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提交 6ab45d2d 编写于 作者: Y yangfei
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add some function

上级 7fbea0e2 df230944
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Showing with 861 addition and 139 deletion
src/common/common.h
浏览文件 @ 6ab45d2d
......@@ -15,6 +15,8 @@ limitations under the License. */
#pragma once
#include <chrono>
namespace paddle_mobile {
using Time = decltype(std::chrono::high_resolution_clock::now());
inline Time time() { return std::chrono::high_resolution_clock::now(); }
......@@ -25,3 +27,5 @@ inline double time_diff(Time t1, Time t2) {
ms counter = std::chrono::duration_cast<ms>(diff);
return counter.count() / 1000.0;
}
}
src/framework/cl/cl_engine.h
浏览文件 @ 6ab45d2d
......@@ -18,8 +18,8 @@ limitations under the License. */
#include <string>
#include "CL/cl.h"
#include "common/log.h"
#include "common/enforce.h"
#include "common/log.h"
#include "framework/cl/cl_deleter.h"
#include "framework/cl/cl_tool.h"
......
src/framework/cl/cl_tool.h
浏览文件 @ 6ab45d2d
......@@ -26,7 +26,8 @@ const char* opencl_error_to_str(cl_int error);
printf( \
"OpenCL error with code %s happened in file %s at line %d. " \
"Exiting.\n", \
opencl_error_to_str(ERR), __FILE__, __LINE__); \
paddle_mobile::framework::opencl_error_to_str(ERR), __FILE__, \
__LINE__); \
}
} // namespace framework
......
src/framework/executor.cpp
浏览文件 @ 6ab45d2d
......@@ -908,10 +908,14 @@ void Executor<GPU_CL, Precision::FP32>::InitMemory() {
for (const auto &var_desc : block->Vars()) {
auto var = program_.scope->Var(var_desc->Name());
if (var_desc->Persistable()) {
auto cl_image = var->template GetMutable<framework::CLImage>();
CLImage *cl_image = nullptr;
if (var_desc->Name() == "feed" || var_desc->Name() == "fetch") {
var->template GetMutable<framework::LoDTensor>();
continue;
} else {
cl_image = var->template GetMutable<framework::CLImage>();
}
char *origin_data =
Get_binary_data(program_.model_path + "/" + var_desc->Name());
char *data = origin_data;
......@@ -928,7 +932,8 @@ void Executor<GPU_CL, Precision::FP32>::InitMemory() {
framework::DDim ddim = framework::make_ddim(desc.Dims());
cl_image->Init(context, tensorInput, ddim);
// has not init
cl_image->SetTensorData(tensorInput, ddim);
delete origin_data;
// paddle_mobile::memory::Free(tensorInput);
......@@ -941,7 +946,7 @@ void Executor<GPU_CL, Precision::FP32>::InitMemory() {
// framework::DDim ddim = framework::make_ddim(desc.Dims());
framework::DDim ddim = cl_image->dims();
DLOG << var_desc->Name();
cl_image->Init(context, ddim);
cl_image->InitEmptyImage(context, ddim);
}
}
}
......@@ -965,9 +970,12 @@ void Executor<GPU_CL, Precision::FP32>::InitCombineMemory() {
for (const auto &var_desc : block->Vars()) {
auto var = program_.scope->Var(var_desc->Name());
if (var_desc->Persistable()) {
auto cl_image = var->template GetMutable<framework::CLImage>();
CLImage *cl_image = nullptr;
if (var_desc->Name() == "feed" || var_desc->Name() == "fetch") {
var->template GetMutable<framework::LoDTensor>();
continue;
} else {
cl_image = var->template GetMutable<framework::CLImage>();
}
cl_context context = program_.scope->GetCLScpoe()->Context();
......@@ -982,7 +990,10 @@ void Executor<GPU_CL, Precision::FP32>::InitCombineMemory() {
float *tensorInput = static_cast<float *>(
paddle_mobile::memory::Alloc(sizeof(float) * numel));
LoadMemory(*var_desc, tensorInput, &origin_data);
cl_image->Init(context, tensorInput, ddim);
// has not init
cl_image->SetTensorData(tensorInput, ddim);
paddle_mobile::memory::Free(tensorInput);
} else {
auto cl_image = var->template GetMutable<framework::CLImage>();
......@@ -991,8 +1002,7 @@ void Executor<GPU_CL, Precision::FP32>::InitCombineMemory() {
const framework::TensorDesc &desc = var_desc->Tensor_desc();
framework::DDim ddim = cl_image->dims();
// framework::DDim ddim = framework::make_ddim(desc.Dims());
cl_image->Init(context, ddim);
cl_image->InitEmptyImage(context, ddim);
}
}
}
......
src/operators/kernel/cl/batchnorm_kernel.cpp
浏览文件 @ 6ab45d2d
......@@ -21,12 +21,67 @@ namespace operators {
template <>
bool BatchNormKernel<GPU_CL, float>::Init(BatchNormParam<GPU_CL> *param) {
this->cl_helper_.AddKernel("batchnorm", "batchnorm_kernel.cl");
const framework::CLImage *mean = param->InputMean();
const framework::CLImage *variance = param->InputVariance();
const framework::CLImage *scale = param->InputScale();
const framework::CLImage *bias = param->InputBias();
const float epsilon = param->Epsilon();
auto mean_ptr = mean->data<float>();
auto variance_ptr = variance->data<float>();
auto scale_ptr = scale->data<float>();
auto bias_ptr = bias->data<float>();
const int C = mean->numel();
float inv_std_ptr[C];
for (int i = 0; i < C; i++) {
inv_std_ptr[i] =
1 / static_cast<float>(pow((variance_ptr[i] + epsilon), 0.5));
}
float *new_scale_ptr = new float[C];
float *new_bias_ptr = new float[C];
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];
}
delete[](new_scale_ptr);
delete[](new_bias_ptr);
framework::CLImage *new_scale = new framework::CLImage();
framework::CLImage *new_bias = new framework::CLImage();
param->SetNewScale(new_scale);
param->SetNewBias(new_bias);
return true;
}
template <>
void BatchNormKernel<GPU_CL, float>::Compute(
const BatchNormParam<GPU_CL> &param) {}
const BatchNormParam<GPU_CL> &param) {
auto kernel = this->cl_helper_.KernelAt(0);
auto default_work_size = this->cl_helper_.DefaultWorkSize(*param.OutputY());
auto input = param.InputX()->GetCLImage();
auto out = param.OutputY()->GetCLImage();
auto new_scale = param.NewScale()->GetCLImage();
auto new_bias = param.NewBias()->GetCLImage();
const int out_height = param.OutputY()->HeightOfOneBlock();
const int out_width = param.OutputY()->WidthOfOneBlock();
clSetKernelArg(kernel, 0, sizeof(int), &out_height);
clSetKernelArg(kernel, 1, sizeof(int), &out_width);
clSetKernelArg(kernel, 2, sizeof(cl_mem), &input);
clSetKernelArg(kernel, 3, sizeof(cl_mem), &new_scale);
clSetKernelArg(kernel, 4, sizeof(cl_mem), &new_bias);
clSetKernelArg(kernel, 5, sizeof(cl_mem), &out);
clEnqueueNDRangeKernel(this->cl_helper_.CLCommandQueue(), kernel, 3, NULL,
default_work_size.data(), NULL, 0, NULL, NULL);
}
template class BatchNormKernel<GPU_CL, float>;
......
