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提交 85210c6b 编写于 作者: Y yangfei
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repair bug of multithreading depthwise_conv3x3(s2)

上级 efa4963e
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Showing with 198 addition and 191 deletion
src/operators/math/depthwise_conv_3x3.cpp
浏览文件 @ 85210c6b
......@@ -1465,180 +1465,187 @@ void DepthwiseConvAddBNRelu3x3s2p1v2(const Tensor *input, const Tensor *filter,
Tensor *output, const Tensor *new_scale,
const Tensor *new_bias, bool if_relu) {
#if __ARM_NEON
#ifdef _OPENMP
const float *newscale_data = new_scale->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 inhxw = input_height * input_width;
const int outhxw = output_height * output_width;
float32x4_t zero = vdupq_n_f32(0.0);
for (int b = 0; b < batch_size; b++) {
#pragma omp parallel for
for (int c = 0; c < input_channel; c++) {
const float *filter_data = filter->data<float>() + c * 9;
const float *input_data = input->data<float>() + c * inhxw;
float *output_data = output->data<float>() + c * outhxw;
float32x4_t vnewbias = vdupq_n_f32(newbias_data[c]);
float32x4_t 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];
int m;
for (m = 1; m < output_width - 2; m = m + 3) {
float *output_ptr = output_data + m;
float32x4x2_t input_buff_mid{}, input_buff_bottom{};
float32x4_t in0, in1, in2, in3, tmp0, tmp1, tmp2, tmp3, out0;
input_buff_mid = vld2q_f32(input_data + (2 * m - 1));
input_buff_bottom = vld2q_f32(input_data + input_width + (2 * m - 1));
in0 = input_buff_mid.val[0];
tmp0 = input_buff_mid.val[1];
tmp1 = vextq_f32(in0, zero, 1);
in2 = input_buff_bottom.val[0];
tmp2 = input_buff_bottom.val[1];
tmp3 = vextq_f32(in2, zero, 1);
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, zero);
}
vst1q_lane_f32(output_ptr, out0, 0);
vst1q_lane_f32(output_ptr + 1, out0, 1);
vst1q_lane_f32(output_ptr + 2, out0, 2);
}
for (m = 1; m < output_width - 2; m += 3) {
}
for (int j = m; j < output_width; j++) {
output_data[j] = input_data[2 * j - 1] * w10 + input_data[2 * j] * w11 +
input_data[2 * j + 1] * w12 +
input_data[2 * j - 1 + input_width] * w20 +
input_data[2 * j + input_width] * w21 +
input_data[2 * j + 1 + input_width] * w22;
output_data[j] = newscale_data[c] * output_data[j] + newbias_data[c];
if (if_relu) {
output_data[j] = output_data[j] < 0 ? 0 : output_data[j];
}
}
for (int i = 1; i < output_height; i += 1) {
for (int m = 1; m < output_width - 2; m += 3) {
float *output_ptr = output_data + i * output_width + m;
float32x4x2_t input_buff_top{}, input_buff_mid{}, input_buff_bottom{};
float32x4_t in0, in1, in2, in3, in4, in5, tmp0, tmp1, tmp2, tmp3,
tmp4, tmp5, out0;
input_buff_top =
vld2q_f32(input_data + (2 * i - 1) * input_width + (2 * m - 1));
input_buff_mid =
vld2q_f32(input_data + (2 * i) * input_width + (2 * m - 1));
input_buff_bottom =
vld2q_f32(input_data + (2 * i + 1) * input_width + (2 * m - 1));
in0 = input_buff_top.val[0];
tmp0 = input_buff_top.val[1];
tmp1 = vextq_f32(in0, zero, 1);
in2 = input_buff_mid.val[0];
tmp2 = input_buff_mid.val[1];
tmp3 = vextq_f32(in2, zero, 1);
in4 = input_buff_bottom.val[0];
tmp4 = input_buff_bottom.val[1];
