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- Add new error types for the TensorFlow Lite compatibility
check
- Try to detect custom operators in SavedModel/Keras models
- Add warning to the advice about models with custom operators
Change-Id: I2f65474eecf2788110acc43585fa300eda80e21b
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- Update TensorFlow dependencies for x86_64
- Adapt unit tests to new TensorFlow version
- Update linters (including pre-commit hooks) and fix issues
- Use conditional import to fix tflite compat code for aarch64
Change-Id: I1a9b080b900ab65e38f7f2552562822bbfdcd259
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Check input model for Arm NN TensorFlow Lite Delegate 22.08 support.
Change-Id: I1253c4c0b294c5283e08f0a39561b922ef0f62e6
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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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- 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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Re-factoring the code base to further merge the old AIET code into MLIA.
- Remove last traces of the backend type 'tool'
- Controlled systems removed, including SSH protocol, controller,
RunningCommand, locks etc.
- Build command / build dir and deploy functionality removed from
Applications and Systems
- Moving working_dir()
- Replace module 'output_parser' with new module 'output_consumer' and
merge Base64 parsing into it
- Change the output consumption to optionally remove (i.e. actually
consume) lines
- Use Base64 parsing in GenericInferenceOutputParser, replacing the
regex-based parsing and remove the now unused regex parsing
- Remove AIET reporting
- Pre-install applications by moving them to src/mlia/resources/backends
- Rename aiet-config.json to backend-config.json
- Move tests from tests/mlia/ to tests/
- Adapt unit tests to code changes
- Dependencies removed: paramiko, filelock, psutil
- Fix bug in corstone.py: The wrong resource directory was used which
broke the functionality to download backends.
- Use f-string formatting.
- Use logging instead of print.
Change-Id: I768bc3bb6b2eda57d219ad01be4a8e0a74167d76
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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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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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