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import torch
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import numpy as np
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from utils.box_utils import bbox_iou, xywh2xyxy
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def trans_vg_eval_val(pred_boxes, gt_boxes):
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batch_size = pred_boxes.shape[0]
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pred_boxes = xywh2xyxy(pred_boxes)
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pred_boxes = torch.clamp(pred_boxes, 0, 1)
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gt_boxes = xywh2xyxy(gt_boxes)
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iou = bbox_iou(pred_boxes, gt_boxes)
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accu = torch.sum(iou >= 0.5) / float(batch_size)
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return iou, accu
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def trans_vg_eval_test(pred_boxes, gt_boxes, sum=True):
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pred_boxes = xywh2xyxy(pred_boxes)
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pred_boxes = torch.clamp(pred_boxes, 0, 1)
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gt_boxes = xywh2xyxy(gt_boxes)
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iou = bbox_iou(pred_boxes, gt_boxes)
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accu = torch.sum(iou >= 0.5) if sum else iou >= 0.5
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return iou, accu
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def eval_category(category_id_list, iou, accu):
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category_id_list = category_id_list.cpu().numpy()
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sub = list(set(category_id_list)).__len__()
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iou = iou.cpu().numpy()
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accu = accu.cpu().numpy()
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category_iou = [0] * sub
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category_accu = [0] * sub
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sub_num = [0] * sub
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for (id, iou_, accu_) in zip(category_id_list, iou, accu):
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category_iou[id-1] += iou_
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category_accu[id-1] += accu_
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sub_num[id-1] += 1
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category_iou = [i / s for i, s in zip(category_iou, sub_num)]
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category_accu = [a / s for a, s in zip(category_accu, sub_num)]
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return category_iou, category_accu
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