text: from init import *
  from utils import *
  from data import *
  from model import *
  from train import PATH
  if __name__=="__main__":
      model = ConvNet().to(device)
      model = torch.load(PATH)
      tes_acc = []
      time_list = [] 
      model.eval()
      ytrue = np.arange(0,64)
      ypred = np.arange(0,64)
      with torch.no_grad():
          for images, box in test_loder:
              box = box.permute(0,3,1,2)
              images, box = images.to(device), box.to(device)
              begin = time.time()
              outputs = model(images)
              end = time.time()
              time_list.append(end-begin)
              box2,outputs = model.trnsform(box,outputs)
              gindex = (box2[:,0,:,:]>0).nonzero(as_tuple=True)
              # pindex = (box[:,0,:,:]>0).nonzero(as_tuple=True)
              g_index = torch.stack([gindex[0],gindex[1],gindex[2]])
              # pindex = torch.stack([pindex[1],pindex[2]])
              accuracy,grd,pred = cal_accuracy(g_index.to(device),box2,outputs)
              ytrue = np.concatenate((ytrue,np.array(grd)),axis = 0)
              ypred = np.concatenate((ypred,np.array(pred)),axis = 0)
              tes_acc.append(accuracy)
      print((sum(tes_acc)/len(tes_acc))*100)
      print((sum(time_list)/len(time_list)))
      