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提交 a57307ef 编写于 作者: Y yangfei
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implement multithreading 3x3 s1 depthwise_conv

上级 bf3f9183
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Showing with 413 addition and 165 deletion
src/operators/math/depthwise_conv_3x3.cpp
浏览文件 @ a57307ef
...@@ -529,6 +529,252 @@ void DepthwiseConvAddBNRelu3x3s1p1(const Tensor *input, const Tensor *filter, ...@@ -529,6 +529,252 @@ void DepthwiseConvAddBNRelu3x3s1p1(const Tensor *input, const Tensor *filter,
const float *newscale_data = new_scale->data<float>(); const float *newscale_data = new_scale->data<float>();
const float *newbias_data = new_bias->data<float>(); const float *newbias_data = new_bias->data<float>();
const int batch_size = static_cast<int>(input->dims()[0]);
const int input_channel = static_cast<int>(input->dims()[1]);
const int input_height = static_cast<int>(input->dims()[2]);
const int input_width = static_cast<int>(input->dims()[3]);
const int output_height = static_cast<int>(output->dims()[2]);
const int output_width = static_cast<int>(output->dims()[3]);
const int hxw = input_height * input_width;
const int l = input_height;
float32x4_t vnewbias = vdupq_n_f32(0.0);
float32x4_t vnewscale = vdupq_n_f32(1.0);
float32x4_t vzero = vdupq_n_f32(0);
for (int b = 0; b < batch_size; b++) {
filter_data = filter->data<float>();
for (int c = 0; c < input_channel; c++) {
vnewbias = vdupq_n_f32(newbias_data[c]);
vnewscale = vdupq_n_f32(newscale_data[c]);
float w00 = filter_data[0];
float w01 = filter_data[1];
float w02 = filter_data[2];
float w10 = filter_data[3];
float w11 = filter_data[4];
float w12 = filter_data[5];
float w20 = filter_data[6];
float w21 = filter_data[7];
float w22 = filter_data[8];
output_data[0] = w11 * input_data[0] + w12 * input_data[1] +
w21 * input_data[l] + w22 * input_data[l + 1];
output_data[l - 1] = w10 * input_data[l - 2] + w11 * input_data[l - 1] +
w20 * input_data[2 * l - 2] +
w21 * input_data[2 * l - 1];
output_data[(l - 1) * l] =
w01 * input_data[(l - 2) * l] + w02 * input_data[(l - 2) * l + 1] +
w11 * input_data[(l - 1) * l] + w12 * input_data[(l - 1) * l + 1];
output_data[l * l - 1] = w00 * input_data[(l - 2) * (l + 1)] +
w01 * input_data[(l - 2) * (l + 1) + 1] +
w10 * input_data[l * l - 2] +
w11 * input_data[l * l - 1];
output_data[0] = output_data[0] * newscale_data[c] + newbias_data[c];
output_data[l - 1] =
output_data[l - 1] * newscale_data[c] + newbias_data[c];
output_data[(l - 1) * l] =
output_data[(l - 1) * l] * newscale_data[c] + newbias_data[c];
output_data[l * l - 1] =
output_data[l * l - 1] * newscale_data[c] + newbias_data[c];
if (if_relu) {
output_data[0] = output_data[0] < 0 ? 0 : output_data[0];
output_data[l - 1] = output_data[l - 1] < 0 ? 0 : output_data[l - 1];
output_data[(l - 1) * l] =
output_data[(l - 1) * l] < 0 ? 0 : output_data[(l - 1) * l];
output_data[l * l - 1] =
output_data[l * l - 1] < 0 ? 0 : output_data[l * l - 1];
}
for (int i = 1; i < l - 1; ++i) {
output_data[i * l] =
w01 * input_data[i * l - l] + w02 * input_data[i * l - l + 1] +
w11 * input_data[i * l] + w12 * input_data[i * l + 1] +
w21 * input_data[i * l + l] + w22 * input_data[i * l + l + 1];
output_data[i * l + l - 1] = w00 * input_data[i * l + l - 1 - l - 1] +
w01 * input_data[i * l + l - 1 - l] +
w10 * input_data[i * l + l - 1 - 1] +
w11 * input_data[i * l + l - 1] +
w20 * input_data[i * l + l - 1 + l - 1] +
w21 * input_data[i * l + l - 1 + l];
output_data[i * l] =
output_data[i * l] * newscale_data[c] + newbias_data[c];
output_data[i * l + l - 1] =
output_data[i * l + l - 1] * newscale_data[c] + newbias_data[c];
if (if_relu) {
