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#pragma once
#include <atomic>
#include <condition_variable>
#include <memory>
#include <mutex>
#include <queue>
#include "caffe2/core/logging.h"
#include "caffe2/core/operator.h"
#include "caffe2/core/stats.h"
#include "caffe2/core/tensor.h"
namespace caffe2 {
// TODO: This is a very naive implementation with a single mutex. We can do the
// atomic index + circular queue optimizations or pull something more
// heavy-weight later
class RebatchingQueue {
public:
RebatchingQueue(size_t capacity, size_t numBlobs);
~RebatchingQueue();
bool enqueueOne(
CPUContext& context,
const std::vector<const TensorCPU*>& inputs);
bool enqueueMany(
CPUContext& context,
const std::vector<const TensorCPU*>& inputs);
bool dequeue(
CPUContext& context,
size_t numElements,
const std::vector<TensorCPU*>& outputs);
size_t capacity() const;
size_t numBlobs() const;
bool isClosed() const;
void close();
private:
bool enqueue(std::vector<std::vector<TensorCPU>> splittedInputs);
bool canWrite() const;
bool canRead() const;
const size_t capacity_;
const size_t numBlobs_;
mutable std::mutex mutex_;
bool isClosed_{false};
uint64_t head_{0};
uint64_t tail_{0};
std::condition_variable cvEmpty_;
std::condition_variable cvOverflow_;
std::vector<std::vector<TensorCPU>> queue_;
};
} // namespace caffe2
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