tmp5 = vextq_f32(in4, zero, 1);
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, zero);
}
vst1q_lane_f32(output_ptr, out0, 0);
vst1q_lane_f32(output_ptr + 1, out0, 1);
vst1q_lane_f32(output_ptr + 2, out0, 2);
}
int m;
for (m = 1; m < output_width - 2; m += 3) {
}
for (int j = m; j < output_width; j++) {
output_data[i * output_width + j] =
input_data[(2 * i - 1) * input_width + 2 * j - 1] * w00 +
input_data[(2 * i - 1) * input_width + 2 * j] * w01 +
input_data[(2 * i - 1) * input_width + 2 * j + 1] * w02 +
input_data[(2 * i) * input_width + 2 * j - 1] * w10 +
input_data[(2 * i) * input_width + 2 * j] * w11 +
input_data[(2 * i) * input_width + 2 * j + 1] * w12 +
input_data[(2 * i + 1) * input_width + 2 * j - 1] * w20 +
input_data[(2 * i + 1) * input_width + 2 * j] * w21 +
input_data[(2 * i + 1) * input_width + 2 * 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];
}
}
}
output_data[0] = input_data[0] * w11 + input_data[1] * w12 +
input_data[input_height] * w21 +
input_data[input_height + 1] * w22;
output_data[0] = newscale_data[c] * output_data[0] + newbias_data[c];
if (if_relu) {
output_data[0] = output_data[0] < 0 ? 0 : output_data[0];
}
for (int i = 1; i < output_height; i++) {
output_data[i * output_width] =
input_data[(2 * i - 1) * input_width] * w01 +
input_data[(2 * i - 1) * input_width + 1] * w02 +
input_data[(2 * i) * input_width] * w11 +
input_data[(2 * i) * input_width + 1] * w12 +
input_data[(2 * i + 1) * input_width] * w21 +
input_data[(2 * i + 1) * input_width + 1] * w22;
output_data[i * output_width] =
newscale_data[c] * output_data[i * output_width] + newbias_data[c];
if (if_relu) {
output_data[i * output_width] = output_data[i * output_width] < 0
? 0
: output_data[i * output_width];
}
}
}
}
#else
//#ifdef _OPENMP
// const float *newscale_data = new_scale->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 inhxw = input_height * input_width;
// const int outhxw = output_height * output_width;
//
// float32x4_t zero = vdupq_n_f32(0.0);
// for (int b = 0; b < batch_size; b++) {
// #pragma omp parallel for
// for (int c = 0; c < input_channel; c++) {
// const float *filter_data = filter->data<float>() + c * 9;
// const float *input_data = input->data<float>() + c * inhxw;
// float *output_data = output->data<float>() + c * outhxw;
// float32x4_t vnewbias = vdupq_n_f32(newbias_data[c]);
// float32x4_t 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];
//
// int m;
// for (m = 1; m < output_width - 2; m = m + 3) {
// float *output_ptr = output_data + m;
// float32x4x2_t input_buff_mid{}, input_buff_bottom{};
// float32x4_t in0, in1, in2, in3, tmp0, tmp1, tmp2, tmp3, out0;
// input_buff_mid = vld2q_f32(input_data + (2 * m - 1));
// input_buff_bottom = vld2q_f32(input_data + input_width + (2 * m -
// 1));
//
// in0 = input_buff_mid.val[0];
// tmp0 = input_buff_mid.val[1];
// tmp1 = vextq_f32(in0, zero, 1);
//
// in2 = input_buff_bottom.val[0];
// tmp2 = input_buff_bottom.val[1];
// tmp3 = vextq_f32(in2, zero, 1);
//
// 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, zero);
// }
// vst1q_lane_f32(output_ptr, out0, 0);
