| /* | |
| * Copyright (c) 2017-2021 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 "arm_compute/core/Types.h" | |
| #include "arm_compute/core/Utils.h" | |
| #include "support/ToolchainSupport.h" | |
| #include "utils/CommonGraphOptions.h" | |
| #include "utils/GraphUtils.h" | |
| #include "utils/Utils.h" | |
| using namespace arm_compute::utils; | |
| using namespace arm_compute::graph::frontend; | |
| using namespace arm_compute::graph_utils; | |
| /** Example demonstrating how to implement InceptionV3's network using the Compute Library's graph API */ | |
| class InceptionV3Example : public Example | |
| { | |
| public: | |
| InceptionV3Example() | |
| : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "InceptionV3") | |
| { | |
| } | |
| 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; | |
| // Get trainable parameters data path | |
| std::string data_path = common_params.data_path; | |
| // Create a preprocessor object | |
| std::unique_ptr<IPreprocessor> preprocessor = std::make_unique<TFPreproccessor>(); | |
| // Create input descriptor | |
| const auto operation_layout = common_params.data_layout; | |
| const TensorShape tensor_shape = permute_shape(TensorShape(299U, 299U, 3U, common_params.batches), DataLayout::NCHW, operation_layout); | |
| TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout); | |
| // Set weights trained layout | |
| const DataLayout weights_layout = DataLayout::NCHW; | |
| graph << common_params.target | |
| << common_params.fast_math_hint | |
| << InputLayer(input_descriptor, get_input_accessor(common_params, std::move(preprocessor), false)) | |
| << ConvolutionLayer(3U, 3U, 32U, | |
| get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_1a_3x3_weights.npy", weights_layout), | |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(2, 2, 0, 0)) | |
| .set_name("Conv2d_1a_3x3/convolution") | |
| << BatchNormalizationLayer(get_weights_accessor(data_path, | |
| "/cnn_data/inceptionv3_model/Conv2d_1a_3x3_BatchNorm_moving_mean.npy"), | |
| get_weights_accessor(data_path, | |
| "/cnn_data/inceptionv3_model/Conv2d_1a_3x3_BatchNorm_moving_variance.npy"), | |
| nullptr, get_weights_accessor(data_path, | |
| "/cnn_data/inceptionv3_model/Conv2d_1a_3x3_BatchNorm_beta.npy"), | |
| 0.001f) | |
| .set_name("Conv2d_1a_3x3/BatchNorm/batchnorm") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_1a_3x3/Relu") | |
| << ConvolutionLayer(3U, 3U, 32U, | |
| get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_2a_3x3_weights.npy", weights_layout), | |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0)) | |
| .set_name("Conv2d_2a_3x3/convolution") | |
| << BatchNormalizationLayer(get_weights_accessor(data_path, | |
| "/cnn_data/inceptionv3_model/Conv2d_2a_3x3_BatchNorm_moving_mean.npy"), | |
| get_weights_accessor(data_path, | |
| "/cnn_data/inceptionv3_model/Conv2d_2a_3x3_BatchNorm_moving_variance.npy"), | |
| nullptr, get_weights_accessor(data_path, | |
| "/cnn_data/inceptionv3_model/Conv2d_2a_3x3_BatchNorm_beta.npy"), | |
| 0.001f) | |
| .set_name("Conv2d_2a_3x3/BatchNorm/batchnorm") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_2a_3x3/Relu") | |
| << ConvolutionLayer(3U, 3U, 64U, | |
| get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_2b_3x3_weights.npy", weights_layout), | |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1)) | |
| .set_name("Conv2d_2b_3x3/convolution") | |
| << BatchNormalizationLayer(get_weights_accessor(data_path, | |
| "/cnn_data/inceptionv3_model/Conv2d_2b_3x3_BatchNorm_moving_mean.npy"), | |
| get_weights_accessor(data_path, | |
| "/cnn_data/inceptionv3_model/Conv2d_2b_3x3_BatchNorm_moving_variance.npy"), | |
| nullptr, get_weights_accessor(data_path, | |
| "/cnn_data/inceptionv3_model/Conv2d_2b_3x3_BatchNorm_beta.npy"), | |
| 0.001f) | |
| .set_name("Conv2d_2b_3x3/BatchNorm/batchnorm") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_2b_3x3/Relu") | |
| << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("MaxPool_3a_3x3/MaxPool") | |
| << ConvolutionLayer(1U, 1U, 80U, | |
| get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_3b_1x1_weights.npy", weights_layout), | |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0)) | |
| .set_name("Conv2d_3b_1x1/convolution") | |
| << BatchNormalizationLayer(get_weights_accessor(data_path, | |
| "/cnn_data/inceptionv3_model/Conv2d_3b_1x1_BatchNorm_moving_mean.npy"), | |
| get_weights_accessor(data_path, | |
| "/cnn_data/inceptionv3_model/Conv2d_3b_1x1_BatchNorm_moving_variance.npy"), | |
| nullptr, get_weights_accessor(data_path, | |
| "/cnn_data/inceptionv3_model/Conv2d_3b_1x1_BatchNorm_beta.npy"), | |
| 0.001f) | |
| .set_name("Conv2d_3b_1x1/BatchNorm/batchnorm") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_3b_1x1/Relu") | |
| << ConvolutionLayer(3U, 3U, 192U, | |
