| Age | Commit message (Collapse) | Author |
|---|
|
Updates Vela Version to 3.11.0 and TensorFlow version to 2.15.1
Required keras import to change:
from keras.api._v2 import keras needed instead of calling tf.keras
Subsequently tf.keras.X needed to change to keras.X
Resolves: MLIA-1107
Signed-off-by: Nathan Bailey <nathan.bailey@arm.com>
Change-Id: I53bcaa9cdad58b0e6c311c8c6490393d33cb18bc
|
|
If a file has the right extension, MLIA previously tried
to load files with invalid content, resulting in confusing
errors. This patch adds better reporting for that scenario
Resolves: MLIA-1051
Signed-off-by: Annie Tallund <annie.tallund@arm.com>
Change-Id: I3f1fd578906a73a58367428f78409866f5da7836
|
|
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>
|
|
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
|
|
- Fix input shape of rewrite replacement:
During and after training of the replacement model for a rewrite the
Keras model is converted and saved in TensorFlow Lite format. If the
input shape does not match the teacher model exactly, e.g. if the
batch size is undefined, the TFLiteConverter adds extra operators
during conversion.
- Fix rewritten model output
- Save the model output with the rewritten operator in the output dir
- Log MAE and NRMSE of the rewrite
- Remove 'verbose' flag from rewrite module and rely on the logging
mechanism to control verbose output.
- Re-factor utility classes for rewrites
- Merge the two TFLiteModel classes
- Move functionality to load/save TensorFlow Lite flatbuffers to
nn/tensorflow/tflite_graph
- Fix issue with unknown shape in datasets
After upgrading to TensorFlow 2.12 the unknown shape of the
TFRecordDataset is causing problems when training the replacement models
for rewrites. By explicitly setting the right shape of the tensors we
can work around the issue.
- Adapt default parameters for rewrites. The training steps especially
had to be increased significantly to be effective.
Resolves: MLIA-895, MLIA-907, MLIA-946, MLIA-979
Signed-off-by: Benjamin Klimczak <benjamin.klimczak@arm.com>
Change-Id: I887ad165aed0f2c6e5a0041f64cec5e6c5ab5c5c
|
|
- Update version dependencies in the tox.ini
- Fix linter issues
Change-Id: I04c3a841ee2646a865dab037701d66c28792f2a4
Signed-off-by: Benjamin Klimczak <benjamin.klimczak@arm.com>
|
|
- 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
|
|
Use "TensorFlow Lite" instead of "TFLite" in
documentation and comments
Change-Id: Ie4450d72fb2e5261d152d72ab8bd94c3da914c46
|
|
- 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
|
|
Add MLIA codebase including sources and tests.
Change-Id: Id41707559bd721edd114793618d12ccd188d8dbd
|
