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提交 6f4b68b5 编写于 作者: xiebaiyuan's avatar xiebaiyuan
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Merge remote-tracking branch 'origin/superresoltion' into superresoltion

上级 51249069 ca16f67b
隐藏空白更改
内联 并排
Showing with 157 addition and 57 deletion
src/operators/kernel/arm/conv_kernel.cpp
浏览文件 @ 6f4b68b5
......@@ -77,15 +77,15 @@ void ConvKernel<CPU, float>::Compute(const ConvParam<CPU> &param) {
break;
case ConvParam<CPU>::EXEC_DEPTHWISE3x3S1P1_FLOAT:
math::DepthwiseConv3x3s1p1(param.Input(), param.Filter(), param.Output(),
nullptr, false);
nullptr, false, false);
break;
case ConvParam<CPU>::EXEC_DEPTHWISE3x3S2P1_FLOAT:
math::DepthwiseConv3x3s2p1v2(param.Input(), param.Filter(),
param.Output(), nullptr, false);
param.Output(), nullptr, false, false);
break;
case ConvParam<CPU>::EXEC_DEPTHWISE3x3S2P0_FLOAT:
math::DepthwiseConv3x3s2p0(param.Input(), param.Filter(), param.Output(),
nullptr, false);
nullptr, false, false);
break;
case ConvParam<CPU>::EXEC_WINOGRAD3X3_FLOAT:
WinogradConv3x3<8, 3>(param);
......
src/operators/kernel/central-arm-func/conv_add_arm_func.h
浏览文件 @ 6f4b68b5
......@@ -122,7 +122,7 @@ void ConvAddCompute(const FusionConvAddParam<CPU> &param) {
param.Filter()->dims()[2] == param.Filter()->dims()[3] &&
param.Filter()->dims()[2] == 3 && param.Strides()[0] == 1) {
math::DepthwiseConv3x3s1p1(param.Input(), param.Filter(), param.Output(),
param.Bias(), true);
param.Bias(), true, false);
} else if (param.Groups() == param.Input()->dims()[1] &&
param.Input()->dims()[1] == param.Output()->dims()[1] &&
param.Filter()->dims()[2] == param.Filter()->dims()[3] &&
......@@ -133,10 +133,10 @@ void ConvAddCompute(const FusionConvAddParam<CPU> &param) {
// param.Output(), false);
if (param.Paddings()[0] == 0) {
math::DepthwiseConv3x3s2p0(param.Input(), param.Filter(), param.Output(),
param.Bias(), true);
param.Bias(), true, false);
} else {
math::DepthwiseConv3x3s2p1v2(param.Input(), param.Filter(),
param.Output(), param.Bias(), true);
param.Output(), param.Bias(), true, false);
}
} else {
ConvAddBasic(param);
......
src/operators/kernel/central-arm-func/conv_add_relu_arm_func.h
浏览文件 @ 6f4b68b5
......@@ -15,6 +15,7 @@ limitations under the License. */
#ifdef FUSION_CONVADDRELU_OP
#pragma once
#include <operators/math/depthwise_conv3x3.h>
#include <vector>
#include "operators/math/conv_func.h"
#include "operators/math/im2col.h"
......@@ -26,7 +27,7 @@ namespace paddle_mobile {
namespace operators {
template <typename Itype, typename Otype>
void ConvAddReluCompute(const FusionConvAddReluParam<CPU> &param) {
void ConvAddReluBasic(const FusionConvAddReluParam<CPU> &param) {
const Tensor *input = param.Input();
Tensor filter = *param.Filter();
Tensor bias = *param.Bias();
......@@ -118,6 +119,34 @@ void ConvAddReluCompute(const FusionConvAddReluParam<CPU> &param) {
}
}
template <typename Itype, typename Otype>
void ConvAddReluCompute(const FusionConvAddReluParam<CPU> &param) {
param.Output()->mutable_data<float>();
if (param.Groups() == param.Input()->dims()[1] &&
param.Input()->dims()[1] == param.Output()->dims()[1] &&
param.Filter()->dims()[2] == param.Filter()->dims()[3] &&
param.Filter()->dims()[2] == 3 && param.Strides()[0] == 1) {
math::DepthwiseConv3x3s1p1(param.Input(), param.Filter(), param.Output(),
param.Bias(), true, true);
} else if (param.Groups() == param.Input()->dims()[1] &&
param.Input()->dims()[1] == param.Output()->dims()[1] &&
param.Filter()->dims()[2] == param.Filter()->dims()[3] &&
param.Filter()->dims()[2] == 3 && param.Strides()[0] == 2) {
// math::DepthwiseConv3x3(param.Input(), param.Strides(),
// param.Paddings(),
// param.Filter(), param.Bias(),
// param.Output(), false);
if (param.Paddings()[0] == 0) {
math::DepthwiseConv3x3s2p0(param.Input(), param.Filter(), param.Output(),
param.Bias(), true, true);
} else {
math::DepthwiseConv3x3s2p1v2(param.Input(), param.Filter(),
param.Output(), param.Bias(), true, true);
}
} else {
ConvAddReluBasic<Itype, Otype>(param);
}
}
} // namespace operators
} // namespace paddle_mobile
......
