From b5d9f1ebc4df28ed2b74a5a7f40a4a71d36e84ad Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E8=91=9B=E5=B3=BB=E6=81=BA?= <202115006@mail.sdu.edu.cn>
Date: Tue, 2 Sep 2025 12:02:51 +0000
Subject: [PATCH] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=207=5Fnetw?=
 =?UTF-8?q?ork=5Ftrain.py?=
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---
 7_network_train.py | 79 ----------------------------------------------
 1 file changed, 79 deletions(-)
 delete mode 100644 7_network_train.py

diff --git a/7_network_train.py b/7_network_train.py
deleted file mode 100644
index 8ebf28e..0000000
--- a/7_network_train.py
+++ /dev/null
@@ -1,79 +0,0 @@
-import torch
-import torch.nn as nn
-from torch.utils.data import DataLoader
-from torch.utils.tensorboard import SummaryWriter
-from torchvision.utils import make_grid, save_image
-import time
-import pandas as pd
-from torchsummary import summary
-from Network.MyDataset import MyDataset
-from utils.train_val_lr import train, validate, adjust_learning_rate
-from Network.Model import Model
-from config import Network_train_Config as cfg
-from config import Path_Config as pcfg
-
-# File paths
-data_file = pcfg.dataset_path
-label_file = pcfg.labelset_path
-impulse_field_file = pcfg.field_impulse
-impulse_sim_file = pcfg.sim_impulse
-
-if __name__ == '__main__':
-    BATCH_SIZE = cfg.BATCH_SIZE
-    LR = cfg.LR  # Initial learning rate
-    EPOCHS = cfg.EPOCHS
-    val_loss_min = cfg.val_loss_min  # Initial high value for validation loss tracking
-    
-    # Model initialization
-    model = Model(inplanes=2, outplanes=1, layers=cfg.network_layers).cuda()
-    optimizer = torch.optim.Adam(model.parameters(), lr=LR)
-    loss_func = nn.MSELoss(reduction='mean')
-    
-    # Create a CSV file to log training progress
-    df = pd.DataFrame(columns=['epoch', 'train Loss', 'val Loss', 'learning rate'])
-    df.to_csv(pcfg.train_val_loss, index=False)
-    
-    # Load dataset
-    dataset = MyDataset(data_file, label_file, impulse_field_file, impulse_sim_file, mode='train', check=False)
-    train_size = int(len(dataset) * cfg.dataset_Proportion)
-    val_size = len(dataset) - train_size
-    train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])
-    
-    # Create data loaders
-    train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=cfg.num_workers, pin_memory=True)
-    val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=cfg.num_workers, pin_memory=True)
-    
-    # TensorBoard writer
-    writer = SummaryWriter(r'Log')
-    
-    print('Dataset loaded successfully.')
-    
-    for epoch in range(EPOCHS):
-        epoch_start_time = time.time()
-        print(f'Starting epoch {epoch}')
-        
-        # Adjust learning rate
-        LR = adjust_learning_rate(optimizer, epoch, LR)
-        
-        # Training and validation
-        train_loss = train(train_loader, model, loss_func, optimizer, epoch)
-        val_loss = validate(val_loader, model, loss_func)
-        
-        print(f'Validation Loss: {val_loss:.6f}')
-        
-        # Save best model
-        if val_loss < val_loss_min:
-            torch.save(model.state_dict(), pcfg.BEST_MODEL_PATH)
-            val_loss_min = val_loss
-        
-        # Periodically save model
-        if epoch % cfg.save_period == 0:
-            torch.save(model.state_dict(), pcfg.LATEST_MODEL_PATH)
-        
-        # Log training details
-        log_data = pd.DataFrame([[epoch, train_loss, val_loss, LR]])
-        log_data.to_csv(pcfg.train_val_loss, mode='a', header=False, index=False)
-        
-        print(f'Epoch {epoch} completed in {time.time() - epoch_start_time:.2f} seconds.')
-
-