- Windows 10，amd64
- 使用 CMAKE GUI + MSBUILD 构建支持 tensorflow GPU 的 C++ 静态库
- 构建成功。
- LABEL_IMAGE 教程示例执行时间:
- ... Main.cc 执行:9.17 秒
- ... Label_image.py 执行(tensorflow):10.34 秒
- ... Label_image.py 执行(tensorflow-gpu):1.62 秒
知道为什么吗？非常感谢
Main.cc 具有较小的自定义:
#define NOMINMAX
#include <fstream>
#include <utility>
#include <vector>
#include "tensorflow/cc/ops/const_op.h"
#include "tensorflow/cc/ops/image_ops.h"
#include "tensorflow/cc/ops/standard_ops.h"
#include "tensorflow/core/framework/graph.pb.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/graph/default_device.h"
#include "tensorflow/core/graph/graph_def_builder.h"
#include "tensorflow/core/lib/core/errors.h"
#include "tensorflow/core/lib/core/stringpiece.h"
#include "tensorflow/core/lib/core/threadpool.h"
#include "tensorflow/core/lib/io/path.h"
#include "tensorflow/core/lib/strings/stringprintf.h"
#include "tensorflow/core/platform/env.h"
#include "tensorflow/core/platform/init_main.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/platform/types.h"
#include "tensorflow/core/public/session.h"
#include "tensorflow/core/util/command_line_flags.h"
// These are all common classes it's handy to reference with no namespace.
using tensorflow::Flag;
using tensorflow::Tensor;
using tensorflow::Status;
using tensorflow::string;
using tensorflow::int32;
static Status ReadEntireFile(tensorflow::Env* env, const string& filename, Tensor* output) {
tensorflow::uint64 file_size = 0;
TF_RETURN_IF_ERROR(env->GetFileSize(filename, &file_size));
string contents;
contents.resize(file_size);
std::unique_ptr<tensorflow::RandomAccessFile> file;
TF_RETURN_IF_ERROR(env->NewRandomAccessFile(filename, &file));
tensorflow::StringPiece data;
TF_RETURN_IF_ERROR(file->Read(0, file_size, &data, &(contents)[0]));
if (data.size() != file_size) {
return tensorflow::errors::DataLoss("Truncated read of '", filename, "' expected ", file_size, " got ", data.size());
}
output->scalar<string>()() = data.ToString();
return Status::OK();
}
// Given an image file name, read in the data, try to decode it as an image,
// resize it to the requested size, and then scale the values as desired.
Status ReadTensorFromImageFile(const string file_name, const int input_height, const int input_width, const float input_mean, const float input_std, std::vector<Tensor>* out_tensors) {
auto root = tensorflow::Scope::NewRootScope();
using namespace ::tensorflow::ops; // NOLINT(build/namespaces)
string input_name = "file_reader";
string output_name = "dim";
// read file_name into a tensor named input
Tensor input(tensorflow::DT_STRING, tensorflow::TensorShape());
TF_RETURN_IF_ERROR(ReadEntireFile(tensorflow::Env::Default(), file_name, &input));
// use a placeholder to read input data
auto file_reader = Placeholder(root.WithOpName("input"), tensorflow::DataType::DT_STRING);
std::vector<std::pair<string, tensorflow::Tensor>> inputs = { { "input", input }, };
// Now try to figure out what kind of file it is and decode it.
const int wanted_channels = 3;
tensorflow::Output image_reader;
if (tensorflow::StringPiece(file_name).ends_with(".png")) {
image_reader = DecodePng(root.WithOpName("png_reader"), file_reader, DecodePng::Channels(wanted_channels));
}
else if (tensorflow::StringPiece(file_name).ends_with(".gif")) {
// gif decoder returns 4-D tensor, remove the first dim
image_reader = Squeeze(root.WithOpName("squeeze_first_dim"), DecodeGif(root.WithOpName("gif_reader"), file_reader));
}
else if (tensorflow::StringPiece(file_name).ends_with(".bmp")) {
image_reader = DecodeBmp(root.WithOpName("bmp_reader"), file_reader);
}
else {
// Assume if it's neither a PNG nor a GIF then it must be a JPEG.
image_reader = DecodeJpeg(root.WithOpName("jpeg_reader"), file_reader, DecodeJpeg::Channels(wanted_channels));
}
// Now cast the image data to float so we can do normal math on it.
auto uint8_caster = Cast(root.WithOpName("uint8_caster"), image_reader, tensorflow::DT_UINT8);
// The convention for image ops in TensorFlow is that all images are expected
// to be in batches, so that they're four-dimensional arrays with indices of
// [batch, height, width, channel]. Because we only have a single image, we
// have to add a batch dimension of 1 to the start with ExpandDims().
auto dims_expander = ExpandDims(root.WithOpName(output_name), uint8_caster, 0);
// Bilinearly resize the image to fit the required dimensions.
//auto resized = ResizeBilinear(root, dims_expander,Const(root.WithOpName("size"), { input_height, input_width }));
// Subtract the mean and divide by the scale.
//Div(root.WithOpName(output_name), Sub(root, resized, { input_mean }),{ input_std });
// This runs the GraphDef network definition that we've just constructed, and
// returns the results in the output tensor.
tensorflow::GraphDef graph;
TF_RETURN_IF_ERROR(root.ToGraphDef(&graph));
tensorflow::SessionOptions options;
std::unique_ptr<tensorflow::Session> session(tensorflow::NewSession(options));
TF_RETURN_IF_ERROR(session->Create(graph));
TF_RETURN_IF_ERROR(session->Run({ inputs }, { output_name }, {}, out_tensors));
return Status::OK();
}
// Reads a model graph definition from disk, and creates a session object you
// can use to run it.