src/operators/kernel/cl/cl_kernel/batchnorm_kernel.cl 0 → 100644
浏览文件 @ 6ab45d2d
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
__kernel void batchnorm(__private const int out_height,
__private const int out_width,
__read_only image2d_t input,
__read_only image2d_t new_scale,
__read_only image2d_t new_bias,
__write_only image2d_t output) {
const int out_c = get_global_id(0);
const int out_w = get_global_id(1);
const int out_nh = get_global_id(2);
const sampler_t sampler =
CLK_NORMALIZED_COORDS_TRUE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST;
half4 new_scale = read_imageh(bn_scale, sampler, (int2)(out_c, 0));
half4 new_bias = read_imageh(bn_bias, sampler, (int2)(out_c, 0));
int pos_x = mad24(out_c, out_width, out_w);
half4 in = read_imageh(input, sampler, (int2)(pos_x, out_nh));
half4 out = mad(in, new_scale, new_bias);
write_imageh(output, (int2)(pos_x, nh), out);
}
src/operators/kernel/cl/cl_kernel/fetch_kernel.cl 0 → 100644
浏览文件 @ 6ab45d2d
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
__kernel void fetch(__private const int in_height,
__private const int in_width,
__private const int size_ch,
__private const int size_block,
__private const int size_batch,
__read_only image2d_t input,
__global float* out) {
const int in_c = get_global_id(0);
const int in_w = get_global_id(1);
const int in_nh = get_global_id(2);
const int in_n = in_nh / in_height;
const int in_h = in_nh % in_height;
const sampler_t sampler =
CLK_NORMALIZED_COORDS_TRUE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST;
const int pos_x = mad24(in_c, in_width, in_w);
half4 in = read_imageh(input, sampler, (int2)(pos_x, in_nh));
const int index = in_n * size_batch + in_c * size_block + in_h * in_width + in_w;
out[index] = convert_float(in.x);
out[index + size_ch] = convert_float(in.y);
out[index + size_ch * 2] = convert_float(in.z);
out[index + size_ch * 3] = convert_float(in.w);
}
src/operators/kernel/cl/cl_kernel/pool_kernel.cl 0 → 100644
浏览文件 @ 6ab45d2d
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#define MIN_VALUE -FLT_MAX
__kernel void pool_max(
__private const int in_height, __private const int in_width,
__private const int out_height, __private const int out_width,
__private const int pad_top, __private const int pad_left,
__private const int stride_h, __private const int stride_w,
__private const int ksize_h, __private const int ksize_w,
__read_only image2d_t input, __write_only image2d_t output) {
const int out_c = get_global_id(0);
const int out_w = get_global_id(1);
const int out_nh = get_global_id(2);
const int out_n = out_nh / out_height;
const int out_h = out_nh % out_height;
const sampler_t sampler =
CLK_NORMALIZED_COORDS_TRUE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST;
int start_h = max(out_h * stride_h - pad_top, 0);
int end_h = min(start_h + ksize_h, in_height);
int start_w = max(out_w * stride_w - pad_left, 0);
int end_w = min(start_w + ksize_w, in_width);
const int pos_in_x = out_c * in_width;
const int pos_in_y = out_n * in_height;
half4 max_value = (half4)(MIN_VALUE);
for (int y = start_h; y < end_h; ++y) {
for (int x = start_w; x < end_w; ++x) {
half4 tmp = read_imageh(input, sampler, (int2)(pos_in_x + x, pos_in_y + y));
max_value = max(max_value, tmp);
}
}
const int pos_out_x = mad24(out_c, out_width, out_w);
write_imageh(output, (int2)(pos_out_x, out_nh), max_value);
}
__kernel void pool_avg(
__private const int in_height, __private const int in_width,
__private const int out_height, __private const int out_width,
__private const int pad_top, __private const int pad_left,
__private const int stride_h, __private const int stride_w,
__private const int ksize_h, __private const int ksize_w,
__read_only image2d_t input, __write_only image2d_t output) {
const int out_c = get_global_id(0);
const int out_w = get_global_id(1);
const int out_nh = get_global_id(2);
const int out_n = out_nh / out_height;
const int out_h = out_nh % out_height;
const sampler_t sampler =
CLK_NORMALIZED_COORDS_TRUE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST;
int start_h = max(out_h * stride_h - pad_top, 0);
int end_h = min(start_h + ksize_h, in_height);
int start_w = max(out_w * stride_w - pad_left, 0);
int end_w = min(start_w + ksize_w, in_width);
const int pos_in_x = out_c * in_width;
const int pos_in_y = out_n * in_height;
half4 sum = (half4)(0.0f);
int num = 0;
for (int y = start_h; y < end_h; ++y) {
for (int x = start_w; x < end_w; ++x) {
sum += read_imageh(input, sampler, (int2)(pos_in_x + x, pos_in_y + y));
num++;
}
}
half4 avg = sum / num;
const int pos_out_x = mad24(out_c, out_width, out_w);
write_imageh(output, (int2)(pos_out_x, out_nh), avg);
}
\ No newline at end of file
src/operators/kernel/cl/cl_kernel/relu.cl 0 → 100644
浏览文件 @ 6ab45d2d
/* 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. */
__kernel void relu(__read_only image2d_t input,
__write_only image2d_t output)
const int x = get_global_id(0);
const int y = get_global_id(1);
const sampler_t sampler = CLK_NORMALIZED_COORDS_TRUE |
CLK_ADDRESS_CLAMP |
CLK_FILTER_NEAREST;
half4 r = read_imageh(input, sampler, int2(x, y));
r = max(half4(0, 0, 0, 0), r);
write_imageh(output, int2(x, y), r);
}
\ No newline at end of file
src/operators/kernel/cl/cl_kernel/reshape.cl 0 → 100644
