diff --git a/Load_process/__pycache__/LoadData.cpython-311.pyc b/Load_process/__pycache__/LoadData.cpython-311.pyc new file mode 100644 index 0000000..56b80ce Binary files /dev/null and b/Load_process/__pycache__/LoadData.cpython-311.pyc differ diff --git a/Load_process/__pycache__/Load_Indepentend.cpython-311.pyc b/Load_process/__pycache__/Load_Indepentend.cpython-311.pyc new file mode 100644 index 0000000..4999f35 Binary files /dev/null and b/Load_process/__pycache__/Load_Indepentend.cpython-311.pyc differ diff --git a/Load_process/__pycache__/Loading_Tools.cpython-311.pyc b/Load_process/__pycache__/Loading_Tools.cpython-311.pyc new file mode 100644 index 0000000..2cbc0d0 Binary files /dev/null and b/Load_process/__pycache__/Loading_Tools.cpython-311.pyc differ diff --git a/Load_process/__pycache__/file_processing.cpython-311.pyc b/Load_process/__pycache__/file_processing.cpython-311.pyc index 7871ace..e103f73 100644 Binary files a/Load_process/__pycache__/file_processing.cpython-311.pyc and b/Load_process/__pycache__/file_processing.cpython-311.pyc differ diff --git a/Model_Loss/Loss.py b/Model_Loss/Loss.py new file mode 100644 index 0000000..3a09fe3 --- /dev/null +++ b/Model_Loss/Loss.py @@ -0,0 +1,15 @@ +from torch import nn +from torch.nn import functional + + +class Entropy_Loss(nn.Module): + def __init__(self): + super(Entropy_Loss, self).__init__() + + def forward(self, outputs, labels): + # 範例: 使用均方誤差作為損失計算 + # outputs = torch.argmax(outputs, 1) + # outputs = outputs.float() + labels = labels.float() + loss = functional.binary_cross_entropy(outputs, labels) + return loss \ No newline at end of file diff --git a/Read_and_process_image/__pycache__/ReadAndProcess.cpython-311.pyc b/Read_and_process_image/__pycache__/ReadAndProcess.cpython-311.pyc index 5efc188..07744b3 100644 Binary files a/Read_and_process_image/__pycache__/ReadAndProcess.cpython-311.pyc and b/Read_and_process_image/__pycache__/ReadAndProcess.cpython-311.pyc differ diff --git a/Read_and_process_image/__pycache__/__init__.cpython-311.pyc b/Read_and_process_image/__pycache__/__init__.cpython-311.pyc index b14b642..ad3439c 100644 Binary files a/Read_and_process_image/__pycache__/__init__.cpython-311.pyc and b/Read_and_process_image/__pycache__/__init__.cpython-311.pyc differ diff --git a/Training_Tools/Tools.py b/Training_Tools/Tools.py index 94828c1..8a01197 100644 --- a/Training_Tools/Tools.py +++ b/Training_Tools/Tools.py @@ -1,5 +1,6 @@ import pandas as pd from sklearn.preprocessing import OneHotEncoder +from torch.nn import functional class Tool: def __init__(self) -> None: @@ -19,6 +20,10 @@ class Tool: self.__Normal_ImageGenerator_Data_Root = "" self.__Comprehensive_Generator_Root = "" + self.Training_Zip = "" + self.Validation_Zip = "" + self.Testing_Zip = "" + self.__Labels = [] self.__OneHot_Encording = [] pass @@ -43,12 +48,17 @@ class Tool: self.__Normal_ImageGenerator_Data_Root = "../Dataset/Training/Normal_ImageGenerator" self.__Comprehensive_Generator_Root = "../Dataset/Training/Comprehensive_ImageGenerator" - def Set_OneHotEncording(self, Content): - Array_To_DataFrame = pd.DataFrame(Content) - onehotencoder = OneHotEncoder() - onehot = onehotencoder.fit_transform(Array_To_DataFrame).toarray() - - self.__OneHot_Encording = onehot + def Set_OneHotEncording(self, content, Number_Of_Classes): + OneHot_labels = functional.one_hot(content, Number_Of_Classes) + return OneHot_labels + + def Set_Zips(self, Datas, Labels, Address_Name): + if Address_Name == "Training": + self.Training_Zip = zip(Datas, Labels) + if Address_Name == "Validation": + self.Validation_Zip = zip(Datas, Labels) + if Address_Name == "Testing": + self.Testing_Zip = zip(Datas, Labels) def Get_Data_Label(self): ''' @@ -80,4 +90,7 @@ class Tool: return self.__Comprehensive_Generator_Root def Get_OneHot_Encording_Label(self): - return self.__OneHot_Encording \ No newline at end of file + return self.__OneHot_Encording + + def Get_Zip(self): + return self.Training_Zip, self.Testing_Zip, self.Validation_Zip \ No newline at end of file diff --git a/_validation/__pycache__/ValidationTheEnterData.cpython-311.pyc b/_validation/__pycache__/ValidationTheEnterData.cpython-311.pyc index 75327b1..f3a8bd9 100644 Binary files a/_validation/__pycache__/ValidationTheEnterData.cpython-311.pyc and b/_validation/__pycache__/ValidationTheEnterData.cpython-311.pyc differ diff --git a/all_models_tools/__pycache__/__init__.cpython-311.pyc b/all_models_tools/__pycache__/__init__.cpython-311.pyc index 1ac5718..e397ef7 100644 Binary files a/all_models_tools/__pycache__/__init__.cpython-311.pyc and b/all_models_tools/__pycache__/__init__.cpython-311.pyc differ diff --git a/all_models_tools/__pycache__/all_model_tools.cpython-311.pyc b/all_models_tools/__pycache__/all_model_tools.cpython-311.pyc index 3114077..ee90445 100644 Binary files a/all_models_tools/__pycache__/all_model_tools.cpython-311.pyc and b/all_models_tools/__pycache__/all_model_tools.cpython-311.pyc differ diff --git a/all_models_tools/all_model_tools.py b/all_models_tools/all_model_tools.py index 924c54f..6ae048e 100644 --- a/all_models_tools/all_model_tools.py +++ b/all_models_tools/all_model_tools.py @@ -1,40 +1,68 @@ -from keras.callbacks import EarlyStopping, ReduceLROnPlateau, ModelCheckpoint -from keras.layers import GlobalAveragePooling2D, Dense, Reshape, Multiply from Load_process.file_processing import Process_File import datetime +import torch -def attention_block(input): - channel = input.shape[-1] +# def attention_block(input): +# channel = input.shape[-1] - GAP = GlobalAveragePooling2D()(input) +# GAP = GlobalAveragePooling2D()(input) - block = Dense(units = channel // 16, activation = "relu")(GAP) - block = Dense(units = channel, activation = "sigmoid")(block) - block = Reshape((1, 1, channel))(block) +# block = Dense(units = channel // 16, activation = "relu")(GAP) +# block = Dense(units = channel, activation = "sigmoid")(block) +# block = Reshape((1, 1, channel))(block) - block = Multiply()([input, block]) +# block = Multiply()([input, block]) - return block +# return block -def call_back(model_name, index): +class EarlyStopping: + def __init__(self, patience=74, verbose=False, delta=0): + self.patience = patience + self.verbose = verbose + self.delta = delta + self.counter = 0 + self.best_loss = None + self.early_stop = False + + def __call__(self, val_loss, model, save_path): + if self.best_loss is None: + self.best_loss = val_loss + self.save_checkpoint(val_loss, model, save_path) + elif val_loss > self.best_loss + self.delta: + self.counter += 1 + if self.verbose: + print(f"EarlyStopping counter: {self.counter} out of {self.patience}") + if self.counter >= self.patience: + self.early_stop = True + else: + self.best_loss = val_loss + self.save_checkpoint(val_loss, model, save_path) + self.counter = 0 + + def save_checkpoint(self, val_loss, model, save_path): + torch.save(model.state_dict(), save_path) + if self.verbose: + print(f"Validation loss decreased ({self.best_loss:.6f} --> {val_loss:.6f}). Saving model to {save_path}") + + +def call_back(model_name, index, optimizer): File = Process_File() 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) + ".weights.h5", model_dir) - model_mckp = ModelCheckpoint(modelfiles, monitor='val_loss', save_best_only=True, save_weights_only = True, mode='auto') + # model_mckp = ModelCheckpoint(modelfiles, monitor='val_loss', save_best_only=True, save_weights_only = True, mode='auto') - earlystop = EarlyStopping(monitor='val_loss', patience=74, verbose=1) # 提早停止 + earlystop = EarlyStopping(patience=74, verbose=True) # 提早停止 - reduce_lr = ReduceLROnPlateau( - monitor = 'val_loss', + reduce_lr = torch.optim.lr_scheduler.ReduceLROnPlateau( + optimizer, factor = 0.94, # 學習率降低的量。 