| get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_4a_3x3_weights.npy", weights_layout), | |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0)) | |
| .set_name("Conv2d_4a_3x3/convolution") | |
| << BatchNormalizationLayer(get_weights_accessor(data_path, | |
| "/cnn_data/inceptionv3_model/Conv2d_4a_3x3_BatchNorm_moving_mean.npy"), | |
| get_weights_accessor(data_path, | |
| "/cnn_data/inceptionv3_model/Conv2d_4a_3x3_BatchNorm_moving_variance.npy"), | |
| nullptr, get_weights_accessor(data_path, | |
| "/cnn_data/inceptionv3_model/Conv2d_4a_3x3_BatchNorm_beta.npy"), | |
| 0.001f) | |
| .set_name("Conv2d_4a_3x3/BatchNorm/batchnorm") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_4a_3x3/Relu") | |
| << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("MaxPool_5a_3x3/MaxPool"); | |
| graph << get_inception_node_A(data_path, "Mixed_5b", weights_layout, 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U), | |
| 32U) | |
| .set_name("Mixed_5b/concat"); | |
| graph << get_inception_node_A(data_path, "Mixed_5c", weights_layout, 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U), | |
| 64U, true) | |
| .set_name("Mixed_5c/concat"); | |
| graph << get_inception_node_A(data_path, "Mixed_5d", weights_layout, 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U), | |
| 64U) | |
| .set_name("Mixed_5d/concat"); | |
| graph << get_inception_node_B(data_path, "Mixed_6a", weights_layout, 384U, std::make_tuple(64U, 96U, 96U)).set_name("Mixed_6a/concat"); | |
| graph << get_inception_node_C(data_path, "Mixed_6b", weights_layout, 192U, std::make_tuple(128U, 128U, 192U), | |
| std::make_tuple(128U, 128U, 128U, 128U, 192U), 192U) | |
| .set_name("Mixed_6b/concat"); | |
| graph << get_inception_node_C(data_path, "Mixed_6c", weights_layout, 192U, std::make_tuple(160U, 160U, 192U), | |
| std::make_tuple(160U, 160U, 160U, 160U, 192U), 192U) | |
| .set_name("Mixed_6c/concat"); | |
| graph << get_inception_node_C(data_path, "Mixed_6d", weights_layout, 192U, std::make_tuple(160U, 160U, 192U), | |
| std::make_tuple(160U, 160U, 160U, 160U, 192U), 192U) | |
| .set_name("Mixed_6d/concat"); | |
| graph << get_inception_node_C(data_path, "Mixed_6e", weights_layout, 192U, std::make_tuple(192U, 192U, 192U), | |
| std::make_tuple(192U, 192U, 192U, 192U, 192U), 192U) | |
| .set_name("Mixed_6e/concat"); | |
| graph << get_inception_node_D(data_path, "Mixed_7a", weights_layout, std::make_tuple(192U, 320U), | |
| std::make_tuple(192U, 192U, 192U, 192U)) | |
| .set_name("Mixed_7a/concat"); | |
| graph << get_inception_node_E(data_path, "Mixed_7b", weights_layout, 320U, std::make_tuple(384U, 384U, 384U), | |
| std::make_tuple(448U, 384U, 384U, 384U), 192U) | |
| .set_name("Mixed_7b/concat"); | |
| graph << get_inception_node_E(data_path, "Mixed_7c", weights_layout, 320U, std::make_tuple(384U, 384U, 384U), | |
| std::make_tuple(448U, 384U, 384U, 384U), 192U, true) | |
| .set_name("Mixed_7c/concat"); | |
| graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 8, operation_layout, PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL))).set_name("Logits/AvgPool_1a_8x8/AvgPool") | |
| << ConvolutionLayer(1U, 1U, 1001U, get_weights_accessor(data_path, | |
| "/cnn_data/inceptionv3_model/Logits_Conv2d_1c_1x1_weights.npy", weights_layout), | |
| get_weights_accessor(data_path, | |
| "/cnn_data/inceptionv3_model/Logits_Conv2d_1c_1x1_biases.npy"), | |
| PadStrideInfo(1, 1, 0, 0)) | |
| .set_name("Logits/Conv2d_1c_1x1/convolution") | |
| << 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; | |
| config.use_synthetic_type = arm_compute::is_data_type_quantized(common_params.data_type); | |
| config.synthetic_type = common_params.data_type; | |
| graph.finalize(common_params.target, config); | |
| return true; | |
| } | |
| void do_run() override | |
| { | |
| graph.run(); | |
| } | |
| private: | |
| CommandLineParser cmd_parser; | |
| CommonGraphOptions common_opts; | |
| CommonGraphParams common_params; | |
| Stream graph; | |
| private: | |
| ConcatLayer get_inception_node_A(const std::string &data_path, std::string &¶m_path, DataLayout weights_layout, | |
| unsigned int a_filt, | |
| std::tuple<unsigned int, unsigned int> b_filters, | |
| std::tuple<unsigned int, unsigned int, unsigned int> c_filters, | |
| unsigned int d_filt, | |
| bool is_name_different = false) | |
| { | |
| std::string total_path = "/cnn_data/inceptionv3_model/" + param_path + "_"; | |
| // This is due to a naming issue in the tf model | |
| std::string conv_id0 = "_0a_"; | |
| std::string conv_id1 = "2d_0b_"; | |
| if(is_name_different) | |
| { | |
| conv_id0 = "_0b_"; | |
| conv_id1 = "_1_0c_"; | |
| } | |
| SubStream i_a(graph); | |
| i_a << ConvolutionLayer( | |
| 1U, 1U, a_filt, | |
| get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy", weights_layout), | |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), | |
