r"""Get an item from registry with key 'name' Args: name (string): Key whose value needs to be retrieved. default: If

Assume the first argument is the name of the argument.

Print information about the VQA annotation file. :return:

Load model from file. Args: path (str): file path

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

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

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

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

Convert a version string into a tuple of integers. This method is usually used for comparing two versions. For pre-release versions: alpha <

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

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 the

Build a pretrained model from default configuration file, specified by model_type. Args: - model_type (str): model type,

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

Returns the state of the scaler as a :class:`dict`.

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

Classify each pixel with fc.

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

Utility for deleting a directory. Useful for cleaning the storage space that contains various training artifacts like checkpoints, data etc.

Build a module from config dict. Args: cfg (dict): Config dict. It should at least contain the key "type". registry (:obj:`Regist

nn.Module: the normalization layer named "norm1"

nn.Module: the normalization layer named "norm2"

Print a log message. Args: msg (str): The message to be logged. logger (logging.Logger | str | None): The logger to be used.

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

Snapshot models

Register a hook into the hook list. The hook will be inserted into a priority queue, with the specified priority (See :class:`Priorit

Load checkpoint from a file or URI. Args: model (Module): Module to load checkpoint. filename (str): Accept local filepath, URL,

Multiple with alpha and add beat with clip.

Whether the input is an string instance. Note: This method is deprecated since python 2 is no longer supported.

B x [T, ...] torch tensors

Convert a ``filepath`` to standard format of petrel oss. If the ``filepath`` is concatenated by ``os.path.join``, in a Windows enviro

Map ``filepath`` to a string path whose prefix will be replaced by :attr:`self.path_mapping`. Args: filepath (str): Path

Get the current training iteration step.

Recursively convert Tensor in inputs from src_type to dst_type. Args: inputs: Inputs that to be casted. src_type (torch.dtype): S

Download a file from a URL and cache it locally. If the file already exists, it is not downloaded again. If distributed, only the main proces

Infer a suitable file client based on the URI and arguments. Args: file_client_args (dict, optional): Arguments to instantiate a

Check if a module is a module wrapper. The following 3 modules in MMCV (and their subclasses) are regarded as module wrappers: DataParallel,

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

Add prefix for dict. Args: inputs (dict): The input dict with str keys. prefix (str): The prefix to add. Returns: d

Get the root logger. The logger will be initialized if it has not been initialized. By default a StreamHandler will be added. If `log_file` i

Loads parameters (but not training state)

Common i/o utility to handle loading data from various file formats. Supported: .pkl, .pickle, .npy, .json For the npy files, we

Return a list of dicts containing instr_id:'xx', path:[(viewpointId, heading_rad, elevation_rad)]

Update the `_params_init_info` in the module if the value of parameters are changed. Args: module (obj:`nn.Module`): The module of Py

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

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

nn.Module: normalization layer after the third convolution layer