IVGCVSW-1806: Refactor Android-NN-Driver ModelToINetworkConverter
* Moved conversion logic into new V1_0 and V1_1 HalPolicy classes
* Extracted common helper functions into ConversionUtils class
Change-Id: I1ab50edc266dd528c0cb22a5cd1aa65e103674d9
diff --git a/1.0/ArmnnDriver.hpp b/1.0/ArmnnDriver.hpp
index 560b0d3..a048973 100644
--- a/1.0/ArmnnDriver.hpp
+++ b/1.0/ArmnnDriver.hpp
@@ -9,67 +9,62 @@
#include "ArmnnDevice.hpp"
#include "ArmnnDriverImpl.hpp"
+#include "HalPolicy.hpp"
+
#include "../ArmnnDriverImpl.hpp"
#include <log/log.h>
namespace armnn_driver
{
-namespace V1_0
+namespace hal_1_0
{
-class ArmnnDriver : public ArmnnDevice, public ::android::hardware::neuralnetworks::V1_0::IDevice
+class ArmnnDriver : public ArmnnDevice, public V1_0::IDevice
{
public:
ArmnnDriver(DriverOptions options)
: ArmnnDevice(std::move(options))
{
- ALOGV("V1_0::ArmnnDriver::ArmnnDriver()");
+ ALOGV("hal_1_0::ArmnnDriver::ArmnnDriver()");
}
~ArmnnDriver() {}
public:
- Return<void> getCapabilities(
- ::android::hardware::neuralnetworks::V1_0::IDevice::getCapabilities_cb cb) override
+ Return<void> getCapabilities(V1_0::IDevice::getCapabilities_cb cb) override
{
- ALOGV("V1_0::ArmnnDriver::getCapabilities()");
+ ALOGV("hal_1_0::ArmnnDriver::getCapabilities()");
- return V1_0::ArmnnDriverImpl::getCapabilities(m_Runtime,
- cb);
+ return hal_1_0::ArmnnDriverImpl::getCapabilities(m_Runtime, cb);
}
- Return<void> getSupportedOperations(
- const ::android::hardware::neuralnetworks::V1_0::Model& model,
- ::android::hardware::neuralnetworks::V1_0::IDevice::getSupportedOperations_cb cb) override
+ Return<void> getSupportedOperations(const V1_0::Model& model,
+ V1_0::IDevice::getSupportedOperations_cb cb) override
{
- ALOGV("V1_0::ArmnnDriver::getSupportedOperations()");
+ ALOGV("hal_1_0::ArmnnDriver::getSupportedOperations()");
- return armnn_driver::ArmnnDriverImpl<HalVersion_1_0>::getSupportedOperations(m_Runtime,
- m_Options,
- model,
- cb);
+ return armnn_driver::ArmnnDriverImpl<HalPolicy>::getSupportedOperations(m_Runtime, m_Options, model, cb);
}
- Return<ErrorStatus> prepareModel(
- const ::android::hardware::neuralnetworks::V1_0::Model& model,
- const android::sp<IPreparedModelCallback>& cb) override
+ Return<ErrorStatus> prepareModel(const V1_0::Model& model,
+ const android::sp<IPreparedModelCallback>& cb) override
{
- ALOGV("V1_0::ArmnnDriver::prepareModel()");
+ ALOGV("hal_1_0::ArmnnDriver::prepareModel()");
- return armnn_driver::ArmnnDriverImpl<HalVersion_1_0>::prepareModel(m_Runtime,
- m_ClTunedParameters,
- m_Options,
- model,
- cb);
+ return armnn_driver::ArmnnDriverImpl<HalPolicy>::prepareModel(m_Runtime,
+ m_ClTunedParameters,
+ m_Options,
+ model,
+ cb);
}
Return<DeviceStatus> getStatus() override
{
- ALOGV("V1_0::ArmnnDriver::getStatus()");
+ ALOGV("hal_1_0::ArmnnDriver::getStatus()");
- return armnn_driver::ArmnnDriverImpl<HalVersion_1_0>::getStatus();
+ return armnn_driver::ArmnnDriverImpl<HalPolicy>::getStatus();
}
};
-} // armnn_driver::namespace V1_0
-} // namespace armnn_driver
+} // namespace hal_1_0
+} // namespace armnn_driver
\ No newline at end of file
diff --git a/1.0/ArmnnDriverImpl.cpp b/1.0/ArmnnDriverImpl.cpp
index c7c0f7e..a35bb0e 100644
--- a/1.0/ArmnnDriverImpl.cpp
+++ b/1.0/ArmnnDriverImpl.cpp
@@ -8,33 +8,27 @@
#include <log/log.h>
-using namespace std;
-using namespace android;
-using namespace android::nn;
-using namespace android::hardware;
-
namespace
{
-const char *g_Float32PerformanceExecTimeName = "ArmNN.float32Performance.execTime";
-const char *g_Float32PerformancePowerUsageName = "ArmNN.float32Performance.powerUsage";
-const char *g_Quantized8PerformanceExecTimeName = "ArmNN.quantized8Performance.execTime";
+const char *g_Float32PerformanceExecTimeName = "ArmNN.float32Performance.execTime";
+const char *g_Float32PerformancePowerUsageName = "ArmNN.float32Performance.powerUsage";
+const char *g_Quantized8PerformanceExecTimeName = "ArmNN.quantized8Performance.execTime";
const char *g_Quantized8PerformancePowerUsageName = "ArmNN.quantized8Performance.powerUsage";
} // anonymous namespace
namespace armnn_driver
{
-namespace V1_0
+namespace hal_1_0
{
-Return<void> ArmnnDriverImpl::getCapabilities(
- const armnn::IRuntimePtr& runtime,
- neuralnetworks::V1_0::IDevice::getCapabilities_cb cb)
+Return<void> ArmnnDriverImpl::getCapabilities(const armnn::IRuntimePtr& runtime,
+ V1_0::IDevice::getCapabilities_cb cb)
{
- ALOGV("V1_0::ArmnnDriverImpl::getCapabilities()");
+ ALOGV("hal_1_0::ArmnnDriverImpl::getCapabilities()");
- neuralnetworks::V1_0::Capabilities capabilities;
+ V1_0::Capabilities capabilities;
if (runtime)
{
capabilities.float32Performance.execTime =
@@ -53,9 +47,9 @@
}
else
{
- capabilities.float32Performance.execTime = 0;
- capabilities.float32Performance.powerUsage = 0;
- capabilities.quantized8Performance.execTime = 0;
+ capabilities.float32Performance.execTime = 0;
+ capabilities.float32Performance.powerUsage = 0;
+ capabilities.quantized8Performance.execTime = 0;
capabilities.quantized8Performance.powerUsage = 0;
cb(ErrorStatus::DEVICE_UNAVAILABLE, capabilities);
@@ -64,5 +58,5 @@
return Void();
}
-} // namespace armnn_driver::V1_0
-} // namespace armnn_driver
+} // namespace hal_1_0
+} // namespace armnn_driver
\ No newline at end of file
diff --git a/1.0/ArmnnDriverImpl.hpp b/1.0/ArmnnDriverImpl.hpp
index a6af74d..7f033e0 100644
--- a/1.0/ArmnnDriverImpl.hpp
+++ b/1.0/ArmnnDriverImpl.hpp
@@ -11,18 +11,18 @@
#include <armnn/ArmNN.hpp>
+namespace V1_0 = ::android::hardware::neuralnetworks::V1_0;
+
namespace armnn_driver
{
-namespace V1_0
+namespace hal_1_0
{
class ArmnnDriverImpl
{
public:
- static Return<void> getCapabilities(
- const armnn::IRuntimePtr& runtime,
- ::android::hardware::neuralnetworks::V1_0::IDevice::getCapabilities_cb cb);
+ static Return<void> getCapabilities(const armnn::IRuntimePtr& runtime, V1_0::IDevice::getCapabilities_cb cb);
};
-} // namespace armnn_driver::V1_0
+} // namespace hal_1_0
} // namespace armnn_driver
diff --git a/1.0/HalPolicy.cpp b/1.0/HalPolicy.cpp
new file mode 100644
index 0000000..d3c6dba
--- /dev/null
+++ b/1.0/HalPolicy.cpp
@@ -0,0 +1,1360 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "HalPolicy.hpp"
+
+namespace armnn_driver
+{
+namespace hal_1_0
+{
+
+bool HalPolicy::ConvertOperation(const Operation& operation, const Model& model, ConversionData& data)
+{
+ switch (operation.type)
+ {
+ case V1_0::OperationType::ADD:
+ return ConvertAdd(operation, model, data);
+ case V1_0::OperationType::AVERAGE_POOL_2D:
+ return ConvertAveragePool2d(operation, model, data);
+ case V1_0::OperationType::CONCATENATION:
+ return ConvertConcatenation(operation, model, data);
+ case V1_0::OperationType::CONV_2D:
+ return ConvertConv2d(operation, model, data);
+ case V1_0::OperationType::DEPTHWISE_CONV_2D:
+ return ConvertDepthwiseConv2d(operation, model, data);
+ case V1_0::OperationType::FLOOR:
+ return ConvertFloor(operation, model, data);
+ case V1_0::OperationType::FULLY_CONNECTED:
+ return ConvertFullyConnected(operation, model, data);
+ case V1_0::OperationType::LOCAL_RESPONSE_NORMALIZATION:
+ return ConvertLocalResponseNormalization(operation, model, data);
