Flush written text to both stdout and a file, if open.

Converts a 3x4 camera matrix to a 4x4

r"""Copies current values of the internal counters to the user-visible state and resets them for the next round. If `keep_previous=Tr

r"""Returns the raw moments that were accumulated for the given statistic between the last two calls to `update()`, or zero if no scal

Appends the a predicted depthmap to the given image

Flush, close possible files, and remove stdout/stderr mirroring.

Stack camera extrinsics and intrinsics into a flattened vector using precomputed statistics. Uses image size to fit the used intrinsics

Unstack camera extrinsics and intrinsics from a flattened vector representation using precomputed statistics. Uses image size to fit the used

Traverses the object name and returns the last (rightmost) python object.

r"""Returns the mean of the scalars that were accumulated for the given statistic between the last two calls to `update()`, or NaN if

Loads model from persistent .pkl file

Stack intrinsic matrices into 4 element vectors

Unstack intrinsic matrices into 3x3 matrices

Metric computation for training loop

Searches for the underlying module behind the name to some python object. Returns the module and the object name (original name with module part r

r"""Test whether the given object or class is persistent, i.e., whether it will save its source code when pickled.

r"""Returns the names of all statistics broadcasted so far that match the regular expression specified at construction time.

Write text to stdout (and a file) and optionally flush.

r"""Check that the given object is pickleable, raising an exception if it is not. This function is expected to be considerably more efficient

r"""Query the source code of a given Python module.

r"""Get or create a Python module for the given source code.

r"""Synchronize the global cumulative counters across devices and processes. Called internally by `Collector.update()`.

r"""Returns the averages accumulated between the last two calls to `update()` as an `dnnlib.EasyDict`. The contents are as follows:

Finds the python object with the given name and calls it as a function.

Generate random images using the given model. Examples: \b # Generate a couple of images and save them as out/*.png python generate_

Calculate evaluation metrics (FID and FD_DINOv2). Examples: \b # Generate 10000 images using 8 GPUs and save them as out/*/*.png tor

Transform an image to be used by a DepthAnythingV2 model

Applies the depth model on the given image and resizes the result to the desired shape

Computes epipolar distance between two images, for pixels of size 'patch_size' Uses the following formula: Given a line $x=a + t n$, the

Finds the python object with the given name.

Determine whether the given object is a top-level function, i.e., defined at module scope using 'def'.

Determine whether the given object is a valid URL string.

r"""Returns the number of scalars that were accumulated for the given statistic between the last two calls to `update()`, or zero if n

r"""Class decorator that extends a given class to save its source code when pickled. Example: from torch_utils import persistence

r"""Broadcasts the given set of scalars to all interested instances of `Collector`, across device and process boundaries. NaNs and Infs are ig

r"""Returns the standard deviation of the scalars that were accumulated for the given statistic between the last two calls to `update(

Warps image based on the geometry and the depth