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使用3d unet,用的brats2020的数据集,python main.py后,targetsize和inputsize会出现维度不匹配的问题,inputsize是[(2,2,32,32,32)],targetsize是[(2,64,32,32)];我把target.reshape([2,2,32,32,32])后成功运行,但是后续调整batsize的的大小,程序又会报错,RuntimeError: shape '[2, 2, 32, 32, 32]' is invalid for input of size 524288,就是inputsize会随着batchsize的改变而改变,这是什么原因呢,因为一般batchsize不就是可以随意改变的吗
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我也是相同问题,这份代码类似的loss设置,我在https://github.com/HiLab-git/SSL4MIS见过。但是我的问题没有解决,我暂时放弃这份代码了
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loss = criterion_ce(outputs, y) + criterion_dice(outputs, y.argmax(dim=1).unsqueeze(1)) 我尝试这样修改,可以跑通,你可以试试,欢迎交流结果
谢谢您的解决方案,我现在也能跑通了,只不过各项指标不太理想。
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使用3d unet,用的brats2020的数据集,python main.py后,targetsize和inputsize会出现维度不匹配的问题,inputsize是[(2,2,32,32,32)],targetsize是[(2,64,32,32)];我把target.reshape([2,2,32,32,32])后成功运行,但是后续调整batsize的的大小,程序又会报错,RuntimeError: shape '[2, 2, 32, 32, 32]' is invalid for input of size 524288,就是inputsize会随着batchsize的改变而改变,这是什么原因呢,因为一般batchsize不就是可以随意改变的吗
The text was updated successfully, but these errors were encountered: