13 namespace armcomputetensorutils
21 return arm_compute::DataType::U8;
23 return arm_compute::DataType::F16;
25 return arm_compute::DataType::F32;
27 return arm_compute::DataType::QASYMM8_SIGNED;
29 return arm_compute::DataType::QASYMM8;
31 return arm_compute::DataType::QSYMM16;
34 return multiScales ? arm_compute::DataType::QSYMM8_PER_CHANNEL : arm_compute::DataType::QSYMM8;
38 return arm_compute::DataType::QSYMM8_PER_CHANNEL;
41 return arm_compute::DataType::S32;
43 BOOST_ASSERT_MSG(
false,
"Unknown data type");
44 return arm_compute::DataType::UNKNOWN;
49 unsigned int originalInputRank,
50 const std::vector<unsigned int>& armnnAxes)
54 if (armnnAxes.empty())
62 outAclCoords.set_num_dimensions(inputDimensions);
63 std::generate(outAclCoords.begin(), outAclCoords.end(), [d = inputDimensions - 1] ()
mutable {
return d--; });
81 outAclCoords.set_num_dimensions(armnnAxes.size());
82 std::transform(armnnAxes.begin(), armnnAxes.end(),
84 [originalInputRank](
unsigned int i){
return originalInputRank - i - 1; });
90 arm_compute::TensorShape BuildArmComputeTensorShape(
const armnn::TensorShape& tensorShape)
92 arm_compute::TensorShape shape;
106 if (shape.num_dimensions() == 0)
108 shape.set_num_dimensions(1);
116 arm_compute::TensorInfo BuildArmComputeTensorInfo(
const armnn::TensorInfo& tensorInfo)
119 const arm_compute::TensorShape aclTensorShape = BuildArmComputeTensorShape(tensorInfo.
GetShape());
122 const arm_compute::QuantizationInfo aclQuantizationInfo = multiScales ?
126 return arm_compute::TensorInfo(aclTensorShape, 1, aclDataType, aclQuantizationInfo);
129 arm_compute::TensorInfo BuildArmComputeTensorInfo(
const armnn::TensorInfo& tensorInfo,
132 arm_compute::TensorInfo aclTensorInfo = BuildArmComputeTensorInfo(tensorInfo);
133 aclTensorInfo.set_data_layout(ConvertDataLayout(dataLayout));
135 return aclTensorInfo;
146 default:
throw InvalidArgumentException(
"Unknown armnn::DataLayout: [" +
147 std::to_string(static_cast<int>(dataLayout)) +
"]");
151 arm_compute::PoolingLayerInfo BuildArmComputePoolingLayerInfo(
const Pooling2dDescriptor& descriptor,
152 bool fpMixedPrecision)
154 using arm_compute::PoolingType;
155 using arm_compute::DimensionRoundingType;
156 using arm_compute::PadStrideInfo;
157 using arm_compute::PoolingLayerInfo;
158 using arm_compute::Size2D;
164 const DataLayout dataLayout = ConvertDataLayout(descriptor.m_DataLayout);
166 bool isGlobalPooling = (descriptor.m_StrideX==0 && descriptor.m_StrideY==0);
170 return arm_compute::PoolingLayerInfo(poolingType, dataLayout);
174 descriptor.m_OutputShapeRounding);
175 const PadStrideInfo padStrideInfo(descriptor.m_StrideX,
176 descriptor.m_StrideY,
177 descriptor.m_PadLeft,
178 descriptor.m_PadRight,
180 descriptor.m_PadBottom,
185 const Size2D poolSize(descriptor.m_PoolWidth, descriptor.m_PoolHeight);
187 return arm_compute::PoolingLayerInfo(poolingType, poolSize, dataLayout, padStrideInfo, excludePadding,
191 arm_compute::NormalizationLayerInfo BuildArmComputeNormalizationLayerInfo(
const NormalizationDescriptor& descriptor)
193 const arm_compute::NormType normType =
195 return arm_compute::NormalizationLayerInfo(normType,
196 descriptor.m_NormSize,
205 arm_compute::PermutationVector aclPerm;
207 unsigned int start = 0;
208 while ((start < perm.
GetSize()) && (start == perm[start]))
213 for (
unsigned int i = start; i < perm.
GetSize(); ++i)
215 aclPerm.set(i - start, perm[i] - start);
222 arm_compute::PermutationVector aclPerm;
223 std::map<unsigned int, unsigned int> permuteMappings;
224 for (
unsigned int i = 0; i < perm.
GetSize(); ++i)
226 permuteMappings[perm[i]] = i;
229 std::vector<unsigned int> permuteVector;
230 for (
unsigned int i = 0; i < perm.
GetSize(); ++i)
232 permuteVector.push_back(permuteMappings.at(i));
235 unsigned int start = 0;
236 while ((start < perm.
GetSize()) && (start == permuteVector[start]))
241 for (
unsigned int i = start; i < perm.
GetSize(); ++i)
243 aclPerm.set(i - start, permuteVector[i] - start);
248 arm_compute::Size2D BuildArmComputeSize2D(
const unsigned int width,
const unsigned int height)
250 return arm_compute::Size2D(width, height);
253 arm_compute::PixelValue GetPixelValue(arm_compute::ITensor& input,
float pixelValue)
255 switch (input.info()->data_type())
257 case arm_compute::DataType::F16:
258 return arm_compute::PixelValue(static_cast<Half>(pixelValue));
259 case arm_compute::DataType::F32:
260 return arm_compute::PixelValue(pixelValue);
261 case arm_compute::DataType::QASYMM8:
262 return arm_compute::PixelValue(static_cast<uint8_t>(pixelValue));
263 case arm_compute::DataType::QSYMM16:
264 return arm_compute::PixelValue(static_cast<int16_t>(pixelValue));
265 case arm_compute::DataType::QSYMM8_PER_CHANNEL:
266 return arm_compute::PixelValue(static_cast<int8_t>(pixelValue));
268 throw InvalidArgumentException(
"Unsupported DataType: [" +
269 std::to_string(static_cast<int>(input.info()->data_type())) +
"]");
const TensorShape & GetShape() const
arm_compute::PoolingType ConvertPoolingAlgorithmToAclPoolingType(PoolingAlgorithm poolingAlgorithm)
#define ARMNN_NO_DEPRECATE_WARN_BEGIN
std::array< unsigned int, MaxNumOfTensorDimensions > Coordinates
The padding fields don't count and are ignored.
arm_compute::NormType ConvertNormalizationAlgorithmChannelToAclNormType(NormalizationAlgorithmChannel channelType)
Copyright (c) 2020 ARM Limited.
std::vector< float > GetQuantizationScales() const
bool HasMultipleQuantizationScales() const
#define ARMNN_NO_DEPRECATE_WARN_END
arm_compute::DimensionRoundingType ConvertOutputShapeRoundingToAclDimensionRoundingType(OutputShapeRounding rounding)
int32_t GetQuantizationOffset() const
float GetQuantizationScale() const
DataType GetDataType() const
unsigned int GetNumDimensions() const