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PaddleDetection
提交 60f70b17 编写于 作者: D dzhwinter
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test=develop

上级 cc02353d
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CMakeLists.txt
浏览文件 @ 60f70b17
......@@ -212,6 +212,7 @@ endif()
include(external/threadpool)
include(flags) # set paddle compile flags
include(cudnn) # set cudnn libraries, must before configure
include(configure) # add paddle env configuration
......@@ -225,9 +226,6 @@ elseif()
set(WITH_ANAKIN OFF CACHE STRING "Anakin is used in MKL only now." FORCE)
endif()
include(flags) # set paddle compile flags
include(cudnn) # set cudnn libraries, must before configure
include(configure) # add paddle env configuration
include(generic) # simplify cmake module
include(package) # set paddle packages
include(ccache) # set ccache for compilation
......
paddle/fluid/inference/api/demo_ci/simple_on_word2vec.cc
浏览文件 @ 60f70b17
......@@ -135,7 +135,6 @@ void MainThreads(int num_threads, bool use_gpu) {
} // namespace paddle
int main(int argc, char** argv) {
FLAGS_dirname = "./word2vec.inference.model";
google::ParseCommandLineFlags(&argc, &argv, true);
paddle::demo::Main(false /* use_gpu*/);
paddle::demo::MainThreads(1, false /* use_gpu*/);
......
paddle/fluid/inference/api/demo_ci/test.cc 已删除 100644 → 0
浏览文件 @ cc02353d
#include<windows.h>
#include <fstream>
#include "inference_icnet.h"
#include <thread>
#include <vector>
#include <string>
#include <iostream>
#include <sstream>
using namespace std;
template <class Type>
Type stringToNum(const string& str)
{
istringstream iss(str);
Type num;
iss >> num;
return num;
}
void test_imgs() {
void *h = init_predictor("./lb/__model__", "./lb/__params__", 0.3f, true, 0);
std::ifstream infile("new_file.list");
std::ofstream ofs("./1.png.output.txt");
std::string temp_s;
std::vector<std::string> all_files;
while (!infile.eof()) {
infile >> temp_s;
all_files.push_back(temp_s);
}
// size_t file_num = all_files.size();
infile.close();
// =============read file list =============
for (size_t f_k = 0; f_k < 1; f_k++) {
// std::string path = "D:\\Paddle\\paddle\\fluid\\inference\\api\\demo_ci\\build\\Release\\";
// std::ifstream in_img(path + all_files[f_k]);
std::string mypath = "D:\\Paddle\\paddle\\fluid\\inference\\api\\demo_ci\\build\\Release\\1.png.txt";
std::cout << "file" << mypath << std::endl;
std::ifstream in_img(mypath);
//std::cout << path + all_files[f_k] << std::endl;
double temp_v;
const int size = 3 * 449 * 581 * 1;
float * data = new float[size];
std::string value;
if (!in_img.is_open()) {
cout << "open failed" << endl;
}
double sum_input = .0;
for (auto i = 0; i < size; i++) {
getline(in_img, value, '\n');
double v = stringToNum<double>(value);
data[i] = static_cast<float>(v);
sum_input += v;
}
std::cout << "sum_input" << sum_input << std::endl;
in_img.close();
const int SIZE = 449 * 581 * 1;
int64_t * p = new int64_t[SIZE]();
int out_size = 0;
//memset(p, 0, size);
predict(h, data, 3, 449, 581, &p, &out_size, 1);
std::cout << "out_size = " << out_size << std::endl;
double out_sum = .0;
for (auto i = 0; i < out_size / sizeof(int64_t); i++) {
out_sum += p[i];
ofs << p[i] << " ";
}
ofs.close();
std::cout << "inferece out sum" << out_sum << std::endl;
delete p;
}
destory_predictor(h);
}
int main(int argc, char** argv) {
//if (true) {
// std::thread t1(func, init_predictor("./infer_model/__model__", "./infer_model/__params__", 0.1f, true, 0));
// std::thread t2(func, init_predictor("./infer_model/__model__", "./infer_model/__params__", 0.1f, true, 0));
// //std::thread t3(func, init_predictor("./infer_model/__model__", "./infer_model/__params__", 0.1f, true, 0));
// //std::thread t4(func, init_predictor("./infer_model/__model__", "./infer_model/__params__", 0.1f, true, 0));
// t1.join();
// t2.join();
// //t3.join();
// //t4.join();
// //Sleep(1);
//}
test_imgs();
return 0;
}
paddle/fluid/inference/api/helper.h
浏览文件 @ 60f70b17
......@@ -97,7 +97,7 @@ static void TensorAssignData(PaddleTensor *tensor,
}
template <typename T>
static int ZeroCopyTensorAssignData(paddle::ZeroCopyTensor *tensor,
static int ZeroCopyTensorAssignData(ZeroCopyTensor *tensor,
const std::vector<std::vector<T>> &data) {
int size{0};
auto *ptr = tensor->mutable_data<T>(PaddlePlace::kCPU);
......
