Skip to content

P
Paddle-Lite

PaddlePaddle / Paddle-Lite

通知 331
Star 4
Fork 1
P
Paddle-Lite
提交 a663dd86 编写于 作者: R Ray Liu 提交者： GitHub
浏览文件
操作

Merge pull request #1217 from hjchen2/dev-latest 

Optimize im2col and implement winograd conv, bring 35% speed up for googlenet
上级 92d3661c ee79fcf4
展开全部 隐藏空白更改
内联 并排
Showing with 3729 addition and 332 deletion
CMakeLists.txt
浏览文件 @ a663dd86
...@@ -5,6 +5,7 @@ option(DEBUGING "enable debug mode" ON) ...@@ -5,6 +5,7 @@ option(DEBUGING "enable debug mode" ON)
option(USE_EXCEPTION "use std exception" ON) option(USE_EXCEPTION "use std exception" ON)
option(SYMBOL_HIDDEN "symbol hidden" OFF) # on when use jni or ios io option(SYMBOL_HIDDEN "symbol hidden" OFF) # on when use jni or ios io
option(LOG_PROFILE "log profile" OFF) option(LOG_PROFILE "log profile" OFF)
# select the platform to build # select the platform to build
option(CPU "armv7 with neon" ON) option(CPU "armv7 with neon" ON)
option(GPU_MALI "mali gpu" OFF) option(GPU_MALI "mali gpu" OFF)
...@@ -15,7 +16,6 @@ if(FPGA) ...@@ -15,7 +16,6 @@ if(FPGA)
option(FPGAV2 "fpga v2" OFF) option(FPGAV2 "fpga v2" OFF)
endif() endif()
project(paddle-mobile) project(paddle-mobile)
file(GLOB_RECURSE PADDLE_MOBILE_CC src/*.cc src/*.cpp src/*.c src/*.mm) file(GLOB_RECURSE PADDLE_MOBILE_CC src/*.cc src/*.cpp src/*.c src/*.mm)
...@@ -247,6 +247,3 @@ elseif(FPGA) ...@@ -247,6 +247,3 @@ elseif(FPGA)
add_subdirectory(test) add_subdirectory(test)
endif() endif()
src/framework/executor.cpp
浏览文件 @ a663dd86
...@@ -30,7 +30,6 @@ limitations under the License. */ ...@@ -30,7 +30,6 @@ limitations under the License. */
#ifdef PADDLE_EXECUTOR_MULTITHREAD #ifdef PADDLE_EXECUTOR_MULTITHREAD
#include <queue> #include <queue>
#include <utility>
#include "common/threadpool.h" #include "common/threadpool.h"
#endif #endif
...@@ -73,7 +72,7 @@ Executor<Dtype, P>::Executor(const framework::Program<Dtype> p, int batch_size, ...@@ -73,7 +72,7 @@ Executor<Dtype, P>::Executor(const framework::Program<Dtype> p, int batch_size,
op->Type(), op->GetInputs(), op->GetOutputs(), op->GetAttrMap(), op->Type(), op->GetInputs(), op->GetOutputs(), op->GetAttrMap(),
program_.scope); program_.scope);
// infer shape to reshape tensor before predict, // infer shape to reshape tensor before predict,
// but for lod tensor, it will need to reshape in runtime // but for lod tensor, it will still need to reshape in runtime
if (!loddable_) { if (!loddable_) {
op_base->InferShape(); op_base->InferShape();
} }
......
src/framework/operator.h
浏览文件 @ a663dd86
...@@ -127,11 +127,6 @@ class OperatorWithKernel : public OperatorBase<Dtype> { ...@@ -127,11 +127,6 @@ class OperatorWithKernel : public OperatorBase<Dtype> {
virtual void InferShape() const = 0; virtual void InferShape() const = 0;
void Init() { void Init() {
// for (auto i : this->inputs_) {
// DLOG << i.first;
// DLOG << i.second;
// }
PADDLE_MOBILE_ENFORCE(kernel_.Init(&param_), " %s kernel init failed", PADDLE_MOBILE_ENFORCE(kernel_.Init(&param_), " %s kernel init failed",
this->type_.c_str()); this->type_.c_str());
} }
......
src/framework/tensor.h
浏览文件 @ a663dd86
...@@ -54,22 +54,6 @@ class Tensor : public TensorBase { ...@@ -54,22 +54,6 @@ class Tensor : public TensorBase {
this->offset_ = inTensor.offset_; this->offset_ = inTensor.offset_;
} }
#ifdef PADDLE_MOBILE_DEBUG
template <typename T>
inline void dump(std::string filename) const {
const T *dataptr = data<T>();
std::ofstream out(filename.c_str());
for (int i = 0; i < numel(); ++i) {
out << dataptr[i] << " ";
}
out << "形状：";
for (int j = 0; j < dims_.size(); ++j) {
out << dims_[j] << " ";
}
out.close();
}
#endif
/*! Resize the dimensions of the memory block. */ /*! Resize the dimensions of the memory block. */
inline Tensor &Resize(const DDim &dims) { inline Tensor &Resize(const DDim &dims) {
dims_ = dims; dims_ = dims;
......
src/operators/dequantize_op.cpp
浏览文件 @ a663dd86
...@@ -22,7 +22,7 @@ namespace operators { ...@@ -22,7 +22,7 @@ namespace operators {
template <typename DeviceType, typename T> template <typename DeviceType, typename T>
void DequantizeOp<DeviceType, T>::InferShape() const { void DequantizeOp<DeviceType, T>::InferShape() const {
const auto& input_dims = this->param_.input_->dims(); const auto& input_dims = this->param_.input_->dims();
this->param_.out_->Resize(input_dims); this->param_.output_->Resize(input_dims);
} }
} // namespace operators } // namespace operators
......
src/operators/kernel/arm/conv_kernel.cpp
浏览文件 @ a663dd86
...@@ -22,12 +22,76 @@ namespace operators { ...@@ -22,12 +22,76 @@ namespace operators {
template <> template <>
bool ConvKernel<CPU, float>::Init(ConvParam<CPU> *param) { bool ConvKernel<CPU, float>::Init(ConvParam<CPU> *param) {
if (param->Filter()->type() == typeid(int8_t)) {
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] < 3 &&
param->Strides()[0] == param->Strides()[1]) {
param->ExecMode() = ConvParam<CPU>::EXEC_DEPTHWISE3x3_INT8;
} else {
param->ExecMode() = ConvParam<CPU>::EXEC_GEMM_INT8;
}
} 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] == 1) {
param->ExecMode() = ConvParam<CPU>::EXEC_DEPTHWISE3x3S1P1_FLOAT;
} 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->ExecMode() = ConvParam<CPU>::EXEC_DEPTHWISE3x3_FLOAT;
#ifndef __aarch64__
} else if (param->Filter()->dims()[2] == param->Filter()->dims()[3] &&
param->Strides()[0] == param->Strides()[1] &&
param->Dilations()[0] == param->Dilations()[1] &&
param->Filter()->dims()[2] == 3 && param->Strides()[0] == 1 &&
param->Dilations()[0] == 1 && param->Output()->dims()[1] >= 16 &&
param->Input()->dims()[1] >= 16 &&
param->Input()->dims()[2] <= 140 /* refered from ncnn */) {
param->ExecMode() = ConvParam<CPU>::EXEC_WINOGRAD3X3_FLOAT;
// transform weight
framework::Tensor *transformed_weight = new framework::Tensor;
operators::math::winograd_transform_weight<8, 3>(*param->Filter(),
transformed_weight);
param->Filter() = transformed_weight;
#endif
} else {
param->ExecMode() = ConvParam<CPU>::EXEC_GEMM_FLOAT;
}
}
return true; return true;
} }
template <> template <>
void ConvKernel<CPU, float>::Compute(const ConvParam<CPU> &param) { void ConvKernel<CPU, float>::Compute(const ConvParam<CPU> &param) {
ConvCompute<float>(param); switch (param.ExecMode()) {
case ConvParam<CPU>::EXEC_GEMM_INT8:
GemmConv<int8_t, int32_t>(param);
break;
case ConvParam<CPU>::EXEC_DEPTHWISE3x3_INT8:
DepthwiseConv3x3<int8_t, int32_t>(param);
break;
case ConvParam<CPU>::EXEC_DEPTHWISE3x3S1P1_FLOAT:
math::DepthwiseConv3x3s1p1(param.Input(), param.Filter(), param.Output(),
nullptr, false);
break;
case ConvParam<CPU>::EXEC_DEPTHWISE3x3_FLOAT:
math::DepthwiseConv3x3(param.Input(), param.Strides(), param.Paddings(),
param.Filter(), nullptr, param.Output(), false);
break;
case ConvParam<CPU>::EXEC_WINOGRAD3X3_FLOAT:
WinogradConv3x3<8, 3>(param);
break;
case ConvParam<CPU>::EXEC_GEMM_FLOAT:
GemmConv<float, float>(param);
break;
default:
PADDLE_MOBILE_THROW_EXCEPTION("Invalid convolution execute mode %d",
param.ExecMode());
}
} }
template class ConvKernel<CPU, float>; template class ConvKernel<CPU, float>;
......
