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| author | Colm Donelan <colm.donelan@arm.com> | 2024-03-29 16:00:24 +0000 |
|---|---|---|
| committer | Colm Donelan <colm.donelan@arm.com> | 2024-04-17 13:46:29 +0000 |
| commit | 4f1771ab4d321afba9f5a52411855b5dc33bf247 (patch) | |
| tree | a5772debacfaaea83b078d923633c10715284d98 /src/armnn/Network.cpp | |
| parent | 4e74df277b80db0be5c19b07d9f59575dc7ef3d6 (diff) | |
| download | armnn-4f1771ab4d321afba9f5a52411855b5dc33bf247.tar.gz | |
MLCE-1248 Removing limitations on zero scale value in quantization.
Currently Arm NN will fail to load models containing quantization
scale value of zero.
Signed-off-by: Colm Donelan <colm.donelan@arm.com>
Change-Id: Ifefcee1279b8667da63d1aa7d42e5d44875f9fbe
Diffstat (limited to 'src/armnn/Network.cpp')
| -rw-r--r-- | src/armnn/Network.cpp | 11 |
1 files changed, 1 insertions, 10 deletions
diff --git a/src/armnn/Network.cpp b/src/armnn/Network.cpp index 6f33fb6a15..810abaa5b9 100644 --- a/src/armnn/Network.cpp +++ b/src/armnn/Network.cpp @@ -818,16 +818,6 @@ bool CheckScaleSetOnQuantizedType(Layer* layer, Optional<std::vector<std::string throw InvalidArgumentException("Per Axis Quantization is not supported in " "Asymmetric Quantization Datatype."); } - if ((!info.HasPerAxisQuantization() && info.GetQuantizationScale() == 0.f) - || (info.HasPerAxisQuantization() && (quantizationScales.end() != - std::find(quantizationScales.begin(), quantizationScales.end(), 0.f)))) { - noErrors = false; - std::stringstream ss; - ss << "output " << i << " of layer " << GetLayerTypeAsCString(layer->GetType()) - << " (" << layer->GetNameStr() << ") is of type" - << " Quantized value but the scale parameter has not been set"; - ReportError(ss.str(), errMessages); - } // Softmax under QuantisedAsymm8 must always be scale (1.0f/256.0f) and offset 0 if (!info.HasPerAxisQuantization() && quantizationDataType == DataType::QAsymmU8 && (info.GetQuantizationScale() != (1.0f / 256.0f) || @@ -841,6 +831,7 @@ bool CheckScaleSetOnQuantizedType(Layer* layer, Optional<std::vector<std::string info.SetQuantizationScale((1.0f / 256.0f)); info.SetQuantizationOffset(0); outputSlot.SetTensorInfo(info); + ReportError(ss.str(), errMessages); } break; default: |