| PadStrideInfo(1, 1, 0, 0)) | |
| .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/convolution") | |
| << BatchNormalizationLayer( | |
| get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), | |
| get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), | |
| nullptr, | |
| get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"), | |
| 0.001f) | |
| .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/BatchNorm/batchnorm") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_0a_1x1/Relu"); | |
| SubStream i_b(graph); | |
| i_b << ConvolutionLayer( | |
| 1U, 1U, std::get<0>(b_filters), | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id0 + "1x1_weights.npy", weights_layout), | |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), | |
| PadStrideInfo(1, 1, 0, 0)) | |
| .set_name(param_path + "/Branch_1/Conv2d" + conv_id0 + "1x1/convolution") | |
| << BatchNormalizationLayer( | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id0 + "1x1_BatchNorm_moving_mean.npy"), | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id0 + "1x1_BatchNorm_moving_variance.npy"), | |
| nullptr, | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id0 + "1x1_BatchNorm_beta.npy"), | |
| 0.001f) | |
| .set_name(param_path + "/Branch_1/Conv2d" + conv_id0 + "1x1/BatchNorm/batchnorm") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d" + conv_id0 + "1x1/Relu") | |
| << ConvolutionLayer( | |
| 5U, 5U, std::get<1>(b_filters), | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv" + conv_id1 + "5x5_weights.npy", weights_layout), | |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), | |
| PadStrideInfo(1, 1, 2, 2)) | |
| .set_name(param_path + "/Branch_1/Conv2d" + conv_id1 + "5x5/convolution") | |
| << BatchNormalizationLayer( | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv" + conv_id1 + "5x5_BatchNorm_moving_mean.npy"), | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv" + conv_id1 + "5x5_BatchNorm_moving_variance.npy"), | |
| nullptr, | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv" + conv_id1 + "5x5_BatchNorm_beta.npy"), | |
| 0.001f) | |
| .set_name(param_path + "/Branch_1/Conv2d" + conv_id1 + "5x5/BatchNorm/batchnorm") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d" + conv_id1 + "5x5/Relu"); | |
| SubStream i_c(graph); | |
| i_c << ConvolutionLayer( | |
| 1U, 1U, std::get<0>(c_filters), | |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_weights.npy", weights_layout), | |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), | |
| PadStrideInfo(1, 1, 0, 0)) | |
| .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/convolution") | |
| << BatchNormalizationLayer( | |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), | |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), | |
| nullptr, | |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_beta.npy"), | |
| 0.001f) | |
| .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/BatchNorm/batchnorm") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0a_1x1/Relu") | |
| << ConvolutionLayer( | |
| 3U, 3U, std::get<1>(c_filters), | |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_weights.npy", weights_layout), | |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), | |
| PadStrideInfo(1, 1, 1, 1)) | |
| .set_name(param_path + "/Branch_2/Conv2d_0b_3x3/convolution") | |
| << BatchNormalizationLayer( | |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"), | |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"), | |
| nullptr, | |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_beta.npy"), | |
| 0.001f) | |
| .set_name(param_path + "/Branch_2/Conv2d_0b_3x3/BatchNorm/batchnorm") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0b_3x3/Relu") | |
| << ConvolutionLayer( | |
| 3U, 3U, std::get<2>(c_filters), | |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_3x3_weights.npy", weights_layout), | |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), | |
| PadStrideInfo(1, 1, 1, 1)) | |
| .set_name(param_path + "/Branch_2/Conv2d_0c_3x3/convolution") | |
| << BatchNormalizationLayer( | |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_3x3_BatchNorm_moving_mean.npy"), | |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_3x3_BatchNorm_moving_variance.npy"), | |
| nullptr, | |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_3x3_BatchNorm_beta.npy"), | |
| 0.001f) | |
| .set_name(param_path + "/Branch_2/Conv2d_0c_3x3/BatchNorm/batcnorm") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0c_3x3/Relu"); | |
| SubStream i_d(graph); | |
| i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, common_params.data_layout, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), | |
| true)) | |
| .set_name(param_path + "/Branch_3/AvgPool_0a_3x3/AvgPool") | |
| << ConvolutionLayer( | |
| 1U, 1U, d_filt, | |