// vst1q_lane_f32(output_ptr + 1, out0, 1);
// vst1q_lane_f32(output_ptr + 2, out0, 2);
// }
// for (m = 1; m < output_width - 2; m += 3) {
// }
// for (int j = m; j < output_width; j++) {
// output_data[j] = input_data[2 * j - 1] * w10 + input_data[2 * j] *
// w11 +
// input_data[2 * j + 1] * w12 +
// input_data[2 * j - 1 + input_width] * w20 +
// input_data[2 * j + input_width] * w21 +
// input_data[2 * j + 1 + input_width] * w22;
// output_data[j] = newscale_data[c] * output_data[j] +
// newbias_data[c]; if (if_relu) {
// output_data[j] = output_data[j] < 0 ? 0 : output_data[j];
// }
// }
//
// for (int i = 1; i < output_height; i += 1) {
// for (int m = 1; m < output_width - 2; m += 3) {
// float *output_ptr = output_data + i * output_width + m;
// float32x4x2_t input_buff_top{}, input_buff_mid{},
// input_buff_bottom{}; float32x4_t in0, in1, in2, in3, in4, in5,
// tmp0, tmp1, tmp2, tmp3,
// tmp4, tmp5, out0;
// input_buff_top =
// vld2q_f32(input_data + (2 * i - 1) * input_width + (2 * m -
// 1));
// input_buff_mid =
// vld2q_f32(input_data + (2 * i) * input_width + (2 * m - 1));
// input_buff_bottom =
// vld2q_f32(input_data + (2 * i + 1) * input_width + (2 * m -
// 1));
//
// in0 = input_buff_top.val[0];
// tmp0 = input_buff_top.val[1];
// tmp1 = vextq_f32(in0, zero, 1);
//
// in2 = input_buff_mid.val[0];
// tmp2 = input_buff_mid.val[1];
// tmp3 = vextq_f32(in2, zero, 1);
//
// in4 = input_buff_bottom.val[0];
// tmp4 = input_buff_bottom.val[1];
// tmp5 = vextq_f32(in4, zero, 1);
//
// 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, zero);
// }
// vst1q_lane_f32(output_ptr, out0, 0);
// vst1q_lane_f32(output_ptr + 1, out0, 1);
// vst1q_lane_f32(output_ptr + 2, out0, 2);
// }
// int m;
// for (m = 1; m < output_width - 2; m += 3) {
// }
// for (int j = m; j < output_width; j++) {
// output_data[i * output_width + j] =
// input_data[(2 * i - 1) * input_width + 2 * j - 1] * w00 +
// input_data[(2 * i - 1) * input_width + 2 * j] * w01 +
// input_data[(2 * i - 1) * input_width + 2 * j + 1] * w02 +
// input_data[(2 * i) * input_width + 2 * j - 1] * w10 +
// input_data[(2 * i) * input_width + 2 * j] * w11 +
// input_data[(2 * i) * input_width + 2 * j + 1] * w12 +
// input_data[(2 * i + 1) * input_width + 2 * j - 1] * w20 +
// input_data[(2 * i + 1) * input_width + 2 * j] * w21 +
// input_data[(2 * i + 1) * input_width + 2 * 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];
// }
// }
// }
// output_data[0] = input_data[0] * w11 + input_data[1] * w12 +
// input_data[input_height] * w21 +
// input_data[input_height + 1] * w22;
//
// output_data[0] = newscale_data[c] * output_data[0] + newbias_data[c];
// if (if_relu) {
// output_data[0] = output_data[0] < 0 ? 0 : output_data[0];
// }
// for (int i = 1; i < output_height; i++) {
// output_data[i * output_width] =
// input_data[(2 * i - 1) * input_width] * w01 +
// input_data[(2 * i - 1) * input_width + 1] * w02 +
// input_data[(2 * i) * input_width] * w11 +
// input_data[(2 * i) * input_width + 1] * w12 +
// input_data[(2 * i + 1) * input_width] * w21 +
// input_data[(2 * i + 1) * input_width + 1] * w22;
//
// output_data[i * output_width] =
