| # Copyright © 2019 Arm Ltd. All rights reserved. |
| # SPDX-License-Identifier: MIT |
| import inspect |
| |
| import pytest |
| |
| import pyarmnn as ann |
| import numpy as np |
| import pyarmnn._generated.pyarmnn as generated |
| |
| |
| def test_activation_descriptor_default_values(): |
| desc = ann.ActivationDescriptor() |
| assert desc.m_Function == ann.ActivationFunction_Sigmoid |
| assert desc.m_A == 0 |
| assert desc.m_B == 0 |
| |
| |
| def test_argminmax_descriptor_default_values(): |
| desc = ann.ArgMinMaxDescriptor() |
| assert desc.m_Function == ann.ArgMinMaxFunction_Min |
| assert desc.m_Axis == -1 |
| |
| |
| def test_batchnormalization_descriptor_default_values(): |
| desc = ann.BatchNormalizationDescriptor() |
| assert desc.m_DataLayout == ann.DataLayout_NCHW |
| np.allclose(0.0001, desc.m_Eps) |
| |
| |
| def test_batchtospacend_descriptor_default_values(): |
| desc = ann.BatchToSpaceNdDescriptor() |
| assert desc.m_DataLayout == ann.DataLayout_NCHW |
| assert [1, 1] == desc.m_BlockShape |
| assert [(0, 0), (0, 0)] == desc.m_Crops |
| |
| |
| def test_batchtospacend_descriptor_assignment(): |
| desc = ann.BatchToSpaceNdDescriptor() |
| desc.m_BlockShape = (1, 2, 3) |
| |
| ololo = [(1, 2), (3, 4)] |
| size_1 = len(ololo) |
| desc.m_Crops = ololo |
| |
| assert size_1 == len(ololo) |
| desc.m_DataLayout = ann.DataLayout_NHWC |
| assert ann.DataLayout_NHWC == desc.m_DataLayout |
| assert [1, 2, 3] == desc.m_BlockShape |
| assert [(1, 2), (3, 4)] == desc.m_Crops |
| |
| |
| @pytest.mark.parametrize("input_shape, value, vtype", [([-1], -1, 'int'), (("one", "two"), "'one'", 'str'), |
| ([1.33, 4.55], 1.33, 'float'), |
| ([{1: "one"}], "{1: 'one'}", 'dict')], ids=lambda x: str(x)) |
| def test_batchtospacend_descriptor_rubbish_assignment_shape(input_shape, value, vtype): |
| desc = ann.BatchToSpaceNdDescriptor() |
| with pytest.raises(TypeError) as err: |
| desc.m_BlockShape = input_shape |
| |
| assert "Failed to convert python input value {} of type '{}' to C type 'j'".format(value, vtype) in str(err.value) |
| |
| |
| @pytest.mark.parametrize("input_crops, value, vtype", [([(1, 2), (3, 4, 5)], '(3, 4, 5)', 'tuple'), |
| ([(1, 'one')], "(1, 'one')", 'tuple'), |
| ([-1], -1, 'int'), |
| ([(1, (1, 2))], '(1, (1, 2))', 'tuple'), |
| ([[1, [1, 2]]], '[1, [1, 2]]', 'list') |
| ], ids=lambda x: str(x)) |
| def test_batchtospacend_descriptor_rubbish_assignment_crops(input_crops, value, vtype): |
| desc = ann.BatchToSpaceNdDescriptor() |
| with pytest.raises(TypeError) as err: |
| desc.m_Crops = input_crops |
| |
| assert "Failed to convert python input value {} of type '{}' to C type".format(value, vtype) in str(err.value) |
| |
| |
| def test_batchtospacend_descriptor_empty_assignment(): |
| desc = ann.BatchToSpaceNdDescriptor() |
| desc.m_BlockShape = [] |
| assert [] == desc.m_BlockShape |
| |
| |
| def test_batchtospacend_descriptor_ctor(): |
| desc = ann.BatchToSpaceNdDescriptor([1, 2, 3], [(4, 5), (6, 7)]) |