src/operators/math/depthwise_conv3x3.cpp
浏览文件 @ 6f4b68b5
......@@ -251,7 +251,7 @@ void DepthwiseConv3x3(const framework::Tensor *input,
void DepthwiseConv3x3s1p1(const framework::Tensor *input,
const framework::Tensor *filter,
framework::Tensor *output, framework::Tensor *bias,
bool if_bias) {
bool if_bias, bool if_relu) {
#if __ARM_NEON
const float *input_data = input->data<float>();
const float *filter_data = filter->data<float>();
......@@ -268,6 +268,15 @@ void DepthwiseConv3x3s1p1(const framework::Tensor *input,
const int c = static_cast<int>(input->dims()[1]);
const int hxw = h * w;
float32x4_t vbias = vdupq_n_f32(0.0);
// leftTop, rightTop, leftBottom, rightBottom
int lt = 0;
int rt = w - 1;
int lb = (h - 1) * w;
int rb = h * w - 1;
float32x4_t zero = vdupq_n_f32(0.0);
for (int b = 0; b < batch_size; ++b) {
const float *filter_data_tmp = filter_data;
......@@ -287,39 +296,51 @@ void DepthwiseConv3x3s1p1(const framework::Tensor *input,
float w21 = filter_data_tmp[7];
float w22 = filter_data_tmp[8];
output_data[0] = w11 * input_data[0] + w12 * input_data[1] +
w21 * input_data[w] + w22 * input_data[w + 1];
output_data[w - 1] = w10 * input_data[w - 2] + w11 * input_data[w - 1] +
w20 * input_data[2 * w - 2] +
w21 * input_data[2 * w - 1];
output_data[(h - 1) * w] =
output_data[lt] = w11 * input_data[0] + w12 * input_data[1] +
w21 * input_data[w] + w22 * input_data[w + 1];
output_data[rt] = w10 * input_data[w - 2] + w11 * input_data[w - 1] +
w20 * input_data[2 * w - 2] +
w21 * input_data[2 * w - 1];
output_data[lb] =
w01 * input_data[(h - 2) * w] + w02 * input_data[(h - 2) * w + 1] +
w11 * input_data[(h - 1) * w] + w12 * input_data[(h - 1) * w + 1];
output_data[h * w - 1] =
output_data[rb] =
w00 * input_data[h * w - w - 2] + w01 * input_data[h * w - w - 1] +
w10 * input_data[h * w - 2] + w11 * input_data[h * w - 1];
if (if_bias) {
output_data[0] += bias_data[j];
output_data[w - 1] += bias_data[j];
output_data[(h - 1) * w] += bias_data[j];
output_data[h * w - 1] += bias_data[j];
output_data[lt] += bias_data[j];
output_data[rt] += bias_data[j];
output_data[lb] += bias_data[j];
output_data[rb] += bias_data[j];
}
if (if_relu) {
output_data[lt] = output_data[lt] < 0 ? 0 : output_data[lt];
output_data[rt] = output_data[rt] < 0 ? 0 : output_data[rt];
output_data[lb] = output_data[lb] < 0 ? 0 : output_data[lb];
output_data[rb] = output_data[rb] < 0 ? 0 : output_data[rb];
}
for (int i = 1; i < h - 1; ++i) {
output_data[i * w] =
int left = i * w;
int right = i * w + w - 1;
output_data[left] =
w01 * input_data[i * w - w] + w02 * input_data[i * w - w + 1] +
w11 * input_data[i * w] + w12 * input_data[i * w + 1] +
w21 * input_data[i * w + w] + w22 * input_data[i * w + w + 1];
output_data[i * w + w - 1] = w00 * input_data[i * w + w - 1 - w - 1] +
w01 * input_data[i * w + w - 1 - w] +
w10 * input_data[i * w + w - 1 - 1] +
w11 * input_data[i * w + w - 1] +
w20 * input_data[i * w + w - 1 + w - 1] +
w21 * input_data[i * w + w - 1 + w];
output_data[right] = w00 * input_data[i * w + w - 1 - w - 1] +
w01 * input_data[i * w + w - 1 - w] +
w10 * input_data[i * w + w - 1 - 1] +
w11 * input_data[i * w + w - 1] +
w20 * input_data[i * w + w - 1 + w - 1] +
w21 * input_data[i * w + w - 1 + w];
