121 lines
4.6 KiB
Python
121 lines
4.6 KiB
Python
import torch
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import sys
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import os
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import time
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# 添加當前目錄到系統路徑
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sys.path.append(os.path.dirname(os.path.abspath(__file__)))
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from experiments.experiment import experiments
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from utils.Stomach_Config import Training_Config, Loading_Config, Save_Result_File_Config
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from Training_Tools.Tools import Tool
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from merge_class.merge import merge
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def test_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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if torch.cuda.is_available():
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print(f"GPU: {torch.cuda.get_device_name(0)}")
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# 創建一個簡單的測試數據集
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class SimpleDataset(torch.utils.data.Dataset):
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def __init__(self, size=10):
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self.size = size
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self.data = torch.randn(size, 3, 256, 256) # 假設輸入是3通道256x256圖像
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self.mask = torch.randint(0, 2, (size, 1, 256, 256)).float() # 二值掩碼
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self.labels = torch.zeros(size, 3) # 假設有3個類別
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self.labels[:, 0] = 1 # 所有樣本都是第一個類別
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print(f"創建了測試數據集，大小: {size}，數據形狀: {self.data.shape}")
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def __len__(self):
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return self.size
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def __getitem__(self, idx):
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return self.data[idx], self.mask[idx], self.labels[idx]
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# 創建測試數據
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print("創建測試數據...")
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test_dataset = SimpleDataset()
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training_data = [test_dataset.data[i] for i in range(test_dataset.size)]
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training_label = [test_dataset.labels[i] for i in range(test_dataset.size)]
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training_mask = [test_dataset.mask[i] for i in range(test_dataset.size)]
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# 初始化工具和配置
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tool = Tool()
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Status = 1
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Label_Length = 3
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# 模擬main.py中的數據處理
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print("\n模擬main.py中的數據處理...")
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Merge = merge()
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# 確保Loading_Config中包含必要的配置項
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required_configs = [
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"Test_Data_Root", "Training_Labels", "Annotation_Root",
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"Label_Image_Labels", "Image enhance processing save root"
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]
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for config in required_configs:
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if config not in Loading_Config:
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print(f"添加缺少的配置項: {config}")
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if config == "Test_Data_Root":
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Loading_Config[config] = "./test_data"
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elif config == "Training_Labels":
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Loading_Config[config] = ["class1", "class2", "class3"]
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elif config == "Annotation_Root":
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Loading_Config[config] = "./annotations"
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elif config == "Label_Image_Labels":
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Loading_Config[config] = ["CA", "Normal"]
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elif config == "Image enhance processing save root":
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Loading_Config[config] = "./enhanced_images"
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# 測試experiments類的初始化
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try:
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print("\n測試experiments類的初始化...")
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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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GastroSegNet_Training_Data=training_data,
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GastroSegNet_Training_Label=training_label,
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GastroSegNet_Training_Mask=training_mask,
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Training_Config=Training_Config,
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Loading_Config=Loading_Config,
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tools=tool,
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Number_Of_Classes=Label_Length,
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status=Status
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)
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print("experiments類初始化成功!")
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# 測試processing_main方法（僅執行部分代碼）
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print("\n測試processing_main方法...")
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# 這裡我們不實際調用processing_main，因為它會執行完整的訓練流程
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# 而是檢查關鍵屬性是否正確設置
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print(f"模型名稱: {experiment.model_name}")
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print(f"實驗名稱: {experiment.experiment_name}")
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print(f"訓練批次大小: {experiment.train_batch_size}")
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print(f"使用設備: {experiment.device}")
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# 模擬processing_main的部分功能
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print("\n模擬processing_main的部分功能...")
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# 這裡我們只模擬一些基本操作，不執行實際的訓練
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print("模擬讀取測試數據...")
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experiment.test = training_data
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experiment.test_label = training_label
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print("\n測試成功!")
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except Exception as e:
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print(f"\n測試失敗: {str(e)}")
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import traceback
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traceback.print_exc()
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if __name__ == "__main__":
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test_main() |