| import cv2 |
| import torch |
| from torch import nn |
| from detectron2.utils.comm import get_world_size |
| from detectron2.structures import pairwise_iou, Boxes |
| |
| import torch.nn.functional as F |
| import numpy as np |
| from detectron2.structures import Boxes, ImageList, Instances |
|
|
| __all__ = ['reduce_sum', '_transpose'] |
|
|
| INF = 1000000000 |
|
|
| def _transpose(training_targets, num_loc_list): |
| ''' |
| This function is used to transpose image first training targets to |
| level first ones |
| :return: level first training targets |
| ''' |
| for im_i in range(len(training_targets)): |
| training_targets[im_i] = torch.split( |
| training_targets[im_i], num_loc_list, dim=0) |
|
|
| targets_level_first = [] |
| for targets_per_level in zip(*training_targets): |
| targets_level_first.append( |
| torch.cat(targets_per_level, dim=0)) |
| return targets_level_first |
|
|
|
|
| def reduce_sum(tensor): |
| world_size = get_world_size() |
| if world_size < 2: |
| return tensor |
| tensor = tensor.clone() |
| torch.distributed.all_reduce(tensor, op=torch.distributed.ReduceOp.SUM) |
| return tensor |
