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3DUNet implemented with pytorch

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您好,最近在用您分享的代码做分割,train的背景dice0=0.9984,目标dice1= 0.6014,但是test的时候dice0=0.6787,dice1=0.0011,且预测出来的标签全黑。我是用4个GPU进行训练和预测的,请问出现这种问题的原因是什么呢?和模型参数的读取有关系?希望得到您的帮助!

您好~如题,我注意到[Unet.py](https://github.com/lee-zq/3DUNet-Pytorch/blob/master/models/UNet.py)里似乎没有涉及到任何比如batch或者instance等等的normalization操作,但我记得原始的3D UNet里应该是有的。请问您这样做是有什么用意吗?还是说encoder某一步里有自带normalization呢? 盼复,十分感谢!

你好,我用你的网盘数据可以训练测试,但是用我自己的CT数据、分割数据,到这一步就卡住报错了: fixd_path = r'D:\Download\3DUNet-Pytorch-master\fixed_data' dataset = Lits_DataSet([16, 64, 64],0.5,fixd_path,mode='train') #batch size data_loader=DataLoader(dataset=dataset,batch_size=2,num_workers=1, shuffle=True) for batch_idx, (data, target) in enumerate(data_loader): print("最大值:") print(torch.max(target.long() , 0)) print(data.shape, target.shape) target = to_one_hot_3d(target.long()) 到to_one_hot_3d()函数里的scatter时报错 torch.Size([2,...

Can you show me your result when submitting LITS on leaderboard? Thankyou very much!

hello, when i run python ./preprocess_LiTS.py in the fix_data() function the following error occured "ERROR (nifti_image_write_hdr_img2): cannot open output file './fixed_datadata/volume-52.nii' ** ERROR (nifti_image_write_hdr_img2): cannot open output file './fixed_datalabel/segmentation-52.nii.gz'",

in dataset_lits_train.py, def get_np_data_3d(),line44: def get_np_data_3d(self, filename, resize_scale=1): data_np = sitk.GetArrayFromImage(sitk.ReadImage(self.dataset_path + '/data/' + filename, sitk.sitkInt16)) if self.resize_scale !=1.0: data_np = ndimage.zoom(data_np, zoom=self.resize_scale, order=3) data_np = data_np.astype(np.float32) # --------不理解的地方二 data_np=data_np/200.0...

你好,想问一下,在运行train.py时有一些输出不是很明白,train dice0,1,2分别是代表什么呢?同理,val dice0,1,2又分别代表什么,如果要评估分割效果,真正的dice系数是看哪一个,谢谢!

I'm trying to run the code, but during training is giving me the error NameError: name 'get_dices' is not defined. Could you please provide help me to resolve this issue?

你好lee-zq, 非常感谢你的方法! 我在训练的时候遇到了这个问题: ``` =======Epoch:1======= 0%| | 0/2 [00:00

Hello, do you have a trained model that I can use directly