see :func:`BaseInstanceMasks.rescale`

The forward function that also takes the RFP features as input.

Scale RoI coordinates by scale factor. Args: rois (torch.Tensor): RoI (Region of Interest), shape (n, 5) scale_factor

Sanitizes the input coordinates so that x1 < x2, x1 != x2, x1 >= 0, and x2 <= image_size. Also converts from relative to absolute coor

Semantic segmengation postprocess. Args: mask_cls (Tensor): Classfication outputs of shape (num_queries, cls_out_

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

Test det bboxes without test-time augmentation. Args: feats (tuple[torch.Tensor]): Multi-level features from the

Simple test for mask head without augmentation.

Obtain mask prediction without augmentation.

Smooth L1 loss. Args: pred (torch.Tensor): The prediction. target (torch.Tensor): The learning target of the prediction.

SSD Head Config.

convert polygon masks to bitmap masks.

See :func:`BaseInstanceMasks.to_tensor`.

Check if detected bboxes are true positive or false positive. Args: det_bbox (ndarray): Detected bboxes of this image, of shape (m, 5).

Check if detected bboxes are true positive or false positive. Args: det_bbox (ndarray): Detected bboxes of this image, of shape (m, 5).

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

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

YoloV3 Head Config.

L2 normalization layer. Args: n_dims (int): Number of dimensions to be normalized scale (float, optional): Defaults t

Bottleneck block for ResNeXt. If style is "pytorch", the stride-two layer is the 3x3 conv layer, if it is "caffe", the stride-two lay

CrossEntropyLoss. Args: use_sigmoid (bool, optional): Whether the prediction uses sigmoid of softmax. Defaults to

str: the string of the module

Set attribute, i.e. self.name = value This reloading prevent the teacher model from being registered as a nn.Module. The teacher modu

Add the fc branch which consists of a sequential of conv layers.

Add default meta keys. We set default meta keys including `pad_shape`, `scale_factor` and `img_norm_cfg` to avoid the case where no `

Add the fc branch which consists of a sequential of fc layers.

Adjust the brightness of image.

Apply Color transformation to image.

Adjust the image contrast.

Run forward function and calculate loss for box head in training.

Run forward function and calculate loss for box head in training.

Run forward function and calculate loss for box head in training.

Run forward function and calculate loss for box head in training.

Run forward function and calculate loss for box head in training.

Run forward function and calculate loss for box head in training.

bbox post-processing method. The boxes would be rescaled to the original image scale and do the nms operation. Usually `with_nms` is

bbox post-processing method. Do the nms operation for bboxes in same level. Args: mlvl_scores (list[Tensor]): Box scores

Args: cls_pred (Tensor): The prediction with shape (num_query, 1, *) or (num_query, *). gt_labels (Te

Initialize predictor layers of a single level head.

Compute the value of gamma according to paper. gamma is the parameter of bernoulli distribution, which controls the number of features