Resnet Global Module for Initialization

Implement DeepLab-V3 model A: stride8 B: stride16 with skip connections

ResNext optimized for the ImageNet dataset, as specified in https://arxiv.org/pdf/1611.05431.pdf

Sequeeze Excitation Module

Computes and stores the average and current value

Cross Entroply NLL Loss

Sampler that restricts data loading to a subset of the dataset. It is especially useful in conjunction with :class:`torch.nn.parallel.Distrib

Global average pooling over the input's spatial dimensions

Identity Residual Block for WideResnet

Take a random crop from the image. First the image or crop size may need to be adjusted if the incoming image is too small... If th

Main Squeeze Excitation Network Module

This class implements Stochastic Gradient Descent with Warm Restarts(SGDR): https://arxiv.org/abs/1608.03983. Set the learning rate of each p

Xception optimized for the ImageNet dataset, as specified in https://arxiv.org/pdf/1610.02357.pdf

Xception optimized for the ImageNet dataset, as specified in https://arxiv.org/pdf/1610.02357.pdf

operations performed: 1x1 x depth 3x3 x depth dilation 6 3x3 x depth dilation 12 3x3 x depth dilation 18 image pool

Set the learning rate of each parameter group using a cosine annealing schedule, where :math:`\eta_{max}` is set to the initial lr and :math:`

Implementation of one layer of GraphSAGE

Image Weighted Cross Entropy Loss