paddle/fluid/operators/CMakeLists.txt
浏览文件 @ 60f70b17
......@@ -291,7 +291,7 @@ op_library(gru_op DEPS sequence2batch gru_compute)
op_library(recurrent_op DEPS executor)
op_library(warpctc_op DEPS dynload_warpctc sequence_padding sequence_scale)
op_library(cos_sim_op DEPS cos_sim_functor)
op_library(parallel_do_op DEPS executor glog)
op_library(parallel_do_op DEPS executor)
op_library(unsqueeze_op DEPS reshape_op)
op_library(squeeze_op DEPS reshape_op)
op_library(extract_rows_op DEPS memory)
......
paddle/fluid/operators/batch_norm_op.cu.cc
浏览文件 @ 60f70b17
......@@ -141,27 +141,6 @@ class BatchNormKernel<platform::CUDADeviceContext, T>
bias->template data<BatchNormParamType<T>>(),
est_mean->template data<BatchNormParamType<T>>(),
est_var->template data<BatchNormParamType<T>>(), epsilon));
VLOG(3) << "before tensor copy";
Tensor mean_, var_, x_, y_;
framework::TensorCopy(*est_mean, platform::CPUPlace(), dev_ctx, &mean_);
framework::TensorCopy(*est_var, platform::CPUPlace(), dev_ctx, &var_);
framework::TensorCopy(*x, platform::CPUPlace(), dev_ctx, &x_);
framework::TensorCopy(*y, platform::CPUPlace(), dev_ctx, &y_);
VLOG(3) << "after tensor copy";
auto check_tensor = [&](const Tensor& check) {
float sum = .0;
for(size_t i=0; i < check.numel(); ++i) {
sum += check.data<float>()[i];
}
return sum;
};
VLOG(3) << "BatchNormKernel";
VLOG(3) << "mean" << check_tensor(mean_);
VLOG(3) << "var" << check_tensor(var_);
VLOG(3) << "x" << check_tensor(x_);
VLOG(3) << "y" << check_tensor(y_);
} else {
// Run training mode.
// obtain running mean and running inv var, and see if we need to
......
paddle/fluid/operators/conv_cudnn_op.cu.cc
浏览文件 @ 60f70b17
......@@ -43,7 +43,6 @@ template <typename T>
class CUDNNConvOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
VLOG(3) << "inside cudnn";
PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
"It must use CUDAPlace.");
auto* input = ctx.Input<Tensor>("Input");
......@@ -60,7 +59,7 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
const T* input_data = input->data<T>();
const T* filter_data = filter->data<T>();
T* output_data = output->mutable_data<T>(ctx.GetPlace());
VLOG(3) << "get all inputs";
// ------------------- cudnn descriptors ---------------------
ScopedTensorDescriptor input_desc;
ScopedTensorDescriptor output_desc;
......@@ -73,7 +72,7 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
cudnnConvolutionDescriptor_t cudnn_conv_desc =
conv_desc.descriptor<T>(paddings, strides, dilations);
VLOG(3) << "create tensor descriptor";
#if CUDNN_VERSION_MIN(7, 0, 1)
// cudnn 7 can support groups, no need to do it mannually
// FIXME(typhoonzero): find a better way to disable groups
......@@ -82,7 +81,7 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
cudnn_conv_desc, groups));
groups = 1;
#endif
VLOG(3) << "before create tensor descriptor";
cudnnTensorDescriptor_t cudnn_input_desc = input_desc.descriptor<T>(
layout, framework::vectorize2int(input->dims()), groups);
cudnnTensorDescriptor_t cudnn_output_desc = output_desc.descriptor<T>(
......@@ -112,7 +111,7 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
output_height = output->dims()[2];
output_width = output->dims()[3];
}
VLOG(3) << "after create tensor descriptor";
int group_offset_in =
input_channels / groups * input_height * input_width * input_depth;
int group_offset_out =
......@@ -129,7 +128,6 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
auto handle = dev_ctx.cudnn_handle();
VLOG(3) << "set cudnn algorithm";
CUDNN_ENFORCE(platform::dynload::cudnnGetConvolutionForwardAlgorithm(
handle, cudnn_input_desc, cudnn_filter_desc, cudnn_conv_desc,
cudnn_output_desc, CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
......@@ -150,7 +148,7 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
cudnn_conv_desc, CUDNN_DEFAULT_MATH));
}
#endif
VLOG(3) << "before get workspace";
// get workspace size able to allocate
CUDNN_ENFORCE(platform::dynload::cudnnGetConvolutionForwardWorkspaceSize(
handle, cudnn_input_desc, cudnn_filter_desc, cudnn_conv_desc,
......@@ -159,6 +157,7 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
// the limit because the algo is overrided to use tensor core.
