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test.py

+import torch
+from tqdm import tqdm
+from opt import opt
+from utils.metrics import evaluate
+import datasets
+from torch.utils.data import DataLoader
+from utils.comm import generate_model
+from utils.metrics import Metrics
+
+
+def test():
+    print('loading data......')
+    test_data = getattr(datasets, opt.dataset)(opt.root, opt.test_data_dir, mode='test')
+    test_dataloader = DataLoader(test_data, batch_size=1, shuffle=False, num_workers=opt.num_workers)
+    total_batch = int(len(test_data) / 1)
+    model = generate_model(opt)
+
+    model.eval()
+
+    # metrics_logger initialization
+    metrics = Metrics(['recall', 'specificity', 'precision', 'F1', 'F2',
+                       'ACC_overall', 'IoU_poly', 'IoU_bg', 'IoU_mean', 'Dice'])
+
+    with torch.no_grad():
+        bar = tqdm(enumerate(test_dataloader), total=total_batch)
+        for i, data in bar:
+            img, gt = data['image'], data['label']
+
+            if opt.use_gpu:
+                img = img.cuda()
+                gt = gt.cuda()
+
+            output = model(img)
+            _recall, _specificity, _precision, _F1, _F2, \
+            _ACC_overall, _IoU_poly, _IoU_bg, _IoU_mean, _Dice = evaluate(output, gt)
+
+            metrics.update(recall= _recall, specificity= _specificity, precision= _precision, 
+                            F1= _F1, F2= _F2, ACC_overall= _ACC_overall, IoU_poly= _IoU_poly, 
+                            IoU_bg= _IoU_bg, IoU_mean= _IoU_mean, Dice = _Dice
+                        )
+
+    metrics_result = metrics.mean(total_batch)
+
+    print("Test Result:")
+    print('recall: %.4f, specificity: %.4f, precision: %.4f, F1: %.4f, F2: %.4f, '
+          'ACC_overall: %.4f, IoU_poly: %.4f, IoU_bg: %.4f, IoU_mean: %.4f'
+          % (metrics_result['recall'], metrics_result['specificity'], metrics_result['precision'],
+             metrics_result['F1'], metrics_result['F2'], metrics_result['ACC_overall'],
+             metrics_result['IoU_poly'], metrics_result['IoU_bg'], metrics_result['IoU_mean'], metric_result['Dice']))
+
+
+if __name__ == '__main__':
+
+    if opt.mode == 'test':
+        print('--- PolypSeg Test---')
+        test()
+
+    print('Done')