// // Copyright © 2021 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // #include "ParserFlatbuffersFixture.hpp" #include // Conv3D support was added in TF 2.5, so for backwards compatibility a hash define is needed. #if defined(ARMNN_POST_TFLITE_2_4) TEST_SUITE("TensorflowLiteParser_Conv3D") { struct SimpleConv3DFixture : public ParserFlatbuffersFixture { explicit SimpleConv3DFixture() { m_JsonString = R"( { "version": 3, "operator_codes": [ { "builtin_code": "CONV_3D" } ], "subgraphs": [ { "tensors": [ { "shape": [ 1, 2, 3, 3, 1 ], "type": "UINT8", "buffer": 0, "name": "inputTensor", "quantization": { "min": [ 0.0 ], "max": [ 255.0 ], "scale": [ 1.0 ], "zero_point": [ 0 ], } }, { "shape": [ 1, 1, 1, 1, 1 ], "type": "UINT8", "buffer": 1, "name": "outputTensor", "quantization": { "min": [ 0.0 ], "max": [ 511.0 ], "scale": [ 2.0 ], "zero_point": [ 0 ], } }, { "shape": [ 2, 3, 3, 1, 1 ], "type": "UINT8", "buffer": 2, "name": "filterTensor", "quantization": { "min": [ 0.0 ], "max": [ 255.0 ], "scale": [ 1.0 ], "zero_point": [ 0 ], } } ], "inputs": [ 0 ], "outputs": [ 1 ], "operators": [ { "opcode_index": 0, "inputs": [ 0, 2 ], "outputs": [ 1 ], "builtin_options_type": "Conv3DOptions", "builtin_options": { "padding": "VALID", "stride_d": 1, "stride_w": 1, "stride_h": 1, "fused_activation_function": "NONE" }, "custom_options_format": "FLEXBUFFERS" } ], } ], "buffers" : [ { }, { }, { "data": [ 2,1,0, 6,2,1, 4,1,2, 1,2,1, 2,0,2, 2,1,1 ], }, { }, ] } )"; SetupSingleInputSingleOutput("inputTensor", "outputTensor"); } }; TEST_CASE_FIXTURE(SimpleConv3DFixture, "ParseSimpleConv3D") { RunTest<5, armnn::DataType::QAsymmU8>( 0, { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, }, // Due to the output scaling we need to half the values. { (1*2 + 2*1 + 3*0 + 4*6 + 5*2 + 6*1 + 7*4 + 8*1 + 9*2 + 10*1 + 11*2 + 12*1 + 13*2 + 14*0 + 15*2 + 16*2 + 17*1 + 18*1) /2 }); } struct Conv3DWithBiasesFixture : public ParserFlatbuffersFixture { explicit Conv3DWithBiasesFixture(const std::string& inputShape, const std::string& outputShape, const std::string& filterShape, const std::string& filterData, const std::string& biasShape, const std::string& biasData, const std::string& strides, const std::string& activation="NONE", const std::string& filterScale="1.0", const std::string& filterZeroPoint="0", const std::string& outputScale="1.0", const std::string& outputZeroPoint="0") { m_JsonString = R"( { "version": 3, "operator_codes": [ { "builtin_code": "CONV_3D" } ], "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": [ )" + outputScale + R"( ], "zero_point": [ )" + outputZeroPoint + R"( ], } }, { "shape": )" + filterShape + R"( , "type": "UINT8", "buffer": 2, "name": "filterTensor", "quantization": { "min": [ 0.0 ], "max": [ 255.0 ], "scale": [ )" + filterScale + R"( ], "zero_point": [ )" + filterZeroPoint + R"( ], } }, { "shape": )" + biasShape + R"( , "type": "INT32", "buffer": 