diff options
Diffstat (limited to 'src/armnn/test/optimizations/FoldPadTests.cpp')
| -rw-r--r-- | src/armnn/test/optimizations/FoldPadTests.cpp | 24 |
1 files changed, 15 insertions, 9 deletions
diff --git a/src/armnn/test/optimizations/FoldPadTests.cpp b/src/armnn/test/optimizations/FoldPadTests.cpp index 11f09e80e0..a598983706 100644 --- a/src/armnn/test/optimizations/FoldPadTests.cpp +++ b/src/armnn/test/optimizations/FoldPadTests.cpp @@ -45,7 +45,7 @@ TEST_CASE("FoldPadLayerIntoConvolution2dLayer") convolution2dDescriptor.m_DataLayout = DataLayout::NHWC; std::vector<float> weightsVector(18); - ConstTensor weights(TensorInfo(4, weightsShape, DataType::Float32), weightsVector); + ConstTensor weights(TensorInfo(4, weightsShape, DataType::Float32, 0.0f, 0, true), weightsVector); Convolution2dLayer* conv2dLayer = graph.AddLayer<Convolution2dLayer>(convolution2dDescriptor, "conv2d"); conv2dLayer->m_Weight = std::make_unique<ScopedTensorHandle>(weights); @@ -122,7 +122,7 @@ TEST_CASE("FoldPadLayerIntoDepthwiseConvolution2dLayer") depthwiseConvolution2dDescriptor.m_DataLayout = DataLayout::NHWC; std::vector<float> weightsVector(18); - ConstTensor weights(TensorInfo(4, weightsShape, DataType::Float32), weightsVector); + ConstTensor weights(TensorInfo(4, weightsShape, DataType::Float32, 0.0f, 0, true), weightsVector); auto* depthwiseConv2dLayer = graph.AddLayer<DepthwiseConvolution2dLayer>(depthwiseConvolution2dDescriptor, "depthwiseConv2d"); @@ -526,7 +526,9 @@ TEST_CASE("FoldPadLayerIntoPooling2dLayer_ExecuteInferenceWithAndWithoutOptimiza NetworkId networkIdentifier; CHECK(run->LoadNetwork(networkIdentifier, std::move(optimizedNetwork)) == Status::Success); - InputTensors inputTensors{{0, ConstTensor(run->GetInputTensorInfo(networkIdentifier, 0), inputData.data())}}; + TensorInfo inputTensorInfo = run->GetInputTensorInfo(networkIdentifier, 0); + inputTensorInfo.SetConstant(true); + InputTensors inputTensors{{0, ConstTensor(inputTensorInfo, inputData.data())}}; // Set the initial values of the data to different values to the golden data just in case the inference fails. std::vector<float> optimizedData(32, -std::numeric_limits<float>::infinity()); @@ -614,10 +616,10 @@ TEST_CASE("FoldPadLayerIntoConv2dLayer_ExecuteInferenceWithAndWithoutOptimizatio 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42}; - TensorInfo weightsInfo(4, weightsShape, DataType::Float32); + TensorInfo weightsInfo(4, weightsShape, DataType::Float32, 0.0f, 0, true); ConstTensor weights(weightsInfo, weightsData); std::vector<float> biasVector = {5, 6, 7, 8}; - TensorInfo biasInfo({4}, DataType::Float32); + TensorInfo biasInfo({4}, DataType::Float32, 0.0f, 0, true); ConstTensor bias(biasInfo, biasVector); Optional<ConstTensor> optionalBias = Optional<ConstTensor>(bias); @@ -644,7 +646,9 @@ TEST_CASE("FoldPadLayerIntoConv2dLayer_ExecuteInferenceWithAndWithoutOptimizatio NetworkId networkIdentifier; CHECK(run->LoadNetwork(networkIdentifier, std::move(optimizedNetwork)) == Status::Success); - InputTensors inputTensors{{0, ConstTensor(run->GetInputTensorInfo(networkIdentifier, 0), inputData.data())}}; + TensorInfo inputTensorInfo = run->GetInputTensorInfo(networkIdentifier, 0); + inputTensorInfo.SetConstant(true); + InputTensors inputTensors{{0, ConstTensor(inputTensorInfo, inputData.data())}}; // Set the initial values of the data to different values to the golden data just in case the inference fails. std::vector<float> optimizedData(100, -std::numeric_limits<float>::infinity()); @@ -732,10 +736,10 @@ TEST_CASE("FoldPadLayerIntoDepthwiseConv2dLayer_ExecuteInferenceWithAndWithoutOp 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42}; - TensorInfo weightsInfo(4, weightsShape, DataType::Float32); + TensorInfo weightsInfo(4, weightsShape, DataType::Float32, 0.0f, 0, true); ConstTensor weights(weightsInfo, weightsData); std::vector<float> biasVector = {5, 6, 7, 8, 9, 10, 11, 12, 5, 6, 7, 8}; - TensorInfo biasInfo({12}, DataType::Float32); + TensorInfo biasInfo({12}, DataType::Float32, 0.0f, 0, true); ConstTensor bias(biasInfo, biasVector); Optional<ConstTensor> optionalBias = Optional<ConstTensor>(bias); @@ -762,7 +766,9 @@ TEST_CASE("FoldPadLayerIntoDepthwiseConv2dLayer_ExecuteInferenceWithAndWithoutOp NetworkId networkIdentifier; CHECK(run->LoadNetwork(networkIdentifier, std::move(optimizedNetwork)) == Status::Success); - InputTensors inputTensors{{0, ConstTensor(run->GetInputTensorInfo(networkIdentifier, 0), inputData.data())}}; + TensorInfo inputTensorInfo = run->GetInputTensorInfo(networkIdentifier, 0); + inputTensorInfo.SetConstant(true); + InputTensors inputTensors{{0, ConstTensor(inputTensorInfo, inputData.data())}}; // Set the initial values of the data to different values to the golden data just in case the inference fails. std::vector<float> optimizedData(300, -std::numeric_limits<float>::infinity()); |