NOTE: this interface is experimental. Args: in_channels: channels of the input features mask_classification:

NOTE: this interface is experimental. Args: input_shape: shapes (channels and stride) of the input features n

NOTE: this interface is experimental. Args: input_shape: shapes (channels and stride) of the input features t

Multi-Scale Deformable Attention Module :param d_model hidden dimension :param n_levels number of feature levels

Args: log_dir (str): the directory to save the output events kwargs: other arguments passed to `torch.utils.tensorboa

NOTE: this interface is experimental. Args: is_train: for training or inference augmentations: a list of augm

NOTE: this interface is experimental. Args: is_train: whether it's used in training or inference augmentatio

NOTE: this interface is experimental. Args: is_train: for training or inference augmentations: a list of augm

Args: dataset_name (str): the name of the dataset. It must have the following metadata associated with it:

Args: evaluators (list): the evaluators to combine.

Args: dataset_name (str): name of the dataset to be evaluated. It must have either the following corresponding me

Args: dataset_name (str): name of the dataset to be evaluated. It must have either the following corresponding me

Args: cfg (CfgNode): instance_mode (ColorMode): parallel (bool): whether to run the model in different pr

Args: img (ndarray): an RGB image of shape (H, W, 3) in range [0, 255]. scale (float): scale the input image

Args: img_rgb: a numpy array of shape (H, W, C), where H and W correspond to the height and width of the image re

_summary_ Yields: list(int): a batch of indics, corresponding to ConcatDataset of src_dataset_ls

Evaluate predictions. Fill self._results with the metrics of the tasks.

Uses the given `dataset_name` argument (instead of the names in cfg), because the standard practice is to evaluate each test set individually

ViT weight initialization, original timm impl (for reproducibility)

ViT weight initialization, original timm impl (for reproducibility)

Add config for BEiT Adapter Backbone.