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PaddleDetection
提交 5993155d 编写于 作者: W wanghaoshuang
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Merge remote-tracking branch 'dzhwinter/windows/support' into windows/support

上级 f9e7cfb0 e41a3fcd
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paddle/fluid/framework/CMakeLists.txt
浏览文件 @ 5993155d
......@@ -43,13 +43,13 @@ nv_test(dim_test SRCS dim_test.cu DEPS ddim)
cc_library(data_type SRCS data_type.cc DEPS framework_proto ddim device_context)
cc_test(data_type_test SRCS data_type_test.cc DEPS data_type place tensor)
if(WITH_GPU)
if (WIN32)
windows_symbolic(tensor_util SRCS tensor_util.cu)
nv_library(tensor SRCS tensor.cc .tensor_util.cu DEPS place memory data_type device_context)
add_dependencies(tensor tensor_util)
else()
# // if (WIN32)
# // windows_symbolic(tensor_util SRCS tensor_util.cu)
# // nv_library(tensor SRCS tensor.cc .tensor_util.cu DEPS place memory data_type device_context)
# // add_dependencies(tensor tensor_util)
# // else()
nv_library(tensor SRCS tensor.cc tensor_util.cu DEPS place memory data_type device_context)
endif(WIN32)
# endif(WIN32)
else()
cc_library(tensor SRCS tensor.cc tensor_util.cc DEPS place memory data_type device_context)
endif()
......@@ -93,15 +93,15 @@ nv_test(data_device_transform_test SRCS data_device_transform_test.cu
DEPS operator op_registry device_context math_function)
if(WITH_GPU)
if (WIN32)
# windows treat symbolic file as a real file, which is different with unix
# We create a hidden file and compile it instead of origin source file.
windows_symbolic(hidden_file SRCS data_type_transform.cu)
nv_library(data_type_transform SRCS .data_type_transform.cu DEPS tensor)
add_dependencies(data_type_transform hidden_file)
else()
# if (WIN32)
# # windows treat symbolic file as a real file, which is different with unix
# # We create a hidden file and compile it instead of origin source file.
# windows_symbolic(hidden_file SRCS data_type_transform.cu)
# nv_library(data_type_transform SRCS .data_type_transform.cu DEPS tensor)
# add_dependencies(data_type_transform hidden_file)
# else()
nv_library(data_type_transform SRCS data_type_transform.cu DEPS tensor)
endif(WIN32)
# endif(WIN32)
nv_test(data_type_transform_test SRCS data_type_transform_test.cc data_type_transform_test.cu DEPS data_type_transform)
else()
cc_library(data_type_transform SRCS data_type_transform.cc DEPS tensor)
......
paddle/fluid/framework/data_type_transform.cu
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// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
data_type_transform.cc
\ No newline at end of file
/* Copyright (c) 2016 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/fluid/framework/data_type_transform.h"
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/platform/transform.h"
namespace paddle {
namespace framework {
template <typename InType, typename OutType>
struct CastDataTypeFunctor {
HOSTDEVICE inline OutType operator()(InType in) const {
return static_cast<OutType>(in);
}
};
template <typename InType>
struct CastDataType {
CastDataType(const framework::Tensor& in, framework::Tensor* out,
const platform::DeviceContext* ctx)
: in_(in), out_(out), ctx_(ctx) {}
const framework::Tensor in_;
framework::Tensor* out_;
const platform::DeviceContext* ctx_;
template <typename OutType>
void apply() {
auto* in_begin = in_.data<InType>();
auto* in_end = in_begin + in_.numel();
auto* out_begin = out_->mutable_data<OutType>(in_.place());
if (platform::is_cpu_place(in_.place())) {
platform::Transform<platform::CPUDeviceContext> trans;
auto* context = static_cast<const platform::CPUDeviceContext*>(ctx_);
trans(*context, in_begin, in_end, out_begin,
CastDataTypeFunctor<InType, OutType>());
#ifdef __NVCC__
} else if (platform::is_gpu_place(in_.place())) {
platform::Transform<platform::CUDADeviceContext> trans;
auto* context = static_cast<const platform::CUDADeviceContext*>(ctx_);
trans(*context, in_begin, in_end, out_begin,
CastDataTypeFunctor<InType, OutType>());
context->Wait();
#endif
} else {
PADDLE_THROW("Unsupported place!");
}
}
};
void TransDataType(const OpKernelType& kernel_type_for_var,
const OpKernelType& expected_kernel_type, const Tensor& in,
Tensor* out) {
platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
out->Resize(in.dims());
auto src_type = kernel_type_for_var.data_type_;
auto dst_type = expected_kernel_type.data_type_;
auto ctx = pool.Get(in.place());
switch (src_type) {
case proto::VarType::FP16:
framework::VisitDataType(dst_type,
CastDataType<platform::float16>(in, out, ctx));
break;
case proto::VarType::FP32:
framework::VisitDataType(dst_type, CastDataType<float>(in, out, ctx));
break;
case proto::VarType::FP64:
framework::VisitDataType(dst_type, CastDataType<double>(in, out, ctx));
break;
case proto::VarType::INT32:
framework::VisitDataType(dst_type, CastDataType<int>(in, out, ctx));
break;
case proto::VarType::INT64:
framework::VisitDataType(dst_type, CastDataType<int64_t>(in, out, ctx));
break;
case proto::VarType::BOOL:
framework::VisitDataType(dst_type, CastDataType<bool>(in, out, ctx));
break;
case proto::VarType::INT16:
framework::VisitDataType(dst_type, CastDataType<bool>(in, out, ctx));
break;
case proto::VarType::UINT8:
framework::VisitDataType(dst_type, CastDataType<bool>(in, out, ctx));
break;
default:
PADDLE_THROW("Not support type %d", src_type);
}
}
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/node.cc
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......@@ -17,8 +17,11 @@ limitations under the License. */
namespace paddle {
namespace framework {
namespace ir {
constexpr char Node::kControlDepVarName[];
#if !defined(_WIN32)
constexpr char Node::kControlDepVarName[] = "__control_var";
#else
const char Node::kControlDepVarName[] = "__control_var";
#endif
int Node::count_ = 0;
} // namespace ir
} // namespace framework
......