if (if_bias) {
output_data[i * w] += bias_data[j];
output_data[i * w + w - 1] += bias_data[j];
output_data[left] += bias_data[j];
output_data[right] += bias_data[j];
}
if (if_relu) {
output_data[left] = output_data[left] < 0 ? 0 : output_data[left];
output_data[right] = output_data[right] < 0 ? 0 : output_data[right];
}
}
......@@ -352,7 +373,9 @@ void DepthwiseConv3x3s1p1(const framework::Tensor *input,
out0 = vmlaq_n_f32(out0, tmp2, w21);
out0 = vmlaq_n_f32(out0, tmp3, w22);
out0 = vaddq_f32(out0, vbias);
if (if_relu) {
out0 = vmaxq_f32(out0, zero);
}
vst1q_f32(output_ptr, out0);
in5 = vld1q_f32(input_tmp_end + 4);
......@@ -370,7 +393,9 @@ void DepthwiseConv3x3s1p1(const framework::Tensor *input,
out0 = vmlaq_n_f32(out0, tmp2, w11);
out0 = vmlaq_n_f32(out0, tmp3, w12);
out0 = vaddq_f32(out0, vbias);
if (if_relu) {
out0 = vmaxq_f32(out0, zero);
}
vst1q_f32(output_ptr + (h - 1) * w, out0);
// can optimize to each 8 stride.
......@@ -399,6 +424,9 @@ void DepthwiseConv3x3s1p1(const framework::Tensor *input,
out0 = vmlaq_n_f32(out0, tmp2, w21);
out0 = vmlaq_n_f32(out0, tmp3, w22);
out0 = vaddq_f32(out0, vbias);
if (if_relu) {
out0 = vmaxq_f32(out0, zero);
}
for (int i = 0; i < c_mid; ++i) {
if (i == 0) {
......@@ -428,6 +456,9 @@ void DepthwiseConv3x3s1p1(const framework::Tensor *input,
out0 = vmlaq_n_f32(out0, tmp2, w11);
out0 = vmlaq_n_f32(out0, tmp3, w12);
out0 = vaddq_f32(out0, vbias);
if (if_relu) {
out0 = vmaxq_f32(out0, zero);
}
for (int i = 0; i < c_mid; ++i) {
if (i == 0) {
......@@ -471,6 +502,9 @@ void DepthwiseConv3x3s1p1(const framework::Tensor *input,
out0 = vmlaq_n_f32(out0, tmp4, w21);
out0 = vmlaq_n_f32(out0, tmp5, w22);
out0 = vaddq_f32(out0, vbias);
if (if_relu) {
out0 = vmaxq_f32(out0, zero);
}
vst1q_f32(output_ptr, out0);
......@@ -502,6 +536,9 @@ void DepthwiseConv3x3s1p1(const framework::Tensor *input,
out0 = vmlaq_n_f32(out0, tmp4, w21);
out0 = vmlaq_n_f32(out0, tmp5, w22);
out0 = vaddq_f32(out0, vbias);
if (if_relu) {
out0 = vmaxq_f32(out0, zero);
}
for (int i = 0; i < c_mid; ++i) {
if (i == 0) {
......@@ -1273,7 +1310,7 @@ void DepthwiseConvAddBNRelu3x3s2p1(const framework::Tensor *input,
void DepthwiseConv3x3s2p1v2(const framework::Tensor *input,
const framework::Tensor *filter,
framework::Tensor *output, framework::Tensor *bias,
bool if_bias) {
bool if_bias, bool if_relu) {
#if __ARM_NEON
const float *input_data = input->data<float>();
const float *filter_data = filter->data<float>();
......@@ -1361,6 +1398,9 @@ void DepthwiseConv3x3s2p1v2(const framework::Tensor *input,
res3 = vaddq_f32(vextq_f32(elewise_res2, zero, 1),
vaddq_f32(elewise_res0, elewise_res1));
res3 = vaddq_f32(res3, vbias);
if (if_relu) {
res3 = vmaxq_f32(res3, zero);
}
vst1q_f32(output_row_ptr, res3);
input_row_ptr += 6;
......@@ -1395,6 +1435,9 @@ void DepthwiseConv3x3s2p1v2(const framework::Tensor *input,
res3 = vaddq_f32(vextq_f32(elewise_res2, zero, 1),
vaddq_f32(elewise_res0, elewise_res1));
res3 = vaddq_f32(res3, vbias);
if (if_relu) {
res3 = vmaxq_f32(res3, zero);
}
if ((w4 != w_times)) {
vst1q_f32(output_row_ptr, res3);
......@@ -1410,12 +1453,18 @@ void DepthwiseConv3x3s2p1v2(const framework::Tensor *input,
output_row_ptr += 3;
}
output_data_tmp[0] = input_const[0] * w11 + input_const[1] * w12 +
