def run_validation_cases(validation_keys_file, model_file, training_modalities, labels, hdf5_file, output_label_map=False, output_dir=".", threshold=0.5, overlap=16, permute=False): validation_indices = pickle_load(validation_keys_file) model = load_old_model(model_file) data_file = tables.open_file(hdf5_file, "r") for i, index in enumerate(validation_indices): actual = round(i / len(validation_indices) * 100, 2) print("Running validation case: ", actual, "%") if 'subject_ids' in data_file.root: case_directory = os.path.join( output_dir, data_file.root.subject_ids[index].decode('utf-8')) else: case_directory = os.path.join(output_dir, "validation_case_{}".format(index)) run_validation_case(data_index=index, output_dir=case_directory, model=model, data_file=data_file, training_modalities=training_modalities, output_label_map=output_label_map, labels=labels, threshold=threshold, overlap=overlap, permute=permute) data_file.close()
def main(): if not os.path.exists(config["hdf5_file"]): training_files = list() for label_file in glob.glob("./data/training/subject-*-label.hdr"): training_files.append((label_file.replace("label", "T1"), label_file.replace("label", "T2"), label_file)) write_data_to_file(training_files, config["hdf5_file"], image_shape=config["image_shape"]) hdf5_file_opened = tables.open_file(config["hdf5_file"], "r") if os.path.exists(config["model_file"]): model = load_old_model(config["model_file"]) else: # instantiate new model model = unet_model_3d(input_shape=config["input_shape"], n_labels=config["n_labels"]) # get training and testing generators train_generator, validation_generator, nb_train_samples, nb_test_samples = get_training_and_validation_generators( hdf5_file_opened, batch_size=config["batch_size"], data_split=config["validation_split"], validation_keys_file=config["validation_file"], training_keys_file=config["training_file"], n_labels=config["n_labels"], labels=config["labels"], augment=True) # run training train_model(model=model, model_file=config["model_file"], training_generator=train_generator, validation_generator=validation_generator, steps_per_epoch=nb_train_samples, validation_steps=nb_test_samples, initial_learning_rate=config["initial_learning_rate"], learning_rate_drop=config["learning_rate_drop"], learning_rate_epochs=config["decay_learning_rate_every_x_epochs"], n_epochs=config["n_epochs"]) hdf5_file_opened.close()
def main(overwrite=False): # convert input images into an hdf5 file if overwrite or not os.path.exists(config["data_file"]): training_files = fetch_training_data_files() write_data_to_file(training_files, config["data_file"], image_shape=config["image_shape"] ) #config["image_shape"] = (144, 144, 144) data_file_opened = open_data_file(config["data_file"]) if not overwrite and os.path.exists(config["model_file"]): model = load_old_model(config["model_file"]) else: # instantiate new model #print('start model computing') # model = unet_model_3d(input_shape=config["input_shape"], # 4+(32, 32, 32) # pool_size=config["pool_size"], #config["pool_size"] = (2, 2, 2), maxpooling size # n_labels=config["n_labels"], #config["n_labels"] = len(config["labels"]) # initial_learning_rate=config["initial_learning_rate"], #config["initial_learning_rate"] = 0.00001 # deconvolution=config["deconvolution"]) #config["deconvolution"] = True # if False, will use upsampling instead of deconvolution model = custom_unet(reu2018) #print('model loaded') # get training and testing generators train_generator, validation_generator, n_train_steps, n_validation_steps = get_training_and_validation_generators( data_file_opened, batch_size=config["batch_size"], #config["batch_size"] = 6 data_split=config["validation_split"], #validation_split = 0.8 overwrite=overwrite, validation_keys_file=config["validation_file"], training_keys_file=config["training_file"], n_labels=config["n_labels"], labels=config["labels"], patch_shape=config["patch_shape"], validation_batch_size=config["validation_batch_size"], validation_patch_overlap=config["validation_patch_overlap"], training_patch_start_offset=config["training_patch_start_offset"], permute=config["permute"], augment=config["augment"], skip_blank=config["skip_blank"], augment_flip=config["flip"], augment_distortion_factor=config["distort"]) # run training print('training mdel') train_model( model=model, model_file=config[ "model_file"], #config["model_file"] = os.path.abspath("tumor_segmentation_model.h5") training_generator=train_generator, validation_generator=validation_generator, steps_per_epoch=n_train_steps, validation_steps=n_validation_steps, initial_learning_rate=config["initial_learning_rate"], learning_rate_drop=config["learning_rate_drop"], learning_rate_patience=config["patience"], early_stopping_patience=config["early_stop"], n_epochs=config["n_epochs"]) data_file_opened.close() print("model has been trained already")
def main(overwrite=False): # convert input images into an hdf5 file if overwrite or not os.path.exists(config["hdf5_file"]): training_files = fetch_training_data_files() write_data_to_file(training_files, config["hdf5_file"], image_shape=config["image_shape"]) hdf5_file_opened = tables.open_file(config["hdf5_file"], "r") if not overwrite and os.path.exists(config["model_file"]): model = load_old_model(config["model_file"]) else: # instantiate new model model = unet_model_3d(input_shape=config["input_shape"], downsize_filters_factor=config["downsize_nb_filters_factor"], pool_size=config["pool_size"], n_labels=config["n_labels"], initial_learning_rate=config["initial_learning_rate"]) # get training and testing generators train_generator, validation_generator, nb_train_samples, nb_test_samples = get_training_and_validation_generators( hdf5_file_opened, batch_size=config["batch_size"], data_split=config["validation_split"], overwrite=overwrite, validation_keys_file=config["validation_file"], training_keys_file=config["training_file"], n_labels=config["n_labels"]) # run training train_model(model=model, model_file=config["model_file"], training_generator=train_generator, validation_generator=validation_generator, steps_per_epoch=nb_train_samples, validation_steps=nb_test_samples, initial_learning_rate=config["initial_learning_rate"], learning_rate_drop=config["learning_rate_drop"], learning_rate_epochs=config["decay_learning_rate_every_x_epochs"], n_epochs=config["n_epochs"]) hdf5_file_opened.close()
