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Multi-scale Transformer Network for Cross-Modality MR Image Synthesis (IEEE TMI)
Hello, can the pre-training weight file EdgeMAE.pth included in the code be directly used in subsequent finetune?
I really appreciate your work and hope to learn more details through the code. I hope you can share the code.Thanks.
发生异常: ValueError
cannot reshape array of size 0 into shape (5,200,200)
File "E:\MT-Net Data\MT-Net\utils\maeloader.py", line 98, in getitem
npy = np.load(self.images[subject])
File "E:\MT-Net Data\MT-Net\pretrain.py", line 42, in
for i,img in enumerate(train_loader):
ValueError: cannot reshape array of size 0 into shape (5,200,200)
总是pretrain一部分以后报这个错误,不知道是什么情况
作者你好,
我看到utils/preprocessing.py中将BraTS的TrainingData二八分作为了测试集和训练集,
想请教为什么不用BraTS提供的ValidationData做测试,
另外论文中描述为用70%的训练数据来训练,这里为什么二八分呢?
Hello,
I've encountered some difficulty understanding how to execute the code even after reviewing the readme file. Could you provide a more concise explanation? Your assistance would be greatly appreciated.
I look forward to your help.
Thank you.
运行pretrain.py报错
FileNotFoundError: [Errno 2] No such file or directory: './data/train/'
然后data目录没找到train文件夹,请问怎么解决呀?
Hello,
Firstly, great work on the project!
I've been reviewing the finetune.py
script and noticed something about the fine-tuning strategy. In the paper, it's mentioned that you "fine-tune the last six layers of the pre-trained encoder while freezing the others." I might be missing something, but in the code, the pretrained encoder (E
) is updating all of its layers:
E = MAE_finetune(img_size=opt.img_size, patch_size=opt.mae_patch_size, embed_dim=opt.encoder_dim, depth=opt.depth,
num_heads=opt.num_heads, in_chans=1, mlp_ratio=opt.mlp_ratio)
I saw the gradients for FC_module
are turned off:
for param in FC_module.parameters():
param.requires_grad = False
The optimizer is initialized to optimize over parameters of both E
and G
:
params = list(E.parameters()) + list(G.parameters())
optimizer = torch.optim.Adam(params)
But I couldn't find any section in the code where the initial layers are frozen.
Could you please clarify if this fine-tuning strategy of only updating the last six layers is an essential step? I'm curious if I can reproduce your results without this specific strategy.
Thank you for your time and assistance!
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