CNNSupervisedTrainer_VGG16.py 11.6 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():
139
                    predictions_ = mx.nd.zeros((batch_size, 1000,), ctx=mx_context)
140

141
                    lossList = []
142
                    predictions_ = self._networks[0](data_)
143
144
145
146
147
                    lossList.append(loss_function(predictions_, predictions_label))

                    loss = 0
                    for element in lossList:
                        loss = loss + element
148

Nicola Gatto's avatar
Nicola Gatto committed
149
150

                loss.backward()
151

152

153
154
                for trainer in trainers:
                    trainer.step(batch_size)
Nicola Gatto's avatar
Nicola Gatto committed
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173

                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):
174
                data_ = batch.data[0].as_in_context(mx_context)
175
176
177
178
179

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

180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213

                def applyBeamSearch(input, depth, width, maxDepth, currProb, netIndex, bestOutput):
                    bestProb = 0.0
                    while depth < maxDepth:
                        depth += 1
                        batchIndex = 0
                        for batchEntry in input:
                            top_k_indices = mx.nd.topk(batchEntry, axis=0, k=width)
                            top_k_values = mx.nd.topk(batchEntry, ret_typ='value', axis=0, k=width)
                            for index in range(top_k_indices.size):

                                #print mx.nd.array(top_k_indices[index])
                                #print top_k_values[index]
                                if depth == 1:
                                    #print mx.nd.array(top_k_indices[index])
                                    result = applyBeamSearch(self._networks[netIndex](mx.nd.array(top_k_indices[index])), depth, width, maxDepth,
                                        currProb * top_k_values[index], netIndex, self._networks[netIndex](mx.nd.array(top_k_indices[index])))
                                else:
                                    result = applyBeamSearch(self._networks[netIndex](mx.nd.array(top_k_indices[index])), depth, width, maxDepth,
                                        currProb * top_k_values[index], netIndex, bestOutput)

                                if depth == maxDepth:
                                    #print currProb
                                    if currProb > bestProb:
                                        bestProb = currProb
                                        bestOutput[batchIndex] = result[batchIndex]
                                        #print "new bestOutput: ", bestOutput

                            batchIndex += 1
                    #print bestOutput
                    #print bestProb
                    return bestOutput


214
                if True: 
215
                    predictions_ = mx.nd.zeros((batch_size, 1000,), ctx=mx_context)
216
217

                    predictions_ = self._networks[0](data_)
218

219
220
221
222
223
224
225
226
227
                out_names=[]
                out_names.append(predictions_)
                predictions = []
                for output_name in out_names:
                    if mx.nd.shape_array(output_name).size > 1:
                        predictions.append(mx.nd.argmax(output_name, axis=1))
                    #ArgMax already applied
                    else:
                        predictions.append(output_name)
228

229
230

                metric.update(preds=predictions, labels=labels)
Nicola Gatto's avatar
Nicola Gatto committed
231
232
233
234
235
            train_metric_score = metric.get()[1]

            test_iter.reset()
            metric = mx.metric.create(eval_metric)
            for batch_i, batch in enumerate(test_iter):
236
                data_ = batch.data[0].as_in_context(mx_context)
237
238
239
240
241

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

242
                if True: 
243
                    predictions_ = mx.nd.zeros((batch_size, 1000,), ctx=mx_context)
244
245

                    predictions_ = self._networks[0](data_)
246

247
248
249
250
251
252
253
254
255
                out_names=[]
                out_names.append(predictions_)
                predictions = []
                for output_name in out_names:
                    if mx.nd.shape_array(output_name).size > 1:
                        predictions.append(mx.nd.argmax(output_name, axis=1))
                    #ArgMax already applied
                    else:
                        predictions.append(output_name)
256

257
                metric.update(preds=predictions, labels=labels)
Nicola Gatto's avatar
Nicola Gatto committed
258
259
260
261
            test_metric_score = metric.get()[1]

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

262

Nicola Gatto's avatar
Nicola Gatto committed
263
            if (epoch - begin_epoch) % checkpoint_period == 0:
264
265
                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
266

267
268
269
        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
270

271
272
    def parameter_path(self, index):
        return self._net_creator._model_dir_ + self._net_creator._model_prefix_ + '_' + str(index)