roywei commented on a change in pull request #14685: [Fit API] improve event
handlers
URL: https://github.com/apache/incubator-mxnet/pull/14685#discussion_r277092803
##########
File path: python/mxnet/gluon/contrib/estimator/estimator.py
##########
@@ -148,63 +145,70 @@ def _is_initialized(self):
return False
return True
- def _batch_fn(self, batch, ctx, is_iterator=False):
- if is_iterator:
- data = batch.data[0]
- label = batch.label[0]
- else:
- data = batch[0]
- label = batch[1]
+ def _get_data_and_label(self, batch, ctx):
+ data = batch[0]
+ label = batch[1]
data = gluon.utils.split_and_load(data, ctx_list=ctx, batch_axis=0)
label = gluon.utils.split_and_load(label, ctx_list=ctx, batch_axis=0)
return data, label
+ def prepare_loss_and_metrics(self):
+ """
+ Based on loss functions and training metrics in estimator
+ Create metric wrappers to record loss values,
+ Create copies of train loss/metric objects to record validation values
+ """
+ if any(not hasattr(self, attribute) for attribute in
+ ['train_metrics', 'val_metrics']):
+ # Use default mx.metric.Accuracy() for
gluon.loss.SoftmaxCrossEntropyLoss()
+ if not self.train_metrics and any([isinstance(l,
gluon.loss.SoftmaxCrossEntropyLoss) for l in self.loss]):
+ self.train_metrics = [Accuracy()]
+ self.val_metrics = []
+ for loss in self.loss:
+ self.train_metrics.append(Loss("Train " + ''.join([i for i in
loss.name if not i.isdigit()])))
+ self.val_metrics.append(Loss("Validation " + ''.join([i for i
in loss.name if not i.isdigit()])))
+ for metric in self.train_metrics:
+ val_metric = copy.deepcopy(metric)
+ metric.name = "Train " + metric.name
+ val_metric.name = "Validation " + val_metric.name
+ self.val_metrics.append(val_metric)
+ return self.train_metrics, self.val_metrics
+
def evaluate(self,
val_data,
- batch_fn=None):
+ val_metrics):
"""Evaluate model on validation data
Parameters
----------
val_data : DataLoader
validation data with data and labels
- batch_fn : function
- custom batch function to extract data and label
- from a data batch and load into contexts(devices)
+ val_metrics : EvalMetric or list of EvalMetrics
+ metrics to update validation result
"""
- for metric in self.val_metrics + self.val_loss_metrics:
+ for metric in val_metrics:
metric.reset()
for _, batch in enumerate(val_data):
- if not batch_fn:
- if isinstance(val_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(val_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)
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:
multi loss will be supported in
https://github.com/apache/incubator-mxnet/pull/14628, let's get the first
version into master and iterate on that.
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