CNNSupervisedTrainer_mnist_mnistClassifier_net.py 19.8 KB
Newer Older
Nicola Gatto's avatar
Nicola Gatto committed
1 2 3 4 5 6
import mxnet as mx
import logging
import numpy as np
import time
import os
import shutil
Sebastian N.'s avatar
Sebastian N. committed
7 8 9
import pickle
import math
import sys
Nicola Gatto's avatar
Nicola Gatto committed
10 11
from mxnet import gluon, autograd, nd

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
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)

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)

Sebastian N.'s avatar
Sebastian N. committed
37 38 39 40 41 42 43 44 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 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116
class SoftmaxCrossEntropyLossIgnoreIndices(gluon.loss.Loss):
    def __init__(self, axis=-1, ignore_indices=[], sparse_label=True, from_logits=False, weight=None, batch_axis=0, **kwargs):
        super(SoftmaxCrossEntropyLossIgnoreIndices, self).__init__(weight, batch_axis, **kwargs)
        self._axis = axis
        self._ignore_indices = ignore_indices
        self._sparse_label = sparse_label
        self._from_logits = from_logits

    def hybrid_forward(self, F, pred, label, sample_weight=None):
        log_softmax = F.log_softmax
        pick = F.pick
        if not self._from_logits:
            pred = log_softmax(pred, self._axis)
        if self._sparse_label:
            loss = -pick(pred, label, axis=self._axis, keepdims=True)
        else:
            label = _reshape_like(F, label, pred)
            loss = -(pred * label).sum(axis=self._axis, keepdims=True)
        # ignore some indices for loss, e.g. <pad> tokens in NLP applications
        for i in self._ignore_indices:
            loss = loss * mx.nd.logical_not(mx.nd.equal(mx.nd.argmax(pred, axis=1), mx.nd.ones_like(mx.nd.argmax(pred, axis=1))*i))
        return loss.mean(axis=self._batch_axis, exclude=True)

@mx.metric.register
class BLEU(mx.metric.EvalMetric):
    N = 4

    def __init__(self, exclude=None, name='bleu', output_names=None, label_names=None):
        super(BLEU, self).__init__(name=name, output_names=output_names, label_names=label_names)

        self._exclude = exclude or []

        self._match_counts = [0 for _ in range(self.N)]
        self._counts = [0 for _ in range(self.N)]

        self._size_ref = 0
        self._size_hyp = 0

    def update(self, labels, preds):
        labels, preds = mx.metric.check_label_shapes(labels, preds, True)

        new_labels = self._convert(labels)
        new_preds = self._convert(preds)

        for label, pred in zip(new_labels, new_preds):
            reference = [word for word in label if word not in self._exclude]
            hypothesis = [word for word in pred if word not in self._exclude]

            self._size_ref += len(reference)
            self._size_hyp += len(hypothesis)

            for n in range(self.N):
                reference_ngrams = self._get_ngrams(reference, n + 1)
                hypothesis_ngrams = self._get_ngrams(hypothesis, n + 1)

                match_count = 0

                for ngram in hypothesis_ngrams:
                    if ngram in reference_ngrams:
                        reference_ngrams.remove(ngram)

                        match_count += 1

                self._match_counts[n] += match_count
                self._counts[n] += len(hypothesis_ngrams)

    def get(self):
        precisions = [sys.float_info.min for n in range(self.N)]

        i = 1

        for n in range(self.N):
            match_counts = self._match_counts[n]
            counts = self._counts[n]

            if counts != 0:
                if match_counts == 0:
                    i *= 2
                    match_counts = 1 / i

117 118
                if (match_counts / counts) > 0:
                    precisions[n] = match_counts / counts
Sebastian N.'s avatar
Sebastian N. committed
119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172

        bleu = self._get_brevity_penalty() * math.exp(sum(map(math.log, precisions)) / self.N)

        return (self.name, bleu)

    def calculate(self):
        precisions = [sys.float_info.min for n in range(self.N)]

        i = 1

        for n in range(self.N):
            match_counts = self._match_counts[n]
            counts = self._counts[n]

            if counts != 0:
                if match_counts == 0:
                    i *= 2
                    match_counts = 1 / i

                precisions[n] = match_counts / counts

        return self._get_brevity_penalty() * math.exp(sum(map(math.log, precisions)) / self.N)

    def _get_brevity_penalty(self):
        if self._size_hyp >= self._size_ref:
            return 1
        else:
            return math.exp(1 - (self._size_ref / self._size_hyp))

