Data instance which defines the basic attributes. Args: impath (str): image path. label (int): class label. domain (int):

A simple neural network composed of a CNN backbone and optionally a head such as mlp for classification.

A registry providing name -> object mapping, to support custom modules. To create a registry (e.g. a backbone registry): .. code-block::

Subclass torch's LayerNorm to handle fp16.

Subclass torch's LayerNorm to handle fp16.

Subclass torch's LayerNorm to handle fp16.

Compute and store the average and current value. Examples:: >>> # 1. Initialize a meter to record loss >>> losses = AverageMeter(

Fully convolutional network.

Gradient reversal layer. It acts as an identity layer in the forward, but reverses the sign of the gradient in the backward.

Normalize data using per-channel mean and standard deviation. Reference: - Ulyanov et al. Instance normalization: The missing in- gredien

Mobile Inverted Residual Bottleneck Block Args: block_args (namedtuple): BlockArgs, see above global_params (namedtuple): Gl

Store the average and current value for a set of metrics. Examples:: >>> # 1. Create an instance of MetricMeter >>> metric = Metr

Attention from `"Dynamic Domain Generalization" <https://github.com/MetaVisionLab/DDG>`_.

Randomly choose one of the best 25 Sub-policies on CIFAR10. Example: >>> policy = CIFAR10Policy() >>> transformed = policy(image)

Conv2dDynamic from `"Dynamic Domain Generalization" <https://github.com/MetaVisionLab/DDG>`_.

Randomly mask out one or more patches from an image. https://github.com/uoguelph-mlrg/Cutout Args: n_holes (int, optional): number o

Add gaussian noise.

Randomly choose one of the best 24 Sub-policies on ImageNet. Example: >>> policy = ImageNetPolicy() >>> transformed = policy(imag

Localization network.

Write console output to external text file. Imported from `<https://github.com/Cysu/open-reid/blob/master/reid/utils/logging.py>`_ Args:

A ResNet class that is similar to torchvision's but contains the following changes: - There are now 3 "stem" convolutions as opposed to 1, wi

A ResNet class that is similar to torchvision's but contains the following changes: - There are now 3 "stem" convolutions as opposed to 1, wi

A ResNet class that is similar to torchvision's but contains the following changes: - There are now 3 "stem" convolutions as opposed to 1, wi

r"""Allows the model to jointly attend to information from different representation subspaces. See `Attention Is All You Need <https://arxiv.o

r"""Allows the model to jointly attend to information from different representation subspaces. See `Attention Is All You Need <https://arxiv.o

Given an image of (height, width), we resize it to (height*1.125, width*1.125), and then perform random cropping. Args: height (int):

Randomly samples N classes each with K instances to form a minibatch of size N*K. Modified from https://github.com/KaiyangZhou/deep-person-re

Randomly samples N domains each with K images to form a minibatch of size N*K. Args: data_source (list): list of Datums. batc

Randomly choose one of the best 25 Sub-policies on SVHN. Example: >>> policy = SVHNPolicy() >>> transformed = policy(image)

Sequential domain sampler, which randomly samples K images from each domain to form a minibatch. Args: data_source (list): list of Da