| assert desc.m_DataLayout == ann.DataLayout_NCHW |
| assert [1, 2, 3] == desc.m_BlockShape |
| assert [(4, 5), (6, 7)] == desc.m_Crops |
| |
| |
| def test_convolution2d_descriptor_default_values(): |
| desc = ann.Convolution2dDescriptor() |
| assert desc.m_PadLeft == 0 |
| assert desc.m_PadTop == 0 |
| assert desc.m_PadRight == 0 |
| assert desc.m_PadBottom == 0 |
| assert desc.m_StrideX == 0 |
| assert desc.m_StrideY == 0 |
| assert desc.m_DilationX == 1 |
| assert desc.m_DilationY == 1 |
| assert desc.m_BiasEnabled == False |
| assert desc.m_DataLayout == ann.DataLayout_NCHW |
| |
| |
| def test_depthtospace_descriptor_default_values(): |
| desc = ann.DepthToSpaceDescriptor() |
| assert desc.m_BlockSize == 1 |
| assert desc.m_DataLayout == ann.DataLayout_NHWC |
| |
| |
| def test_depthwise_convolution2d_descriptor_default_values(): |
| desc = ann.DepthwiseConvolution2dDescriptor() |
| assert desc.m_PadLeft == 0 |
| assert desc.m_PadTop == 0 |
| assert desc.m_PadRight == 0 |
| assert desc.m_PadBottom == 0 |
| assert desc.m_StrideX == 0 |
| assert desc.m_StrideY == 0 |
| assert desc.m_DilationX == 1 |
| assert desc.m_DilationY == 1 |
| assert desc.m_BiasEnabled == False |
| assert desc.m_DataLayout == ann.DataLayout_NCHW |
| |
| |
| def test_detectionpostprocess_descriptor_default_values(): |
| desc = ann.DetectionPostProcessDescriptor() |
| assert desc.m_MaxDetections == 0 |
| assert desc.m_MaxClassesPerDetection == 1 |
| assert desc.m_DetectionsPerClass == 1 |
| assert desc.m_NmsScoreThreshold == 0 |
| assert desc.m_NmsIouThreshold == 0 |
| assert desc.m_NumClasses == 0 |
| assert desc.m_UseRegularNms == False |
| assert desc.m_ScaleH == 0 |
| assert desc.m_ScaleW == 0 |
| assert desc.m_ScaleX == 0 |
| assert desc.m_ScaleY == 0 |
| |
| |
| def test_fakequantization_descriptor_default_values(): |
| desc = ann.FakeQuantizationDescriptor() |
| np.allclose(6, desc.m_Max) |
| np.allclose(-6, desc.m_Min) |
| |
| |
| def test_fully_connected_descriptor_default_values(): |
| desc = ann.FullyConnectedDescriptor() |
| assert desc.m_BiasEnabled == False |
| assert desc.m_TransposeWeightMatrix == False |
| |
| |
| def test_instancenormalization_descriptor_default_values(): |
| desc = ann.InstanceNormalizationDescriptor() |
| assert desc.m_Gamma == 1 |
| assert desc.m_Beta == 0 |
| assert desc.m_DataLayout == ann.DataLayout_NCHW |
| np.allclose(1e-12, desc.m_Eps) |
| |
| |
| def test_lstm_descriptor_default_values(): |
| desc = ann.LstmDescriptor() |
| assert desc.m_ActivationFunc == 1 |
| assert desc.m_ClippingThresCell == 0 |
| assert desc.m_ClippingThresProj == 0 |
| assert desc.m_CifgEnabled == True |
| assert desc.m_PeepholeEnabled == False |
| assert desc.m_ProjectionEnabled == False |
| assert desc.m_LayerNormEnabled == False |
| |
| |
| def test_l2normalization_descriptor_default_values(): |
| desc = ann.L2NormalizationDescriptor() |
| assert desc.m_DataLayout == ann.DataLayout_NCHW |
