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Dear FreeSurfer Developers,
I am trying to run the new “NextBrain" probabilistic atlas
mri_histo_atlas_segment model, which internally calls mri_synthseg with
additional parameters `--threads 8 --cpu --parc --robust` as a first step.
My system: MacBook Pro 2021 with M1 pro chip, currently running macOS Monterey
12.6
I installed the most up-to-date developmental FS version:
freesurfer-macOS-darwin_arm64-dev-20240211-4e530af
Currently, I am definitely getting stuck on the mri_synthseg step, so for now
my issue is related to SynthSeg, not to the NextBrain package.
This is what I did so far, assuming I am in the SUBJECTS folder:
1. The example for mri_hist_atlas_segment, `mri_histo_atlas_segment
bert/mri/orig.mgz bert/mri/histo_atlas_segmentation/ 1 8` will return an error
without displaying the error message.
2. I reproduced the error by running it’s analysis steps one by one: SynthSeg
returns an error right after running it with any kind of configuration (e.g.,
`mri_synthseg --i bert/mri/orig.mgz --o
mri/synthseg.mgz`): `module 'numpy' has no attribute ‘float128’. This is
traceable to line 1595 of freesurfer/dev/python/scripts/mri_synthseg. The error
generally seems to be a known issue on Apple chips. I fixed it with replacing
`np.float128` with `np.longfloat`.
3. Now, SynthSeg starts to set itself up but quits with:
```
Node: 'model_1/unet_bn_down_0/FusedBatchNormV3'
Detected at node 'model_1/unet_bn_down_0/FusedBatchNormV3' defined at (most
recent call last):
File "/Applications/freesurfer/dev/python/scripts/mri_synthseg", line 2642,
in <module>
main()
File "/Applications/freesurfer/dev/python/scripts/mri_synthseg", line 162,
in main
predict(
File "/Applications/freesurfer/dev/python/scripts/mri_synthseg", line 323,
in predict
post_patch_segmentation = net.predict(image)
File
"/Applications/freesurfer/dev/python/miniconda3_apple_dist/py3_8_13/lib/python3.8/site-packages/keras/src/utils/traceback_utils.py",
line 65, in error_handler
return fn(*args, **kwargs)
File
"/Applications/freesurfer/dev/python/miniconda3_apple_dist/py3_8_13/lib/python3.8/site-packages/keras/src/engine/training.py",
line 2554, in predict
tmp_batch_outputs = self.predict_function(iterator)
File
"/Applications/freesurfer/dev/python/miniconda3_apple_dist/py3_8_13/lib/python3.8/site-packages/keras/src/engine/training.py",
line 2341, in predict_function
return step_function(self, iterator)
File
"/Applications/freesurfer/dev/python/miniconda3_apple_dist/py3_8_13/lib/python3.8/site-packages/keras/src/engine/training.py",
line 2327, in step_function
outputs = model.distribute_strategy.run(run_step, args=(data,))
File
"/Applications/freesurfer/dev/python/miniconda3_apple_dist/py3_8_13/lib/python3.8/site-packages/keras/src/engine/training.py",
line 2315, in run_step
outputs = model.predict_step(data)
File
"/Applications/freesurfer/dev/python/miniconda3_apple_dist/py3_8_13/lib/python3.8/site-packages/keras/src/engine/training.py",
line 2283, in predict_step
return self(x, training=False)
File
"/Applications/freesurfer/dev/python/miniconda3_apple_dist/py3_8_13/lib/python3.8/site-packages/keras/src/utils/traceback_utils.py",
line 65, in error_handler
return fn(*args, **kwargs)
File
"/Applications/freesurfer/dev/python/miniconda3_apple_dist/py3_8_13/lib/python3.8/site-packages/keras/src/engine/training.py",
line 569, in __call__
return super().__call__(*args, **kwargs)
File
"/Applications/freesurfer/dev/python/miniconda3_apple_dist/py3_8_13/lib/python3.8/site-packages/keras/src/utils/traceback_utils.py",
line 65, in error_handler
return fn(*args, **kwargs)
File
"/Applications/freesurfer/dev/python/miniconda3_apple_dist/py3_8_13/lib/python3.8/site-packages/keras/src/engine/base_layer.py",
line 1150, in __call__
outputs = call_fn(inputs, *args, **kwargs)
File
"/Applications/freesurfer/dev/python/miniconda3_apple_dist/py3_8_13/lib/python3.8/site-packages/keras/src/utils/traceback_utils.py",