    @staticmethod
    def _get_ngrams(sentence, n):
        ngrams = []

        if len(sentence) >= n:
            for i in range(len(sentence) - n + 1):
                ngrams.append(sentence[i:i+n])

        return ngrams

    @staticmethod
    def _convert(nd_list):
        if len(nd_list) == 0:
            return []

        new_list = [[] for _ in range(nd_list[0].shape[0])]

        for element in nd_list:
            for i in range(element.shape[0]):
                new_list[i].append(element[i].asscalar())

        return new_list



173
class CNNSupervisedTrainer_mnist_mnistClassifier_net:
Sebastian N.'s avatar
Sebastian N. committed
174
    def __init__(self, data_loader, net_constructor):
Nicola Gatto's avatar
Nicola Gatto committed
175 176
        self._data_loader = data_loader
        self._net_creator = net_constructor
Sebastian N.'s avatar
Sebastian N. committed
177
        self._networks = {}
Nicola Gatto's avatar
Nicola Gatto committed
178 179 180 181

    def train(self, batch_size=64,
              num_epoch=10,
              eval_metric='acc',
Sebastian N.'s avatar
Updated  
Sebastian N. committed
182
              eval_metric_params={},
183 184
              loss ='softmax_cross_entropy',
              loss_params={},
Nicola Gatto's avatar
Nicola Gatto committed
185 186 187 188 189
              optimizer='adam',
              optimizer_params=(('learning_rate', 0.001),),
              load_checkpoint=True,
              context='gpu',
              checkpoint_period=5,
Sebastian N.'s avatar
Sebastian N. committed
190
              save_attention_image=False,
Sebastian N.'s avatar
Sebastian N. committed
191
              use_teacher_forcing=False,
Nicola Gatto's avatar
Nicola Gatto committed
192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208
              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(
Sebastian N.'s avatar
Sebastian N. committed
209 210 211
                                                   optimizer_params['step_size'],
                                                   factor=optimizer_params['learning_rate_decay'],
                                                   stop_factor_lr=min_learning_rate)
Nicola Gatto's avatar
Nicola Gatto committed
212 213 214
            del optimizer_params['step_size']
            del optimizer_params['learning_rate_decay']

Sebastian N.'s avatar
Sebastian N. committed
215 216
        train_batch_size = batch_size
        test_batch_size = batch_size
Nicola Gatto's avatar
Nicola Gatto committed
217

Sebastian N.'s avatar
Sebastian N. committed
218
        train_iter, train_test_iter, test_iter, data_mean, data_std, train_images, test_images = self._data_loader.load_data(train_batch_size, test_batch_size)
Sebastian N.'s avatar
Sebastian N. committed
219 220 221 222 223

        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
224 225 226 227 228 229 230 231

        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_)

Sebastian N.'s avatar
Sebastian N. committed
232
        self._networks = self._net_creator.networks
Nicola Gatto's avatar
Nicola Gatto committed
233 234 235 236 237 238 239

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

Sebastian N.'s avatar
Sebastian N. committed
240
        trainers = [mx.gluon.Trainer(network.collect_params(), optimizer, optimizer_params) for network in self._networks.values() if len(network.collect_params().values()) != 0]
Nicola Gatto's avatar
Nicola Gatto committed
241

242 243
        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
Sebastian N.'s avatar
Sebastian N. committed
244
        ignore_indices = [loss_params['ignore_indices']] if 'ignore_indices' in loss_params else []
245 246
        if loss == 'softmax_cross_entropy':
            fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
Sebastian N.'s avatar
Sebastian N. committed
247
            loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(from_logits=fromLogits, sparse_label=sparseLabel)
248
        elif loss == 'softmax_cross_entropy_ignore_indices':
Sebastian N.'s avatar
Sebastian N. committed
249
            fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
Sebastian N.'s avatar
Sebastian N. committed
250
            loss_function = SoftmaxCrossEntropyLossIgnoreIndices(ignore_indices=ignore_indices, from_logits=fromLogits, sparse_label=sparseLabel)
251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275
        elif loss == 'sigmoid_binary_cross_entropy':
            loss_function = mx.gluon.loss.SigmoidBinaryCrossEntropyLoss()
        elif loss == 'cross_entropy':
            loss_function = CrossEntropyLoss(sparse_label=sparseLabel)
        elif loss == 'l2':
            loss_function = mx.gluon.loss.L2Loss()
        elif loss == 'l1':
            loss_function = mx.gluon.loss.L2Loss()
        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)
        elif loss == 'log_cosh':
            loss_function = LogCoshLoss()
        else:
            logging.error("Invalid loss parameter.")
Nicola Gatto's avatar
Nicola Gatto committed
276 277 278 279 280 281 282 283

        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):
                with autograd.record():
Sebastian N.'s avatar
Sebastian N. committed
284 285 286 287 288 289 290
                    labels = [batch.label[i].as_in_context(mx_context) for i in range(1)]

                    image_ = batch.data[0].as_in_context(mx_context)

                    predictions_ = mx.nd.zeros((train_batch_size, 10,), ctx=mx_context)


