/**************************************************************************** * Generated by ACUITY 6.6.0 * Match timvx 1.1.30 * * Neural Network appliction network definition source file ****************************************************************************/ #include "vx_mobilenet.h" #include #include #include namespace { char *get_const_data(const char *data_file_name) { std::ifstream fin(data_file_name, std::ios::in | std::ios::binary); if (fin) { fin.seekg(0, std::ios::end); int size = fin.tellg(); fin.seekg(0, std::ios::beg); char *buffer = new char [size]; std::cout<<"File "<> mobilenet::input_size_list = {{3 , 224 , 224 , 1}}; std::vector mobilenet::input_bytes_list = {3 * 224 * 224 * 1 * sizeof(input_0_type)}; std::vector> mobilenet::output_size_list = {{1001 , 1}}; std::vector> mobilenet::inputs_tensor; std::vector> mobilenet::outputs_tensor; void mobilenet::construct_graph ( std::shared_ptr graph, const char *data_file_name ) { char *coef_data_ptr = get_const_data(data_file_name); tim::vx::Quantization permute_33_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.0078125, 128); tim::vx::TensorSpec permute_33_out0_spec(tim::vx::DataType::UINT8, {}, tim::vx::TensorAttribute::TRANSIENT, permute_33_out0_quant); auto permute_33_out0 = graph->CreateTensor(permute_33_out0_spec); tim::vx::Quantization convolution_1_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0); tim::vx::TensorSpec convolution_1_out0_spec(tim::vx::DataType::UINT8, {}, tim::vx::TensorAttribute::TRANSIENT, convolution_1_out0_quant); auto convolution_1_out0 = graph->CreateTensor(convolution_1_out0_spec); tim::vx::ShapeType convolution_1_weight_shape({3,3,3,32}); tim::vx::Quantization convolution_1_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.02182667888700962, 151); tim::vx::TensorSpec convolution_1_weight_spec(tim::vx::DataType::UINT8, convolution_1_weight_shape, tim::vx::TensorAttribute::CONSTANT, convolution_1_weight_quant); auto convolution_1_weight = graph->CreateTensor(convolution_1_weight_spec, coef_data_ptr + 1029156); tim::vx::ShapeType convolution_1_bias_shape({32}); tim::vx::Quantization convolution_1_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00017052092880476266, 0); tim::vx::TensorSpec convolution_1_bias_spec(tim::vx::DataType::INT32, convolution_1_bias_shape, tim::vx::TensorAttribute::CONSTANT, convolution_1_bias_quant); auto convolution_1_bias = graph->CreateTensor(convolution_1_bias_spec, coef_data_ptr + 1029028); tim::vx::Quantization convolution_2_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0); tim::vx::TensorSpec convolution_2_out0_spec(tim::vx::DataType::UINT8, {}, tim::vx::TensorAttribute::TRANSIENT, convolution_2_out0_quant); auto convolution_2_out0 = graph->CreateTensor(convolution_2_out0_spec); tim::vx::ShapeType convolution_2_weight_shape({3,3,32,1}); tim::vx::Quantization convolution_2_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.29219913482666016, 110); tim::vx::TensorSpec convolution_2_weight_spec(tim::vx::DataType::UINT8, convolution_2_weight_shape, tim::vx::TensorAttribute::CONSTANT, convolution_2_weight_quant); auto convolution_2_weight = graph->CreateTensor(convolution_2_weight_spec, coef_data_ptr + 3172868); tim::vx::ShapeType convolution_2_bias_shape({32}); tim::vx::Quantization convolution_2_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.006875000894069672, 0); tim::vx::TensorSpec convolution_2_bias_spec(tim::vx::DataType::INT32, convolution_2_bias_shape, tim::vx::TensorAttribute::CONSTANT, convolution_2_bias_quant); auto convolution_2_bias = graph->CreateTensor(convolution_2_bias_spec, coef_data_ptr + 3172740); tim::vx::Quantization convolution_3_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0); tim::vx::TensorSpec convolution_3_out0_spec(tim::vx::DataType::UINT8, {}, tim::vx::TensorAttribute::TRANSIENT, convolution_3_out0_quant); auto convolution_3_out0 = graph->CreateTensor(convolution_3_out0_spec); tim::vx::ShapeType convolution_3_weight_shape({1,1,32,64}); tim::vx::Quantization convolution_3_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.030420949682593346, 121); tim::vx::TensorSpec convolution_3_weight_spec(tim::vx::DataType::UINT8, convolution_3_weight_shape, tim::vx::TensorAttribute::CONSTANT, convolution_3_weight_quant); auto convolution_3_weight = graph->CreateTensor(convolution_3_weight_spec, coef_data_ptr + 3173412); tim::vx::ShapeType convolution_3_bias_shape({64}); tim::vx::Quantization convolution_3_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.0007157585932873189, 0); tim::vx::TensorSpec convolution_3_bias_spec(tim::vx::DataType::INT32, convolution_3_bias_shape, tim::vx::TensorAttribute::CONSTANT, convolution_3_bias_quant); auto convolution_3_bias = graph->CreateTensor(convolution_3_bias_spec, coef_data_ptr + 3173156); tim::vx::Quantization convolution_4_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0); tim::vx::TensorSpec convolution_4_out0_spec(tim::vx::DataType::UINT8, {}, tim::vx::TensorAttribute::TRANSIENT, convolution_4_out0_quant); auto convolution_4_out0 = graph->CreateTensor(convolution_4_out0_spec); tim::vx::ShapeType convolution_4_weight_shape({3,3,64,1}); tim::vx::Quantization convolution_4_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.40277284383773804, 130); tim::vx::TensorSpec convolution_4_weight_spec(tim::vx::DataType::UINT8, convolution_4_weight_shape, tim::vx::TensorAttribute::CONSTANT, convolution_4_weight_quant); auto convolution_4_weight = graph->CreateTensor(convolution_4_weight_spec, coef_data_ptr + 3175716); tim::vx::ShapeType convolution_4_bias_shape({64}); tim::vx::Quantization convolution_4_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.009476631879806519, 0); tim::vx::TensorSpec convolution_4_bias_spec(tim::vx::DataType::INT32, convolution_4_bias_shape, tim::vx::TensorAttribute::CONSTANT, convolution_4_bias_quant); auto convolution_4_bias = graph->CreateTensor(convolution_4_bias_spec, coef_data_ptr + 3175460); tim::vx::Quantization convolution_5_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0); tim::vx::TensorSpec convolution_5_out0_spec(tim::vx::DataType::UINT8, {}, tim::vx::TensorAttribute::TRANSIENT, convolution_5_out0_quant); auto convolution_5_out0 = graph->CreateTensor(convolution_5_out0_spec); tim::vx::ShapeType convolution_5_weight_shape({1,1,64,128}); tim::vx::Quantization convolution_5_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.015148180536925793, 104); tim::vx::TensorSpec convolution_5_weight_spec(tim::vx::DataType::UINT8, convolution_5_weight_shape, tim::vx::TensorAttribute::CONSTANT, convolution_5_weight_quant); auto convolution_5_weight = graph->CreateTensor(convolution_5_weight_spec, coef_data_ptr + 3176804); tim::vx::ShapeType convolution_5_bias_shape({128}); tim::vx::Quantization convolution_5_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00035641362774185836, 0); tim::vx::TensorSpec convolution_5_bias_spec(tim::vx::DataType::INT32, convolution_5_bias_shape, tim::vx::TensorAttribute::CONSTANT, convolution_5_bias_quant); auto convolution_5_bias = graph->CreateTensor(convolution_5_bias_spec, coef_data_ptr + 3176292); tim::vx::Quantization convolution_6_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0); tim::vx::TensorSpec convolution_6_out0_spec(tim::vx::DataType::UINT8, {}, tim::vx::TensorAttribute::TRANSIENT, convolution_6_out0_quant); auto convolution_6_out0 = graph->CreateTensor(convolution_6_out0_spec); tim::vx::ShapeType convolution_6_weight_shape({3,3,128,1}); tim::vx::Quantization convolution_6_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.06053730100393295, 160); tim::vx::TensorSpec convolution_6_weight_spec(tim::vx::DataType::UINT8, convolution_6_weight_shape, tim::vx::TensorAttribute::CONSTANT, convolution_6_weight_quant); auto convolution_6_weight = graph->CreateTensor(convolution_6_weight_spec, coef_data_ptr + 3185508); tim::vx::ShapeType convolution_6_bias_shape({128}); tim::vx::Quantization convolution_6_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00142435054294765, 0); tim::vx::TensorSpec convolution_6_bias_spec(tim::vx::DataType::INT32, convolution_6_bias_shape, tim::vx::TensorAttribute::CONSTANT, convolution_6_bias_quant); auto convolution_6_bias = graph->CreateTensor(convolution_6_bias_spec, coef_data_ptr + 3184996); tim::vx::Quantization convolution_7_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0); tim::vx::TensorSpec convolution_7_out0_spec(tim::vx::DataType::UINT8, {}, tim::vx::TensorAttribute::TRANSIENT, convolution_7_out0_quant); auto convolution_7_out0 = graph->CreateTensor(convolution_7_out0_spec); tim::vx::ShapeType convolution_7_weight_shape({1,1,128,128}); tim::vx::Quantization convolution_7_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.013755458407104015, 94); tim::vx::TensorSpec convolution_7_weight_spec(tim::vx::DataType::UINT8, convolution_7_weight_shape, tim::vx::TensorAttribute::CONSTANT, convolution_7_weight_quant); auto convolution_7_weight = graph->CreateTensor(convolution_7_weight_spec, coef_data_ptr + 3187172); tim::vx::ShapeType convolution_7_bias_shape({128}); tim::vx::Quantization convolution_7_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00032364498474635184, 0); tim::vx::TensorSpec convolution_7_bias_spec(tim::vx::DataType::INT32, convolution_7_bias_shape, tim::vx::TensorAttribute::CONSTANT, convolution_7_bias_quant); auto convolution_7_bias = graph->CreateTensor(convolution_7_bias_spec, coef_data_ptr + 3186660); tim::vx::Quantization convolution_8_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0); tim::vx::TensorSpec convolution_8_out0_spec(tim::vx::DataType::UINT8, {}, tim::vx::TensorAttribute::TRANSIENT, convolution_8_out0_quant); auto convolution_8_out0 = graph->CreateTensor(convolution_8_out0_spec); tim::vx::ShapeType convolution_8_weight_shape({3,3,128,1}); tim::vx::Quantization convolution_8_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.01675807684659958, 123); tim::vx::TensorSpec convolution_8_weight_spec(tim::vx::DataType::UINT8, convolution_8_weight_shape, tim::vx::TensorAttribute::CONSTANT, convolution_8_weight_quant); auto convolution_8_weight = graph->CreateTensor(convolution_8_weight_spec, coef_data_ptr + 3204068); tim::vx::ShapeType convolution_8_bias_shape({128}); tim::vx::Quantization convolution_8_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.0003942920302506536, 0); tim::vx::TensorSpec convolution_8_bias_spec(tim::vx::DataType::INT32, convolution_8_bias_shape, tim::vx::TensorAttribute::CONSTANT, convolution_8_bias_quant); auto convolution_8_bias = graph->CreateTensor(convolution_8_bias_spec, coef_data_ptr + 3203556); tim::vx::Quantization convolution_9_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0); tim::vx::TensorSpec convolution_9_out0_spec(tim::vx::DataType::UINT8, {}, tim::vx::TensorAttribute::TRANSIENT, convolution_9_out0_quant); auto convolution_9_out0 = graph->CreateTensor(convolution_9_out0_spec); tim::vx::ShapeType convolution_9_weight_shape({1,1,128,256}); tim::vx::Quantization convolution_9_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.007601846940815449, 151); tim::vx::TensorSpec convolution_9_weight_spec(tim::vx::DataType::UINT8, convolution_9_weight_shape, tim::vx::TensorAttribute::CONSTANT, convolution_9_weight_quant); auto convolution_9_weight = graph->CreateTensor(convolution_9_weight_spec, coef_data_ptr + 3206244); tim::vx::ShapeType convolution_9_bias_shape({256}); tim::vx::Quantization convolution_9_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00017885988927446306, 0); tim::vx::TensorSpec convolution_9_bias_spec(tim::vx::DataType::INT32, convolution_9_bias_shape, tim::vx::TensorAttribute::CONSTANT, convolution_9_bias_quant); auto convolution_9_bias = graph->CreateTensor(convolution_9_bias_spec, coef_data_ptr + 3205220); tim::vx::Quantization