/* * Copyright (c) 2018-2021 Arm Limited. * * SPDX-License-Identifier: MIT * * Permission is hereby granted, free of charge, to any person obtaining a copy * of this software and associated documentation files (the "Software"), to * deal in the Software without restriction, including without limitation the * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or * sell copies of the Software, and to permit persons to whom the Software is * furnished to do so, subject to the following conditions: * * The above copyright notice and this permission notice shall be included in all * copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ #include "arm_compute/graph.h" #include "support/ToolchainSupport.h" #include "utils/CommonGraphOptions.h" #include "utils/GraphUtils.h" #include "utils/Utils.h" using namespace arm_compute; using namespace arm_compute::utils; using namespace arm_compute::graph::frontend; using namespace arm_compute::graph_utils; /** Example demonstrating how to implement MobileNetV2's network using the Compute Library's graph API */ class GraphMobilenetV2Example : public Example { public: GraphMobilenetV2Example() : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "MobileNetV2") { } GraphMobilenetV2Example(const GraphMobilenetV2Example &) = delete; GraphMobilenetV2Example &operator=(const GraphMobilenetV2Example &) = delete; ~GraphMobilenetV2Example() override = default; bool do_setup(int argc, char **argv) override { // Parse arguments cmd_parser.parse(argc, argv); cmd_parser.validate(); // Consume common parameters common_params = consume_common_graph_parameters(common_opts); // Return when help menu is requested if(common_params.help) { cmd_parser.print_help(argv[0]); return false; } // Print parameter values std::cout << common_params << std::endl; // Create input descriptor const TensorShape tensor_shape = permute_shape(TensorShape(224U, 224U, 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; // Create core graph if(arm_compute::is_data_type_float(common_params.data_type)) { create_graph_float(input_descriptor); } else { create_graph_qasymm8(input_descriptor); } // Create common tail graph << ReshapeLayer(TensorShape(1001U)).set_name("Predictions/Reshape") << SoftmaxLayer().set_name("Predictions/Softmax") << OutputLayer(get_output_accessor(common_params, 5)); // Finalize graph GraphConfig config; config.num_threads = common_params.threads; config.use_tuner = common_params.enable_tuner; config.tuner_mode = common_params.tuner_mode; config.tuner_file = common_params.tuner_file; config.mlgo_file = common_params.mlgo_file; graph.finalize(common_params.target, config); return true; } void do_run() override { // Run graph graph.run(); } private: CommandLineParser cmd_parser; CommonGraphOptions common_opts; CommonGraphParams common_params; Stream graph; private: enum class IsResidual { Yes, No }; enum class HasExpand { Yes, No }; private: void create_graph_float(TensorDescriptor &input_descriptor) { // Create model path const std::string model_path = "/cnn_data/mobilenet_v2_1.0_224_model/"; // Create a preprocessor object std::unique_ptr preprocessor = std::make_unique(); // Get trainable parameters data path std::string data_path = common_params.data_path; // Add model path to data path 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, get_weights_accessor(data_path, "Conv_weights.npy", DataLayout::NCHW), std::unique_ptr(nullptr), PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL)) .set_name("Conv") << BatchNormalizationLayer(get_weights_accessor(data_path, "Conv_BatchNorm_moving_mean.npy"), get_weights_accessor(data_path, "Conv_BatchNorm_moving_variance.npy"), get_weights_accessor(data_path, "Conv_BatchNorm_gamma.npy"), get_weights_accessor(data_path, "Conv_BatchNorm_beta.npy"), 0.0010000000474974513f) .set_name("Conv/BatchNorm") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)) .set_name("Conv/Relu6"); get_expanded_conv_float(data_path, "expanded_conv", 32U, 