// // Copyright © 2017 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // #include "ParserFlatbuffersFixture.hpp" TEST_SUITE("TensorflowLiteParser_DepthwiseConvolution2D") { struct DepthwiseConvolution2dFixture : public ParserFlatbuffersFixture { explicit DepthwiseConvolution2dFixture(const std::string& inputShape, const std::string& outputShape, const std::string& filterShape, const std::string& filterData, const std::string& strides, const std::string& paddingType, const std::string biasShape = "", const std::string biasData = "") { std::string inputTensors = "[ 0, 2 ]"; std::string biasTensor = ""; std::string biasBuffer = ""; if (biasShape.size() > 0 && biasData.size() > 0) { inputTensors = "[ 0, 2, 3 ]"; biasTensor = R"( { "shape": )" + biasShape + R"( , "type": "INT32", "buffer": 3, "name": "biasTensor", "quantization": { "min": [ 0.0 ], "max": [ 255.0 ], "scale": [ 1.0 ], "zero_point": [ 0 ], } } )"; biasBuffer = R"( { "data": )" + biasData + R"(, }, )"; } m_JsonString = R"( { "version": 3, "operator_codes": [ { "builtin_code": "DEPTHWISE_CONV_2D" } ], "subgraphs": [ { "tensors": [ { "shape": )" + inputShape + R"(, "type": "UINT8", "buffer": 0, "name": "inputTensor", "quantization": { "min": [ 0.0 ], "max": [ 255.0 ], "scale": [ 1.0 ], "zero_point": [ 0 ], } }, { "shape": )" + outputShape + R"(, "type": "UINT8", "buffer": 1, "name": "outputTensor", "quantization": { "min": [ 0.0 ], "max": [ 511.0 ], "scale": [ 2.0 ], "zero_point": [ 0 ], } }, { "shape": )" + filterShape + R"(, "type": "UINT8", "buffer": 2, "name": "filterTensor", "quantization": { "min": [ 0.0 ], "max": [ 255.0 ], "scale": [ 1.0 ], "zero_point": [ 0 ], } }, )" + biasTensor + R"( ], "inputs": [ 0 ], "outputs": [ 1 ], "operators": [ { "opcode_index": 0, "inputs": )" + inputTensors + R"(, "outputs": [ 1 ], "builtin_options_type": "DepthwiseConv2DOptions", "builtin_options": { "padding": ")" + paddingType + R"(", "stride_w": )" + strides+ R"(, "stride_h": )" + strides+ R"(, "depth_multiplier": 1, "fused_activation_function": "NONE" }, "custom_options_format": "FLEXBUFFERS" } ], } ], "buffers" : [ { }, { }, { "data": )" + filterData + R"(, }, )" + biasBuffer + R"( ] } )"; SetupSingleInputSingleOutput("inputTensor", "outputTensor"); } }; struct DepthwiseConvolution2dSameFixture : DepthwiseConvolution2dFixture { DepthwiseConvolution2dSameFixture() : DepthwiseConvolution2dFixture("[ 1, 3, 3, 1 ]", // inputShape "[ 1, 3, 3, 1 ]", // outputShape "[ 1, 3, 3, 1 ]", // filterShape "[ 9,8,7, 6,5,4, 3,2,1 ]", // filterData "1", // stride w and h "SAME") // padding type {} }; TEST_CASE_FIXTURE(DepthwiseConvolution2dSameFixture, "ParseDepthwiseConv2DSame") { RunTest<4, armnn::DataType::QAsymmU8>( 0, { 0, 1, 2, 3, 4, 5, 6, 7, 8 }, // the expected values were generated using the example python implementation at // https://eli.thegreenplace.net/2018/depthwise-separable-convolutions-for-machine-learning/ // divide the expected values by the output scale, as it is not 1.0 { 14/2, 35/2, 38/2, 57/2, 120/2, 111/2, 110/2, 197/2, 158/2 }); } struct DepthwiseConvolution2dValidFixture : DepthwiseConvolution2dFixture { DepthwiseConvolution2dValidFixture () : DepthwiseConvolution2dFixture("[ 1, 3, 3, 1 ]", // inputShape "[ 1, 1, 1, 1 ]", // outputShape "[ 1, 3, 3, 1 ]", // filterShape "[ 9,8,7, 6,5,4, 3,2,1 ]", // filterData "1", // stride w and h "VALID") // padding type {} }; TEST_CASE_FIXTURE(DepthwiseConvolution2dValidFixture, "ParseDepthwiseConv2DValid") { RunTest<4, armnn::DataType::QAsymmU8>( 0, { 0, 1, 2, 3, 4, 5, 6, 7, 8 }, // divide the expected values by the output scale, as it is not 1.0 { 120/2 }); } struct DepthwiseConvolution2dSameBiasFixture : DepthwiseConvolution2dFixture { DepthwiseConvolution2dSameBiasFixture() : DepthwiseConvolution2dFixture("[ 1, 3, 3, 1 ]", // inputShape "[ 1, 3, 3, 1 ]", // outputShape "[ 1, 3, 3, 1 ]", // filterShape "[ 9,8,7, 6,5,4, 3,2,1 ]", // filterData "1", // stride w and h "SAME", // padding type "[ 1 ]", // biasShape "[ 10, 0, 0, 0 ]") // biasData {} }; TEST_CASE_FIXTURE(DepthwiseConvolution2dSameBiasFixture, "ParseDepthwiseConv2DSameBias") { RunTest<4, armnn::DataType::QAsymmU8>( 0, { 0, 1, 2, 3, 4, 5, 6, 7, 8 }, // divide the expected values by the output scale, as it is not 1.0 { ( 14+10)/2, ( 35+10)/2, ( 38+10)/2, ( 57+10)/2, (120+10)/2, (111+10)/2, (110+10)/2, (197+10)/2, (158+10)/2 }); } struct DynamicDepthwiseConvolution2dSameBiasFixture : DepthwiseConvolution2dFixture { DynamicDepthwiseConvolution2dSameBiasFixture() : DepthwiseConvolution2dFixture("[ 1, 3, 3, 1 ]", // inputShape "[ ]", // outputShape "[ 1, 3, 3, 1 ]", // filterShape "[ 9,8,7, 6,5,4, 3,2,1 ]", // filterData "1", // stride w and h "SAME", // padding type "[ 1 ]", // biasShape "[ 10, 0, 0, 0 ]") // biasData {} }; TEST_CASE_FIXTURE(DynamicDepthwiseConvolution2dSameBiasFixture, "ParseDynamicDepthwiseConv2DSameBias") { RunTest<4, armnn::DataType::QAsymmU8, armnn::DataType::QAsymmU8>(0, { { "inputTensor", { 0, 1, 2, 3, 4, 5, 6, 7, 8 } } }, { { "outputTensor", { ( 14+10)/2, ( 35+10)/2, ( 38+10)/2, ( 57+10)/2, (120+10)/2, (111+10)/2, (110+10)/2, (197+10)/2, (158+10)/2 } } }, true); } struct DepthwiseConvolution2dFixture2 : public ParserFlatbuffersFixture { explicit DepthwiseConvolution2dFixture2(const std::string& inputShape, const std::string& outputShape, const std::string& filterShape, const std::string& filterData, const std::string& strides, const std::string& paddingType, const std::string biasShape = "", const std::string biasData = "", const std::string filter_quant_min = "[ 0.0 ]", const std::string filter_quant_max = "[ 255.0 ]", const std::string filter_quant_scale = "[ 1.0 ]", const std::string filter_quant_zero_point = "[ 0 ]", const std::string filter_quant_axis = "", const std::string output_scale = "[ 1.0 ]") { std::string inputTensors = "[ 0, 2 ]"; std::string biasTensor = ""; std::string biasBuffer = ""; if (biasShape.size() > 0 && biasData.size() > 0) { inputTensors = "[ 0, 2, 3 ]"; biasTensor = R"( { "shape": )" + biasShape + R"( , "type": "INT32", "buffer": 3, "name": "biasTensor", "quantization": { "min": [ 0.0 ], "max": [ 255.0 ], "scale": [ 1.0 ], "zero_point": [ 0 ], } } )"; biasBuffer = R"( { "data": )" + biasData + R"(, }, )"; } std::string filter_qantization = R"( "min": )" + filter_quant_min + R"(, "max": )" + filter_quant_max + R"(, "scale": )" + filter_quant_scale + R"(, "zero_point": )" + filter_quant_zero_point; // A given quantization axis indicates if per channel quantization is used for filters if (filter_quant_axis.size() > 0) { filter_qantization += R"(, "quantized_dimension": )" + filter_quant_axis; } m_JsonString = R"( { "version": 3, "operator_codes": [ { "builtin_code": "DEPTHWISE_CONV_2D" } ], "subgraphs": [ { "tensors": [ { "shape": )" + inputShape + R"(, "type": "INT8", "buffer": 0, "name": "inputTensor", "quantization": { "min": [ 0.0 ], "max": [ 255.0 ], "scale": [ 1.0 ], "zero_point": [ 0 ], } }, { "shape": )" + outputShape + R"(, "type": "INT8", "buffer": 1, "name": "outputTensor", "quantization": { "min": [ 0.0 ], "max": [ 511.0 ], "scale": )" + output_scale + R"(, "zero_point": [ 0 ], } }, { "shape": )" + filterShape + R"(, "type": "INT8", "buffer": 2, "name": "filterTensor", "quantization": {)" + filter_qantization + R"( } }, )" + biasTensor + R"( ], "inputs": [ 0 ], "outputs": [ 1 ], "operators": [ { "opcode_index": 0, "inputs": )" + inputTensors + R"(, "outputs": [ 1 ], "builtin_options_type": "DepthwiseConv2DOptions", "builtin_options": { "padding": ")" + paddingType + R"(", "stride_w": )" + strides+ R"(, "stride_h": )" + strides+ R"(, "depth_multiplier": 1, "fused_activation_function": "NONE" }, "custom_options_format": "FLEXBUFFERS" } ], } ], "buffers" : [ { }, { }, { "data": )" + filterData + R"(, }, )" + biasBuffer + R"( ] } )"; SetupSingleInputSingleOutput("inputTensor", "outputTensor"); } }; // No quantization meaning scale=1.0 and offset=0.0 and tensor quantization struct DepthwiseConvolution2dNoQuantFixture : DepthwiseConvolution2dFixture2 { DepthwiseConvolution2dNoQuantFixture() : DepthwiseConvolution2dFixture2("[ 1, 3, 3, 3 ]", // inputShape "[ 1, 3, 3, 3 ]", // outputShape "[ 1, 3, 3, 3 ]", // filterShape "[ 9,8,7, 6,5,4, 3,2,1, " "9,8,7, 6,5,4, 3,2,1, " "9,8,7, 6,5,4, 3,2,1 ]", // filterData "1", // stride w and h "SAME", // padding type "", // bias shape "" // bias data ) {} }; // No quantization meaning scale=1.0 and offset=0.0 and tensor quantization TEST_CASE_FIXTURE(DepthwiseConvolution2dNoQuantFixture, "ParseDepthwiseConv2DNoQuant") { RunTest<4, armnn::DataType::QAsymmS8>( 0, { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1}, { 18, 14, 10, 36, 30, 24, 30, 26, 22, 27, 21, 15, 54, 45, 36, 45, 39, 33, 18, 14, 10, 36, 30, 24, 30, 26, 22}); } // Uses per channel quantization on weights but with scales = 1.0 and offsets = 0.0 struct DepthwiseConvolution2dNoChannelQuantFixture : DepthwiseConvolution2dFixture2 { DepthwiseConvolution2dNoChannelQuantFixture() : DepthwiseConvolution2dFixture2("[ 1, 3, 3, 3 ]", // inputShape "[ 1, 3, 3, 3 ]", // outputShape "[ 1, 3, 3, 3 ]", // filterShape "[ 9,8,7, 6,5,4, 3,2,1, 9,8,7, 6,5,4, 3,2,1, 9,8,7, 6,5,4, 3,2,1 ]", //filterData "1", // stride w and h "SAME", // padding type "", // bias shape "", // bias data "[ 0.0 ]", // filter quantization min values "[ 255.0 ]", // filter quantization max values "[ 1.0, 1.0, 1.0]", // filter quantization scales "[ 0, 0, 0]", // filter quantization zero-points "3" // filter quantized axis // (in case of per channel quantization) ) {} }; // Uses per channel quantization on weights but with scales = 1.0 and offsets = 0.0 TEST_CASE_FIXTURE(DepthwiseConvolution2dNoChannelQuantFixture, "ParseDepthwiseConv2DFilterNoChannelQuant") { RunTest<4, armnn::DataType::QAsymmS8>( 0, { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1}, { 18, 14, 10, 36, 30, 24, 30, 26, 22, 27, 21, 15, 54, 45, 36, 45, 39, 33, 18, 14, 10, 36, 30, 24, 30, 26, 22}); } // Uses per channel quantization on weights but all scales are set to the same value struct DepthwiseConvolution2dWeightsPerChannelQuantFixture : DepthwiseConvolution2dFixture2 { DepthwiseConvolution2dWeightsPerChannelQuantFixture() : DepthwiseConvolution2dFixture2("[ 1, 3, 3, 3 ]", // inputShape "[ 1, 3, 3, 3 ]", // outputShape "[ 1, 3, 3, 3 ]", // filterShape // filterData is [ 9,8,7, 6,5,4, 3,2,1, 9,8,7, 6,5,4, 3,2,1, 9,8,7, 6,5,4, 3,2,1 ] // quantized per channel with q_dim=3 "[36, 32, 28, 24, 20, 16, 12, 8, 4, 36, 32, 28, 24, " "20, 16, 12, 8, 4, 36, 32, 28, 24, 20, 16, 12, 8, 4]", "1", // stride w and h "SAME", // padding type "", // bias shape "", // bias data "[ 0.0 ]", // filter quantization min values "[ 255.0 ]", // filter quantization max values "[ 0.25, 0.25, 0.25]", // filter quantization scales "[ 0, 0, 0]", // filter quantization zero-points "3" // filter quantized axis // (in case of per channel quantization) ) {} }; // Weights are per channel quantized but all scales are set to the same value TEST_CASE_FIXTURE(DepthwiseConvolution2dWeightsPerChannelQuantFixture, "ParseDepthwiseConv2DFilterWeightsPerChannelQuant") { RunTest<4, armnn::DataType::QAsymmS8>( 0, { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1}, { 18, 14, 10, 36, 30, 24, 30, 26, 22, 27, 21, 15, 54, 45, 36, 45, 39, 33, 18, 14, 10, 36, 30, 24, 30, 26, 22}); } // Uses per channel quantization on weights all scales are different in this test struct DepthwiseConvolution2dWeightsPerChannelQuant1Fixture : DepthwiseConvolution2dFixture2 { DepthwiseConvolution2dWeightsPerChannelQuant1Fixture() : DepthwiseConvolution2dFixture2("[ 1, 3, 3, 3 ]", // inputShape "[ 1, 3, 3, 3 ]", // outputShape "[ 1, 3, 3, 3 ]", // filterShape // filterData is [ 9,8,7, 6,5,4, 3,2,1, 9,8,7, 6,5,4, 3,2,1, 9,8,7, 6,5,4, 3,2,1 ] // quantized per channel with q_dim=3 "[36, 40, 70, 24, 25, 40, 12, 10, 10, 36, 40, 70, 24, " "25, 40, 12, 10, 10, 36, 40, 70, 24, 25, 40, 12, 10, 10]", "1", // stride w and h "SAME", // padding type "", // bias