commit 4f46a976f3654abd42841d05b3b686c3b8d726b5 Author: zhousha <736730048@qq.com> Date: Mon Sep 15 16:17:15 2025 +0800 update diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..4c8b8eb --- /dev/null +++ b/.gitignore @@ -0,0 +1,2 @@ +*.zip +.DS_Store diff --git a/026_0010.jpg b/026_0010.jpg new file mode 100755 index 0000000..5571278 Binary files /dev/null and b/026_0010.jpg differ diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000..d107e63 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,23 @@ +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"] + diff --git a/README.md b/README.md new file mode 100644 index 0000000..449388c --- /dev/null +++ b/README.md @@ -0,0 +1,29 @@ +# 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" +``` diff --git a/model_test_caltech_http_mr100.py b/model_test_caltech_http_mr100.py new file mode 100644 index 0000000..5597531 --- /dev/null +++ b/model_test_caltech_http_mr100.py @@ -0,0 +1,148 @@ +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)