+ case V1_0::OperationType::LOGISTIC:
+ return ConvertLogistic(operation, model, data);
+ case V1_0::OperationType::LSTM:
+ return ConvertLstm(operation, model, data);
+ case V1_0::OperationType::L2_NORMALIZATION:
+ return ConvertL2Normalization(operation, model, data);
+ case V1_0::OperationType::L2_POOL_2D:
+ return ConvertL2Pool2d(operation, model, data);
+ case V1_0::OperationType::MAX_POOL_2D:
+ return ConvertMaxPool2d(operation, model, data);
+ case V1_0::OperationType::MUL:
+ return ConvertMul(operation, model, data);
+ case V1_0::OperationType::RELU:
+ return ConvertReLu(operation, model, data);
+ case V1_0::OperationType::RELU1:
+ return ConvertReLu1(operation, model, data);
+ case V1_0::OperationType::RELU6:
+ return ConvertReLu6(operation, model, data);
+ case V1_0::OperationType::SOFTMAX:
+ return ConvertSoftmax(operation, model, data);
+ case V1_0::OperationType::TANH:
+ return ConvertTanH(operation, model, data);
+ case V1_0::OperationType::RESHAPE:
+ return ConvertReshape(operation, model, data);
+ case V1_0::OperationType::RESIZE_BILINEAR:
+ return ConvertResizeBilinear(operation, model, data);
+ default:
+ return Fail("%s: Operation type %s not supported in ArmnnDriver",
+ __func__, toString(operation.type).c_str());
+ }
+}
+
+bool HalPolicy::ConvertAdd(const Operation& operation, const Model& model, ConversionData& data)
+{
+ LayerInputHandle input0 = ConvertToLayerInputHandle(operation, 0, model, data);
+ LayerInputHandle input1 = ConvertToLayerInputHandle(operation, 1, model, data);
+
+ if (!input0.IsValid() || !input1.IsValid())
+ {
+ return Fail("%s: Operation has invalid inputs", __func__);
+ }
+
+ // The FuseActivation parameter is always the input index 2
+ // and it should be optional
+ ActivationFn activationFunction;
+ if (!GetOptionalInputActivation(operation, 2, activationFunction, model, data))
+ {
+ return Fail("%s: Operation has invalid inputs", __func__);
+ }
+
+ const Operand* outputOperand = GetOutputOperand(operation, 0, model);
+ if (!outputOperand)
+ {
+ return false;
+ }
+
+ const armnn::TensorInfo outInfo = GetTensorInfoForOperand(*outputOperand);
+
+ if (!IsLayerSupported(__func__,
+ armnn::IsAdditionSupported,
+ data.m_Compute,
+ input0.GetTensorInfo(),
+ input1.GetTensorInfo(),
+ outInfo))
+ {
+ return false;
+ }
+
+ armnn::IConnectableLayer* const startLayer = data.m_Network->AddAdditionLayer();
+ armnn::IConnectableLayer* const endLayer = ProcessActivation(outInfo, activationFunction, startLayer, data);
+
+ const armnn::TensorInfo& inputTensorInfo0 = input0.GetTensorInfo();
+ const armnn::TensorInfo& inputTensorInfo1 = input1.GetTensorInfo();
+
+ if (endLayer != nullptr)
+ {
+ BroadcastTensor(input0, input1, startLayer, *data.m_Network);
+ return SetupAndTrackLayerOutputSlot(operation, 0, *endLayer, model, data);
+ }
+ else
+ {
+ return Fail("%s: ProcessActivation failed", __func__);
+ }
+}
+
+bool HalPolicy::ConvertAveragePool2d(const Operation& operation, const Model& model, ConversionData& data)
+{
+ return ConvertPooling2d(operation, __func__, armnn::PoolingAlgorithm::Average, model, data);
+}
+
+bool HalPolicy::ConvertConcatenation(const Operation& operation, const Model& model, ConversionData& data)
+{
+ // The first N (0..N-1) inputs are tensors. The Nth input is the concatenation axis.
+ if (operation.inputs.size() <= 1)
+ {
+ return Fail("%s: Operation has insufficient arguments", __func__);
+ }
+
+ // Get inputs and outputs
+ const std::size_t numInputTensors = operation.inputs.size() - 1;
+
+ int32_t concatDim;
+ if (!GetInputScalar(operation, numInputTensors, OperandType::INT32, concatDim, model, data))
+ {
+ return Fail("%s: Operation has invalid inputs", __func__);
+ }
+
+ const Operand* const outputOperand = GetOutputOperand(operation, 0, model);
+ if (!outputOperand)
+ {
+ return Fail("%s: Operation has no outputs", __func__);
+ }
+
+
+ armnn::TensorInfo outputInfo = GetTensorInfoForOperand(*outputOperand);
+ armnn::TensorShape outputShape = outputInfo.GetShape();
+
+ //
+ // handle negative concat dims along the lines of tensorflow as described here:
+ // https://www.tensorflow.org/api_docs/python/tf/concat
+ // "negative axis refers to axis + rank(values)-th dimension"
+ //
+ if (concatDim < 0)
+ {
+ concatDim += outputShape.GetNumDimensions();
+ }
+
+ if (concatDim >= static_cast<int32_t>(outputShape.GetNumDimensions()) || concatDim < 0)
+ {
+ return Fail("%s: Operation has invalid concat axis: %d", __func__, concatDim);
+ }
+
+ std::vector<LayerInputHandle> inputHandles;
+ std::vector<armnn::TensorShape> inputShapes;
+
+ inputHandles.reserve(numInputTensors);
+ inputShapes.reserve(numInputTensors);
+
+ bool inputsHaveBeenReshaped = false;
+ unsigned int tensorDimensionsAdded = 0;
+
+ for (uint32_t i = 0; i < numInputTensors; ++i)
+ {
+ const Operand* const operand = GetInputOperand(operation, i, model);
+ if (!operand)
+ {
+ return Fail("%s: Operation has invalid inputs", __func__);
+ }
+
+ armnn::TensorShape operandShape = GetTensorShapeForOperand(*operand);
+ LayerInputHandle operandInputHandle = ConvertToLayerInputHandle(operation, i, model, data);
+
+ if (operandShape.GetNumDimensions() == 0)
+ {
+ return Fail("%s: Operands with rank 0 are not supported", __func__);
+ }
+
+ if (RequiresReshape(operandShape))
+ {
+ inputsHaveBeenReshaped = true;
+
+ armnn::TensorInfo reshapeInfo = operandInputHandle.GetTensorInfo();
+
+ // Expand the tensor to three dimensions
+ if (operandShape.GetNumDimensions() == 2)
+ {
+ reshapeInfo.SetShape(armnn::TensorShape({1, operandShape[0], operandShape[1]}));
+ tensorDimensionsAdded = 1;
+ }
+ else
+ {
+ reshapeInfo.SetShape(armnn::TensorShape({1, 1, operandShape[0]}));
+ tensorDimensionsAdded = 2;
+ }
+
+ armnn::IConnectableLayer& newReshape = AddReshapeLayer(
+ *data.m_Network,
+ operandInputHandle,
+ reshapeInfo
+ );
+
+ // Point to the reshape operation rather then the input operation
+ operandShape = reshapeInfo.GetShape();
+ operandInputHandle = LayerInputHandle(true, &newReshape.GetOutputSlot(0), reshapeInfo);
+ }
+
+ inputShapes.emplace_back(operandShape);
+ inputHandles.emplace_back(operandInputHandle);
+
+ if (!inputHandles.back().IsValid())
+ {
+ return Fail("%s: Operation has invalid inputs", __func__);
+ }
+ }
+
+ BOOST_ASSERT(inputShapes.size() == inputHandles.size());
+
+ if (inputsHaveBeenReshaped)
+ {
+ // Adjust the concatenation dimension by the amount of dimensions added (if any)
+ concatDim += tensorDimensionsAdded;
+
+ // Add extra dimensions to the output shape to reflect the addition of the reshape layers
+ if (tensorDimensionsAdded == 1)
+ {
+ outputShape = armnn::TensorShape({1, outputShape[0], outputShape[1]});
+ }
+ else if (tensorDimensionsAdded == 2)
+ {
+ outputShape = armnn::TensorShape({1, 1, outputShape[0], outputShape[1]});
+ }
+ }
+
+ // Get the pair of permutations required for the concatenation
+ std::pair<armnn::PermutationVector, armnn::PermutationVector> permutationPair =
+ std::make_pair(IdentityPermutation4D, IdentityPermutation4D);
+
+ CreatePermutationParameters(inputShapes[0].GetNumDimensions(), concatDim, permutationPair);
+
