142 lines
6.6 KiB
Python
142 lines
6.6 KiB
Python
from experiments.experiment import experiments
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import torch
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import numpy as np
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from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score
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from Load_process.LoadData import Loding_Data_Root
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import os
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import seaborn as sns
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import datetime
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from Load_process.file_processing import Process_File
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from Load_process.Load_Indepentend import Load_Indepentend_Data
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from Training_Tools.PreProcess import Training_Precesses
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from Training_Tools.Tools import Tool
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import matplotlib.figure as figure
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import matplotlib.backends.backend_agg as agg
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from Calculate_Process.Calculate import Calculate
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import argparse
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import json
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from utils.Stomach_Config import Training_Config, Loading_Config
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from model_data_processing.processing import shuffle_data
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if __name__ == "__main__":
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# 解析命令行参数
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parser = argparse.ArgumentParser(description='评估单一类别的准确度、精确度、召回率和F1值')
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parser.add_argument('--target_class', type=int, default=0, help='要评估的目标类别索引 (0, 1, 2)')
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args = parser.parse_args()
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# 测试GPU是否可用
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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print(f"使用设备: {device}")
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# 设置GPU
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if torch.cuda.is_available():
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torch.cuda.set_device(0)
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print(f"GPU: {torch.cuda.get_device_name(0)}")
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# 初始化对象
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tool = Tool()
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Status = 1 # 決定要使用什麼資料集
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# 取得One-hot encording 的資料
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tool.Set_OneHotEncording(Loading_Config["Training_Labels"])
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Encording_Label = tool.Get_OneHot_Encording_Label()
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Label_Length = len(Loading_Config["Training_Labels"])
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# 检查目标类别是否有效
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target_class = args.target_class
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training_labels = Loading_Config["Training_Labels"]
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if target_class < 0 or target_class >= len(training_labels):
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print(f"错误: 目标类别索引 {target_class} 无效，必须在 0 到 {len(training_labels)-1} 之间")
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exit(1)
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print(f"正在评估类别: {training_labels[target_class]}")
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load = Loding_Data_Root(Loading_Config["Training_Labels"], Loading_Config["Train_Data_Root"], Loading_Config["ImageGenerator_Data_Root"])
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cut_image = Load_Indepentend_Data(Loading_Config["Training_Labels"], Encording_Label)
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# 创建模型
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experiment = experiments(Training_Config, Loading_Config, tool, 3, "Test")
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model = experiment.construct_model()
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# 加载模型权重
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Model_Weight = [
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"../Result/save_the_best_model/Xception Skin trains Stomach Cancer Dataset, and uses WeightRandomSampler Change HSV Channel of V is -150/Xception/best_model( 2025-05-19 )-_fold0.pt",
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"../Result/save_the_best_model/Xception Skin trains Stomach Cancer Dataset, and uses WeightRandomSampler Change HSV Channel of V is -150/Xception/best_model( 2025-05-19 )-_fold1.pt",
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"../Result/save_the_best_model/Xception Skin trains Stomach Cancer Dataset, and uses WeightRandomSampler Change HSV Channel of V is -150/Xception/best_model( 2025-05-19 )-_fold2.pt",
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"../Result/save_the_best_model/Xception Skin trains Stomach Cancer Dataset, and uses WeightRandomSampler Change HSV Channel of V is -150/Xception/best_model( 2025-05-19 )-_fold3.pt",
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"../Result/save_the_best_model/Xception Skin trains Stomach Cancer Dataset, and uses WeightRandomSampler Change HSV Channel of V is -150/Xception/best_model( 2025-05-19 )-_fold4.pt"
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]
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Calculate_Tool = [Calculate() for i in range(3)]
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i = 0
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for path in Model_Weight:
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if os.path.exists(path):
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model.load_state_dict(torch.load(path))
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print("读取权重完成\n")
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model.eval() # 设置为评估模式
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# 预处理对象
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preprocess = Training_Precesses(256)
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cut_image.process_main(Loading_Config["Test_Data_Root"]) # 呼叫處理test Data與Validation Data的function
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test, test_label = cut_image.test, cut_image.test_label
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test, test_label = shuffle_data(test, test_label)
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# 只评估目标类别
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# 转换为PyTorch张量并移动到设备
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test_dataset = preprocess.Setting_DataSet(cut_image.test, cut_image.test_label, "Transform")
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test_loader = preprocess.Dataloader_Sampler(test_dataset, 1, False)
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all_results = []
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all_labels = []
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# 使用PyTorch的预测方式
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with torch.no_grad(): # 不计算梯度
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for inputs, labels, _, _ in test_loader:
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inputs = inputs.to(device)
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labels = labels.to(device)
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outputs = model(inputs)
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_, predicted = torch.max(outputs, 1)
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all_results.append(predicted.cpu().numpy())
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all_labels.append(np.argmax(labels.cpu().numpy(), axis=1))
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# 合并结果
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Predict = np.concatenate(all_results)
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y_test = np.concatenate(all_labels)
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print(f"{training_labels[target_class]} 预测结果: {Predict}\n")
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# 计算评估指标
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accuracy = accuracy_score(y_test, Predict)
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precision = precision_score(y_test, Predict, average=None)
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recall = recall_score(y_test, Predict, average=None)
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F1 = f1_score(y_test, Predict, average=None)
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# 计算每个类别的准确率
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class_accuracies = []
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for class_idx in range(len(training_labels)):
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class_mask = (y_test == class_idx)
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class_accuracy = accuracy_score(y_test[class_mask], Predict[class_mask])
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class_accuracies.append(class_accuracy)
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print(f"运行 {i+1}:\n")
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print(f"整体准确率 (Accuracy): {accuracy:.4f}")
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for class_idx in range(len(training_labels)):
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print(f"类别 {training_labels[class_idx]} 的评估指标:")
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print(f" 准确率 (Accuracy): {class_accuracies[class_idx]:.4f}")
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print(f" 精确率 (Precision): {precision[class_idx]:.4f}")
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print(f" 召回率 (Recall): {recall[class_idx]:.4f}")
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print(f" F1值: {F1[class_idx]:.4f}\n")
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Calculate_Tool[class_idx].Append_numbers(0, class_accuracies[class_idx], precision[class_idx], recall[class_idx], 0, F1[class_idx])
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i += 1
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# 计算平均值和标准差
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for i in range(3):
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Calculate_Tool[i].Calculate_Mean()
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Calculate_Tool[i].Calculate_Std()
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print(f"\n{training_labels[target_class]} 类别的评估结果:")
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print(Calculate_Tool[i].Output_Style())
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