Open joshuagohos opened 1 year ago
rossbar
commented
1 year ago Thanks for reporting @joshuagohos . Can you share how the environment was created (for example, did you use conda or pip)? Also can you share what version of scikit-image is installed in your environment?
joshuagohos
commented
1 year ago In Colab, I used ! pip install deepcell.
Checking the scikit-image version in Colab, I get:
import skimage
skimage.__version__0.18.3
The package installation output for ! pip install deepcell is pasted here:
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Collecting deepcell
Downloading DeepCell-0.12.4.tar.gz (143 kB)
|████████████████████████████████| 143 kB 4.1 MB/s
Installing build dependencies ... done
Getting requirements to build wheel ... done
Preparing wheel metadata ... done
Requirement already satisfied: opencv-python-headless<5 in /usr/local/lib/python3.8/dist-packages (from deepcell) (4.6.0.66)
Requirement already satisfied: scikit-learn>=0.20.4 in /usr/local/lib/python3.8/dist-packages (from deepcell) (1.0.2)
Requirement already satisfied: scipy<2,>=1.2.3 in /usr/local/lib/python3.8/dist-packages (from deepcell) (1.7.3)
Collecting deepcell-tracking~=0.6.1
Downloading DeepCell_Tracking-0.6.4.tar.gz (37 kB)
Collecting pydot<2,>=1.4.2
Downloading pydot-1.4.2-py2.py3-none-any.whl (21 kB)
Requirement already satisfied: matplotlib in /usr/local/lib/python3.8/dist-packages (from deepcell) (3.2.2)
Collecting tensorflow~=2.8.0
Downloading tensorflow-2.8.4-cp38-cp38-manylinux2010_x86_64.whl (498.0 MB)
|████████████████████████████████| 498.0 MB 11 kB/s
Collecting tensorflow-addons~=0.16.1
Downloading tensorflow_addons-0.16.1-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.1 MB)
|████████████████████████████████| 1.1 MB 57.9 MB/s
Requirement already satisfied: scikit-image>=0.14.5 in /usr/local/lib/python3.8/dist-packages (from deepcell) (0.18.3)
Collecting deepcell-toolbox~=0.12.0
Downloading DeepCell_Toolbox-0.12.0.tar.gz (39 kB)
Installing build dependencies ... done
Getting requirements to build wheel ... done
Preparing wheel metadata ... done
Requirement already satisfied: numpy<1.24,>=1.16.6 in /usr/local/lib/python3.8/dist-packages (from deepcell) (1.21.6)
Collecting jupyter<2,>=1.0.0
Downloading jupyter-1.0.0-py2.py3-none-any.whl (2.7 kB)
Collecting spektral~=1.0.4
Downloading spektral-1.0.8-py3-none-any.whl (123 kB)
|████████████████████████████████| 123 kB 32.6 MB/s
Requirement already satisfied: pandas in /usr/local/lib/python3.8/dist-packages (from deepcell-toolbox~=0.12.0->deepcell) (1.3.5)
Requirement already satisfied: tqdm in /usr/local/lib/python3.8/dist-packages (from deepcell-toolbox~=0.12.0->deepcell) (4.64.1)
Requirement already satisfied: networkx>=2.1 in /usr/local/lib/python3.8/dist-packages (from deepcell-toolbox~=0.12.0->deepcell) (2.8.8)
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Requirement already satisfied: ipywidgets in /usr/local/lib/python3.8/dist-packages (from jupyter<2,>=1.0.0->deepcell) (7.7.1)
Requirement already satisfied: nbconvert in /usr/local/lib/python3.8/dist-packages (from jupyter<2,>=1.0.0->deepcell) (5.6.1)
Collecting qtconsole
Downloading qtconsole-5.4.0-py3-none-any.whl (121 kB)
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Requirement already satisfied: ipykernel in /usr/local/lib/python3.8/dist-packages (from jupyter<2,>=1.0.0->deepcell) (5.3.4)
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Collecting numpy<1.24,>=1.16.6
Downloading numpy-1.19.5-cp38-cp38-manylinux2010_x86_64.whl (14.9 MB)
|████████████████████████████████| 14.9 MB 24.6 MB/s
Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from spektral~=1.0.4->deepcell) (2.23.0)
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Collecting tensorboard<2.9,>=2.8
Downloading tensorboard-2.8.0-py3-none-any.whl (5.8 MB)
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Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.8/dist-packages (from tensorflow~=2.8.0->deepcell) (2.1.1)
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Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.8/dist-packages (from tensorflow~=2.8.0->deepcell) (3.3.0)
Collecting tensorflow~=2.8.0
Downloading tensorflow-2.8.3-cp38-cp38-manylinux2010_x86_64.whl (498.4 MB)
|████████████████████████████████| 498.4 MB 25 kB/s
Downloading tensorflow-2.8.2-cp38-cp38-manylinux2010_x86_64.whl (498.0 MB)
|████████████████████████████████| 498.0 MB 20 kB/s
Downloading tensorflow-2.8.1-cp38-cp38-manylinux2010_x86_64.whl (498.0 MB)
|████████████████████████████████| 498.0 MB 32 kB/s
Downloading tensorflow-2.8.0-cp38-cp38-manylinux2010_x86_64.whl (497.6 MB)
|████████████████████████████████| 497.6 MB 1.9 kB/s
Collecting tf-estimator-nightly==2.8.0.dev2021122109
Downloading tf_estimator_nightly-2.8.0.dev2021122109-py2.py3-none-any.whl (462 kB)
|████████████████████████████████| 462 kB 62.8 MB/s
INFO: pip is looking at multiple versions of numpy to determine which version is compatible with other requirements. This could take a while.