浏览文件 @ 6ab45d2d
/* 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. */
__kernel void reshape(__read_only image2d_t input,
__write_only image2d_t output,
__private const int d0,
__private const int d1,
__private const int d2,
__private const int d3,
__private const int x0,
__private const int x1,
__private const int x2,
__private const int x3) {
const int x = get_global_id(0);
const int y = get_global_id(1);
int obx = x / x3;
int oby = y / x2;
int ox = x % x3;
int oy = y % x2;
const sampler_t sampler = CLK_NORMALIZED_COORDS_TRUE |
CLK_ADDRESS_CLAMP |
CLK_FILTER_NEAREST;
half4 r;
for (int i = 0; i < 4; i++) {
int t = obx * 4 + i;
if (t > x1) break;
int oindex = oby * x1 * x2 * x3 + t * x2 * x3 + ox * x3 + oy;
int i0, i1, i2, i3;
int i3 = oindex % d3; oindex /= d3;
int i2 = oindex % d2; oindex /= d2;
int i1 = oindex % d1; oindex /= d1;
int i0 = oindex;
int ix = (i1 / 4) * d3 + i3;
int iy = i0 * d2 + i2;
r[i] = read_imageh(input, sampler, int2(ix, iy))[i1%4];
}
write_imageh(output, int2(x, y), r);
}
\ No newline at end of file
src/operators/kernel/cl/cl_kernel/softmax.cl 0 → 100644
浏览文件 @ 6ab45d2d
/* 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. */
__kernel void softmax(__read_only image2d_t input,
__write_only image2d_t output,
__private const int d0,
__private const int d1,
__private const int d2,
__private const int d3) {
const int z = get_global_id(0);
const int x = get_global_id(1);
const int y = get_global_id(2);
const sampler_t sampler = CLK_NORMALIZED_COORDS_TRUE |
CLK_ADDRESS_CLAMP |
CLK_FILTER_NEAREST;
half4 maxv = read_imageh(input, sampler, int2(z * d3, y));
half4 buf[d3] = {piece};
for (int i = 1; i < d3; i++) {
buf[i] = read_imageh(input, sampler, int2(z * d3 + i, y));
maxv = max(maxv, buf[i]);
}
float4 sum = 0;
for (int i = 0; i < d3; i++) {
buf[i] = exp(buf[i] - maxv);
sum += buf[i];
}
half4 r = buf[x] / sum;
write_imageh(output, int2(z * d3 + x, y), r);
}
src/operators/kernel/cl/conv_add_bn_relu_kernel.cpp
浏览文件 @ 6ab45d2d
......@@ -16,6 +16,7 @@ limitations under the License. */
#include "operators/kernel/conv_add_bn_relu_kernel.h"
#include "framework/cl/cl_image.h"
#include "framework/cl/cl_tool.h"
namespace paddle_mobile {
namespace operators {
......@@ -56,15 +57,15 @@ bool ConvAddBNReluKernel<GPU_CL, float>::Init(
framework::CLImage *new_scale = new framework::CLImage();
new_scale->Init(this->cl_helper_.CLContext(), new_scale_ptr,
variance->dims());
new_scale->SetTensorData(new_scale_ptr, variance->dims());
new_scale->InitCLImage(this->cl_helper_.CLContext());
framework::CLImage *new_bias = new framework::CLImage();
new_bias->Init(this->cl_helper_.CLContext(), new_bias_ptr, variance->dims());
new_bias->SetTensorData(new_bias_ptr, variance->dims());
new_bias->InitCLImage(this->cl_helper_.CLContext());
param->SetNewScale(new_scale);
param->SetNewBias(new_bias);
PADDLE_MOBILE_ENFORCE(
......@@ -115,26 +116,32 @@ void ConvAddBNReluKernel<GPU_CL, float>::Compute(
int output_width = param.Output()->WidthOfOneBlock();
int output_height = param.Output()->HeightOfOneBlock();
clSetKernelArg(kernel, 0, sizeof(int), &c_block);
clSetKernelArg(kernel, 1, sizeof(int), &w);
clSetKernelArg(kernel, 2, sizeof(int), &nh);
clSetKernelArg(kernel, 3, sizeof(cl_mem), &input);
clSetKernelArg(kernel, 4, sizeof(cl_mem), &filter);
clSetKernelArg(kernel, 5, sizeof(cl_mem), &biase);
clSetKernelArg(kernel, 6, sizeof(cl_mem), &new_scale);
clSetKernelArg(kernel, 7, sizeof(cl_mem), &new_bias);
clSetKernelArg(kernel, 8, sizeof(cl_mem), &output);
clSetKernelArg(kernel, 9, sizeof(int), &stride);
clSetKernelArg(kernel, 10, sizeof(int), &offset);
clSetKernelArg(kernel, 11, sizeof(int), &input_c);
clSetKernelArg(kernel, 12, sizeof(int), &dilation);
clSetKernelArg(kernel, 13, sizeof(int), &input_width);
clSetKernelArg(kernel, 14, sizeof(int), &input_height);
clSetKernelArg(kernel, 15, sizeof(int), &output_width);
clSetKernelArg(kernel, 16, sizeof(int), &output_height);
cl_int status;
status = clSetKernelArg(kernel, 0, sizeof(int), &c_block);
status = clSetKernelArg(kernel, 1, sizeof(int), &w);
status = clSetKernelArg(kernel, 2, sizeof(int), &nh);
status = clSetKernelArg(kernel, 3, sizeof(cl_mem), &input);
status = clSetKernelArg(kernel, 4, sizeof(cl_mem), &filter);
status = clSetKernelArg(kernel, 5, sizeof(cl_mem), &biase);
status = clSetKernelArg(kernel, 6, sizeof(cl_mem), &new_scale);
status = clSetKernelArg(kernel, 7, sizeof(cl_mem), &new_bias);
status = clSetKernelArg(kernel, 8, sizeof(cl_mem), &output);
status = clSetKernelArg(kernel, 9, sizeof(int), &stride);
status = clSetKernelArg(kernel, 10, sizeof(int), &offset);
status = clSetKernelArg(kernel, 11, sizeof(int), &input_c);
status = clSetKernelArg(kernel, 12, sizeof(int), &dilation);
status = clSetKernelArg(kernel, 13, sizeof(int), &input_width);
status = clSetKernelArg(kernel, 14, sizeof(int), &input_height);
status = clSetKernelArg(kernel, 15, sizeof(int), &output_width);
status = clSetKernelArg(kernel, 16, sizeof(int), &output_height);
CL_CHECK_ERRORS(status);
status =
clEnqueueNDRangeKernel(this->cl_helper_.CLCommandQueue(), kernel, 3, NULL,
default_work_size.data(), NULL, 0, NULL, NULL);
CL_CHECK_ERRORS(status);
}
template class ConvAddBNReluKernel<GPU_CL, float>;
......