| get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy", weights_layout), | |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), | |
| PadStrideInfo(1, 1, 0, 0)) | |
| .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/convolution") | |
| << BatchNormalizationLayer( | |
| get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_mean.npy"), | |
| get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_variance.npy"), | |
| nullptr, | |
| get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_beta.npy"), | |
| 0.001f) | |
| .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/BatchNorm/batchnorm") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_3/Conv2d_0b_1x1/Relu"); | |
| return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d)); | |
| } | |
| ConcatLayer get_inception_node_B(const std::string &data_path, std::string &¶m_path, DataLayout weights_layout, | |
| unsigned int a_filt, | |
| std::tuple<unsigned int, unsigned int, unsigned int> b_filters) | |
| { | |
| std::string total_path = "/cnn_data/inceptionv3_model/" + param_path + "_"; | |
| SubStream i_a(graph); | |
| i_a << ConvolutionLayer( | |
| 3U, 3U, a_filt, | |
| get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_1x1_weights.npy", weights_layout), | |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), | |
| PadStrideInfo(2, 2, 0, 0)) | |
| .set_name(param_path + "/Branch_0/Conv2d_1a_1x1/convolution") | |
| << BatchNormalizationLayer( | |
| get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_1x1_BatchNorm_moving_mean.npy"), | |
| get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_1x1_BatchNorm_moving_variance.npy"), | |
| nullptr, | |
| get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_1x1_BatchNorm_beta.npy"), | |
| 0.001f) | |
| .set_name(param_path + "/Branch_0/Conv2d_1a_1x1/BatchNorm/batchnorm") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_1a_1x1/Relu"); | |
| SubStream i_b(graph); | |
| i_b << ConvolutionLayer( | |
| 1U, 1U, std::get<0>(b_filters), | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy", weights_layout), | |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), | |
| PadStrideInfo(1, 1, 0, 0)) | |
| .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/convolution") | |
| << BatchNormalizationLayer( | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), | |
| nullptr, | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"), | |
| 0.001f) | |
| .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/BatchNorm/batchnorm") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0a_1x1/Relu") | |
| << ConvolutionLayer( | |
| 3U, 3U, std::get<1>(b_filters), | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_weights.npy", weights_layout), | |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), | |
| PadStrideInfo(1, 1, 1, 1)) | |
| .set_name(param_path + "/Branch_1/Conv2d_0b_3x3/convolution") | |
| << BatchNormalizationLayer( | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"), | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"), | |
| nullptr, | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_beta.npy"), | |
| 0.001f) | |
| .set_name(param_path + "/Branch_1/Conv2d_0b_3x3/BatchNorm/batchnorm") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0b_3x3/Relu") | |
| << ConvolutionLayer( | |
| 3U, 3U, std::get<2>(b_filters), | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_1x1_weights.npy", weights_layout), | |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), | |
| PadStrideInfo(2, 2, 0, 0)) | |
| .set_name(param_path + "/Branch_1/Conv2d_1a_1x1/convolution") | |
| << BatchNormalizationLayer( | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_1x1_BatchNorm_moving_mean.npy"), | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_1x1_BatchNorm_moving_variance.npy"), | |
| nullptr, | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_1x1_BatchNorm_beta.npy"), | |
| 0.001f) | |
| .set_name(param_path + "/Branch_1/Conv2d_1a_1x1/BatchNorm/batchnorm") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_1a_1x1/Relu"); | |
| SubStream i_c(graph); | |
| i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, common_params.data_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name(param_path + "/Branch_2/MaxPool_1a_3x3/MaxPool"); | |
| return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c)); | |
| } | |
| ConcatLayer get_inception_node_C(const std::string &data_path, std::string &¶m_path, DataLayout weights_layout, | |
| unsigned int a_filt, | |
| std::tuple<unsigned int, unsigned int, unsigned int> b_filters, | |
| std::tuple<unsigned int, unsigned int, unsigned int, unsigned int, unsigned int> c_filters, | |
| unsigned int d_filt) | |
| { | |
| std::string total_path = "/cnn_data/inceptionv3_model/" + param_path + "_"; | |
| SubStream i_a(graph); | |
| i_a << ConvolutionLayer( | |
