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提交 06be382a 编写于 作者: L liuruilong
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add Central arm folder

上级 e600acf8
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Showing with 498 addition and 402 deletion
src/operators/fusion_conv_add.h
浏览文件 @ 06be382a
......@@ -68,11 +68,23 @@ class FusionConvAddOp : public framework::OperatorWithKernel<
};
#ifdef PADDLE_MOBILE_CPU
#ifndef CONV_ADD_REGISTER
static framework::FusionOpRegistrar convadd_registrar(
new FusionConvAddMatcher());
new FusionConvAddMatcher());
#define CONV_ADD_REGISTER
#endif
#endif
#ifdef PADDLE_MOBILE_MALI_GPU
#ifndef CONV_ADD_REGISTER
static framework::FusionOpRegistrar convadd_registrar(
new FusionConvAddMatcher());
#define CONV_ADD_REGISTER
#endif
#endif
#ifdef PADDLE_MOBILE_FPGA
#endif
......
src/operators/fusion_conv_add_relu_op.h
浏览文件 @ 06be382a
......@@ -64,8 +64,13 @@ class FusionConvAddReluOp : public framework::OperatorWithKernel<
};
#ifdef PADDLE_MOBILE_CPU
#ifndef CONV_ADD_RELU_REGISTER
#define CONV_ADD_RELU_REGISTER
// static framework::FusionOpRegistrar fusion_conv_add_relu_registrar(new
// FusionConvAddReluOpMatcher());
#endif
#endif
#ifdef PADDLE_MOBILE_MALI_GPU
#endif
......
src/operators/fusion_fc_op.h
浏览文件 @ 06be382a
......@@ -66,11 +66,19 @@ class FusionFcOp
};
#ifdef PADDLE_MOBILE_CPU
#ifndef CONV_CPU_REGISTER
#define CONV_CPU_REGISTER
static framework::FusionOpRegistrar fc_registrar(new FusionFcMatcher());
#endif
#endif
#ifdef PADDLE_MOBILE_MALI_GPU
// static framework::FusionOpRegistrar fc_registrar(new FusionFcMatcher());
#ifndef CONV_CPU_REGISTER
#define CONV_CPU_REGISTER
static framework::FusionOpRegistrar fc_registrar(new FusionFcMatcher());
#endif
#endif
#ifdef PADDLE_MOBILE_FPGA
#endif
......
src/operators/kernel/arm/batchnorm_kernel.cpp
浏览文件 @ 06be382a
......@@ -17,6 +17,7 @@ limitations under the License. */
#pragma once
#include "operators/kernel/batchnorm_kernel.h"
#include "operators/kernel/central-arm-func/batchnorm_func.h"
namespace paddle_mobile {
namespace operators {
......@@ -28,215 +29,7 @@ bool BatchNormKernel<CPU, float>::Init(const BatchNormParam &para) const {
template <>
void BatchNormKernel<CPU, float>::Compute(const BatchNormParam &param) const {
const Tensor *input_x = param.InputX();
auto input_x_ptr = input_x->data<float>();
const auto &x_dims = input_x->dims();
const int N = x_dims[0];
const int C = x_dims[1];
const int H = x_dims[2];
const int W = x_dims[3];
const int stride0 = C * H * W;
const int stride1 = H * W;
const int stride2 = W;
Tensor *out = param.OutputY();
auto out_ptr = out->mutable_data<float>();
const float epsilon = param.Epsilon();
const Tensor *mean = param.InputMean();
const Tensor *variance = param.InputVariance();
const Tensor *scale = param.InputScale();
const Tensor *bias = param.InputBias();
auto mean_ptr = mean->data<float>();
auto variance_ptr = variance->data<float>();
auto scale_ptr = scale->data<float>();
auto bias_ptr = bias->data<float>();
// Tensor inv_std;
// auto inv_std_ptr = inv_std.mutable_data<float>(make_ddim({C}));
PADDLE_MOBILE_ENFORCE(C == variance->numel(),
"C must equal to variance.numel()");
int HXW = H * W;
if (HXW > 32) {
int NXC = N * C;
float *inv_std_ptr = new float[NXC * 4];
float *volatile new_scale_ptr = new float[NXC * 4];
float *volatile new_bias_ptr = new float[NXC * 4];
/// std = (var + epsilon).sqrt();
/// inv_std = 1 / std;
for (int i = 0; i < C * 4; i += 4) {
int index = i / 4;
inv_std_ptr[i] =
1 / static_cast<float>(pow((variance_ptr[index] + epsilon), 0.5));
inv_std_ptr[i + 1] = inv_std_ptr[i];
inv_std_ptr[i + 2] = inv_std_ptr[i];
inv_std_ptr[i + 3] = inv_std_ptr[i];
