cetmann / iunets Goto Github PK

may you share the artificial dataset of the 3D ’foam phantoms’, please

In Tutorial 1: Training iUNets in Pytorch, for the An invertible toy problem, when Training the model, the loss was computed by comparing the model output output = model(x) and y = torch.flip(x, (3,)) instead of x. Why the third dimension should be flipped? Thank you!

Hi @cetmann , thanks for providing such a valuable toolbox for the community!

There was one interesting finding when playing with the code, say, when we save a model of size (16, 32, 64, 128) | (2, 2, 2, 2) and create a new model of (16,32,64,128) | (2,2,3,3), the new model can still load the checkpoints of the previous one. I have never seen this phenomenon in non-invertible CNN. Do you have some clues of this interesting behavior?

I really appreciate any help you can provide.

To use iunet as a normalizing flow, please help add the support.

Hi, I hope you are well.

Thanks for this repository and your great work.

I would appreciate some insight about how you obtained these matrices:
https://github.com/cetmann/iunets/blob/master/iunets/layers.py#L590-L608

Regards

Hi,

First, I want to thank you for releasing the code early. This is a really interesting work and I believe it has a very high potential. I worked on through your codes for MNIST and I am impressed by the memory it saves during training compared to standard UNet.

I find that the code for BraTS is missing and it would be great if you could post your version of implementing iUNet for BraTS dataset.

Thank You !

Hello,
I'd like to reproduce your code about Brain Tumor Segmentation, how should I operate?

Hello, thanks for releasing this work.
We just wonder what is the point to make the UNet invertible in terms of "normalized flow" ?

( Since the "decoder" is not the invertible function of encoder, such design seems make iUNet indifferent with the standard UNet, except for efficient memory usage. However, the paper also leave a lot of paragraph to describe the invertible condition of normalized flow, I guess this work may have more contribute in normalized flow.

Hi! I tried to run iUNet tutorial on my machine. However, it failed and return a TypeError. TypeError: __init__() missing 1 required positional argument: 'in_channels'. Could you assist me with this?

Thanks!

Hello,
I tried to run iunet using a test_mnist file.
But this error occurred. How can I solve this error?

RuntimeError Traceback (most recent call last)
in
3 x, y = padding(x.to(device)), y.to(device)
4 optimizer.zero_grad()
----> 5 output = model(x)
6 loss = loss_fn(output, y)
7 loss.backward()
/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in call(self, *input, **kwargs)
538 result = self._slow_forward(*input, **kwargs)
539 else:
--> 540 result = self.forward(*input, **kwargs)
541 for hook in self._forward_hooks.values():
542 hook_result = hook(self, input, result)

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/container.py in forward(self, input)
98 def forward(self, input):
99 for module in self:
--> 100 input = module(input)
101 return input
102

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in call(self, *input, **kwargs)
538 result = self._slow_forward(*input, **kwargs)
539 else:
--> 540 result = self.forward(*input, **kwargs)
541 for hook in self._forward_hooks.values():
542 hook_result = hook(self, input, result)

/mnt/dongsoo/RSNA/Models/iUNET/iunets-master/iunets-master/iunets/networks.py in forward(self, x)
186 # RevNet L
187 for j in range(depth):
--> 188 x = self.module_L[i]j
189
190 # Downsampling L

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in call(self, *input, **kwargs)
538 result = self._slow_forward(*input, **kwargs)
539 else:
--> 540 result = self.forward(*input, **kwargs)
541 for hook in self._forward_hooks.values():
542 hook_result = hook(self, input, result)

/usr/local/lib/python3.6/dist-packages/memcnn/models/revop.py in forward(self, *xin)
162 self.num_bwd_passes,
163 len(xin),
--> 164 *(xin + tuple([p for p in self._fn.parameters() if p.requires_grad])))
165 if not self.keep_input:
166 if not pytorch_version_one_and_above:

/usr/local/lib/python3.6/dist-packages/memcnn/models/revop.py in forward(ctx, fn, fn_inverse, keep_input, num_bwd_passes, num_inputs, *inputs_and_weights)
26 # Makes a detached copy which shares the storage
27 x = [element.detach() for element in inputs]
---> 28 outputs = ctx.fn(*x)
29
30 if not isinstance(outputs, tuple):

RuntimeError: OrderedDict mutated during iteration