IVGCVSW-4931 Update NN Driver to support dynamic tensors
* Change NN Driver m_Network to now have ShapeInferenceMethod::InferAndValidate
* Implement dynamic tensor support for:
- ArgMinMax layer
- Pooling2d layer
- Activation layer
* Skip dynamic tensor tests for any HAL other than 1.3
Change-Id: Icf66c968e49cdd4822b8c79c5f18b3f9e97dc53f
Signed-off-by: Finn Williams <Finn.Williams@Arm.com>
Signed-off-by: Teresa Charlin <teresa.charlinreyes@arm.com>
diff --git a/ArmnnPreparedModel.cpp b/ArmnnPreparedModel.cpp
index f990d3b..8aa28d7 100644
--- a/ArmnnPreparedModel.cpp
+++ b/ArmnnPreparedModel.cpp
@@ -48,7 +48,7 @@
for (unsigned int d = 0; d < tensorInfo.GetNumDimensions(); ++d)
{
- if (requestArg.dimensions[d] != tensorInfo.GetShape()[d])
+ if (requestArg.dimensions[d] != 0 && requestArg.dimensions[d] != tensorInfo.GetShape()[d])
{
ALOGE("Mismatched size for dimension %d (request argument: %u, expected %u)",
d, requestArg.dimensions[d], tensorInfo.GetShape()[d]);
diff --git a/ArmnnPreparedModel_1_2.cpp b/ArmnnPreparedModel_1_2.cpp
index 76ef426..dd5bdae 100644
--- a/ArmnnPreparedModel_1_2.cpp
+++ b/ArmnnPreparedModel_1_2.cpp
@@ -80,7 +80,7 @@
for (unsigned int d = 0; d < tensorInfo.GetNumDimensions(); ++d)
{
- if (requestArg.dimensions[d] != tensorInfo.GetShape()[d])
+ if (requestArg.dimensions[d] != 0 && requestArg.dimensions[d] != tensorInfo.GetShape()[d])
{
ALOGE("Mismatched size for dimension %d (request argument: %u, expected %u)",
d, requestArg.dimensions[d], tensorInfo.GetShape()[d]);
@@ -259,10 +259,17 @@
}
const size_t outputSize = outputTensorInfo.GetNumBytes();
+
+ if (outputArg.location.length < outputSize)
+ {
+ ALOGW("ArmnnPreparedModel_1_2::Execute failed: outputArg.location.length < outputSize");
+ return V1_0::ErrorStatus::OUTPUT_INSUFFICIENT_SIZE;
+ }
+
const size_t bufferSize = memPools.at(outputArg.location.poolIndex).getHidlMemory().size();
if (bufferSize < outputSize)
{
- ALOGW("ArmnnPreparedModel_1_2::Execute failed");
+ ALOGW("ArmnnPreparedModel_1_2::Execute failed: bufferSize < outputSize");
return V1_0::ErrorStatus::OUTPUT_INSUFFICIENT_SIZE;
}
diff --git a/ArmnnPreparedModel_1_3.cpp b/ArmnnPreparedModel_1_3.cpp
index c7adc6c..a27c7a3 100644
--- a/ArmnnPreparedModel_1_3.cpp
+++ b/ArmnnPreparedModel_1_3.cpp
@@ -101,7 +101,7 @@
for (unsigned int d = 0; d < tensorInfo.GetNumDimensions(); ++d)
{
- if (requestArg.dimensions[d] != tensorInfo.GetShape()[d])
+ if (requestArg.dimensions[d] != 0 && requestArg.dimensions[d] != tensorInfo.GetShape()[d])
{
ALOGE("Mismatched size for dimension %d (request argument: %u, expected %u)",
d, requestArg.dimensions[d], tensorInfo.GetShape()[d]);
@@ -309,6 +309,13 @@
ALOGW("ArmnnPreparedModel_1_3::executeFenced parameter loopTimeoutDuration is set but not supported.");
}
+ if (!android::nn::validateRequest(request, m_Model, /*allowUnspecifiedOutput=*/false))
+ {
+ ALOGV("ArmnnPreparedModel_1_3::executeFenced outputs must be specified for fenced execution ");
+ cb(ErrorStatus::INVALID_ARGUMENT, hidl_handle(nullptr), nullptr);
+ return Void();
+ }
+
ExecutionContext_1_3 ctx;
if (measureTiming == MeasureTiming::YES)
{
@@ -319,12 +326,6 @@
ALOGV("ArmnnPreparedModel_1_3::executeFenced(): %s", GetModelSummary(m_Model).c_str());
m_RequestCount++;
- if (!android::nn::validateRequest(request, m_Model))
- {
- cb(ErrorStatus::INVALID_ARGUMENT, hidl_handle(nullptr), nullptr);
- return Void();
- }
-
if (!m_RequestInputsAndOutputsDumpDir.empty())
{
ALOGD("Dumping inputs and outputs for request %" PRIuPTR, reinterpret_cast<std::uintptr_t>(&cb));
@@ -442,7 +443,7 @@
{
const auto& outputArg = request.outputs[i];
- const armnn::TensorInfo outputTensorInfo = m_Runtime->GetOutputTensorInfo(m_NetworkId, i);
+ armnn::TensorInfo outputTensorInfo = m_Runtime->GetOutputTensorInfo(m_NetworkId, i);
const armnn::Tensor outputTensor = GetTensorForRequestArgument(outputArg, outputTensorInfo, memPools);
if (outputTensor.GetMemoryArea() == nullptr)
{
@@ -450,16 +451,40 @@
return V1_3::ErrorStatus::GENERAL_FAILURE;
}
+ unsigned int count = 0;
+ std::for_each(outputArg.dimensions.begin(), outputArg.dimensions.end(), [&](auto dim)
+ {
+ if (dim != 0)
+ {
+ outputTensorInfo.GetShape()[count] = dim;
+ }
+ else
+ {
+ outputTensorInfo.GetShape()[count] = outputArg.dimensions.size();
+ }
+
+ count++;
+ });
+
const size_t outputSize = outputTensorInfo.GetNumBytes();
+
+ outputs.emplace_back(i, outputTensor);
+ outputShapes[i] = ComputeShape(outputTensorInfo);
+
+ if (outputArg.location.length < outputSize)
+ {
+ ALOGW("ArmnnPreparedModel_1_3::Execute failed");
+ outputShapes[i].isSufficient = false;
+ return V1_3::ErrorStatus::OUTPUT_INSUFFICIENT_SIZE;
+ }
+
const size_t bufferSize = memPools.at(outputArg.location.poolIndex).getHidlMemory().size();
if (bufferSize < outputSize)
{
ALOGW("ArmnnPreparedModel_1_3::Execute failed");
+ outputShapes[i].isSufficient = false;
return V1_3::ErrorStatus::OUTPUT_INSUFFICIENT_SIZE;
}
-
- outputs.emplace_back(i, outputTensor);
- outputShapes[i] = ComputeShape(outputTensorInfo);
}
return V1_3::ErrorStatus::NONE;
diff --git a/ConversionUtils.cpp b/ConversionUtils.cpp
index 4c77396..b03ffbd 100644
--- a/ConversionUtils.cpp
+++ b/ConversionUtils.cpp
@@ -38,6 +38,15 @@
}
}
+void LayerInputHandle::Disconnect(armnn::IInputSlot& inputSlot)
+{
+ ARMNN_ASSERT(IsValid());
+ if (m_OutputSlot)
+ {
+ m_OutputSlot->Disconnect(inputSlot);
+ }
+}
+
const armnn::TensorInfo& LayerInputHandle::GetTensorInfo() const
{
return m_TensorInfo;
diff --git a/ConversionUtils.hpp b/ConversionUtils.hpp
index 5dc9993..474d1a5 100644
--- a/ConversionUtils.hpp
+++ b/ConversionUtils.hpp
@@ -62,6 +62,8 @@
void Connect(armnn::IInputSlot& inputSlot);
+ void Disconnect(armnn::IInputSlot& inputSlot);
+
const armnn::TensorInfo& GetTensorInfo() const;
private:
@@ -1380,7 +1382,8 @@
uint32_t layerOutputIndex,
const HalModel& model,
ConversionData& data,
- const armnn::TensorInfo* overrideOutputInfo = nullptr)
+ const armnn::TensorInfo* overrideOutputInfo = nullptr,
+ const std::function <void (const armnn::TensorInfo&, bool&)>& validateFunc = nullptr)
{
using HalOperand = typename HalPolicy::Operand;
@@ -1392,18 +1395,39 @@
armnn::IOutputSlot& outputSlot = layer.GetOutputSlot(layerOutputIndex);
- const uint32_t operandIndex = operation.outputs[operationOutputIndex];
- data.m_OutputSlotForOperand[operandIndex] = &outputSlot;
-
- if (overrideOutputInfo == nullptr)
+ bool isSupported = false;
+ if (validateFunc &&
+ layer.GetInputSlot(0).GetConnection() &&
+ IsDynamicTensor(outputSlot.GetTensorInfo()))
{
- outputSlot.SetTensorInfo(GetTensorInfoForOperand(*outputOperand));
+ outputSlot.IsTensorInfoSet();
+ validateFunc(outputSlot.GetTensorInfo(), isSupported);
+
+ if(!isSupported)
+ {
+ for (unsigned int inputSlotIndex = 0; inputSlotIndex < layer.GetNumInputSlots(); ++inputSlotIndex)
+ {
+ layer.GetInputSlot(inputSlotIndex).GetConnection()->Disconnect(layer.GetInputSlot(inputSlotIndex));
+ }
+
+ return false;
+ }
}
else
{
- outputSlot.SetTensorInfo(*overrideOutputInfo);
+ if (overrideOutputInfo == nullptr)
+ {
+ outputSlot.SetTensorInfo(GetTensorInfoForOperand(*outputOperand));
+ }
+ else
+ {
+ outputSlot.SetTensorInfo(*overrideOutputInfo);
+ }
}
+ const uint32_t operandIndex = operation.outputs[operationOutputIndex];
+ data.m_OutputSlotForOperand[operandIndex] = &outputSlot;
+
return true;
}
@@ -1452,7 +1476,8 @@
armnn::IConnectableLayer& layer,
const HalModel& model,
ConversionData& data,
- const armnn::TensorInfo* overrideOutputInfo = nullptr)
+ const armnn::TensorInfo* overrideOutputInfo = nullptr,
+ const std::function <void (const armnn::TensorInfo&, bool&)>& validateFunc = nullptr)
{
return SetupAndTrackLayerOutputSlot<HalPolicy>(operation,
outputIndex,
@@ -1460,7 +1485,8 @@
outputIndex,
model,
data,
- overrideOutputInfo);
+ overrideOutputInfo,
+ validateFunc);
}
template<typename HalPolicy,
@@ -1487,19 +1513,29 @@
}
const armnn::TensorInfo& outInfo = GetTensorInfoForOperand(*outputOperand);
- if (IsDynamicTensor(outInfo))
- {
- return Fail("%s: Dynamic output tensors are not supported", __func__);
- }
bool isSupported = false;
- FORWARD_LAYER_SUPPORT_FUNC(__func__,
- IsActivationSupported,
- data.m_Backends,
- isSupported,
- input.GetTensorInfo(),
- outInfo,
- activationDesc);
+
+ auto validateFunc = [&](const armnn::TensorInfo& outInfo, bool& isSupported)
+ {
+ FORWARD_LAYER_SUPPORT_FUNC(__func__,
+ IsActivationSupported,
+ data.m_Backends,
+ isSupported,
+ input.GetTensorInfo(),
+ outInfo,
+ activationDesc);
+ };
+
+ if(IsDynamicTensor(outInfo))
+ {
+ isSupported = AreDynamicTensorsSupported();
+ }
+ else
+ {
+ validateFunc(outInfo, isSupported);
+ }
+
if (!isSupported)
{
return false;
@@ -1509,7 +1545,7 @@
ARMNN_ASSERT(layer != nullptr);
input.Connect(layer->GetInputSlot(0));
- return SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 0, *layer, model, data);
+ return SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 0, *layer, model, data, nullptr, validateFunc);
}
template<typename HalPolicy,
@@ -1634,11 +1670,6 @@
const armnn::TensorInfo& inputInfo = input.GetTensorInfo();
const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
- if (IsDynamicTensor(outputInfo))
- {
- return Fail("%s: Dynamic output tensors are not supported", __func__);
- }
-
armnn::Pooling2dDescriptor desc;
desc.m_PoolType = poolType;
desc.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor;
@@ -1697,13 +1728,28 @@
}
bool isSupported = false;
- FORWARD_LAYER_SUPPORT_FUNC(__func__,
- IsPooling2dSupported,
- data.m_Backends,
- isSupported,
- inputInfo,
- outputInfo,
- desc);
+
+ auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported)
+ {