shape "", // bias data "[ 0.0 ]", // filter quantization min values "[ 255.0 ]", // filter quantization max values "[ 0.25, 0.2, 0.1]", // filter quantization scales "[ 0, 0, 0]", // filter quantization zero-points "3" // filter quantized axis // (in case of per channel quantization) ) {} }; // Uses per channel quantization on weights all scales are different in this test TEST_CASE_FIXTURE(DepthwiseConvolution2dWeightsPerChannelQuant1Fixture, "ParseDepthwiseConv2DFilterWeightsPerChannelQuant1") { RunTest<4, armnn::DataType::QAsymmS8>( 0, { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1}, { 18, 14, 10, 36, 30, 24, 30, 26, 22, 27, 21, 15, 54, 45, 36, 45, 39, 33, 18, 14, 10, 36, 30, 24, 30, 26, 22}); } // Uses per channel quantization on weights all scales are different in this test // Uses different shape for weights and input compared to the other tests above struct DepthwiseConvolution2dWeightsPerChannelQuant2Fixture : DepthwiseConvolution2dFixture2 { DepthwiseConvolution2dWeightsPerChannelQuant2Fixture() : DepthwiseConvolution2dFixture2("[ 1, 4, 4, 4 ]", // inputShape "[ 1, 4, 4, 4 ]", // outputShape "[ 1, 2, 2, 4 ]", // filterShape // filterData is [ 9,8,7,6, 5,4,3,2, 1,9,8,7, 6,5,4,3 ] // quantized per channel with q_dim=3 "[36, 40, 70, 20, 20, 20, 30, 6, 4, 45, 80, 23, 24, 25, 40, 10]", "1", // stride w and h "SAME", // padding type "", // bias shape "", // bias data "[ 0.0 ]", // filter quantization min values "[ 255.0 ]", // filter quantization max values "[ 0.25, 0.2, 0.1, 0.3]", // filter quantization scales "[ 0, 0, 0, 0]", // filter quantization zero-points "3" // filter quantized axis // (in case of per channel quantization) ) {} }; // Uses per channel quantization on weights all scales are different in this test // Uses different shape for weights and input compared to the other tests above TEST_CASE_FIXTURE(DepthwiseConvolution2dWeightsPerChannelQuant2Fixture, "ParseDepthwiseConv2DFilterWeightsPerChannelQuant2") { RunTest<4, armnn::DataType::QAsymmS8>( 0, { 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1}, { 21, 26, 22, 18, 21, 26, 22, 18, 21, 26, 22, 18, 10, 17, 15, 13, 21, 26, 22, 18, 21, 26, 22, 18, 21, 26, 22, 18, 10, 17, 15, 13, 21, 26, 22, 18, 21, 26, 22, 18, 21, 26, 22, 18, 10, 17, 15, 13, 14, 12, 10, 8, 14, 12, 10, 8, 14, 12, 10, 8, 9, 8, 7, 6}); } // Test for depthwise_multiplier different to one (M > 1) struct DepthwiseConvolution2dWeightsPerChannelQuant4Fixture : DepthwiseConvolution2dFixture2 { DepthwiseConvolution2dWeightsPerChannelQuant4Fixture() : DepthwiseConvolution2dFixture2("[ 1, 4, 4, 4 ]", // inputShape "[ 1, 4, 4, 16 ]", // outputShape "[ 1, 2, 2, 16 ]", // filterShape // filter data is [ 9,8,7,6, 5,4,3,2, 1,9,8,7, 6,5,4,3, // 9,8,7,6, 5,4,3,2, 1,9,8,7, 6,5,4,3, // 9,8,7,6, 5,4,3,2, 1,9,8,7, 6,5,4,3, // 9,8,7,6, 5,4,3,2, 1,9,8,7, 6,5,4,3 ] // quantized per channel with q_dim=3 "[36, 40, 70, 20, 20, 20, 30, 6, 4, 45, 80, 23, 24, 25, 40, 10, " "36, 40, 70, 20, 20, 20, 30, 6, 4, 45, 80, 23, 24, 25, 40, 10, " "36, 40, 70, 20, 20, 20, 30, 6, 4, 45, 80, 23, 24, 25, 40, 10, " "36, 40, 70, 20, 20, 20, 30, 6, 4, 45, 80, 23, 24, 25, 40, 10]", "1", // stride w and h "SAME", // padding type "", // bias shape "", // bias data "[ 0.0 ]", // filter quantization min values "[ 255.0 ]", // filter quantization max values "[ 0.25, 0.2, 0.1, 0.3," "0.25, 0.2, 0.1, 0.3," "0.25, 0.2, 0.1, 0.3," "0.25, 0.2, 0.1, 0.3]", // filter quantization scales "[ 0, 0, 0, 0]", // filter quantization zero-points "3" // filter quantized axis // (in case of per channel quantization) ) {} }; // Test for depthwise_multiplier different to one (M > 1) TEST_CASE_FIXTURE(DepthwiseConvolution2dWeightsPerChannelQuant4Fixture, "ParseDepthwiseConv2DFilterWeightsPerChannelQuant4") { RunTest<4, armnn::DataType::QAsymmS8>( 0, { 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1}, { 36, 32, 28, 24, 20, 16, 12, 8, 4, 36, 32, 28, 24, 20, 16, 12, 36, 32, 28, 24, 20, 16, 12, 8, 4, 36, 32, 28, 24, 20, 16, 12, 