Convert model output tensor to index-list. Args: output (tensor): The model outputs with size: N * T * C. img_metas (l

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

Generate P_hat and inv_delta_C for later.

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

Calculate recall, precision, f1-score. Args: origin (int): Original entities in groundtruth. found (int): Predicted entities from

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.

Transform outputs for a batch item into bbox predictions. Args: cls_score_list (list[Tensor]): Box scores for a single scale leve

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.

Get next index from dataset.

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.

Extract local maximum pixel with given kernel. Args: heat (Tensor): Target heatmap. kernel (int): Kernel size of max

Mask refining process with point head in testing.

Generate mesh grid of x and y. Args: x (torch.Tensor): Grids of x dimension. y (torch.Tensor): Grids of y dimension.

Parse the raw outputs (losses) of the network. Args: losses (dict): Raw outputs 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

Get top k positions from heatmap. Args: scores (Tensor): Target heatmap with shape [batch, num_classes, height, w

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 offest 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):

Check whether the training dataset is compatible with head. Args: runner (obj:`EpochBasedRunner`): Epoch based Runner.

Check whether the dataset in val epoch is compatible with head. Args: runner (obj:`EpochBasedRunner`): Epoch based Runner.

Convert Bitmasks to tensor. Args: bitmasks (list[BitmapMasks]): The BitmapMasks list. Each item is for one img.

Convert Bitmasks to tensor. Args: bitmasks (list[BitmapMasks]): The BitmapMasks list. Each item is for one img.

Build encoder for scene text recognizer.

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

Build preprocessor for scene text recognizer.

Builder for Transformer.

credit - https://github.com/Yonghongwei/Gradient-Centralization

Collect the information of the running environments.

Collect the information of the running environments.

Convert various input in BGR order to normalized RGB matplotlib color tuples, Args: color (:obj:`Color`/str/tuple/int/ndarray): Color

Run code concurrently in different streams. :param streamqueue: asyncio.Queue instance. Queue tasks define the pool of streams used for conc

3x3 convolution with padding

Gets entities from preds. Args: preds (list): Sequence of preds. masks (tensor): The valid part is 1 and the invalid

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

see :func:`BaseInstanceMasks.crop_and_resize`

Decoding predictions of DbNet to instances. This is partially adapted from https://github.com/MhLiao/DB. Args: preds (Tensor): The he

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

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

Calculate the Euclidean distance matrix. Args: A (ndarray): The point sequence. B (ndarray): The point sequence with the same dim

Evaluate the text recognition performance with metric: word accuracy and 1-N.E.D. See https://rrc.cvc.uab.es/?ch=14&com=tasks for details. Ar

Evaluation in COCO protocol. Args: results (list[list | tuple]): Testing results of the dataset. metric (str | list[s

Judge whether to perform_evaluation after this epoch. Returns: bool: The flag indicating whether to perform evaluation.

Decoding predictions of FCENet to instances. Args: preds (list(Tensor)): The head output tensors. fourier_degree (int): The maxim

Find out detected results whose score > score_thr. Args: results (ndarray): The results matrix of shape (n, k). score (ndarray):

Find invalid polygons. Args: results (dict): The dict containing gt_mask. Returns: ignore_tags (list[bool]):

Placeholder to format result to dataset-specific output.

Transform Fourier coefficient maps to polygon maps. Args: real_maps (tensor): A map composed of the real parts of the

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