python code from: https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filte

python code from: https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filte

Get sigmas --- the set of noise levels for SMLD from config files. Args: config: A ConfigDict object parsed from the config file Returns:

Create a linear module.

Compute alpha_t of a given continuous-time label t in [0, T].

Compute alpha_t of a given continuous-time label t in [0, T].

The noise predicition model function that is used for DPM-Solver.

Create the reverse-time SDE/ODE. Args: score_fn: A time-dependent score-based model that takes x and t and returns the score. probabi

Create the reverse-time SDE/ODE. Args: score_fn: A time-dependent score-based model that takes x and t and returns the score. probabi

Singlestep solver DPM-Solver-2 from time `s` to time `t`. Args: x: A jnp.DeviceArray. The initial value at time `s`.

Singlestep solver DPM-Solver-2 from time `s` to time `t`. Args: x: A pytorch tensor. The initial value at time `s`.

Singlestep solver DPM-Solver-2 from time `s` to time `t`. Args: x: A pytorch tensor. The initial value at time `s`.

Singlestep solver DPM-Solver-2 from time `s` to time `t`. Args: x: A pytorch tensor. The initial value at time `s`.

Singlestep solver DPM-Solver-2 from time `s` to time `t`. Args: x: A pytorch tensor. The initial value at time `s`.

Singlestep solver DPM-Solver-2 from time `s` to time `t`. Args: x: A jnp.DeviceArray. The initial value at time `s`.

Zero out the parameters of a module and return it.

Wraps `torchvision.models.inception_v3` Skips default weight inititialization if supported by torchvision version. See https://github.com/mse

Args: x: HxWxC image, [0, 1] sf: down-scale factor Return: bicubicly downsampled LR image

Args: x: HxWxC image, [0, 1] sf: down-scale factor Return: bicubicly downsampled LR image

Crop the center of an image to the given size.

Crop the center of an image to the given size.

Evaluate a function without caching intermediate activations, allowing for reduced memory at the expense of extra compute in the backward pas

Evaluate a function without caching intermediate activations, allowing for reduced memory at the expense of extra compute in the backward pas

If comm is provided, average all numerical stats across that comm

Convert the torso of the model to float16.

A counter for the `thop` package to count the operations in an attention operation. Meant to be used like: macs, params = thop.pr

A counter for the `thop` package to count the operations in an attention operation. Meant to be used like: macs, params = thop.pr

Return the data prediction model (with thresholding).

Return the data prediction model (with corrector).

Return the data prediction model (with corrector).

Return the data prediction model (with corrector).

Return the data prediction model (with corrector).

Return the data prediction model (with thresholding).

:param h: height :param w: width :return: normalized distance to image border, wtith min distance = 0 at border and

Download a file from a url and place it in root. Args: url (str): URL to download file from root (str): Directory to place downlo

The drift function of the reverse-time SDE.

Modify update functions of predictor & corrector to incorporate information of gray-scale images.

Modify update functions of predictor & corrector to incorporate information of gray-scale images.

Modify the update function of predictor & corrector to incorporate data information.

Modify the update function of predictor & corrector to incorporate data information.

Create a function to give the output of the score-based model. Args: model: The score model. train: `True` for training and `False` for eva

Create a function to give the output of the score-based model. Args: model: A `flax.linen.Module` object the represent the architecture of scor

Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time. For discrete-time DPMs, we convert `t_continuou

Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time. For discrete-time DPMs, we convert `t_continuou

Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time. For discrete-time DPMs, we convert `t_continuou

Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time. For discrete-time DPMs, we convert `t_continuou

Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time. For discrete-time DPMs, we convert `t_continuou

Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time. For discrete-time DPMs, we convert `t_continuou

Wraps `score_fn` so that the model output corresponds to a real time-dependent score function. Args: sde: An `sde_lib.SDE` object that represen