Hi, you really did an excelent job, and thanks for your sharing. I have already prepared all datasets except KiTS19, other datasets work well on your code, they all got right results. I followed the steps in KiTS19, but this result run by evaluate.py and postp.py is wrong. So, would you please tell me more details about how to get right KiTS19? Thanks!

Hi. @jianpengz ,， Thx very much for your work. Currently, I just use this repro, and occur some problems when downloading the dataset as your instruction. 1.) I do not find any data related with Kidney dataset; 2) I just download Lits and found that are different with your data folder format in the lits/MOTS/MOTS_train/test.txt

Hello, thank you very much for the code you provided. I encountered some problems during training. During the training process, a dimensional abnormality error occurred at a certain epoch. What is the reason for this? torch.Size([1, 64, 32, 96, 96]) skip1 : shape torch.Size([1, 64, 32, 96, 95]) 16 22:01:46 WRN A exception occurred during Engine initialization, give up running process Traceback (most recent call last): File "train.py", line 253, in main() File "train.py", line 188, in main preds = model(images, task_ids) File "/home/test/anaconda3/envs/qy/lib/python3.6/site-packages/torch/nn/modules/module.py", line 547, in call result = self.forward(*input, **kwargs) File "/home/test/anaconda3/envs/qy/lib/python3.6/site-packages/apex-0.1-py3.6.egg/apex/parallel/distributed.py", line 560, in forward result = self.module(*inputs, **kwargs) File "/home/test/anaconda3/envs/qy/lib/python3.6/site-packages/torch/nn/modules/module.py", line 547, in call result = self.forward(*input, **kwargs) File "/data/qy/code/DoDNet/a_DynConv/unet3D_DynConv882.py", line 230, in forward x = x + skip1 RuntimeError: The size of tensor a (96) must match the size of tensor b (95) at non-singleton dimension 4

it's a great honor for me to read your paper, when i run your code i find it shows 'cannot import name 'parse_devices' from 'utils.pyt_utils'', i checked the pyt_utils.py but didn't find the defination of parse_devices. i don't how to solve it ? thanks for your help.

Hi, thank you a lot for your contribution. I am trying to reproduce the results but find that on dataset 2Hippocampus, you have 242 + 61 = 303 images but in imagesTr folder under Task04_Hippocampus downloaded from MSD, I can only find 260 in total. How do you get labeled data from cases in imagesTs?

张老师好，您给的代码中a_DynConv文件夹下没有train_Dynconv.py文件，是需要运行train.py吗？python train.py后出现

Traceback (most recent call last):
  File "train.py", line 30, in <module>
    from engine import Engine
  File "../engine.py", line 9, in <module>
    from utils.logger import get_logger
Traceback (most recent call last):
ModuleNotFoundError:   File "train.py", line 30, in <module>
No module named 'utils.logger'
    from engine import Engine
  File "../engine.py", line 9, in <module>
    from utils.logger import get_logger
ModuleNotFoundError: No module named 'utils.logger'

但是pip install utils.logger 又没有找到该module

ERROR: Could not find a version that satisfies the requirement utils.logger
ERROR: No matching distribution found for utils.logger

我想着这个utils.logger是不是您少公开的文件？

期待您的回复，谢谢

The link that's given for the 0Liver data does not have the same format as the rest of the data with the imagesTr/labelsTr/imagesTs structure the rest of the datasets have. In the train.txt file, the 0Liver is given that structure. Where can I find the data for 0Liver that has this structuring?

Also, for the 1Kidney preprocessing, should line 28 on respacing.py be dirs1 and not i_dirs1 so that the preprocessing for 1Kidey goes through this loop because the i_dirs1 is each specific case? Also, when going through this loop the data does not save in the spacing folder with origin as a sub folder. Is this an issue, or can I just delete the origin folder from the train.txt file?

@jianpengz i can run your code use your command in pycharm terminal ,but when i run the train.py directly it shows 'RuntimeError:' as the picture shows,can you tell me how to solve it ?thanks for your time and kindness.