new_scale_ptr[i] = inv_std_ptr[i] * scale_ptr[index];
new_scale_ptr[i + 1] = new_scale_ptr[i];
new_scale_ptr[i + 2] = new_scale_ptr[i];
new_scale_ptr[i + 3] = new_scale_ptr[i];
new_bias_ptr[i] =
bias_ptr[index] - mean_ptr[index] * inv_std_ptr[i] * scale_ptr[index];
new_bias_ptr[i + 1] = new_bias_ptr[i];
new_bias_ptr[i + 2] = new_bias_ptr[i];
new_bias_ptr[i + 3] = new_bias_ptr[i];
}
for (int j = C * 4; j < NXC * 4; ++j) {
new_scale_ptr[j] = new_scale_ptr[j - C * 4];
new_bias_ptr[j] = new_bias_ptr[j - C * 4];
}
asm volatile(
"subs %[N], %[N], #1 \n\t"
"blt end_n_%= \n\t"
"loop_n_%=: \n\t"
"subs %[C], %[C], #1 \n\t"
"blt end_c_%= \n\t"
"loop_c_%=: \n\t"
"vld1.32 {q9}, [%[new_scale_ptr]]! \n\t"
"vld1.32 {q10}, [%[new_bias_ptr]]! \n\t"
"mov r6, %[HXW] \n\t"
"subs r6, r6, #32 \n\t"
"blt end_hw_%= \n\t"
"loop_hw_%=: \n\t"
"vld1.32 {q1, q2}, [%[input_x_ptr]]! \n\t"
"vld1.32 {q3, q4}, [%[input_x_ptr]]! \n\t"
"vld1.32 {q5, q6}, [%[input_x_ptr]]! \n\t"
"vld1.32 {q7, q8}, [%[input_x_ptr]]! \n\t"
"vmul.f32 q1, q1, q9 \n\t"
"vmul.f32 q2, q2, q9 \n\t"
"vmul.f32 q3, q3, q9 \n\t"
"vmul.f32 q4, q4, q9 \n\t"
"vmul.f32 q5, q5, q9 \n\t"
"vmul.f32 q6, q6, q9 \n\t"
"vmul.f32 q7, q7, q9 \n\t"
"vmul.f32 q8, q8, q9 \n\t"
"vadd.f32 q1, q1, q10 \n\t"
"vadd.f32 q2, q2, q10 \n\t"
"vadd.f32 q3, q3, q10 \n\t"
"vadd.f32 q4, q4, q10 \n\t"
"vadd.f32 q5, q5, q10 \n\t"
"vadd.f32 q6, q6, q10 \n\t"
"vadd.f32 q7, q7, q10 \n\t"
"vadd.f32 q8, q8, q10 \n\t"
"vst1.32 {q1, q2}, [%[out_ptr]]! \n\t"
"vst1.32 {q3, q4}, [%[out_ptr]]! \n\t"
"vst1.32 {q5, q6}, [%[out_ptr]]! \n\t"
"vst1.32 {q7, q8}, [%[out_ptr]]! \n\t"
"subs r6, r6, #32 \n\t"
"bge loop_hw_%= \n\t"
"end_hw_%=: \n\t"
"cmp r6, #0 \n\t"
"bge end_remainder_%= \n\t"
"mov r5, #4 \n\t"
"mul r6, r6, r5 \n\t"
"add %[input_x_ptr], %[input_x_ptr], r6 \n\t"
"vld1.32 {q1, q2}, [%[input_x_ptr]]! \n\t"
"vld1.32 {q3, q4}, [%[input_x_ptr]]! \n\t"
"vld1.32 {q5, q6}, [%[input_x_ptr]]! \n\t"
"vld1.32 {q7, q8}, [%[input_x_ptr]]! \n\t"
"vmul.f32 q1, q1, q9 \n\t"
"vmul.f32 q2, q2, q9 \n\t"
"vmul.f32 q3, q3, q9 \n\t"
"vmul.f32 q4, q4, q9 \n\t"
"vmul.f32 q5, q5, q9 \n\t"
"vmul.f32 q6, q6, q9 \n\t"
"vmul.f32 q7, q7, q9 \n\t"
"vmul.f32 q8, q8, q9 \n\t"
"vadd.f32 q1, q1, q10 \n\t"
"vadd.f32 q2, q2, q10 \n\t"
"vadd.f32 q3, q3, q10 \n\t"
"vadd.f32 q4, q4, q10 \n\t"
"vadd.f32 q5, q5, q10 \n\t"
"vadd.f32 q6, q6, q10 \n\t"
"vadd.f32 q7, q7, q10 \n\t"
"vadd.f32 q8, q8, q10 \n\t"
"add %[out_ptr], %[out_ptr], r6 \n\t"
"vst1.32 {q1, q2}, [%[out_ptr]]! \n\t"
"vst1.32 {q3, q4}, [%[out_ptr]]! \n\t"
"vst1.32 {q5, q6}, [%[out_ptr]]! \n\t"
"vst1.32 {q7, q8}, [%[out_ptr]]! \n\t"
"end_remainder_%=: \n\t"
"subs %[C], %[C], #1 \n\t"
"bge loop_c_%= \n\t"
"end_c_%=: \n\t"
"subs %[N], %[N], #1 \n\t"
"bge loop_n_%= \n\t"
"end_n_%=: \n\t"
:
: [input_x_ptr] "r"(input_x_ptr), [out_ptr] "r"(out_ptr),
[new_scale_ptr] "r"(new_scale_ptr), [new_bias_ptr] "r"(new_bias_ptr),
[N] "r"(N), [C] "r"(C), [HXW] "r"(HXW)
: "memory", "q0", "q1", "q2", "q3", "q4", "q5", "q6", "q7", "q8", "q9",
"q10", "r5", "r6");
delete[] inv_std_ptr;
delete[] new_scale_ptr;
delete[] new_bias_ptr;
} else {
float *inv_std_ptr = new float[C];
for (int i = 0; i < C; i++) {
inv_std_ptr[i] =
1 / static_cast<float>(pow((variance_ptr[i] + epsilon), 0.5));
}
Tensor new_scale;
auto new_scale_ptr = new_scale.mutable_data<float>(make_ddim({C}));
Tensor new_bias;
auto new_bias_ptr = new_bias.mutable_data<float>(make_ddim({C}));
/// ((x - est_mean) * (inv_var) * scale + bias equal to
/// (x * inv_var * scale) + (bias - est_mean * inv_var * scale)
for (int i = 0; i < C; i++) {
new_scale_ptr[i] = inv_std_ptr[i] * scale_ptr[i];
new_bias_ptr[i] =
bias_ptr[i] - mean_ptr[i] * inv_std_ptr[i] * scale_ptr[i];
{
for (int n = 0; n < N; n++) {
for (int h = 0; h < H; h++) {
int tmp_index = n * stride0 + i * stride1 + h * stride2;
for (int w = 0; w < W; w++) {
int index = tmp_index + w;
out_ptr[index] =
input_x_ptr[index] * new_scale_ptr[i] + new_bias_ptr[i];
}
}
}
}
}
delete[] inv_std_ptr;
// DLOG << "input[2,5,1,0](input[102]) ,channel 5 :";
// DLOG << "input_x_ptr : " << input_x_ptr[102];
// DLOG << "variance : " << variance_ptr[5];
// DLOG << "inv_std_ptr : " << inv_std_ptr[5];
// DLOG << "new_scale_ptr : " << new_scale_ptr[5];
// DLOG << "new_bias_ptr : " << new_bias_ptr[5];
// DLOG << "out_ptr : " << out_ptr[102];
}
BatchnormCompute<float>(param);
}
} // namespace operators
......
src/operators/kernel/arm/conv_add_relu_kernel.cpp
浏览文件 @ 06be382a
......@@ -15,6 +15,7 @@ limitations under the License. */
#ifdef FUSION_CONVADD_RELU_OP
#include "operators/kernel/conv_add_relu_kernel.h"
#include "operators/kernel/central-arm-func/conv_add_relu_func.h"
namespace paddle_mobile {
namespace operators {
......@@ -28,92 +29,7 @@ bool ConvAddReluKernel<CPU, float>::Init(
template <>
void ConvAddReluKernel<CPU, float>::Compute(
const FusionConvAddReluParam &param) const {
const Tensor *input = param.Input();
Tensor filter = *param.Filter();
Tensor bias = *param.Bias();
int axis = param.Axis();
Tensor *output = param.Output();
math::expand_bias(bias, axis, output->dims());
output->ShareDataWith(bias);
int groups = param.Groups();
std::vector<int> strides = param.Strides();
std::vector<int> paddings = param.Paddings();
std::vector<int> dilations = param.Dilations();
const int batch_size = static_cast<int>(input->dims()[0]);
std::vector<int64_t> filter_shape_vec(framework::vectorize(filter.dims()));
std::vector<int64_t> output_shape_vec(framework::vectorize(output->dims()));
size_t data_dim = filter_shape_vec.size() - 2;
std::vector<int64_t> col_shape_vec(1 + 2 * data_dim);
col_shape_vec[0] = input->dims()[1] / groups;
for (size_t j = 0; j < data_dim; ++j) {
col_shape_vec[j + 1] = filter_shape_vec[j + 2];
col_shape_vec[j + 1 + data_dim] = output_shape_vec[j + 2];
}
framework::DDim col_shape(framework::make_ddim(col_shape_vec));
framework::DDim col_matrix_shape =
framework::flatten_to_2d(col_shape, data_dim + 1);
bool is_expand =
math::IsExpand(filter_shape_vec, strides, paddings, dilations);
Tensor col;
Tensor col_matrix;
if (is_expand) {
col.mutable_data<float>(col_shape);
col_matrix.ShareDataWith(col);
col_matrix.Resize(col_matrix_shape);
}
framework::DDim input_shape = framework::slice_ddim(
input->dims(), 1, static_cast<int>(input->dims().size()));
framework::DDim filter_matrix_shape = {filter.dims()[0],
filter.numel() / filter.dims()[0]};
filter.Resize(filter_matrix_shape);
framework::DDim output_matrix_shape = {
output->dims()[1],
output->numel() / (output->dims()[0] * output->dims()[1])};
// convolution operator: im2col(or vol2col) + gemm
int in_step = static_cast<int>(input->dims()[1]) / groups;
int out_step = static_cast<int>(output->dims()[1]) / groups;
math::Vol2ColFunctor<CPU, float> vol2col;
math::Im2ColFunctor<math::ColFormat::kCFO, CPU, float> im2col;
for (int i = 0; i < batch_size; i++) {
Tensor in_batch = input->Slice(i, i + 1).Resize(input_shape);
Tensor out_batch = output->Slice(i, i + 1).Resize(output_matrix_shape);
for (int g = 0; g < groups; g++) {
Tensor in_slice = in_batch.Slice(g * in_step, (g + 1) * in_step);
if (!is_expand) {
col.ShareDataWith(in_slice);
col_matrix.ShareDataWith(col);
col_matrix.Resize(col_matrix_shape);
} else if (data_dim == 2U) {
// im2col
im2col(in_slice, dilations, strides,
std::vector<int>{paddings[0], paddings[1], paddings[0],
paddings[1]},
&col);
} else if (data_dim == 3U) {
// vol2col
