def on_loader_start(self, state: RunnerState):
scheduler = state.get_key(key="scheduler",
inner_key=self.scheduler_key)
if state.loader_name.startswith("train") and \
isinstance(scheduler, OneCycleLR) and self.mode == "batch":
scheduler.recalculate(loader_len=state.loader_len,
current_step=state.stage_epoch)
def on_stage_start(self, state: RunnerState):
optimizer = state.get_key(key="optimizer",
inner_key=self.optimizer_key)
assert optimizer is not None
lr = optimizer.defaults["lr"]
momentum = get_optimizer_momentum(optimizer)
state.set_key(lr, "lr", inner_key=self.optimizer_key)
state.set_key(momentum, "momentum", inner_key=self.optimizer_key)
def on_batch_end(self, state: RunnerState) -> None:
loss = state.get_key(key="loss")
loss = self._preprocess_loss(loss)
loss = self.loss_fn(loss)
state.metrics.add_batch_value(metrics_dict={
self.prefix: loss.item(),
})
_add_loss_to_state(self.prefix, state, loss)
def step(self, state: RunnerState):
scheduler = state.get_key(key="scheduler",
inner_key=self.scheduler_key)
valid_metric = \
safitty.get(state.metrics.valid_values, self.reduce_metric)
lr, momentum = self._scheduler_step(scheduler=scheduler,
valid_metric=valid_metric)
state.set_key(lr, key="lr", inner_key=self.scheduler_key)
state.set_key(momentum, key="momentum", inner_key=self.scheduler_key)
def on_batch_end(self, state: RunnerState):
criterion = state.get_key(key="criterion",
inner_key=self.criterion_key)
loss = self._compute_loss(state, criterion) * self.multiplier
state.metrics.add_batch_value(metrics_dict={
self.prefix: loss.item(),
})
self._add_loss_to_state(state, loss)
def on_stage_start(self, state: RunnerState):
scheduler = state.get_key(key="scheduler",
inner_key=self.scheduler_key)
assert scheduler is not None
if self.mode is None:
if isinstance(scheduler, BatchScheduler):
self.mode = "batch"
else:
self.mode = "epoch"
if isinstance(scheduler, OneCycleLR) and self.mode == "batch":
scheduler.reset()
def on_batch_end(self, state: RunnerState):
if state.loader_name.startswith("train"):
criterion = state.get_key(key="criterion",
inner_key=self.criterion_key)
else:
criterion = nn.CrossEntropyLoss()
loss = self._compute_loss(state, criterion) * self.multiplier
state.metrics.add_batch_value(metrics_dict={
self.prefix: loss.item(),
})
self._add_loss_to_state(state, loss)