Source code for niftynet.utilities.user_parameters_default

# -*- coding: utf-8 -*-
"""
This module defines niftynet parameters and their defaults.
"""
from __future__ import (absolute_import, division, print_function,
                        unicode_literals)

import os

from niftynet.engine.image_window_dataset import SMALLER_FINAL_BATCH_MODE
from niftynet.io.image_loader import SUPPORTED_LOADERS
from niftynet.io.image_sets_partitioner import SUPPORTED_PHASES
from niftynet.utilities.user_parameters_helper import (
    float_array, int_array, spatial_atleast3d, spatialnumarray, str2boolean,
    str_array)
from niftynet.utilities.util_import import require_module

DEFAULT_INFERENCE_OUTPUT = os.path.join('.', 'output')
DEFAULT_EVALUATION_OUTPUT = os.path.join('.', 'evaluation')
DEFAULT_DATASET_SPLIT_FILE = os.path.join('.', 'dataset_split.csv')
DEFAULT_HISTOGRAM_REF_FILE = os.path.join('.', 'histogram_ref_file.txt')
DEFAULT_MODEL_DIR = None
DEFAULT_EVENT_HANDLERS = ('model_saver', 'model_restorer', 'sampler_threading',
                          'apply_gradients', 'output_interpreter',
                          'console_logger', 'tensorboard_logger')

DEFAULT_ITERATION_GENERATOR = 'iteration_generator'


