workspace_allocator;
copy_make_border_3d(bottom_blob, bottom_blob_bordered, pad_top, pad_bottom, pad_left, pad_right, pad_front, pad_behind, BORDER_CONSTANT, pad_value, opt_b);
}
else if (pad_left == -233 && pad_right == -233 && pad_top == -233 && pad_bottom == -233 && pad_front == -233 && pad_behind == -233)
{
// tensorflow padding=SAME or onnx padding=SAME_UPPER
int wpad = kernel_extent_w + (w - 1) / stride_w * stride_w - w;
int hpad = kernel_extent_h + (h - 1) / stride_h * stride_h - h;
int dpad = kernel_extent_d + (d - 1) / stride_d * stride_d - d;
if (wpad > 0 || hpad > 0 || dpad > 0)
{
Option opt_b = opt;
opt_b.blob_allocator = opt.workspace_allocator;
copy_make_border_3d(bottom_blob, bottom_blob_bordered, hpad / 2, hpad - hpad / 2, wpad / 2, wpad - wpad / 2, dpad / 2, dpad - dpad / 2, BORDER_CONSTANT, pad_value, opt_b);
}
}
else if (pad_left == -234 && pad_right == -234 && pad_top == -234 && pad_bottom == -234 && pad_front == -234 && pad_behind == -234)
{
// onnx padding=SAME_LOWER
int wpad = kernel_extent_w + (w - 1) / stride_w * stride_w - w;
int hpad = kernel_extent_h + (h - 1) / stride_h * stride_h - h;
int dpad = kernel_extent_d + (d - 1) / stride_d * stride_d - d;
if (wpad > 0 || hpad > 0 || dpad > 0)
{
Option opt_b = opt;
opt_b.blob_allocator = opt.workspace_allocator;
copy_make_border_3d(bottom_blob, bottom_blob_bordered, hpad - hpad / 2, hpad / 2, wpad - wpad / 2, wpad / 2, dpad / 2, dpad - dpad / 2, BORDER_CONSTANT, pad_value, opt_b);
}
}
}
} // namespace ncnn
|