input_const[in_w] * w21 +
input_const[in_w + 1] * w22;
// leftTop, rightTop, leftBottom, rightBottom
int lt = 0;
int rt = out_w - 1;
int lb = out_w * (out_h - 1);
int rb = out_h * out_w - 1;
output_data_tmp[lt] = input_const[0] * w11 + input_const[1] * w12 +
input_const[in_w] * w21 +
input_const[in_w + 1] * w22;
out2in_mid = (out_w - 1) * 2;
output_data_tmp[out_w - 1] =
output_data_tmp[rt] =
w10 * input_const[out2in_mid - 1] + w11 * input_const[out2in_mid] +
w20 * input_const[out2in_mid + in_w - 1] +
w21 * input_const[out2in_mid + in_w] +
......@@ -1424,7 +1473,7 @@ void DepthwiseConv3x3s2p1v2(const framework::Tensor *input,
out2in_mid = (out_h - 1) * 2 * in_w;
output_data_tmp[out_w * (out_h - 1)] =
output_data_tmp[lb] =
w01 * input_const[out2in_mid - in_w] +
w02 * input_const[out2in_mid - in_w + 1] +
w11 * input_const[out2in_mid] + w12 * input_const[out2in_mid + 1] +
......@@ -1432,7 +1481,7 @@ void DepthwiseConv3x3s2p1v2(const framework::Tensor *input,
w22 * input_const[out2in_mid + in_w + 1]);
out2in_mid = (out_h - 1) * 2 * in_w + (out_w - 1) * 2;
output_data_tmp[out_h * out_w - 1] =
output_data_tmp[rb] =
w00 * input_const[out2in_mid - in_w - 1] +
w01 * input_const[out2in_mid - in_w] +
w10 * input_const[out2in_mid - 1] + w11 * input_const[out2in_mid] +
......@@ -1443,22 +1492,30 @@ void DepthwiseConv3x3s2p1v2(const framework::Tensor *input,
(1 - if_pad_r) * (1 - if_pad_b) * w22 *
input_const[out2in_mid + in_w + 1];
if (if_bias) {
output_data_tmp[0] += bias_data[j];
output_data_tmp[out_w - 1] += bias_data[j];
output_data_tmp[out_w * (out_h - 1)] += bias_data[j];
output_data_tmp[out_h * out_w - 1] += bias_data[j];
output_data_tmp[lt] += bias_data[j];
output_data_tmp[rt] += bias_data[j];
output_data_tmp[lb] += bias_data[j];
output_data_tmp[rb] += bias_data[j];
}
if (if_relu) {
output_data_tmp[lt] = output_data_tmp[lt] < 0 ? 0 : output_data_tmp[lt];
output_data_tmp[rt] = output_data_tmp[rt] < 0 ? 0 : output_data_tmp[rt];
output_data_tmp[lb] = output_data_tmp[lb] < 0 ? 0 : output_data_tmp[lb];
output_data_tmp[rb] = output_data_tmp[rb] < 0 ? 0 : output_data_tmp[rb];
}
for (int i = 1; i < out_h - 1; i++) {
out2in_mid = i * 2 * in_w;
output_data_tmp[i * out_w] = w01 * input_const[out2in_mid - in_w] +
w02 * input_const[out2in_mid - in_w + 1] +
w11 * input_const[out2in_mid] +
w12 * input_const[out2in_mid + 1] +
w21 * input_const[out2in_mid + in_w] +
w22 * input_const[out2in_mid + in_w + 1];
int left = i * out_w;
output_data_tmp[left] = w01 * input_const[out2in_mid - in_w] +
w02 * input_const[out2in_mid - in_w + 1] +
w11 * input_const[out2in_mid] +
w12 * input_const[out2in_mid + 1] +
w21 * input_const[out2in_mid + in_w] +
w22 * input_const[out2in_mid + in_w + 1];
out2in_mid = i * 2 * in_w + (out_w - 1) * 2;
output_data_tmp[i * out_w + out_w - 1] =
int right = i * out_w + out_w - 1;
output_data_tmp[right] =
w00 * input_const[out2in_mid - in_w - 1] +
w01 * input_const[out2in_mid - in_w] +
w10 * input_const[out2in_mid - 1] + w11 * input_const[out2in_mid] +
......@@ -1468,8 +1525,14 @@ void DepthwiseConv3x3s2p1v2(const framework::Tensor *input,
w12 * input_const[out2in_mid + 1] +
w22 * input_const[out2in_mid + in_w + 1]);
if (if_bias) {
output_data_tmp[i * out_w] += bias_data[j];