src/operators/kernel/arm/dequantize_kernel.cpp
浏览文件 @ a663dd86
...@@ -31,7 +31,7 @@ bool DequantizeKernel<CPU, float>::Init(DequantizeParam<CPU> *param) { ...@@ -31,7 +31,7 @@ bool DequantizeKernel<CPU, float>::Init(DequantizeParam<CPU> *param) {
template <> template <>
void DequantizeKernel<CPU, float>::Compute(const DequantizeParam<CPU> &param) { void DequantizeKernel<CPU, float>::Compute(const DequantizeParam<CPU> &param) {
const Tensor *input = param.input_; const Tensor *input = param.input_;
Tensor *output = param.out_; Tensor *output = param.output_;
float activation_scale = param.activation_scale_->data<float>()[0]; float activation_scale = param.activation_scale_->data<float>()[0];
float weight_scale = param.weight_scale_; float weight_scale = param.weight_scale_;
const int32_t *x = input->data<const int32_t>(); const int32_t *x = input->data<const int32_t>();
...@@ -43,11 +43,15 @@ void DequantizeKernel<CPU, float>::Compute(const DequantizeParam<CPU> &param) { ...@@ -43,11 +43,15 @@ void DequantizeKernel<CPU, float>::Compute(const DequantizeParam<CPU> &param) {
size_t loop = size >> 4; size_t loop = size >> 4;
size_t remain = size & 0xF; size_t remain = size & 0xF;
float32x4_t s = vdupq_n_f32(scale); float32x4_t s = vdupq_n_f32(scale);
#pragma omp parallel for
for (size_t i = 0; i < loop; ++i) { for (size_t i = 0; i < loop; ++i) {
int32x4_t r0 = vld1q_s32(x); const int32_t *local_x = x + (i << 4);
int32x4_t r1 = vld1q_s32(x + 4); float *local_y = y + (i << 4);
int32x4_t r2 = vld1q_s32(x + 8); int32x4_t r0 = vld1q_s32(local_x);
int32x4_t r3 = vld1q_s32(x + 12); int32x4_t r1 = vld1q_s32(local_x + 4);
int32x4_t r2 = vld1q_s32(local_x + 8);
int32x4_t r3 = vld1q_s32(local_x + 12);
float32x4_t f0 = vcvtq_f32_s32(r0); float32x4_t f0 = vcvtq_f32_s32(r0);
float32x4_t f1 = vcvtq_f32_s32(r1); float32x4_t f1 = vcvtq_f32_s32(r1);
float32x4_t f2 = vcvtq_f32_s32(r2); float32x4_t f2 = vcvtq_f32_s32(r2);
...@@ -56,14 +60,14 @@ void DequantizeKernel<CPU, float>::Compute(const DequantizeParam<CPU> &param) { ...@@ -56,14 +60,14 @@ void DequantizeKernel<CPU, float>::Compute(const DequantizeParam<CPU> &param) {
f1 = vmulq_f32(f1, s); f1 = vmulq_f32(f1, s);
f2 = vmulq_f32(f2, s); f2 = vmulq_f32(f2, s);
f3 = vmulq_f32(f3, s); f3 = vmulq_f32(f3, s);
vst1q_f32(y, f0); vst1q_f32(local_y, f0);
vst1q_f32(y + 4, f1); vst1q_f32(local_y + 4, f1);
vst1q_f32(y + 8, f2); vst1q_f32(local_y + 8, f2);
vst1q_f32(y + 12, f3); vst1q_f32(local_y + 12, f3);
x += 16;
y += 16;
} }
size = remain; size = remain;
x += (loop << 4);
y += (loop << 4);
#endif #endif
for (size_t i = 0; i < size; ++i) { for (size_t i = 0; i < size; ++i) {
y[i] = x[i] * scale; y[i] = x[i] * scale;
......
src/operators/kernel/central-arm-func/conv_add_arm_func.h
浏览文件 @ a663dd86
...@@ -17,7 +17,7 @@ limitations under the License. */ ...@@ -17,7 +17,7 @@ limitations under the License. */
#include <vector> #include <vector>
#include "operators/math/conv_func.h" #include "operators/math/conv_func.h"
#include "operators/math/depthwise_conv_3x3.h" #include "operators/math/depthwise_conv3x3.h"
#include "operators/math/im2col.h" #include "operators/math/im2col.h"
#include "operators/math/math_function.h" #include "operators/math/math_function.h"
#include "operators/math/vol2col.h" #include "operators/math/vol2col.h"
......
src/operators/kernel/central-arm-func/conv_add_bn_relu_arm_func.h
浏览文件 @ a663dd86
...@@ -17,7 +17,7 @@ limitations under the License. */ ...@@ -17,7 +17,7 @@ limitations under the License. */
#pragma once #pragma once
#include <vector> #include <vector>
#include "operators/math/depthwise_conv_3x3.h" #include "operators/math/depthwise_conv3x3.h"
#include "operators/math/im2col.h" #include "operators/math/im2col.h"
#include "operators/math/math_function.h" #include "operators/math/math_function.h"
#include "operators/math/vol2col.h" #include "operators/math/vol2col.h"
......
src/operators/kernel/central-arm-func/conv_arm_func.h
浏览文件 @ a663dd86
...@@ -17,18 +17,19 @@ limitations under the License. */ ...@@ -17,18 +17,19 @@ limitations under the License. */
#pragma once #pragma once
#include <vector> #include <vector>
#include "operators/math/conv_func.h" #include "operators/math/conv_func.h"
#include "operators/math/depthwise_conv_3x3.h" #include "operators/math/depthwise_conv3x3.h"
#include "operators/math/im2col.h" #include "operators/math/im2col.h"
#include "operators/math/math_function.h" #include "operators/math/math_function.h"
#include "operators/math/pad.h" #include "operators/math/pad.h"
#include "operators/math/vol2col.h" #include "operators/math/vol2col.h"
#include "operators/math/winograd/winograd_transform.h"
#include "operators/op_param.h" #include "operators/op_param.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace operators { namespace operators {
template <typename Itype, typename Otype> template <typename Itype, typename Otype>
inline void ConvBasic(const ConvParam<CPU> &param) { inline void GemmConv(const ConvParam<CPU> &param) {
const Tensor *input = param.Input(); const Tensor *input = param.Input();
Tensor filter = *param.Filter(); Tensor filter = *param.Filter();
Tensor *output = param.Output(); Tensor *output = param.Output();
...@@ -38,10 +39,7 @@ inline void ConvBasic(const ConvParam<CPU> &param) { ...@@ -38,10 +39,7 @@ inline void ConvBasic(const ConvParam<CPU> &param) {
const std::vector<int> paddings = param.Paddings(); const std::vector<int> paddings = param.Paddings();
const std::vector<int> dilations = param.Dilations(); const 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> filter_shape_vec(framework::vectorize(filter.dims()));
std::vector<int64_t> output_shape_vec(framework::vectorize(output->dims())); std::vector<int64_t> output_shape_vec(framework::vectorize(output->dims()));
size_t data_dim = filter_shape_vec.size() - 2; size_t data_dim = filter_shape_vec.size() - 2;
std::vector<int64_t> col_shape_vec(1 + 2 * data_dim); std::vector<int64_t> col_shape_vec(1 + 2 * data_dim);
...@@ -82,6 +80,7 @@ inline void ConvBasic(const ConvParam<CPU> &param) { ...@@ -82,6 +80,7 @@ inline void ConvBasic(const ConvParam<CPU> &param) {
math::Vol2ColFunctor<CPU, Itype> vol2col; math::Vol2ColFunctor<CPU, Itype> vol2col;
math::Im2ColFunctor<math::ColFormat::kCFO, CPU, Itype> im2col; math::Im2ColFunctor<math::ColFormat::kCFO, CPU, Itype> im2col;
const int batch_size = static_cast<int>(input->dims()[0]);
for (int i = 0; i < batch_size; i++) { for (int i = 0; i < batch_size; i++) {
Tensor in_batch = input->Slice(i, i + 1).Resize(input_shape); Tensor in_batch = input->Slice(i, i + 1).Resize(input_shape);
Tensor out_batch = output->Slice(i, i + 1).Resize(output_matrix_shape); Tensor out_batch = output->Slice(i, i + 1).Resize(output_matrix_shape);
...@@ -99,7 +98,6 @@ inline void ConvBasic(const ConvParam<CPU> &param) { ...@@ -99,7 +98,6 @@ inline void ConvBasic(const ConvParam<CPU> &param) {
std::vector<int>{paddings[0], paddings[1], paddings[0], std::vector<int>{paddings[0], paddings[1], paddings[0],
paddings[1]}, paddings[1]},
&col); &col);
} else if (data_dim == 3U) { } else if (data_dim == 3U) {
// vol2col // vol2col
vol2col(in_slice, dilations, strides, paddings, &col); vol2col(in_slice, dilations, strides, paddings, &col);
...@@ -116,25 +114,86 @@ inline void ConvBasic(const ConvParam<CPU> &param) { ...@@ -116,25 +114,86 @@ inline void ConvBasic(const ConvParam<CPU> &param) {
} }
} }
template <typename P> template <int tile, int kernel>
void ConvCompute(const ConvParam<CPU> &param) { inline void WinogradConv3x3(const ConvParam<CPU> &param) {
if (param.Input()->type() == typeid(int8_t)) { const Tensor *input = param.Input();
ConvBasic<int8_t, int32_t>(param); const Tensor *filter = param.Filter();
} else { Tensor *output = param.Output();
if (param.Groups() == param.Input()->dims()[1] && output->mutable_data<float>();
param.Input()->dims()[1] == param.Output()->dims()[1] && int batch_size = input->dims()[0];
param.Filter()->dims()[2] == param.Filter()->dims()[3] && int groups = param.Groups();
param.Filter()->dims()[2] == 3 && param.Strides()[0] == 1) { const std::vector<int> &paddings = param.Paddings();
math::DepthwiseConv3x3s1p1(param.Input(), param.Filter(), param.Output(),
nullptr, false); auto winograd_pad = [&](int width, int pad) {
} else if (param.Groups() == param.Input()->dims()[1] && int output_tile = tile - kernel + 1;
param.Input()->dims()[1] == param.Output()->dims()[1] && // int tiles = (width + pad - kernel) / output_tile + 1;
param.Filter()->dims()[2] == param.Filter()->dims()[3] && // return (tiles - 1) * output_tile + tile - width;
param.Filter()->dims()[2] == 3) { int pad_width = (width + 2 * pad - kernel) / output_tile * output_tile;
math::DepthwiseConv3x3(param.Input(), param.Strides(), param.Paddings(), return pad_width + tile - width;
param.Filter(), nullptr, param.Output(), false); };
math::PadFunctor<CPU, float> pad;
Tensor input_pad;
framework::Tensor transformed_input;
for (int i = 0; i < batch_size; ++i) {
Tensor in_batch = input->Slice(i, i + 1);
Tensor out_batch = output->Slice(i, i + 1);
// int pad_bottom = winograd_pad(in_batch.dims()[2], paddings[0]);
// int pad_right = winograd_pad(in_batch.dims()[3], paddings[1]);
int pad_bottom = paddings[0];
int pad_right = paddings[1];
if (paddings[0] || paddings[1] || pad_bottom || pad_right) {
framework::DDim pad_shape = in_batch.dims();
pad_shape[2] += paddings[0] + pad_bottom;
pad_shape[3] += paddings[1] + pad_right;
input_pad.mutable_data<float>(pad_shape);
pad(in_batch, paddings[0], pad_bottom, paddings[1], pad_right,
&input_pad);
} else {
input_pad = in_batch;
}
// tile input and transform
math::winograd_transform_input<tile, kernel>(input_pad, &transformed_input);
// caculate output
math::winograd_transform_output<tile, kernel>(transformed_input, *filter,
output);
}
}
template <typename Itype, typename Otype>
inline void DepthwiseConv3x3(const ConvParam<CPU> &param) {
const Tensor *input = param.Input();
const Tensor *filter = param.Filter();
Tensor *output = param.Output();
output->mutable_data<Otype>();
const std::vector<int> &paddings = param.Paddings();
const std::vector<int> &strides = param.Strides();
const int batch_size = static_cast<int>(input->dims()[0]);
Tensor input_pad;
math::PadFunctor<CPU, Itype> pad;
for (int i = 0; i < batch_size; i++) {
Tensor in_batch = input->Slice(i, i + 1);
Tensor out_batch = output->Slice(i, i + 1);
if (paddings[0] || paddings[1]) {
framework::DDim pad_shape = in_batch.dims();
pad_shape[2] += 2 * paddings[0];
pad_shape[3] += 2 * paddings[1];
input_pad.mutable_data<float>(pad_shape);
pad(in_batch, paddings[0], paddings[0], paddings[1], paddings[1],
&input_pad);
} else {
input_pad = in_batch;
}
if (strides[0] == 1) {
math::DepthwiseConv3x3s1<Itype, Otype>(input_pad, *filter, &out_batch);
} else if (strides[0] == 2) {
math::DepthwiseConv3x3s2<Itype, Otype>(input_pad, *filter, &out_batch);
} else { } else {
ConvBasic<float, float>(param); // math::DepthwiseConv3x3<Itype, Otype>(input_pad, *filter,
// &out_batch);
PADDLE_MOBILE_THROW_EXCEPTION(
"Depthwise conv with generic strides has not been implemented.");
} }
} }
} }
......