[docs]def add_application_args(parser): """ Common keywords for all applications :param parser: :return: """ parser.add_argument( "--cuda_devices", metavar='', help="Set CUDA_VISIBLE_DEVICES variable, e.g. '0,1,2,3'; " "leave blank to use the system default value", type=str, default='') parser.add_argument( "--num_threads", help="Set number of preprocessing threads", metavar='', type=int, default=2) parser.add_argument( "--num_gpus", help="Set number of training GPUs", metavar='', type=int, default=1) parser.add_argument( "--model_dir", metavar='', help="Directory to save/load intermediate training models and logs", default=DEFAULT_MODEL_DIR) parser.add_argument( "--dataset_split_file", metavar='', help="File assigning subjects to training/validation/inference subsets", default=DEFAULT_DATASET_SPLIT_FILE) parser.add_argument( "--event_handler", metavar='', help="String(s) representing event handler module(s)", type=str_array, default=DEFAULT_EVENT_HANDLERS) parser.add_argument( "--iteration_generator", metavar='', help='String representing an iteration generator class', type=str, default=DEFAULT_ITERATION_GENERATOR) return parser
[docs]def add_inference_args(parser): """ keywords defined for inference action :param parser: :return: """ parser.add_argument( "--spatial_window_size", type=spatial_atleast3d, help="Specify the spatial size of the input data (ndims <= 3)", default=()) parser.add_argument( "--inference_iter", metavar='', help="[Inference only] Use the checkpoint at this iteration for " "inference", type=int, default=-1) parser.add_argument( "--dataset_to_infer", metavar='', help="[Inference only] which data set to compute inference for", choices=list(SUPPORTED_PHASES) + [''], default='') parser.add_argument( "--save_seg_dir", metavar='', help="[Inference only] Prediction directory name", # without '/' default=DEFAULT_INFERENCE_OUTPUT) parser.add_argument( "--output_postfix", metavar='', help="[Inference only] Prediction filename postfix", default="_niftynet_out") parser.add_argument( "--output_interp_order", metavar='', help="[Inference only] interpolation order of the network output", type=int, default=0) parser.add_argument( "--border", metavar='', help="[Inference only] Width of borders to crop for segmented patch", type=spatialnumarray, default=(0, 0, 0)) parser.add_argument( "--fill_constant", help="[Inference only] Output fill value " "used fill borders of output images.", type=float, default=0.0) return parser
[docs]def add_evaluation_args(parser): """ keywords defined for evaluation action :param parser: :return: """ parser.add_argument( "--evaluations", metavar='', help="[Evaluation only] List of evaluations to generate", default='') parser.add_argument( "--save_csv_dir", metavar='', help="[Evaluation only] Directory to save evaluation metrics", default=DEFAULT_EVALUATION_OUTPUT) return parser
[docs]def add_input_data_args(parser): """ keywords defined for input data specification section :param parser: :return: """ parser.add_argument( "--csv_file", metavar='', type=str, help="Input list of subjects in csv files", default='') parser.add_argument( "--path_to_search", metavar='', type=str, help="Input data folder to find a list of input image files", default='') parser.add_argument( "--filename_contains", metavar='', type=str_array, help="keywords in input file names, matched filenames will be used.") parser.add_argument( "--filename_not_contains", metavar='', type=str_array, help="keywords in input file names, negatively matches filenames", default='') parser.add_argument( "--filename_removefromid", metavar='', type=str, help="Regular expression for extracting subject id from filename, " "matched pattern will be removed from the file names " "to form the subject id", default='') parser.add_argument( "--interp_order", type=int, choices=[0, 1, 2, 3], default=1, help="interpolation order of the input images") parser.add_argument( "--loader", type=str, choices=list(SUPPORTED_LOADERS), default=None, help="Image loader to use from {}. " "Leave blank to try all loaders.".format(list(SUPPORTED_LOADERS))) parser.add_argument( "--pixdim", type=float_array, default=(), help="voxel width along each dimension") parser.add_argument( "--axcodes", type=str_array, default=(), help="labels for positive end of voxel axes, possible labels are" " ('L','R'),('P','A'),('I','S')" " *see also nibabel.orientations.ornt2axcodes") parser.add_argument( "--spatial_window_size", type=spatial_atleast3d, help="specify the spatial size of the input data (ndims <= 3)", default=()) return parser
[docs]def add_network_args(parser): """ keywords defined for network specification :param parser: :return: """ import niftynet.layer.binary_masking import niftynet.layer.activation import niftynet.utilities.histogram_standardisation as hist_std_module parser.add_argument( "--name", help="Choose a net from NiftyNet/niftynet/network/ or from " "user specified module string", metavar='') parser.add_argument( "--activation_function", help="Specify activation function types", choices=list(niftynet.layer.activation.SUPPORTED_OP), metavar='TYPE_STR', default='relu') parser.add_argument( "--batch_size", metavar='', help="Set batch size of the net", type=int, default=2) parser.add_argument( "--smaller_final_batch_mode", metavar='TYPE_STR', help="If True, allow the final batch to be smaller " "if there are insufficient items left in the queue, " "and the batch size will be undetermined during " "graph construction.", choices=list(SMALLER_FINAL_BATCH_MODE), default='pad') parser.add_argument( "--decay", help="[Training only] Set weight decay", type=float, default=0.0) parser.add_argument( "--reg_type", metavar='TYPE_STR', help="[Training only] Specify regulariser type_str", type=str, default='L2') parser.add_argument( "--volume_padding_size", metavar='', help="Set padding size of each volume (in all dimensions)", type=spatialnumarray, default=(0, 0, 0)) parser.add_argument( "--volume_padding_mode", metavar='', help="Set which type of numpy padding to do, see " "https://docs.scipy.org/doc/numpy-1.14.0/" "reference/generated/numpy.pad.html " "for details", type=str, default='minimum') parser.add_argument( "--window_sampling", metavar='TYPE_STR', help="How to sample patches from each loaded image:" " 'uniform': fixed size uniformly distributed," " 'resize': resize image to the patch size.", choices=['uniform', 'resize', 'balanced', 'weighted'], default='uniform') parser.add_argument( "--queue_length", help="Set size of preprocessing buffer queue", metavar='', type=int, default=5) parser.add_argument( "--multimod_foreground_type", choices=list( niftynet.layer.binary_masking.SUPPORTED_MULTIMOD_MASK_TYPES), help="Way of combining the foreground masks from different " "modalities. 