+ outputShape = armnnUtils::Permuted(outputShape, permutationPair.first);
+ outputInfo.SetShape(outputShape);
+
+ // this is no-op for identity swizzles, otherwise it replaces both
+ // the handles and shapes with the swizzled layer output handles and shapes
+ SwizzleInputs(*data.m_Network, inputHandles, inputShapes, permutationPair.first);
+
+ // Create an armnn merger layer descriptor - this will also perform validation on the input shapes
+ armnn::OriginsDescriptor mergerDescriptor;
+ try
+ {
+ // The merger descriptor is always created across the only supported concat
+ // dimension, which is 0 or 1
+ mergerDescriptor =
+ armnn::CreateMergerDescriptorForConcatenation(
+ inputShapes.begin(), inputShapes.end(), concatDim);
+ }
+ catch (const armnn::Exception& error)
+ {
+ return Fail("%s: Error preparing merger descriptor. %s", __func__, error.what());
+ }
+
+ // Validate the output shape is correct given the input shapes based on the
+ // only valid concat dimension which is 0 or 1
+ if (!ValidateConcatOutputShape(inputShapes, outputShape, concatDim))
+ {
+ return Fail("%s: Error validating the output shape for concat", __func__);
+ }
+
+ std::vector<const armnn::TensorInfo*> inputTensorInfos;
+ std::transform(inputHandles.begin(), inputHandles.end(), std::back_inserter(inputTensorInfos),
+ [](const LayerInputHandle& h) -> const armnn::TensorInfo*{ return &h.GetTensorInfo(); });
+ if (!IsLayerSupported(__func__,
+ armnn::IsMergerSupported,
+ data.m_Compute,
+ inputTensorInfos,
+ mergerDescriptor))
+ {
+ return false;
+ }
+
+ armnn::IConnectableLayer* layer = data.m_Network->AddMergerLayer(mergerDescriptor);
+ assert(layer != nullptr);
+ layer->GetOutputSlot(0).SetTensorInfo(outputInfo);
+
+ // Connect inputs to the layer
+ const int numInputSlots = layer->GetNumInputSlots();
+ assert(static_cast<std::size_t>(numInputSlots) == inputHandles.size());
+ for (int i = 0; i < numInputSlots; ++i)
+ {
+ // connect the input directly to the merge (concat) layer
+ inputHandles[static_cast<unsigned int>(i)].Connect(layer->GetInputSlot(i));
+ }
+
+ // Add permutation layer and connect the output to it, the permutation becomes the output layer
+ armnn::IConnectableLayer& deswizzleLayer = AddPermuteLayer(*data.m_Network,
+ layer->GetOutputSlot(0),
+ permutationPair.second);
+ layer = &deswizzleLayer;
+
+ if (inputsHaveBeenReshaped)
+ {
+ armnn::TensorInfo afterConcatInfo = layer->GetOutputSlot(0).GetTensorInfo();
+
+ // Undo the reshape knowing the amount of dimensions added
+ if (tensorDimensionsAdded == 1)
+ {
+ afterConcatInfo.SetShape(armnn::TensorShape({ afterConcatInfo.GetShape()[1],
+ afterConcatInfo.GetShape()[2] }));
+ }
+ else if (tensorDimensionsAdded == 2)
+ {
+ afterConcatInfo.SetShape(armnn::TensorShape({ afterConcatInfo.GetShape()[2],
+ afterConcatInfo.GetShape()[3] }));
+ }
+
+ layer = &AddReshapeLayer(
+ *data.m_Network,
+ layer->GetOutputSlot(0),
+ afterConcatInfo
+ );
+ }
+
+ return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data);
+}
+
+bool HalPolicy::ConvertConv2d(const Operation& operation, const Model& model, ConversionData& data)
+{
+ LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data);
+ if (!input.IsValid())
+ {
+ return Fail("%s: Operation has invalid inputs", __func__);
+ }
+
+ const Operand* output = GetOutputOperand(operation, 0, model);
+ if (!output)
+ {
+ return Fail("%s: Could not read output 0", __func__);
+ }
+
+ const armnn::TensorInfo& inputInfo = input.GetTensorInfo();
+ const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
+
+ const armnn::TensorInfo swizzledInputInfo = armnnUtils::Permuted(inputInfo, NHWCToArmNN);
+ const armnn::TensorInfo swizzledOutputInfo = armnnUtils::Permuted(outputInfo, NHWCToArmNN);
+
+ // ArmNN does not currently support non-fixed weights or bias
+ const ConstTensorPin weightsPin = ConvertOperationInputToConstTensorPin(operation, 1, model, data, NHWCToArmNN);
+ const ConstTensorPin biasPin = ConvertOperationInputToConstTensorPin(operation, 2, model, data);
+
+ if (!weightsPin.IsValid() || !biasPin.IsValid())
+ {
+ return Fail("%s: Operation has invalid inputs", __func__);
+ }
+
+ armnn::ConstTensor weights = weightsPin.GetConstTensor();
+ armnn::ConstTensor bias = biasPin.GetConstTensor();
+ SanitizeBiasQuantizationScale(bias.GetInfo(), weights.GetInfo(), swizzledInputInfo);
+
+ armnn::Convolution2dDescriptor desc;
+ ActivationFn activation;
+
+ if (operation.inputs.size() == 10)
+ {
+ if (!GetInputScalar(operation, 3, OperandType::INT32, desc.m_PadLeft, model, data) ||
+ !GetInputScalar(operation, 4, OperandType::INT32, desc.m_PadRight, model, data) ||
+ !GetInputScalar(operation, 5, OperandType::INT32, desc.m_PadTop, model, data) ||
+ !GetInputScalar(operation, 6, OperandType::INT32, desc.m_PadBottom, model, data) ||
+ !GetInputScalar(operation, 7, OperandType::INT32, desc.m_StrideX, model, data) ||
+ !GetInputScalar(operation, 8, OperandType::INT32, desc.m_StrideY, model, data) ||
+ !GetInputActivationFunction(operation, 9, activation, model, data))
+ {
+ return Fail("%s: Operation has invalid inputs", __func__);
+ }
+ }
+ else if (operation.inputs.size() == 7)
+ {
+ android::nn::PaddingScheme paddingScheme;
+ if (!GetInputPaddingScheme(operation, 3, paddingScheme, model, data) ||
+ !GetInputScalar(operation, 4, OperandType::INT32, desc.m_StrideX, model, data) ||
+ !GetInputScalar(operation, 5, OperandType::INT32, desc.m_StrideY, model, data) ||
+ !GetInputActivationFunction(operation, 6, activation, model, data))
+ {
+ return Fail("%s: Operation has invalid inputs", __func__);
+ }
+
+ const uint32_t kernelX = weights.GetShape()[3];
+ const uint32_t kernelY = weights.GetShape()[2];
+ const uint32_t inputX = swizzledInputInfo.GetShape()[3];
+ const uint32_t inputY = swizzledInputInfo.GetShape()[2];
+
+ CalcPadding(inputX, kernelX, desc.m_StrideX, desc.m_PadLeft, desc.m_PadRight, paddingScheme);
+ CalcPadding(inputY, kernelY, desc.m_StrideY, desc.m_PadTop, desc.m_PadBottom, paddingScheme);
+ }
+ else
+ {
+ return Fail("%s: Unsupported number of operation inputs", __func__);
+ }
+
+ desc.m_BiasEnabled = true;
+ auto biases = boost::make_optional(bias.GetInfo());
+
+ if (!IsLayerSupported(__func__,
+ armnn::IsConvolution2dSupported,
+ data.m_Compute,
+ swizzledInputInfo,
+ swizzledOutputInfo,
+ desc,
+ weights.GetInfo(),
+ biases))
+ {
+ return false;
+ }
+
+ armnn::IConnectableLayer* startLayer = data.m_Network->AddConvolution2dLayer(desc, weights, bias);
+ armnn::IConnectableLayer* endLayer = ProcessActivation(swizzledOutputInfo, activation, startLayer, data);
+
+ if (endLayer != nullptr)
+ {
+ armnn::IConnectableLayer& outSwizzleLayer =
+ SwizzleInDeswizzleOut(*data.m_Network, input, *startLayer, *endLayer);
+ return SetupAndTrackLayerOutputSlot(operation, 0, outSwizzleLayer, model, data);
+ }
+ else
+ {
+ return Fail("%s: ProcessActivation failed", __func__);
+ }
+}
+
+bool HalPolicy::ConvertDepthwiseConv2d(const Operation& operation, const Model& model, ConversionData& data)
+{
+ LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data);
+ if (!input.IsValid())
+ {
+ return Fail("%s: Operation has invalid inputs", __func__);
+ }
+