src/operators/kernel/central-arm-func/conv_bn_add_relu_arm_func.h
浏览文件 @ a663dd86
...@@ -17,7 +17,7 @@ limitations under the License. */ ...@@ -17,7 +17,7 @@ limitations under the License. */
#pragma once #pragma once
#include <vector> #include <vector>
#include "operators/math/depthwise_conv_3x3.h" #include "operators/math/depthwise_conv3x3.h"
#include "operators/math/im2col.h" #include "operators/math/im2col.h"
#include "operators/math/math_function.h" #include "operators/math/math_function.h"
#include "operators/math/vol2col.h" #include "operators/math/vol2col.h"
......
src/operators/kernel/central-arm-func/conv_bn_relu_arm_func.h
浏览文件 @ a663dd86
...@@ -16,13 +16,15 @@ limitations under the License. */ ...@@ -16,13 +16,15 @@ limitations under the License. */
#pragma once #pragma once
#include <vector> #include <vector>
#include "operators/math/depthwise_conv_3x3.h" #include "operators/math/depthwise_conv3x3.h"
#include "operators/math/im2col.h" #include "operators/math/im2col.h"
#include "operators/math/math_function.h" #include "operators/math/math_function.h"
#include "operators/math/vol2col.h" #include "operators/math/vol2col.h"
#include "operators/op_param.h" #include "operators/op_param.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace operators { namespace operators {
void ConvBNReluBasic(const FusionConvBNReluParam<CPU> &param) { void ConvBNReluBasic(const FusionConvBNReluParam<CPU> &param) {
const Tensor *input = param.Input(); const Tensor *input = param.Input();
Tensor filter = *param.Filter(); Tensor filter = *param.Filter();
......
src/operators/kernel/central-arm-func/depthwise_conv_arm_func.h
浏览文件 @ a663dd86
...@@ -15,10 +15,9 @@ limitations under the License. */ ...@@ -15,10 +15,9 @@ limitations under the License. */
#ifdef DEPTHWISECONV_OP #ifdef DEPTHWISECONV_OP
#pragma once #pragma once
#include <operators/math/depthwise_conv_3x3.h>
#include <vector> #include <vector>
#include "operators/kernel/central-arm-func/conv_arm_func.h" #include "operators/kernel/central-arm-func/conv_arm_func.h"
#include "operators/math/depthwise_conv3x3.h"
#include "operators/op_param.h" #include "operators/op_param.h"
namespace paddle_mobile { namespace paddle_mobile {
...@@ -44,7 +43,7 @@ void DepthwiseConvCompute(const ConvParam<CPU> &param) { ...@@ -44,7 +43,7 @@ void DepthwiseConvCompute(const ConvParam<CPU> &param) {
Bias, false); Bias, false);
} else { } else {
ConvBasic<float, float>(param); GemmConv<float, float>(param);
} }
} }
......
src/operators/kernel/central-arm-func/dwconv_bn_relu_arm_func.h
浏览文件 @ a663dd86
...@@ -16,13 +16,15 @@ limitations under the License. */ ...@@ -16,13 +16,15 @@ limitations under the License. */
#pragma once #pragma once
#include <vector> #include <vector>
#include "operators/math/depthwise_conv_3x3.h" #include "operators/math/depthwise_conv3x3.h"
#include "operators/math/im2col.h" #include "operators/math/im2col.h"
#include "operators/math/math_function.h" #include "operators/math/math_function.h"
#include "operators/math/vol2col.h" #include "operators/math/vol2col.h"
#include "operators/op_param.h" #include "operators/op_param.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace operators { namespace operators {
void DWConvBNReluBasic(const FusionDWConvBNReluParam<CPU> &param) { void DWConvBNReluBasic(const FusionDWConvBNReluParam<CPU> &param) {
const Tensor *input = param.Input(); const Tensor *input = param.Input();
Tensor filter = *param.Filter(); Tensor filter = *param.Filter();
......
src/operators/kernel/conv_add_kernel.h
浏览文件 @ a663dd86
...@@ -24,7 +24,7 @@ limitations under the License. */ ...@@ -24,7 +24,7 @@ limitations under the License. */
#include "framework/ddim.h" #include "framework/ddim.h"
#include "framework/operator.h" #include "framework/operator.h"
#include "operators/math/conv_func.h" #include "operators/math/conv_func.h"
#include "operators/math/depthwise_conv_3x3.h" #include "operators/math/depthwise_conv3x3.h"
#include "operators/math/im2col.h" #include "operators/math/im2col.h"
#include "operators/math/math_function.h" #include "operators/math/math_function.h"
#include "operators/math/vol2col.h" #include "operators/math/vol2col.h"
......
src/operators/math/depthwise_conv_3x3.cpp → src/operators/math/depthwise_conv3x3.cpp
浏览文件 @ a663dd86
...@@ -11,18 +11,22 @@ distributed under the License is distributed on an "AS IS" BASIS, ...@@ -11,18 +11,22 @@ distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. */ limitations under the License. */
#include "operators/math/depthwise_conv_3x3.h"
#include "operators/math/depthwise_conv3x3.h"
#include <vector>
#if __ARM_NEON #if __ARM_NEON
#include <arm_neon.h> #include <arm_neon.h>
#endif #endif
#include <vector>
namespace paddle_mobile { namespace paddle_mobile {
namespace operators { namespace operators {
namespace math { namespace math {
void DepthwiseConv3x3(const Tensor *input, vector<int> strides,
vector<int> paddings, const Tensor *filter, Tensor *bias, void DepthwiseConv3x3(const framework::Tensor *input,
Tensor *output, bool if_bias) { const std::vector<int> &strides,
const std::vector<int> &paddings,
const framework::Tensor *filter, framework::Tensor *bias,
framework::Tensor *output, bool if_bias) {
const int batch_size = input->dims()[0]; const int batch_size = input->dims()[0];
const int input_height = input->dims()[2]; const int input_height = input->dims()[2];
...@@ -67,12 +71,12 @@ void DepthwiseConv3x3(const Tensor *input, vector<int> strides, ...@@ -67,12 +71,12 @@ void DepthwiseConv3x3(const Tensor *input, vector<int> strides,
for (int pw = 0; pw < output_width; pw++) { for (int pw = 0; pw < output_width; pw++) {
hstart = ph * stride_height - padding_height; hstart = ph * stride_height - padding_height;
wstart = pw * stride_width - padding_width; wstart = pw * stride_width - padding_width;
hend = min(hstart + _kernel_size, input_height + padding_height); hend = std::min(hstart + _kernel_size, input_height + padding_height);
wend = min(wstart + _kernel_size, input_width + padding_width); wend = std::min(wstart + _kernel_size, input_width + padding_width);
hstart = max(hstart, 0); hstart = std::max(hstart, 0);
wstart = max(wstart, 0); wstart = std::max(wstart, 0);
hend = min(hend, input_height); hend = std::min(hend, input_height);
wend = min(wend, input_width); wend = std::min(wend, input_width);
pos1 = input_data + hstart * input_width + wstart; pos1 = input_data + hstart * input_width + wstart;
pos2 = input_data + (hstart + 1) * input_width + wstart; pos2 = input_data + (hstart + 1) * input_width + wstart;
pos3 = input_data + (hstart + 2) * input_width + wstart; pos3 = input_data + (hstart + 2) * input_width + wstart;
...@@ -244,12 +248,14 @@ void DepthwiseConv3x3(const Tensor *input, vector<int> strides, ...@@ -244,12 +248,14 @@ void DepthwiseConv3x3(const Tensor *input, vector<int> strides,
} }
} }
void DepthwiseConv3x3s1p1(const Tensor *input, const Tensor *filter, void DepthwiseConv3x3s1p1(const framework::Tensor *input,
Tensor *output, Tensor *bias, bool if_bias) { const framework::Tensor *filter,
framework::Tensor *output, framework::Tensor *bias,
bool if_bias) {
#if __ARM_NEON #if __ARM_NEON
const float *input_data = input->data<float>(); const float *input_data = input->data<float>();
const float *filter_data = filter->data<float>(); const float *filter_data = filter->data<float>();
float *output_data = output->data<float>(); float *output_data = output->mutable_data<float>();
const float *bias_data; const float *bias_data;
if (if_bias) { if (if_bias) {
bias_data = bias->data<float>(); bias_data = bias->data<float>();
...@@ -517,9 +523,12 @@ void DepthwiseConv3x3s1p1(const Tensor *input, const Tensor *filter, ...@@ -517,9 +523,12 @@ void DepthwiseConv3x3s1p1(const Tensor *input, const Tensor *filter,
#endif #endif
} }
void DepthwiseConvAddBNRelu3x3s1p1(const Tensor *input, const Tensor *filter, void DepthwiseConvAddBNRelu3x3s1p1(const framework::Tensor *input,
Tensor *output, const Tensor *new_scale, const framework::Tensor *filter,
const Tensor *new_bias, bool if_relu) { framework::Tensor *output,
const framework::Tensor *new_scale,
const framework::Tensor *new_bias,
bool if_relu) {
#if __ARM_NEON #if __ARM_NEON
const float *input_data = input->data<float>(); const float *input_data = input->data<float>();
const float *filter_data = filter->data<float>(); const float *filter_data = filter->data<float>();
...@@ -1059,9 +1068,12 @@ void DepthwiseConvAddBNRelu3x3s1p1(const Tensor *input, const Tensor *filter, ...@@ -1059,9 +1068,12 @@ void DepthwiseConvAddBNRelu3x3s1p1(const Tensor *input, const Tensor *filter,
} }
/// w!=h not fix /// w!=h not fix
void DepthwiseConvAddBNRelu3x3s2p1(const Tensor *input, const Tensor *filter, void DepthwiseConvAddBNRelu3x3s2p1(const framework::Tensor *input,
Tensor *output, const Tensor *new_scale, const framework::Tensor *filter,
const Tensor *new_bias, bool if_relu) { framework::Tensor *output,
const framework::Tensor *new_scale,
const framework::Tensor *new_bias,
bool if_relu) {
#if __ARM_NEON #if __ARM_NEON
const int batch_size = input->dims()[0]; const int batch_size = input->dims()[0];
...@@ -1107,12 +1119,12 @@ void DepthwiseConvAddBNRelu3x3s2p1(const Tensor *input, const Tensor *filter, ...@@ -1107,12 +1119,12 @@ void DepthwiseConvAddBNRelu3x3s2p1(const Tensor *input, const Tensor *filter,
for (int pw = 0; pw < output_width; pw++) { for (int pw = 0; pw < output_width; pw++) {
hstart = ph * stride_height - padding_height; hstart = ph * stride_height - padding_height;
wstart = pw * stride_width - padding_width; wstart = pw * stride_width - padding_width;
hend = min(hstart + _kernel_size, input_height + padding_height); hend = std::min(hstart + _kernel_size, input_height + padding_height);
wend = min(wstart + _kernel_size, input_width + padding_width); wend = std::min(wstart + _kernel_size, input_width + padding_width);
hstart = max(hstart, 0); hstart = std::max(hstart, 0);
wstart = max(wstart, 0); wstart = std::max(wstart, 0);
hend = min(hend, input_height); hend = std::min(hend, input_height);
wend = min(wend, input_width); wend = std::min(wend, input_width);
pos1 = input_data + hstart * input_width + wstart; pos1 = input_data + hstart * input_width + wstart;
pos2 = input_data + (hstart + 1) * input_width + wstart; pos2 = input_data + (hstart + 1) * input_width + wstart;
pos3 = input_data + (hstart + 2) * input_width + wstart; pos3 = input_data + (hstart + 2) * input_width + wstart;
...@@ -1258,8 +1270,10 @@ void DepthwiseConvAddBNRelu3x3s2p1(const Tensor *input, const Tensor *filter, ...@@ -1258,8 +1270,10 @@ void DepthwiseConvAddBNRelu3x3s2p1(const Tensor *input, const Tensor *filter,
#endif #endif
} }
void DepthwiseConv3x3s2p1v2(const Tensor *input, const Tensor *filter, void DepthwiseConv3x3s2p1v2(const framework::Tensor *input,
Tensor *output, Tensor bias, bool if_bias) { const framework::Tensor *filter,
framework::Tensor *output, framework::Tensor bias,
bool if_bias) {
#if __ARM_NEON #if __ARM_NEON
const float *input_data = input->data<float>(); const float *input_data = input->data<float>();
const float *filter_data = filter->data<float>(); const float *filter_data = filter->data<float>();
...@@ -1463,9 +1477,12 @@ void DepthwiseConv3x3s2p1v2(const Tensor *input, const Tensor *filter, ...@@ -1463,9 +1477,12 @@ void DepthwiseConv3x3s2p1v2(const Tensor *input, const Tensor *filter,
#endif #endif
} }
void DepthwiseConvAddBNRelu3x3s2p1v2(const Tensor *input, const Tensor *filter, void DepthwiseConvAddBNRelu3x3s2p1v2(const framework::Tensor *input,
Tensor *output, const Tensor *new_scale, const framework::Tensor *filter,
const Tensor *new_bias, bool if_relu) { framework::Tensor *output,
const framework::Tensor *new_scale,
const framework::Tensor *new_bias,
bool if_relu) {
#if __ARM_NEON #if __ARM_NEON
// #ifdef _OPENMP // #ifdef _OPENMP
// const float *newscale_data = new_scale->data<float>(); // const float *newscale_data = new_scale->data<float>();
...@@ -1886,8 +1903,10 @@ void DepthwiseConvAddBNRelu3x3s2p1v2(const Tensor *input, const Tensor *filter, ...@@ -1886,8 +1903,10 @@ void DepthwiseConvAddBNRelu3x3s2p1v2(const Tensor *input, const Tensor *filter,
#endif #endif
} }
void DepthwiseConv3x3s2p0(const Tensor *input, const Tensor *filter, void DepthwiseConv3x3s2p0(const framework::Tensor *input,
Tensor *output, Tensor bias, bool if_bias) { const framework::Tensor *filter,
framework::Tensor *output, framework::Tensor bias,
bool if_bias) {
#if __ARM_NEON #if __ARM_NEON
const int batch_size = static_cast<int>(input->dims()[0]); const int batch_size = static_cast<int>(input->dims()[0]);
......
src/operators/math/depthwise_conv3x3.h 0 → 100644
浏览文件 @ a663dd86
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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
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. */
#pragma once
#include <algorithm>
#include <vector>
#include "framework/tensor.h"
#include "operators/math/conv_func.h"
namespace paddle_mobile {
namespace operators {
namespace math {
void DepthwiseConv3x3(const framework::Tensor *input,
const std::vector<int> &strides,
const std::vector<int> &paddings,
const framework::Tensor *filter, framework::Tensor *bias,
framework::Tensor *output, bool if_bias);
void DepthwiseConv3x3s1p1(const framework::Tensor *input,
const framework::Tensor *filter,
framework::Tensor *output, framework::Tensor *bias,
bool if_bias);
void DepthwiseConvAddBNRelu3x3s1p1(const framework::Tensor *input,
const framework::Tensor *filter,
framework::Tensor *output,
const framework::Tensor *new_scale,
const framework::Tensor *new_bias,
bool if_relu);
void DepthwiseConvAddBNRelu3x3s2p1(const framework::Tensor *input,
const framework::Tensor *filter,
framework::Tensor *output,
const framework::Tensor *new_scale,
const framework::Tensor *new_bias,
bool if_relu);
void DepthwiseConv3x3s2p1v2(const framework::Tensor *input,
const framework::Tensor *filter,
framework::Tensor *output, framework::Tensor bias,
bool if_bias);
void DepthwiseConvAddBNRelu3x3s2p1v2(const framework::Tensor *input,
const framework::Tensor *filter,
framework::Tensor *output,
const framework::Tensor *new_scale,
const framework::Tensor *new_bias,
bool if_relu);
void DepthwiseConv3x3s2p0(const framework::Tensor *input,
const framework::Tensor *filter,
framework::Tensor *output, framework::Tensor bias,
bool if_bias);
// TODO(hjchen2) need to be implemented
// template<typename Itype, typename Otype>
// void DepthwiseConv3x3(const framework::Tensor *input,
// const framework::Tensor *filter,
// const std::vector<int> &strides,
// framework::Tensor *output);
template <typename Itype, typename Otype>
void DepthwiseConv3x3s1(const framework::Tensor &input,
const framework::Tensor &filter,
framework::Tensor *output);
template <typename Itype, typename Otype>
void DepthwiseConv3x3s2(const framework::Tensor &input,
const framework::Tensor &filter,
framework::Tensor *output);
} // namespace math
} // namespace operators
} // namespace paddle_mobile
src/operators/math/depthwise_conv3x3_int8.cpp 0 → 100644
浏览文件 @ a663dd86
此差异已折叠。
src/operators/math/gemm.cpp
浏览文件 @ a663dd86
...@@ -26,79 +26,6 @@ limitations under the License. */ ...@@ -26,79 +26,6 @@ limitations under the License. */
namespace paddle_mobile { namespace paddle_mobile {
namespace operators { namespace operators {
namespace math { namespace math {
/*int MC = 0;
int KC = 0;
int NC = 0;
float *packedA;
float *packedB;
float *packedC;
float *zero;
typedef void (*FnPack)(int, int, int, const float *, int, float *);
typedef void (*FnAddDot)(int, const float *, const float *, float *, int);
FnPack procPackA;
FnPack procPackB;
FnAddDot procAddDot;*/
/*
// 将A矩阵分块复制到连续内存(ColMajor)
void PackMatrixA(int m, int k, int m_tail, const float *A, int lda,
float *buffer) {
int i, j;
const float *Aij;
for (i = 0; i < m - m_tail; i += MR) {
for (j = 0; j < k; ++j) {
Aij = &A(i, j);
*buffer++ = *Aij;
*buffer++ = *(Aij + 1);
*buffer++ = *(Aij + 2);
*buffer++ = *(Aij + 3);
}
}
if (m_tail != 0) {
for (j = 0; j < k; ++j) {
Aij = &A(m - m_tail, j);
for (i = 0; i < m_tail; ++i) {
*buffer++ = *(Aij + i);
}
for (i = m_tail; i < MR; ++i) {
*buffer++ = 0;
}
}
}
}
// 将B矩阵分块复制到连续内存(ColMajor)
void PackMatrixB(int k, int n, int n_tail, const float *B, int ldb,
float *buffer) {
int i, j;
const float *Bj, *Bj1, *Bj2, *Bj3;
for (j = 0; j < n - n_tail; j += NR) {
Bj = &B(0, j);
Bj1 = &B(0, j + 1);
Bj2 = &B(0, j + 2);
Bj3 = &B(0, j + 3);
for (i = 0; i < k; ++i) {
*buffer++ = *Bj++;
*buffer++ = *Bj1++;
*buffer++ = *Bj2++;
*buffer++ = *Bj3++;
}
}
if (n_tail != 0) {
for (i = 0; i < k; ++i) {
for (int j = n - n_tail; j < n; ++j) {
*buffer++ = B(i, j);
}
for (int j = n; j < n + (NR - n_tail); ++j) {
*buffer++ = 0;
}
}
}
}
*/
// 将A矩阵分块复制到连续内存(RowMajor) // 将A矩阵分块复制到连续内存(RowMajor)
void Gemm::PackMatrixA_4r(int m, int k, int m_tail, const float *A, int lda, void Gemm::PackMatrixA_4r(int m, int k, int m_tail, const float *A, int lda,
......
