215 lines
7.9 KiB
Python
215 lines
7.9 KiB
Python
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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 BeitImageProcessor, BeitForImageClassification
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# 根据模型实际架构选择类
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from transformers import ViTForImageClassification, BeitForImageClassification
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from tqdm import tqdm
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from transformers import AutoConfig
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from transformers import AutoImageProcessor, AutoModelForImageClassification
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import os
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import random
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import time # 新增导入时间模块
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# 支持 Iluvatar GPU 加速，若不可用则使用 CPU
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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print(f"当前使用的设备: {device}") # 添加调试信息
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# 若有多块 GPU，可使用 DataParallel 进行并行计算
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if torch.cuda.device_count() > 1:
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print(f"使用 {torch.cuda.device_count()} 块 GPU 进行计算")
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class COCOImageClassifier:
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def __init__(self, model_path: str, local_image_paths: list):
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"""初始化COCO图像分类器"""
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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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# 将模型移动到设备
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self.model = self.model.to(device)
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print(f"模型是否在 GPU 上: {next(self.model.parameters()).is_cuda}") # 添加调试信息
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# 若有多块 GPU，使用 DataParallel
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if 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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self.local_image_paths = local_image_paths
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def predict_image_path(self, image_path: str, top_k: int = 5) -> dict:
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"""
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预测本地图片文件对应的图片类别
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Args:
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image_path: 本地图片文件路径
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top_k: 返回置信度最高的前k个类别
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Returns:
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包含预测结果的字典
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"""
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try:
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# 打开图片
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image = Image.open(image_path).convert("RGB")
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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(device)
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# 模型推理
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with torch.no_grad():
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outputs = self.model(**inputs)
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# 获取预测结果
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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(top_k, dim=1)
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# 整理结果
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predictions = []
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for i in range(top_k):
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class_idx = top_indices[0, i].item()
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confidence = top_probs[0, i].item()
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predictions.append({
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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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})
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return {
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"image_path": image_path,
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"predictions": predictions
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}
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except Exception as e:
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print(f"处理图片文件 {image_path} 时出错: {e}")
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return None
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def batch_predict(self, limit: int = 20, top_k: int = 5) -> list:
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"""
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批量预测本地图片
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Args:
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limit: 限制处理的图片数量
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top_k: 返回置信度最高的前k个类别
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Returns:
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包含所有预测结果的列表
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"""
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results = []
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local_image_paths = self.local_image_paths[:limit]
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print(f"开始预测 {len(local_image_paths)} 张本地图片...")
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start_time = time.time() # 记录开始时间
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for image_path in tqdm(local_image_paths):
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result = self.predict_image_path(image_path, top_k)
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if result:
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results.append(result)
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end_time = time.time() # 记录结束时间
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total_time = end_time - start_time # 计算总时间
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images_per_second = len(results) / total_time # 计算每秒处理的图片数量
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print(f"模型每秒可以处理 {images_per_second:.2f} 张图片")
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return results
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def save_results(self, results: list, output_file: str = "caltech_predictions.json"):
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"""
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保存预测结果到JSON文件
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Args:
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results: 预测结果列表
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output_file: 输出文件名
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"""
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with open(output_file, 'w', encoding='utf-8') as f:
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json.dump(results, f, ensure_ascii=False, indent=2)
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print(f"结果已保存到 {output_file}")
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# 主程序
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if __name__ == "__main__":
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# 替换为本地模型路径
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LOCAL_MODEL_PATH = "/home/zhoushasha/models/microsoft_beit_base_patch16_224_pt22k_ft22k"
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# 替换为Caltech 256数据集文件夹路径
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CALTECH_256_PATH = "/home/zhoushasha/models/256ObjectCategoriesNew"
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local_image_paths = []
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true_labels = {}
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# 遍历Caltech 256数据集中的每个文件夹
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for folder in os.listdir(CALTECH_256_PATH):
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folder_path = os.path.join(CALTECH_256_PATH, folder)
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if os.path.isdir(folder_path):
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# 获取文件夹名称中的类别名称
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class_name = folder.split('.', 1)[1]
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# 获取文件夹中的所有图片文件
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image_files = [os.path.join(folder_path, f) for f in os.listdir(folder_path) if f.endswith(('.jpg', '.jpeg', '.png'))]
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# 随机选择3张图片
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selected_images = random.sample(image_files, min(3, len(image_files)))
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for image_path in selected_images:
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local_image_paths.append(image_path)
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true_labels[image_path] = class_name
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# 创建分类器实例
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classifier = COCOImageClassifier(LOCAL_MODEL_PATH, local_image_paths)
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# 批量预测
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results = classifier.batch_predict(limit=len(local_image_paths), top_k=3)
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# 保存结果
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classifier.save_results(results)
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# 打印简要统计
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print(f"\n处理完成: 成功预测 {len(results)} 张图片")
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if results:
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print("\n示例预测结果:")
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sample = results[0]
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print(f"图片路径: {sample['image_path']}")
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for i, pred in enumerate(sample['predictions'], 1):
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print(f"{i}. {pred['class_name']} (置信度: {pred['confidence']:.2%})")
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correct_count = 0
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total_count = len(results)
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# 统计每个类别的实际样本数和正确预测数
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class_actual_count = {}
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class_correct_count = {}
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for prediction in results:
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image_path = prediction['image_path']
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top1_prediction = max(prediction['predictions'], key=lambda x: x['confidence'])
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predicted_class = top1_prediction['class_name'].lower()
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true_class = true_labels.get(image_path).lower()
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# 统计每个类别的实际样本数
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if true_class not in class_actual_count:
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class_actual_count[true_class] = 0
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class_actual_count[true_class] += 1
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# 检查预测类别中的每个单词是否包含真实标签
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words = predicted_class.split()
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for word in words:
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if true_class in word:
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correct_count += 1
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# 统计每个类别的正确预测数
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if true_class not in class_correct_count:
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class_correct_count[true_class] = 0
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class_correct_count[true_class] += 1
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break
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accuracy = correct_count / total_count
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print(f"\nAccuracy: {accuracy * 100:.2f}%")
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# 计算每个类别的召回率
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recall_per_class = {}
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for class_name in class_actual_count:
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if class_name in class_correct_count:
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recall_per_class[class_name] = class_correct_count[class_name] / class_actual_count[class_name]
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else:
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recall_per_class[class_name] = 0
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# 计算平均召回率
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average_recall = sum(recall_per_class.values()) / len(recall_per_class)
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print(f"\nAverage Recall: {average_recall * 100:.2f}%")
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