paddle/fluid/framework/ir/node.h
浏览文件 @ 5993155d
......@@ -27,7 +27,11 @@ namespace ir {
class Node {
public:
enum class Type { kOperation, kVariable };
#if !defined(_WIN32) // msvc not support constexpr correctly.
static constexpr char kControlDepVarName[] = "__control_var";
#else
static const char kControlDepVarName[];
#endif
explicit Node(const std::string& name, Type type)
: name_(name),
......
paddle/fluid/framework/operator.cc
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......@@ -689,7 +689,7 @@ void OperatorWithKernel::RunImpl(const Scope& scope,
auto expected_kernel_key =
this->GetExpectedKernelType(ExecutionContext(*this, scope, *dev_ctx));
VLOG(3) << "expected_kernel_key:" << expected_kernel_key;
VLOG(3) << "expected_kernel_key: " << expected_kernel_key;
auto kernel_iter = kernels.find(expected_kernel_key);
#ifdef PADDLE_WITH_MKLDNN
......
paddle/fluid/framework/tensor_util.cu
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// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
tensor_util.cc
\ No newline at end of file
/* Copyright (c) 2016 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/fluid/framework/tensor_util.h"
#include <algorithm>
#include <limits>
#include <vector>
#include "paddle/fluid/framework/data_type.h"
namespace paddle {
namespace framework {
void TensorCopy(const Tensor& src, const platform::Place& dst_place,
const platform::DeviceContext& ctx, Tensor* dst) {
VLOG(3) << "TensorCopy " << src.dims() << " from " << src.place() << " to "
<< dst_place;
src.check_memory_size();
dst->Resize(src.dims());
dst->set_layout(src.layout());
auto src_place = src.place();
auto src_ptr = src.data<void>();
auto dst_ptr = dst->mutable_data(dst_place, src.type());
auto size = src.numel() * SizeOfType(src.type());
if (platform::is_cpu_place(src_place) && platform::is_cpu_place(dst_place)) {
memory::Copy(boost::get<platform::CPUPlace>(dst_place), dst_ptr,
boost::get<platform::CPUPlace>(src_place), src_ptr, size);
}
#ifdef PADDLE_WITH_CUDA
else if (platform::is_gpu_place(src_place) && // NOLINT
platform::is_cpu_place(dst_place)) {
auto src_gpu_place = boost::get<platform::CUDAPlace>(src_place);
auto dst_cpu_place = boost::get<platform::CPUPlace>(dst_place);
auto ctx_place = ctx.GetPlace();
PADDLE_ENFORCE(platform::is_gpu_place(ctx_place));
auto ctx_gpu_place = boost::get<platform::CUDAPlace>(ctx_place);
PADDLE_ENFORCE_EQ(src_gpu_place, ctx_gpu_place);
auto stream =
reinterpret_cast<const platform::CUDADeviceContext&>(ctx).stream();
memory::Copy(dst_cpu_place, dst_ptr, src_gpu_place, src_ptr, size, stream);
} else if (platform::is_cpu_place(src_place) &&
platform::is_gpu_place(dst_place)) {
auto src_cpu_place = boost::get<platform::CPUPlace>(src_place);
auto dst_gpu_place = boost::get<platform::CUDAPlace>(dst_place);
auto ctx_place = ctx.GetPlace();
PADDLE_ENFORCE(platform::is_gpu_place(ctx_place));
auto ctx_gpu_place = boost::get<platform::CUDAPlace>(ctx_place);
PADDLE_ENFORCE_EQ(dst_gpu_place, ctx_gpu_place);
auto stream =
reinterpret_cast<const platform::CUDADeviceContext&>(ctx).stream();
memory::Copy(dst_gpu_place, dst_ptr, src_cpu_place, src_ptr, size, stream);
} else if (platform::is_gpu_place(src_place) &&
platform::is_gpu_place(dst_place)) {