Error while training on Hepatic Vessels and Pancreas:

Traceback (most recent call last): File "train.py", line 250, in <module> main() File "train.py", line 177, in main for iter, batch in enumerate(trainloader): File "C:\Users\shafi\AppData\Local\Programs\Python\Python37\lib\site-packages\torch\utils\data\dataloader.py", line 359, in __iter__ return self._get_iterator() File "C:\Users\shafi\AppData\Local\Programs\Python\Python37\lib\site-packages\torch\utils\data\dataloader.py", line 305, in _get_iterator return _MultiProcessingDataLoaderIter(self) File "C:\Users\shafi\AppData\Local\Programs\Python\Python37\lib\site-packages\torch\utils\data\dataloader.py", line 918, in __init__ w.start() File "C:\Users\shafi\AppData\Local\Programs\Python\Python37\lib\multiprocessing\process.py", line 112, in start self._popen = self._Popen(self) File "C:\Users\shafi\AppData\Local\Programs\Python\Python37\lib\multiprocessing\context.py", line 223, in _Popen return _default_context.get_context().Process._Popen(process_obj) File "C:\Users\shafi\AppData\Local\Programs\Python\Python37\lib\multiprocessing\context.py", line 322, in _Popen return Popen(process_obj) File "C:\Users\shafi\AppData\Local\Programs\Python\Python37\lib\multiprocessing\popen_spawn_win32.py", line 89, in __init__ reduction.dump(process_obj, to_child) File "C:\Users\shafi\AppData\Local\Programs\Python\Python37\lib\multiprocessing\reduction.py", line 60, in dump ForkingPickler(file, protocol).dump(obj) BrokenPipeError: [Errno 32] Broken pipe

The MSD Liver dataset was used for training： DoDNet/a_DynConv/MOTSDataset.py", line 471, in my_collate data_dict = tr_transforms(**data_dict) TypeError: call() got an unexpected keyword argument 'image'

Hello, thank you very much for the code. I have a question about re_spacing.py. The code does not process the 0liver data set. Does it mean that there is no need to perform additional processing on the downloaded data, just modify the file name directly.

Traceback (most recent call last): File "/home/shiya.xu/papers/DoDNet/a_DynConv/train.py", line 250, in main() File "/home/shiya.xu/papers/DoDNet/a_DynConv/train.py", line 185, in main preds = model(images, task_ids) File "/home/shiya.xu/anaconda3/envs/pyy/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1190, in _call_impl return forward_call(*input, **kwargs) File "/home/shiya.xu/anaconda3/envs/pyy/lib/python3.10/site-packages/apex/parallel/distributed.py", line 564, in forward result = self.module(*inputs, **kwargs) File "/home/shiya.xu/anaconda3/envs/pyy/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1190, in _call_impl return forward_call(*input, **kwargs) File "/home/shiya.xu/papers/DoDNet/a_DynConv/unet3D_DynConv882.py", line 224, in forward x = x + skip1 RuntimeError: The size of tensor a (96) must match the size of tensor b (95) at non-singleton dimension 3 WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 26535 closing signal SIGTERM ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 1 (pid: 26536) of binary: /home/shiya.xu/anaconda3/envs/pyy/bin/python

Hi,

I downloaded the chekcpoint and did the inference but found that the Dice of Kidney (and Kidney's tumor) is close to 0! Like the following figure shows. Did anyone meet the same issue? Can anyone help to explain the reason? Thanks a lot.