def main(overwrite=False): # convert input images into an hdf5 file if overwrite or not os.path.exists(config["data_file"]): training_files, subject_ids = fetch_training_data_files( return_subject_ids=True) write_data_to_file(training_files, config["data_file"], image_shape=config["image_shape"], subject_ids=subject_ids) data_file_opened = open_data_file(config["data_file"]) if not overwrite and os.path.exists(config["model_file"]): print("Loading old model file from the location: ", config["model_file"]) model = load_old_model(config["model_file"]) else: # instantiate new model print("Creating new model at the location: ", config["model_file"]) model = isensee2017_model( input_shape=config["input_shape"], n_labels=config["n_labels"], initial_learning_rate=config["initial_learning_rate"], n_base_filters=config["n_base_filters"]) # get training and testing generators train_generator, validation_generator, n_train_steps, n_validation_steps = get_training_and_validation_generators( data_file_opened, batch_size=config["batch_size"], data_split=config["validation_split"], overwrite=overwrite, validation_keys_file=config["validation_file"], training_keys_file=config["training_file"], n_labels=config["n_labels"], labels=config["labels"], patch_shape=config["patch_shape"], validation_batch_size=config["validation_batch_size"], validation_patch_overlap=config["validation_patch_overlap"], training_patch_start_offset=config["training_patch_start_offset"], permute=config["permute"], augment=config["augment"], skip_blank=config["skip_blank"], augment_flip=config["flip"], augment_distortion_factor=config["distort"]) print("Running the Training. Model file:", config["model_file"]) # run training train_model(model=model, model_file=config["model_file"], training_generator=train_generator, validation_generator=validation_generator, steps_per_epoch=n_train_steps, validation_steps=n_validation_steps, initial_learning_rate=config["initial_learning_rate"], learning_rate_drop=config["learning_rate_drop"], learning_rate_patience=config["patience"], early_stopping_patience=config["early_stop"], n_epochs=config["n_epochs"]) data_file_opened.close()
def main(overwrite=False): # convert input images into an hdf5 file # 若有则加载旧数据集,注意,此时image_shape为之前设置的 if overwrite or not os.path.exists(config["data_file"]): training_files, subject_ids = fetch_training_data_files(return_subject_ids=True) write_data_to_file(training_files, config["data_file"], image_shape=config["image_shape"], subject_ids=subject_ids) data_file_opened = open_data_file(config["data_file"]) # 加载/创建模型文件 if not overwrite and os.path.exists(config["model_file"]): model = load_old_model(config["model_file"]) else: # instantiate new model model = att_res_ds_unet.att_res_ds_unet_model(input_shape=config["input_shape"], n_labels=config["n_labels"], initial_learning_rate=config["initial_learning_rate"], n_base_filters=config["n_base_filters"]) from keras.utils.vis_utils import plot_model plot_model(model, to_file='att_res_ds_uet.png', show_shapes=True) # get training and testing generators # ../unet3d/generator.py # 创建生成器(generator),用于后面训练 train_generator, validation_generator, n_train_steps, n_validation_steps = get_training_and_validation_generators( data_file_opened, batch_size=config["batch_size"], data_split=config["validation_split"], overwrite=overwrite, validation_keys_file=config["validation_file"], training_keys_file=config["training_file"], n_labels=config["n_labels"], labels=config["labels"], patch_shape=config["patch_shape"], validation_batch_size=config["validation_batch_size"], validation_patch_overlap=config["validation_patch_overlap"], training_patch_start_offset=config["training_patch_start_offset"], permute=config["permute"], augment=config["augment"], skip_blank=config["skip_blank"], augment_flip=config["flip"], augment_distortion_factor=config["distort"]) # run training # ../unet3d/training.py # 训练一个keras模型 train_model(model=model, model_file=config["model_file"], training_generator=train_generator, validation_generator=validation_generator, steps_per_epoch=n_train_steps, validation_steps=n_validation_steps, initial_learning_rate=config["initial_learning_rate"], learning_rate_drop=config["learning_rate_drop"], learning_rate_patience=config["patience"], early_stopping_patience=config["early_stop"], n_epochs=config["n_epochs"]) data_file_opened.close()
def main(overwrite=False): # convert input images into an hdf5 file if overwrite or not os.path.exists(config["data_file"]): training_files = fetch_training_data_files() print("Number of Training file Found:", len(training_files)) write_data_to_file(training_files, config["data_file"], image_shape=config["image_shape"]) print("Opening data file.") data_file_opened = open_data_file(config["data_file"]) if not overwrite and os.path.exists(config["model_file"]): print("Loading existing model file.") model = load_old_model(config["model_file"]) else: # instantiate new model print("Instantiating new model file.") model = unet_model_3d(input_shape=config["input_shape"], pool_size=config["pool_size"], n_labels=config["n_labels"], initial_learning_rate=config["initial_learning_rate"], deconvolution=config["deconvolution"]) # get training and testing generators print("Getting training and testing generators.") train_generator, validation_generator, n_train_steps, n_validation_steps = get_training_and_validation_generators( data_file_opened, batch_size=config["batch_size"], data_split=config["validation_split"], overwrite=overwrite, validation_keys_file=config["validation_file"], training_keys_file=config["training_file"], n_labels=config["n_labels"], labels=config["labels"], patch_shape=config["patch_shape"], validation_batch_size=config["validation_batch_size"], validation_patch_overlap=config["validation_patch_overlap"], training_patch_start_offset=config["training_patch_start_offset"], permute=config["permute"], augment=config["augment"], skip_blank=config["skip_blank"], augment_flip=config["flip"], augment_distortion_factor=config["distort"]) # run training print("Running the training......") train_model(model=model, model_file=config["model_file"], training_generator=train_generator, validation_generator=validation_generator, steps_per_epoch=n_train_steps, validation_steps=n_validation_steps, initial_learning_rate=config["initial_learning_rate"], learning_rate_drop=config["learning_rate_drop"], learning_rate_patience=config["patience"], early_stopping_patience=config["early_stop"], n_epochs=config["n_epochs"]) data_file_opened.close() print("Training DONE")
def validate(model_file, ibis_data, input_shape=(96, 96, 96), batch_size=2, data_split=0.8, num_gpus=None, only_aa=False): model = load_old_model(model_file) validate_data = IBISGenerator(ibis_data, input_shape=input_shape, batch_size=batch_size, only_aa=only_aa, start_index=data_split, shuffle=False) data_gen = validate_data.generate() y_true = [] y_pred = [] num_batches = validate_data.data.shape[0] / batch_size for i in xrange(num_batches): print i, "of", num_batches - 1 X, y = data_gen.next() y_true_sample = y.flatten().astype(int) y_pred_sample = model.predict_on_batch(X).flatten().astype(int) y_true += y_true_sample.tolist() y_pred += y_pred_sample.tolist() print np.where(y_true_sample == 1) print np.where(y_pred_sample == 1) tpr, fpr, _ = roc_curve(y_true_sample, y_pred_sample) roc_auc = auc(fpr, tpr) print "Batch ROCAUC:", roc_auc tpr, fpr, _ = roc_curve(y_true_sample, y_pred_sample.tolist()) roc_auc = auc(fpr, tpr) print "Total TPR, FPR:", tpr, fpr model_name = os.path.splitext(os.path.basename(model_file))[0] pp = PdfPages("model_evaluation-{}.pdf".format(model_name)) fig = plt.figure() ax = fig.add_subplot(111) ax.plot(fpr, tpr, lw=3., label="ROC (AUC: {})".format(roc_auc)) ax.set_xlabel("False Positive Rate") ax.set_ylabel("True Positive Rate") ax.set_xlim([0, 1.0]) ax.set_ylim([0.0, 1.05]) fig.suptitle("{} Model Evaluation".format(model_name), fontsize=20) pp.savefig() pp.close() return roc_auc
