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// Copyright 2019 The Chromium Authors. All rights reserved.
// Use of this source code is governed by a BSD-style license that can be
// found in the LICENSE file.
#include "media/learning/impl/learning_task_controller_helper.h"
#include <memory>
#include <utility>
#include "base/bind.h"
#include "base/threading/sequenced_task_runner_handle.h"
namespace media {
namespace learning {
LearningTaskControllerHelper::LearningTaskControllerHelper(
const LearningTask& task,
AddExampleCB add_example_cb,
SequenceBoundFeatureProvider feature_provider)
: task_(task),
feature_provider_(std::move(feature_provider)),
task_runner_(base::SequencedTaskRunnerHandle::Get()),
add_example_cb_(std::move(add_example_cb)) {}
LearningTaskControllerHelper::~LearningTaskControllerHelper() = default;
void LearningTaskControllerHelper::BeginObservation(base::UnguessableToken id,
FeatureVector features) {
auto& pending_example = pending_examples_[id];
// Start feature prediction, so that we capture the current values.
if (!feature_provider_.is_null()) {
feature_provider_.Post(
FROM_HERE, &FeatureProvider::AddFeatures, std::move(features),
base::BindOnce(&LearningTaskControllerHelper::OnFeaturesReadyTrampoline,
task_runner_, AsWeakPtr(), id));
} else {
pending_example.example.features = std::move(features);
pending_example.features_done = true;
}
}
void LearningTaskControllerHelper::CompleteObservation(
base::UnguessableToken id,
const ObservationCompletion& completion) {
auto iter = pending_examples_.find(id);
if (iter == pending_examples_.end())
return;
iter->second.example.target_value = completion.target_value;
iter->second.example.weight = completion.weight;
iter->second.target_done = true;
ProcessExampleIfFinished(std::move(iter));
}
void LearningTaskControllerHelper::CancelObservation(
base::UnguessableToken id) {
auto iter = pending_examples_.find(id);
if (iter == pending_examples_.end())
return;
// This would have to check for pending predictions, if we supported them, and
// defer destruction until the features arrive.
pending_examples_.erase(iter);
}
// static
void LearningTaskControllerHelper::OnFeaturesReadyTrampoline(
scoped_refptr<base::SequencedTaskRunner> task_runner,
base::WeakPtr<LearningTaskControllerHelper> weak_this,
base::UnguessableToken id,
FeatureVector features) {
// TODO(liberato): this would benefit from promises / deferred data.
auto cb = base::BindOnce(&LearningTaskControllerHelper::OnFeaturesReady,
std::move(weak_this), id, std::move(features));
if (!task_runner->RunsTasksInCurrentSequence()) {
task_runner->PostTask(FROM_HERE, std::move(cb));
} else {
std::move(cb).Run();
}
}
void LearningTaskControllerHelper::OnFeaturesReady(base::UnguessableToken id,
FeatureVector features) {
PendingExampleMap::iterator iter = pending_examples_.find(id);
// It's possible that OnLabelCallbackDestroyed has already run. That's okay
// since we don't support prediction right now.
if (iter == pending_examples_.end())
return;
iter->second.example.features = std::move(features);
iter->second.features_done = true;
ProcessExampleIfFinished(std::move(iter));
}
void LearningTaskControllerHelper::ProcessExampleIfFinished(
PendingExampleMap::iterator iter) {
if (!iter->second.features_done || !iter->second.target_done)
return;
add_example_cb_.Run(std::move(iter->second.example));
pending_examples_.erase(iter);
// TODO(liberato): If we receive FeatureVector f1 then f2, and start filling
// in features for a prediction, and if features become available in the order
// f2, f1, and we receive a target value for f2 before f1's features are
// complete, should we insist on deferring training with f2 until we start
// prediction on f1? I suppose that we could just insist that features are
// provided in the same order they're received, and it's automatic.
}
} // namespace learning
} // namespace media
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