From da67cb9f4c00bd01f9f6a16383c8259236194820 Mon Sep 17 00:00:00 2001
From: zhanghuan <1262329256@qq.com>
Date: Thu, 11 Sep 2025 17:56:25 +0800
Subject: [PATCH] =?UTF-8?q?=E7=8E=B0=E5=9C=A8=E7=BB=9F=E4=B8=80=E4=BD=BF?=
 =?UTF-8?q?=E7=94=A8Torch=E4=B8=AD=E7=9A=84interpolate=E8=BF=99=E4=B8=AA?=
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 =?UTF-8?q?=EF=BC=8C=E5=BA=94=E8=AF=A5=E5=B0=B1=E5=8F=AF=E4=BB=A5=E4=BA=86?=
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---
 net/food_net.py      | 18 ++++++++++++++----
 settings/settings.py |  2 +-
 2 files changed, 15 insertions(+), 5 deletions(-)

diff --git a/net/food_net.py b/net/food_net.py
index 368ec63..01aac77 100644
--- a/net/food_net.py
+++ b/net/food_net.py
@@ -54,7 +54,7 @@ class FoodCNN(nn.Module):
         
     def preprocess_image(self, image):
         """
-        预处理单张图片
+        预处理单张图片（使用与移动端相同的插值方法）
         
         Args:
             image: PIL Image 或 numpy array
@@ -69,10 +69,20 @@ class FoodCNN(nn.Module):
             else:
                 raise ValueError("输入必须是PIL Image或numpy array")
         
-        # 应用预处理变换
-        processed = self.preprocess(image)
+        # 转换为tensor（不做resize）
+        tensor = transforms.ToTensor()(image)
         # 添加batch维度
-        return processed.unsqueeze(0)
+        tensor = tensor.unsqueeze(0)
+        
+        # 使用与移动端相同的插值方法缩放到32x32
+        tensor = F.interpolate(tensor, size=(32, 32), mode='bilinear', align_corners=False)
+        
+        # ImageNet标准化
+        mean = torch.tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1)
+        std = torch.tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1)
+        tensor = (tensor - mean) / std
+        
+        return tensor
     
     def mobile_preprocess(self, x):
         """
diff --git a/settings/settings.py b/settings/settings.py
index b98788c..e1153a8 100644
--- a/settings/settings.py
+++ b/settings/settings.py
@@ -15,7 +15,7 @@ VAL_DATA_DIR = os.path.join(DATASET_DIR, 'val')
 TEST_DATA_DIR = os.path.join(DATASET_DIR, 'test')
 
 # 模型保存路径
-MODEL_DIR = os.path.join(BASE_DIR, 'model', '09')
+MODEL_DIR = os.path.join(BASE_DIR, 'model', '10')
 BEST_MODEL_PATH = os.path.join(MODEL_DIR, 'best_food_model.pth')
 TRAINING_CURVES_PATH = os.path.join(MODEL_DIR, 'training_curves.png')
 TRAINING_RESULTS_PATH = os.path.join(MODEL_DIR, 'training_results.txt')