def main(overwrite=False): # convert input images into an hdf5 file if overwrite or not os.path.exists(config["data_file"]): training_files = fetch_training_data_files() write_data_to_file(training_files, config["data_file"], image_shape=config["image_shape"]) data_file_opened = open_data_file(config["data_file"]) if not overwrite and os.path.exists(config["model_file"]): model = load_old_model(config["model_file"]) else: # instantiate new model model = unet_model_3d( input_shape=config["input_shape"], pool_size=config["pool_size"], n_labels=config["n_labels"], initial_learning_rate=config["initial_learning_rate"], deconvolution=config["deconvolution"]) # get training and testing generators train_generator, validation_generator, n_train_steps, n_validation_steps = get_training_and_validation_generators( data_file_opened, batch_size=config["batch_size"], data_split=config["validation_split"], overwrite=overwrite, validation_keys_file=config["validation_file"], training_keys_file=config["training_file"], n_labels=config["n_labels"], labels=config["labels"], patch_shape=config["patch_shape"], validation_batch_size=config["validation_batch_size"], validation_patch_overlap=config["validation_patch_overlap"], training_patch_start_offset=config["training_patch_start_offset"], permute=config["permute"], augment=config["augment"], skip_blank=config["skip_blank"], augment_flip=config["flip"], augment_distortion_factor=config["distortion_factor"], augment_rotation_factor=config["rotation_factor"], mirror=config["mirror"]) # run training train_model(model=model, model_file=config["model_file"], training_generator=train_generator, validation_generator=validation_generator, steps_per_epoch=n_train_steps, validation_steps=n_validation_steps, initial_learning_rate=config["initial_learning_rate"], learning_rate_drop=config["learning_rate_drop"], learning_rate_patience=config["patience"], early_stopping_patience=config["early_stop"], n_epochs=config["n_epochs"], logging_path=config["logging_path"]) data_file_opened.close()
def main(overwrite=False): if not overwrite and os.path.exists(config["model_file"]): model = load_old_model(config["model_file"]) else: # instantiate new model model = isensee2017_model(input_shape=config["input_shape"], n_labels=config["n_labels"], initial_learning_rate=config["initial_learning_rate"], n_base_filters=config["n_base_filters"]) # get training and validation generators train_generator, validation_generator, n_train_steps, n_validation_steps = get_training_and_validation_generators( npy_path=config["npy_path"], subject_ids_file=config["subject_ids_file"], batch_size=config["batch_size"], validation_batch_size=config["validation_batch_size"], n_labels=config["n_labels"], labels=config["labels"], training_keys_file=config["training_keys_file"], validation_keys_file=config["validation_keys_file"], data_split=config["validation_split"], overwrite=overwrite, augment=config["augment"], augment_flip=config["flip"], augment_distortion_factor=config["distort"], permute=config["permute"], image_shape=config["image_shape"], patch_shape=config["patch_shape"], validation_patch_overlap=config["validation_patch_overlap"], training_patch_start_offset=config["training_patch_start_offset"], skip_blank=config["skip_blank"] ) # run training train_model(model=model, model_file=config["model_file"], training_generator=train_generator, validation_generator=validation_generator, steps_per_epoch=n_train_steps, validation_steps=n_validation_steps, initial_learning_rate=config["initial_learning_rate"], learning_rate_drop=config["learning_rate_drop"], learning_rate_patience=config["patience"], early_stopping_patience=config["early_stop"], n_epochs=config["n_epochs"])
def main(config=None): model = load_old_model(config, re_compile=False) data_file_opened = open_data_file(config["data_file"]) validation_idxs = pickle_load(config['validation_file']) validation_generator = data_generator(data_file_opened, validation_idxs, batch_size=config['validation_batch_size'], n_labels=config['n_labels'], labels=config['labels'], skip_blank=config['skip_blank'], shuffle_index_list=False) steps = math.ceil(len(validation_idxs) / config['validation_batch_size']) results = model.evaluate(validation_generator, steps=steps, verbose=1) metrics_names = model.metrics_names for i, x in enumerate(metrics_names): print('{}: {}'.format(x, results[i])) data_file_opened.close()
def segmentation_for_patient(subject_fd, config, output_path, model=None, mode='size_same_input'): if model is None: model = load_old_model(config) subject_name = os.path.basename(subject_fd) image_mris, original_affine, foreground = get_subject_tensor( subject_fd, subject_name) if mode == 'size_same_input': slices = get_slices(foreground) subject_data_fixed_size, affine = crop_subject_modals( image_mris, input_shape, slices) elif mode == 'size_interpolate': target_shape = tuple(config['inference_shape']) subject_data_fixed_size, affine = resize_modal_image( image_mris, target_shape) else: print('Do not support mode {} for inference'.format(mode)) return subject_tensor = normalize_data(subject_data_fixed_size) subject_tensor = np.expand_dims(subject_tensor, axis=0) output_predict = predict(model, subject_tensor, affine) if mode == 'size_same_input': output = restore_dimension(output_predict, slices, original_affine) elif mode == 'size_interpolate': output = resize(output_predict, new_shape=original_shape, interpolation='nearest') else: print('Do not support mode {} for inference'.format(mode)) return output_fd = os.path.join(output_path, subject_name) if not os.path.exists(output_fd): os.makedirs(output_fd) output_file = os.path.join( output_fd, '{}_prediction{}'.format(subject_name, extension)) output.to_filename(output_file) print('Patient {} is done !'.format(subject_fd))
def main(overwrite=False): # # convert input images into an hdf5 file # if overwrite or not os.path.exists(config["data_file"]): # training_files = fetch_training_data_files() # # write_data_to_file(training_files, config["data_file"], image_shape=config["image_shape"]) # data_file_opened = open_data_file(config["data_file"]) if not overwrite and os.path.exists(config["model_file"]): model = load_old_model(config["model_file"]) else: # instantiate new model model = unet_model_3d( input_shape=config["input_shape"], pool_size=config["pool_size"], n_labels=config["n_labels"], initial_learning_rate=config["initial_learning_rate"], deconvolution=config["deconvolution"]) from keras.utils.vis_utils import plot_model plot_model(model, to_file='original_uet.png', show_shapes=True)
