fix

imgsearchimg/api/Dockerfile

@@ -0,0 +1,35 @@
+# 使用Python 3.11.11作为基础镜像
+# FROM python:3.11.11-slim
+# FROM swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/wallies/python-cuda:3.11-cuda11.8-runtime
+FROM registry.cn-hangzhou.aliyuncs.com/serverless_devs/pytorch:22.12-py3
+
+RUN pip config set global.index-url https://mirrors.aliyun.com/pypi/simple/
+RUN sed -i 's|http://archive.ubuntu.com/ubuntu|http://mirrors.aliyun.com/ubuntu|g' /etc/apt/sources.list && \
+    sed -i 's|http://security.ubuntu.com/ubuntu|http://mirrors.aliyun.com/ubuntu|g' /etc/apt/sources.list
+
+# 设置工作目录
+WORKDIR /app
+
+# 设置环境变量
+ENV PYTHONUNBUFFERED=1 \
+    PYTHONDONTWRITEBYTECODE=1 \
+    PIP_NO_CACHE_DIR=1
+
+# 安装系统依赖
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    build-essential \
+    libmagic1 \
+    && rm -rf /var/lib/apt/lists/*
+
+# 复制项目文件
+COPY . .
+
+
+# 安装Python依赖
+RUN pip install --no-cache-dir -r requirements.txt
+
+
+EXPOSE 5001
+
+# 设置默认命令
+CMD ["python", "func_test.py"]

imgsearchimg/api/image_search.py

@@ -27,8 +27,8 @@ class ImageSearchEngine:
         # 初始化一个id生成计数
         self.faiss_id_max = 0
      
-        # 强制使用CPU设备
-        self.device = torch.device("cpu")
+        # 检查GPU是否可用（仅用于PyTorch模型）
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
         print(f"使用设备: {self.device}")
         
         # 定义基础预处理转换
@@ -39,11 +39,12 @@ class ImageSearchEngine:
         ])
         
     
-        # 加载预训练的ResNet模型并保持全精度
+        # 加载预训练的ResNet模型
         self.model = models.resnet50(weights=ResNet50_Weights.IMAGENET1K_V2)
         # 移除最后的全连接层
         self.model = torch.nn.Sequential(*list(self.model.children())[:-1])
-        self.model = self.model.float().to(self.device).eval()
+        self.model = self.model.to(self.device)
+        self.model.eval()
         
         # 初始化FAISS索引（2048是ResNet50的特征维度）
         self.dimension = 2048
@@ -126,9 +127,6 @@ class ImageSearchEngine:
             多尺度特征向量，处理失败返回None
         """
         try:
-            # 三重精度保障
-            self.model = self.model.float()
-            
             # 获取原图信息
             orig_w, orig_h = image.size
             max_edge = max(orig_w, orig_h)
@@ -136,14 +134,14 @@ class ImageSearchEngine:
 
             # 动态调整策略 -------------------------------------------
             # 策略1：根据最大边长确定基准尺寸
-            base_size = min(max_edge, 2048)  # 最大尺寸限制
+            base_size = min(max_edge, 3000)  # 不超过模型支持的最大尺寸
             
             # 策略2：自动生成窗口尺寸（等比数列）
             min_size = 224  # 最小特征尺寸
-            num_scales = 3  # 采样点数
+            num_scales = 4  # 固定采样点数
             scale_factors = np.logspace(0, 1, num_scales, base=2)
             window_sizes = [int(base_size * f) for f in scale_factors]
-            window_sizes = sorted({min(max(s, min_size), 2048) for s in window_sizes})
+            window_sizes = sorted({min(max(s, min_size), 3000) for s in window_sizes})
             
             # 策略3：根据长宽比调整尺寸组合
             if aspect_ratio > 1.5:  # 宽幅图像
@@ -160,8 +158,9 @@ class ImageSearchEngine:
                 self.base_transform
             ])
             
-            # 
-            img_base = base_transform(image).unsqueeze(0).to(torch.float32).to(self.device)
+            # 半精度加速
+            self.model.half()
+            img_base = base_transform(image).unsqueeze(0).to(self.device).half()
 
             # 动态特征提取 ------------------------------------------
             features = []
@@ -169,21 +168,21 @@ class ImageSearchEngine:
                 # 保持长宽比的重采样
                 target_size = (int(size*aspect_ratio), size) if aspect_ratio > 1 else (size, int(size/aspect_ratio))
                 
