Dict as attribute trick.

A single linear layer which does a 1x1 conv

r"""The network that extracts features to compute the perceptual loss. Args: network (nn.Sequential) : The network that extracts features

r"""Perceptual loss initialization. Args: network (str) : The name of the loss network: 'vgg16' | 'vgg19'. layers (str or list of

Meter is to keep track of statistics along steps. Meters write values for purpose like printing average values. Meters can be flushed to log f

Define the output layer

A named tuple describing a ResNet block.

r"""Configuration class. This should include every human specifiable hyperparameter values for your training.

Conv Up Layer. Bilinear upsample + Conv. Args: in_channels (int): Channel number of the input. out_channels (int): Channel number

(convolution => [BN] => ReLU) * 2

Define an Residual block for different types

Borrowed from https://github.com/facebookresearch/pytorch3d Notice: x,y,z are in image space, normalized can only render squared

Alg: https://www.scratchapixel.com/lessons/3d-basic-rendering/rasterization-practical-implementation Notice: x,y,z are in image space, no

docstring for ADAINDecoder

Computes and stores the average and current value

takes 2 distances, puts through fc layers, spits out value between [0,1] (if use_sigmoid is True)

borrowed from https://github.com/soubhiksanyal/FLAME_PyTorch/blob/master/FLAME.py Given flame parameters this class generates a differentiabl

FLAME texture: https://github.com/TimoBolkart/TF_FLAME/blob/ade0ab152300ec5f0e8555d6765411555c5ed43d/sample_texture.py#L64 FLAME texture

docstring for FineDecoder

docstring for Encoder

Downsampling block for use in encoder.

r"""Flatten the tensor

This HTML class allows us to save images and write texts into a single HTML file. It consists of functions such as <add_header> (add a text head

r"""Creates a criterion that measures Learned Perceptual Image Patch Similarity (LPIPS). Arguments: net_type (str): the network type t