auto src_gpu_place = boost::get<platform::CUDAPlace>(src_place);
auto dst_gpu_place = boost::get<platform::CUDAPlace>(dst_place);
auto ctx_place = ctx.GetPlace();
PADDLE_ENFORCE(platform::is_gpu_place(ctx_place));
auto stream =
reinterpret_cast<const platform::CUDADeviceContext&>(ctx).stream();
if (platform::is_same_place(src_place, dst_place)) {
memory::Copy(dst_gpu_place, dst_ptr, src_gpu_place, src_ptr, size,
stream);
} else {
if (platform::is_same_place(ctx_place, src_place)) {
memory::Copy(dst_gpu_place, dst_ptr, src_gpu_place, src_ptr, size,
stream);
platform::DeviceContextPool::Instance().Get(src.place())->Wait();
} else if (platform::is_same_place(ctx_place, dst_place)) {
platform::DeviceContextPool::Instance().Get(src.place())->Wait();
memory::Copy(dst_gpu_place, dst_ptr, src_gpu_place, src_ptr, size,
stream);
} else {
PADDLE_THROW("ctx is not belong to dst_gpu_place or src_gpu_place.");
}
}
}
#endif
}
void TensorCopy(const Tensor& src, const platform::Place& dst_place,
Tensor* dst) {
platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
const platform::DeviceContext* dev_ctx;
if (platform::is_gpu_place(dst_place)) {
dev_ctx = pool.Get(dst_place);
} else {
dev_ctx = pool.Get(src.place());
}
TensorCopy(src, dst_place, *dev_ctx, dst);
}
void TensorCopySync(const Tensor& src, const platform::Place& dst_place,
Tensor* dst) {
VLOG(3) << "TensorCopySync " << src.dims() << " from " << src.place()
<< " to " << dst_place;
src.check_memory_size();
dst->Resize(src.dims());
dst->set_layout(src.layout());
auto src_place = src.place();
auto src_ptr = src.data<void>();
auto dst_ptr = dst->mutable_data(dst_place, src.type());
auto size = src.numel() * SizeOfType(src.type());
if (platform::is_cpu_place(src_place) && platform::is_cpu_place(dst_place)) {
memory::Copy(boost::get<platform::CPUPlace>(dst_place), dst_ptr,
boost::get<platform::CPUPlace>(src_place), src_ptr, size);
}
#ifdef PADDLE_WITH_CUDA
else if (platform::is_gpu_place(src_place) && // NOLINT
platform::is_cpu_place(dst_place)) {
auto src_gpu_place = boost::get<platform::CUDAPlace>(src_place);
auto dst_cpu_place = boost::get<platform::CPUPlace>(dst_place);
memory::Copy(dst_cpu_place, dst_ptr, src_gpu_place, src_ptr, size, nullptr);
} else if (platform::is_cpu_place(src_place) &&
platform::is_gpu_place(dst_place)) {
auto src_cpu_place = boost::get<platform::CPUPlace>(src_place);
auto dst_gpu_place = boost::get<platform::CUDAPlace>(dst_place);
memory::Copy(dst_gpu_place, dst_ptr, src_cpu_place, src_ptr, size, nullptr);
} else if (platform::is_gpu_place(src_place) &&
platform::is_gpu_place(dst_place)) {
auto src_gpu_place = boost::get<platform::CUDAPlace>(src_place);
auto dst_gpu_place = boost::get<platform::CUDAPlace>(dst_place);
memory::Copy(dst_gpu_place, dst_ptr, src_gpu_place, src_ptr, size, nullptr);
}
#endif
}
template <typename Predicate, typename DevCtx>
struct AnyDTypeVisitor {
Predicate predicate_;
const Tensor& tensor_;
const DevCtx& ctx_;
Tensor* out_;
AnyDTypeVisitor(Predicate predicate, const Tensor& tensor, const DevCtx& ctx,
Tensor* out)
: predicate_(predicate), tensor_(tensor), ctx_(ctx), out_(out) {}
template <typename T>
void apply() const {
auto t = EigenVector<T>::Flatten(tensor_);
auto o = EigenScalar<bool>::From(*out_);
// return any of predicate_(t) is true.
