The forward expects a NestedTensor, which consists of: - samples.tensor: batched images, of shape [batch_size x 3 x H x W]

_summary_ Args: x (_type_): _description_ text_dict (_type_): _description_ { 'encoded_te

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

Input: - src: [bs, sum(hi*wi), 256] - pos: pos embed for src. [bs, sum(hi*wi), 256] - spatial_shapes: h,w

Input: - tgt: nq, bs, d_model - memory: hw, bs, d_model - pos: hw, bs, d_model - refpoints_un

Input: - tgt/tgt_query_pos: nq, bs, d_model -

_summary_ Args: v (_type_): bs, n_img, dim l (_type_): bs, n_text, dim attention_mask_v (_type_, optional

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.

r""" Args: query, key, value: map a query and a set of key-value pairs to an output. See "Attention Is All You Need" for more

Args: text_attention_mask: bs, num_token memory_text: bs, num_token, d_model Raises: Ru

: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

query: (n_context, queryL, d) context: (n_context, sourceL, d)

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

Try import the specified package. If the package not installed, try use pip to install and import if success. Parameters ---------- p

initialize the retargeting slider

_summary_ Args: keypoints (torch.Tensor): ..., 51

L1-normalize columns of X

Create directory recursively if not exists. Similar to `makedir -p`, you can skip checking existence before this function. Parameters ---

Compute the bounding boxes around the provided masks The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spati

get three Euler angles from Rotation Matrix Args: R: (3,3). rotation matrix Returns: x: pitch y: yaw z: roll

Merge list into cfg_dict Merge the dict parsed by MultipleKVAction into this cfg. Examples: >>> options = {'model.backbo

Args: input_dict (dict): all the values will be reduced average (bool): whether to do average or sum Reduce the values in the

Register a module build function. Args: module (:obj:`nn.Module`): Module to be registered.

Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002. Args: inputs: A float tensor of arbitrary shape.

Warning: does not synchronize the deque!