Recursively delete a directory tree. Args: dir_path (str or Path): A directory to be removed. Examples: >>>

Apply function to a list of arguments. Note: This function applies the ``func`` to multiple inputs and map the multiple outputs o

close an opened object.

Detach all tensors in data.

Build a Config instance from config file. Args: filename (str or Path): Name of config file. use_predefined_variables

pop property in data and metainfo as the same as python.

Build activation layer. Args: cfg (dict): The activation layer config, which should contain: - type (str): Layer type.

Reload ``new_ones`` from self.tensor.

Decode the predicted bboxes according to prediction and base boxes.

Yield the values of the dictionary. If class attribute ``lazy`` is False, the value of ``LazyObject`` or ``LazyAttr`` will be built a

Modified from pytorch3d and add `model_type` in __class__.__init__.

r"""Concatenate all file paths. Join one or more filepath components intelligently. The return value is the concatenation of filepath

Remove a file. Args: filepath (str, Path): Path to be removed.

Convert keypoints following the mapping correspondence between src and dst keypoints definition. Supported conventions by now: agora, coco, smplx,

Build normalization layer. Args: cfg (dict): The norm layer config, which should contain: - type (str): Layer type.

Overrides this method to set the :attr:`device` Returns: nn.Module: The model itself.

Convert all tensor to np.narray in data.

Get the value of the key. If class attribute ``lazy`` is True, the LazyObject will be built and returned. Args: key (str)

Build convolution layer. Args: cfg (None or dict): The conv layer config, which should contain: - type (str): Layer type.

Build normalization layer. Args: cfg (dict): The norm layer config, which should contain: - type (str): Layer type.

Convert targets by image to targets by feature level. [target_img0, target_img1] -> [target_level0, target_level1, ...]

Reload ``unbind`` from self.tensor.

Whether the buffer is empty or not.

Register a module. A record will be added to ``self._module_dict``, whose key is the class name or the specified name, and value is t

Concatenate two human_data. All keys will be kept it the returned human_data. If either value from human_data_0 or human_data_1 matches

Check whether a file path is a directory. Args: filepath (str or Path): Path to be checked whether it is a direct

Return a file backend based on the prefix of uri or backend_args. Args: uri (str or Path): Uri to be parsed that contains the file prefix

Convert values in numpy.ndarray type to torch.Tensor, and move Tensors to the target device. All keys will exist in the returned dict.

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

Update a new data sample.

Call all hooks. Args: fn_name (str): The function name in each hook to be called, such as "before_train_epoch".

Reload ``device`` from self.tensor.

Downsample mesh.

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

Build loss.

Reload ``new_empty`` from self.tensor.

Performs a dilation searching step. Args: model (nn.Module): pytorch model work_dir (str): Directory to save the sear

Check whether a file path is a directory. Args: filepath (str or Path): Path to be checked whether it is a direct

Special method for set union field, used as property.setter functions.

Unmap a subset of item (data) back to the original set of items (of size count)

Create new camera class which contains each input camera N times. Args: N: number of new copies of each camera. Returns:

Convert rotation matrixs to axis angles. Args: matrix (Union[torch.Tensor, numpy.ndarray]): input shape should be (..., 3

Set or change key-value pairs in ``metainfo_field`` by parameter ``metainfo``. Args: metainfo (dict): A dict contains the

A wrapper of ``Optimizer.zero_grad``.

dict: a dictionary of info about the object

Read the next frame. If the next frame have been decoded before and in the cache, then return it directly, otherwise decode, cache an

Obtain the mean of tensor on different GPUs.

Convert torch tensor to numpy.ndarray. Args: x (Tensor | Sequence[Tensor]): A single tensor or a sequence of tensors

Override this method to make sure the LazyObject will not be built during updating.

Write image data. Args: grid (Optional[List], optional): Grid to write. Defaults to None. data (Optional[np.ndarray],

Dump config to file or return config text. Args: file (str or Path, optional): If not specified, then the object is d

Check whether a file path is a file. Args: filepath (str or Path): Path to be checked whether it is a file. Returns:

Convert a list of bboxes to roi format. Args: bbox_list (List[Union[Tensor, :obj:`BaseBoxes`]): a list of bboxes correspondin

Loads the loss_scaler state dict. Args: state_dict (dict): scaler state.

Sample positive and negative bboxes. This is a simple implementation of bbox sampling given candidates, assigning results and ground

Set or change key-value pairs in ``data_field`` by parameter ``data``. Args: data (dict): A dict contains annotations of

Merge list into cfg_dict. Merge the dict parsed by MultipleKVAction into this cfg. Args: options (dict): dict of configs

Build a module from config dict when it is a class configuration, or call a function from config dict when it is a function configuration. Ex

Dump data to json/yaml/pickle strings or files. This method provides a unified api for dumping data as strings or to files, and also supports

int: A number to set random modules.

Dumps config into a file or returns a string representation of the config. If a file argument is given, saves the config to that file

Dump keys and items to an npz file. Args: npz_path (str): Path to a dumped npz file. overwrite (bool,

Convert 6D rotation representation to 3x3 rotation matrix. Based on Zhou et al., "On the Continuity of Rotation Representations in Neural Net

Transform inputs for decoder. Args: inputs (list[Tensor]): List of multi-level img features. Returns: Tensor

Check whether a file path is a file. Args: filepath (str or Path): Path to be checked whether it is a file. Returns:

r"""Concatenate all file paths. Join one or more filepath components intelligently. The return value is the concatenation of filepath and any