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

Saturation distortion.

Set random seed. Args: seed (int): Seed to be used. deterministic (bool): Whether to set the deterministic option for

Placeholder for single image test.

Generate the valid flags of points of a single feature map. Args: featmap_size (tuple[int]): The size of feature maps, arrange as

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

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

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

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

Deserialize tensor to picklable python object.

Wrap models with separate MMDistributedDataParallel. It only wraps the modules with parameters.

Returns the tokenized representation of given input string(s) Parameters ---------- texts : Union[str, List[str]] An input st

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

Launch segmentor training.

Inference with full image.

Inference with full image.

Inference with full image.

Inference with full image.

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

Args: cls_pred (Tensor): Predicted classification logits, shape [num_query, num_class]. gt_labels (Te

Args: cls_pred (Tensor): Predicted classfication logits in shape (N1, H, W), dtype=torch.float32. gt_

Args: cls_pred (Tensor): Predicted classification logits, shape [num_query, num_class]. gt_labels (Te

Args: mask_preds (Tensor): Mask prediction logits in shape (N1, H, W). gt_masks (Tensor): Ground truth in shape (N2,

Args: cls_pred (Tensor): Predicted classification logits, shape [num_query, num_class]. gt_labels (Te

Args: cls_pred (Tensor): Predicted classfication logits in shape (N1, H, W), dtype=torch.float32. gt_

Args: cls_pred (Tensor): Predicted classification logits, shape [num_query, num_class]. gt_labels (Te

Args: mask_preds (Tensor): Mask prediction logits in shape (N1, H, W). gt_masks (Tensor): Ground truth in shape (N2,

Args: cls_pred (Tensor): Predicted classification logits. gt_labels (Tensor): Labels. Returns: Te

Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text` and `kwargs` arguments

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

Get item from original dataset.