Sample pixels that have high loss or with low prediction confidence. Args: seg_logit (torch.Tensor): segmentation logits, shape (

Scatter with support for kwargs dictionary.

Take action (same interface as makeActions)

Convert objects of various python types to :obj:`torch.Tensor`. Supported types are: :class:`numpy.ndarray`, :class:`torch.Tensor`, :class:`S

r"""Fills the input Tensor with values drawn from a truncated normal distribution. The values are effectively drawn from the normal distributi

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

This function is taken from the original tf repo. It ensures that all layers have a channel number that is divisible by 8 It can be seen

This function is taken from the original tf repo. It ensures that all layers have a channel number that is divisible by 8 It can be seen

Make each stage.

Make transition layer.

Scale the data in the corresponding channel.

Calculate accuracy according to the prediction and target. Args: pred (torch.Tensor): The model prediction, shape (N, num_class, ...)

Run all_gather on arbitrary picklable data (not necessarily tensors) Args: data: any picklable object Returns: list[data]

Allreduce parameters. Args: params (list[torch.Parameters]): List of parameters or buffers of a model. coalesce (bool

Convert various input to color tuples. Args: color (:obj:`Color`/str/tuple/int/ndarray): Color inputs Returns: tuple[int]: A

Performs all_gather operation on the provided tensors. *** Warning ***: torch.distributed.all_gather has no gradient.

Convert a video with ffmpeg. This provides a general api to ffmpeg, the executed command is:: `ffmpeg -y <pre_options> -i <in_file> <opt

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

Evaluate the model on a given split. Args: split_name (str): name of the split to evaluate on. cur_epoch (in

Evaluate each agent trajectory based on how close it got to the goal location the path contains [view_id, angle, vofv]

Placeholder for Forward function for training.

Forward function for training. Args: img (Tensor): Input images. img_metas (list[dict]): List of image info dict wher

Converts all `BatchNorm2d` and `SyncBatchNorm` layers of provided module into `FrozenBatchNorm2d`. If `module` is itself an instance of eithe

Get class weight for loss function. Args: class_weight (list[float] | str | None): If class_weight is a str, take it as a fil

Makes broadcastable attention and causal masks so that future and masked tokens are ignored. Arguments: attention_mask (

Read data from a given ``filepath`` with 'r' mode. Args: filepath (str or Path): Path to read data. encoding (str): T

Resize image to a given size. Args: img (ndarray): The input image. size (tuple[int]): Target size (w, h). return_scale (

Show an image. Args: img (str or ndarray): The image to be displayed. win_name (str): The window name. wait_time (int): V

Write image to file. Args: img (ndarray): Image array to be written. file_path (str): Image file path. params (None or li

Inference with slide/whole style. Args: img (Tensor): The input image of shape (N, 3, H, W). img_meta (dict): Image i

load checkpoint through HTTP or HTTPS scheme path. In distributed setting, this function only download checkpoint at local rank 0. Args:

make plugins for block. Args: in_channels (int): Input channels of plugin. plugins (list[dict]): List of plugins cfg

Create the directory if it does not exist.

:param x: start :param y: end :return: the path from x to y [v1, v2, ..., v_n, y]

r"""Register an item to registry with key 'name' Args: name: Key with which the item will be registered. Usage::

Organizes datasets by split. Args: datasets: dict of torch.utils.data.Dataset objects by name. Returns: Dict of dataset

Warmup the learning rate

Since built-in ``any`` works only when the element of iterable is not iterable, implement the function.

perform evaluation and save ckpt.

Freeze stages param and norm stats.

Sample from fine grained features. Args: prev_output (list[Tensor]): Prediction of previous decode head. points (Tens

Sample from fine grained features. Args: x (list[Tensor]): Feature pyramid from by neck or backbone. points (Tensor):