61 lines
2.3 KiB
Python
Executable File
61 lines
2.3 KiB
Python
Executable File
from experiments.experiment import experiments
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from Image_Process.load_and_ImageGenerator import Load_ImageGenerator
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from Read_and_process_image.ReadAndProcess import Read_image_and_Process_image
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from Training_Tools.Tools import Tool
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from Load_process.LoadData import Load_Data_Prepare, Load_Data_Tools
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from Calculate_Process.Calculate import Calculate
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from merge_class.merge import merge
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import torch
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if __name__ == "__main__":
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# 測試GPU是否可用
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flag = torch.cuda.is_available()
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if not flag:
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print("CUDA不可用\n")
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else:
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print(f"CUDA可用，數量為{torch.cuda.device_count()}\n")
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Load_Tools = Load_Data_Tools()
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Status = 2 # 決定要使用什麼資料集
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# 要換不同資料集就要改
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tool = Tool()
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tool.Set_Labels()
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tool.Set_Save_Roots()
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Labels = tool.Get_Data_Label()
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Trainig_Root, Testing_Root, Validation_Root = tool.Get_Save_Roots(Status) # 一般的
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Generator_Root = tool.Get_Generator_Save_Roots(Status)
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# 取得One-hot encording 的資料
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tool.Set_OneHotEncording(Labels)
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Encording_Label = tool.Get_OneHot_Encording_Label()
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Label_Length = len(Labels)
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Gneerator_size = 0
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Prepare = Load_Data_Prepare()
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loading_data = Load_ImageGenerator(Trainig_Root, Testing_Root, Validation_Root, Generator_Root, Labels)
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image_processing = Read_image_and_Process_image()
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Merge = merge()
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Calculate_Tool = Calculate()
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counter = 5
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Number_Of_Count = 2
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for i in range(0, counter, 1): # 做規定次數的訓練
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# 讀取資料
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Image_Data, Total_Data_List = loading_data.process_main(Label_Length)
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Training_Data = Load_Tools.Get_Balance_Data(Image_Data, Total_Data_List)
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experiment = experiments(tool, Total_Data_List, Status, Number_Of_Count)
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loss, accuracy, precision, recall, AUC, f = experiment.processing_main(Training_Data, i) # 執行訓練方法
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Calculate_Tool.Append_numbers(loss, accuracy, precision, recall, AUC, f)
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print("實驗結果")
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print("--------------------------------------------")
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print("平均值: ")
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print(Calculate_Tool.Calculate_Mean())
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print("標準差: ")
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print(Calculate_Tool.Calculate_Std())
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print("結果: ")
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print(Calculate_Tool.Output_Style())
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