Collecting numpy<1.24,>=1.16.6
Downloading numpy-1.19.4-cp38-cp38-manylinux2010_x86_64.whl (14.5 MB)
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Downloading numpy-1.19.3-cp38-cp38-manylinux2010_x86_64.whl (14.9 MB)
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Downloading numpy-1.19.2-cp38-cp38-manylinux2010_x86_64.whl (14.5 MB)
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Downloading numpy-1.19.1-cp38-cp38-manylinux2010_x86_64.whl (14.5 MB)
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Downloading numpy-1.19.0-cp38-cp38-manylinux2010_x86_64.whl (14.6 MB)
|████████████████████████████████| 14.6 MB 148 kB/s
Downloading numpy-1.18.5-cp38-cp38-manylinux1_x86_64.whl (20.6 MB)
|████████████████████████████████| 20.6 MB 1.1 MB/s
Downloading numpy-1.18.4-cp38-cp38-manylinux1_x86_64.whl (20.7 MB)
|████████████████████████████████| 20.7 MB 1.4 MB/s
INFO: pip is looking at multiple versions of numpy to determine which version is compatible with other requirements. This could take a while.
Downloading numpy-1.18.3-cp38-cp38-manylinux1_x86_64.whl (20.6 MB)
|████████████████████████████████| 20.6 MB 92.6 MB/s
Downloading numpy-1.18.2-cp38-cp38-manylinux1_x86_64.whl (20.6 MB)
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Downloading numpy-1.18.1-cp38-cp38-manylinux1_x86_64.whl (20.6 MB)
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Downloading numpy-1.18.0-cp38-cp38-manylinux1_x86_64.whl (20.6 MB)
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Downloading numpy-1.17.5-cp38-cp38-manylinux1_x86_64.whl (20.5 MB)
|████████████████████████████████| 20.5 MB 1.2 MB/s
INFO: This is taking longer than usual. You might need to provide the dependency resolver with stricter constraints to reduce runtime. If you want to abort this run, you can press Ctrl + C to do so. To improve how pip performs, tell us what happened here: https://pip.pypa.io/surveys/backtracking
Downloading numpy-1.17.4-cp38-cp38-manylinux1_x86_64.whl (20.5 MB)
|████████████████████████████████| 20.5 MB 38.9 MB/s
Downloading numpy-1.17.3-cp38-cp38-manylinux1_x86_64.whl (20.5 MB)
|████████████████████████████████| 20.5 MB 1.2 MB/s
INFO: pip is looking at multiple versions of spektral to determine which version is compatible with other requirements. This could take a while.
Collecting spektral~=1.0.4
Downloading spektral-1.0.7-py3-none-any.whl (122 kB)
|████████████████████████████████| 122 kB 62.0 MB/s
Downloading spektral-1.0.6-py3-none-any.whl (114 kB)
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Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.8/dist-packages (from tensorflow~=2.8.0->deepcell) (4.4.0)
Collecting tensorflow-estimator<2.9,>=2.8
Downloading tensorflow_estimator-2.8.0-py2.py3-none-any.whl (462 kB)
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Collecting keras<2.9,>=2.8.0rc0
Downloading keras-2.8.0-py2.py3-none-any.whl (1.4 MB)
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Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.8/dist-packages (from tensorflow~=2.8.0->deepcell) (0.28.0)
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Installing collected packages: jedi, tensorflow-estimator, tensorboard, qtpy, keras, tensorflow, qtconsole, deepcell-toolbox, tensorflow-addons, spektral, pydot, jupyter, deepcell-tracking, deepcell
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Successfully installed deepcell-0.12.4 deepcell-toolbox-0.12.0 deepcell-tracking-0.6.4 jedi-0.18.2 jupyter-1.0.0 keras-2.8.0 pydot-1.4.2 qtconsole-5.4.0 qtpy-2.3.0 spektral-1.0.6 tensorboard-2.8.0 tensorflow-2.8.4 tensorflow-addons-0.16.1 tensorflow-estimator-2.8.0Interesting - that install log looks like a lot of old versions and unnecessary downloads. For example the cascading back through versions of numpy all the way back to 1.17 is probably dominating install time. Unfortunately it's always tough to say what causes things like this due to transitive dependencies.
