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[Inference] Update go inference api based on new capi. (#33113)

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go/README_cn.md 已删除 100644 → 0
浏览文件 @ ab0272eb
# Paddle 预测golang API
## 安装
首先cmake编译时打开`-DON_INFER=ON`,在编译目录下得到``paddle_inference_c_install_dir``,将该目录移动到当前目录中并重命名为`paddle_c`
## 在Go中使用Paddle预测
首先创建预测配置
``` go
config := paddle.NewAnalysisConfig()
config.SetModel(model_file, params_file)
config.SwitchUseFeedFetchOps(false)
config.SwitchSpecifyInputNames(true)
```
创建predictor
``` go
predictor := paddle.NewPredictor(config)
```
获取输入Tensor和输出Tensor
``` go
inputs = predictor.GetInputTensors()
```
设置输入数据(假设只有一个输入)
``` go
input := inputs[0]
input.SetValue(data)
input.Reshape([]int32{1, 3, 300, 300})
```
运行预测
``` go
predictor.ZeroCopyRun()
```
获取输入Tensor的真实值
``` go
output := outputs[0]
predictor.GetZeroCopyOutput(output)
value := reflect.ValueOf(output.Value())
shape, dtype := paddle.ShapeAndTypeOf(value)
output_data := value.Interface().([][]float32)
```
## 示例
源码见[mobilenet](./demo/mobilenet.go)
下载[数据](https://paddle-inference-dist.cdn.bcebos.com/mobilenet-test-model-data.tar.gz)并解压到当前目录
运行
```bash
go mod init github.com/paddlepaddle
export LD_LIBRARY_PATH=`pwd`/paddle_c/paddle/lib:$LD_LIBRARY_PATH
go run ./demo/mobilenet.go
```
go/demo/mobilenet.go 已删除 100644 → 0
浏览文件 @ ab0272eb
// Copyright (c) 2019 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.
package main
import "github.com/paddlepaddle/paddle"
import "strings"
import "io/ioutil"
import "strconv"
import "reflect"
func main() {
config := paddle.NewAnalysisConfig()
config.SetModel("data/model/__model__", "data/model/__params__")
config.DisableGlogInfo()
config.SwitchUseFeedFetchOps(false)
config.SwitchSpecifyInputNames(true)
predictor := paddle.NewPredictor(config)
println("============== paddle inference ==============")
println("input num: ", predictor.GetInputNum())
println("input name: ", predictor.GetInputNames()[0])
println("output num: ", predictor.GetOutputNum())
println("output name: ", predictor.GetInputNames()[0])
println("============== run inference =================")
input := predictor.GetInputTensors()[0]
output := predictor.GetOutputTensors()[0]
filename := "data/data.txt"
data := ReadData(filename)
input.SetValue(data[:1 * 3 * 300 * 300])
input.Reshape([]int32{1, 3, 300, 300})
predictor.SetZeroCopyInput(input)
predictor.ZeroCopyRun()
predictor.GetZeroCopyOutput(output)
println("============= parse output ===================")
output_val := output.Value()
value := reflect.ValueOf(output_val)
shape, dtype := paddle.ShapeAndTypeOf(value)
switch dtype {
case paddle.PaddleDType(paddle.FLOAT32):
v := value.Interface().([][]float32)
println("v: ", v[0][0], v[0][1], "...")
case paddle.PaddleDType(paddle.UINT8):
v := value.Interface().([][]uint8)
println("v: ", v[0][0], v[0][1], "...")
case paddle.PaddleDType(paddle.INT32):
v := value.Interface().([][]int32)
println("v: ", v[0][0], v[0][1], "...")
case paddle.PaddleDType(paddle.INT64):
v := value.Interface().([][]int64)
println("v: ", v[0][0], v[0][1], "...")
}
println(shape[0], shape[1])
println(output.Shape()[0])
}
func ReadData(filename string) []float32 {
file_bytes, _ := ioutil.ReadFile(filename)
data_slice := strings.Split(string(file_bytes), " ")
var result []float32
for _, n := range data_slice {
r, _ := strconv.ParseFloat(n, 32)
result = append(result, float32(r))
}
return result
}
go/demo/mobilenet_c.cc 已删除 100644 → 0
浏览文件 @ ab0272eb
// Copyright (c) 2019 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 <paddle_c_api.h>
#include <stdio.h>
#include <stdlib.h>
void SetConfig(PD_AnalysisConfig *);
void ReadData(float *data, int size);
int main(int argc, char *argv[]) {
PD_AnalysisConfig *config = PD_NewAnalysisConfig();
SetConfig(config);
PD_Predictor *predictor = PD_NewPredictor(config);
int input_num = PD_GetInputNum(predictor);
printf("Input num: %d\n", input_num);
int output_num = PD_GetOutputNum(predictor);
printf("Output num: %d\n", output_num);
PD_ZeroCopyTensor input;
PD_InitZeroCopyTensor(&input);
input.name = const_cast<char *>(PD_GetInputName(predictor, 0)); // NOLINT
input.data.capacity = sizeof(float) * 1 * 3 * 300 * 300;
input.data.length = input.data.capacity;
input.data.data = malloc(input.data.capacity);
int shape[] = {1, 3, 300, 300};
input.shape.data = static_cast<int *>(shape);
input.shape.capacity = sizeof(shape);
input.shape.length = sizeof(shape);
input.dtype = PD_FLOAT32;
ReadData((float *)input.data.data, 1 * 3 * 300 * 300); // NOLINT
float *data = (float *)input.data.data; // NOLINT
PD_SetZeroCopyInput(predictor, &input);
int *shape_ptr = (int *)input.shape.data; // NOLINT
PD_ZeroCopyRun(predictor);
PD_ZeroCopyTensor output;
PD_InitZeroCopyTensor(&output);
output.name = const_cast<char *>(PD_GetOutputName(predictor, 0)); // NOLINT
PD_GetZeroCopyOutput(predictor, &output);
PD_DestroyZeroCopyTensor(&output);
PD_DeleteAnalysisConfig(config);
PD_DeletePredictor(predictor);
return 0;
}
void SetConfig(PD_AnalysisConfig *config) {
PD_SetModel(config, "data/model/__model__", "data/model/__params__");
PD_SwitchUseFeedFetchOps(config, false);
PD_SwitchSpecifyInputNames(config, true);
PD_DisableGlogInfo(config);
// PD_SwitchIrOptim(config, false);
}
void ReadData(float *data, int n) {
FILE *fp = fopen("data/data.txt", "r");
for (int i = 0; i < n; i++) {
fscanf(fp, "%f", &data[i]);
}
fclose(fp);
}
go/demo/mobilenet_c_exp.cc 已删除 100644 → 0
浏览文件 @ ab0272eb
// Copyright (c) 2019 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 <pd_inference_api.h>
#include <stdio.h>
#include <stdlib.h>
void ReadData(float* data, int size);
int main(int argc, char* argv[]) {
PD_Config* config = PD_ConfigCreate();
PD_ConfigSetModel(config, "data/model/__model__", "data/model/__params__");
PD_ConfigDisableGlogInfo(config);
PD_Predictor* predictor = PD_PredictorCreate(config);
// config has destroyed in PD_PredictorCreate
config = NULL;
int input_num = PD_PredictorGetInputNum(predictor);
printf("Input num: %d\n", input_num);
int output_num = PD_PredictorGetOutputNum(predictor);
printf("Output num: %d\n", output_num);
PD_OneDimArrayCstr* input_names = PD_PredictorGetInputNames(predictor);
PD_Tensor* input_tensor =
PD_PredictorGetInputHandle(predictor, input_names->data[0]);
PD_OneDimArrayCstrDestroy(input_names);
input_names = NULL;
int32_t shape[] = {1, 3, 300, 300};
float* data = (float*)malloc(sizeof(float) * 1 * 3 * 300 * 300); // NOLINT
ReadData(data, 1 * 3 * 300 * 300); // NOLINT
PD_TensorReshape(input_tensor, 4, shape);
PD_TensorCopyFromCpuFloat(input_tensor, data);
free(data);
data = NULL;
PD_PredictorRun(predictor);
PD_OneDimArrayCstr* output_names = PD_PredictorGetOutputNames(predictor);
PD_Tensor* output_tensor =
PD_PredictorGetOutputHandle(predictor, output_names->data[0]);
PD_OneDimArrayCstrDestroy(output_names);
output_names = nullptr;
PD_OneDimArrayInt32* out_shape = PD_TensorGetShape(output_tensor);
int32_t size = 1;
for (size_t index = 0; index < out_shape->size; ++index) {
size = size * out_shape->data[index];
}
PD_OneDimArrayInt32Destroy(out_shape);
out_shape = NULL;
data = (float*)malloc(sizeof(float) * size); // NOLINT
PD_TensorCopyToCpuFloat(output_tensor, data);
free(data);
data = NULL;
PD_TensorDestroy(output_tensor);
output_tensor = NULL;
PD_TensorDestroy(input_tensor);
input_tensor = NULL;
PD_PredictorDestroy(predictor);
predictor = NULL;
return 0;
}
void ReadData(float* data, int n) {
FILE* fp = fopen("data/data.txt", "r");
for (int i = 0; i < n; i++) {
fscanf(fp, "%f", &data[i]);
}
fclose(fp);
}
go/paddle/config.go 已删除 100644 → 0
浏览文件 @ ab0272eb
// Copyright (c) 2019 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.
