def train_network(self, train_dataset, eval_dataset, epochs: int):
num_batches = int(len(train_dataset) / self.minibatch_size)
logger.info(
"Read in batch data set of size {} examples. Data split "
"into {} batches of size {}.".format(
len(train_dataset), num_batches, self.minibatch_size
)
)
start_time = time.time()
for epoch in range(epochs):
train_dataset.reset_iterator()
data_streamer = DataStreamer(train_dataset, pin_memory=self.trainer.use_gpu)
preprocess_handler = self.preprocess_handler
dtype = self.trainer.dtype
def preprocess(batch):
tdp = preprocess_handler.preprocess(batch)
tdp.set_type(dtype)
return tdp
feed_pages(
data_streamer,
len(train_dataset),
epoch,
self.minibatch_size,
self.trainer.use_gpu,
TrainingPageHandler(self.trainer),
batch_preprocessor=preprocess,
)
if hasattr(self.trainer, "q_network_cpe"):
# TODO: Add CPE support to DDPG/SAC, Parametric DQN (once moved to modular)
eval_dataset.reset_iterator()
data_streamer = DataStreamer(
eval_dataset, pin_memory=self.trainer.use_gpu
)
eval_page_handler = EvaluationPageHandler(
self.trainer, self.evaluator, self
)
feed_pages(
data_streamer,
len(eval_dataset),
epoch,
self.minibatch_size,
self.trainer.use_gpu,
eval_page_handler,
batch_preprocessor=preprocess,
)
SummaryWriterContext.increase_global_step()
through_put = (len(train_dataset) * epochs) / (time.time() - start_time)
logger.info(
"Training finished. Processed ~{} examples / s.".format(round(through_put))
)
def test_global_step(self):
with TemporaryDirectory() as tmp_dir:
writer = SummaryWriter(tmp_dir)
writer.add_scalar = MagicMock()
with summary_writer_context(writer):
SummaryWriterContext.add_scalar("test", torch.ones(1))
SummaryWriterContext.increase_global_step()
SummaryWriterContext.add_scalar("test", torch.zeros(1))
writer.add_scalar.assert_has_calls([
call("test", torch.ones(1), global_step=0),
call("test", torch.zeros(1), global_step=1),
])
self.assertEqual(2, len(writer.add_scalar.mock_calls))
def test_global_step(self):
with TemporaryDirectory() as tmp_dir:
writer = SummaryWriter(tmp_dir)
writer.add_scalar = MagicMock()
with summary_writer_context(writer):
SummaryWriterContext.add_scalar("test", torch.ones(1))
SummaryWriterContext.increase_global_step()
SummaryWriterContext.add_scalar("test", torch.zeros(1))
writer.add_scalar.assert_has_calls(
[
call("test", torch.ones(1), global_step=0),
call("test", torch.zeros(1), global_step=1),
]
)
self.assertEqual(2, len(writer.add_scalar.mock_calls))
def handle(self, tdp: PreprocessedTrainingBatch) -> None:
SummaryWriterContext.increase_global_step()
self.trainer_or_evaluator.train(tdp)
def handle(self, tdp: TrainingDataPage) -> None:
SummaryWriterContext.increase_global_step()
self.trainer.train(tdp)