214 lines
7.0 KiB
Python
214 lines
7.0 KiB
Python
import requests
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import json
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import torch
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from PIL import Image
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from io import BytesIO
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from transformers import AutoImageProcessor, AutoModelForImageClassification
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import os
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import time
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import subprocess
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from flask import Flask, request, jsonify
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class ImageClassifier:
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def __init__(self, model_path: str, device: torch.device):
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"""初始化图像分类器，指定设备"""
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# 模型路径有效性校验
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if not os.path.exists(model_path):
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raise ValueError(f"模型路径不存在: {model_path}")
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if not os.path.isdir(model_path):
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raise ValueError(f"模型路径不是目录: {model_path}")
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# 检查模型必要文件
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required_files = ["config.json", "pytorch_model.bin"]
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missing_files = [f for f in required_files if not os.path.exists(os.path.join(model_path, f))]
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if missing_files:
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raise ValueError(f"模型路径缺少必要文件: {missing_files}")
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self.processor = AutoImageProcessor.from_pretrained(model_path)
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self.model = AutoModelForImageClassification.from_pretrained(model_path)
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# 将模型移动到指定设备
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self.model = self.model.to(device)
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self.device = device
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# 检查设备类型并打印信息
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if device.type == "cuda":
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if is_metax_gpu():
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print(f"模型是否在沐曦GPU上: {next(self.model.parameters()).device.type == 'cuda'}")
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else:
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print(f"模型是否在NVIDIA GPU上: {next(self.model.parameters()).is_cuda}")
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else:
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print(f"模型在 {device.type.upper()} 上运行")
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# 多卡处理
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if device.type == "cuda" and torch.cuda.device_count() > 1:
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self.model = torch.nn.DataParallel(self.model)
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self.id2label = self.model.module.config.id2label if hasattr(self.model, 'module') else self.model.config.id2label
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def predict_single_image(self, image: Image.Image) -> dict:
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"""预测单张PIL图片"""
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try:
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# 预处理
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inputs = self.processor(images=image, return_tensors="pt")
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# 将输入数据移动到设备
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inputs = inputs.to(self.device)
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# 模型推理
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start_time = time.time()
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with torch.no_grad():
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ts = time.time()
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outputs = self.model(** inputs)
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print('muxi T1', time.time() - ts, flush=True)
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ts = time.time()
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for i in range(1000):
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outputs = self.model(**inputs)
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print('muxi T2', time.time() - ts, flush=True)
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processing_time = time.time() - start_time
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# 获取预测结果（只取置信度最高的一个）
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logits = outputs.logits
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probs = torch.nn.functional.softmax(logits, dim=1)
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top_probs, top_indices = probs.topk(1, dim=1)
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# 整理结果
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class_idx = top_indices[0, 0].item()
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confidence = top_probs[0, 0].item()
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return {
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"class_id": class_idx,
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"class_name": self.id2label[class_idx],
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"confidence": confidence,
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"device_used": str(self.device),
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"processing_time": processing_time
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}
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except Exception as e:
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print(f"处理图片时出错: {e}")
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return {
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"class_id": -1,
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"class_name": "error",
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"confidence": 0.0,
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"device_used": str(self.device),
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"processing_time": 0.0,
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"error": str(e)
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}
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def is_metax_gpu():
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"""检查是否为沐曦GPU"""
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try:
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# 检查系统命令 mx-smi
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result = subprocess.run(['which', 'mx-smi'], capture_output=True, text=True)
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if result.returncode == 0:
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return True
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# 检查PCI设备信息
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result = subprocess.run(['lspci'], capture_output=True, text=True)
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if 'MetaX' in result.stdout or 'MXC' in result.stdout or '1e66' in result.stdout:
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return True
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except:
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pass
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return False
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def check_metax_available():
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"""检查沐曦GPU是否可用"""
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return torch.cuda.is_available() and is_metax_gpu()
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def get_device():
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"""获取最佳可用设备（优先沐曦GPU）"""
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# 首先检查沐曦GPU
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if check_metax_available():
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print("检测到沐曦GPU可用")
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return torch.device("cuda:0")
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# 然后检查NVIDIA GPU
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elif torch.cuda.is_available():
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print("检测到NVIDIA GPU可用")
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return torch.device("cuda:0")
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# 最后使用CPU
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else:
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print("未检测到加速设备，使用CPU")
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return torch.device("cpu")
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def setup_metax_environment():
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"""设置沐曦GPU环境"""
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if check_metax_available():
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print("正在设置沐曦GPU环境...")
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try:
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os.environ['MX_VISIBLE_DEVICES'] = '0' # 使用第一张沐曦卡
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print("沐曦GPU环境设置完成")
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except Exception as e:
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print(f"设置沐曦环境时出错: {e}")
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# 初始化服务
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app = Flask(__name__)
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MODEL_PATH = os.environ.get("MODEL_PATH", "/model") # 模型路径（环境变量或默认路径）
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# 设置沐曦环境并初始化分类器
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setup_metax_environment()
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device = get_device()
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classifier = ImageClassifier(MODEL_PATH, device)
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@app.route('/v1/private/s782b4996', methods=['POST'])
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def predict_single():
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"""接收单张图片并返回预测结果"""
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if 'image' not in request.files:
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return jsonify({
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"prediction": {
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"class_id": -1,
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"class_name": "error",
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"confidence": 0.0,
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"device_used": str(device),
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"processing_time": 0.0,
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"error": "请求中未包含图片"
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},
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"status": "error"
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}), 400
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image_file = request.files['image']
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try:
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image = Image.open(BytesIO(image_file.read())).convert("RGB")
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# 获取预测结果
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prediction_result = classifier.predict_single_image(image)
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# 构建响应
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response = {
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"prediction": prediction_result,
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"status": "success"
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}
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return jsonify(response)
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except Exception as e:
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return jsonify({
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"prediction": {
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"class_id": -1,
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"class_name": "error",
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"confidence": 0.0,
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"device_used": str(device),
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"processing_time": 0.0,
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"error": str(e)
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},
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"status": "error"
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}), 500
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@app.route('/health', methods=['GET'])
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def health_check():
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"""健康检查接口"""
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return jsonify({
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"status": "healthy",
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"metax_available": check_metax_available(),
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"device_used": str(device),
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"cpu_threads": torch.get_num_threads()
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}), 200
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if __name__ == "__main__":
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app.run(host='0.0.0.0', port=80, debug=False)
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