new_lr = lr * factor patience = 2, # 沒有改進的時期數，之後學習率將降低 verbose = 0, - mode = 'auto', + mode = 'min', min_lr = 0 # 學習率下限 ) - callbacks_list = [model_mckp, earlystop, reduce_lr] - return callbacks_list \ No newline at end of file + return modelfiles, earlystop, reduce_lr \ No newline at end of file diff --git a/draw_tools/__pycache__/Grad_cam.cpython-311.pyc b/draw_tools/__pycache__/Grad_cam.cpython-311.pyc new file mode 100644 index 0000000..ece6623 Binary files /dev/null and b/draw_tools/__pycache__/Grad_cam.cpython-311.pyc differ diff --git a/draw_tools/__pycache__/__init__.cpython-311.pyc b/draw_tools/__pycache__/__init__.cpython-311.pyc index 747e1cf..2095704 100644 Binary files a/draw_tools/__pycache__/__init__.cpython-311.pyc and b/draw_tools/__pycache__/__init__.cpython-311.pyc differ diff --git a/draw_tools/__pycache__/draw.cpython-311.pyc b/draw_tools/__pycache__/draw.cpython-311.pyc index f8d3ce6..497eb21 100644 Binary files a/draw_tools/__pycache__/draw.cpython-311.pyc and b/draw_tools/__pycache__/draw.cpython-311.pyc differ diff --git a/draw_tools/draw.py b/draw_tools/draw.py index e72d0d3..07b3c2c 100644 --- a/draw_tools/draw.py +++ b/draw_tools/draw.py @@ -5,21 +5,21 @@ import matplotlib.figure as figure import matplotlib.backends.backend_agg as agg from Load_process.file_processing import Process_File -def plot_history(history_value, file_name, model_name): +def plot_history(Epochs, Losses, Accuracys, file_name, model_name): File = Process_File() plt.figure(figsize=(16,4)) plt.subplot(1,2,1) - plt.plot(history_value.history['accuracy']) - plt.plot(history_value.history['val_accuracy']) + plt.plot(range(1, Epochs + 1), Losses[0]) + plt.plot(range(1, Epochs + 1), Losses[1]) plt.ylabel('Accuracy') plt.xlabel('epoch') plt.legend(['Train','Validation'], loc='upper left') plt.title('Model Accuracy') plt.subplot(1,2,2) - plt.plot(history_value.history['loss']) - plt.plot(history_value.history['val_loss']) + plt.plot(range(1, Epochs + 1), Accuracys[0]) + plt.plot(range(1, Epochs + 1), Accuracys[1]) plt.ylabel('loss') plt.xlabel('epoch') plt.legend(['Train','Validation'], loc='upper left') diff --git a/experiments/Model_All_Step.py b/experiments/Model_All_Step.py new file mode 100644 index 0000000..527bb6a --- /dev/null +++ b/experiments/Model_All_Step.py @@ -0,0 +1,131 @@ +from tqdm import tqdm +from torch.nn import functional +import torch +from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score +from torchmetrics.functional import auroc +import torch.optim as optim + +from all_models_tools.all_model_tools import call_back +from Model_Loss.Loss import Entropy_Loss + + +class All_Step: + def __init__(self, Training_Data_And_Label, Test_Data_And_Label, Validation_Data_And_Label, Model, Epoch, Number_Of_Classes): + self.Training_Data_And_Label = Training_Data_And_Label + self.Test_Data_And_Label = Test_Data_And_Label + self.Validation_Data_And_Label = Validation_Data_And_Label + + self.Model = Model + self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + + self.Epoch = Epoch + self.Number_Of_Classes = Number_Of_Classes + + pass + + 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) + + criterion = Entropy_Loss() # 使用自定義的損失函數 + train_losses = [] + val_losses = [] + train_accuracies = [] + val_accuracies = [] + + for epoch in range(self.Epoch): + self.Model.train() + running_loss = 0.0 + all_train_preds = [] + all_train_labels = [] + + epoch_iterator = tqdm(self.Training_Data_And_Label, desc= "Training (Epoch %d)" % epoch) + + + for inputs, labels in epoch_iterator: + # labels = np.reshape(labels, (int(labels.shape[0]), 1)) + inputs, OneHot_labels = inputs.to(self.device), OneHot_labels.to(self.device) + # inputs, labels = inputs.cuda(), labels.cuda() + + Optimizer.zero_grad() + outputs = self.Model(inputs) + loss = criterion(outputs, OneHot_labels) + loss.backward() + Optimizer.step() + running_loss += loss.item() + + # 收集訓練預測和標籤 + _, preds = torch.max(outputs, 1) + all_train_preds.extend(preds.cpu().numpy()) + all_train_labels.extend(labels.cpu().numpy()) + + Training_Loss = running_loss/len(self.Training_Data_And_Label) + + # all_train_labels = torch.FloatTensor(all_train_labels) + # all_train_labels = torch.argmax(all_train_labels, 1) + train_accuracy = accuracy_score(all_train_labels, all_train_preds) + + train_losses.append(Training_Loss) + train_accuracies.append(train_accuracy) + + print(f"Epoch [{epoch+1}/{self.epoch}], Loss: {Training_Loss:.4f}, Accuracy: {train_accuracy:0.2f}", end = ' ') + + self.Model.eval() + val_loss = 0.0 + all_val_preds = [] + all_val_labels = [] + + with torch.no_grad(): + for inputs, labels in self.Validation_Data_And_Label: + inputs, OneHot_labels = inputs.to(self.device), labels.to(self.device) + + outputs = self.Model(inputs) + loss = criterion(outputs, OneHot_labels) + val_loss += loss.item() + + # 驗證預測與標籤 + _, preds = torch.max(outputs, 1) + all_val_preds.extend(preds.cpu().numpy()) + all_val_labels.extend(labels.cpu().numpy()) + + # 計算驗證損失與準確率 + val_loss /= len(list(self.Validation_Data_And_Label)) + val_accuracy = accuracy_score(all_val_labels, all_val_preds) + + val_losses.append(val_loss) + val_accuracies.append(val_accuracy) + print(f"Epoch [{epoch+1}/{self.epoch}], Loss: {val_loss:.4f}, Accuracy: {val_accuracy:0.2f}") + + early_stopping(val_loss, self.Model, model_path) + if early_stopping.early_stop: + print("Early stopping triggered. Training stopped.") + break + + # 學習率調整 + scheduler.step(val_loss) + + return train_losses, val_losses, train_accuracies, val_accuracies + + def Evaluate_Model(self, cnn_model): + # 測試模型 + cnn_model.eval() + True_Label, Predict_Label = [], [] + loss = 0.0 + with torch.no_grad(): + for images, labels in self.Test_Data_And_Label: + images, OneHot_labels = images.to(self.device), OneHot_labels.to(self.device) + + outputs = cnn_model(images) + _, predicted = torch.max(outputs, 1) + Predict_Label.extend(predicted.cpu().numpy()) + True_Label.extend(labels.cpu().numpy()) + + loss /= len(self.Test_Data_And_Label) + + accuracy = accuracy_score(True_Label, Predict_Label) + precision = precision_score(True_Label, Predict_Label) + recall = recall_score(True_Label, Predict_Label) + AUC = auroc(True_Label, Predict_Label, task = ["Stomatch_Cancer", "Normal"]) + f1 = f1_score(True_Label, Predict_Label) + return loss, accuracy, precision, recall, AUC, f1, True_Label, Predict_Label \ No newline at end of file diff --git a/experiments/__pycache__/__init__.cpython-311.pyc b/experiments/__pycache__/__init__.cpython-311.pyc index 7c47fb4..d2d2096 100644 Binary files a/experiments/__pycache__/__init__.cpython-311.pyc and b/experiments/__pycache__/__init__.cpython-311.pyc differ diff --git a/experiments/__pycache__/experiment.cpython-311.pyc b/experiments/__pycache__/experiment.cpython-311.pyc index b48f212..df1dd24 100644 Binary files a/experiments/__pycache__/experiment.cpython-311.pyc and b/experiments/__pycache__/experiment.cpython-311.pyc differ diff --git a/experiments/experiment.py b/experiments/experiment.py index 77d3145..cc40169 100644 --- a/experiments/experiment.py +++ b/experiments/experiment.py @@ -1,38 +1,49 @@ from all_models_tools.all_model_tools