36, 32, 28, 24, 20, 16, 12, 8, 4, 36, 32, 28, 24, 20, 16, 12, 18, 16, 14, 12, 10, 8, 6, 4, 2, 18, 16, 14, 12, 10, 8, 6, 36, 32, 28, 24, 20, 16, 12, 8, 4, 36, 32, 28, 24, 20, 16, 12, 36, 32, 28, 24, 20, 16, 12, 8, 4, 36, 32, 28, 24, 20, 16, 12, 36, 32, 28, 24, 20, 16, 12, 8, 4, 36, 32, 28, 24, 20, 16, 12, 18, 16, 14, 12, 10, 8, 6, 4, 2, 18, 16, 14, 12, 10, 8, 6, 36, 32, 28, 24, 20, 16, 12, 8, 4, 36, 32, 28, 24, 20, 16, 12, 36, 32, 28, 24, 20, 16, 12, 8, 4, 36, 32, 28, 24, 20, 16, 12, 36, 32, 28, 24, 20, 16, 12, 8, 4, 36, 32, 28, 24, 20, 16, 12, 18, 16, 14, 12, 10, 8, 6, 4, 2, 18, 16, 14, 12, 10, 8, 6, 18, 16, 14, 12, 10, 8, 6, 4, 2, 18, 16, 14, 12, 10, 8, 6, 18, 16, 14, 12, 10, 8, 6, 4, 2, 18, 16, 14, 12, 10, 8, 6, 18, 16, 14, 12, 10, 8, 6, 4, 2, 18, 16, 14, 12, 10, 8, 6, 9, 8, 7, 6, 5, 4, 3, 2, 1, 9, 8, 7, 6, 5, 4, 3}); } struct DepthwiseConvolution2dWeightsPerChannelQuant6Fixture : DepthwiseConvolution2dFixture2 { DepthwiseConvolution2dWeightsPerChannelQuant6Fixture() : DepthwiseConvolution2dFixture2("[ 1, 4, 4, 4 ]", // inputShape "[ 1, 4, 4, 16 ]", // outputShape "[ 1, 2, 2, 16 ]", // filterShape // filter data is [ 3,4,1,1,1,3,3,2,1,4,3,4,1,2,2,4, // 2,0,3,1,0,2,4,3,4,3,0,1,3,4,4,1, // 3,3,2,0,0,0,1,3,3,2,4,4,3,1,1,3, // 1,0,0,2,3,0,1,1,4,2,2,1,2,3,2,0] // quantized per channel with q_dim=3 "[12,20,10, 3, 4,15,30, 6, 4,20,30,12, 4,10,20,12," " 8, 0,30, 3, 0,10,40, 9,16,15, 0, 3,12,20,40, 3," " 12,15,20, 0, 0, 0,10, 9,12,10,40,12,12, 5,10, 9," " 4, 0, 0, 6,12, 0,10, 3,16,10,20, 3, 8,15,20, 0]", "1", // stride w and h "SAME", // padding type "", // bias shape "", // bias data "[ 0.0 ]", // filter quantization min values "[ 255.0 ]", // filter quantization max values "[ 0.25, 0.2, 0.1, 0.333333333," "0.25, 0.2, 0.1, 0.333333333," "0.25, 0.2, 0.1, 0.333333333," "0.25, 0.2, 0.1, 0.333333333]", // filter quantization scales "[ 0, 0, 0, 0]", // filter quantization zero-points "3" // filter quantized axis // (in case of per channel quantization) ) {} }; TEST_CASE_FIXTURE(DepthwiseConvolution2dWeightsPerChannelQuant6Fixture, "ParseDepthwiseConv2DFilterWeightsPerChannelQuant6") { RunTest<4, armnn::DataType::QAsymmS8>( 0, { 1,0,1,2,0,4,4,0,2,1,2,0,1,3,3,0, 1,2,2,3,3,4,1,1,2,4,1,3,4,2,0,2, 0,3,1,3,4,3,2,0,1,2,3,3,0,2,4,2, 1,2,1,4,3,4,1,3,1,0,2,3,1,3,2,0}, { 9, 7, 3, 7,12, 8,22,22,27,22,13,17,13,10, 9,17, 15, 9,12, 6,16,14,24,27,19,26,18,23, 9,10, 7, 3, 18,14, 9,11, 7, 9,21,25,17,19,10,15,13, 9, 7, 9, 15,16, 9, 1, 3, 9,11,12, 3,12, 9,12, 6, 2, 2, 6, 13, 4,10,12,11,14,28,28,17,17,14,15,15,13,13,22, 26,24,17, 7,10,20,33,31,23,17,17,16,16,23,20, 7, 17,11,16, 6,10,16,24,22,26,18,23,20,22,23,21,23, 12,16, 4, 4, 2, 6, 8,10,12, 8,16,16, 8, 6, 6,14, 14, 3,14,10,15,15,27,25,16,14, 9,11,21,19,16,24, 24,25,13, 7, 3,13,21,24,25,23,14,17,24,24,21,12, 7, 7, 3, 3,11,10,17,13,33,32,21,26,18,17,17,23, 3, 3, 2, 0, 2, 6, 9,13,10,20,20,24, 2, 4, 4, 8, 9, 4,10, 4, 2,14,22,16, 5, 7, 3, 5,13,20,20,19, 11,12, 6, 4, 4,12,12, 8, 9,10, 3, 6,12,18,18,15, 5, 4, 4, 2, 0, 6,12, 9,10,14, 6,10, 3, 6, 6,12, 3, 4, 1, 1, 3, 9, 9, 6, 2, 8, 6, 8, 0, 0, 0, 0}); } struct DepthwiseConvolution2dWeightsPerChannelQuant1_1Fixture : DepthwiseConvolution2dFixture2 { DepthwiseConvolution2dWeightsPerChannelQuant1_1Fixture() : DepthwiseConvolution2dFixture2("[ 1, 3, 3, 3 ]", // inputShape "[ 1, 3, 3, 3 ]", // outputShape "[ 1, 3, 3, 3 ]", // filterShape // filterData is [ 1,4,0,2,4,3,1,0,1, // 3,0,4,0,1,3,4,2,4, // 3,0,3,4,4,0,3,4,2] // quantized per channel with q_dim=3 "[ 4,20, 0, 8,20,30, 4, 0,10,12," " 0,40, 0, 5,30,16,10,40,12, 0," "30,16,20, 0,12,20,20]", "1", // stride w and h "SAME", // padding type "", // bias shape "", // bias data "[ 0.0 ]", // filter quantization min values "[ 255.0 ]", // filter quantization max values "[ 0.25, 0.2, 0.1]", // filter quantization scales "[ 0, 0, 0]", // filter quantization zero-points "3" // filter quantized axis // (in case of per channel