Sample points for testing. Find ``num_points`` most uncertain points from ``uncertainty_map``. Args: seg_logits (Tensor)

Sample points for training. Sample points in [0, 1] x [0, 1] coordinate space based on their uncertainty. The uncertainties are calcu

Get the position from a period list. It will return the index of the right-closest number in the period list. For example, the cumulative_per

Get priority value. Args: priority (int or str or :obj:`Priority`): Priority. Returns: int: The priority value.

Get the type and mode of points sampler. Args: sampler_type (str): The type of points sampler. The valid value are "D-FPS", "

Get spatial resolution of input feature map considering exporting to onnx mode. Args: x (torch.Tensor): Input tensor, shape (N, C, H,

Returns a dictionary containing a whole state of the module. Both parameters and persistent buffers (e.g. running averages) are included. Key

Returns a dictionary containing a whole state of the module. Both parameters and persistent buffers (e.g. running averages) are included. Key

Get the total training step/epoch.

Convert a grayscale image to BGR image. Args: img (ndarray): The input image. Returns: ndarray: The converted BGR image.

Hue distortion.

Inplace normalize an image with mean and std. Args: img (ndarray): Image to be normalized. mean (ndarray): The mean to be used fo

Pad the given image to a certain shape or pad on all sides with specified padding mode and padding value. Args: img (ndarray): Image

Import modules from the given list of strings. Args: imports (list | str | None): The given module names to be imported. allow_fa

Infer abbreviation from the class name. When we build a norm layer with `build_norm_layer()`, we want to preserve the norm type in variable n

Infer abbreviation from the class name. This method will infer the abbreviation to map class types to abbreviations. Rule 1: If the clas

Infer the scope of registry. The name of the package where registry is defined will be returned. Example: # in mmdet/mod

Inference image(s) with the segmentor. Args: model (nn.Module): The loaded segmentor. imgs (str/ndarray or list[str/ndarray]): Ei

Initialize a segmentor from config file. Args: config (str or :obj:`mmcv.Config`): Config file path or the config object.

Initialize the weights in backbone and heads. Args: pretrained (str, optional): Path to pre-trained weights. Defa

Initialize weight of later layer.

Default initialization for Parameters of Module.

Initialize a module. Args: module (``torch.nn.Module``): the module will be initialized. init_cfg (dict | list[dict]): initializa

Calculate intersection and Union. Args: pred_label (ndarray | str): Prediction segmentation map or predict result filename.

Tell the input variable is a scalar or not. Args: val: Input variable. include_np (bool): Whether include 0-d np.ndar

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

Load annotation from directory. Args: img_dir (str): Path to image directory img_suffix (str): Suffix of images.

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

load checkpoint through URL scheme path. Args: filename (str): checkpoint file name with given prefix map_location (s

In distributed setting, this function only download checkpoint at local rank 0.

In distributed setting, this function only download checkpoint at local rank 0.

Load state_dict to a module. This method is modified from :meth:`torch.nn.Module.load_state_dict`. Default value for ``strict`` is set to ``F

Compute segmentation loss.

Compute segmentation loss.

Compute ``pam_cam``, ``pam``, ``cam`` loss.

Build a color wheel. Args: bins(list or tuple, optional): Specify the number of bins for each color range, corresponding to s

Stack InvertedResidual blocks to build a layer for MobileNetV2. Args: out_channels (int): out_channels of block. num_

Pack all blocks in a stage into a ``ResLayer``.

make plugins for ResNet 'stage_idx'th stage . Currently we support to insert 'context_block', 'empirical_attention_block', 'nonlocal_

CPU version of multi-scale deformable attention. Args: value (Tensor): The value has shape (bs, num_keys, mum_heads, embed_di

nn.Module: the normalization layer named "norm0"

nn.Module: the normalization layer named "norm1"

nn.Module: normalization layer after the first convolution layer

nn.Module: the normalization layer named "norm1"

nn.Module: normalization layer after the second convolution layer

nn.Module: normalization layer after the second convolution layer

Normalize input grid from [-1, 1] to [0, 1] Args: grid (Tensor): The grid to be normalize, range [-1, 1]. Returns: Tensor: Nor

PAM feature classification.

Parse the prefix of a uri. Args: uri (str | Path): Uri to be parsed that contains the file prefix. Examples:

Patch the forward method of a module. Args: func (callable): The original forward method. src_type (torch.dtype): Type of input a

Recursively convert normalization layers from FP16 to FP32. Args: module (nn.Module): The modules to be converted in FP16. Returns:

Get testing data after pipeline. Args: idx (int): Index of data. Returns: dict: Testing data after pipeline

Get training data and annotations after pipeline. Args: idx (int): Index of data. Returns: dict: Training da

Print a model with FLOPs for each layer. Args: model (nn.Module): The model to be printed. total_flops (float): Total FLOPs of th

Forward function of PSP module.

Save data to a given ``filepath``. Args: obj (bytes): Data to be saved. filepath (str or Path): Path to write data.

Quantize an array of (-inf, inf) to [0, levels-1]. Args: arr (ndarray): Input array. min_val (scalar): Minimum value to be clippe

Quantize flow to [0, 255]. After this step, the size of flow will be much smaller, and can be dumped as jpeg images. Args: flow

Randomly select an img_scale from given candidates. Args: img_scales (list[tuple]): Images scales for selection. Returns

Reduce loss as specified. Args: loss (Tensor): Elementwise loss tensor. reduction (str): Options are "none", "mean" and "sum".

Reduce mean when distributed training.

Register a hook from its cfg. Args: hook_cfg (dict): Hook config. It should have at least keys 'type' and 'priority