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| author | Johan Alfvén <johan.alfven@arm.com> | 2022-09-15 15:50:30 +0200 |
|---|---|---|
| committer | Fredrik Svedberg <fredrik.svedberg@arm.com> | 2022-09-27 12:19:24 +0000 |
| commit | 5060ff53f5ac2382e04a68d7772bd71a36f63845 (patch) | |
| tree | e46e109573ca9066288119b76e3aec86a30e9727 /ethosu/vela/tflite_supported_operators.py | |
| parent | e84ed6b6663bb9158cd87d11cb21b48abed1033d (diff) | |
| download | ethos-u-vela-5060ff53f5ac2382e04a68d7772bd71a36f63845.tar.gz | |
MLBEDSW-6961: Bypass functionality for memory ops
- In order to solve output diffs, the Reshape op was pushed
to the CPU. The problem was that the Mean op ifm shape
was replaced by the Reshape op ifm shape.
- This limitation is now removed. Changed implementation
how memory only ops are bypassed. Always replace the memory
only op ifm tensor with its ofm tensor. By doing this
the ifm tensor for the operator that is after the memory only
op is never changed.
Signed-off-by: Johan Alfven <johan.alfven@arm.com>
Change-Id: Ibcdebf33fd9b7a37f90984a129500b5dac52e5ea
Diffstat (limited to 'ethosu/vela/tflite_supported_operators.py')
| -rw-r--r-- | ethosu/vela/tflite_supported_operators.py | 9 |
1 files changed, 0 insertions, 9 deletions
diff --git a/ethosu/vela/tflite_supported_operators.py b/ethosu/vela/tflite_supported_operators.py index d42caf58..3d04def3 100644 --- a/ethosu/vela/tflite_supported_operators.py +++ b/ethosu/vela/tflite_supported_operators.py @@ -317,7 +317,6 @@ class TFLiteSupportedOperators: # Reshape specific checks: self.specific_constraints[Op.Reshape].append(TFLiteSupportedOperators.constraint_reshape_shape_constant) - self.specific_constraints[Op.Reshape].append(TFLiteSupportedOperators.constraint_reshape_before_mean) def is_operator_supported(self, op): ext_type = optype_to_builtintype(op.type) @@ -887,11 +886,3 @@ class TFLiteSupportedOperators: extra = ", ".join(extra) return valid, f"Op has non-const input(s): {extra}" - - @staticmethod - def constraint_reshape_before_mean(op): - "Reshape on NPU not supported before MEAN operator" - for next_op in op.outputs[0].consumers(): - if next_op is not None and next_op.type == Op.Mean: - return False, "" - return True, "" |