convolution_10_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0); tim::vx::TensorSpec convolution_10_out0_spec(tim::vx::DataType::UINT8, {}, tim::vx::TensorAttribute::TRANSIENT, convolution_10_out0_quant); auto convolution_10_out0 = graph->CreateTensor(convolution_10_out0_spec); tim::vx::ShapeType convolution_10_weight_shape({3,3,256,1}); tim::vx::Quantization convolution_10_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.04105526953935623, 129); tim::vx::TensorSpec convolution_10_weight_spec(tim::vx::DataType::UINT8, convolution_10_weight_shape, tim::vx::TensorAttribute::CONSTANT, convolution_10_weight_quant); auto convolution_10_weight = graph->CreateTensor(convolution_10_weight_spec, coef_data_ptr + 3240036); tim::vx::ShapeType convolution_10_bias_shape({256}); tim::vx::Quantization convolution_10_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.0009659679490141571, 0); tim::vx::TensorSpec convolution_10_bias_spec(tim::vx::DataType::INT32, convolution_10_bias_shape, tim::vx::TensorAttribute::CONSTANT, convolution_10_bias_quant); auto convolution_10_bias = graph->CreateTensor(convolution_10_bias_spec, coef_data_ptr + 3239012); tim::vx::Quantization convolution_11_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0); tim::vx::TensorSpec convolution_11_out0_spec(tim::vx::DataType::UINT8, {}, tim::vx::TensorAttribute::TRANSIENT, convolution_11_out0_quant); auto convolution_11_out0 = graph->CreateTensor(convolution_11_out0_spec); tim::vx::ShapeType convolution_11_weight_shape({1,1,256,256}); tim::vx::Quantization convolution_11_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.006431614048779011, 122); tim::vx::TensorSpec convolution_11_weight_spec(tim::vx::DataType::UINT8, convolution_11_weight_shape, tim::vx::TensorAttribute::CONSTANT, convolution_11_weight_quant); auto convolution_11_weight = graph->CreateTensor(convolution_11_weight_spec, coef_data_ptr + 3243364); tim::vx::ShapeType convolution_11_bias_shape({256}); tim::vx::Quantization convolution_11_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00015132607950363308, 0); tim::vx::TensorSpec convolution_11_bias_spec(tim::vx::DataType::INT32, convolution_11_bias_shape, tim::vx::TensorAttribute::CONSTANT, convolution_11_bias_quant); auto convolution_11_bias = graph->CreateTensor(convolution_11_bias_spec, coef_data_ptr + 3242340); tim::vx::Quantization convolution_12_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0); tim::vx::TensorSpec convolution_12_out0_spec(tim::vx::DataType::UINT8, {}, tim::vx::TensorAttribute::TRANSIENT, convolution_12_out0_quant); auto convolution_12_out0 = graph->CreateTensor(convolution_12_out0_spec); tim::vx::ShapeType convolution_12_weight_shape({3,3,256,1}); tim::vx::Quantization convolution_12_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.013460792601108551, 122); tim::vx::TensorSpec convolution_12_weight_spec(tim::vx::DataType::UINT8, convolution_12_weight_shape, tim::vx::TensorAttribute::CONSTANT, convolution_12_weight_quant); auto convolution_12_weight = graph->CreateTensor(convolution_12_weight_spec, coef_data_ptr + 3309924); tim::vx::ShapeType convolution_12_bias_shape({256}); tim::vx::Quantization convolution_12_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.0003167119575664401, 0); tim::vx::TensorSpec convolution_12_bias_spec(tim::vx::DataType::INT32, convolution_12_bias_shape, tim::vx::TensorAttribute::CONSTANT, convolution_12_bias_quant); auto convolution_12_bias = graph->CreateTensor(convolution_12_bias_spec, coef_data_ptr + 3308900); tim::vx::Quantization convolution_13_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0); tim::vx::TensorSpec convolution_13_out0_spec(tim::vx::DataType::UINT8, {}, tim::vx::TensorAttribute::TRANSIENT, convolution_13_out0_quant); auto convolution_13_out0 = graph->CreateTensor(convolution_13_out0_spec); tim::vx::ShapeType convolution_13_weight_shape({1,1,256,512}); tim::vx::Quantization convolution_13_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.00917122047394514, 109); tim::vx::TensorSpec convolution_13_weight_spec(tim::vx::DataType::UINT8, convolution_13_weight_shape, tim::vx::TensorAttribute::CONSTANT, convolution_13_weight_quant); auto convolution_13_weight = graph->CreateTensor(convolution_13_weight_spec, coef_data_ptr + 3314276); tim::vx::ShapeType convolution_13_bias_shape({512}); tim::vx::Quantization convolution_13_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00021578485029749572, 0); tim::vx::TensorSpec convolution_13_bias_spec(tim::vx::DataType::INT32, convolution_13_bias_shape, tim::vx::TensorAttribute::CONSTANT, convolution_13_bias_quant); auto convolution_13_bias = graph->CreateTensor(convolution_13_bias_spec, coef_data_ptr + 3312228); tim::vx::Quantization convolution_14_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0); tim::vx::TensorSpec convolution_14_out0_spec(tim::vx::DataType::UINT8, {}, tim::vx::TensorAttribute::TRANSIENT, convolution_14_out0_quant); auto convolution_14_out0 = graph->CreateTensor(convolution_14_out0_spec); tim::vx::ShapeType convolution_14_weight_shape({3,3,512,1}); tim::vx::Quantization convolution_14_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.036934755742549896, 132); tim::vx::TensorSpec convolution_14_weight_spec(tim::vx::DataType::UINT8, convolution_14_weight_shape, tim::vx::TensorAttribute::CONSTANT, convolution_14_weight_quant); auto convolution_14_weight = graph->CreateTensor(convolution_14_weight_spec, coef_data_ptr + 3447396); tim::vx::ShapeType convolution_14_bias_shape({512}); tim::vx::Quantization convolution_14_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.0008690185495652258, 0); tim::vx::TensorSpec convolution_14_bias_spec(tim::vx::DataType::INT32, convolution_14_bias_shape, tim::vx::TensorAttribute::CONSTANT, convolution_14_bias_quant); auto convolution_14_bias = graph->CreateTensor(convolution_14_bias_spec, coef_data_ptr + 