output_data[i * l] = output_data[i * l] < 0 ? 0 : output_data[i * l];
output_data[i * l + l - 1] =
output_data[i * l + l - 1] < 0 ? 0 : output_data[i * l + l - 1];
}
}
int m;
for (m = 1; m < output_width - 4; m += 4) {
float *output_ptr = output_data + m;
float32x4_t in0, in1, in2, in3, tmp0, tmp1, tmp2, tmp3, out0;
in0 = vld1q_f32(input_data + m - 1);
in1 = vld1q_f32(input_data + m + 3);
in2 = vld1q_f32(input_data + input_width + m - 1);
in3 = vld1q_f32(input_data + input_width + m + 3);
tmp0 = vextq_f32(in0, in1, 1);
tmp1 = vextq_f32(in0, in1, 2);
tmp2 = vextq_f32(in2, in3, 1);
tmp3 = vextq_f32(in2, in3, 2);
out0 = vmulq_n_f32(in0, w10);
out0 = vmlaq_n_f32(out0, tmp0, w11);
out0 = vmlaq_n_f32(out0, tmp1, w12);
out0 = vmlaq_n_f32(out0, in2, w20);
out0 = vmlaq_n_f32(out0, tmp2, w21);
out0 = vmlaq_n_f32(out0, tmp3, w22);
out0 = vmlaq_f32(vnewbias, vnewscale, out0);
if (if_relu) {
out0 = vmaxq_f32(out0, vzero);
}
vst1q_f32(output_ptr, out0);
}
for (m = 1; (m + 3) < output_width - 1; m = m + 4) {
}
for (int j = m; j < output_width - 1; j++) {
output_data[j] = input_data[j - 1] * w10 + input_data[j] * w11 +
input_data[j + 1] * w12 +
input_data[input_width + j - 1] * w20 +
input_data[input_width + j] * w21 +
input_data[input_width + j + 1] * w22;
output_data[j] = output_data[j] * newscale_data[c] + newbias_data[c];
if (if_relu) {
output_data[j] = output_data[j] < 0 ? 0 : output_data[j];
}
}
for (m = 1; (m + 3) < output_width - 1; m = m + 4) {
float *output_ptr =
output_data + (output_height - 1) * output_width + m;
float32x4_t in0, in1, in2, in3, tmp0, tmp1, tmp2, tmp3, out0;
in0 = vld1q_f32(input_data + (output_height - 2) * input_width + m - 1);
in1 = vld1q_f32(input_data + (output_height - 2) * input_width + m + 3);
in2 = vld1q_f32(input_data + (output_height - 1) * input_width + m - 1);
in3 = vld1q_f32(input_data + (output_height - 1) * input_width + m + 3);
tmp0 = vextq_f32(in0, in1, 1);
tmp1 = vextq_f32(in0, in1, 2);
tmp2 = vextq_f32(in2, in3, 1);
tmp3 = vextq_f32(in2, in3, 2);
out0 = vmulq_n_f32(in0, w00);
out0 = vmlaq_n_f32(out0, tmp0, w01);
out0 = vmlaq_n_f32(out0, tmp1, w02);
out0 = vmlaq_n_f32(out0, in2, w10);
out0 = vmlaq_n_f32(out0, tmp2, w11);
out0 = vmlaq_n_f32(out0, tmp3, w12);
out0 = vmlaq_f32(vnewbias, vnewscale, out0);
if (if_relu) {
out0 = vmaxq_f32(out0, vzero);
}
vst1q_f32(output_ptr, out0);
}
for (m = 1; (m + 3) < output_width - 1; m = m + 4) {
}
for (int j = m; j < output_width - 1; j++) {
output_data[(output_height - 1) * input_width + j] =
input_data[(output_height - 2) * input_width + j - 1] * w00 +
input_data[(output_height - 2) * input_width + j] * w01 +
input_data[(output_height - 2) * input_width + j + 1] * w02 +
input_data[(output_height - 1) * input_width + j - 1] * w10 +
input_data[(output_height - 1) * input_width + j] * w11 +
input_data[(output_height - 1) * input_width + j + 1] * w12;
output_data[(output_height - 1) * output_width + j] =
output_data[(output_height - 1) * output_width + j] *
newscale_data[c] +
newbias_data[c];
if (if_relu) {
output_data[(output_height - 1) * output_width + j] =
output_data[(output_height - 1) * output_width + j] < 0
? 0
: output_data[(output_height - 1) * output_width + j];
}
}
#pragma omp parallel for
for (int i = 1; i < output_height - 1; i++) {
for (int m = 1; (m + 3) < output_width - 1; m = m + 4) {
float *output_ptr = output_data + i * output_width + m;
float32x4_t in0, in1, in2, in3, in4, in5, tmp0, tmp1, tmp2, tmp3,
tmp4, tmp5, out0;
in0 = vld1q_f32(input_data + (i - 1) * input_width + m - 1);
in1 = vld1q_f32(input_data + (i - 1) * input_width + m + 3);
in2 = vld1q_f32(input_data + i * input_width + m - 1);
