CNNSupervisedTrainer_VGG16.py 17.7 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 ``````@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 N. 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 N. committed Oct 30, 2019 172 173 `````` `````` Sebastian Nickels committed May 26, 2019 174 ``````class CNNSupervisedTrainer_VGG16: `````` Sebastian N. 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 N. 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 N. 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, `````` Nicola Gatto committed Apr 08, 2019 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 `````` 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'] `````` Sebastian N. committed Nov 07, 2019 215 216 `````` train_batch_size = batch_size test_batch_size = batch_size `````` Nicola Gatto committed Apr 08, 2019 217 `````` `````` Sebastian N. committed Nov 07, 2019 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. committed Jun 21, 2019 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 committed Apr 08, 2019 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. committed Jun 21, 2019 232 `````` self._networks = self._net_creator.networks `````` Nicola Gatto committed Apr 08, 2019 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. committed Oct 31, 2019 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 committed Apr 08, 2019 241 `````` `````` Eyüp Harputlu committed Jun 05, 2019 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 `````` Christian Fuß committed Nov 05, 2019 244 245 246 `````` #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) `````` Eyüp Harputlu committed Jun 05, 2019 247 248 `````` if loss == 'softmax_cross_entropy': fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False `````` Christian Fuß committed Nov 05, 2019 249 250 `````` ignore_indices = [2] loss_function = SoftmaxCrossEntropyLossIgnoreIndices(ignore_indices=ignore_indices, from_logits=fromLogits, sparse_label=sparseLabel) `````` Eyüp Harputlu committed Jun 05, 2019 251 `````` elif loss == 'sigmoid_binary_cross_entropy': `````` Nicola Gatto committed Apr 08, 2019 252 `````` loss_function = mx.gluon.loss.SigmoidBinaryCrossEntropyLoss() `````` Eyüp Harputlu committed Jun 05, 2019 253 254 255 `````` elif loss == 'cross_entropy': loss_function = CrossEntropyLoss(sparse_label=sparseLabel) elif loss == 'l2': `````` Nicola Gatto committed Apr 08, 2019 256 `````` loss_function = mx.gluon.loss.L2Loss() `````` Eyüp Harputlu committed Jun 05, 2019 257 `````` elif loss == 'l1': `````` Nicola Gatto committed Apr 08, 2019 258 `````` loss_function = mx.gluon.loss.L2Loss() `````` Eyüp Harputlu committed Jun 05, 2019 259 260 261 262 263 264 265 266 267 268 269 270 271 `````` 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 272 273 `````` elif loss == 'log_cosh': loss_function = LogCoshLoss() `````` Eyüp Harputlu committed Jun 05, 2019 274 275 `````` else: logging.error("Invalid loss parameter.") `````` Nicola Gatto committed Apr 08, 2019 276 277 278 279 280 281 282 `````` 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 Nov 07, 2019 283 284 `````` with autograd.record(): labels = [batch.label[i].as_in_context(mx_context) for i in range(1)] `````` Sebastian Nickels committed May 26, 2019 285 `````` `````` Sebastian N. committed Nov 07, 2019 286 287 288 `````` data_ = batch.data[0].as_in_context(mx_context) predictions_ = mx.nd.zeros((train_batch_size, 1000,), ctx=mx_context) `````` Christian Fuß committed Oct 08, 2019 289 `````` `````` Sebastian N. committed Aug 12, 2019 290 `````` `````` Christian Fuß committed Sep 10, 2019 291 `````` lossList = [] `````` Sebastian N. committed Nov 07, 2019 292 `````` `````` Sebastian N. committed Aug 12, 2019 293 `````` predictions_ = self._networks[0](data_) `````` Sebastian N. committed Nov 07, 2019 294 295 `````` lossList.append(loss_function(predictions_, labels[0])) `````` Sebastian Nickels committed Jun 06, 2019 296 `````` `````` Christian Fuß committed Sep 10, 2019 297 298 299 `````` loss = 0 for element in lossList: loss = loss + element `````` Nicola Gatto committed Apr 08, 2019 300 301 `````` loss.backward() `````` Sebastian N. committed Jun 21, 2019 302 303 304 `````` for trainer in trainers: trainer.step(batch_size) `````` Nicola Gatto committed Apr 08, 2019 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 `````` 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. committed Nov 07, 2019 321 `````` train_test_iter.reset() `````` Sebastian N. committed Oct 18, 2019 322 `````` metric = mx.metric.create(eval_metric, **eval_metric_params) `````` Sebastian N. committed Nov 07, 2019 323 `````` for batch_i, batch in enumerate(train_test_iter): `````` Christian Fuß committed Nov 12, 2019 324 `````` if True: `````` Sebastian N. committed Nov 07, 2019 325 `````` labels = [batch.label[i].as_in_context(mx_context) for i in range(1)] `````` Sebastian Nickels committed Jun 06, 2019 326 `````` `````` Sebastian N. committed Nov 07, 2019 327 `````` data_ = batch.data[0].as_in_context(mx_context) `````` Sebastian Nickels committed Jun 06, 2019 328 `````` `````` Sebastian N. committed Nov 07, 2019 329 `````` predictions_ = mx.nd.zeros((test_batch_size, 1000,), ctx=mx_context) `````` Sebastian N. committed Aug 12, 2019 330 `````` `````` Sebastian Nickels committed May 26, 2019 331 `````` `````` Sebastian