diff --git a/gpu/pretrain.py b/gpu/pretrain.py
deleted file mode 100755
index 191da3c..0000000
--- a/gpu/pretrain.py
+++ /dev/null
@@ -1,128 +0,0 @@
-#!/usr/bin/python
-#coding=utf-8
-'''
-If there are Chinese comments in the code，please add at the beginning：
-#!/usr/bin/python
-#coding=utf-8   
-
-1，The dataset structure of the single-dataset in this example
- MnistDataset_torch.zip
-  ├── test
-  └── train  
-
-2，Due to the adaptability of a100, before using the training environment, please use the recommended image of the 
-platform with cuda 11.Then adjust the code and submit the image.
-The image of this example is: dockerhub.pcl.ac.cn:5000/user-images/openi:cuda111_python37_pytorch191
-In the training environment, the uploaded dataset will be automatically placed in the /dataset directory. 
-Note: the paths are different when selecting a single dataset and multiple datasets.
-(1)If it is a single dataset: if MnistDataset_torch.zip is selected, 
-   the dataset directory is /dataset/train, /dataset/test;
-   If it is a multiple dataset: if MnistDataset_torch.zip is selected, 
-   the dataset directory is /dataset/MnistDataset_torch/train, /dataset/MnistDataset_torch/test;
-
-(2)If the pre-training model file is selected, the selected pre-training model will be 
-automatically placed in the /pretrainmodel directory.
-for example:
-    If the model file is selected, the calling method is: '/pretrainmodel/' + args.pretrainmodelname
-
-The model download path is under /model by default. Please specify the model output location to /model, 
-and the Qizhi platform will provide file downloads under the /model directory.
-'''
-
-
-from model import Model
-import numpy as np
-import torch
-from torchvision.datasets import mnist
-from torch.nn import CrossEntropyLoss
-from torch.optim import SGD
-from torch.utils.data import DataLoader
-from torchvision.transforms import ToTensor
-import argparse
-import os
-
-# Training settings
-parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
-#The dataset location is placed under /dataset
-parser.add_argument('--traindata', default="/dataset/train" ,help='path to train dataset')
-parser.add_argument('--testdata', default="/dataset/test" ,help='path to test dataset')
-parser.add_argument('--epoch_size', type=int, default=10, help='how much epoch to train')
-parser.add_argument('--batch_size', type=int, default=256, help='how much batch_size in epoch')
-#获取模型文件名称
-parser.add_argument('--ckpt_url', default="", help='pretrain model path')
-
-# 参数声明
-WORKERS = 0   # dataloder线程数
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-model = Model().to(device)
-optimizer = SGD(model.parameters(), lr=1e-1)
-cost = CrossEntropyLoss()
-
-# 模型训练
-def train(model, train_loader, epoch):
-    model.train()
-    train_loss = 0
-    for i, data in enumerate(train_loader, 0):
-        x, y = data
-        x = x.to(device)
-        y = y.to(device)
-        optimizer.zero_grad()
-        y_hat = model(x)
-        loss = cost(y_hat, y)
-        loss.backward()
-        optimizer.step()
-        train_loss += loss
-    loss_mean = train_loss / (i+1)
-    print('Train Epoch: {}\t Loss: {:.6f}'.format(epoch, loss_mean.item()))
- 
-# 模型测试
-def test(model, test_loader, test_data):
-    model.eval()
-    test_loss = 0
-    correct = 0
-    with torch.no_grad():
-        for i, data in enumerate(test_loader, 0):
-            x, y = data
-            x = x.to(device)
-            y = y.to(device)
-            optimizer.zero_grad()
-            y_hat = model(x)
-            test_loss += cost(y_hat, y).item()
-            pred = y_hat.max(1, keepdim=True)[1]
-            correct += pred.eq(y.view_as(pred)).sum().item()
-        test_loss /= (i+1)
-        print('Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
-            test_loss, correct, len(test_data), 100. * correct / len(test_data)))
-def main():
-    # 如果有保存的模型，则加载模型，并在其基础上继续训练
-    if os.path.exists(args.ckpt_url):
-        checkpoint = torch.load(args.ckpt_url)
-        model.load_state_dict(checkpoint['model'])
-        optimizer.load_state_dict(checkpoint['optimizer'])
-        start_epoch = checkpoint['epoch']
-        print('加载 epoch {} 权重成功！'.format(start_epoch))
-    else:
-        start_epoch = 0
-        print('无保存模型，将从头开始训练！')
- 
-    for epoch in range(start_epoch+1, epochs):
-        train(model, train_loader, epoch)
-        test(model, test_loader, test_dataset)
-        # 保存模型
-        state = {'model':model.state_dict(), 'optimizer':optimizer.state_dict(), 'epoch':epoch}
-        torch.save(state, '/model/mnist_epoch{}.pkl'.format(epoch))
-
-if __name__ == '__main__':
-    args, unknown = parser.parse_known_args()
-    #log output
-    print('cuda is available:{}'.format(torch.cuda.is_available()))  
-    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-    batch_size = args.batch_size
-    epochs = args.epoch_size
-    train_dataset = mnist.MNIST(root=args.traindata, train=True, transform=ToTensor(),download=False)
-    test_dataset = mnist.MNIST(root=args.testdata, train=False, transform=ToTensor(),download=False)
-    train_loader = DataLoader(train_dataset, batch_size=batch_size)
-    test_loader = DataLoader(test_dataset, batch_size=batch_size)
-    main()
-
-
diff --git a/gpu/pretrain_for_c2net.py b/gpu/pretrain_for_c2net.py
deleted file mode 100755
index fba79d3..0000000
--- a/gpu/pretrain_for_c2net.py
+++ /dev/null
@@ -1,144 +0,0 @@
-#!/usr/bin/python
-#coding=utf-8
-'''
-If there are Chinese comments in the code，please add at the beginning：
-#!/usr/bin/python
-#coding=utf-8   
-
-In the training environment, 
-(1)the code will be automatically placed in the /tmp/code directory, 
-(2)the uploaded dataset will be automatically placed in the /tmp/dataset directory
-Note: the paths are different when selecting a single dataset and multiple datasets.
-(1)If it is a single dataset: if MnistDataset_torch.zip is selected, 
-   the dataset directory is /tmp/dataset/train, /dataset/test;
-
-The dataset structure of the single dataset in the training image in this example:
-  tmp
-    ├──dataset 
-         ├── test
-         └── train 
-
-If multiple datasets are selected, such as MnistDataset_torch.zip and checkpoint_epoch1_0.73.zip, 
-the dataset directory is /tmp/dataset/MnistDataset_torch/train, /tmp/dataset/MnistDataset_torch/test
-and /tmp/dataset/checkpoint_epoch1_0.73/mnist_epoch1_0.73.pkl
-The dataset structure in the training image for multiple datasets in this example:
-tmp
-  ├──dataset
-     ├── MnistDataset_torch
-     |     ├── test
-     |     └── train 
-     └── checkpoint_epoch1_0.73 
-           ├── mnist_epoch1_0.73.pkl
-(3)the model download path is under /tmp/output by default, please specify the model output location to /tmp/output, 
-qizhi platform will provide file downloads under the /tmp/output directory.
-(4)If the pre-training model file is selected, the selected pre-training model will be 
-automatically placed in the /tmp/pretrainmodel directory.
-for example:
-    If the model file is selected, the calling method is: '/pretrainmodel/' + args.pretrainmodelname
-
-In addition, if you want to get the model file after each training, you can call the uploader_for_gpu tool, 
-which is written as: 
-import os
-os.system("cd /tmp/script_for_grampus/ &&./uploader_for_gpu " + "/tmp/output/")
-'''
-
-
-from model import Model
-import numpy as np
-import torch
-from torchvision.datasets import mnist
-from torch.nn import CrossEntropyLoss
-from torch.optim import SGD
-from torch.utils.data import DataLoader
-from torchvision.transforms import ToTensor
-import argparse
-import os
-
-# Training settings
-parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
-#The dataset location is placed under /dataset
-parser.add_argument('--traindata', default="/tmp/dataset/train" ,help='path to train dataset')
-parser.add_argument('--testdata', default="/tmp/dataset/test" ,help='path to test dataset')
-parser.add_argument('--epoch_size', type=int, default=10, help='how much epoch to train')
-parser.add_argument('--batch_size', type=int, default=256, help='how much batch_size in epoch')
-#获取模型文件名称
-parser.add_argument('--ckpt_url', default="", help='pretrain model path')
-
-# 参数声明
-WORKERS = 0   # dataloder线程数
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-model = Model().to(device)
-optimizer = SGD(model.parameters(), lr=1e-1)