| 1U, 1U, a_filt, | |
| get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy", weights_layout), | |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), | |
| PadStrideInfo(1, 1, 0, 0)) | |
| .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/convolution") | |
| << BatchNormalizationLayer( | |
| get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), | |
| get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), | |
| nullptr, | |
| get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"), | |
| 0.001f) | |
| .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/BatchNorm/batchnorm") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_0a_1x1/Relu"); | |
| SubStream i_b(graph); | |
| i_b << ConvolutionLayer( | |
| 1U, 1U, std::get<0>(b_filters), | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy", weights_layout), | |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), | |
| PadStrideInfo(1, 1, 0, 0)) | |
| .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/convolution") | |
| << BatchNormalizationLayer( | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), | |
| nullptr, | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"), | |
| 0.001f) | |
| .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/BatchNorm/batchnorm") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0a_1x1/Relu") | |
| << ConvolutionLayer( | |
| 7U, 1U, std::get<1>(b_filters), | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_weights.npy", weights_layout), | |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), | |
| PadStrideInfo(1, 1, 3, 0)) | |
| .set_name(param_path + "/Branch_1/Conv2d_0b_1x7/convolution") | |
| << BatchNormalizationLayer( | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_mean.npy"), | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_variance.npy"), | |
| nullptr, | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_beta.npy"), | |
| 0.001f) | |
| .set_name(param_path + "/Branch_1/Conv2d_0b_1x7/BatchNorm/batchnorm") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0b_1x7/Relu") | |
| << ConvolutionLayer( | |
| 1U, 7U, std::get<2>(b_filters), | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_weights.npy", weights_layout), | |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), | |
| PadStrideInfo(1, 1, 0, 3)) | |
| .set_name(param_path + "/Branch_1/Conv2d_0c_7x1/convolution") | |
| << BatchNormalizationLayer( | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_mean.npy"), | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_variance.npy"), | |
| nullptr, | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_beta.npy"), | |
| 0.001f) | |
| .set_name(param_path + "/Branch_1/Conv2d_0c_7x1/BatchNorm/batchnorm") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_0c_7x1/Relu"); | |
| SubStream i_c(graph); | |
| i_c << ConvolutionLayer( | |
| 1U, 1U, std::get<0>(c_filters), | |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_weights.npy", weights_layout), | |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), | |
| PadStrideInfo(1, 1, 0, 0)) | |
| .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/convolution") | |
| << BatchNormalizationLayer( | |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), | |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), | |
| nullptr, | |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_beta.npy"), | |
| 0.001f) | |
| .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/BatchNorm/batchnorm") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0a_1x1/Relu") | |
| << ConvolutionLayer( | |
| 1U, 7U, std::get<1>(c_filters), | |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_7x1_weights.npy", weights_layout), | |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), | |
| PadStrideInfo(1, 1, 0, 3)) | |
| .set_name(param_path + "/Branch_2/Conv2d_0b_7x1/convolution") | |
| << BatchNormalizationLayer( | |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_7x1_BatchNorm_moving_mean.npy"), | |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_7x1_BatchNorm_moving_variance.npy"), | |
| nullptr, | |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_7x1_BatchNorm_beta.npy"), | |
| 0.001f) | |
| .set_name(param_path + "/Branch_2/Conv2d_0b_7x1/BatchNorm/batchnorm") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0b_7x1/Relu") | |
| << ConvolutionLayer( | |
| 7U, 1U, std::get<2>(c_filters), | |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x7_weights.npy", weights_layout), | |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), | |
| PadStrideInfo(1, 1, 3, 0)) | |
| .set_name(param_path + "/Branch_2/Conv2d_0c_1x7/convolution") | |
| << BatchNormalizationLayer( | |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x7_BatchNorm_moving_mean.npy"), | |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x7_BatchNorm_moving_variance.npy"), | |