'and' is the intersection, 'or' is the union " "and 'multi' permits each modality to use its own mask.", default='and') parser.add_argument( "--histogram_ref_file", metavar='', type=str, help="A reference file of histogram for intensity normalisation", default=DEFAULT_HISTOGRAM_REF_FILE) parser.add_argument( "--norm_type", help="Type of normalisation to perform", type=str, default='percentile', choices=list(hist_std_module.SUPPORTED_CUTPOINTS)) parser.add_argument( "--cutoff", help="Cutoff values for the normalisation process", type=float_array, default=(0.01, 0.99)) parser.add_argument( "--foreground_type", choices=list(niftynet.layer.binary_masking.SUPPORTED_MASK_TYPES), help="type_str of foreground masking strategy used", default='otsu_plus') parser.add_argument( "--normalisation", help="Indicates if the normalisation must be performed", type=str2boolean, default=False) parser.add_argument( "--rgb_normalisation", help="Indicates if RGB histogram equilisation should be performed", type=str2boolean, default=False) parser.add_argument( "--whitening", help="Indicates if the whitening of the data should be applied", type=str2boolean, default=False) parser.add_argument( "--normalise_foreground_only", help="Indicates whether a foreground mask should be applied when" " normalising volumes", type=str2boolean, default=False) parser.add_argument( "--weight_initializer", help="Set the initializer for the weight parameters", type=str, default='he_normal') parser.add_argument( "--bias_initializer", help="Set the initializer for the bias parameters", type=str, default='zeros') parser.add_argument( "--keep_prob", help="Probability that each element is kept " "if dropout is supported by the network", type=float, default=1.0) yaml = require_module('yaml', mandatory=False) if yaml: parser.add_argument( "--weight_initializer_args", help="Pass arguments to the initializer for the weight parameters", type=yaml.load, default={}) parser.add_argument( "--bias_initializer_args", help="Pass arguments to the initializer for the bias parameters", type=yaml.load, default={}) return parser
[docs]def add_training_args(parser): """ keywords defined for the training action :param parser: :return: """ parser.add_argument( "--optimiser", help="Choose an optimiser for computing graph gradients and applying", type=str, default='adam') parser.add_argument( "--sample_per_volume", help="[Training only] Set number of samples to take from " "each image that was loaded in a given training epoch", metavar='', type=int, default=1) parser.add_argument( "--rotation_angle", help="The min/max angles of rotation when rotation " "augmentation is enabled", type=float_array, default=()) parser.add_argument( "--rotation_angle_x", help="The min/max angles of the x rotation when rotation " "augmentation is enabled", type=float_array, default=()) parser.add_argument( "--rotation_angle_y", help="The min/max angles of the y rotation when rotation " "augmentation is enabled", type=float_array, default=()) parser.add_argument( "--rotation_angle_z", help="The min/max angles of the z rotation when rotation " "augmentation is enabled", type=float_array, default=()) parser.add_argument( "--scaling_percentage", help="The spatial scaling factor in [min_percentage, max_percentage]", type=float_array, default=()) parser.add_argument( "--isotropic_scaling", help="Indicates if the same random scaling factor should be applied " "to each dimension", type=str2boolean, default=False) parser.add_argument( "--antialiasing", help="Indicates if antialiasing must be performed " "when randomly scaling the input images", type=str2boolean, default=True) parser.add_argument( "--bias_field_range", help="[Training only] The range of bias field coeffs in [min_coeff, " "max_coeff]", type=float_array, default=()) parser.add_argument( "--bf_order", help="[Training only] maximal polynomial order to use for the " "creation of the bias field augmentation", metavar='', type=int, default=3) parser.add_argument( "--random_flipping_axes", help="The axes which can be flipped to augment the data. Supply as " "comma-separated values within single quotes, e.g. '0,1'. Note " "that these are 0-indexed, so choose some combination of 0, 1.", type=int_array, default=-1) # elastic deformation parser.add_argument( "--do_elastic_deformation", help="Enables elastic deformation", type=str2boolean, default=False) parser.add_argument( "--num_ctrl_points", help="Number of control points for the elastic deformation", type=int, default=4) parser.add_argument( "--deformation_sigma", help="The standard deviation for elastic deformation.", type=float, default=15) parser.add_argument( "--proportion_to_deform", help="What fraction of samples to deform elastically.", type=float, default=0.5) parser.add_argument( "--lr", help="[Training only] Set learning rate", type=float, default=0.01) parser.add_argument( "--loss_type", metavar='TYPE_STR', help="[Training only] Specify loss type_str", default='Dice') parser.add_argument( "--starting_iter", metavar='', help="[Training only] Resume from iteration n", type=int, default=0) parser.add_argument( "--save_every_n", metavar='', help="[Training only] Model saving frequency", type=int, default=500) parser.add_argument( "--tensorboard_every_n", metavar='', help="[Training only] Tensorboard summary frequency", type=int, default=20) parser.add_argument( "--max_iter", metavar='', help="[Training only] Total number of iterations", type=int, default=10000) parser.add_argument( "--max_checkpoints", help="Maximum number of model checkpoints that will be saved", type=int, default=100) parser.add_argument( "--validation_every_n", help="Validate every n iterations", type=int, default=-1) parser.add_argument( "--validation_max_iter", help="Number of validation batches to run", type=int, default=1) parser.add_argument( "--exclude_fraction_for_validation", help="Fraction of dataset to use for validation", type=float, default=0.) parser.add_argument( "--exclude_fraction_for_inference", help="Fraction of dataset to use for inference", type=float, default=0.) parser.add_argument( "--vars_to_restore", help="regex strings matching variable names to restore", type=str, default='') parser.add_argument( "--vars_to_freeze", help="regex strings matching variable to be fixed during training", type=str, default='') return parser
SUPPORTED_DEFAULT_SECTIONS = { 'SYSTEM': add_application_args, 'NETWORK': add_network_args, 'TRAINING': add_training_args, 'INFERENCE': add_inference_args, 'EVALUATION': add_evaluation_args, }