Release 18.08
diff --git a/include/armnn/Types.hpp b/include/armnn/Types.hpp
index c9a4bf1..fe1fcb4 100644
--- a/include/armnn/Types.hpp
+++ b/include/armnn/Types.hpp
@@ -22,9 +22,10 @@
enum class DataType
{
- Float32 = 0,
- QuantisedAsymm8 = 1,
- Signed32 = 2
+ Float16 = 0,
+ Float32 = 1,
+ QuantisedAsymm8 = 2,
+ Signed32 = 3
};
enum class ActivationFunction
@@ -33,7 +34,7 @@
TanH = 1,
Linear = 2,
ReLu = 3,
- BoundedReLu = 4, //< min(a, max(b, input))
+ BoundedReLu = 4, ///< min(a, max(b, input))
SoftReLu = 5,
LeakyReLu = 6,
Abs = 7,
@@ -51,16 +52,18 @@
///
/// The padding method modifies the output of pooling layers.
/// In both supported methods, the values are ignored (they are
-/// not even zeros which would make a difference for max pooling
+/// not even zeroes, which would make a difference for max pooling
/// a tensor with negative values). The difference between
-/// IgnoreValue and Exclude is that the former count the padding
+/// IgnoreValue and Exclude is that the former counts the padding
/// fields in the divisor of Average and L2 pooling, while
/// Exclude does not.
///
enum class PaddingMethod
{
- IgnoreValue = 0, // The padding fields count, but ignored
- Exclude = 1 // The padding fields don't count and ignored
+ /// The padding fields count, but are ignored
+ IgnoreValue = 0,
+ /// The padding fields don't count and are ignored
+ Exclude = 1
};
enum class NormalizationAlgorithmChannel
@@ -71,8 +74,10 @@
enum class NormalizationAlgorithmMethod
{
- LocalBrightness = 0, /* Krichevsky 2012: Local Brightness Normalization */
- LocalContrast = 1 /* Jarret 2009: Local Contrast Normalization */
+ /// Krichevsky 2012: Local Brightness Normalization
+ LocalBrightness = 0,
+ /// Jarret 2009: Local Contrast Normalization
+ LocalContrast = 1
};
enum class OutputShapeRounding
@@ -83,15 +88,20 @@
enum class Compute
{
- CpuRef = 0, // CPU Execution: Reference C++ kernels
- CpuAcc = 1, // CPU Execution: NEON: ArmCompute
- GpuAcc = 2, // GPU Execution: OpenCL: ArmCompute
+ /// CPU Execution: Reference C++ kernels
+ CpuRef = 0,
+ /// CPU Execution: NEON: ArmCompute
+ CpuAcc = 1,
+ /// GPU Execution: OpenCL: ArmCompute
+ GpuAcc = 2,
Undefined = 5
};
-struct DeviceSpec
+class IDeviceSpec
{
- Compute DefaultComputeDevice;
+protected:
+ IDeviceSpec() {};
+ virtual ~IDeviceSpec() {};
};
/// Type of identifiers for bindable layers (inputs, outputs).
@@ -105,10 +115,10 @@
using ArrayType = std::array<ValueType, MaxNumOfTensorDimensions>;
using ConstIterator = typename ArrayType::const_iterator;
- /// @param dimMappings Indicates how to translate tensor elements from a given source into the target destination,
+ /// @param dimMappings - Indicates how to translate tensor elements from a given source into the target destination,
/// when source and target potentially have different memory layouts.
///
- /// E.g. For a 4-d tensor laid out in memory with format (Batch Element, Height, Width, Channels),
+ /// E.g. For a 4-d tensor laid out in a memory with the format (Batch Element, Height, Width, Channels),
/// which is to be passed as an input to ArmNN, each source dimension is mapped to the corresponding
/// ArmNN dimension. The Batch dimension remains the same (0 -> 0). The source Height dimension is mapped
/// to the location of the ArmNN Height dimension (1 -> 2). Similar arguments are made for the Width and
@@ -152,7 +162,7 @@
SizeType m_NumDimMappings;
};
-// Define LayerGuid type.
+/// Define LayerGuid type.
using LayerGuid = unsigned int;
}