IVGCVSW-3583 Fix Skipped Batch_To_Space Hal 1.2 Tests
Signed-off-by: Finn Williams <Finn.Williams@arm.com>
Change-Id: I3f2928ba86a9d306a7eb400db3a420e42cf3fa7e
diff --git a/1.1/HalPolicy.cpp b/1.1/HalPolicy.cpp
index 93ee70e..2fab474 100644
--- a/1.1/HalPolicy.cpp
+++ b/1.1/HalPolicy.cpp
@@ -605,77 +605,7 @@
bool HalPolicy::ConvertBatchToSpaceNd(const Operation& operation, const Model& model, ConversionData& data)
{
ALOGV("hal_1_1::HalPolicy::ConvertBatchToSpaceNd()");
-
- LayerInputHandle input = ConvertToLayerInputHandle<hal_1_1::HalPolicy>(operation, 0, model, data);
- if (!input.IsValid())
- {
- return Fail("%s: Operation has invalid inputs", __func__);
- }
-
- const Operand* output = GetOutputOperand<hal_1_1::HalPolicy>(operation, 0, model);
- if (!output)
- {
- return Fail("%s: Could not read output 0", __func__);
- }
-
- const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
- if (IsDynamicTensor(outputInfo))
- {
- return Fail("%s: Dynamic output tensors are not supported", __func__);
- }
-
- const Operand* blockOperand = GetInputOperand<hal_1_1::HalPolicy>(operation, 1, model);
- if (!blockOperand)
- {
- return Fail("%s: Could not read input 1", __func__);
- }
-
- // Convert the block operand to int32
- std::vector<int32_t> block;
- if (!GetTensorInt32Values<hal_1_1::HalPolicy>(*blockOperand, block, model, data))
- {
- return Fail("%s: Input 1 has invalid values", __func__);
- }
-
- const armnn::TensorInfo& inputInfo = input.GetTensorInfo();
-
- unsigned int rank = inputInfo.GetNumDimensions();
- if (rank != 4)
- {
- Fail("%s: Only inputs with rank equal to 4 are supported", __func__);
- }
-
- if (std::any_of(block.cbegin(), block.cend(), [](int32_t i){ return i < 1; }))
- {
- return Fail("%s: Block sizes for each spatial dimension of the input tensor must be"
- " greater than or equal to 1", __func__);
- }
-
- armnn::BatchToSpaceNdDescriptor batchToSpaceNdDesc;
- batchToSpaceNdDesc.m_BlockShape.assign(block.cbegin(), block.cend());
- batchToSpaceNdDesc.m_DataLayout = armnn::DataLayout::NHWC;
-
- // Setting crops to 0,0 0,0 as it is not supported in Android NN API
- batchToSpaceNdDesc.m_Crops = {{0, 0}, {0, 0}};
-
- bool isSupported = false;
- FORWARD_LAYER_SUPPORT_FUNC(__func__,
- IsBatchToSpaceNdSupported,
- data.m_Backends,
- isSupported,
- inputInfo,
- outputInfo,
- batchToSpaceNdDesc);
- if (!isSupported)
- {
- return false;
- }
-
- armnn::IConnectableLayer* const layer = data.m_Network->AddBatchToSpaceNdLayer(batchToSpaceNdDesc);
- assert(layer != nullptr);
- input.Connect(layer->GetInputSlot(0));
-
- return SetupAndTrackLayerOutputSlot<hal_1_1::HalPolicy>(operation, 0, *layer, model, data);
+ return ::ConvertBatchToSpaceNd<hal_1_1::HalPolicy>(operation, model, data);
}
} // namespace hal_1_1
diff --git a/1.2/HalPolicy.cpp b/1.2/HalPolicy.cpp
index fe571df..4372c16 100644
--- a/1.2/HalPolicy.cpp
+++ b/1.2/HalPolicy.cpp
@@ -55,7 +55,6 @@
}
switch (static_cast<V1_1::OperationType>(operationType))
{
- case V1_1::OperationType::BATCH_TO_SPACE_ND:
case V1_1::OperationType::DIV:
case V1_1::OperationType::MEAN:
case V1_1::OperationType::SPACE_TO_BATCH_ND:
@@ -128,6 +127,8 @@
{
case V1_2::OperationType::AVERAGE_POOL_2D:
return ConvertAveragePool2d(operation, model, data);
+ case V1_2::OperationType::BATCH_TO_SPACE_ND:
+ return ConvertBatchToSpaceNd(operation, model, data);
case V1_2::OperationType::CONV_2D:
return ConvertConv2d(operation, model, data);
case V1_2::OperationType::DEPTHWISE_CONV_2D:
@@ -182,6 +183,12 @@
return ConvertPooling2d<hal_1_2::HalPolicy>(operation, __func__, armnn::PoolingAlgorithm::Average, model, data);
}