src/operators/math/im2col.cpp
浏览文件 @ a663dd86
...@@ -22,6 +22,70 @@ namespace paddle_mobile { ...@@ -22,6 +22,70 @@ namespace paddle_mobile {
namespace operators { namespace operators {
namespace math { namespace math {
void ExtractToImg(const float *im_data, float *col_data, const int im_height,
const int im_width, const int col_height, const int col_width,
const int padding_h, const int padding_w, const int stride_h,
const int stride_w, const int kh, const int kw) {
int h = padding_h - kh;
int w = padding_w - kw;
int col_start_height = h > 0 ? (h + stride_h - 1) / stride_h : 0;
int col_start_width = w > 0 ? (w + stride_w - 1) / stride_w : 0;
int start_height = kh + col_start_height * stride_h - padding_h;
int start_width = kw + col_start_width * stride_w - padding_w;
int end_height = (col_height - col_start_height) * stride_h + start_height;
end_height = end_height > im_height ? im_height : end_height;
int end_width = (col_width - col_start_width) * stride_w + start_width;
end_width = end_width > im_width ? im_width : end_width;
int extract = (end_width - start_width + stride_w - 1) / stride_w;
im_data += start_height * im_width + start_width;
col_data += col_start_height * col_width + col_start_width;
for (int i = start_height; i < end_height; i += stride_h) {
if (stride_w == 1) {
memcpy(col_data, im_data, extract * sizeof(float));
} else if (stride_w == 2) {
int s = 0;
#if __ARM_NEON
for (; s < extract - 3; s += 4) {
float32x4x2_t img = vld2q_f32(im_data + s * 2);
vst1q_f32(col_data + s, img.val[0]);
}
#endif
for (; s < extract; ++s) {
col_data[s] = im_data[s * 2];
}
} else if (stride_w == 3) {
int s = 0;
#if __ARM_NEON
for (; s < extract - 3; s += 4) {
float32x4x3_t img = vld3q_f32(im_data + s * 3);
vst1q_f32(col_data + s, img.val[0]);
}
#endif
for (; s < extract; ++s) {
col_data[s] = im_data[s * 3];
}
} else if (stride_w == 4) {
int s = 0;
#if __ARM_NEON
for (; s < extract - 3; s += 4) {
float32x4x4_t img = vld4q_f32(im_data + s * 4);
vst1q_f32(col_data + s, img.val[0]);
}
#endif
for (; s < extract; ++s) {
col_data[s] = im_data[s * 4];
}
} else {
PADDLE_MOBILE_THROW_EXCEPTION("stride_w must be one of 1, 2, 3 and 4.");
}
im_data += im_width * stride_h;
col_data += col_width;
}
}
/* /*
* im = [input_channels, input_height, input_width] * im = [input_channels, input_height, input_width]
* col = * col =
...@@ -363,7 +427,27 @@ void Im2ColFunctor<ColFormat::kCFO, CPU, float>::operator()( ...@@ -363,7 +427,27 @@ void Im2ColFunctor<ColFormat::kCFO, CPU, float>::operator()(
col_data += 9 * oosize; col_data += 9 * oosize;
im_data += isize * isize; im_data += isize * isize;
} }
} else if (stride[0] <= 4 && dilation[0] == 1 && dilation[0] == dilation[1]) {
int im_spatial_size = im_height * im_width;
int col_spatial_size = col_height * col_width;
// pad 0
memset(col_data, 0, col->numel() * sizeof(float));
#pragma omp parallel for
for (int ic = 0; ic < im_channels; ++ic) {
const float *local_im_data = im_data + ic * im_spatial_size;
float *local_col_data =
col_data + ic * filter_height * filter_width * col_spatial_size;
for (int kh = 0; kh < filter_height; ++kh) {
for (int kw = 0; kw < filter_width; ++kw) {
ExtractToImg(local_im_data, local_col_data, im_height, im_width,
col_height, col_width, padding[0], padding[1], stride[0],
stride[1], kh, kw);
local_col_data += col_spatial_size;
}
}
}
} else { } else {
#endif
for (int c = 0; c < channels_col; ++c) { for (int c = 0; c < channels_col; ++c) {
int w_offset = c % filter_width; int w_offset = c % filter_width;
int h_offset = (c / filter_width) % filter_height; int h_offset = (c / filter_width) % filter_height;
...@@ -382,25 +466,7 @@ void Im2ColFunctor<ColFormat::kCFO, CPU, float>::operator()( ...@@ -382,25 +466,7 @@ void Im2ColFunctor<ColFormat::kCFO, CPU, float>::operator()(
} }
} }
} }
} #if __ARM_NEON
#else
for (int c = 0; c < channels_col; ++c) {
int w_offset = c % filter_width;
int h_offset = (c / filter_width) % filter_height;
int c_im = c / (filter_width * filter_height);
for (int h = 0; h < col_height; ++h) {
int im_row_idx = h * stride[0] - padding[0] + h_offset * dilation[0];
for (int w = 0; w < col_width; ++w) {
int im_col_idx = w * stride[1] - padding[1] + w_offset * dilation[1];
int col_idx = (c * col_height + h) * col_width + w;
int im_idx = (im_row_idx + c_im * im_height) * im_width + im_col_idx;
col_data[col_idx] = (im_row_idx < 0 || im_row_idx >= im_height ||
im_col_idx < 0 || im_col_idx >= im_width)
? static_cast<float>(0)
: im_data[im_idx];
}
}
} }
#endif #endif
} }
...@@ -489,21 +555,26 @@ void Im2ColFunctor<ColFormat::kCFO, CPU, int8_t>::operator()( ...@@ -489,21 +555,26 @@ void Im2ColFunctor<ColFormat::kCFO, CPU, int8_t>::operator()(
int channels_col = im_channels * filter_height * filter_width; int channels_col = im_channels * filter_height * filter_width;
const int8_t *im_data = im.data<int8_t>(); const int8_t *im_data = im.data<int8_t>();
int8_t *col_data = col->data<int8_t>(); int8_t *col_data = col->mutable_data<int8_t>();
#if defined(__ARM_NEON__) || defined(__ARM_NEON) #if defined(__ARM_NEON__) || defined(__ARM_NEON)
if (stride[0] <= 4 && dilation[0] == 1 && dilation[0] == dilation[1]) { if (stride[0] <= 4 && dilation[0] == 1 && dilation[0] == dilation[1]) {
int im_spatial_size = im_height * im_width;
int col_spatial_size = col_height * col_width;
// pad 0 // pad 0
memset(col_data, 0, col->numel() * sizeof(int8_t)); memset(col_data, 0, col->numel() * sizeof(int8_t));
#pragma omp parallel for
for (int ic = 0; ic < im_channels; ++ic) { for (int ic = 0; ic < im_channels; ++ic) {
const int8_t *local_im_data = im_data + ic * im_spatial_size;
int8_t *local_col_data =
col_data + ic * filter_height * filter_width * col_spatial_size;
for (int kh = 0; kh < filter_height; ++kh) { for (int kh = 0; kh < filter_height; ++kh) {
for (int kw = 0; kw < filter_width; ++kw) { for (int kw = 0; kw < filter_width; ++kw) {
ExtractToImg(im_data, col_data, im_height, im_width, col_height, ExtractToImg(local_im_data, local_col_data, im_height, im_width,
col_width, padding[0], padding[1], stride[0], stride[1], col_height, col_width, padding[0], padding[1], stride[0],
kh, kw); stride[1], kh, kw);
col_data += col_height * col_width; local_col_data += col_spatial_size;
} }
} }
im_data += im_height * im_width;
} }
} else { } else {
#endif #endif
......
src/operators/math/pad.cpp
浏览文件 @ a663dd86
...@@ -21,10 +21,12 @@ namespace math { ...@@ -21,10 +21,12 @@ namespace math {
template <typename T> template <typename T>
class PadFunctor<CPU, T> { class PadFunctor<CPU, T> {
public: public:
void operator()(const framework::Tensor &input, const int pad_h, void operator()(const framework::Tensor &input, const int pad_top,
const int pad_w, framework::Tensor *output) { const int pad_bottom, const int pad_left, const int pad_right,
framework::Tensor *output) {
const T *in_data = input.data<T>(); const T *in_data = input.data<T>();
T *out_data = output->mutable_data<T>(); T *out_data = output->mutable_data<T>();
// should check output shape is valid for such pad parameters
const framework::DDim &input_shape = input.dims(); const framework::DDim &input_shape = input.dims();
const framework::DDim &output_shape = output->dims(); const framework::DDim &output_shape = output->dims();
// fill output with 0 // fill output with 0
...@@ -32,13 +34,13 @@ class PadFunctor<CPU, T> { ...@@ -32,13 +34,13 @@ class PadFunctor<CPU, T> {
// should make sure the shape of output is match with input // should make sure the shape of output is match with input
for (int i = 0; i < input_shape[0]; ++i) { for (int i = 0; i < input_shape[0]; ++i) {
for (int c = 0; c < input_shape[1]; ++c) { for (int c = 0; c < input_shape[1]; ++c) {
out_data += pad_h * output_shape[3]; out_data += pad_top * output_shape[3];
for (int h = 0; h < input_shape[2]; ++h) { for (int h = 0; h < input_shape[2]; ++h) {
memcpy(out_data + pad_w, in_data, sizeof(T) * input_shape[3]); memcpy(out_data + pad_left, in_data, sizeof(T) * input_shape[3]);
out_data += output_shape[3]; out_data += output_shape[3];
in_data += input_shape[3]; in_data += input_shape[3];
} }
out_data += pad_h * output_shape[3]; out_data += pad_bottom * output_shape[3];
} }
} }
} }
......
src/operators/math/pad.h
浏览文件 @ a663dd86
...@@ -22,8 +22,9 @@ namespace math { ...@@ -22,8 +22,9 @@ namespace math {
template <typename DeviceType, typename T> template <typename DeviceType, typename T>
class PadFunctor { class PadFunctor {
public: public:
void operator()(const framework::Tensor &input, const int pad_h, void operator()(const framework::Tensor &input, const int pad_top,
const int pad_w, framework::Tensor *output); const int pad_bottom, const int pad_left, const int pad_right,
framework::Tensor *output);
}; };
} // namespace math } // namespace math
......