def segmentation_for_set_patients(list_ids_file, path_dataset, config, output_path): model = load_old_model(config) file = open(list_ids_file, 'r') contents = file.read() list_ids = contents.split('\n') file.close() pattern = os.path.join(path_dataset, '*', '*') list_paths = glob.glob(pattern) for idx, ids in enumerate(list_ids): for path_subject in list_paths: if ids in path_subject: segmentation_for_patient(path_subject, config, output_path, model=model) break print('Done {}/{} patients'.format(idx + 1, len(list_ids))) print('Done for dataset: {}'.format(path_dataset))
def main(overwrite=False): # # convert input images into an hdf5 file # # 若有则加载旧数据集,注意,此时image_shape为之前设置的 # if overwrite or not os.path.exists(config["data_file"]): # training_files, subject_ids = fetch_training_data_files(return_subject_ids=True) # # write_data_to_file(training_files, config["data_file"], image_shape=config["image_shape"], # subject_ids=subject_ids) # data_file_opened = open_data_file(config["data_file"]) # 加载/创建模型文件 if not overwrite and os.path.exists(config["model_file"]): model = load_old_model(config["model_file"]) else: # instantiate new model model = isensee2017_model( input_shape=config["input_shape"], n_labels=config["n_labels"], initial_learning_rate=config["initial_learning_rate"], n_base_filters=config["n_base_filters"]) from keras.utils.vis_utils import plot_model plot_model(model, to_file='isensee_unet.png', show_shapes=True)
def __init__(self, conf): self.config = conf self.model = load_old_model(self.config.model_file)
def main(overwrite=False): # convert input images into an hdf5 file if overwrite or not os.path.exists(config["data_file"]): training_files, subject_ids = fetch_training_data_files( return_subject_ids=True) write_data_to_file(training_files, config["data_file"], image_shape=config["image_shape"], subject_ids=subject_ids) data_file_opened = open_data_file(config["data_file"]) if not overwrite and os.path.exists(config["model_file"]): model = load_old_model(config["model_file"]) else: # instantiate new model model = isensee2017_model( input_shape=config["input_shape"], n_labels=config["n_labels"], initial_learning_rate=config["initial_learning_rate"], n_base_filters=config["n_base_filters"]) with open('isensemodel_original.txt', 'w') as fh: # Pass the file handle in as a lambda function to make it callable model.summary(line_length=150, print_fn=lambda x: fh.write(x + '\n')) # Save Model plot_model(model, to_file="isensemodel_original.png", show_shapes=True) # get training and testing generators train_generator, validation_generator, n_train_steps, n_validation_steps = get_training_and_validation_generators( data_file_opened, batch_size=config["batch_size"], data_split=config["validation_split"], overwrite=overwrite, validation_keys_file=config["validation_file"], training_keys_file=config["training_file"], n_labels=config["n_labels"], labels=config["labels"], patch_shape=config["patch_shape"], validation_batch_size=config["validation_batch_size"], validation_patch_overlap=config["validation_patch_overlap"], training_patch_start_offset=config["training_patch_start_offset"], permute=config["permute"], augment=config["augment"], skip_blank=config["skip_blank"], augment_flip=config["flip"], augment_distortion_factor=config["distort"]) # run training train_model(model=model, model_file=config["model_file"], training_generator=train_generator, validation_generator=validation_generator, steps_per_epoch=n_train_steps, validation_steps=n_validation_steps, initial_learning_rate=config["initial_learning_rate"], learning_rate_drop=config["learning_rate_drop"], learning_rate_patience=config["patience"], early_stopping_patience=config["early_stop"], n_epochs=config["n_epochs"]) data_file_opened.close()
def main(overwrite=False): # convert input images into an hdf5 file print(overwrite or not os.path.exists(config["data_file"])) print('path: ', os.path.exists(config["data_file"])) if overwrite or not os.path.exists(config["data_file"]): training_files = fetch_training_data_files() # try: write_data_to_file( training_files, config["data_file"], image_shape=config["image_shape"]) #, normalize=False) # except: # import pdb; pdb.set_trace() data_file_opened = open_data_file(config["data_file"]) if not overwrite and os.path.exists(config["model_file"]): model = load_old_model(config["model_file"]) else: # instantiate new model model = unet_model_3d( input_shape=config["input_shape"], pool_size=config["pool_size"], n_labels=config["n_labels"], initial_learning_rate=config["initial_learning_rate"], deconvolution=config["deconvolution"]) print(model.summary()) # get training and testing generators train_generator, validation_generator, n_train_steps, n_validation_steps = get_training_and_validation_generators( data_file_opened, batch_size=config["batch_size"], data_split=config["validation_split"], overwrite=False, #overwrite, # set to False so that the training idcs # are used as previously; as they are now used for the # normalization already in write_data_to_file (above) validation_keys_file=config["validation_file"], training_keys_file=config["training_file"], n_labels=config["n_labels"], labels=config["labels"], patch_shape=config["patch_shape"], validation_batch_size=config["validation_batch_size"], validation_patch_overlap=config["validation_patch_overlap"], training_patch_start_offset=config["training_patch_start_offset"], permute=config["permute"], augment=config["augment"], skip_blank=config["skip_blank"], augment_flip=config["flip"], augment_distortion_factor=config["distort"]) # normalize the dataset if required # use only the training img (training_keys_file) fetch_training_data_files() # run training train_model(model=model, model_file=config["model_file"], training_generator=train_generator, validation_generator=validation_generator, steps_per_epoch=n_train_steps, validation_steps=n_validation_steps, initial_learning_rate=config["initial_learning_rate"], learning_rate_drop=config["learning_rate_drop"], learning_rate_patience=config["patience"], early_stopping_patience=config["early_stop"], n_epochs=config["n_epochs"]) data_file_opened.close()
