enginex-bi_series-vc-cnn/model_test_caltech_cpu1.py

import requests
import json
import torch
from PIL import Image
from io import BytesIO
from transformers import AutoImageProcessor, AutoModelForImageClassification
from tqdm import tqdm
import os
import random
import time

# 强制使用CPU
device = torch.device("cpu")
print(f"当前使用的设备: {device}")

class COCOImageClassifier:
    def __init__(self, model_path: str, local_image_paths: list):
        """初始化COCO图像分类器"""
        self.processor = AutoImageProcessor.from_pretrained(model_path)
        self.model = AutoModelForImageClassification.from_pretrained(model_path)
        
        # 将模型移动到CPU
        self.model = self.model.to(device)
        self.id2label = self.model.config.id2label
        self.local_image_paths = local_image_paths

    def predict_image_path(self, image_path: str, top_k: int = 5) -> dict:
        """
        预测本地图片文件对应的图片类别

        Args:
            image_path: 本地图片文件路径
            top_k: 返回置信度最高的前k个类别

        Returns:
            包含预测结果的字典
        """
        try:
            # 打开图片
            image = Image.open(image_path).convert("RGB")

            # 预处理
            inputs = self.processor(images=image, return_tensors="pt")

            # 将输入数据移动到CPU
            inputs = inputs.to(device)

            # 模型推理
            with torch.no_grad():
                outputs = self.model(**inputs)

            # 获取预测结果
            logits = outputs.logits
            probs = torch.nn.functional.softmax(logits, dim=1)
            top_probs, top_indices = probs.topk(top_k, dim=1)

            # 整理结果
            predictions = []
            for i in range(top_k):
                class_idx = top_indices[0, i].item()
                confidence = top_probs[0, i].item()
                predictions.append({
                    "class_id": class_idx,
                    "class_name": self.id2label[class_idx],
                    "confidence": confidence
                })

            return {
                "image_path": image_path,
                "predictions": predictions
            }

        except Exception as e:
            print(f"处理图片文件 {image_path} 时出错: {e}")
            return None

    def batch_predict(self, limit: int = 20, top_k: int = 5) -> list:
        """
        批量预测本地图片

        Args:
            limit: 限制处理的图片数量
            top_k: 返回置信度最高的前k个类别

        Returns:
            包含所有预测结果的列表
        """
        results = []
        local_image_paths = self.local_image_paths[:limit]

        print(f"开始预测 {len(local_image_paths)} 张本地图片...")
        start_time = time.time()
        for image_path in tqdm(local_image_paths):
            result = self.predict_image_path(image_path, top_k)
            if result:
                results.append(result)
        end_time = time.time()

        # 计算吞吐量
        throughput = len(results) / (end_time - start_time)
        print(f"模型每秒可以处理 {throughput:.2f} 张图片")

        return results

    def save_results(self, results: list, output_file: str = "celtech_cpu_predictions.json"):
        """
        保存预测结果到JSON文件

        Args:
            results: 预测结果列表
            output_file: 输出文件名
        """
        with open(output_file, 'w', encoding='utf-8') as f:
            json.dump(results, f, ensure_ascii=False, indent=2)

        print(f"结果已保存到 {output_file}")

# 主程序
if __name__ == "__main__":
    # 替换为本地模型路径
    LOCAL_MODEL_PATH = "/home/zhoushasha/models/microsoft_beit_base_patch16_224_pt22k_ft22k"

    # 替换为Caltech 256数据集文件夹路径 New
    CALTECH_256_PATH = "/home/zhoushasha/models/256ObjectCategoriesNew"

    local_image_paths = []
    true_labels = {}

    # 遍历Caltech 256数据集中的每个文件夹
    for folder in os.listdir(CALTECH_256_PATH):
        folder_path = os.path.join(CALTECH_256_PATH, folder)
        if os.path.isdir(folder_path):
            # 获取文件夹名称中的类别名称
            class_name = folder.split('.', 1)[1]
            # 获取文件夹中的所有图片文件
            image_files = [os.path.join(folder_path, f) for f in os.listdir(folder_path) if f.endswith(('.jpg', '.jpeg', '.png'))]
            # 随机选择3张图片
            selected_images = random.sample(image_files, min(3, len(image_files)))
            for image_path in selected_images:
                local_image_paths.append(image_path)
                true_labels[image_path] = class_name

    # 创建分类器实例
    classifier = COCOImageClassifier(LOCAL_MODEL_PATH, local_image_paths)

    # 批量预测
    results = classifier.batch_predict(limit=len(local_image_paths), top_k=3)

    # 保存结果
    classifier.save_results(results)

    # 打印简要统计
    print(f"\n处理完成: 成功预测 {len(results)} 张图片")
    if results:
        print("\n示例预测结果:")
        sample = results[0]
        print(f"图片路径: {sample['image_path']}")
        for i, pred in enumerate(sample['predictions'], 1):
            print(f"{i}. {pred['class_name']} (置信度: {pred['confidence']:.2%})")

    correct_count = 0
    total_count = len(results)
    class_true_positives = {}
    class_false_negatives = {}

    for prediction in results:
        image_path = prediction['image_path']
        top1_prediction = max(prediction['predictions'], key=lambda x: x['confidence'])
        predicted_class = top1_prediction['class_name'].lower()
        true_class = true_labels.get(image_path).lower()

        if true_class not in class_true_positives:
            class_true_positives[true_class] = 0
            class_false_negatives[true_class] = 0

        # 检查预测类别中的每个单词是否包含真实标签
        words = predicted_class.split()
        for word in words:
            if true_class in word:
                correct_count += 1
                class_true_positives[true_class] += 1
                break
        else:
            class_false_negatives[true_class] += 1

    accuracy = correct_count / total_count
    print(f"\nAccuracy: {accuracy * 100:.2f}%")

    # 计算召回率
    total_true_positives = 0
    total_false_negatives = 0
    for class_name in class_true_positives:
        total_true_positives += class_true_positives[class_name]
        total_false_negatives += class_false_negatives[class_name]

    recall = total_true_positives / (total_true_positives + total_false_negatives)
    print(f"Recall: {recall * 100:.2f}%")