def create_tflite_network(model_file: str, backends: list = ['CpuAcc', 'CpuRef']):
- """Creates a network from an onnx model file.
+ """Creates a network from a tflite model file.
Args:
model_file (str): Path of the model file.
def create_onnx_network(model_file: str, backends: list = ['CpuAcc', 'CpuRef']):
- """Creates a network from a tflite model file.
+ """Creates a network from an onnx model file.
Args:
model_file (str): Path of the model file.
raise RuntimeError(message)
try:
- from ._generated.pyarmnn_tfliteparser import ITfLiteParser
+ from ._generated.pyarmnn_tfliteparser import ITfLiteParser, TfLiteParserOptions
except ImportError as err:
logger = logging.getLogger(__name__)
message = "Your ArmNN library instance does not support TF lite models parser functionality. "
from ._generated.pyarmnn import UnaryOperation_Abs, UnaryOperation_Exp, UnaryOperation_Sqrt, UnaryOperation_Rsqrt, \
UnaryOperation_Neg, ElementwiseUnaryDescriptor
from ._generated.pyarmnn import Convolution2dDescriptor, DepthToSpaceDescriptor, DepthwiseConvolution2dDescriptor, \
- DetectionPostProcessDescriptor, FakeQuantizationDescriptor, FullyConnectedDescriptor, \
- InstanceNormalizationDescriptor, LstmDescriptor, L2NormalizationDescriptor, MeanDescriptor
+ DetectionPostProcessDescriptor, FakeQuantizationDescriptor, FillDescriptor, FullyConnectedDescriptor, \
+ GatherDescriptor, InstanceNormalizationDescriptor, LstmDescriptor, L2NormalizationDescriptor, MeanDescriptor
from ._generated.pyarmnn import NormalizationAlgorithmChannel_Across, NormalizationAlgorithmChannel_Within, \
NormalizationAlgorithmMethod_LocalBrightness, NormalizationAlgorithmMethod_LocalContrast, NormalizationDescriptor
from ._generated.pyarmnn import PadDescriptor
list: A list of the output tensor names for the given model.
") GetSubgraphOutputTensorNames;
std::vector<std::string> GetSubgraphOutputTensorNames(size_t subgraphId);
+
+ %feature("flatnested");
+ %feature("docstring",
+ "
+ Options for TfLiteParser.
+
+ Contains:
+ m_StandInLayerForUnsupported (bool): Add StandInLayers as placeholders for unsupported operators.
+ Default: False
+ m_InferAndValidate (bool): Infer output shape of operations based on their input shape. Default: False
+ ")TfLiteParserOptions;
+ struct TfLiteParserOptions
+ {
+ TfLiteParserOptions();
+
+ bool m_StandInLayerForUnsupported;
+ bool m_InferAndValidate;
+ };
};
%extend ITfLiteParser {
// This is not a substitution of the default constructor of the Armnn class. It tells swig to create custom __init__
// method for ITfLiteParser python object that will use static factory method to do the job.
- ITfLiteParser() {
- return armnnTfLiteParser::ITfLiteParser::CreateRaw();
+ ITfLiteParser(const armnnTfLiteParser::ITfLiteParser::TfLiteParserOptions* options = nullptr) {
+ if (options) {
+ return armnnTfLiteParser::ITfLiteParser::CreateRaw(
+ armnn::Optional<armnnTfLiteParser::ITfLiteParser::TfLiteParserOptions>(*options));
+ } else {
+ return armnnTfLiteParser::ITfLiteParser::CreateRaw();
+ }
}
// The following does not replace a real destructor of the Armnn class.
}
-}
+} // end of namespace armnnTfLiteParser
+
// Clear exception typemap.
%exception;
%feature("docstring",
"
- Returns Arm NN library major version. The year of the release.
+ Returns Arm NN library major version.
Returns:
str: Major version of Arm NN installed.
%feature("docstring",
"
- Returns Arm NN library minor version. Month of the year of the release.
+ Returns Arm NN library minor version.
Returns:
str: Minor version of Arm NN installed.