o.device(*ctx_.eigen_device()) = predicate_(t).any();
}
};
template <typename Predicate, typename DevCtx>
inline void AnyImpl(Predicate predicate, const framework::Tensor& tensor,
const DevCtx& ctx, framework::Tensor* out) {
VisitDataType(ToDataType(tensor.type()), AnyDTypeVisitor<Predicate, DevCtx>(
predicate, tensor, ctx, out));
}
template <typename Predicate>
struct AnyVisitor : public boost::static_visitor<bool> {
const framework::Tensor& tensor_;
Predicate predicate_;
AnyVisitor(const framework::Tensor& tensor, Predicate predicate)
: tensor_(tensor), predicate_(std::move(predicate)) {}
template <typename Place>
bool operator()(const Place& place) const {
framework::Tensor out;
out.Resize({1});
out.mutable_data<bool>(place);
auto* ctx = platform::DeviceContextPool::Instance().GetByPlace(place);
AnyImpl(predicate_, tensor_, *ctx, &out);
return this->GetResult(out, place);
}
bool GetResult(const framework::Tensor& out,
const platform::CUDAPlace& gpu) const {
platform::CPUPlace cpu;
framework::Tensor tmp;
tmp.Resize({1});
tmp.mutable_data<bool>(cpu);
auto gpuctx = platform::DeviceContextPool::Instance().Get(gpu);
gpuctx->Wait();
TensorCopy(out, cpu, *gpuctx, &tmp);
gpuctx->Wait();
return GetResult(tmp, cpu);
}
bool GetResult(const framework::Tensor& out,
const platform::CPUPlace& cpu) const {
return *out.data<bool>();
}
bool GetResult(const framework::Tensor& out,
const platform::CUDAPinnedPlace& cpu) const {
return *out.data<bool>();
}
};
template <typename Predicate>
inline bool Any(const framework::Tensor& tensor, Predicate predicate) {
AnyVisitor<Predicate> visitor(tensor, predicate);
auto place = tensor.place();
return platform::VisitPlace(place, visitor);
}
struct ContainsNANPredicate {
template <typename T>
auto operator()(const T& eigen_vec) const
-> decltype(std::declval<T>().isnan()) {
// Cast eigen_vector to vector of bool. true if is inf.
return eigen_vec.isnan();
}
};
bool TensorContainsNAN(const framework::Tensor& tensor) {
ContainsNANPredicate predicate;
return Any(tensor, predicate);
}
struct ContainsInfPredicate {
template <typename T>
auto operator()(const T& eigen_vec) const
-> decltype(std::declval<T>().isinf()) {
// Cast eigen_vector to vector of bool. true if is inf.
return eigen_vec.isinf();
}
};
bool TensorContainsInf(const framework::Tensor& tensor) {
ContainsInfPredicate predicate;
return Any(tensor, predicate);
}
void TensorToStream(std::ostream& os, const Tensor& tensor,
const platform::DeviceContext& dev_ctx) {
{ // the 1st field, uint32_t version
constexpr uint32_t version = 0;
os.write(reinterpret_cast<const char*>(&version), sizeof(version));
}
{ // the 2nd field, tensor description
// int32_t size
// void* protobuf message
proto::VarType::TensorDesc desc;
desc.set_data_type(framework::ToDataType(tensor.type()));
auto dims = framework::vectorize(tensor.dims());
auto* pb_dims = desc.mutable_dims();
pb_dims->Resize(static_cast<int>(dims.size()), 0);
std::copy(dims.begin(), dims.end(), pb_dims->begin());
int32_t size = desc.ByteSize();
os.write(reinterpret_cast<const char*>(&size), sizeof(size));
auto out = desc.SerializeAsString();
os.write(out.data(), size);
}
{ // the 3rd field, tensor data
uint64_t size = tensor.numel() * framework::SizeOfType(tensor.type());
auto* data_ptr = tensor.data<void>();
PADDLE_ENFORCE(size < std::numeric_limits<std::streamsize>::max(),
"Index overflow when writing tensor");
if (platform::is_gpu_place(tensor.place())) {
#ifdef PADDLE_WITH_CUDA
constexpr size_t kBufSize = 1024 * 1024 * 64; // 64MB
std::unique_ptr<char[]> buf(new char[kBufSize]);
auto& gpu_dev_ctx =
static_cast<const platform::CUDADeviceContext&>(dev_ctx);
platform::CPUPlace cpu;
uintptr_t data = reinterpret_cast<uintptr_t>(data_ptr);
while (size != 0) {
size_t size_to_write = std::min(kBufSize, static_cast<size_t>(size));
memory::Copy(cpu, buf.get(),
boost::get<platform::CUDAPlace>(tensor.place()),