src/operators/kernel/cl/conv_add_kernel.cpp
浏览文件 @ 6ab45d2d
......@@ -65,24 +65,31 @@ void ConvAddKernel<GPU_CL, float>::Compute(
int output_width = param.Output()->WidthOfOneBlock();
int output_height = param.Output()->HeightOfOneBlock();
clSetKernelArg(kernel, 0, sizeof(int), &c_block);
clSetKernelArg(kernel, 1, sizeof(int), &w);
clSetKernelArg(kernel, 2, sizeof(int), &nh);
clSetKernelArg(kernel, 3, sizeof(cl_mem), &input);
clSetKernelArg(kernel, 4, sizeof(cl_mem), &filter);
clSetKernelArg(kernel, 5, sizeof(cl_mem), &biase);
clSetKernelArg(kernel, 6, sizeof(cl_mem), &output);
clSetKernelArg(kernel, 7, sizeof(int), &stride);
clSetKernelArg(kernel, 8, sizeof(int), &offset);
clSetKernelArg(kernel, 9, sizeof(int), &input_c);
clSetKernelArg(kernel, 10, sizeof(int), &dilation);
clSetKernelArg(kernel, 11, sizeof(int), &input_width);
clSetKernelArg(kernel, 12, sizeof(int), &input_height);
clSetKernelArg(kernel, 13, sizeof(int), &output_width);
clSetKernelArg(kernel, 14, sizeof(int), &output_height);
cl_int status;
status = clSetKernelArg(kernel, 0, sizeof(int), &c_block);
status = clSetKernelArg(kernel, 1, sizeof(int), &w);
status = clSetKernelArg(kernel, 2, sizeof(int), &nh);
status = clSetKernelArg(kernel, 3, sizeof(cl_mem), &input);
status = clSetKernelArg(kernel, 4, sizeof(cl_mem), &filter);
status = clSetKernelArg(kernel, 5, sizeof(cl_mem), &biase);
status = clSetKernelArg(kernel, 6, sizeof(cl_mem), &output);
status = clSetKernelArg(kernel, 7, sizeof(int), &stride);
status = clSetKernelArg(kernel, 8, sizeof(int), &offset);
status = clSetKernelArg(kernel, 9, sizeof(int), &input_c);
status = clSetKernelArg(kernel, 10, sizeof(int), &dilation);
status = clSetKernelArg(kernel, 11, sizeof(int), &input_width);
status = clSetKernelArg(kernel, 12, sizeof(int), &input_height);
status = clSetKernelArg(kernel, 13, sizeof(int), &output_width);
status = clSetKernelArg(kernel, 14, sizeof(int), &output_height);
CL_CHECK_ERRORS(status);
status =
clEnqueueNDRangeKernel(this->cl_helper_.CLCommandQueue(), kernel, 3, NULL,
default_work_size.data(), NULL, 0, NULL, NULL);
CL_CHECK_ERRORS(status);
}
template class ConvAddKernel<GPU_CL, float>;
......
src/operators/kernel/cl/conv_kernel.cpp
浏览文件 @ 6ab45d2d
......@@ -21,63 +21,69 @@ namespace operators {
template <>
bool ConvKernel<GPU_CL, float>::Init(ConvParam<GPU_CL> *param) {
// PADDLE_MOBILE_ENFORCE(
// param->Filter()->dims()[2] == param->Filter()->dims()[3] &&
// param->Paddings()[0] == param->Paddings()[1],
// "need equal");
// int offset = static_cast<int>(param->Filter()->dims()[2]) / 2 -
// static_cast<int>(param->Paddings()[1]);
// param->SetOffset(offset);
//
// if (param->Filter()->WidthOfOneBlock() == 1 &&
// param->Filter()->HeightOfOneBlock() == 1) {
// this->cl_helper_.AddKernel("conv_1x1", "conv_add_bn_relu_kernel.cl");
// } else if (param->Filter()->dims()[1] == 1) {
// this->cl_helper_.AddKernel("depth_conv_3x3",
// "conv_add_bn_relu_kernel.cl");
// } else if (param->Filter()->WidthOfOneBlock() == 3 &&
// param->Filter()->HeightOfOneBlock() == 3) {
// this->cl_helper_.AddKernel("conv_3x3", "conv_add_bn_relu_kernel.cl");
// } else {
// PADDLE_MOBILE_THROW_EXCEPTION(" not support ");
// }
PADDLE_MOBILE_ENFORCE(
param->Filter()->dims()[2] == param->Filter()->dims()[3] &&
param->Paddings()[0] == param->Paddings()[1],
"need equal");
int offset = static_cast<int>(param->Filter()->dims()[2]) / 2 -
static_cast<int>(param->Paddings()[1]);
param->SetOffset(offset);
if (param->Filter()->WidthOfOneBlock() == 1 &&
param->Filter()->HeightOfOneBlock() == 1) {
this->cl_helper_.AddKernel("conv_1x1", "conv_add_bn_relu_kernel.cl");
} else if (param->Filter()->dims()[1] == 1) {
this->cl_helper_.AddKernel("depth_conv_3x3", "conv_add_bn_relu_kernel.cl");
} else if (param->Filter()->WidthOfOneBlock() == 3 &&
param->Filter()->HeightOfOneBlock() == 3) {
this->cl_helper_.AddKernel("conv_3x3", "conv_add_bn_relu_kernel.cl");
} else {
PADDLE_MOBILE_THROW_EXCEPTION(" not support ");
}
return true;
}
template <>
void ConvKernel<GPU_CL, float>::Compute(const ConvParam<GPU_CL> &param) {
// auto kernel = this->cl_helper_.KernelAt(0);
// auto default_work_size =
// this->cl_helper_.DefaultWorkSize(*param.Output()); int c_block =
// default_work_size[0]; int w = default_work_size[1]; int nh =
// default_work_size[2]; auto input = param.Input()->GetCLImage(); auto
// filter = param.Filter()->GetCLImage(); auto output = param.Output(); int
// stride = param.Strides()[0]; int offset = param.Offset(); int input_c =
// param.Input()->CBlock(); int dilation = param.Dilations()[0]; int
// input_width = param.Input()->WidthOfOneBlock(); int input_height =
// param.Input()->HeightOfOneBlock();
//
// clSetKernelArg(kernel, 0, sizeof(int), &c_block);
// clSetKernelArg(kernel, 1, sizeof(int), &w);
// clSetKernelArg(kernel, 2, sizeof(int), &nh);
// clSetKernelArg(kernel, 3, sizeof(cl_mem), &input);
// clSetKernelArg(kernel, 4, sizeof(cl_mem), &filter);
// clSetKernelArg(kernel, 5, sizeof(cl_mem), &output);
// clSetKernelArg(kernel, 6, sizeof(int), &stride);
// clSetKernelArg(kernel, 7, sizeof(int), &offset);
// clSetKernelArg(kernel, 8, sizeof(int), &input_c);
// clSetKernelArg(kernel, 9, sizeof(int), &dilation);
// clSetKernelArg(kernel, 10, sizeof(int), &input_width);
// clSetKernelArg(kernel, 11, sizeof(int), &input_height);
//
// clEnqueueNDRangeKernel(this->cl_helper_.CLCommandQueue(), kernel, 3, NULL,
// default_work_size.data(), NULL, 0, NULL, NULL);
// auto kernel = this->cl_helper_.KernelAt(0);
// size_t global_work_size[3] = {1, 2, 3};
// clEnqueueNDRangeKernel(this->cl_helper_.CLCommandQueue(), kernel, 3, NULL,
// global_work_size, NULL, 0, NULL, NULL);
auto kernel = this->cl_helper_.KernelAt(0);
auto default_work_size = this->cl_helper_.DefaultWorkSize(*param.Output());
int c_block = default_work_size[0];
int w = default_work_size[1];
int nh = default_work_size[2];
auto input = param.Input()->GetCLImage();
auto filter = param.Filter()->GetCLImage();
auto output = param.Output();
int stride = param.Strides()[0];
int offset = param.Offset();
int input_c = param.Input()->CBlock();
int dilation = param.Dilations()[0];
int input_width = param.Input()->WidthOfOneBlock();
int input_height = param.Input()->HeightOfOneBlock();
cl_int status;
status = clSetKernelArg(kernel, 0, sizeof(int), &c_block);
status = clSetKernelArg(kernel, 1, sizeof(int), &w);
status = clSetKernelArg(kernel, 2, sizeof(int), &nh);
status = clSetKernelArg(kernel, 3, sizeof(cl_mem), &input);
status = clSetKernelArg(kernel, 4, sizeof(cl_mem), &filter);
status = clSetKernelArg(kernel, 5, sizeof(cl_mem), &output);
status = clSetKernelArg(kernel, 6, sizeof(int), &stride);
status = clSetKernelArg(kernel, 7, sizeof(int), &offset);
status = clSetKernelArg(kernel, 8, sizeof(int), &input_c);
status = clSetKernelArg(kernel, 9, sizeof(int), &dilation);
status = clSetKernelArg(kernel, 10, sizeof(int), &input_width);
status = clSetKernelArg(kernel, 11, sizeof(int), &input_height);
CL_CHECK_ERRORS(status);
status =
clEnqueueNDRangeKernel(this->cl_helper_.CLCommandQueue(), kernel, 3, NULL,
default_work_size.data(), NULL, 0, NULL, NULL);
CL_CHECK_ERRORS(status);
}
template class ConvKernel<GPU_CL, float>;
......
