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提交 835dd0d5 编写于 作者: R Ray Liu 提交者： GitHub
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Merge pull request #1222 from Eclipsess/develop 

fix #1221 temp fix dw3x3 w!=h
上级 ac06a518 8a821627
隐藏空白更改
内联 并排
Showing with 76 addition and 67 deletion
src/operators/kernel/central-arm-func/conv_add_arm_func.h
浏览文件 @ 835dd0d5
......@@ -124,7 +124,8 @@ void ConvAddCompute(const FusionConvAddParam<CPU> &param) {
} 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) {
param.Filter()->dims()[2] == 3 && param.Strides()[0] == 2 &&
param.Input()->dims()[2] == param.Input()->dims()[3]) {
// math::DepthwiseConv3x3(param.Input(), param.Strides(),
// param.Paddings(),
// param.Filter(), param.Bias(),
......
src/operators/kernel/central-arm-func/conv_add_bn_relu_arm_func.h
浏览文件 @ 835dd0d5
......@@ -118,14 +118,16 @@ void ConvAddBNReluCompute(const FusionConvAddBNReluParam<CPU> &param) {
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) {
param.Filter()->dims()[2] == 3 && param.Strides()[0] == 1 &&
param.Input()->dims()[2] == param.Input()->dims()[3]) {
math::DepthwiseConvAddBNRelu3x3s1p1(param.Input(), param.Filter(),
param.Output(), param.NewScale(),
param.NewBias(), 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) {
param.Filter()->dims()[2] == 3 && param.Strides()[0] == 2 &&
param.Input()->dims()[2] == param.Input()->dims()[3]) {
// math::DepthwiseConvAddBNRelu3x3s2p1(param.Input(), param.Filter(),
// param.Output(), param.NewScale(),
// param.NewBias(), 1);
......
src/operators/kernel/central-arm-func/conv_arm_func.h
浏览文件 @ 835dd0d5
......@@ -130,7 +130,8 @@ void ConvCompute(const ConvParam<CPU> &param) {
} 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.Filter()->dims()[2] == 3 &&
param.Input()->dims()[2] == param.Input()->dims()[3]) {
math::DepthwiseConv3x3(param.Input(), param.Strides(), param.Paddings(),
param.Filter(), nullptr, param.Output(), false);
} else {
......
src/operators/kernel/central-arm-func/conv_bn_add_relu_arm_func.h
浏览文件 @ 835dd0d5
......@@ -122,14 +122,16 @@ void ConvBNAddReluCompute(const FusionConvBNAddReluParam<CPU> &param) {
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) {
param.Filter()->dims()[2] == 3 && param.Strides()[0] == 1 &&
param.Input()->dims()[2] == param.Input()->dims()[3]) {
math::DepthwiseConvAddBNRelu3x3s1p1(param.Input(), param.Filter(),
param.Output(), param.NewScale(),
param.NewBias(), 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) {
param.Filter()->dims()[2] == 3 && param.Strides()[0] == 2 &&
param.Input()->dims()[2] == param.Input()->dims()[3]) {
// math::DepthwiseConvAddBNRelu3x3s2p1(param.Input(), param.Filter(),
// param.Output(), param.NewScale(),
// param.NewBias(), 1);
......
src/operators/kernel/central-arm-func/conv_bn_relu_arm_func.h
浏览文件 @ 835dd0d5
......@@ -117,14 +117,16 @@ void ConvBNReluCompute(const FusionConvBNReluParam<CPU> &param) {
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) {
param.Filter()->dims()[2] == 3 && param.Strides()[0] == 1 &&
param.Input()->dims()[2] == param.Input()->dims()[3]) {
math::DepthwiseConvAddBNRelu3x3s1p1(param.Input(), param.Filter(),
param.Output(), param.NewScale(),
param.NewBias(), 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) {
param.Filter()->dims()[2] == 3 && param.Strides()[0] == 2 &&
param.Input()->dims()[2] == param.Input()->dims()[3]) {
// math::DepthwiseConvAddBNRelu3x3s2p1(param.Input(), param.Filter(),
// param.Output(), param.NewScale(),
// param.NewBias(), 1);
......
src/operators/kernel/central-arm-func/depthwise_conv_arm_func.h
浏览文件 @ 835dd0d5
......@@ -36,7 +36,8 @@ void DepthwiseConvCompute(const ConvParam<CPU> &param) {
} 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) {
param.Filter()->dims()[2] == 3 && param.Strides()[0] == 2 &&
param.Input()->dims()[2] == param.Input()->dims()[3]) {
// math::DepthwiseConv3x3(param.Input(), param.Strides(),
// param.Paddings(),
// param.Filter(), &Bias, param.Output(), false);
......
