xamyzhao

Thanks for the clarification. In (3), you used the generator to select random source examples? In [L444-447](https://github.com/xamyzhao/brainstorm/blob/edb5f95eb1f6dfb80066782fb18c79400bd6abb0/src/segmenter_model.py#L444-447), X_aug is created by warping the source image to randomly sampled unlabeled images....

Thanks for the code -- that seems okay to me. Have you checked that each unlabeled_X is a differently-shaped example? Also, does your flow_aug_model work if you give it two...

I haven't received your email yet, but I can try to repro the issue with my data. What version of tensorflow and keras are you using?

When I run the segmenter with `--aug_rand` , I am seeing randomly warped examples. What do you mean by "there is no effect mentioned in the paper"? Are your augmented...

Got your email. I agree that the results of `--aug_sas` look strange. For each transformed example, can you also visualize the source and target images? That will help us figure...

I got your email. I agree that the generated examples do not look like the target examples at all. It seems like you might be loading the wrong model --...

@jelly571 you'll probably need to modify the input volume size (to the model), either by using a different dataset or by cropping/resizing each example.

Hi, thanks for your interest! You'll likely have to modify the _make_results_im function in either transform_models.py (https://github.com/xamyzhao/brainstorm/blob/master/src/transform_models.py#L620) or segmenter_model.py (https://github.com/xamyzhao/brainstorm/blob/master/src/segmenter_model.py#L777).

@cue1997 if you search for the term "160, 192, 224" in this repository, you can see the places where this input size is defined: https://github.com/xamyzhao/brainstorm/search?q=160%2C+192%2C+224. You'll likely want to update...

Are you sure you are loading your own trained model? Your first error sounds like your model is still expecting the old input shape, which implies that you are loading...