package paddle
// #cgo CFLAGS: -I${SRCDIR}/../paddle_c/paddle/include
// #cgo LDFLAGS: -L${SRCDIR}/../paddle_c/paddle/lib -lpaddle_inference_c
// #include <stdbool.h>
// #include <stdlib.h>
// #include <paddle_c_api.h>
import "C"
import "runtime"
import "unsafe"
type Precision C.Precision
const (
Precision_FLOAT32 Precision = C.kFloat32
Precision_INT8 Precision = C.kInt8
Precision_HALF Precision = C.kHalf
)
type AnalysisConfig struct {
c *C.PD_AnalysisConfig
}
func NewAnalysisConfig() *AnalysisConfig {
c_config := C.PD_NewAnalysisConfig()
config := &AnalysisConfig{c: c_config}
runtime.SetFinalizer(config, (*AnalysisConfig).finalize)
return config
}
func (config *AnalysisConfig) finalize() {
C.PD_DeleteAnalysisConfig(config.c)
}
func (config *AnalysisConfig) SetModel(model, params string) {
//C.printString((*C.char)(unsafe.Pointer(&s[0])))
c_model := C.CString(model)
defer C.free(unsafe.Pointer(c_model))
var c_params *C.char
if params == "" {
c_params = nil
} else {
c_params = C.CString(params)
defer C.free(unsafe.Pointer(c_params))
}
C.PD_SetModel(config.c, c_model, c_params)
}
func (config *AnalysisConfig) ModelDir() string {
return C.GoString(C.PD_ModelDir(config.c))
}
func (config *AnalysisConfig) ProgFile() string {
return C.GoString(C.PD_ProgFile(config.c))
}
func (config *AnalysisConfig) ParamsFile() string {
return C.GoString(C.PD_ParamsFile(config.c))
}
func (config *AnalysisConfig) EnableUseGpu(memory_pool_init_size_mb int, device_id int) {
C.PD_EnableUseGpu(config.c, C.int(memory_pool_init_size_mb), C.int(device_id))
}
func (config *AnalysisConfig) DisableGpu() {
C.PD_DisableGpu(config.c)
}
func (config *AnalysisConfig) UseGpu() bool {
return ConvertCBooleanToGo(C.PD_UseGpu(config.c))
}
func (config *AnalysisConfig) GpuDeviceId() int {
return int(C.PD_GpuDeviceId(config.c))
}
func (config *AnalysisConfig) MemoryPoolInitSizeMb() int {
return int(C.PD_MemoryPoolInitSizeMb(config.c))
}
func (config *AnalysisConfig) FractionOfGpuMemoryForPool() float32 {
return float32(C.PD_FractionOfGpuMemoryForPool(config.c))
}
func (config *AnalysisConfig) EnableCudnn() {
C.PD_EnableCUDNN(config.c)
}
func (config *AnalysisConfig) CudnnEnabled() bool {
return ConvertCBooleanToGo(C.PD_CudnnEnabled(config.c))
}
func (config *AnalysisConfig) SwitchIrOptim(x bool) {
C.PD_SwitchIrOptim(config.c, C.bool(x))
}
func (config *AnalysisConfig) IrOptim() bool {
return ConvertCBooleanToGo(C.PD_IrOptim(config.c))
}
func (config *AnalysisConfig) SwitchUseFeedFetchOps(x bool) {
C.PD_SwitchUseFeedFetchOps(config.c, C.bool(x))
}
func (config *AnalysisConfig) UseFeedFetchOpsEnabled() bool {
return ConvertCBooleanToGo(C.PD_UseFeedFetchOpsEnabled(config.c))
}
func (config *AnalysisConfig) SwitchSpecifyInputNames(x bool) {
C.PD_SwitchSpecifyInputNames(config.c, C.bool(x))
}
func (config *AnalysisConfig) SpecifyInputName() bool {
return ConvertCBooleanToGo(C.PD_SpecifyInputName(config.c))
}
func (config *AnalysisConfig) EnableTensorRtEngine(workspace_size int, max_batch_size int, min_subgraph_size int, precision Precision, use_static bool, use_calib_mode bool) {
C.PD_EnableTensorRtEngine(config.c, C.int(workspace_size), C.int(max_batch_size), C.int(min_subgraph_size), C.Precision(precision), C.bool(use_static), C.bool(use_calib_mode))
}
func (config *AnalysisConfig) TensorrtEngineEnabled() bool {
return ConvertCBooleanToGo(C.PD_TensorrtEngineEnabled(config.c))
}
func (config *AnalysisConfig) SwitchIrDebug(x bool) {
C.PD_SwitchIrDebug(config.c, C.bool(x))
}
func (config *AnalysisConfig) EnableMkldnn() {
C.PD_EnableMKLDNN(config.c)
}
func (config *AnalysisConfig) MkldnnEnabled() bool {
return ConvertCBooleanToGo(C.PD_MkldnnEnabled(config.c))
}
func (config *AnalysisConfig) SetCpuMathLibraryNumThreads(n int) {
C.PD_SetCpuMathLibraryNumThreads(config.c, C.int(n))
}
func (config *AnalysisConfig) CpuMathLibraryNumThreads() int {
return int(C.PD_CpuMathLibraryNumThreads(config.c))
}
func (config *AnalysisConfig) EnableMkldnnQuantizer() {
C.PD_EnableMkldnnQuantizer(config.c)
}
func (config *AnalysisConfig) EnableMkldnnBfloat16() {
C.PD_EnableMkldnnBfloat16(config.c)
}
func (config *AnalysisConfig) MkldnnQuantizerEnabled() bool {
return ConvertCBooleanToGo(C.PD_MkldnnQuantizerEnabled(config.c))
}
func (config *AnalysisConfig) MkldnnBfloat16Enabled() bool {
return ConvertCBooleanToGo(C.PD_MkldnnBfloat16Enabled(config.c))
}
// SetModelBuffer
// ModelFromMemory
func (config *AnalysisConfig) EnableMemoryOptim() {
C.PD_EnableMemoryOptim(config.c)
}
func (config *AnalysisConfig) MemoryOptimEnabled() bool {
return ConvertCBooleanToGo(C.PD_MemoryOptimEnabled(config.c))
}
func (config *AnalysisConfig) EnableProfile() {
C.PD_EnableProfile(config.c)
}
func (config *AnalysisConfig) ProfileEnabled() bool {
return ConvertCBooleanToGo(C.PD_ProfileEnabled(config.c))
}
func (config *AnalysisConfig) DisableGlogInfo() {
C.PD_DisableGlogInfo(config.c)
}
func (config *AnalysisConfig) DeletePass(pass string) {
c_pass := C.CString(pass)
defer C.free(unsafe.Pointer(c_pass))
C.PD_DeletePass(config.c, c_pass)
}
func (config *AnalysisConfig) SetInValid() {
C.PD_SetInValid(config.c)
}
func (config *AnalysisConfig) IsValid() bool {
return ConvertCBooleanToGo(C.PD_IsValid(config.c))
}
go/paddle/predictor.go 已删除 100644 → 0
浏览文件 @ ab0272eb
// Copyright (c) 2019 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.