16U, PadStrideInfo(1, 1, 1, 1)); get_expanded_conv_float(data_path, "expanded_conv_1", 16U, 24U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), HasExpand::Yes); get_expanded_conv_float(data_path, "expanded_conv_2", 24U, 24U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes); get_expanded_conv_float(data_path, "expanded_conv_3", 24U, 32U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), HasExpand::Yes); get_expanded_conv_float(data_path, "expanded_conv_4", 32U, 32U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes); get_expanded_conv_float(data_path, "expanded_conv_5", 32U, 32U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes); get_expanded_conv_float(data_path, "expanded_conv_6", 32U, 64U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), HasExpand::Yes); get_expanded_conv_float(data_path, "expanded_conv_7", 64U, 64U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes); get_expanded_conv_float(data_path, "expanded_conv_8", 64U, 64U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes); get_expanded_conv_float(data_path, "expanded_conv_9", 64U, 64U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes); get_expanded_conv_float(data_path, "expanded_conv_10", 64U, 96U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes); get_expanded_conv_float(data_path, "expanded_conv_11", 96U, 96U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes); get_expanded_conv_float(data_path, "expanded_conv_12", 96U, 96U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes); get_expanded_conv_float(data_path, "expanded_conv_13", 96U, 160U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), HasExpand::Yes); get_expanded_conv_float(data_path, "expanded_conv_14", 160U, 160U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes); get_expanded_conv_float(data_path, "expanded_conv_15", 160U, 160U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes); get_expanded_conv_float(data_path, "expanded_conv_16", 160U, 320U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes); graph << ConvolutionLayer(1U, 1U, 1280U, get_weights_accessor(data_path, "Conv_1_weights.npy", DataLayout::NCHW), std::unique_ptr(nullptr), PadStrideInfo(1, 1, 0, 0)) .set_name("Conv_1") << BatchNormalizationLayer(get_weights_accessor(data_path, "Conv_1_BatchNorm_moving_mean.npy"), get_weights_accessor(data_path, "Conv_1_BatchNorm_moving_variance.npy"), get_weights_accessor(data_path, "Conv_1_BatchNorm_gamma.npy"), get_weights_accessor(data_path, "Conv_1_BatchNorm_beta.npy"), 0.0010000000474974513f) .set_name("Conv_1/BatchNorm") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)) .set_name("Conv_1/Relu6") << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, common_params.data_layout)).set_name("Logits/AvgPool") << 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 get_expanded_conv_float(const std::string &data_path, std::string &¶m_path, unsigned int input_channels, unsigned int output_channels, PadStrideInfo dwc_pad_stride_info, HasExpand has_expand = HasExpand::No, IsResidual is_residual = IsResidual::No, unsigned int expansion_size = 6) { std::string total_path = param_path + "_"; SubStream left(graph); // Add expand node if(has_expand == HasExpand::Yes) { left << ConvolutionLayer(1U, 1U, input_channels * expansion_size, get_weights_accessor(data_path, total_path + "expand_weights.npy", DataLayout::NCHW), std::unique_ptr(nullptr), PadStrideInfo(1, 1, 0, 0)) .set_name(param_path + "/expand/Conv2D") << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "expand_BatchNorm_moving_mean.npy"), get_weights_accessor(data_path, total_path + "expand_BatchNorm_moving_variance.npy"), get_weights_accessor(data_path, total_path + "expand_BatchNorm_gamma.npy"), get_weights_accessor(data_path, total_path + "expand_BatchNorm_beta.npy"), 0.0010000000474974513f) .set_name(param_path + "/expand/BatchNorm") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)) .set_name(param_path + "/expand/Relu6"); } // Add depthwise node left << DepthwiseConvolutionLayer(3U, 3U, get_weights_accessor(data_path, total_path + "depthwise_depthwise_weights.npy", DataLayout::NCHW), std::unique_ptr(nullptr), dwc_pad_stride_info) .set_name(param_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"), 0.0010000000474974513f) .set_name(param_path + "/depthwise/BatchNorm") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)) .set_name(param_path + "/depthwise/Relu6"); // Add project node left << ConvolutionLayer(1U, 1U, output_channels, get_weights_accessor(data_path, total_path + "project_weights.npy", DataLayout::NCHW), std::unique_ptr(nullptr), PadStrideInfo(1, 1, 0, 0)) .set_name(param_path + "/project/Conv2D") << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "project_BatchNorm_moving_mean.npy"), get_weights_accessor(data_path, total_path + "project_BatchNorm_moving_variance.npy"), get_weights_accessor(data_path, total_path + "project_BatchNorm_gamma.npy"), get_weights_accessor(data_path, total_path + "project_BatchNorm_beta.npy"), 0.0010000000474974513) .set_name(param_path + "/project/BatchNorm"); if(is_residual == IsResidual::Yes) { // Add residual node SubStream right(graph); graph << EltwiseLayer(std::move(left), std::move(right), EltwiseOperation::Add).set_name(param_path + "/add"); } else { graph.forward_tail(left.tail_node()); } } void create_graph_qasymm8(TensorDescriptor &input_descriptor) { // Create model path const std::string model_path = "/cnn_data/mobilenet_v2_1.0_224_quantized_model/"; // Get trainable parameters data path std::string data_path = common_params.data_path; // Add model path to data path if(!data_path.empty()) { data_path += model_path; } const QuantizationInfo in_quant_info = QuantizationInfo(0.0078125f, 128); const QuantizationInfo mid_quant_info = QuantizationInfo(0.023528477177023888f, 128); const std::vector conv_weights_quant_info = { QuantizationInfo(0.03396892547607422f, 122), // Conv QuantizationInfo(0.005167067516595125f, 125), // Conv1 QuantizationInfo(0.0016910821432247758f, 113) // Conv2d_1c_1x1 }; // Pointwise expand convolution quantization info const std::vector pwc_q = { QuantizationInfo(0.254282623529f, 129), // expand_0 (Dummy) QuantizationInfo(0.009758507832884789f, 127), // expand_1 QuantizationInfo(0.0036556976847350597f, 144), // expand_2 QuantizationInfo(0.0029988749884068966f, 104), // expand_3 QuantizationInfo(0.0019244228024035692f, 128), // expand_4 QuantizationInfo(0.0013649158645421267f, 135), // expand_5 QuantizationInfo(0.0019170437008142471f, 127), // expand_6 QuantizationInfo(0.0015538912266492844f, 125), // expand_7 QuantizationInfo(0.0014702979242429137f, 134), // expand_8 QuantizationInfo(0.0013733493397012353f, 127), // expand_9 QuantizationInfo(0.0016282502328976989f, 131), // expand_10 QuantizationInfo(0.0016309921629726887f, 134), // expand_11 QuantizationInfo(0.0018258779309689999f, 138), // expand_12 QuantizationInfo(0.0013828007504343987f, 123), // expand_13 QuantizationInfo(0.0020222084131091833f, 135), // expand_14 QuantizationInfo(0.04281935095787048f, 102), // expand_15 QuantizationInfo(0.002046825597062707f, 135) // expand_16 }; // Depthwise expand convolution quantization info const std::vector dwc_q = { QuantizationInfo(0.3436955213546753f, 165), // expand_0 QuantizationInfo(0.020969120785593987f, 109), // expand_1 QuantizationInfo(0.16981913149356842f, 52), // expand_2 QuantizationInfo(0.017202870920300484f, 143), // expand_3 QuantizationInfo(0.06525065749883652f, 118), // expand_4 QuantizationInfo(0.07909784466028214f, 95), // expand_5 QuantizationInfo(0.010087885893881321f, 127), // expand_6 QuantizationInfo(0.06092711538076401f, 110), // expand_7 QuantizationInfo(0.052407849580049515f, 133), // expand_8 QuantizationInfo(0.04077887907624245f, 155), // expand_9 QuantizationInfo(0.031107846647500992f, 143), // expand_10 QuantizationInfo(0.07080810517072678f, 66), // expand_11 QuantizationInfo(0.07448793947696686f, 159), // expand_12 QuantizationInfo(0.01525793131440878f, 92), // expand_13 QuantizationInfo(0.04166752099990845f, 147), // expand_14 QuantizationInfo(0.04281935095787048f, 102), // expand_15 QuantizationInfo(0.16456253826618195, 201) // expand_16 }; // Project convolution quantization info const std::vector prwc_q = { QuantizationInfo(0.03737175464630127f, 140), // expand_0 QuantizationInfo(0.0225360207259655f, 156), // expand_1 QuantizationInfo(0.02740888111293316f, 122), // expand_2 QuantizationInfo(0.016844693571329117f, 111), // expand_3 QuantizationInfo(0.019062912091612816f, 146), // expand_4 QuantizationInfo(0.018293123692274094f, 128), // expand_5 QuantizationInfo(0.014601286500692368f, 147), // expand_6 QuantizationInfo(0.016782939434051514f, 124), // expand_7 QuantizationInfo(0.012898261658847332f, 125), // expand_8 QuantizationInfo(0.019561484456062317f, 144), // expand_9 QuantizationInfo(0.007436311338096857f, 129), // expand_10 QuantizationInfo(0.00838223285973072f, 136), // expand_11 QuantizationInfo(0.023982593789696693f, 154), // expand_12 QuantizationInfo(0.009447949007153511f, 140), // expand_13 QuantizationInfo(0.00789870135486126f, 139), // expand_14 QuantizationInfo(0.03697410225868225f, 131), // expand_15 QuantizationInfo(0.008009289391338825f, 111) // expand_16 }; graph << InputLayer(input_descriptor.set_quantization_info(in_quant_info), get_weights_accessor(data_path, common_params.image)) << ConvolutionLayer( 3U, 3U, 32U, get_weights_accessor(data_path, "Conv_weights.npy"), get_weights_accessor(data_path, "Conv_bias.npy"), PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), 1, conv_weights_quant_info.at(0), mid_quant_info) .set_name("Conv") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)).set_name("Conv/Relu6") << DepthwiseConvolutionLayer(3U, 3U, get_weights_accessor(data_path, "expanded_conv_depthwise_depthwise_weights.npy"), get_weights_accessor(data_path, "expanded_conv_depthwise_depthwise_biases.npy"), PadStrideInfo(1, 1, 1, 1), 1, dwc_q.at(0)) .set_name("expanded_conv/depthwise/depthwise") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)).set_name("expanded_conv/depthwise/Relu6") << ConvolutionLayer(1U, 1U, 16U, get_weights_accessor(data_path, "expanded_conv_project_weights.npy"), get_weights_accessor(data_path, "expanded_conv_project_biases.npy"), PadStrideInfo(1, 1, 0, 0), 1, prwc_q.at(0)) .set_name("expanded_conv/project/Conv2D"); get_expanded_conv_qasymm8(data_path, "expanded_conv_1", IsResidual::No, 96U, 24U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), pwc_q.at(1), dwc_q.at(1), prwc_q.at(1)); get_expanded_conv_qasymm8(data_path, "expanded_conv_2", IsResidual::Yes, 144U, 24U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(2), dwc_q.at(2), prwc_q.at(2)); get_expanded_conv_qasymm8(data_path, "expanded_conv_3", IsResidual::No, 144U, 32U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), pwc_q.at(3), dwc_q.at(3), prwc_q.at(3)); get_expanded_conv_qasymm8(data_path, "expanded_conv_4", IsResidual::Yes, 192U, 32U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(4), dwc_q.at(4), prwc_q.at(4)); get_expanded_conv_qasymm8(data_path, "expanded_conv_5", IsResidual::Yes, 192U, 32U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(5), dwc_q.at(5), prwc_q.at(5)); get_expanded_conv_qasymm8(data_path, "expanded_conv_6", IsResidual::No, 192U, 64U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), pwc_q.at(6), dwc_q.at(6), prwc_q.at(6)); get_expanded_conv_qasymm8(data_path, "expanded_conv_7", IsResidual::Yes, 384U, 64U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(7), dwc_q.at(7), prwc_q.at(7)); get_expanded_conv_qasymm8(data_path, "expanded_conv_8", IsResidual::Yes, 384U, 64U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(8), dwc_q.at(8), prwc_q.at(8)); get_expanded_conv_qasymm8(data_path, "expanded_conv_9", IsResidual::Yes, 384U, 64U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(9), dwc_q.at(9), prwc_q.at(9)); get_expanded_conv_qasymm8(data_path, "expanded_conv_10", IsResidual::No, 