output_data_tmp[i * out_w + out_w - 1] += bias_data[j];
output_data_tmp[left] += bias_data[j];
output_data_tmp[right] += bias_data[j];
}
if (if_relu) {
output_data_tmp[left] =
output_data_tmp[left] < 0 ? 0 : output_data_tmp[left];
output_data_tmp[right] =
output_data_tmp[right] < 0 ? 0 : output_data_tmp[right];
}
}
filter_data_tmp += 9;
......@@ -1909,7 +1972,7 @@ void DepthwiseConvAddBNRelu3x3s2p1v2(const framework::Tensor *input,
void DepthwiseConv3x3s2p0(const framework::Tensor *input,
const framework::Tensor *filter,
framework::Tensor *output, framework::Tensor *bias,
bool if_bias) {
bool if_bias, bool if_relu) {
#if __ARM_NEON
const int batch_size = static_cast<int>(input->dims()[0]);
......@@ -1977,6 +2040,9 @@ void DepthwiseConv3x3s2p0(const framework::Tensor *input,
if (if_bias) {
out0 = vaddq_f32(out0, biasv);
}
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);
......@@ -1985,7 +2051,8 @@ void DepthwiseConv3x3s2p0(const framework::Tensor *input,
for (m = 0; m < output_width - 2; m += 3) {
}
for (int j = m; j < output_width; j++) {
output_data[i * output_width + j] =
int index = i * output_width + j;
output_data[index] =
input_data[(2 * i) * input_width + 2 * j] * w00 +
input_data[(2 * i) * input_width + 2 * j + 1] * w01 +
input_data[(2 * i) * input_width + 2 * j + 2] * w02 +
......@@ -1996,7 +2063,11 @@ void DepthwiseConv3x3s2p0(const framework::Tensor *input,
input_data[(2 * i + 2) * input_width + 2 * j + 1] * w21 +
input_data[(2 * i + 2) * input_width + 2 * j + 2] * w22;
if (if_bias) {
output_data[i * output_width + j] += *bias_data;
output_data[index] += *bias_data;
}
if (if_relu) {
output_data[index] =
output_data[index] < 0 ? 0 : output_data[index];
}
}
}
......
src/operators/math/depthwise_conv3x3.h
浏览文件 @ 6f4b68b5
......@@ -32,7 +32,7 @@ void DepthwiseConv3x3(const framework::Tensor *input,
void DepthwiseConv3x3s1p1(const framework::Tensor *input,
const framework::Tensor *filter,
framework::Tensor *output, framework::Tensor *bias,
bool if_bias);
bool if_bias, bool if_relu);
void DepthwiseConvAddBNRelu3x3s1p1(const framework::Tensor *input,
const framework::Tensor *filter,
......@@ -51,7 +51,7 @@ void DepthwiseConvAddBNRelu3x3s2p1(const framework::Tensor *input,
void DepthwiseConv3x3s2p1v2(const framework::Tensor *input,
const framework::Tensor *filter,
framework::Tensor *output, framework::Tensor *bias,
bool if_bias);
bool if_bias, bool if_relu);
void DepthwiseConvAddBNRelu3x3s2p1v2(const framework::Tensor *input,
const framework::Tensor *filter,
......@@ -63,7 +63,7 @@ void DepthwiseConvAddBNRelu3x3s2p1v2(const framework::Tensor *input,
void DepthwiseConv3x3s2p0(const framework::Tensor *input,
const framework::Tensor *filter,
framework::Tensor *output, framework::Tensor *bias,
bool if_bias);
bool if_bias, bool if_relu);
// TODO(hjchen2) need to be implemented
// template<typename Itype, typename Otype>
......
tools/build.sh
浏览文件 @ 6f4b68b5
......@@ -162,7 +162,7 @@ build_for_ios() {
fi
cd "${BUILD_DIR}"
make -j 8
cp ../../../src/ios_io/PaddleMobileCPU.h ./build/PaddleMobileCPU.h
cp ../../../src/io/ios_io/PaddleMobileCPU.h ./build/PaddleMobileCPU.h
cd ./build
# 生成符号表
ranlib *.a
......
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