Returns True if version `lhs` is later or equal to `rhs`. Args: lhs: version name to compare with `rhs`, return True if later or equ

Discard positive and negative points according to discard probability Args: keys: The ``keys`` parameter will be used to

r""" Aggregates the output of `_compute_tensor`. Args: reduction: define mode of reduction to the metrics, will only appl

Verify hash signature of specified file. Args: filepath: path of source file to verify hash value. val: expected hash value

`np.clip` with equivalent implementation for torch.

This function converts the boxes in src_mode to the dst_mode. Args: boxes: source bounding boxes, Nx4 or Nx6 torch tensor or ndarray

Utility to convert the input data to a cupy array. If passing a dictionary, list or tuple, recursively check every item and convert it to cup

Return a Composition with a flattened list of transforms. Nested ``Compose`` objects that share the same ``map_items`` setting as the

Get all case stats. Caller of the DataAnalyser class. The function initiates multiple GPU or CPU processes of the internal _get_all_c

Returns an image as a numpy array (complete with channel as dim 0) with contents that iterate like an arange.

Extract data array and metadata from loaded image and return them. This function returns two objects, first is numpy array of image d

Get the default applied operations. Returns: default applied operations.

Get a fresh results dictionary containing fresh tuples of ProfileResult objects.

import the history of the bundleAlgo objects as a list of algo dicts. each algo_dict has keys name (folder name), algo (bundleAlgo), is_train

Determine if the object is an iterable sequence and is not a string.

Load model weights or TorchScript module of a bundle. Args: name: bundle name. If `None` and `url` is `None`, it must be provided in

Load image/label paths of decathlon challenge from JSON file JSON file should follow the format of the Medical Segmentation Decathlon datalis

Normalize the id string to consistently use `cls.sep`. Args: id: id string to be normalized.

Convert the ``affine`` between the `RAS` and `LPS` orientation by flipping the first two spatial dimensions. Args: affine: a 2D

Plot 2D or 3D image on the TensorBoard, 3D image will be converted to GIF image. Note: Plot 3D or 2D image(with more than 3 channels) as

Read image data from specified file or files, it can read a list of data files and stack them together as multi-channel data in `get_

Register image reader to load image file and metadata. Args: reader: reader instance to be registered with this loader.

Return the padding value needed to ensure a convolution using the given kernel size produces an output of the same shape as the input for a s

Args: input_noise: random noise, of the same shape as the desired latent representation. autoencoder_model: first sta

Args: img: data to be transformed, assuming `img` is channel-first and padding doesn't apply to the channel dim. to_p

get the last part of the path (removing the trailing slash if it exists)

Check boundaries for Signal transforms

Close the pandas `TextFileReader` iterable objects. If the input src is file path, TextFileReader was created internally, need to clo

Args: input: the shape should be BNH[WD]. target: the shape should be BNH[WD].

Args: input: the shape should be BNHW[D]. target: the shape should be BNHW[D].

Read the project setup.cfg file to determine Versioneer config.

Get the project root directory. We require that all commands are run from the project root, i.e. the directory that contains setup.py, setup.

a local folder to the testing algorithm template or a url to the compressed template file. Default to None, which effectively uses bundle_gen

Return a boolean indicating whether the given callable `obj` has the `keywords` in its signature.

Check whether the config is an executable expression string. Currently, a string starts with ``"$"`` character is interpreted as an e

Verify whether the specified file or files format match supported suffixes. If supported suffixes is None, skip the verification and return T

Pad `img` for a given an amount of padding in each dimension. `torch.nn.functional.pad` is used unless the mode or kwargs are not available

Read the metadata from specified JSON or YAML file. The metadata as a dictionary will be stored at ``self.config["_meta_"]``.

Reset the dataset items with specified `func`. Args: data: if not None, execute `func` on it, default to `self.src`.

Args: input_noise: random noise, of the same shape as the desired sample. diffusion_model: model to sample from.

Set config by ``id``. Args: config: config to set at location ``id``. id: id to specify the expected positio

Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference. Args: num_infe

Start the background thread to replace training items for every epoch.

Tensor initialization with truncated normal distribution. Based on: https://github.com/rwightman/pytorch-image-models Args: tensor

Update the `args` dictionary with the input `kwargs`. For dict data, recursively update the content based on the keys. Example::

Add a path and its parent directory to the list if the path is a valid directory.

Dimensional collapse score via SVD effective rank. Uses the entropy-based effective rank from Roy & Vetterli (2007): ``eff_rank = sum(sv) / m

Examine if the package version is compatible with the MONAI version in the metadata.

Search for the item with `key` in `config_dict`. Returns: the first occurrence of `key` in a breadth first search.

Helper for initializing ConstantPadNd with SAME padding similar to Tensorflow. Uses output of _get_same_padding_conv_nd() to get the padding

Pop args from the `src` dictionary based on specified keys in `args` and (key, default value) pairs in `kwargs`.

Add a ``ConfigItem`` to the resolver. Args: item: a ``ConfigItem``.

Add noise to the original samples. Args: original_samples: original samples noise: noise to add to samples

Export the Algo object to a JSON file (pickle-free serialization). Args: algo: Algo-like object (typically BundleAlgo or subclass).

Register a set of Ignite Event-Handlers to a specified Ignite engine. Args: engine: Ignite Engine, it can be a trainer,

Check whether the input array has pending operations, raise an error or warn when it has. Args: input_array: input array to be check

Given input and output affine, compute appropriate shapes in the output space based on the input array's shape. This function also return

Get the nested value in a list of dictionary that shares the same structure. Args: data_list: a list of dictionary {key1: {key2: np.n

Args: pred: the shape should be BCH(WD) Raises: ValueError: When ``self.reduction`` is not one of ["mean", "

Args: pred: Predicted dense displacement field (DDF) with shape BCH[WD], where C is the number of

Returns the algo output paths to find the algo scripts and configs.

Given an image of dimensions `image_size`, return a patch size tuple taking the dimension from `patch_size` if this is not 0/None. Otherwise,

Get the project version from whatever source is available. Returns dict with two keys: 'version' and 'full'.

Initialize routine to parse configuration files and extract main components such as trainer, evaluator, and filters. Args:

Check whether this config represents a `class` or `function` that is to be instantiated. Args: config: input config cont

Load the module object from the given Torchscript filename or stream, and convert the stored JSON metadata back to a dict object. This will p