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# Copyright (c) OpenMMLab. All rights reserved.
# Copied from
# 'https://github.com/qinzhi-0110/'
# 'Temporal-Context-Aggregation-Network-Pytorch/'
# 'blob/main/utils.py'
# TODO: refactor
import torch
def batch_iou(proposals, gt_boxes):
len_proposals = proposals[:, 1] - proposals[:, 0]
int_xmin = torch.max(proposals[:, 0], gt_boxes[:, 0])
int_xmax = torch.min(proposals[:, 1], gt_boxes[:, 1])
inter_len = torch.clamp(int_xmax - int_xmin, min=0.)
union_len = len_proposals - inter_len + gt_boxes[:, 1] - gt_boxes[:, 0]
jaccard = inter_len / (union_len + 0.00001)
return jaccard
def bbox_xw_transform_inv(boxes, deltas, dx_w, dw_w):
widths = boxes[:, 1] - boxes[:, 0]
ctr_x = boxes[:, 0] + 0.5 * widths
dx = deltas[:, 0] * dx_w
dw = deltas[:, 1] * dw_w
pred_ctr_x = dx * widths + ctr_x
pred_w = torch.exp(dw) * widths
pred_boxes = deltas.clone()
# x1
pred_boxes[:, 0] = pred_ctr_x - 0.5 * pred_w
# x2
pred_boxes[:, 1] = pred_ctr_x + 0.5 * pred_w
return pred_boxes
def bbox_xw_transform_batch(ex_rois, gt_rois):
ex_widths = torch.clamp(ex_rois[:, 1] - ex_rois[:, 0], min=0.00001)
ex_ctr_x = ex_rois[:, 0] + 0.5 * ex_widths
gt_widths = torch.clamp(gt_rois[:, 1] - gt_rois[:, 0], min=0.00001)
gt_ctr_x = gt_rois[:, 0] + 0.5 * gt_widths
targets_dx = (gt_ctr_x - ex_ctr_x) / ex_widths
targets_dw = torch.log(gt_widths / ex_widths)
targets = torch.stack((targets_dx, targets_dw), dim=1)
return targets
def bbox_se_transform_batch(ex_rois, gt_rois):
ex_widths = torch.clamp(ex_rois[:, 1] - ex_rois[:, 0], min=0.00001)
s_offset = gt_rois[:, 0] - ex_rois[:, 0]
e_offset = gt_rois[:, 1] - ex_rois[:, 1]
targets_s = s_offset / ex_widths
targets_e = e_offset / ex_widths
targets = torch.stack((targets_s, targets_e), dim=1)
return targets
def bbox_se_transform_inv(boxes, deltas, dse_w):
widths = boxes[:, 1] - boxes[:, 0]
s_offset = deltas[:, 0] * widths * dse_w
e_offset = deltas[:, 1] * widths * dse_w
pred_boxes = deltas.clone()
pred_boxes[:, 0] = boxes[:, 0] + s_offset
pred_boxes[:, 1] = boxes[:, 1] + e_offset
return pred_boxes
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