package paddle
// #cgo CFLAGS: -I${SRCDIR}/../paddle_c/paddle/include
// #cgo LDFLAGS: -L${SRCDIR}/../paddle_c/paddle/lib -lpaddle_inference_c
// #include <stdbool.h>
// #include "paddle_c_api.h"
import "C"
import "reflect"
import "runtime"
import "unsafe"
type Predictor struct {
c *C.PD_Predictor
}
func NewPredictor(config *AnalysisConfig) *Predictor {
c_predictor := C.PD_NewPredictor((*config).c)
predictor := &Predictor{c: c_predictor}
runtime.SetFinalizer(predictor, (*Predictor).finalize)
return predictor
}
func (predictor *Predictor) finalize() {
C.PD_DeletePredictor(predictor.c)
}
func DeletePredictor(predictor *Predictor) {
C.PD_DeletePredictor(predictor.c)
}
func (predictor *Predictor) GetInputNum() int {
return int(C.PD_GetInputNum(predictor.c))
}
func (predictor *Predictor) GetOutputNum() int {
return int(C.PD_GetOutputNum(predictor.c))
}
func (predictor *Predictor) GetInputName(n int) string {
return C.GoString(C.PD_GetInputName(predictor.c, C.int(n)))
}
func (predictor *Predictor) GetOutputName(n int) string {
return C.GoString(C.PD_GetOutputName(predictor.c, C.int(n)))
}
func (predictor *Predictor) GetInputTensors() [](*ZeroCopyTensor) {
var result [](*ZeroCopyTensor)
for i := 0; i < predictor.GetInputNum(); i++ {
tensor := NewZeroCopyTensor()
tensor.c.name = C.PD_GetInputName(predictor.c, C.int(i))
result = append(result, tensor)
}
return result
}
func (predictor *Predictor) GetOutputTensors() [](*ZeroCopyTensor) {
var result [](*ZeroCopyTensor)
for i := 0; i < predictor.GetOutputNum(); i++ {
tensor := NewZeroCopyTensor()
tensor.c.name = C.PD_GetOutputName(predictor.c, C.int(i))
result = append(result, tensor)
}
return result
}
func (predictor *Predictor) GetInputNames() []string {
names := make([]string, predictor.GetInputNum())
for i := 0; i < len(names); i++ {
names[i] = predictor.GetInputName(i)
}
return names
}
func (predictor *Predictor) GetOutputNames() []string {
names := make([]string, predictor.GetOutputNum())
for i := 0; i < len(names); i++ {
names[i] = predictor.GetOutputName(i)
}
return names
}
func (predictor *Predictor) SetZeroCopyInput(tensor *ZeroCopyTensor) {
C.PD_SetZeroCopyInput(predictor.c, tensor.c)
}
func (predictor *Predictor) GetZeroCopyOutput(tensor *ZeroCopyTensor) {
C.PD_GetZeroCopyOutput(predictor.c, tensor.c)
tensor.name = C.GoString(tensor.c.name)
var shape []int32
shape_hdr := (*reflect.SliceHeader)(unsafe.Pointer(&shape))
shape_hdr.Data = uintptr(unsafe.Pointer(tensor.c.shape.data))
shape_hdr.Len = int(tensor.c.shape.length / C.sizeof_int)
shape_hdr.Cap = int(tensor.c.shape.length / C.sizeof_int)
tensor.Reshape(shape)
}
func (predictor *Predictor) ZeroCopyRun() {
C.PD_ZeroCopyRun(predictor.c)
}
go/paddle/tensor.go 已删除 100644 → 0
浏览文件 @ ab0272eb
// Copyright (c) 2019 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.
package paddle
// #cgo CFLAGS: -I${SRCDIR}/../paddle_c/paddle/include
// #cgo LDFLAGS: -L${SRCDIR}/../paddle_c/paddle/lib -lpaddle_inference_c
// #include <stdbool.h>
// #include <stdlib.h>
// #include <string.h>
// #include <paddle_c_api.h>
import "C"
import "runtime"
import "reflect"
import "unsafe"
import (
"bytes"
"encoding/binary"
)
type PaddleDType C.PD_DataType
const (
FLOAT32 PaddleDType = C.PD_FLOAT32
INT32 PaddleDType = C.PD_INT32
INT64 PaddleDType = C.PD_INT64
UINT8 PaddleDType = C.PD_UINT8
UNKDTYPE PaddleDType = C.PD_UNKDTYPE
)
var types = []struct {
gotype reflect.Type
dtype PaddleDType
}{
{reflect.TypeOf(float32(0)), FLOAT32},
{reflect.TypeOf(int32(0)), INT32},
{reflect.TypeOf(int64(0)), INT64},
{reflect.TypeOf(uint8(0)), UINT8},
}
func TypeOfShape(dtype PaddleDType, shape []int32) reflect.Type {
var ret reflect.Type
for _, t := range types {
if dtype == PaddleDType(t.dtype) {
ret = t.gotype
break
}
}
if ret == nil {
panic(bug("Data %v type is not support", dtype))
}
for range shape {
ret = reflect.SliceOf(ret)
}
return ret
}
type ZeroCopyTensor struct {
c *C.PD_ZeroCopyTensor
name string
shape []int32
}
func NewZeroCopyTensor() *ZeroCopyTensor {
c_tensor := C.PD_NewZeroCopyTensor()
tensor := &ZeroCopyTensor{c: c_tensor}
runtime.SetFinalizer(tensor, (*ZeroCopyTensor).finalize)
return tensor
}
func (tensor *ZeroCopyTensor) finalize() {
C.PD_DeleteZeroCopyTensor(tensor.c)
}
func (tensor *ZeroCopyTensor) Shape() []int32 {
return tensor.shape
}
func (tensor *ZeroCopyTensor) Name() string {
return C.GoString(tensor.c.name)
}
func (tensor *ZeroCopyTensor) Rename(name string) {
tensor.name = name
tensor.c.name = (*C.char)(unsafe.Pointer(tensor.c.name))
//tensor.c.name = C.CString(tensor.name)
//defer C.free(unsafe.Pointer(tensor.c.name))
}
func (tensor *ZeroCopyTensor) Reshape(shape []int32) {
tensor.shape = make([]int32, len(shape))
copy(tensor.shape, shape)
length := C.sizeof_int * C.size_t(len(shape))
if tensor.c.shape.capacity < C.size_t(length) {
if tensor.c.shape.capacity != C.size_t(0) {
C.free(tensor.c.shape.data)
}
tensor.c.shape.data = C.malloc(length)
tensor.c.shape.capacity = length
}
tensor.c.shape.length = length
C.memcpy(tensor.c.shape.data, unsafe.Pointer(&shape[0]), length)
}
func (tensor *ZeroCopyTensor) DataType() PaddleDType {
return PaddleDType(tensor.c.dtype)
}
func (tensor *ZeroCopyTensor) SetValue(value interface{}) {
val := reflect.ValueOf(value)
shape, dtype := ShapeAndTypeOf(val)
tensor.Reshape(shape)
num := numel(shape)
length := C.size_t(SizeofDataType(dtype) * num)
if tensor.c.data.capacity < length {
if tensor.c.data.capacity != C.size_t(0) {
C.free(tensor.c.data.data)
}
tensor.c.data.data = C.malloc(length)
tensor.c.data.capacity = length
}
tensor.c.data.length = length
switch dtype {
case PaddleDType(UINT8):
data := val.Interface().([]uint8)
C.memcpy(tensor.c.data.data, unsafe.Pointer(&data[0]), length)
case PaddleDType(INT32):
data := val.Interface().([]int32)
C.memcpy(tensor.c.data.data, unsafe.Pointer(&data[0]), length)
case PaddleDType(INT64):
data := val.Interface().([]int64)
C.memcpy(tensor.c.data.data, unsafe.Pointer(&data[0]), length)
case PaddleDType(FLOAT32):
data := val.Interface().([]float32)
C.memcpy(tensor.c.data.data, unsafe.Pointer(&data[0]), length)
}
tensor.c.dtype = C.PD_DataType(dtype)
}
func TypeOf(dtype PaddleDType, shape []int32) reflect.Type {
var ret reflect.Type
for _, t := range types {
if t.dtype == dtype {
ret = t.gotype
break
}
}
for range shape {
ret = reflect.SliceOf(ret)
}
return ret
}
func (tensor *ZeroCopyTensor) Value() interface{} {
t := TypeOf(PaddleDType(tensor.c.dtype), tensor.shape)
value := reflect.New(t)
c_bytes := tensor.c.data.data
length := tensor.c.data.length
var slice []byte
if unsafe.Sizeof(unsafe.Pointer(nil)) == 8 {
slice = (*[1<<50 - 1]byte)(unsafe.Pointer(c_bytes))[:length:length]
} else {
slice = (*[1 << 30]byte)(unsafe.Pointer(c_bytes))[:length:length]
}
r := bytes.NewReader(slice)
DecodeTensor(r, tensor.Shape(), t, value)
return reflect.Indirect(value).Interface()
}
func Endian() binary.ByteOrder {
buf := [2]byte{}
*(*uint16)(unsafe.Pointer(&buf[0])) = uint16(0xABCD)
var endian binary.ByteOrder
switch buf {
case [2]byte{0xCD, 0xAB}:
endian = binary.LittleEndian
case [2]byte{0xAB, 0xCD}:
endian = binary.BigEndian
default:
panic("Could not determine native endianness.")
