diff options
Diffstat (limited to 'examples/graph_mobilenet.cpp')
| -rw-r--r-- | examples/graph_mobilenet.cpp | 320 |
1 files changed, 176 insertions, 144 deletions
diff --git a/examples/graph_mobilenet.cpp b/examples/graph_mobilenet.cpp index 4630dc958a..e3a6ef116d 100644 --- a/examples/graph_mobilenet.cpp +++ b/examples/graph_mobilenet.cpp @@ -22,6 +22,7 @@ * SOFTWARE. */ #include "arm_compute/graph.h" + #include "support/ToolchainSupport.h" #include "utils/CommonGraphOptions.h" #include "utils/GraphUtils.h" @@ -36,14 +37,13 @@ using namespace arm_compute::graph_utils; class GraphMobilenetExample : public Example { public: - GraphMobilenetExample() - : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "MobileNetV1") + GraphMobilenetExample() : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "MobileNetV1") { // Add model id option model_id_opt = cmd_parser.add_option<SimpleOption<int>>("model-id", 0); model_id_opt->set_help("Mobilenet model id (0: 1.0_224, else: 0.75_160"); } - GraphMobilenetExample(const GraphMobilenetExample &) = delete; + GraphMobilenetExample(const GraphMobilenetExample &) = delete; GraphMobilenetExample &operator=(const GraphMobilenetExample &) = delete; ~GraphMobilenetExample() override = default; bool do_setup(int argc, char **argv) override @@ -56,7 +56,7 @@ public: common_params = consume_common_graph_parameters(common_opts); // Return when help menu is requested - if(common_params.help) + if (common_params.help) { cmd_parser.print_help(argv[0]); return false; @@ -72,15 +72,17 @@ public: unsigned int spatial_size = (model_id == 0 || common_params.data_type == DataType::QASYMM8) ? 224 : 160; // Create input descriptor - const TensorShape tensor_shape = permute_shape(TensorShape(spatial_size, spatial_size, 3U, common_params.batches), DataLayout::NCHW, common_params.data_layout); - TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout); + const TensorShape tensor_shape = + permute_shape(TensorShape(spatial_size, spatial_size, 3U, common_params.batches), DataLayout::NCHW, + common_params.data_layout); + TensorDescriptor input_descriptor = + TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout); // Set graph hints - graph << common_params.target - << common_params.fast_math_hint; + graph << common_params.target << common_params.fast_math_hint; // Create core graph - if(arm_compute::is_data_type_float(common_params.data_type)) + if (arm_compute::is_data_type_float(common_params.data_type)) { create_graph_float(input_descriptor, model_id); } @@ -90,8 +92,7 @@ public: } // Create common tail - graph << ReshapeLayer(TensorShape(1001U)).set_name("Reshape") - << SoftmaxLayer().set_name("Softmax") + graph << ReshapeLayer(TensorShape(1001U)).set_name("Reshape") << SoftmaxLayer().set_name("Softmax") << OutputLayer(get_output_accessor(common_params, 5)); // Finalize graph @@ -115,14 +116,15 @@ public: private: CommandLineParser cmd_parser; CommonGraphOptions common_opts; - SimpleOption<int> *model_id_opt{ nullptr }; + SimpleOption<int> *model_id_opt{nullptr}; CommonGraphParams common_params; Stream graph; void create_graph_float(TensorDescriptor &input_descriptor, int model_id) { float depth_scale = (model_id == 0) ? 1.f : 0.75; - std::string model_path = (model_id == 0) ? "/cnn_data/mobilenet_v1_1_224_model/" : "/cnn_data/mobilenet_v1_075_160_model/"; + std::string model_path = + (model_id == 0) ? "/cnn_data/mobilenet_v1_1_224_model/" : "/cnn_data/mobilenet_v1_075_160_model/"; // Create a preprocessor object std::unique_ptr<IPreprocessor> preprocessor = std::make_unique<TFPreproccessor>(); @@ -131,47 +133,68 @@ private: std::string data_path = common_params.data_path; // Add model path to data path - if(!data_path.empty()) + if (!data_path.empty()) { data_path += model_path; } - graph << InputLayer(input_descriptor, - get_input_accessor(common_params, std::move(preprocessor), false)) - << ConvolutionLayer( - 3U, 3U, 32U * depth_scale, - get_weights_accessor(data_path, "Conv2d_0_weights.npy", DataLayout::NCHW), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR)) - .set_name("Conv2d_0") - << BatchNormalizationLayer( - get_weights_accessor(data_path, "Conv2d_0_BatchNorm_moving_mean.npy"), - get_weights_accessor(data_path, "Conv2d_0_BatchNorm_moving_variance.npy"), - get_weights_accessor(data_path, "Conv2d_0_BatchNorm_gamma.npy"), - get_weights_accessor(data_path, "Conv2d_0_BatchNorm_beta.npy"), - 0.001f) - .set_name("Conv2d_0/BatchNorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)).set_name("Conv2d_0/Relu6"); - graph << get_dwsc_node_float(data_path, "Conv2d_1", 64 * depth_scale, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0)); - graph << get_dwsc_node_float(data_path, "Conv2d_2", 128 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); - graph << get_dwsc_node_float(data_path, "Conv2d_3", 128 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); - graph << get_dwsc_node_float(data_path, "Conv2d_4", 256 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); - graph << get_dwsc_node_float(data_path, "Conv2d_5", 256 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); - graph << get_dwsc_node_float(data_path, "Conv2d_6", 512 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); - graph << get_dwsc_node_float(data_path, "Conv2d_7", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); - graph << get_dwsc_node_float(data_path, "Conv2d_8", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); - graph << get_dwsc_node_float(data_path, "Conv2d_9", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); - graph << get_dwsc_node_float(data_path, "Conv2d_10", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); - graph << get_dwsc_node_float(data_path, "Conv2d_11", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); - graph << get_dwsc_node_float(data_path, "Conv2d_12", 1024 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); - graph << get_dwsc_node_float(data_path, "Conv2d_13", 1024 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0)); - graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, common_params.data_layout)).set_name("Logits/AvgPool_1a") - << ConvolutionLayer( - 1U, 1U, 1001U, - get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_weights.npy", DataLayout::NCHW), - get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_biases.npy"), - PadStrideInfo(1, 1, 0, 0)) - .set_name("Logits/Conv2d_1c_1x1"); + graph << InputLayer(input_descriptor, get_input_accessor(common_params, std::move(preprocessor), false)) + << ConvolutionLayer(3U, 3U, 32U * depth_scale, + get_weights_accessor(data_path, "Conv2d_0_weights.npy", DataLayout::NCHW), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), + PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR)) + .set_name("Conv2d_0") + << BatchNormalizationLayer(get_weights_accessor(data_path, "Conv2d_0_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, "Conv2d_0_BatchNorm_moving_variance.npy"), + get_weights_accessor(data_path, "Conv2d_0_BatchNorm_gamma.npy"), + get_weights_accessor(data_path, "Conv2d_0_BatchNorm_beta.npy"), 0.001f) + .set_name("Conv2d_0/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)) + .set_name("Conv2d_0/Relu6"); + graph << get_dwsc_node_float(data_path, "Conv2d_1", 64 * depth_scale, PadStrideInfo(1, 1, 1, 1), + PadStrideInfo(1, 1, 0, 0)); + graph << get_dwsc_node_float(data_path, "Conv2d_2", 128 * depth_scale, + PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), + PadStrideInfo(1, 1, 0, 0)); + graph << get_dwsc_node_float(data_path, "Conv2d_3", 128 * depth_scale, + PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), + PadStrideInfo(1, 1, 0, 0)); + graph << get_dwsc_node_float(data_path, "Conv2d_4", 256 * depth_scale, + PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), + PadStrideInfo(1, 1, 0, 0)); + graph << get_dwsc_node_float(data_path, "Conv2d_5", 256 * depth_scale, + PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), + PadStrideInfo(1, 1, 0, 0)); + graph << get_dwsc_node_float(data_path, "Conv2d_6", 512 * depth_scale, + PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), + PadStrideInfo(1, 1, 0, 0)); + graph << get_dwsc_node_float(data_path, "Conv2d_7", 512 * depth_scale, + PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), + PadStrideInfo(1, 1, 0, 0)); + graph << get_dwsc_node_float(data_path, "Conv2d_8", 512 * depth_scale, + PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), + PadStrideInfo(1, 1, 0, 0)); + graph << get_dwsc_node_float(data_path, "Conv2d_9", 512 * depth_scale, + PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), + PadStrideInfo(1, 1, 0, 0)); + graph << get_dwsc_node_float(data_path, "Conv2d_10", 512 * depth_scale, + PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), + PadStrideInfo(1, 1, 0, 0)); + graph << get_dwsc_node_float(data_path, "Conv2d_11", 512 * depth_scale, + PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), + PadStrideInfo(1, 1, 0, 0)); + graph << get_dwsc_node_float(data_path, "Conv2d_12", 1024 * depth_scale, + PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), + PadStrideInfo(1, 1, 0, 0)); + graph << get_dwsc_node_float(data_path, "Conv2d_13", 1024 * depth_scale, + PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), + PadStrideInfo(1, 1, 0, 0)); + graph + << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, common_params.data_layout)).set_name("Logits/AvgPool_1a") + << ConvolutionLayer( + 1U, 1U, 1001U, get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_weights.npy", DataLayout::NCHW), + get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_biases.npy"), PadStrideInfo(1, 1, 0, 0)) + .set_name("Logits/Conv2d_1c_1x1"); } void create_graph_qasymm(TensorDescriptor &input_descriptor) @@ -180,7 +203,7 @@ private: std::string data_path = common_params.data_path; // Add model path to data path - if(!data_path.empty()) + if (!data_path.empty()) { data_path += "/cnn_data/mobilenet_qasymm8_model/"; } @@ -188,19 +211,16 @@ private: // Quantization info taken from the AndroidNN QASYMM8 MobileNet example const QuantizationInfo in_quant_info = QuantizationInfo(0.0078125f, 128); - const std::vector<QuantizationInfo> conv_weights_quant_info = - { + const std::vector<QuantizationInfo> conv_weights_quant_info = { QuantizationInfo(0.02182667888700962f, 151), // conv0 QuantizationInfo(0.004986600950360298f, 74) // conv14 }; - const std::vector<QuantizationInfo> conv_out_quant_info = - { + const std::vector<QuantizationInfo> conv_out_quant_info = { QuantizationInfo(0.023528477177023888f, 0), // conv0 QuantizationInfo(0.16609922051429749f, 66) // conv14 }; - const std::vector<QuantizationInfo> depth_weights_quant_info = - { + const std::vector<QuantizationInfo> depth_weights_quant_info = { QuantizationInfo(0.29219913482666016f, 110), // dwsc1 QuantizationInfo(0.40277284383773804f, 130), // dwsc2 QuantizationInfo(0.06053730100393295f, 160), // dwsc3 @@ -216,8 +236,7 @@ private: QuantizationInfo(0.12616927921772003f, 211) // dwsc13 }; - const std::vector<QuantizationInfo> point_weights_quant_info = - { + const std::vector<QuantizationInfo> point_weights_quant_info = { QuantizationInfo(0.030420949682593346f, 121), // dwsc1 QuantizationInfo(0.015148180536925793f, 104), // dwsc2 QuantizationInfo(0.013755458407104015f, 94), // dwsc3 @@ -235,108 +254,121 @@ private: graph << InputLayer(input_descriptor.set_quantization_info(in_quant_info), get_input_accessor(common_params, nullptr, false)) - << ConvolutionLayer( - 3U, 3U, 32U, - get_weights_accessor(data_path, "Conv2d_0_weights.npy"), - get_weights_accessor(data_path, "Conv2d_0_bias.npy"), - PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), - 1, conv_weights_quant_info.at(0), conv_out_quant_info.at(0)) - .set_name("Conv2d_0") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)).set_name("Conv2d_0/Relu6"); - graph << get_dwsc_node_qasymm(data_path, "Conv2d_1", 64U, PadStrideInfo(1U, 1U, 1U, 1U), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(0), point_weights_quant_info.at(0)); - graph << get_dwsc_node_qasymm(data_path, "Conv2d_2", 128U, PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(1), - point_weights_quant_info.at(1)); - graph << get_dwsc_node_qasymm(data_path, "Conv2d_3", 128U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(2), - point_weights_quant_info.at(2)); - graph << get_dwsc_node_qasymm(data_path, "Conv2d_4", 256U, PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(3), - point_weights_quant_info.at(3)); - graph << get_dwsc_node_qasymm(data_path, "Conv2d_5", 256U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(4), - point_weights_quant_info.at(4)); - graph << get_dwsc_node_qasymm(data_path, "Conv2d_6", 512U, PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(5), - point_weights_quant_info.at(5)); - graph << get_dwsc_node_qasymm(data_path, "Conv2d_7", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(6), - point_weights_quant_info.at(6)); - graph << get_dwsc_node_qasymm(data_path, "Conv2d_8", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(7), - point_weights_quant_info.at(7)); - graph << get_dwsc_node_qasymm(data_path, "Conv2d_9", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(8), - point_weights_quant_info.at(8)); - graph << get_dwsc_node_qasymm(data_path, "Conv2d_10", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(9), - point_weights_quant_info.at(9)); - graph << get_dwsc_node_qasymm(data_path, "Conv2d_11", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(10), - point_weights_quant_info.at(10)); - graph << get_dwsc_node_qasymm(data_path, "Conv2d_12", 1024U, PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(11), - point_weights_quant_info.at(11)); - graph << get_dwsc_node_qasymm(data_path, "Conv2d_13", 1024U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(12), - point_weights_quant_info.at(12)) - << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, common_params.data_layout)).set_name("Logits/AvgPool_1a") - << ConvolutionLayer( - 