CNNSupervisedTrainer_VGG16.py 8.89 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

Eyüp Harputlu's avatar
Eyüp Harputlu committed
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
class CrossEntropyLoss(gluon.loss.Loss):
    def __init__(self, axis=-1, sparse_label=True, weight=None, batch_axis=0, **kwargs):
        super(CrossEntropyLoss, self).__init__(weight, batch_axis, **kwargs)
        self._axis = axis
        self._sparse_label = sparse_label

    def hybrid_forward(self, F, pred, label, sample_weight=None):
        pred = F.log(pred)
        if self._sparse_label:
            loss = -F.pick(pred, label, axis=self._axis, keepdims=True)
        else:
            label = gluon.loss._reshape_like(F, label, pred)
            loss = -F.sum(pred * label, axis=self._axis, keepdims=True)
        loss = gluon.loss._apply_weighting(F, loss, self._weight, sample_weight)
        return F.mean(loss, axis=self._batch_axis, exclude=True)

Eyüp Harputlu's avatar
Eyüp Harputlu committed
25
26
27
28
29
30
31
32
33
class LogCoshLoss(gluon.loss.Loss):
    def __init__(self, weight=None, batch_axis=0, **kwargs):
        super(LogCoshLoss, self).__init__(weight, batch_axis, **kwargs)

    def hybrid_forward(self, F, pred, label, sample_weight=None):
        loss = F.log(F.cosh(pred - label))
        loss = gluon.loss._apply_weighting(F, loss, self._weight, sample_weight)
        return F.mean(loss, axis=self._batch_axis, exclude=True)

34
class CNNSupervisedTrainer_VGG16:
35
    def __init__(self, data_loader, net_constructor):
Nicola Gatto's avatar
Nicola Gatto committed
36
37
        self._data_loader = data_loader
        self._net_creator = net_constructor
38
        self._networks = {}
Nicola Gatto's avatar
Nicola Gatto committed
39
40
41
42

    def train(self, batch_size=64,
              num_epoch=10,
              eval_metric='acc',
Eyüp Harputlu's avatar
Eyüp Harputlu committed
43
44
              loss ='softmax_cross_entropy',
              loss_params={},
Nicola Gatto's avatar
Nicola Gatto committed
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
              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)
75
76
77
78
79

        if normalize:
            self._net_creator.construct(context=mx_context, data_mean=data_mean, data_std=data_std)
        else:
            self._net_creator.construct(context=mx_context)
Nicola Gatto's avatar
Nicola Gatto committed
80
81
82
83
84
85
86
87

        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_)

88
        self._networks = self._net_creator.networks
Nicola Gatto's avatar
Nicola Gatto committed
89
90
91
92
93
94
95

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

96
        trainers = [mx.gluon.Trainer(network.collect_params(), optimizer, optimizer_params) for network in self._networks.values()]
Nicola Gatto's avatar
Nicola Gatto committed
97

