20250308 Commits: K-Fold has been finish, but sampler has some question to solve

Image_Process/Image_Generator.py

@@ -3,7 +3,7 @@ from _validation.ValidationTheEnterData import validation_the_enter_data
 from Load_process.file_processing import Process_File
 from Load_process.LoadData import Load_Data_Prepare
 from torchvision import transforms
-from Training_Tools.Tools import Tool
+from Training_Tools.PreProcess import Training_Precesses
 import numpy as np
 from PIL import Image
 import torch
@@ -51,7 +51,7 @@ class Image_generator():
         '''
         File = Process_File()
         image_processing = Read_image_and_Process_image(self.Image_Size)
-        tool = Tool()
+        tool = Training_Precesses("", "", "", "")
         Classes = []
         Transform = self.Generator_Content(stardand)
 
@@ -60,7 +60,7 @@ class Image_generator():
             save_root = File.Make_Save_Root(label, save_roots) # 合併路徑
 
             Classes = image_processing.make_label_list(len(image), "1")
-            Training_Dataset = tool.Convert_Data_To_DataSet_And_Put_To_Dataloader(image, Classes, 1, False)
+            Training_Dataset = tool.Combine_Signal_Dataset_To_DataLoader(image, Classes, 1, False)
 
             if File.JudgeRoot_MakeDir(save_root): # 判斷要存的資料夾存不存在，不存在則創立
                 print("The file is exist.This Script is not creating new fold.")

Image_Process/load_and_ImageGenerator.py

@@ -19,10 +19,9 @@ Parmeter
     herpes_data: 合併herpes Dataset的資料成一個List
     MonkeyPox_data: 合併MonkeyPox DataSet 的資料成一個List
     '''
-    def __init__(self, Training_Root,Test_Root, Validation_Root, Generator_Root, Labels, Image_Size) -> None:
+    def __init__(self, Training_Root,Test_Root, Generator_Root, Labels, Image_Size) -> None:
         self.Training_Root = Training_Root
         self.TestRoot = Test_Root
-        self.ValidationRoot = Validation_Root
         self.GeneratoRoot = Generator_Root
         self.Labels = Labels
         self.Image_Size = Image_Size
@@ -37,13 +36,9 @@ Parmeter
         Generator = Image_generator(self.GeneratoRoot, self.Labels, self.Image_Size)
 
         # 將測試資料獨立出來
-        test_size = 0.1
+        test_size = 0.2
         Indepentend.IndependentData_main(self.TestRoot, test_size)
 
-        # 將驗證資料獨立出來
-        test_size = 0.1
-        Indepentend.IndependentData_main(self.ValidationRoot, test_size)
-
         if not File.Judge_File_Exist(self.GeneratoRoot): # 檔案若不存在
             # 確定我要多少個List
             Prepare.Set_Data_Content([], Data_Length)

Load_process/Load_Indepentend.py

@@ -17,7 +17,7 @@ class Load_Indepentend_Data():
         self.OneHot_Encording = OneHot_Encording
         pass
 
-    def process_main(self, Test_data_root, Validation_data_root):
+    def process_main(self, Test_data_root):
         self.test, self.test_label = self.get_Independent_image(Test_data_root)
         print("\ntest_labels有" + str(len(self.test_label)) + "筆資料\n")
 

Processing_image.py

@@ -14,7 +14,7 @@ if __name__ == "__main__":
     tool.Set_Labels()
     tool.Set_Save_Roots()
     Labels = tool.Get_Data_Label()
-    Trainig_Root, Testing_Root, Validation_Root = tool.Get_Save_Roots(2)
+    Trainig_Root, Testing_Root = tool.Get_Save_Roots(2)
 
     load = Loding_Data_Root(Labels, Trainig_Root, "")
     Data_Root = load.get_Image_data_roots(Trainig_Root)

Training_Tools/PreProcess.py

@@ -1,4 +1,4 @@
-from torch.utils.data import Dataset, DataLoader, RandomSampler
+from torch.utils.data import Dataset, DataLoader, RandomSampler, WeightedRandomSampler
 import torchvision.transforms as transforms
 import torch
 