| nullptr, | |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x7_BatchNorm_beta.npy"), | |
| 0.001f) | |
| .set_name(param_path + "/Branch_2/Conv2d_0c_1x7/BatchNorm/batchnorm") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0c_1x7/Relu") | |
| << ConvolutionLayer( | |
| 1U, 7U, std::get<3>(c_filters), | |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_7x1_weights.npy", weights_layout), | |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), | |
| PadStrideInfo(1, 1, 0, 3)) | |
| .set_name(param_path + "/Branch_2/Conv2d_0d_7x1/convolution") | |
| << BatchNormalizationLayer( | |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_7x1_BatchNorm_moving_mean.npy"), | |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_7x1_BatchNorm_moving_variance.npy"), | |
| nullptr, | |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_7x1_BatchNorm_beta.npy"), | |
| 0.001f) | |
| .set_name(param_path + "/Branch_2/Conv2d_0d_7x1/BatchNorm/batchnorm") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0d_7x1/Relu") | |
| << ConvolutionLayer( | |
| 7U, 1U, std::get<4>(c_filters), | |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_1x7_weights.npy", weights_layout), | |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), | |
| PadStrideInfo(1, 1, 3, 0)) | |
| .set_name(param_path + "/Branch_2/Conv2d_0e_1x7/convolution") | |
| << BatchNormalizationLayer( | |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_1x7_BatchNorm_moving_mean.npy"), | |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_1x7_BatchNorm_moving_variance.npy"), | |
| nullptr, | |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_1x7_BatchNorm_beta.npy"), | |
| 0.001f) | |
| .set_name(param_path + "/Branch_2/Conv2d_0e_1x7/BatchNorm/batchnorm") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0e_1x7/Relu"); | |
| SubStream i_d(graph); | |
| i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, common_params.data_layout, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), | |
| true)) | |
| .set_name(param_path + "/Branch_3/AvgPool_0a_3x3/AvgPool") | |
| << ConvolutionLayer( | |
| 1U, 1U, d_filt, | |
| get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy", weights_layout), | |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), | |
| PadStrideInfo(1, 1, 0, 0)) | |
| .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/convolution") | |
| << BatchNormalizationLayer( | |
| get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_mean.npy"), | |
| get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_variance.npy"), | |
| nullptr, | |
| get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_beta.npy"), | |
| 0.001f) | |
| .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/BatchNorm/batchnorm") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_3/Conv2d_0b_1x1/Relu"); | |
| return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d)); | |
| } | |
| ConcatLayer get_inception_node_D(const std::string &data_path, std::string &¶m_path, DataLayout weights_layout, | |
| std::tuple<unsigned int, unsigned int> a_filters, | |
| std::tuple<unsigned int, unsigned int, unsigned int, unsigned int> b_filters) | |
| { | |
| std::string total_path = "/cnn_data/inceptionv3_model/" + param_path + "_"; | |
| SubStream i_a(graph); | |
| i_a << ConvolutionLayer( | |
| 1U, 1U, std::get<0>(a_filters), | |
| get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy", weights_layout), | |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), | |
| PadStrideInfo(1, 1, 0, 0)) | |
| .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/convolution") | |
| << BatchNormalizationLayer( | |
| get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), | |
| get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), | |
| nullptr, | |
| get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"), | |
| 0.001f) | |
| .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/BatchNorm/batchnorm") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_0a_1x1/Relu") | |
| << ConvolutionLayer( | |
| 3U, 3U, std::get<1>(a_filters), | |
| get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_weights.npy", weights_layout), | |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), | |
| PadStrideInfo(2, 2, 0, 0)) | |
| .set_name(param_path + "/Branch_0/Conv2d_1a_3x3/convolution") | |
| << BatchNormalizationLayer( | |
| get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"), | |
| get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"), | |
| nullptr, | |
| get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_beta.npy"), | |
| 0.001f) | |
| .set_name(param_path + "/Branch_0/Conv2d_1a_3x3/BatchNorm/batchnorm") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_1a_3x3/Relu"); | |
| SubStream i_b(graph); | |
| i_b << ConvolutionLayer( | |
