commit 60692ce197e94275ffd246620795ea020a8ad8b5
author Teresa Charlin <teresa.charlinreyes@arm.com> Wed May 11 17:01:50 2022 +0100
committer Teresa Charlin <teresa.charlinreyes@arm.com> Wed May 11 19:39:03 2022 +0100
tree 8b274f00ca341db9f5f6b19c945b4fc6651d190f
parent 0302723e409df80bebd83883ce0aad07fece54c5

IVGCVSW-6949 Remove use of member variables in RefDepthwiseConv2d workload

* Fixes Segmentation fault in RefDepthwiseConvolution2d workload
 originated by IVGCVSW-6127 ConstTensorsAsInput DepthwiseConvolution2d



Signed-off-by: Teresa Charlin <teresa.charlinreyes@arm.com>
Change-Id: I019377777ae384bcd193ecab7b8cdf8266e79f45

src/backends/reference/workloads/RefDepthwiseConvolution2dWorkload.cpp

diff --git a/src/backends/reference/workloads/RefDepthwiseConvolution2dWorkload.cpp b/src/backends/reference/workloads/RefDepthwiseConvolution2dWorkload.cpp
index c1c3916..22538d2 100644
--- a/src/backends/reference/workloads/RefDepthwiseConvolution2dWorkload.cpp
+++ b/src/backends/reference/workloads/RefDepthwiseConvolution2dWorkload.cpp
@@ -64,8 +64,6 @@
 
 void RefDepthwiseConvolution2dWorkload::ExecuteAsync(WorkingMemDescriptor &workingMemDescriptor)
 {
-    PostAllocationConfigure(workingMemDescriptor.m_Inputs, workingMemDescriptor.m_Outputs);
-
     Execute(workingMemDescriptor.m_Inputs, workingMemDescriptor.m_Outputs);
 }
 
@@ -73,22 +71,22 @@
                                                 std::vector<ITensorHandle*> outputs) const
 {
     ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefDepthwiseConvolution2dWorkload_Execute");
-    std::unique_ptr<Decoder<float>> pBiasDecoder{};
 
-    std::unique_ptr<Decoder<float>> inputDecoder = MakeDecoder<float>(GetTensorInfo(inputs[0]), inputs[0]->Map());
-    std::unique_ptr<Encoder<float>> OutputEncoder = MakeEncoder<float>(GetTensorInfo(outputs[0]), outputs[0]->Map());
-
-    const TensorShape& inputShape = GetTensorInfo(inputs[0]).GetShape();
+    const TensorShape& inputShape  = GetTensorInfo(inputs[0]).GetShape();
     const TensorShape& outputShape = GetTensorInfo(outputs[0]).GetShape();
+    const TensorShape& filterShape = GetTensorInfo(inputs[1]).GetShape();
 
-    m_FilterDecoder->Reset(inputs[1]->Map());
+    std::unique_ptr<Decoder<float>> inputDecoder  = MakeDecoder<float>(GetTensorInfo(inputs[0]), inputs[0]->Map());
+    std::unique_ptr<Encoder<float>> outputEncoder = MakeEncoder<float>(GetTensorInfo(outputs[0]), outputs[0]->Map());
+    std::unique_ptr<Decoder<float>> filterDecoder = MakeDecoder<float>(GetTensorInfo(inputs[1]), inputs[1]->Map());
+    std::unique_ptr<Decoder<float>> biasDecoder{};
     if (m_Data.m_Parameters.m_BiasEnabled)
     {
-        m_BiasDecoder->Reset(inputs[2]->Map());
+       biasDecoder = MakeDecoder<float>(GetTensorInfo(inputs[2]), inputs[2]->Map());
     }
 
-    Convolve(inputShape, *inputDecoder, outputShape, *OutputEncoder,
-             m_FilterShape, *m_FilterDecoder, m_Data.m_Parameters.m_BiasEnabled, m_BiasDecoder.get(),
+    Convolve(inputShape, *inputDecoder, outputShape, *outputEncoder,
+             filterShape, *filterDecoder, m_Data.m_Parameters.m_BiasEnabled, biasDecoder.get(),
              m_Data.m_Parameters.m_DataLayout, m_Data.m_Parameters.m_PadTop, m_Data.m_Parameters.m_PadLeft,
              m_Data.m_Parameters.m_StrideX, m_Data.m_Parameters.m_StrideY,
              m_Data.m_Parameters.m_DilationX,