def run(img_folder, img_extension='dcm', img_size=[288, 224], img_scale=4095, multi_view=False, do_featurewise_norm=True, featurewise_mean=398.5, featurewise_std=627.8, batch_size=16, samples_per_epoch=160, nb_epoch=20, balance_classes=.0, all_neg_skip=0., pos_cls_weight=1.0, nb_init_filter=64, init_filter_size=7, init_conv_stride=2, pool_size=3, pool_stride=2, weight_decay=.0001, alpha=1., l1_ratio=.5, inp_dropout=.0, hidden_dropout=.0, init_lr=.01, val_size=.2, lr_patience=5, es_patience=10, resume_from=None, net='resnet50', load_val_ram=False, exam_tsv='./metadata/exams_metadata.tsv', img_tsv='./metadata/images_crosswalk.tsv', best_model='./modelState/dm_resnet_best_model.h5', final_model="NOSAVE"): '''Run ResNet training on mammograms using an exam or image list Args: featurewise_mean, featurewise_std ([float]): they are estimated from 1152 x 896 images. Using different sized images give very close results. For png, mean=7772, std=12187. ''' # Read some env variables. random_seed = int(os.getenv('RANDOM_SEED', 12345)) nb_worker = int(os.getenv('NUM_CPU_CORES', 4)) gpu_count = int(os.getenv('NUM_GPU_DEVICES', 1)) # Setup training and validation data. # Load image or exam lists and split them into train and val sets. meta_man = DMMetaManager(exam_tsv=exam_tsv, img_tsv=img_tsv, img_folder=img_folder, img_extension=img_extension) if multi_view: exam_list = meta_man.get_flatten_exam_list() exam_train, exam_val = train_test_split( exam_list, test_size=val_size, random_state=random_seed, stratify=meta_man.exam_labs(exam_list)) val_size_ = len(exam_val) * 2 # L and R. else: img_list, lab_list = meta_man.get_flatten_img_list() img_train, img_val, lab_train, lab_val = train_test_split( img_list, lab_list, test_size=val_size, random_state=random_seed, stratify=lab_list) val_size_ = len(img_val) # Create image generator. img_gen = DMImageDataGenerator(horizontal_flip=True, vertical_flip=True) if do_featurewise_norm: img_gen.featurewise_center = True img_gen.featurewise_std_normalization = True img_gen.mean = featurewise_mean img_gen.std = featurewise_std else: img_gen.samplewise_center = True img_gen.samplewise_std_normalization = True if multi_view: train_generator = img_gen.flow_from_exam_list( exam_train, target_size=(img_size[0], img_size[1]), target_scale=img_scale, batch_size=batch_size, balance_classes=balance_classes, all_neg_skip=all_neg_skip, shuffle=True, seed=random_seed, class_mode='binary') if load_val_ram: val_generator = img_gen.flow_from_exam_list( exam_val, target_size=(img_size[0], img_size[1]), target_scale=img_scale, batch_size=val_size_, validation_mode=True, class_mode='binary') else: val_generator = img_gen.flow_from_exam_list( exam_val, target_size=(img_size[0], img_size[1]), target_scale=img_scale, batch_size=batch_size, validation_mode=True, class_mode='binary') else: train_generator = img_gen.flow_from_img_list( img_train, lab_train, target_size=(img_size[0], img_size[1]), target_scale=img_scale, batch_size=batch_size, balance_classes=balance_classes, all_neg_skip=all_neg_skip, shuffle=True, seed=random_seed, class_mode='binary') if load_val_ram: val_generator = img_gen.flow_from_img_list( img_val, lab_val, target_size=(img_size[0], img_size[1]), target_scale=img_scale, batch_size=val_size_, validation_mode=True, class_mode='binary') else: val_generator = img_gen.flow_from_img_list( img_val, lab_val, target_size=(img_size[0], img_size[1]), target_scale=img_scale, batch_size=batch_size, validation_mode=True, class_mode='binary') # Load validation set into RAM. if load_val_ram: validation_set = next(val_generator) if not multi_view and len(validation_set[0]) != val_size_: raise Exception elif