commit b85547024490d4c89538dcebfb0a2730e18a6d8d
author David Monahan <david.monahan@arm.com> Thu Apr 25 16:03:38 2019 +0100
committer David Monahan <david.monahan@arm.com> Mon Apr 29 07:15:23 2019 +0000
tree a6962f8c2392c4be7edd2b00249ff6a28bfdcf30
parent 04a8b05b25d3b752040a262a2725fa59753dd9b5

IVGCVSW-2657: Fix to force correct quantisation parameters for QASYMM8 Softmax
Signed-off-by: David Monahan <david.monahan@arm.com>

Change-Id: I0989ea843714ba1d5da756bb87ddefa3706b07eb

src/armnn/Network.cpp

diff --git a/src/armnn/Network.cpp b/src/armnn/Network.cpp
index 0bd8d4b..a38bcf1 100644
--- a/src/armnn/Network.cpp
+++ b/src/armnn/Network.cpp
@@ -114,8 +114,8 @@
     bool noErrors = true;
     unsigned int numOutputs = layer->GetNumOutputSlots();
     for (unsigned int i = 0; i < numOutputs; i++) {
-        const OutputSlot &outputSlot = layer->GetOutputSlot(i);
-        const TensorInfo &info = outputSlot.GetTensorInfo();
+        OutputSlot& outputSlot = layer->GetOutputSlot(i);
+        TensorInfo info = outputSlot.GetTensorInfo();
         if (DataType::QuantisedAsymm8 == info.GetDataType()) {
             if (0.f == info.GetQuantizationScale()) {
                 noErrors = false;
@@ -125,6 +125,20 @@
                    << " Quantized 8 bit but its 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.GetQuantizationScale() != (1.0f / 256.0f) ||
+                 info.GetQuantizationOffset() != 0) &&
+                 layer->GetType() == armnn::LayerType::Softmax)
+            {
+                std::stringstream ss;
+                ss << "Quantization parameters for Softmax layer (Scale: " <<
+                info.GetQuantizationScale() << " and Offset: " << info.GetQuantizationOffset() <<
+                ") are incorrect and have been updated to Scale: 0.00390625 and Offset: 0";
+                BOOST_LOG_TRIVIAL(warning) << ss.str();
+                info.SetQuantizationScale((1.0f /256.0f));
+                info.SetQuantizationOffset(0);
+                outputSlot.SetTensorInfo(info);
+            }
         }
     }
     return noErrors;