CNNSupervisedTrainer_VGG16.py 17.4 KB
 Nicola Gatto committed Apr 08, 2019 1 2 3 4 5 6 ``````import mxnet as mx import logging import numpy as np import time import os import shutil `````` Christian Fuß committed Oct 08, 2019 7 ``````import pickle `````` Sebastian N. committed Oct 18, 2019 8 9 ``````import math import sys `````` Nicola Gatto committed Apr 08, 2019 10 11 ``````from mxnet import gluon, autograd, nd `````` Eyüp Harputlu committed Jun 05, 2019 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 ``````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 committed Jun 24, 2019 28 29 30 31 32 33 34 35 36 ``````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. committed Oct 30, 2019 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 ``````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) #loss = _apply_weighting(F, loss, self._weight, sample_weight) # ignore some indices for loss, e.g. 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) `````` Sebastian N. committed Oct 18, 2019 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 ``````@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 precisions[n] = match_counts / counts 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 `````` Christian Fuß committed Oct 08, 2019 170 `````` `````` Sebastian N. committed Oct 30, 2019 171 172 `````` `````` Sebastian Nickels committed May 26, 2019 173 ``````class CNNSupervisedTrainer_VGG16: `````` Christian Fuß committed Sep 24, 2019 174 `````` def applyBeamSearch(input, length, width, maxLength, currProb, netIndex, bestOutput): `````` Christian Fuß committed Sep 23, 2019 175 `````` bestProb = 0.0 `````` Christian Fuß committed Sep 24, 2019 176 177 `````` while length < maxLength: length += 1 `````` Christian Fuß committed Sep 23, 2019 178 179 180 181 182 183 184 185 `````` 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] `````` Christian Fuß committed Sep 24, 2019 186 `````` if length == 1: `````` Christian Fuß committed Sep 23, 2019 187 `````` #print mx.nd.array(top_k_indices[index]) `````` Christian Fuß committed Sep 24, 2019 188 `````` result = applyBeamSearch(self._networks[netIndex](mx.nd.array(top_k_indices[index])), length, width, maxLength, `````` Christian Fuß committed Sep 23, 2019 189 190 `````` currProb * top_k_values[index], netIndex, self._networks[netIndex](mx.nd.array(top_k_indices[index]))) else: `````` Christian Fuß committed Sep 24, 2019 191 `````` result = applyBeamSearch(self._networks[netIndex](mx.nd.array(top_k_indices[index])), length, width, maxLength, `````` Christian Fuß committed Sep 23, 2019 192 193 `````` currProb * top_k_values[index], netIndex, bestOutput) `````` Christian Fuß committed Sep 24, 2019 194 `````` if length == maxLength: `````` Christian Fuß committed Sep 23, 2019 195 196 197 198 199 200 201 202 203 204 205 206 `````` #print currProb if currProb > bestProb: bestProb = currProb bestOutput[batchIndex] = result[batchIndex] #print "new bestOutput: ", bestOutput batchIndex += 1 #print bestOutput #print bestProb return bestOutput `````` Sebastian N. committed Jun 21, 2019 207 `````` def __init__(self, data_loader, net_constructor): `````` Nicola Gatto committed Apr 08, 2019 208 209 `````` self._data_loader = data_loader self._net_creator = net_constructor `````` Sebastian N. committed Jun 21, 2019 210 `````` self._networks = {} `````` Nicola Gatto committed Apr 08, 2019 211 212 213 214 `````` def train(self, batch_size=64, num_epoch=10, eval_metric='acc', `````` Sebastian N. committed Oct 18, 2019 215 `````` eval_metric_params={}, `````` Eyüp Harputlu committed Jun 05, 2019 216 217 `````` loss ='softmax_cross_entropy', loss_params={}, `````` Nicola Gatto committed Apr 08, 2019 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 `````` 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) `````` Sebastian N. committed Jun 21, 2019 248 249 250 251 252 `````` 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 committed Apr 08, 2019 253 254 255 256 257 258 259 260 `````` 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. committed Jun 21, 2019 