+ const Operand* output = GetOutputOperand(operation, 0, model);
+ if (!output)
+ {
+ return Fail("%s: Could not read output 0", __func__);
+ }
+
+ const armnn::TensorInfo& inputInfo = input.GetTensorInfo();
+ const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
+
+ const armnn::TensorInfo swizzledInputInfo = armnnUtils::Permuted(inputInfo, NHWCToArmNN);
+ const armnn::TensorInfo swizzledOutputInfo = armnnUtils::Permuted(outputInfo, NHWCToArmNN);
+
+ // ArmNN does not currently support non-fixed weights or bias
+
+ // Find the shape of the weights tensor. In AndroidNN this will be [ 1, H, W, I * M ]
+ // but in ArmNN it needs to be [ M, I, H, W ]
+ const Operand* weightsOperand = GetInputOperand(operation, 1, model);
+
+ if (weightsOperand == nullptr)
+ {
+ return Fail("%s: Operand is invalid", __func__);
+ }
+
+ // Reinterpret weight data as [ H, W, I, M ]
+ armnn::TensorShape weightsShape({ weightsOperand->dimensions[1], weightsOperand->dimensions[2],
+ inputInfo.GetShape()[3],
+ weightsOperand->dimensions[3] / inputInfo.GetShape()[3] });
+
+ // Swizzle weight data [ H, W, I, M ] -> [ M, I, H, W ]
+ const armnn::PermutationVector HWIMToMIHW = { 2U, 3U, 1U, 0U };
+ ConstTensorPin weightsPin =
+ ConvertOperationInputToConstTensorPin(operation, 1, model, data, HWIMToMIHW, &weightsShape);
+
+ // Bias is a 1D tensor
+ ConstTensorPin biasPin = ConvertOperationInputToConstTensorPin(operation, 2, model, data);
+
+ if (!weightsPin.IsValid() || !biasPin.IsValid())
+ {
+ return Fail("%s: Operation has invalid inputs", __func__);
+ }
+
+ armnn::ConstTensor weights = weightsPin.GetConstTensor();
+ armnn::ConstTensor bias = biasPin.GetConstTensor();
+ SanitizeBiasQuantizationScale(bias.GetInfo(), weights.GetInfo(), swizzledInputInfo);
+
+ armnn::DepthwiseConvolution2dDescriptor desc;
+ ActivationFn activation;
+
+ if (operation.inputs.size() == 11)
+ {
+ if (!GetInputScalar(operation, 3, OperandType::INT32, desc.m_PadLeft, model, data) ||
+ !GetInputScalar(operation, 4, OperandType::INT32, desc.m_PadRight, model, data) ||
+ !GetInputScalar(operation, 5, OperandType::INT32, desc.m_PadTop, model, data) ||
+ !GetInputScalar(operation, 6, OperandType::INT32, desc.m_PadBottom, model, data) ||
+ !GetInputScalar(operation, 7, OperandType::INT32, desc.m_StrideX, model, data) ||
+ !GetInputScalar(operation, 8, OperandType::INT32, desc.m_StrideY, model, data) ||
+ !GetInputActivationFunction(operation, 10, activation, model, data))
+ {
+ return Fail("%s: Operation has invalid inputs", __func__);
+ }
+ }
+ else if (operation.inputs.size() == 8)
+ {
+ android::nn::PaddingScheme paddingScheme;
+ if (!GetInputPaddingScheme(operation, 3, paddingScheme, model, data) ||
+ !GetInputScalar(operation, 4, OperandType::INT32, desc.m_StrideX, model, data) ||
+ !GetInputScalar(operation, 5, OperandType::INT32, desc.m_StrideY, model, data) ||
+ !GetInputActivationFunction(operation, 7, activation, model, data))
+ {
+ return Fail("%s: Operation has invalid inputs", __func__);
+ }
+
+ const uint32_t kernelX = weights.GetShape()[3];
+ const uint32_t kernelY = weights.GetShape()[2];
+ const uint32_t inputX = swizzledInputInfo.GetShape()[3];
+ const uint32_t inputY = swizzledInputInfo.GetShape()[2];
+
+ CalcPadding(inputX, kernelX, desc.m_StrideX, desc.m_PadLeft, desc.m_PadRight, paddingScheme);
+ CalcPadding(inputY, kernelY, desc.m_StrideY, desc.m_PadTop, desc.m_PadBottom, paddingScheme);
+ }
+ else
+ {
+ return Fail("%s: Unsupported number of operation inputs", __func__);
+ }
+
+ desc.m_BiasEnabled = true;
+ auto biases = boost::make_optional(bias.GetInfo());
+
+ if (!IsLayerSupported(__func__,
+ armnn::IsDepthwiseConvolutionSupported,
+ data.m_Compute,
+ swizzledInputInfo,
+ swizzledOutputInfo,
+ desc,
+ weights.GetInfo(),
+ biases))
+ {
+ return false;
+ }
+
+ armnn::IConnectableLayer* startLayer = data.m_Network->AddDepthwiseConvolution2dLayer(desc, weights, bias);
+ armnn::IConnectableLayer* endLayer = ProcessActivation(swizzledOutputInfo, activation, startLayer, data);
+
+ if (endLayer != nullptr)
+ {
+ armnn::IConnectableLayer& outSwizzleLayer =
+ SwizzleInDeswizzleOut(*data.m_Network, input, *startLayer, *endLayer);
+ return SetupAndTrackLayerOutputSlot(operation, 0, outSwizzleLayer, model, data);
+ }
+ else
+ {
+ return Fail("%s: ProcessActivation failed", __func__);
+ }
+}
+
+bool HalPolicy::ConvertFloor(const Operation& operation, const Model& model, ConversionData& data)
+{
+ LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data);
+ if (!input.IsValid())
+ {
+ return Fail("%s: Operation has invalid inputs", __func__);
+ }
+
+ const Operand* const outputOperand = GetOutputOperand(operation, 0, model);
+ if (!outputOperand)
+ {
+ return Fail("%s: Operation has invalid outputs", __func__);
+ }
+
+ if (!IsLayerSupported(__func__,
+ armnn::IsFloorSupported,
+ data.m_Compute,
+ input.GetTensorInfo(),
+ GetTensorInfoForOperand(*outputOperand)))
+ {
+ return false;
+ }
+
+ armnn::IConnectableLayer* layer = data.m_Network->AddFloorLayer();
+ assert(layer != nullptr);
+ input.Connect(layer->GetInputSlot(0));
+
+ return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data);
+}
+
+bool HalPolicy::ConvertFullyConnected(const Operation& operation, const Model& model, ConversionData& data)
+{
+ LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data);
+ if (!input.IsValid())
+ {
+ return Fail("%s: Operation has invalid inputs", __func__);
+ }
+
+ const Operand* output = GetOutputOperand(operation, 0, model);
+ if (!output)
+ {
+ return Fail("%s: Could not read output 0", __func__);
+ }
+
+ const armnn::TensorInfo& inputInfo = input.GetTensorInfo();
+ const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
+
+ // ArmNN does not currently support non-fixed weights or bias
+ ConstTensorPin weightsPin = ConvertOperationInputToConstTensorPin(operation, 1, model, data); // 2D
+ ConstTensorPin biasPin = ConvertOperationInputToConstTensorPin(operation, 2, model, data); // 1D
+
+ if (!weightsPin.IsValid() || !biasPin.IsValid())
+ {
+ return Fail("%s: Operation has invalid inputs", __func__);
+ }
+
+ armnn::ConstTensor weights = weightsPin.GetConstTensor();
+ armnn::ConstTensor bias = biasPin.GetConstTensor();
+
+ armnn::TensorInfo reshapedInfo = inputInfo;
+ if (inputInfo.GetNumDimensions() > 2U)
+ {
+ unsigned int dim0 = inputInfo.GetShape()[0];
+ unsigned int dim1 = inputInfo.GetShape()[1];
+
+ for (unsigned int i = 2U; i < inputInfo.GetNumDimensions(); ++i)
+ {
+ dim1 *= inputInfo.GetShape()[i];
+ }
+
+ unsigned int divisor = weights.GetInfo().GetShape()[1] / dim1;
+ if(dim0 % divisor != 0)
+ {
+ return Fail("%s: Failed to deduce tensor shape", __func__);
+ }
+
+ reshapedInfo.SetShape(armnn::TensorShape({dim0 / divisor, dim1 * divisor}));
+ }
+
+ // ensuring that the bias value is within 1% of the weights input (small float differences can exist)
+ SanitizeBiasQuantizationScale(bias.GetInfo(), weights.GetInfo(), reshapedInfo);
+
+ ActivationFn activationFunction;
+ if (!GetInputActivationFunction(operation, 3, activationFunction, model, data))
+ {
+ return Fail("%s: Operation has invalid inputs", __func__);
+ }
+
+ armnn::FullyConnectedDescriptor desc;
