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91 lines
3.5 KiB
91 lines
3.5 KiB
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#pragma once
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#include <map>
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#include <string>
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#include <thread> // NOLINT
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#include <vector>
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#include "paddle/fluid/framework/lod_tensor.h"
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#include "paddle/fluid/inference/io.h"
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void ThreadedRunInference(
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const std::unique_ptr<paddle::framework::ProgramDesc>& inference_program,
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paddle::framework::Executor* executor, paddle::framework::Scope* scope,
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const int thread_id,
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const std::vector<paddle::framework::LoDTensor*>& cpu_feeds,
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const std::vector<paddle::framework::LoDTensor*>& cpu_fetchs) {
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auto copy_program = std::unique_ptr<paddle::framework::ProgramDesc>(
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new paddle::framework::ProgramDesc(*inference_program));
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std::string feed_holder_name = "feed_" + paddle::string::to_string(thread_id);
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std::string fetch_holder_name =
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"fetch_" + paddle::string::to_string(thread_id);
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copy_program->SetFeedHolderName(feed_holder_name);
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copy_program->SetFetchHolderName(fetch_holder_name);
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// 3. Get the feed_target_names and fetch_target_names
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const std::vector<std::string>& feed_target_names =
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copy_program->GetFeedTargetNames();
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const std::vector<std::string>& fetch_target_names =
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copy_program->GetFetchTargetNames();
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// 4. Prepare inputs: set up maps for feed targets
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std::map<std::string, const paddle::framework::LoDTensor*> feed_targets;
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for (size_t i = 0; i < feed_target_names.size(); ++i) {
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// Please make sure that cpu_feeds[i] is right for feed_target_names[i]
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feed_targets[feed_target_names[i]] = cpu_feeds[i];
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}
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// 5. Define Tensor to get the outputs: set up maps for fetch targets
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std::map<std::string, paddle::framework::LoDTensor*> fetch_targets;
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for (size_t i = 0; i < fetch_target_names.size(); ++i) {
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fetch_targets[fetch_target_names[i]] = cpu_fetchs[i];
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}
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// 6. Run the inference program
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executor->Run(*copy_program, scope, feed_targets, fetch_targets, true,
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feed_holder_name, fetch_holder_name);
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}
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template <typename Place>
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void TestMultiThreadInference(
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const std::string& dirname,
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const std::vector<std::vector<paddle::framework::LoDTensor*>>& cpu_feeds,
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const std::vector<std::vector<paddle::framework::LoDTensor*>>& cpu_fetchs,
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const int num_threads) {
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// 1. Define place, executor, scope
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auto place = Place();
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auto executor = paddle::framework::Executor(place);
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auto* scope = new paddle::framework::Scope();
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// 2. Initialize the inference_program and load parameters
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std::unique_ptr<paddle::framework::ProgramDesc> inference_program =
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paddle::inference::Load(executor, *scope, dirname);
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std::vector<std::thread*> threads;
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for (int i = 0; i < num_threads; ++i) {
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threads.push_back(new std::thread(
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ThreadedRunInference, std::ref(inference_program), &executor, scope, i,
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std::ref(cpu_feeds[i]), std::ref(cpu_fetchs[i])));
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}
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for (int i = 0; i < num_threads; ++i) {
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threads[i]->join();
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delete threads[i];
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}
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delete scope;
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}
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