1U, 1U, 1001U, - get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_weights.npy"), - get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_bias.npy"), - PadStrideInfo(1U, 1U, 0U, 0U), 1, conv_weights_quant_info.at(1), conv_out_quant_info.at(1)) - .set_name("Logits/Conv2d_1c_1x1"); + << ConvolutionLayer(3U, 3U, 32U, get_weights_accessor(data_path, "Conv2d_0_weights.npy"), + get_weights_accessor(data_path, "Conv2d_0_bias.npy"), + PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), 1, + conv_weights_quant_info.at(0), conv_out_quant_info.at(0)) + .set_name("Conv2d_0") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)) + .set_name("Conv2d_0/Relu6"); + graph << get_dwsc_node_qasymm(data_path, "Conv2d_1", 64U, PadStrideInfo(1U, 1U, 1U, 1U), + PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(0), + point_weights_quant_info.at(0)); + graph << get_dwsc_node_qasymm( + data_path, "Conv2d_2", 128U, PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), + PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(1), point_weights_quant_info.at(1)); + graph << get_dwsc_node_qasymm( + data_path, "Conv2d_3", 128U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), + PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(2), point_weights_quant_info.at(2)); + graph << get_dwsc_node_qasymm( + data_path, "Conv2d_4", 256U, PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), + PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(3), point_weights_quant_info.at(3)); + graph << get_dwsc_node_qasymm( + data_path, "Conv2d_5", 256U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), + PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(4), point_weights_quant_info.at(4)); + graph << get_dwsc_node_qasymm( + data_path, "Conv2d_6", 512U, PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), + PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(5), point_weights_quant_info.at(5)); + graph << get_dwsc_node_qasymm( + data_path, "Conv2d_7", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), + PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(6), point_weights_quant_info.at(6)); + graph << get_dwsc_node_qasymm( + data_path, "Conv2d_8", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), + PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(7), point_weights_quant_info.at(7)); + graph << get_dwsc_node_qasymm( + data_path, "Conv2d_9", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), + PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(8), point_weights_quant_info.at(8)); + graph << get_dwsc_node_qasymm( + data_path, "Conv2d_10", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), + PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(9), point_weights_quant_info.at(9)); + graph << get_dwsc_node_qasymm( + data_path, "Conv2d_11", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), + PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(10), point_weights_quant_info.at(10)); + graph << get_dwsc_node_qasymm( + data_path, "Conv2d_12", 1024U, PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), + PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(11), point_weights_quant_info.at(11)); + graph + << get_dwsc_node_qasymm( + data_path, "Conv2d_13", 1024U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), + PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(12), point_weights_quant_info.at(12)) + << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, common_params.data_layout)).set_name("Logits/AvgPool_1a") + << ConvolutionLayer(1U, 1U, 1001U, get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_weights.npy"), + get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_bias.npy"), + PadStrideInfo(1U, 1U, 0U, 0U), 1, conv_weights_quant_info.at(1), + conv_out_quant_info.at(1)) + .set_name("Logits/Conv2d_1c_1x1"); } - ConcatLayer get_dwsc_node_float(const std::string &data_path, std::string &¶m_path, - unsigned int conv_filt, - PadStrideInfo dwc_pad_stride_info, PadStrideInfo conv_pad_stride_info) + ConcatLayer get_dwsc_node_float(const std::string &data_path, + std::string &¶m_path, + unsigned int conv_filt, + PadStrideInfo dwc_pad_stride_info, + PadStrideInfo conv_pad_stride_info) { std::string total_path = param_path + "_"; SubStream sg(graph); sg << DepthwiseConvolutionLayer( - 3U, 3U, - get_weights_accessor(data_path, total_path + "depthwise_depthwise_weights.npy", DataLayout::NCHW), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - dwc_pad_stride_info) - .set_name(total_path + "depthwise/depthwise") + 3U, 3U, + get_weights_accessor(data_path, total_path + "depthwise_depthwise_weights.npy", DataLayout::NCHW), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), dwc_pad_stride_info) + .set_name(total_path + "depthwise/depthwise") << BatchNormalizationLayer( - get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_moving_mean.npy"), - get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_moving_variance.npy"), - get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_gamma.npy"), - get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_beta.npy"), - 0.001f) - .set_name(total_path + "depthwise/BatchNorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)).set_name(total_path + "depthwise/Relu6") - << ConvolutionLayer( - 1U, 1U, conv_filt, - get_weights_accessor(data_path, total_path + "pointwise_weights.npy", DataLayout::NCHW), - std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), - conv_pad_stride_info) - .set_name(total_path + "pointwise/Conv2D") + get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_moving_variance.npy"), + get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_gamma.npy"), + get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_beta.npy"), 0.001f) + .set_name(total_path + "depthwise/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)) + .set_name(total_path + "depthwise/Relu6") + << ConvolutionLayer(1U, 1U, conv_filt, + get_weights_accessor(data_path, total_path + "pointwise_weights.npy", DataLayout::NCHW), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), conv_pad_stride_info) + .set_name(total_path + "pointwise/Conv2D") << BatchNormalizationLayer( - get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_moving_mean.npy"), - get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_moving_variance.npy"), - get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_gamma.npy"), - get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_beta.npy"), - 0.001f) - .set_name(total_path + "pointwise/BatchNorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)).set_name(total_path + "pointwise/Relu6"); + get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_moving_variance.npy"), + get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_gamma.npy"), + get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_beta.npy"), 0.001f) + .set_name(total_path + "pointwise/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)) + .set_name(total_path + "pointwise/Relu6"); return ConcatLayer(std::move(sg)); } - ConcatLayer get_dwsc_node_qasymm(const std::string &data_path, std::string &¶m_path, + ConcatLayer get_dwsc_node_qasymm(const std::string &data_path, + std::string &¶m_path, const unsigned int conv_filt, - PadStrideInfo dwc_pad_stride_info, PadStrideInfo conv_pad_stride_info, - QuantizationInfo depth_weights_quant_info, QuantizationInfo point_weights_quant_info) + PadStrideInfo dwc_pad_stride_info, + PadStrideInfo conv_pad_stride_info, + QuantizationInfo depth_weights_quant_info, + QuantizationInfo point_weights_quant_info) { std::string total_path = param_path + "_"; SubStream sg(graph); - sg << DepthwiseConvolutionLayer( - 3U, 3U, - get_weights_accessor(data_path, total_path + "depthwise_weights.npy"), - get_weights_accessor(data_path, total_path + "depthwise_bias.npy"), - dwc_pad_stride_info, 1, std::move(depth_weights_quant_info)) - .set_name(total_path + "depthwise/depthwise") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)).set_name(total_path + "depthwise/Relu6") - << ConvolutionLayer( - 1U, 1U, conv_filt, - get_weights_accessor(data_path, total_path + "pointwise_weights.npy"), - get_weights_accessor(data_path, total_path + "pointwise_bias.npy"), - conv_pad_stride_info, 1, std::move(point_weights_quant_info)) - .set_name(total_path + "pointwise/Conv2D") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)).set_name(total_path + "pointwise/Relu6"); + sg << DepthwiseConvolutionLayer(3U, 3U, get_weights_accessor(data_path, total_path + "depthwise_weights.npy"), + get_weights_accessor(data_path, total_path + "depthwise_bias.npy"), + dwc_pad_stride_info, 1, std::move(depth_weights_quant_info)) + .set_name(total_path + "depthwise/depthwise") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)) + .set_name(total_path + "depthwise/Relu6") + << ConvolutionLayer(1U, 1U, conv_filt, get_weights_accessor(data_path, total_path + "pointwise_weights.npy"), + get_weights_accessor(data_path, total_path + "pointwise_bias.npy"), conv_pad_stride_info, + 1, std::move(point_weights_quant_info)) + .set_name(total_path + "pointwise/Conv2D") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)) + .set_name(total_path + "pointwise/Relu6"); return ConcatLayer(std::move(sg)); } |