+ FORWARD_LAYER_SUPPORT_FUNC(__func__,
+ IsPooling2dSupported,
+ data.m_Backends,
+ isSupported,
+ inputInfo,
+ outputInfo,
+ desc);
+
+ };
+
+ if(IsDynamicTensor(outputInfo))
+ {
+ isSupported = AreDynamicTensorsSupported();
+ }
+ else
+ {
+ validateFunc(outputInfo, isSupported);
+ }
+
if (!isSupported)
{
return false;
@@ -1723,7 +1769,12 @@
input.Connect(pooling2dLayer->GetInputSlot(0));
- return SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 0, *endLayer, model, data);
+ if (!isSupported)
+ {
+ return false;
+ }
+
+ return SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 0, *endLayer, model, data, nullptr, validateFunc);
}
template<typename HalPolicy,
@@ -1842,23 +1893,33 @@
const armnn::TensorInfo& inputInfo0 = input0.GetTensorInfo();
const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
- if (IsDynamicTensor(outputInfo))
- {
- return Fail("%s: Dynamic output tensors are not supported", __func__);
- }
armnn::ArgMinMaxDescriptor descriptor;
descriptor.m_Function = argMinMaxFunction;
descriptor.m_Axis = axis;
bool isSupported = false;
- FORWARD_LAYER_SUPPORT_FUNC(__func__,
- IsArgMinMaxSupported,
- data.m_Backends,
- isSupported,
- inputInfo0,
- outputInfo,
- descriptor);
+
+ auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported)
+ {
+ FORWARD_LAYER_SUPPORT_FUNC(__func__,
+ IsArgMinMaxSupported,
+ data.m_Backends,
+ isSupported,
+ inputInfo0,
+ outputInfo,
+ descriptor);
+ };
+
+ if(IsDynamicTensor(outputInfo))
+ {
+ isSupported = AreDynamicTensorsSupported();
+ }
+ else
+ {
+ validateFunc(outputInfo, isSupported);
+ }
+
if (!isSupported)
{
return false;
@@ -1869,7 +1930,7 @@
input0.Connect(layer->GetInputSlot(0));
- return SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 0, *layer, model, data);
+ return SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 0, *layer, model, data, nullptr, validateFunc);
}
template<typename HalPolicy,
diff --git a/ModelToINetworkConverter.cpp b/ModelToINetworkConverter.cpp
index 24fb490..0d6ddc3 100644
--- a/ModelToINetworkConverter.cpp
+++ b/ModelToINetworkConverter.cpp
@@ -59,8 +59,17 @@
totalPoolSize += pool.size();
}
+ using NetworkOptions = std::vector<armnn::BackendOptions>;
+ NetworkOptions networkOptions;
+ armnn::BackendOptions shapeInferenceMethodOption("ShapeInferenceMethod",
+ {
+ { "InferAndValidate", true }
+ });
+
+ networkOptions.push_back(shapeInferenceMethodOption);
+
// Create armnn::INetwork
- m_Data.m_Network = armnn::INetwork::Create();
+ m_Data.m_Network = armnn::INetwork::Create(networkOptions);
// add operations to it
// track which layer outputs each operand
diff --git a/Utils.cpp b/Utils.cpp
index 8a2812a..db1b6e6 100644
--- a/Utils.cpp
+++ b/Utils.cpp
@@ -80,7 +80,8 @@
armnn::TensorInfo GetTensorInfoForOperand(const V1_0::Operand& operand)
{
- armnn::DataType type;
+ using namespace armnn;
+ DataType type;
switch (operand.type)
{
@@ -97,7 +98,30 @@
throw UnsupportedOperand<V1_0::OperandType>(operand.type);
}
- armnn::TensorInfo ret(operand.dimensions.size(), operand.dimensions.data(), type);
+ TensorInfo ret;
+ if (operand.dimensions.size() == 0)
+ {
+ TensorShape tensorShape(Dimensionality::NotSpecified);
+ ret = TensorInfo(tensorShape, type);
+ }
+ else