+ desc.m_TransposeWeightMatrix = true;
+ desc.m_BiasEnabled = true;
+
+ if (!IsLayerSupported(__func__,
+ armnn::IsFullyConnectedSupported,
+ data.m_Compute,
+ inputInfo,
+ outputInfo,
+ weights.GetInfo(),
+ bias.GetInfo(),
+ desc))
+ {
+ return false;
+ }
+
+ armnn::IConnectableLayer* startLayer = data.m_Network->AddFullyConnectedLayer(desc, weights, bias);
+ armnn::IConnectableLayer* endLayer = ProcessActivation(outputInfo, activationFunction, startLayer, data);
+
+ if (endLayer != nullptr)
+ {
+ if (inputInfo.GetNumDimensions() > 2U)
+ {
+ armnn::ReshapeDescriptor reshapeDescriptor;
+ reshapeDescriptor.m_TargetShape = reshapedInfo.GetShape();
+
+ armnn::IConnectableLayer* reshapeLayer = data.m_Network->AddReshapeLayer(reshapeDescriptor);
+ assert(reshapeLayer != nullptr);
+ input.Connect(reshapeLayer->GetInputSlot(0));
+ reshapeLayer->GetOutputSlot(0).SetTensorInfo(reshapedInfo);
+ reshapeLayer->GetOutputSlot(0).Connect(startLayer->GetInputSlot(0));
+ }
+ else
+ {
+ input.Connect(startLayer->GetInputSlot(0));
+ }
+
+ return SetupAndTrackLayerOutputSlot(operation, 0, *endLayer, model, data);
+ }
+ else
+ {
+ return Fail("%s: ProcessActivation failed", __func__);
+ }
+}
+
+bool HalPolicy::ConvertLocalResponseNormalization(const Operation& operation,
+ const Model& model,
+ ConversionData& data)
+{
+ LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data);
+ if (!input.IsValid())
+ {
+ return Fail("%s: Operation has invalid inputs", __func__);
+ }
+
+ const Operand* output = GetOutputOperand(operation, 0, model);
+ if (!output)
+ {
+ return Fail("%s: Could not read output 0", __func__);
+ }
+
+ const armnn::TensorInfo& inputInfo = input.GetTensorInfo();
+ const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
+
+ const armnn::TensorInfo swizzledInputInfo = armnnUtils::Permuted(inputInfo, NHWCToArmNN);
+ const armnn::TensorInfo swizzledOutputInfo = armnnUtils::Permuted(outputInfo, NHWCToArmNN);
+
+ armnn::NormalizationDescriptor descriptor;
+
+ descriptor.m_NormChannelType = armnn::NormalizationAlgorithmChannel::Across;
+ descriptor.m_NormMethodType = armnn::NormalizationAlgorithmMethod::LocalBrightness;
+
+ if (!input.IsValid() ||
+ !GetInputScalar(operation, 1, OperandType::INT32, descriptor.m_NormSize, model, data) ||
+ !GetInputFloat32(operation, 2, descriptor.m_K, model, data) ||
+ !GetInputFloat32(operation, 3, descriptor.m_Alpha, model, data) ||
+ !GetInputFloat32(operation, 4, descriptor.m_Beta, model, data))
+ {
+ return Fail("%s: Operation has invalid inputs", __func__);
+ }
+
+ // ArmNN expects normSize to be the full size of the normalization
+ // window rather than the radius as in AndroidNN.
+ descriptor.m_NormSize = 1 + (2 * descriptor.m_NormSize);
+
+ if (!IsLayerSupported(__func__,
+ armnn::IsNormalizationSupported,
+ data.m_Compute,
+ swizzledInputInfo,
+ swizzledOutputInfo,
+ descriptor))
+ {
+ return false;
+ }
+
+
+ armnn::IConnectableLayer* layer = data.m_Network->AddNormalizationLayer(descriptor);
+ assert(layer != nullptr);
+ layer->GetOutputSlot(0).SetTensorInfo(swizzledOutputInfo);
+
+ armnn::IConnectableLayer& outSwizzleLayer = SwizzleInDeswizzleOut(*data.m_Network, input, *layer);
+
+ return SetupAndTrackLayerOutputSlot(operation, 0, outSwizzleLayer, model, data);
+}
+
+bool HalPolicy::ConvertLogistic(const Operation& operation, const Model& model, ConversionData& data)
+{
+ armnn::ActivationDescriptor desc;
+ desc.m_Function = armnn::ActivationFunction::Sigmoid;
+
+ return ConvertToActivation(operation, __func__, desc, model, data);
+}
+
+bool HalPolicy::ConvertLstm(const Operation& operation, const Model& model, ConversionData& data)
+{
+ // Inputs:
+ // 00: The input: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, input_size], where
+ // “batch_size” corresponds to the batching dimension, and “input_size” is the size of the input.
+ LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data);
+ if (!input.IsValid())
+ {
+ return Fail("%s: Could not read input 0: input", __func__);
+ }
+ // 18: The output state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size].
+ LayerInputHandle outputStateIn = ConvertToLayerInputHandle(operation, 18, model, data);
+ if (!outputStateIn.IsValid())
+ {
+ return Fail("%s: Could not read input 18: outputStateIn", __func__);
+ }
+ // 19: The cell state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units].
+ LayerInputHandle cellStateIn = ConvertToLayerInputHandle(operation, 19, model, data);
+ if (!cellStateIn.IsValid())
+ {
+ return Fail("%s: Could not read input 19: cellStateIn", __func__);
+ }
+
+ // Get the mandatory input tensors:
+ // 02: The input-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape
+ // [num_units, input_size].
+ const ConstTensorPin inputToForgetWeightsPin = ConvertOperationInputToConstTensorPin(operation, 2, model, data);
+ // 03: The input-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units, input_size].
+ const ConstTensorPin inputToCellWeightsPin = ConvertOperationInputToConstTensorPin(operation, 3, model, data);
+ // 04: The input-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape
+ // [num_units, input_size].
+ const ConstTensorPin inputToOutputWeightsPin = ConvertOperationInputToConstTensorPin(operation, 4, model, data);
+ // 06: The recurrent-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape
+ // [num_units, output_size].
+ const ConstTensorPin recurrentToForgetWeightsPin =
+ ConvertOperationInputToConstTensorPin(operation, 6, model, data);
+ // 07: The recurrent-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape
+ // [num_units, output_size].
+ const ConstTensorPin recurrentToCellWeightsPin = ConvertOperationInputToConstTensorPin(operation, 7, model, data);
+ // 08: The recurrent-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape
+ // [num_units, output_size].
+ const ConstTensorPin recurrentToOutputWeightsPin =
+ ConvertOperationInputToConstTensorPin(operation, 8, model, data);
+ // 13: The forget gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units].
+ const ConstTensorPin forgetGateBiasPin = ConvertOperationInputToConstTensorPin(operation, 13, model, data);
+ // 14: The cell bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units].
+ const ConstTensorPin cellBiasPin = ConvertOperationInputToConstTensorPin(operation, 14, model, data);
+ // 15: The output gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units].
+ const ConstTensorPin outputGateBiasPin = ConvertOperationInputToConstTensorPin(operation, 15, model, data);
+
+ if (!inputToForgetWeightsPin.IsValid() ||
+ !inputToCellWeightsPin.IsValid() ||
+ !inputToOutputWeightsPin.IsValid() ||
+ !recurrentToForgetWeightsPin.IsValid() ||
+ !recurrentToCellWeightsPin.IsValid() ||
+ !recurrentToOutputWeightsPin.IsValid() ||
+ !forgetGateBiasPin.IsValid() ||
+ !cellBiasPin.IsValid() ||
+ !outputGateBiasPin.IsValid())
+ {
+ return Fail("%s: Operation has invalid tensor inputs", __func__);
+ }
+
+ // Get the optional input tensors:
+ // 01: The input-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape
+ // [num_units, input_size], where “num_units” corresponds to the number of cell units.