src/operators/kernel/cl/depthwise_conv_kernel.cpp
浏览文件 @ 6ab45d2d
......@@ -55,23 +55,30 @@ void DepthwiseConvKernel<GPU_CL, float>::Compute(
int output_width = param.Output()->WidthOfOneBlock();
int output_height = param.Output()->HeightOfOneBlock();
clSetKernelArg(kernel, 0, sizeof(int), &c_block);
clSetKernelArg(kernel, 1, sizeof(int), &w);
clSetKernelArg(kernel, 2, sizeof(int), &nh);
clSetKernelArg(kernel, 3, sizeof(cl_mem), &input);
clSetKernelArg(kernel, 4, sizeof(cl_mem), &filter);
clSetKernelArg(kernel, 5, sizeof(cl_mem), &output);
clSetKernelArg(kernel, 6, sizeof(int), &stride);
clSetKernelArg(kernel, 7, sizeof(int), &offset);
clSetKernelArg(kernel, 8, sizeof(int), &input_c);
clSetKernelArg(kernel, 9, sizeof(int), &dilation);
clSetKernelArg(kernel, 10, sizeof(int), &input_width);
clSetKernelArg(kernel, 11, sizeof(int), &input_height);
clSetKernelArg(kernel, 12, sizeof(int), &output_width);
clSetKernelArg(kernel, 13, sizeof(int), &output_height);
cl_int status;
status = clSetKernelArg(kernel, 0, sizeof(int), &c_block);
status = clSetKernelArg(kernel, 1, sizeof(int), &w);
status = clSetKernelArg(kernel, 2, sizeof(int), &nh);
status = clSetKernelArg(kernel, 3, sizeof(cl_mem), &input);
status = clSetKernelArg(kernel, 4, sizeof(cl_mem), &filter);
status = clSetKernelArg(kernel, 5, sizeof(cl_mem), &output);
status = clSetKernelArg(kernel, 6, sizeof(int), &stride);
status = clSetKernelArg(kernel, 7, sizeof(int), &offset);
status = clSetKernelArg(kernel, 8, sizeof(int), &input_c);
status = clSetKernelArg(kernel, 9, sizeof(int), &dilation);
status = clSetKernelArg(kernel, 10, sizeof(int), &input_width);
status = clSetKernelArg(kernel, 11, sizeof(int), &input_height);
status = clSetKernelArg(kernel, 12, sizeof(int), &output_width);
status = clSetKernelArg(kernel, 13, sizeof(int), &output_height);
CL_CHECK_ERRORS(status);
status =
clEnqueueNDRangeKernel(this->cl_helper_.CLCommandQueue(), kernel, 3, NULL,
default_work_size.data(), NULL, 0, NULL, NULL);
CL_CHECK_ERRORS(status);
}
template class DepthwiseConvKernel<GPU_CL, float>;
......
src/operators/kernel/cl/feed_kernel.cpp
浏览文件 @ 6ab45d2d
......@@ -27,6 +27,7 @@ bool FeedKernel<GPU_CL, float>::Init(FeedParam<GPU_CL> *param) {
template <>
void FeedKernel<GPU_CL, float>::Compute(const FeedParam<GPU_CL> &param) {
<<<<<<< HEAD
auto kernel = this->cl_helper_.KernelAt(0);
cl_int status;
auto output = param.Out();
......@@ -38,6 +39,19 @@ void FeedKernel<GPU_CL, float>::Compute(const FeedParam<GPU_CL> &param) {
int height = output->dims()[2];
int width = output->dims()[3];
DLOG << output->dims();
=======
DLOG << "feed_kernel";
auto kernel = this->cl_helper_.KernelAt(0);
cl_int status;
auto output = param.Out();
auto input = param.InputX();
DLOG << " input: " << input;
const float *input_data = input->data<float>();
cl_mem cl_image = output->GetCLImage();
int height = output->dims()[2];
int width = output->dims()[3];
>>>>>>> df230944d11f0f09aea4c2c6bc0489d8667fa8ca
status = clSetKernelArg(kernel, 0, sizeof(cl_mem), &input_data);
status = clSetKernelArg(kernel, 0, sizeof(cl_mem), &cl_image);
status = clSetKernelArg(kernel, 0, sizeof(cl_mem), &width);
......