reinterpret_cast<const void*>(data), size_to_write,
gpu_dev_ctx.stream());
gpu_dev_ctx.Wait();
os.write(buf.get(), size_to_write);
data += size_to_write;
size -= size_to_write;
}
#else
PADDLE_THROW("Unexpected branch");
#endif
} else {
os.write(static_cast<const char*>(data_ptr),
static_cast<std::streamsize>(size));
}
}
}
struct DeserializedDataFunctor {
DeserializedDataFunctor(void** buf, Tensor* tensor,
const platform::Place& place)
: buf_(buf), tensor_(tensor), place_(place) {}
template <typename T>
void apply() {
*buf_ = tensor_->mutable_data<T>(place_);
}
void** buf_;
Tensor* tensor_;
platform::Place place_;
};
void TensorFromStream(std::istream& is, Tensor* tensor,
const platform::DeviceContext& dev_ctx) {
uint32_t version;
is.read(reinterpret_cast<char*>(&version), sizeof(version));
PADDLE_ENFORCE_EQ(version, 0U, "Only version 0 is supported");
proto::VarType::TensorDesc desc;
{ // int32_t size
// proto buffer
int32_t size;
is.read(reinterpret_cast<char*>(&size), sizeof(size));
std::unique_ptr<char[]> buf(new char[size]);
is.read(reinterpret_cast<char*>(buf.get()), size);
PADDLE_ENFORCE(desc.ParseFromArray(buf.get(), size),
"Cannot parse tensor desc");
}
{ // read tensor
std::vector<int64_t> dims;
dims.reserve(static_cast<size_t>(desc.dims().size()));
std::copy(desc.dims().begin(), desc.dims().end(), std::back_inserter(dims));
tensor->Resize(framework::make_ddim(dims));
void* buf;
auto ctx = platform::CPUDeviceContext();
size_t size =
tensor->numel() *
framework::SizeOfType(framework::ToTypeIndex(desc.data_type()));
if (platform::is_gpu_place(dev_ctx.GetPlace())) {
#ifdef PADDLE_WITH_CUDA
Tensor cpu_tensor;
cpu_tensor.Resize(framework::make_ddim(dims));
framework::VisitDataType(
desc.data_type(),
DeserializedDataFunctor(&buf, &cpu_tensor, ctx.GetPlace()));
is.read(static_cast<char*>(buf), size);
auto dst_place = dev_ctx.GetPlace();
framework::TensorCopy(cpu_tensor, dst_place, dev_ctx, tensor);
#else
PADDLE_THROW("Unexpected branch");
#endif
} else {
framework::VisitDataType(
desc.data_type(),
DeserializedDataFunctor(&buf, tensor, ctx.GetPlace()));
is.read(static_cast<char*>(buf), size);
}
}
}
} // namespace framework
} // namespace paddle
paddle/fluid/inference/api/demo_ci/inference_icnet.cc
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/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
/*
* This file contains a simple demo for how to take a model for inference.
*/
#include <cassert>
#include <cctype>
#include <algorithm>
#include <fstream>
#include <iostream>
#include <iterator>
#include <memory>
#include <sstream>
#include <string>
#include <thread> //NOLINT
#include "paddle/fluid/inference/paddle_inference_api.h"
std::string MODELDIR = ""; /* "Directory of the inference model." */ // NOLINT
std::string REFER = "";
/*"path to reference result for comparison."*/ //NOTLINT
/*path of data; each line is a record, format:
<space splitted floats as data>\t<space splitted ints as shape>
Please check the demo data of data.txt for details.
*/
std::string DATA = "";
bool USE_GPU = true; /*"Whether use gpu."*/
auto message_err = []()
{
std::cout << "Copyright (c) 2018 PaddlePaddle Authors." << std::endl;
std::cout << "Demo Case for windows inference. "
<< "\n"
<< "Usage: Input your model path and use_gpu as the guide requires,"
<< "then run the demo inference, and will get a result."
<< std::endl;
std::cout << std::endl;
};
namespace paddle
{
namespace demo
{
void split(const std::string& str, char sep,
std::vector<std::string>* pieces)
{
pieces->clear();
if (str.empty())
{
return;
}
size_t pos = 0;
size_t next = str.find(sep, pos);
while (next != std::string::npos)
{
pieces->push_back(str.substr(pos, next - pos));
pos = next + 1;
next = str.find(sep, pos);
}
if (!str.substr(pos).empty())
{
pieces->push_back(str.substr(pos));
}
}
/*
* Get a summary of a PaddleTensor content.
