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author | Tim Hall <tim.hall@arm.com> | 2022-03-03 17:43:16 +0000 |
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committer | Tim Hall <tim.hall@arm.com> | 2022-04-04 14:25:01 +0100 |
commit | a3fe665803c0f72000f9dda249446d5a0d03240f (patch) | |
tree | 1de76663d1a1d5a39cf795eecbc99d5479382735 /ethosu/vela/tensor.py | |
parent | 68df8a1f5469daac53b7a418d92204f7026e4228 (diff) | |
download | ethos-u-vela-a3fe665803c0f72000f9dda249446d5a0d03240f.tar.gz |
vela: Minor refactordev/mlbedsw-6271
- Changed comments to docstring on QuantizationParams
- Simplified op type to op name conversion
Signed-off-by: Tim Hall <tim.hall@arm.com>
Change-Id: I2fdf5922cc17944c9bd37917a85fdfe50a1e651d
Diffstat (limited to 'ethosu/vela/tensor.py')
-rw-r--r-- | ethosu/vela/tensor.py | 10 |
1 files changed, 6 insertions, 4 deletions
diff --git a/ethosu/vela/tensor.py b/ethosu/vela/tensor.py index 783f459e..38b0e430 100644 --- a/ethosu/vela/tensor.py +++ b/ethosu/vela/tensor.py @@ -269,9 +269,10 @@ class QuantizationParameters: return np.subtract(values, self.zero_point) * self.scale_f32 def is_scaling_equal(self, other: Optional["QuantizationParameters"]) -> bool: - # quantisation parameter scaling is not equal if 'other' is None because - # it implies that the tensor it belongs to is not quantised. otherwise, - # it depends upon whether the scale and zero point are equal + """ + Returns True if the scale and zero point of self and other are equal. If other is None then the scaling is + not considered equal because the tensor is assumed to not be quantised and False will be returned + """ if not isinstance(other, QuantizationParameters): return False @@ -279,12 +280,13 @@ class QuantizationParameters: return self.scale_f32 == other.scale_f32 and self.zero_point == other.zero_point def is_valid(self) -> bool: - # quantisation parameters are consider valid if they have a scale and zero point + """Return True if the quantisation parameters have a scale and zero point""" return self.scale_f32 is not None and self.zero_point is not None def is_per_axis(self) -> bool: """Returns True if either the scale, zero point, minimum or maximum values have more than one value""" + for attr in ("scale_f32", "zero_point", "min", "max"): if np.size(getattr(self, attr)) > 1: return True |