@@ -29,6 +29,12 @@ class Training_Precesses:
         self.Training_Labels = Training_Labels
         self.Testing_Datas = Testing_Datas
         self.Testing_Labels = Testing_Labels
+
+        seed = 42  # 設定任意整數作為種子
+        # 產生隨機種子產生器
+        self.generator = torch.Generator()
+        self.generator.manual_seed(seed)
+
         pass
 
     def Total_Data_Combine_To_DataLoader(self, Batch_Size):
@@ -40,14 +46,15 @@ class Training_Precesses:
 
         return Training_DataLoader, Testing_DataLoader
     
-    def Convert_Data_To_DataSet(self, Datas : list, Labels : list, status : bool = True):
-        seed = 42  # 設定任意整數作為種子
-        # 產生隨機種子產生器
-        generator = torch.Generator()
-        generator.manual_seed(seed)
+    def Combine_Signal_Dataset_To_DataLoader(self, datas : list, Labels : list, Batch_Size, status : bool = True):
+        dataset = self.Convert_Data_To_DataSet(datas, Labels, status)
+        sampler = WeightedRandomSampler(dataset, generator = self.generator) # 創建Sampler
+        Dataloader = DataLoader(dataset = dataset, batch_size = Batch_Size, num_workers = 0, pin_memory=True, sampler = sampler)
+        return Dataloader
 
+    def Convert_Data_To_DataSet(self, Datas : list, Labels : list, status : bool = True):
         # 創建 Dataset
         list_dataset = ListDataset(Datas, Labels, status)
-        # sampler = RandomSampler(list_dataset, generator = generator) # 創建Sampler
+
 
         return list_dataset

Training_Tools/Tools.py

@@ -13,10 +13,6 @@ class Tool:
         self.__Normal_Test_Data_Root = ""
         self.__Comprehensive_Testing_Root = ""
 
-        self.__ICG_Validation_Data_Root = ""
-        self.__Normal_Validation_Data_Root = ""
-        self.__Comprehensive_Validation_Root = ""
-
         self.__ICG_ImageGenerator_Data_Root = ""
         self.__Normal_ImageGenerator_Data_Root = ""
         self.__Comprehensive_Generator_Root = ""
@@ -37,10 +33,6 @@ class Tool:
         self.__Normal_Test_Data_Root = "../Dataset/Training/Normal_TestData"
         self.__Comprehensive_Testing_Root = "../Dataset/Training/Comprehensive_TestData"
 
-        self.__ICG_Validation_Data_Root = "../Dataset/Training/CA_ICG_ValidationData"
-        self.__Normal_Validation_Data_Root = "../Dataset/Training/Normal_ValidationData"
-        self.__Comprehensive_Validation_Root = "../Dataset/Training/Comprehensive_ValidationData"
-
         self.__ICG_ImageGenerator_Data_Root = "../Dataset/Training/ICG_ImageGenerator"
         self.__Normal_ImageGenerator_Data_Root = "../Dataset/Training/Normal_ImageGenerator"
         self.__Comprehensive_Generator_Root = "../Dataset/Training/Comprehensive_ImageGenerator"
@@ -68,11 +60,11 @@ class Tool:
             若choose != 1 || choose != 2 => 會回傳四個結果
         '''
         if choose == 1:
-            return self.__ICG_Training_Root, self.__ICG_Test_Data_Root, self.__ICG_Validation_Data_Root
+            return self.__ICG_Training_Root, self.__ICG_Test_Data_Root
         if choose == 2:
-            return self.__Normal_Training_Root, self.__Normal_Test_Data_Root, self.__Normal_Validation_Data_Root
+            return self.__Normal_Training_Root, self.__Normal_Test_Data_Root
         else:
-            return self.__Comprehensive_Training_Root, self.__Comprehensive_Testing_Root, self.__Comprehensive_Validation_Root
+            return self.__Comprehensive_Training_Root, self.__Comprehensive_Testing_Root
         
     def Get_Generator_Save_Roots(self, choose):
         '''回傳結果為Train, test, validation'''

all_models_tools/all_model_tools.py

@@ -50,7 +50,7 @@ def call_back(model_name, index, optimizer):
 
     model_dir = '../Result/save_the_best_model/' + model_name
     File.JudgeRoot_MakeDir(model_dir)
-    modelfiles = File.Make_Save_Root('best_model( ' + str(datetime.date.today()) + " )-" +  str(index) + ".pt", model_dir)
+    modelfiles = File.Make_Save_Root('best_model( ' + str(datetime.date.today()) + " )-" +  index + ".pt", model_dir)
     
     # model_mckp = ModelCheckpoint(modelfiles, monitor='val_loss', save_best_only=True, save_weights_only = True, mode='auto')
 

experiments/Model_All_Step.py

@@ -1,20 +1,26 @@
 from tqdm import tqdm
-import torch
 from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
-from sklearn.model_selection import KFold
 from torchmetrics.functional import auroc
-import torch.optim as optim
-import numpy as np
+from sklearn.model_selection import KFold
+from sklearn.metrics import confusion_matrix
+
 from all_models_tools.all_model_tools import call_back
 from Model_Loss.Loss import Entropy_Loss
 from merge_class.merge import merge
-from draw_tools.Grad_cam import GradCAM
-from torch.utils.data import Subset, DataLoader
+from Training_Tools.PreProcess import ListDataset
+from Load_process.file_processing import Process_File
+from draw_tools.draw import plot_history, draw_heatmap
+from Load_process.file_processing import Process_File
+
 import time
+import torch.optim as optim
+import numpy as np
+import torch
+import pandas as pd
 