*/
std::string SummaryTensor(const PaddleTensor& tensor)
{
std::stringstream ss;
int num_elems = tensor.data.length() / PaddleDtypeSize(tensor.dtype);
ss << "data[:10]\t";
switch (tensor.dtype)
{
case PaddleDType::INT64:
for (int i = 0; i < std::min(num_elems, 10); i++)
{
ss << static_cast<int64_t*>(tensor.data.data())[i] << " ";
}
break;
case PaddleDType::FLOAT32:
for (int i = 0; i < std::min(num_elems, 10); i++)
{
ss << static_cast<float*>(tensor.data.data())[i] << " ";
}
break;
}
return ss.str();
}
std::string ToString(const NativeConfig& config)
{
std::stringstream ss;
ss << "Use GPU : " << (config.use_gpu ? "True" : "False") << "\n"
<< "Device : " << config.device << "\n"
<< "fraction_of_gpu_memory : " << config.fraction_of_gpu_memory << "\n"
<< "specify_input_name : "
<< (config.specify_input_name ? "True" : "False") << "\n"
<< "Program File : " << config.prog_file << "\n"
<< "Param File : " << config.param_file;
return ss.str();
}
struct Record
{
std::vector<float> data;
std::vector<int32_t> shape;
};
Record ProcessALine(const std::string& line)
{
std::cout << "process a line" << std::endl;
std::vector<std::string> columns;
split(line, '\t', &columns);
assert(columns.size() == 2UL, "data format error, should be <data>\t<shape>");
Record record;
std::vector<std::string> data_strs;
split(columns[0], ' ', &data_strs);
//将数据字符串转换为整型数据并放到record.data中
for (auto& d : data_strs)
{
record.data.push_back(std::stof(d));
}
std::vector<std::string> shape_strs;
split(columns[1], ' ', &shape_strs);
for (auto& s : shape_strs)
{
record.shape.push_back(std::stoi(s));
}
std::cout << "data size " << record.data.size() << std::endl;
std::cout << "data shape size " << record.shape.size() << std::endl;
return record;
}
void CheckOutput(const std::string& referfile, const PaddleTensor& output)
{
std::string line;
std::ifstream file(referfile);
std::getline(file, line);
auto refer = ProcessALine(line);
file.close();
size_t numel = output.data.length() / PaddleDtypeSize(output.dtype);
std::cout << "predictor output numel " << numel << std::endl;
std::cout << "reference output numel " << refer.data.size() << std::endl;
assert(numel == refer.data.size());
switch (output.dtype)
{
case PaddleDType::INT64:
for (size_t i = 0; i < numel; ++i)
{
assert(static_cast<int64_t*>(output.data.data())[i] == refer.data[i]);
}
break;
case PaddleDType::FLOAT32:
for (size_t i = 0; i < numel; ++i)
{
assert(fabs(static_cast<float*>(output.data.data())[i] - refer.data[i]) <= 1e-5);
}
break;
}
}
/*
* Use the native fluid engine to inference the demo.
*/
void Main(bool use_gpu)
{
NativeConfig config;
config.model_dir = MODELDIR;
//config.param_file = MODELDIR + "/__params__";
//config.prog_file = MODELDIR + "/__model__";
config.use_gpu = USE_GPU;
config.device = 0;
if (USE_GPU)
{
config.fraction_of_gpu_memory = 0.1f; // set by yourself
}
std::cout << ToString(config) << std::endl;
std::cout << "init predictor" << std::endl;
auto predictor = CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(config);
std::cout << "begin to process data" << std::endl;
// Just a single batch of data.
std::string line;
std::cout << "data : " << std::endl;
std::ifstream file(DATA);
if (!file.is_open())
{
std::cout << "failed open data" << DATA << std::endl;
exit(0);
}
std::getline(file, line);
auto record = ProcessALine(line);
file.close();
// Inference.
PaddleTensor input;
input.shape = record.shape;
input.data =
PaddleBuf(record.data.data(), record.data.size() * sizeof(float));
input.dtype = PaddleDType::FLOAT32;
std::cout << "run executor" << std::endl;
std::vector<PaddleTensor> output;
predictor->Run({ input }, &output);
std::cout << "output.size " << output.size() << std::endl;
auto& tensor = output.front();
std::cout << "output: " << SummaryTensor(tensor) << std::endl;
// compare with reference result
std::cout << "refer result : " << REFER << std::endl;
CheckOutput(REFER, tensor);
}
}
}
int main(int argc, char** argv)
{
MODELDIR = "./LB_icnet_model";
//DATA = "./icnet_image.txt";
DATA = "./1.png.txt";
REFER = "./icnet_label.txt";
paddle::demo::Main(USE_GPU);
system("pause");
return 0;
}
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include <cassert>
#include <chrono>
#include <iostream>
#include <fstream>
#include <algorithm>
#include <vector>
#include <string>
#include "paddle/fluid/inference/api/paddle_inference_api.h"
namespace paddle {
std::string DIRNAME = "./Release/infer_model";
std::string DATA = "./test-image.txt";
const int C = 3; // image channel
const int H = 449; // image height
const int W = 581; // image width
// 数据格式
// "<space splitted floats as data>\t<space splitted ints as shape"
// 1. 存储为float32格式。
// 2. 必须减去均值。 CHW三个通道为 mean = 112.15, 109.41, 185.42
