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test_Ablation.py

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+import os
+#os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+import torch
+import torch.nn.functional as F
+import numpy as np
+from utils import data_loader
+from tqdm import tqdm
+from utils.metrics import Evaluator
+# from network.Net import HANet_v2
+from PIL import Image
+from network.Net2 import HANet_v2,HANet_v3,HANet_v4
+from network.Net3 import HANet_v5,HANet_v6,HANet_v6_Ablation
+from network.CD_Model import HANModel3
+from network.SemiModel import SemiModel
+from network import Ablation_model
+
+import time
+start=time.time()
+
+def test(test_loader, Eva_test, save_path, net):
+    print("Strat validing!")
+
+
+    net.train(False)
+    net.eval()
+    for i, (A, B, mask, filename) in enumerate(tqdm(test_loader)):
+        with torch.no_grad():
+            A = A.cuda()
+            B = B.cuda()
+            Y = mask.cuda()
+            preds = net(A,B)
+            output = F.sigmoid(preds[1])
+            output[output >= 0.5] = 1
+            output[output < 0.5] = 0
+            pred = output.data.cpu().numpy().astype(int)
+            target = Y.cpu().numpy()
+
+            for i in range(output.shape[0]):
+                probs_array = (torch.squeeze(output[i])).data.cpu().numpy()
+                final_mask = probs_array * 255
+                final_mask = final_mask.astype(np.uint8)
+                final_savepath = save_path + filename[i] + '.png'
+                im = Image.fromarray(final_mask)
+                im.save(final_savepath)
+
+            Eva_test.add_batch(target, pred)
+    print('target.shape', target.shape)
+    print('pred.shape', pred.shape)
+
+
+    IoU = Eva_test.Intersection_over_Union()
+    Pre = Eva_test.Precision()
+    Recall = Eva_test.Recall()
+    F1 = Eva_test.F1()
+    OA=Eva_test.OA()
+    Kappa=Eva_test.Kappa()
+
+    # print('[Test] IoU: %.4f, Precision:%.4f, Recall: %.4f, F1: %.4f' % (IoU[1], Pre[1], Recall[1], F1[1]))
+    print('[Test] F1: %.4f, Precision:%.4f, Recall: %.4f, OA: %.4f, Kappa: %.4f,IoU: %.4f' % ( F1[1],Pre[1],Recall[1],OA[1],Kappa[1],IoU[1]))
+    # print('F1-Score: {:.2f}\nPrecision: {:.2f}\nRecall: {:.2f}\nOA: {:.2f}\nKappa: {:.2f}\nIoU: {:.2f}\n}'.format(F1[1] * 100, Pre[1] * 100, Recall[1] * 100, OA[1] * 100, Kappa[1] * 100, IoU[1] * 100))
+    print('F1-Score: Precision: Recall: OA: Kappa: IoU: ')
+    # print('{:.2f}\{:.2f}\{:.2f}\{:.2f}\{:.2f}\{:.2f}'.format(F1[1] * 100, Pre[1] * 100, Recall[1] * 100, OA[1] * 100, Kappa[1] * 100,IoU[1] * 100))
+    print('{:.2f} {:.2f} {:.2f} {:.2f} {:.2f} {:.2f}\n'.format(F1[1] * 100, Pre[1] * 100, Recall[1] * 100, OA[1] * 100, Kappa[1] * 100,IoU[1] * 100))
+    print('{:.2f} {:.2f} {:.2f} {:.2f} {:.2f} {:.2f}\n'.format(F1[0] * 100, Pre[0] * 100, Recall[0] * 100, OA[0] * 100, Kappa[0] * 100,IoU[0] * 100))
+
+
+if __name__ == '__main__':
+    import argparse
+
+    # 'SemiModel_without_GCM_3_4_5'
+    # "SemiModel_without_GCM_1_2_3"
+    # "SemiModel_without_all_GCM"
+    # "SemiModel_without_Refine"
+    # 'SemiModel_without_agg_init'
+    # 'SemiModel_without_agg_final'
+    # 'Full'
+
+    Ablation_name = 'SemiModel_without_GCM_3_4_5'
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--batchsize', type=int, default=16, help='training batch size')
+    parser.add_argument('--trainsize', type=int, default=256, help='training dataset size')
+    parser.add_argument('--gpu_id', type=str, default='2', help='train use gpu')  #修改这里！！！
+    parser.add_argument('--data_name', type=str, default='LEVIR', #修改这里！！！
+                        help='the test rgb images root')
+    parser.add_argument('--model_name', type=str, default='SemiModel_noema04', #修改这里！！！
+                        help='the test rgb images root')
+    # parser.add_argument('--save_path', type=str, default='/data/chengxi.han/Sigma122/HANet_Model3-Semi/output/test_wokers/')  # 半监督
+    # parser.add_argument('--save_path', type=str, default='./test_result/WHU/WHU-labeled-10-semiloss-1.1-2/')
+    # parser.add_argument('--save_path', type=str, default='./test_result/LEVIR/LEVIR-WHU-Student-30%-100Epo/')