Eyüp Harputlu's avatar
Eyüp Harputlu committed
98
99
100
101
102
103
        margin = loss_params['margin'] if 'margin' in loss_params else 1.0
        sparseLabel = loss_params['sparse_label'] if 'sparse_label' in loss_params else True
        if loss == 'softmax_cross_entropy':
            fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
            loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(from_logits=fromLogits, sparse_label=sparseLabel)
        elif loss == 'sigmoid_binary_cross_entropy':
Nicola Gatto's avatar
Nicola Gatto committed
104
            loss_function = mx.gluon.loss.SigmoidBinaryCrossEntropyLoss()
Eyüp Harputlu's avatar
Eyüp Harputlu committed
105
106
107
        elif loss == 'cross_entropy':
            loss_function = CrossEntropyLoss(sparse_label=sparseLabel)
        elif loss == 'l2':
Nicola Gatto's avatar
Nicola Gatto committed
108
            loss_function = mx.gluon.loss.L2Loss()
Eyüp Harputlu's avatar
Eyüp Harputlu committed
109
        elif loss == 'l1':
Nicola Gatto's avatar
Nicola Gatto committed
110
            loss_function = mx.gluon.loss.L2Loss()
Eyüp Harputlu's avatar
Eyüp Harputlu committed
111
112
113
114
115
116
117
118
119
120
121
122
123
        elif loss == 'huber':
            rho = loss_params['rho'] if 'rho' in loss_params else 1
            loss_function = mx.gluon.loss.HuberLoss(rho=rho)
        elif loss == 'hinge':
            loss_function = mx.gluon.loss.HingeLoss(margin=margin)
        elif loss == 'squared_hinge':
            loss_function = mx.gluon.loss.SquaredHingeLoss(margin=margin)
        elif loss == 'logistic':
            labelFormat = loss_params['label_format'] if 'label_format' in loss_params else 'signed'
            loss_function = mx.gluon.loss.LogisticLoss(label_format=labelFormat)
        elif loss == 'kullback_leibler':
            fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else True
            loss_function = mx.gluon.loss.KLDivLoss(from_logits=fromLogits)
Eyüp Harputlu's avatar
Eyüp Harputlu committed
124
125
        elif loss == 'log_cosh':
            loss_function = LogCoshLoss()
Eyüp Harputlu's avatar
Eyüp Harputlu committed
126
127
        else:
            logging.error("Invalid loss parameter.")
Nicola Gatto's avatar
Nicola Gatto committed
128
129
130
131
132
133
134

        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):
135
                data_ = batch.data[0].as_in_context(mx_context)
136
                predictions_label = batch.label[0].as_in_context(mx_context)
137

Nicola Gatto's avatar
Nicola Gatto committed
138
                with autograd.record():
Sebastian Nickels's avatar
Sebastian Nickels committed
139
                    predictions_ = mx.nd.zeros((1000,), ctx=mx_context)
140
141

                    predictions_ = self._networks[0](data_)
142

143
                    loss = \
144
                        loss_function(predictions_, predictions_label)
Nicola Gatto's avatar
Nicola Gatto committed
145
146

                loss.backward()
147
148
149

                for trainer in trainers:
                    trainer.step(batch_size)
Nicola Gatto's avatar
Nicola Gatto committed
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168

                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):
169
                data_ = batch.data[0].as_in_context(mx_context)
170
171
172
173
174

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

175
                if True:
Sebastian Nickels's avatar
Sebastian Nickels committed
176
                    predictions_ = mx.nd.zeros((1000,), ctx=mx_context)
177
178

                    predictions_ = self._networks[0](data_)
179

180
                predictions = [
181
                    mx.nd.argmax(predictions_, axis=1)
182
183
184
                ]

                metric.update(preds=predictions, labels=labels)
Nicola Gatto's avatar
Nicola Gatto committed
185
186
187
188
189
            train_metric_score = metric.get()[1]

            test_iter.reset()
            metric = mx.metric.create(eval_metric)
            for batch_i, batch in enumerate(test_iter):
190
                data_ = batch.data[0].as_in_context(mx_context)
191
192
193
194
195

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

196
                if True:
Sebastian Nickels's avatar
Sebastian Nickels committed
197
                    predictions_ = mx.nd.zeros((1000,), ctx=mx_context)
198
199

                    predictions_ = self._networks[0](data_)
200

201
                predictions = [
202
                    mx.nd.argmax(predictions_, axis=1)
203
                ]
204

205
                metric.update(preds=predictions, labels=labels)
Nicola Gatto's avatar
Nicola Gatto committed
206
207
208
209
210
            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:
211
212
                for i, network in self._networks.items():
                    network.save_parameters(self.parameter_path(i) + '-' + str(epoch).zfill(4) + '.params')
Nicola Gatto's avatar
Nicola Gatto committed
213

214
215
216
        for i, network in self._networks.items():
            network.save_parameters(self.parameter_path(i) + '-' + str(num_epoch + begin_epoch).zfill(4) + '.params')
            network.export(self.parameter_path(i) + '_newest', epoch=0)
Nicola Gatto's avatar
Nicola Gatto committed
217

218
    def parameter_path(self, index):
Bernhard Rumpe's avatar
BR-sy    
Bernhard Rumpe committed
219
        return self._net_creator._model_dir_ + self._net_creator._model_prefix_ + '_' + str(index)