Utility function to apply conv + BN. # Arguments x: input tensor. filters: filters in `Conv2D`. num_row: height of the con

Utility function to apply conv + BN. # Arguments x: input tensor. filters: filters in `Conv2D`. kernel_size: kernel size a

This only applies for num_conv_per_stage and convolutional_upsampling=True not real vram consumption. just a constant term to which t

:param spacing: :param patch_size: :param min_feature_map_size: min edge length of feature maps in bottleneck :return:

unpacks all npz files in a folder to npy (whatever you want to have unpacked must be saved unter key) :param folder: :param threads:

This is what actually configures the entire experiment. The plans file is generated by experiment planning :return:

:param spacing: :param patch_size: :param min_feature_map_size: min edge length of feature maps in bottleneck :return:

net_output must be (b, c, x, y(, z))) gt must be a label map (shape (b, 1, x, y(, z)) OR shape (b, x, y(, z))) or one hot encoding (b, c, x,

test = predicted image :param test_ref_pairs: :param evaluator: :param labels: must be a dict of int-> str or a list of int :para

:param data: :param do_mirroring: :param mirror_axes: :param use_sliding_window: :param step_size: :p

Set the reference segmentation.

Set the test segmentation.

Extension of Conv2D layer with batchnorm

square_volumes will square the weight term. The paper recommends square_volumes=True; I don't (just an intuition)

Help find layer in Keras model by name Args: model: Keras `Model` layer_name: str, name of layer Returns: index

This is a suggestion for if your dataset is a dictionary (my personal standard) :return:

this is only valid for the brats data in here where the labels are 1, 2, and 3. The original brats data have a different labeling convention!

Retrieves the correct output directory for the nnU-Net model described by the input parameters :param model: :param task: :param tra

TODO

:param base: :param gt_labels_folder: subfolder of base with niftis of ground truth labels :param raw_subfolder_name: subfolder of base w

returns a dictionary that contains pointers to conv, nonlin and norm ops and the default kwargs I like to use :return:

For prediction of test cases just set training=False, this will prevent loading of training data and training batchgenerator initiali

loads the relevant part of the pkl file that is needed for applying postprocessing :param pkl_file: :return:

separate_z=True will resample with order 0 along z :param data: :param new_shape: :param is_seg: :param axis: :param order:

:param cval_seg: :param cval_data: :param data: :param seg: :param original_spacing: :param target_spacing: :param order_

Compute metrics for segmentations.

Adds a Inception-ResNet block. This function builds 3 types of Inception-ResNet blocks mentioned in the paper, controlled by the `block_type`

This is specific to the U-Net and must be adapted for other network architectures :return:

used for if the checkpoint is already in ram :param checkpoint: :param train: :return:

This is a utility for writing segmentations to nifto and npz. It requires the data to have been preprocessed by GenericPreprocessor because i