PADDLE_ENFORCE_LE(workspace_size_in_bytes, workspace_size_limit,
"workspace_size to be allocated exceeds the limit");
// ------------------- cudnn conv forward ---------------------
ScalingParamType<T> alpha = 1.0f, beta = 0.0f;
for (int i = 0; i < groups; i++) {
......@@ -312,6 +311,7 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
cudnn_filter_desc, filter_algo, &tmp_size));
workspace_size_in_bytes = std::max(workspace_size_in_bytes, tmp_size);
}
// ------------------- cudnn conv backward data ---------------------
ScalingParamType<T> alpha = 1.0f, beta = 0.0f;
if (input_grad) {
......
paddle/fluid/operators/fetch_op.cc
浏览文件 @ 60f70b17
......@@ -42,8 +42,6 @@ class FetchOp : public framework::OperatorBase {
"Cannot find out_var in scope, out_var_name is %s",
out_name);
VLOG(3) << "fetch_var ptr " << fetch_var << " is " << (fetch_var == nullptr);
VLOG(3) << "out_var ptr " << out_var << " is " << (out_var == nullptr);
auto col = static_cast<size_t>(Attr<int>("col"));
auto *fetch_list = out_var->GetMutable<framework::FeedFetchList>();
......
paddle/fluid/operators/label_smooth_op.cc
浏览文件 @ 60f70b17
......@@ -34,7 +34,7 @@ class LabelSmoothOp : public framework::OperatorWithKernel {
auto in_dims = ctx->GetInputDim("X");
if (ctx->HasInput("PriorDist")) {
auto noise_dims = ctx->GetInputDim("PriorDist");
int64_t noise_numel = paddle::framework::product(noise_dims);
auto noise_numel = paddle::framework::product(noise_dims);
PADDLE_ENFORCE(
in_dims[1] == noise_numel,
"The number of elements in Input(PriorDist) must be equal to the "
......
paddle/fluid/operators/load_combine_op.cc
浏览文件 @ 60f70b17
......@@ -12,7 +12,7 @@ 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 <fstream>
#include <vector>
#include <memory>
#include "paddle/fluid/framework/data_type_transform.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/device_context.h"
......@@ -33,10 +33,15 @@ class LoadCombineOp : public framework::OperatorBase {
const platform::Place &place) const override {
auto filename = Attr<std::string>("file_path");
auto load_as_fp16 = Attr<bool>("load_as_fp16");
std::ifstream fin(filename, std::ios_base::in | std::ios_base::binary);
//std::ifstream fin(filename, std::ios_base::in);
PADDLE_ENFORCE(!fin.bad(),
auto format = Attr<std::string>("format");
std::unique_ptr<std::ifstream> fin;
if (format == "windows") {
fin.reset(new std::ifstream(filename,
std::ios_base::in | std::ios_base::binary));
} else {
fin.reset(new std::ifstream(filename));
}
PADDLE_ENFORCE(static_cast<bool>(*fin),
"Cannot open file %s for load_combine op", filename);
auto out_var_names = Outputs("Out");
......@@ -48,32 +53,20 @@ class LoadCombineOp : public framework::OperatorBase {
auto &dev_ctx = *pool.Get(place);
for (size_t i = 0; i < out_var_names.size(); i++) {
VLOG(3) << "load variable " << out_var_names[i];
auto *out_var = scope.FindVar(out_var_names[i]);
PADDLE_ENFORCE(out_var != nullptr, "Output variable %s cannot be found",
out_var_names[i]);
auto *tensor = out_var->GetMutable<framework::LoDTensor>();
VLOG(3) << "Get Tensor";
// Error checking
PADDLE_ENFORCE(!fin.bad(), "Cannot read more from file %s",
PADDLE_ENFORCE(static_cast<bool>(*fin), "Cannot read more from file %s",
filename);
VLOG(3) << "before deserialization";
// Get data from fin to tensor
DeserializeFromStream(fin, tensor, dev_ctx);
// VLOG(3) << "after deserialization";
// framework::Tensor check;
// framework::TensorCopy(*tensor, platform::CPUPlace(), dev_ctx, &check);
// float sum = .0;
// for(size_t i=0; i < check.numel(); ++i) {
// if(std::type_index(check.type()) == std::type_index(typeid(int64_t))) {
// sum += static_cast<float>(check.data<int64_t>()[i]);
// } else {
// sum += check.data<float>()[i];
// }
// }
// VLOG(3) << "sum result" << sum;
DeserializeFromStream(*fin, tensor, dev_ctx);
auto in_dtype = framework::ToDataType(tensor->type());
auto out_dtype =
load_as_fp16 ? framework::proto::VarType::FP16 : in_dtype;
......@@ -93,9 +86,7 @@ class LoadCombineOp : public framework::OperatorBase {
tensor = out_var->GetMutable<framework::LoDTensor>();
tensor->set_lod(fp16_tensor.lod());
tensor->ShareDataWith(fp16_tensor);
}
VLOG(3) << "load " << out_var_names[i] << " finished";
}
}
};
......@@ -119,6 +110,18 @@ class LoadCombineOpProtoMaker : public framework::OpProtoAndCheckerMaker {
"LoDTensors will be loaded from \"file_path\".")
.AddCustomChecker(
[](const std::string &path) { return !path.empty(); });
AddAttr<std::string>("format",
R"DOC((windows|linux)" "saved model file format
windows and linux file newline symbol is
different. windows(newline is \n\r) or linux(newline is \r)
So if you set attribute format to windows, then we saved model file in binary.
It can be used both linux and windows. If you set format to linux,
it will save file in normal file, newline symbol is \r. Need to note
that these two format is not inter-compatible.)DOC")
.SetDefault("linux")
.AddCustomChecker([](const std::string &s) {
return s == "windows" || s == "linux";
});
AddComment(R"DOC(
LoadCombine Operator.
......
paddle/fluid/operators/load_op.cc
浏览文件 @ 60f70b17
......@@ -12,6 +12,7 @@ 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 <fstream>
#include <memory>
#include "paddle/fluid/framework/data_type_transform.h"
#include "paddle/fluid/framework/op_registry.h"
......@@ -34,8 +35,15 @@ class LoadOp : public framework::OperatorBase {
// FIXME(yuyang18): We save variable to local file now, but we should change
// it to save an output stream.
auto filename = Attr<std::string>("file_path");
std::ifstream fin(filename);
PADDLE_ENFORCE(static_cast<bool>(fin), "Cannot open file %s for load op",
auto format = Attr<std::string>("format");
std::unique_ptr<std::ifstream> fin;
if (format == "windows") {
fin.reset(new std::ifstream(filename,
std::ios_base::in | std::ios_base::binary));
} else {
fin.reset(new std::ifstream(filename));
}
PADDLE_ENFORCE(static_cast<bool>(*fin), "Cannot open file %s for load op",
filename);
auto out_var_name = Output("Out");
......@@ -44,9 +52,9 @@ class LoadOp : public framework::OperatorBase {
out_var_name);
if (out_var->IsType<framework::LoDTensor>()) {
LoadLodTensor(fin, place, out_var);
LoadLodTensor(*fin, place, out_var);
} else if (out_var->IsType<framework::SelectedRows>()) {
LoadSelectedRows(fin, place, out_var);
LoadSelectedRows(*fin, place, out_var);
} else {
PADDLE_ENFORCE(
false,
......@@ -110,6 +118,18 @@ class LoadOpProtoMaker : public framework::OpProtoAndCheckerMaker {
R"(Variable will be loaded from "file_path")")
.AddCustomChecker(
[](const std::string &path) { return !path.empty(); });
AddAttr<std::string>("format",
R"DOC((windows|linux)" "saved model file format
windows and linux file newline symbol is
different. windows(newline is \n\r) or linux(newline is \r)
So if you set attribute format to windows, then we saved model file in binary.
