Create a summary writer logging to log_dir.

Args: ap_iou_thresh: float between 0 and 1.0 IoU threshold to judge whether a prediction is positive.

Args: ap_iou_thresh: float between 0 and 1.0 IoU threshold to judge whether a prediction is positive.

Votes generation from seed point features. Args: vote_facotr: int number of votes generated from each seed point

Initializes the model. Parameters: backbone: torch module of the backbone to be used. See backbone.py transformer: to

Args: gamma: Weighting parameter to balance loss for hard and easy examples. alpha: Weighting parameter to balance lo

object candidate point prediction from seed point features. Args: seed_feature_dim: int number of channels of see

Convert continuous angle to discrete class [optinal] also small regression number from class center angle to current angle.

r""" Parameters ---------- grad_out : torch.Tensor (B, c, n) tensor with gradients of ouputs Returns

r""" Parameters ---------- grad_out : torch.Tensor (B, C, npoint, nsample) tensor of the gradients of the output

Compute 2D bounding box IoU. Input: box1: tuple of (xmin,ymin,xmax,ymax) box2: tuple of (xmin,ymin,xmax,ymax) Output:

Use accumulated predictions and groundtruths to compute Average Precision.

Dump results. Args: end_points: dict {..., pred_mask} pred_mask is a binary mask array of size (batch_size, num_

Generic functions to compute precision/recall for object detection for multiple classes. Input: pred_all: map of {img_id:

r""" Uses iterative furthest point sampling to select a set of npoint features that have the largest minimum distance Paramet

r""" Parameters ---------- features : torch.Tensor (B, C, N) tensor idx : torch.Tensor (B, n

r""" Find the three nearest neighbors of unknown in known Parameters ---------- unknown : torch.Tensor

r""" Performs weight linear interpolation on 3 features Parameters ---------- features : torch.Tensor

r""" Parameters ---------- features : torch.Tensor (B, C, N) tensor of features to group idx : torch.Tens

r""" Parameters ---------- radius : float radius of the balls nsample : int maximum number of

r""" Parameters ---------- xyz : torch.Tensor xyz coordinates of the features (B, N, 3) new_xyz : torch.Te

r""" Parameters ---------- xyz : torch.Tensor xyz coordinates of the features (B, N, 3) new_xyz : torch.Te

r""" Parameters ---------- xyz : torch.Tensor (B, N, 3) tensor of the xyz coordinates of the features feat

r""" Parameters ---------- xyz : torch.Tensor (B, N, 3) tensor of the xyz coordinates of the features feat

r""" Parameters ---------- xyz : torch.Tensor (B, N, 3) tensor of the xyz coordinates of the features feat

r""" Parameters ---------- unknown : torch.Tensor (B, n, 3) tensor of the xyz positions of the unknown features

r""" Propagate features from xyz1 to xyz2. Parameters ---------- xyz2 : torch.Tensor (B, N2, 3) tensor of the xyz

r""" Forward pass of the network Parameters ---------- pointcloud: Variable(torch.cuda.FloatTensor)

:param query: B C Pq :param key: B C Pk :param query_pos: B Pq 3/6 :param key_pos: B Pk 3/6 :param value_pos:

Forward pass. Arguments: seed_xyz: (batch_size, num_seed, 3) Pytorch tensor seed_features: (batch_size, feature_dim,

Args: input: (B, #proposals, #classes) float tensor. Predicted logits for each class target: (B, #pro