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

Regress the bbox for the predicted class. Used in Cascade R-CNN. Args: rois (Tensor): shape (n, 4) or (n, 5) label (T

See :func:`BaseInstanceMasks.to_tensor`.

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

Parse char boxes annotations. Args: annotations (list[dict]): Annotations of one image, where each dict is for one

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

Check whether the anchors are inside the border. Args: flat_anchors (torch.Tensor): Flatten anchors, shape (n, 4). valid_flags (t

Convert bbox coordinates from (cx, cy, w, h) to (x1, y1, x2, y2). Args: bbox (Tensor): Shape (n, 4) for bboxes. Returns: Ten

Jitter on the coordinates of bounding box. Args: points_x (list[float | int]): List of y for four vertices. points_y (list[float

Build backbone.

Crop text region with their bounding box. Args: src_img (np.array): The original image. box (list[float | int]): Points of quadra

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

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

Use `get_logger` method in mmcv to get the root logger. The logger will be initialized if it has not been initialized. By default a StreamHan

Convert indexes to text strings. Args: indexes (list[list[int]]): [[1,2,3,3,4], [5,4,6,3,7]]. Returns: string

Draw bboxes and class labels (with scores) on an image. Args: img (str or ndarray): The image to be displayed. bboxes (ndarray):

Judge the input polygon is invalid or not. It is invalid if its area smaller than 1 or the shorter side of its minimum bounding box smaller

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

nn.Module: normalization layer after the third convolution layer

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`

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

Test without augmentation. Args: x (tuple[Tensor]): Features from the upstream network, each is a 4D-tensor.

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.

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

Parse annotations of boxes, texts and labels for one image. Args: annotations (list[dict]): Annotations of one image, where

Calculate overlap between two set of bboxes. If ``is_aligned `` is ``False``, then calculate the overlaps between each bbox of bboxes1 and bb

Build label convertor for scene text recognizer.

Builder of IoU calculator.

Check if the image has orientation information. If yes, ignore it by converting the image format to png, and return new filename, otherwise re

Convert segmentation into text boundaries. Args: results (tuple): The result tuple. The first element is segmentati

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

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

Test model with multiple gpus. This method tests model with multiple gpus and collects the results under two different modes: gpu and cpu mod

Output the worst results for debugging. Args: img_results (list[dict]): Image result list. img_infos (list[dict]): Image informat

Box head forward function used in both training and testing.

Box head forward function used in both training and testing.

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 decoder for scene text recognizer.

Build neck.

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

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,

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

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

Embed features. This code was partially adapted from https://github.com/GXYM/DRRG licensed under the MIT license. Args: input_feats (

see :func:`BaseInstanceMasks.flip`

Generate text center region mask and geometry attribute maps. Args: img_size (tuple): The image size (height, width).

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

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

Get class names of a dataset.

Concat multi-level feature maps by image. [feature_level0, feature_level1...] -> [feature_image0, feature_image1...] Convert the shape of eac

Make plugins for ResNet ``stage_idx`` th stage. Currently we support to insert ``context_block``, ``empirical_attention_block``, ``no

nn.Module: the normalization layer named "norm2"

Number of output classes.

Converting the points set into bounding box. :param pts: the input points sets (fields), each points set (fields) is represented

Convert a text mask represented by point coordinates sequence into a text boundary. Args: points (ndarray): Mask index of size (n, 2)

Prepare an example input image for ``generate_inputs_and_wrap_model``. Args: input_config (dict): customized config describing the exampl

Rescale boundaries via scale_factor. Args: boundaries (list[list[float]]): The boundary list. Each boundary with size