It can be used both linux and windows. If you set format to linux,
it will save file in normal file, newline symbol is \r. Need to note
that these two format is not inter-compatible.)DOC")
.SetDefault("linux")
.AddCustomChecker([](const std::string &s) {
return s == "windows" || s == "linux";
});
AddComment(
"Load operator will load a LoDTensor / SelectedRows variable from disk "
"file.");
......
paddle/fluid/operators/save_combine_op.cc
浏览文件 @ 60f70b17
......@@ -14,6 +14,7 @@ limitations under the License. */
#include <stdint.h>
#include <fstream>
#include <memory>
#include <numeric>
#include <sstream>
#include "paddle/fluid/framework/data_type.h"
......@@ -41,6 +42,7 @@ class SaveCombineOp : public framework::OperatorBase {
auto filename = Attr<std::string>("file_path");
auto overwrite = Attr<bool>("overwrite");
auto save_as_fp16 = Attr<bool>("save_as_fp16");
auto format = Attr<std::string>("format");
bool is_present = FileExists(filename);
if (is_present && !overwrite) {
......@@ -49,8 +51,14 @@ class SaveCombineOp : public framework::OperatorBase {
}
MkDirRecursively(DirName(filename).c_str());
std::ofstream fout(filename, std::ios_base::out | std::ios_base::binary);
PADDLE_ENFORCE(static_cast<bool>(fout), "Cannot open %s to write",
std::unique_ptr<std::ofstream> fout;
if (format == "windows") {
fout.reset(new std::ofstream(filename,
std::ios_base::out | std::ios_base::binary));
} else {
fout.reset(new std::ofstream(filename));
}
PADDLE_ENFORCE(static_cast<bool>(*fout), "Cannot open %s to write",
filename);
auto inp_var_names = Inputs("X");
......@@ -86,12 +94,11 @@ class SaveCombineOp : public framework::OperatorBase {
// copy LoD info to the new tensor
out.set_lod(tensor.lod());
framework::TransDataType(in_kernel_type, out_kernel_type, tensor, &out);
framework::SerializeToStream(fout, out, dev_ctx);
framework::SerializeToStream(*fout, out, dev_ctx);
} else {
framework::SerializeToStream(fout, tensor, dev_ctx);
framework::SerializeToStream(*fout, tensor, dev_ctx);
}
}
fout.close();
}
};
......@@ -124,6 +131,18 @@ to a file on disk.
"The \"file_path\" where the LoDTensor variables will be saved.")
.AddCustomChecker(
[](const std::string &path) { return !path.empty(); });
AddAttr<std::string>("format",
R"DOC((windows|linux)" "saved model file format
windows and linux file newline symbol is
different. windows(newline is \n\r) or linux(newline is \r)
So if you set attribute format to windows, then we saved model file in binary.
It can be used both linux and windows. If you set format to linux,
it will save file in normal file, newline symbol is \r. Need to note
that these two format is not inter-compatible.)DOC")
.SetDefault("linux")
.AddCustomChecker([](const std::string &s) {
return s == "windows" || s == "linux";
});
}
};
......
paddle/fluid/operators/save_op.cc
浏览文件 @ 60f70b17
......@@ -14,6 +14,7 @@ limitations under the License. */
#include <stdint.h>
#include <fstream>
#include <memory>
#include <numeric>
#include "paddle/fluid/framework/data_type.h"
......@@ -64,6 +65,7 @@ class SaveOp : public framework::OperatorBase {
framework::Variable *var) const {
auto filename = Attr<std::string>("file_path");
auto overwrite = Attr<bool>("overwrite");
auto format = Attr<std::string>("format");
if (FileExists(filename) && !overwrite) {
PADDLE_THROW("%s is existed, cannot save to it when overwrite=false",
......@@ -80,8 +82,14 @@ class SaveOp : public framework::OperatorBase {
// FIXME(yuyang18): We save variable to local file now, but we should change
// it to save an output stream.