-cost = CrossEntropyLoss()
-
-# 模型训练
-def train(model, train_loader, epoch):
-    model.train()
-    train_loss = 0
-    for i, data in enumerate(train_loader, 0):
-        x, y = data
-        x = x.to(device)
-        y = y.to(device)
-        optimizer.zero_grad()
-        y_hat = model(x)
-        loss = cost(y_hat, y)
-        loss.backward()
-        optimizer.step()
-        train_loss += loss
-    loss_mean = train_loss / (i+1)
-    print('Train Epoch: {}\t Loss: {:.6f}'.format(epoch, loss_mean.item()))
- 
-# 模型测试
-def test(model, test_loader, test_data):
-    model.eval()
-    test_loss = 0
-    correct = 0
-    with torch.no_grad():
-        for i, data in enumerate(test_loader, 0):
-            x, y = data
-            x = x.to(device)
-            y = y.to(device)
-            optimizer.zero_grad()
-            y_hat = model(x)
-            test_loss += cost(y_hat, y).item()
-            pred = y_hat.max(1, keepdim=True)[1]
-            correct += pred.eq(y.view_as(pred)).sum().item()
-        test_loss /= (i+1)
-        print('Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
-            test_loss, correct, len(test_data), 100. * correct / len(test_data)))
-def main():
-    # 如果有保存的模型，则加载模型，并在其基础上继续训练
-    if os.path.exists(args.ckpt_url):
-        checkpoint = torch.load(args.ckpt_url)
-        model.load_state_dict(checkpoint['model'])
-        optimizer.load_state_dict(checkpoint['optimizer'])
-        start_epoch = checkpoint['epoch']
-        print('加载 epoch {} 权重成功！'.format(start_epoch))
-    else:
-        start_epoch = 0
-        print('无保存模型，将从头开始训练！')
- 
-    for epoch in range(start_epoch+1, epochs):
-        train(model, train_loader, epoch)
-        test(model, test_loader, test_dataset)
-        # 保存模型
-        state = {'model':model.state_dict(), 'optimizer':optimizer.state_dict(), 'epoch':epoch}
-        torch.save(state, '/tmp/output/mnist_epoch{}.pkl'.format(epoch))
-        #After calling uploader_for_gpu, after each epoch training, the result file under /tmp/output will be sent back to Qizhi
-        os.system("cd /tmp/script_for_grampus/ &&./uploader_for_gpu " + "/tmp/output/")
-
-if __name__ == '__main__':
-    args, unknown = parser.parse_known_args()
-    #log output
-    print('cuda is available:{}'.format(torch.cuda.is_available()))  
-    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-    batch_size = args.batch_size
-    epochs = args.epoch_size
-    train_dataset = mnist.MNIST(root=args.traindata, train=True, transform=ToTensor(),download=False)
-    test_dataset = mnist.MNIST(root=args.testdata, train=False, transform=ToTensor(),download=False)
-    train_loader = DataLoader(train_dataset, batch_size=batch_size)
-    test_loader = DataLoader(test_dataset, batch_size=batch_size)
-    main()
-
-
-        
\ No newline at end of file
diff --git a/gpu/pretrain_wjtest.py b/gpu/pretrain_wjtest.py
deleted file mode 100755
index a791b21..0000000
--- a/gpu/pretrain_wjtest.py
+++ /dev/null
@@ -1,127 +0,0 @@
-#!/usr/bin/python
-#coding=utf-8
-'''
-If there are Chinese comments in the code，please add at the beginning：
-#!/usr/bin/python
-#coding=utf-8   
-
-1，The dataset structure of the single-dataset in this example
- MnistDataset_torch.zip
-  ├── test
-  └── train  
-
-2，Due to the adaptability of a100, before using the training environment, please use the recommended image of the 
-platform with cuda 11.Then adjust the code and submit the image.
-The image of this example is: dockerhub.pcl.ac.cn:5000/user-images/openi:cuda111_python37_pytorch191
-In the training environment, the uploaded dataset will be automatically placed in the /dataset directory. 
-Note: the paths are different when selecting a single dataset and multiple datasets.
-(1)If it is a single dataset: if MnistDataset_torch.zip is selected, 
-   the dataset directory is /dataset/train, /dataset/test;
-   If it is a multiple dataset: if MnistDataset_torch.zip is selected, 
-   the dataset directory is /dataset/MnistDataset_torch/train, /dataset/MnistDataset_torch/test;
-
-(2)If the pre-training model file is selected, the selected pre-training model will be 
-automatically placed in the /pretrainmodel directory.