+ const ConstTensorPin inputToInputWeightsPin = ConvertOperationInputToConstTensorPin(operation, 1, model, data);
+ // 05: The recurrent-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape
+ // [num_units, output_size], where “output_size” corresponds to either the number of cell units (i.e.,
+ // “num_units”), or the second dimension of the “projection_weights”, if defined.
+ const ConstTensorPin recurrentToInputWeightsPin = ConvertOperationInputToConstTensorPin(operation, 5, model, data);
+ // 09: The cell-to-input weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units].
+ const ConstTensorPin cellToInputWeightsPin = ConvertOperationInputToConstTensorPin(operation, 9, model, data);
+ // 10: The cell-to-forget weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units].
+ const ConstTensorPin cellToForgetWeightsPin = ConvertOperationInputToConstTensorPin(operation, 10, model, data);
+ // 11: The cell-to-output weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units].
+ const ConstTensorPin cellToOutputWeightsPin = ConvertOperationInputToConstTensorPin(operation, 11, model, data);
+ // 12: The input gate bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units].
+ const ConstTensorPin inputGateBiasPin = ConvertOperationInputToConstTensorPin(operation, 12, model, data);
+ // 16: The projection weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape
+ // [output_size, num_units].
+ const ConstTensorPin projectionWeightsPin = ConvertOperationInputToConstTensorPin(operation, 16, model, data);
+ // 17: The projection bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [output_size].
+ const ConstTensorPin projectionBiasPin = ConvertOperationInputToConstTensorPin(operation, 17, model, data);
+
+ if ((!inputToInputWeightsPin.IsValid() && !inputToInputWeightsPin.IsOptional()) ||
+ (!recurrentToInputWeightsPin.IsValid() && !recurrentToInputWeightsPin.IsOptional()) ||
+ (!cellToInputWeightsPin.IsValid() && !cellToInputWeightsPin.IsOptional()) ||
+ (!cellToForgetWeightsPin.IsValid() && !cellToForgetWeightsPin.IsOptional()) ||
+ (!cellToOutputWeightsPin.IsValid() && !cellToOutputWeightsPin.IsOptional()) ||
+ (!inputGateBiasPin.IsValid() && !inputGateBiasPin.IsOptional()) ||
+ (!projectionWeightsPin.IsValid() && !projectionWeightsPin.IsOptional()) ||
+ (!projectionBiasPin.IsValid() && !projectionBiasPin.IsOptional()))
+ {
+ return Fail("%s: Operation has invalid tensor inputs", __func__);
+ }
+
+ // Get the mandatory input scalars (actually 1-D tensors of size 1):
+ // 20: The activation function: A value indicating the activation function:
+ // 0: None; 1: Relu; 3: Relu6; 4: Tanh; 6: Sigmoid.
+ // 21: The clipping threshold: for the cell state, such that values are bound within [-cell_clip, cell_clip].
+ // If set to 0.0 then clipping is disabled.
+ // 22: The clipping threshold: for the output from the projection layer, such that values are bound within
+ // [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled.
+ ActivationFn activation;
+ float cellClip;
+ float projClip;
+ if (!GetInputActivationFunctionFromTensor(operation, 20, activation, model, data) ||
+ !GetInputScalar(operation, 21, OperandType::FLOAT32, cellClip, model, data) ||
+ !GetInputScalar(operation, 22, OperandType::FLOAT32, projClip, model, data))
+ {
+ return Fail("%s: Operation has invalid scalar inputs", __func__);
+ }
+
+ // Outputs:
+ // 00: The scratch buffer: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units * 4] with
+ // CIFG, or [batch_size, num_units * 3] without CIFG.
+ const Operand* scratchBuffer = GetOutputOperand(operation, 0, model);
+ if (!scratchBuffer)
+ {
+ return Fail("%s: Could not read output 0: scratchBuffer", __func__);
+ }
+ // 01: The output state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size].
+ const Operand* outputStateOut = GetOutputOperand(operation, 1, model);
+ if (!outputStateOut)
+ {
+ return Fail("%s: Could not read output 1: outputStateOut", __func__);
+ }
+ // 02: The cell state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units].
+ const Operand* cellStateOut = GetOutputOperand(operation, 2, model);
+ if (!cellStateOut)
+ {
+ return Fail("%s: Could not read output 2: cellStateOut", __func__);
+ }
+ // 03: The output: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. This is
+ // effectively the same as the current “output state (out)” value.
+ const Operand* output = GetOutputOperand(operation, 3, model);
+ if (!output)
+ {
+ return Fail("%s: Could not read output 3: output", __func__);
+ }
+
+ // set the params structure for the AddLstmLayer call
+ armnn::LstmInputParams params;
+ params.m_InputToInputWeights = inputToInputWeightsPin.GetConstTensorPtr();
+ params.m_InputToForgetWeights = inputToForgetWeightsPin.GetConstTensorPtr();
+ params.m_InputToCellWeights = inputToCellWeightsPin.GetConstTensorPtr();
+ params.m_InputToOutputWeights = inputToOutputWeightsPin.GetConstTensorPtr();
+ params.m_RecurrentToInputWeights = recurrentToInputWeightsPin.GetConstTensorPtr();
+ params.m_RecurrentToForgetWeights = recurrentToForgetWeightsPin.GetConstTensorPtr();
+ params.m_RecurrentToCellWeights = recurrentToCellWeightsPin.GetConstTensorPtr();
+ params.m_RecurrentToOutputWeights = recurrentToOutputWeightsPin.GetConstTensorPtr();
+ params.m_CellToInputWeights = cellToInputWeightsPin.GetConstTensorPtr();
+ params.m_CellToForgetWeights = cellToForgetWeightsPin.GetConstTensorPtr();
+ params.m_CellToOutputWeights = cellToOutputWeightsPin.GetConstTensorPtr();
+ params.m_InputGateBias = inputGateBiasPin.GetConstTensorPtr();
+ params.m_ForgetGateBias = forgetGateBiasPin.GetConstTensorPtr();
+ params.m_CellBias = cellBiasPin.GetConstTensorPtr();
+ params.m_OutputGateBias = outputGateBiasPin.GetConstTensorPtr();
+ params.m_ProjectionWeights = projectionWeightsPin.GetConstTensorPtr();
+ params.m_ProjectionBias = projectionBiasPin.GetConstTensorPtr();
+
+ // set the layer descriptor
+ armnn::LstmDescriptor desc;
+ desc.m_ActivationFunc = activation;
+ desc.m_ClippingThresCell = cellClip;
+ desc.m_ClippingThresProj = projClip;
+ desc.m_CifgEnabled = (params.m_InputToInputWeights == nullptr ||
+ params.m_RecurrentToInputWeights == nullptr ||
+ params.m_InputGateBias == nullptr);
+ desc.m_PeepholeEnabled = (params.m_CellToForgetWeights != nullptr ||
+ params.m_CellToOutputWeights != nullptr);
+ desc.m_ProjectionEnabled = (params.m_ProjectionWeights != nullptr);
+
+ // validate the optional input groups
+ if (desc.m_CifgEnabled &&
+ (params.m_InputToInputWeights != nullptr ||
+ params.m_RecurrentToInputWeights != nullptr ||
+ params.m_InputGateBias != nullptr))
+ {
+ return Fail("%s: All, or none, of input-to-input weights, recurrent-to-input weights,"
+ " and input gate bias must be provided", __func__);
+ }
+
+ if (!desc.m_ProjectionEnabled && params.m_ProjectionBias != nullptr)
+ {
+ return Fail("%s: projection bias should not be provided without projection weights", __func__);
+ }
+
+ if (desc.m_PeepholeEnabled &&
+ (params.m_CellToForgetWeights == nullptr ||
+ params.m_CellToOutputWeights == nullptr ||
+ (!desc.m_CifgEnabled && params.m_CellToInputWeights == nullptr)))
+ {
+ return Fail("%s: All, or none, of cell-to-forget weights and cell-to-output weights must be provided"
+ " and, if CIFG is not enabled, cell-to-input weights must also be provided", __func__);
+ }
+
+ // Check if the layer is supported
+ // Inputs
+ const armnn::TensorInfo& inputInfo = input.GetTensorInfo();
+ const armnn::TensorInfo& outputStateInInfo = outputStateIn.GetTensorInfo();
+ const armnn::TensorInfo& cellStateInInfo = cellStateIn.GetTensorInfo();
+
+ // Outputs
