Initialize the weights in detector. Args: pretrained (str, optional): Path to pre-trained weights. Defaults to No

Calculates the intersection-over-union of two bounding boxes. Args: bbox1 (numpy.array, list of floats): bounding box in

Inner product of the input space.

Check if is one of the norms.

Importance-based Sample Reweighting (ISR_P), positive part. Args: cls_score (Tensor): Predicted classification scores. bbox_pred

Map from level to values based on max_value.

nn.Module: normalization layer after the first convolution layer

Overlay mask over image. Source: https://github.com/albertomontesg/davis-interactive/blob/master/davisinteractive/utils/visualization.py This

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

Randomly negate value based on random_negative_prob.

See :func:`BaseInstanceMasks.rescale`.

RetinanNet Head Config.

Sample positive and negative bboxes. This is a simple implementation of bbox sampling given candidates, assigning results and ground

Expand an array of boxes by a given scale. Args: bboxes (Tensor): Shape (m, 4) scale (float): The scale factor of bboxes Ret

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

Test only det bboxes without augmentation. Args: x (tuple[Tensor]): Feature maps of all scale level. img_metas (list[

Simple test for mask head without augmentation.

Run the model in onnxruntime env. Args: feat (list[Tensor]): A list of tensors from torch.rand, each is a 4D-tensor. Ret

args: net - The network model to train objective - Loss functions loss_weight - Weights for each training

str: a "nice" summary string describing this module

Box head forward function used in both training and testing.

Check whether the `num_classes` in head matches the length of `CLASSSES` in `dataset`. Args: runner (obj:`EpochBasedRunne

Crop image with a given center and size, then paste the cropped image to a blank image with two centers align. This function is equiv

Check whether the center of each box is in the patch. Args: patch (list[int]): The cropped area, [left, top, right, bottom].

Fuse global context feats with roi feats.

Generates the gaussian label density centered at target_bb args: target_bb - target bounding box (num_images, 4) returns

Generates the gaussian label function centered at target_bb args: target_bb - target bounding box (num_images, 4) return

Generates the gaussian label function centered at target_bb args: target_bb - target bounding box (num_images, 4) return

Generates the gaussian label function centered at target_bb args: target_bb - target bounding box (num_images, 4) return

Taken from DiMP processing Generates the gaussian label function centered at target_bb args: target_bb - target bounding

Return an activation function given a string

Transform outputs for a batch item into bbox predictions. Args: mlvl_cls_scores (list[Tensor]): Each element in the list is

Transform outputs for a single batch item into bbox predictions. Args: cls_scores (list[Tensor]): Box iou-aware scores for a sing

Get final border for the target size. This function generates a ``final_border`` according to image's shape. The area between ``final

Find the predefined detector config directory.

Load a configuration as a python module.

Sample fine grained feats from each level feature map and concatenate them together.

Estimate uncertainty based on pred logits. We estimate uncertainty as L1 distance between 0.0 and the logits prediction in 'mask_pred

Initialize rule, key_indicator, comparison_func, and best score. Args: rule (str | None): Comparison rule for best score.