 
 class All_Step:
-    def __init__(self, PreProcess_Classes_Data, Batch, Model, Epoch, Number_Of_Classes, Model_Name):
+    def __init__(self, PreProcess_Classes_Data, Batch, Model, Epoch, Number_Of_Classes, Model_Name, Experiment_Name):
         self.PreProcess_Classes_Data = PreProcess_Classes_Data
         self.Training_DataLoader, self.Test_Dataloader = self.PreProcess_Classes_Data.Total_Data_Combine_To_DataLoader(Batch)
 
@@ -25,48 +31,67 @@ class All_Step:
         self.Number_Of_Classes = Number_Of_Classes
 
         self.Model_Name = Model_Name
-
-        pass
+        self.Experiment_Name = Experiment_Name
 
     def Training_Step(self, model_name, counter):
-        # 定義優化器，並設定 weight_decay 參數來加入 L2 正則化
-        Optimizer = optim.SGD(self.Model.parameters(), lr=0.045, momentum = 0.9, weight_decay=0.1)
-        model_path, early_stopping, scheduler = call_back(model_name, counter, Optimizer)
+        # Lists to store metrics across all folds
+        all_fold_train_losses = []
+        all_fold_val_losses = []
+        all_fold_train_accuracies = []
+        all_fold_val_accuracies = []
 
-        criterion = Entropy_Loss()  # 使用自定義的損失函數
+        # Define K-fold cross-validator
+        K_Fold = KFold(n_splits=5, shuffle=True, random_state=42)
+
+        File = Process_File()
+
+        # Get the underlying dataset from PreProcess_Classes_Data
+        training_dataset = ListDataset(data_list = self.PreProcess_Classes_Data.Training_Datas, labels_list = self.PreProcess_Classes_Data.Training_Labels, status = True)
+
+        # K-Fold loop
+        for fold, (train_idx, val_idx) in enumerate(K_Fold.split(training_dataset)):
+            print(f"\nStarting Fold {fold + 1}/5")
+
+            # Create training and validation subsets for this fold
+            train_subset = torch.utils.data.Subset(training_dataset, train_idx)
+            val_subset = torch.utils.data.Subset(training_dataset, val_idx)
+
+            # Wrap subsets in DataLoaders (use same batch size as original)
+            batch_size = self.Training_DataLoader.batch_size
+            train_loader = torch.utils.data.DataLoader(train_subset, batch_size=batch_size, shuffle=True)
+            val_loader = torch.utils.data.DataLoader(val_subset, batch_size=batch_size, shuffle=False)
+
+            # Reinitialize model and optimizer for each fold
+            self.Model = self.Model.__class__(self.Number_Of_Classes).to(self.device)  # Reinitialize model
+            Optimizer = optim.SGD(self.Model.parameters(), lr=0.045, momentum=0.9, weight_decay=0.1)
+            model_path, early_stopping, scheduler = call_back(model_name, str(counter) + f"_fold{fold}", Optimizer)
+
+            criterion = Entropy_Loss()  # Custom loss function
             Merge_Function = merge()
+
+            # Lists to store metrics for this fold
             train_losses = []
             val_losses = []
             train_accuracies = []
             val_accuracies = []
-        Total_Epoch = 0
 
-        K_Flod = KFold(n_splits = 5, shuffle = True, random_state = 42)
-
-        for epoch in range(self.Epoch): # 訓練迴圈
-            self.Model.train() # 開始訓練
+            # Epoch loop
+            for epoch in range(self.Epoch):
+                self.Model.train()  # Start training
                 running_loss = 0.0
                 all_train_preds = []
                 all_train_labels = []
                 processed_samples = 0
 
-            # 計算每個 epoch 的起始時間
+                # Calculate epoch start time
                 start_time = time.time()
-            total_samples = len(self.Training_DataLoader)
-            train_subset = ""
-            val_subset = ""  
+                total_samples = len(train_subset)  # Total samples in subset, not DataLoader
 
-            for fold, (train_idx, vali_idx) in enumerate( K_Flod.split(self.PreProcess_Classes_Data.Training_Datas)):
-                # Create training and validation subsets for this fold
-                train_subset = Subset(self.Training_DataLoader, train_idx)
-                val_subset = Subset(self.Training_DataLoader, vali_idx)
-
-                Training_Data = DataLoader(train_subset, self.Training_DataLoader.batch_size, num_workers = 0, pin_memory=True, shuffle = True)
-
-                epoch_iterator = tqdm(Training_Data, desc=f"Epoch [{epoch}/{self.Epoch}]")
+                # Progress bar for training batches
+                epoch_iterator = tqdm(train_loader, desc=f"Fold {fold + 1}/5, Epoch [{epoch + 1}/{self.Epoch}]")
 
                 for inputs, labels in epoch_iterator:
-                    inputs, labels = torch.as_tensor(inputs).to(self.device), torch.as_tensor(labels).to(self.device)
+                    inputs, labels = inputs.to(self.device), labels.to(self.device)  # Already tensors from DataLoader
 
                     Optimizer.zero_grad()
                     outputs = self.Model(inputs)
@@ -75,85 +100,112 @@ class All_Step:
                     Optimizer.step()
                     running_loss += loss.item()
 
-                    # 收集訓練預測和標籤
-                    Output_Values, Output_Indexs = torch.max(outputs, dim = 1)
-                    True_Indexs = np.argmax(labels.cpu().numpy(), 1)
+                    # Collect training predictions and labels
+                    Output_Values, Output_Indexs = torch.max(outputs, dim=1)
+                    True_Indexs = np.argmax(labels.cpu().numpy(), axis=1)
 
                     all_train_preds.append(Output_Indexs.cpu().numpy())
                     all_train_labels.append(True_Indexs)
 
-                    processed_samples += len(inputs)
+                    processed_samples += inputs.size(0)  # Use size(0) for batch size
 
-                    # 計算當前進度
+                    # Calculate progress and timing
                     progress = (processed_samples / total_samples) * 100
-                    
-                    # 計算經過時間和剩餘時間
                     elapsed_time = time.time() - start_time
                     iterations_per_second = processed_samples / elapsed_time if elapsed_time > 0 else 0
                     eta = (total_samples - processed_samples) / iterations_per_second if iterations_per_second > 0 else 0
                     time_str = f"{int(elapsed_time//60):02d}:{int(elapsed_time%60):02d}<{int(eta//60):02d}:{int(eta%60):02d}"
 
-                    # 計算當前批次的精確度（這裡需要根據你的具體需求調整）
+                    # Calculate batch accuracy
                     batch_accuracy = (Output_Indexs.cpu().numpy() == True_Indexs).mean()
 
-                    # 更新進度條顯示
-                    epoch_iterator.set_description(f"Epoch [{epoch}/{self.Epoch}]")
+                    # Update progress bar
                     epoch_iterator.set_postfix_str(
-                        f"{processed_samples}/{total_samples} [{time_str}, {iterations_per_second:.2f}it/s, " +
-                        f"acc={batch_accuracy:.3f}, loss={loss.item():.3f}, ]"
+                        f"{processed_samples}/{total_samples} [{time_str}, {iterations_per_second:.2f}it/s, "
+                        f"acc={batch_accuracy:.3f}, loss={loss.item():.3f}]"
                     )
 
                 epoch_iterator.close()
 
+                # Merge predictions and labels
                 all_train_preds = Merge_Function.merge_data_main(all_train_preds, 0, len(all_train_preds))
                 all_train_labels = Merge_Function.merge_data_main(all_train_labels, 0, len(all_train_labels))
 
-                Training_Loss = running_loss / len(self.Training_DataLoader)
+                Training_Loss = running_loss / len(train_loader)
                 train_accuracy = accuracy_score(all_train_labels, all_train_preds)
 
                 train_losses.append(Training_Loss)
                 train_accuracies.append(train_accuracy)
 
+                # Validation step
                 self.Model.eval()
                 val_loss = 0.0
                 all_val_preds = []
                 all_val_labels = []
 
                 with torch.no_grad():
-                    for inputs, labels in val_subset:                    
-                        inputs, labels = torch.as_tensor(inputs).to(self.device), torch.as_tensor(labels).to(self.device)
-
+                    for inputs, labels in val_loader:
+                        inputs, labels = inputs.to(self.device), labels.to(self.device)
                         outputs = self.Model(inputs)
                         loss = criterion(outputs, labels)
                         val_loss += loss.item()
 
-                        # 收集訓練預測和標籤
-                        Output_Values, Output_Indexs = torch.max(outputs, dim = 1)
-                        True_Indexs = np.argmax(labels.cpu().numpy(), 1)
+                        # Collect validation predictions and labels
+                        Output_Values, Output_Indexs = torch.max(outputs, dim=1)
+                        True_Indexs = np.argmax(labels.cpu().numpy(), axis=1)
 
                         all_val_preds.append(Output_Indexs.cpu().numpy())
                         all_val_labels.append(True_Indexs)
 
