CNNSupervisedTrainer_VGG16.py 6.15 KB
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import mxnet as mx
import logging
import numpy as np
import time
import os
import shutil
from mxnet import gluon, autograd, nd

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class CNNSupervisedTrainer_VGG16:
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    def __init__(self, data_loader, net_constructor, net=None):
        self._data_loader = data_loader
        self._net_creator = net_constructor
        self._net = net

    def train(self, batch_size=64,
              num_epoch=10,
              eval_metric='acc',
              optimizer='adam',
              optimizer_params=(('learning_rate', 0.001),),
              load_checkpoint=True,
              context='gpu',
              checkpoint_period=5,
              normalize=True):
        if context == 'gpu':
            mx_context = mx.gpu()
        elif context == 'cpu':
            mx_context = mx.cpu()
        else:
            logging.error("Context argument is '" + context + "'. Only 'cpu' and 'gpu are valid arguments'.")

        if 'weight_decay' in optimizer_params:
            optimizer_params['wd'] = optimizer_params['weight_decay']
            del optimizer_params['weight_decay']
        if 'learning_rate_decay' in optimizer_params:
            min_learning_rate = 1e-08
            if 'learning_rate_minimum' in optimizer_params:
                min_learning_rate = optimizer_params['learning_rate_minimum']
                del optimizer_params['learning_rate_minimum']
            optimizer_params['lr_scheduler'] = mx.lr_scheduler.FactorScheduler(
                                                   optimizer_params['step_size'],
                                                   factor=optimizer_params['learning_rate_decay'],
                                                   stop_factor_lr=min_learning_rate)
            del optimizer_params['step_size']
            del optimizer_params['learning_rate_decay']


        train_iter, test_iter, data_mean, data_std = self._data_loader.load_data(batch_size)
        if self._net is None:
            if normalize:
                self._net_creator.construct(
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                    context=mx_context, data_mean=data_mean, data_std=data_std)
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            else:
                self._net_creator.construct(context=mx_context)

        begin_epoch = 0
        if load_checkpoint:
            begin_epoch = self._net_creator.load(mx_context)
        else:
            if os.path.isdir(self._net_creator._model_dir_):
                shutil.rmtree(self._net_creator._model_dir_)

        self._net = self._net_creator.net

        try:
            os.makedirs(self._net_creator._model_dir_)
        except OSError:
            if not os.path.isdir(self._net_creator._model_dir_):
                raise

        trainer = mx.gluon.Trainer(self._net.collect_params(), optimizer, optimizer_params)

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        loss_functions = {}

        for output_name, last_layer in self._net.last_layers.items():
            if last_layer == 'softmax':
                loss_functions[output_name] = mx.gluon.loss.SoftmaxCrossEntropyLoss()
            elif last_layer == 'sigmoid':
                loss_functions[output_name] = mx.gluon.loss.SigmoidBinaryCrossEntropyLoss()
            elif last_layer == 'linear':
                loss_functions[output_name] = mx.gluon.loss.L2Loss()
            else:
                loss_functions[output_name] = mx.gluon.loss.L2Loss()
                logging.warning("Invalid last layer, defaulting to L2 loss")
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        speed_period = 50
        tic = None

        for epoch in range(begin_epoch, begin_epoch + num_epoch):
            train_iter.reset()
            for batch_i, batch in enumerate(train_iter):
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                data_data = batch.data[0].as_in_context(mx_context)
                predictions_label = batch.label[0].as_in_context(mx_context)
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                with autograd.record():
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                    predictions_output = self._net(data_data)

                    loss = loss_functions['predictions'](predictions_output, predictions_label)
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                loss.backward()
                trainer.step(batch_size)

                if tic is None:
                    tic = time.time()
                else:
                    if batch_i % speed_period == 0:
                        try:
                            speed = speed_period * batch_size / (time.time() - tic)
                        except ZeroDivisionError:
                            speed = float("inf")

                        logging.info("Epoch[%d] Batch[%d] Speed: %.2f samples/sec" % (epoch, batch_i, speed))

                        tic = time.time()

            tic = None

            train_iter.reset()
            metric = mx.metric.create(eval_metric)
            for batch_i, batch in enumerate(train_iter):
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                data_data = batch.data[0].as_in_context(mx_context)

                labels = [
                    batch.label[0].as_in_context(mx_context)
                ]

                predictions_output = self._net(data_data)
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                predictions = [
                    mx.nd.argmax(predictions_output, axis=1)
                ]

                metric.update(preds=predictions, labels=labels)
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            train_metric_score = metric.get()[1]

            test_iter.reset()
            metric = mx.metric.create(eval_metric)
            for batch_i, batch in enumerate(test_iter):
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                data_data = batch.data[0].as_in_context(mx_context)

                labels = [
                    batch.label[0].as_in_context(mx_context)
                ]

                predictions_output = self._net(data_data)

                predictions = [
                    mx.nd.argmax(predictions_output, axis=1)
                ]
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                metric.update(preds=predictions, labels=labels)
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            test_metric_score = metric.get()[1]

            logging.info("Epoch[%d] Train: %f, Test: %f" % (epoch, train_metric_score, test_metric_score))

            if (epoch - begin_epoch) % checkpoint_period == 0:
                self._net.save_parameters(self.parameter_path() + '-' + str(epoch).zfill(4) + '.params')

        self._net.save_parameters(self.parameter_path() + '-' + str(num_epoch + begin_epoch).zfill(4) + '.params')
        self._net.export(self.parameter_path() + '_newest', epoch=0)

    def parameter_path(self):
        return self._net_creator._model_dir_ + self._net_creator._model_prefix_