def on_epoch_start(self, state: State):
if self.checkpoint_resume and state.stage_epoch == 0:
state.epoch += 1
state.stage_epoch = state.stage_epoch + self.checkpoint_stage_epoch
state.checkpoint_data = {'stage_epoch': state.stage_epoch}
if self.master:
self.step.start(1, name=state.stage)
self.step.start(2,
name=f'epoch {state.stage_epoch}',
index=state.stage_epoch)
def on_batch_start(self, state: State):
data = state.input['features']
inp1 = state.input['h1_targets']
inp2 = state.input['h2_targets']
inp3 = state.input['h3_targets']
if not state.loader_name.startswith("train"):
return
data, targets = mixup(data, inp1, inp2, inp3, self.alpha)
state.input['features'] = data
state.input['targets'] = targets
def on_batch_start(self, state: State):
"""
Mixes data according to Cutmix algorithm.
:param state: current state
:return: void
"""
if not self.is_needed:
return
if self.alpha > 0:
self.lam = np.random.beta(self.alpha, self.alpha)
else:
self.lam = 1
self.index = torch.randperm(state.batch_in[self.fields[0]].shape[0])
self.index.to(state.device)
bbx1, bby1, bbx2, bby2 = \
self._rand_bbox(state.batch_in[self.fields[0]].shape, self.lam)
for f in self.fields:
state.batch_in[f][:, :, bbx1:bbx2, bby1:bby2] = \
state.batch_in[f][self.index, :, bbx1:bbx2, bby1:bby2]
self.lam = 1 - ((bbx2 - bbx1) * (bby2 - bby1) /
(state.batch_in[self.fields[0]].shape[-1] *
state.batch_in[self.fields[0]].shape[-2]))
def on_batch_start(self, state: State):
step = state.step - self.loader_step_start
if self.last_batch_logged and \
step != state.batch_size * state.loader_len:
if (now() - self.last_batch_logged).total_seconds() < 10:
return
task = self.get_parent_task()
task.batch_index = int(step / state.batch_size)
task.batch_total = state.loader_len
if task.batch_index > task.batch_total:
state.step = state.batch_size
task.batch_index = 1
task.loader_name = state.loader_name
duration = int((now() - self.loader_started_time).total_seconds())
task.epoch_duration = duration
task.epoch_time_remaining = int(
duration *
(task.batch_total / task.batch_index)) - task.epoch_duration
if state.loss is not None:
# noinspection PyUnresolvedReferences
task.loss = float(state.loss['loss'])
self.task_provider.update()
self.last_batch_logged = now()
def _compute_metric(self, state: State):
criterion_kwargs = {
self.real_data_criterion_key:
state.batch_in[self.input_key],
self.fake_data_criterion_key:
state.batch_out[self.output_key],
self.critic_criterion_key:
state.model[self.critic_model_key],
self.condition_args_criterion_key:
[state.batch_in[key] for key in self.condition_keys]
}
criterion = state.get_attr("criterion", self.criterion_key)
return criterion(**criterion_kwargs)
def do_cutmix(self, state: State):
bbx1, bby1, bbx2, bby2 = rand_bbox(state.input[self.fields[0]].shape, self.lam)
for f in self.fields:
state.input[f][:, :, bbx1:bbx2, bby1:bby2] = state.input[f][
self.index, :, bbx1:bbx2, bby1:bby2
]
self.lam = 1 - (
(bbx2 - bbx1)
* (bby2 - bby1)
/ (
state.input[self.fields[0]].shape[-1]
* state.input[self.fields[0]].shape[-2]
)
)
def on_batch_start(self, state: State):
if not self.is_needed:
return
if self.alpha > 0:
self.lam = np.random.beta(self.alpha, self.alpha)
else:
self.lam = 1
self.index = torch.randperm(state.batch_in[self.fields[0]].shape[0])
self.index.to(state.device)
for f in self.fields:
state.batch_in[f] = self.lam * state.batch_in[f] + \
(1 - self.lam) * state.batch_in[f][self.index]
def on_batch_start(self, state: State):
if not self.is_needed:
return
if self.alpha > 0:
self.lam = np.random.beta(self.alpha, self.alpha)
else:
self.lam = 1
self.index = torch.randperm(state.input[self.fields[0]].shape[0])
self.index.to(state.device)
bbx1, bby1, bbx2, bby2 = rand_bbox(state.input[self.input_key].size(), self.lam)
for f in self.fields:
state.input[f][:, :, bbx1:bbx2, bby1:bby2] = state.input[f][self.index, :, bbx1:bbx2, bby1:bby2]
def on_epoch_end(self, state: State) -> None:
if state.stage.startswith("infer"):
return
score = state.metric_manager.valid_values[self.metric]
if self.best_score is None:
self.best_score = score
if self.is_better(score, self.best_score):
self.num_bad_epochs = 0
self.best_score = score
else:
self.num_bad_epochs += 1
if self.num_bad_epochs >= self.patience:
print(f"Early stop at {state.stage_epoch} epoch")
state.early_stop = True
def on_batch_end(self, state: State):
"""On batch end event"""
super().on_batch_end(state)
if not state.need_backward:
return
optimizer = state.get_key(key="optimizer",
inner_key=self.optimizer_key)
need_gradient_step = \
self._accumulation_counter % self.accumulation_steps == 0
if need_gradient_step:
for group in optimizer.param_groups:
for param in group["params"]:
param.data.clamp_(min=-self.weight_clamp_value,
max=self.weight_clamp_value)
def on_batch_start(self, state: State):
"""Batch start hook.
Args:
state (State): current state
"""
if not self.is_needed:
return
if self.alpha > 0:
self.lam = np.random.beta(self.alpha, self.alpha)
else:
self.lam = 1
self.index = torch.randperm(state.input[self.fields[0]].shape[0])
self.index.to(state.device)
for f in self.fields:
state.input[f] = (self.lam * state.input[f] +
(1 - self.lam) * state.input[f][self.index])
def on_batch_end(self, state: State) -> None:
"""On batch end event.
Args:
state (State): current state
"""
super().on_batch_end(state)
if not state.is_train_loader:
return
optimizer = state.get_attr(key="optimizer",
inner_key=self.optimizer_key)
need_gradient_step = (self._accumulation_counter %
self.accumulation_steps == 0)
if need_gradient_step:
for group in optimizer.param_groups:
for param in group["params"]:
param.data.clamp_(
min=-self.weight_clamp_value,
max=self.weight_clamp_value,
)
def on_batch_start(self, state: State):
state.prev_batch_metrics = state.batch_metrics
super().on_batch_start(state)
def on_stage_start(self, state: State):
state.loggers = {'console': VerboseLogger()}
def do_mixup(self, state: State):
for f in self.fields:
state.input[f] = (
self.lam * state.input[f] + (1 - self.lam) * state.input[f][self.index]
)