| np.allclose(1e-12, desc.m_Eps) |
| |
| |
| def test_mean_descriptor_default_values(): |
| desc = ann.MeanDescriptor() |
| assert desc.m_KeepDims == False |
| |
| |
| def test_normalization_descriptor_default_values(): |
| desc = ann.NormalizationDescriptor() |
| assert desc.m_NormChannelType == ann.NormalizationAlgorithmChannel_Across |
| assert desc.m_NormMethodType == ann.NormalizationAlgorithmMethod_LocalBrightness |
| assert desc.m_NormSize == 0 |
| assert desc.m_Alpha == 0 |
| assert desc.m_Beta == 0 |
| assert desc.m_K == 0 |
| assert desc.m_DataLayout == ann.DataLayout_NCHW |
| |
| |
| def test_origin_descriptor_default_values(): |
| desc = ann.ConcatDescriptor() |
| assert 0 == desc.GetNumViews() |
| assert 0 == desc.GetNumDimensions() |
| assert 1 == desc.GetConcatAxis() |
| |
| |
| def test_origin_descriptor_incorrect_views(): |
| desc = ann.ConcatDescriptor(2, 2) |
| with pytest.raises(RuntimeError) as err: |
| desc.SetViewOriginCoord(1000, 100, 1000) |
| assert "Failed to set view origin coordinates." in str(err.value) |
| |
| |
| def test_origin_descriptor_ctor(): |
| desc = ann.ConcatDescriptor(2, 2) |
| value = 5 |
| for i in range(desc.GetNumViews()): |
| for j in range(desc.GetNumDimensions()): |
| desc.SetViewOriginCoord(i, j, value+i) |
| desc.SetConcatAxis(1) |
| |
| assert 2 == desc.GetNumViews() |
| assert 2 == desc.GetNumDimensions() |
| assert [5, 5] == desc.GetViewOrigin(0) |
| assert [6, 6] == desc.GetViewOrigin(1) |
| assert 1 == desc.GetConcatAxis() |
| |
| |
| def test_pad_descriptor_default_values(): |
| desc = ann.PadDescriptor() |
| assert desc.m_PadValue == 0 |
| |
| |
| def test_permute_descriptor_default_values(): |
| pv = ann.PermutationVector((0, 2, 3, 1)) |
| desc = ann.PermuteDescriptor(pv) |
| assert desc.m_DimMappings.GetSize() == 4 |
| assert desc.m_DimMappings[0] == 0 |
| assert desc.m_DimMappings[1] == 2 |
| assert desc.m_DimMappings[2] == 3 |
| assert desc.m_DimMappings[3] == 1 |
| |
| |
| def test_pooling_descriptor_default_values(): |
| desc = ann.Pooling2dDescriptor() |
| assert desc.m_PoolType == ann.PoolingAlgorithm_Max |
| assert desc.m_PadLeft == 0 |
| assert desc.m_PadTop == 0 |
| assert desc.m_PadRight == 0 |
| assert desc.m_PadBottom == 0 |
| assert desc.m_PoolHeight == 0 |
| assert desc.m_PoolWidth == 0 |
| assert desc.m_StrideX == 0 |
| assert desc.m_StrideY == 0 |
| assert desc.m_OutputShapeRounding == ann.OutputShapeRounding_Floor |
| assert desc.m_PaddingMethod == ann.PaddingMethod_Exclude |
| assert desc.m_DataLayout == ann.DataLayout_NCHW |
| |
| |
| def test_reshape_descriptor_default_values(): |
| desc = ann.ReshapeDescriptor() |
| # check the empty Targetshape |
| assert desc.m_TargetShape.GetNumDimensions() == 0 |
| |
| |
| def test_slice_descriptor_default_values(): |
| desc = ann.SliceDescriptor() |
| assert desc.m_TargetWidth == 0 |
| assert desc.m_TargetHeight == 0 |
| assert desc.m_Method == ann.ResizeMethod_NearestNeighbor |
