Multi-Scale Deformable Attention Module :param d_model hidden dimension :param n_levels number of feature levels

Multi-Scale Deformable Attention Module :param d_model hidden dimension :param n_levels number of feature levels

str: the string of the module

Input: - srcs: List of multi features [bs, ci, hi, wi] - masks: List of multi masks [bs, hi, wi] - refpoi

:param query (N, Length_{query}, C) :param key (N, 1, C) :param reference_poin

:param query (N, Length_{query}, C) :param reference_points (N, Length_{query}, n_levels, 2), range

Forward function. Args: x: input features with shape of (num_windows*B, N, C) mask: (0/-inf) mask with shape of (num_

Forward function. Args: x: Input feature, tensor size (B, H*W, C). H, W: Spatial resolution of the input feature.

Forward function. Args: x: Input feature, tensor size (B, H*W, C). H, W: Spatial resolution of the input feature.

Forward function. Args: x: Input feature, tensor size (B, H*W, C). H, W: Spatial resolution of the input feature.

Forward function.

Generalized IoU from https://giou.stanford.edu/ The boxes should be in [x0, y0, x1, y1] format Returns a [N, M] pairwise matrix, where

Generalized IoU from https://giou.stanford.edu/ Input: - boxes1, boxes2: N,4 Output: - giou: N, 4

get_gaussian_map_from_points B,C,H,W -> B,N,2 float(0, 1) float(0, 1) softargmax function Args: hm (float): Input images(

return the dicf contained in args. e.g: >>> with open(path, 'w') as f: json.dump(get_raw_dict(args), f, indent=2