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| author | Fredrik Svedberg <fredrik.svedberg@arm.com> | 2022-07-06 14:54:12 +0200 |
|---|---|---|
| committer | Fredrik Svedberg <fredrik.svedberg@arm.com> | 2022-07-15 13:20:47 +0000 |
| commit | 1156317a8a8df1bc0c25a54db8475d84495f1a79 (patch) | |
| tree | a34f60c1d874bcac6e65052e39fca1608f3b72fb /ethosu/vela/tflite_graph_optimiser.py | |
| parent | 93f492bae9c4dd16a1f64b851b237263695ee03e (diff) | |
| download | ethos-u-vela-1156317a8a8df1bc0c25a54db8475d84495f1a79.tar.gz | |
MLBEDSW-6703 Add SHAPE operator to supported operators
Added SHAPE operator to the supported operators report.
Updated the constraints for QUANTIZE and SHAPE operator.
Also fixed RESHAPE consuming statically optimised shape.
Signed-off-by: Fredrik Svedberg <fredrik.svedberg@arm.com>
Change-Id: I1d964d602d3f361a0f16dae8133197280dd84c48
Diffstat (limited to 'ethosu/vela/tflite_graph_optimiser.py')
| -rw-r--r-- | ethosu/vela/tflite_graph_optimiser.py | 8 |
1 files changed, 3 insertions, 5 deletions
diff --git a/ethosu/vela/tflite_graph_optimiser.py b/ethosu/vela/tflite_graph_optimiser.py index f2a8c803..b1a56605 100644 --- a/ethosu/vela/tflite_graph_optimiser.py +++ b/ethosu/vela/tflite_graph_optimiser.py @@ -1408,10 +1408,8 @@ def optimise_quantize(op: Operation, arch, nng): if input_values.ndim == 0: input_values = np.array([input_values]) - # requantized int8 to int8 - if (ifm.dtype == DataType.int8 and ofm.dtype == DataType.int8) or ( - ifm.dtype == DataType.int16 and ofm.dtype == DataType.int16 - ): + # requantized int8 to int8 or int16 to int16 + if ifm.dtype == ofm.dtype == DataType.int8 or ifm.dtype == ofm.dtype == DataType.int16: # scale needs to use double precision to match TFLite reference kernel effective_scale = np.float64(ifm.quantization.scale_f32) / np.float64(ofm.quantization.scale_f32) @@ -1495,7 +1493,7 @@ def supported_operator_check(op, arch, nng): def tflite_optimise_graph(nng, arch): - # Compile time optimisations + # Compile time static optimisations optimisation_list = [optimise_quantize, convert_shape_op_to_constant_tensor] for idx, sg in enumerate(nng.subgraphs): |