Compute the root sum of squares (rss) of the data (typically done for multi-coil MRI samples) Args: x: Input tensor spatial_

Run the bundle workflow, it can be a training, evaluation or inference.

Compute the scaling matrix according to the new spatial size Args: spatial_size: original spatial size. new_spatial_size: ne

Set the input data and run deterministic transforms to generate cache content. Note: should call this func after an entire epoch and

Set the bundle ensemble method Args: ensemble_method_name: the name of the ensemble method. Only two methods are support

Resample ``self.data_obj`` if needed. This method assumes ``self.data_obj`` is a 'channel-last' ndarray. Args: meta_dic

Calculate the factors (divisors) that the input image shape must be divisible by

Apply soft clip to the input array or tensor. The intensity values will be soft clipped according to f(x) = x + (1/sharpness_factor)*soft

Split the id string into a list of strings by `cls.sep`. Args: id: id string to be split. last: whether to s

Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion process from the learned m

Summarize the input list of data and generates a report ready for json/yaml export. Args: data: a list of data dicts.

Test the ability to save `net` in ONNX format, reload it and validate with runtime. The value `inputs` is forward-passed through the `net` wi

Run the training on a single GPU with one specified configuration provided. Note: if CUDA_VISIBLE_DEVICES is already set and gpu_id r

Computing unravel coordinates from indices. Args: idx: a sequence of indices to unravel. shape: shape of array/tensor. Retur

Verify whether the specified `filename` is supported by pynrrd reader. Args: filename: file name or a list of file names

Issue the warning message `msg`.

To make column norm of `affine` the same as `scale`. If diagonal is False, returns an affine that combines orthogonal rotation and the new s

Zoom boxes Args: boxes: bounding boxes, Nx4 or Nx6 torch tensor or ndarray. The box mode is assumed to be StandardMode zoom:

Args: spatial_dims: number of spatial dimensions of the input image. kernel_size: the kernel size of both pooling ope

Args: spatial_dims: number of spatial dimensions. in_channels: number of input channels. out_channels: nu

Args: spatial_dims: number of spatial dimensions. channels: channels pooling: use MaxPool if True, stride

Remap the voxel labels in the input data dictionary based on the specified mapping. This list of local -> global label mappings will

Args: output_dtype: output data type. affine_lps_to_ras: whether to convert the affine matrix from "LPS" to "RAS". De

Args: patch_size: size of patches to generate slices for, 0/None selects whole dimension start_pos: starting positio

Args: keys: keys of the corresponding items to be transformed. See also: :py:class:`monai.transforms.compose.MapT

Args: to_onehot_y : whether to convert `y` into the one-hot format. Defaults to False. delta : weight of the backgrou

Prints a representation of the tensor. Prepends "meta" to ``torch.Tensor.__repr__``. Use ``print_verbose`` for associated met

This internal function applies affine matrices to the point coordinate Args: points: point coordinates, Nx2 or Nx3 torch tensor or n

argmax for N-D array → returns coordinate vector (z,y,x) or (y,x).

Return the real config id this proxy writes to, resolving all ``$@ref`` hops transitively.

This internal function computes the intersection and union area of two set of boxes. Args: boxes1: bounding boxes, Nx4 or Nx6 torch

cache the original function by name, so that the decorator doesn't shadow it.

Resolve ``key`` as a nested config id. Args: key: the child key/index. Returns: The parsed child co

Check optimizer doesn't already have scheduler.

Combine dicom series (a list of pydicom dataset objects). Their data arrays will be stacked together at a new dimension as the last d

sliding window batch spatial scaling indexing for multi-resolution outputs.

Args: alpha: generalised number of true positives of target class. flat_target: the target tensor. wasser

Compute the Sobel gradients of the horizontal vertical map (HoVerMap). More specifically, it will compute horizontal gradient of the input hor

Estimate a covariance matrix of the variables. Args: input_data: A 1-D or 2-D array containing multiple variables and observations.

pop the original function from cache.

use depth first search to find all path activation combination

Retrieve file from `url` to `filepath`, optionally showing a progress bar.

Compute and fill the cache content from data source. Args: indices: target indices in the `self.data` source to compute

Generate contour coordinates. Given the previous and current coordinates of border positions, returns the int pixel that marks the ex

Returns the block object defining a layer of the UNet structure including the implementation of the skip between encoding (down) and

Compute the log-likelihood of a Gaussian distribution discretizing to a given image. Code adapted from https://github.com/openai/impr