CNNSupervisedTrainer_MultipleStreams.py 22.8 KB
 Julian Dierkes committed Jan 03, 2020 1 2 3 4 5 6 7 8 9 10 11 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 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 ``````import mxnet as mx import logging import numpy as np import time import os import shutil import pickle import math import sys from mxnet import gluon, autograd, nd 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) 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: `````` Julian Treiber committed Apr 13, 2020 53 `````` label = gluon.loss._reshape_like(F, label, pred) `````` Julian Dierkes committed Jan 03, 2020 54 55 56 57 58 59 `````` loss = -(pred * label).sum(axis=self._axis, keepdims=True) # 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) * mx.nd.equal(mx.nd.argmax(pred, axis=1), label)) return loss.mean(axis=self._batch_axis, exclude=True) `````` Julian Treiber committed Apr 13, 2020 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 ``````class DiceLoss(gluon.loss.Loss): def __init__(self, axis=-1, sparse_label=True, from_logits=False, weight=None, batch_axis=0, **kwargs): super(DiceLoss, self).__init__(weight, batch_axis, **kwargs) self._axis = axis self._sparse_label = sparse_label self._from_logits = from_logits def dice_loss(self, F, pred, label): smooth = 1. pred_y = F.argmax(pred, axis = self._axis) intersection = pred_y * label score = (2 * F.mean(intersection, axis=self._batch_axis, exclude=True) + smooth) \ / (F.mean(label, axis=self._batch_axis, exclude=True) + F.mean(pred_y, axis=self._batch_axis, exclude=True) + smooth) return - F.log(score) def hybrid_forward(self, F, pred, label, sample_weight=None): if not self._from_logits: pred = F.log_softmax(pred, self._axis) 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) diceloss = self.dice_loss(F, pred, label) return F.mean(loss, axis=self._batch_axis, exclude=True) + diceloss `````` Julian Dierkes committed Jan 03, 2020 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 ``````@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 if (match_counts / counts) > 0: 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 class CNNSupervisedTrainer_MultipleStreams: def __init__(self, data_loader, net_constructor): self._data_loader = data_loader self._net_creator = net_constructor self._networks = {} def train(self, batch_size=64, num_epoch=10, eval_metric='acc', eval_metric_params={}, eval_train=False, loss ='softmax_cross_entropy', loss_params={}, optimizer='adam', optimizer_params=(('learning_rate', 0.001),), load_checkpoint=True, checkpoint_period=5, log_period=50, context='gpu', save_attention_image=False, use_teacher_forcing=False, normalize=True, preprocessing = False): 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 preprocessing: preproc_lib = "CNNPreprocessor_MultipleStreams_executor" train_iter, test_iter, data_mean, data_std, train_images, test_images = self._data_loader.load_preprocessed_data(batch_size, preproc_lib) else: train_iter, test_iter, data_mean, data_std, train_images, test_images = self._data_loader.load_data(batch_size) 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'] 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) 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_) self._networks = self._net_creator.networks try: os.makedirs(self._net_creator._model_dir_) except OSError: if not os.path.isdir(self._net_creator._model_dir_): raise trainers = [mx.gluon.Trainer(network.collect_params(), optimizer, optimizer_params) for network in self._networks.values() if len(network.collect_params().values()) != 0] 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 ignore_indices = [loss_params['ignore_indices']] if 'ignore_indices' in loss_params else [] `````` Julian Treiber committed Apr 13, 2020 278 `````` loss_axis = loss_params['loss_axis'] if 'loss_axis' in loss_params else -1 `````` Julian Treiber committed Apr 13, 2020 279 `````` batch_axis = loss_params['batch_axis'] if 'batch_axis' in loss_params else 0 `````` Julian Dierkes committed Jan 03, 2020 280 281 `````` if loss == 'softmax_cross_entropy': fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False `````` Julian Treiber committed Apr 13, 2020 282 `````` loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(axis=loss_axis, from_logits=fromLogits, sparse_label=sparseLabel, batch_axis=batch_axis) `````` Julian Dierkes committed Jan 03, 2020 283 284 `````` elif loss == 'softmax_cross_entropy_ignore_indices': fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False `````` Julian Treiber committed Apr 13, 2020 285 `````` loss_function = SoftmaxCrossEntropyLossIgnoreIndices(axis=loss_axis, ignore_indices=ignore_indices, from_logits=fromLogits, sparse_label=sparseLabel, batch_axis=batch_axis) `````` Julian Dierkes committed Jan 03, 2020 286 287 288 `````` elif loss == 'sigmoid_binary_cross_entropy': loss_function = mx.gluon.loss.SigmoidBinaryCrossEntropyLoss() elif loss == 'cross_entropy': `````` Julian Treiber committed Apr 13, 2020 289 `````` loss_function = CrossEntropyLoss(axis=loss_axis, sparse_label=sparseLabel, batch_axis=batch_axis) `````` Julian Treiber committed Apr 13, 2020 290 291 292 `````` elif loss == 'dice_loss': fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False dice_weight = loss_params['dice_weight'] if 'dice_weight' in loss_params else None `````` Julian Treiber committed Apr 13, 2020 293 `````` loss_function = DiceLoss(axis=loss_axis, from_logits=fromLogits, weight=dice_weight, sparse_label=sparseLabel, batch_axis=batch_axis) `````` Julian Dierkes committed Jan 03, 2020 