For this particular case, after you've run pip install deepcell, can you try pip install --upgrade scikit-image? That should bring you up to a version that supports the correct call signatures for the function in question.
joshuagohos
commented
1 year ago Ok. I've added it in the following:
! pip install deepcell ! pip install --upgrade scikit-image
Now, checking the version yields:
import skimage skimage.__version__
0.19.3
I am now able to run app.predict() based on the NuclearSegmentation application. So it works insofar as the previous bug is now gone.
But, one caveat is that it seems to give me different segmentation results than what I got before when trying on my own image data.
Before the above error and using the previous scikit-image version, applying nuclear-application segmentation gave me this segmentation:
But with this update of scikit-image, the same app.predict() from. Nuclear Segmentation, I get:
The color scaling rendering is a little different, but the segmentation is applied on the same base image.
Moreover, both are different from what I get from the online DeepCell predict -> label result:
Again, apologies for the different color rendering, but they are all of the same base image.
I've reflected this in another issue intro-to-deepcell#45
rossbar
commented
1 year ago I am now able to run app.predict() based on the NuclearSegmentation application. So it works insofar as the previous bug is now gone.
Great, thanks for checking!
But, one caveat is that it seems to give me different segmentation results than what I got before when trying on my own image data.
The scope here is much larger than the original issue. There are many potential sources of variability: model versions, data versions, any additions or alterations vis-a-vis model training (if applicable), pre- or post- processing steps that involve random processes, dependency management, etc.
At this point I don't have any concrete suggestions for a fully reproducible workflow for your use-case. Some (non-expert) suggestions that I'd offer:
Before the above error and using the previous scikit-image version, applying nuclear-application segmentation gave me this segmentation:
pip install deepcell==0.12.3. Though beware that this assumes you are running in a "clean" environment on colab, which doesn't seem to be the case in the above build log.
msschwartz21
commented
1 year ago I just want to add that there was an update to the NuclearSegmentation model between deepcell 0.12.3 and deepcell 0.12.4 so I would second Ross's suggestion to revert to the previous version with deepcell==0.12.3.
joshuagohos
commented
1 year ago Thanks. Concur with your suggestions and will try them in Colab. Are the Docker container versions more recommended then in this case?
joshuagohos
commented
1 year ago I confirm that reverting to deepcell==0.12.3 reinstates my previous segmentation results. Thanks! The only issue remaining is the comparability between the kiosk/online versions and the notebook application version.
msschwartz21
commented
1 year ago I checked and the same models should be deployed in both deepcell 0.12.3 and in the kiosk. We'll look into possible differences after the holidays and let you know if we find anything.
joshuagohos
commented
1 year ago Thanks. If it helps, I found that changing the resolution parameter in the notebook command modulates the segmentation result to be closer to the online version.
In the online version, I set the image resolution to 1 micron to get the result.
In the notebook, if I do app.predict(myimagearray, image_mpp=1.0), this yields the segmentation that is quite different. But if I do image_mpp = 1.3 the segmentation approaches the online one.
Is the online version applying some additional rescaling beyond the initial 1 micron setting that the noteboook version does not do?
In any case, happy holidays!
Running segmentations_predictions = app.predict(X_train, image_mpp=0.5) throws:
TypeError: h_maxima() got an unexpected keyword argument 'footprint'
Referencing /usr/local/lib/python3.8/dist-packages/deepcell_toolbox/deep_watershed.py
This was not present before (the commands worked fine). It just occurred around 12PM 21 Dec 2022. This same error also applies to the Nuclear-Application and Cytoplasm-Application.
This error occurs as I am trying out the notebook https://github.com/vanvalenlab/deepcell-tf/tree/master/notebooks/applications/Mesmer-Application.ipynb.
Here is the snapshow of the above ipynb as I ran it in Colab.