+ {
+ bool dimensionsSpecificity[5] = { true, true, true, true, true };
+ int count = 0;
+ std::for_each(operand.dimensions.data(),
+ operand.dimensions.data() + operand.dimensions.size(),
+ [&](const unsigned int val)
+ {
+ if (val == 0)
+ {
+ dimensionsSpecificity[count] = false;
+ }
+ count++;
+ });
+
+ TensorShape tensorShape(operand.dimensions.size(), operand.dimensions.data(), dimensionsSpecificity);
+ ret = TensorInfo(tensorShape, type);
+ }
ret.SetQuantizationScale(operand.scale);
ret.SetQuantizationOffset(operand.zeroPoint);
@@ -143,7 +167,31 @@
throw UnsupportedOperand<V1_2::OperandType>(operand.type);
}
- TensorInfo ret(operand.dimensions.size(), operand.dimensions.data(), type);
+ TensorInfo ret;
+ if (operand.dimensions.size() == 0)
+ {
+ TensorShape tensorShape(Dimensionality::NotSpecified);
+ ret = TensorInfo(tensorShape, type);
+ }
+ else
+ {
+ bool dimensionsSpecificity[5] = { true, true, true, true, true };
+ int count = 0;
+ std::for_each(operand.dimensions.data(),
+ operand.dimensions.data() + operand.dimensions.size(),
+ [&](const unsigned int val)
+ {
+ if (val == 0)
+ {
+ dimensionsSpecificity[count] = false;
+ }
+ count++;
+ });
+
+ TensorShape tensorShape(operand.dimensions.size(), operand.dimensions.data(), dimensionsSpecificity);
+ ret = TensorInfo(tensorShape, type);
+ }
+
if (perChannel)
{
// ExtraParams is expected to be of type channelQuant
@@ -219,7 +267,29 @@
}
else
{
- ret = TensorInfo(operand.dimensions.size(), operand.dimensions.data(), type);
+ if (operand.dimensions.size() == 0)
+ {
+ TensorShape tensorShape(Dimensionality::NotSpecified);
+ ret = TensorInfo(tensorShape, type);
+ }
+ else
+ {
+ bool dimensionsSpecificity[5] = { true, true, true, true, true };
+ int count = 0;
+ std::for_each(operand.dimensions.data(),
+ operand.dimensions.data() + operand.dimensions.size(),
+ [&](const unsigned int val)
+ {
+ if (val == 0)
+ {
+ dimensionsSpecificity[count] = false;
+ }
+ count++;
+ });
+
+ TensorShape tensorShape(operand.dimensions.size(), operand.dimensions.data(), dimensionsSpecificity);
+ ret = TensorInfo(tensorShape, type);
+ }
}
if (perChannel)
@@ -501,10 +571,22 @@
return fileName;
}
-bool IsDynamicTensor(const armnn::TensorInfo& outputInfo)
+bool IsDynamicTensor(const armnn::TensorInfo& tensorInfo)
{
- // Dynamic tensors have at least one 0-sized dimension
- return outputInfo.GetNumElements() == 0u;
+ if (tensorInfo.GetShape().GetDimensionality() == armnn::Dimensionality::NotSpecified)
+ {
+ return true;
+ }
+ return !tensorInfo.GetShape().AreAllDimensionsSpecified();
+}
+
+bool AreDynamicTensorsSupported()
+{
+#if defined(ARMNN_ANDROID_NN_V1_3)
+ return true;
+#else
+ return false;
+#endif
}
std::string GetFileTimestamp()
@@ -568,7 +650,4 @@
#endif
}
}
-
-
-
} // namespace armnn_driver
diff --git a/Utils.hpp b/Utils.hpp
index d58d273..86eb6db 100644
--- a/Utils.hpp
+++ b/Utils.hpp
@@ -143,6 +143,9 @@
/// Checks if a tensor info represents a dynamic tensor
bool IsDynamicTensor(const armnn::TensorInfo& outputInfo);
+/// Checks for ArmNN support of dynamic tensors.
+bool AreDynamicTensorsSupported(void);
+
std::string GetFileTimestamp();
#if defined(ARMNN_ANDROID_NN_V1_2) || defined(ARMNN_ANDROID_NN_V1_3)