-                val_loss /= len(val_subset)
+                # Merge predictions and labels
+                all_val_preds = Merge_Function.merge_data_main(all_val_preds, 0, len(all_val_preds))
+                all_val_labels = Merge_Function.merge_data_main(all_val_labels, 0, len(all_val_labels))
+
+                val_loss /= len(val_loader)
                 val_accuracy = accuracy_score(all_val_labels, all_val_preds)
 
                 val_losses.append(val_loss)
                 val_accuracies.append(val_accuracy)
-                # print(f"Val_loss: {val_loss:.4f}, Val_accuracy: {val_accuracy:0.2f}\n")
 
+                # Early stopping
                 early_stopping(val_loss, self.Model, model_path)
                 if early_stopping.early_stop:
-                    print("Early stopping triggered. Training stopped.")
-                    Total_Epoch = epoch
+                    print(f"Early stopping triggered in Fold {fold + 1} at epoch {epoch + 1}")
+                    Total_Epoch = epoch + 1
                     break
 
-                # 學習率調整
+                # Learning rate adjustment
                 scheduler.step(val_loss)
 
-        return train_losses, val_losses, train_accuracies, val_accuracies, Total_Epoch
+            else:  # If no early stopping
+                Total_Epoch = self.Epoch
 
-    def Evaluate_Model(self, cnn_model, counter):
-        # 測試模型
+            # Store fold results
+            all_fold_train_losses.append(train_losses)
+            all_fold_val_losses.append(val_losses)
+            all_fold_train_accuracies.append(train_accuracies)
+            all_fold_val_accuracies.append(val_accuracies)
+
+            Losses = [train_losses, val_losses]
+            Accuracies = [train_accuracies, val_accuracies]
+            plot_history(Total_Epoch, Losses, Accuracies, "train" + str(fold), self.Experiment_Name) # 將訓練結果化成圖，並將化出來的圖丟出去儲存
+
+        # Aggregate results across folds
+        avg_train_losses = np.mean([losses[-1] for losses in all_fold_train_losses])
+        avg_val_losses = np.mean([losses[-1] for losses in all_fold_val_losses])
+        avg_train_accuracies = np.mean([acc[-1] for acc in all_fold_train_accuracies])
+        avg_val_accuracies = np.mean([acc[-1] for acc in all_fold_val_accuracies])
+
+        print(f"\nCross-Validation Results:")
+        print(f"Avg Train Loss: {avg_train_losses:.4f}, Avg Val Loss: {avg_val_losses:.4f}")
+        print(f"Avg Train Acc: {avg_train_accuracies:.4f}, Avg Val Acc: {avg_val_accuracies:.4f}")
+
+        File.Save_TXT_File(content = f"\nCross-Validation Results:\nAvg Train Loss: {avg_train_losses:.4f}, Avg Val Loss: {avg_val_losses:.4f}\nAvg Train Acc: {avg_train_accuracies:.4f}, Avg Val Acc: {avg_val_accuracies:.4f}\n", File_Name = "Training_Average_Result")
+
+        pass
+
+    def Evaluate_Model(self, cnn_model, Model_Name, counter):
+        # (Unchanged Evaluate_Model method)
         cnn_model.eval()
         True_Label, Predict_Label = [], []
         True_Label_OneHot, Predict_Label_OneHot = [], []
@@ -162,33 +214,54 @@ class All_Step:
         with torch.no_grad():
             for images, labels in self.Test_Dataloader:
                 images, labels = torch.tensor(images).to(self.device), torch.tensor(labels).to(self.device)
-
                 outputs = cnn_model(images)
-
-                # 收集訓練預測和標籤
                 Output_Values, Output_Indexs = torch.max(outputs, 1)
                 True_Indexs = np.argmax(labels.cpu().numpy(), 1)
 
                 True_Label.append(Output_Indexs.cpu().numpy())
                 Predict_Label.append(True_Indexs)
 
-                Predict_Label_OneHot.append(torch.tensor(outputs, dtype = torch.float32).cpu().numpy()[0])
-                True_Label_OneHot.append(torch.tensor(labels, dtype = torch.int).cpu().numpy()[0])
-
-                # # 創建 GradCAM 實例
-                # Layers = cnn_model.base_model.body.conv4.pointwise
-                # grad_cam = GradCAM(cnn_model, target_layer="base_model")
-                # # 可視化 Grad-CAM
-                # grad_cam.visualize(outputs, images, target_class = 3, File_Name = counter, model_name = self.Model_Name)
+                Predict_Label_OneHot.append(torch.tensor(outputs, dtype=torch.float32).cpu().numpy()[0])
+                True_Label_OneHot.append(torch.tensor(labels, dtype=torch.int).cpu().numpy()[0])
 
         loss /= len(self.Test_Dataloader)
 