src/operators/kernel/central-arm-func/dwconv_bn_relu_arm_func.h
浏览文件 @ 835dd0d5
......@@ -115,14 +115,16 @@ void DWConvBNReluCompute(const FusionDWConvBNReluParam<CPU> &param) {
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) {
param.Filter()->dims()[2] == 3 && param.Strides()[0] == 1 &&
param.Input()->dims()[2] == param.Input()->dims()[3]) {
math::DepthwiseConvAddBNRelu3x3s1p1(param.Input(), param.Filter(),
param.Output(), param.NewScale(),
param.NewBias(), 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) {
param.Filter()->dims()[2] == 3 && param.Strides()[0] == 2 &&
param.Input()->dims()[2] == param.Input()->dims()[3]) {
// math::DepthwiseConvAddBNRelu3x3s2p1(param.Input(), param.Filter(),
// param.Output(), param.NewScale(),
// param.NewBias(), 1);
......
src/operators/math/depthwise_conv_3x3.cpp
浏览文件 @ 835dd0d5
......@@ -257,8 +257,7 @@ void DepthwiseConv3x3s1p1(const Tensor *input, const Tensor *filter,
const int h = static_cast<int>(input->dims()[2]);
const int w = static_cast<int>(input->dims()[3]);
const int l = h;
// const int l = h;
const int batch_size = static_cast<int>(input->dims()[0]);
const int c = static_cast<int>(input->dims()[1]);
const int hxw = h * w;
......@@ -271,7 +270,7 @@ void DepthwiseConv3x3s1p1(const Tensor *input, const Tensor *filter,
vbias = vdupq_n_f32(bias_data[j]);
}
int l_mid = l - 2; // l=1->l_mid=-1,l=2->l_mid=0
int w_mid = w - 2; // l=1->l_mid=-1,l=2->l_mid=0
float w00 = filter_data_tmp[0];
float w01 = filter_data_tmp[1];
float w02 = filter_data_tmp[2];
......@@ -283,39 +282,38 @@ void DepthwiseConv3x3s1p1(const Tensor *input, const Tensor *filter,
float w22 = filter_data_tmp[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];
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] =
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] =
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[l - 1] += bias_data[j];
output_data[(l - 1) * l] += bias_data[j];
output_data[l * l - 1] += 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];
}
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];
for (int i = 1; i < h - 1; ++i) {
output_data[i * w] =
w01 * input_data[i * w - w] + w02 * input_data[i * w - w + 1] +
w11 * input_data[i * w] + w12 * input_data[i * w + w] +
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];
if (if_bias) {
output_data[i * l] += bias_data[j];
output_data[i * l + l - 1] += bias_data[j];
output_data[i * w] += bias_data[j];
output_data[i * w + w - 1] += bias_data[j];
}
}
......@@ -325,15 +323,15 @@ void DepthwiseConv3x3s1p1(const Tensor *input, const Tensor *filter,
float32x4_t in0, in1, in2, in3, in4, in5, in6, in7, tmp0, tmp1, tmp2,
tmp3, tmp4, tmp5, out0;
in0 = vld1q_f32(input_tmp);
in2 = vld1q_f32(input_tmp + l);
const float *input_tmp_end = input_tmp + (l - 2) * l;
in2 = vld1q_f32(input_tmp + w);
const float *input_tmp_end = input_tmp + (h - 2) * w;
in4 = vld1q_f32(input_tmp_end);
in6 = vld1q_f32(input_tmp_end + l);
int c_mid = l_mid;
in6 = vld1q_f32(input_tmp_end + w);
int c_mid = w_mid;
auto output_ptr = output_data + 1;
for (; c_mid > 3; c_mid -= 4) {
in1 = vld1q_f32(input_tmp + 4);
in3 = vld1q_f32(input_tmp + l + 4);
in3 = vld1q_f32(input_tmp + w + 4);
tmp0 = vextq_f32(in0, in1, 1);
tmp1 = vextq_f32(in0, in1, 2);
......@@ -352,7 +350,7 @@ void DepthwiseConv3x3s1p1(const Tensor *input, const Tensor *filter,
vst1q_f32(output_ptr, out0);
in5 = vld1q_f32(input_tmp_end + 4);
in7 = vld1q_f32(input_tmp_end + l + 4);
in7 = vld1q_f32(input_tmp_end + w + 4);
tmp0 = vextq_f32(in4, in5, 1);
tmp1 = vextq_f32(in4, in5, 2);
......@@ -367,7 +365,7 @@ void DepthwiseConv3x3s1p1(const Tensor *input, const Tensor *filter,
out0 = vmlaq_n_f32(out0, tmp3, w12);
out0 = vaddq_f32(out0, vbias);
vst1q_f32(output_ptr + (l - 1) * l, out0);
vst1q_f32(output_ptr + (h - 1) * w, out0);
// can optimize to each 8 stride.