384U, 96U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(10), dwc_q.at(10), prwc_q.at(10)); get_expanded_conv_qasymm8(data_path, "expanded_conv_11", IsResidual::Yes, 576U, 96U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(11), dwc_q.at(11), prwc_q.at(11)); get_expanded_conv_qasymm8(data_path, "expanded_conv_12", IsResidual::Yes, 576U, 96U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(12), dwc_q.at(12), prwc_q.at(12)); get_expanded_conv_qasymm8(data_path, "expanded_conv_13", IsResidual::No, 576U, 160U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), pwc_q.at(13), dwc_q.at(13), prwc_q.at(13)); get_expanded_conv_qasymm8(data_path, "expanded_conv_14", IsResidual::Yes, 960U, 160U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(14), dwc_q.at(14), prwc_q.at(14)); get_expanded_conv_qasymm8(data_path, "expanded_conv_15", IsResidual::Yes, 960U, 160U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(15), dwc_q.at(15), prwc_q.at(15)); get_expanded_conv_qasymm8(data_path, "expanded_conv_16", IsResidual::No, 960U, 320U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(16), dwc_q.at(16), prwc_q.at(16)); graph << ConvolutionLayer(1U, 1U, 1280U, get_weights_accessor(data_path, "Conv_1_weights.npy"), get_weights_accessor(data_path, "Conv_1_biases.npy"), PadStrideInfo(1, 1, 0, 0), 1, conv_weights_quant_info.at(1)) .set_name("Conv_1") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)).set_name("Conv_1/Relu6") << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, common_params.data_layout)).set_name("Logits/AvgPool") << ConvolutionLayer(1U, 1U, 1001U, get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_weights.npy"), get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_biases.npy"), PadStrideInfo(1, 1, 0, 0), 1, conv_weights_quant_info.at(2)) .set_name("Logits/Conv2d_1c_1x1"); } void get_expanded_conv_qasymm8(const std::string &data_path, std::string &¶m_path, IsResidual is_residual, unsigned int input_channels, unsigned int output_channels, PadStrideInfo dwc_pad_stride_info, const QuantizationInfo &pwi, const QuantizationInfo &dwi, const QuantizationInfo &pji) { std::string total_path = param_path + "_"; SubStream left(graph); left << ConvolutionLayer(1U, 1U, input_channels, get_weights_accessor(data_path, total_path + "project_weights.npy"), get_weights_accessor(data_path, total_path + "project_biases.npy"), PadStrideInfo(1, 1, 0, 0), 1, pwi) .set_name(param_path + "/Conv2D") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)).set_name(param_path + "/Conv2D/Relu6") << DepthwiseConvolutionLayer(3U, 3U, get_weights_accessor(data_path, total_path + "depthwise_depthwise_weights.npy"), get_weights_accessor(data_path, total_path + "depthwise_depthwise_biases.npy"), dwc_pad_stride_info, 1, dwi) .set_name(param_path + "/depthwise/depthwise") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)).set_name(param_path + "/depthwise/Relu6") << ConvolutionLayer(1U, 1U, output_channels, get_weights_accessor(data_path, total_path + "project_weights.npy"), get_weights_accessor(data_path, total_path + "project_biases.npy"), PadStrideInfo(1, 1, 0, 0), 1, pji) .set_name(param_path + "/project/Conv2D"); if(is_residual == IsResidual::Yes) { // Add residual node SubStream right(graph); graph << EltwiseLayer(std::move(left), std::move(right), EltwiseOperation::Add).set_name(param_path + "/add"); } else { graph.forward_tail(left.tail_node()); } } }; /** Main program for MobileNetV2 * * Model is based on: * https://arxiv.org/abs/1801.04381 * "MobileNetV2: Inverted Residuals and Linear Bottlenecks" * Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen * * Provenance: https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.0_224.tgz * * @note To list all the possible arguments execute the binary appended with the --help option * * @param[in] argc Number of arguments * @param[in] argv Arguments */ int main(int argc, char **argv) { return arm_compute::utils::run_example(argc, argv); }