update

.gitignore

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+*.zip
+.DS_Store

Dockerfile

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+FROM harbor-contest.4pd.io/luxinlong02/sherpa-onnx-offline-asr:1.12.5-mr100-corex-4.3.0-zh-en
+
+WORKDIR /workspace/
+COPY ./model_test_caltech_http_mr100.py /workspace/
+COPY ./microsoft_beit_base_patch16_224_pt22k_ft22k /model
+
+# Try common distros; the first that exists will succeed
+RUN (apt-get update && apt-get install -y python3-pip) || \
+    (microdnf install -y python3-pip) || \
+    (dnf install -y python3-pip) || \
+    (yum install -y python3-pip) || \
+    (apk add --no-cache py3-pip)
+
+# 安装transformers 4.46.3
+RUN python3 -m pip install --no-cache-dir transformers==4.46.3
+
+
+RUN python3 -m pip install flask==3.1.1
+
+EXPOSE 80
+
+ENTRYPOINT ["python3", "model_test_caltech_http_mr100.py"]
+

README.md

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+# enginex-ascend-910-vc-cnn
+
+运行于【天数智芯-智铠】系列算力卡的【视觉分类】引擎，基于 CNN 架构，支持 BEiT、MobileViT 等流行模型
+
+## QuickStart
+
+1、从 modelscope上下载视觉分类的模型，例如 microsoft/beit-base-patch16-224
+```python
+modelscope download --model microsoft/beit-base-patch16-224 README.md --local_dir /mnt/contest_ceph/zhoushasha/models/microsoft/beit_base_patch16_224_pt22k_ft22k
+```
+
+2、使用Dockerfile生成镜像
+联系天数智芯-智铠厂商，获取基础镜像corex:4.3.0
+使用 Dockerfile 生成 镜像
+```python
+docker build -f Dockerfile -t mr100-my:v3 .
+```
+注意 Dockerfile 中已预先将模型 microsoft_beit_base_patch16_224_pt22k_ft22k 放在了 /model 下面
+
+3、启动docker
+```python
+
+```
+
+4、测试服务
+```python
+curl -X POST http://localhost:10086/v1/private/s782b4996 \
+>   -F "image=@/home/zhoushasha/models/026_0010.jpg"
+```

model_test_caltech_http_mr100.py

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+import torch
+import time
+import os
+from PIL import Image
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+from flask import Flask, request, jsonify
+from io import BytesIO
+
+# 设置CPU核心数（仅用于可能的底层优化，不影响GPU推理）
+os.environ["OMP_NUM_THREADS"] = "4"
+os.environ["MKL_NUM_THREADS"] = "4"
+os.environ["NUMEXPR_NUM_THREADS"] = "4"
+os.environ["OPENBLAS_NUM_THREADS"] = "4"
+os.environ["VECLIB_MAXIMUM_THREADS"] = "4"
+torch.set_num_threads(4)
+
+# 设备配置 - 只关注GPU
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+print(f"当前设备: {device}")
+print(f"CPU核心数设置: {torch.get_num_threads()}")
+
+class ImageClassifier:
+    def __init__(self, model_path: str):
+        self.processor = AutoImageProcessor.from_pretrained(model_path)
+        
+        # 仅加载GPU模型
+        if device.type == "cuda":
+            self.model = AutoModelForImageClassification.from_pretrained(model_path).to(device)
+        else:
+            self.model = None  # 无GPU时模型为None
+        
+        # 保存id2label映射（从模型配置获取）
+        if self.model:
+            self.id2label = self.model.config.id2label
+        else:
+            self.id2label = None
+
+    def _predict_with_model(self, image) -> dict:
+        """使用GPU执行预测"""
+        try:
+            # 检查GPU模型是否可用
+            if not self.model or device.type != "cuda":
+                return {
+                    "class_id": -1,
+                    "class_name": "error",
+                    "confidence": 0.0,
+                    "device_used": str(device),
+                    "processing_time": 0.0,
+                    "error": "CUDA设备不可用或模型未加载"
+                }
+            
+            # 记录开始时间
+            start_time = time.perf_counter()
+            
+            # 处理图片并移动到GPU
+            inputs = self.processor(images=image, return_tensors="pt").to(device)
+            
+            with torch.no_grad():
+                ts = time.time()
+                outputs = self.model(** inputs)
+                print('mr100 T1', time.time() - ts, flush=True)
+                
+                ts = time.time()
+                for i in range(1000):
+                    outputs = self.model(**inputs)
+                print('mr100 T2', time.time() - ts, flush=True)
+                
+            logits = outputs.logits
+            probs = torch.nn.functional.softmax(logits, dim=1)
+            max_prob, max_idx = probs.max(dim=1)
+            class_idx = max_idx.item()
+            
+            # 计算处理时间
+            processing_time = round(time.perf_counter() - start_time, 6)
+            
+            return {
+                "class_id": class_idx,
+                "class_name": self.id2label[class_idx],
+                "confidence": float(max_prob.item()),
+                "device_used": str(device),
+                "processing_time": processing_time
+            }
+        except Exception as e:
+            return {
+                "class_id": -1,
+                "class_name": "error",
+                "confidence": 0.0,
+                "device_used": str(device),
+                "processing_time": 0.0,
+                "error": str(e)
+            }
+
+    def predict_single_image(self, image) -> dict:
+        """预测单张图片，仅使用GPU"""
+        results = {"status": "success"}
+        results["prediction"] = self._predict_with_model(image)
+        return results
+
+# 初始化服务
+app = Flask(__name__)
+MODEL_PATH = os.environ.get("MODEL_PATH", "/model")  # 模型路径（环境变量或默认路径）
+classifier = ImageClassifier(MODEL_PATH)
+
+@app.route('/v1/private/s782b4996', methods=['POST'])
+def predict_single():
+    """接收单张图片并返回GPU预测结果"""
+    if 'image' not in request.files:
+        return jsonify({
+            "status": "error",
+            "prediction": {
+                "class_id": -1,
+                "class_name": "error",
+                "confidence": 0.0,
+                "device_used": str(device),
+                "processing_time": 0.0,
+                "error": "请求中未包含图片"
+            }
+        }), 400
+    
+    image_file = request.files['image']
+    try:
+        image = Image.open(BytesIO(image_file.read())).convert("RGB")
+        result = classifier.predict_single_image(image)
+        return jsonify(result)
+    except Exception as e:
+        return jsonify({
+            "status": "error",
+            "prediction": {
+                "class_id": -1,
+                "class_name": "error",
+                "confidence": 0.0,
+                "device_used": str(device),
+                "processing_time": 0.0,
+                "error": str(e)
+            }
+        }), 500
+
+@app.route('/health', methods=['GET'])
+def health_check():
+    return jsonify({
+        "status": "healthy", 
+        "cuda_available": device.type == "cuda",
+        "device_used": str(device),
+        "cpu_threads": torch.get_num_threads()
+    }), 200
+
+if __name__ == "__main__":
+    app.run(host='0.0.0.0', port=80, debug=False)