+    # parser.add_argument('--save_path', type=str, default='./test_result/S2Looking/S2Looking-supervised-100-Teacher/')
+    # parser.add_argument('--save_path', type=str, default='./test_result-supervised/SYSU/SemiHANet-SYSU-Sepuervised-100%-Teacher/')
+    # parser.add_argument('--save_path', type=str, default='./test_result/LEVIR/SemiHANet-WHU-LEVIR-Teacher-30%-100Epo-F1/')
+    # parser.add_argument('--save_path', type=str,default='./test_result/LEVIR/SemiHANet-WHU-LEVIR-Student-30%-100Epo-F1/')
+    # parser.add_argument('--save_path', type=str,default='./test_result/LEVIRsup-WHUunsup/SemiHANet-LEVIRsup-WHUunsup-Teacher-30/')
+    # parser.add_argument('--save_path', type=str,default='./test_result/WHUsup-LEVIRunsup/SemiHANet-WHUsup-LEVIRunsup-Student-30/')
+    parser.add_argument('--save_path', type=str, default='./test_result/AblationStudy/LEVIR-5-without_GCM_A_B_C-Teacher/')
+
+    # 半监督影像保存路径
+    # parser.add_argument('--save_path', type=str, default='./test_result-supervised/') #全监督的影像保存路径
+    opt = parser.parse_args()
+
+    # set the device for training
+    if opt.gpu_id == '0':
+        os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+        print('USE GPU 0')
+    elif opt.gpu_id == '1':
+        os.environ["CUDA_VISIBLE_DEVICES"] = "1"
+        print('USE GPU 1')
+    if opt.gpu_id == '2':
+        os.environ["CUDA_VISIBLE_DEVICES"] = "2"
+        print('USE GPU 2')
+    if opt.gpu_id == '3':
+        os.environ["CUDA_VISIBLE_DEVICES"] = "3"
+        print('USE GPU 3')
+
+    if opt.data_name == 'LEVIR':
+        opt.test_root = '/data/chengxi.han/data/LEVIR CD Dataset256/test/'
+        # opt.test_root = '/data/chengxi.han/data/LEVIR-CD-Dataset256/test256-sub/'
+        # opt.test_root = '/data/chengxi.han/data/LEVIR CD Dataset256/test256-sub-331/'
+        # opt.test_root = '/data/chengxi.han/data/LEVIR-CD-Dataset256/test256-sub-21/'
+        # opt.test_root = '/data/chengxi.han/data/LEVIR-CD-Dataset256/test256-sub-1419/'
+    elif opt.data_name == 'WHU':
+        # opt.test_root = '/data/chengxi.han/data/Building change detection dataset256/test/'
+        opt.test_root = '/data/chengxi.han/data/WHU-CD-256-Semi/test/'
+        # opt.test_root = '/data/chengxi.han/data/Building change detection dataset256/test-sub/'
+        # opt.test_root = '/data/chengxi.han/data/Building change detection dataset256/test-sub-61-3/'
+
+    elif opt.data_name == 'CDD':
+        opt.test_root = '/data/chengxi.han/data/CDD_ChangeDetectionDataset/Real/subset/test/'
+        # opt.test_root = '/data/chengxi.han/data/Building change detection dataset256/test-sub-CDD402/'
+    elif opt.data_name == 'DSIFN':
+        opt.test_root = '/data/chengxi.han/data/DSIFN256/test/'
+    elif opt.data_name == 'SYSU':
+        opt.test_root = '/data/chengxi.han/data/SYSU-CD/test/'
+        # opt.test_root = '/data/chengxi.han/data/SYSU-CD/test-sub/'
+    elif opt.data_name == 'S2Looking':
+        opt.test_root = '/data/chengxi.han/data/S2Looking256/test/'
+        # opt.test_root = '/data/chengxi.han/data/S2Looking256/test-sub/'
+    elif opt.data_name == 'GoogleGZ':
+        opt.test_root = '/data/chengxi.han/data/Google_GZ_CD256/test/'
+    elif opt.data_name == 'LEVIRsup-WHUunsup':
+        opt.test_root = '/data/chengxi.han/data/WHU-LEVIR-CD-256-Semi/test/'
+
+    opt.save_path = opt.save_path + opt.data_name + '/' + opt.model_name + '/'
+    test_loader = data_loader.get_test_loader(opt.test_root, opt.batchsize, opt.trainsize, num_workers=2, shuffle=False, pin_memory=True)
+    Eva_test = Evaluator(num_class=2)
+
+    if opt.model_name == 'HANet_v2':
+        model = HANet_v2().cuda()
+    elif opt.model_name == 'HANet_v3':
+        model = HANet_v3().cuda()
+    elif opt.model_name == 'HANet_v4':
+        model = HANet_v4().cuda()
+    elif opt.model_name == 'HANet_v5':
+        model = HANet_v5().cuda()
+    elif opt.model_name == 'HANet_v6':
+        model = HANet_v6().cuda()
+    elif opt.model_name == 'HANet_v6_Ablation':
+        model = HANet_v6_Ablation().cuda()
+    elif opt.model_name == 'HANModel3':
+        model = HANModel3().cuda()
+    elif opt.model_name == 'SemiModel_noema04':
+        model = SemiModel().cuda()
