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["data_file"]): training_files = fetch_training_data_files() print(training_files) write_data_to_file(training_files, config["data_file"], image_shape=config["image_shape"]) data_file_opened = open_data_file(config["data_file"]) # get training and testing generators - generate pickel files containing IDS 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"]) data_file_opened.close()
def main(): kwargs = vars(parse_args()) prediction_dir = os.path.abspath(kwargs.pop("prediction_dir")) output_label_map = not kwargs.pop("no_label_map") for key, value in kwargs.items(): if value: if key == "modalities": config["training_modalities"] = value else: config[key] = value filenames, subject_ids = fetch_brats_2020_files(config["training_modalities"], group="Validation", include_truth=False, return_subject_ids=True) if not os.path.exists(config["data_file"]): write_data_to_file(filenames, config["data_file"], image_shape=config["image_shape"], subject_ids=subject_ids, save_truth=False) pickle_dump(list(range(len(subject_ids))), config["validation_file"]) run_validation_cases(validation_keys_file=config["validation_file"], model_file=config["model_file"], training_modalities=config["training_modalities"], labels=config["labels"], hdf5_file=config["data_file"], output_label_map=output_label_map, output_dir=prediction_dir, test=False, output_basename=kwargs["output_basename"], permute=config["permute"]) for filename_list, subject_id in zip(filenames, subject_ids): prediction_filename = os.path.join(prediction_dir, kwargs["output_basename"].format(subject=subject_id)) print("Resampling:", prediction_filename) ref = nib.load(filename_list[0]) pred = nib.load(prediction_filename) pred_resampled = resample_to_img(pred, ref, interpolation="nearest") pred_resampled.to_filename(prediction_filename)
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(): 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(args): prediction_dir = os.path.abspath("./headneck/prediction_test/" + args.organ.lower()) if not os.path.exists(prediction_dir): os.makedirs(prediction_dir) test_data_files, subject_ids = fetch_test_data_files( return_subject_ids=True) if not os.path.exists(config["data_file"]): write_data_to_file(test_data_files, config["data_file"], image_shape=config["image_shape"], subject_ids=subject_ids) if not os.path.exists(config["test_file"]): test_list = list(range(len(subject_ids))) pickle_dump(test_list, config["test_file"]) run_validation_cases(validation_keys_file=config["test_file"], model_file=config["model_file"], training_modalities=config["training_modalities"], labels=config["labels"], hdf5_file=config["data_file"], output_label_map=True, output_dir=prediction_dir) header = ("Background", "Organ") masking_functions = (get_background_mask, get_organ_mask) rows = list() prediction_path = "./headneck/prediction_test/" + args.organ.lower() + "/" for case_folder in glob.glob(prediction_path + "*/"): truth_file = os.path.join(case_folder, "truth.nii.gz") truth_image = nib.load(truth_file) truth = truth_image.get_data() prediction_file = os.path.join(case_folder, "prediction.nii.gz") prediction_image = nib.load(prediction_file) prediction = prediction_image.get_data() rows.append([ dice_coefficient(func(truth), func(prediction)) for func in masking_functions ]) df = pd.DataFrame.from_records(rows, columns=header) df.to_csv(prediction_path + "headneck_scores.csv") scores = dict() for index, score in enumerate(df.columns): values = df.values.T[index] scores[score] = values[np.isnan(values) == False] plt.boxplot(list(scores.values()), labels=list(scores.keys())) plt.ylabel("Dice Coefficient") plt.savefig(prediction_path + "test_scores_boxplot.png") plt.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 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 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() 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=True): # 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)
def _save_new_h5_datafile(data_file_h5, new_image_shape): training_files = _fetch_training_data_files('private') # write all the data files into the hdf5 file # if necessary crop the data to the new dimensions (if less than original) # or add the 0 layer around it (if more than original) write_data_to_file(training_files, data_file_h5, image_shape=new_image_shape)
def main(): # convert_brats_data(pre_config["data_original"], pre_config["data_preprocessed"], overwrite=False) subject_ids, training_files = fetch_training_data_files( pre_config["data_preprocessed"]) pickle_dump(subject_ids, pre_config["subject_ids_file"]) write_data_to_file(pre_config["npy_path"], subject_ids, training_files, image_shape=pre_config["image_shape"])
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.source_data_file) or not os.path.exists(self.config.target_data_file): ''' We write two files, one with source samples and one with target samples. ''' source_data_files, target_data_files, subject_ids_source, subject_ids_target = self.fetch_training_data_files(return_subject_ids=True) if not os.path.exists(self.config.source_data_file) or overwrite_data: write_data_to_file(source_data_files, self.config.source_data_file, image_shape=self.config.image_shape, subject_ids=subject_ids_source) if not os.path.exists(self.config.target_data_file) or overwrite_data: write_data_to_file(target_data_files, self.config.target_data_file, image_shape=self.config.image_shape, subject_ids=subject_ids_target) else: print("Reusing previously written data file. Set overwrite_data to True to overwrite this file.") source_data = open_data_file(self.config.source_data_file) target_data = open_data_file(self.config.target_data_file) # instantiate new model, compile = False because the