vol2col(in_slice, dilations, strides, paddings, &col);
}
// gemm
Tensor out_slice = out_batch.Slice(g * out_step, (g + 1) * out_step);
Tensor filter_slice = filter.Slice(g * out_step, (g + 1) * out_step);
math::matmul<float>(filter_slice, false, col_matrix, false,
static_cast<float>(1), &out_slice,
static_cast<float>(1), true);
}
}
ConvAddReluCompute<float>(param);
}
template class ConvAddReluKernel<CPU, float>;
......
src/operators/kernel/arm/conv_kernel.cpp
浏览文件 @ 06be382a
......@@ -15,6 +15,7 @@ limitations under the License. */
#ifdef CONV_OP
#include "operators/kernel/conv_kernel.h"
#include "operators/kernel/central-arm-func/conv_func.h"
namespace paddle_mobile {
namespace operators {
......@@ -26,88 +27,7 @@ bool ConvKernel<CPU, float>::Init(const ConvParam &para) const {
template <>
void ConvKernel<CPU, float>::Compute(const ConvParam &param) const {
const Tensor *input = param.Input();
Tensor filter = *param.Filter();
Tensor *output = param.Output();
output->mutable_data<float>();
int groups = param.Groups();
std::vector<int> strides = param.Strides();
std::vector<int> paddings = param.Paddings();
std::vector<int> dilations = param.Dilations();
const int batch_size = static_cast<int>(input->dims()[0]);
std::vector<int64_t> filter_shape_vec(framework::vectorize(filter.dims()));
std::vector<int64_t> output_shape_vec(framework::vectorize(output->dims()));
size_t data_dim = filter_shape_vec.size() - 2;
std::vector<int64_t> col_shape_vec(1 + 2 * data_dim);
col_shape_vec[0] = input->dims()[1] / groups;
for (size_t j = 0; j < data_dim; ++j) {
col_shape_vec[j + 1] = filter_shape_vec[j + 2];
col_shape_vec[j + 1 + data_dim] = output_shape_vec[j + 2];
}
framework::DDim col_shape(framework::make_ddim(col_shape_vec));
framework::DDim col_matrix_shape =
framework::flatten_to_2d(col_shape, data_dim + 1);
bool is_expand = IsExpand(filter_shape_vec, strides, paddings, dilations);
Tensor col;
Tensor col_matrix;
if (is_expand) {
col.mutable_data<float>(col_shape);
col_matrix.ShareDataWith(col);
col_matrix.Resize(col_matrix_shape);
}
framework::DDim input_shape = framework::slice_ddim(
input->dims(), 1, static_cast<int>(input->dims().size()));
framework::DDim filter_matrix_shape = {filter.dims()[0],
filter.numel() / filter.dims()[0]};
filter.Resize(filter_matrix_shape);
framework::DDim output_matrix_shape = {
output->dims()[1],
output->numel() / (output->dims()[0] * output->dims()[1])};
// convolution operator: im2col(or vol2col) + gemm
int in_step = static_cast<int>(input->dims()[1]) / groups;
int out_step = static_cast<int>(output->dims()[1]) / groups;
math::Vol2ColFunctor<CPU, float> vol2col;
math::Im2ColFunctor<math::ColFormat::kCFO, CPU, float> im2col;
for (int i = 0; i < batch_size; i++) {
Tensor in_batch = input->Slice(i, i + 1).Resize(input_shape);
Tensor out_batch = output->Slice(i, i + 1).Resize(output_matrix_shape);
for (int g = 0; g < groups; g++) {
Tensor in_slice = in_batch.Slice(g * in_step, (g + 1) * in_step);
if (!is_expand) {
col.ShareDataWith(in_slice);
col_matrix.ShareDataWith(col);
col_matrix.Resize(col_matrix_shape);
} else if (data_dim == 2U) {
// im2col
im2col(in_slice, dilations, strides,
std::vector<int>{paddings[0], paddings[1], paddings[0],
paddings[1]},
&col);
} else if (data_dim == 3U) {
// vol2col
vol2col(in_slice, dilations, strides, paddings, &col);
}
// gemm
Tensor out_slice = out_batch.Slice(g * out_step, (g + 1) * out_step);
Tensor filter_slice = filter.Slice(g * out_step, (g + 1) * out_step);
math::matmul<float>(filter_slice, false, col_matrix, false,
static_cast<float>(1), &out_slice,
static_cast<float>(0));
}
}
ConvCompute<float>(param);
}
template class ConvKernel<CPU, float>;
......
