CNNSupervisedTrainer_mnist_mnistClassifier_net.py 6.17 KB
Newer Older
Nicola Gatto's avatar
Nicola Gatto committed
1 2 3 4 5 6 7 8
import mxnet as mx
import logging
import numpy as np
import time
import os
import shutil
from mxnet import gluon, autograd, nd

9
class CNNSupervisedTrainer_mnist_mnistClassifier_net:
Nicola Gatto's avatar
Nicola Gatto committed
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
    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(
51
                    context=mx_context, data_mean=data_mean, data_std=data_std)
Nicola Gatto's avatar
Nicola Gatto committed
52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71
            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)

72 73 74 75 76 77 78 79 80 81 82 83
        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")
Nicola Gatto's avatar
Nicola Gatto committed
84 85 86 87 88 89 90

        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):
91 92
                image_data = batch.data[0].as_in_context(mx_context)
                predictions_label = batch.label[0].as_in_context(mx_context)
93

Nicola Gatto's avatar
Nicola Gatto committed
94
                with autograd.record():
95 96 97
                    predictions_output = self._net(image_data)

                    loss = loss_functions['predictions'](predictions_output, predictions_label)
Nicola Gatto's avatar
Nicola Gatto committed
98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119

                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):
120 121 122 123 124 125 126
                image_data = batch.data[0].as_in_context(mx_context)

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

                predictions_output = self._net(image_data)
127

128 129 130 131 132
                predictions = [
                    mx.nd.argmax(predictions_output, axis=1)
                ]

                metric.update(preds=predictions, labels=labels)
Nicola Gatto's avatar
Nicola Gatto committed
133 134 135 136 137
            train_metric_score = metric.get()[1]

            test_iter.reset()
            metric = mx.metric.create(eval_metric)
            for batch_i, batch in enumerate(test_iter):
138 139 140 141 142 143 144 145 146 147 148
                image_data = batch.data[0].as_in_context(mx_context)

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

                predictions_output = self._net(image_data)

                predictions = [
                    mx.nd.argmax(predictions_output, axis=1)
                ]
149

150
                metric.update(preds=predictions, labels=labels)
Nicola Gatto's avatar
Nicola Gatto committed
151 152 153 154 155 156 157 158 159 160 161 162
            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_