+ const armnn::TensorInfo& scratchBufferInfo = GetTensorInfoForOperand(*scratchBuffer);
+ const armnn::TensorInfo& outputStateOutInfo = GetTensorInfoForOperand(*outputStateOut);
+ const armnn::TensorInfo& cellStateOutInfo = GetTensorInfoForOperand(*cellStateOut);
+ const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
+
+ // Basic parameters
+ const armnn::TensorInfo& inputToForgetWeights = params.m_InputToForgetWeights->GetInfo();
+ const armnn::TensorInfo& inputToCellWeights = params.m_InputToCellWeights->GetInfo();
+ const armnn::TensorInfo& inputToOutputWeights = params.m_InputToOutputWeights->GetInfo();
+ const armnn::TensorInfo& recurrentToForgetWeights = params.m_RecurrentToForgetWeights->GetInfo();
+ const armnn::TensorInfo& recurrentToCellWeights = params.m_RecurrentToCellWeights->GetInfo();
+ const armnn::TensorInfo& recurrentToOutputWeights = params.m_RecurrentToOutputWeights->GetInfo();
+ const armnn::TensorInfo& forgetGateBias = params.m_ForgetGateBias->GetInfo();
+ const armnn::TensorInfo& cellBias = params.m_CellBias->GetInfo();
+ const armnn::TensorInfo& outputGateBias = params.m_OutputGateBias->GetInfo();
+
+ //Optional parameters
+ const armnn::TensorInfo* inputToInputWeights = nullptr;
+ const armnn::TensorInfo* recurrentToInputWeights = nullptr;
+ const armnn::TensorInfo* cellToInputWeights = nullptr;
+ const armnn::TensorInfo* inputGateBias = nullptr;
+ const armnn::TensorInfo* projectionWeights = nullptr;
+ const armnn::TensorInfo* projectionBias = nullptr;
+ const armnn::TensorInfo* cellToForgetWeights = nullptr;
+ const armnn::TensorInfo* cellToOutputWeights = nullptr;
+
+ if(!desc.m_CifgEnabled)
+ {
+ inputToInputWeights = &(params.m_InputToInputWeights->GetInfo());
+ recurrentToInputWeights = &(params.m_RecurrentToInputWeights->GetInfo());
+ if (params.m_CellToInputWeights != nullptr)
+ {
+ cellToInputWeights = &(params.m_CellToInputWeights->GetInfo());
+ }
+ inputGateBias = &(params.m_InputGateBias->GetInfo());
+ }
+
+ if(desc.m_ProjectionEnabled)
+ {
+ projectionWeights = &(params.m_ProjectionWeights->GetInfo());
+ if (params.m_ProjectionBias != nullptr)
+ {
+ projectionBias = &(params.m_ProjectionBias->GetInfo());
+ }
+ }
+
+ if(desc.m_PeepholeEnabled)
+ {
+ cellToForgetWeights = &(params.m_CellToForgetWeights->GetInfo());
+ cellToOutputWeights = &(params.m_CellToOutputWeights->GetInfo());
+ }
+
+ if (!IsLayerSupported(__func__,
+ armnn::IsLstmSupported,
+ data.m_Compute,
+ inputInfo,
+ outputStateInInfo,
+ cellStateInInfo,
+ scratchBufferInfo,
+ outputStateOutInfo,
+ cellStateOutInfo,
+ outputInfo,
+ desc,
+ inputToForgetWeights,
+ inputToCellWeights,
+ inputToOutputWeights,
+ recurrentToForgetWeights,
+ recurrentToCellWeights,
+ recurrentToOutputWeights,
+ forgetGateBias,
+ cellBias,
+ outputGateBias,
+ inputToInputWeights,
+ recurrentToInputWeights,
+ cellToInputWeights,
+ inputGateBias,
+ projectionWeights,
+ projectionBias,
+ cellToForgetWeights,
+ cellToOutputWeights))
+ {
+ return false;
+ }
+
+ // Add the layer
+ armnn::IConnectableLayer* layer = data.m_Network->AddLstmLayer(desc, params, "Lstm");
+
+ input.Connect(layer->GetInputSlot(0));
+ outputStateIn.Connect(layer->GetInputSlot(1));
+ cellStateIn.Connect(layer->GetInputSlot(2));
+
+ return (SetupAndTrackLayerOutputSlot(operation, 0, *layer, 0, model, data) &&
+ SetupAndTrackLayerOutputSlot(operation, 1, *layer, 1, model, data) &&
+ SetupAndTrackLayerOutputSlot(operation, 2, *layer, 2, model, data) &&
+ SetupAndTrackLayerOutputSlot(operation, 3, *layer, 3, model, data));
+}
+
+bool HalPolicy::ConvertL2Normalization(const Operation& operation, const Model& model, ConversionData& data)
+{
+ LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data);
+ if (!input.IsValid())
+ {
+ return Fail("%s: Operation has invalid inputs", __func__);
+ }
+
+ const Operand* output = GetOutputOperand(operation, 0, model);
+ if (!output)
+ {
+ return Fail("%s: Could not read output 0", __func__);
+ }
+
+ const armnn::TensorInfo& inputInfo = input.GetTensorInfo();
+ const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
+
+ const armnn::TensorInfo swizzledInputInfo = armnnUtils::Permuted(inputInfo, NHWCToArmNN);
+ const armnn::TensorInfo swizzledOutputInfo = armnnUtils::Permuted(outputInfo, NHWCToArmNN);
+
+ if (!IsLayerSupported(__func__,
+ armnn::IsL2NormalizationSupported,
+ data.m_Compute,
+ swizzledInputInfo,
+ swizzledOutputInfo))
+ {
+ return false;
+ }
+
+ armnn::IConnectableLayer* layer = data.m_Network->AddL2NormalizationLayer();
+ assert(layer != nullptr);
+ layer->GetOutputSlot(0).SetTensorInfo(swizzledOutputInfo);
+
+ armnn::IConnectableLayer& outSwizzleLayer = SwizzleInDeswizzleOut(*data.m_Network, input, *layer);
+
+ return SetupAndTrackLayerOutputSlot(operation, 0, outSwizzleLayer, model, data);
+}
+
+bool HalPolicy::ConvertL2Pool2d(const Operation& operation, const Model& model, ConversionData& data)
+{
+ return ConvertPooling2d(operation, __func__, armnn::PoolingAlgorithm::L2, model, data);
+}
+
+bool HalPolicy::ConvertMaxPool2d(const Operation& operation, const Model& model, ConversionData& data)
+{
+ return ConvertPooling2d(operation, __func__, armnn::PoolingAlgorithm::Max, model, data);
+}
+
+bool HalPolicy::ConvertMul(const Operation& operation, const Model& model, ConversionData& data)
+{
+ LayerInputHandle input0 = ConvertToLayerInputHandle(operation, 0, model, data);
+ LayerInputHandle input1 = ConvertToLayerInputHandle(operation, 1, model, data);
+
+ if (!input0.IsValid() || !input1.IsValid())
+ {
+ return Fail("%s: Operation has invalid inputs", __func__);
+ }
+
+ // The FuseActivation parameter is always the input index 2
+ // and it should be optional
+ ActivationFn activationFunction;
+ if (!GetOptionalInputActivation(operation, 2, activationFunction, model, data))
+ {
+ return Fail("%s: Operation has invalid inputs", __func__);
+ }
+
+ const Operand* outputOperand = GetOutputOperand(operation, 0, model);
+
+ if (outputOperand == nullptr)
+ {
+ return false;
+ }
+
+ const armnn::TensorInfo& outInfo = GetTensorInfoForOperand(*outputOperand);
+
+ if (!IsLayerSupported(__func__,
+ armnn::IsMultiplicationSupported,
+ data.m_Compute,
+ input0.GetTensorInfo(),
+ input1.GetTensorInfo(),
+ outInfo))
+ {
+ return false;
+ }
+
+ armnn::IConnectableLayer* const startLayer = data.m_Network->AddMultiplicationLayer();
+ armnn::IConnectableLayer* const endLayer = ProcessActivation(outInfo, activationFunction, startLayer, data);
+
+ const armnn::TensorInfo& inputTensorInfo0 = input0.GetTensorInfo();
+ const armnn::TensorInfo& inputTensorInfo1 = input1.GetTensorInfo();
+
+ if (endLayer != nullptr)
+ {
+ BroadcastTensor(input0, input1, startLayer, *data.m_Network);
+ return SetupAndTrackLayerOutputSlot(operation, 0, *endLayer, model, data);
+ }
+ else
+ {
+ return Fail("%s: ProcessActivation failed", __func__);
+ }
+}
+
+bool HalPolicy::ConvertReLu(const Operation& operation, const Model& model, ConversionData& data)
+{
+ armnn::ActivationDescriptor desc;
+ desc.m_Function = armnn::ActivationFunction::ReLu;
+
+ return ConvertToActivation(operation, __func__, desc, model, data);
+}
+
+bool HalPolicy::ConvertReLu1(const Operation& operation, const Model& model, ConversionData& data)
+{
+ armnn::ActivationDescriptor desc;
+ desc.m_Function = armnn::ActivationFunction::BoundedReLu;
+ desc.m_A = 1.0f;
+ desc.m_B = -1.0f;
+
+ return ConvertToActivation(operation, __func__, desc, model, data);
+}
+
+bool HalPolicy::ConvertReLu6(const Operation& operation, const Model& model, ConversionData& data)
+{
+ armnn::ActivationDescriptor desc;
+ desc.m_Function = armnn::ActivationFunction::BoundedReLu;
+ desc.m_A = 6.0f;
+
