r"""`Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression <https://arxiv.org/abs/1902.09630>`_. Args:

A general separation scheme for gmm model. It separates a GMM distribution of candidate samples into three parts, 0 1 and uncertain a

General visualization GT and result function. Args: img (str or ndarray): The image to be displayed.) annotation (dict): Ground truth

Initialize assigner and sampler.

Initialize ``bbox_head``

Initialize layers of the DeformableDetrTransformer.

Initialize ``mask_head``

Initialize ``point_head``

Load the pretrained model in teacher detector. Args: pretrained (str, optional): Path to pre-trained weights. Defa

IoU loss. Computing the IoU loss between a set of predicted bboxes and target bboxes. The loss is calculated as negative log of IoU. Arg

r"""Loss function for knowledge distilling using KL divergence. Args: pred (Tensor): Predicted logits with shape (N, n + 1). soft

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

Compute deltas of proposals w.r.t. gt in the MMDet V1.x manner. We usually compute the deltas of x, y, w, h of proposals w.r.t ground truth b

Apply deltas to shift/scale base boxes in the MMDet V1.x manner. Typically the rois are anchor or proposed bounding boxes and the deltas are

Load annotation from annotation file.

Load annotation from COCO style annotation file. Args: ann_file (str): Path of annotation file. Returns: lis

Load proposal from proposal file.

Compute losses of the head.

loss() is implemented in StageCascadeRPNHead.

Loss function. Only outputs from the last feature level are used for computing losses by default. Args: all_cls_

Compute loss of a single scale level. Args: anchors (Tensor): Box reference for each scale level with shape (N, n

Benchmark between async and synchronous inference interfaces. Sample runs for 20 demo images on K80 GPU, model - mask_rcnn_r50_fpn_1x: async

In Darknet backbone, ConvLayer is usually followed by ResBlock. This function will make that. The Conv layers always have 3x3 filters with

Stack InvertedResidual blocks to build a layer for MobileNetV2. Args: out_channels (int): out_channels of block. num_

Pack all blocks in a stage into a ``ResLayer`` for DetectoRS.

Build Trident Res Layers.

Map rois to corresponding feature levels by scales. - scale < finest_scale * 2: level 0 - finest_scale * 2 <= scale < finest_scale *

Export mask branch to onnx which supports batch inference. Args: x (tuple[Tensor]): Feature maps of all scale level.

Compute mask target for positive proposals in multiple images. Args: pos_proposals_list (list[Tensor]): Positive proposals in multiple

Merge augmented detection bboxes and scores. Args: aug_bboxes (list[Tensor]): shape (n, 4*#class) aug_scores (list[Te

Merge augmented proposals (multiscale, flip, etc.) Args: aug_proposals (list[Tensor]): proposals from different testing schem

Merge augmented detection bboxes and score. Args: aug_results (list[list[Tensor]]): Det_bboxes and det_labels of each

Merge augmented detection bboxes and score. Args: aug_results (list[list[Tensor]]): Det_bboxes and det_labels of each

Merge bbox predictions of each branch.

Converts MMDetection model (config + checkpoint) to TorchServe `.mar`. Args: config_file: In MMDetection config format.

Convert keys in pycls pretrained moco models to mmdet style.

Warpper of mse loss.

Compute negative bag loss. :math:`FL((1 - P_{a_{j} \in A_{+}}) * (1 - P_{j}^{bg}))`. :math:`P_{a_{j} \in A_{+}}`: Box_probability of

nn.Module: the normalization layer named "norm0"

nn.Module: the normalization layer named "norm1"

nn.Module: normalization layer after the first convolution layer

nn.Module: the normalization layer named "norm1"

nn.Module: normalization layer after the first convolution layer

nn.Module: normalization layer after the second convolution layer

nn.Module: normalization layer after the second convolution layer

Transform network output for a batch into bbox predictions. Args: rois (Tensor): Boxes to be transformed. Has sha