See :func:`BaseInstanceMasks.pad`.

Extract a multi-scale single image tensor from a multi-scale batch tensor based on batch index. Note: The default value of detach is True, be

Build roi extractor.

Evaluate mAP of a dataset. Args: det_results (list[list]): [[cls1_det, cls2_det, ...], ...]. The outer list indicates images,

Filter results using score threshold and topk candidates. Args: scores (Tensor): The scores, shape (num_bboxes, K). score_thr (fl

Return the class containing the corresponding attributes according to the fpn_test_step_names.

Log image size. Args: img_scale (tuple): Image size to be logged. shape_order (str, optional): The order of image shape.

Show RPN proposals on the image. Args: data (str or np.ndarray): Image filename or loaded image. result (Tensor or tu

Test function without test time augmentation. Args: imgs (list[torch.Tensor]): List of multiple images img_metas (lis

Generate sparse anchors according to the ``prior_idxs``. Args: prior_idxs (Tensor): The index of corresponding anchors

Transpose and gather feature according to index. Args: feat (Tensor): Target feature map. ind (Tensor): Target coord index.

Replace ResNet50 with ResNet18 in config.

Build backbone.

Builder of IoU calculator.

Deprecated sample_per_gpu in cfg.data.

Apply transformation `pred_bboxes` to `boxes`. Args: bboxes (torch.Tensor): Basic boxes. Shape (B, N, 4) or (N, 4) pr

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

Format the results to json (standard format for COCO evaluation). Args: results (list[tuple | numpy.ndarray]): Testing results of

Get COCO annotation by index. Args: idx (int): Index of data. Returns: dict: Annotation info of specified in

Initialize a detector from config file. Args: config (str, :obj:`Path`, or :obj:`mmcv.Config`): Config file path, :obj:`Path`

Load anns with the specified ids. self.anns is a list of annotation lists instead of a list of annotations. Args:

Merge augmented mask prediction. Args: aug_masks (list[ndarray]): shape (n, #class, h, w) img_shapes (list[ndarray]): shape (3, )

Regress the bbox for the predicted class. Used in Cascade R-CNN. Args: rois (Tensor): Rois from `rpn_head` or last stage

Set user-defined new property.

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

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

Calculate overlap between two set of bboxes. FP16 Contributed by https://github.com/open-mmlab/mmdetection/pull/4889 Note: Assume bbo

Build PyTorch DataLoader. In distributed training, each GPU/process has a dataloader. In non-distributed training, there is only one dataload

Builder of IoU calculator.

Build neck.

Check if norm layer is in correct train state.

Apply transformation `pred_bboxes` to `boxes`. Args: bboxes (torch.Tensor): Basic boxes.Shape (B, N, 4) or (N, 4) pre

Find the latest checkpoint from the working directory. Args: path(str): The path to find checkpoints. suffix(str): File extension

Get custom activation of cls_score. Args: cls_score (torch.Tensor): The prediction with shape (N, C + 2). Returns:

make plugins for block. Args: in_channels (int): Input channels of plugin. plugins (list[dict]): List of plugins cfg

Returns: list: Contains all keys in meta_info_fields.

Refine bboxes during training. Args: rois (Tensor): Shape (n*bs, 5), where n is image number per GPU, and bs is t

see :func:`BaseInstanceMasks.resize`

Setup multi-processing environment variables.

Simple forward test function.

See :func:`BaseInstanceMasks.to_tensor`.

Box head forward function used in both training and testing.

Box head forward function used in both training and testing.

Create a superset of inputs needed to run test or train batches. Args: input_shape (tuple): input batch dimensions n

Create sample results that can be passed to BBoxHead.get_targets.

Mask head forward function used in both training and testing.

Create a dummy onnx::NonMaxSuppression op while exporting to ONNX. This function helps exporting to onnx with batch and multiclass NMS op. It

See :func:`BaseInstanceMasks.crop`.

Encode bitmap mask to RLE code. Args: mask_results (list | tuple[list]): bitmap mask results. In mask scoring rcnn, mask_resu

Directly extract features from the backbone+neck.

Extract features from multiple images. Args: imgs (list[torch.Tensor]): A list of images. The images are augmente

Generate the coordinate. Args: featmap_sizes (tuple): The feature to be calculated, of shape (N, C, W, H). device (st

Get category ids of concatenated dataset by index. Args: idx (int): Index of data. Returns: list[int]: All c

Get COCO category ids by index. Args: idx (int): Index of data. Returns: list[int]: All categories in the im

Get user-defined property.

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

Interpolate the `source` to the shape of the `target`. The `source` must be a Tensor, but the `target` can be a Tensor or a np.ndarray with t

Make divisible function. This function rounds the channel number to the nearest value that can be divisible by the divisor. It is taken from

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

nn.Module: normalization layer after the third convolution layer

Parse the package dependencies listed in a requirements file but strips specific versioning information. Args: fname (str): path to r

Prepare results dict for pipeline.

Makes a function retry itself after encountering pytorch's CUDA OOM error. The implementation logic is referred to https://gi

Update a dict to `data_fields`. Args: data (dict): A dict contains annotations of image or model predictions. Def

Convert objects of various python types to :obj:`torch.Tensor`. Supported types are: :class:`numpy.ndarray`, :class:`torch.Tensor`, :class:`S

Box head forward function used in both training and testing.

Check data in fields from two results are same.

A context which ignores CUDA OOM exception from pytorch. Code is modified from <https://github.com/facebookresearch/detectron2/blob/main/dete

Mask head forward function used in both training and testing.

Get the flag indicate whether anchor centers are inside regions.

Map bboxes from testing scale to original image scale.

Build linear layer. Args: cfg (None or dict): The linear layer config, which should contain: - type (str): Layer type.

Calculate region of the box defined by the ratio, the ratio is from the center of the box to every edge.

Calculate a proportional bbox region. The bbox center are fixed and the new h' and w' is h * ratio and w * ratio. Args: bbox (Tensor

Clip boxes dynamically for onnx. Since torch.clamp cannot have dynamic `min` and `max`, we scale the boxes by 1/max_shape and clamp in the

Coerces input into a random number generator. If the input is None, then a global random state is returned. If the input is a numeric value,