+ return ConvertToActivation(operation, __func__, desc, model, data);
+}
+
+bool HalPolicy::ConvertSoftmax(const Operation& operation, const Model& model, ConversionData& data)
+{
+ LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data);
+ if (!input.IsValid())
+ {
+ return Fail("%s: Operation has invalid inputs", __func__);
+ }
+
+ const Operand* outputOperand = GetOutputOperand(operation, 0, model);
+ if (!outputOperand)
+ {
+ return Fail("%s: Operation has no outputs", __func__);
+ }
+
+ const armnn::TensorInfo outInfo = GetTensorInfoForOperand(*outputOperand);
+
+ armnn::SoftmaxDescriptor desc;
+ if (!GetInputFloat32(operation, 1, desc.m_Beta, model, data))
+ {
+ return Fail("%s: Operation has invalid inputs", __func__);
+ }
+
+ if (!IsLayerSupported(__func__,
+ armnn::IsSoftmaxSupported,
+ data.m_Compute,
+ input.GetTensorInfo(),
+ outInfo,
+ desc))
+ {
+ return false;
+ }
+
+ armnn::IConnectableLayer* layer = data.m_Network->AddSoftmaxLayer(desc);
+ assert(layer != nullptr);
+ input.Connect(layer->GetInputSlot(0));
+
+ return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data);
+}
+
+bool HalPolicy::ConvertTanH(const Operation& operation, const Model& model, ConversionData& data)
+{
+ armnn::ActivationDescriptor desc;
+ desc.m_Function = armnn::ActivationFunction::TanH;
+ desc.m_A = 1.0f; // android nn does not support tanH parameters
+ desc.m_B = 1.0f; // set to 1.0f for unity scaling
+
+ return ConvertToActivation(operation, __func__, desc, model, data);
+}
+
+bool HalPolicy::ConvertReshape(const Operation& operation, const Model& model, ConversionData& data)
+{
+ const Operand* inputOperand = GetInputOperand(operation, 0, model);
+ const Operand* requestedShapeOperand = GetInputOperand(operation, 1, model);
+ const Operand* outputOperand = GetOutputOperand(operation, 0, model);
+
+ if (inputOperand == nullptr
+ || requestedShapeOperand == nullptr
+ || outputOperand == nullptr)
+ {
+ return Fail("%s: Operation has invalid inputs", __func__);
+ }
+
+
+ if (requestedShapeOperand->dimensions.size() != 1)
+ {
+ return Fail("%s: Input 1 expected to be one-dimensional (found %i dimensions)",
+ __func__, requestedShapeOperand->dimensions.size());
+ }
+
+ std::vector<int32_t> targetDimensions;
+ if (!GetTensorInt32Values(*requestedShapeOperand, targetDimensions, model, data))
+ {
+ return Fail("%s: Could not read values of input 1", __func__);
+ }
+
+ const Shape inputOperandShape = GetOperandShape(*inputOperand);
+
+ Shape requestedShape;
+ // targetDimensions may contain special values (e.g. -1). reshapePrepare() is an AndroidNN provided utility
+ // function that resolves these values into a fully specified tensor shape.
+ if (!reshapePrepare(inputOperandShape, targetDimensions.data(), targetDimensions.size(), &requestedShape))
+ {
+ return Fail("%s: Failed to resolve the requested shape", __func__);
+ }
+
+ const Shape outputOperandShape = GetOperandShape(*outputOperand);
+ if (!SameShape(requestedShape, outputOperandShape))
+ {
+ return Fail("%s: Shape of output operand does not match resolved requested shape", __func__);
+ }
+
+ LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data);
+ if (!input.IsValid())
+ {
+ return Fail("%s: Could not read input 0", __func__);
+ }
+
+ if (!IsLayerSupported(__func__,
+ armnn::IsReshapeSupported,
+ data.m_Compute,
+ input.GetTensorInfo()))
+ {
+ return false;
+ }
+
+
+ armnn::ReshapeDescriptor reshapeDescriptor;
+ reshapeDescriptor.m_TargetShape = armnn::TensorShape(requestedShape.dimensions.size(),
+ requestedShape.dimensions.data());
+
+ armnn::IConnectableLayer* layer = data.m_Network->AddReshapeLayer(reshapeDescriptor);
+ assert(layer != nullptr);
+ input.Connect(layer->GetInputSlot(0));
+
+ return SetupAndTrackLayerOutputSlot(operation, 0, *layer, model, data);
+}
+
+bool HalPolicy::ConvertResizeBilinear(const Operation& operation, const Model& model, ConversionData& data)
+{
+ LayerInputHandle input = ConvertToLayerInputHandle(operation, 0, model, data);
+ if (!input.IsValid())
+ {
+ return Fail("%s: Could not read input 0", __func__);
+ }
+
+ const Operand* output = GetOutputOperand(operation, 0, model);
+ if (!output)
+ {
+ return Fail("%s: Could not read output 0", __func__);
+ }
+
+ const armnn::TensorInfo& inputInfo = input.GetTensorInfo();
+ const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
+
+ const armnn::TensorInfo swizzledInputInfo = armnnUtils::Permuted(inputInfo, NHWCToArmNN);
+ const armnn::TensorInfo swizzledOutputInfo = armnnUtils::Permuted(outputInfo, NHWCToArmNN);
+
+ if (!IsLayerSupported(__func__,
+ armnn::IsResizeBilinearSupported,
+ data.m_Compute,
+ swizzledInputInfo))
+ {
+ return false;
+ }
+
+ armnn::ResizeBilinearDescriptor desc;
+
+ if ( !GetInputScalar(operation, 1, OperandType::INT32, desc.m_TargetHeight, model, data)
+ || !GetInputScalar(operation, 2, OperandType::INT32, desc.m_TargetWidth, model, data))
+ {
+ return Fail("%s: Operation has invalid inputs", __func__);
+ }
+
+ armnn::IConnectableLayer* layer = data.m_Network->AddResizeBilinearLayer(desc);
+ assert(layer != nullptr);
+ layer->GetOutputSlot(0).SetTensorInfo(swizzledOutputInfo);
+
+ armnn::IConnectableLayer& outSwizzleLayer = SwizzleInDeswizzleOut(*data.m_Network, input, *layer);
+
+ return SetupAndTrackLayerOutputSlot(operation, 0, outSwizzleLayer, model, data);
+
+}
+
+} // namespace hal_1_0
+} // namespace armnn_driver
\ No newline at end of file
diff --git a/1.0/HalPolicy.hpp b/1.0/HalPolicy.hpp
new file mode 100644
index 0000000..c596075
--- /dev/null
+++ b/1.0/HalPolicy.hpp
@@ -0,0 +1,75 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include "ConversionUtils.hpp"
+
+#include <HalInterfaces.h>
+
+namespace V1_0 = ::android::hardware::neuralnetworks::V1_0;
+
+namespace armnn_driver
+{
+namespace hal_1_0
+{
+
+class HalPolicy
+{
+public:
+ using Model = V1_0::Model;
+ using Operation = V1_0::Operation;
+ using getSupportedOperations_cb = V1_0::IDevice::getSupportedOperations_cb;
+
+ static bool ConvertOperation(const Operation& operation, const Model& model, ConversionData& data);
+
+private:
+ static bool ConvertAdd(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertAveragePool2d(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertConcatenation(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertConv2d(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertDepthwiseConv2d(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertFloor(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertFullyConnected(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertLocalResponseNormalization(const Operation& operation,
+ const Model& model,
+ ConversionData& data);
+
+ static bool ConvertLogistic(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertLstm(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertL2Normalization(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertL2Pool2d(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertMaxPool2d(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertMul(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertReLu(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertReLu1(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertReLu6(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertSoftmax(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertTanH(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertReshape(const Operation& operation, const Model& model, ConversionData& data);
+
+ static bool ConvertResizeBilinear(const Operation& operation, const Model& model, ConversionData& data);
+};
+
+} // namespace hal_1_0
+} // namespace armnn_driver