Sebastian N.'s avatar
Sebastian N. committed
291 292
                    nd.waitall()

Sebastian N.'s avatar
Sebastian N. committed
293
                    lossList = []
294 295

                    predictions_ = self._networks[0](image_)
296

Sebastian N.'s avatar
Sebastian N. committed
297
                    lossList.append(loss_function(predictions_, labels[0]))
298

Sebastian N.'s avatar
Sebastian N. committed
299 300 301
                    loss = 0
                    for element in lossList:
                        loss = loss + element
Nicola Gatto's avatar
Nicola Gatto committed
302 303

                loss.backward()
Sebastian N.'s avatar
Sebastian N. committed
304 305 306

                for trainer in trainers:
                    trainer.step(batch_size)
Nicola Gatto's avatar
Nicola Gatto committed
307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322

                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

Sebastian N.'s avatar
Sebastian N. committed
323 324 325
            train_test_iter.reset()
            metric = mx.metric.create(eval_metric, **eval_metric_params)
            for batch_i, batch in enumerate(train_test_iter):
326
                if True: 
Sebastian N.'s avatar
Sebastian N. committed
327
                    labels = [batch.label[i].as_in_context(mx_context) for i in range(1)]
328

Sebastian N.'s avatar
Sebastian N. committed
329 330 331
                    image_ = batch.data[0].as_in_context(mx_context)

                    predictions_ = mx.nd.zeros((test_batch_size, 10,), ctx=mx_context)
332

333

Sebastian N.'s avatar
Sebastian N. committed
334 335
                    nd.waitall()

Sebastian N.'s avatar
Sebastian N. committed
336 337
                    outputs = []
                    attentionList=[]
338
                    predictions_ = self._networks[0](image_)
339

Sebastian N.'s avatar
Sebastian N. committed
340 341 342 343 344 345
                    outputs.append(predictions_)


                    if save_attention_image == "True":
                        import matplotlib.pyplot as plt
                        logging.getLogger('matplotlib').setLevel(logging.ERROR)
346

Sebastian N.'s avatar
Sebastian N. committed
347
                        plt.clf()
Sebastian N.'s avatar
Sebastian N. committed
348
                        fig = plt.figure(figsize=(15,15))
Sebastian N.'s avatar
Sebastian N. committed
349
                        max_length = len(labels)-1
350

Sebastian N.'s avatar
Sebastian N. committed
351 352 353
                        if(os.path.isfile('src/test/resources/training_data/Show_attend_tell/dict.pkl')):
                            with open('src/test/resources/training_data/Show_attend_tell/dict.pkl', 'rb') as f:
                                dict = pickle.load(f)
354

Sebastian N.'s avatar
Sebastian N. committed
355 356 357 358

                        ax = fig.add_subplot(max_length//3, max_length//4, 1)
                        ax.imshow(train_images[0+test_batch_size*(batch_i)].transpose(1,2,0))

Sebastian N.'s avatar
Sebastian N. committed
359 360 361 362 363
                        for l in range(max_length):
                            attention = attentionList[l]
                            attention = mx.nd.slice_axis(attention, axis=0, begin=0, end=1)
                            attention = mx.nd.squeeze(attention)
                            attention_resized = np.resize(attention.asnumpy(), (8, 8))
Sebastian N.'s avatar
Sebastian N. committed
364
                            ax = fig.add_subplot(max_length//3, max_length//4, l+2)
365 366 367 368 369 370
                            if int(labels[l+1][0].asscalar()) > len(dict):
                                ax.set_title("<unk>")
                                img = ax.imshow(train_images[0+test_batch_size*(batch_i)].transpose(1,2,0))
                                ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent())
                                break
                            elif dict[int(labels[l+1][0].asscalar())] == "<end>":
Sebastian N.'s avatar
Sebastian N. committed
371 372 373 374 375 376 377 378
                                ax.set_title(".")
                                img = ax.imshow(train_images[0+test_batch_size*(batch_i)].transpose(1,2,0))
                                ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent())
                                break
                            else:
                                ax.set_title(dict[int(labels[l+1][0].asscalar())])
                                img = ax.imshow(train_images[0+test_batch_size*(batch_i)].transpose(1,2,0))
                                ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent())
Sebastian N.'s avatar
Sebastian N. committed
379 380 381 382 383 384 385 386 387 388 389 390 391 392 393