src/operators/kernel/cl/fetch_kernel.cpp
浏览文件 @ 6ab45d2d
......@@ -19,11 +19,45 @@ namespace operators {
template <>
bool FetchKernel<GPU_CL, float>::Init(FetchParam<GPU_CL> *param) {
this->cl_helper_.AddKernel("fetch", "fetch_kernel.cl");
return true;
}
template <>
void FetchKernel<GPU_CL, float>::Compute(const FetchParam<GPU_CL> &param) {}
void FetchKernel<GPU_CL, float>::Compute(const FetchParam<GPU_CL> &param) {
auto kernel = this->cl_helper_.KernelAt(0);
auto default_work_size = this->cl_helper_.DefaultWorkSize(*param.InputX());
auto input = param.InputX()->GetCLImage();
auto *out = param.Out();
const auto &dims = param.InputX()->dims();
const int N = dims[0];
const int C = dims[1];
const int in_height = dims[2];
const int in_width = dims[3];
int size_ch = in_height * in_width;
int size_block = size_ch * 4;
int size_batch = size_ch * C;
// need create outputBuffer
cl_image_format imageFormat;
imageFormat.image_channel_order = CL_RGBA;
imageFormat.image_channel_data_type = CL_FLOAT;
cl_mem outputBuffer;
clSetKernelArg(kernel, 0, sizeof(int), &in_height);
clSetKernelArg(kernel, 1, sizeof(int), &in_width);
clSetKernelArg(kernel, 2, sizeof(int), &size_ch);
clSetKernelArg(kernel, 3, sizeof(int), &size_block);
clSetKernelArg(kernel, 4, sizeof(int), &size_batch);
clSetKernelArg(kernel, 5, sizeof(cl_mem), &input);
clSetKernelArg(kernel, 6, sizeof(cl_mem), &outputBuffer);
clEnqueueNDRangeKernel(this->cl_helper_.CLCommandQueue(), kernel, 3, NULL,
default_work_size.data(), NULL, 0, NULL, NULL);
}
template class FetchKernel<GPU_CL, float>;
......
src/operators/kernel/cl/pool_kernel.cpp
浏览文件 @ 6ab45d2d
......@@ -21,11 +21,51 @@ namespace operators {
template <>
bool PoolKernel<GPU_CL, float>::Init(PoolParam<GPU_CL> *param) {
std::string pooling_type = param->PoolingType();
this->cl_helper_.AddKernel("pool_" + pooling_type, "pool_kernel.cl");
return true;
}
template <>
void PoolKernel<GPU_CL, float>::Compute(const PoolParam<GPU_CL> &param) {}
void PoolKernel<GPU_CL, float>::Compute(const PoolParam<GPU_CL> &param) {
auto kernel = this->cl_helper_.KernelAt(0);
auto default_work_size = this->cl_helper_.DefaultWorkSize(*param.Output());
auto input = param.Input()->GetCLImage();
auto out = param.Output()->GetCLImage();
const int in_height = param.Input()->HeightOfOneBlock();
const int in_width = param.Input()->WidthOfOneBlock();
const int out_height = param.Output()->HeightOfOneBlock();
const int out_width = param.Output()->WidthOfOneBlock();
std::string pooling_type = param.PoolingType();
std::vector<int> ksize = param.Ksize();
std::vector<int> strides = param.Strides();
std::vector<int> paddings = param.Paddings();
const int pad_top = paddings[0];
const int pad_left = paddings[1];
const int stride_h = strides[0];
const int stride_w = strides[1];
const int ksize_h = ksize[0];
const int ksize_w = ksize[1];
clSetKernelArg(kernel, 0, sizeof(cl_int), &in_height);
clSetKernelArg(kernel, 1, sizeof(cl_int), &in_width);
clSetKernelArg(kernel, 2, sizeof(cl_int), &out_height);
clSetKernelArg(kernel, 3, sizeof(cl_int), &out_width);
clSetKernelArg(kernel, 4, sizeof(cl_int), &pad_top);
clSetKernelArg(kernel, 5, sizeof(cl_int), &pad_left);
clSetKernelArg(kernel, 6, sizeof(cl_int), &stride_h);
clSetKernelArg(kernel, 7, sizeof(cl_int), &stride_w);
clSetKernelArg(kernel, 8, sizeof(cl_int), &ksize_h);
clSetKernelArg(kernel, 9, sizeof(cl_int), &ksize_w);
clSetKernelArg(kernel, 10, sizeof(cl_mem), &input);
clSetKernelArg(kernel, 11, sizeof(cl_mem), &out);
clEnqueueNDRangeKernel(this->cl_helper_.CLCommandQueue(), kernel, 3, NULL,
default_work_size.data(), NULL, 0, NULL, NULL);
}
template class PoolKernel<GPU_CL, float>;
......
src/operators/kernel/cl/relu_kernel.cpp
浏览文件 @ 6ab45d2d
......@@ -11,6 +11,7 @@ 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 RELU_OP
#include "operators/kernel/relu_kernel.h"
......@@ -18,14 +19,28 @@ namespace paddle_mobile {
namespace operators {
template <>
bool ReluKernel<GPU_CL, float>::Init(ReluParam<GPU_CL> *param) {
bool ReluKernel<GPU_CL, float>::Init(ReluParam<GPU_CL>* param) {
this->cl_helper_.AddKernel("relu", "relu.cl");
return true;
}
template <>
void ReluKernel<GPU_CL, float>::Compute(const ReluParam<GPU_CL> &param) {}
void ReluKernel<GPU_CL, float>::Compute(const ReluParam<GPU_CL>& param) {
auto kernel = this->cl_helper_.KernelAt(0);
const auto* input = param.InputX();
auto* output = param.Out();
auto default_work_size = this->cl_helper_.DefaultWorkSize(*output);
auto inputImage = input->GetCLImage();
auto outputImage = output->GetCLImage();
clSetKernelArg(kernel, 0, sizeof(cl_mem), &inputImage);
clSetKernelArg(kernel, 1, sizeof(cl_mem), &outputImage);
const size_t work_size[2] = {input->ImageWidth(), input->ImageHeight()};
clEnqueueNDRangeKernel(this->cl_helper_.CLCommandQueue(), kernel, 3, NULL,
work_size, NULL, 0, NULL, NULL);
}
template class ReluKernel<GPU_CL, float>;
} // namespace operators
} // namespace paddle_mobile
#endif
src/operators/kernel/cl/reshape_kernel.cpp
浏览文件 @ 6ab45d2d
......@@ -19,11 +19,36 @@ namespace operators {
template <>
bool ReshapeKernel<GPU_CL, float>::Init(ReshapeParam<GPU_CL> *param) {
this->cl_helper_.AddKernel("reshape", "reshape.cl");
return true;
}
template <>
void ReshapeKernel<GPU_CL, float>::Compute(const ReshapeParam<GPU_CL> &param) {}
void ReshapeKernel<GPU_CL, float>::Compute(const ReshapeParam<GPU_CL> &param) {
auto kernel = this->cl_helper_.KernelAt(0);
const auto *input = param.InputX();
auto *output = param.Out();
auto inputImage = input->GetCLImage();
auto outputImage = output->GetCLImage();
clSetKernelArg(kernel, 0, sizeof(cl_mem), &inputImage);
clSetKernelArg(kernel, 1, sizeof(cl_mem), &outputImage);
const auto &inputDim = input->dims();
const auto &outputDim = output->dims();
int dims[4] = {inputDim[0], inputDim[1], inputDim[2], inputDim[3]};
int odims[4] = {outputDim[0], outputDim[1], outputDim[2], outputDim[3]};
clSetKernelArg(kernel, 2, sizeof(int), dims);
clSetKernelArg(kernel, 3, sizeof(int), dims + 1);
clSetKernelArg(kernel, 4, sizeof(int), dims + 2);
clSetKernelArg(kernel, 5, sizeof(int), dims + 3);
clSetKernelArg(kernel, 6, sizeof(int), odims);
clSetKernelArg(kernel, 7, sizeof(int), odims + 1);
clSetKernelArg(kernel, 8, sizeof(int), odims + 2);
clSetKernelArg(kernel, 9, sizeof(int), odims + 3);
const size_t work_size[2] = {output->ImageWidth(), output->ImageHeight()};
clEnqueueNDRangeKernel(this->cl_helper_.CLCommandQueue(), kernel, 2, NULL,
work_size, NULL, 0, NULL, NULL);
}
template class ReshapeKernel<GPU_CL, float>;
......