src/operators/kernel/central-arm-func/batchnorm_func.h
浏览文件 @ 06be382a
//
// Created by liuRuiLong on 2018/6/28.
//
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#ifndef PADDLE_MOBILE_BATCHNORM_FUNC_H
#define PADDLE_MOBILE_BATCHNORM_FUNC_H
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
#endif //PADDLE_MOBILE_BATCHNORM_FUNC_H
http://www.apache.org/licenses/LICENSE-2.0
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. */
#ifdef BATCHNORM_OP
#pragma once
#include "operators/op_param.h"
namespace paddle_mobile {
namespace operators {
template<typename P>
void BatchnormCompute(const BatchNormParam &param) {
const Tensor *input_x = param.InputX();
auto input_x_ptr = input_x->data<float>();
const auto &x_dims = input_x->dims();
const int N = x_dims[0];
const int C = x_dims[1];
const int H = x_dims[2];
const int W = x_dims[3];
const int stride0 = C * H * W;
const int stride1 = H * W;
const int stride2 = W;
Tensor *out = param.OutputY();
auto out_ptr = out->mutable_data<float>();
const float epsilon = param.Epsilon();
const Tensor *mean = param.InputMean();
const Tensor *variance = param.InputVariance();
const Tensor *scale = param.InputScale();
const Tensor *bias = param.InputBias();
auto mean_ptr = mean->data<float>();
auto variance_ptr = variance->data<float>();
auto scale_ptr = scale->data<float>();
auto bias_ptr = bias->data<float>();
// Tensor inv_std;
// auto inv_std_ptr = inv_std.mutable_data<float>(make_ddim({C}));
PADDLE_MOBILE_ENFORCE(C == variance->numel(),
"C must equal to variance.numel()");
int HXW = H * W;
if (HXW > 32) {
int NXC = N * C;
float *inv_std_ptr = new float[NXC * 4];
float *volatile new_scale_ptr = new float[NXC * 4];
float *volatile new_bias_ptr = new float[NXC * 4];
/// std = (var + epsilon).sqrt();
/// inv_std = 1 / std;
for (int i = 0; i < C * 4; i += 4) {
int index = i / 4;
inv_std_ptr[i] =
1 / static_cast<float>(pow((variance_ptr[index] + epsilon), 0.5));
inv_std_ptr[i + 1] = inv_std_ptr[i];
inv_std_ptr[i + 2] = inv_std_ptr[i];
inv_std_ptr[i + 3] = inv_std_ptr[i];
new_scale_ptr[i] = inv_std_ptr[i] * scale_ptr[index];
new_scale_ptr[i + 1] = new_scale_ptr[i];
new_scale_ptr[i + 2] = new_scale_ptr[i];
new_scale_ptr[i + 3] = new_scale_ptr[i];
new_bias_ptr[i] =
bias_ptr[index] - mean_ptr[index] * inv_std_ptr[i] * scale_ptr[index];
new_bias_ptr[i + 1] = new_bias_ptr[i];
new_bias_ptr[i + 2] = new_bias_ptr[i];
new_bias_ptr[i + 3] = new_bias_ptr[i];
}
for (int j = C * 4; j < NXC * 4; ++j) {
new_scale_ptr[j] = new_scale_ptr[j - C * 4];
new_bias_ptr[j] = new_bias_ptr[j - C * 4];
}
asm volatile(
"subs %[N], %[N], #1 \n\t"
"blt end_n_%= \n\t"
"loop_n_%=: \n\t"
"subs %[C], %[C], #1 \n\t"
"blt end_c_%= \n\t"
"loop_c_%=: \n\t"
"vld1.32 {q9}, [%[new_scale_ptr]]! \n\t"
"vld1.32 {q10}, [%[new_bias_ptr]]! \n\t"
"mov r6, %[HXW] \n\t"
"subs r6, r6, #32 \n\t"
"blt end_hw_%= \n\t"
"loop_hw_%=: \n\t"
"vld1.32 {q1, q2}, [%[input_x_ptr]]! \n\t"
"vld1.32 {q3, q4}, [%[input_x_ptr]]! \n\t"
"vld1.32 {q5, q6}, [%[input_x_ptr]]! \n\t"
"vld1.32 {q7, q8}, [%[input_x_ptr]]! \n\t"
"vmul.f32 q1, q1, q9 \n\t"
