I have used your code and tested it to predict the segmentation results for validation set. Then I submitted the generated segmentation files on cbica evaluation portal for BraTS 2020 validation segmentation task. My job is completed but in zip file I am not receiving any csv file with segmentation scores. Results-733562074827703492.zip

Please help.

My dear friend, when I run your code, there are only label 1,2,4 in my result . All the background are label 1, I was puzzled. I just only download your code here , then change the path to my environment, and edit the parallel code into single GPU.
My platform is Ubuntu20.04 + RXT 3090.

Have you ever encountered the same problem ?

Would you please give me some suggestions?

Many thanks in advance!

Wenxuan您好， 在train.py中，有： train_loader = DataLoader(dataset=train_set, sampler=train_sampler, batch_size=args.batch_size // num_gpu, drop_last=True, num_workers=args.num_workers, pin_memory=True) 此处，我不是太明白batch_size这里为什么要用args.batch_size除以num_gpu？ args.batch_size 为8, 再除以gpu数量4, 等于2, 相当于每个gpu得到的数据batch_size为2？ 难道不是每个gpu得到的数据batch_size就是args.batch_size 吗？ 谢谢，并祝好！

My dear friend, sorry to bother you.

I'm a greenhand in medical image segmentation, maybe this function seems very easy for you , but I can not understand it clearly.

Both 中文 and English are OK, ^__^

Many thanks in advance

Hi, Great work. But there are few small problems that puzzle me. 1) The image shape changes from 240240155 to 240240160，what are the considerations here? 2) I can't understand the intmd_encoder_outputs of the encoder which is the output intmd_x of transformer, what's the difference between xand intmd_x? Besides, the encoder_output seems to be not used in Decoder.

x, intmd_x = self.transformer(x)

def forward(self, x, auxillary_output_layers=[1, 2, 3, 4]):

     x1_1, x2_1, x3_1, encoder_output, intmd_encoder_outputs = self.encode(x)

     decoder_output = self.decode(
         x1_1, x2_1, x3_1, encoder_output, intmd_encoder_outputs, auxillary_output_layers
     )

     if auxillary_output_layers is not None:
         auxillary_outputs = {}
         for i in auxillary_output_layers:
             val = str(2 * i - 1)
             _key = 'Z' + str(i)
             auxillary_outputs[_key] = intmd_encoder_outputs[val]

         return decoder_output

     return decoder_output

Hi, I had meet "RuntimeError: CUDA out of memory" when I reccurent, with "python -m torch.distributed.launch --nproc_per_node=2 --master_port 20011 train.py --gpu 0,1", and my env is RTX2080ti. could you tell me what is nvidia device you used? thanks you very much!

Hello wenxuan. I hope you recognize me. I had raised an issue earlier about predictions on brats 2020 data. I need your help in resuming the training. I am training model on google colab which shuts after 12 hours. So I want to resume it from the same point where it gets stopped.

Traceback (most recent call last): File "train.py", line 290, in main_worker() File "train.py", line 114, in main_worker log_args(log_file) File "train.py", line 271, in log_args fh = logging.FileHandler(log_file) File "/usr/lib/python3.7/logging/init.py", line 1087, in init StreamHandler.init(self, self._open()) File "/usr/lib/python3.7/logging/init.py", line 1116, in _open return open(self.baseFilename, self.mode, encoding=self.encoding) FileNotFoundError: [Errno 2] No such file or directory: '/content/drive/MyDrive/Colab Notebooks/Wenxuan_segmentation/log/TransBTS2021-06-26.txt'

Hi Dude , good afternoon. I'm reading your code. To my understanding , Transformer.py is for define the transformer encoder.
I'm wondering whether there is any API that we can use directly instead of tedious coding.

I just found torch.nn.TransformerEncoder exists. Is it usable in this project? Have you ever thought about this problem before?

Many thanks!

Hello, I run the tranBTS code for 3D medical images, the shape of the generated attention weight is (Batch_size,num_heads, n_patches,n_patches ), is this not applicable to 3D images? Thank you in advance!

First of all, thank you for your paper and code. Actually, I am a bit puzzled about the training settings. As mentioned in the paper, the model was trained for 6000 epochs, with batch size 8 on 4 RTX GPU. Indeed I am not quite familiar with this dataset and wonder the scale of it? how many iterations/ steps in each epoch? if the dataset is small, why it takes so many epochs to converge ? Thanks again!

I noticed that you used the BraTS data set in preprocess.py to process the 4 dimensions of the MRI image. I would like to ask that there is only one image.nii and one segmentation.nii in KiTS, and there is no 4-dimensional .nii , May I ask how to do preprocessing for this piece? my solution: Is it possible to just remove this part of the code? Or copy 4 copies of image.nii and use this code? Hope to get your reply, best wishes!

I notice that the output of the model contains 4 classes, but there's only 3 classes of tumor. I'm wondering what is the purpose of the dimension zero?

I divided my data into training sets, validation sets, and test sets. When training, I use "mode=train", but when I learn the code, I find that the validation set is not in training, and there is no parameter adjustment. In test.py, although there is an option to opt out of validation at training time, the setup of a single dataset file does not satisfy both validation and testing. 我将我的数据分为训练集、验证集和测试集。训练时，使用"mode=train"，但我学习代码时发现验证集并没有进入训练，没有进行参数调整.在test.py中，尽管有选择在训练时未进行验证的选项，但只有一个数据集文件的设置， 并不能同时满足验证和测试。