3445348); tim::vx::Quantization convolution_15_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0); tim::vx::TensorSpec convolution_15_out0_spec(tim::vx::DataType::UINT8, {}, tim::vx::TensorAttribute::TRANSIENT, convolution_15_out0_quant); auto convolution_15_out0 = graph->CreateTensor(convolution_15_out0_spec); tim::vx::ShapeType convolution_15_weight_shape({1,1,512,512}); tim::vx::Quantization convolution_15_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.005300046876072884, 140); tim::vx::TensorSpec convolution_15_weight_spec(tim::vx::DataType::UINT8, convolution_15_weight_shape, tim::vx::TensorAttribute::CONSTANT, convolution_15_weight_quant); auto convolution_15_weight = graph->CreateTensor(convolution_15_weight_spec, coef_data_ptr + 3454052); tim::vx::ShapeType convolution_15_bias_shape({512}); tim::vx::Quantization convolution_15_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00012470202636905015, 0); tim::vx::TensorSpec convolution_15_bias_spec(tim::vx::DataType::INT32, convolution_15_bias_shape, tim::vx::TensorAttribute::CONSTANT, convolution_15_bias_quant); auto convolution_15_bias = graph->CreateTensor(convolution_15_bias_spec, coef_data_ptr + 3452004); tim::vx::Quantization convolution_16_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0); tim::vx::TensorSpec convolution_16_out0_spec(tim::vx::DataType::UINT8, {}, tim::vx::TensorAttribute::TRANSIENT, convolution_16_out0_quant); auto convolution_16_out0 = graph->CreateTensor(convolution_16_out0_spec); tim::vx::ShapeType convolution_16_weight_shape({3,3,512,1}); tim::vx::Quantization convolution_16_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.042609862983226776, 94); tim::vx::TensorSpec convolution_16_weight_spec(tim::vx::DataType::UINT8, convolution_16_weight_shape, tim::vx::TensorAttribute::CONSTANT, convolution_16_weight_quant); auto convolution_16_weight = graph->CreateTensor(convolution_16_weight_spec, coef_data_ptr + 3718244); tim::vx::ShapeType convolution_16_bias_shape({512}); tim::vx::Quantization convolution_16_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.0010025452356785536, 0); tim::vx::TensorSpec convolution_16_bias_spec(tim::vx::DataType::INT32, convolution_16_bias_shape, tim::vx::TensorAttribute::CONSTANT, convolution_16_bias_quant); auto convolution_16_bias = graph->CreateTensor(convolution_16_bias_spec, coef_data_ptr + 3716196); tim::vx::Quantization convolution_17_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0); tim::vx::TensorSpec convolution_17_out0_spec(tim::vx::DataType::UINT8, {}, tim::vx::TensorAttribute::TRANSIENT, convolution_17_out0_quant); auto convolution_17_out0 = graph->CreateTensor(convolution_17_out0_spec); tim::vx::ShapeType convolution_17_weight_shape({1,1,512,512}); tim::vx::Quantization convolution_17_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.0049632852897048, 127); tim::vx::TensorSpec convolution_17_weight_spec(tim::vx::DataType::UINT8, convolution_17_weight_shape, tim::vx::TensorAttribute::CONSTANT, convolution_17_weight_quant); auto convolution_17_weight = graph->CreateTensor(convolution_17_weight_spec, coef_data_ptr + 3724900); tim::vx::ShapeType convolution_17_bias_shape({512}); tim::vx::Quantization convolution_17_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00011677854490699247, 0); tim::vx::TensorSpec convolution_17_bias_spec(tim::vx::DataType::INT32, convolution_17_bias_shape, tim::vx::TensorAttribute::CONSTANT, convolution_17_bias_quant); auto convolution_17_bias = graph->CreateTensor(convolution_17_bias_spec, coef_data_ptr + 3722852); tim::vx::Quantization convolution_18_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0); tim::vx::TensorSpec convolution_18_out0_spec(tim::vx::DataType::UINT8, {}, tim::vx::TensorAttribute::TRANSIENT, convolution_18_out0_quant); auto convolution_18_out0 = graph->CreateTensor(convolution_18_out0_spec); tim::vx::ShapeType convolution_18_weight_shape({3,3,512,1}); tim::vx::Quantization convolution_18_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.028358859941363335, 127); tim::vx::TensorSpec convolution_18_weight_spec(tim::vx::DataType::UINT8, convolution_18_weight_shape, tim::vx::TensorAttribute::CONSTANT, convolution_18_weight_quant); auto convolution_18_weight = graph->CreateTensor(convolution_18_weight_spec, coef_data_ptr + 3989092); tim::vx::ShapeType convolution_18_bias_shape({512}); tim::vx::Quantization convolution_18_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.0006672407616861165, 0); tim::vx::TensorSpec convolution_18_bias_spec(tim::vx::DataType::INT32, convolution_18_bias_shape, tim::vx::TensorAttribute::CONSTANT, convolution_18_bias_quant); auto convolution_18_bias = graph->CreateTensor(convolution_18_bias_spec, coef_data_ptr + 3987044); tim::vx::Quantization convolution_19_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0); tim::vx::TensorSpec convolution_19_out0_spec(tim::vx::DataType::UINT8, {}, tim::vx::TensorAttribute::TRANSIENT, convolution_19_out0_quant); auto convolution_19_out0 = graph->CreateTensor(convolution_19_out0_spec); tim::vx::ShapeType convolution_19_weight_shape({1,1,512,512}); tim::vx::Quantization convolution_19_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.007770895957946777, 89); tim::vx::TensorSpec convolution_19_weight_spec(tim::vx::DataType::UINT8, convolution_19_weight_shape, tim::vx::TensorAttribute::CONSTANT, convolution_19_weight_quant); auto convolution_19_weight = graph->CreateTensor(convolution_19_weight_spec, coef_data_ptr + 3995748); tim::vx::ShapeType convolution_19_bias_shape({512}); tim::vx::Quantization convolution_19_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00018283734971191734, 0); tim::vx::TensorSpec convolution_19_bias_spec(tim::vx::DataType::INT32, convolution_19_bias_shape, tim::vx::TensorAttribute::CONSTANT, convolution_19_bias_quant); auto convolution_19_bias = graph->CreateTensor(convolution_19_bias_spec, coef_data_ptr + 3993700); tim::vx::Quantization convolution_20_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0); tim::vx::TensorSpec convolution_20_out0_spec(tim::vx::DataType::UINT8, {}, tim::vx::TensorAttribute::TRANSIENT, convolution_20_out0_quant); auto convolution_20_out0 = graph->CreateTensor(convolution_20_out0_spec); tim::vx::ShapeType convolution_20_weight_shape({3,3,512,1}); tim::vx::Quantization convolution_20_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.024329448118805885, 134); tim::vx::TensorSpec convolution_20_weight_spec(tim::vx::DataType::UINT8, convolution_20_weight_shape, tim::vx::TensorAttribute::CONSTANT, convolution_20_weight_quant); auto convolution_20_weight = graph->CreateTensor(convolution_20_weight_spec, coef_data_ptr + 1032068); tim::vx::ShapeType convolution_20_bias_shape({512}); tim::vx::Quantization convolution_20_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.0005724348593503237, 0); tim::vx::TensorSpec convolution_20_bias_spec(tim::vx::DataType::INT32, convolution_20_bias_shape, tim::vx::TensorAttribute::CONSTANT, convolution_20_bias_quant); auto convolution_20_bias = graph->CreateTensor(convolution_20_bias_spec, coef_data_ptr + 1030020); tim::vx::Quantization convolution_21_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0); tim::vx::TensorSpec convolution_21_out0_spec(tim::vx::DataType::UINT8, {}, tim::vx::TensorAttribute::TRANSIENT, convolution_21_out0_quant); auto convolution_21_out0 = graph->CreateTensor(convolution_21_out0_spec); tim::vx::ShapeType convolution_21_weight_shape({1,1,512,512}); tim::vx::Quantization convolution_21_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.009658650495111942, 99); tim::vx::TensorSpec convolution_21_weight_spec(tim::vx::DataType::UINT8, convolution_21_weight_shape, tim::vx::TensorAttribute::CONSTANT, convolution_21_weight_quant); auto convolution_21_weight = graph->CreateTensor(convolution_21_weight_spec, coef_data_ptr + 1038724); tim::vx::ShapeType convolution_21_bias_shape({512}); tim::vx::Quantization convolution_21_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00022725333110429347, 0); tim::vx::TensorSpec convolution_21_bias_spec(tim::vx::DataType::INT32, convolution_21_bias_shape, tim::vx::TensorAttribute::CONSTANT, convolution_21_bias_quant); auto convolution_21_bias = graph->CreateTensor(convolution_21_bias_spec, coef_data_ptr + 1036676); tim::vx::Quantization convolution_22_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0); tim::vx::TensorSpec convolution_22_out0_spec(tim::vx::DataType::UINT8, {}, tim::vx::TensorAttribute::TRANSIENT, convolution_22_out0_quant); auto convolution_22_out0 = graph->CreateTensor(convolution_22_out0_spec); tim::vx::ShapeType convolution_22_weight_shape({3,3,512,1}); tim::vx::Quantization convolution_22_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.019366811960935593, 106); tim::vx::TensorSpec convolution_22_weight_spec(tim::vx::DataType::UINT8, convolution_22_weight_shape, tim::vx::TensorAttribute::CONSTANT, convolution_22_weight_quant); auto convolution_22_weight = graph->CreateTensor(convolution_22_weight_spec, coef_data_ptr + 1302916); tim::vx::ShapeType convolution_22_bias_shape({512}); tim::vx::Quantization convolution_22_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.0004556716012302786, 0); tim::vx::TensorSpec convolution_22_bias_spec(tim::vx::DataType::INT32, convolution_22_bias_shape, tim::vx::TensorAttribute::CONSTANT, convolution_22_bias_quant); auto convolution_22_bias = graph->CreateTensor(convolution_22_bias_spec, coef_data_ptr + 1300868); tim::vx::Quantization convolution_23_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0); tim::vx::TensorSpec convolution_23_out0_spec(tim::vx::DataType::UINT8, {}, tim::vx::TensorAttribute::TRANSIENT, convolution_23_out0_quant); auto convolution_23_out0 = graph->CreateTensor(convolution_23_out0_spec); tim::vx::ShapeType convolution_23_weight_shape({1,1,512,512}); tim::vx::Quantization convolution_23_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.005446993745863438, 153); tim::vx::TensorSpec convolution_23_weight_spec(tim::vx::DataType::UINT8, convolution_23_weight_shape, tim::vx::TensorAttribute::CONSTANT, convolution_23_weight_quant); auto convolution_23_weight = graph->CreateTensor(convolution_23_weight_spec, coef_data_ptr + 1309572); tim::vx::ShapeType convolution_23_bias_shape({512}); tim::vx::Quantization convolution_23_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00012815947411581874, 0); tim::vx::TensorSpec convolution_23_bias_spec(tim::vx::DataType::INT32, convolution_23_bias_shape, tim::vx::TensorAttribute::CONSTANT, convolution_23_bias_quant); auto convolution_23_bias = graph->CreateTensor(convolution_23_bias_spec, coef_data_ptr + 1307524); tim::vx::Quantization convolution_24_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0); tim::vx::TensorSpec convolution_24_out0_spec(tim::vx::DataType::UINT8, {}, tim::vx::TensorAttribute::TRANSIENT, convolution_24_out0_quant); auto convolution_24_out0 = graph->CreateTensor(convolution_24_out0_spec); tim::vx::ShapeType convolution_24_weight_shape({3,3,512,1}); tim::vx::Quantization convolution_24_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.007835594937205315, 126); tim::vx::TensorSpec convolution_24_weight_spec(tim::vx::DataType::UINT8, convolution_24_weight_shape, tim::vx::TensorAttribute::CONSTANT, convolution_24_weight_quant); auto convolution_24_weight = graph->CreateTensor(convolution_24_weight_spec, coef_data_ptr + 1573764); tim::vx::ShapeType convolution_24_bias_shape({512}); tim::vx::Quantization convolution_24_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00018435961101204157, 0); tim::vx::TensorSpec convolution_24_bias_spec(tim::vx::DataType::INT32, convolution_24_bias_shape, tim::vx::TensorAttribute::CONSTANT, convolution_24_bias_quant); auto convolution_24_bias = graph->CreateTensor(convolution_24_bias_spec, coef_data_ptr + 1571716); tim::vx::Quantization convolution_25_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0); tim::vx::TensorSpec convolution_25_out0_spec(tim::vx::DataType::UINT8, {}, tim::vx::TensorAttribute::TRANSIENT, convolution_25_out0_quant); auto convolution_25_out0 = graph->CreateTensor(convolution_25_out0_spec); tim::vx::ShapeType convolution_25_weight_shape({1,1,512,1024}); tim::vx::Quantization convolution_25_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.00817922968417406, 130); tim::vx::TensorSpec convolution_25_weight_spec(tim::vx::DataType::UINT8, convolution_25_weight_shape, tim::vx::TensorAttribute::CONSTANT, convolution_25_weight_quant); auto convolution_25_weight = graph->CreateTensor(convolution_25_weight_spec, coef_data_ptr + 1582468); tim::vx::ShapeType convolution_25_bias_shape({1024}); tim::vx::Quantization convolution_25_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.0001924448151839897, 0); tim::vx::TensorSpec convolution_25_bias_spec(tim::vx::DataType::INT32, convolution_25_bias_shape, tim::vx::TensorAttribute::CONSTANT, convolution_25_bias_quant); auto convolution_25_bias = graph->CreateTensor(convolution_25_bias_spec, coef_data_ptr + 1578372); tim::vx::Quantization convolution_26_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0); tim::vx::TensorSpec convolution_26_out0_spec(tim::vx::DataType::UINT8, {}, tim::vx::TensorAttribute::TRANSIENT, convolution_26_out0_quant); auto convolution_26_out0 = graph->CreateTensor(convolution_26_out0_spec); tim::vx::ShapeType convolution_26_weight_shape({3,3,1024,1}); tim::vx::Quantization convolution_26_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.12616927921772003, 211); tim::vx::TensorSpec convolution_26_weight_spec(tim::vx::DataType::UINT8, convolution_26_weight_shape, tim::vx::TensorAttribute::CONSTANT, convolution_26_weight_quant); auto convolution_26_weight = graph->CreateTensor(convolution_26_weight_spec, coef_data_ptr + 2110852); tim::vx::ShapeType convolution_26_bias_shape({1024}); tim::vx::Quantization convolution_26_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.002968570915982127, 0); tim::vx::TensorSpec convolution_26_bias_spec(tim::vx::DataType::INT32, convolution_26_bias_shape, tim::vx::TensorAttribute::CONSTANT, convolution_26_bias_quant); auto convolution_26_bias = graph->CreateTensor(convolution_26_bias_spec, coef_data_ptr + 2106756); tim::vx::Quantization convolution_27_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0); tim::vx::TensorSpec convolution_27_out0_spec(tim::vx::DataType::UINT8, {}, tim::vx::TensorAttribute::TRANSIENT, convolution_27_out0_quant); auto convolution_27_out0 = graph->CreateTensor(convolution_27_out0_spec); tim::vx::ShapeType convolution_27_weight_shape({1,1,1024,1024}); tim::vx::Quantization convolution_27_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.018048152327537537, 95); tim::vx::TensorSpec convolution_27_weight_spec(tim::vx::DataType::UINT8, convolution_27_weight_shape, tim::vx::TensorAttribute::CONSTANT, convolution_27_weight_quant); auto convolution_27_weight = graph->CreateTensor(convolution_27_weight_spec, coef_data_ptr + 2124164); tim::vx::ShapeType convolution_27_bias_shape({1024}); tim::vx::Quantization convolution_27_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.000424645550083369, 0); tim::vx::TensorSpec convolution_27_bias_spec(tim::vx::DataType::INT32, convolution_27_bias_shape, tim::vx::TensorAttribute::CONSTANT, convolution_27_bias_quant); auto convolution_27_bias = graph->CreateTensor(convolution_27_bias_spec, coef_data_ptr + 2120068); tim::vx::Quantization pooling_28_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0); tim::vx::TensorSpec pooling_28_out0_spec(tim::vx::DataType::UINT8, {}, tim::vx::TensorAttribute::TRANSIENT, pooling_28_out0_quant); auto pooling_28_out0 = graph->CreateTensor(pooling_28_out0_spec); tim::vx::Quantization convolution_29_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.16609922051429749, 66); tim::vx::TensorSpec convolution_29_out0_spec(tim::vx::DataType::UINT8, {}, tim::vx::TensorAttribute::TRANSIENT, convolution_29_out0_quant); auto convolution_29_out0 = graph->CreateTensor(convolution_29_out0_spec); tim::vx::ShapeType convolution_29_weight_shape({1,1,1024,1001}); tim::vx::Quantization convolution_29_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.004986600950360298, 74); tim::vx::TensorSpec convolution_29_weight_spec(tim::vx::DataType::UINT8, convolution_29_weight_shape, tim::vx::TensorAttribute::CONSTANT, convolution_29_weight_quant); auto convolution_29_weight = graph->CreateTensor(convolution_29_weight_spec, coef_data_ptr + 4004); tim::vx::ShapeType convolution_29_bias_shape({1001}); tim::vx::Quantization convolution_29_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00011732713028322905, 0); tim::vx::TensorSpec convolution_29_bias_spec(tim::vx::DataType::INT32, convolution_29_bias_shape, tim::vx::TensorAttribute::CONSTANT, convolution_29_bias_quant); auto convolution_29_bias = graph->CreateTensor(convolution_29_bias_spec, coef_data_ptr + 0); tim::vx::Quantization permute_34_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.16609922051429749, 66); tim::vx::TensorSpec permute_34_out0_spec(tim::vx::DataType::UINT8, {}, tim::vx::TensorAttribute::TRANSIENT, permute_34_out0_quant); auto permute_34_out0 = graph->CreateTensor(permute_34_out0_spec); tim::vx::Quantization reshape_30_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.16609922051429749, 66); tim::vx::TensorSpec reshape_30_out0_spec(tim::vx::DataType::UINT8, {}, tim::vx::TensorAttribute::TRANSIENT, reshape_30_out0_quant); auto reshape_30_out0 = graph->CreateTensor(reshape_30_out0_spec); tim::vx::ShapeType input_0_shape({3,224,224,1}); tim::vx::Quantization input_0_quant(tim::vx::QuantType::ASYMMETRIC, 0.0078125, 128); tim::vx::TensorSpec