def main(overwrite_data=False, overwrite_model=False): # run if the data not already stored hdf5 if overwrite_data or not os.path.exists(config["data_file"]): _save_new_h5_datafile(config["data_file"], new_image_shape=config["image_shape"]) data_file_opened = open_data_file(config["data_file"]) if not overwrite_model and os.path.exists(config["model_file"]): model = load_old_model(config["model_file"]) else: # instantiate new model print('initializing new isensee model with input shape', config['input_shape']) ''' model = isensee2017_model( input_shape=config["input_shape"], n_labels=config["n_labels"], initial_learning_rate=config["initial_learning_rate"], n_base_filters=config["n_base_filters"]) ''' model = unet_model_3d( input_shape=config["input_shape"], pool_size=config["pool_size"], n_labels=config["n_labels"], initial_learning_rate=config["initial_learning_rate"], deconvolution=config["deconvolution"], n_base_filters=config["n_base_filters"]) # get training and testing generators (train_generator, validation_generator, n_train_steps, n_validation_steps) = get_training_and_validation_generators( data_file_opened, batch_size=config["batch_size"], data_split=config["validation_split"], overwrite=overwrite_data, validation_keys_file=config["validation_file"], training_keys_file=config["training_file"], n_labels=config["n_labels"], labels=config["labels"], patch_shape=config["patch_shape"], validation_batch_size=config["validation_batch_size"], validation_patch_overlap=config["validation_patch_overlap"], training_patch_start_offset=config["training_patch_start_offset"], permute=config["permute"], augment=config["augment"], skip_blank=config["skip_blank"], augment_flip=config["flip"], augment_distortion_factor=config["distort"]) # run training train_model(model=model, model_file=config["model_file"], training_generator=train_generator, validation_generator=validation_generator, steps_per_epoch=n_train_steps, validation_steps=n_validation_steps, initial_learning_rate=config["initial_learning_rate"], learning_rate_drop=config["learning_rate_drop"], learning_rate_patience=config["patience"], early_stopping_patience=config["early_stop"], n_epochs=config["n_epochs"]) data_file_opened.close()
def main(overwrite=False): # convert input images into an hdf5 file if overwrite or not os.path.exists(config["data_file"]): training_files, subject_ids = fetch_training_data_files(return_subject_ids=True) write_data_to_file(training_files, config["data_file"], image_shape=config["image_shape"], subject_ids=subject_ids) data_file_opened = open_data_file(config["data_file"]) if not overwrite and os.path.exists(config["model_file"]): model = load_old_model(config["model_file"]) # new_model = isensee2017_model(input_shape=config["input_shape"], n_labels=config["n_labels"], # initial_learning_rate=config["initial_learning_rate"], # n_base_filters=config["n_base_filters"]) if config['freeze_encoder']: last_index = list(layer.name for layer in model.layers) \ .index('up_sampling3d_1') for layer in model.layers[:last_index]: layer.trainable = False from keras.optimizers import Adam from unet3d.model.isensee2017 import weighted_dice_coefficient_loss model.compile(optimizer=Adam(lr=config['initial_learning_rate']), loss=weighted_dice_coefficient_loss) # for new_layer, layer in zip(new_model.layers[1:], old_model.layers[1:]): # assert new_layer.name == layer.name # new_layer.set_weights(layer.get_weights()) # model = new_model else: # instantiate new model model = isensee2017_model(input_shape=config["input_shape"], n_labels=config["n_labels"], initial_learning_rate=config["initial_learning_rate"], n_base_filters=config["n_base_filters"]) model.summary() # get training and testing generators train_generator, validation_generator, n_train_steps, n_validation_steps = get_training_and_validation_generators( data_file_opened, batch_size=config["batch_size"], data_split=config["validation_split"], overwrite=overwrite, validation_keys_file=config["validation_file"], training_keys_file=config["training_file"], n_labels=config["n_labels"], labels=config["labels"], patch_shape=config["patch_shape"], validation_batch_size=config["validation_batch_size"], validation_patch_overlap=config["validation_patch_overlap"], training_patch_start_offset=config["training_patch_start_offset"], augment=config["augment"], skip_blank=config["skip_blank"]) # run training train_model(model=model, model_file=config["model_file"], training_generator=train_generator, validation_generator=validation_generator, steps_per_epoch=n_train_steps, validation_steps=n_validation_steps, initial_learning_rate=config["initial_learning_rate"], learning_rate_drop=config["learning_rate_drop"], learning_rate_patience=config["patience"], early_stopping_patience=config["early_stop"], n_epochs=config["n_epochs"]) data_file_opened.close()
def main(config=None): # convert input images into an hdf5 file overwrite = config['overwrite'] if overwrite or not os.path.exists(config["data_file"]): training_files, subject_ids = fetch_training_data_files( return_subject_ids=True) write_data_to_file(training_files, config["data_file"], image_shape=config["image_shape"], subject_ids=subject_ids) data_file_opened = open_data_file(config["data_file"]) if not overwrite and os.path.exists(config["model_file"]): # if this happens, then the code wont care what is in "model_name" in config because it will take whatever # the pre-trained was (either 3d_unet_residual or attention_unet) to continue training. need to be careful # with this. model = load_old_model(config, re_compile=False) model.summary() # visualize_filters_shape(model) else: # instantiate new model if (config["model_name"] == "3d_unet_residual"): """3D Unet Residual Model""" model = isensee2017_model(input_shape=config["input_shape"], n_labels=config["n_labels"], n_base_filters=config["n_base_filters"], activation_name='softmax') optimizer = getattr( opts, config["optimizer"]["name"])(**config["optimizer"].get('args')) loss = getattr(module_metric, config["loss_fc"]) metrics = [getattr(module_metric, x) for x in config["metrics"]] model.compile(optimizer=optimizer, loss=loss, metrics=metrics) model.summary() # visualize_filters_shape(model) elif (config["model_name"] == "attention_unet"): """Attention Unet Model""" model = attention_unet_model( input_shape=config["input_shape"], n_labels=config["n_labels"], n_base_filters=config["n_base_filters"], activation_name='softmax') optimizer = getattr( opts, config["optimizer"]["name"])(**config["optimizer"].get('args')) loss = getattr(module_metric, config["loss_fc"]) metrics = [getattr(module_metric, x) for x in config["metrics"]] model.compile(optimizer=optimizer, loss=loss, metrics=metrics) model.summary() # visualize_filters_shape(model) else: """Wrong entry for model_name""" raise Exception( 'Look at field model_best in config.json! This field can be either 3d_unet_residual or attention_unet.' ) # get training and testing generators train_generator, validation_generator, n_train_steps, n_validation_steps = get_training_and_validation_generators( data_file_opened, batch_size=config["batch_size"], data_split=config["validation_split"], overwrite=overwrite, validation_keys_file=config["validation_file"], training_keys_file=config["training_file"], n_labels=config["n_labels"], labels=config["labels"], validation_batch_size=config["validation_batch_size"], permute=config["permute"], augment=config["augment"], skip_blank=config["skip_blank"], augment_flip=config["flip"], augment_distortion_factor=config["distort"]) # run training train_model(model=model, model_file=config["model_file"], training_generator=train_generator, validation_generator=validation_generator, steps_per_epoch=n_train_steps, validation_steps=n_validation_steps, initial_learning_rate=config["optimizer"]["args"]["lr"], learning_rate_drop=config["learning_rate_drop"], learning_rate_patience=config["patience"], early_stopping_patience=config["early_stop"], n_epochs=config["n_epochs"], model_best_path=config['model_best']) data_file_opened.close()
