You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
196 lines
6.9 KiB
196 lines
6.9 KiB
/* 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 <time.h>
|
|
#include "paddle/fluid/framework/lod_tensor.h"
|
|
#include "paddle/fluid/inference/io.h"
|
|
#include "paddle/fluid/platform/profiler.h"
|
|
|
|
template <typename T>
|
|
void SetupTensor(paddle::framework::LoDTensor& input,
|
|
paddle::framework::DDim dims,
|
|
T lower,
|
|
T upper) {
|
|
srand(time(0));
|
|
T* input_ptr = input.mutable_data<T>(dims, paddle::platform::CPUPlace());
|
|
for (int i = 0; i < input.numel(); ++i) {
|
|
input_ptr[i] =
|
|
(static_cast<T>(rand()) / static_cast<T>(RAND_MAX)) * (upper - lower) +
|
|
lower;
|
|
}
|
|
}
|
|
|
|
template <typename T>
|
|
void SetupTensor(paddle::framework::LoDTensor& input,
|
|
paddle::framework::DDim dims,
|
|
std::vector<T>& data) {
|
|
CHECK_EQ(paddle::framework::product(dims), static_cast<int64_t>(data.size()));
|
|
T* input_ptr = input.mutable_data<T>(dims, paddle::platform::CPUPlace());
|
|
memcpy(input_ptr, data.data(), input.numel() * sizeof(T));
|
|
}
|
|
|
|
template <typename T>
|
|
void SetupLoDTensor(paddle::framework::LoDTensor& input,
|
|
paddle::framework::LoD& lod,
|
|
T lower,
|
|
T upper) {
|
|
input.set_lod(lod);
|
|
int dim = lod[0][lod[0].size() - 1];
|
|
SetupTensor<T>(input, {dim, 1}, lower, upper);
|
|
}
|
|
|
|
template <typename T>
|
|
void SetupLoDTensor(paddle::framework::LoDTensor& input,
|
|
paddle::framework::DDim dims,
|
|
paddle::framework::LoD lod,
|
|
std::vector<T>& data) {
|
|
const size_t level = lod.size() - 1;
|
|
CHECK_EQ(dims[0], static_cast<int64_t>((lod[level]).back()));
|
|
input.set_lod(lod);
|
|
SetupTensor<T>(input, dims, data);
|
|
}
|
|
|
|
template <typename T>
|
|
void CheckError(paddle::framework::LoDTensor& output1,
|
|
paddle::framework::LoDTensor& output2) {
|
|
// Check lod information
|
|
EXPECT_EQ(output1.lod(), output2.lod());
|
|
|
|
EXPECT_EQ(output1.dims(), output2.dims());
|
|
EXPECT_EQ(output1.numel(), output2.numel());
|
|
|
|
T err = static_cast<T>(0);
|
|
if (typeid(T) == typeid(float)) {
|
|
err = 1E-3;
|
|
} else if (typeid(T) == typeid(double)) {
|
|
err = 1E-6;
|
|
} else {
|
|
err = 0;
|
|
}
|
|
|
|
size_t count = 0;
|
|
for (int64_t i = 0; i < output1.numel(); ++i) {
|
|
if (fabs(output1.data<T>()[i] - output2.data<T>()[i]) > err) {
|
|
count++;
|
|
}
|
|
}
|
|
EXPECT_EQ(count, 0U) << "There are " << count << " different elements.";
|
|
}
|
|
|
|
template <typename Place>
|
|
void TestInference(const std::string& dirname,
|
|
const std::vector<paddle::framework::LoDTensor*>& cpu_feeds,
|
|
std::vector<paddle::framework::LoDTensor*>& cpu_fetchs,
|
|
const int repeat = 1,
|
|
const bool is_combined = false) {
|
|
// 1. Define place, executor, scope
|
|
auto place = Place();
|
|
auto executor = paddle::framework::Executor(place);
|
|
auto* scope = new paddle::framework::Scope();
|
|
|
|
// Profile the performance
|
|
paddle::platform::ProfilerState state;
|
|
if (paddle::platform::is_cpu_place(place)) {
|
|
state = paddle::platform::ProfilerState::kCPU;
|
|
} else {
|
|
#ifdef PADDLE_WITH_CUDA
|
|
state = paddle::platform::ProfilerState::kCUDA;
|
|
// The default device_id of paddle::platform::CUDAPlace is 0.
