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TFLiteConverter was producing log messages in the output that was not
possible to capture and redirect to logging.
The solution/workaround is to run it as a subprocess.
This change required some refactoring around existing invocations of
the converter.
Change-Id: I394bd0d49d36e6686cfcb9d658e4aad05326cb87
Signed-off-by: Gergely Nagy <gergely.nagy@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 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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- Add ability to intercept low level TensorFlow output
- Produce advice for the models that could not be
converted to the TensorFlow Lite format
- Refactor utility functions for TensorFlow Lite
conversion
- Add TensorFlow Lite compatibility checker
Change-Id: I47d120d2619ced7b143bc92c5184515b81c0220d
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Use "TensorFlow Lite" instead of "TFLite" in
documentation and comments
Change-Id: Ie4450d72fb2e5261d152d72ab8bd94c3da914c46
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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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Use TFLITE_BUILTINS instead of TFLITE_BUILTINS_INT8 to make the
quantization non-strict.
Note: De facto this does not change the behavior of MLIA because the
TFLITE_BUILTINS_INT8 is not correctly applied with the new quantizer.
See: https://github.com/tensorflow/tensorflow/issues/56535
Change-Id: Ia0782ba22c5e9223fa10fec71c16aee60b79bb57
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Add MLIA codebase including sources and tests.
Change-Id: Id41707559bd721edd114793618d12ccd188d8dbd
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