in3 = vld1q_f32(input_data + i * input_width + m + 3);
in4 = vld1q_f32(input_data + (i + 1) * input_width + m - 1);
in5 = vld1q_f32(input_data + (i + 1) * input_width + m + 3);
tmp0 = vextq_f32(in0, in1, 1);
tmp1 = vextq_f32(in0, in1, 2);
tmp2 = vextq_f32(in2, in3, 1);
tmp3 = vextq_f32(in2, in3, 2);
tmp4 = vextq_f32(in4, in5, 1);
tmp5 = vextq_f32(in4, in5, 2);
out0 = vmulq_n_f32(in0, w00);
out0 = vmlaq_n_f32(out0, tmp0, w01);
out0 = vmlaq_n_f32(out0, tmp1, w02);
out0 = vmlaq_n_f32(out0, in2, w10);
out0 = vmlaq_n_f32(out0, tmp2, w11);
out0 = vmlaq_n_f32(out0, tmp3, w12);
out0 = vmlaq_n_f32(out0, in4, w20);
out0 = vmlaq_n_f32(out0, tmp4, w21);
out0 = vmlaq_n_f32(out0, tmp5, w22);
out0 = vmlaq_f32(vnewbias, vnewscale, out0);
if (if_relu) {
out0 = vmaxq_f32(out0, vzero);
}
vst1q_f32(output_ptr, out0);
}
int m;
for (m = 1; (m + 3) < output_width - 1; m = m + 4) {
}
for (int j = m; j < output_width - 1; j++) {
output_data[i * output_width + j] =
input_data[(i - 1) * input_width + j - 1] * w00 +
input_data[(i - 1) * input_width + j] * w01 +
input_data[(i - 1) * input_width + j + 1] * w02 +
input_data[(i)*input_width + j - 1] * w10 +
input_data[(i)*input_width + j] * w11 +
input_data[(i)*input_width + j + 1] * w12 +
input_data[(i + 1) * input_width + j - 1] * w20 +
input_data[(i + 1) * input_width + j] * w21 +
input_data[(i + 1) * input_width + j + 1] * w22;
output_data[i * output_width + j] =
newscale_data[c] * output_data[i * output_width + j] +
newbias_data[c];
if (if_relu) {
output_data[i * output_width + j] =
output_data[i * output_width + j] < 0
? 0
: output_data[i * output_width + j];
}
}
}
input_data = input_data + hxw;
output_data = output_data + hxw;
filter_data = filter_data + 9;
}
}
/*
const float *input_data = input->data<float>();
const float *filter_data = filter->data<float>();
float *output_data = output->data<float>();
const float *newscale_data = new_scale->data<float>();
const float *newbias_data = new_bias->data<float>();
const int h = static_cast<int>(input->dims()[2]); const int h = static_cast<int>(input->dims()[2]);
const int w = static_cast<int>(input->dims()[3]); const int w = static_cast<int>(input->dims()[3]);
const int l = h; const int l = h;
...@@ -605,8 +851,8 @@ void DepthwiseConvAddBNRelu3x3s1p1(const Tensor *input, const Tensor *filter, ...@@ -605,8 +851,8 @@ void DepthwiseConvAddBNRelu3x3s1p1(const Tensor *input, const Tensor *filter,
output_data[i * l + l - 1] * newscale_data[j] + newbias_data[j]; output_data[i * l + l - 1] * newscale_data[j] + newbias_data[j];
if (if_relu) { if (if_relu) {
output_data[i * l] = output_data[i * l] < 0 ? 0 : output_data[i * l]; output_data[i * l] = output_data[i * l] < 0 ? 0 : output_data[i *
output_data[i * l + l - 1] = l]; output_data[i * l + l - 1] =
output_data[i * l + l - 1] < 0 ? 0 : output_data[i * l + l - 1]; output_data[i * l + l - 1] < 0 ? 0 : output_data[i * l + l - 1];
} }
} }
...@@ -738,6 +984,7 @@ void DepthwiseConvAddBNRelu3x3s1p1(const Tensor *input, const Tensor *filter, ...@@ -738,6 +984,7 @@ void DepthwiseConvAddBNRelu3x3s1p1(const Tensor *input, const Tensor *filter,
} }
// mid // mid
for (int i = 0; i < l - 2; ++i) { for (int i = 0; i < l - 2; ++i) {
auto output_ptr = output_data + (i + 1) * l + 1; auto output_ptr = output_data + (i + 1) * l + 1;
input_tmp = input_data + i * l; input_tmp = input_data + i * l;
...@@ -820,6 +1067,7 @@ void DepthwiseConvAddBNRelu3x3s1p1(const Tensor *input, const Tensor *filter, ...@@ -820,6 +1067,7 @@ void DepthwiseConvAddBNRelu3x3s1p1(const Tensor *input, const Tensor *filter,
filter_data_tmp += 9; filter_data_tmp += 9;
} }
} }
*/
#endif #endif
} }
......
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