-for example:
-    If the model file is selected, the calling method is: '/pretrainmodel/' + args.pretrainmodelname
-
-The model download path is under /model by default. Please specify the model output location to /model, 
-and the Qizhi platform will provide file downloads under the /model directory.
-'''
-
-
-from model import Model
-import numpy as np
-import torch
-from torchvision.datasets import mnist
-from torch.nn import CrossEntropyLoss
-from torch.optim import SGD
-from torch.utils.data import DataLoader
-from torchvision.transforms import ToTensor
-import argparse
-import os
-
-# Training settings
-parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
-#The dataset location is placed under /dataset
-parser.add_argument('--traindata', default="/dataset/train" ,help='path to train dataset')
-parser.add_argument('--testdata', default="/dataset/test" ,help='path to test dataset')
-parser.add_argument('--epoch_size', type=int, default=10, help='how much epoch to train')
-parser.add_argument('--batch_size', type=int, default=256, help='how much batch_size in epoch')
-#获取模型文件名称
-parser.add_argument('--ckpt_url', default="", help='pretrain model path')
-
-# 参数声明
-WORKERS = 0   # dataloder线程数
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-model = Model().to(device)
-optimizer = SGD(model.parameters(), lr=1e-1)
-cost = CrossEntropyLoss()
-
-# 模型训练
-def train(model, train_loader, epoch):
-    model.train()
-    train_loss = 0
-    for i, data in enumerate(train_loader, 0):
-        x, y = data
-        x = x.to(device)
-        y = y.to(device)
-        optimizer.zero_grad()
-        y_hat = model(x)
-        loss = cost(y_hat, y)
-        loss.backward()
-        optimizer.step()
-        train_loss += loss
-    loss_mean = train_loss / (i+1)
-    print('Train Epoch: {}\t Loss: {:.6f}'.format(epoch, loss_mean.item()))
- 
-# 模型测试
-def test(model, test_loader, test_data):
-    model.eval()
-    test_loss = 0
-    correct = 0
-    with torch.no_grad():
-        for i, data in enumerate(test_loader, 0):
-            x, y = data
-            x = x.to(device)
-            y = y.to(device)
-            optimizer.zero_grad()
-            y_hat = model(x)
-            test_loss += cost(y_hat, y).item()
-            pred = y_hat.max(1, keepdim=True)[1]
-            correct += pred.eq(y.view_as(pred)).sum().item()
-        test_loss /= (i+1)
-        print('Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
-            test_loss, correct, len(test_data), 100. * correct / len(test_data)))
-def main():
-    # 如果有保存的模型，则加载模型，并在其基础上继续训练
-    print('------ckpt_url is: ', args.ckpt_url);
-    if os.path.exists(args.ckpt_url):
-        checkpoint = torch.load(args.ckpt_url)
-        model.load_state_dict(checkpoint['model'])
-        optimizer.load_state_dict(checkpoint['optimizer'])
-        start_epoch = checkpoint['epoch']
-        print('加载 epoch {} 权重成功！'.format(start_epoch))
-    else:
-        start_epoch = 0
-        print('无保存模型，将从头开始训练！')
- 
-    for epoch in range(start_epoch+1, epochs):
-        train(model, train_loader, epoch)
-        test(model, test_loader, test_dataset)
-        # 保存模型
-        state = {'model':model.state_dict(), 'optimizer':optimizer.state_dict(), 'epoch':epoch}
-        torch.save(state, '/model/mnist_epoch{}.pkl'.format(epoch))
-
-if __name__ == '__main__':
-    args, unknown = parser.parse_known_args()
-    #log output
-    print('cuda is available:{}'.format(torch.cuda.is_available()))  
-    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-    batch_size = args.batch_size
-    epochs = args.epoch_size
-    train_dataset = mnist.MNIST(root=args.traindata, train=True, transform=ToTensor(),download=False)
-    test_dataset = mnist.MNIST(root=args.testdata, train=False, transform=ToTensor(),download=False)
-    train_loader = DataLoader(train_dataset, batch_size=batch_size)
-    test_loader = DataLoader(test_dataset, batch_size=batch_size)
-    main()
\ No newline at end of file