commit b218b5c6478d6c371cfd597c778826b96005a528 Author: zhousha <736730048@qq.com> Date: Thu Sep 18 15:32:10 2025 +0800 update 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_muxi b/Dockerfile_muxi new file mode 100644 index 0000000..9c7f407 --- /dev/null +++ b/Dockerfile_muxi @@ -0,0 +1,17 @@ +FROM mxc500-torch2.4-py310:mc2.33.0.6-ubuntu22.04-amd64 + +WORKDIR /workspace/ +COPY ./model_test_caltech_http_muxi.py /workspace/ +COPY ./microsoft_beit_base_patch16_224_pt22k_ft22k /model + +# 安装transformers 4.46.3 +RUN /opt/conda/bin/python3 -m pip install --upgrade pip +RUN /opt/conda/bin/python3 -m pip install --no-cache-dir transformers==4.46.3 +RUN /opt/conda/bin/python3 -m pip install flask==3.1.1 + + +EXPOSE 80 + +ENTRYPOINT ["/opt/conda/bin/python3", "model_test_caltech_http_muxi.py"] + + diff --git a/README.md b/README.md new file mode 100644 index 0000000..54d5bb8 --- /dev/null +++ b/README.md @@ -0,0 +1,33 @@ +# 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 +docker run -it \ + -p 10086:80 \ + --device=/dev/iluvatar0:/dev/iluvatar0 \ + --name mr_translation_test \ + mr100-my:v3 +``` + +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_muxi.py b/model_test_caltech_http_muxi.py new file mode 100644 index 0000000..063919d --- /dev/null +++ b/model_test_caltech_http_muxi.py @@ -0,0 +1,213 @@ +import requests +import json +import torch +from PIL import Image +from io import BytesIO +from transformers import AutoImageProcessor, AutoModelForImageClassification +import os +import time +import subprocess +from flask import Flask, request, jsonify + +class ImageClassifier: + def __init__(self, model_path: str, device: torch.device): + """初始化图像分类器，指定设备""" + # 模型路径有效性校验 + if not os.path.exists(model_path): + raise ValueError(f"模型路径不存在: {model_path}") + if not os.path.isdir(model_path): + raise ValueError(f"模型路径不是目录: {model_path}") + + # 检查模型必要文件 + required_files = ["config.json", "pytorch_model.bin"] + missing_files = [f for f in required_files if not os.path.exists(os.path.join(model_path, f))] + if missing_files: + raise ValueError(f"模型路径缺少必要文件: {missing_files}") + + self.processor = AutoImageProcessor.from_pretrained(model_path) + self.model = AutoModelForImageClassification.from_pretrained(model_path) + + # 将模型移动到指定设备 + self.model = self.model.to(device) + self.device = device + + # 检查设备类型并打印信息 + if device.type == "cuda": + if is_metax_gpu(): + print(f"模型是否在沐曦GPU上: {next(self.model.parameters()).device.type == 'cuda'}") + else: + print(f"模型是否在NVIDIA GPU上: {next(self.model.parameters()).is_cuda}") + else: + print(f"模型在 {device.type.upper()} 上运行") + + # 多卡处理 + if device.type == "cuda" and torch.cuda.device_count() > 1: + self.model = torch.nn.DataParallel(self.model) + + self.id2label = self.model.module.config.id2label if hasattr(self.model, 'module') else self.model.config.id2label + + def predict_single_image(self, image: Image.Image) -> dict: + """预测单张PIL图片""" + try: + # 预处理 + inputs = self.processor(images=image, return_tensors="pt") + + # 将输入数据移动到设备 + inputs = inputs.to(self.device) + + # 模型推理 + start_time = time.time() + + + with torch.no_grad(): + ts = time.time() + outputs = self.model(** inputs) + print('muxi T1', time.time() - ts, flush=True) + + ts = time.time() + for i in range(1000): + outputs = self.model(**inputs) + print('muxi T2', time.time() - ts, flush=True) + + processing_time = time.time() - start_time + + # 获取预测结果（只取置信度最高的一个） + logits = outputs.logits + probs = torch.nn.functional.softmax(logits, dim=1) + top_probs, top_indices = probs.topk(1, dim=1) + + # 整理结果 + class_idx = top_indices[0, 0].item() + confidence = top_probs[0, 0].item() + + return { + "class_id": class_idx, + "class_name": self.id2label[class_idx], + "confidence": confidence, + "device_used": str(self.device), + "processing_time": processing_time + } + + except Exception as e: + print(f"处理图片时出错: {e}") + return { + "class_id": -1, + "class_name": "error", + "confidence": 0.0, + "device_used": str(self.device), + "processing_time": 0.0, + "error": str(e) + } + +def is_metax_gpu(): + """检查是否为沐曦GPU""" + try: + # 检查系统命令 mx-smi + result = subprocess.run(['which', 'mx-smi'], capture_output=True, text=True) + if result.returncode == 0: + return True + + # 检查PCI设备信息 + result = subprocess.run(['lspci'], capture_output=True, text=True) + if 'MetaX' in result.stdout or 'MXC' in result.stdout or '1e66' in result.stdout: + return True + except: + pass + return False + +def check_metax_available(): + """检查沐曦GPU是否可用""" + return torch.cuda.is_available() and is_metax_gpu() + +def get_device(): + """获取最佳可用设备（优先沐曦GPU）""" + # 首先检查沐曦GPU + if check_metax_available(): + print("检测到沐曦GPU可用") + return torch.device("cuda:0") + + # 然后检查NVIDIA GPU + elif torch.cuda.is_available(): + print("检测到NVIDIA GPU可用") + return torch.device("cuda:0") + + # 最后使用CPU + else: + print("未检测到加速设备，使用CPU") + return torch.device("cpu") + +def setup_metax_environment(): + """设置沐曦GPU环境""" + if check_metax_available(): + print("正在设置沐曦GPU环境...") + try: + os.environ['MX_VISIBLE_DEVICES'] = '0' # 使用第一张沐曦卡 + print("沐曦GPU环境设置完成") + except Exception as e: + print(f"设置沐曦环境时出错: {e}") + +# 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） + +# 设置沐曦环境并初始化分类器 +setup_metax_environment() +device = get_device() +classifier = ImageClassifier(MODEL_PATH, device) + +@app.route('/v1/private/s782b4996', methods=['POST']) +def predict_single(): + """接收单张图片并返回预测结果""" + if 'image' not in request.files: + return jsonify({ + "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" + }, + "status": "error" + }), 400 + + image_file = request.files['image'] + try: + image = Image.open(BytesIO(image_file.read())).convert("RGB") + + # 获取预测结果 + prediction_result = classifier.predict_single_image(image) + + # 构建响应 + response = { + "prediction": prediction_result, + "status": "success" + } + + return jsonify(response) + + except Exception as e: + return jsonify({ + "prediction": { + "class_id": -1, + "class_name": "error", + "confidence": 0.0, + "device_used": str(device), + "processing_time": 0.0, + "error": str(e) + }, + "status": "error" + }), 500 + +@app.route('/health', methods=['GET']) +def health_check(): + """健康检查接口""" + return jsonify({ + "status": "healthy", + "metax_available": check_metax_available(), + "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)