-        True_Label_OneHot = torch.tensor(True_Label_OneHot, dtype = torch.int)
-        Predict_Label_OneHot = torch.tensor(Predict_Label_OneHot, dtype = torch.float32)
+        True_Label_OneHot = torch.tensor(True_Label_OneHot, dtype=torch.int)
+        Predict_Label_OneHot = torch.tensor(Predict_Label_OneHot, dtype=torch.float32)
 
         accuracy = accuracy_score(True_Label, Predict_Label)
-        precision = precision_score(True_Label, Predict_Label, average = "macro")
-        recall = recall_score(True_Label, Predict_Label, average = "macro")
-        AUC = auroc(Predict_Label_OneHot, True_Label_OneHot, num_labels = self.Number_Of_Classes, task = "multilabel", average = "macro")
-        f1 = f1_score(True_Label, Predict_Label, average = "macro")
-        return loss, accuracy, precision, recall, AUC, f1, True_Label, Predict_Label
+        precision = precision_score(True_Label, Predict_Label, average="macro")
+        recall = recall_score(True_Label, Predict_Label, average="macro")
+        AUC = auroc(Predict_Label_OneHot, True_Label_OneHot, num_labels=self.Number_Of_Classes, task="multilabel", average="macro")
+        f1 = f1_score(True_Label, Predict_Label, average="macro")
+
+        Matrix = self.record_matrix_image(True_Label, Predict_Label, Model_Name, counter)
+        print(self.record_everyTime_test_result(loss, accuracy, precision, recall, AUC, f1, counter, self.Experiment_Name, Matrix)) # 紀錄當前訓練完之後的預測結果，並輸出成csv檔
+
+        pass
+    
+    def record_matrix_image(self, True_Labels, Predict_Labels, model_name, index):
+        '''劃出混淆矩陣(熱力圖)'''
+        # 計算混淆矩陣
+        matrix = confusion_matrix(True_Labels, Predict_Labels)
+        draw_heatmap(matrix, model_name, index) # 呼叫畫出confusion matrix的function
+
+        return matrix
+    
+    def record_everyTime_test_result(self, loss, accuracy, precision, recall, auc, f, indexs, model_name, Matrix):
+        '''記錄我單次的訓練結果並將它輸出到檔案中'''
+        File = Process_File()
+
+        Dataframe = pd.DataFrame(
+                {
+                    "model_name" : str(model_name),
+                    "loss" : "{:.2f}".format(loss), 
+                    "precision" : "{:.2f}%".format(precision * 100), 
+                    "recall" : "{:.2f}%".format(recall * 100),
+                    "accuracy" : "{:.2f}%".format(accuracy * 100), 
+                    "f" : "{:.2f}%".format(f * 100), 
+                    "AUC" : "{:.2f}%".format(auc * 100)
+                }, index = [indexs])
+        File.Save_CSV_File("train_result", Dataframe)
+
+        return Dataframe

experiments/experiment.py

@@ -1,13 +1,11 @@
-from draw_tools.draw import plot_history, draw_heatmap
-from Load_process.Load_Indepentend import Load_Indepentend_Data
-from _validation.ValidationTheEnterData import validation_the_enter_data
-from Load_process.file_processing import Process_File
-from sklearn.metrics import confusion_matrix
+from torchinfo import summary
+
+from Training_Tools.PreProcess import Training_Precesses
 from experiments.pytorch_Model import ModifiedXception
 from experiments.Model_All_Step import All_Step
-from Training_Tools.PreProcess import Training_Precesses
-from torchinfo import summary
-import pandas as pd
+from Load_process.Load_Indepentend import Load_Indepentend_Data
+from _validation.ValidationTheEnterData import validation_the_enter_data
+
 import numpy as np
 import torch
 import torch.nn as nn
@@ -63,10 +61,10 @@ class experiments():
         pass
 
     def processing_main(self, Training_Data, Training_Label, counter):
-        Train, Test, Validation = self.Topic_Tool.Get_Save_Roots(self.Status) # 要換不同資料集就要改
+        Train, Test = self.Topic_Tool.Get_Save_Roots(self.Status) # 要換不同資料集就要改
 
         start = time.time()
-        self.cut_image.process_main(Test, Validation) # 呼叫處理test Data與Validation Data的function
+        self.cut_image.process_main(Test) # 呼叫處理test Data與Validation Data的function
         end = time.time()
         print("讀取testing與validation資料(154)執行時間：%f 秒\n" % (end - start))
 
@@ -80,21 +78,14 @@ class experiments():
         for name, parameters in cnn_model.named_parameters():
             print(f"Layer Name: {name}, Parameters: {parameters.size()}")
 
-        step = All_Step(PreProcess, self.train_batch_size, cnn_model, self.epoch, self.Number_Of_Classes, self.model_name)
+        step = All_Step(PreProcess, self.train_batch_size, cnn_model, self.epoch, self.Number_Of_Classes, self.model_name, self.experiment_name)
         print("\n\n\n讀取訓練資料(70000)執行時間：%f 秒\n\n" % (end - start))
 