| assert desc.m_DataLayout == ann.DataLayout_NCHW |
| |
| |
| def test_resize_descriptor_default_values(): |
| desc = ann.ResizeDescriptor() |
| assert desc.m_TargetWidth == 0 |
| assert desc.m_TargetHeight == 0 |
| assert desc.m_Method == ann.ResizeMethod_NearestNeighbor |
| assert desc.m_DataLayout == ann.DataLayout_NCHW |
| |
| |
| def test_spacetobatchnd_descriptor_default_values(): |
| desc = ann.SpaceToBatchNdDescriptor() |
| assert desc.m_DataLayout == ann.DataLayout_NCHW |
| |
| |
| def test_spacetodepth_descriptor_default_values(): |
| desc = ann.SpaceToDepthDescriptor() |
| assert desc.m_BlockSize == 1 |
| assert desc.m_DataLayout == ann.DataLayout_NHWC |
| |
| |
| def test_stack_descriptor_default_values(): |
| desc = ann.StackDescriptor() |
| assert desc.m_Axis == 0 |
| assert desc.m_NumInputs == 0 |
| # check the empty Inputshape |
| assert desc.m_InputShape.GetNumDimensions() == 0 |
| |
| |
| def test_slice_descriptor_default_values(): |
| desc = ann.SliceDescriptor() |
| desc.m_Begin = [1, 2, 3, 4, 5] |
| desc.m_Size = (1, 2, 3, 4) |
| |
| assert [1, 2, 3, 4, 5] == desc.m_Begin |
| assert [1, 2, 3, 4] == desc.m_Size |
| |
| |
| def test_slice_descriptor_ctor(): |
| desc = ann.SliceDescriptor([1, 2, 3, 4, 5], (1, 2, 3, 4)) |
| |
| assert [1, 2, 3, 4, 5] == desc.m_Begin |
| assert [1, 2, 3, 4] == desc.m_Size |
| |
| |
| def test_strided_slice_descriptor_default_values(): |
| desc = ann.StridedSliceDescriptor() |
| desc.m_Begin = [1, 2, 3, 4, 5] |
| desc.m_End = [6, 7, 8, 9, 10] |
| desc.m_Stride = (10, 10) |
| desc.m_BeginMask = 1 |
| desc.m_EndMask = 2 |
| desc.m_ShrinkAxisMask = 3 |
| desc.m_EllipsisMask = 4 |
| desc.m_NewAxisMask = 5 |
| |
| assert [1, 2, 3, 4, 5] == desc.m_Begin |
| assert [6, 7, 8, 9, 10] == desc.m_End |
| assert [10, 10] == desc.m_Stride |
| assert 1 == desc.m_BeginMask |
| assert 2 == desc.m_EndMask |
| assert 3 == desc.m_ShrinkAxisMask |
| assert 4 == desc.m_EllipsisMask |
| assert 5 == desc.m_NewAxisMask |
| |
| |
| def test_strided_slice_descriptor_ctor(): |
| desc = ann.StridedSliceDescriptor([1, 2, 3, 4, 5], [6, 7, 8, 9, 10], (10, 10)) |
| desc.m_Begin = [1, 2, 3, 4, 5] |
| desc.m_End = [6, 7, 8, 9, 10] |
| desc.m_Stride = (10, 10) |
| |
| assert [1, 2, 3, 4, 5] == desc.m_Begin |
| assert [6, 7, 8, 9, 10] == desc.m_End |
| assert [10, 10] == desc.m_Stride |
| |
| |
| def test_softmax_descriptor_default_values(): |
| desc = ann.SoftmaxDescriptor() |
| assert desc.m_Axis == -1 |
| np.allclose(1.0, desc.m_Beta) |
| |
| |
| def test_space_to_batch_nd_descriptor_default_values(): |
| desc = ann.SpaceToBatchNdDescriptor() |
| assert [1, 1] == desc.m_BlockShape |
| assert [(0, 0), (0, 0)] == desc.m_PadList |
| assert ann.DataLayout_NCHW == desc.m_DataLayout |
| |
| |
| def test_space_to_batch_nd_descriptor_assigned_values(): |
| desc = ann.SpaceToBatchNdDescriptor() |
| desc.m_BlockShape = (90, 100) |
| desc.m_PadList = [(1, 2), (3, 4)] |
| assert [90, 100] == desc.m_BlockShape |