// newscale_data[c] * output_data[i * output_width] +
// newbias_data[c];
// if (if_relu) {
// output_data[i * output_width] = output_data[i * output_width] < 0
// ? 0
// : output_data[i *
// output_width];
// }
// }
// }
// }
//
//#else
const float *input_data = input->data<float>();
const float *filter_data = filter->data<float>();
......@@ -1646,9 +1653,6 @@ void DepthwiseConvAddBNRelu3x3s2p1v2(const Tensor *input, const Tensor *filter,
const float *newscale_data = new_scale->data<float>();
const float *newbias_data = new_bias->data<float>();
float32x4_t vnewbias = vdupq_n_f32(0.0);
float32x4_t vnewscale = vdupq_n_f32(1.0);
const int in_h = static_cast<int>(input->dims()[2]);
const int in_w = static_cast<int>(input->dims()[3]);
const int out_h = static_cast<int>(output->dims()[2]);
......@@ -1660,22 +1664,22 @@ void DepthwiseConvAddBNRelu3x3s2p1v2(const Tensor *input, const Tensor *filter,
const int if_pad = in_l - 1 == (out_l - 1) * 2 ? 1 : 0;
const int batch_size = static_cast<int>(input->dims()[0]);
const int c = static_cast<int>(input->dims()[1]);
const float *input_row_ptr;
float *output_row_ptr;
const int w_times = (out_w - 2) / 3;
float32x4x2_t input_buff_mid{}, input_buff_bottom[w_times + 1];
float32x4_t elewise_res0, elewise_res1, elewise_res2, res3;
int out2in_mid;
float32x4_t zero = vdupq_n_f32(0.0);
for (int b = batch_size; b > 0; --b) {
const float *filter_data_tmp = filter_data;
for (int j = 0; j < c; ++j) {
#pragma omp parallel for
for (int j = 0; j < c; j++) {
const float *input_row_ptr;
float *output_row_ptr;
float32x4x2_t input_buff_mid{}, input_buff_bottom[w_times + 1];
float32x4_t elewise_res0, elewise_res1, elewise_res2, res3;
int out2in_mid;
float32x4_t vnewbias = vdupq_n_f32(0.0);
float32x4_t vnewscale = vdupq_n_f32(1.0);
auto output_data_tmp = output_data + j * out_h * out_w;
auto input_data_tmp = input_data + j * in_h * in_w;
auto input_const = input_data_tmp;
const float *filter_data_tmp = filter_data + 9 * j;
vnewbias = vdupq_n_f32(newbias_data[j]);
vnewscale = vdupq_n_f32(newscale_data[j]);
......@@ -1726,7 +1730,9 @@ void DepthwiseConvAddBNRelu3x3s2p1v2(const Tensor *input, const Tensor *filter,
if (if_relu) {
res3 = vmaxq_f32(res3, zero);
}
vst1q_f32(output_row_ptr, res3);
vst1q_lane_f32(output_row_ptr, res3, 0);
vst1q_lane_f32(output_row_ptr + 1, res3, 1);
vst1q_lane_f32(output_row_ptr + 2, res3, 2);
input_row_ptr += 6;
output_row_ptr += 3;
......@@ -1765,7 +1771,9 @@ void DepthwiseConvAddBNRelu3x3s2p1v2(const Tensor *input, const Tensor *filter,
res3 = vmaxq_f32(res3, zero);
}
if ((w4 != w_times)) {
vst1q_f32(output_row_ptr, res3);
vst1q_lane_f32(output_row_ptr, res3, 0);
vst1q_lane_f32(output_row_ptr + 1, res3, 1);
vst1q_lane_f32(output_row_ptr + 2, res3, 2);
} else {
if (out_l - 2 - w_times * 3 == 1) {
vst1q_lane_f32(output_row_ptr, res3, 0);
......@@ -1865,12 +1873,11 @@ void DepthwiseConvAddBNRelu3x3s2p1v2(const Tensor *input, const Tensor *filter,
: output_data_tmp[i * out_l + out_l - 1];
}
}
filter_data_tmp += 9;
}
input_data += inhxw * c;
output_data += outhxw * c;
}
#endif
//#endif
#endif
}
......
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