def add_to_queue(self, trainer: "pl.Trainer",
queue: torch.multiprocessing.SimpleQueue) -> None:
"""Appends the :attr:`trainer.callback_metrics` dictionary to the given queue. To avoid issues with memory
sharing, we cast the data to numpy.
Args:
queue: the instance of the queue to append the data.
"""
callback_metrics: dict = apply_to_collection(
trainer.callback_metrics, torch.Tensor, lambda x: x.cpu().numpy(
)) # send as numpy to avoid issues with memory sharing
queue.put(callback_metrics)
def get_from_queue(self, trainer: "pl.Trainer",
queue: torch.multiprocessing.SimpleQueue) -> None:
"""Retrieve the :attr:`trainer.callback_metrics` dictionary from the given queue. To preserve consistency,
we cast back the data to ``torch.Tensor``.
Args:
queue: the instance of the queue from where to get the data.
"""
# NOTE: `add_to_queue` needs to be called before
callback_metrics: dict = queue.get()
trainer.callback_metrics.update(
apply_to_collection(callback_metrics, np.ndarray,
lambda x: torch.tensor(x)))
def get_from_queue(self, trainer: Trainer,
queue: torch.multiprocessing.SimpleQueue) -> None:
self.new_test_val = queue.get()
return super().get_from_queue(trainer, queue)
def add_to_queue(self, trainer: Trainer,
queue: torch.multiprocessing.SimpleQueue) -> None:
queue.put("new_test_val")
return super().add_to_queue(trainer, queue)
def get_from_queue(self, queue: torch.multiprocessing.SimpleQueue) -> None:
self.test_val = queue.get()
return super().get_from_queue(queue)
def add_to_queue(self, queue: torch.multiprocessing.SimpleQueue) -> None:
queue.put("test_val")
return super().add_to_queue(queue)