| 1U, 1U, std::get<0>(b_filters), | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy", weights_layout), | |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), | |
| PadStrideInfo(1, 1, 0, 0)) | |
| .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/convolution") | |
| << BatchNormalizationLayer( | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), | |
| nullptr, | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"), | |
| 0.001f) | |
| .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/BatchNorm/batchnorm") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0a_1x1/Relu") | |
| << ConvolutionLayer( | |
| 7U, 1U, std::get<1>(b_filters), | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_weights.npy", weights_layout), | |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), | |
| PadStrideInfo(1, 1, 3, 0)) | |
| .set_name(param_path + "/Branch_1/Conv2d_0b_1x7/convolution") | |
| << BatchNormalizationLayer( | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_mean.npy"), | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_variance.npy"), | |
| nullptr, | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_beta.npy"), | |
| 0.001f) | |
| .set_name(param_path + "/Branch_1/Conv2d_0b_1x7/BatchNorm/batchnorm") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0b_1x7/Relu") | |
| << ConvolutionLayer( | |
| 1U, 7U, std::get<2>(b_filters), | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_weights.npy", weights_layout), | |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), | |
| PadStrideInfo(1, 1, 0, 3)) | |
| .set_name(param_path + "/Branch_1/Conv2d_0c_7x1/convolution") | |
| << BatchNormalizationLayer( | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_mean.npy"), | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_variance.npy"), | |
| nullptr, | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_beta.npy"), | |
| 0.001f) | |
| .set_name(param_path + "/Branch_1/Conv2d_0c_7x1/BatchNorm/batchnorm") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0c_7x1/Relu") | |
| << ConvolutionLayer( | |
| 3U, 3U, std::get<3>(b_filters), | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_weights.npy", weights_layout), | |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), | |
| PadStrideInfo(2, 2, 0, 0)) | |
| .set_name(param_path + "/Branch_1/Conv2d_1a_3x3/convolution") | |
| << BatchNormalizationLayer( | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"), | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"), | |
| nullptr, | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_beta.npy"), | |
| 0.001f) | |
| .set_name(param_path + "/Branch_1/Conv2d_1a_3x3/BatchNorm/batchnorm") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_1a_3x3/Relu"); | |
| SubStream i_c(graph); | |
| i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, common_params.data_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name(param_path + "/Branch_2/MaxPool_1a_3x3/MaxPool"); | |
| return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c)); | |
| } | |
| ConcatLayer get_inception_node_E(const std::string &data_path, std::string &¶m_path, DataLayout weights_layout, | |
| unsigned int a_filt, | |
| std::tuple<unsigned int, unsigned int, unsigned int> b_filters, | |
| std::tuple<unsigned int, unsigned int, unsigned int, unsigned int> c_filters, | |
| unsigned int d_filt, | |
| bool is_name_different = false) | |
| { | |
| // This is due to a naming issue in the tf model | |
| std::string conv_id = "_0b_"; | |
| if(is_name_different) | |
| { | |
| conv_id = "_0c_"; | |
| } | |
| std::string total_path = "/cnn_data/inceptionv3_model/" + param_path + "_"; | |
| SubStream i_a(graph); | |
| i_a << ConvolutionLayer( | |
| 1U, 1U, a_filt, | |
| get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy", weights_layout), | |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), | |
| PadStrideInfo(1, 1, 0, 0)) | |
| .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/convolution") | |
| << BatchNormalizationLayer( | |
| get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), | |
| get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), | |
| nullptr, | |
| get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"), | |
| 0.001f) | |
| .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/BatchNorm/batchnorm") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_0a_1x1/Relu"); | |
| SubStream i_b(graph); | |
| i_b << ConvolutionLayer( | |
| 1U, 1U, std::get<0>(b_filters), | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy", weights_layout), | |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), | |
| PadStrideInfo(1, 1, 0, 0)) | |
| .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/convolution") | |
| << BatchNormalizationLayer( | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), | |