| assert [(1, 2), (3, 4)] == desc.m_PadList |
| assert ann.DataLayout_NCHW == desc.m_DataLayout |
| |
| |
| def test_space_to_batch_nd_descriptor_ctor(): |
| desc = ann.SpaceToBatchNdDescriptor((1, 2, 3), [(1, 2), (3, 4)]) |
| assert [1, 2, 3] == desc.m_BlockShape |
| assert [(1, 2), (3, 4)] == desc.m_PadList |
| assert ann.DataLayout_NCHW == desc.m_DataLayout |
| |
| |
| def test_transpose_convolution2d_descriptor_default_values(): |
| desc = ann.DepthwiseConvolution2dDescriptor() |
| assert desc.m_PadLeft == 0 |
| assert desc.m_PadTop == 0 |
| assert desc.m_PadRight == 0 |
| assert desc.m_PadBottom == 0 |
| assert desc.m_StrideX == 0 |
| assert desc.m_StrideY == 0 |
| assert desc.m_BiasEnabled == False |
| assert desc.m_DataLayout == ann.DataLayout_NCHW |
| |
| |
| def test_view_descriptor_default_values(): |
| desc = ann.SplitterDescriptor() |
| assert 0 == desc.GetNumViews() |
| assert 0 == desc.GetNumDimensions() |
| |
| |
| def test_view_descriptor_incorrect_input(): |
| desc = ann.SplitterDescriptor(2, 3) |
| with pytest.raises(RuntimeError) as err: |
| desc.SetViewOriginCoord(1000, 100, 1000) |
| assert "Failed to set view origin coordinates." in str(err.value) |
| |
| with pytest.raises(RuntimeError) as err: |
| desc.SetViewSize(1000, 100, 1000) |
| assert "Failed to set view size." in str(err.value) |
| |
| |
| def test_view_descriptor_ctor(): |
| desc = ann.SplitterDescriptor(2, 3) |
| value_size = 1 |
| value_orig_coord = 5 |
| for i in range(desc.GetNumViews()): |
| for j in range(desc.GetNumDimensions()): |
| desc.SetViewOriginCoord(i, j, value_orig_coord+i) |
| desc.SetViewSize(i, j, value_size+i) |
| |
| assert 2 == desc.GetNumViews() |
| assert 3 == desc.GetNumDimensions() |
| assert [5, 5] == desc.GetViewOrigin(0) |
| assert [6, 6] == desc.GetViewOrigin(1) |
| assert [1, 1] == desc.GetViewSizes(0) |
| assert [2, 2] == desc.GetViewSizes(1) |
| |
| |
| def test_createdescriptorforconcatenation_ctor(): |
| input_shape_vector = [ann.TensorShape((2, 1)), ann.TensorShape((3, 1)), ann.TensorShape((4, 1))] |
| desc = ann.CreateDescriptorForConcatenation(input_shape_vector, 0) |
| assert 3 == desc.GetNumViews() |
| assert 0 == desc.GetConcatAxis() |
| assert 2 == desc.GetNumDimensions() |
| c = desc.GetViewOrigin(1) |
| d = desc.GetViewOrigin(0) |
| |
| |
| def test_createdescriptorforconcatenation_wrong_shape_for_axis(): |
| input_shape_vector = [ann.TensorShape((1, 2)), ann.TensorShape((3, 4)), ann.TensorShape((5, 6))] |
| with pytest.raises(RuntimeError) as err: |
| desc = ann.CreateDescriptorForConcatenation(input_shape_vector, 0) |
| |
| assert "All inputs to concatenation must be the same size along all dimensions except the concatenation dimension" in str( |
| err.value) |
| |
| |
| @pytest.mark.parametrize("input_shape_vector", [([-1, "one"]), |
| ([1.33, 4.55]), |
| ([{1: "one"}])], ids=lambda x: str(x)) |
| def test_createdescriptorforconcatenation_rubbish_assignment_shape_vector(input_shape_vector): |