import call_back from Read_and_process_image.ReadAndProcess import Read_image_and_Process_image from draw_tools.draw import plot_history, Confusion_Matrix_of_Two_Classification -from keras import regularizers from Load_process.Load_Indepentend import Load_Indepentend_Data from _validation.ValidationTheEnterData import validation_the_enter_data -from keras.layers import GlobalAveragePooling2D, Dense, Dropout -from keras.applications import Xception from Load_process.file_processing import Process_File from merge_class.merge import merge from draw_tools.Grad_cam import Grad_CAM from sklearn.metrics import confusion_matrix -from keras.models import Model -from keras.optimizers import SGD +from experiments.pytorch_Model import ModifiedXception from Image_Process.Image_Generator import Image_generator +from Model_All_Step import All_Step import pandas as pd -import keras import numpy as np +import torch +import torch.nn as nn import time class experiments(): - def __init__(self, tools, status): - ''' - parmeter: - * validation_obj : 驗證物件 - * cut_image : 切割影像物件 - * image_processing : 讀檔與讀檔資料處理物件 - * merge : 合併物件 - * model_name: 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) - * generator_batch_size: 每一批次要讀多少檔出來 - * epoch: 訓練次數 - * train_batch_size: 訓練時要多少批次的影像為1組 - * generator_batch_size: 減少圖片數量對GPU記憶體的用量, 減少張數用的 - * experiment_name : 本次實驗名稱 - * convolution_name : Pre-train model 的最後一層Convolotion的名稱 + def __init__(self, tools, Number_Of_Classes, status): + ''' + # 實驗物件 + + ## 說明: + * 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 + + ## parmeter: + * Topic_Tool: 讀取訓練、驗證、測試的資料集與Label等等的內容 + * cut_image: 呼叫切割影像物件 + * merge: 合併的物件 + * 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) + * experiment_name: 實驗名稱 + * epoch: 訓練次數 + * train_batch_size: 訓練資料的batch + * convolution_name: Grad-CAM的最後一層的名稱 + * Number_Of_Classes: Label的類別 + * Status: 選擇現在資料集的狀態 + * device: 決定使用GPU或CPU + + ## Method: + * processing_main: 實驗物件的進入點 + * construct_model: 決定實驗用的Model + * Training_Step: 訓練步驟，開始進行訓練驗證的部分 + * Evaluate_Model: 驗證模型的準確度 + * record_matrix_image: 劃出混淆矩陣(熱力圖) + * record_everyTime_test_result: 記錄我單次的訓練結果並將它輸出到檔案中 ''' self.Topic_Tool = tools @@ -49,99 +60,72 @@ class experiments(): self.epoch = 10000 self.train_batch_size = 128 self.layers = 1 - self.convolution_name = self.get_layer_name(self.model_name) + self.convolution_name = "block14_sepconv2" + self.Number_Of_Classes = Number_Of_Classes self.Grad = "" self.Status = status + self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') pass def processing_main(self, train, train_label, counter): Train, Test, Validation = 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 end = time.time() print("讀取testing與validation資料(154)執行時間：%f 秒\n" % (end - start)) - Generator = Image_generator("", "") + # Generator = Image_generator("", "") # 將處理好的test Data 與 Validation Data 丟給這個物件的變數 self.test, self.test_label = self.cut_image.test, self.cut_image.test_label self.validation, self.validation_label = self.cut_image.validation, self.cut_image.validation_label + self.Topic_Tool.Set_Zips(train, train_label, "Training") + self.Topic_Tool.Set_Zips(self.test, self.test_label, "Testing") + self.Topic_Tool.Set_Zips(self.validation, self.validation_label, "Validation") - self.Grad = Grad_CAM(self.Topic_Tool.Get_Data_Label(), self.test_label, self.experiment_name, self.convolution_name) + self.Training_Zip, self.Testing_Zip, self.Validation_Zip = self.Topic_Tool.Get_Zip() + + # self.Grad = Grad_CAM(self.Topic_Tool.Get_Data_Label(), self.test_label, self.experiment_name, self.convolution_name) cnn_model = self.construct_model() # 呼叫讀取模型的function + step = All_Step(self.Training_Zip, self.Testing_Zip, self.Validation_Zip, cnn_model, self.epoch, self.Number_Of_Classes) # 