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

Call function to load multi-view image from files. Args: results (dict): Result dict containing multi-view image filenames.

Call function to normalize images. Args: results (dict): Result dict from loading pipeline. Returns: dict: Nor

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

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

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

Args: lines_pred (Tensor): predicted normalized lines: [num_query, num_points, 2] gt_lines (Tensor):

Args: lines_pred (Tensor): predicted normalized lines: [num_query, num_points, 2] gt_lines (Tensor):

Args: bbox_pred (Tensor): Predicted boxes with normalized coordinates (cx, cy, w, h), which are all in range [0,

Args: bboxes (Tensor): Predicted boxes with unnormalized coordinates (x1, y1, x2, y2). Shape [num_query, 4].

Args: lines_pred (Tensor): predicted normalized lines: [nP, num_points, 2] lines_gt (Tensor): Ground

Args: bbox_pred: nproposal, 4*2, pos_dim bbox_gt: ngt, 4*2 Returns: cost: nproposal, ngt

Call function to normalize images. Args: results (dict): Result dict from loading pipeline. Returns: dict: Nor

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

Args: bbox_pred (Tensor): Predicted boxes with normalized coordinates (cx, cy, w, h), which are all in range [0,

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

Call function to normalize images. Args: results (dict): Result dict from loading pipeline. Returns: dict: Nor

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

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

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

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

Args: index (int): Index Returns: tuple: (sample, target) where target is class_index of the target class.

Args: index (int): Index Returns: tuple: (image, target) where target is class_index of the target class.

Get item from infos according to the given index. Returns: dict: Data dictionary of the corresponding index.

Get item from infos according to the given index. Returns: dict: Data dictionary of the corresponding index.

Get item from infos according to the given index. Returns: dict: Data dictionary of the corresponding index.

DCNv3 Module :param channels :param kernel_size :param stride :param pad :param dilation :par

Wraps a Pytorch module to get intermediate values Arguments: model {nn.module} -- The Pytorch module to call return_l

DCNv3 Module :param channels :param kernel_size :param stride :param pad :param dilation :par