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_r264084500
########## File path: python/mxnet/gluon/estimator/estimator.py ########## @@ -0,0 +1,203 @@ +# 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: + warnings.warn("No trainer specified, default SGD optimizer with learning rate 0.001 is used.") + self.trainers = [gluon.Trainer(self.net.collect_params(), 'sgd', {'learning_rate': 0.001})] + + 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 _batch_fn(self, batch, ctx): + data = gluon.utils.split_and_load(batch[0], ctx_list=ctx, batch_axis=0) + label = gluon.utils.split_and_load(batch[1], ctx_list=ctx, batch_axis=0) + return data, label + + def fit(self, train_data, + val_data=None, + epochs=1, + batch_size=None, + event_handlers=None): + + if not batch_size: + batch_size = 32 * len(self.context) + + 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(self)) + + # TODO: handle validation logic and update train stats + do_validation = False + if val_data: + do_validation = True + + # training begin + for handler in event_handlers: + handler.train_begin() + + for epoch in range(epochs): + # epoch begin + self.train_stats["epochs"].append(epoch) + self.train_stats["learning_rate"].append(self.trainers[0].learning_rate) + + for handler in event_handlers: + handler.epoch_begin() + + for metric in self.metrics + self.loss_metrics: + metric.reset() + + for i, batch in enumerate(train_data): + data, label = self._batch_fn(batch, self.context) + + # batch begin + for handler in event_handlers: + handler.batch_begin() + + 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)]) + + for loss in losses: + for l in loss: + l.backward() + + # update metrics + for metric in self.metrics: + metric.update(label, pred) + self.train_stats['batch_' + metric.name] = metric.get()[1] + for loss, loss_metric, in zip(losses, self.loss_metrics): + loss_metric.update(0, [l for l in loss]) + self.train_stats['batch_' + loss_metric.name] = loss_metric.get()[1] + + self.train_stats['step'] = str(batch_size * (i + 1)) + '/' + str(len(train_data._dataset)) + + for trainer in self.trainers: + trainer.step(batch_size) + + # batch end + for handler in event_handlers: + handler.batch_end() + + for metric in self.metrics + self.loss_metrics: + self.train_stats['train_' + metric.name].append(metric.get()[1]) Review comment: Validation metrics would also be needed in train_stats. 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