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 # if os.path.exists(model_dir): # 如果這個檔案存在 # cnn_model.load_weights(model_dir) # 將模型權重讀出來 - # print("讀出權重\n") - - Optimizer = SGD(learning_rate = 0.045, momentum = 0.9) # 決定優化器與學習率 - - cnn_model.compile( - loss = "binary_crossentropy", - optimizer = Optimizer, - metrics= - [ - 'accuracy', - keras.metrics.Precision(name='precision'), - keras.metrics.Recall(name='recall'), - keras.metrics.AUC(name = 'AUC'), - ] - ) - - train_data = Generator.Generator_Content(5) # 叫入ImageGeneratorr的物件，為了要讓我訓練時可以分批次讀取資料，GPU記憶體才不會爆 - cnn_model.summary() # 顯示模型架構 + # print("讀出權重\n") print("\n\n\n讀取訓練資料(70000)執行時間：%f 秒\n\n" % (end - start)) - history = cnn_model.fit( - train_data.flow(train, train_label, batch_size = self.generator_batch_size), - # x = train, - # y = train_label, - epochs = self.epoch, - batch_size = self.train_batch_size, - validation_data = (self.validation, self.validation_label), - callbacks = call_back(self.experiment_name, counter) # 呼叫 call back list - # callbacks = call_back("best_model", self.counter) # 呼叫 call back list - ) - Matrix = self.record_matrix_image(cnn_model, self.experiment_name, counter) # 紀錄混淆矩陣的function - loss, accuracy, precision, recall, AUC = cnn_model.evaluate(self.test, self.test_label) # 預測結果 + train_losses, val_losses, train_accuracies, val_accuracies = step.Training_Step(self.model_name, counter) + loss, accuracy, precision, recall, AUC, f1, True_Label, Predict_Label = step.Evaluate_Model(cnn_model) - # 防分母為0的時候 - if recall == 0 or precision == 0: - f = 0 - else: - f = (1 + 0.5 * 0.5) * ((recall * precision) / (0.5 * 0.5 * recall + precision)) - - print(self.record_everyTime_test_result(loss, accuracy, precision, recall, AUC, f, 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 + 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)) # 紀錄當前訓練完之後的預測結果，並輸出成csv檔 - plot_history(history, "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 - self.Grad.process_main(cnn_model, counter, self.test) + Losses = [train_losses, val_losses] + Accuracyes = [train_accuracies, val_accuracies] + 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 + # self.Grad.process_main(cnn_model, counter, self.test) - return loss, accuracy, precision, recall, AUC, f + return loss, accuracy, precision, recall, AUC, f1 def construct_model(self): '''決定我這次訓練要用哪個model''' - xception = Xception(include_top = False, weights = "imagenet", input_shape = (512, 512, 3)) - Flatten = GlobalAveragePooling2D()(xception.output) - output = Dense(units = 1370, activation = "relu", kernel_regularizer = regularizers.L2())(Flatten) - output = Dropout(0.6)(output) - output = Dense(units = 2, activation = "softmax")(output) + cnn_model = ModifiedXception() - cnn_model = Model(inputs = xception.input, outputs = output) + if torch.cuda.device_count() > 1: + cnn_model = nn.DataParallel(cnn_model) + cnn_model = cnn_model.to(self.device) return cnn_model - def record_matrix_image(self, cnn_model : Model, model_name, index): + def record_matrix_image(self, True_Labels, Predict_Labels, model_name, index): '''劃出混淆矩陣(熱力圖)''' - result = cnn_model.predict(self.test) # 利用predict function來預測結果 - result = np.argmax(result, axis = 1) # 將預測出來的結果從one-hot encoding轉成label-encoding - y_test = np.argmax(self.test_label, axis = 1) - matrix = confusion_matrix(result, y_test, labels = [0, 1]) # 丟入confusion matrix的function中，以形成混淆矩陣 + # 計算混淆矩陣 + matrix = confusion_matrix(True_Labels, Predict_Labels) Confusion_Matrix_of_Two_Classification(model_name, matrix, index) # 呼叫畫出confusion matrix的function return matrix.real @@ -163,37 +147,4 @@ class experiments(): File.Save_CSV_File("train_result", Dataframe) # File.Save_TXT_File("Matrix_Result : " + str(Matrix), model_name + "_train" + str(indexs)) - return Dataframe - - def get_layer_name(self, model_name): - if(self.validation_obj.validation_string(model_name, "VGG19")): - return "block5_conv4" - if(self.validation_obj.validation_string(model_name, "ResNet50")): - return "conv5_block3_3_conv" - if(self.validation_obj.validation_string(model_name, "Xception")): - return "block14_sepconv2" - if(self.validation_obj.validation_string(model_name, "DenseNet121")): - return "conv5_block16_concat" - if(self.validation_obj.validation_string(model_name, "InceptionResNetV2")): - return "conv_7b" - if(self.validation_obj.validation_string(model_name, "InceptionV3")): - return "conv2d_93" - if(self.validation_obj.validation_string(model_name, "MobileNet")): - return "conv_pw_13" - if(self.validation_obj.validation_string(model_name, "MobileNetV2")): - return "Conv_1" - if(self.validation_obj.validation_string(model_name, "NASNetLarge")): - return "separable_conv_2_normal_left5_18" - if(self.validation_obj.validation_string(model_name, "ResNet101")): - return "conv5_block3_3_conv" - if(self.validation_obj.validation_string(model_name, "ResNet101V2")): - return "conv5_block3_3_conv" - if(self.validation_obj.validation_string(model_name, "ResNet152")): - return "conv5_block3_3_conv" - if(self.validation_obj.validation_string(model_name, "ResNet152V2")): - return "conv5_block3_out" - if(self.validation_obj.validation_string(model_name, "ResNet50v2")): - return "conv5_block3_out" - if(self.validation_obj.validation_string(model_name, "VGG16")): - return "block5_conv3" - + return Dataframe \ No newline at end of file diff --git a/main.py b/main.py index 0379c41..c54eefc 100644 --- a/main.py +++ b/main.py @@ -6,18 +6,17 @@ from model_data_processing.processing import shuffle_data from Load_process.LoadData import Load_Data_Prepare from Calculate_Process.Calculate import Calculate from merge_class.merge import merge -import tensorflow as tf import time +import torch import os if __name__ == "__main__": # 測試GPU是否可用 - print('TensorFlow version:', tf.__version__) - physical_devices = tf.config.experimental.list_physical_devices('GPU') - print(physical_devices) - assert len(physical_devices) > 0, "Not enough GPU hardware devices available" - tf.config.experimental.set_memory_growth(physical_devices[0], True) - os.environ["CUDA_VISIBLE_DEVICES"]='0' + flag = torch.cuda.is_available() + if not flag: + print("CUDA不可用\n") + else: + print(f"CUDA可用，數量為{torch.cuda.device_count()}\n") Status = 2 # 決定要使用什麼資料集 # 要換不同資料集就要改 @@ -86,6 +85,7 @@ if __name__ == "__main__": print(len(training_data)) 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 + training_data = image_processing.normalization(training_data) end = time.time() print("\n\n\n讀取訓練資料(70000)執行時間：%f 秒\n\n" % (end - start)) diff --git a/merge_class/__pycache__/merge.cpython-311.pyc b/merge_class/__pycache__/merge.cpython-311.pyc index 83892d8..be701db 100644 Binary files a/merge_class/__pycache__/merge.cpython-311.pyc and b/merge_class/__pycache__/merge.cpython-311.pyc differ diff --git a/model_data_processing/__pycache__/__init__.cpython-311.pyc b/model_data_processing/__pycache__/__init__.cpython-311.pyc index ae4bbc7..58b90c4 100644 Binary files a/model_data_processing/__pycache__/__init__.cpython-311.pyc and b/model_data_processing/__pycache__/__init__.cpython-311.pyc differ diff --git a/model_data_processing/__pycache__/processing.cpython-311.pyc b/model_data_processing/__pycache__/processing.cpython-311.pyc index 9da029d..6406322 100644 Binary files a/model_data_processing/__pycache__/processing.cpython-311.pyc and b/model_data_processing/__pycache__/processing.cpython-311.pyc differ