157 lines
7.1 KiB
Python
157 lines
7.1 KiB
Python
from experiments.experiment import experiments
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from Image_Process.Image_Generator import Image_generator
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from Training_Tools.Tools import Tool
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from model_data_processing.processing import make_label_list, Read_Image_Root_And_Image_Enhance
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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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from model_data_processing.processing_for_cut_image import Cut_Indepentend_Data
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from Load_process.LoadData import Loding_Data_Root
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from Load_process.file_processing import Process_File
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from utils.Stomach_Config import Training_Config, Loading_Config
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from Image_Process.Image_Mask_Ground_Truth_Processing import XMLAnnotationProcessor
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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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# 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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print(f"GPU: {torch.cuda.get_device_name(0)}")
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tool = Tool()
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Status = 1
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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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Prepare = Load_Data_Prepare()
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Indepentend = Cut_Indepentend_Data(Loading_Config["Train_Data_Root"], Loading_Config["Training_Labels"])
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Merge = merge()
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Calculate_Tool = Calculate()
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file = Process_File()
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Train_Size = 0
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# 讀取資料
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# 將測試資料獨立出來
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test_size = 0.2
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Indepentend.IndependentData_main(Loading_Config["Test_Data_Root"], test_size)
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# # 創建處理切割的部分
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# if not file.JudgeRoot_MakeDir(Loading_Config['Annotation_Training_Root']) and not file.JudgeRoot_MakeDir(Loading_Config['Annotation_Testing_Root']):
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# processor_train = XMLAnnotationProcessor(
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# dataset_root = Loading_Config["Train_Data_Root"],
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# )
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# processor_test = XMLAnnotationProcessor(
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# dataset_root = Loading_Config["Test_Data_Root"],
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# )
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# # 設定自訂樣式（可選）
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# processor_train.set_drawing_style(
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# box_color=(0, 0, 255), # 紅色邊界框
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# text_color=(255, 255, 255), # 白色文字
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# box_thickness=3,
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# font_scale=0.7
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# )
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# processor_test.set_drawing_style(
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# box_color=(0, 0, 255), # 紅色邊界框
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# text_color=(255, 255, 255), # 白色文字
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# box_thickness=3,
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# font_scale=0.7
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# )
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# print("XML標註處理器已準備就绪")
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# for Label_Iamge_List in Loading_Config["Label_Image_Labels"]:
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# if Label_Iamge_List == "CA":
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# Label = "stomach_cancer_Crop"
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# else:
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# Label = "Have_Question_Crop"
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# training_results = processor_train.process_multiple_xml(f"../Label_Image/{Label_Iamge_List}", Loading_Config['Annotation_Training_Root'], Label)
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# testing_results = processor_test.process_multiple_xml(f"../Label_Image/{Label_Iamge_List}", Loading_Config['Annotation_Testing_Root'], Label)
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# else:
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# print("Training and Testing annoation is exist!!!!")
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# # 讀取切割完成後的檔案
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# print("Mask Ground truth is Finished\n")
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# Mask_load = Loding_Data_Root(Loading_Config["XML_Loading_Label"], Loading_Config['Annotation_Training_Root'], "")
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# Mask_Data_Dict_Data = Mask_load.process_main(False)
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# Total_Size_Lists = []
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# print("Mask資料集總數")
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# for label in Loading_Config["XML_Loading_Label"]:
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# Train_Size += len(Mask_Data_Dict_Data[label])
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# Total_Size_Lists.append(len(Mask_Data_Dict_Data[label]))
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# print(f"Labels: {label}, 總數為: {len(Mask_Data_Dict_Data[label])}")
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# print("總共有 " + str(Train_Size) + " 筆資料")
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# 讀取原始資料集
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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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Data_Dict_Data = load.process_main(False)
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# # 製作資料增強資料
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# if not file.Judge_File_Exist(Loading_Config['Image enhance processing save root']):
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# for label in Loading_Config["Training_Labels"]:
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# Read_Image_Root_And_Image_Enhance(Data_Dict_Data[label], f"{Loading_Config['Image enhance processing save root']}/{label}")
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# tmp_load = Loding_Data_Root(Loading_Config["Training_Labels"], Loading_Config['Image enhance processing save root'], Loading_Config["ImageGenerator_Data_Root"])
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# Data_Dict_Data = tmp_load.process_main(False)
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Total_Size_List = []
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print("前處理後資料集總數")
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for label in Loading_Config["Training_Labels"]:
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Train_Size += len(Data_Dict_Data[label])
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Total_Size_List.append(len(Data_Dict_Data[label]))
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print(f"Labels: {label}, 總數為: {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(make_label_list(Total_Size_List[i], 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(Loading_Config["Training_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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# Mask_Keys = list(Mask_Data_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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# Training_Mask_Data = Merge.merge_all_image_data(Mask_Data_Dict_Data[Mask_Keys[0]], Mask_Data_Dict_Data[Mask_Keys[1]]) # 將訓練資料合併成一個list
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# for i in range(2, len(Mask_Keys)):
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# Training_Mask_Data = Merge.merge_all_image_data(Training_Mask_Data, Mask_Data_Dict_Data[Mask_Keys[i]]) # 將訓練資料合併成一個list
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experiment = experiments(
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Xception_Training_Data=Training_Data,
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Xception_Training_Label=Training_Label,
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Xception_Training_Mask_Data=None,
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status=Status
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)
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start = time.time()
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experiment.processing_main() # 執行訓練方法
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end = time.time()
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print(f"\n\n\n訓練時間：{end - start} 秒\n\n") |