150 lines
7.5 KiB
Python
150 lines
7.5 KiB
Python
from all_models_tools.all_model_tools import call_back
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from Read_and_process_image.ReadAndProcess import Read_image_and_Process_image
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from draw_tools.draw import plot_history, Confusion_Matrix_of_Two_Classification
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from Load_process.Load_Indepentend import Load_Indepentend_Data
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from _validation.ValidationTheEnterData import validation_the_enter_data
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from Load_process.file_processing import Process_File
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from merge_class.merge import merge
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from draw_tools.Grad_cam import Grad_CAM
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from sklearn.metrics import confusion_matrix
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from experiments.pytorch_Model import ModifiedXception
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from Image_Process.Image_Generator import Image_generator
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from Model_All_Step import All_Step
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import pandas as pd
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import numpy as np
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import torch
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import torch.nn as nn
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import time
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class experiments():
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def __init__(self, tools, Number_Of_Classes, status):
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'''
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# 實驗物件
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## 說明:
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* 用於開始訓練pytorch的物件，裡面分為數個方法，負責處理實驗過程的種種
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## parmeter:
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* Topic_Tool: 讀取訓練、驗證、測試的資料集與Label等等的內容
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* cut_image: 呼叫切割影像物件
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* merge: 合併的物件
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* model_name: 模型名稱，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: 實驗名稱
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* epoch: 訓練次數
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* train_batch_size: 訓練資料的batch
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* convolution_name: Grad-CAM的最後一層的名稱
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* Number_Of_Classes: Label的類別
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* Status: 選擇現在資料集的狀態
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* device: 決定使用GPU或CPU
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## Method:
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* processing_main: 實驗物件的進入點
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* construct_model: 決定實驗用的Model
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* Training_Step: 訓練步驟，開始進行訓練驗證的部分
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* Evaluate_Model: 驗證模型的準確度
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* record_matrix_image: 劃出混淆矩陣(熱力圖)
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* record_everyTime_test_result: 記錄我單次的訓練結果並將它輸出到檔案中
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'''
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self.Topic_Tool = tools
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self.validation_obj = validation_the_enter_data() # 呼叫驗證物件
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self.cut_image = Load_Indepentend_Data(self.Topic_Tool.Get_Data_Label(), self.Topic_Tool.Get_OneHot_Encording_Label()) # 呼叫切割影像物件
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self.image_processing = Read_image_and_Process_image()
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self.merge = merge()
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self.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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self.experiment_name = "Xception Skin to train Normal stomach cancer"
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# self.file_name = "Remove background of Chickenpox with normal image"
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self.generator_batch_size = 50
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self.epoch = 10000
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self.train_batch_size = 128
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self.layers = 1
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self.convolution_name = "block14_sepconv2"
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self.Number_Of_Classes = Number_Of_Classes
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self.Grad = ""
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self.Status = status
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self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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pass
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def processing_main(self, train, train_label, counter):
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Train, Test, Validation = self.Topic_Tool.Get_Save_Roots(self.Status) # 要換不同資料集就要改
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start = time.time()
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self.cut_image.process_main(Test, Validation) # 呼叫處理test Data與Validation Data的function
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end = time.time()
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print("讀取testing與validation資料(154)執行時間：%f 秒\n" % (end - start))
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# Generator = Image_generator("", "")
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# 將處理好的test Data 與 Validation Data 丟給這個物件的變數
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self.test, self.test_label = self.cut_image.test, self.cut_image.test_label
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self.validation, self.validation_label = self.cut_image.validation, self.cut_image.validation_label
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self.Topic_Tool.Set_Zips(train, train_label, "Training")
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self.Topic_Tool.Set_Zips(self.test, self.test_label, "Testing")
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self.Topic_Tool.Set_Zips(self.validation, self.validation_label, "Validation")
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self.Training_Zip, self.Testing_Zip, self.Validation_Zip = self.Topic_Tool.Get_Zip()
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# self.Grad = Grad_CAM(self.Topic_Tool.Get_Data_Label(), self.test_label, self.experiment_name, self.convolution_name)
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cnn_model = self.construct_model() # 呼叫讀取模型的function
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step = All_Step(self.Training_Zip, self.Testing_Zip, self.Validation_Zip, cnn_model, self.epoch, self.Number_Of_Classes)
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# model_dir = '../save_the_best_model/Topic/Remove background with Normal image/best_model( 2023-10-17 )-2.h5' # 這是一個儲存模型權重的路徑，每一個模型都有一個自己權重儲存的檔
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# if os.path.exists(model_dir): # 如果這個檔案存在
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# cnn_model.load_weights(model_dir) # 將模型權重讀出來
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# print("讀出權重\n")
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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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train_losses, val_losses, train_accuracies, val_accuracies = step.Training_Step(self.model_name, counter)
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loss, accuracy, precision, recall, AUC, f1, True_Label, Predict_Label = step.Evaluate_Model(cnn_model)
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self.record_matrix_image(True_Label, Predict_Label, self.model_name, counter)
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print(self.record_everyTime_test_result(loss, accuracy, precision, recall, AUC, f1, counter, self.experiment_name)) # 紀錄當前訓練完之後的預測結果，並輸出成csv檔
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Losses = [train_losses, val_losses]
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Accuracyes = [train_accuracies, val_accuracies]
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plot_history(self.epoch, Losses, Accuracyes, "train" + str(counter), self.experiment_name) # 將訓練結果化成圖，並將化出來的圖丟出去儲存
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# self.Grad.process_main(cnn_model, counter, self.test)
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return loss, accuracy, precision, recall, AUC, f1
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def construct_model(self):
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'''決定我這次訓練要用哪個model'''
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cnn_model = ModifiedXception()
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if torch.cuda.device_count() > 1:
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cnn_model = nn.DataParallel(cnn_model)
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cnn_model = cnn_model.to(self.device)
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return cnn_model
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def record_matrix_image(self, True_Labels, Predict_Labels, model_name, index):
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'''劃出混淆矩陣(熱力圖)'''
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# 計算混淆矩陣
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matrix = confusion_matrix(True_Labels, Predict_Labels)
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Confusion_Matrix_of_Two_Classification(model_name, matrix, index) # 呼叫畫出confusion matrix的function
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return matrix.real
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def record_everyTime_test_result(self, loss, accuracy, precision, recall, auc, f, indexs, model_name, Matrix):
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'''記錄我單次的訓練結果並將它輸出到檔案中'''
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File = Process_File()
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Dataframe = pd.DataFrame(
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{
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"model_name" : str(model_name),
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"loss" : "{:.2f}".format(loss),
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"precision" : "{:.2f}%".format(precision * 100),
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"recall" : "{:.2f}%".format(recall * 100),
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"accuracy" : "{:.2f}%".format(accuracy * 100),
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"f" : "{:.2f}%".format(f * 100),
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"AUC" : "{:.2f}%".format(auc * 100)
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}, index = [indexs])
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File.Save_CSV_File("train_result", Dataframe)
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# File.Save_TXT_File("Matrix_Result : " + str(Matrix), model_name + "_train" + str(indexs))
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return Dataframe |