src/operators/math/depthwise_conv_3x3.h → src/operators/math/winograd/winograd_transform.h
浏览文件 @ a663dd86
...@@ -12,40 +12,31 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. ...@@ -12,40 +12,31 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. */ limitations under the License. */
#ifdef CONV_OP
#pragma once #pragma once
#include <algorithm>
#include <vector>
#include "framework/tensor.h" #include "framework/tensor.h"
#include "operators/math/conv_func.h"
namespace paddle_mobile { namespace paddle_mobile {
namespace operators { namespace operators {
namespace math { namespace math {
using framework::Tensor;
using std::max; template <int tile, int kernel>
using std::min; void winograd_transform_weight(const framework::Tensor &weight,
using std::vector; framework::Tensor *output);
void DepthwiseConv3x3(const Tensor *input, vector<int> strides, template <int tile, int kernel>
vector<int> paddings, const Tensor *filter, Tensor *bias, void winograd_transform_input(const framework::Tensor &input,
Tensor *output, bool if_bias); framework::Tensor *output);
void DepthwiseConv3x3s1p1(const Tensor *input, const Tensor *filter,
Tensor *output, Tensor *bias, bool if_bias); template <int tile, int kernel>
void DepthwiseConvAddBNRelu3x3s1p1(const Tensor *input, const Tensor *filter, void winograd_transform_output(const framework::Tensor &input,
Tensor *output, const Tensor *new_scale, const framework::Tensor &weight,
const Tensor *new_bias, bool if_relu); framework::Tensor *output);
void DepthwiseConvAddBNRelu3x3s2p1(const Tensor *input, const Tensor *filter,
Tensor *output, const Tensor *new_scale,
const Tensor *new_bias, bool if_relu);
void DepthwiseConv3x3s2p1v2(const Tensor *input, const Tensor *filter,
Tensor *output, Tensor bias, bool if_bias);
void DepthwiseConvAddBNRelu3x3s2p1v2(const Tensor *input, const Tensor *filter,
Tensor *output, const Tensor *new_scale,
const Tensor *new_bias, bool if_relu);
void DepthwiseConv3x3s2p0(const Tensor *input, const Tensor *filter,
Tensor *output, Tensor bias, bool if_bias);
} // namespace math } // namespace math
} // namespace operators } // namespace operators
} // namespace paddle_mobile } // namespace paddle_mobile
#endif
src/operators/math/winograd/winograd_transform_f6k3.cpp 0 → 100644
浏览文件 @ a663dd86
此差异已折叠。
src/operators/op_param.h
浏览文件 @ a663dd86
...@@ -405,9 +405,9 @@ class ConvParam : public OpParam { ...@@ -405,9 +405,9 @@ class ConvParam : public OpParam {
const RType *Input() const { return input_; } const RType *Input() const { return input_; }
RType *Filter() const { return filter_; } RType *&Filter() const { return filter_; }
RType *Output() const { return output_; } RType *&Output() const { return output_; }
const vector<int> &Strides() const { return strides_; } const vector<int> &Strides() const { return strides_; }
...@@ -415,6 +415,19 @@ class ConvParam : public OpParam { ...@@ -415,6 +415,19 @@ class ConvParam : public OpParam {
const vector<int> &Dilations() const { return dilations_; } const vector<int> &Dilations() const { return dilations_; }
enum ExecMode {
EXEC_INVALID = 0,
EXEC_GEMM_FLOAT,
EXEC_DEPTHWISE3x3S1P1_FLOAT,
EXEC_DEPTHWISE3x3_FLOAT,
EXEC_WINOGRAD3X3_FLOAT,
EXEC_WINOGRAD5X5_FLOAT,
EXEC_GEMM_INT8,
EXEC_DEPTHWISE3x3_INT8,
};
ExecMode &ExecMode() const { return exec_mode_; }
const int &Groups() const { return groups; } const int &Groups() const { return groups; }
#ifdef PADDLE_MOBILE_CL #ifdef PADDLE_MOBILE_CL
...@@ -426,11 +439,12 @@ class ConvParam : public OpParam { ...@@ -426,11 +439,12 @@ class ConvParam : public OpParam {
private: private:
RType *input_; RType *input_;
RType *output_; mutable RType *output_;
RType *filter_; mutable RType *filter_;
vector<int> strides_; vector<int> strides_;
vector<int> paddings_; vector<int> paddings_;
vector<int> dilations_; vector<int> dilations_;
mutable enum ExecMode exec_mode_;
int groups; int groups;
#ifdef PADDLE_MOBILE_CL #ifdef PADDLE_MOBILE_CL
...@@ -2509,7 +2523,7 @@ class QuantizeParam : public OpParam { ...@@ -2509,7 +2523,7 @@ class QuantizeParam : public OpParam {
QuantizeParam(const VariableNameMap &inputs, const VariableNameMap &outputs, QuantizeParam(const VariableNameMap &inputs, const VariableNameMap &outputs,
const AttributeMap &attrs, const Scope &scope) { const AttributeMap &attrs, const Scope &scope) {
input_ = InputXFrom<GType>(inputs, scope); input_ = InputXFrom<GType>(inputs, scope);
out_ = OutFrom<GType>(outputs, scope); output_ = OutFrom<GType>(outputs, scope);
// online // online
// scale = max(abs(x)) // scale = max(abs(x))
online_scale_ = GetVarValue<GType>("OutScale", outputs, scope); online_scale_ = GetVarValue<GType>("OutScale", outputs, scope);
...@@ -2528,8 +2542,7 @@ class QuantizeParam : public OpParam { ...@@ -2528,8 +2542,7 @@ class QuantizeParam : public OpParam {
// op input // op input
RType *input_; RType *input_;
// op output // op output
RType *out_; RType *output_;
//
RType *online_scale_; RType *online_scale_;
// if static scale or not // if static scale or not
bool is_static_ = false; bool is_static_ = false;
...@@ -2537,7 +2550,11 @@ class QuantizeParam : public OpParam { ...@@ -2537,7 +2550,11 @@ class QuantizeParam : public OpParam {
float static_scale_ = 1.0f; float static_scale_ = 1.0f;
// round method type // round method type
// nearest_zero and nearest_even is valid currently // nearest_zero and nearest_even is valid currently
RoundType round_type_ = ROUND_NEAREST_AWAY_ZERO; // RoundType round_type_ = ROUND_NEAREST_AWAY_ZERO;
RoundType round_type_ = ROUND_NEAREST_TOWARDS_ZERO;
// optional paddings
std::vector<int> paddings_;
int8_t padding_val_;
}; };
#endif #endif
...@@ -2551,7 +2568,7 @@ class DequantizeParam : public OpParam { ...@@ -2551,7 +2568,7 @@ class DequantizeParam : public OpParam {
DequantizeParam(const VariableNameMap &inputs, const VariableNameMap &outputs, DequantizeParam(const VariableNameMap &inputs, const VariableNameMap &outputs,
const AttributeMap &attrs, const Scope &scope) { const AttributeMap &attrs, const Scope &scope) {
input_ = InputXFrom<GType>(inputs, scope); input_ = InputXFrom<GType>(inputs, scope);
out_ = OutFrom<GType>(outputs, scope); output_ = OutFrom<GType>(outputs, scope);
activation_scale_ = GetVarValue<GType>("Scale", inputs, scope); activation_scale_ = GetVarValue<GType>("Scale", inputs, scope);
// dequantization is performed as x = x / static_scale / online_scale // dequantization is performed as x = x / static_scale / online_scale
if (HasAttr("weight_scale", attrs)) { if (HasAttr("weight_scale", attrs)) {
...@@ -2565,7 +2582,7 @@ class DequantizeParam : public OpParam { ...@@ -2565,7 +2582,7 @@ class DequantizeParam : public OpParam {
// op input // op input
RType *input_; RType *input_;
// op output // op output
RType *out_; RType *output_;
RType *activation_scale_; RType *activation_scale_;
float weight_scale_; float weight_scale_;
}; };
......
src/operators/quantize_op.cpp
浏览文件 @ a663dd86
...@@ -22,8 +22,8 @@ namespace operators { ...@@ -22,8 +22,8 @@ namespace operators {
template <typename DeviceType, typename T> template <typename DeviceType, typename T>
void QuantizeOp<DeviceType, T>::InferShape() const { void QuantizeOp<DeviceType, T>::InferShape() const {
const auto& input_dims = this->param_.input_->dims(); const auto &input_dims = this->param_.input_->dims();
this->param_.out_->Resize(input_dims); this->param_.output_->Resize(input_dims);
auto scale_dims = framework::make_ddim(std::vector<int>{1}); auto scale_dims = framework::make_ddim(std::vector<int>{1});
this->param_.online_scale_->Resize(scale_dims); this->param_.online_scale_->Resize(scale_dims);
} }
......
test/CMakeLists.txt
浏览文件 @ a663dd86
...@@ -155,7 +155,7 @@ if (NOT FOUND_MATCH) ...@@ -155,7 +155,7 @@ if (NOT FOUND_MATCH)
target_link_libraries(test-googlenet-quali paddle-mobile) target_link_libraries(test-googlenet-quali paddle-mobile)
# gen test # gen test
ADD_EXECUTABLE(test-conv-op operators/test_cov_op.cpp test_helper.h test_include.h executor_for_test.h) ADD_EXECUTABLE(test-conv-op operators/test_conv_op.cpp test_helper.h test_include.h executor_for_test.h)
target_link_libraries(test-conv-op paddle-mobile) target_link_libraries(test-conv-op paddle-mobile)
# gen test # gen test
...@@ -242,10 +242,6 @@ if (NOT FOUND_MATCH) ...@@ -242,10 +242,6 @@ if (NOT FOUND_MATCH)
ADD_EXECUTABLE(test-dequantize-op operators/test_dequantize_op.cpp test_helper.h test_include.h) ADD_EXECUTABLE(test-dequantize-op operators/test_dequantize_op.cpp test_helper.h test_include.h)
target_link_libraries(test-dequantize-op paddle-mobile) target_link_libraries(test-dequantize-op paddle-mobile)
# test int8 conv op
ADD_EXECUTABLE(test-int8-conv-op operators/test_int8_conv_op.cpp test_helper.h test_include.h)
target_link_libraries(test-int8-conv-op paddle-mobile)
# gen test log # gen test log
ADD_EXECUTABLE(test-log common/test_log.cpp) ADD_EXECUTABLE(test-log common/test_log.cpp)
target_link_libraries(test-log paddle-mobile) target_link_libraries(test-log paddle-mobile)
...@@ -368,6 +364,10 @@ if (NOT FOUND_MATCH) ...@@ -368,6 +364,10 @@ if (NOT FOUND_MATCH)
ADD_EXECUTABLE(test-multi-process net/test_multi_inference_predict.cpp test_helper.h test_include.h) ADD_EXECUTABLE(test-multi-process net/test_multi_inference_predict.cpp test_helper.h test_include.h)
target_link_libraries(test-multi-process paddle-mobile) target_link_libraries(test-multi-process paddle-mobile)
# gen test benchmark
ADD_EXECUTABLE(test-benchmark net/test_benchmark.cpp)
target_link_libraries(test-benchmark paddle-mobile)
# gen test # gen test
ADD_EXECUTABLE(test-eng net/test_eng.cpp test_helper.h test_include.h) ADD_EXECUTABLE(test-eng net/test_eng.cpp test_helper.h test_include.h)
target_link_libraries(test-eng paddle-mobile) target_link_libraries(test-eng paddle-mobile)
......
