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

:param x: img of size (bs, c, h, w) :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])

Update target parameters to be closer to those of source parameters using an exponential moving average. :param target_params: the targe

A fast approximation of the cumulative distribution function of the standard normal.

A fast approximation of the cumulative distribution function of the standard normal.

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

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

Compute what the p_mean_variance output would have been, should the model's score function be conditioned by cond_fn. See con

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

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

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

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

Defaults for image and classifier training.

embed_dim: output dimension for each position pos: a list of positions to be encoded: size (M,) out: (M, D)

embed_dim: output dimension for each position pos: a list of positions to be encoded: size (M,) out: (M, D)

grid_size: int of the grid height and width return: pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/

Copy model parameters into a (differently-shaped) list of full-precision parameters.

Defaults for image training.

Compute the KL divergence between two gaussians. Shapes are automatically broadcasted, so batches can be compared to scalars, among othe

Compute the KL divergence between two gaussians. Shapes are automatically broadcasted, so batches can be compared to scalars, among other

Diffuse the data for a given number of diffusion steps. In other words, sample from q(x_t | x_0). :param x_start: the initi

Diffuse the data for a given number of diffusion steps. In other words, sample from q(x_t | x_0). :param x_start: the initial

Restore the parameters stored with the `store` method. Useful to validate the model with EMA parameters without affecting the

This function disables printing when not in master process

shift pixel for super-resolution with different scale factors Args: x: WxHxC or WxH sf: scale factor upper_left: shift dir