def _default_iteration_writer(self, engine: Engine, writer: SummaryWriter):
"""Execute iteration level event write operation based on Ignite engine.state data.
Default is to write the loss value of current iteration.
Args:
engine (ignite.engine): Ignite Engine, it can be a trainer, validator or evaluator.
writer (SummaryWriter): TensorBoard writer, created in TensorBoardHandler.
"""
loss = self.output_transform(engine.state.output)
if loss is None:
return # do nothing if output is empty
if isinstance(loss, dict):
for name in sorted(loss):
value = loss[name]
if not is_scalar(value):
warnings.warn('ignoring non-scalar output in TensorBoardStatsHandler,'
' make sure `output_transform(engine.state.output)` returns'
' a scalar or dictionary of key and scalar pairs to avoid this warning.'
' {}:{}'.format(name, type(value)))
continue # not plot multi dimensional output
writer.add_scalar(name, value.item() if torch.is_tensor(value) else value, engine.state.iteration)
elif is_scalar(loss): # not printing multi dimensional output
writer.add_scalar(self.tag_name, loss.item() if torch.is_tensor(loss) else loss, engine.state.iteration)
else:
warnings.warn('ignoring non-scalar output in TensorBoardStatsHandler,'
' make sure `output_transform(engine.state.output)` returns'
' a scalar or a dictionary of key and scalar pairs to avoid this warning.'
' {}'.format(type(loss)))
writer.flush()
def _default_iteration_print(self, engine: Engine):
"""Execute iteration log operation based on Ignite engine.state data.
Print the values from ignite state.logs dict.
Default behavior is to print loss from output[1], skip if output[1] is not loss.
Args:
engine (ignite.engine): Ignite Engine, it can be a trainer, validator or evaluator.
"""
loss = self.output_transform(engine.state.output)
if loss is None:
return # no printing if the output is empty
out_str = ''
if isinstance(loss, dict): # print dictionary items
for name in sorted(loss):
value = loss[name]
if not is_scalar(value):
warnings.warn(
'ignoring non-scalar output in StatsHandler,'
' make sure `output_transform(engine.state.output)` returns'
' a scalar or dictionary of key and scalar pairs to avoid this warning.'
' {}:{}'.format(name, type(value)))
continue # not printing multi dimensional output
out_str += self.key_var_format.format(
name,
value.item() if torch.is_tensor(value) else value)
else:
if is_scalar(loss): # not printing multi dimensional output
out_str += self.key_var_format.format(
self.tag_name,
loss.item() if torch.is_tensor(loss) else loss)
else:
warnings.warn(
'ignoring non-scalar output in StatsHandler,'
' make sure `output_transform(engine.state.output)` returns'
' a scalar or a dictionary of key and scalar pairs to avoid this warning.'
' {}'.format(type(loss)))
if not out_str:
return # no value to print
num_iterations = engine.state.epoch_length
current_iteration = (engine.state.iteration - 1) % num_iterations + 1
current_epoch = engine.state.epoch
num_epochs = engine.state.max_epochs
base_str = "Epoch: {}/{}, Iter: {}/{} --".format(
current_epoch, num_epochs, current_iteration, num_iterations)
self.logger.info(' '.join([base_str, out_str]))