}
return endian
}
func DecodeTensor(r *bytes.Reader, shape []int32, t reflect.Type, ptr reflect.Value) {
switch t.Kind() {
case reflect.Uint8, reflect.Int32, reflect.Int64, reflect.Float32:
binary.Read(r, Endian(), ptr.Interface())
case reflect.Slice:
value := reflect.Indirect(ptr)
value.Set(reflect.MakeSlice(t, int(shape[0]), int(shape[0])))
if len(shape) == 1 && value.Len() > 0 {
switch value.Index(0).Kind() {
case reflect.Uint8, reflect.Int32, reflect.Int64, reflect.Float32:
binary.Read(r, Endian(), value.Interface())
return
}
}
for i := 0; i < value.Len(); i++ {
DecodeTensor(r, shape[1:], t.Elem(), value.Index(i).Addr())
}
}
}
func SizeofDataType(dtype PaddleDType) int32 {
switch dtype {
case UINT8:
return int32(C.sizeof_uchar)
case INT32:
return int32(C.sizeof_int)
case INT64:
return int32(C.sizeof_longlong)
case FLOAT32:
return int32(C.sizeof_float)
}
return -1
}
func ShapeAndTypeOf(val reflect.Value) (shape []int32, dt PaddleDType) {
gotype := val.Type()
for gotype.Kind() == reflect.Array || gotype.Kind() == reflect.Slice {
shape = append(shape, int32(val.Len()))
if val.Len() > 0 {
val = val.Index(0)
}
gotype = gotype.Elem()
}
for _, t := range types {
if gotype.Kind() == t.gotype.Kind() {
return shape, PaddleDType(t.dtype)
}
}
return shape, dt
}
paddle/fluid/inference/api/details/zero_copy_tensor.cc
浏览文件 @ c7e3c918
......@@ -104,6 +104,8 @@ DataType Tensor::type() const {
return DataType::INT32;
} else if (type == paddle::framework::proto::VarType::UINT8) {
return DataType::UINT8;
} else if (type == paddle::framework::proto::VarType::INT8) {
return DataType::INT8;
}
return DataType::FLOAT32;
}
......
paddle/fluid/inference/api/paddle_analysis_config.h
浏览文件 @ c7e3c918
......@@ -31,6 +31,7 @@
#include <unordered_set>
#include <utility>
#include <vector>
#include "paddle_infer_declare.h" // NOLINT
/*! \file */
......@@ -177,6 +178,26 @@ struct PD_INFER_DECL AnalysisConfig {
///
void DisableGpu();
///
/// \brief Turn on XPU.
///
/// \param l3_workspace_size The size of the video memory allocated by the l3
/// cache, the maximum is 16M.
/// \param locked Whether the allocated L3 cache can be locked. If false,
/// it means that the L3 cache is not locked, and the allocated L3
/// cache can be shared by multiple models, and multiple models
/// sharing the L3 cache will be executed sequentially on the card.
/// \param autotune Whether to autotune the conv operator in the model. If
/// true, when the conv operator of a certain dimension is executed
/// for the first time, it will automatically search for a better
/// algorithm to improve the performance of subsequent conv operators
/// of the same dimension.
/// \param autotune_file Specify the path of the autotune file. If
/// autotune_file is specified, the algorithm specified in the
/// file will be used and autotune will not be performed again.
/// \param precision Calculation accuracy of multi_encoder
/// \param adaptive_seqlen Is the input of multi_encoder variable length
///
void EnableXpu(int l3_workspace_size = 0xfffc00, bool locked = false,
bool autotune = true, const std::string& autotune_file = "",
const std::string& precision = "int16",
......
paddle/fluid/inference/capi_exp/pd_common.h
浏览文件 @ c7e3c918
......@@ -71,5 +71,5 @@ PD_ENUM(PD_PlaceType){PD_PLACE_UNK = -1, PD_PLACE_CPU, PD_PLACE_GPU,
PD_ENUM(PD_DataType){
PD_DATA_UNK = -1, PD_DATA_FLOAT32, PD_DATA_INT32,
PD_DATA_INT64, PD_DATA_UINT8,
PD_DATA_INT64, PD_DATA_UINT8, PD_DATA_INT8,
};
paddle/fluid/inference/capi_exp/pd_config.cc
浏览文件 @ c7e3c918
......@@ -14,6 +14,8 @@
#include "paddle/fluid/inference/capi_exp/pd_config.h"
#include "paddle/fluid/inference/api/paddle_inference_api.h"
#include "paddle/fluid/inference/capi_exp/pd_types.h"
#include "paddle/fluid/inference/capi_exp/utils_internal.h"
#include "paddle/fluid/platform/enforce.h"
#define CHECK_NULL_POINTER_PARM(param) \
......@@ -125,10 +127,14 @@ PD_Bool PD_ConfigUseGpu(__pd_keep PD_Config* pd_config) {
}
void PD_ConfigEnableXpu(__pd_keep PD_Config* pd_config,
int32_t l3_workspace_size) {
int32_t l3_workspace_size, PD_Bool locked,
PD_Bool autotune, const char* autotune_file,
const char* precision, PD_Bool adaptive_seqlen) {
CHECK_AND_CONVERT_PD_CONFIG;
config->EnableXpu(l3_workspace_size);
config->EnableXpu(l3_workspace_size, locked, autotune, autotune_file,
precision, adaptive_seqlen);
}
PD_Bool PD_ConfigUseXpu(__pd_keep PD_Config* pd_config) {
CHECK_AND_CONVERT_PD_CONFIG;
return config->use_xpu();
......@@ -378,5 +384,24 @@ void PD_ConfigPartiallyRelease(__pd_keep PD_Config* pd_config) {
CHECK_AND_CONVERT_PD_CONFIG;
config->PartiallyRelease();
}
void PD_ConfigDeletePass(__pd_keep PD_Config* pd_config, const char* pass) {
CHECK_AND_CONVERT_PD_CONFIG;
config->pass_builder()->DeletePass(pass);
}
void PD_ConfigInsertPass(__pd_keep PD_Config* pd_config, size_t idx,
const char* pass) {
CHECK_AND_CONVERT_PD_CONFIG;
config->pass_builder()->InsertPass(idx, pass);
}
void PD_ConfigAppendPass(__pd_keep PD_Config* pd_config, const char* pass) {
CHECK_AND_CONVERT_PD_CONFIG;
config->pass_builder()->AppendPass(pass);
}
__pd_give PD_OneDimArrayCstr* PD_ConfigAllPasses(
__pd_keep PD_Config* pd_config) {
CHECK_AND_CONVERT_PD_CONFIG;
std::vector<std::string> passes = config->pass_builder()->AllPasses();
return paddle_infer::CvtVecToOneDimArrayCstr(passes);
}
} // extern "C"
paddle/fluid/inference/capi_exp/pd_config.h
浏览文件 @ c7e3c918
......@@ -25,6 +25,7 @@
#pragma once
#include "pd_common.h" // NOLINT
#include "pd_types.h" // NOLINT
typedef struct PD_Config PD_Config;
......@@ -154,10 +155,27 @@ PADDLE_CAPI_EXPORT extern PD_Bool PD_ConfigUseGpu(
/// \brief Turn on XPU.
///
/// \param[in] pd_onfig config
/// \param[in] l3_workspace_size l3 workspace size.
/// \param l3_workspace_size The size of the video memory allocated by the l3
/// cache, the maximum is 16M.
/// \param locked Whether the allocated L3 cache can be locked. If false,
/// it means that the L3 cache is not locked, and the allocated L3
/// cache can be shared by multiple models, and multiple models
/// sharing the L3 cache will be executed sequentially on the card.
