将分类的类别也应用在训练程序中，更好的解耦。

train/train_food_classifier.py

@@ -105,8 +105,8 @@ def test(model, test_loader, device, class_names):
     model.eval()
     correct = 0
     total = 0
-    class_correct = list(0. for i in range(3))
-    class_total = list(0. for i in range(3))
+    class_correct = list(0. for i in range(settings.NUM_CLASSES))
+    class_total = list(0. for i in range(settings.NUM_CLASSES))
     
     with torch.no_grad():
         test_bar = tqdm(test_loader, desc='测试中')
@@ -305,8 +305,8 @@ if __name__ == '__main__':
         f.write("各类别测试准确率:\n")
         f.write("-" * 30 + "\n")
         model.eval()
-        class_correct = list(0. for i in range(3))
-        class_total = list(0. for i in range(3))
+        class_correct = list(0. for i in range(settings.NUM_CLASSES))
+        class_total = list(0. for i in range(settings.NUM_CLASSES))
         
         with torch.no_grad():
             for data, target in test_loader: