roywei commented on a change in pull request #14629: [MXNET-1333] Estimator and Fit API URL: https://github.com/apache/incubator-mxnet/pull/14629#discussion_r279517266
########## File path: python/mxnet/gluon/contrib/estimator/estimator.py ########## @@ -0,0 +1,363 @@ +# 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, unused-variable +"""Gluon Estimator""" + +import copy +import warnings +import weakref + +from .event_handler import MetricHandler, ValidationHandler, LoggingHandler +from .event_handler import TrainBegin, EpochBegin, BatchBegin, BatchEnd, EpochEnd, TrainEnd +from .... import gluon, autograd +from ....context import Context, cpu, gpu, num_gpus +from ....metric import EvalMetric, Loss, Accuracy + +__all__ = ['Estimator'] + + +class Estimator(object): + """Estimator Class for easy model training + + :py:class:`Estimator` can be used to facilitate the training & validation process + + + Parameters + ---------- + loss : gluon.loss.Loss or list of gluon.loss.Loss + Loss(objective functions) to calculate during training + metrics : EvalMetric or list of EvalMetric + Metrics for evaluating models + initializer : Initializer + initializer to initialize the network + trainer : Trainer + Trainer to apply optimizer on network parameters + context : Context or list of Context + device(s) to run the training on + """ + + def __init__(self, net, + loss, + metrics=None, + initializer=None, + trainer=None, + context=None): + + self.net = net + self.loss = self._check_loss(loss) + self.train_metrics = self._check_metrics(metrics) + + self.context = self._check_context(context) + self._initialize(initializer) + self.trainer = self._check_trainer(trainer) + + def _check_loss(self, loss): + if isinstance(loss, gluon.loss.Loss): + loss = [loss] + elif isinstance(loss, list) or all([isinstance(l, gluon.loss.Loss) for l in loss]): + loss = loss + else: + raise ValueError("loss must be a Loss or a list of Loss, " + "refer to gluon.loss.Loss:{}".format(loss)) + return loss + + def _check_metrics(self, metrics): + if isinstance(metrics, EvalMetric): + metrics = [metrics] + else: + metrics = metrics or [] + if not all([isinstance(metric, EvalMetric) for metric in metrics]): + raise ValueError("metrics must be a Metric or a list of Metric, " + "refer to mxnet.metric.EvalMetric:{}".format(metrics)) + return metrics + + def _check_context(self, context): + # handle context + if isinstance(context, Context): + context = [context] + elif isinstance(context, list) and all([isinstance(c, Context) for c in context]): + context = context + elif 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(1)] ") + context = [gpu(0)] + else: + context = [cpu()] + else: + raise ValueError("context must be a Context or a list of Context, " + "refer to mxnet.Context:{}".format(context)) + return context + + def _initialize(self, initializer): + # initialize the network + if initializer: + if self._is_initialized(): + # if already initialized, re-init with user specified initializer + warnings.warn("Network already initialized, re-initializing with %s. " + "You don't need to pass initializer if you already " + "initialized your net." % type(initializer).__name__) + self.net.initialize(init=initializer, ctx=self.context, force_reinit=True) + else: + # initialize with user specified initializer + self.net.initialize(init=initializer, ctx=self.context, force_reinit=False) + else: + if not self._is_initialized(): + self.net.initialize(ctx=self.context) + + def _check_trainer(self, trainer): + # handle trainer + if not trainer: + warnings.warn("No trainer specified, default SGD optimizer " + "with learning rate 0.001 is used.") + trainer = gluon.Trainer(self.net.collect_params(), + 'sgd', {'learning_rate': 0.001}) + elif not isinstance(trainer, gluon.Trainer): + raise ValueError("Trainer must be a Gluon Trainer instance, refer to " + "gluon.Trainer:{}".format(trainer)) + return trainer + + def _is_initialized(self): + param_dict = self.net.collect_params() + for param in param_dict: + try: + param_dict[param].list_ctx() + except RuntimeError: + return False + return True + + 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 + Returns train_metrics and val_metrics + + """ + 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(''.join([i for i in loss.name if not i.isdigit()]))) Review comment: Do you think removing trailing numbers will suffice? ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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