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 `````` 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.") tic = None for epoch in range(begin_epoch, begin_epoch + num_epoch): loss_total = 0 train_iter.reset() for batch_i, batch in enumerate(train_iter): with autograd.record(): labels = [batch.label[i].as_in_context(mx_context) for i in range(2)] data_0_ = batch.data[0].as_in_context(mx_context) data_1_ = batch.data[1].as_in_context(mx_context) pred_ = [mx.nd.zeros((batch_size, 4,), ctx=mx_context) for i in range(2)] nd.waitall() lossList = [] pred_[0] = self._networks[0](data_0_) lossList.append(loss_function(pred_[0], labels[0])) pred_[1] = self._networks[1](data_1_) lossList.append(loss_function(pred_[1], labels[1])) loss = 0 for element in lossList: loss = loss + element loss.backward() loss_total += loss.sum().asscalar() for trainer in trainers: trainer.step(batch_size) if tic is None: tic = time.time() else: if batch_i % log_period == 0: try: speed = log_period * batch_size / (time.time() - tic) except ZeroDivisionError: speed = float("inf") loss_avg = loss_total / (batch_size * log_period) loss_total = 0 logging.info("Epoch[%d] Batch[%d] Speed: %.2f samples/sec Loss: %.5f" % (epoch, batch_i, speed, loss_avg)) tic = time.time() tic = None if eval_train: train_iter.reset() metric = mx.metric.create(eval_metric, **eval_metric_params) for batch_i, batch in enumerate(train_iter): labels = [batch.label[i].as_in_context(mx_context) for i in range(2)] data_0_ = batch.data[0].as_in_context(mx_context) data_1_ = batch.data[1].as_in_context(mx_context) pred_ = [mx.nd.zeros((batch_size, 4,), ctx=mx_context) for i in range(2)] nd.waitall() outputs = [] attentionList=[] pred_[0] = self._networks[0](data_0_) outputs.append(pred_[0]) pred_[1] = self._networks[1](data_1_) outputs.append(pred_[1]) if save_attention_image == "True": import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt logging.getLogger('matplotlib').setLevel(logging.ERROR) 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) plt.clf() fig = plt.figure(figsize=(15,15)) max_length = len(labels)-1 ax = fig.add_subplot(max_length//3, max_length//4, 1) ax.imshow(train_images[0+batch_size*(batch_i)].transpose(1,2,0)) for l in range(max_length): attention = attentionList[l] attention = mx.nd.slice_axis(attention, axis=0, begin=0, end=1).squeeze() attention_resized = np.resize(attention.asnumpy(), (8, 8)) ax = fig.add_subplot(max_length//3, max_length//4, l+2) if int(labels[l+1][0].asscalar()) > len(dict): ax.set_title("") elif dict[int(labels[l+1][0].asscalar())] == "": ax.set_title(".") img = ax.imshow(train_images[0+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+batch_size*(batch_i)].transpose(1,2,0)) ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent()) 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) metric.update(preds=predictions, labels=labels) train_metric_score = metric.get()[1] else: train_metric_score = 0 test_iter.reset() metric = mx.metric.create(eval_metric, **eval_metric_params) for batch_i, batch in enumerate(test_iter): `````` Julian Treiber committed Apr 13, 2020 455 `````` if True: `````` Julian Dierkes committed Jan 03, 2020 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 `````` labels = [batch.label[i].as_in_context(mx_context) for i in range(2)] data_0_ = batch.data[0].as_in_context(mx_context) data_1_ = batch.data[1].as_in_context(mx_context) pred_ = [mx.nd.zeros((batch_size, 4,), ctx=mx_context) for i in range(2)] nd.waitall() outputs = [] attentionList=[] pred_[0] = self._networks[0](data_0_) outputs.append(pred_[0]) pred_[1] = self._networks[1](data_1_) outputs.append(pred_[1]) if save_attention_image == "True": if not eval_train: import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt logging.getLogger('matplotlib').setLevel(logging.ERROR) 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) plt.clf() fig = plt.figure(figsize=(15,15)) max_length = len(labels)-1 ax = fig.add_subplot(max_length//3, max_length//4, 1) ax.imshow(test_images[0+batch_size*(batch_i)].transpose(1,2,0)) for l in range(max_length): attention = attentionList[l] attention = mx.nd.slice_axis(attention, axis=0, begin=0, end=1).squeeze() attention_resized = np.resize(attention.asnumpy(), (8, 8)) ax = fig.add_subplot(max_length//3, max_length//4, l+2) if int(mx.nd.slice_axis(outputs[l+1], axis=0, begin=0, end=1).squeeze().asscalar()) > len(dict): ax.set_title("") elif dict[int(mx.nd.slice_axis(outputs[l+1], axis=0, begin=0, end=1).squeeze().asscalar())] == "": ax.set_title(".") img = ax.imshow(test_images[0+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(outputs[l+1], axis=0, begin=0, end=1).squeeze().asscalar())]) img = ax.imshow(test_images[0+batch_size*(batch_i)].transpose(1,2,0)) ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent()) plt.tight_layout() target_dir = 'target/attention_images' if not os.path.exists(target_dir): os.makedirs(target_dir) plt.savefig(target_dir + '/attention_test.png') plt.close() predictions = [] for output_name in outputs: `````` 521 `````` predictions.append(output_name) `````` Julian Dierkes committed Jan 03, 2020 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 `````` metric.update(preds=predictions, labels=labels) 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: for i, network in self._networks.items(): network.save_parameters(self.parameter_path(i) + '-' + str(epoch).zfill(4) + '.params') 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) def parameter_path(self, index): return self._net_creator._model_dir_ + self._net_creator._model_prefix_ + '_' + str(index)``````