input_0_spec(tim::vx::DataType::UINT8, input_0_shape, tim::vx::TensorAttribute::INPUT, input_0_quant); auto input_0 = graph->CreateTensor(input_0_spec); tim::vx::ShapeType output_32_shape({1001,1}); // tim::vx::Quantization output_32_quant(tim::vx::QuantType::ASYMMETRIC, 0.00390625, 0); // tim::vx::TensorSpec output_32_spec(tim::vx::DataType::UINT8, output_32_shape, // tim::vx::TensorAttribute::OUTPUT, output_32_quant); tim::vx::TensorSpec output_32_spec(tim::vx::DataType::FLOAT32, output_32_shape, tim::vx::TensorAttribute::OUTPUT); auto output_32 = graph->CreateTensor(output_32_spec); mobilenet::inputs_tensor.push_back(input_0); mobilenet::outputs_tensor.push_back(output_32); auto permute_33 = graph->CreateOperation ( std::vector({1,2,0,3})); // perm auto convolution_1 = graph->CreateOperation ( 32, // weights tim::vx::PadType::NONE, // padding std::array({3,3}), // ksize std::array({2,2}), // stride std::array({1,1}), // dilation std::array({0,1,0,1}), // pad 0); // multiplier auto convolution_2 = graph->CreateOperation ( 32, // weights tim::vx::PadType::NONE, // padding std::array({3,3}), // ksize std::array({1,1}), // stride std::array({1,1}), // dilation std::array({1,1,1,1}), // pad 1.0); // multiplier auto convolution_3 = graph->CreateOperation ( 64, // weights tim::vx::PadType::NONE, // padding std::array({1,1}), // ksize std::array({1,1}), // stride std::array({1,1}), // dilation std::array({0,0,0,0}), // pad 0); // multiplier auto convolution_4 = graph->CreateOperation ( 64, // weights tim::vx::PadType::NONE, // padding std::array({3,3}), // ksize std::array({2,2}), // stride std::array({1,1}), // dilation std::array({0,1,0,1}), // pad 1.0); // multiplier auto convolution_5 = graph->CreateOperation ( 128, // weights tim::vx::PadType::NONE, // padding std::array({1,1}), // ksize std::array({1,1}), // stride std::array({1,1}), // dilation std::array({0,0,0,0}), // pad 0); // multiplier auto convolution_6 = graph->CreateOperation ( 128, // weights tim::vx::PadType::NONE, // padding std::array({3,3}), // ksize std::array({1,1}), // stride std::array({1,1}), // dilation std::array({1,1,1,1}), // pad 1.0); // multiplier auto convolution_7 = graph->CreateOperation ( 128, // weights tim::vx::PadType::NONE, // padding std::array({1,1}), // ksize std::array({1,1}), // stride std::array({1,1}), // dilation std::array({0,0,0,0}), // pad 0); // multiplier auto convolution_8 = graph->CreateOperation ( 128, // weights tim::vx::PadType::NONE, // padding std::array({3,3}), // ksize std::array({2,2}), // stride std::array({1,1}), // dilation std::array({0,1,0,1}), // pad 1.0); // multiplier auto convolution_9 = graph->CreateOperation ( 256, // weights tim::vx::PadType::NONE, // padding std::array({1,1}), // ksize std::array({1,1}), // stride std::array({1,1}), // dilation std::array({0,0,0,0}), // pad 0); // multiplier auto convolution_10 = graph->CreateOperation ( 256, // weights tim::vx::PadType::NONE, // padding std::array({3,3}), // ksize std::array({1,1}), // stride std::array({1,1}), // dilation std::array({1,1,1,1}), // pad 1.0); // multiplier auto convolution_11 = graph->CreateOperation ( 256, // weights tim::vx::PadType::NONE, // padding std::array({1,1}), // ksize std::array({1,1}), // stride std::array({1,1}), // dilation std::array({0,0,0,0}), // pad 0); // multiplier auto convolution_12 = graph->CreateOperation ( 256, // weights tim::vx::PadType::NONE, // padding std::array({3,3}), // ksize std::array({2,2}), // stride std::array({1,1}), // dilation std::array({0,1,0,1}), // pad 1.0); // multiplier auto convolution_13 = graph->CreateOperation ( 512, // weights tim::vx::PadType::NONE, // padding std::array({1,1}), // ksize std::array({1,1}), // stride std::array({1,1}), // dilation std::array({0,0,0,0}), // pad 0); // multiplier auto convolution_14 = graph->CreateOperation ( 512, // weights tim::vx::PadType::NONE, // padding std::array({3,3}), // ksize std::array({1,1}), // stride std::array({1,1}), // dilation std::array({1,1,1,1}), // pad 1.0); // multiplier auto convolution_15 = graph->CreateOperation ( 512, // weights tim::vx::PadType::NONE, // padding std::array({1,1}), // ksize std::array({1,1}), // stride std::array({1,1}), // dilation std::array({0,0,0,0}), // pad 0); // multiplier auto convolution_16 = graph->CreateOperation ( 512, // weights tim::vx::PadType::NONE, // padding std::array({3,3}), // ksize std::array({1,1}), // stride std::array({1,1}), // dilation std::array({1,1,1,1}), // pad 1.0); // multiplier auto convolution_17 = graph->CreateOperation ( 512, // weights tim::vx::PadType::NONE, // padding std::array({1,1}), // ksize std::array({1,1}), // stride std::array({1,1}), // dilation std::array({0,0,0,0}), // pad 0); // multiplier auto convolution_18 = graph->CreateOperation ( 512, // weights tim::vx::PadType::NONE, // padding std::array({3,3}), // ksize std::array({1,1}), // stride std::array({1,1}), // dilation std::array({1,1,1,1}), // pad 1.0); // multiplier auto convolution_19 = graph->CreateOperation ( 512, // weights tim::vx::PadType::NONE, // padding std::array({1,1}), // ksize std::array({1,1}), // stride std::array({1,1}), // dilation std::array({0,0,0,0}), // pad 0); // multiplier auto convolution_20 = graph->CreateOperation ( 512, // weights tim::vx::PadType::NONE, // padding std::array({3,3}), // ksize std::array({1,1}), // stride std::array({1,1}), // dilation std::array({1,1,1,1}), // pad 1.0); // multiplier auto convolution_21 = graph->CreateOperation ( 512, // weights tim::vx::PadType::NONE, // padding std::array({1,1}), // ksize std::array({1,1}), // stride std::array({1,1}), // dilation std::array({0,0,0,0}), // pad 0); // multiplier auto convolution_22 = graph->CreateOperation ( 512, // weights tim::vx::PadType::NONE, // padding std::array({3,3}), // ksize std::array({1,1}), // stride std::array({1,1}), // dilation std::array({1,1,1,1}), // pad 1.0); // multiplier auto convolution_23 = graph->CreateOperation ( 