quantization) ) {} }; TEST_CASE_FIXTURE(DepthwiseConvolution2dWeightsPerChannelQuant1_1Fixture, "ParseDepthwiseConv2DFilterWeightsPerChannelQuant1_1") { RunTest<4, armnn::DataType::QAsymmS8>( 0, { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1}, { 11,11, 9,17,11,16,10, 5,10, 14,15,13,21,19,20,13,13,13, 7, 7,11,11,11,15, 6, 9,10}); } // Same with input different to 1 struct DepthwiseConvolution2dWeightsPerChannelQuant1_2Fixture : DepthwiseConvolution2dFixture2 { DepthwiseConvolution2dWeightsPerChannelQuant1_2Fixture() : DepthwiseConvolution2dFixture2("[ 1, 3, 3, 3 ]", // inputShape "[ 1, 3, 3, 3 ]", // outputShape "[ 1, 3, 3, 3 ]", // filterShape // filterData is [ 1,4,0,2,4,3,1,0,1, // 3,0,4,0,1,3,4,2,4, // 3,0,3,4,4,0,3,4,2] // quantized per channel with q_dim=3 "[ 4,20, 0, 8,20,30, 4, 0,10,12," " 0,40, 0, 5,30,16,10,40,12, 0," "30,16,20, 0,12,20,20]", "1", // stride w and h "SAME", // padding type "", // bias shape "", // bias data "[ 0.0 ]", // filter quantization min values "[ 255.0 ]", // filter quantization max values "[ 0.25, 0.2, 0.1]", // filter quantization scales "[ 0, 0, 0]", // filter quantization zero-points "3" // filter quantized axis // (in case of per channel quantization) ) {} }; TEST_CASE_FIXTURE(DepthwiseConvolution2dWeightsPerChannelQuant1_2Fixture, "ParseDepthwiseConv2DFilterWeightsPerChannelQuant1_2") { RunTest<4, armnn::DataType::QAsymmS8>( 0, { 3,2,0,0,4,3,0,1,2, 0,1,3,0,4,2,2,2,3, 2,4,3,2,0,4,3,4,0}, { 0,30,16,15,30,32, 8, 9,24, 20,33,28,34,48,50,18,38,35, 8, 8,36,20,28,33,10,28,25}); } struct DepthwiseConvolution2dWeightsPerChannelQuant4_1Fixture : DepthwiseConvolution2dFixture2 { DepthwiseConvolution2dWeightsPerChannelQuant4_1Fixture() : DepthwiseConvolution2dFixture2("[ 1, 4, 4, 4 ]", // inputShape "[ 1, 4, 4, 16 ]", // outputShape "[ 1, 2, 2, 16 ]", // filterShape // filter data is [ 3,4,1,1,1,3,3,2,1,4,3,4,1,2,2,4, // 2,0,3,1,0,2,4,3,4,3,0,1,3,4,4,1, // 3,3,2,0,0,0,1,3,3,2,4,4,3,1,1,3, // 1,0,0,2,3,0,1,1,4,2,2,1,2,3,2,0 ] // quantized per channel with q_dim=3 "[12,20,10, 3, 4,15,30, 6, 4,20,30,13, 4,10,20,13," " 8, 0,30, 3, 0,10,40,10,16,15, 0, 3,12,20,40, 3," " 12,15,20, 0, 0, 0,10,10,12,10,40,13,12, 5,10,10," " 4, 0, 0, 6,12, 0,10, 3,16,10,20, 3, 8,15,20, 0]", "1", // stride w and h "SAME", // padding type "", // bias shape "", // bias data "[ 0.0 ]", // filter quantization min values "[ 255.0 ]", // filter quantization max values "[ 0.25, 0.2, 0.1, 0.3," "0.25, 0.2, 0.1, 0.3," "0.25, 0.2, 0.1, 0.3," "0.25, 0.2, 0.1, 0.3]", // filter quantization scales "[ 0, 0, 0, 0]", // filter quantization zero-points "3" // filter quantized axis // (in case of per channel quantization) ) {} }; TEST_CASE_FIXTURE(DepthwiseConvolution2dWeightsPerChannelQuant4_1Fixture, "ParseDepthwiseConv2DFilterWeightsPerChannelQuant4_1") { RunTest<4, armnn::DataType::QAsymmS8>( 0, { 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1}, { 9, 7, 6, 4, 4, 5, 9, 9,12,11, 9,10, 9,10, 9, 8, 9, 7, 6, 4, 4, 5, 9, 9,12,11, 9,10, 9,10, 9, 8, 9, 7, 6, 4, 4, 5, 9, 9,12,11, 9,10, 9,10, 9, 8, 6, 7, 3, 1, 1, 3, 4, 5, 4, 6, 7, 8, 4, 3, 3, 7, 9, 7, 6, 4, 4, 5, 9, 9,12,11, 9,10, 9,10, 9, 8, 9, 7, 6, 4, 4, 5, 9, 9,12,11, 9,10, 9,10, 9, 8, 9, 7, 6, 4, 4, 5, 9, 9,12,11, 9,10, 9,10, 9, 8, 6, 7, 3, 1, 1, 3, 4, 5, 4, 6, 7, 8, 4, 3, 3, 7, 9, 7, 6, 4, 4, 5, 9, 9,12,11, 9,10, 9,10, 9, 8, 9, 7, 6, 4, 4, 5, 9, 9,12,11, 9,10, 9,10, 9, 8, 9, 7, 6, 4, 4, 5, 9, 9,12,11, 9,10, 9,10, 9, 8, 6, 7, 3, 1, 1, 3, 4, 5, 4, 6, 7, 8, 4, 3, 3, 7, 5, 4, 4, 2, 1, 5, 7, 5, 5, 7, 3, 5, 4, 6, 6, 5, 5, 4, 4, 2, 1, 5, 7, 5, 5, 7, 3, 5, 4, 6, 6, 5, 5, 4, 4, 2, 1, 5, 7, 5, 5, 7, 3, 5, 4, 6, 6, 5, 3, 4, 1, 1, 1, 3, 3, 2, 1, 4, 3, 4, 1, 2, 2, 4}); } struct DepthwiseConvolution2dWeightsPerChannelQuant4_2Fixture : DepthwiseConvolution2dFixture2 { DepthwiseConvolution2dWeightsPerChannelQuant4_2Fixture() : DepthwiseConvolution2dFixture2("[ 1, 4, 4, 4 ]", // inputShape "[ 1, 4, 4, 16 ]", // outputShape "[ 1, 2, 2, 16 ]", // filterShape // filter data is [ 3,4,1,1,1,3,3,2,1,4,3,4,1,2,2,4, // 2,0,3,1,0,2,4,3,4,3,0,1,3,4,4,1, // 