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Rework doctrings in rewrite functions based on recent changes
Resolves MLIA-944
Signed-off-by: Nathan Bailey <nathan.bailey@arm.com>
Change-Id: I31a37e17a296f8a16d0db408d48c6de65c05300e
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Implements CLI and API changes for the new conv2d rewrite targets
Resolves: MLIA-1157
Signed-off-by: Nathan Bailey <nathan.bailey@arm.com>
Change-Id: I03c7a3a536d2f0a805b4689a9d96b95f8b4ab86c
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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
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- 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
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* Define replacement function fully_connected layer
* Define RewriteConfiguration and Rewriter to integrate
rewrite module into mlia optimize command
* Fix a bug in the ethos_u/data_collection.py file
* Fix a bug in join.py
* Remove diff_stats and use diff instead, added related
changes around this to ensure e2e tests passing
* Add unit tests for all changes
* Fix bug in diff_stats function
* The bug was caused by a dividing by numpy array
of all zeros. The previous way of handling it
did not consider the all zeros case but only
dealt with partially zeros
* unit tests added.
* Fix the bug in rewrite/core/graph_edit/join.py
* Remove the possibility of passing None to append_relabel
function because it is immutable
* The bug happened when empty dictionary was passed in the
append_relabel function and the function overwrites the
reference of operator_map which caused the dictionary
was not updated after the function call
Resolves: MLIA-749, MLIA-864, MLIA-866
Change-Id: I1ab426996232f182345e6e98033d5dcb32aea08c
Signed-off-by: Benjamin Klimczak <benjamin.klimczak@arm.com>
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