133 lines
6.1 KiB
Python
133 lines
6.1 KiB
Python
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 model_data_processing.processing import shuffle_data
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from Load_process.LoadData import Load_Data_Prepare
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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 time
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import torch
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import os
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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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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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experiment = experiments(tool, Status)
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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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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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Data_Dict_Data = loading_data.process_main(Label_Length)
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Data_Dict_Data = shuffle_data(Data_Dict_Data, Labels, 2)
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tmp = []
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Train_Size = 0
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if len(Data_Dict_Data[Labels[0]]) >= len(Data_Dict_Data[Labels[1]]):
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Train_Size = len(Data_Dict_Data[Labels[1]])
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for j in range(Train_Size):
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tmp.append(Data_Dict_Data[Labels[0]][j])
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Data_Dict_Data[Labels[0]] = tmp
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else:
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Train_Size = len(Data_Dict_Data[Labels[0]])
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for j in range(Train_Size):
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tmp.append(Data_Dict_Data[Labels[1]][j])
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Data_Dict_Data[Labels[1]] = tmp
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# 輸出內容
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print("Negative Data有 " + str(len(Data_Dict_Data[Labels[1]])) + " 筆資料")
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print("Positive Data有 " + str(len(Data_Dict_Data[Labels[0]])) + " 筆資料")
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print("總共有 " + str(len(Data_Dict_Data[Labels[0]]) + len(Data_Dict_Data[Labels[1]])) + " 筆資料")
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# 做出跟資料相同數量的Label
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Negative_Num = image_processing.make_label_list(Train_Size, Encording_Label[1])
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Positive_Num = image_processing.make_label_list(Train_Size, Encording_Label[0])
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# 將資料做成Dict的資料型態
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Prepare.Set_Final_Dict_Data(Labels, Data_Dict_Data, [Positive_Num, Negative_Num], 2)
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Final_Dict_Data = Prepare.Get_Final_Data_Dict()
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keys = list(Final_Dict_Data.keys())
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training_data = Merge.merge_all_image_data(Final_Dict_Data[keys[0]], Final_Dict_Data[keys[1]]) # 將訓練資料合併成一個list
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training_label = Merge.merge_all_image_data(Final_Dict_Data[keys[2]], Final_Dict_Data[keys[3]]) #將訓練資料的label合併成一個label的list
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start = time.time()
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trains_Data_Image = image_processing.Data_Augmentation_Image(training_data) # 多執行續讀檔
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total_trains, train_label = shuffle_data(trains_Data_Image, training_label) # 將資料打亂
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training_data = list(total_trains) # 轉換資料型態
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print(len(training_data))
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training_data, train_label = image_processing.image_data_processing(training_data, train_label) # 將讀出來的檔做正規化。降label轉成numpy array 格式
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training_data = image_processing.normalization(training_data)
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end = time.time()
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print("\n\n\n讀取訓練資料(70000)執行時間：%f 秒\n\n" % (end - start))
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# 針對其他資料執行值方圖等化加去背
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# with ProcessPoolExecutor() as executor: ## 默认为1，多執行續
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# trains = list(executor.map(adaptive_histogram_equalization, trains_another))
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# # 針對正常資料做值方圖等化加去背
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# train_normal = list(executor.map(image_processing.get_data, normal)) # 多執行續讀檔
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# normal = list(executor.map(Remove_Background, train_normal)) # 多執行續去背
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# normal_data = list(executor.map(adaptive_histogram_equalization, normal))
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# # 針對猴痘水痘進行讀檔
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# Chickenpox = list(executor.map(image_processing.get_data, chickenpox)) # 多執行續讀檔
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# train_chickenpox = list(executor.map(shapen, Chickenpox))
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# Monkeypox = list(executor.map(image_processing.get_data, monkeypox)) # 多執行續讀檔
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# train_monkeypox = list(executor.map(shapen, Monkeypox)) # 銳化
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# for All_Normal_Data in normal_data:
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# trains.append(All_Normal_Data)
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# training_label.append([0, 0, 0, 0, 0, 1, 0])
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# for All_Chhickenpox_Data in train_chickenpox:
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# trains.append(All_Chhickenpox_Data)
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# training_label.append([0, 0, 0, 1, 0, 0, 0])
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# for All_Monkeypox_Data in train_monkeypox:
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# trains.append(All_Monkeypox_Data)
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# training_label.append([0, 0, 0, 0, 1, 0, 0])
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loss, accuracy, precision, recall, AUC, f = experiment.processing_main(training_data, train_label, 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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