MedSegDiff
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Problems during sample
I run the segmentation_sample.py, and meet the problem:
Logging to /root/autodl-tmp/MedSegDif/med_results/img_out/
creating model and diffusion...
sampling...
no dpm-solver
/root/miniconda3/lib/python3.8/site-packages/torch/nn/functional.py:1709: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.
warnings.warn("nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.")
Traceback (most recent call last):
File "MedSegDif/med_scripts/segmentation_sample.py", line 163, in
I know that it is because x_start.shape is not equal to x_t.shape. However, My dataset is similar to ISICDataset, so I feel very strange. Thanks a lot if you can reply.
I run the segmentation_sample.py, and meet the problem:
Logging to /root/autodl-tmp/MedSegDif/med_results/img_out/ creating model and diffusion... sampling... no dpm-solver /root/miniconda3/lib/python3.8/site-packages/torch/nn/functional.py:1709: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead. warnings.warn("nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.") Traceback (most recent call last): File "MedSegDif/med_scripts/segmentation_sample.py", line 163, in main() File "MedSegDif/med_scripts/segmentation_sample.py", line 109, in main sample, x_noisy, org, cal, cal_out = sample_fn( File "/root/autodl-tmp/./MedSegDif/med_guided_diffusion/gaussian_diffusion.py", line 553, in p_sample_loop_known for sample in self.p_sample_loop_progressive( File "/root/autodl-tmp/./MedSegDif/med_guided_diffusion/gaussian_diffusion.py", line 624, in p_sample_loop_progressive out = self.p_sample( File "/root/autodl-tmp/./MedSegDif/med_guided_diffusion/gaussian_diffusion.py", line 435, in p_sample out = self.p_mean_variance( File "/root/autodl-tmp/./MedSegDif/med_guided_diffusion/respace.py", line 90, in p_mean_variance return super().p_mean_variance(self._wrap_model(model), *args, **kwargs) File "/root/autodl-tmp/./MedSegDif/med_guided_diffusion/gaussian_diffusion.py", line 319, in p_mean_variance model_mean, _, _ = self.q_posterior_mean_variance( File "/root/autodl-tmp/./MedSegDif/med_guided_diffusion/gaussian_diffusion.py", line 219, in q_posterior_mean_variance assert x_start.shape == x_t.shape AssertionError
I know that it is because x_start.shape is not equal to x_t.shape. However, My dataset is similar to ISICDataset, so I feel very strange. Thanks a lot if you can reply.
Have you solved it? I have the same doubt, thanks!
I try to print x_start.shape and x_t.shape however there is an error occurred before I can get x_start.
Traceback (most recent call last): File "F:\dpm\MedSegDiff-master-v2\v2\scripts\segmentation_sample.py", line 194, in
main() File "F:\dpm\MedSegDiff-master-v2\v2\scripts\segmentation_sample.py", line 116, in main sample, x_noisy, org, cal, cal_out = sample_fn( File "F:\dpm\MedSegDiff-master-v2\v2\scripts..\guided_diffusion\gaussian_diffusion.py", line 566, in p_sample_loop_known for sample in self.p_sample_loop_progressive( File "F:\dpm\MedSegDiff-master-v2\v2\scripts..\guided_diffusion\gaussian_diffusion.py", line 651, in p_sample_loop_progressive out = self.p_sample( File "F:\dpm\MedSegDiff-master-v2\v2\scripts..\guided_diffusion\gaussian_diffusion.py", line 446, in p_sample out = self.p_mean_variance( File "F:\dpm\MedSegDiff-master-v2\v2\scripts..\guided_diffusion\respace.py", line 90, in p_mean_variance return super().p_mean_variance(self._wrap_model(model), *args, **kwargs) File "F:\dpm\MedSegDiff-master-v2\v2\scripts..\guided_diffusion\gaussian_diffusion.py", line 325, in p_mean_variance self._predict_xstart_from_eps(x_t=x, t=t, eps=model_output) File "F:\dpm\MedSegDiff-master-v2\v2\scripts..\guided_diffusion\gaussian_diffusion.py", line 350, in _predict_xstart_from_eps assert x_t.shape == eps.shape AssertionError
I print x_t.shape and eps.shape, and result is like this: x_t.shape= torch.Size([1, 1, 64, 64]) eps.shape= torch.Size([1, 2, 64, 64])
I suppose to use the right dataset step by step in Readme.md. The eps[:, 0, :, :] is totally the same as eps[:, 1; :, :]. After clipping eps to [1,1,64,64] by deleting either of them, program can work. I can't understand why their shapes are not consistant. Therefore, I can't figure out whether I can get the right answer by simple clipping conducted by myself without theory support.
I try to print x_start.shape and x_t.shape however there is an error occurred before I can get x_start.
Traceback (most recent call last): File "F:\dpm\MedSegDiff-master-v2\v2\scripts\segmentation_sample.py", line 194, in main() File "F:\dpm\MedSegDiff-master-v2\v2\scripts\segmentation_sample.py", line 116, in main sample, x_noisy, org, cal, cal_out = sample_fn( File "F:\dpm\MedSegDiff-master-v2\v2\scripts..\guided_diffusion\gaussian_diffusion.py", line 566, in p_sample_loop_known for sample in self.p_sample_loop_progressive( File "F:\dpm\MedSegDiff-master-v2\v2\scripts..\guided_diffusion\gaussian_diffusion.py", line 651, in p_sample_loop_progressive out = self.p_sample( File "F:\dpm\MedSegDiff-master-v2\v2\scripts..\guided_diffusion\gaussian_diffusion.py", line 446, in p_sample out = self.p_mean_variance( File "F:\dpm\MedSegDiff-master-v2\v2\scripts..\guided_diffusion\respace.py", line 90, in p_mean_variance return super().p_mean_variance(self._wrap_model(model), *args, **kwargs) File "F:\dpm\MedSegDiff-master-v2\v2\scripts..\guided_diffusion\gaussian_diffusion.py", line 325, in p_mean_variance self._predict_xstart_from_eps(x_t=x, t=t, eps=model_output) File "F:\dpm\MedSegDiff-master-v2\v2\scripts..\guided_diffusion\gaussian_diffusion.py", line 350, in _predict_xstart_from_eps assert x_t.shape == eps.shape AssertionError
I print x_t.shape and eps.shape, and result is like this: x_t.shape= torch.Size([1, 1, 64, 64]) eps.shape= torch.Size([1, 2, 64, 64])
I suppose to use the right dataset step by step in Readme.md. The is totally the same as . After clipping eps to by deleting either of them, program can work. I can't understand why their shapes are not consistant. Therefore, I can't figure out whether I can get the right answer by simple clipping conducted by myself without theory support.
eps[:, 0, :, :]``eps[:, 1; :, :]``[1,1,64,64]
Hello! I have encountered a type-related issue with the ISIC dataset. Specifically, I'm getting the following error:
"assert x_t.shape == eps.shape AssertionError"
Could you please help me identify which line of code needs to be modified to make the project run successfully?
Why is it that the result of my last training in BraTS is a MRI diagram?
