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It/keras3 pytorch #396
It/keras3 pytorch #396
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… tox with TF backend
Co-authored-by: Igor Tatarnikov <[email protected]>
* Support single z-stack tif file for input. * Fix commit hook. * Apply review suggestions.
* remove modular asv benchmarks * recover old structure * remove asv-specific lines from gitignore and manifest * prune benchmarks
…ditional dependencies on M1 (#408) * naive attempt at adapting to silicon mac CI * run include guard test on Silicon CI * double-check hdf5 is needed
* Replace coord map values with numba list/tuple for optim. * Switch to fortran layout for faster update of last dim. * Cache kernel. * jit ball filter. * Put z as first axis to speed z rolling (row-major memory). * Unroll recursion (no perf impact either way). * Parallelize cell cluster splitting. * Parallelize walking for full images. * Cleanup docs and pep8 etc. * Add pre-commit fixes. * Fix parallel always being selected and numba function 1st class warning. * Run hook. * Older python needs Union instead of |. * Accept review suggestion. * Address review changes. * num_threads must be an int. --------- Co-authored-by: Matt Einhorn <[email protected]>
updates: - [github.com/pre-commit/pre-commit-hooks: v4.5.0 → v4.6.0](pre-commit/pre-commit-hooks@v4.5.0...v4.6.0) - [github.com/astral-sh/ruff-pre-commit: v0.3.5 → v0.4.3](astral-sh/ruff-pre-commit@v0.3.5...v0.4.3) - [github.com/psf/black: 24.3.0 → 24.4.2](psf/black@24.3.0...24.4.2) Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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Looking good @IgorTatarnikov ! Mainly minor comments or questions in the review. Some higher-level comments below.
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I know you say in the description that docs are not required atm, but maybe a quick edit to the README is useful so that we don't forget when we return to this. Could be just stating the need to install a backend, the need to configure the backend (via env variable for example) if developing, how to run tests locally with all backends etc.
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I ran the tests locally using
tox
with the three backends and got some errors fortorch
that seem to relate to Apple chips. I wrote some more details and a possible workaround in the review. -
The comparison with the previous performance looks great! Not the focus of this review, but I was thinking for the blogpost it would be more relevant to compare the
torch
implementation and the previous TF implementation more explicitly. Right now the comparison is more like: given an implementation, how does default model compare to retrained model.
Co-authored-by: sfmig <[email protected]>
for more information, see https://pre-commit.ci
* Simplify model download * Update model cache
# Conflicts: # .github/workflows/test_and_deploy.yml # .github/workflows/test_include_guard.yaml # cellfinder/core/tools/prep.py
* remove pytest-lazy-fixture as dev dependency and skip test (with WG temp fix) * Test Keras is present (#374) * check if Keras present * change TF to Keras in CI * remove comment * change dependencies in pyproject.toml for Keras 3.0 * Migrate to Keras 3.0 with TF backend (#373) * remove pytest-lazy-fixture as dev dependency and skip test (with WG temp fix) * change tensorflow dependency for cellfinder * replace keras imports from tensorflow to just keras imports * add keras import and reorder * add keras and TF 2.16 to pyproject.toml * comment out TF version check for now * change checkpoint filename for compliance with keras 3. remove use_multiprocessing=False from fit() as it is no longer an input. test_train() passing * add multiprocessing parameters to cube generator constructor and remove from fit() signature (keras3 change) * apply temp garbage collector fix * skip troublesome test * skip running tests on CI on windows * remove commented out TF check * clean commented out code. Explicitly pass use_multiprocessing=False (as before) * remove str conversion before model.save * raise test_detection error for sonarcloud happy * skip running tests on windows on CI * remove filename comment and small edits * Replace TF references in comments and warning messages (#378) * change some old references to TF for the import check * change TF cached model to Keras * Cellfinder with Keras 3.0 and jax backend (#379) * replace tensorflow Tensor with keras tensor * add case for TF prep in prep_model_weights * add different backends to pyproject.toml * add backend configuration to cellfinder init file. tests passing with jax locally * define extra dependencies for cellfinder with different backends. run tox with TF backend * run tox using TF and JAX backend * install TF in brainmapper environment before running tests in CI * add backends check to cellfinder init file * clean up comments * fix tf-nightly import check * specify TF backend in include guard check * clarify comment * remove 'backend' from dependencies specifications * Apply suggestions from code review Co-authored-by: Igor Tatarnikov <[email protected]> --------- Co-authored-by: Igor Tatarnikov <[email protected]> * Run cellfinder with JAX in Windows tests in CI (#382) * use jax backend in brainmapper tests in CI * skip TF backend on windows * fix pip install cellfinder for brainmapper CI tests * add keras env variable for brainmapper CLI tests * fix prep_model_weights * It/keras3 pytorch (#396) * replace tensorflow Tensor with keras tensor * add case for TF prep in prep_model_weights * add different backends to pyproject.toml * add backend configuration to cellfinder init file. tests passing with jax locally * define extra dependencies for cellfinder with different backends. run tox with TF backend * run tox using TF and JAX backend * install TF in brainmapper environment before running tests in CI * add backends check to cellfinder init file * clean up comments * fix tf-nightly import check * specify TF backend in include guard check * clarify comment * remove 