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()