}
%include "typemaps/vectors.i"
+%include "stdint.i"
%typemap(out) const uint32_t*
%{
%feature("docstring",
"
+ A descriptor for the Fill layer. Creates a tensor filled with a scalar value.
+
+ Contains:
+ m_Value (float): Value the tensor will be filled with.
+
+ ") FillDescriptor;
+struct FillDescriptor
+{
+ FillDescriptor();
+ FillDescriptor(const float& value);
+
+ bool operator ==(const FillDescriptor& rhs) const;
+
+ float m_Value;
+};
+
+%feature("docstring",
+ "
+ A descriptor for the Gather layer.
+
+ Contains:
+ m_Axis (int32_t): The axis from where to gather values from.
+
+ ") GatherDescriptor;
+struct GatherDescriptor
+{
+ GatherDescriptor();
+
+ GatherDescriptor(int32_t axis);
+
+ bool operator ==(const GatherDescriptor& rhs) const
+ {
+ return m_Axis == rhs.m_Axis;
+ }
+
+ int32_t m_Axis;
+};
+
+%feature("docstring",
+ "
A descriptor for the FullyConnected layer. See `INetwork.AddFullyConnectedLayer()`.
Contains:
Default: `ResizeMethod_NearestNeighbor`.
m_DataLayout (int): The data layout to be used (`DataLayout_NCHW`, `DataLayout_NHWC`). Default: `DataLayout_NCHW`.
m_AlignCorners (bool): Align corners or not when resizing. If True, corner pixel values are preserved after resizing.
- Default: False
+ Default: False.
+ m_HalfPixelCenters (bool): If true, calculates the pixels from the center instead of from the edge.
+ Default: False.
") ResizeDescriptor;
struct ResizeDescriptor
ResizeMethod m_Method;
DataLayout m_DataLayout;
bool m_AlignCorners;
+ bool m_HalfPixelCenters;
bool operator ==(const ResizeDescriptor& rhs) const;
};
m_StrideY (int): Underlying C++ data type is uint32_t. Stride value when proceeding through input for the height dimension. Default: 0.
m_BiasEnabled (bool): Enable/disable bias. Default: false.
m_DataLayout (int): The data layout to be used (`DataLayout_NCHW`, `DataLayout_NHWC`). Default: `DataLayout_NCHW`.
+ m_OutputShapeEnabled (bool): Set to true if output shape is specified. Will prevent output shape inference.
+ m_OutputShape (list of int): Output shape if it has been specified.
") TransposeConvolution2dDescriptor;
struct TransposeConvolution2dDescriptor
{
TransposeConvolution2dDescriptor();
- uint32_t m_PadLeft;
- uint32_t m_PadRight;
- uint32_t m_PadTop;
- uint32_t m_PadBottom;
- uint32_t m_StrideX;
- uint32_t m_StrideY;
- bool m_BiasEnabled;
- DataLayout m_DataLayout;
+ uint32_t m_PadLeft;
+ uint32_t m_PadRight;
+ uint32_t m_PadTop;
+ uint32_t m_PadBottom;
+ uint32_t m_StrideX;
+ uint32_t m_StrideY;
+ bool m_BiasEnabled;
+ DataLayout m_DataLayout;
+ bool m_OutputShapeEnabled;
+ std::vector<unsigned int> m_OutputShape;
bool operator ==(const TransposeConvolution2dDescriptor& rhs) const;
};
Contains:
m_debug (bool): Add debug data for easier troubleshooting.
- m_ReduceFp32ToFp16 (bool): Reduce Fp32 data to Fp16 for faster processing.
+ m_ReduceFp32ToBf16 (bool): Reduces Fp32 network to BFloat16 (Bf16) for faster processing. Layers
+ that can not be reduced will be left in Fp32.
+ m_ReduceFp32ToFp16 (bool): Reduces Fp32 network to Fp16 for faster processing. Layers
+ that can not be reduced will be left in Fp32.