std::ofstream fout(filename);
PADDLE_ENFORCE(static_cast<bool>(fout), "Cannot open %s to write",
std::unique_ptr<std::ofstream> fout;
if (format == "windows") {
fout.reset(new std::ofstream(filename,
std::ios_base::out | std::ios_base::binary));
} else {
fout.reset(new std::ofstream(filename));
}
PADDLE_ENFORCE(static_cast<bool>(*fout), "Cannot open %s to write",
filename);
auto save_as_fp16 = Attr<bool>("save_as_fp16");
......@@ -95,11 +103,10 @@ class SaveOp : public framework::OperatorBase {
framework::TransDataType(in_kernel_type, out_kernel_type, tensor, &out);
// copy LoD info to the new tensor
out.set_lod(tensor.lod());
framework::SerializeToStream(fout, out, dev_ctx);
framework::SerializeToStream(*fout, out, dev_ctx);
} else {
framework::SerializeToStream(fout, tensor, dev_ctx);
framework::SerializeToStream(*fout, tensor, dev_ctx);
}
fout.close();
}
void SaveSelectedRows(const framework::Scope &scope,
......@@ -110,6 +117,7 @@ class SaveOp : public framework::OperatorBase {
lt_var != nullptr,
"Can not find variable kLookupTablePath for SaveSelectedRows");
std::string filename = lt_var->data();
auto format = Attr<std::string>("format");
VLOG(4) << "SaveSelectedRows get File name: " << filename;
MkDirRecursively(DirName(filename).c_str());
......@@ -122,11 +130,16 @@ class SaveOp : public framework::OperatorBase {
// FIXME(yuyang18): We save variable to local file now, but we should change
// it to save an output stream.
std::ofstream fout(filename);
PADDLE_ENFORCE(static_cast<bool>(fout), "Cannot open %s to write",
std::unique_ptr<std::ofstream> fout;
if (format == "windows") {
fout.reset(new std::ofstream(filename,
std::ios_base::out | std::ios_base::binary));
} else {
fout.reset(new std::ofstream(filename));
}
PADDLE_ENFORCE(static_cast<bool>(*fout), "Cannot open %s to write",
filename);
framework::SerializeToStream(fout, selectedRows, dev_ctx);
fout.close();
framework::SerializeToStream(*fout, selectedRows, dev_ctx);
}
};
......@@ -154,6 +167,18 @@ This operator will serialize and write LoDTensor / SelectedRows variable to file
"The \"file_path\" where the variable will be saved.")
.AddCustomChecker(
[](const std::string &path) { return !path.empty(); });
AddAttr<std::string>("format",
R"DOC((windows|linux)" "saved model file format
windows and linux file newline symbol is
different. windows(newline is \n\r) or linux(newline is \r)
So if you set attribute format to windows, then we saved model file in binary.
It can be used both linux and windows. If you set format to linux,
it will save file in normal file, newline symbol is \r. Need to note
that these two format is not inter-compatible.)DOC")
.SetDefault("linux")
.AddCustomChecker([](const std::string &s) {
return s == "windows" || s == "linux";
});
}
};
......
paddle/fluid/platform/init.cc
浏览文件 @ 60f70b17
......@@ -94,9 +94,7 @@ void InitDevices(bool init_p2p, const std::vector<int> devices) {
int count = 0;
#ifdef PADDLE_WITH_CUDA
try {
VLOG(3) << "get cuda count";
count = platform::GetCUDADeviceCount();
VLOG(3) << "get cuda pass";
} catch (const std::exception &exp) {
LOG(WARNING) << "Compiled with WITH_GPU, but no GPU found in runtime.";
}
......@@ -109,14 +107,11 @@ void InitDevices(bool init_p2p, const std::vector<int> devices) {
}
places.emplace_back(platform::CUDAPlace(devices[i]));
}
VLOG(3) << "before p2p";
if (init_p2p) {
InitP2P(devices);
}
VLOG(3) << "p2p pass";
places.emplace_back(platform::CPUPlace());
platform::DeviceContextPool::Init(places);
VLOG(3) << "init pass";
#ifndef PADDLE_WITH_MKLDNN
platform::SetNumThreads(FLAGS_paddle_num_threads);
#endif
......
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