| with pytest.raises(TypeError) as err: |
| desc = ann.CreateDescriptorForConcatenation(input_shape_vector, 0) |
| |
| assert "in method 'CreateDescriptorForConcatenation', argument 1 of type 'std::vector< armnn::TensorShape,std::allocator< armnn::TensorShape > >'" in str( |
| err.value) |
| |
| |
| generated_classes = inspect.getmembers(generated, inspect.isclass) |
| generated_classes_names = list(map(lambda x: x[0], generated_classes)) |
| @pytest.mark.parametrize("desc_name", ['ActivationDescriptor', |
| 'ArgMinMaxDescriptor', |
| 'PermuteDescriptor', |
| 'SoftmaxDescriptor', |
| 'ConcatDescriptor', |
| 'SplitterDescriptor', |
| 'Pooling2dDescriptor', |
| 'FullyConnectedDescriptor', |
| 'Convolution2dDescriptor', |
| 'DepthwiseConvolution2dDescriptor', |
| 'DetectionPostProcessDescriptor', |
| 'NormalizationDescriptor', |
| 'L2NormalizationDescriptor', |
| 'BatchNormalizationDescriptor', |
| 'InstanceNormalizationDescriptor', |
| 'BatchToSpaceNdDescriptor', |
| 'FakeQuantizationDescriptor', |
| 'ResizeDescriptor', |
| 'ReshapeDescriptor', |
| 'SpaceToBatchNdDescriptor', |
| 'SpaceToDepthDescriptor', |
| 'LstmDescriptor', |
| 'MeanDescriptor', |
| 'PadDescriptor', |
| 'SliceDescriptor', |
| 'StackDescriptor', |
| 'StridedSliceDescriptor', |
| 'TransposeConvolution2dDescriptor']) |
| class TestDescriptorMassChecks: |
| |
| def test_desc_implemented(self, desc_name): |
| assert desc_name in generated_classes_names |
| |
| def test_desc_equal(self, desc_name): |
| desc_class = next(filter(lambda x: x[0] == desc_name ,generated_classes))[1] |
| |
| assert desc_class() == desc_class() |
| |
| |
| |
| generated_classes = inspect.getmembers(generated, inspect.isclass) |
| generated_classes_names = list(map(lambda x: x[0], generated_classes)) |
| @pytest.mark.parametrize("desc_name", ['ActivationDescriptor', |
| 'ArgMinMaxDescriptor', |
| 'PermuteDescriptor', |
| 'SoftmaxDescriptor', |
| 'ConcatDescriptor', |
| 'SplitterDescriptor', |
| 'Pooling2dDescriptor', |
| 'FullyConnectedDescriptor', |
| 'Convolution2dDescriptor', |
| 'DepthwiseConvolution2dDescriptor', |
| 'DetectionPostProcessDescriptor', |
| 'NormalizationDescriptor', |
| 'L2NormalizationDescriptor', |
| 'BatchNormalizationDescriptor', |
| 'InstanceNormalizationDescriptor', |
| 'BatchToSpaceNdDescriptor', |
| 'FakeQuantizationDescriptor', |
| 'ResizeDescriptor', |
| 'ReshapeDescriptor', |
| 'SpaceToBatchNdDescriptor', |
| 'SpaceToDepthDescriptor', |
| 'LstmDescriptor', |
| 'MeanDescriptor', |
| 'PadDescriptor', |
| 'SliceDescriptor', |
| 'StackDescriptor', |
| 'StridedSliceDescriptor', |
| 'TransposeConvolution2dDescriptor']) |
| class TestDescriptorMassChecks: |
| |
| def test_desc_implemented(self, desc_name): |
| assert desc_name in generated_classes_names |
| |
| def test_desc_equal(self, desc_name): |
| desc_class = next(filter(lambda x: x[0] == desc_name ,generated_classes))[1] |
| |
| assert desc_class() == desc_class() |
| |