len(validation_set[0][0]) != val_size_ \ or len(validation_set[0][1]) != val_size_: raise Exception # Create model. if resume_from is not None: model = load_model(resume_from, custom_objects={ 'sensitivity': DMMetrics.sensitivity, 'specificity': DMMetrics.specificity }) else: if multi_view: builder = MultiViewResNetBuilder else: builder = ResNetBuilder if net == 'resnet18': model = builder.build_resnet_18( (1, img_size[0], img_size[1]), 1, nb_init_filter, init_filter_size, init_conv_stride, pool_size, pool_stride, weight_decay, alpha, l1_ratio, inp_dropout, hidden_dropout) elif net == 'resnet34': model = builder.build_resnet_34( (1, img_size[0], img_size[1]), 1, nb_init_filter, init_filter_size, init_conv_stride, pool_size, pool_stride, weight_decay, alpha, l1_ratio, inp_dropout, hidden_dropout) elif net == 'resnet50': model = builder.build_resnet_50( (1, img_size[0], img_size[1]), 1, nb_init_filter, init_filter_size, init_conv_stride, pool_size, pool_stride, weight_decay, alpha, l1_ratio, inp_dropout, hidden_dropout) elif net == 'dmresnet14': model = builder.build_dm_resnet_14( (1, img_size[0], img_size[1]), 1, nb_init_filter, init_filter_size, init_conv_stride, pool_size, pool_stride, weight_decay, alpha, l1_ratio, inp_dropout, hidden_dropout) elif net == 'dmresnet47rb5': model = builder.build_dm_resnet_47rb5( (1, img_size[0], img_size[1]), 1, nb_init_filter, init_filter_size, init_conv_stride, pool_size, pool_stride, weight_decay, alpha, l1_ratio, inp_dropout, hidden_dropout) elif net == 'dmresnet56rb6': model = builder.build_dm_resnet_56rb6( (1, img_size[0], img_size[1]), 1, nb_init_filter, init_filter_size, init_conv_stride, pool_size, pool_stride, weight_decay, alpha, l1_ratio, inp_dropout, hidden_dropout) elif net == 'dmresnet65rb7': model = builder.build_dm_resnet_65rb7( (1, img_size[0], img_size[1]), 1, nb_init_filter, init_filter_size, init_conv_stride, pool_size, pool_stride, weight_decay, alpha, l1_ratio, inp_dropout, hidden_dropout) elif net == 'resnet101': model = builder.build_resnet_101( (1, img_size[0], img_size[1]), 1, nb_init_filter, init_filter_size, init_conv_stride, pool_size, pool_stride, weight_decay, alpha, l1_ratio, inp_dropout, hidden_dropout) elif net == 'resnet152': model = builder.build_resnet_152( (1, img_size[0], img_size[1]), 1, nb_init_filter, init_filter_size, init_conv_stride, pool_size, pool_stride, weight_decay, alpha, l1_ratio, inp_dropout, hidden_dropout) if gpu_count > 1: model = make_parallel(model, gpu_count) # Model training. sgd = SGD(lr=init_lr, momentum=0.9, decay=0.0, nesterov=True) model.compile(optimizer=sgd, loss='binary_crossentropy', metrics=[DMMetrics.sensitivity, DMMetrics.specificity]) reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=lr_patience, verbose=1) early_stopping = EarlyStopping(monitor='val_loss', patience=es_patience, verbose=1) if load_val_ram: auc_checkpointer = DMAucModelCheckpoint(best_model, validation_set, batch_size=batch_size) else: auc_checkpointer = DMAucModelCheckpoint(best_model, val_generator, nb_test_samples=val_size_) # checkpointer = ModelCheckpoint( # best_model, monitor='val_loss', verbose=1, save_best_only=True) hist = model.fit_generator( train_generator, samples_per_epoch=samples_per_epoch, nb_epoch=nb_epoch, class_weight={ 0: 1.0, 1: pos_cls_weight }, validation_data=validation_set if load_val_ram else val_generator, nb_val_samples=val_size_, callbacks=[reduce_lr, early_stopping, auc_checkpointer], nb_worker=nb_worker, pickle_safe=True, # turn on pickle_safe to avoid a strange error. verbose=2) # Training report. min_loss_locs, = np.where( hist.history['val_loss'] == min(hist.history['val_loss'])) best_val_loss = hist.history['val_loss'][min_loss_locs[0]] best_val_sensitivity = hist.history['val_sensitivity'][min_loss_locs[0]] best_val_specificity = hist.history['val_specificity'][min_loss_locs[0]] print "\n==== Training summary ====" print "Minimum val loss achieved at epoch:", min_loss_locs[0] + 1 print "Best val loss:", best_val_loss print "Best val sensitivity:", best_val_sensitivity print "Best val specificity:", best_val_specificity if final_model != "NOSAVE": model.save(final_model) return hist
