Concat the predictions of all :obj:`InstanceData` in the list. Args: instances_list (list[:obj:`InstanceData`]): A list

dict: a dictionary of info about the object

Apply same name function to all tensors in data_fields.

Apply same name function to all tensors in data_fields.

Apply same name function to all tensors in data_fields.

See :func:`BaseInstanceMasks.expand`.

Apply function to a list of arguments. Note: This function applies the ``func`` to multiple inputs and map the multiple outputs o

Build loss.

Decode the predicted bboxes according to prediction and base boxes.

Apply same name function to all tensors in data_fields.

Convert targets by image to targets by feature level. [target_img0, target_img1] -> [target_level0, target_level1, ...]

Returns: list: Contains all keys in data_fields.

Convert a list of bboxes to roi format. Args: bbox_list (list[Tensor]): a list of bboxes corresponding to a batch of images.

Unmap a subset of item (data) back to the original set of items (of size count)

Transform inputs for decoder. Args: inputs (list[Tensor]): List of multi-level img features. Returns: Tensor

Classify each pixel with fc.

Change the device of the data inplace. Example: >>> self = SamplingResult.random() >>> print(f'self = {self.to(None)}

Build head.

Apply same name function to all tensors in data_fields.

See :func:`BaseInstanceMasks.pad`.

Obtain the mean of tensor on different GPUs.

Sample positive and negative bboxes. This is a simple implementation of bbox sampling given candidates, assigning results and ground

Assign gt to bboxes. The assignment is done in following steps 1. compute iou between all bbox (bbox of all pyramid levels) and gt

Calculate the ious between each bbox of bboxes1 and bboxes2. Args: bboxes1 (ndarray): Shape (n, 4) bboxes2 (ndarray): Shape (k, 4

Extract features. Args: img (torch.Tensor): Image tensor with shape (n, c, h ,w). Returns: list[torch.Tensor

Builder of box assigner.

Builder of box sampler.

nn.Module: the normalization layer named "norm1"

Convert detection results to a list of numpy arrays. Args: bboxes (torch.Tensor | np.ndarray): shape (n, 5) labels (torch.Tensor

See :func:`BaseInstanceMasks.flip`.

Generate grid anchors in multiple feature levels. Args: featmap_sizes (list[tuple]): List of feature map sizes in

Loss function of DIIHead, get loss of all images. Args: cls_score (Tensor): Classification prediction results of

Encode deltas between bboxes and ground truth boxes.

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

Returns: list: Contains all values in data_fields.

Calculate the ground truth for all samples in a batch according to the sampling_results. Almost the same as the implementation in bbo

nn.Module: the normalization layer named "norm2"

Convert a version string into a tuple of integers. This method is usually used for comparing two versions. For pre-release versions: alpha <

Get the root logger. The logger will be initialized if it has not been initialized. By default a StreamHandler will be added. If `log_file` i

Get segmentation masks from mask_pred and bboxes. Args: mask_pred (Tensor or ndarray): shape (n, #class, h, w). F

Return a new results with same image meta information. Args: meta_info (dict, optional): A dict contains the meta information

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

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

Mask head forward function used in both training and testing.

Map bboxes from the original image scale to testing scale.

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

NMS for multi-class bboxes. Args: multi_bboxes (Tensor): shape (n, #class*4) or (n, 4) multi_scores (Tensor): shape (n, #class),

Initialize conv sequential for CornerHead.

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

Evaluate in VOC protocol. Args: results (list[list | tuple]): Testing results of the dataset. metric (str | list[str]

Get targets for PAA head. This method is almost the same as `AnchorHead.get_targets()`. We direct return the results from _get_target

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

Box forward function used in both training and testing.

Calculate accuracy according to the prediction and target. Args: pred (torch.Tensor): The model prediction, shape (N, num_class)

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

Builder of box coder.

Build datasets.

Replace the string "${key}" with the corresponding value. Replace the "${key}" with the value of ori_cfg.key in the config. And support repla

Update data root according to env MMDET_DATASETS. If set env MMDET_DATASETS, update cfg.data_root according to MMDET_DATASETS. Otherwise, usi

Add prefix for dict. Args: inputs (dict): The input dict with str keys. prefix (str): The prefix to add. Returns: d

Build detector.

Build roi extractor.

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

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

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

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

Build backbone.

Builder of IoU calculator.

Build segmentor.

Compute losses of the head. Args: cls_scores (list[Tensor]): Cls and quality scores for each scale level has shap

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