"vmul.f32 q2, q2, q9 \n\t"
"vmul.f32 q3, q3, q9 \n\t"
"vmul.f32 q4, q4, q9 \n\t"
"vmul.f32 q5, q5, q9 \n\t"
"vmul.f32 q6, q6, q9 \n\t"
"vmul.f32 q7, q7, q9 \n\t"
"vmul.f32 q8, q8, q9 \n\t"
"vadd.f32 q1, q1, q10 \n\t"
"vadd.f32 q2, q2, q10 \n\t"
"vadd.f32 q3, q3, q10 \n\t"
"vadd.f32 q4, q4, q10 \n\t"
"vadd.f32 q5, q5, q10 \n\t"
"vadd.f32 q6, q6, q10 \n\t"
"vadd.f32 q7, q7, q10 \n\t"
"vadd.f32 q8, q8, q10 \n\t"
"vst1.32 {q1, q2}, [%[out_ptr]]! \n\t"
"vst1.32 {q3, q4}, [%[out_ptr]]! \n\t"
"vst1.32 {q5, q6}, [%[out_ptr]]! \n\t"
"vst1.32 {q7, q8}, [%[out_ptr]]! \n\t"
"subs r6, r6, #32 \n\t"
"bge loop_hw_%= \n\t"
"end_hw_%=: \n\t"
"cmp r6, #0 \n\t"
"bge end_remainder_%= \n\t"
"mov r5, #4 \n\t"
"mul r6, r6, r5 \n\t"
"add %[input_x_ptr], %[input_x_ptr], r6 \n\t"
"vld1.32 {q1, q2}, [%[input_x_ptr]]! \n\t"
"vld1.32 {q3, q4}, [%[input_x_ptr]]! \n\t"
"vld1.32 {q5, q6}, [%[input_x_ptr]]! \n\t"
"vld1.32 {q7, q8}, [%[input_x_ptr]]! \n\t"
"vmul.f32 q1, q1, q9 \n\t"
"vmul.f32 q2, q2, q9 \n\t"
"vmul.f32 q3, q3, q9 \n\t"
"vmul.f32 q4, q4, q9 \n\t"
"vmul.f32 q5, q5, q9 \n\t"
"vmul.f32 q6, q6, q9 \n\t"
"vmul.f32 q7, q7, q9 \n\t"
"vmul.f32 q8, q8, q9 \n\t"
"vadd.f32 q1, q1, q10 \n\t"
"vadd.f32 q2, q2, q10 \n\t"
"vadd.f32 q3, q3, q10 \n\t"
"vadd.f32 q4, q4, q10 \n\t"
"vadd.f32 q5, q5, q10 \n\t"
"vadd.f32 q6, q6, q10 \n\t"
"vadd.f32 q7, q7, q10 \n\t"
"vadd.f32 q8, q8, q10 \n\t"
"add %[out_ptr], %[out_ptr], r6 \n\t"
"vst1.32 {q1, q2}, [%[out_ptr]]! \n\t"
"vst1.32 {q3, q4}, [%[out_ptr]]! \n\t"
"vst1.32 {q5, q6}, [%[out_ptr]]! \n\t"
"vst1.32 {q7, q8}, [%[out_ptr]]! \n\t"
"end_remainder_%=: \n\t"
"subs %[C], %[C], #1 \n\t"
"bge loop_c_%= \n\t"
"end_c_%=: \n\t"
"subs %[N], %[N], #1 \n\t"
"bge loop_n_%= \n\t"
"end_n_%=: \n\t"
:
: [input_x_ptr] "r"(input_x_ptr), [out_ptr] "r"(out_ptr),
[new_scale_ptr] "r"(new_scale_ptr), [new_bias_ptr] "r"(new_bias_ptr),
[N] "r"(N), [C] "r"(C), [HXW] "r"(HXW)
: "memory", "q0", "q1", "q2", "q3", "q4", "q5", "q6", "q7", "q8", "q9",
"q10", "r5", "r6");
delete[] inv_std_ptr;
delete[] new_scale_ptr;
delete[] new_bias_ptr;
} else {
float *inv_std_ptr = new float[C];
for (int i = 0; i < C; i++) {
inv_std_ptr[i] =
1 / static_cast<float>(pow((variance_ptr[i] + epsilon), 0.5));
}
Tensor new_scale;
auto new_scale_ptr = new_scale.mutable_data<float>(framework::make_ddim({C}));
Tensor new_bias;
auto new_bias_ptr = new_bias.mutable_data<float>(framework::make_ddim({C}));
/// ((x - est_mean) * (inv_var) * scale + bias equal to
/// (x * inv_var * scale) + (bias - est_mean * inv_var * scale)
for (int i = 0; i < C; i++) {
new_scale_ptr[i] = inv_std_ptr[i] * scale_ptr[i];
new_bias_ptr[i] =
bias_ptr[i] - mean_ptr[i] * inv_std_ptr[i] * scale_ptr[i];
{
for (int n = 0; n < N; n++) {
for (int h = 0; h < H; h++) {
int tmp_index = n * stride0 + i * stride1 + h * stride2;
for (int w = 0; w < W; w++) {
int index = tmp_index + w;
out_ptr[index] =
input_x_ptr[index] * new_scale_ptr[i] + new_bias_ptr[i];
}
}
}
}
}
delete[] inv_std_ptr;
}
}
}
}
#endif
src/operators/kernel/central-arm-func/conv_add_relu_func.h
浏览文件 @ 06be382a
//
// Created by liuRuiLong on 2018/6/28.