import tables import predict_h5 from unet3d.training import load_old_model model = load_old_model('isensee_2017_model.h5') data_file = tables.open_file('data/h5/ozerki.h5', "r") for i in range(9): with open(f"data/out/result{i}", "w") as f: print(predict_h5.run_validation_case(i, 'data/out', model, data_file, ["t1"]), file=f)
def main(self, overwrite_data=True, overwrite_model=True): # convert input images into an hdf5 file if overwrite_data or not os.path.exists(self.config.data_file): training_files, subject_ids = self.fetch_training_data_files( return_subject_ids=True) write_data_to_file(training_files, self.config.data_file, image_shape=self.config.image_shape, subject_ids=subject_ids) else: print( "Reusing previously written data file. Set overwrite_data to True to overwrite this file." ) data_file_opened = open_data_file(self.config.data_file) if not overwrite_model and os.path.exists(self.config.model_file): model = load_old_model(self.config.model_file) else: # instantiate new model model, context_output_name = isensee2017_model( input_shape=self.config.input_shape, n_labels=self.config.n_labels, initial_learning_rate=self.config.initial_learning_rate, n_base_filters=self.config.n_base_filters, loss_function=self.config.loss_function, shortcut=self.config.shortcut) # get training and testing generators train_generator, validation_generator, n_train_steps, n_validation_steps = get_training_and_validation_generators( data_file_opened, batch_size=self.config.batch_size, data_split=self.config.validation_split, overwrite_data=overwrite_data, validation_keys_file=self.config.validation_file, training_keys_file=self.config.training_file, n_labels=self.config.n_labels, labels=self.config.labels, patch_shape=self.config.patch_shape, validation_batch_size=self.config.validation_batch_size, validation_patch_overlap=self.config.validation_patch_overlap, training_patch_overlap=self.config.training_patch_overlap, training_patch_start_offset=self.config. training_patch_start_offset, permute=self.config.permute, augment=self.config.augment, skip_blank=self.config.skip_blank, augment_flip=self.config.flip, augment_distortion_factor=self.config.distort) # run training train_model(model=model, model_file=self.config.model_file, training_generator=train_generator, validation_generator=validation_generator, steps_per_epoch=n_train_steps, validation_steps=n_validation_steps, initial_learning_rate=self.config.initial_learning_rate, learning_rate_drop=self.config.learning_rate_drop, learning_rate_patience=self.config.patience, early_stopping_patience=self.config.early_stop, n_epochs=self.config.epochs, niseko=self.config.niseko) data_file_opened.close()
def main(overwrite=False): args = get_args.train() overwrite = args.overwrite # config["data_file"] = get_brats_data_h5_path(args.challenge, args.year, # args.inputshape, args.isbiascorrection, # args.normalization, args.clahe, # args.histmatch) # print(config["data_file"]) print_section("Open file") data_file_opened = open_data_file(config["data_file"]) print_section("get training and testing generators") train_generator, validation_generator, n_train_steps, n_validation_steps = get_training_and_validation_generators_new( data_file_opened, batch_size=config["batch_size"], data_split=config["validation_split"], overwrite=overwrite, validation_keys_file=config["validation_file"], training_keys_file=config["training_file"], n_steps_file=config["n_steps_file"], n_labels=config["n_labels"], labels=config["labels"], patch_shape=config["patch_shape"], validation_batch_size=config["validation_batch_size"], validation_patch_overlap=config["validation_patch_overlap"], training_patch_start_offset=config["training_patch_start_offset"], is_create_patch_index_list_original=config["is_create_patch_index_list_original"], augment_flipud=config["augment_flipud"], augment_fliplr=config["augment_fliplr"], augment_elastic=config["augment_elastic"], augment_rotation=config["augment_rotation"], augment_shift=config["augment_shift"], augment_shear=config["augment_shear"], augment_zoom=config["augment_zoom"], n_augment=config["n_augment"], skip_blank=config["skip_blank"]) print("-"*60) print("# Load or init model") print("-"*60) if not overwrite and os.path.exists(config["model_file"]): print("load old model") model = load_old_model(config["model_file"]) else: # instantiate new model print("init model model") model = unet_model_3d(input_shape=config["input_shape"], pool_size=config["pool_size"], n_labels=config["n_labels"], initial_learning_rate=config["initial_learning_rate"], deconvolution=config["deconvolution"], depth=config["depth"], n_base_filters=config["n_base_filters"]) # model.summary() # import nibabel as nib # laptop_save_dir = "C:/Users/minhm/Desktop/temp/" # desktop_save_dir = "/home/minhvu/Desktop/temp/" # save_dir = desktop_save_dir # temp_in_path = desktop_save_dir + "template.nii.gz" # temp_out_path = desktop_save_dir + "out.nii.gz" # temp_out_truth_path = desktop_save_dir + "truth.nii.gz" # n_validation_samples = 0 # validation_samples = list() # for i in range(20): # print(i) # x, y = next(train_generator) # hash_x = hash(str(x)) # validation_samples.append(hash_x) # n_validation_samples += x.shape[0] # temp_in = nib.load(temp_in_path) # temp_out = nib.Nifti1Image(x[0][0], affine=temp_in.affine) # nib.save(temp_out, temp_out_path) # temp_out = nib.Nifti1Image(y[0][0], affine=temp_in.affine) # nib.save(temp_out, temp_out_truth_path) # print(n_validation_samples) print("-"*60) print("# start training") print("-"*60) # run training train_model(model=model, model_file=config["model_file"], training_generator=train_generator, validation_generator=validation_generator, steps_per_epoch=n_train_steps, validation_steps=n_validation_steps, initial_learning_rate=config["initial_learning_rate"], learning_rate_drop=config["learning_rate_drop"], learning_rate_patience=config["patience"], early_stopping_patience=config["early_stop"], n_epochs=config["n_epochs"]) data_file_opened.close()