/// \param autotune Whether to autotune the conv operator in the model. If
/// true, when the conv operator of a certain dimension is executed
/// for the first time, it will automatically search for a better
/// algorithm to improve the performance of subsequent conv operators
/// of the same dimension.
/// \param autotune_file Specify the path of the autotune file. If
/// autotune_file is specified, the algorithm specified in the
/// file will be used and autotune will not be performed again.
/// \param precision Calculation accuracy of multi_encoder
/// \param adaptive_seqlen Is the input of multi_encoder variable length
///
PADDLE_CAPI_EXPORT extern void PD_ConfigEnableXpu(
__pd_keep PD_Config* pd_config, int32_t l3_workspace_size);
__pd_keep PD_Config* pd_config, int32_t l3_workspace_size, PD_Bool locked,
PD_Bool autotune, const char* autotune_file, const char* precision,
PD_Bool adaptive_seqlen);
///
/// \brief A boolean state telling whether the XPU is turned on.
///
......@@ -565,6 +583,35 @@ PADDLE_CAPI_EXPORT extern PD_Bool PD_ConfigIsValid(
///
PADDLE_CAPI_EXPORT extern void PD_ConfigPartiallyRelease(
__pd_keep PD_Config* pd_config);
///
/// \brief Delete all passes that has a certain type 'pass'.
///
/// \param[in] pass the certain pass type to be deleted.
///
PADDLE_CAPI_EXPORT extern void PD_ConfigDeletePass(
__pd_keep PD_Config* pd_config, const char* pass);
///
/// \brief Insert a pass to a specific position
///
/// \param[in] idx the position to insert.
/// \param[in] pass the new pass.
///
PADDLE_CAPI_EXPORT extern void PD_ConfigInsertPass(
__pd_keep PD_Config* pd_config, size_t idx, const char* pass);
///
/// \brief Append a pass to the end of the passes
///
/// \param[in] pass the new pass.
///
PADDLE_CAPI_EXPORT extern void PD_ConfigAppendPass(
__pd_keep PD_Config* pd_config, const char* pass);
///
/// \brief Get information of passes.
///
/// \return Return list of the passes.
///
PADDLE_CAPI_EXPORT extern __pd_give PD_OneDimArrayCstr* PD_ConfigAllPasses(
__pd_keep PD_Config* pd_config);
#ifdef __cplusplus
} // extern "C"
......
paddle/fluid/inference/capi_exp/pd_predictor.cc
浏览文件 @ c7e3c918
......@@ -106,4 +106,9 @@ void PD_PredictorDestroy(__pd_take PD_Predictor* pd_predictor) {
delete pd_predictor;
}
const char* PD_GetVersion() {
static std::string version = paddle_infer::GetVersion();
return version.c_str();
}
} // extern "C"
paddle/fluid/inference/capi_exp/pd_predictor.h
浏览文件 @ c7e3c918
......@@ -143,6 +143,13 @@ PADDLE_CAPI_EXPORT extern uint64_t PD_PredictorTryShrinkMemory(
PADDLE_CAPI_EXPORT extern void PD_PredictorDestroy(
__pd_take PD_Predictor* pd_predictor);
///
/// \brief Get version info.
///
/// \return version
///
PADDLE_CAPI_EXPORT extern const char* PD_GetVersion();
#ifdef __cplusplus
} // extern "C"
#endif
paddle/fluid/inference/capi_exp/pd_utils.cc
浏览文件 @ c7e3c918
......@@ -196,6 +196,8 @@ DataType CvtToCxxDatatype(PD_DataType data_type) {
return DataType::INT32;
case PD_DATA_UINT8:
return DataType::UINT8;
case PD_DATA_INT8:
return DataType::INT8;
default:
PADDLE_THROW(paddle::platform::errors::InvalidArgument(
"Unsupport paddle data type %d.", data_type));
......
paddle/fluid/inference/goapi/README.md 0 → 100644
浏览文件 @ c7e3c918
# Paddle Inference golang API
Paddle Inference golang API 基于 [capi](../capi_exp) 和 cgo 实现，需要您提前准备好C预测库。
## 安装
1. 确认使用Paddle的CommitId
您可以通过`git log -1`的方式，确认您使用的Paddle版本的CommitId
2. 使用`go get`获取golang paddle api
```
# 此处使用上一步记录的CommitId，假设为76e5724
COMMITID=76e5724
go get -d -v github.com/paddlepaddle/paddle/paddle/fluid/inference/goapi@${COMMITID}
```
3. 下载C预测库
您可以选择直接下载[paddle_inference_c](https://github.com/PaddlePaddle/Paddle-Inference-Demo/blob/master/docs/user_guides/download_lib.md)预测库，或通过源码编译的方式安装，源码编译方式参考官网文档，注意这里cmake编译时打开`-DON_INFER=ON`,在编译目录下得到`paddle_inference_c_install_dir`。
4. 软链
go1.15新增了`GOMODCACHE`环境变量，`go get`默认会将代码下载到`GOMODCACHE`目录下，您可以通过`go env | grep GOMODCACHE`的方式，查看该路径，在官网发布的docker镜像中该路径一般默认为`/root/gopath/pkg/mod`，进入到golang api代码路径建立软连接，将c预测库命名为`paddle_inference_c`。
```bash
eval $(go env | grep GOMODCACHE)
# 按需修改最后的goapi版本号
cd ${GOMODCACHE}/github.com/paddlepaddle/paddle/paddle/fluid/inference/goapi\@v0.0.0-20210517084506-76e5724c16a5/
ln -s ${PADDLE_C_DOWNLOAD_DIR}/paddle_inference_c_install_dir paddle_inference_c
```
5. 运行单测，验证
```
bash test.sh
```
## 在Go中使用Paddle预测
首先创建预测配置
```go
config := paddle.NewConfig()
config.SetModel(model_file, params_file)
```
创建predictor
```go
predictor := paddle.NewPredictor(config)
```
获取输入Tensor和输出Tensor
```go
inNames := predictor.GetInputNames()
inHandle = predictor.GetInputHandle(inNames[0])
outNames := predictor.GetOutputNames()
outHandle := predictor.GetOutputHandle(outNames[0])
```
设置输入数据(假设只有一个输入)
```go
data := make([]float32, 1*3*224*224)
for i := 0; i < len(data); i++ {
data[i] = float32(i%255) * 0.1
}
inHandle.Reshape([]int32{1, 3, 224, 224})
inHandle.CopyFromCpu(data)
```
设置Lod
```go
lod := make([][]uint, 2)
for i:=0; i < len(lod); i++ {
lod[i] = make([]uint, 2)
// 设置输入...
lod[i][0] = 0
lod[i][0] = 10
}
inHandle.SetLod(lod)
```
运行预测
```go
predictor.Run()
```
获取输入Tensor的真实值
```go
func numElements(shape []int32) int32 {
n := int32(1)
for _, v := range shape {
n *= v
}
return n
}
outData := make([]float32, numElements(outHandle.Shape()))
outHandle.CopyToCpu(outData)
fmt.Println(outHandle.Lod())
```
## 示例
Demo示例见[Paddle-Inference-Demo](https://github.com/PaddlePaddle/Paddle-Inference-Demo/tree/master/go)
paddle/fluid/inference/goapi/config.go 0 → 100644
浏览文件 @ c7e3c918
此差异已折叠。
paddle/fluid/inference/goapi/config_test.go 0 → 100644
浏览文件 @ c7e3c918
// Copyright (c) 2021 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.
package paddle
import "testing"
func TestNewConfig(t *testing.T) {
config := NewConfig()
config.SetProgFile("model")
config.SetParamsFile("params")
config.SetOptimCacheDir("cache")
config.DisableFCPadding()
t.Logf("UseFcPadding:%+v", config.UseFcPadding())
// It will break when we have no xpu env.