input_tmp += 4;
......@@ -380,8 +378,8 @@ void DepthwiseConv3x3s1p1(const Tensor *input, const Tensor *filter,
}
// top right pad
float32x4_t pad0 = vdupq_n_f32(input_data[l - 1]);
float32x4_t pad1 = vdupq_n_f32(input_data[2 * l - 1]);
float32x4_t pad0 = vdupq_n_f32(input_data[w - 1]);
float32x4_t pad1 = vdupq_n_f32(input_data[2 * w - 1]);
tmp0 = vextq_f32(in0, pad0, 1);
tmp1 = vextq_f32(in0, pad0, 2);
......@@ -409,8 +407,8 @@ void DepthwiseConv3x3s1p1(const Tensor *input, const Tensor *filter,
}
// bottom right pad
float32x4_t pad2 = vdupq_n_f32(input_data[l * l - 1 - l]);
float32x4_t pad3 = vdupq_n_f32(input_data[l * l - 1]);
float32x4_t pad2 = vdupq_n_f32(input_data[h * w - 1 - w]);
float32x4_t pad3 = vdupq_n_f32(input_data[h * w - 1]);
tmp0 = vextq_f32(in4, pad2, 1);
tmp1 = vextq_f32(in4, pad2, 2);
......@@ -427,28 +425,28 @@ void DepthwiseConv3x3s1p1(const Tensor *input, const Tensor *filter,
for (int i = 0; i < c_mid; ++i) {
if (i == 0) {
vst1q_lane_f32(output_ptr + (l - 1) * l + i, out0, 0);
vst1q_lane_f32(output_ptr + (h - 1) * w + i, out0, 0);
}
if (i == 1) {
vst1q_lane_f32(output_ptr + (l - 1) * l + i, out0, 1);
vst1q_lane_f32(output_ptr + (h - 1) * w + i, out0, 1);
}
if (i == 2) {
vst1q_lane_f32(output_ptr + (l - 1) * l + i, out0, 2);
vst1q_lane_f32(output_ptr + (h - 1) * w + i, out0, 2);
}
}
// mid
for (int i = 0; i < l - 2; ++i) {
auto output_ptr = output_data + (i + 1) * l + 1;
input_tmp = input_data + i * l;
for (int i = 0; i < h - 2; ++i) {
auto output_ptr = output_data + (i + 1) * w + 1;
input_tmp = input_data + i * w;
auto in0_tmp = vld1q_f32(input_tmp);
auto in2_tmp = vld1q_f32(input_tmp + l);
auto in4_tmp = vld1q_f32(input_tmp + l + l);
c_mid = l_mid;
auto in2_tmp = vld1q_f32(input_tmp + w);
auto in4_tmp = vld1q_f32(input_tmp + w + w);
c_mid = w_mid;
for (; c_mid > 3; c_mid -= 4) {
auto in1_tmp = vld1q_f32(input_tmp + 4);
auto in3_tmp = vld1q_f32(input_tmp + l + 4);
auto in5_tmp = vld1q_f32(input_tmp + l + l + 4);
auto in3_tmp = vld1q_f32(input_tmp + w + 4);
auto in5_tmp = vld1q_f32(input_tmp + w + w + 4);
tmp0 = vextq_f32(in0_tmp, in1_tmp, 1);
tmp1 = vextq_f32(in0_tmp, in1_tmp, 2);
......@@ -477,9 +475,9 @@ void DepthwiseConv3x3s1p1(const Tensor *input, const Tensor *filter,
in4_tmp = in5_tmp;
}
float32x4_t pad0 = vdupq_n_f32(input_data[i * l + l - 1]);
float32x4_t pad1 = vdupq_n_f32(input_data[i * l + l - 1 + l]);
float32x4_t pad2 = vdupq_n_f32(input_data[i * l + l - 1 + l + l]);
float32x4_t pad0 = vdupq_n_f32(input_data[i * w + w - 1]);
float32x4_t pad1 = vdupq_n_f32(input_data[i * w + w - 1 + w]);
float32x4_t pad2 = vdupq_n_f32(input_data[i * w + w - 1 + w + w]);
tmp0 = vextq_f32(in0_tmp, pad0, 1);
tmp1 = vextq_f32(in0_tmp, pad0, 2);
......
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