test/framework/test_load_memory.cpp
浏览文件 @ a663dd86
...@@ -12,10 +12,11 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. ...@@ -12,10 +12,11 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. */ limitations under the License. */
#include <iostream>
#include <string> #include <string>
#include "../test_helper.h" #include "../test_helper.h"
#include "../test_include.h" #include "../test_include.h"
static size_t ReadBuffer(const char *file_name, uint8_t **out) { static size_t ReadBuffer(const char *file_name, uint8_t **out) {
FILE *fp; FILE *fp;
fp = fopen(file_name, "rb"); fp = fopen(file_name, "rb");
......
test/net/test_benchmark.cpp 0 → 100644
浏览文件 @ a663dd86
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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
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. */
#include <iostream>
#include "../test_helper.h"
#include "../test_include.h"
int main(int argc, char* argv[]) {
if (argc < 4) {
std::cout << "Usage: " << std::endl
<< "./test_benchmark fluid_model feed_shape thread_num [use_fuse]"
<< std::endl;
std::cout << "use_fuse: optional, bool, default is 1\n";
return 1;
}
bool optimize = true;
char* fluid_model = argv[1];
char* feed_shape = argv[2];
int thread_num = atoi(argv[3]);
if (argc == 5) {
optimize = atoi(argv[4]);
}
paddle_mobile::PaddleMobile<paddle_mobile::CPU> paddle_mobile;
paddle_mobile.SetThreadNum(thread_num);
auto time1 = time();
if (paddle_mobile.Load(fluid_model, optimize)) {
auto time2 = time();
std::cout << "load cost :" << time_diff(time1, time2) << "ms\n";
paddle_mobile::framework::Tensor input;
std::shared_ptr<paddle_mobile::framework::Tensor> output;
std::vector<int64_t> dims{1, 3, 224, 224};
if (feed_shape) {
sscanf(feed_shape, "%d,%d,%d,%d", &dims[0], &dims[1], &dims[2], &dims[3]);
}
std::cout << "feed shape: [" << dims[0] << ", " << dims[1] << ", "
<< dims[2] << ", " << dims[3] << "]\n";
paddle_mobile::framework::DDim in_shape =
paddle_mobile::framework::make_ddim(dims);
SetupTensor<float>(&input, in_shape, 0.f, 255.f);
// warmup
for (int i = 0; i < 10; ++i) {
output = paddle_mobile.Predict(input);
}
auto time3 = time();
for (int i = 0; i < 10; ++i) {
output = paddle_mobile.Predict(input);
}
auto time4 = time();
std::cout << "predict cost :" << time_diff(time3, time4) / 10 << "ms\n";
}
return 0;
}
test/net/test_googlenet.cpp
浏览文件 @ a663dd86
...@@ -20,12 +20,11 @@ int main() { ...@@ -20,12 +20,11 @@ int main() {
#ifdef PADDLE_MOBILE_FPGA #ifdef PADDLE_MOBILE_FPGA
paddle_mobile::PaddleMobile<paddle_mobile::FPGA> paddle_mobile; paddle_mobile::PaddleMobile<paddle_mobile::FPGA> paddle_mobile;
#endif #endif
#ifdef PADDLE_MOBILE_CPU #ifdef PADDLE_MOBILE_CPU
paddle_mobile::PaddleMobile<paddle_mobile::CPU> paddle_mobile; paddle_mobile::PaddleMobile<paddle_mobile::CPU> paddle_mobile;
#endif #endif
paddle_mobile.SetThreadNum(4); paddle_mobile.SetThreadNum(1);
bool optimize = true; bool optimize = true;
auto time1 = time(); auto time1 = time();
if (paddle_mobile.Load(g_googlenet, optimize)) { if (paddle_mobile.Load(g_googlenet, optimize)) {
...@@ -36,7 +35,7 @@ int main() { ...@@ -36,7 +35,7 @@ int main() {
std::vector<float> output; std::vector<float> output;
std::vector<int64_t> dims{1, 3, 224, 224}; std::vector<int64_t> dims{1, 3, 224, 224};
GetInput<float>(g_test_image_1x3x224x224, &input, dims); GetInput<float>(g_test_image_1x3x224x224, &input, dims);
// 预热十次 // warmup
for (int i = 0; i < 10; ++i) { for (int i = 0; i < 10; ++i) {
output = paddle_mobile.Predict(input, dims); output = paddle_mobile.Predict(input, dims);
} }
...@@ -46,8 +45,7 @@ int main() { ...@@ -46,8 +45,7 @@ int main() {
} }
auto time4 = time(); auto time4 = time();
std::cout << "predict cost :" << time_diff(time3, time4) / 10 << "ms" std::cout << "predict cost: " << time_diff(time3, time4) / 10 << "ms\n";
<< std::endl;
} }
return 0; return 0;
} }
test/operators/test_int8_conv_op.cpp → test/operators/test_conv_op.cpp
浏览文件 @ a663dd86
...@@ -18,7 +18,7 @@ limitations under the License. */ ...@@ -18,7 +18,7 @@ limitations under the License. */
namespace paddle_mobile { namespace paddle_mobile {
// Reference convolution for checking results: // Reference convolution from Caffe for checking results.
// accumulate through explicit loops over input, output, and filters. // accumulate through explicit loops over input, output, and filters.
template <typename Itype, typename Otype> template <typename Itype, typename Otype>
void conv2d(const framework::Tensor *input, const framework::Tensor *filter, void conv2d(const framework::Tensor *input, const framework::Tensor *filter,
...@@ -129,7 +129,7 @@ void conv2d(const framework::Tensor *input, const framework::Tensor *filter, ...@@ -129,7 +129,7 @@ void conv2d(const framework::Tensor *input, const framework::Tensor *filter,
} }
template <typename Itype, typename Otype, int Kernel, int Pad, int Stride> template <typename Itype, typename Otype, int Kernel, int Pad, int Stride>
int TestConvOp() { int TestConvOp(int in_channels, int in_height, int in_width, int out_channels) {
int kernel_h = Kernel; int kernel_h = Kernel;
int kernel_w = Kernel; int kernel_w = Kernel;
int pad_h = Pad; int pad_h = Pad;
...@@ -140,10 +140,10 @@ int TestConvOp() { ...@@ -140,10 +140,10 @@ int TestConvOp() {
int dilation_w = 1; int dilation_w = 1;
int batch_size = 1; int batch_size = 1;
int input_c = 3; int input_c = in_channels;
int input_h = 100; int input_h = in_height;
int input_w = 100; int input_w = in_width;
int output_c = 10; int output_c = out_channels;
framework::DDim input_shape = framework::DDim input_shape =
framework::make_ddim({batch_size, input_c, input_h, input_w}); framework::make_ddim({batch_size, input_c, input_h, input_w});
framework::DDim filter_shape = framework::DDim filter_shape =
...@@ -158,7 +158,7 @@ int TestConvOp() { ...@@ -158,7 +158,7 @@ int TestConvOp() {
auto input_var = scope.get()->Var("input"); auto input_var = scope.get()->Var("input");
auto input = input_var->template GetMutable<framework::LoDTensor>(); auto input = input_var->template GetMutable<framework::LoDTensor>();
SetupTensor<Itype>(input, input_shape, -20, 20); SetupTensor<Itype>(input, input_shape, -20.0, 20.0);
auto filter_var = scope.get()->Var("filter"); auto filter_var = scope.get()->Var("filter");
auto filter = filter_var->template GetMutable<framework::LoDTensor>(); auto filter = filter_var->template GetMutable<framework::LoDTensor>();
...@@ -174,8 +174,9 @@ int TestConvOp() { ...@@ -174,8 +174,9 @@ int TestConvOp() {
auto *op = new operators::ConvOp<CPU, float>("conv2d", inputs, outputs, attrs, auto *op = new operators::ConvOp<CPU, float>("conv2d", inputs, outputs, attrs,
scope); scope);
// struct timespec ts_begin, ts_end;
op->InferShape(); op->InferShape();
op->Init();
// struct timespec ts_begin, ts_end;
// warmup // warmup
// op->Run(); // op->Run();
// clock_gettime(CLOCK_MONOTONIC, &ts_begin); // clock_gettime(CLOCK_MONOTONIC, &ts_begin);
...@@ -202,9 +203,16 @@ int TestConvOp() { ...@@ -202,9 +203,16 @@ int TestConvOp() {
const Otype *output_data = output->data<Otype>(); const Otype *output_data = output->data<Otype>();
Otype *output_cmp_data = output_cmp.data<Otype>(); Otype *output_cmp_data = output_cmp.data<Otype>();
for (int i = 0; i < output->numel(); ++i) { for (int i = 0; i < output->numel(); ++i) {
PADDLE_MOBILE_ENFORCE(output_data[i] == output_cmp_data[i], float gap = output_data[i] - output_cmp_data[i];
PADDLE_MOBILE_ENFORCE(std::abs(gap / (output_data[i] + 1e-5)) < 1e-3,
"output[%d] = %d, output_cmp[%d] = %d", i, "output[%d] = %d, output_cmp[%d] = %d", i,
output_data[i], i, output_cmp_data[i]); output_data[i], i, output_cmp_data[i]);
// if (std::abs(gap / (output_data[i] + 1e-5)) > 1e-3) {
// LOG(kLOG_INFO) << "output_data[" << i << "] = " << output_data[i]
// << ", output_cmp_data[" << i << "] = " <<
// output_cmp_data[i];
// return 1;
// }
} }
delete op; delete op;
return 0; return 0;
...@@ -212,68 +220,88 @@ int TestConvOp() { ...@@ -212,68 +220,88 @@ int TestConvOp() {
} // namespace paddle_mobile } // namespace paddle_mobile