+bool HalPolicy::ConvertBatchToSpaceNd(const Operation& operation, const Model& model, ConversionData& data)
+{
+ ALOGV("hal_1_2::HalPolicy::ConvertBatchToSpaceNd()");
+ return ::ConvertBatchToSpaceNd<hal_1_2::HalPolicy>(operation, model, data);
+}
+
bool HalPolicy::ConvertConv2d(const Operation& operation, const Model& model, ConversionData& data)
{
ALOGV("hal_1_2::HalPolicy::ConvertConv2d()");
diff --git a/1.2/HalPolicy.hpp b/1.2/HalPolicy.hpp
index 5d6158a..e4719e8 100644
--- a/1.2/HalPolicy.hpp
+++ b/1.2/HalPolicy.hpp
@@ -33,6 +33,8 @@
private:
static bool ConvertAveragePool2d(const Operation& operation, const Model& model, ConversionData& data);
+ static bool ConvertBatchToSpaceNd(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);
diff --git a/ConversionUtils.hpp b/ConversionUtils.hpp
index fc6d365..946bc95 100644
--- a/ConversionUtils.hpp
+++ b/ConversionUtils.hpp
@@ -1800,5 +1800,90 @@
return Fail("%s: ProcessActivation failed", __func__);
}
+template<typename HalPolicy,
+ typename HalOperation = typename HalPolicy::Operation,
+ typename HalModel = typename HalPolicy::Model>
+bool ConvertBatchToSpaceNd(const HalOperation& operation,
+ const HalModel& model,
+ ConversionData& data)
+{
+ using HalOperand = typename HalPolicy::Operand;
+ using HalOperandType = typename HalPolicy::OperandType;
+
+ LayerInputHandle input = ConvertToLayerInputHandle<HalPolicy>(operation, 0, model, data);
+ if (!input.IsValid())
+ {
+ return Fail("%s: Operation has invalid inputs", __func__);
+ }
+
+ const HalOperand* output = GetOutputOperand<HalPolicy>(operation, 0, model);
+ if (!output)
+ {
+ return Fail("%s: Could not read output 0", __func__);
+ }
+
+ const armnn::TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
+ if (IsDynamicTensor(outputInfo))
+ {
+ return Fail("%s: Dynamic output tensors are not supported", __func__);
+ }
+
+ const HalOperand* blockOperand = GetInputOperand<HalPolicy>(operation, 1, model);
+ if (!blockOperand)
+ {
+ return Fail("%s: Could not read input 1", __func__);
+ }
+
+ // Convert the block operand to int32
+ std::vector<int32_t> block;
+ if (!GetTensorInt32Values<HalPolicy>(*blockOperand, block, model, data))
+ {
+ return Fail("%s: Input 1 has invalid values", __func__);
+ }
+
+ const armnn::TensorInfo& inputInfo = input.GetTensorInfo();
+
+ unsigned int rank = inputInfo.GetNumDimensions();
+ if (rank != 4)
+ {
+ Fail("%s: Only inputs with rank equal to 4 are supported", __func__);
+ }
+
+ if (std::any_of(block.cbegin(), block.cend(), [](int32_t i){ return i < 1; }))
+ {
+ return Fail("%s: Block sizes for each spatial dimension of the input tensor must be"
+ " greater than or equal to 1", __func__);
+ }
+
+ armnn::BatchToSpaceNdDescriptor batchToSpaceNdDesc;
+ batchToSpaceNdDesc.m_BlockShape.assign(block.cbegin(), block.cend());
+ batchToSpaceNdDesc.m_DataLayout = armnn::DataLayout::NHWC;
+
+ if (Is12Operand(*output))
+ {
+ batchToSpaceNdDesc.m_DataLayout = OptionalDataLayout<HalPolicy>(operation, 3, model, data);
+ }
+ // Setting crops to 0,0 0,0 as it is not supported in Android NN API
+ batchToSpaceNdDesc.m_Crops = {{0, 0}, {0, 0}};
+
+ bool isSupported = false;
+ FORWARD_LAYER_SUPPORT_FUNC(__func__,
+ IsBatchToSpaceNdSupported,
+ data.m_Backends,
+ isSupported,
+ inputInfo,
+ outputInfo,
+ batchToSpaceNdDesc);
+ if (!isSupported)
+ {
+ return false;
+ }
+
+ armnn::IConnectableLayer* const layer = data.m_Network->AddBatchToSpaceNdLayer(batchToSpaceNdDesc);
+ assert(layer != nullptr);
+ input.Connect(layer->GetInputSlot(0));
+
+ return SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 0, *layer, model, data);
+}
} // namespace armnn_driver