Args: mask_preds (Tensor): Mask prediction in shape (N1, H, W). gt_masks (Tensor): Ground truth in shape (N2, H, W)

Builder of box sampler.

Calculate dice loss, which is proposed in `V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation <https://arxiv

Generate proposals from encoded memory. Args: memory (Tensor) : The output of encoder, has shape (bs, num_key, em

Get the position embedding of proposal.

Get the reference points used in decoder. Args: spatial_shapes (Tensor): The shape of all feature maps, has shape

Returns a dictionary containing a whole state of the module. Both parameters and persistent buffers (e.g. running averages) are included. Key

Returns a dictionary containing a whole state of the module. Both parameters and persistent buffers (e.g. running averages) are included. Key

Compute classification and mask targets for all images for a decoder layer. Args: cls_scores_list (list[Tensor]): Mask sc

Compute classification and mask targets for all images for a decoder layer. Args: cls_scores_list (list[Tensor]): Mask sc

Get ``num_points`` most uncertain points with random points during train. Sample points in [0, 1] x [0, 1] coordinate space based on their

Estimate uncertainty based on pred logits. We estimate uncertainty as L1 distance between 0.0 and the logits prediction in 'mask_pred' for th

Get the valid radios of feature maps of all level.

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

Initialize layers of the DeformableDetrTransformer.

Initialize the weights in backbone. Args: pretrained (str, optional): Path to pre-trained weights. Defaults to No

In distributed setting, this function only download checkpoint at local rank 0.

In distributed setting, this function only download checkpoint at local rank 0.

In distributed setting, this function only download checkpoint at local rank 0.

In distributed setting, this function only download checkpoint at local rank 0.

Load state_dict to a module. This method is modified from :meth:`torch.nn.Module.load_state_dict`. Default value for ``strict`` is set to ``F

Load state_dict to a module. This method is modified from :meth:`torch.nn.Module.load_state_dict`. Default value for ``strict`` is set to ``F

Loss function. Args: all_cls_scores (Tensor): Classification scores for all decoder layers with shape [num_decode

Loss function. Args: all_cls_scores (Tensor): Classification scores for all decoder layers with shape [num_decode

Calculate naive dice loss, the coefficient in the denominator is the first power instead of the second power. Args: pred (torch.Tenso

Serialize picklable python object to tensor.

Randomly sample an img_scale when ``ratio_range`` is specified. A ratio will be randomly sampled from the range specified by ``ratio_

Randomly select an img_scale from given candidates. Args: img_scales (list[tuple]): Images scales for selection. Returns

Generate the valid flags of points of a single feature map. Args: featmap_size (tuple[int]): The size of feature maps, arrange as

Inference by sliding-window with overlap. If h_crop > h_img or w_crop > w_img, the small patch will be used to decode without padding

Inference by sliding-window with overlap. If h_crop > h_img or w_crop > w_img, the small patch will be used to decode without padding

Deserialize tensor to picklable python object.

Copy a model state_dict to cpu. Args: state_dict (OrderedDict): Model weights on GPU. Returns: OrderedDict: Model weights on

Copy a model state_dict to cpu. Args: state_dict (OrderedDict): Model weights on GPU. Returns: OrderedDict: Model weights on

Copy a model state_dict to cpu. Args: state_dict (OrderedDict): Model weights on GPU. Returns: OrderedDict: Model weights on

Copy a model state_dict to cpu. Args: state_dict (OrderedDict): Model weights on GPU. Returns: OrderedDict: Model weights on

Inference with full image.

Inference with full image.

Args: x: (B, H, W, C) window_size (int): window size Returns: windows: (num_windows*B, window_size, window_size, C)

Args: x: (B, H, W, C) window_size (int): window size Returns: windows: (num_windows*B, window_size, window_size, C)

Args: x: (B, H, W, C) window_size (int): window size Returns: windows: (num_windows*B, window_size, window_size, C)

Args: windows: (num_windows*B, window_size, window_size, C) window_size (int): Window size H (int): Height of image

Args: windows: (num_windows*B, window_size, window_size, C) window_size (int): Window size H (int): Height of image

Args: windows: (num_windows*B, window_size, window_size, C) window_size (int): Window size H (int): Height of image

Call function to load images into results. Args: results (dict): A result dict contains the file name of the image

Call function to resize images, bounding boxes, masks, semantic segmentation map. Args: results (dict): Result dict from

Call function to pad images, masks, semantic segmentation maps. Args: results (dict): Result dict from loading pipeline.

Call function to process the image with gamma correction. Args: results (dict): Result dict from loading pipeline. Retur

Call function to transform and format common fields in results. Args: results (dict): Result dict contains the data to convert.