Flatten [N, C, H, W] shape tensor to [N, L, C] shape tensor. Args: x (Tensor): The input tensor of shape [N, C, H, W] before conversion.

nn.Module: normalization layer after the first convolution layer

Randomly negate value based on random_negative_prob.

See :func:`BaseInstanceMasks.resize`.

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`,

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

Test with single GPU by progressive mode. Args: model (nn.Module): Model to be tested. data_loader (utils.data.Dataloader): Pytor

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

Convert the model into training mode will keeping the normalization layer freezed.

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

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].

Forward function for feature maps before classifying each pixel with ``self.cls_seg`` fc. Args: inputs (list[Tensor]): Li

Freeze stages param and norm stats.

Freeze stages param and norm stats.

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

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

Sample from fine grained features. Args: prev_output (list[Tensor]): Prediction of previous decode head. points (Tens

Get model evaluation results.

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

Sample from fine grained features. Args: x (list[Tensor]): Feature pyramid from by neck or backbone. points (Tensor):

Get list of structured dict for logging bounding boxes to W&B. Args: bboxes (list): List of bounding box coordinates in

Get list of structured dict for logging masks to W&B. Args: masks (list): List of masks. labels (int): List of label

Initialize the W&B Tables for model evaluation.

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:

Log model checkpoint as W&B Artifact. Args: model_path (str): Path of the checkpoint to log. aliases (list): List of

Log the W&B Tables for model evaluation. The table will be logged multiple times creating new version. Use this to compare models at

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

Calculate global coordinate of mosaic image and local coordinate of cropped sub-image. Args: loc (str): Index for the sub

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

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

Display or save image with groung truths and predictions from a model. Args: dataset (Dataset): A PyTorch dataset.

Convert instance segmentation results to COCO json style.

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 DistributedDataParallel module by device type. If device is cuda, return a MMDistributedDataParallel model; if device is mlu, return a

Build DistributedDataParallel module by device type. If device is cuda, return a MMDistributedDataParallel module; if device is mlu, return a

build DataParallel module by device type. if device is cuda, return a MMDataParallel model; if device is mlu, return a MLUDataParallel model.

build DataParallel module by device type. if device is cuda, return a MMDataParallel module; if device is mlu, return a MLUDataParallel modul

Build head.

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

Build optimizer, set weight decay of normalization to 0 by default.

Compute reweight_factor for regression and classification loss.