+
+    if Ablation_name == 'SemiModel_without_GCM_3_4_5':
+        model = Ablation_model.SemiModel_without_GCM_3_4_5().cuda()
+        ema_model = Ablation_model.SemiModel_without_GCM_3_4_5().cuda()
+    elif Ablation_name == "SemiModel_without_GCM_1_2_3":
+        model = Ablation_model.SemiModel_without_GCM_1_2_3().cuda()
+        ema_model = Ablation_model.SemiModel_without_GCM_1_2_3().cuda()
+    elif Ablation_name == "SemiModel_without_all_GCM":
+        model = Ablation_model.SemiModel_without_all_GCM().cuda()
+        ema_model = Ablation_model.SemiModel_without_all_GCM().cuda()
+    elif Ablation_name == "SemiModel_without_Refine":
+        model = Ablation_model.SemiModel_without_Refine().cuda()
+        ema_model = Ablation_model.SemiModel_without_Refine().cuda()
+    elif Ablation_name == 'SemiModel_without_agg_init':
+        model = Ablation_model.SemiModel_without_agg_init().cuda()
+        ema_model = Ablation_model.SemiModel_without_agg_init().cuda()
+    elif Ablation_name == 'SemiModel_without_agg_final':
+        model = Ablation_model.SemiModel_without_agg_final().cuda()
+        ema_model = Ablation_model.SemiModel_without_agg_final().cuda()
+
+    elif Ablation_name == 'Full':
+        model = SemiModel().cuda()
+        ema_model = SemiModel().cuda()
+
+    # opt.load = './output/' + opt.data_name + '/' + opt.model_name + '_best_iou.pth'
+    # opt.load = './output/' + opt.data_name + '/' + opt.model_name + '_best_student_iou.pth' #半监督
+    # opt.load = './output/' + opt.data_name + '-5%/' + opt.model_name + '_best_teacher_iou.pth' #半监督
+    # opt.load = './output-supervised/' + opt.data_name + '/' + opt.model_name + '_best_student_iou.pth'  # #全监督的
+    # opt.load = './output-supervised/' + opt.data_name + '/' + opt.model_name + '_best_teacher_iou.pth' # #全监督的
+
+    # iou 83.84 10% GoogleGZ
+    # opt.load = './output/test_wokers/load1' + opt.data_name + '/' + opt.model_name + '_best_teacher_iou.pth' #半监督
+
+
+
+    # parser.add_argument('--save_path', type=str,default='./output/test_wokers/load1/') #半监督的路径
+
+#-------测试老师的模型teacher-------
+    # save_path = './output/S2Looking-5/'
+    # save_path = './output/SYSU-Sepuervised-100%/'
+    # save_path = './output/test_wokers/load1/WHU-30-100Epo/'
+    # save_path = './output-supervised/SYSU-30/'  # 全监督的
+    # save_path = './output/LEVIRsup-WHUunsup-30/'
+    # save_path = './output/WHUsup-LEVIRunsup-30/'
+    save_path = './output/LEVIR-5-without_GCM_A_B_C/'
+    # save_path = save_path + '/' + opt.model_name
+    save_path = save_path + opt.data_name + '/' + opt.model_name
+    opt.load = save_path + '_train1_' + '_best_teacher_iou.pth'
+    if opt.load is not None:
+        model.load_state_dict(torch.load(opt.load))
+        print('load model from ', opt.load)
+
+    # print('路径是：WHU-labeled-5-semiloss-0.8')
+
+
+    #-------测试学生的模型student-------
+    # # save_path = './output/S2Looking-5/'
+    # # save_path = './output/SYSU-Sepuervised-100%/'
+    # # save_path = './output/test_wokers/load1/' #半监督的
+    # # save_path = './output-supervised/LEVIR-5' #全监督的
+    # # save_path = './output/test_wokers/load1/WHU-30-100Epo'
+    # # save_path = './output/LEVIRsup-WHUunsup-30/'
+    # # save_path = './output/WHUsup-LEVIRunsup-30/'
+    # save_path = './output/S2Looking-supervised-100/'
+    #
+    # # save_path = save_path +  opt.model_name
+    # save_path = save_path + opt.data_name + '/' + opt.model_name
+    # # opt.load = save_path + opt.data_name + '/' + opt.model_name+ '_train1_' + '_best_student_iou.pth'
+    #
+    # # save_path = save_path + opt.model_name
+    # opt.load = save_path + '_train1_'+ '_best_student_iou.pth'
+    # # opt.load ='./output/LEVIR-5%/SemiModel_noema04_best_teacher_iou.pth'
+    # if opt.load is not None:
+    #     print('load model from ', opt.load)
+    #     checkpoint_stud = torch.load(opt.load)
+    #     model.load_state_dict(checkpoint_stud['best_student_net '])
+    #
+
+
+
+
+    save_path = opt.save_path
+    if not os.path.exists(save_path):
+        os.makedirs(save_path)
+
+    test(test_loader, Eva_test, opt.save_path, model)
+
+end=time.time()
+print('程序测试test的时间为:',end-start)