                        plt.tight_layout()
                        target_dir = 'target/attention_images'
                        if not os.path.exists(target_dir):
                                    os.makedirs(target_dir)
                        plt.savefig(target_dir + '/attention_train.png')
                        plt.close()

                predictions = []
                for output_name in outputs:
                    if mx.nd.shape_array(mx.nd.squeeze(output_name)).size > 1:
                        predictions.append(mx.nd.argmax(output_name, axis=1))
                    else:
                        predictions.append(output_name)
394 395

                metric.update(preds=predictions, labels=labels)
Nicola Gatto's avatar
Nicola Gatto committed
396 397 398
            train_metric_score = metric.get()[1]

            test_iter.reset()
Sebastian N.'s avatar
Sebastian N. committed
399
            metric = mx.metric.create(eval_metric, **eval_metric_params)
Nicola Gatto's avatar
Nicola Gatto committed
400
            for batch_i, batch in enumerate(test_iter):
401
                if True: 
Sebastian N.'s avatar
Sebastian N. committed
402
                    labels = [batch.label[i].as_in_context(mx_context) for i in range(1)]
403

Sebastian N.'s avatar
Sebastian N. committed
404 405 406
                    image_ = batch.data[0].as_in_context(mx_context)

                    predictions_ = mx.nd.zeros((test_batch_size, 10,), ctx=mx_context)
407

408

Sebastian N.'s avatar
Sebastian N. committed
409 410
                    nd.waitall()

Sebastian N.'s avatar
Sebastian N. committed
411 412
                    outputs = []
                    attentionList=[]
413
                    predictions_ = self._networks[0](image_)
414

Sebastian N.'s avatar
Sebastian N. committed
415 416 417 418 419
                    outputs.append(predictions_)


                    if save_attention_image == "True":
                        plt.clf()
Sebastian N.'s avatar
Sebastian N. committed
420
                        fig = plt.figure(figsize=(15,15))
Sebastian N.'s avatar
Sebastian N. committed
421
                        max_length = len(labels)-1
422

Sebastian N.'s avatar
Sebastian N. committed
423 424 425 426

                        ax = fig.add_subplot(max_length//3, max_length//4, 1)
                        ax.imshow(test_images[0+test_batch_size*(batch_i)].transpose(1,2,0))

Sebastian N.'s avatar
Sebastian N. committed
427 428 429 430 431
                        for l in range(max_length):
                            attention = attentionList[l]
                            attention = mx.nd.slice_axis(attention, axis=0, begin=0, end=1)
                            attention = mx.nd.squeeze(attention)
                            attention_resized = np.resize(attention.asnumpy(), (8, 8))
Sebastian N.'s avatar
Sebastian N. committed
432
                            ax = fig.add_subplot(max_length//3, max_length//4, l+2)
433 434 435 436 437 438
                            if int(mx.nd.slice_axis(outputs[l+1], axis=0, begin=0, end=1).squeeze().asscalar()) > len(dict):
                                ax.set_title("<unk>")
                                img = ax.imshow(test_images[0+test_batch_size*(batch_i)].transpose(1,2,0))
                                ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent())
                                break
                            elif dict[int(mx.nd.slice_axis(outputs[l+1], axis=0, begin=0, end=1).squeeze().asscalar())] == "<end>":
Sebastian N.'s avatar
Sebastian N. committed
439 440 441 442 443
                                ax.set_title(".")
                                img = ax.imshow(test_images[0+test_batch_size*(batch_i)].transpose(1,2,0))
                                ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent())
                                break
                            else:
444
                                ax.set_title(dict[int(mx.nd.slice_axis(outputs[l+1], axis=0, begin=0, end=1).squeeze().asscalar())])
Sebastian N.'s avatar
Sebastian N. committed
445 446
                                img = ax.imshow(test_images[0+test_batch_size*(batch_i)].transpose(1,2,0))
                                ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent())
Sebastian N.'s avatar
Sebastian N. committed
447 448 449 450 451 452 453 454 455 456 457 458 459


                        plt.tight_layout()
                        plt.savefig(target_dir + '/attention_test.png')
                        plt.close()

                predictions = []
                for output_name in outputs:
                    if mx.nd.shape_array(mx.nd.squeeze(output_name)).size > 1:
                        predictions.append(mx.nd.argmax(output_name, axis=1))
                    #ArgMax already applied
                    else:
                        predictions.append(output_name)
460

461
                metric.update(preds=predictions, labels=labels)
Nicola Gatto's avatar
Nicola Gatto committed
462 463 464 465
            test_metric_score = metric.get()[1]

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

Sebastian N.'s avatar
Sebastian N. committed
466

Nicola Gatto's avatar
Nicola Gatto committed
467
            if (epoch - begin_epoch) % checkpoint_period == 0:
Sebastian N.'s avatar
Sebastian N. committed
468 469
                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
470

Sebastian N.'s avatar
Sebastian N. committed
471 472 473
        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
474

Sebastian N.'s avatar
Sebastian N. committed
475
    def parameter_path(self, index):
476
        return self._net_creator._model_dir_ + self._net_creator._model_prefix_ + '_' + str(index)