`Fashion-MNIST <https://github.com/zalandoresearch/fashion-mnist>`_ Dataset. Args: root (string): Root directory of dataset where ``Fashi

Track a series of values and provide access to smoothed values over a window or the global series average.

In timm it is implemented as self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) B, N, C = x.shape qkv = self.qkv(x).reshape(B, N, 3, self.

Transformer Block

Transformer Block

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

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

Multi-Headed Dot Product Attention

Multi-Headed Dot Product Attention

FeedForward Neural Networks for each position

FeedForward Neural Networks for each position

Adds (optionally learned) positional embeddings to the inputs.

Adds (optionally learned) positional embeddings to the inputs.

Transformer with Self-Attentive Blocks

Transformer with Self-Attentive Blocks

In timm it is implemented as self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) B, N, C = x.shape qkv = self.qkv(x).reshape(B, N, 3, self.

In timm it is implemented as self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) B, N, C = x.shape qkv = self.qkv(x).reshape(B, N, 3, self.

In timm it is implemented as self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) B, N, C = x.shape qkv = self.qkv(x).reshape(B, N, 3, self.

Computes and stores the average and current value

`Fashion-MNIST <https://github.com/zalandoresearch/fashion-mnist>`_ Dataset. Args: root (string): Root directory of dataset where ``Fashi

Applies low-rank adaptation to a vision transformer. Args: vit_model: a vision transformer model, see base_vit.py r: rank of LoRA

Applies low-rank adaptation to a vision transformer. Args: vit_model: a vision transformer model, see base_vit.py r: rank of LoRA

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

Args: name (str): Model name, e.g. 'B_16' pretrained (bool): Load pretrained weights in_channels (int): Number of channel

Args: name (str): Model name, e.g. 'B_16' pretrained (bool): Load pretrained weights in_channels (int): Number of channel