src/operators/kernel/cl/softmax_kernel.cpp
浏览文件 @ 6ab45d2d
......@@ -21,11 +21,30 @@ namespace operators {
template <>
bool SoftmaxKernel<GPU_CL, float>::Init(SoftmaxParam<GPU_CL> *param) {
this->cl_helper_.AddKernel("softmax", "softmax.cl");
return true;
}
template <>
void SoftmaxKernel<GPU_CL, float>::Compute(const SoftmaxParam<GPU_CL> &param) {}
void SoftmaxKernel<GPU_CL, float>::Compute(const SoftmaxParam<GPU_CL> &param) {
auto kernel = this->cl_helper_.KernelAt(0);
auto default_work_size = this->cl_helper_.DefaultWorkSize(*(param.Out()));
const auto *input = param.InputX();
auto *output = param.Out();
auto inputImage = input->GetCLImage();
auto outputImage = output->GetCLImage();
clSetKernelArg(kernel, 0, sizeof(cl_mem), &inputImage);
clSetKernelArg(kernel, 1, sizeof(cl_mem), &outputImage);
const auto &inputDim = input->dims();
int dims[4] = {inputDim[0], inputDim[1], inputDim[2], inputDim[3]};
clSetKernelArg(kernel, 2, sizeof(int), dims);
clSetKernelArg(kernel, 3, sizeof(int), dims + 1);
clSetKernelArg(kernel, 4, sizeof(int), dims + 2);
clSetKernelArg(kernel, 5, sizeof(int), dims + 3);
clEnqueueNDRangeKernel(this->cl_helper_.CLCommandQueue(), kernel, 3, NULL,
default_work_size.data(), NULL, 0, NULL, NULL);
}
template class SoftmaxKernel<GPU_CL, float>;
......
src/operators/op_param.h
浏览文件 @ 6ab45d2d
......@@ -614,6 +614,14 @@ class BatchNormParam : OpParam {
const string &DataFormat() const { return data_format_; }
void SetNewScale(RType *new_scale) { new_scale_ = new_scale; }
void SetNewBias(RType *new_bias) { new_bias_ = new_bias; }
const RType *NewScale() const { return new_scale_; }
const RType *NewBias() const { return new_bias_; }
private:
RType *input_x_;
RType *output_y_;
......@@ -625,6 +633,8 @@ class BatchNormParam : OpParam {
float momentum_;
bool is_test_;
string data_format_;
RType *new_bias_;
RType *new_scale_;
};
#endif
......@@ -936,10 +946,21 @@ class FetchParam : public OpParam {
FetchParam(const VariableNameMap &inputs, const VariableNameMap &outputs,
const AttributeMap &attrs, const Scope &scope) {
input_x_ = InputXFrom<GType>(inputs, scope);
<<<<<<< HEAD
out_ = OutFrom<LoDTensor>(outputs, scope);
}
const RType *InputX() const { return input_x_; }
Tensor *Out() const { return out_; }
=======
out_ = OutFrom(outputs, scope);
}
const RType *InputX() const { return input_x_; }
Tensor *Out() const { return out_; }
static Tensor *OutFrom(const VariableNameMap &outputs, const Scope &scope) {
return GetVarValue<Tensor>("Out", outputs, scope);
}
>>>>>>> df230944d11f0f09aea4c2c6bc0489d8667fa8ca
private:
RType *input_x_;
......
test/net/test_googlenet.cpp
浏览文件 @ 6ab45d2d
......@@ -29,8 +29,8 @@ int main() {
bool optimize = true;
auto time1 = time();
if (paddle_mobile.Load(g_googlenet, optimize)) {
auto time2 = time();
std::cout << "load cost :" << time_diff(time1, time2) << "ms" << std::endl;
auto time2 = paddle_mobile::time();
std::cout << "load cost :" << paddle_mobile::time_diff(time1, time2) << "ms" << std::endl;
std::vector<float> input;
std::vector<int64_t> dims{1, 3, 224, 224};
GetInput<float>(g_test_image_1x3x224x224, &input, dims);
......
test/net/test_mobilenet_GPU.cpp
浏览文件 @ 6ab45d2d
......@@ -19,14 +19,14 @@ limitations under the License. */
int main() {
paddle_mobile::PaddleMobile<paddle_mobile::GPU_CL> paddle_mobile;
// paddle_mobile.SetThreadNum(4);
auto time1 = time();
auto time1 = paddle_mobile::time();
// auto isok = paddle_mobile.Load(std::string(g_mobilenet_detect) + "/model",
// std::string(g_mobilenet_detect) + "/params", true);
auto isok = paddle_mobile.Load(g_mobilenet, false);
if (isok) {
auto time2 = time();
std::cout << "load cost :" << time_diff(time1, time1) << "ms" << std::endl;
auto time2 = paddle_mobile::time();
std::cout << "load cost :" << paddle_mobile::time_diff(time1, time1) << "ms" << std::endl;
std::vector<float> input;
std::vector<int64_t> dims{1, 3, 224, 224};
......@@ -42,13 +42,13 @@ int main() {
for (int i = 0; i < 10; ++i) {
auto vec_result = paddle_mobile.Predict(input, dims);
}
auto time3 = time();
auto time3 = paddle_mobile::time();
for (int i = 0; i < 10; ++i) {
auto vec_result = paddle_mobile.Predict(input, dims);
}
DLOG << vec_result;
auto time4 = time();
std::cout << "predict cost :" << time_diff(time3, time4) / 10 << "ms"
auto time4 = paddle_mobile::time();
std::cout << "predict cost :" << paddle_mobile::time_diff(time3, time4) / 10 << "ms"
<< std::endl;
}
......