compilation is made in JDOT.py 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, depth=self.config.depth, compile=False) # get training and testing generators if not self.config.depth_jdot: context_output_name = [] jd = JDOT(model, config=self.config, source_data=source_data, target_data=target_data, context_output_name=context_output_name) # m = jd.load_old_model(self.config.model_file) # print(m) if self.config.load_base_model: print("Loading trained model") jd.load_old_model(os.path.abspath("Data/saved_models/model_center_"+self.config.source_center)+".h5") elif not self.config.overwrite_model: jd.load_old_model(self.config.model_file) else: print("Creating new model, this will overwrite your old model") jd.compile_model() if self.config.train_jdot: jd.train_model(self.config.epochs) else: jd.train_model_on_source(self.config.epochs) jd.evaluate_model() source_data.close() target_data.close()
def main(): kwargs = vars(parse_args()) prediction_dir = os.path.abspath(kwargs.pop("prediction_dir")) output_label_map = not kwargs.pop("no_label_map") for key, value in kwargs.items(): if value: if key == "modalities": config["training_modalities"] = value else: config[key] = value validate_path = kwargs["validate_path"] subject_ids = list() filenames = list() blacklist = [] for root, dirs, files in os.walk(validate_path): for f in files: subject_id = f.split('.')[0] if subject_id not in blacklist: subject_ids.append(subject_id) subject_files = list() subject_files.append(validate_path + '/' + f) filenames.append(tuple(subject_files)) if not os.path.exists(config["data_file"]): write_data_to_file(filenames, config["data_file"], image_shape=config["image_shape"], subject_ids=subject_ids, save_truth=False) pickle_dump(list(range(len(subject_ids))), config["validation_file"]) run_validation_cases(validation_keys_file=config["validation_file"], model_file=config["model_file"], training_modalities=config["training_modalities"], labels=config["labels"], hdf5_file=config["data_file"], output_label_map=output_label_map, output_dir=prediction_dir, test=False, output_basename=kwargs["output_basename"], permute=config["permute"]) for filename_list, subject_id in zip(filenames, subject_ids): prediction_filename = os.path.join( prediction_dir, kwargs["output_basename"].format(subject=subject_id)) print("Resampling:", prediction_filename) ref = nib.load(filename_list[0]) pred = nib.load(prediction_filename) pred_resampled = resample_to_img(pred, ref, interpolation="nearest") pred_resampled.to_filename(prediction_filename)
def main(self, overwrite_data=True): self.config.validation_split = 0.0 self.config.data_file = os.path.abspath("Data/generated_data/" + self.config.data_set + "_testing.h5") self.config.training_file = os.path.abspath("Data/generated_data/" + self.config.data_set + "_testing.pkl") self.config.validation_file = os.path.abspath( "Data/generated_data/" + self.config.data_set + "_testing_validation_ids.pkl") # convert input images into an hdf5 file if overwrite_data or not os.path.exists(self.config.data_file): testing_files, subject_ids = self.fetch_testing_data_files( return_subject_ids=True) write_data_to_file(testing_files, self.config.data_file, image_shape=self.config.image_shape, subject_ids=subject_ids) data_file_opened = open_data_file(self.config.data_file) testing_split, _ = get_validation_split( data_file_opened, data_split=0, overwrite_data=self.config.overwrite_data, training_file=self.config.training_file, validation_file=self.config.validation_file) # 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_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) data_file_opened.close()
def fetch_training_data_files(self): data_files = list() for subject_dir in glob.glob( os.path.join(os.path.dirname(__file__), "../Data/data_" + self.config.data_set, "training", "*")): subject_center = subject_dir[ -9: -7] # Retrieve for the MICCAI16 data-set the center of the patient self.ids.append(os.path.basename(subject_dir)) subject_files = list() for modality in self.config.training_modalities + [ "./" + self.config.GT ]: # Autre solution ? "/ManualSegmentation/ pour miccai16" subject_files.append( os.path.join(subject_dir, modality + ".nii.gz")) # + "/Preprocessed/ pour miccai16 data_files.append(tuple(subject_files)) write_data_to_file(data_files, self.config.source_data_file, image_shape=self.config.image_shape, subject_ids=self.ids)
def prepare_data(args): data_dir = get_h5_training_dir(BRATS_DIR, "data") # make dir if not os.path.exists(data_dir): print_separator() print("making dir", data_dir) os.makedirs(data_dir) print_section("convert input images into an hdf5 file") data_filename = get_training_h5_filename(datatype="data", args=args) print(data_filename) data_file_path = os.path.join(data_dir, data_filename) print("save to", data_file_path) dataset = get_dataset( is_test=args.is_test, is_bias_correction=args.is_bias_correction, is_denoise=args.is_denoise) print("reading folder:", dataset) if args.overwrite or not os.path.exists(data_file_path): training_files = fetch_training_data_files(dataset) write_data_to_file(training_files, data_file_path, config=config, image_shape=get_shape_from_string(args.image_shape), brats_dir=BRATS_DIR, crop=args.crop, is_normalize=args.is_normalize, is_hist_match=args.is_hist_match, dataset=dataset, is_denoise=args.is_denoise)
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=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 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()
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_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(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(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_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()