/
generator.py
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/
generator.py
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import os
import copy
from random import shuffle
import itertools
import numpy as np
from utils import pickle_dump, pickle_load
from patches import compute_patch_indices, get_random_nd_index, get_patch_from_3d_data
from augment import augment_data, random_permutation_x_y
def get_training_and_validation_generators(data_file, batch_size, n_labels, training_keys_file, validation_keys_file,
data_split=0.8, overwrite=False, labels=None, augment=False,
augment_flip=True, augment_distortion_factor=0.25, patch_shape=None,
validation_patch_overlap=0, training_patch_start_offset=None,
validation_batch_size=None, skip_blank=True, permute=False):
"""
Creates the training and validation generators that can be used when training the model.
:param skip_blank: If True, any blank (all-zero) label images/patches will be skipped by the data generator.
:param validation_batch_size: Batch size for the validation data.
:param training_patch_start_offset: Tuple of length 3 containing integer values. Training data will randomly be
offset by a number of pixels between (0, 0, 0) and the given tuple. (default is None)
:param validation_patch_overlap: Number of pixels/voxels that will be overlapped in the validation data. (requires
patch_shape to not be None)
:param patch_shape: Shape of the data to return with the generator. If None, the whole image will be returned.
(default is None)
:param augment_flip: if True and augment is True, then the data will be randomly flipped along the x, y and z axis
:param augment_distortion_factor: if augment is True, this determines the standard deviation from the original
that the data will be distorted (in a stretching or shrinking fashion). Set to None, False, or 0 to prevent the
augmentation from distorting the data in this way.
:param augment: If True, training data will be distorted on the fly so as to avoid over-fitting.
:param labels: List or tuple containing the ordered label values in the image files. The length of the list or tuple
should be equal to the n_labels value.
Example: (10, 25, 50)
The data generator would then return binary truth arrays representing the labels 10, 25, and 30 in that order.
:param data_file: hdf5 file to load the data from.
:param batch_size: Size of the batches that the training generator will provide.
:param n_labels: Number of binary labels.
:param training_keys_file: Pickle file where the index locations of the training data will be stored.
:param validation_keys_file: Pickle file where the index locations of the validation data will be stored.
:param data_split: How the training and validation data will be split. 0 means all the data will be used for
validation and none of it will be used for training. 1 means that all the data will be used for training and none
will be used for validation. Default is 0.8 or 80%.
:param overwrite: If set to True, previous files will be overwritten. The default mode is false, so that the
training and validation splits won't be overwritten when rerunning model training.
:param permute: will randomly permute the data (data must be 3D cube)
:return: Training data generator, validation data generator, number of training steps, number of validation steps
"""
if not validation_batch_size:
validation_batch_size = batch_size
training_list, validation_list = get_validation_split(data_file,
data_split=data_split,
overwrite=overwrite,
training_file=training_keys_file,
validation_file=validation_keys_file)
training_generator = data_generator(data_file, training_list,
batch_size=batch_size,
n_labels=n_labels,
labels=labels,
augment=augment,
augment_flip=augment_flip,
augment_distortion_factor=augment_distortion_factor,
patch_shape=patch_shape,
patch_overlap=0,
patch_start_offset=training_patch_start_offset,
skip_blank=skip_blank,
permute=permute)
validation_generator = data_generator(data_file, validation_list,
batch_size=validation_batch_size,
n_labels=n_labels,
labels=labels,
patch_shape=patch_shape,
patch_overlap=validation_patch_overlap,
skip_blank=skip_blank)
# Set the number of training and testing samples per epoch correctly
num_training_steps = get_number_of_steps(get_number_of_patches(data_file, training_list, patch_shape,
skip_blank=skip_blank,
patch_start_offset=training_patch_start_offset,
patch_overlap=0), batch_size)
print("Number of training steps: ", num_training_steps)
num_validation_steps = get_number_of_steps(get_number_of_patches(data_file, validation_list, patch_shape,
skip_blank=skip_blank,
patch_overlap=validation_patch_overlap),
validation_batch_size)
print("Number of validation steps: ", num_validation_steps)
return training_generator, validation_generator, num_training_steps, num_validation_steps
def get_number_of_steps(n_samples, batch_size):
if n_samples <= batch_size:
return n_samples
elif np.remainder(n_samples, batch_size) == 0:
return n_samples//batch_size
else:
return n_samples//batch_size + 1
def get_validation_split(data_file, training_file, validation_file, data_split=0.8, overwrite=False):
"""
Splits the data into the training and validation indices list.
:param data_file: pytables hdf5 data file
:param training_file:
:param validation_file:
:param data_split:
:param overwrite:
:return:
"""
if overwrite or not os.path.exists(training_file):
print("Creating validation split...")
nb_samples = data_file.root.data.shape[0]
sample_list = list(range(nb_samples))
training_list, validation_list = split_list(sample_list, split=data_split)
pickle_dump(training_list, training_file)
pickle_dump(validation_list, validation_file)
return training_list, validation_list
else:
print("Loading previous validation split...")
