199 lines
6.5 KiB
Python
199 lines
6.5 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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from tqdm import tqdm
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import os
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import random
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import time
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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，指定指定设备"""
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# 增加模型路径有效性详细校验
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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"] # 基础模型文件
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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相应信息
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if device.type == "cuda":
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print(f"模型是否在 GPU 上: {next(self.model.parameters()).is_cuda}")
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elif device.type == "npu":
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print(f"模型是否在 NPU 上: {next(self.model.parameters()).device.type == 'npu'}")
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else:
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print(f"模型在 {device.type.upper()} 上运行")
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# 若在GPU/NPU且有多块，使用DataParallel
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if device.type in ["cuda", "npu"] and (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('ascend 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('ascend 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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device_type = "npu" if self.device.type == "npu" else "cuda"
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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 check_ascend_available():
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"""检查昇腾NPU是否可用"""
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try:
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# 检查是否有昇腾相关的Python包
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import torch_npu
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# 检查NPU设备是否可用
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if hasattr(torch, 'npu') and torch.npu.is_available():
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return True
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except ImportError:
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pass
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return False
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def get_device():
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"""获取最佳可用设备"""
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# 首先检查昇腾NPU
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if check_ascend_available():
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print("检测到昇腾NPU可用")
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return torch.device("npu: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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# 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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# 获取设备并初始化分类器
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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NPU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NPU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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device_type = "npu" if device.type == "npu" else "cuda"
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return jsonify({
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"status": "healthy",
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"npu_available": device.type == "npu",
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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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