// config.EnableXpu(100)
// t.Logf("EnableXpu, UseXpu:%+v ", config.UseXpu())
config.SwitchIrOptim(true)
t.Logf("IrOptim:%+v", config.IrOptim())
config.EnableUseGpu(100, 0)
t.Logf("use_gpu:%+v, gpu_id:%+v", config.UseGpu(), config.GpuDeviceId())
t.Logf("MemoryPoolInitSizeMb:%+v, FractionOfGpuMemoryForPool:%+v", config.MemoryPoolInitSizeMb(), config.FractionOfGpuMemoryForPool())
config.EnableTensorRtEngine(1024, 16, 3, PrecisionFloat32, false, false)
t.Logf("TensorRtEngineEnabled:%+v", config.TensorRtEngineEnabled())
minInputShape := map[string][]int32{
"image": []int32{-1, 3, 100, 100},
"shape": []int32{-1, 2},
}
maxInputShape := map[string][]int32{
"image": []int32{-1, 3, 608, 608},
"shape": []int32{-1, 2},
}
optInputShape := map[string][]int32{
"image": []int32{-1, 3, 406, 406},
"shape": []int32{-1, 2},
}
config.SetTRTDynamicShapeInfo(minInputShape, maxInputShape, optInputShape, false)
config.EnableTensorRtOSS()
t.Logf("TensorrtOssEnabled:%+v", config.TensorrtOssEnabled())
config.EnableTensorRtDLA(0)
t.Logf("TensorrtDlaEnabled:%+v", config.TensorrtDlaEnabled())
config.DisableTensorRtOPs([]string{"mul", "fc"})
config.EnableGpuMultiStream()
t.Logf("ThreadLocalStreamEnabled:%+v", config.ThreadLocalStreamEnabled())
config.SwitchIrDebug(false)
config.EnableMKLDNN()
config.EnableMemoryOptim()
t.Logf("MemoryOptimEnabled:%+v", config.MemoryOptimEnabled())
config.EnableProfile()
t.Logf("ProfileEnabled:%+v", config.ProfileEnabled())
config.DisableGlogInfo()
t.Logf("GlogInfoDisabled:%+v", config.GlogInfoDisabled())
t.Logf("IsValid:%+v", config.IsValid())
config.AppendPass("test_pass")
t.Logf("After AppendPass, AllPasses:%+v", config.AllPasses())
config.DeletePass("test_pass")
t.Logf("After DeletePass, AllPasses:%+v", config.AllPasses())
}
func TestLite(t *testing.T) {
config := NewConfig()
config.SetModel("model", "params")
t.Log(config.ProgFile())
t.Log(config.ParamsFile())
config.EnableLiteEngine(PrecisionFloat32, true, []string{}, []string{})
t.Logf("LiteEngineEnabled:%+v", config.LiteEngineEnabled())
}
func TestMkldnn(t *testing.T) {
config := NewConfig()
config.SetModelDir("modelDir")
t.Log(config.ModelDir())
config.EnableMKLDNN()
t.Logf("MkldnnEnabled:%+v", config.MkldnnEnabled())
config.SetMkldnnCacheCapacity(4)
config.SetCpuMathLibraryNumThreads(4)
t.Logf("CpuMathLibraryNumThreads:%+v", config.CpuMathLibraryNumThreads())
config.SetMKLDNNOp([]string{"fc", "conv"})
config.EnableMkldnnQuantizer()
t.Logf("MkldnnQuantizerEnabled:%+v", config.MkldnnQuantizerEnabled())
config.EnableMkldnnBfloat16()
t.Logf("MkldnnBfloat16Enabled:%+v", config.MkldnnBfloat16Enabled())
config.SetBfloat16Op([]string{"fc", "mul"})
}
paddle/fluid/inference/goapi/go.mod 0 → 100644
浏览文件 @ c7e3c918
module github.com/jiweibo/paddle/paddle/fluid/inference/goapi
go 1.15
paddle/fluid/inference/goapi/lib.go 0 → 100644
浏览文件 @ c7e3c918
// Copyright (c) 2021 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.
package paddle
// #cgo CFLAGS: -I${SRCDIR}/paddle_inference_c/paddle/include
// #cgo LDFLAGS: -L${SRCDIR}/paddle_inference_c/paddle/lib -lpaddle_inference_c
import "C"
paddle/fluid/inference/goapi/predictor.go 0 → 100644
浏览文件 @ c7e3c918
// Copyright (c) 2021 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.
package paddle
// #include "pd_predictor.h"
// #include "pd_tensor.h"
// #include "pd_common.h"
// #include "pd_types.h"
// #include "pd_utils.h"
// #include <stdlib.h>
// #include <string.h>
import "C"
import (
"runtime"
"unsafe"
)
type Predictor struct {
c *C.PD_Predictor
}
///
/// \brief Create a new Predictor
///
/// \param[in] Config config
/// \return new predicor.
///
func NewPredictor(config *Config) *Predictor {
cPredictor := C.PD_PredictorCreate(config.c)
predictor := &Predictor{c: cPredictor}
runtime.SetFinalizer(predictor, func(predictor *Predictor) {
C.PD_PredictorDestroy(predictor.c)
})
return predictor
}
///
/// \brief Clone a new Predictor
///
/// \return new predictor.
///
func (p *Predictor) Clone() *Predictor {
cPredictor := C.PD_PredictorClone(p.c)
predictor := &Predictor{c: cPredictor}
runtime.SetFinalizer(predictor, func(predictor *Predictor) {
C.PD_PredictorDestroy(predictor.c)
})
return predictor
}
///
/// \brief Get the input number
///
/// \return input number
///
func (p *Predictor) GetInputNum() uint {
return uint(C.PD_PredictorGetInputNum(p.c))
}
///
/// \brief Get the output number
///
/// \return output number
///
func (p *Predictor) GetOutputNum() uint {
return uint(C.PD_PredictorGetOutputNum(p.c))
}
///
/// \brief Get the input names
///
/// \return input names
///
func (p *Predictor) GetInputNames() []string {
cNames := C.PD_PredictorGetInputNames(p.c)
numNames := int(cNames.size)
names := cvtToGoSliceString(numNames, cNames.data)
C.PD_OneDimArrayCstrDestroy(cNames)
return names
}
///
/// \brief Get the output names
///
/// \return output names
///
func (p *Predictor) GetOutputNames() []string {
cNames := C.PD_PredictorGetOutputNames(p.c)
numNames := int(cNames.size)
names := cvtToGoSliceString(numNames, cNames.data)
C.PD_OneDimArrayCstrDestroy(cNames)
return names
}
///
/// \brief Get the Input Tensor object
///
/// \param[in] name input name
/// \return input tensor
///
func (p *Predictor) GetInputHandle(name string) *Tensor {
cName := C.CString(name)
cHandle := C.PD_PredictorGetInputHandle(p.c, cName)
C.free(unsafe.Pointer(cName))
handle := &Tensor{c: cHandle}
runtime.SetFinalizer(handle, func(handle *Tensor) {
C.PD_TensorDestroy(handle.c)
})
return handle
}
///
/// \brief Get the Output Tensor object
///
/// \param[in] name output name
/// \return output tensor
///
func (p *Predictor) GetOutputHandle(name string) *Tensor {
cName := C.CString(name)
cHandle := C.PD_PredictorGetOutputHandle(p.c, cName)
C.free(unsafe.Pointer(cName))
handle := &Tensor{c: cHandle}
runtime.SetFinalizer(handle, func(handle *Tensor) {
C.PD_TensorDestroy(handle.c)
})
return handle
}
///
/// \brief Run the prediction engine
///
func (p *Predictor) Run() {
C.PD_PredictorRun(p.c)
}
///
/// \brief Clear the intermediate tensors of the predictor
///
func (p *Predictor) ClearIntermediateTensor() {
C.PD_PredictorClearIntermediateTensor(p.c)
}
///
/// \brief Release all tmp tensor to compress the size of the memory pool.
/// The memory pool is considered to be composed of a list of chunks, if
/// the chunk is not occupied, it can be released.
///
/// \return Number of bytes released. It may be smaller than the actual
/// released memory, because part of the memory is not managed by the
/// MemoryPool.
///
func (p *Predictor) TryShrinkMemory() {
C.PD_PredictorTryShrinkMemory(p.c)
}
paddle/fluid/inference/goapi/predictor_test.go 0 → 100644
浏览文件 @ c7e3c918
// Copyright (c) 2021 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.
package paddle
import (
"io/ioutil"
"os"
"testing"
)
func TestNewPredictor(t *testing.T) {
t.Logf("Version:\n%+v", Version())
config := NewConfig()
config.SetModel("./mobilenetv1/inference.pdmodel", "./mobilenetv1/inference.pdiparams")
config.EnableUseGpu(100, 0)
predictor := NewPredictor(config)
inNames := predictor.GetInputNames()
t.Logf("InputNames:%+v", inNames)
outNames := predictor.GetOutputNames()
t.Logf("OutputNames:%+v", outNames)
inHandle := predictor.GetInputHandle(inNames[0])
inHandle.Reshape([]int32{1, 3, 224, 224})
t.Logf("inHandle name:%+v, shape:%+v", inHandle.Name(), inHandle.Shape())
var lod [][]uint
lod = append(lod, []uint{0, 1, 2})
lod = append(lod, []uint{1, 2, 3, 4})
inHandle.SetLod(lod)
t.Logf("inHandle Lod:%+v", inHandle.Lod())
data := make([]float32, numElements([]int32{1, 3, 224, 224}))
for i := 0; i < int(numElements([]int32{1, 3, 224, 224})); i++ {
data[i] = float32(i%255) * 0.1
}
inHandle.CopyFromCpu(data)
t.Logf("inHandle Type:%+v", inHandle.Type())
predictor.Run()
outHandle := predictor.GetOutputHandle(outNames[0])
t.Logf("outHandle name:%+v", outHandle.Name())
outShape := outHandle.Shape()
t.Logf("outHandle Shape:%+v", outShape)
outData := make([]float32, numElements(outShape))
outHandle.CopyToCpu(outData)
t.Log(outData)
cloned := predictor.Clone()
t.Logf("InputNum:%+v", cloned.GetInputNum())
t.Logf("OutputNum:%+v", cloned.GetInputNum())
cloned.ClearIntermediateTensor()
}
func TestFromBuffer(t *testing.T) {
modelFile, err := os.Open("./mobilenetv1/inference.pdmodel")
if err != nil {
t.Fatal(err)
}
paramsFile, err := os.Open("./mobilenetv1/inference.pdiparams")
if err != nil {
t.Fatal(err)
}
defer modelFile.Close()
defer paramsFile.Close()
model, err := ioutil.ReadAll(modelFile)
if err != nil {
t.Fatal(err)
}
params, err := ioutil.ReadAll(paramsFile)
if err != nil {
t.Fatal(err)
}
config := NewConfig()
config.SetModelBuffer(string(model), string(params))
predictor := NewPredictor(config)
inNames := predictor.GetInputNames()
outNames := predictor.GetOutputNames()
inHandle := predictor.GetInputHandle(inNames[0])
inHandle.Reshape([]int32{1, 3, 224, 224})
data := make([]float32, numElements([]int32{1, 3, 224, 224}))
for i := 0; i < int(numElements([]int32{1, 3, 224, 224})); i++ {
data[i] = float32(i%255) * 0.1
}
inHandle.CopyFromCpu(data)
predictor.Run()
outHandle := predictor.GetOutputHandle(outNames[0])
outShape := outHandle.Shape()
t.Logf("outHandle Shape:%+v", outShape)
outData := make([]float32, numElements(outShape))
outHandle.CopyToCpu(outData)
t.Log(outData)
}
func numElements(shape []int32) int32 {
n := int32(1)
for _, v := range shape {
n *= v
}
return n
}
paddle/fluid/inference/goapi/tensor.go 0 → 100644
浏览文件 @ c7e3c918
// Copyright (c) 2021 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.
package paddle
// #include "pd_tensor.h"
// #include "pd_utils.h"
// #include "pd_types.h"
// #include "pd_common.h"
// #include "stdlib.h"
import "C"
import (
"fmt"
"reflect"
"unsafe"
)
type DataType C.PD_DataType
const (
Unk DataType = C.PD_DATA_UNK
Float32 DataType = C.PD_DATA_FLOAT32
Int32 DataType = C.PD_DATA_INT32
Int64 DataType = C.PD_DATA_INT64
Uint8 DataType = C.PD_DATA_UINT8
Int8 DataType = C.PD_DATA_INT8
)
type PlaceType C.PD_PlaceType
const (
UnkPlace PlaceType = C.PD_PLACE_UNK
CpuPlace PlaceType = C.PD_PLACE_CPU
GpuPlace PlaceType = C.PD_PLACE_GPU
XpuPlace PlaceType = C.PD_PLACE_XPU
)
type Tensor struct {
c *C.PD_Tensor
}
///
/// \brief Reset the shape of the tensor.
/// Generally it's only used for the input tensor.
///
/// \param[in] shape The shape to set.
///
func (t *Tensor) Reshape(shape []int32) {
C.PD_TensorReshape(t.c, C.size_t(len(shape)), (*C.int32_t)(unsafe.Pointer(&shape[0])))
}
///
/// \brief Get the tensor shape
///
/// \return The tensor shape.
///
func (t *Tensor) Shape() []int32 {
cData := C.PD_TensorGetShape(t.c)
length := int(cData.size)
defer C.PD_OneDimArrayInt32Destroy(cData)
return cvtToGoSliceInt32(length, cData.data)
}
///
/// \brief Set the tensor lod information
/// \param[in] pd_tensor tensor.
/// \param[in] lod lod information.
///
func (t *Tensor) SetLod(lod [][]uint) {
cLod := (*C.struct_PD_TwoDimArraySize)(C.malloc(C.size_t(C.sizeof_struct_PD_TwoDimArraySize)))
length := len(lod)
cLod.size = C.size_t(uint(length))
var lodList = make([]*C.struct_PD_OneDimArraySize, length+1)
for i, v := range lod {
oneDimArray := (*C.struct_PD_OneDimArraySize)(C.malloc(C.size_t(C.sizeof_struct_PD_OneDimArraySize)))
defer C.free(unsafe.Pointer(oneDimArray))
tmpLength := len(v)
oneDimArray.size = C.size_t(uint(tmpLength))
tmpC := (*C.size_t)(C.malloc(C.size_t(C.sizeof_size_t * tmpLength)))
defer C.free(unsafe.Pointer(tmpC))
tmpSlice := (*[1 << 27]C.size_t)(unsafe.Pointer(tmpC))[:tmpLength:tmpLength]
for j, w := range v {
tmpSlice[j] = C.size_t(w)
}
oneDimArray.data = tmpC
lodList[i] = oneDimArray
}
cLod.data = (**C.struct_PD_OneDimArraySize)(unsafe.Pointer(&lodList[0]))
C.PD_TensorSetLod(t.c, cLod)
C.free(unsafe.Pointer(cLod))
// C.PD_TwoDimArraySizeDestroy(cLod)
}
///
/// \brief Get the tensor lod information
///
/// \return the lod information.
///
func (t *Tensor) Lod() [][]uint {
cLod := C.PD_TensorGetLod(t.c)
length := int(cLod.size)
res := make([][]uint, length)
if length == 0 {
return res
}
cLodSlice := (*[1 << 27]*C.struct_PD_OneDimArraySize)(unsafe.Pointer(cLod.data))[:length:length]
for i := 0; i < length; i++ {
size := uint(cLodSlice[i].size)
lod := make([]uint, size)
tmpSlice := (*[1 << 27]C.size_t)(unsafe.Pointer(cLodSlice[i].data))[:size:size]
for j, v := range tmpSlice {
lod[j] = uint(v)
}
res[i] = lod
}
C.PD_TwoDimArraySizeDestroy(cLod)
return res
}
///
/// \brief Get the tensor data type
/// \param[in] pd_tensor tensor.
/// \return the tensor data type.
///
func (t *Tensor) Type() DataType {
cDtype := C.PD_TensorGetDataType(t.c)
return DataType(cDtype)
}
///
/// \brief Get the tensor name
///
/// \return the tensor name.
///
func (t *Tensor) Name() string {
return C.GoString(C.PD_TensorGetName(t.c))
}
///
/// \brief Copy the host memory to tensor data.
/// It's usually used to set the input tensor data.
///
/// \param[in] value
///
func (t *Tensor) CopyFromCpu(value interface{}) {
val := reflect.ValueOf(value)
dtype, _ := dataTypeOf(val)
switch dtype {
case Float32:
data := val.Interface().([]float32)
C.PD_TensorCopyFromCpuFloat(t.c, (*C.float)(unsafe.Pointer(&data[0])))
case Int32:
data := val.Interface().([]int32)
C.PD_TensorCopyFromCpuInt32(t.c, (*C.int32_t)(unsafe.Pointer(&data[0])))
case Int64:
data := val.Interface().([]int64)
C.PD_TensorCopyFromCpuInt64(t.c, (*C.int64_t)(unsafe.Pointer(&data[0])))
case Uint8:
data := val.Interface().([]uint8)
C.PD_TensorCopyFromCpuUint8(t.c, (*C.uint8_t)(unsafe.Pointer(&data[0])))
case Int8:
data := val.Interface().([]int8)
C.PD_TensorCopyFromCpuInt8(t.c, (*C.int8_t)(unsafe.Pointer(&data[0])))
}
}
///
/// \brief Copy the tensor data to the host memory.