-        train_losses, val_losses, train_accuracies, val_accuracies, Epoch = step.Training_Step(self.model_name, counter)
-        loss, accuracy, precision, recall, AUC, f1, True_Label, Predict_Label = step.Evaluate_Model(cnn_model, counter)
-
-        Matrix = self.record_matrix_image(True_Label, Predict_Label, self.model_name, counter)
-        print(self.record_everyTime_test_result(loss, accuracy, precision, recall, AUC, f1, counter, self.experiment_name, Matrix)) # 紀錄當前訓練完之後的預測結果，並輸出成csv檔
-            
-        Losses = [train_losses, val_losses]
-        Accuracies = [train_accuracies, val_accuracies]
-        plot_history(Epoch + 1, Losses, Accuracies, "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
+        step.Training_Step(self.model_name, counter)
+        step.Evaluate_Model(cnn_model, self.model_name, counter)
         # self.Grad.process_main(cnn_model, counter, Testing_Dataset)
 
-        return loss, accuracy, precision, recall, AUC, f1
+        pass
 
     def construct_model(self):
         '''決定我這次訓練要用哪個model'''
@@ -105,30 +96,3 @@ class experiments():
 
         cnn_model = cnn_model.to(self.device)
         return cnn_model
-
-    def record_matrix_image(self, True_Labels, Predict_Labels, model_name, index):
-        '''劃出混淆矩陣(熱力圖)'''
-        # 計算混淆矩陣
-        matrix = confusion_matrix(True_Labels, Predict_Labels)
-        draw_heatmap(matrix, model_name, index) # 呼叫畫出confusion matrix的function
-
-        return matrix
-
-    def record_everyTime_test_result(self, loss, accuracy, precision, recall, auc, f, indexs, model_name, Matrix):
-        '''記錄我單次的訓練結果並將它輸出到檔案中'''
-        File = Process_File()
-
-        Dataframe = pd.DataFrame(
-                {
-                    "model_name" : str(model_name),
-                    "loss" : "{:.2f}".format(loss), 
-                    "precision" : "{:.2f}%".format(precision * 100), 
-                    "recall" : "{:.2f}%".format(recall * 100),
-                    "accuracy" : "{:.2f}%".format(accuracy * 100), 
-                    "f" : "{:.2f}%".format(f * 100), 
-                    "AUC" : "{:.2f}%".format(auc * 100)
-                }, index = [indexs])
-        File.Save_CSV_File("train_result", Dataframe)
-        # File.Save_TXT_File("Matrix_Result : " + str(Matrix), model_name + "_train" + str(indexs))
-
-        return Dataframe

experiments/pytorch_Model.py

@@ -1,8 +1,4 @@
-import torch
 import torch.nn as nn
-import torch.optim as optim
-import torchvision.transforms as transforms
-from torchvision import models
 import timm
 
 

main.py

@@ -6,6 +6,7 @@ from model_data_processing.processing import Balance_Process
 from Load_process.LoadData import Load_Data_Prepare
 from Calculate_Process.Calculate import Calculate
 from merge_class.merge import merge
+
 import time
 import torch
 
@@ -24,7 +25,7 @@ if __name__ == "__main__":
 
     Status = 1 # 決定要使用什麼資料集
     Labels = tool.Get_Data_Label()
-    Trainig_Root, Testing_Root, Validation_Root = tool.Get_Save_Roots(Status) # 一般的
+    Trainig_Root, Testing_Root = tool.Get_Save_Roots(Status) # 一般的
     Generator_Root = tool.Get_Generator_Save_Roots(Status)
 
     # 取得One-hot encording 的資料
@@ -41,21 +42,25 @@ if __name__ == "__main__":
     Image_Size = 256
 
     Prepare = Load_Data_Prepare()
-    loading_data = Load_ImageGenerator(Trainig_Root, Testing_Root, Validation_Root, Generator_Root, Labels, Image_Size)
+    loading_data = Load_ImageGenerator(Trainig_Root, Testing_Root, Generator_Root, Labels, Image_Size)
     experiment = experiments(Image_Size, Model_Name, Experiment_Name, Epoch, Train_Batch_Size, tool, Classification, Status)
     image_processing = Read_image_and_Process_image(Image_Size)
     Merge = merge()
     Calculate_Tool = Calculate()
  
-    counter = 5
+    counter = 1
     Batch_Size = 128
+    Train_Size = 0
     
     for Run_Range in range(0, counter, 1): # 做規定次數的訓練
         # 讀取資料
         Data_Dict_Data = loading_data.process_main(Label_Length)
-        Data_Dict_Data, Train_Size = Balance_Process(Data_Dict_Data, Labels)
+        # Data_Dict_Data, Train_Size = Balance_Process(Data_Dict_Data, Labels)
  
