Placeholder for encode images with backbone and decode into a semantic segmentation map of the same size as input.

Encode bitmap mask to RLE code. Args: mask_results (list | tuple[list]): bitmap mask results. In mask scoring rcnn, mask_resu

Generates all extensions of a given prefix token in the Trie. Example: ```python >>> trie = Trie() >>> trie

Extra repr.

Extra repr.

Placeholder for extract features from images.

Forward function. Args: inputs (tuple): Input data. kwargs (dict): Args for ``mmcv.parallel.scatter_g

Calls either :func:`forward_train` or :func:`forward_test` depending on whether ``return_loss`` is ``True``. Note this setting will c

Forward function.

Forward function.

Forward function.

Forward function.

Forward function.

Forward function for `SinePositionalEncoding`. Args: mask (Tensor): ByteTensor mask. Non-zero values representing

Forward function for `LearnedPositionalEncoding`. Args: mask (Tensor): ByteTensor mask. Non-zero values representing

Args: x (Tensor): Has shape (B, H*W, C_in). input_size (tuple[int]): The spatial shape of x, arrange as (H, W).

Forward function for `TransformerCoder`. Returns: Tensor: forwarded results with shape [num_query, bs, embed_dims].

Forward function for `TransformerDecoder`. Args: query (Tensor): Input query with shape `(num_query, bs, embed_di

Forward function for `Transformer`. Args: x (Tensor): Input query with shape [bs, c, h, w] where c = embed_dims.

Forward function for `TransformerDecoder`. Args: query (Tensor): Input query with shape `(num_query, bs, embed_di

Forward function for `Transformer`. Args: mlvl_feats (list(Tensor)): Input queries from different level. Each ele

Forward function for `DynamicConv`. Args: param_feature (Tensor): The feature can be used to generate the paramet

Args: x (Tensor): Has shape (B, C, H, W). In most case, C is 3. Returns: tuple: Contains merged results and

Args: x (Tensor): Has shape (B, H*W, C_in). input_size (tuple[int]): The spatial shape of x, arrange as (H, W).

Forward function.

Forward function.

Forward function.

Forward function.

Forward function.

Forward function.

Forward function.

Forward function.

Forward function.

Forward function. Args: feats (list[Tensor]): Multi scale Features from the upstream network, each is a 4D-tensor

Forward function. Args: feats (list[Tensor]): Features from the upstream network, each is a 4D-tensor.

Forward function. Args: feats (list[Tensor]): Multi scale Features from the upstream network, each is a 4D-tensor

Forward function.

Forward function.

Forward function.

Forward function.

Args: feats (list[Tensor]): Feature maps of each level. Each has shape of (batch_size, c, h, w). Returns

Add padding to `x` Args: x (Tensor): Input tensor has shape (B, C, H, W). Returns: Tensor: The tensor with a

Args: x (Tensor): Has shape (B, C, H, W). In most case, C is 3. Returns: tuple: Contains merged results and

Args: x (Tensor): Has shape (B, H*W, C_in). input_size (tuple[int]): The spatial shape of x, arrange as (H, W).

Forward function for `MultiheadAttention`. **kwargs allow passing a more general data flow when combining with other operations in `t

Forward function for `FFN`. The function would add x to the output tensor if residue is None.

Args: feats (list[Tensor]): Feature maps of each level. Each has shape of (batch_size, c, h, w). Returns

Args: feats (list[Tensor]): Feature maps of each level. Each has shape of [bs, c, h, w]. img_metas (l

Args: feats (list[Tensor]): Feature maps of each level. Each has shape of [bs, c, h, w]. img_metas (l

Forward function. Args: pred (torch.Tensor): The prediction, has a shape (n, *). target (torch.Tensor): The label of

Forward function to calculate accuracy. Args: pred (torch.Tensor): Prediction of models. target (torch.Tensor): Targe

Forward function.

Forward function. Args: pred (torch.Tensor): The prediction. target (torch.Tensor): The learning label of the predict