| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include <boost/test/unit_test.hpp> |
| #include "ParserFlatbuffersFixture.hpp" |
| #include "../TfLiteParser.hpp" |
| |
| #include <string> |
| #include <iostream> |
| |
| BOOST_AUTO_TEST_SUITE(TensorflowLiteParser) |
| |
| 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 |
| {} |
| }; |
| |
| BOOST_FIXTURE_TEST_CASE(ParseDepthwiseConv2DSame, DepthwiseConvolution2dSameFixture) |
| { |
| 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 |
| {} |
| }; |
| |
| BOOST_FIXTURE_TEST_CASE(ParseDepthwiseConv2DValid, DepthwiseConvolution2dValidFixture) |
| { |
| 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 |
| {} |
| }; |
| |
| BOOST_FIXTURE_TEST_CASE(ParseDepthwiseConv2DSameBias, DepthwiseConvolution2dSameBiasFixture) |
| { |
| 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 |
| {} |
| }; |
| |
| BOOST_FIXTURE_TEST_CASE(ParseDynamicDepthwiseConv2DSameBias, DynamicDepthwiseConvolution2dSameBiasFixture) |
| { |
| 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 = "" |
| ) |
| { |
| 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": [ 1.0 ], |
| "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 |
| BOOST_FIXTURE_TEST_CASE(ParseDepthwiseConv2DNoQuant, DepthwiseConvolution2dNoQuantFixture) |
| { |
| 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 |
| BOOST_FIXTURE_TEST_CASE(ParseDepthwiseConv2DFilterNoChannelQuant, DepthwiseConvolution2dNoChannelQuantFixture) |
| { |
| 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 |
| BOOST_FIXTURE_TEST_CASE(ParseDepthwiseConv2DFilterWeightsPerChannelQuant, |
| DepthwiseConvolution2dWeightsPerChannelQuantFixture) |
| { |
| 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 |
| BOOST_FIXTURE_TEST_CASE(ParseDepthwiseConv2DFilterWeightsPerChannelQuant1, |
| DepthwiseConvolution2dWeightsPerChannelQuant1Fixture) |
| { |
| 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 |
| BOOST_FIXTURE_TEST_CASE(ParseDepthwiseConv2DFilterWeightsPerChannelQuant2, |
| DepthwiseConvolution2dWeightsPerChannelQuant2Fixture) |
| { |
| 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) |
| BOOST_FIXTURE_TEST_CASE(ParseDepthwiseConv2DFilterWeightsPerChannelQuant4, |
| DepthwiseConvolution2dWeightsPerChannelQuant4Fixture) |
| { |
| 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}); |
| } |
| |
| BOOST_AUTO_TEST_SUITE_END() |