-        print("總共有 " + str(Train_Size * 3) + " 筆資料")
+        for label in Labels:
+            Train_Size += len(Data_Dict_Data[label])
+        
+        print("總共有 " + str(Train_Size) + " 筆資料")
 
         # 做出跟資料相同數量的Label
         Classes = []
@@ -88,15 +93,5 @@ if __name__ == "__main__":
         end = time.time()
         print("\n\n\n讀取訓練資料(70000)執行時間：%f 秒\n\n" % (end - start))          
         
-        loss, accuracy, precision, recall, AUC, f = experiment.processing_main(Training_Data, Training_Label, Run_Range) # 執行訓練方法
-        Calculate_Tool.Append_numbers(loss, accuracy, precision, recall, AUC, f)
-
-    print("實驗結果")
-    print("--------------------------------------------")
-    print("平均值: ")
-    print(Calculate_Tool.Calculate_Mean())
-    print("標準差: ")
-    print(Calculate_Tool.Calculate_Std())
-    print("結果: ")
-    print(Calculate_Tool.Output_Style())
+        experiment.processing_main(Training_Data, Training_Label, Run_Range) # 執行訓練方法
     

test.ipynb

@@ -1859,19 +1859,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(array([0, 3, 4, 5, 6, 7]), array([1, 2])), (array([0, 1, 2, 4, 6, 7]), array([3, 5])), (array([1, 2, 3, 5, 6, 7]), array([0, 4])), (array([0, 1, 2, 3, 4, 5, 6]), array([7])), (array([0, 1, 2, 3, 4, 5, 7]), array([6]))]\n",
-      "[0 1 2 3 4 7] [5 6]\n",
-      "[0 1 2 5 6 7] [3 4]\n",
-      "[1 2 3 4 5 6] [0 7]\n",
-      "[0 2 3 4 5 6 7] [1]\n",
-      "[0 1 3 4 5 6 7] [2]\n"
+      "[(array([0, 2, 3, 4, 5, 6]), array([1, 7])), (array([0, 1, 2, 4, 5, 7]), array([3, 6])), (array([0, 1, 3, 4, 6, 7]), array([2, 5])), (array([0, 1, 2, 3, 5, 6, 7]), array([4])), (array([1, 2, 3, 4, 5, 6, 7]), array([0]))]\n",
+      "0 [0 2 3 4 5 6] [1 7]\n",
+      "1 [0 1 3 4 6 7] [2 5]\n",
+      "2 [1 2 4 5 6 7] [0 3]\n",
+      "3 [0 1 2 3 5 6 7] [4]\n",
+      "4 [0 1 2 3 4 5 7] [6]\n"
      ]
     }
    ],
@@ -1881,10 +1881,8 @@
     "k = KFold(n_splits = 5, shuffle = True)\n",
     "a = [1, 2, 3, 4 ,5, 6,7, 8]\n",
     "\n",
-    "print(list(k.split(a)))\n",
-    "\n",
-    "for d, b in k.split(a):\n",
-    "    print(d, b)"
+    "for d, (b, c) in enumerate(k.split(a)):\n",
+    "    print(d, b, c)"
    ]
   },
   {
@@ -1892,7 +1890,42 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "from torch.utils.data import Subset, DataLoader, Dataset\n",
+    "\n",
+    "class ListDataset(Dataset):\n",
+    "    def __init__(self, data_list, labels_list, status):\n",
+    "        self.data = data_list\n",
+    "        self.labels = labels_list\n",
+    "        self.status = status\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return len(self.data)\n",
+    "\n",
+    "    def __getitem__(self, idx):\n",
+    "        sample = self.data[idx]  \n",
+    "\n",
+    "        if self.status:\n",
+    "            from Image_Process.Image_Generator import Image_generator\n",
+    "            ImageGenerator = Image_generator(\"\", \"\", 12)\n",
+    "            Transform = ImageGenerator.Generator_Content(5)\n",
+    "            sample = Transform(sample)\n",
+    "\n",
+    "        label = self.labels[idx]\n",
+    "        return sample, label\n",
+    "\n",
+    "k = KFold(n_splits = 5, shuffle = True)\n",
+    "a = [1, 2, 3, 4 ,5, 6,7, 8]\n",
+    "label = [10, 20, 30, 40, 50,60, 70, 80]\n",
+    "\n",
+    "f = ListDataset(a, label, True)\n",
+    "\n",
+    "for d, (b, c) in enumerate(k.split(a)):\n",
+    "    # Create training and validation subsets for this fold\n",
+    "    train_subset = torch.utils.data.Subset(training_dataset, train_idx)\n",
+    "    val_subset = torch.utils.data.Subset(training_dataset, val_idx)\n",
+    "\n"
+   ]
   }
  ],
  "metadata": {