roywei commented on a change in pull request #14685: [Fit API] improve event
handlers
URL: https://github.com/apache/incubator-mxnet/pull/14685#discussion_r277094415
##########
File path: python/mxnet/gluon/contrib/estimator/estimator.py
##########
@@ -226,111 +230,72 @@ def fit(self, train_data,
custom batch function to extract data and label
from a data batch and load into contexts(devices)
"""
-
- self.max_epoch = epochs
- self.stop_training = False
- self.processed_samples = None
- self.batch_idx = 0
-
+ self.max_epochs = epochs
event_handlers = event_handlers or []
# provide default logging handler
- if not event_handlers or \
- not any(isinstance(handler, LoggingHandler) for handler in
event_handlers):
- event_handlers.append(LoggingHandler())
- warnings.warn("No Event Handler specified, default
`LoggingHandler()` "
- "is used with
verbose=LoggingHandler.LOG_VERBOSITY_PER_EPOCH. "
- "Please look at gluon.estimator.event_handler for
more detail.")
+ if not event_handlers:
+ train_metrics, val_metrics = self.prepare_loss_and_metrics()
+ event_handlers.append(MetricHandler(train_metrics=train_metrics))
+ if val_data:
+ event_handlers.append(ValidationHandler(val_data=val_data,
eval_fn=self.evaluate,
+
val_metrics=val_metrics))
+ event_handlers.append(LoggingHandler(train_metrics=train_metrics,
+ val_metrics=val_metrics))
+ warnings.warn("No Event Handler specified, default %s are used. "
+ "Please look at
gluon.contrib.estimator.event_handler for more detail." %
+ ", ".join([handler.__class__.__name__ for handler in
event_handlers]))
+
+ event_handlers.sort(key=lambda handler: getattr(handler, 'rank', 0),
reverse=True)
train_begin, epoch_begin, batch_begin, \
batch_end, epoch_end, train_end =
self._categorize_handlers(event_handlers)
- # passing estimator to event handlers so they can access estimator
information
- # when a event is triggered
- for handler in event_handlers:
- handler.estimator = self
-
+ # only pass a weak reference to all event handlers
+ estimator_ref = weakref.proxy(self)
# training begin
for handler in train_begin:
- handler.train_begin()
+ handler.train_begin(estimator_ref)
- for epoch in range(self.max_epoch):
+ for epoch in range(epochs):
# epoch begin
- self.current_epoch = epoch
- # Number of samples trained after every batch
- completed_samples = 0
-
for handler in epoch_begin:
- handler.epoch_begin()
-
- for metric in self.train_metrics + self.train_loss_metrics:
- metric.reset()
+ handler.epoch_begin(estimator_ref)
for i, batch in enumerate(train_data):
- if not batch_fn:
- if isinstance(train_data, gluon.data.DataLoader):
- data, label = self._batch_fn(batch, self.context)
- else:
- raise ValueError("You are using a custom iteration,
please also provide "
- "batch_fn to extract data and label.
Alternatively, you "
- "can provide the data as
gluon.data.DataLoader")
- else:
- data, label = batch_fn(batch, self.context)
+ if not isinstance(train_data, gluon.data.DataLoader):
+ raise ValueError("Estimator only support input as Gluon
DataLoader. Alternatively, you "
+ "can transform your DataIter or any
NDArray into Gluon DataLoader. "
+ "Refer to gluon.data.dataloader")
+ data, label = self._get_data_and_label(batch, self.context)
batch_size = batch[0].shape[0]
# batch begin
for handler in batch_begin:
- handler.batch_begin()
+ handler.batch_begin(estimator_ref, batch=batch)
with autograd.record():
pred = [self.net(x) for x in data]
- losses = []
- for loss in self.loss:
- losses.append([loss(y_hat, y) for y_hat, y in
zip(pred, label)])
+ loss = [self.loss[0](y_hat, y) for y_hat, y in zip(pred,
label)]
Review comment:
as above https://issues.apache.org/jira/browse/MXNET-1395
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