Comments (6)
Hi @islandLZ. The problem seems to be the last version of the architecture was strictly designed to be 3d while your inputs are 2d. I've just committed a version of the architecture with a 'dim' argument with '2d' or '3d' inputs. Give that a try and see if it works for you.
Thank you for your work. I will try the 'dim' you just set up now。
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I success
Hi @islandLZ. The problem seems to be the last version of the architecture was strictly designed to be 3d while your inputs are 2d. I've just committed a version of the architecture with a 'dim' argument with '2d' or '3d' inputs. Give that a try and see if it works for you.
Your work has been successful.
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When I change n_channels = 3 , I meet an other erro!
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Hi @islandLZ. The problem seems to be the last version of the architecture was strictly designed to be 3d while your inputs are 2d. I've just committed a version of the architecture with a 'dim' argument with '2d' or '3d' inputs. Give that a try and see if it works for you.
from mednext.
I set this:
class MedNeXt(nn.Module):
def __init__(self,
in_channels: int = 3,
n_channels: int = 32,
n_classes: int = 3,
exp_r: list = [3,4,8,8,8,8,8,4,3], # Expansion ratio as in Swin Transformers
kernel_size: int = 5, # Ofcourse can test kernel_size
enc_kernel_size: int = None,
dec_kernel_size: int = None,
deep_supervision: bool = False, # Can be used to test deep supervision
do_res: bool = True, # Can be used to individually test residual connection
do_res_up_down: bool = True, # Additional 'res' connection on up and down convs
checkpoint_style: str = 'outside_block', # Either inside block or outside block
block_counts: list = [3,4,8,8,8,8,8,4,3], # Can be used to test staging ratio:# [3,3,9,3] in Swin as opposed to [2,2,2,2,2] in nnUNet
norm_type = 'group',
):
.......
then I have made modifications to my data format:
[1,3,512,512] -> [1,3,64,64,64]
So that the network can operate normally.
But I am still testing whether this method is feasible。
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I'm glad to hear that it helped!
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Related Issues (9)
- Deep supervision HOT 2
- Valid accuracy HOT 4
- several import errors HOT 2
- nnUNet_plan_and_preprocess: command not found HOT 3
- How to train MedNeXt HOT 2
- Cannot find 'nnunet_mednext.network_architecture.custom_modules.custom_networks' HOT 2
- Excessive GPU memory consumption HOT 2
- hello, can you upload both the trained weights or the predicted results of MedNeXt on KiTS19 datasets? It may not cost your extra time. Becasue we will compare your results at qualitative and quantitative aspects. Thanks! HOT 1
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