text: import time
  import numpy as np
  import tensorflow as tf
  #import matplotlib.pyplot as plt
  #from alexnet import *
  #from vgg16net_rcnn import Vgg16Net_RCNN
  #from vgg16net_ssd import Vgg16Net_SSD
  #from resnet101_mask_rcnn import Resnet101_MaskRCNN
  #from vgg16_ctpn_crnn_ctc import Vgg16_CTPN_CRNN_CTC
  #from resnet50_dssd_leye import Resnet50_DSSD_LEYE
  from resnet101_fcis import Resnet101_FCIS
  from Mybase.solver import Solver
  
  
  def test():
      #model = MyCifar10Net(num_class = 10, reg = 0.001, dropout = 0.5, wscale = 0.01, dtype = tf.float32)
      #model = MyAlexNet(num_class = 10, reg = 0.001, dropout = 0.7, wscale = 0.01, dtype = tf.float32)
      #model = Cifar10_ResNet(num_layers = 44, num_class = 10, reg = 2e-4, dropout = 0.5, dtype = tf.float32)
      #model = Vgg16Net_RCNN(num_classes = 21, reg = 2e-4, dropout = 0.7, dtype = tf.float32)
      #model = Resnet101_MaskRCNN(num_classes=81, reg=1e-4, dropout=0.6, dtype=tf.float32)
      #model = Resnet101_MaskRCNN(num_classes=1000, reg=1e-4, dropout=0.6, dtype=tf.float32)
      #model = Vgg16_CTPN_CRNN_CTC(num_classes=1000, reg=5e-4, dropout=0.5, dtype=tf.float32)
      #mdl = Resnet50_DSSD_LEYE(cls_num=81, reg=1e-4, drp=0.5, typ=tf.float32)
      mdl = Resnet101_FCIS(cls_num=21, reg=1e-4, drp=0.5, typ=tf.float32)
      
      sov = Solver(mdl,
                   opm_cfg={
                       "decay_rule": "fixed",
                       #"optim_rule": "adam",
                       #"lr_base": 0.0002,
                       "optim_rule": "momentum",
                       "lr_base":  0.00005, #0.004
                       "momentum": 0.9,
                       #"optim_rule": "adadelta",
                       #"lr_base": 0.01,
                   },
                   bat_siz     =  2, epc_num = 10000,
                   itr_per_prt = 20, prt_ena = True,
                   mov_ave_dca = 0.99,
                   epc_per_dca = 1)
      #print("TRAINING...")
      #sov.train()
      #print("TESTING...")
      sov.test()
      sov.display_detections(show=False, save=True)
      #solver.show_loss_acc()
      """
      plt.subplot(2, 1, 1)
      plt.title('Training loss')
      plt.xlabel('Iteration')
  
      plt.subplot(2, 1, 2)
      plt.title('accuracy')
      plt.xlabel('Epoch')
      
      #plt.subplot(3, 1, 3)
      #plt.title('Validation accuracy')
      #plt.xlabel('Epoch')
      
      plt.subplot(2, 1, 1)
      plt.plot(solver.loss_history, 'o')
  
      plt.subplot(2, 1, 2)
      plt.plot(solver.train_acc_history, '-o', label="train_acc")
      plt.plot(solver.val_acc_history, '-o', label="val_acc")
  
      for i in [1, 2]:
          plt.subplot(2, 1, i)
          plt.legend(loc='upper center', ncol=4)
      
      plt.gcf().set_size_inches(15, 15)
      plt.show()
      """
  