-                # CPU兼容的插值
+                # GPU加速的智能插值
                 img_tensor = torch.nn.functional.interpolate(
                     img_base, 
                     size=target_size,
-                    mode='bilinear',
+                    mode= 'area' if size < base_size else 'bicubic',  # 下采样用area，上采样用bicubic
                     align_corners=False
-                ).to(torch.float32)
+                )
 
                 # 自适应归一化（保持原图统计特性）
                 if hasattr(self, 'adaptive_normalize'):
                     img_tensor = self.adaptive_normalize(img_tensor)
 
                 # 混合精度推理
-                with torch.no_grad():
-                    feature = self.model(img_tensor).to(torch.float32)
+                with torch.no_grad(), torch.cuda.amp.autocast():
+                    feature = self.model(img_tensor)
                 
                 features.append(feature.squeeze().float())
 
@@ -204,6 +203,48 @@ class ImageSearchEngine:
 
 
 
+    def _extract_multi_scale_features_bak(self, image: Image.Image) -> Optional[torch.Tensor]:
+        """提取多尺度特征。
+        
+        Args:
+            image: PIL图片对象
+            
+        Returns:
+            多尺度特征向量，如果处理失败返回None
+        """
+        try:
+            features_list = []
+            window_sizes = [256, 512,1024,1560,2048,2560,3000]
+            # 多尺度转换 - 增加更多尺度
+            #self.multi_scale_sizes = [224, 384, 512, 768, 1024, 1536,2048,3000]
+            for size in window_sizes:
+                # 调整图片大小
+                transform = transforms.Compose([
+                    transforms.Resize((size, size), interpolation=transforms.InterpolationMode.LANCZOS),
+                    self.base_transform
+                ])
+                
+                # 应用变换
+                img_tensor = transform(image).unsqueeze(0).to(self.device)
+                
+                # 提取特征
+                with torch.no_grad():
+                    feature = self.model(img_tensor)
+                
+                features_list.append(feature.squeeze())
+            
+            # 计算加权平均，较大尺度的权重更高
+            weights = torch.linspace(1, 2, len(features_list)).to(self.device)
+            weights = weights / weights.sum()
+            
+            weighted_features = torch.stack([f * w for f, w in zip(features_list, weights)])
+            final_feature = weighted_features.sum(dim=0)
+            
+            return final_feature
+            
+        except Exception as e:
+            print(f"提取多尺度特征时出错: {e}")
+            return None
 
     def _extract_sliding_window_features(self, image: Image.Image) -> Optional[torch.Tensor]:
         """优化版滑动窗口特征提取（动态调整+批量处理）
@@ -215,9 +256,6 @@ class ImageSearchEngine:
             滑动窗口特征向量，处理失败返回None
         """
         try:
-            # 三重精度保障
-            self.model = self.model.float()
-            
             # 获取原图信息
             orig_w, orig_h = image.size
             aspect_ratio = orig_w / orig_h
@@ -229,10 +267,10 @@ class ImageSearchEngine:
                 int(2 ** np.round(np.log2(max_dim * 0.1))),  # 约10%尺寸
                 int(2 ** np.floor(np.log2(max_dim * 0.5))),  # 约50%尺寸
                 int(2 ** np.ceil(np.log2(max_dim)))          # 接近原图尺寸
-            } & {256, 512, 1024, 2048})  # 与预设尺寸取交集
+            } & {256, 512, 1024, 2048, 3000})  # 与预设尺寸取交集
             
             # 智能步长调整（窗口尺寸越大步长越大）
-            stride_ratios = {256:0.5, 512:0.4, 1024:0.3, 2048:0.2}
+            stride_ratios = {256:0.5, 512:0.4, 1024:0.3, 2048:0.2, 3000:0.15}
             
             # 预处理优化 --------------------------------------------
             # 生成基准图像（最大窗口尺寸）
@@ -241,11 +279,15 @@ class ImageSearchEngine:
                         (max_win_size, int(max_win_size / aspect_ratio))
             
             transform = transforms.Compose([
-                transforms.Resize(base_size[::-1], interpolation=transforms.InterpolationMode.BILINEAR),
+                transforms.Resize(base_size[::-1], interpolation=transforms.InterpolationMode.LANCZOS),
                 self.base_transform
             ])
-            base_img = transform(image).to(torch.float32).to(self.device)
+            base_img = transform(image).to(self.device)
             
+            # 半精度加速
+            self.model.half()
+            base_img = base_img.half()
+
             # 批量特征提取 ------------------------------------------
             all_features = []
             for win_size in window_sizes:
@@ -258,7 +300,7 @@ class ImageSearchEngine:
                 num_w = (w - win_size) // stride + 1
                 