/// It's usually used to get the output tensor data.
///
/// \param[value] data The tensor will copy the data to the address.
///
func (t *Tensor) CopyToCpu(value interface{}) {
val := reflect.ValueOf(value)
dtype, _ := dataTypeOf(val)
switch dtype {
case Float32:
data := val.Interface().([]float32)
C.PD_TensorCopyToCpuFloat(t.c, (*C.float)(unsafe.Pointer(&data[0])))
case Int32:
data := val.Interface().([]int32)
C.PD_TensorCopyToCpuInt32(t.c, (*C.int32_t)(unsafe.Pointer(&data[0])))
case Int64:
data := val.Interface().([]int64)
C.PD_TensorCopyToCpuInt64(t.c, (*C.int64_t)(unsafe.Pointer(&data[0])))
case Uint8:
data := val.Interface().([]uint8)
C.PD_TensorCopyToCpuUint8(t.c, (*C.uint8_t)(unsafe.Pointer(&data[0])))
case Int8:
data := val.Interface().([]int8)
C.PD_TensorCopyToCpuInt8(t.c, (*C.int8_t)(unsafe.Pointer(&data[0])))
}
}
var types = []struct {
typ reflect.Type
dataType C.PD_DataType
}{
{reflect.TypeOf(float32(0)), C.PD_DATA_FLOAT32},
{reflect.TypeOf(int32(0)), C.PD_DATA_INT32},
{reflect.TypeOf(int64(0)), C.PD_DATA_INT64},
{reflect.TypeOf(uint8(0)), C.PD_DATA_UINT8},
{reflect.TypeOf(int8(0)), C.PD_DATA_INT8},
}
func dataTypeOf(val reflect.Value) (dt DataType, err error) {
typ := val.Type()
for typ.Kind() == reflect.Array || typ.Kind() == reflect.Slice {
if val.Len() > 0 {
val = val.Index(0)
}
typ = typ.Elem()
}
for _, t := range types {
if typ.Kind() == t.typ.Kind() {
return DataType(t.dataType), nil
}
}
return dt, fmt.Errorf("unsupported type %v", typ)
}
paddle/fluid/inference/goapi/test.sh 0 → 100644
浏览文件 @ c7e3c918
#!/bin/bash
# Copyright (c) 2020 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.
# 1. download the mobilenetv1 model to test config and predictor
if [ ! -d mobilenetv1 ]; then
wget https://paddle-inference-dist.bj.bcebos.com/Paddle-Inference-Demo/mobilenetv1.tgz
tar xzf mobilenetv1.tgz
fi
# 2. set LD_LIBRARY_PATH
export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:$PWD/paddle_inference_c/paddle/lib
# 3. go test
go test -v ./...
go/demo/mobilenet_cxx.cc → paddle/fluid/inference/goapi/utils.go
浏览文件 @ c7e3c918
// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
// Copyright (c) 2021 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.
......@@ -11,37 +11,51 @@
// 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 <paddle_inference_api.h>
#include <fstream>
#include <iostream>
void SetConfig(paddle::AnalysisConfig *);
package paddle
int main(int argc, char *argv[]) {
paddle::AnalysisConfig config;
SetConfig(&config);
auto predictor = paddle::CreatePaddlePredictor(config);
auto input_name = predictor->GetInputNames()[0];
auto input = predictor->GetInputTensor(input_name);
std::cout << predictor->GetOutputNames()[0] << std::endl;
std::vector<int> shape{1, 3, 300, 300};
input->Reshape(std::move(shape));
std::vector<float> data(1 * 300 * 300 * 3);
std::ifstream fin("data/data.txt");
for (int i = 0; i < data.size(); i++) {
fin >> data[i];
}
// #include <stdint.h>
// #include <stdlib.h>
import "C"
import (
"unsafe"
)
input->copy_from_cpu(data.data());
predictor->ZeroCopyRun();
auto output_name = predictor->GetOutputNames()[0];
auto output = predictor->GetOutputTensor(output_name);
return 0;
func cvtPDBoolToGo(b C.int8_t) bool {
var cFalse C.int8_t
if b != cFalse {
return true
}
return false
}
void SetConfig(paddle::AnalysisConfig *config) {
config->SetModel("data/model/__model__", "data/model/__params__");
config->SwitchUseFeedFetchOps(false);
config->SwitchSpecifyInputNames(true);
config->SwitchIrOptim(false);
func cvtGoBoolToPD(b bool) C.int8_t {
if b == false {
return 0
}
return 1
}
func cvtToGoSliceString(length int, str **C.char) []string {
if str == nil {
return nil
}
tmpSlice := (*[1 << 27]*C.char)(unsafe.Pointer(str))[:length:length]
goStrings := make([]string, length)
for i, s := range tmpSlice {
goStrings[i] = C.GoString(s)
}
return goStrings
}
func cvtToGoSliceInt32(length int, data *C.int32_t) []int32 {
if data == nil {
return nil
}
tmpSlice := (*[1 << 27]C.int32_t)(unsafe.Pointer(data))[:length:length]
res := make([]int32, length)
for i, s := range tmpSlice {
res[i] = int32(s)
}
return res
}
go/paddle/common.go → paddle/fluid/inference/goapi/version.go
浏览文件 @ c7e3c918
// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
// Copyright (c) 2021 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.
......@@ -14,29 +14,13 @@
package paddle
// #cgo CFLAGS: -I${SRCDIR}/../paddle_c/paddle/include
// #cgo LDFLAGS: -L${SRCDIR}/../paddle_c/paddle/lib -lpaddle_inference_c
// #include <stdbool.h>
// #include <paddle_c_api.h>
// #include "pd_common.h"
// #include "pd_predictor.h"
// #include "pd_types.h"
// #include "pd_utils.h"
import "C"
import "fmt"
func ConvertCBooleanToGo(b C.bool) bool {
var c_false C.bool
if b != c_false {
return true
}
return false
}
func numel(shape []int32) int32 {
n := int32(1)
for _, d := range shape {
n *= d
}
return n
}
func bug(format string, args ...interface{}) error {
return fmt.Errorf("Bug %v", fmt.Sprintf(format, args...))
func Version() string {
cVersion := C.PD_GetVersion()
return C.GoString(cVersion)
}
paddle/scripts/paddle_build.sh
浏览文件 @ c7e3c918
......@@ -1985,6 +1985,26 @@ EOF
fi
}
function test_go_inference_api() {
cat <<EOF
========================================
Testing go inference api ...
========================================
EOF
# ln paddle_inference_c lib
cd ${PADDLE_ROOT}/build
ln -s ${PADDLE_ROOT}/build/paddle_inference_c_install_dir/ ${PADDLE_ROOT}/paddle/fluid/inference/goapi/paddle_inference_c
# run go test
cd ${PADDLE_ROOT}/paddle/fluid/inference/goapi
bash test.sh
EXIT_CODE=$?
if [[ "$EXIT_CODE" != "0" ]]; then
exit 8;
fi
}
function test_fluid_lib_train() {
cat <<EOF
========================================
......@@ -2226,6 +2246,8 @@ function main() {
gen_fluid_lib ${parallel_number}
test_fluid_lib
#test_fluid_lib_train
#go inference test
test_go_inference_api
;;
test_train)
gen_fluid_lib ${parallel_number}
......
tools/dockerfile/Dockerfile.ubuntu
浏览文件 @ c7e3c918
......@@ -123,7 +123,7 @@ RUN rm Python-$version.tgz setuptools-40.6.2.zip setuptools-50.3.2.zip pip-20.0.
# Install Go and glide
WORKDIR /home
RUN wget -qO- https://paddle-ci.cdn.bcebos.com/go1.8.1.linux-amd64.tar.gz | \
RUN wget -qO- https://paddle-ci.gz.bcebos.com/go1.15.12.linux-amd64.tar.gz | \
tar -xz -C /usr/local && \
mkdir /root/gopath && \
mkdir /root/gopath/bin && \
......
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