增加了数据增强离线的程序（可扩充数据集），将图片压缩从32*32，调整为224*224.

train/train_food_classifier.py

@@ -30,17 +30,27 @@ print(f"使用设备: {device}")
 
 
 # 数据预处理 - 必须包含Resize以保证batch中tensor尺寸一致，只有Normalize由模型内部完成
+# 把32，32换成224，224
+# transform_train = transforms.Compose([
+#     transforms.Resize((224, 224)),
+#     transforms.RandomHorizontalFlip(p=0.5),
+#     transforms.RandomRotation(10),
+#     transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1),
+#     transforms.ToTensor(),
+#     # 注意：只有Normalize由模型内部处理
+# ])
 transform_train = transforms.Compose([
-    transforms.Resize((32, 32)),  # 必须保留，确保batch中tensor尺寸一致
+    transforms.Resize((224, 224)),
     transforms.RandomHorizontalFlip(p=0.5),
     transforms.RandomRotation(10),
-    transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1),
+    transforms.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.02),
     transforms.ToTensor(),
     # 注意：只有Normalize由模型内部处理
 ])
 
+# 把缩放成32*32，修改为224*224
 transform_test = transforms.Compose([
-    transforms.Resize((32, 32)),  # 必须保留，确保batch中tensor尺寸一致
+    transforms.Resize((224, 224)),  # 必须保留，确保batch中tensor尺寸一致
     transforms.ToTensor(),
     # 注意：只有Normalize由模型内部处理
 ])
@@ -129,7 +139,7 @@ def test(model, test_loader, device, class_names):
             })
     
     print(f'\n测试集总体准确率: {100.*correct/total:.2f}%')
-    for i in range(3):
+    for i in range(settings.NUM_CLASSES):
         if class_total[i] > 0:
             print(f'{class_names[i]} 准确率: {100.*class_correct[i]/class_total[i]:.2f}%')