Apply median filter to an image. Args: in_tensor: input tensor; median filtering will be applied to the last `spatial_dims` dimensio

converting the dense displacement field from the MONAI space to the ITK Args: image: itk image of array shape 2D: (H, W) or 3D: (D, H

warp with MONAI Args: img: numpy array of shape (D, H, W) ddf: numpy array of shape (3, D, H, W) Returns: warped

must be defined for deepcopy to work See: - https://pytorch.org/docs/stable/generated/torch.Tensor.new_empty.html#torch-

Get number of resnet backbone output feature maps channel.

Get number of resnet backbone output feature maps. Since every backbone contains the same 5 output feature maps, the number list should be `[

Functional implementation of changing the input image's orientation into the specified based on `spatial_ornt`. This function operates eagerl

Functional implementation of padding a MetaTensor. This function operates eagerly or lazily according to ``lazy`` (default ``False``). `

Pad the input images, so that the output spatial sizes are divisible by `size_divisible`. It pads them at the end to create a (B, C, H, W) or

Function version of :py:class:`monai.transforms.croppad.batch.PadListDataCollate`. Same as MONAI's ``list_data_collate``, except any tensors

Implements parsing of 'output_lists' arg of trt_compile(). Args: ret: plain list of Tensors output_lists: list of output group

Split the dataset into N partitions based on the given class labels. It can make sure the same ratio of classes in every partition. Other

Patch the loaded bundle config with a new handler logic to enable experiment tracking features. Args: parser: loaded con

Split pep440 version string at the post-release segment. Returns the release segments before the post-release and the post-release version nu

Split pep440 version string at the post-release segment. Returns the release segments before the post-release and the post-release version nu

Performs experiment planning and preprocessing before the training. Args: fpe: [OPTIONAL] Name of the Dataset Fingerprin

Generate a configuration file that specifies the details of the experiment. Args: pl: [OPTIONAL] Name of the Experiment

Plot metrics on a single graph with running averages plotted for selected keys. The values in `graphmap` should be lists of (timepoint, value

Plot metric graph data with images below into figure `fig`. The intended use is for the graph data to be metrics from a training run and the

Generate a plot of the current status of the contained engine whose loss and metrics were tracked by `logger`. The function `plot_fun

Point-based window inferer that takes an input image, a set of points, and a model, and returns a segmented image. The inferer algorithm crop

Postprocessing to generate detection result from classification logits and box regression. Use self.box_selector to select the final

Analyse the data analysis report and check if the algorithm needs to be skipped. This function is overriden within algo. Args

If transform requires 2D data, then convert to 2D by selecting the middle of the last dimension.

Use the trained model to predict the outputs with a given input image. Args: predict_files: a list of paths to files to

Use this to run inference with nnU-Net. This function is used when you want to manually specify a folder containing a trained nnU

Read test data and output model predictions.

Given the logits from a network, computing the segmentation by thresholding all values above 0 if multi-labels task, computing the `argmax` a

Prepare the ground truth for evaluation based on the binary tumor mask

Prepare the probability map for detection evaluation.

Print GPU info to `file`. Args: file: `print()` text stream file. Defaults to `sys.stdout`.

Print system info to `file`. Requires the optional library, `psutil`. Args: file: `print()` text stream file. Defaults to `sys.stdou

Decorator which can be applied to a function which profiles any calls to it. All calls to decorated callables must be done within the

Wrapper around anything iterable to profile how long it takes to generate items.

Push a MONAI bundle to the Hugging Face Hub. Typical usage examples: .. code-block:: bash python -m monai.bundle push_to_hf_hu

(1) Set random variable to apply the transform. (2) Get alpha from uniform distribution.

Helper method to sample both the location and intensity of the spikes. When not working channel wise (channel_wise=False) it use the

Performs the learning rate range test. Args: train_loader: training set data loader. val_loader: validation data load

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