Status LoadGraph(const string& graph_file_name, std::unique_ptr<tensorflow::Session>* session) {
tensorflow::GraphDef graph_def;
Status load_graph_status = ReadBinaryProto(tensorflow::Env::Default(), graph_file_name, &graph_def);
if (!load_graph_status.ok()) {return tensorflow::errors::NotFound("Failed to load compute graph at '",graph_file_name, "'");}
tensorflow::SessionOptions options;
session->reset(tensorflow::NewSession(options));
Status session_create_status = (*session)->Create(graph_def);
if (!session_create_status.ok()) {return session_create_status; }
return Status::OK();
}
int main(int argc, char* argv[]) {
// These are the command-line flags the program can understand.
// They define where the graph and input data is located, and what kind of
// input the model expects. If you train your own model, or use something
// other than inception_v3, then you'll need to update these.
string image = "tensorflow/examples/label_image/data/grace_hopper.jpg";
string graph = "tensorflow/examples/label_image/data/faster_rcnn_resnet101_coco_11_06_2017/frozen_inference_graph.pb";
string labels = "/tensorflow/tensorflow/examples/label_image/data/faster_rcnn_resnet101_coco_11_06_2017/graph.pbtxt";
int32 input_width = 299;
int32 input_height = 299;
float input_mean = 0;
float input_std = 255;
string input_layer = "image_tensor:0";
std::vector<string> output_layer = { "detection_boxes:0", "detection_scores:0", "detection_classes:0", "num_detections:0" };
string o_layer = "detection_boxes:0, detection_scores : 0, detection_classes : 0, num_detections : 0"; //dummy for Flag structure
bool self_test = false;
string root_dir = "/tensorflow/";
std::vector<Flag> flag_list = {
Flag("image", &image, "image to be processed"),
Flag("graph", &graph, "graph to be executed"),
Flag("labels", &labels, "name of file containing labels"),
Flag("input_width", &input_width, "resize image to this width in pixels"),
Flag("input_height", &input_height,
"resize image to this height in pixels"),
Flag("input_mean", &input_mean, "scale pixel values to this mean"),
Flag("input_std", &input_std, "scale pixel values to this std deviation"),
Flag("input_layer", &input_layer, "name of input layer"),
Flag("output_layer", &o_layer, "name of output layer"),
Flag("self_test", &self_test, "run a self test"),
Flag("root_dir", &root_dir,
"interpret image and graph file names relative to this directory"),
};
string usage = tensorflow::Flags::Usage(argv[0], flag_list);
const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
if (!parse_result) {
LOG(ERROR) << usage;
return -1;
}
// We need to call this to set up global state for TensorFlow.
tensorflow::port::InitMain(argv[0], &argc, &argv);
if (argc > 1) {
LOG(ERROR) << "Unknown argument " << argv[1] << "\n" << usage;
return -1;
}
// First we load and initialize the model.
std::unique_ptr<tensorflow::Session> session;
string graph_path = tensorflow::io::JoinPath(root_dir, graph);
Status load_graph_status = LoadGraph(graph_path, &session);
if (!load_graph_status.ok()) {
LOG(ERROR) << load_graph_status;
return -1;
}
// Get the image from disk as a float array of numbers, resized and normalized
// to the specifications the main graph expects.
std::vector<Tensor> resized_tensors;
string image_path = tensorflow::io::JoinPath(root_dir, image);
//-------------------------------------
LOG(ERROR) << "Detection Basla....";
Status read_tensor_status = ReadTensorFromImageFile(image_path, input_height, input_width, input_mean, input_std, &resized_tensors);
if (!read_tensor_status.ok()) {
LOG(ERROR) << read_tensor_status;
return -1;
}
const Tensor resized_tensor = resized_tensors[0];
// Actually run the image through the model.
std::vector<Tensor> outputs;
Status run_status = session->Run({ { input_layer, resized_tensor } }, { output_layer }, {}, &outputs);
LOG(ERROR) << "Detection Bit......";
//-----------------------------------------
if (!run_status.ok()) {
LOG(ERROR) << "Running model failed: " << run_status;
return -1;
}
tensorflow::TTypes<float>::Flat scores = outputs[1].flat<float>();
tensorflow::TTypes<float>::Flat classes = outputs[2].flat<float>();
tensorflow::TTypes<float>::Flat num_detections = outputs[3].flat<float>();
auto boxes = outputs[0].flat_outer_dims<float, 3>();
LOG(ERROR) << "num_detections:" << num_detections(0) << "," << outputs[0].shape().DebugString();
for (size_t i = 0; i < num_detections(0) && i < 20; ++i)
{
if (scores(i) > 0.5)
{
LOG(ERROR) << i << ",score:" << scores(i) << ",class:" << classes(i) << ",box:" << "," << boxes(0, i, 0) << "," << boxes(0, i, 1) << "," << boxes(0, i, 2) << "," << boxes(0, i, 3);
}
}
return 0;
}
最佳答案
- 成功构建后，我运行代码并收到“_pywrap_tensorflow_internal.pyd not found”消息。
- 我搜索了 PC，在 phython/tensorflow 路径中找到了一个。
- 我将那个复制到执行路径，除了 gpu 使用之外一切正常
- 突然有什么话对我耳语； “嘿你不朽!!你应该是最近生成的 pywrap_tensorflow_internal.dll 并将其重命名为 _pywrap_tensorflow_internal.pyd 并将其复制到执行路径。
- 正在使用 GPU
关于c++ - Tensorflow C++ 不使用 GPU，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/47762137/