|
|
// Users can get the device_id using:
|
|
// int device_id = place.GetDeviceId();
|
|
paddle::platform::SetDeviceId(0);
|
|
#else
|
|
PADDLE_THROW("'CUDAPlace' is not supported in CPU only device.");
|
|
#endif
|
|
}
|
|
|
|
// 2. Initialize the inference_program and load parameters
|
|
std::unique_ptr<paddle::framework::ProgramDesc> inference_program;
|
|
|
|
// Enable the profiler
|
|
paddle::platform::EnableProfiler(state);
|
|
{
|
|
paddle::platform::RecordEvent record_event(
|
|
"init_program",
|
|
paddle::platform::DeviceContextPool::Instance().Get(place));
|
|
|
|
if (is_combined) {
|
|
// All parameters are saved in a single file.
|
|
// Hard-coding the file names of program and parameters in unittest.
|
|
// The file names should be consistent with that used in Python API
|
|
// `fluid.io.save_inference_model`.
|
|
std::string prog_filename = "__model_combined__";
|
|
std::string param_filename = "__params_combined__";
|
|
inference_program =
|
|
paddle::inference::Load(executor,
|
|
*scope,
|
|
dirname + "/" + prog_filename,
|
|
dirname + "/" + param_filename);
|
|
} else {
|
|
// Parameters are saved in separate files sited in the specified
|
|
// `dirname`.
|
|
inference_program = paddle::inference::Load(executor, *scope, dirname);
|
|
}
|
|
}
|
|
// Disable the profiler and print the timing information
|
|
paddle::platform::DisableProfiler(paddle::platform::EventSortingKey::kDefault,
|
|
"load_program_profiler.txt");
|
|
paddle::platform::ResetProfiler();
|
|
|
|
// 3. Get the feed_target_names and fetch_target_names
|
|
const std::vector<std::string>& feed_target_names =
|
|
inference_program->GetFeedTargetNames();
|
|
const std::vector<std::string>& fetch_target_names =
|
|
inference_program->GetFetchTargetNames();
|
|
|
|
// 4. Prepare inputs: set up maps for feed targets
|
|
std::map<std::string, const paddle::framework::LoDTensor*> feed_targets;
|
|
for (size_t i = 0; i < feed_target_names.size(); ++i) {
|
|
// Please make sure that cpu_feeds[i] is right for feed_target_names[i]
|
|
feed_targets[feed_target_names[i]] = cpu_feeds[i];
|
|
}
|
|
|
|
// 5. Define Tensor to get the outputs: set up maps for fetch targets
|
|
std::map<std::string, paddle::framework::LoDTensor*> fetch_targets;
|
|
for (size_t i = 0; i < fetch_target_names.size(); ++i) {
|
|
fetch_targets[fetch_target_names[i]] = cpu_fetchs[i];
|
|
}
|
|
|
|
// 6. Run the inference program
|
|
{
|
|
// Ignore the profiling results of the first run
|
|
executor.Run(*inference_program, scope, feed_targets, fetch_targets);
|
|
|
|
// Enable the profiler
|
|
paddle::platform::EnableProfiler(state);
|
|
|
|
// Run repeat times to profile the performance
|
|
for (int i = 0; i < repeat; ++i) {
|
|
paddle::platform::RecordEvent record_event(
|
|
"run_inference",
|
|
paddle::platform::DeviceContextPool::Instance().Get(place));
|
|
|
|
executor.Run(*inference_program, scope, feed_targets, fetch_targets);
|
|
}
|
|
|
|
// Disable the profiler and print the timing information
|
|
paddle::platform::DisableProfiler(
|
|
paddle::platform::EventSortingKey::kDefault,
|
|
"run_inference_profiler.txt");
|
|
paddle::platform::ResetProfiler();
|
|
}
|
|
|
|
delete scope;
|
|
}
|