Initialization following [Magneto](http://arxiv.org/abs/2210.06423)

Main Routine. Construct and optionally push a RADIO model to Hugging Face. Usage: python3 -m hf_hub --model <model-name> --checkpoint-p

Main Routine. Pull a model from HuggingFace and make sure its output features match those of the corresponding TorchHub model. Usage:

Computes the RankMe (http://arxiv.org/abs/2210.02885) and LiDAR (http://arxiv.org/abs/2312.04000) estimates of the rank of the produced embed

Computes the RankMe (http://arxiv.org/abs/2210.02885) and LiDAR (http://arxiv.org/abs/2312.04000) estimates of the rank of the produced embed

Computes the RankMe (http://arxiv.org/abs/2210.02885) and LiDAR (http://arxiv.org/abs/2312.04000) estimates of the rank of the produced embed

Computes the PCA features for every frame in a supplied video and renders them into a new video.

Process images with radio1d_size animation. First pass: Extract PCA stats with max radio1d_size. Second pass: Generate frames for all rad

Taken from https://towardsdatascience.com/augmentation-methods-using-albumentations-and-pytorch-35cd135382f8

Straight-through estimator for the rounding operation.

Sample token counts for each sample in a batch. Uses torch.multinomial for sampling, ensuring reproducibility with torch.manual_seed(). Args

Slice variable numbers of 1D tokens per sample. Args: x: Input tensor of shape (B, N, C) where N = num_prefix + num_spatial num_t

Training routine.

Intercept call.

ViT-Huge model (ViT-H/16) from original paper (https://arxiv.org/abs/2010.11929).

Args: img_size (int, tuple): input image size patch_size (int, tuple): patch size in_chans (int): number

Global Resolution Attention Block , see README for details Attention with subsampling to get a bigger receptive field for attention

Args: input_resolution: input image resolution window_size: window size compression_ratio: compression ra

Args: in_features: input features dimension. hidden_features: hidden features dimension. out_features: ou

Args: dim: feature size dimension. shuffle: idea with keep_dim: bool argument for maintaining the resolut

Args: in_chans: number of input channels. in_dim: intermediate feature size dimension to speed up stem. d

Args: dim: feature size dimension. depth: number of layers in each stage. input_resolution: input image r

Hi Resolution neck to support output of high res features that are useful for dense tasks. depths - total number of layers in the bas

Args: dim: feature size dimension. depths: number of layers in each stage. window_size: window size in ea

Args: dim: Number of input channels. out_dim: Number of output channels (or 2 * dim if None) norm_layer:

Args: dim: Number of input channels. out_dim: Number of output channels (or dim // 2 if None) norm_layer:

Args: input_embed_dim: Embedding dimension of input (after encoder downscaling). target_embed_dim: Target embedding d

Args: patch_size: Patch size. in_chans: Number of image input channels. embed_dim: Transformer embedding