nn.Module: normalization layer after the first convolution layer

Randomly negate value based on random_negative_prob.

Downsample the first feat and upsample last feat in feats.

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

Add meta information. Args: meta_info (dict): A dict contains the meta information of image. such as `img_shape`,

Setup multi-processing environment variables.

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

Generate grid anchors of a single level. Note: This function is usually called by method ``self.grid_priors``. Args:

str: a "nice" summary string describing this module

Assign gt to priors using SimOTA. Args: pred_scores (Tensor): Classification scores of one image, a 2D-Tensor with

Initialize conv layers of a single level head.

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

Fuse global context feats with roi feats.

Get adaptive scales according to areas. The scale range is [0.5, 1.0]. When the area is less than ``'min_area'``, the scale is 0.5 while the

Transform outputs of a single image into bbox predictions. Args: cls_score_list (list[Tensor]): Box scores from all scale

Transform outputs for a single batch item into bbox predictions. Args: tl_heat (Tensor): Top-left corner heatmap for current leve

Sample fine grained feats from each level feature map and concatenate them together. Args: x (tuple[Tensor]): Feature map

Interpolate the `source` (4D) to the shape of the `target`.

Private function to load bounding box annotations. Args: results (dict): Result dict from :obj:`mmdet.CustomDataset`. Re

Private function to load label annotations. Args: results (dict): Result dict from :obj:`mmdet.CustomDataset`. Returns:

Mask refining process with point head in testing. Args: x (tuple[Tensor]): Feature maps of all scale level. rois (Ten

Parse the raw outputs (losses) of the network. Args: losses (dict): Raw output of the network, which usually contain

Randomly sample an img_scale according to ``ratio_range`` and ``multiscale_mode``. If ``ratio_range`` is specified, a ratio will be s

Convert instance segmentation results to COCO json style.

Set flag according to image aspect ratio. Images with aspect ratio greater than 1 will be set as group 1, otherwise group 0.

Swap the parameter of model with parameter in ema_buffer.

Handle empty batch dimension to adaptive_avg_pool2d. Args: input (tensor): 4D tensor. output_size (int, tuple[int,int]): the targ

Add ground truth as assigned results. Args: gt_labels (torch.Tensor): Labels of gt boxes

Import a module from albumentations. It inherits some of :func:`build_from_cfg` logic. Args: cfg (dict): Config dict. It

Get offset for deformable conv based on anchor shape NOTE: currently support deformable kernel_size=3 and dilation=1 Args:

Attach a text label above each bar in *rects*, displaying its height.

Flip bboxes horizontally or vertically. Args: bboxes (Tensor): Shape (..., 4*k) img_shape (tuple): Image shape. direction

Split batch bbox prediction back to each image.

Rescale bounding box w.r.t. scale_factor. Args: bboxes (Tensor): Shape (n, 4) for bboxes or (n, 5) for rois scale_factor (float):

Build a model from config and load the given checkpoint. Args: config_path (str): the OpenMMLab config for the model we want to

Collect the information of the running environments.

See :func:`BaseInstanceMasks.crop`.

Crop predicted masks by zeroing out everything not in the predicted bbox. Args: masks (Tensor): shape [H, W, N].

Decode distance prediction to bounding box. Args: points (Tensor): Shape (B, N, 2) or (N, 2). distance (Tensor): Distance from th

Draw labels on the axes. Args: ax (matplotlib.Axes): The input axes. labels (ndarray): The labels with the shape of (n, ).

Directly extract features from the backbone and neck.

flip tensor base on flip_direction. Args: src_tensor (Tensor): input feature map, shape (B, C, H, W). flip_direction (str): The f

Placeholder of forward function. Returns: dict[str, Tensor]: A dictionary, including features and predicted score

Forward features from the upstream network. Args: feats (tuple[Tensor]): Features from the upstream network, each is

Args: x (Tensor): Has shape (B, C, H, W). In most case, C is 3. Returns: tuple: Contains merged results and

Forward for head part which is called after every decoder layer. Args: decoder_out (Tensor): in shape (num_queries, batch_size, c

r"""Generate 2D gaussian radius. This function is modified from the `official github repo <https://github.com/princeton-vl/CornerNet-Lite/blo

Generate base anchors. Returns: list(torch.Tensor): Base anchors of a feature grid in multiple \ feature levels.

Base on the previous bboxes and regression values, we compute the regressed bboxes and generate the grids on the bboxes. :param reg:

Generate base anchors of a single level. Args: base_size (int | float): Basic size of an anchor. scales (torch.Tensor

Get squares according to feature map sizes and guided anchors. Args: featmap_sizes (list[tuple]): Multi-level feature map sizes.

Get squares according to feature map sizes and guided anchors. Args: featmap_sizes (list[tuple]): Multi-level feature map sizes.

Get annotation of concatenated dataset by index. Args: idx (int): Index of data. Returns: dict: Annotation i

Get OpenImages annotation by index. Args: idx (int): Index of data. Returns: dict: Annotation info of specif

get_bboxes() is implemented in StageCascadeRPNHead.

Get class names of a dataset.

Get class names of current dataset. Args: classes (Sequence[str] | str | None): If classes is None, use default C

Get classes name from file. Args: label_file (str): File path of the label description file that maps the classes

Get custom classification channels. Args: num_classes (int): The number of classes. Returns: int: The custom

Get image metas from pipeline.

Get points according to feature map sizes. Args: featmap_sizes (list[tuple]): Multi-level feature map sizes. img_meta

Get hierarchy for classes. Args: hierarchy_file (sty): File path to the hierarchy for classes. Returns: ndar

Get ``num_points`` most uncertain points during test. Args: mask_pred (Tensor): A tensor of shape (num_rois, num_classes,

Get sampled approxs and inside flags according to feature map sizes. Args: featmap_sizes (list[tuple]): Multi-level feature map s

Compute corresponding GT box and classification targets for proposals. Args: proposals_list (list[list]): Multi level poi

Compute semantic segmentation targets for each image. Args: segm_pred (Tensor): Predicted semantic segmentation map

Get ``num_points`` most uncertain points with random points during train. Sample points in [0, 1] x [0, 1] coordinate space based on their

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

Get a tensor of bboxes centered at (0, 0). Returns: Tensor: Tensor of bboxes with shape (num_bboxes, 4) in [xmin, ymi

Inference image(s) with the detector. Args: model (nn.Module): The loaded detector. imgs (str/ndarray or list[str/ndarray] or tup