Stomach_Cancer_Pytorch/testing.py
2024-11-19 12:16:22 +00:00

48 lines
1.6 KiB
Python
Executable File

# import paramiko
# from scp import SCPClient
# import os
# import pexpect
# def createSSHClient(server, port, user, password):
# client = paramiko.SSHClient()
# client.load_system_host_keys()
# client.set_missing_host_key_policy(paramiko.AutoAddPolicy)
# client.connect(server, port, user, password)
# return client
# ssh = createSSHClient("10.1.29.28", 31931, "root", "whitekirin")
# # os.mkdir("Original_ResNet101V2_with_NPC_Augmentation_Image")
# # with open("Original_ResNet101V2_with_NPC_Augmentation_Image_train3.txt", "w") as file:
# # pass
# with SCPClient(ssh.get_transport()) as scp:
# scp.get("/mnt/c/張晉嘉/stomach_cancer/Original_ResNet101V2_with_NPC_Augmentation_Image_train3.txt", "/raid/whitekirin/stomach_cancer/Model_result/save_the_train_result(2024-10-05)/Original_ResNet101V2_with_NPC_Augmentation_Image_train3.txt")
from Training_Tools.Tools import Tool
from torchvision.datasets import ImageFolder
from torch.utils.data import DataLoader, Subset
import torchvision.transforms as transforms
import numpy as np
transform = transforms.Compose([
transforms.Resize((64, 64)),
transforms.ToTensor(),
])
tool = Tool()
tool.Set_Labels() # 要換不同資料集就要改
tool.Set_Save_Roots()
Train, Test, Validation = tool.Get_Save_Roots(1)
train_dataset = ImageFolder(root=Train, transform=transform)
balanced_loader = DataLoader(train_dataset, batch_size=32, shuffle=True, num_workers = 4)
tests = []
for i, (test, labels) in enumerate(balanced_loader):
tests.append(labels)
print(tests)