N. committed Nov 07, 2019 332 `````` outputs = [] `````` Christian Fuß committed Nov 05, 2019 333 `````` attentionList=[] `````` Sebastian N. committed Aug 12, 2019 334 `````` predictions_ = self._networks[0](data_) `````` Sebastian N. committed Nov 07, 2019 335 `````` `````` Christian Fuß committed Sep 23, 2019 336 `````` outputs.append(predictions_) `````` Sebastian Nickels committed May 26, 2019 337 `````` `````` Christian Fuß committed Nov 05, 2019 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 `````` if save_attention_image == "True": import matplotlib.pyplot as plt logging.getLogger('matplotlib').setLevel(logging.ERROR) plt.clf() fig = plt.figure(figsize=(10,10)) 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) 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)) ax = fig.add_subplot(max_length//3, max_length//4, l+1) ax.set_title(dict[int(labels[l+1][0].asscalar())]) `````` Christian Fuß committed Nov 12, 2019 358 `````` img = ax.imshow(train_images[0+test_batch_size*(batch_i)].transpose(1,2,0)) `````` Christian Fuß committed Nov 05, 2019 359 360 361 362 363 364 365 366 367 368 `````` 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() `````` Christian Fuß committed Sep 09, 2019 369 `````` predictions = [] `````` Christian Fuß committed Sep 23, 2019 370 `````` for output_name in outputs: `````` Sebastian N. committed Oct 30, 2019 371 `````` if mx.nd.shape_array(mx.nd.squeeze(output_name)).size > 1: `````` Christian Fuß committed Sep 09, 2019 372 373 374 `````` predictions.append(mx.nd.argmax(output_name, axis=1)) else: predictions.append(output_name) `````` Sebastian Nickels committed Jun 06, 2019 375 376 `````` metric.update(preds=predictions, labels=labels) `````` Nicola Gatto committed Apr 08, 2019 377 378 379 `````` train_metric_score = metric.get()[1] test_iter.reset() `````` Sebastian N. committed Oct 18, 2019 380 `````` metric = mx.metric.create(eval_metric, **eval_metric_params) `````` Nicola Gatto committed Apr 08, 2019 381 `````` for batch_i, batch in enumerate(test_iter): `````` Christian Fuß committed Nov 12, 2019 382 `````` if True: `````` Sebastian N. committed Nov 07, 2019 383 `````` labels = [batch.label[i].as_in_context(mx_context) for i in range(1)] `````` Sebastian Nickels committed Jun 06, 2019 384 `````` `````` Sebastian N. committed Nov 07, 2019 385 `````` data_ = batch.data[0].as_in_context(mx_context) `````` Sebastian Nickels committed Jun 06, 2019 386 `````` `````` Sebastian N. committed Nov 07, 2019 387 `````` predictions_ = mx.nd.zeros((test_batch_size, 1000,), ctx=mx_context) `````` Sebastian N. committed Aug 12, 2019 388 `````` `````` Sebastian N. committed Jul 03, 2019 389 `````` `````` Sebastian N. committed Nov 07, 2019 390 `````` outputs = [] `````` Christian Fuß committed Nov 05, 2019 391 `````` attentionList=[] `````` Sebastian N. committed Aug 12, 2019 392 `````` predictions_ = self._networks[0](data_) `````` Sebastian N. committed Nov 07, 2019 393 `````` `````` Christian Fuß committed Sep 23, 2019 394 `````` outputs.append(predictions_) `````` Sebastian N. committed Jul 03, 2019 395 `````` `````` Christian Fuß committed Nov 05, 2019 396 397 398 399 400 401 402 403 404 405 406 407 408 409 `````` if save_attention_image == "True": plt.clf() fig = plt.figure(figsize=(10,10)) max_length = len(labels)-1 for l in range(max_length): attention = attentionList[l] attention = mx.nd.slice_axis(attention, axis=2, begin=0, end=1) 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)) ax = fig.add_subplot(max_length//3, max_length//4, l+1) ax.set_title(dict[int(mx.nd.slice_axis(mx.nd.argmax(outputs[l+1], axis=1), axis=0, begin=0, end=1).asscalar())]) `````` Christian Fuß committed Nov 12, 2019 410 `````` img = ax.imshow(test_images[0+test_batch_size*(batch_i)].transpose(1,2,0)) `````` Christian Fuß committed Nov 05, 2019 411 412 413 414 415 416 417 `````` ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent()) plt.tight_layout() plt.savefig(target_dir + '/attention_test.png') plt.close() `````` Christian Fuß committed Sep 09, 2019 418 `````` predictions = [] `````` Christian Fuß committed Sep 23, 2019 419 `````` for output_name in outputs: `````` Sebastian N. committed Oct 30, 2019 420 `````` if mx.nd.shape_array(mx.nd.squeeze(output_name)).size > 1: `````` Christian Fuß committed Sep 09, 2019 421 422 423 424 `````` predictions.append(mx.nd.argmax(output_name, axis=1)) #ArgMax already applied else: predictions.append(output_name) `````` Sebastian Nickels committed May 26, 2019 425 `````` `````` Sebastian Nickels committed Jun 06, 2019 426 `````` metric.update(preds=predictions, labels=labels) `````` Nicola Gatto committed Apr 08, 2019 427 428 429 430 `````` 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 431 `````` `````` Nicola Gatto committed Apr 08, 2019 432 `````` if (epoch - begin_epoch) % checkpoint_period == 0: `````` Sebastian N. committed Jun 21, 2019 433 434 `````` 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 435 `````` `````` Sebastian N. committed Jun 21, 2019 436 437 438 `````` 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 439 `````` `````` Sebastian N. committed Jun 21, 2019 440 `````` def parameter_path(self, index): `````` Bernhard Rumpe committed Aug 23, 2019 441 `` return self._net_creator._model_dir_ + self._net_creator._model_prefix_ + '_' + str(index)``