Map bboxes from the original image scale to testing scale.

Get the initial target model parameters given the few-shot labels

Get root logger. Args: log_file (str, optional): File path of log. Defaults to None. log_level (int, optional): The level of logg

Convert a mask tensor to one or more bounding boxes. Note: This function is a bit new, make sure it does what it says. /Andreas :param mask:

args: search_area_factor - The size of the search region relative to the target size. output_sz - The size (width, h

Grab configs necessary to create a detector. These are deep copied to allow for safe modification of parameters without influencing other tes

Initialize conv sequential for CornerHead.

Load indexed image with given filename. Used to read segmentation annotations.

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

Random select some elements from the gallery. If `gallery` is a Tensor, the returned indices will be a Tensor; If `gallery` is a ndar

Extract transformed image samples. args: im: Image. pos: Center position for extraction. scale: Image scale to extract fea

Convert masks to the format of ndarray.

Box forward function used in both training and testing.

Mask head forward function used in both training and testing.

Samples num_ids frames between min_id and max_id for which target is visible args: visible - 1d Tensor indicating whether target

Assign gt to bboxes. This method assign a gt bbox to every bbox (proposal/anchor), each bbox will be assigned with -1, or a semi-posi

Create complex tensor from real and imaginary part.

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

Display a 2D tensor. args: fig_num: Figure number. title: Title of figure.

Apply the target model to obtain the mask encodings

Builder of box coder.

Build roi extractor.

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

Get a single or set of datasets.

All inputs in original image coordinates. Generates a box in the cropped image sample reference frame, in the format used by the IoUNet.

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

Inner product of the output space.

nn.Module: the normalization layer named "norm2"

Sample an image patch. args: im: Image pos: center position of crop sample_sz: size to crop output_sz: size to re

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

Apply element-wise weight and reduce loss. Args: loss (Tensor): Element-wise loss. weight (Tensor): Element-wise weights.

Build a module. Args: cfg (dict, list[dict]): The config of modules, is is either a dict or a list of configs. regist

Convert Mask to ndarray.. Args: mask (:obj:`BitmapMasks` or :obj:`PolygonMasks` or torch.Tensor or np.ndarray): The mask to be co

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

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

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

Replace the ImageToTensor transform in a data pipeline to DefaultFormatBundle, which is normally useful in batch inference. Args: pip

Extract image patches at multiple scales. args: im: Image. pos: Center position for extraction. scales: Image scales to ex

Samples num_ids frames between min_id and max_id for which target is visible args: visible - 1d Tensor indicating whether target

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

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

Extract classification features based on the input backbone features.

Args: imgs (List[Tensor]): the outer list indicates test-time augmentations and inner Tensor should have a shape

Get IoU prediction features from a 4 or 5 dimensional backbone input.

Get modulation vectors for the targets. args: feat: Backbone features from reference images. Dims (batch, feature_dim, H, W).

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

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

Pointwise complex multiplication of real tensor a with complex tensor b.

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