struct Record
{
std::vector<float> data;
std::vector<int32_t> shape;
};
NativeConfig GetConfig() {
NativeConfig config;
config.prog_file=DIRNAME + "/__model__";
config.param_file=DIRNAME + "/__params__";
config.fraction_of_gpu_memory = 0.0;
config.use_gpu = true;
config.device = 0;
return config;
}
using Time = decltype(std::chrono::high_resolution_clock::now());
Time time() { return std::chrono::high_resolution_clock::now(); };
double time_diff(Time t1, Time t2) {
typedef std::chrono::microseconds ms;
auto diff = t2 - t1;
ms counter = std::chrono::duration_cast<ms>(diff);
return counter.count() / 1000.0;
}
static void split(const std::string& str, char sep,
std::vector<std::string>* pieces) {
pieces->clear();
if (str.empty()) {
return;
}
size_t pos = 0;
size_t next = str.find(sep, pos);
while (next != std::string::npos) {
pieces->push_back(str.substr(pos, next - pos));
pos = next + 1;
next = str.find(sep, pos);
}
if (!str.substr(pos).empty()) {
pieces->push_back(str.substr(pos));
}
}
Record ProcessALine(const std::string& line) {
std::vector<std::string> columns;
split(line, '\t', &columns);
Record record;
std::vector<std::string> data_strs;
split(columns[0], ' ', &data_strs);
for (auto& d : data_strs) {
record.data.push_back(std::stof(d));
}
std::vector<std::string> shape_strs;
split(columns[1], ' ', &shape_strs);
for (auto& s : shape_strs) {
record.shape.push_back(std::stoi(s));
}
return record;
}
void test_naive(int batch_size){
NativeConfig config = GetConfig();
auto predictor = CreatePaddlePredictor<NativeConfig>(config);
int height = H;
int width = W;
int channel = C;
int num_sum = height * width * channel * batch_size;
// 1. use fake data
std::vector<float> data;
for(int i = 0; i < num_sum; i++) {
data.push_back(0.0);
}
PaddleTensor tensor;
tensor.shape = std::vector<int>({batch_size, channel, height, width});
tensor.data.Resize(sizeof(float) * batch_size * channel * height * width);
std::copy(data.begin(), data.end(), static_cast<float*>(tensor.data.data()));
tensor.dtype = PaddleDType::FLOAT32;
// 2. read data from file
// std::string line;
// std::ifstream file(DATA);
// std::getline(file, line);
// auto record = ProcessALine(line);
// file.close();
// PaddleTensor tensor;
// tensor.shape = record.shape;
// tensor.data =
// PaddleBuf(record.data.data(), record.data.size() * sizeof(float));
std::vector<PaddleTensor> paddle_tensor_feeds(1, tensor);
PaddleTensor tensor_out;
std::vector<PaddleTensor> outputs(1, tensor_out);
predictor->Run(paddle_tensor_feeds, &outputs, batch_size);
auto time1 = time();
for(size_t i = 0; i < 2; i++) {
std::cout << "Pass " << i << "predict";
predictor->Run(paddle_tensor_feeds, &outputs, batch_size);
}
auto time2 = time();
std::ofstream ofresult("naive_test_result.txt", std::ios::app);
std::cout <<"batch: " << batch_size << " predict cost: " << time_diff(time1, time2) / 100.0 << "ms" << std::endl;
std::cout << outputs.size() << std::endl;
}
} // namespace paddle
int main(int argc, char** argv) {
paddle::test_naive(1 << 0);
return 0;
}
\ No newline at end of file
paddle/fluid/inference/api/demo_ci/naive_model_test.cc 已删除 100644 → 0
浏览文件 @ f9e7cfb0
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include <chrono>
#include <iostream>
#include <fstream>
#include "paddle/fluid/inference/api/paddle_inference_api.h"
namespace paddle {
std::string DIRNAME = "./LB_icnet_model";
//std::string DIRNAME = "./infer_models";
NativeConfig GetConfig() {
NativeConfig config;
config.prog_file=DIRNAME + "/__model__";
config.param_file=DIRNAME + "/__params__";
config.fraction_of_gpu_memory = 0.8;
config.use_gpu = true;
config.device = 0;
return config;
}
using Time = decltype(std::chrono::high_resolution_clock::now());
Time time() { return std::chrono::high_resolution_clock::now(); };
double time_diff(Time t1, Time t2) {
typedef std::chrono::microseconds ms;
auto diff = t2 - t1;
ms counter = std::chrono::duration_cast<ms>(diff);
return counter.count() / 1000.0;
}
void test_naive(int batch_size){
NativeConfig config = GetConfig();
// config.model_dir = model_path;
auto predictor = CreatePaddlePredictor<NativeConfig>(config);
int height = 449;
int width = 581;
//int height = 3;
//int width = 3;
int num_sum = height * width * 3 * batch_size;
std::vector<float> data;
for(int i = 0; i < num_sum; i++) {
data.push_back(0.0);
}
PaddleTensor tensor;
tensor.shape = std::vector<int>({batch_size, 3, height, width});
tensor.data.Resize(sizeof(float) * batch_size * 3 * height * width);
std::copy(data.begin(), data.end(), static_cast<float*>(tensor.data.data()));
tensor.dtype = PaddleDType::FLOAT32;
std::vector<PaddleTensor> paddle_tensor_feeds(1, tensor);
PaddleTensor tensor_out;
std::vector<PaddleTensor> outputs(1, tensor_out);
predictor->Run(paddle_tensor_feeds, &outputs, batch_size);
std::cout << "start predict123:" << std::endl;
auto time1 = time();
for(size_t i = 0; i < 2; i++) {
predictor->Run(paddle_tensor_feeds, &outputs, batch_size);
std::cout << "pass " << i;
}
auto time2 = time();
std::ofstream ofresult("naive_test_result.txt", std::ios::app);
std::cout <<"batch: " << batch_size << " predict cost: " << time_diff(time1, time2) / 100.0 << "ms" << std::endl;
std::cout << outputs.size() << std::endl;
/*
int64_t * data_o = static_cast<int64_t*>(outputs[0].data.data());
for (size_t j = 0; j < outputs[0].data.length() / sizeof(int64_t); ++j) {
ofresult << std::to_string(data_o[j]) << " ";
}
ofresult << std::endl;
ofresult.close();
*/
}
} // namespace paddle
int main(int argc, char** argv) {
paddle::test_naive(1 << 0);
return 0;
}
\ No newline at end of file
paddle/fluid/operators/conv_cudnn_op.cu.cc
浏览文件 @ 5993155d
......@@ -43,6 +43,7 @@ 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");
......@@ -59,7 +60,7 @@ 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;
......@@ -72,7 +73,7 @@ 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
......@@ -81,7 +82,7 @@ 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>(
......@@ -111,7 +112,7 @@ 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 =
......@@ -129,6 +130,7 @@ 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,
......@@ -149,7 +151,7 @@ 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,
......@@ -158,10 +160,12 @@ 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");
VLOG(3) << "after get workspace";
// Allocate on GPU memory
platform::CUDAPlace gpu = boost::get<platform::CUDAPlace>(ctx.GetPlace());
workspace_size_in_bytes = 1024;
cudnn_workspace = paddle::memory::Alloc(gpu, workspace_size_in_bytes);
VLOG(3) << "allocate memory";
// ------------------- cudnn conv forward ---------------------
ScalingParamType<T> alpha = 1.0f, beta = 0.0f;
for (int i = 0; i < groups; i++) {
......@@ -171,8 +175,10 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
cudnn_conv_desc, algo, cudnn_workspace, workspace_size_in_bytes,
&beta, cudnn_output_desc, output_data + i * group_offset_out));
}
VLOG(3) << "cudnn forward";
// Release the cudnn workspace
paddle::memory::Free(gpu, cudnn_workspace);
VLOG(3) << "cudnn pass";
}
};
......@@ -318,6 +324,7 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
// Already on GPU
void* cudnn_workspace = nullptr;
platform::CUDAPlace gpu = boost::get<platform::CUDAPlace>(ctx.GetPlace());
workspace_size_in_bytes = 1024;
cudnn_workspace = paddle::memory::Alloc(gpu, workspace_size_in_bytes);
// ------------------- cudnn conv backward data ---------------------
ScalingParamType<T> alpha = 1.0f, beta = 0.0f;
......
paddle/fluid/operators/load_combine_op.cc
浏览文件 @ 5993155d
......@@ -33,8 +33,8 @@ class LoadCombineOp : public framework::OperatorBase {
auto filename = Attr<std::string>("file_path");
auto load_as_fp16 = Attr<bool>("load_as_fp16");
std::ifstream fin(filename);
PADDLE_ENFORCE(static_cast<bool>(fin),
std::ifstream fin(filename, std::ios_base::in | std::ios_base::binary);
PADDLE_ENFORCE(!fin.bad(),
"Cannot open file %s for load_combine op", filename);
auto out_var_names = Outputs("Out");
......@@ -46,20 +46,21 @@ 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 " << 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(static_cast<bool>(fin), "Cannot read more from file %s",
PADDLE_ENFORCE(!fin.bad(), "Cannot read more from file %s",
filename);
VLOG(3) << "before deserialization";
// Get data from fin to tensor
DeserializeFromStream(fin, tensor, dev_ctx);
DeserializeFromStream(fin, tensor, dev_ctx);
VLOG(3) << "after deserialization";
auto in_dtype = framework::ToDataType(tensor->type());
auto out_dtype =
load_as_fp16 ? framework::proto::VarType::FP16 : in_dtype;
......@@ -80,6 +81,7 @@ class LoadCombineOp : public framework::OperatorBase {
tensor->set_lod(fp16_tensor.lod());
tensor->ShareDataWith(fp16_tensor);
}
VLOG(3) << "load " << out_var_names[i] << " finished";
}
}
};
......
paddle/fluid/platform/cudnn_helper.h
浏览文件 @ 5993155d
......@@ -59,6 +59,7 @@ inline const char* cudnnGetErrorString(cudnnStatus_t status) {
#define CUDNN_VERSION_MIN(major, minor, patch) \
(CUDNN_VERSION >= ((major)*1000 + (minor)*100 + (patch)))
#if !defined(_WIN32)
#define CUDNN_ENFORCE(condition) \
do { \
cudnnStatus_t status = condition; \
......@@ -66,6 +67,9 @@ inline const char* cudnnGetErrorString(cudnnStatus_t status) {
PADDLE_THROW(::paddle::platform::cudnnGetErrorString(status)); \
} \
} while (false)
#else
#define CUDNN_ENFORCE(condition)
#endif
enum class DataLayout { // Not use
kNHWC,
......
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