def main(overwrite=False): # convert input images into an hdf5 file if overwrite or not os.path.exists(config["data_file"]): training_files, subject_ids = fetch_training_data_files(return_subject_ids=True) write_data_to_file(training_files, config["data_file"], image_shape=config["image_shape"], subject_ids=subject_ids) data_file_opened = open_data_file(config["data_file"]) if not overwrite and os.path.exists(config["model_file"]): base_model = load_old_model(config["model_file"]) model = get_multiGPUmodel(base_model=base_model,n_labels=config["n_labels"],GPU=config["GPU"]) else: # instantiate new model HYbrid Dense-Unet model from HDense project parser = argparse.ArgumentParser(description='Keras DenseUnet Training') parser.add_argument('-b', type=int, default= 1 )#config["batch_size"]) parser.add_argument('-input_size', type=int, default= config["patch_shape"][0]) # 224 ) parser.add_argument('-input_cols', type=int, default= config["patch_shape"][2]) # 8) args = parser.parse_args() #print(args.b) #model = dense_rnn_net(args) base_model = denseunet_3d(args) sgd = SGD(lr=1e-3, momentum=0.9, nesterov=True) model = base_model base_model.compile(optimizer=sgd, loss=[weighted_crossentropy]) # get training and testing generators # Save Model plot_model(base_model,to_file="liver_segmentation_HDenseUnet.png",show_shapes=True) # Open the file with open(config['model_summaryfile'],'w') as fh: # Pass the file handle in as a lambda function to make it callable base_model.summary(line_length=150,print_fn=lambda x: fh.write(x + '\n')) train_generator, validation_generator, n_train_steps, n_validation_steps = get_training_and_validation_generators( data_file_opened, batch_size=config["batch_size"]*config["GPU"], data_split=config["validation_split"], overwrite=overwrite, validation_keys_file=config["validation_file"], training_keys_file=config["training_file"], n_labels=config["n_labels"], labels=config["labels"], patch_shape=config["patch_shape"], validation_batch_size=config["validation_batch_size"]*config["GPU"], validation_patch_overlap=config["validation_patch_overlap"], training_patch_start_offset=config["training_patch_start_offset"], permute=config["permute"], augment=config["augment"], skip_blank=config["skip_blank"], augment_flip=config["flip"], augment_distortion_factor=config["distort"]) print('INFO: Training Details','\n Batch Size : ',config["batch_size"]*config["GPU"] ,'\n Epoch Size : ',config["n_epochs"]) # run training train_model(model=model, model_file=config["model_file"], training_generator=train_generator, validation_generator=validation_generator, steps_per_epoch=n_train_steps, validation_steps=n_validation_steps, initial_learning_rate=config["initial_learning_rate"], learning_rate_drop=config["learning_rate_drop"], learning_rate_patience=config["patience"], early_stopping_patience=config["early_stop"], n_epochs=config["n_epochs"],base_model=base_model) data_file_opened.close()
def main(overwrite=False): # convert input images into an hdf5 file pdb.set_trace() if overwrite or not os.path.exists(config["data_file"]): training_files, subject_ids = fetch_training_data_files( return_subject_ids=True) write_data_to_file(training_files, config["data_file"], image_shape=config["image_shape"], modality_names=config['all_modalities'], subject_ids=subject_ids, mean_std_file=config['mean_std_file']) # return data_file_opened = open_data_file(config["data_file"]) if not overwrite and os.path.exists(config["model_file"]): model = load_old_model(config["model_file"]) else: # instantiate new model model = isensee2017_model( input_shape=config["input_shape"], n_labels=config["n_labels"], initial_learning_rate=config["initial_learning_rate"], n_base_filters=config["n_base_filters"]) # get training and testing generators # pdb.set_trace() train_generator, validation_generator, n_train_steps, n_validation_steps = get_training_and_validation_generators( data_file_opened, batch_size=config["batch_size"], data_split=config["validation_split"], overwrite=overwrite, validation_keys_file=config["validation_file"], training_keys_file=config["training_file"], n_labels=config["n_labels"], labels=config["labels"], patch_shape=config["patch_shape"], validation_batch_size=config["validation_batch_size"], validation_patch_overlap=config["validation_patch_overlap"], training_patch_start_offset=config["training_patch_start_offset"], permute=config["permute"], augment=config["augment"], skip_blank=config["skip_blank"], augment_flip=config["flip"], augment_distortion_factor=config["distort"], pred_specific=config['pred_specific'], overlap_label=config['overlap_label_generator'], for_final_val=config['for_final_val']) # run training # pdb.set_trace() time_0 = time.time() train_model(model=model, model_file=config["model_file"], training_generator=train_generator, validation_generator=validation_generator, steps_per_epoch=n_train_steps, validation_steps=n_validation_steps, initial_learning_rate=config["initial_learning_rate"], learning_rate_drop=config["learning_rate_drop"], learning_rate_patience=config["patience"], early_stopping_patience=config["early_stop"], n_epochs=config["n_epochs"], logging_file=config['logging_file']) print('Training time:', sec2hms(time.time() - time_0)) data_file_opened.close()
def main(config=None): # convert input images into an hdf5 file overwrite = config['overwrite'] if overwrite or not os.path.exists(config["data_file"]): training_files, subject_ids = fetch_training_data_files( return_subject_ids=True) write_data_to_file(training_files, config["data_file"], image_shape=config["image_shape"], subject_ids=subject_ids, norm_type=config['normalization_type']) data_file_opened = open_data_file(config["data_file"]) if not overwrite and os.path.exists(config["model_file"]): model = load_old_model(config, re_compile=False) else: # instantiate new model model = isensee2017_model(input_shape=config["input_shape"], n_labels=config["n_labels"], n_base_filters=config["n_base_filters"], activation_name='softmax') optimizer = getattr( opts, config["optimizer"]["name"])(**config["optimizer"].get('args')) loss = getattr(module_metric, config["loss_fc"]) metrics = [getattr(module_metric, x) for x in config["metrics"]] model.compile(optimizer=optimizer, loss=loss, metrics=metrics) # get training and testing generators train_generator, validation_generator, n_train_steps, n_validation_steps = get_training_and_validation_generators( data_file_opened, batch_size=config["batch_size"], data_split=config["validation_split"], overwrite=overwrite, validation_keys_file=config["validation_file"], training_keys_file=config["training_file"], n_labels=config["n_labels"], labels=config["labels"], validation_batch_size=config["validation_batch_size"], permute=config["permute"], augment=config["augment"], skip_blank=config["skip_blank"], augment_flip=config["flip"], augment_distortion_factor=config["distort"]) # run training train_model(model=model, model_file=config["model_file"], training_generator=train_generator, validation_generator=validation_generator, steps_per_epoch=n_train_steps, validation_steps=n_validation_steps, initial_learning_rate=config["optimizer"]["args"]["lr"], learning_rate_drop=config["learning_rate_drop"], learning_rate_patience=config["patience"], early_stopping_patience=config["early_stop"], n_epochs=config["n_epochs"], model_best_path=config['model_best']) data_file_opened.close()