                 # 调整窗口数量上限（防止显存溢出）
-                MAX_WINDOWS = 16  # 最大窗口数
+                MAX_WINDOWS = 32  # 根据显存调整
                 if num_h * num_w > MAX_WINDOWS:
                     stride = int(np.sqrt(h * w * win_size**2 / MAX_WINDOWS))
                     num_h = (h - win_size) // stride + 1
@@ -277,11 +319,11 @@ class ImageSearchEngine:
                     continue
 
                 # 批量处理（自动分块防止OOM）
-                BATCH_SIZE = 4  # 批处理大小
-                with torch.no_grad():
+                BATCH_SIZE = 8  # 根据显存调整
+                with torch.no_grad(), torch.cuda.amp.autocast():
                     for i in range(0, len(windows), BATCH_SIZE):
-                        batch = torch.stack(windows[i:i+BATCH_SIZE]).to(torch.float32)
-                        features = self.model(batch).to(torch.float32)
+                        batch = torch.stack(windows[i:i+BATCH_SIZE])
+                        features = self.model(batch)
                         all_features.append(features.cpu().float())  # 转移至CPU释放显存
 
             # 特征融合 ---------------------------------------------
@@ -291,13 +333,72 @@ class ImageSearchEngine:
             final_feature = torch.cat([f.view(-1, f.shape[-1]) for f in all_features], dim=0)
             final_feature = final_feature.mean(dim=0).to(self.device)
 
-            return final_feature.float()
+            return final_feature
 
         except Exception as e:
             print(f"滑动窗口特征提取失败: {e}")
             return None
 
 
+    def _extract_sliding_window_features_bak(self, image: Image.Image) -> Optional[torch.Tensor]:
+        """使用滑动窗口提取特征。
+        
+        Args:
+            image: PIL图片对象
+            
+        Returns:
+            滑动窗口特征向量，如果处理失败返回None
+        """
+        try:
+            window_sizes = [256, 512,1024,1560,2048,2560,3000]
+            stride_ratio = 0.25  # 步长比例
+            
+            features_list = []
+
+            for window_size in window_sizes:
+                # 调整图片大小，保持宽高比
+                aspect_ratio = image.size[0] / image.size[1]
+                if aspect_ratio > 1:
+                    new_width = int(window_size * aspect_ratio)
+                    new_height = window_size
+                else:
+                    new_width = window_size
+                    new_height = int(window_size / aspect_ratio)
+                
+                transform = transforms.Compose([
+                    transforms.Resize((new_height, new_width), interpolation=transforms.InterpolationMode.LANCZOS),
+                    self.base_transform
+                ])
+                
+                # 转换图片
+                img_tensor = transform(image)
+                
+                # 计算步长
+                stride = int(window_size * stride_ratio)
+                
+                # 使用滑动窗口提取特征
+                for i in range(0, img_tensor.size(1) - window_size + 1, stride):
+                    for j in range(0, img_tensor.size(2) - window_size + 1, stride):
+                        window = img_tensor[:, i:i+window_size, j:j+window_size].unsqueeze(0).to(self.device)
+                        
+                        with torch.no_grad():
+                            feature = self.model(window)
+                        
+                        features_list.append(feature.squeeze())
+            
+            # 如果没有提取到特征，返回None
+            if not features_list:
+                return None
+            
+            # 计算所有特征的平均值
+            final_feature = torch.stack(features_list).mean(dim=0)
+            
+            return final_feature
+            
+        except Exception as e:
+            print(f"提取滑动窗口特征时出错: {e}")
+            return None
+
     def extract_features(self, img: Image.Image) -> np.ndarray:
         """结合多尺度和滑动窗口提取特征。
         
@@ -663,6 +764,7 @@ class ImageSearchEngine:
 
         except Exception as e:
             print(f"删除 product_id 为 {product_id} 的记录时出错: {e}")
+            traceback.print_exc()
             return False
 
     def get_index_size(self) -> int:

sku3d/.drone.yml

@@ -1,183 +0,0 @@
----
-kind: pipeline
-type: docker
-name: default
-steps:
-- name: 1.submodules
-  image: alpine/git
-  commands:
-  - git submodule update --init --recursive
-
-- name: 2.building
-  image: golang:1.17
-  environment:
-    GOOS: linux
-    GOARCH: amd64
-    GO111MODULE: on
-    CGO_ENABLED: 0
-    GOPROXY: https://proxy.golang.com.cn,direct
-  
-  commands:
-  - cd sku3d
-  - go version
-  - go env
-  - go build
-
-- name: 3.dockerbuild(test)  
-  image: plugins/docker
-  settings:
-    username: infish2018
-    password: infish@2022
-    repo: registry.cn-chengdu.aliyuncs.com/sku3d/maintest
-    registry: registry.cn-chengdu.aliyuncs.com
-    mirror: https://17itejgx.mirror.aliyuncs.com
-
-    tags:
-      - latest
-      - '1.0.13'
-    context: sku3d/
-    dockerfile: sku3d/Dockerfile
-  when:
-    branch:
-    - test
-
-- name: 4.ssh-deploy(test)
-  image: appleboy/drone-ssh
-  settings:
-    host:
-      - 124.71.139.24
-    username: root
-    password:
-      from_secret: serverpassword
-    port: 22
-    command_timeout: 3m
-    script:
-      - cd /root/services/compose-sku3d-test
-      - docker pull registry.cn-chengdu.aliyuncs.com/sku3d/maintest:latest
-      - docker-compose up -d
-      - docker-compose ps
-  when:
-    branch:
-    - test
-
-- name: 3.dockerbuild(release)  
-  image: plugins/docker
-  settings:
-    username: infish2018
-    password: infish@2022
-    repo: registry.cn-chengdu.aliyuncs.com/sku3d/main
-    registry: registry.cn-chengdu.aliyuncs.com
-    mirror: https://17itejgx.mirror.aliyuncs.com
-
-    tags:
-      - latest
-      - '1.0.13'
-    context: sku3d/
-    dockerfile: sku3d/Dockerfile
-  when:
-    branch:
-    - release
-
-- name: 4.ssh-deploy(release)
-  image: appleboy/drone-ssh
-  settings:
-    host:
-      - 124.71.139.24
-    username: root
-    password:
-      from_secret: serverpassword
-    port: 22
-    command_timeout: 3m
-    script:
-      - cd /root/services/platform-release
-      - docker pull registry.cn-chengdu.aliyuncs.com/sku3d/main:latest
-      - docker-compose up -d
-      - docker-compose ps
-  when:
-    branch:
-    - release
-
-- name: 3.dockerbuild(dadongtest)  
-  image: plugins/docker
-  settings:
-    username: infish2018
-    password: infish@2022
-    repo: registry.cn-chengdu.aliyuncs.com/waibao/dadongmaintest
-    registry: registry.cn-chengdu.aliyuncs.com
-    mirror: https://17itejgx.mirror.aliyuncs.com
-
-    tags:
-      - latest
-      - '0.0.1'
-    context: sku3d/
-    dockerfile: sku3d/Dockerfile
-  when:
-    branch:
-    - dadongtest
-
-- name: 4.ssh-deploy(dadongtest)
-  image: appleboy/drone-ssh
-  settings:
-    host:
-      - 124.71.170.116
-    username: root
-    password:
-      from_secret: dadongpassword
-    port: 22
-    command_timeout: 3m
-    script:
-      - cd /root/sku3d-test
-      - docker pull registry.cn-chengdu.aliyuncs.com/waibao/dadongmaintest:latest
-      - docker-compose up -d
-      - docker-compose ps
-  when:
-    branch:
-    - dadongtest
-
-- name: 3.dockerbuild(dadongrelease)  
-  image: plugins/docker
-  settings:
-    username: infish2018
-    password: infish@2022
-    repo: registry.cn-chengdu.aliyuncs.com/waibao/dadongmain
-    registry: registry.cn-chengdu.aliyuncs.com
-    mirror: https://17itejgx.mirror.aliyuncs.com
-
-    tags:
-      - latest
-      - '0.0.1'
-    context: sku3d/
-    dockerfile: sku3d/Dockerfile
-  when:
-    branch:
-    - dadongrelease
-
-- name: 4.ssh-deploy(dadongrelease)
-  image: appleboy/drone-ssh
-  settings:
-    host:
-      - 124.71.170.116
-    username: root
-    password:
-      from_secret: dadongpassword
-    port: 22
-    command_timeout: 3m
-    script:
-      - cd /root/sku3d
-      - docker pull registry.cn-chengdu.aliyuncs.com/waibao/dadongmain:latest
-      - docker-compose up -d
-      - docker-compose ps
-  when:
-    branch:
-    - dadongrelease
-
-trigger:
-  branch:
-  - test
-  - release
-  - dadongrelease
-  - dadongtest
-  
-  event:
-  - push
-  

sku3d/.gitmodules

@@ -1,3 +0,0 @@
-[submodule "comm"]
-	path = comm
-	url = http://124.70.149.18:10880/service/comm.git