x: (N, L, patch_size**2 *3) imgs: (N, 3, H, W)

Evaluate coco keypoint results. The pose prediction results will be saved in ``${res_folder}/result_keypoints.json``. Note:

Args: dim (int): Number of input channels. num_heads (int): Number of attention heads in each ViT block.

Args: dim (int): Number of input channels. num_heads (int): Number of attention heads in each ViT block.

Post-processing to turn output segm image to class index map Args: segm: (H, W, 3) Returns:

Post-processing to turn output segm image to class index map Args: pred_path: path to a (H, W, 3) image dis

Args: images: (bs, 256, 192, 3) resize: whether to resize to (64, 48) Returns: heatmaps: (bs, 1

Args: images: (bs, 256, 192, 3) resize: whether to resize to (64, 48) Returns: heatmaps: (bs, 1

Args: target: ndarray (17, 256, 192) target_weight: ndarray (17, 1) metas: dict Returns: an RGB image, R en

Args: target: ndarray (17, 256, 192) target_weight: ndarray (17, 1) metas: dict Returns: an RGB image, R en

embed_dim: output dimension for each position pos: a list of positions to be encoded: size (M,) out: (M, D)

Calculate absolute positional embeddings. If needed, resize embeddings and remove cls_token dimension for the original embeddings. Ar

Calculate absolute positional embeddings. If needed, resize embeddings and remove cls_token dimension for the original embeddings. Ar

Get relative positional embeddings according to the relative positions of query and key sizes. Args: q_size (int): size of qu

Get relative positional embeddings according to the relative positions of query and key sizes. Args: q_size (int): size of qu

Initialize random seed. If the seed is not set, the seed will be automatically randomized, and then broadcast to all processes to prevent som

Matrix NMS for multi-class masks. Args: masks (Tensor): Has shape (num_instances, h, w) labels (Tensor): Labels of correspond

Post-processing to turn output segm image to class index map Args: pred_path: path to a (H, W, 3) image Return

This function disables printing when not in master process

x: (N, L, patch_size**2 *3) imgs: (N, 3, H, W)

Initialisation for DatasetFromSampler.

Args: kernel_size (Tuple): kernel size of the projection layer. stride (Tuple): stride of the projection layer.

Args: kernel_size (Tuple): kernel size of the projection layer. stride (Tuple): stride of the projection layer.

Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`. https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e

Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`. https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e

Calculate the centroid coordinates of the mask. Args: mask (Tensor): The mask to be calculated, shape (h, w). esp (fl

Modified from https://github.com/CSAILVision/semantic-segmentation-pytorch/blob/master/mit_semseg/utils.py

Implement a simple combining logic following "combine_semantic_and_instance_predictions.py" in panopticapi to produce panoptic segmentati

Evaluate coco keypoint results. The pose prediction results will be saved in ``${res_folder}/result_keypoints.json``. Note:

tgts: [N, 3, H, W] pred: [N, 3, H, W] mask: [N, L], 0 is keep, 1 is remove, valid: [N, 3, H, W]

tgts: [N, 3, H, W] pred: [N, 3, H, W] mask: [N, L], 0 is keep, 1 is remove, valid: [N, 3, H, W]

Defines the computation performed at every call when testing.

Defines the computation performed at every call when testing.

Defines the computation performed at every call when testing.

Defines the computation performed at every call when testing.