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import torch
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def batch_iou(proposals, gt_boxes):
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len_proposals = proposals[:, 1] - proposals[:, 0]
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int_xmin = torch.max(proposals[:, 0], gt_boxes[:, 0])
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int_xmax = torch.min(proposals[:, 1], gt_boxes[:, 1])
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inter_len = torch.clamp(int_xmax - int_xmin, min=0.)
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union_len = len_proposals - inter_len + gt_boxes[:, 1] - gt_boxes[:, 0]
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jaccard = inter_len / (union_len + 0.00001)
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return jaccard
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def bbox_xw_transform_inv(boxes, deltas, dx_w, dw_w):
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widths = boxes[:, 1] - boxes[:, 0]
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ctr_x = boxes[:, 0] + 0.5 * widths
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dx = deltas[:, 0] * dx_w
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dw = deltas[:, 1] * dw_w
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pred_ctr_x = dx * widths + ctr_x
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pred_w = torch.exp(dw) * widths
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pred_boxes = deltas.clone()
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pred_boxes[:, 0] = pred_ctr_x - 0.5 * pred_w
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pred_boxes[:, 1] = pred_ctr_x + 0.5 * pred_w
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return pred_boxes
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def bbox_xw_transform_batch(ex_rois, gt_rois):
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ex_widths = torch.clamp(ex_rois[:, 1] - ex_rois[:, 0], min=0.00001)
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ex_ctr_x = ex_rois[:, 0] + 0.5 * ex_widths
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gt_widths = torch.clamp(gt_rois[:, 1] - gt_rois[:, 0], min=0.00001)
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gt_ctr_x = gt_rois[:, 0] + 0.5 * gt_widths
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targets_dx = (gt_ctr_x - ex_ctr_x) / ex_widths
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targets_dw = torch.log(gt_widths / ex_widths)
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targets = torch.stack((targets_dx, targets_dw), dim=1)
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return targets
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def bbox_se_transform_batch(ex_rois, gt_rois):
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ex_widths = torch.clamp(ex_rois[:, 1] - ex_rois[:, 0], min=0.00001)
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s_offset = gt_rois[:, 0] - ex_rois[:, 0]
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e_offset = gt_rois[:, 1] - ex_rois[:, 1]
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targets_s = s_offset / ex_widths
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targets_e = e_offset / ex_widths
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targets = torch.stack((targets_s, targets_e), dim=1)
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return targets
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def bbox_se_transform_inv(boxes, deltas, dse_w):
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widths = boxes[:, 1] - boxes[:, 0]
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s_offset = deltas[:, 0] * widths * dse_w
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e_offset = deltas[:, 1] * widths * dse_w
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pred_boxes = deltas.clone()
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pred_boxes[:, 0] = boxes[:, 0] + s_offset
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pred_boxes[:, 1] = boxes[:, 1] + e_offset
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return pred_boxes
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