File "train.py", line 266, in <module> main() File "train.py", line 151, in main args.reload_path, map_location=torch.device('cpu'))) File "C:\Users\shafi\AppData\Local\Programs\Python\Python37\lib\site-packages\torch\nn\modules\module.py", line 1407, in load_state_dict self.__class__.__name__, "\n\t".join(error_msgs))) RuntimeError: Error(s) in loading state_dict for unet3D: Missing key(s) in state_dict: "conv1.weight", "layer0.0.gn1.weight", "layer0.0.gn1.bias", "layer0.0.conv1.weight", "layer0.0.gn2.weight", "layer0.0.gn2.bias", "layer0.0.conv2.weight", "layer1.0.gn1.weight", "layer1.0.gn1.bias", "layer1.0.conv1.weight", "layer1.0.gn2.weight", "layer1.0.gn2.bias", "layer1.0.conv2.weight", "layer1.0.downsample.0.weight", "layer1.0.downsample.0.bias", "layer1.0.downsample.2.weight", "layer1.1.gn1.weight", "layer1.1.gn1.bias", "layer1.1.conv1.weight", "layer1.1.gn2.weight", "layer1.1.gn2.bias", "layer1.1.conv2.weight", "layer2.0.gn1.weight", "layer2.0.gn1.bias", "layer2.0.conv1.weight", "layer2.0.gn2.weight", "layer2.0.gn2.bias", "layer2.0.conv2.weight", "layer2.0.downsample.0.weight", "layer2.0.downsample.0.bias", "layer2.0.downsample.2.weight", "layer2.1.gn1.weight", "layer2.1.gn1.bias", "layer2.1.conv1.weight", "layer2.1.gn2.weight", "layer2.1.gn2.bias", "layer2.1.conv2.weight", "layer3.0.gn1.weight", "layer3.0.gn1.bias", "layer3.0.conv1.weight", "layer3.0.gn2.weight", "layer3.0.gn2.bias", "layer3.0.conv2.weight", "layer3.0.downsample.0.weight", "layer3.0.downsample.0.bias", "layer3.0.downsample.2.weight", "layer3.1.gn1.weight", "layer3.1.gn1.bias", "layer3.1.conv1.weight", "layer3.1.gn2.weight", "layer3.1.gn2.bias", "layer3.1.conv2.weight", "layer4.0.gn1.weight", "layer4.0.gn1.bias", "layer4.0.conv1.weight", "layer4.0.gn2.weight", "layer4.0.gn2.bias", "layer4.0.conv2.weight", "layer4.0.downsample.0.weight", "layer4.0.downsample.0.bias", "layer4.0.downsample.2.weight", "layer4.1.gn1.weight", "layer4.1.gn1.bias", "layer4.1.conv1.weight", "layer4.1.gn2.weight", "layer4.1.gn2.bias", "layer4.1.conv2.weight", "fusionConv.0.weight", "fusionConv.0.bias", "fusionConv.2.weight", "x8_resb.0.gn1.weight", "x8_resb.0.gn1.bias", "x8_resb.0.conv1.weight", "x8_resb.0.gn2.weight", "x8_resb.0.gn2.bias", "x8_resb.0.conv2.weight", "x8_resb.0.downsample.0.weight", "x8_resb.0.downsample.0.bias", "x8_resb.0.downsample.2.weight", "x4_resb.0.gn1.weight", "x4_resb.0.gn1.bias", "x4_resb.0.conv1.weight", "x4_resb.0.gn2.weight", "x4_resb.0.gn2.bias", "x4_resb.0.conv2.weight", "x4_resb.0.downsample.0.weight", "x4_resb.0.downsample.0.bias", "x4_resb.0.downsample.2.weight", "x2_resb.0.gn1.weight", "x2_resb.0.gn1.bias", "x2_resb.0.conv1.weight", "x2_resb.0.gn2.weight", "x2_resb.0.gn2.bias", "x2_resb.0.conv2.weight", "x2_resb.0.downsample.0.weight", "x2_resb.0.downsample.0.bias", "x2_resb.0.downsample.2.weight", "x1_resb.0.gn1.weight", "x1_resb.0.gn1.bias", "x1_resb.0.conv1.weight", "x1_resb.0.gn2.weight", "x1_resb.0.gn2.bias", "x1_resb.0.conv2.weight", "precls_conv.0.weight", "precls_conv.0.bias", "precls_conv.2.weight", "precls_conv.2.bias", "GAP.0.weight", "GAP.0.bias", "controller.weight", "controller.bias". Unexpected key(s) in state_dict: "module.conv1.weight", "module.layer0.0.gn1.weight", "module.layer0.0.gn1.bias", "module.layer0.0.conv1.weight", "module.layer0.0.gn2.weight", "module.layer0.0.gn2.bias", "module.layer0.0.conv2.weight", "module.layer1.0.gn1.weight", "module.layer1.0.gn1.bias", "module.layer1.0.conv1.weight", "module.layer1.0.gn2.weight", "module.layer1.0.gn2.bias", "module.layer1.0.conv2.weight", "module.layer1.0.downsample.0.weight", "module.layer1.0.downsample.0.bias", "module.layer1.0.downsample.2.weight", "module.layer1.1.gn1.weight", "module.layer1.1.gn1.bias", "module.layer1.1.conv1.weight", "module.layer1.1.gn2.weight", "module.layer1.1.gn2.bias", "module.layer1.1.conv2.weight", "module.layer2.0.gn1.weight", "module.layer2.0.gn1.bias", "module.layer2.0.conv1.weight", "module.layer2.0.gn2.weight", "module.layer2.0.gn2.bias", "module.layer2.0.conv2.weight", "module.layer2.0.downsample.0.weight", "module.layer2.0.downsample.0.bias", "module.layer2.0.downsample.2.weight", "module.layer2.1.gn1.weight", "module.layer2.1.gn1.bias", "module.layer2.1.conv1.weight", "module.layer2.1.gn2.weight", "module.layer2.1.gn2.bias", "module.layer2.1.conv2.weight", "module.layer3.0.gn1.weight", "module.layer3.0.gn1.bias", "module.layer3.0.conv1.weight", "module.layer3.0.gn2.weight", "module.layer3.0.gn2.bias", "module.layer3.0.conv2.weight", "module.layer3.0.downsample.0.weight", "module.layer3.0.downsample.0.bias", "module.layer3.0.downsample.2.weight", "module.layer3.1.gn1.weight", "module.layer3.1.gn1.bias", "module.layer3.1.conv1.weight", "module.layer3.1.gn2.weight", "module.layer3.1.gn2.bias", "module.layer3.1.conv2.weight", "module.layer4.0.gn1.weight", "module.layer4.0.gn1.bias", "module.layer4.0.conv1.weight", "module.layer4.0.gn2.weight", "module.layer4.0.gn2.bias", "module.layer4.0.conv2.weight", "module.layer4.0.downsample.0.weight", "module.layer4.0.downsample.0.bias", "module.layer4.0.downsample.2.weight", "module.layer4.1.gn1.weight", "module.layer4.1.gn1.bias", "module.layer4.1.conv1.weight", "module.layer4.1.gn2.weight", "module.layer4.1.gn2.bias", "module.layer4.1.conv2.weight", "module.fusionConv.0.weight", "module.fusionConv.0.bias", "module.fusionConv.2.weight", "module.x8_resb.0.gn1.weight", "module.x8_resb.0.gn1.bias", "module.x8_resb.0.conv1.weight", "module.x8_resb.0.gn2.weight", "module.x8_resb.0.gn2.bias", "module.x8_resb.0.conv2.weight", "module.x8_resb.0.downsample.0.weight", "module.x8_resb.0.downsample.0.bias", "module.x8_resb.0.downsample.2.weight", "module.x4_resb.0.gn1.weight", "module.x4_resb.0.gn1.bias", "module.x4_resb.0.conv1.weight", "module.x4_resb.0.gn2.weight", "module.x4_resb.0.gn2.bias", "module.x4_resb.0.conv2.weight", "module.x4_resb.0.downsample.0.weight", "module.x4_resb.0.downsample.0.bias", "module.x4_resb.0.downsample.2.weight", "module.x2_resb.0.gn1.weight", "module.x2_resb.0.gn1.bias", "module.x2_resb.0.conv1.weight", "module.x2_resb.0.gn2.weight", "module.x2_resb.0.gn2.bias", "module.x2_resb.0.conv2.weight", "module.x2_resb.0.downsample.0.weight", "module.x2_resb.0.downsample.0.bias", "module.x2_resb.0.downsample.2.weight", "module.x1_resb.0.gn1.weight", "module.x1_resb.0.gn1.bias", "module.x1_resb.0.conv1.weight", "module.x1_resb.0.gn2.weight", "module.x1_resb.0.gn2.bias", "module.x1_resb.0.conv2.weight", "module.precls_conv.0.weight", "module.precls_conv.0.bias", "module.precls_conv.2.weight", "module.precls_conv.2.bias", "module.GAP.0.weight", "module.GAP.0.bias", "module.controller.weight", "module.controller.bias".

A shell script to run to download all of the MOTS data gdown is a requirement (can be installed with pip install gdown) takes target_path as an argument to download the data do the following: chmod +x load_mots.sh ./load_mots.sh target_path