CNNSupervisedTrainer_Alexnet.py 19.1 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 Nickels 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 Nickels 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 Nickels 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 ``````@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 `````` Christian Fuß committed Nov 12, 2019 118 119 `````` if (match_counts / counts) > 0: precisions[n] = match_counts / counts `````` Sebastian Nickels committed Oct 18, 2019 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 `````` 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 171 `````` `````` Sebastian Nickels committed Oct 30, 2019 172 173 `````` `````` Sebastian Nickels committed May 26, 2019 174 ``````class CNNSupervisedTrainer_Alexnet: `````` Sebastian Nickels committed Jun 21, 2019 175 `````` def __init__(self, data_loader, net_constructor): `````` Nicola Gatto committed Apr 08, 2019 176 177 `````` self._data_loader = data_loader self._net_creator = net_constructor `````` Sebastian Nickels committed Jun 21, 2019 178 `````` self._networks = {} `````` Nicola Gatto committed Apr 08, 2019 179 180 181 182 `````` def train(self, batch_size=64, num_epoch=10, eval_metric='acc', `````` Sebastian Nickels committed Oct 18, 2019 183 `````` eval_metric_params={}, `````` Eyüp Harputlu committed Jun 05, 2019 184 185 `````` loss ='softmax_cross_entropy', loss_params={}, `````` Nicola Gatto committed Apr 08, 2019 186 187 188 189 190 `````` optimizer='adam', optimizer_params=(('learning_rate', 0.001),), load_checkpoint=True, context='gpu', checkpoint_period=5, `````` Christian Fuß committed Nov 05, 2019 191 `````` save_attention_image=False, `````` 192 `````` use_teacher_forcing=False, `````` Nicola Gatto committed Apr 08, 2019 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 `````` 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'] `````` 216 217 `````` train_batch_size = batch_size test_batch_size = batch_size `````` Nicola Gatto committed Apr 08, 2019 218 `````` `````` Sebastian Nickels committed Nov 07, 2019 219 `````` 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 Nickels committed Jun 21, 2019 220 221 222 223 224 `````` 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 225 226 227 228 229 230 231 232 `````` 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 Nickels committed Jun 21, 2019 233 `````` self._networks = self._net_creator.networks `````` Nicola Gatto committed Apr 08, 2019 234 235 236 237 238 239 240 `````` try: os.makedirs(self._net_creator._model_dir_) except OSError: if not os.path.isdir(self._net_creator._model_dir_): raise `````` Sebastian Nickels committed Oct 31, 2019 241 `````` 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 committed Apr 08, 2019 242 `````` `````` Eyüp Harputlu committed Jun 05, 2019 243 244 `````` 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 `````` 245 `````` ignore_indices = [loss_params['ignore_indices']] if 'ignore_indices' in loss_params else [] `````` Eyüp Harputlu committed Jun 05, 2019 246 247 `````` if loss == 'softmax_cross_entropy': fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False `````` 248 249 250 `````` loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(from_logits=fromLogits, sparse_label=sparseLabel) if loss == 'softmax_cross_entropy_ignore_indices': fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False `````` Christian Fuß committed Nov 05, 2019 251 `````` loss_function = SoftmaxCrossEntropyLossIgnoreIndices(ignore_indices=ignore_indices, from_logits=fromLogits, sparse_label=sparseLabel) `````` Eyüp Harputlu committed Jun 05, 2019 252 `````` elif loss == 'sigmoid_binary_cross_entropy': `````` Nicola Gatto committed Apr 08, 2019 253 `````` loss_function = mx.gluon.loss.SigmoidBinaryCrossEntropyLoss() `````` Eyüp Harputlu committed Jun 05, 2019 254 255 256 `````` elif loss == 'cross_entropy': loss_function = CrossEntropyLoss(sparse_label=sparseLabel) elif loss == 'l2': `````` Nicola Gatto committed Apr 08, 2019 257 `````` loss_function = mx.gluon.loss.L2Loss() `````` Eyüp Harputlu committed Jun 05, 2019 258 `````` elif loss == 'l1': `````` Nicola Gatto committed Apr 08, 2019 259 `````` loss_function = mx.gluon.loss.L2Loss() `````` Eyüp Harputlu committed Jun 05, 2019 260 261 262 263 264 265 266 267 268 269 270 271 272 `````` 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 273 274 `````` elif loss == 'log_cosh': loss_function = LogCoshLoss() `````` Eyüp Harputlu committed Jun 05, 2019 275 276 `````` else: logging.error("Invalid loss parameter.") `````` Nicola Gatto committed Apr 08, 2019 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): `````` 284 285 `````` with autograd.record(): labels = [batch.label[i].as_in_context(mx_context) for i in range(1)] `````` Sebastian Nickels committed May 26, 2019 286 `````` `````` 287 288 289 `````` data_ = batch.data[0].as_in_context(mx_context) predictions_ = mx.nd.zeros((train_batch_size, 10,), ctx=mx_context) `````` Christian Fuß committed Oct 08, 2019 290 `````` `````` Sebastian Nickels committed Aug 12, 2019 291 `````` `````` 292 293 `````` nd.waitall() `````` Christian Fuß committed Sep 10, 2019 294 `````` lossList = [] `````` 295 `````` `````` Sebastian Nickels committed Aug 12, 2019 296 `````` predictions_ = self._networks[0](data_) `````` 297 298 `````` lossList.append(loss_function(predictions_, labels[0])) `````` Sebastian Nickels committed Jun 06, 2019 299 `````` `````` Christian Fuß committed Sep 10, 2019 300 301 302 `````` loss = 0 for element in lossList: loss = loss + element `````` Nicola Gatto committed Apr 08, 2019 303 304 `````` loss.backward() `````` Sebastian Nickels committed Jun 21, 2019 305 306 307 `````` for trainer in trainers: trainer.step(batch_size) `````` Nicola Gatto committed Apr 08, 2019 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 `````` 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 `````` 324 `````` train_test_iter.reset() `````` Sebastian Nickels committed Oct 18, 2019 325 `````` metric = mx.metric.create(eval_metric, **eval_metric_params) `````` 326 `````` for batch_i, batch in enumerate(train_test_iter): `````` 327 `````` if True: `````` 328 `````` labels = [batch.label[i].as_in_context(mx_context) for i in range(1)] `````` Sebastian Nickels committed Jun 06, 2019 329 `````` `````` 330 `````` data_ = batch.data[0].as_in_context(mx_context) `````` Sebastian Nickels committed Jun 06, 2019 331 `````` `````` 332 `````` predictions_ = mx.nd.zeros((test_batch_size, 10,), ctx=mx_context) `````` Sebastian Nickels committed Aug 12, 2019 333 `````` `````` Sebastian Nickels committed May 26, 2019 334 `````` `````` 335 336 `````` nd.waitall() `````` 337 `````` outputs = [] `````` Christian Fuß committed Nov 05, 2019 338 `````` attentionList=[] `````` Sebastian Nickels committed Aug 12, 2019 339 `````` predictions_ = self._networks[0](data_) `````` 340 `````` `````` Christian Fuß committed Sep 23, 2019 341 `````` outputs.append(predictions_) `````` Sebastian Nickels committed May 26, 2019 342 `````` `````` Christian Fuß committed Nov 05, 2019 343 344 345 346 347 348 `````` if save_attention_image == "True": import matplotlib.pyplot as plt logging.getLogger('matplotlib').setLevel(logging.ERROR) plt.clf() `````` 349 `````` fig = plt.figure(figsize=(15,15)) `````` Christian Fuß committed Nov 05, 2019 350 351 352 353 354 355 `````` max_length = len(labels)-1 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) `````` 356 357 358 359 `````` 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)) `````` Christian Fuß committed Nov 05, 2019 360 361 362 363 364 `````` 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)) `````` 365 366 367 368 369 370 371 372 373 374 `````` ax = fig.add_subplot(max_length//3, max_length//4, l+2) if dict[int(labels[l+1][0].asscalar())] == "": 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()) `````` Christian Fuß committed Nov 05, 2019 375 376 377 378 379 380 381 382 383 `````` 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() `````` Christian Fuß committed Sep 09, 2019 384 `````` predictions = [] `````` Christian Fuß committed Sep 23, 2019 385 `````` for output_name in outputs: `````` Sebastian Nickels committed Oct 30, 2019 386 `````` if mx.nd.shape_array(mx.nd.squeeze(output_name)).size > 1: `````` Christian Fuß committed Sep 09, 2019 387 388 389 `````` predictions.append(mx.nd.argmax(output_name, axis=1)) else: predictions.append(output_name) `````` Sebastian Nickels committed Jun 06, 2019 390 391 `````` metric.update(preds=predictions, labels=labels) `````` Nicola Gatto committed Apr 08, 2019 392 393 394 `````` train_metric_score = metric.get()[1] test_iter.reset() `````` Sebastian Nickels committed Oct 18, 2019 395 `````` metric = mx.metric.create(eval_metric, **eval_metric_params) `````` Nicola Gatto committed Apr 08, 2019 396 `````` for batch_i, batch in enumerate(test_iter): `````` 397 `````` if True: `````` 398 `````` labels = [batch.label[i].as_in_context(mx_context) for i in range(1)] `````` Sebastian Nickels committed Jun 06, 2019 399 `````` `````` 400 `````` data_ = batch.data[0].as_in_context(mx_context) `````` Sebastian Nickels committed Jun 06, 2019 401 `````` `````` 402 `````` predictions_ = mx.nd.zeros((test_batch_size, 10,), ctx=mx_context) `````` Sebastian Nickels committed Aug 12, 2019 403 `````` `````` 404 `````` `````` 405 406 `````` nd.waitall() `````` 407 `````` outputs = [] `````` Christian Fuß committed Nov 05, 2019 408 `````` attentionList=[] `````` Sebastian Nickels committed Aug 12, 2019 409 `````` predictions_ = self._networks[0](data_) `````` 410 `````` `````` Christian Fuß committed Sep 23, 2019 411 `````` outputs.append(predictions_) `````` 412 `````` `````` Christian Fuß committed Nov 05, 2019 413 414 415 `````` if save_attention_image == "True": plt.clf() `````` 416 `````` fig = plt.figure(figsize=(15,15)) `````` Christian Fuß committed Nov 05, 2019 417 418 `````` max_length = len(labels)-1 `````` 419 420 421 422 `````` 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)) `````` Christian Fuß committed Nov 05, 2019 423 424 425 426 427 `````` 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)) `````` 428 429 430 431 432 433 434 435 436 437 `````` ax = fig.add_subplot(max_length//3, max_length//4, l+2) if dict[int(mx.nd.slice_axis(mx.nd.argmax(outputs[l+1], axis=1), axis=0, begin=0, end=1).asscalar())] == "": 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: ax.set_title(dict[int(mx.nd.slice_axis(mx.nd.argmax(outputs[l+1], axis=1), axis=0, begin=0, end=1).asscalar())]) 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()) `````` Christian Fuß committed Nov 05, 2019 438 439 440 441 442 443 `````` plt.tight_layout() plt.savefig(target_dir + '/attention_test.png') plt.close() `````` Christian Fuß committed Sep 09, 2019 444 `````` predictions = [] `````` Christian Fuß committed Sep 23, 2019 445 `````` for output_name in outputs: `````` Sebastian Nickels committed Oct 30, 2019 446 `````` if mx.nd.shape_array(mx.nd.squeeze(output_name)).size > 1: `````` Christian Fuß committed Sep 09, 2019 447 448 449 450 `````` predictions.append(mx.nd.argmax(output_name, axis=1)) #ArgMax already applied else: predictions.append(output_name) `````` Sebastian Nickels committed May 26, 2019 451 `````` `````` Sebastian Nickels committed Jun 06, 2019 452 `````` metric.update(preds=predictions, labels=labels) `````` Nicola Gatto committed Apr 08, 2019 453 454 455 456 `````` 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 457 `````` `````` Nicola Gatto committed Apr 08, 2019 458 `````` if (epoch - begin_epoch) % checkpoint_period == 0: `````` Sebastian Nickels committed Jun 21, 2019 459 460 `````` 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 461 `````` `````` Sebastian Nickels committed Jun 21, 2019 462 463 464 `````` 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 465 `````` `````` Sebastian Nickels committed Jun 21, 2019 466 `````` def parameter_path(self, index): `````` Bernhard Rumpe committed Aug 23, 2019 467 `` return self._net_creator._model_dir_ + self._net_creator._model_prefix_ + '_' + str(index)``