97 lines
4.1 KiB
Python
97 lines
4.1 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 Balance_Process
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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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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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# 參數設定
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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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Status = 1 # 決定要使用什麼資料集
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Labels = tool.Get_Data_Label()
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Trainig_Root, Testing_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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Classification = 3 # 分類數量
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Model_Name = "Xception" # 取名，告訴我我是用哪個模型(可能是預處理模型/自己設計的模型)
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Experiment_Name = "Xception Skin to train Normal stomach cancer"
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Epoch = 10000
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Train_Batch_Size = 64
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Image_Size = 256
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Prepare = Load_Data_Prepare()
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loading_data = Load_ImageGenerator(Trainig_Root, Testing_Root, Generator_Root, Labels, Image_Size)
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experiment = experiments(Image_Size, Model_Name, Experiment_Name, Epoch, Train_Batch_Size, tool, Classification, Status)
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image_processing = Read_image_and_Process_image(Image_Size)
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Merge = merge()
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Calculate_Tool = Calculate()
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counter = 1
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Batch_Size = 128
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Train_Size = 0
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for Run_Range 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, Train_Size = Balance_Process(Data_Dict_Data, Labels)
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for label in Labels:
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Train_Size += len(Data_Dict_Data[label])
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print("總共有 " + str(Train_Size) + " 筆資料")
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# 做出跟資料相同數量的Label
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Classes = []
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i = 0
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for encording in Encording_Label:
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Classes.append(image_processing.make_label_list(Train_Size, encording))
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i += 1
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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, Classes, Label_Length)
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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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for i in range(2, Label_Length):
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training_data = Merge.merge_all_image_data(training_data, Final_Dict_Data[keys[i]]) # 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[Label_Length]], Final_Dict_Data[keys[Label_Length + 1]]) #將訓練資料的label合併成一個label的list
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for i in range(Label_Length + 2, 2 * Label_Length):
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training_label = Merge.merge_all_image_data(training_label, Final_Dict_Data[keys[i]]) # 將訓練資料合併成一個list
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start = time.time()
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trains_Data_Image = image_processing.Data_Augmentation_Image(training_data) # 讀檔
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Training_Data, Training_Label = image_processing.image_data_processing(trains_Data_Image, training_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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# training_data = training_data.permute(0, 3, 1, 2)
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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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experiment.processing_main(Training_Data, Training_Label, Run_Range) # 執行訓練方法
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