512, // weights tim::vx::PadType::NONE, // padding std::array({1,1}), // ksize std::array({1,1}), // stride std::array({1,1}), // dilation std::array({0,0,0,0}), // pad 0); // multiplier auto convolution_24 = graph->CreateOperation ( 512, // weights tim::vx::PadType::NONE, // padding std::array({3,3}), // ksize std::array({2,2}), // stride std::array({1,1}), // dilation std::array({0,1,0,1}), // pad 1.0); // multiplier auto convolution_25 = graph->CreateOperation ( 1024, // weights tim::vx::PadType::NONE, // padding std::array({1,1}), // ksize std::array({1,1}), // stride std::array({1,1}), // dilation std::array({0,0,0,0}), // pad 0); // multiplier auto convolution_26 = graph->CreateOperation ( 1024, // weights tim::vx::PadType::NONE, // padding std::array({3,3}), // ksize std::array({1,1}), // stride std::array({1,1}), // dilation std::array({1,1,1,1}), // pad 1.0); // multiplier auto convolution_27 = graph->CreateOperation ( 1024, // weights tim::vx::PadType::NONE, // padding std::array({1,1}), // ksize std::array({1,1}), // stride std::array({1,1}), // dilation std::array({0,0,0,0}), // pad 0); // multiplier auto pooling_28 = graph->CreateOperation ( tim::vx::PoolType::AVG, // type std::array({0,0,0,0}), // pad std::array({7,7}), // ksize std::array({2,2}), // stride tim::vx::RoundType::FLOOR); // round_type auto convolution_29 = graph->CreateOperation ( 1001, // weights tim::vx::PadType::NONE, // padding std::array({1,1}), // ksize std::array({1,1}), // stride std::array({1,1}), // dilation std::array({0,0,0,0}), // pad 0); // multiplier auto permute_34 = graph->CreateOperation ( std::vector({2,0,1,3})); // perm auto reshape_30 = graph->CreateOperation ( std::vector({1001,1})); // size auto softmax_31 = graph->CreateOperation ( 1.0, // beta 0); // axis (*permute_33) .BindInputs({input_0}) .BindOutputs({permute_33_out0}); (*convolution_1) .BindInputs({permute_33_out0, convolution_1_weight, convolution_1_bias}) .BindOutputs({convolution_1_out0}); (*convolution_2) .BindInputs({convolution_1_out0, convolution_2_weight, convolution_2_bias}) .BindOutputs({convolution_2_out0}); (*convolution_3) .BindInputs({convolution_2_out0, convolution_3_weight, convolution_3_bias}) .BindOutputs({convolution_3_out0}); (*convolution_4) .BindInputs({convolution_3_out0, convolution_4_weight, convolution_4_bias}) .BindOutputs({convolution_4_out0}); (*convolution_5) .BindInputs({convolution_4_out0, convolution_5_weight, convolution_5_bias}) .BindOutputs({convolution_5_out0}); (*convolution_6) .BindInputs({convolution_5_out0, convolution_6_weight, convolution_6_bias}) .BindOutputs({convolution_6_out0}); (*convolution_7) .BindInputs({convolution_6_out0, convolution_7_weight, convolution_7_bias}) .BindOutputs({convolution_7_out0}); (*convolution_8) .BindInputs({convolution_7_out0, convolution_8_weight, convolution_8_bias}) .BindOutputs({convolution_8_out0}); (*convolution_9) .BindInputs({convolution_8_out0, convolution_9_weight, convolution_9_bias}) .BindOutputs({convolution_9_out0}); (*convolution_10) .BindInputs({convolution_9_out0, convolution_10_weight, convolution_10_bias}) .BindOutputs({convolution_10_out0}); (*convolution_11) .BindInputs({convolution_10_out0, convolution_11_weight, convolution_11_bias}) .BindOutputs({convolution_11_out0}); (*convolution_12) .BindInputs({convolution_11_out0, convolution_12_weight, convolution_12_bias}) .BindOutputs({convolution_12_out0}); (*convolution_13) .BindInputs({convolution_12_out0, convolution_13_weight, convolution_13_bias}) .BindOutputs({convolution_13_out0}); (*convolution_14) .BindInputs({convolution_13_out0, convolution_14_weight, convolution_14_bias}) .BindOutputs({convolution_14_out0}); (*convolution_15) .BindInputs({convolution_14_out0, convolution_15_weight, convolution_15_bias}) .BindOutputs({convolution_15_out0}); (*convolution_16) .BindInputs({convolution_15_out0, convolution_16_weight, convolution_16_bias}) .BindOutputs({convolution_16_out0}); (*convolution_17) .BindInputs({convolution_16_out0, convolution_17_weight, convolution_17_bias}) .BindOutputs({convolution_17_out0}); (*convolution_18) .BindInputs({convolution_17_out0, convolution_18_weight, convolution_18_bias}) .BindOutputs({convolution_18_out0}); (*convolution_19) .BindInputs({convolution_18_out0, convolution_19_weight, convolution_19_bias}) .BindOutputs({convolution_19_out0}); (*convolution_20) .BindInputs({convolution_19_out0, convolution_20_weight, convolution_20_bias}) .BindOutputs({convolution_20_out0}); (*convolution_21) .BindInputs({convolution_20_out0, convolution_21_weight, convolution_21_bias}) .BindOutputs({convolution_21_out0}); (*convolution_22) .BindInputs({convolution_21_out0, convolution_22_weight, convolution_22_bias}) .BindOutputs({convolution_22_out0}); (*convolution_23) .BindInputs({convolution_22_out0, convolution_23_weight, convolution_23_bias}) .BindOutputs({convolution_23_out0}); (*convolution_24) .BindInputs({convolution_23_out0, convolution_24_weight, convolution_24_bias}) .BindOutputs({convolution_24_out0}); (*convolution_25) .BindInputs({convolution_24_out0, convolution_25_weight, convolution_25_bias}) .BindOutputs({convolution_25_out0}); (*convolution_26) .BindInputs({convolution_25_out0, convolution_26_weight, convolution_26_bias}) .BindOutputs({convolution_26_out0}); (*convolution_27) .BindInputs({convolution_26_out0, convolution_27_weight, convolution_27_bias}) .BindOutputs({convolution_27_out0}); (*pooling_28) .BindInputs({convolution_27_out0}) .BindOutputs({pooling_28_out0}); (*convolution_29) .BindInputs({pooling_28_out0, convolution_29_weight, convolution_29_bias}) .BindOutputs({convolution_29_out0}); (*permute_34) .BindInputs({convolution_29_out0}) .BindOutputs({permute_34_out0}); (*reshape_30) .BindInputs({permute_34_out0}) .BindOutputs({reshape_30_out0}); (*softmax_31) .BindInputs({reshape_30_out0}) .BindOutputs({output_32}); free(coef_data_ptr); } } // namespace acuitylite