int main() { int main(int argc, char *argv[]) {
if (argc < 5) {
LOG(paddle_mobile::kLOG_INFO)
<< "Usage:\n"
<< " ./test-int8-conv-op in_channels in_height in_width out_channels\n"
<< " params:\n"
<< " -in_channels: int, input image's channels\n"
<< " -in_height: int, input image's height\n"
<< " -in_width: int, input image's width\n"
<< " -out_channels: int, conv output channels\n";
return 1;
}
int in_channels = atoi(argv[1]);
int in_height = atoi(argv[2]);
int in_width = atoi(argv[3]);
int out_channels = atoi(argv[4]);
// kernel = 3, pad = 1, stride = 1
LOG(paddle_mobile::kLOG_INFO) << "float, kernel=3, pad=1, stride=1";
paddle_mobile::TestConvOp<float, float, 3, 1, 1>(in_channels, in_height,
in_width, out_channels);
// kernel = 7, pad = 0, stride = 2 // kernel = 7, pad = 0, stride = 2
LOG(paddle_mobile::kLOG_INFO) << "int8, kernel=7, pad=0, stride=2"; LOG(paddle_mobile::kLOG_INFO) << "int8, kernel=7, pad=0, stride=2";
paddle_mobile::TestConvOp<int8_t, int32_t, 7, 0, 2>(); paddle_mobile::TestConvOp<int8_t, int32_t, 7, 0, 2>(in_channels, in_height,
in_width, out_channels);
// kernel = 7, pad = 1, stride = 2 // kernel = 7, pad = 1, stride = 2
LOG(paddle_mobile::kLOG_INFO) << "int8, kernel=7, pad=1, stride=2"; LOG(paddle_mobile::kLOG_INFO) << "int8, kernel=7, pad=1, stride=2";
paddle_mobile::TestConvOp<int8_t, int32_t, 7, 1, 2>(); paddle_mobile::TestConvOp<int8_t, int32_t, 7, 1, 2>(in_channels, in_height,
in_width, out_channels);
// kernel = 7, pad = 3, stride = 2 // kernel = 7, pad = 3, stride = 2
LOG(paddle_mobile::kLOG_INFO) << "int8, kernel=7, pad=3, stride=2"; LOG(paddle_mobile::kLOG_INFO) << "int8, kernel=7, pad=3, stride=2";
paddle_mobile::TestConvOp<int8_t, int32_t, 7, 3, 2>(); paddle_mobile::TestConvOp<int8_t, int32_t, 7, 3, 2>(in_channels, in_height,
in_width, out_channels);
// kernel = 7, pad = 0, stride = 1 // kernel = 7, pad = 0, stride = 1
LOG(paddle_mobile::kLOG_INFO) << "int8, kernel=7, pad=0, stride=1"; LOG(paddle_mobile::kLOG_INFO) << "int8, kernel=7, pad=0, stride=1";
paddle_mobile::TestConvOp<int8_t, int32_t, 7, 0, 1>(); paddle_mobile::TestConvOp<int8_t, int32_t, 7, 0, 1>(in_channels, in_height,
in_width, out_channels);
// kernel = 7, pad = 1, stride = 1 // kernel = 7, pad = 1, stride = 1
LOG(paddle_mobile::kLOG_INFO) << "int8, kernel=7, pad=1, stride=1"; LOG(paddle_mobile::kLOG_INFO) << "int8, kernel=7, pad=1, stride=1";
paddle_mobile::TestConvOp<int8_t, int32_t, 7, 1, 1>(); paddle_mobile::TestConvOp<int8_t, int32_t, 7, 1, 1>(in_channels, in_height,
in_width, out_channels);
// kernel = 7, pad = 3, stride = 1 // kernel = 7, pad = 3, stride = 1
LOG(paddle_mobile::kLOG_INFO) << "int8, kernel=7, pad=3, stride=1"; LOG(paddle_mobile::kLOG_INFO) << "int8, kernel=7, pad=3, stride=1";
paddle_mobile::TestConvOp<int8_t, int32_t, 7, 3, 1>(); paddle_mobile::TestConvOp<int8_t, int32_t, 7, 3, 1>(in_channels, in_height,
in_width, out_channels);
// kernel = 7, pad = 5, stride = 3 // kernel = 7, pad = 5, stride = 3
LOG(paddle_mobile::kLOG_INFO) << "int8, kernel=7, pad=5, stride=3"; LOG(paddle_mobile::kLOG_INFO) << "int8, kernel=7, pad=5, stride=3";
paddle_mobile::TestConvOp<int8_t, int32_t, 7, 5, 3>(); paddle_mobile::TestConvOp<int8_t, int32_t, 7, 5, 3>(in_channels, in_height,
in_width, out_channels);
// kernel = 7, pad = 3, stride = 4 // kernel = 7, pad = 3, stride = 4
LOG(paddle_mobile::kLOG_INFO) << "int8, kernel=7, pad=3, stride=4"; LOG(paddle_mobile::kLOG_INFO) << "int8, kernel=7, pad=3, stride=4";
paddle_mobile::TestConvOp<int8_t, int32_t, 7, 3, 4>(); paddle_mobile::TestConvOp<int8_t, int32_t, 7, 3, 4>(in_channels, in_height,
LOG(paddle_mobile::kLOG_INFO) << "\n"; in_width, out_channels);
// kernel = 3, pad = 0, stride = 1 // kernel = 3, pad = 0, stride = 1
LOG(paddle_mobile::kLOG_INFO) << "int8, kernel=3, pad=0, stride=1"; LOG(paddle_mobile::kLOG_INFO) << "int8, kernel=3, pad=0, stride=1";
paddle_mobile::TestConvOp<int8_t, int32_t, 3, 0, 1>(); paddle_mobile::TestConvOp<int8_t, int32_t, 3, 0, 1>(in_channels, in_height,
in_width, out_channels);
// kernel = 3, pad = 0, stride = 1 // kernel = 3, pad = 0, stride = 1
LOG(paddle_mobile::kLOG_INFO) << "float, kernel=3, pad=0, stride=1"; LOG(paddle_mobile::kLOG_INFO) << "float, kernel=3, pad=0, stride=1";
paddle_mobile::TestConvOp<float, float, 3, 0, 1>(); paddle_mobile::TestConvOp<float, float, 3, 0, 1>(in_channels, in_height,
LOG(paddle_mobile::kLOG_INFO) << "\n"; in_width, out_channels);
// kernel = 3, pad = 1, stride = 1 // kernel = 3, pad = 1, stride = 1
LOG(paddle_mobile::kLOG_INFO) << "int8, kernel=3, pad=1, stride=1"; LOG(paddle_mobile::kLOG_INFO) << "int8, kernel=3, pad=1, stride=1";
paddle_mobile::TestConvOp<int8_t, int32_t, 3, 1, 1>(); paddle_mobile::TestConvOp<int8_t, int32_t, 3, 1, 1>(in_channels, in_height,
in_width, out_channels);
// kernel = 3, pad = 1, stride = 1 // kernel = 3, pad = 1, stride = 1
LOG(paddle_mobile::kLOG_INFO) << "float, kernel=3, pad=1, stride=1"; LOG(paddle_mobile::kLOG_INFO) << "float, kernel=3, pad=1, stride=1";
paddle_mobile::TestConvOp<float, float, 3, 1, 1>(); paddle_mobile::TestConvOp<float, float, 3, 1, 1>(in_channels, in_height,
LOG(paddle_mobile::kLOG_INFO) << "\n"; in_width, out_channels);
// kernel = 5, pad = 0, stride = 1 // kernel = 5, pad = 0, stride = 1
LOG(paddle_mobile::kLOG_INFO) << "int8, kernel=5, pad=0, stride=1"; LOG(paddle_mobile::kLOG_INFO) << "int8, kernel=5, pad=0, stride=1";
paddle_mobile::TestConvOp<int8_t, int32_t, 5, 0, 1>(); paddle_mobile::TestConvOp<int8_t, int32_t, 5, 0, 1>(in_channels, in_height,
in_width, out_channels);
// kernel = 5, pad = 0, stride = 1 // kernel = 5, pad = 0, stride = 1
LOG(paddle_mobile::kLOG_INFO) << "float, kernel=5, pad=0, stride=1"; LOG(paddle_mobile::kLOG_INFO) << "float, kernel=5, pad=0, stride=1";
paddle_mobile::TestConvOp<float, float, 5, 0, 1>(); paddle_mobile::TestConvOp<float, float, 5, 0, 1>(in_channels, in_height,
LOG(paddle_mobile::kLOG_INFO) << "\n"; in_width, out_channels);
// kernel = 5, pad = 2, stride = 1 // kernel = 5, pad = 2, stride = 1
LOG(paddle_mobile::kLOG_INFO) << "int8, kernel=5, pad=2, stride=1"; LOG(paddle_mobile::kLOG_INFO) << "int8, kernel=5, pad=2, stride=1";
paddle_mobile::TestConvOp<int8_t, int32_t, 5, 2, 1>(); paddle_mobile::TestConvOp<int8_t, int32_t, 5, 2, 1>(in_channels, in_height,
in_width, out_channels);
// kernel = 5, pad = 2, stride = 1 // kernel = 5, pad = 2, stride = 1
LOG(paddle_mobile::kLOG_INFO) << "float, kernel=5, pad=2, stride=1"; LOG(paddle_mobile::kLOG_INFO) << "float, kernel=5, pad=2, stride=1";
paddle_mobile::TestConvOp<float, float, 5, 2, 1>(); paddle_mobile::TestConvOp<float, float, 5, 2, 1>(in_channels, in_height,
in_width, out_channels);
} }
test/operators/test_cov_op.cpp 已删除 100644 → 0
浏览文件 @ 92d3661c
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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
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. */
#include "../test_include.h"
#include "operators/conv_op.h"
int main() {
paddle_mobile::framework::Loader<paddle_mobile::GPU_MALI> loader;
// ../models/image_classification_resnet.inference.model
auto program = loader.Load(g_googlenet);
PADDLE_MOBILE_ENFORCE(program.originProgram != nullptr,
"program file read fail");
Executor4Test<paddle_mobile::GPU_MALI, paddle_mobile::operators::ConvOp<
paddle_mobile::GPU_MALI, float>>
executor(program, "conv2d");
paddle_mobile::framework::Tensor input;
GetInput<float>(g_test_image_1x3x224x224, &input, {1, 3, 224, 224});
// // use SetupTensor if not has local input image .
// SetupTensor<float>(&input, {1, 3, 224, 224}, static_cast<float>(0),
// static_cast<float>(1));
auto out_ddim = paddle_mobile::framework::make_ddim({1, 64, 112, 112});
auto output = executor.Predict(input, "data", "conv2d_0.tmp_0", out_ddim);
auto output_ptr = output->data<float>();
for (int j = 0; j < 20; ++j) {
DLOG << " value of output: " << output_ptr[j];
}
return 0;
}
tools/build.sh
浏览文件 @ a663dd86
...@@ -212,4 +212,4 @@ else ...@@ -212,4 +212,4 @@ else
else else
build_error "$1" build_error "$1"
fi fi
fi fi
\ No newline at end of file
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或