[GitHub] [incubator-mxnet] ankkhedia commented on a change in pull request #14346: [MXNet-1334][WIP][Fit API]base class for estimator and eventhandler

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ankkhedia commented on a change in pull request #14346: [MXNet-1334][WIP][Fit API]base class for estimator and eventhandler
URL: https://github.com/apache/incubator-mxnet/pull/14346#discussion_r264084052
 
 

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 File path: python/mxnet/gluon/estimator/estimator.py
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+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# coding: utf-8
+# pylint: disable=wildcard-import
+"""Gluon Estimator"""
+
+
+import warnings
+
+from .event_handler import LoggingHandler
+from ... import *
+from ... import gluon, autograd
+from ...context import cpu, gpu, num_gpus
+from ...metric import EvalMetric, Loss
+
+__all__ = ['Estimator']
+
+
+class Estimator(object):
+    """
+    Estimator Class for easy model training
+    TODO: update doc
+    """
+
+    def __init__(self, net,
+                 loss=None,
+                 metrics=None,
+                 initializer=None,
+                 trainers=None,
+                 context=None):
+
+        self.net = net
+        if isinstance(loss, gluon.loss.Loss):
+            self.loss = [loss]
+        else:
+            self.loss = loss or []
+        if isinstance(metrics, EvalMetric):
+            self.metrics = [metrics]
+        else:
+            self.metrics = metrics or []
+
+        self.initializer = initializer
+        # store training statistics
+        self.train_stats = {}
+        self.train_stats['epochs'] = []
+        self.train_stats['learning_rate'] = []
+        # time used for each epoch
+        self.train_stats['step'] = ''
+        for metric in self.metrics:
+            # record a history of metrics over each epoch
+            self.train_stats['train_' + metric.name] = []
+            # only record the latest metric numbers after each batch
+            self.train_stats['batch_' + metric.name] = 0.
+        self.loss_metrics = []
+        # using the metric wrapper for loss to record loss value
+        for loss in self.loss:
+            self.loss_metrics.append(Loss(loss.name))
+            self.train_stats['train_' + loss.name] = []
+            # only record the latest loss numbers after each batch
+            self.train_stats['batch_' + loss.name] = 0.
+
+        # handle context
+        if isinstance(context, Context):
+            self.context = [context]
+        if not context:
+            if num_gpus() > 0:
+                # only use 1 GPU by default
+                if num_gpus() > 1:
+                    warnings.warn("You have multiple GPUs, gpu(0) will be used by default."
+                                  "To utilize all your GPUs, specify context as a list of gpus, e.g. context=[mx.gpu(0), mx.gpu(2)] ")
+                self.context = [gpu(0)]
+            else:
+                self.context = [cpu()]
+
+        # initialize the network
+        if self.initializer:
+            if self._is_initialized():
+                # if already initialized, re-init with user specified initializer
+                warnings.warn("You have already initialized your net, it will be forced re-initialized "
+                              "with the initializer you speficied. You don't need to pass initializer if you alraedy initialized your net.")
+                self.net.initialize(init=self.initializer, ctx=self.context, force_reinit=True)
+            else:
+                # initialize with user specified initializer
+                self.net.initialize(init=self.initializer, ctx=self.context, force_reinit=False)
+        else:
+            if not self._is_initialized():
+                self.net.initialize(ctx=self.context)
+
+        # handle trainers
+        if isinstance(trainers, gluon.Trainer):
+            self.trainers = [trainers]
+        else:
+            self.trainers = trainers or []
+        if not self.trainers:
 
 Review comment:
   Are we dealing with multiple trainer case over here (e.g. Multi task classification)

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