vis_root (string): Root directory of images (e.g. coco/images/) ann_root (string): directory to store the annotation file

Add children for a registry. The ``registry`` will be added as children based on its scope. The parent registry could build objects f

Run forward function and calculate loss for auxiliary head in training.

Check branches configuration.

Build :obj:`ConcatDataset by.

Construct candidate dict.

Convert trainId to id for cityscapes.

Convert segmentation label to onehot. Args: seg_label (Tensor): Segmentation label of shape (N, H, W). num_classes (i

Run forward function and calculate loss for decode head in inference.

Run forward function and calculate loss for decode head in training.

Download annotation files if necessary. All the vision-language datasets should have annotations of unified format. storage_

Dump the initialization information to a file named `initialization.log.json` in workdir. Args: logger_name (str): The na

Evaluation in Cityscapes protocol. Args: results (list): Testing results of the dataset. logger (logging.Logger | str

Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.

Expand onehot labels to match the size of prediction.

Fuse conv and bn into one module. Args: conv (nn.Module): Conv to be fused. bn (nn.Module): BN to be fused. Returns:

Finds a loader that supports the given path. Falls back to the local loader if no other loader is found. Args: path (str)

Generate the shear matrix for transformation. Args: magnitude (int | float): The magnitude used for shear. direction (str): The f

Generate the translate matrix. Args: offset (int | float): The offset used for translate. direction (str): The translate directio

Check if params contain NaN.

Initialize ``auxiliary_head``

Initialize ``decode_head``

Initialize slurm distributed training environment. If argument ``port`` is not specified, then the master port will be system environment var

Check and initialize input transforms. The in_channels, in_index and input_transform must match. Specifically, when input_transform i

Construct language variable.

Load checkpoint from somewhere (modelzoo, file, url). Args: filename (str): Accept local filepath, URL, ``torchvision://xxx``,

Resume from a checkpoint.

Resume from a checkpoint.

Load partial pretrained model with specific prefix. Args: prefix (str): The prefix of sub-module. filename (str): Accept local fi

load graph from self.scan, Store the graph {scan_id: graph} in self.graphs Store the shortest path {scan_id: {view_id_x: {vie

Load weights from .npz checkpoints for official Google Brain Flax implementation

Extract precomputed features into variable.