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import torch
import numpy as np
from utils.box_utils import bbox_iou, xywh2xyxy
def trans_vg_eval_val(pred_boxes, gt_boxes):
batch_size = pred_boxes.shape[0]
pred_boxes = xywh2xyxy(pred_boxes)
pred_boxes = torch.clamp(pred_boxes, 0, 1)
gt_boxes = xywh2xyxy(gt_boxes)
iou = bbox_iou(pred_boxes, gt_boxes)
accu = torch.sum(iou >= 0.5) / float(batch_size)
return iou, accu
def trans_vg_eval_test(pred_boxes, gt_boxes, sum=True):
pred_boxes = xywh2xyxy(pred_boxes)
pred_boxes = torch.clamp(pred_boxes, 0, 1)
gt_boxes = xywh2xyxy(gt_boxes)
iou = bbox_iou(pred_boxes, gt_boxes)
accu = torch.sum(iou >= 0.5) if sum else iou >= 0.5
return iou, accu
def eval_category(category_id_list, iou, accu):
# category_id_list包含 [1,2,3...8]id子类，此处需要 -1 表示从0编码
category_id_list = category_id_list.cpu().numpy()
sub = list(set(category_id_list)).__len__()
iou = iou.cpu().numpy()
accu = accu.cpu().numpy()
category_iou = [0] * sub
category_accu = [0] * sub
sub_num = [0] * sub
for (id, iou_, accu_) in zip(category_id_list, iou, accu):
category_iou[id-1] += iou_
category_accu[id-1] += accu_
sub_num[id-1] += 1
category_iou = [i / s for i, s in zip(category_iou, sub_num)]
category_accu = [a / s for a, s in zip(category_accu, sub_num)]
return category_iou, category_accu
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