3, "name": "biasTensor", "quantization": { "min": [ 0.0 ], "max": [ 255.0 ], "scale": [ 1.0 ], "zero_point": [ 0 ], } } ], "inputs": [ 0 ], "outputs": [ 1 ], "operators": [ { "opcode_index": 0, "inputs": [ 0, 2, 3 ], "outputs": [ 1 ], "builtin_options_type": "Conv3DOptions", "builtin_options": { "padding": "SAME", "stride_d": )" + strides + R"(, "stride_w": )" + strides + R"(, "stride_h": )" + strides + R"(, "fused_activation_function": )" + activation + R"( }, "custom_options_format": "FLEXBUFFERS" } ], } ], "buffers" : [ { }, { }, { "data": )" + filterData + R"(, }, { "data": )" + biasData + R"(, }, ] } )"; SetupSingleInputSingleOutput("inputTensor", "outputTensor"); } }; struct SimpleConv3DWithBiasesFixture : Conv3DWithBiasesFixture { SimpleConv3DWithBiasesFixture() : Conv3DWithBiasesFixture("[ 1, 2, 2, 2, 1 ]", // inputShape "[ 1, 2, 2, 2, 1 ]", // outputShape "[ 2, 2, 2, 1, 1 ]", // filterShape "[ 2,1, 1,0, 0,1, 1,1 ]", // filterData "[ 1 ]", // biasShape "[ 5, 0, 0, 0 ]", // biasData "1") // stride d, w and h {} }; TEST_CASE_FIXTURE(SimpleConv3DWithBiasesFixture, "ParseConv3DWithBias") { RunTest<5, armnn::DataType::QAsymmU8>(0, { 1, 2, 3, 4, 5, 6, 7, 8 }, { 33, 21, 23, 13, 28, 25, 27, 21 }); } TEST_CASE_FIXTURE(SimpleConv3DWithBiasesFixture, "ParseDynamicConv3DWithBias") { RunTest<5, armnn::DataType::QAsymmU8, armnn::DataType::QAsymmU8>(0, { { "inputTensor", { 2, 4, 6, 8, 10, 12, 14, 16 } } }, { { "outputTensor", { 61, 37, 41, 21, 51, 45, 49, 37 } } }, true); } struct Relu6Conv3DWithBiasesFixture : Conv3DWithBiasesFixture { Relu6Conv3DWithBiasesFixture() : Conv3DWithBiasesFixture("[ 1, 2, 2, 2, 1 ]", // inputShape "[ 1, 2, 2, 2, 1 ]", // outputShape "[ 2, 2, 2, 1, 1 ]", // filterShape "[ 2,1, 1,0, 0,1, 1,1 ]", // filterData "[ 1 ]", // biasShape "[ 0, 0, 0, 0 ]", // biasData "1", // stride d, w, and h "RELU6", // activation "1.0", // filter scale "0", // filter zero point "2.0", // output scale "0") // output zero point {} }; TEST_CASE_FIXTURE(Relu6Conv3DWithBiasesFixture, "ParseConv3DAndRelu6WithBias") { uint8_t relu6Min = 6 / 2; // Divide by output scale RunTest<5, armnn::DataType::QAsymmU8>( 0, { 1, 2, 3, 4, 5, 6, 7, 8 }, // RELU6 cuts output values at +6 { std::min(relu6Min, static_cast((1*2 + 2*1 + 3*1 + 4*0 + 5*0 + 6*1 + 7*1 + 8*1)/2)), std::min(relu6Min, static_cast((2*2 + 0*1 + 0*1 + 0*0 + 0*0 + 0*1 + 8*1 + 0*1)/2)), std::min(relu6Min, static_cast((3*2 + 0*1 + 0*1 + 0*0 + 0*0 + 8*1 + 0*1 + 0*1)/2)), std::min(relu6Min, static_cast((4*2 + 0*1 + 0*1 + 0*0 + 8*0 + 0*1 + 0*1 + 0*1)/2)), std::min(relu6Min, static_cast((5*2 + 0*1 + 0*1 + 8*0 + 0*0 + 0*1 + 0*1 + 0*1)/2)), std::min(relu6Min, static_cast((6*2 + 0*1 + 8*1 + 0*0 + 0*0 + 0*1 + 0*1 + 0*1)/2)), std::min(relu6Min, static_cast((7*2 + 8*1 + 0*1 + 0*0 + 0*0 + 0*1 + 0*1 + 0*1)/2)), std::min(relu6Min, static_cast((8*2 + 0*1 + 0*1 + 0*0 + 0*0 + 0*1 + 0*1 + 0*1)/2)) }); } } #endif