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Updating to Vela 3.10 which requires TensorFlow 2.14 which requires
Python 3.9 (dropping support for Python 3.8).
Resolves: MLIA-997
Change-Id: Id60bd08f7156a8efa204ef71ba81590edf0e3b28
Signed-off-by: Benjamin Klimczak <benjamin.klimczak@arm.com>
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If the input model for rewriting is quantized:
- Record de-quantized TFRecords
- enable writing de-quantized calibration data for the training
- re-generate augmented training data, if needed
- Use quantization-aware training (QAT) to train the replacement models
- Check if replacement model is quantized:
If source model is quantized, we make sure rewrite's output model
is quantized too. Right now, only int8 is supported so raising
an error if any other datatype is present in the output.
Resolves: MLIA-907, MLIA-908, MLIA-927
Signed-off-by: Benjamin Klimczak <benjamin.klimczak@arm.com>
Change-Id: Icb4070a9e6f1fdb5ce36120d73823986e89ac955
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* Add flags for rewrite (--rewrite, --rewrite-start,
--rewrite-end, --rewrite-target)
* Refactor CLI interfaces to accept tflite models with optimize for
rewrite, keras models with optimize for clustering and pruning
* Refactor and move common.py and select.py out of the folder
nn/tensorflow/optimizations
* Add file nn/rewrite/core/rewrite.py as placeholder
* Update/add unit tests
* Refactor OptimizeModel in ethos_u/data_collection.py
for accepting tflite model case
* Extend the logic so that if "--rewrite" is specified, we don't add
pruning to also accept TFLite models.
* Update README.md
Resolves: MLIA-750, MLIA-854, MLIA-865
Signed-off-by: Benjamin Klimczak <benjamin.klimczak@arm.com>
Change-Id: I67d85f71fa253d2bad4efe304ad8225970b9622c
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- Add a PruningPolicy to skip layers that are not
supported by the Keras pruning API
- Make dataset generation more generic to support
use-cases beyond classification
Signed-off-by: Annie Tallund <annie.tallund@arm.com>
Change-Id: I198dae2b53860f449f2fdbc71575babceed1ffcf
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- Update version dependencies in the tox.ini
- Fix linter issues
Change-Id: I04c3a841ee2646a865dab037701d66c28792f2a4
Signed-off-by: Benjamin Klimczak <benjamin.klimczak@arm.com>
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- Enable deferred annotations evaluation
- Use builtin types for type hints whenever possible
- Use | syntax for union types
- Rename mlia.core._typing into mlia.core.typing
Change-Id: I3f6ffc02fa069c589bdd9e8bddbccd504285427a
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- mypy issue: to make the comment #type: ignore platform specific,
flags like platform.machine() cannot be recognized by mypy, so we
cannot isolate the specific lines of code that fail mypy tests
- numpy issue: for numpy version < 1.20, the function np.unique
has not been type annotated, which caused mypy throwing the error
when we run our unit tests in aarch64
- because of the above two reasons, we use function decorator to
turn off type checking for entire functions to remove all
annotations so that the mypy error for certain lines can be silented
Change-Id: Id91e65ef7677b78b4c9c85b8412229e3672e3a66
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- Update TensorFlow to version 2.8 (now supported by Vela 3.4)
- Adapt existing codebase to preserve intermediary tensors in the interpreter in order to avoid errors when trying to print all of them in the future.
- Ignore types for numpy methods that do not have typing annotations in their definitions. This is needed because otherwise mypy would consider the calling function to also be untyped.
Change-Id: I943ac196fd4e378f5238949b15c23a2d628c8b5e
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Upgrade Vela version from 3.3.0 to 3.4.0.
- Adapt code to use new typing notation by replacing `numpy.array` with `numpy.ndarray` where necessary.
Change-Id: I035e9564d448652aa09a52d79c71ef09663ea776
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Add MLIA codebase including sources and tests.
Change-Id: Id41707559bd721edd114793618d12ccd188d8dbd
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