3,3,2,0,0,0,1,3,3,2,4,4,3,1,1,3, // 1,0,0,2,3,0,1,1,4,2,2,1,2,3,2,0 ] // quantized per channel with q_dim=3 "[12,20,10, 3, 4,15,30, 6, 4,20,30,13, 4,10,20,13," " 8, 0,30, 3, 0,10,40,10,16,15, 0, 3,12,20,40, 3," " 12,15,20, 0, 0, 0,10,10,12,10,40,13,12, 5,10,10," " 4, 0, 0, 6,12, 0,10, 3,16,10,20, 3, 8,15,20, 0]", "1", // stride w and h "SAME", // padding type "", // bias shape "", // bias data "[ 0.0 ]", // filter quantization min values "[ 255.0 ]", // filter quantization max values "[ 0.25, 0.2, 0.1, 0.3," "0.25, 0.2, 0.1, 0.3," "0.25, 0.2, 0.1, 0.3," "0.25, 0.2, 0.1, 0.3]", // filter quantization scales "[ 0, 0, 0, 0]", // filter quantization zero-points "3" // filter quantized axis // (in case of per channel quantization) ) {} }; TEST_CASE_FIXTURE(DepthwiseConvolution2dWeightsPerChannelQuant4_2Fixture, "ParseDepthwiseConv2DFilterWeightsPerChannelQuant4_2") { RunTest<4, armnn::DataType::QAsymmS8>( 0, { 3,3,3,4, 4,4,0,0, 0,3,4,3, 0,2,2,3, 3,0,3,0, 0,3,2,1, 4,1,2,2, 0,0,0,4, 3,2,2,2, 2,1,0,4, 4,3,2,4, 3,2,0,0, 4,1,4,4, 1,0,4,3, 3,2,0,3, 1,1,0,2}, { 26,21,21, 7,12,17,28,21,20,22,25,26, 6,11,10,16, 16,16, 4,12, 7,18,28,27,30,20,12,14,16,19,17, 6, 12,12, 8, 0, 3,13,18,15,18,26,20,26,26,32,28,21, 0, 0, 0, 0, 2, 6, 6, 4, 2, 8, 6, 8,15,10,10,24, 20,21, 9, 7, 3, 6,15,16,17,22,17,22,17,18,14, 7, 18, 6,16,12,12,11,17,15,18,18,10,12,27,26,22,18, 27,28,12,10, 7, 3, 8,13, 8,12,14,16,26,24,24,24, 9, 9, 6, 0, 0, 0, 2, 6, 0, 0, 0, 0, 4, 8, 8,16, 26,24,17, 7, 2, 8,11,10,30,24,30,28,32,33,30,24, 20,11,16,12, 7, 9,17,13,20,14,16,18,31,36,33,29, 28,25,19, 9, 6,13,20,19, 2, 8, 6, 8,17,17,15,25, 12,15, 5, 3, 2, 6, 7, 7, 0, 0, 0, 0, 6, 2, 2, 6, 14,16, 7, 5, 1, 3, 3, 2,20,28,12,20,13,20,20,19, 9, 4,10, 4, 0, 4, 8, 6, 4,16,12,16,12,18,18,15, 11,12, 6, 4, 2, 8,10, 7, 0, 0, 0, 0, 9,14,14,14, 3, 4, 1, 1, 1, 3, 3, 2, 0, 0, 0, 0, 2, 4, 4, 8}); } struct DepthwiseConvolution2dWeightsPerChannelQuant4_5Fixture : DepthwiseConvolution2dFixture2 { DepthwiseConvolution2dWeightsPerChannelQuant4_5Fixture() : DepthwiseConvolution2dFixture2("[ 1, 4, 4, 4 ]", // inputShape "[ 1, 4, 4, 16 ]", // outputShape "[ 1, 2, 2, 16 ]", // filterShape // filter data is [ 1, 4, 9, 16, 25, 36, // 49, 64, 81, 100, 121, 144, // 169, 196, 225, 256, 17, 36, // 57, 80, 105, 132, 161, 192, // 225, 260, 297, 336, 377, 420, // 465, 512, 33, 68, 105, 144, // 185, 228, 273, 320, 369, 420, // 473, 528, 585, 644, 705, 768, // 49, 100, 153, 208, 265, 324, // 385, 448, 513, 580, 649, 720, // 793, 868, 945,1024 ] // quantized per channel with q_dim=3 "[ 1, 2, 3, 4, 5, 6, 7, 8, 9,10,11,12,13,14,15,16," " 17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32," " 33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48," "49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64]", "1", // stride w and h "SAME", // padding type "", // bias shape "", // bias data "[ 0.0 ]", // filter quantization min values "[ 255.0 ]", // filter quantization max values "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10,11,12,13,14,15,16]", // filter quantization scales "[ 0, 0, 0, 0]", // filter quantization zero-points "3", // filter quantized axis // (in case of per channel quantization) "[ 100.0 ]" // output scale ) {} }; // Test for depthwise_multiplier different to one (M > 1) TEST_CASE_FIXTURE(DepthwiseConvolution2dWeightsPerChannelQuant4_5Fixture, "ParseDepthwiseConv2DFilterWeightsPerChannelQuant4_5") { RunTest<4, armnn::DataType::QAsymmS8>( 0, { 1,1,1,2,2,2,1,2,1,2,2,1,2,2,1,1,1,1,1,1,1,2,2,2, 1,2,2,2,1,1,1,2,1,1,1,1,2,1,2,1,2,1,1,2,1,2,1,1, 1,2,2,1,2,2,1,1,2,1,2,1,1,2,1,2}, { 1, 2, 3, 5, 9,11,14,16,17,19,21,24,32,36,39,43, 1, 2, 3, 4,11,14,17,20,22,26,29,33,34,38,42,46, 1, 2, 3, 5, 8,11,13,16,16,18,21,24,33,36,39,43, 0, 0, 1, 1, 2, 3, 3, 4, 4, 5, 5, 6,13,14,16,17, 1, 3, 4, 6, 6, 8,10,12,19,22,24,27,23,25,28,30, 1, 3, 5, 8, 7, 8,10,12,18,21,24,27,32,36,39,43, 1, 2, 4, 5, 8,10,13,15,12,14,16,18,30,33,37,40, 0, 0, 1, 1, 3, 4, 5, 7, 4, 5, 5, 6, 9,10,11,12, 1, 3, 5, 7,10,12,15,17,17,20,23,25,19,21,23,25, 