") OptimizerOptions;
struct OptimizerOptions
{
OptimizerOptions();
- OptimizerOptions(bool reduceFp32ToFp16, bool debug);
+ OptimizerOptions(bool reduceFp32ToFp16,
+ bool debug,
+ bool reduceFp32ToBf16 = false);
+ bool m_ReduceFp32ToBf16;
bool m_ReduceFp32ToFp16;
bool m_Debug;
};
armnn::IConnectableLayer* AddDivisionLayer(const char* name = nullptr);
%feature("docstring",
- "
- Adds an Elementwise Unary layer to the network. Type of unary operation to use is decided by elementwiseUnaryDescriptor. Unary operations supported are (Abs, Exp, Neg, Rsqrt, Sqrt)
+ "
+ Adds an Elementwise Unary layer to the network. Type of unary operation to use is decided by elementwiseUnaryDescriptor. Unary operations supported are (Abs, Exp, Neg, Rsqrt, Sqrt)
- Args:
- elementwiseUnaryDescriptor (ElementwiseUnaryDescriptor): ElementwiseUnaryDescriptor to configure the choice of unary operation added to the network.
- name (str): Optional name for the layer.
+ Args:
+ elementwiseUnaryDescriptor (ElementwiseUnaryDescriptor): ElementwiseUnaryDescriptor to configure the choice of unary operation added to the network.
+ name (str): Optional name for the layer.
- Returns:
- IConnectableLayer: Interface for configuring the layer.
- ") AddElementwiseUnaryLayer;
+ Returns:
+ IConnectableLayer: Interface for configuring the layer.
+ ") AddElementwiseUnaryLayer;
armnn::IConnectableLayer* AddElementwiseUnaryLayer(const ElementwiseUnaryDescriptor& elementwiseUnaryDescriptor,
const char* name = nullptr);
%feature("docstring",
"
+ Add a Fill layer to the network.
+
+ Args:
+ FillDescriptor (FillDescriptor): Descriptor for the fill operation.
+ name (str): Optional name for the layer.
+
+ Returns:
+ IConnectableLayer: Interface for configuring the layer.
+ ") AddFillLayer;
+ armnn::IConnectableLayer* AddFillLayer(const FillDescriptor& fillDescriptor,
+ const char* name = nullptr);
+
+ %feature("docstring",
+ "
Adds a Floor layer to the network.
Args:
Add Gather layer to the network.
Args:
+ descriptor (GatherDescriptor): Descriptor for the gather operation.
name (str): Optional name for the layer.
Returns:
IConnectableLayer: Interface for configuring the layer.
") AddGatherLayer;
- armnn::IConnectableLayer* AddGatherLayer(const char* name = nullptr);
+ armnn::IConnectableLayer* AddGatherLayer(const GatherDescriptor& descriptor,
+ const char* name = nullptr);
%feature("docstring",
"
armnn::IConnectableLayer* AddQuantizedLstmLayer(const armnn::QuantizedLstmInputParams& params,
const char* name = nullptr);
+
+ %feature("docstring",
+ "
+ Adds a Rank layer to the network.
+
+ Args:
+ name (str): Optional name for the layer.
+
+ Returns:
+ IConnectableLayer: Interface for configuring the layer.
+ ") AddRankLayer;
+ armnn::IConnectableLayer* AddRankLayer(const char* name = nullptr);
+
+
%feature("docstring",
"
Adds a Reshape layer to the network.