line 96, in error_handler
return fn(*args, **kwargs)
File
"/Applications/freesurfer/dev/python/miniconda3_apple_dist/py3_8_13/lib/python3.8/site-packages/keras/src/engine/functional.py",
line 512, in call
return self._run_internal_graph(inputs, training=training, mask=mask)
File
"/Applications/freesurfer/dev/python/miniconda3_apple_dist/py3_8_13/lib/python3.8/site-packages/keras/src/engine/functional.py",
line 669, in _run_internal_graph
outputs = node.layer(*args, **kwargs)
File
"/Applications/freesurfer/dev/python/miniconda3_apple_dist/py3_8_13/lib/python3.8/site-packages/keras/src/utils/traceback_utils.py",
line 65, in error_handler
return fn(*args, **kwargs)
File
"/Applications/freesurfer/dev/python/miniconda3_apple_dist/py3_8_13/lib/python3.8/site-packages/keras/src/engine/base_layer.py",
line 1150, in __call__
outputs = call_fn(inputs, *args, **kwargs)
File
"/Applications/freesurfer/dev/python/miniconda3_apple_dist/py3_8_13/lib/python3.8/site-packages/keras/src/utils/traceback_utils.py",
line 96, in error_handler
return fn(*args, **kwargs)
File
"/Applications/freesurfer/dev/python/miniconda3_apple_dist/py3_8_13/lib/python3.8/site-packages/keras/src/layers/normalization/batch_normalization.py",
line 597, in call
outputs = self._fused_batch_norm(
File
"/Applications/freesurfer/dev/python/miniconda3_apple_dist/py3_8_13/lib/python3.8/site-packages/keras/src/layers/normalization/batch_normalization.py",
line 990, in _fused_batch_norm
output, mean, variance = control_flow_util.smart_cond(
File
"/Applications/freesurfer/dev/python/miniconda3_apple_dist/py3_8_13/lib/python3.8/site-packages/keras/src/utils/control_flow_util.py",
line 108, in smart_cond
return tf.__internal__.smart_cond.smart_cond(
File
"/Applications/freesurfer/dev/python/miniconda3_apple_dist/py3_8_13/lib/python3.8/site-packages/keras/src/layers/normalization/batch_normalization.py",
line 979, in _fused_batch_norm_inference
return tf.compat.v1.nn.fused_batch_norm(
Node: 'model_1/unet_bn_down_0/FusedBatchNormV3'
2 root error(s) found.
(0) INVALID_ARGUMENT: input must be 4-dimensional[1,256,256,256,24]
[[{{node model_1/unet_bn_down_0/FusedBatchNormV3}}]]
[[model_1/model/gaussian_blur/ExpandDims_27/_546]]
(1) INVALID_ARGUMENT: input must be 4-dimensional[1,256,256,256,24]
[[{{node model_1/unet_bn_down_0/FusedBatchNormV3}}]]
0 successful operations.
0 derived errors ignored. [Op:__inference_predict_function_4927]
resuming program execution
```
4. After further messing around with the mri_synthseg script, I can get past
that point by changing all lines that call the `unet` function from `net =
unet(nb_features=24, input_shape=[None, None, None, 1], ...)` to `net =
unet(nb_features=24, input_shape=[1, None, None, None, 1], ...)` or similar
(e.g. line 627, 645, 667, 684, …). However, then it crashes at a later point
with other errors apparently related to data dimensionality.
I would really appreciate any suggestions! I know that running the analysis on
a MacBook might not be the best choice anyways, but before I try to set it up
on our institute HTC (which might be a whole other challenge), I though I try
it that way. Also, I figured someone might be interested in the error report.
Thanks a lot!
Best wishes,
Leon
____________
Leon D. Lotter, MD
Clinician Scientist PhD candidate
INM-7: Brain and Behaviour, Research Centre Juelich
Institute of Systems Neuroscience, University Hospital Duesseldorf
Max Planck School of Cognition, Leipzig
https://secure-web.cisco.com/1dp_Q8wZIqLOEUSqDix0kribzUPxw3EWu7oCDRmyzTKfFU2N5NcFU_FQDtXD3RHguKH3rOPl5RZbh5zUERmXh0JL_uTf8XCZ-iK1CmVbdIQ6d-0PbsqILWALHnNa3mSJQYuXqCCLq1PBrkbaqhmMpUgC4buckbJvWIM0kXNZ6udMZswsvN4cLcoQ_u5G1rEnR06RSQwI052YN7J0Nl3-tXSI0yYH98IwnEOZvu24YrhhPCkNJBnT6fnClaNC3ZakUbiigvU9wZ-CGIcHAdkCj84EU9G3_K2R8KXZsiykoZh09uET4O7lRsnXESAcj7Hf1/https%3A%2F%2Fleondlotter.de
leondlot...@gmail.com
l.lot...@fz-juelich.de
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