//
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#ifndef PADDLE_MOBILE_CONV_ADD_RELU_FUNC_H
#define PADDLE_MOBILE_CONV_ADD_RELU_FUNC_H
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
#endif //PADDLE_MOBILE_CONV_ADD_RELU_FUNC_H
http://www.apache.org/licenses/LICENSE-2.0
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. */
#ifdef FUSION_CONVADD_RELU_OP
#pragma once
#include "operators/op_param.h"
namespace paddle_mobile {
namespace operators {
template<typename P>
void ConvAddReluCompute(const FusionConvAddReluParam &param) {
const Tensor *input = param.Input();
Tensor filter = *param.Filter();
Tensor bias = *param.Bias();
int axis = param.Axis();
Tensor *output = param.Output();
math::expand_bias(bias, axis, output->dims());
output->ShareDataWith(bias);
int groups = param.Groups();
std::vector<int> strides = param.Strides();
std::vector<int> paddings = param.Paddings();
std::vector<int> dilations = param.Dilations();
const int batch_size = static_cast<int>(input->dims()[0]);
std::vector<int64_t> filter_shape_vec(framework::vectorize(filter.dims()));
std::vector<int64_t> output_shape_vec(framework::vectorize(output->dims()));
size_t data_dim = filter_shape_vec.size() - 2;
std::vector<int64_t> col_shape_vec(1 + 2 * data_dim);
col_shape_vec[0] = input->dims()[1] / groups;
for (size_t j = 0; j < data_dim; ++j) {
col_shape_vec[j + 1] = filter_shape_vec[j + 2];
col_shape_vec[j + 1 + data_dim] = output_shape_vec[j + 2];
}
framework::DDim col_shape(framework::make_ddim(col_shape_vec));
framework::DDim col_matrix_shape =
framework::flatten_to_2d(col_shape, data_dim + 1);
bool is_expand =
math::IsExpand(filter_shape_vec, strides, paddings, dilations);
Tensor col;
Tensor col_matrix;
if (is_expand) {
col.mutable_data<float>(col_shape);
col_matrix.ShareDataWith(col);
col_matrix.Resize(col_matrix_shape);
}
framework::DDim input_shape = framework::slice_ddim(
input->dims(), 1, static_cast<int>(input->dims().size()));
framework::DDim filter_matrix_shape = {filter.dims()[0],
filter.numel() / filter.dims()[0]};
filter.Resize(filter_matrix_shape);
framework::DDim output_matrix_shape = {
output->dims()[1],
output->numel() / (output->dims()[0] * output->dims()[1])};
// convolution operator: im2col(or vol2col) + gemm
int in_step = static_cast<int>(input->dims()[1]) / groups;
int out_step = static_cast<int>(output->dims()[1]) / groups;
math::Vol2ColFunctor<CPU, float> vol2col;
math::Im2ColFunctor<math::ColFormat::kCFO, CPU, float> im2col;
for (int i = 0; i < batch_size; i++) {
Tensor in_batch = input->Slice(i, i + 1).Resize(input_shape);
Tensor out_batch = output->Slice(i, i + 1).Resize(output_matrix_shape);
for (int g = 0; g < groups; g++) {
Tensor in_slice = in_batch.Slice(g * in_step, (g + 1) * in_step);
if (!is_expand) {
col.ShareDataWith(in_slice);
col_matrix.ShareDataWith(col);
col_matrix.Resize(col_matrix_shape);
} else if (data_dim == 2U) {
// im2col
im2col(in_slice, dilations, strides,
std::vector<int>{paddings[0], paddings[1], paddings[0],
paddings[1]},
&col);
} else if (data_dim == 3U) {
// vol2col
vol2col(in_slice, dilations, strides, paddings, &col);
}
// gemm
Tensor out_slice = out_batch.Slice(g * out_step, (g + 1) * out_step);
Tensor filter_slice = filter.Slice(g * out_step, (g + 1) * out_step);
math::matmul<float>(filter_slice, false, col_matrix, false,
static_cast<float>(1), &out_slice,
static_cast<float>(1), true);
}
}
}
}
}
#endif
src/operators/kernel/central-arm-func/conv_func.h
浏览文件 @ 06be382a
//
// Created by liuRuiLong on 2018/6/28.
//
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#ifndef PADDLE_MOBILE_CONV_FUNC_H
#define PADDLE_MOBILE_CONV_FUNC_H
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
#endif //PADDLE_MOBILE_CONV_FUNC_H
http://www.apache.org/licenses/LICENSE-2.0
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. */
#ifdef CONV_OP
#pragma once
#include "operators/op_param.h"
namespace paddle_mobile {
namespace operators {
template<typename P>
void ConvCompute(const ConvParam &param) {
const Tensor *input = param.Input();
Tensor filter = *param.Filter();
Tensor *output = param.Output();
output->mutable_data<float>();
int groups = param.Groups();
std::vector<int> strides = param.Strides();
std::vector<int> paddings = param.Paddings();
std::vector<int> dilations = param.Dilations();
const int batch_size = static_cast<int>(input->dims()[0]);
std::vector<int64_t> filter_shape_vec(framework::vectorize(filter.dims()));
std::vector<int64_t> output_shape_vec(framework::vectorize(output->dims()));
size_t data_dim = filter_shape_vec.size() - 2;
std::vector<int64_t> col_shape_vec(1 + 2 * data_dim);
col_shape_vec[0] = input->dims()[1] / groups;
for (size_t j = 0; j < data_dim; ++j) {
col_shape_vec[j + 1] = filter_shape_vec[j + 2];
col_shape_vec[j + 1 + data_dim] = output_shape_vec[j + 2];
}
framework::DDim col_shape(framework::make_ddim(col_shape_vec));
framework::DDim col_matrix_shape =
framework::flatten_to_2d(col_shape, data_dim + 1);
bool is_expand = IsExpand(filter_shape_vec, strides, paddings, dilations);
Tensor col;
Tensor col_matrix;
if (is_expand) {
col.mutable_data<float>(col_shape);
col_matrix.ShareDataWith(col);
col_matrix.Resize(col_matrix_shape);
}
framework::DDim input_shape = framework::slice_ddim(
input->dims(), 1, static_cast<int>(input->dims().size()));
framework::DDim filter_matrix_shape = {filter.dims()[0],
filter.numel() / filter.dims()[0]};
filter.Resize(filter_matrix_shape);
framework::DDim output_matrix_shape = {
output->dims()[1],
output->numel() / (output->dims()[0] * output->dims()[1])};
// convolution operator: im2col(or vol2col) + gemm
int in_step = static_cast<int>(input->dims()[1]) / groups;
int out_step = static_cast<int>(output->dims()[1]) / groups;
math::Vol2ColFunctor<CPU, float> vol2col;
math::Im2ColFunctor<math::ColFormat::kCFO, CPU, float> im2col;
for (int i = 0; i < batch_size; i++) {
Tensor in_batch = input->Slice(i, i + 1).Resize(input_shape);
Tensor out_batch = output->Slice(i, i + 1).Resize(output_matrix_shape);
for (int g = 0; g < groups; g++) {
Tensor in_slice = in_batch.Slice(g * in_step, (g + 1) * in_step);
if (!is_expand) {
col.ShareDataWith(in_slice);
col_matrix.ShareDataWith(col);
col_matrix.Resize(col_matrix_shape);
} else if (data_dim == 2U) {
// im2col
im2col(in_slice, dilations, strides,
std::vector<int>{paddings[0], paddings[1], paddings[0],
paddings[1]},
&col);
} else if (data_dim == 3U) {
// vol2col
vol2col(in_slice, dilations, strides, paddings, &col);
}
// gemm
Tensor out_slice = out_batch.Slice(g * out_step, (g + 1) * out_step);
Tensor filter_slice = filter.Slice(g * out_step, (g + 1) * out_step);
math::matmul<float>(filter_slice, false, col_matrix, false,
static_cast<float>(1), &out_slice,
static_cast<float>(0));
}
}
}
}
}
#endif
tools/android-debug-script/push2android.sh
浏览文件 @ 06be382a
#!/usr/bin/env sh
push_fn () {
MODELS_PATH="../test/models/*"
MODELS_SRC="../test/models"
IMAGE_PATH="../test/images/*"
EXE_FILE="../test/build/*"
MODELS_PATH="../../test/models/*"
MODELS_SRC="../../test/models"
IMAGE_PATH="../../test/images/*"
EXE_FILE="../../test/build/*"
EXE_DIR="data/local/tmp/bin"
adb shell mkdir ${EXE_DIR}
MODELS_DIR="data/local/tmp/models"
......@@ -14,9 +14,14 @@ do
adb shell mkdir ${MODELS_DIR}"/"${file}
done
if [[ -d "../../src/operators/kernel/mali/ACL_Android/build" ]]; then
ACL_BUILD_PATH="../../src/operators/kernel/mali/ACL_Android/build/*"
adb push ${ACL_BUILD_PATH} ${EXE_DIR}
fi
IMAGES_DIR="data/local/tmp/images"
adb shell mkdir ${IMAGES_DIR}
LIB_PATH="../build/release/arm-v7a/build/*"
LIB_PATH="../../build/release/arm-v7a/build/*"
adb push ${EXE_FILE} ${EXE_DIR}
adb push ${LIB_PATH} ${EXE_DIR}
if [[ $1 != "npm" ]]; then
......
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