'backend' from dependencies specifications * Apply suggestions from code review Co-authored-by: Igor Tatarnikov <[email protected]> * PyTorch runs utilizing multiple cores * PyTorch fix with default models * Tests run on every push for now * Run test on torch backend only * Fixed guard test to set torch as KERAS_BACKEND * KERAS_BACKEND env variable set directly in test_include_guard.yaml * Run test on python 3.11 * Remove tf-nightly from __init__ version check * Added 3.11 to legacy tox config * Changed legacy tox config for real this time * Don't set the wrong max_processing value * Torch is now set as the default backend * Tests only run with torch, updated comments * Unpinned torch version * Add codecov token (#403) * add codecov token * generate xml coverage report * add timeout to testing jobs * Allow turning off classification or detection in GUI (#402) * Allow turning off classification or detection in GUI. * Fix test. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Refactor to fix code analysis errors. * Ensure array is always 2d. * Apply suggestions from code review Co-authored-by: Igor Tatarnikov <[email protected]> --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Igor Tatarnikov <[email protected]> * Support single z-stack tif file for input (#397) * Support single z-stack tif file for input. * Fix commit hook. * Apply review suggestions. * Remove modular asv benchmarks (#406) * remove modular asv benchmarks * recover old structure * remove asv-specific lines from gitignore and manifest * prune benchmarks * Adapt CI so it covers both new and old Macs, and installs required additional dependencies on M1 (#408) * naive attempt at adapting to silicon mac CI * run include guard test on Silicon CI * double-check hdf5 is needed * Optimize cell detection (#398) (#407) * Replace coord map values with numba list/tuple for optim. * Switch to fortran layout for faster update of last dim. * Cache kernel. * jit ball filter. * Put z as first axis to speed z rolling (row-major memory). * Unroll recursion (no perf impact either way). * Parallelize cell cluster splitting. * Parallelize walking for full images. * Cleanup docs and pep8 etc. * Add pre-commit fixes. * Fix parallel always being selected and numba function 1st class warning. * Run hook. * Older python needs Union instead of |. * Accept review suggestion. * Address review changes. * num_threads must be an int. --------- Co-authored-by: Matt Einhorn <[email protected]> * [pre-commit.ci] pre-commit autoupdate (#412) updates: - [github.com/pre-commit/pre-commit-hooks: v4.5.0 → v4.6.0](pre-commit/pre-commit-hooks@v4.5.0...v4.6.0) - [github.com/astral-sh/ruff-pre-commit: v0.3.5 → v0.4.3](astral-sh/ruff-pre-commit@v0.3.5...v0.4.3) - [github.com/psf/black: 24.3.0 → 24.4.2](psf/black@24.3.0...24.4.2) Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * Apply suggestions from code review Co-authored-by: sfmig <[email protected]> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Simplify model download (#414) * Simplify model download * Update model cache * Remove jax and tf tests * Standardise the data types for inputs to all be float32 * Force torch to use CPU on arm based macOS during tests * Added PYTORCH_MPS_HIGH_WATERMARK_RATION env variable * Set env variables in test setup * Try to set the default device to cpu in the test itself * Add device call to Conv3D to force cpu * Revert changes, request one cpu left free * Revers the numb cores, don't use arm based mac runner * Merged main, removed torch flags on cellfinder install for guards and brainmapper * Lowercase Torch * Change cache directory --------- Co-authored-by: sfmig <[email protected]> Co-authored-by: Kimberly Meechan <[email protected]> Co-authored-by: Matt Einhorn <[email protected]> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Alessandro Felder <[email protected]> Co-authored-by: Adam Tyson <[email protected]> * Set pooling padding to valid by default on all MaxPooling3D layers * Removed tf error suppression and other tf related functions * Force torch to use cpu device when CELLFINDER_TEST_DEVICE env variable set to cpu * Added nev variable to test step * Use the GITHUB ACTIONS environemntal variable instead * Added docstring for fixture setting device to cpu on arm based mac * Revert changes to no_free_cpus being fixture, and default param * Fixed typo in test_and_deploy.yml * Set multiprocessing to false for the data generators * Update all cache steps to match * Remove reference to TF * Make sure tests can run locally when GITHUB_ACTIONS env variable is missing2 * Removed warning when backend is not configured * Set the label tensor to be float32 to ensure compatibility with mps * Always set KERAS_BACKEND to torch on init * Remove code in __init__ checking for if backend is installed --------- Co-authored-by: sfmig <[email protected]> Co-authored-by: Kimberly Meechan <[email protected]> Co-authored-by: Matt Einhorn <[email protected]> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Alessandro Felder <[email protected]> Co-authored-by: Adam Tyson <[email protected]>
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Please fill out as much of this template as you can, but if you have any problems or questions, just leave a comment and we will help out :)
Description
What is this PR
Why is this PR needed?
Migrating away from tensorflow backend.
What does this PR do?
Makes torch the default backend, CI tests will only run with a torch backend on python 3.9, 3.10 and 3.11.
References
Please reference any existing issues/PRs that relate to this PR.
How has this PR been tested?
Tested locally and on CI.
Is this a breaking change?
Hopefully not? If people want to continue using a tensorflow installation all they have to do is run
export KERAS_BACKEND="tensorflow"
and make suretensorflow>=2.16.1
Does this PR require an update to the documentation?
Not this one. The PR from
cellfinder-to-keras-3
tomain
will definitely need extra documentation. Might be worth timing the release of the next version with a blog post explaining the changes.Checklist: