Register default and custom hooks for training. Default and custom hooks include: +----------------------+-------------------------+

Convert roi based relative point coordinates to image based absolute point coordinates. Args: rois (Tensor): RoIs or BBoxes, shape (N

Write the segmentation results to images. Args: results (list[list | tuple | ndarray]): Testing results of the da

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

Saturation distortion.

Visualize the segmentation results on the image. Args: model (nn.Module): The loaded segmentor. img (str or np.ndarray): Image fi

Placeholder for single image test.

Total time since the timer is started. Returns (float): Time in seconds.

Inference by sliding-window with overlap. If h_crop > h_img or w_crop > w_img, the small patch will be used to decode without padding

Split scope and key. The first scope will be split from key. Examples: >>> Registry.split_scope_key('mmdet.ResNet')

Convert tensor to 3-channel images. Args: tensor (torch.Tensor): Tensor that contains multiple images, shape ( N, C, H, W).

Calculate Total Intersection and Union. Args: results (list[ndarray] | list[str]): List of prediction segmentation maps or li

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

Copy a model state_dict to cpu. Args: state_dict (OrderedDict): Model weights on GPU. Returns: OrderedDict: Model weights on

Copy a model state_dict to cpu. Args: state_dict (OrderedDict): Model weights on GPU. Returns: OrderedDict: Model weights on

Inference with full image.

Args: x: (B, H, W, C) window_size (int): window size Returns: windows: (num_windows*B, window_size, window_size, C)

Args: windows: (num_windows*B, window_size, window_size, C) window_size (int): Window size H (int): Height of image

Write pfm file. Args: path (str): pathto file image (array): data scale (int, optional): Scale. Defaults to 1.

Call function to load images into results. Args: results (dict): A result dict contains the file name of the imag

Call function to apply test time augment transforms on results. Args: results (dict): Result dict contains the data to transform.

Call function to resize images, bounding boxes, masks, semantic segmentation map. Args: results (dict): Result dict from

Call function to flip bounding boxes, masks, semantic segmentation maps. Args: results (dict): Result dict from loading p

Call function to pad images, masks, semantic segmentation maps. Args: results (dict): Result dict from loading pipeline.

Call function to normalize images. Args: results (dict): Result dict from loading pipeline. Returns: dict: N

Call function to rerange images. Args: results (dict): Result dict from loading pipeline. Returns: dict: Rera

Call function to Use CLAHE method process images. Args: results (dict): Result dict from loading pipeline. Returns:

Call function to randomly crop images, semantic segmentation maps. Args: results (dict): Result dict from loading pipeline.

Call function to rotate image, semantic segmentation maps. Args: results (dict): Result dict from loading pipeline. Retu

Call function to convert RGB image to grayscale image. Args: results (dict): Result dict from loading pipeline. Returns:

Call function to process the image with gamma correction. Args: results (dict): Result dict from loading pipeline. Retur

Call function to scale the semantic segmentation map. Args: results (dict): Result dict from loading pipeline. Returns:

Call function to perform photometric distortion on images. Args: results (dict): Result dict from loading pipeline. Retu

Call functions to load image and get image meta information. Args: results (dict): Result dict from :obj:`mmseg.CustomDataset`.

Call function to load multiple types annotations. Args: results (dict): Result dict from :obj:`mmseg.CustomDataset`. Ret

Call function to apply transforms sequentially. Args: data (dict): A result dict contains the data to transform. Returns

Call function to convert data in results to :obj:`torch.Tensor`. Args: results (dict): Result dict contains the data to convert.

Call function to convert image in results to :obj:`torch.Tensor` and transpose the channel order. Args: results (dict): R

Call function to convert image in results to :obj:`torch.Tensor` and transpose the channel order. Args: results (dict): R

Call function to convert data in results to :obj:`mmcv.DataContainer`. Args: results (dict): Result dict contains the dat

Call function to transform and format common fields in results. Args: results (dict): Result dict contains the data to convert.

Call function to collect keys in results. The keys in ``meta_keys`` will be converted to :obj:mmcv.DataContainer. Args: r

Get training/test data after pipeline. Args: idx (int): Index of data. Returns: dict: Training/test data (wi

Get item from original dataset.