From 7612bd6cc385dfbf54f831a6349f3a9363c6d0a2 Mon Sep 17 00:00:00 2001 From: Jan Eilers Date: Tue, 6 Apr 2021 17:29:03 +0100 Subject: IVGCVSW-5842 Remove cross-wiring in depthwise * Reading tensor infos won't allow a permutation vector anymore. The permutation only changed the quantization dimension not the shape and was therefore misleading * The permutation of the full tensor info is now performed in armnnUtils::Permuted * Changed TfLite Parser depthwise parsing function * Added unit tests to TfLite Parser with more random data * Changed TfLite Delegate depthwise parsing function * Added unit test to the delegate with per channel quantization !android-nn-driver:5412 Signed-off-by: Jan Eilers Change-Id: I1f985ee69547bcaf16a72201e00a6b6fe1ef9a97 --- .../test/DepthwiseConvolution2D.cpp | 424 ++++++++++++++++++++- 1 file changed, 408 insertions(+), 16 deletions(-) (limited to 'src/armnnTfLiteParser/test') diff --git a/src/armnnTfLiteParser/test/DepthwiseConvolution2D.cpp b/src/armnnTfLiteParser/test/DepthwiseConvolution2D.cpp index 7380d884fd..95ad2d5ee9 100644 --- a/src/armnnTfLiteParser/test/DepthwiseConvolution2D.cpp +++ b/src/armnnTfLiteParser/test/DepthwiseConvolution2D.cpp @@ -225,19 +225,19 @@ BOOST_FIXTURE_TEST_CASE(ParseDynamicDepthwiseConv2DSameBias, DynamicDepthwiseCon 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& 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 = ""; @@ -301,7 +301,7 @@ struct DepthwiseConvolution2dFixture2 : public ParserFlatbuffersFixture "quantization": { "min": [ 0.0 ], "max": [ 511.0 ], - "scale": [ 1.0 ], + "scale": )" + output_scale + R"(, "zero_point": [ 0 ], } }, @@ -381,12 +381,12 @@ struct DepthwiseConvolution2dNoChannelQuantFixture : DepthwiseConvolution2dFixtu : 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 + "[ 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 + "[ 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 @@ -582,4 +582,396 @@ BOOST_FIXTURE_TEST_CASE(ParseDepthwiseConv2DFilterWeightsPerChannelQuant4, 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) + ) + {} +}; + + +BOOST_FIXTURE_TEST_CASE(ParseDepthwiseConv2DFilterWeightsPerChannelQuant6, + DepthwiseConvolution2dWeightsPerChannelQuant6Fixture) +{ + 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) + ) + {} +}; + + +BOOST_FIXTURE_TEST_CASE(ParseDepthwiseConv2DFilterWeightsPerChannelQuant1_1, + DepthwiseConvolution2dWeightsPerChannelQuant1_1Fixture) +{ + 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) + ) + {} +}; + + +BOOST_FIXTURE_TEST_CASE(ParseDepthwiseConv2DFilterWeightsPerChannelQuant1_2, + DepthwiseConvolution2dWeightsPerChannelQuant1_2Fixture) +{ + 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) + ) + {} +}; + + +BOOST_FIXTURE_TEST_CASE(ParseDepthwiseConv2DFilterWeightsPerChannelQuant4_1, + DepthwiseConvolution2dWeightsPerChannelQuant4_1Fixture) +{ + 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) + ) + {} +}; + + +BOOST_FIXTURE_TEST_CASE(ParseDepthwiseConv2DFilterWeightsPerChannelQuant4_2, + DepthwiseConvolution2dWeightsPerChannelQuant4_2Fixture) +{ + 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) +BOOST_FIXTURE_TEST_CASE(ParseDepthwiseConv2DFilterWeightsPerChannelQuant4_5, + DepthwiseConvolution2dWeightsPerChannelQuant4_5Fixture) +{ + 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) +BOOST_FIXTURE_TEST_CASE(ParseDepthwiseConv2DFilterWeightsPerChannelQuant4_3_1, + DepthwiseConvolution2dWeightsPerChannelQuant4_3_1Fixture) +{ + 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}); +} + BOOST_AUTO_TEST_SUITE_END() -- cgit v1.2.1