tools/web-exporter/CMakeLists.txt 0 → 100644
浏览文件 @ 6ab45d2d
cmake_minimum_required(VERSION 3.6)
project(web-exporter)
set(CMAKE_CXX_STANDARD 11)
file(GLOB PADDLE_MOBILE_CPP_FILES
"../../src/common/*.c"
"../../src/common/*.cpp"
"../../src/memory/*.cpp"
"../../src/framework/*.c"
"../../src/framework/*.cpp"
"../../src/framework/program/*.cpp"
"../../src/framework/program/program-optimize/*.cpp"
)
file(GLOB EXPORT_CPP_FILES "*.cpp")
add_executable(web-exporter ${PADDLE_MOBILE_CPP_FILES} ${EXPORT_CPP_FILES})
target_include_directories(web-exporter PRIVATE "../../src")
target_link_libraries(web-exporter)
\ No newline at end of file
tools/web-exporter/export-nodejs.cpp 0 → 100644
浏览文件 @ 6ab45d2d
#include "export.h"
inline std::string indent(int i) {
return std::string(i, ' ');
}
void export_nodejs(ProgramPtr program, ScopePtr scope, std::ostream & os) {
os << "module.exports.program = {\n";
os << indent(2) << var2str("blocks") << ": [\n";
for (const auto& block: program->Blocks()) {
os << indent(4) << "{\n";
os << indent(6) << var2str("vars") << ": {\n";
for (const auto& var: block->Vars()) {
const auto& dim = var->Tensor_desc().Dims();
os << indent(8) << var2str(var->Name()) << ": {\n";
os << indent(10) << var2str("dim") << ": " << var2str(dim) << ",\n";
os << indent(10) << var2str("persistable") << ": " << var2str(var->Persistable()) << "\n";
os << indent(8) << "},\n";
}
os << indent(6) << "},\n";
os << indent(6) << var2str("ops") << ": [\n";
for (const auto& op: block->Ops()) {
os << indent(8) << "{\n";
os << indent(10) << var2str("type") << ": " << var2str(op->Type()) << ",\n";
os << indent(10) << var2str("inputs") << ": {\n";
for (const auto& kv: op->GetInputs()) {
os << indent(12) << var2str(kv.first) << ": " << var2str(kv.second) << ",\n";
}
os << indent(10) << "},\n";
os << indent(10) << var2str("outputs") << ": {\n";
for (const auto& kv: op->GetInputs()) {
os << indent(12) << var2str(kv.first) << ": " << var2str(kv.second) << ",\n";
}
os << indent(10) << "},\n";
os << indent(10) << var2str("attrs") << ": {\n";
for (const auto& kv: op->GetAttrMap()) {
os << indent(12) << var2str(kv.first) << ": ";
os << decltype(kv.second)::ApplyVistor(VarVisitor(), kv.second) << ",\n";
}
os << indent(10) << "},\n";
os << indent(8) << "},\n";
}
os << indent(6) << "],\n";
os << indent(4) << "},\n";
}
os << indent(2) << "]\n";
os << "}\n";
}
tools/web-exporter/export-scope.cpp 0 → 100644
浏览文件 @ 6ab45d2d
#include <cstdio>
#include "export.h"
void export_scope(ProgramPtr program, ScopePtr scope, const std::string & dirname) {
for (const auto& block: program->Blocks()) {
for (const auto& var: block->Vars()) {
if (var->Name() == "feed" || var->Name() == "fetch") {
continue;
}
if (var->Persistable()) {
auto* v = scope->FindVar(var->Name());
assert(v != nullptr);
int count = 1;
for (auto n: var->Tensor_desc().Dims()) {
count *= n;
}
auto* tensor = v->GetMutable<paddle_mobile::framework::LoDTensor>();
const float * p = tensor->mutable_data<float>();
std::string para_file_name = dirname + '/' + var->Name();
FILE *para_file = fopen(para_file_name.c_str(), "w");
assert(p != nullptr);
fwrite(p, sizeof(float), count, para_file);
fclose(para_file);
// std::cout << "==> " << var->Name() << " " << count << "\n";
// for (int i = 0; i < count; i++) {
// std::cout << p[i] << ", ";
// }
// std::cout << "\n";
}
}
}
}
tools/web-exporter/export.cpp 0 → 100644
浏览文件 @ 6ab45d2d
#include "export.h"
#include <sys/stat.h>
#include <sys/types.h>
class FakeExecutor : public paddle_mobile::framework::Executor<paddle_mobile::CPU, paddle_mobile::Precision::FP32> {
public:
FakeExecutor(const paddle_mobile::framework::Program<paddle_mobile::CPU> p) {
program_ = p;
batch_size_ = 1;
use_optimize_ = true;
loddable_ = false;
if (use_optimize_) {
to_predict_program_ = program_.optimizeProgram;
} else {
to_predict_program_ = program_.originProgram;
}
auto *variable_ptr = program_.scope->Var("batch_size");
variable_ptr[0].SetValue<int>(1);
if (program_.combined) {
InitCombineMemory();
} else {
InitMemory();
}
}
};
int main(int argc, char** argv) {
if (argc != 3) {
std::cout << "Usage: " << argv[0] << " <combined-modle-dir> <output-dir>\n";
return -1;
}
std::string model_dir = argv[1];
std::string model_path = model_dir + "/model";
std::string para_path = model_dir + "/params";
std::string out_dir = argv[2];
std::string out_model_js = out_dir + "/model.js";
std::string out_para_dir = out_dir + "/paras";
mkdir(out_dir.c_str(), S_IRWXU|S_IRWXG|S_IRWXO);
mkdir(out_para_dir.c_str(), S_IRWXU|S_IRWXG|S_IRWXO);
std::cout << "loading " << model_path << " & " << para_path << "\n";
paddle_mobile::framework::Loader<> loader;
auto program = loader.Load(model_path, para_path, true);
FakeExecutor executor(program);
auto optimizedProgram = program.optimizeProgram;
export_scope(optimizedProgram, program.scope, out_para_dir);
std::ofstream fs(out_model_js.c_str());
export_nodejs(optimizedProgram, program.scope, fs);
fs.close();
return 0;
}
tools/web-exporter/export.h 0 → 100644
浏览文件 @ 6ab45d2d
#pragma once
#include <iostream>
#include <vector>
#include <memory>
#include <string>
#include <ostream>
#include <fstream>
#include "framework/loader.h"
#include "framework/executor.h"
#include "framework/scope.h"
#include "framework/program/program_desc.h"
// using paddle_mobile::framework::ProgramDesc;
// using paddle_mobile::framework::Scope;
using ProgramPtr = std::shared_ptr<paddle_mobile::framework::ProgramDesc>;
using ScopePtr = std::shared_ptr<paddle_mobile::framework::Scope>;
void export_nodejs(ProgramPtr program, ScopePtr scope, std::ostream & os = std::cout);
void export_scope(ProgramPtr program, ScopePtr scope, const std::string & dirname = ".");
template <typename T>
inline std::string var2str(const T & v) {
return std::to_string(v);
}
template <>
inline std::string var2str(const std::string & v) {
return "\"" + v + "\"";
}
inline std::string var2str(const char* v) {
return var2str<std::string>(v);
}
inline std::string var2str(const bool v) {
return v ? "true" : "false";
}
template <typename T>
std::string var2str(const std::vector<T> & v) {
std::string r = "[";
auto s = v.size();
for (int i = 0; i < s; i++) {
if (i) r += ", ";
r += var2str(v[i]);
}
return r + "]";
}
struct VarVisitor {
using type_t = decltype(var2str(0));
template <typename T>
type_t operator()(const T & v) {
return var2str(v);
}
};
\ No newline at end of file
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