from meta import DMMetaManager meta_man = DMMetaManager(img_folder='preprocessedData/png_288x224/', img_extension='png') exam_list = meta_man.get_flatten_exam_list() img_list = meta_man.get_flatten_img_list() from dm_image import DMImageDataGenerator img_gen = DMImageDataGenerator(featurewise_center=True, featurewise_std_normalization=True) img_gen.mean = 7772. img_gen.std = 12187. datgen_exam = img_gen.flow_from_exam_list(exam_list, target_size=(288, 224), batch_size=8, shuffle=False, seed=123) datgen_image = img_gen.flow_from_img_list(img_list[0], img_list[1], target_size=(288, 224), batch_size=32, shuffle=False, seed=123) import numpy as np
def run(img_folder, img_extension='png', img_size=[288, 224], multi_view=False, do_featurewise_norm=True, featurewise_mean=7772., featurewise_std=12187., batch_size=16, samples_per_epoch=160, nb_epoch=20, val_size=.2, balance_classes=0., all_neg_skip=False, pos_cls_weight=1.0, alpha=1., l1_ratio=.5, init_lr=.01, lr_patience=2, es_patience=4, exam_tsv='./metadata/exams_metadata.tsv', img_tsv='./metadata/images_crosswalk.tsv', dl_state='./modelState/resnet50_288_best_model.h5', best_model='./modelState/enet_288_best_model.h5', final_model="NOSAVE"): # Read some env variables. random_seed = int(os.getenv('RANDOM_SEED', 12345)) nb_worker = int(os.getenv('NUM_CPU_CORES', 4)) gpu_count = int(os.getenv('NUM_GPU_DEVICES', 1)) # Setup training and validation data. meta_man = DMMetaManager(exam_tsv=exam_tsv, img_tsv=img_tsv, img_folder=img_folder, img_extension=img_extension) if multi_view: exam_list = meta_man.get_flatten_exam_list() exam_train, exam_val = train_test_split( exam_list, test_size=val_size, random_state=random_seed, stratify=meta_man.exam_labs(exam_list)) val_size_ = len(exam_val) * 2 # L and R. else: img_list, lab_list = meta_man.get_flatten_img_list() img_train, img_val, lab_train, lab_val = train_test_split( img_list, lab_list, test_size=val_size, random_state=random_seed, stratify=lab_list) val_size_ = len(img_val) img_gen = DMImageDataGenerator(horizontal_flip=True, vertical_flip=True) if do_featurewise_norm: img_gen.featurewise_center = True img_gen.featurewise_std_normalization = True img_gen.mean = featurewise_mean img_gen.std = featurewise_std else: img_gen.samplewise_center = True img_gen.samplewise_std_normalization = True if multi_view: train_generator = img_gen.flow_from_exam_list( exam_train, target_size=(img_size[0], img_size[1]), batch_size=batch_size, balance_classes=balance_classes, all_neg_skip=all_neg_skip, shuffle=True, seed=random_seed, class_mode='binary') val_generator = img_gen.flow_from_exam_list(exam_val, target_size=(img_size[0], img_size[1]), batch_size=batch_size, validation_mode=True, class_mode='binary') else: train_generator = img_gen.flow_from_img_list( img_train, lab_train, target_size=(img_size[0], img_size[1]), batch_size=batch_size, balance_classes=balance_classes, all_neg_skip=all_neg_skip, shuffle=True, seed=random_seed, class_mode='binary') val_generator = img_gen.flow_from_img_list(img_val, lab_val, target_size=(img_size[0], img_size[1]), batch_size=batch_size, validation_mode=True, class_mode='binary') # Deep learning model. dl_model = load_model(dl_state, custom_objects={ 'sensitivity': DMMetrics.sensitivity, 'specificity': DMMetrics.specificity }) # Dummy compilation to turn off the "uncompiled" error when model was run on multi-GPUs. # dl_model.compile(optimizer='sgd', loss='binary_crossentropy') reprlayer_model = Model(input=dl_model.input, output=dl_model.get_layer(index=-2).output) if gpu_count > 1: reprlayer_model = make_parallel(reprlayer_model, gpu_count) # Setup test data in RAM. X_test, y_test = dlrepr_generator(reprlayer_model, val_generator, val_size_) # import pdb; pdb.set_trace() # Evaluat DL model on the test data. val_generator.reset() dl_test_pred = dl_model.predict_generator(val_generator, val_samples=val_size_, nb_worker=1, pickle_safe=False) # Set nb_worker to >1 can cause: # either inconsistent result when pickle_safe is False, # or broadcasting error when pickle_safe is True. # This seems to be a Keras bug!! # Further note: the broadcasting error may only happen when val_size_ # is not divisible by batch_size. try: dl_auc = roc_auc_score(y_test, dl_test_pred) dl_loss = log_loss(y_test, dl_test_pred) except ValueError: dl_auc = 0. dl_loss = np.inf print "\nAUROC by the DL model: %.4f, loss: %.4f" % (dl_auc, dl_loss) # import pdb; pdb.set_trace() # Elastic net training. target_classes = np.array([0, 1]) sgd_clf = SGDClassifier(loss='log', penalty='elasticnet', alpha=alpha, l1_ratio=l1_ratio, verbose=0, n_jobs=nb_worker, learning_rate='constant', eta0=init_lr, random_state=random_seed, class_weight={ 0: 1.0, 1: pos_cls_weight }) curr_lr = init_lr best_epoch = 0 best_auc = 0. min_loss = np.inf min_loss_epoch = 0 for epoch in xrange(nb_epoch): samples_seen = 0 X_list = [] y_list = [] epoch_start = time.time() while samples_seen < samples_per_epoch: X, y = next(train_generator) X_repr = reprlayer_model.predict_on_batch(X) sgd_clf.partial_fit(X_repr, y, classes=target_classes) samples_seen += len(y) X_list.append(X_repr) y_list.append(y) # The training X, y are expected to change for each epoch due to # image random sampling and class balancing. X_train_epo = np.concatenate(X_list) y_train_epo = np.concatenate(y_list) # End of epoch summary. pred_prob = sgd_clf.predict_proba(X_test)[:, 1] train_prob = sgd_clf.predict_proba(X_train_epo)[:, 1] try: auc = roc_auc_score(y_test, pred_prob) crossentropy_loss = log_loss(y_test, pred_prob) except ValueError: auc = 0. crossentropy_loss = np.inf try: train_loss = log_loss(y_train_epo, train_prob) except ValueError: train_loss = np.inf wei_sparseness = np.mean(sgd_clf.coef_ == 0) epoch_span = time.time() - epoch_start print ("%ds - Epoch=%d, auc=%.4f, train_loss=%.4f, test_loss=%.4f, " "weight sparsity=%.4f") % \ (epoch_span, epoch + 1, auc, train_loss, crossentropy_loss, wei_sparseness) # Model checkpoint, reducing learning rate and early stopping. if auc > best_auc: best_epoch = epoch + 1 best_auc = auc if best_model != "NOSAVE": with open(best_model, 'w') as best_state: pickle.dump(sgd_clf, best_state) if crossentropy_loss < min_loss: min_loss = crossentropy_loss min_loss_epoch = epoch + 1 else: if epoch + 1 - min_loss_epoch >= es_patience: print 'Early stopping criterion has reached. Stop training.' break if epoch + 1 - min_loss_epoch >= lr_patience: curr_lr *= .1 sgd_clf.set_params(eta0=curr_lr) print "Reducing learning rate to: %s" % (curr_lr) # End of training summary print ">>> Found best AUROC: %.4f at epoch: %d, saved to: %s <<<" % \ (best_auc, best_epoch, best_model) print ">>> Found best val loss: %.4f at epoch: %d. <<<" % \ (min_loss, min_loss_epoch) #### Save elastic net model!! #### if final_model != "NOSAVE": with open(final_model, 'w') as final_state: pickle.dump(sgd_clf, final_state)