return pickle_load(training_file), pickle_load(validation_file)
def split_list(input_list, split=0.8, shuffle_list=True):
if shuffle_list:
shuffle(input_list)
n_training = int(len(input_list) * split)
training = input_list[:n_training]
testing = input_list[n_training:]
return training, testing
def data_generator(data_file, index_list, batch_size=1, n_labels=1, labels=None, augment=False, augment_flip=True,
augment_distortion_factor=0.25, patch_shape=None, patch_overlap=0, patch_start_offset=None,
shuffle_index_list=True, skip_blank=True, permute=False):
orig_index_list = index_list
while True:
x_list = list()
y_list = list()
if patch_shape:
index_list = create_patch_index_list(orig_index_list, data_file.root.data.shape[-3:], patch_shape,
patch_overlap, patch_start_offset)
else:
index_list = copy.copy(orig_index_list)
if shuffle_index_list:
shuffle(index_list)
while len(index_list) > 0:
index = index_list.pop()
add_data(x_list, y_list, data_file, index, augment=augment, augment_flip=augment_flip,
augment_distortion_factor=augment_distortion_factor, patch_shape=patch_shape,
skip_blank=skip_blank, permute=permute)
if len(x_list) == batch_size or (len(index_list) == 0 and len(x_list) > 0):
yield convert_data(x_list, y_list, n_labels=n_labels, labels=labels)
x_list = list()
y_list = list()
def get_number_of_patches(data_file, index_list, patch_shape=None, patch_overlap=0, patch_start_offset=None,
skip_blank=True):
if patch_shape:
index_list = create_patch_index_list(index_list, data_file.root.data.shape[-3:], patch_shape, patch_overlap,
patch_start_offset)
count = 0
for index in index_list:
x_list = list()
y_list = list()
add_data(x_list, y_list, data_file, index, skip_blank=skip_blank, patch_shape=patch_shape)
if len(x_list) > 0:
count += 1
return count
else:
return len(index_list)
def create_patch_index_list(index_list, image_shape, patch_shape, patch_overlap, patch_start_offset=None):
patch_index = list()
for index in index_list:
if patch_start_offset is not None:
random_start_offset = np.negative(get_random_nd_index(patch_start_offset))
patches = compute_patch_indices(image_shape, patch_shape, overlap=patch_overlap, start=random_start_offset)
else:
patches = compute_patch_indices(image_shape, patch_shape, overlap=patch_overlap)
patch_index.extend(itertools.product([index], patches))
return patch_index
def add_data(x_list, y_list, data_file, index, augment=False, augment_flip=False, augment_distortion_factor=0.25,
patch_shape=False, skip_blank=True, permute=False):
"""
Adds data from the data file to the given lists of feature and target data
:param skip_blank: Data will not be added if the truth vector is all zeros (default is True).
:param patch_shape: Shape of the patch to add to the data lists. If None, the whole image will be added.
:param x_list: list of data to which data from the data_file will be appended.
:param y_list: list of data to which the target data from the data_file will be appended.
:param data_file: hdf5 data file.
:param index: index of the data file from which to extract the data.
:param augment: if True, data will be augmented according to the other augmentation parameters (augment_flip and
augment_distortion_factor)
:param augment_flip: if True and augment is True, then the data will be randomly flipped along the x, y and z axis
:param augment_distortion_factor: if augment is True, this determines the standard deviation from the original
that the data will be distorted (in a stretching or shrinking fashion). Set to None, False, or 0 to prevent the
augmentation from distorting the data in this way.
:param permute: will randomly permute the data (data must be 3D cube)
:return:
"""
data, truth = get_data_from_file(data_file, index, patch_shape=patch_shape)
if augment:
if patch_shape is not None:
affine = data_file.root.affine[index[0]]
else:
affine = data_file.root.affine[index]
data, truth = augment_data(data, truth, affine, flip=augment_flip, scale_deviation=augment_distortion_factor)
if permute:
if data.shape[-3] != data.shape[-2] or data.shape[-2] != data.shape[-1]:
raise ValueError("To utilize permutations, data array must be in 3D cube shape with all dimensions having "
"the same length.")
data, truth = random_permutation_x_y(data, truth[np.newaxis])
else:
truth = truth[np.newaxis]
if not skip_blank or np.any(truth != 0):
x_list.append(data)
y_list.append(truth)
def get_data_from_file(data_file, index, patch_shape=None):
if patch_shape:
index, patch_index = index
data, truth = get_data_from_file(data_file, index, patch_shape=None)
x = get_patch_from_3d_data(data, patch_shape, patch_index)
y = get_patch_from_3d_data(truth, patch_shape, patch_index)
else:
x, y = data_file.root.data[index], data_file.root.truth[index, 0]
return x, y
def convert_data(x_list, y_list, n_labels=1, labels=None):
x = np.asarray(x_list)
y = np.asarray(y_list)
if n_labels == 1:
y[y > 0] = 1
elif n_labels > 1:
y = get_multi_class_labels(y, n_labels=n_labels, labels=labels)
return x, y
def get_multi_class_labels(data, n_labels, labels=None):
"""
Translates a label map into a set of binary labels.
:param data: numpy array containing the label map with shape: (n_samples, 1, ...).
:param n_labels: number of labels.
:param labels: integer values of the labels.
:return: binary numpy array of shape: (n_samples, n_labels, ...)
"""
new_shape = [data.shape[0], n_labels] + list(data.shape[2:])
y = np.zeros(new_shape, np.int8)
for label_index in range(n_labels):
if labels is not None:
y[:, label_index][data[:, 0] == labels[label_index]] = 1
else:
y[:, label_index][data[:, 0] == (label_index + 1)] = 1
return y