261 `````` self._networks = self._net_creator.networks `````` Nicola Gatto committed Apr 08, 2019 262 263 264 265 266 267 268 `````` try: os.makedirs(self._net_creator._model_dir_) except OSError: if not os.path.isdir(self._net_creator._model_dir_): raise `````` Sebastian N. committed Jun 21, 2019 269 `````` trainers = [mx.gluon.Trainer(network.collect_params(), optimizer, optimizer_params) for network in self._networks.values()] `````` Nicola Gatto committed Apr 08, 2019 270 `````` `````` Eyüp Harputlu committed Jun 05, 2019 271 272 273 274 275 276 `````` 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 committed Apr 08, 2019 277 `````` loss_function = mx.gluon.loss.SigmoidBinaryCrossEntropyLoss() `````` Eyüp Harputlu committed Jun 05, 2019 278 279 280 `````` elif loss == 'cross_entropy': loss_function = CrossEntropyLoss(sparse_label=sparseLabel) elif loss == 'l2': `````` Nicola Gatto committed Apr 08, 2019 281 `````` loss_function = mx.gluon.loss.L2Loss() `````` Eyüp Harputlu committed Jun 05, 2019 282 `````` elif loss == 'l1': `````` Nicola Gatto committed Apr 08, 2019 283 `````` loss_function = mx.gluon.loss.L2Loss() `````` Eyüp Harputlu committed Jun 05, 2019 284 285 286 287 288 289 290 291 292 293 294 295 296 `````` 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 committed Jun 24, 2019 297 298 `````` elif loss == 'log_cosh': loss_function = LogCoshLoss() `````` Eyüp Harputlu committed Jun 05, 2019 299 300 `````` else: logging.error("Invalid loss parameter.") `````` Nicola Gatto committed Apr 08, 2019 301 302 303 304 305 306 307 `````` 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): `````` Sebastian N. committed Aug 12, 2019 308 `````` data_ = batch.data[0].as_in_context(mx_context) `````` Sebastian Nickels committed Jun 06, 2019 309 `````` predictions_label = batch.label[0].as_in_context(mx_context) `````` Sebastian Nickels committed May 26, 2019 310 `````` `````` Christian Fuß committed Oct 08, 2019 311 312 `````` outputs=[] `````` Nicola Gatto committed Apr 08, 2019 313 `````` with autograd.record(): `````` Sebastian N. committed Sep 06, 2019 314 `````` predictions_ = mx.nd.zeros((batch_size, 1000,), ctx=mx_context) `````` Sebastian N. committed Aug 12, 2019 315 `````` `````` Christian Fuß committed Sep 10, 2019 316 `````` lossList = [] `````` Sebastian N. committed Aug 12, 2019 317 `````` predictions_ = self._networks[0](data_) `````` Christian Fuß committed Sep 10, 2019 318 319 320 321 322 `````` lossList.append(loss_function(predictions_, predictions_label)) loss = 0 for element in lossList: loss = loss + element `````` Sebastian Nickels committed Jun 06, 2019 323 `````` `````` Nicola Gatto committed Apr 08, 2019 324 `````` loss.backward() `````` Sebastian N. committed Jun 21, 2019 325 326 327 `````` for trainer in trainers: trainer.step(batch_size) `````` Nicola Gatto committed Apr 08, 2019 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 `````` 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() `````` Sebastian N. committed Oct 18, 2019 345 `````` metric = mx.metric.create(eval_metric, **eval_metric_params) `````` Nicola Gatto committed Apr 08, 2019 346 `````` for batch_i, batch in enumerate(train_iter): `````` Sebastian N. committed Aug 12, 2019 347 `````` data_ = batch.data[0].as_in_context(mx_context) `````` Sebastian Nickels committed Jun 06, 2019 348 349 350 351 352 `````` labels = [ batch.label[0].as_in_context(mx_context) ] `````` Christian Fuß committed Sep 23, 2019 353 `````` outputs=[] `````` Christian Fuß committed Sep 15, 2019 354 `````` `````` Sebastian N. committed Oct 18, 2019 355 `````` if True: `````` Sebastian N. committed Sep 06, 2019 356 `````` predictions_ = mx.nd.zeros((batch_size, 1000,), ctx=mx_context) `````` Sebastian N. committed Aug 12, 2019 357 358 `````` predictions_ = self._networks[0](data_) `````` Christian Fuß committed Sep 23, 2019 359 `````` outputs.append(predictions_) `````` Sebastian Nickels committed May 26, 2019 360 `````` `````` Christian Fuß committed Sep 09, 2019 361 `````` predictions = [] `````` Christian Fuß committed Sep 23, 2019 362 `````` for output_name in outputs: `````` Sebastian N. committed Oct 30, 2019 363 `````` if mx.nd.shape_array(mx.nd.squeeze(output_name)).size > 1: `````` Christian Fuß committed Sep 09, 2019 364 365 366 367 `````` predictions.append(mx.nd.argmax(output_name, axis=1)) #ArgMax already applied else: predictions.append(output_name) `````` Christian Fuß committed Aug 28, 2019 368 `````` `````` Sebastian N. committed Oct 30, 2019 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 `````` ''' #Compute BLEU and NIST Score if data folder contains a dictionary -> NLP dataset 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) import nltk.translate.bleu_score import nltk.translate.nist_score prediction = [] for index in range(batch_size): sentence = '' for entry in predictions: sentence += dict[int(entry[index].asscalar())] + ' ' prediction.append(sentence) for index in range(batch_size): sentence = '' for batchEntry in batch.label: sentence += dict[int(batchEntry[index].asscalar())] + ' ' print("############################") print("label1: ", sentence) print("prediction1: ", prediction[index]) BLEUscore = nltk.translate.bleu_score.sentence_bleu([sentence], prediction[index]) NISTscore = nltk.translate.nist_score.sentence_nist([sentence], prediction[index]) print("BLEU: ", BLEUscore) print("NIST: ", NISTscore) print("############################") ''' `````` Sebastian Nickels committed Jun 06, 2019 400 `````` metric.update(preds=predictions, labels=labels) `````` Nicola Gatto committed Apr 08, 2019 401 402 403 `````` train_metric_score = metric.get()[1] test_iter.reset() `````` Sebastian N. committed Oct 18, 2019 404 `````` metric = mx.metric.create(eval_metric, **eval_metric_params) `````` Nicola Gatto committed Apr 08, 2019 405 `````` for batch_i, batch in enumerate(test_iter): `````` Sebastian N. committed Aug 12, 2019 406 `````` data_ = batch.data[0].as_in_context(mx_context) `````` Sebastian Nickels committed Jun 06, 2019 407 408 409 410 411 `````` labels = [ batch.label[0].as_in_context(mx_context) ] `````` Christian Fuß committed Sep 23, 2019 412 413 `````` outputs=[] `````` Sebastian N. committed Oct 30, 2019 414 `````` if True: `````` Sebastian N. committed Sep 06, 2019 415 `````` predictions_ = mx.nd.zeros((batch_size, 1000,), ctx=mx_context) `````` Sebastian N. committed Aug 12, 2019 416 417 `````` predictions_ = self._networks[0](data_) `````` Christian Fuß committed Sep 23, 2019 418 `````` outputs.append(predictions_) `````` Sebastian N. committed Jul 03, 2019 419 `````` `````` Christian Fuß committed Sep 09, 2019 420 `````` predictions = [] `````` Christian Fuß committed Sep 23, 2019 421 `````` for output_name in outputs: `````` Sebastian N. committed Oct 30, 2019 422 `````` if mx.nd.shape_array(mx.nd.squeeze(output_name)).size > 1: `````` Christian Fuß committed Sep 09, 2019 423 424 425 426 `````` predictions.append(mx.nd.argmax(output_name, axis=1)) #ArgMax already applied else: predictions.append(output_name) `````` Sebastian Nickels committed May 26, 2019 427 `````` `````` Sebastian Nickels committed Jun 06, 2019 428 `````` metric.update(preds=predictions, labels=labels) `````` Nicola Gatto committed Apr 08, 2019 429 430 431 432 `````` test_metric_score = metric.get()[1] logging.info("Epoch[%d] Train: %f, Test: %f" % (epoch, train_metric_score, test_metric_score)) `````` Christian Fuß committed Aug 28, 2019 433 `````` `````` Nicola Gatto committed Apr 08, 2019 434 `````` if (epoch - begin_epoch) % checkpoint_period == 0: `````` Sebastian N. committed Jun 21, 2019 435 436 `````` for i, network in self._networks.items(): network.save_parameters(self.parameter_path(i) + '-' + str(epoch).zfill(4) + '.params') `````` Nicola Gatto committed Apr 08, 2019 437 `````` `````` Sebastian N. committed Jun 21, 2019 438 439 440 `````` 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 committed Apr 08, 2019 441 `````` `````` Sebastian N. committed Jun 21, 2019 442 443 `````` def parameter_path(self, index): return self._net_creator._model_dir_ + self._net_creator._model_prefix_ + '_' + str(index)``````