Size of the full vocabulary with the added tokens.

str: a string that describes the module

str: a string that describes the module

Converts an index (integer) in a token (str) using the vocab.

Converts a token (str) in an id using the vocab.

perform evaluation and save ckpt.

perform evaluation and save ckpt.

Compute classification and mask targets for one image. Args: cls_score (Tensor): Mask score logits from a single decoder layer

Compute classification and mask targets for one image. Args: cls_score (Tensor): Mask score logits from a single decoder layer

Compute classification and mask targets for one image. Args: cls_score (Tensor): Mask score logits from a single decoder layer

Detects whether the optional user-specified attention_mask & the automatically created causal mask can be ignored in case PyTorch's S

Inserts one token to an ordered list if it does not already exist. Note: token_list must be sorted.

Checks whether the last character in text is one of a punctuation, control or whitespace character.

Checks whether the first character in text is one of a punctuation, control or whitespace character.

Sample negative samples.

Sample positive samples.

Returns a tokenized string. We de-activated the `add_dummy_prefix` option, thus the sentencepiece internals will always strip any

add model config path or file and update registry

Returns the added tokens in the vocabulary as a dictionary of index to AddedToken. Returns: `Dict[str, int]`: The added

Returns the sorted mapping from string to index. The added tokens encoder is cached for performance optimisation in `self._added_toke

ADE20K class names for external use.

ADE20K palette for external use.

Apply all reduce function for python dict object. The code is modified from https://github.com/Megvii- BaseDetection/YOLOX/blob/main/yolox/ut

Allreduce gradients. Args: params (list[torch.Parameters]): List of parameters of a model coalesce (bool, optional): Whether allr

Computes one-to-one matching based on the weighted costs. This method assign each query prediction to a ground truth or background. T

Test with augmentations. Only rescale=True is supported.

Test with augmentations. Only rescale=True is supported.

Test with augmentations. Only rescale=True is supported.

Test with augmentations. Only rescale=True is supported.

Test with augmentations. Only rescale=True is supported.

This method forwards all its arguments to SiglipTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please refer to the docstring of t

torch.Tensor: concatenated positive and negative boxes

Calculate the binary CrossEntropy loss. Args: pred (torch.Tensor): The prediction with shape (N, 1). label (torch.Tensor): The le

Build backbone.

Builder of box coder.

Build head.

Build neck.

Build model.

Build Transformer.

Cityscapes class names for external use.

Cityscapes palette for external use.

Converts a sequence of tokens (string) in a single string.

Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make use of token type ids

cross_entropy. The wrapper function for :func:`F.cross_entropy` Args: pred (torch.Tensor): The prediction with shape (N, 1). labe