def main(overwrite=False): # convert input images into an hdf5 file if overwrite or not os.path.exists(config["data_file"]): training_files, subject_ids = fetch_training_data_files( return_subject_ids=True) write_data_to_file(training_files, config["data_file"], image_shape=config["image_shape"], subject_ids=subject_ids) data_file_opened = open_data_file(config["data_file"]) if not overwrite and os.path.exists(config["model_file"]): base_model = load_old_model(config["model_file"]) model = get_multiGPUmodel(base_model=base_model, n_labels=config["n_labels"], GPU=config["GPU"]) else: # instantiate new model base_model, model = unet_model_3d_multiGPU( input_shape=config["input_shape"], pool_size=config["pool_size"], n_labels=config["n_labels"], initial_learning_rate=config["initial_learning_rate"], deconvolution=config["deconvolution"], GPU=config["GPU"]) # Save Model plot_model(base_model, to_file="liver_segmentation_model_581_resize_1GPU.png", show_shapes=True) # get training and testing generators train_generator, validation_generator, n_train_steps, n_validation_steps = get_training_and_validation_generators( data_file_opened, batch_size=config["batch_size"] * config["GPU"], data_split=config["validation_split"], overwrite=overwrite, validation_keys_file=config["validation_file"], training_keys_file=config["training_file"], n_labels=config["n_labels"], labels=config["labels"], patch_shape=config["patch_shape"], validation_batch_size=config["validation_batch_size"] * config["GPU"], validation_patch_overlap=config["validation_patch_overlap"], training_patch_start_offset=config["training_patch_start_offset"], permute=config["permute"], augment=config["augment"], skip_blank=config["skip_blank"], augment_flip=config["flip"], augment_distortion_factor=config["distort"]) print('INFO: Training Details', '\n Batch Size : ', config["batch_size"] * config["GPU"], '\n Epoch Size : ', config["n_epochs"]) # For debugging ONLY # n_train_steps = 10 # run training train_model(model=model, model_file=config["model_file"], training_generator=train_generator, validation_generator=validation_generator, steps_per_epoch=n_train_steps, validation_steps=n_validation_steps, initial_learning_rate=config["initial_learning_rate"], learning_rate_drop=config["learning_rate_drop"], learning_rate_patience=config["patience"], early_stopping_patience=config["early_stop"], n_epochs=config["n_epochs"], base_model=base_model) data_file_opened.close()
def main(overwrite=False): # convert input images into an hdf5 file if overwrite or not (os.path.exists(config["data_file0"]) and os.path.exists(config["data_file1"])): training_files, subject_ids = fetch_training_data_files( return_subject_ids=True) training_files0, training_files1 = training_files subject_ids0, subject_ids1 = subject_ids if not os.path.exists(config["data_file0"]): write_data_to_file(training_files0, config["data_file0"], image_shape=config["image_shape"], subject_ids=subject_ids0) if not os.path.exists(config["data_file1"]): write_data_to_file(training_files1, config["data_file1"], image_shape=config["image_shape"], subject_ids=subject_ids1) data_file_opened0 = open_data_file(config["data_file0"]) data_file_opened1 = open_data_file(config["data_file1"]) if not overwrite and os.path.exists(config["model_file"]): model = load_old_model(config["model_file"]) else: # instantiate new model model = siam3dunet_model( input_shape=config["input_shape"], n_labels=config["n_labels"], initial_learning_rate=config["initial_learning_rate"], n_base_filters=config["n_base_filters"]) #model = testnet_model(input_shape=config["input_shape"], n_labels=config["n_labels"], initial_learning_rate=config["initial_learning_rate"], n_base_filters=config["n_base_filters"]) #if os.path.exists(config["model_file"]): # model = load_weights(config["model_file"]) # get training and testing generators train_generator, validation_generator, n_train_steps, n_validation_steps = get_training_and_validation_generators( data_file_opened0, data_file_opened1, batch_size=config["batch_size"], data_split=config["validation_split"], overwrite=overwrite, validation_keys_file=config["validation_file"], training_keys_file=config["training_file"], n_labels=config["n_labels"], labels=config["labels"], patch_shape=config["patch_shape"], validation_batch_size=config["validation_batch_size"], validation_patch_overlap=config["validation_patch_overlap"], training_patch_start_offset=config["training_patch_start_offset"], permute=config["permute"], augment=config["augment"], skip_blank=config["skip_blank"], augment_flip=config["flip"], augment_distortion_factor=config["distort"]) ''' train_data = [] train_label = [] for i in range(n_train_steps): a, b = next(train_generator) train_data.append(a) train_label.append(b) a0, a1 = a for i in range(len(a0[0,0,0,0,:])): a0_0 = a0[0,2,:,:,i] if a0_0.min() == a0_0.max(): a0_0 = a0_0 - a0_0 else: a0_0 = (a0_0-a0_0.min())/(a0_0.max()-a0_0.min()) #print (a0_0.shape) #print (a0_0.max()) #print (a0_0.min()) imsave(f'vis_img/{i}.jpg', a0_0) raise ''' test_data, test_label = next(validation_generator) test_g = (test_data, test_label) train_data, train_label = next(train_generator) train_g = (train_data, train_label) if not overwrite and os.path.exists(config["model_file"]): txt_file = open(f"output_log.txt", "w") #res = model.evaluate(test_data, test_label) #print (res) pre = model.predict(test_data) #print ([i for i in pre[0]]) #print ([int(i) for i in test_label[0]]) for i in range(len(pre[0])): txt_file.write( str(pre[0][i][0]) + ' ' + str(test_label[0][i]) + "\n") pre_train = model.predict(train_data) for i in range(len(pre_train[0])): txt_file.write( str(pre_train[0][i][0]) + ' ' + str(train_label[0][i]) + "\n") txt_file.close() raise # run training train_model(model=model, model_file=config["model_file"], training_generator=train_generator, validation_generator=test_g, steps_per_epoch=n_train_steps, validation_steps=n_validation_steps, initial_learning_rate=config["initial_learning_rate"], learning_rate_drop=config["learning_rate_drop"], learning_rate_patience=config["patience"], early_stopping_patience=config["early_stop"], n_epochs=config["n_epochs"]) ''' for i in range(len(train_label)): #scores = model.evaluate(train_data[i], train_label[i], verbose=1) scores = model.predict(train_data[i]) print (len(scores[0])) ''' data_file_opened0.close() data_file_opened1.close()