np.allclose(-6, desc.m_Min)
+def test_fill_descriptor_default_values():
+ desc = ann.FillDescriptor()
+ np.allclose(0, desc.m_Value)
+
+
+def test_gather_descriptor_default_values():
+ desc = ann.GatherDescriptor()
+ assert desc.m_Axis == 0
+
+
def test_fully_connected_descriptor_default_values():
desc = ann.FullyConnectedDescriptor()
assert desc.m_BiasEnabled == False
def test_transpose_convolution2d_descriptor_default_values():
- desc = ann.DepthwiseConvolution2dDescriptor()
+ desc = ann.TransposeConvolution2dDescriptor()
assert desc.m_PadLeft == 0
assert desc.m_PadTop == 0
assert desc.m_PadRight == 0
assert desc.m_StrideY == 0
assert desc.m_BiasEnabled == False
assert desc.m_DataLayout == ann.DataLayout_NCHW
+ assert desc.m_OutputShapeEnabled == False
def test_view_descriptor_default_values():
'StackDescriptor',
'StridedSliceDescriptor',
'TransposeConvolution2dDescriptor',
- 'ElementwiseUnaryDescriptor'])
+ 'ElementwiseUnaryDescriptor',
+ 'FillDescriptor',
+ 'GatherDescriptor'])
class TestDescriptorMassChecks:
def test_desc_implemented(self, desc_name):
'StackDescriptor',
'StridedSliceDescriptor',
'TransposeConvolution2dDescriptor',
- 'ElementwiseUnaryDescriptor'])
+ 'ElementwiseUnaryDescriptor',
+ 'FillDescriptor',
+ 'GatherDescriptor'])
class TestDescriptorMassChecks:
def test_desc_implemented(self, desc_name):
import pytest
import pyarmnn as ann
+def test_optimizer_options_default_values():
+ opt = ann.OptimizerOptions()
+ assert opt.m_ReduceFp32ToFp16 == False
+ assert opt.m_Debug == False
+ assert opt.m_ReduceFp32ToBf16 == False
+
+def test_optimizer_options_set_values1():
+ opt = ann.OptimizerOptions(True, True)
+ assert opt.m_ReduceFp32ToFp16 == True
+ assert opt.m_Debug == True
+ assert opt.m_ReduceFp32ToBf16 == False
+
+def test_optimizer_options_set_values2():
+ opt = ann.OptimizerOptions(False, False, True)
+ assert opt.m_ReduceFp32ToFp16 == False
+ assert opt.m_Debug == False
+ assert opt.m_ReduceFp32ToBf16 == True
@pytest.fixture(scope="function")
def get_runtime(shared_data_folder, network_file):
'AddDivisionLayer',
'AddElementwiseUnaryLayer',
'AddFloorLayer',
+ 'AddFillLayer',
'AddFullyConnectedLayer',
'AddGatherLayer',
'AddInputLayer',
'AddPreluLayer',
'AddQuantizeLayer',
'AddQuantizedLstmLayer',
+ 'AddRankLayer',
'AddReshapeLayer',
'AddResizeLayer',
'AddSliceLayer',
import numpy as np
+def test_TfLiteParserOptions_default_values():
+ parserOptions = ann.TfLiteParserOptions()
+ assert parserOptions.m_InferAndValidate == False
+ assert parserOptions.m_StandInLayerForUnsupported == False
+
+
@pytest.fixture()
def parser(shared_data_folder):
"""
assert parser.thisown
+def test_tflite_parser_with_optional_options():
+ parserOptions = ann.TfLiteParserOptions()
+ parserOptions.m_InferAndValidate = True
+ parser = ann.ITfLiteParser(parserOptions)
+ assert parser.thisown
+
+
+def create_with_opt() :
+ parserOptions = ann.TfLiteParserOptions()
+ parserOptions.m_InferAndValidate = True
+ return ann.ITfLiteParser(parserOptions)
+
+def test_tflite_parser_with_optional_options_out_of_scope(shared_data_folder):
+ parser = create_with_opt()
+ network = parser.CreateNetworkFromBinaryFile(os.path.join(shared_data_folder, "mock_model.tflite"))
+
+ graphs_count = parser.GetSubgraphCount()
+ graph_id = graphs_count - 1
+
+ input_names = parser.GetSubgraphInputTensorNames(graph_id)
+ input_binding_info = parser.GetNetworkInputBindingInfo(graph_id, input_names[0])
+
+ output_names = parser.GetSubgraphOutputTensorNames(graph_id)
+
+ preferred_backends = [ann.BackendId('CpuAcc'), ann.BackendId('CpuRef')]
+
+ options = ann.CreationOptions()
+ runtime = ann.IRuntime(options)
+
+ opt_network, messages = ann.Optimize(network, preferred_backends, runtime.GetDeviceSpec(), ann.OptimizerOptions())
+ assert 0 == len(messages)
+
+ net_id, messages = runtime.LoadNetwork(opt_network)
+ assert "" == messages
+
+
def test_tflite_get_sub_graph_count(parser):
graphs_count = parser.GetSubgraphCount()
assert graphs_count == 1
projects / platform / upstream / armnn.git / commitdiff