2, 4, 6, 8, 7, 9,11,13,17,20,23,25,23,25,28,30, 1, 2, 4, 6, 9,11,14,16,15,17,20,22,28,31,35,38, 0, 0, 1, 1, 4, 5, 6, 7, 4, 5, 5, 6,13,14,16,17, 0, 0, 1, 1, 2, 3, 4, 5, 3, 4, 5, 6, 5, 6, 6, 7, 0, 0, 1, 1, 1, 2, 2, 3, 5, 6, 7, 8, 5, 6, 6, 7, 0, 0, 0, 1, 2, 3, 3, 4, 3, 4, 5, 6, 9,10,11,12, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 3, 3, 4, 5}); } struct DepthwiseConvolution2dWeightsPerChannelQuant4_3_1Fixture : DepthwiseConvolution2dFixture2 { DepthwiseConvolution2dWeightsPerChannelQuant4_3_1Fixture() : DepthwiseConvolution2dFixture2("[ 1, 4, 4, 4 ]", // inputShape "[ 1, 4, 4, 16 ]", // outputShape "[ 1, 2, 2, 16 ]", // filterShape // filter data is [ 3,4,1,1,1,3,3,2,1,4,3,4,1,2,2,4, // 2,0,3,1,0,2,4,3,4,3,0,1,3,4,4,1, // 3,3,2,0,0,0,1,3,3,2,4,4,3,1,1,3, // 1,0,0,2,3,0,1,1,4,2,2,1,2,3,2,0 ] // quantized per channel with q_dim=3 "[12,20,10, 3, 2,24, 9,10, 5,16,30,12, 3,10, 4,32," " 8, 0,30, 3, 0,16,12,15,20,12, 0, 3, 9,20, 8, 8," " 12,15,20, 0, 0, 0, 3,15,15, 8,40,12, 9, 5, 2,24," " 4, 0, 0, 6, 6, 0, 3, 5,20, 8,20, 3, 6,15, 4, 0]", "1", // stride w and h "SAME", // padding type "", // bias shape "", // bias data "[ 0.0 ]", // filter quantization min values "[ 255.0 ]", // filter quantization max values "[0.25, 0.2, 0.1, 0.3333333333, " "0.5, 0.125, 0.33333333, 0.2, " "0.2, 0.25, 0.1, 0.333333333, " "0.3333333333, 0.2, 0.5, 0.125]", // filter quantization scales "[ 0, 0, 0, 0]", // filter quantization zero-points "3" // filter quantized axis // (in case of per channel quantization) ) {} }; // Test for depthwise_multiplier different to one (M > 1) TEST_CASE_FIXTURE(DepthwiseConvolution2dWeightsPerChannelQuant4_3_1Fixture, "ParseDepthwiseConv2DFilterWeightsPerChannelQuant4_3_1") { RunTest<4, armnn::DataType::QAsymmS8>( 0, { 3,3,3,4, 4,4,0,0, 0,3,4,3, 0,2,2,3, 3,0,3,0, 0,3,2,1, 4,1,2,2, 0,0,0,4, 3,2,2,2, 2,1,0,4, 4,3,2,4, 3,2,0,0, 4,1,4,4, 1,0,4,3, 3,2,0,3, 1,1,0,2}, { 26,21,21, 7,12,17,28,21,20,22,25,26, 6,11,10,16, 16,16, 4,12, 7,18,28,27,30,20,12,14,16,19,17, 6, 12,12, 8, 0, 3,13,18,15,18,26,20,26,26,32,28,21, 0, 0, 0, 0, 2, 6, 6, 4, 2, 8, 6, 8,15,10,10,24, 20,21, 9, 7, 3, 6,15,16,17,22,17,22,17,18,14, 7, 18, 6,16,12,12,11,17,15,18,18,10,12,27,26,22,18, 27,28,12,10, 7, 3, 8,13, 8,12,14,16,26,24,24,24, 9, 9, 6, 0, 0, 0, 2, 6, 0, 0, 0, 0, 4, 8, 8,16, 26,24,17, 7, 2, 8,11,10,30,24,30,28,32,33,30,24, 20,11,16,12, 7, 9,17,13,20,14,16,18,31,36,33,29, 28,25,19, 9, 6,13,20,19, 2, 8, 6, 8,17,17,15,25, 12,15, 5, 3, 2, 6, 7, 7, 0, 0, 0, 0, 6, 2, 2, 6, 14,16, 7, 5, 1, 3, 3, 2,20,28,12,20,13,20,20,19, 9, 4,10, 4, 0, 4, 8, 6, 4,16,12,16,12,18,18,15, 11,12, 6, 4, 2, 8,10, 7, 0, 0, 0, 0, 9,14,14,14, 3, 4, 1, 1, 1, 3, 3, 2, 0, 0, 0, 0, 2, 4, 4, 8}); } struct DepthwiseConvolution2dWeightsPerChannelQuant4_3_2Fixture : DepthwiseConvolution2dFixture2 { DepthwiseConvolution2dWeightsPerChannelQuant4_3_2Fixture() : DepthwiseConvolution2dFixture2("[ 1, 2, 2, 2 ]", // inputShape "[ 1, 2, 2, 4 ]", // outputShape "[ 1, 3, 3, 4 ]", // filterShape // filter data is [ 0,1,2,3,4,5,6,7,8, // 0,1,2,3,4,5,6,7,8, // 0,1,2,3,4,5,6,7,8, // 0,1,2,3,4,5,6,7,8 ] // quantized per channel with q_dim=3 "[0, 5,20, 9,16,25,60,21,32," " 0,10, 6,12,20,50,18,28,40," " 0, 3, 8,15,40,15,24,35,80," " 0, 4,10,30,12,20,30,70,24]", "1", // stride w and h "SAME", // padding type "", // bias shape "", // bias data "[ 0.0 ]", // filter quantization min values "[ 255.0 ]", // filter quantization max values "[0.25, 0.2, 0.1, 0.3333333333]", // filter quantization scales "[ 0, 0, 0, 0]", // filter quantization zero-points "3" // filter quantized axis // (in case of per channel quantization) ) {} }; // An easy test with M > 1 for debugging TEST_CASE_FIXTURE(DepthwiseConvolution2dWeightsPerChannelQuant4_3_2Fixture, "ParseDepthwiseConv2DFilterWeightsPerChannelQuant4_3_2") { RunTest<4, armnn::DataType::QAsymmS8>( 0, { 0,1,2,3,4,5,6,7}, { 38,50,76,92,44,56,66,37,56,50,37,53,62,74,45,61}); } } // end of TEST_SUITE("TensorflowLiteParser_DepthwiseConvolution2D")