Load model from file. Args: path (str): file path

Create a 1D, 2D, or 3D convolution module.

Create a linear module.

Make a standard normalization layer. :param channels: number of input channels. :return: an nn.Module for normalization.

Init. Args: scale_factor (float): scaling mode (str): interpolation mode

Imagenet Superresolution Dataloader Performs following ops in order: 1. crops a crop of size s from image either as random o

Guidance for training (drop) or inference, please define the null input for the grounding tokenizer

batch should be the output from dataset. Please define here how to process the batch and prepare the input only for the grou

Zero out the parameters of a module and return it.

Projector MLP Args: in_features (int): input channels out_features (int): output channels mlp_dim (int, o

Inference interface for the model with padding augmentation Padding augmentation fixes the boundary artifacts in the output depth map

1x1 convolution

3x3 convolution with padding

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