| nullptr, | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"), | |
| 0.001f) | |
| .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/BatchNorm/batchnorm") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0a_1x1/Relu"); | |
| SubStream i_b1(i_b); | |
| i_b1 << ConvolutionLayer( | |
| 3U, 1U, std::get<1>(b_filters), | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x3_weights.npy", weights_layout), | |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), | |
| PadStrideInfo(1, 1, 1, 0)) | |
| .set_name(param_path + "/Branch_1/Conv2d_0b_1x3/convolution") | |
| << BatchNormalizationLayer( | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x3_BatchNorm_moving_mean.npy"), | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x3_BatchNorm_moving_variance.npy"), | |
| nullptr, | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x3_BatchNorm_beta.npy"), | |
| 0.001f) | |
| .set_name(param_path + "/Branch_1/Conv2d_0b_1x3/BatchNorm/batchnorm") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0b_1x3/Relu"); | |
| SubStream i_b2(i_b); | |
| i_b2 << ConvolutionLayer( | |
| 1U, 3U, std::get<2>(b_filters), | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id + "3x1_weights.npy", weights_layout), | |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), | |
| PadStrideInfo(1, 1, 0, 1)) | |
| .set_name(param_path + "/Branch_1/Conv2d" + conv_id + "3x1/convolution") | |
| << BatchNormalizationLayer( | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id + "3x1_BatchNorm_moving_mean.npy"), | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id + "3x1_BatchNorm_moving_variance.npy"), | |
| nullptr, | |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id + "3x1_BatchNorm_beta.npy"), | |
| 0.001f) | |
| .set_name(param_path + "/Branch_1/Conv2d" + conv_id + "3x1/BatchNorm/batchnorm") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d" + conv_id + "3x1/Relu"); | |
| // Merge b1 and b2 | |
| i_b << ConcatLayer(std::move(i_b1), std::move(i_b2)).set_name(param_path + "/Branch_1/concat"); | |
| SubStream i_c(graph); | |
| i_c << ConvolutionLayer( | |
| 1U, 1U, std::get<0>(c_filters), | |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_weights.npy", weights_layout), | |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), | |
| PadStrideInfo(1, 1, 0, 0)) | |
| .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/convolution") | |
| << BatchNormalizationLayer( | |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), | |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), | |
| nullptr, | |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_beta.npy"), | |
| 0.001f) | |
| .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/BatchNorm/batchnorm") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0a_1x1/Relu") | |
| << ConvolutionLayer( | |
| 3U, 3U, std::get<1>(c_filters), | |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_weights.npy", weights_layout), | |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), | |
| PadStrideInfo(1, 1, 1, 1)) | |
| .set_name(param_path + "/Branch_2/Conv2d_0b_3x3/convolution") | |
| << BatchNormalizationLayer( | |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"), | |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"), | |
| nullptr, | |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_beta.npy"), | |
| 0.001f) | |
| .set_name(param_path + "/Branch_2/Conv2d_0b_3x3/BatchNorm/batchnorm") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0b_3x3/Relu"); | |
| SubStream i_c1(i_c); | |
| i_c1 << ConvolutionLayer( | |
| 3U, 1U, std::get<2>(c_filters), | |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x3_weights.npy", weights_layout), | |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), | |
| PadStrideInfo(1, 1, 1, 0)) | |
| .set_name(param_path + "/Branch_2/Conv2d_0c_1x3/convolution") | |
| << BatchNormalizationLayer( | |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x3_BatchNorm_moving_mean.npy"), | |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x3_BatchNorm_moving_variance.npy"), | |
| nullptr, | |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x3_BatchNorm_beta.npy"), | |
| 0.001f) | |
| .set_name(param_path + "/Branch_2/Conv2d_0c_1x3/BatchNorm/batchnorm") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0c_1x3/Relu"); | |
| SubStream i_c2(i_c); | |
| i_c2 << ConvolutionLayer( | |
| 1U, 3U, std::get<3>(c_filters), | |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_3x1_weights.npy", weights_layout), | |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), | |
| PadStrideInfo(1, 1, 0, 1)) | |
| .set_name(param_path + "/Branch_2/Conv2d_0d_3x1/convolution") | |
| << BatchNormalizationLayer( | |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_3x1_BatchNorm_moving_mean.npy"), | |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_3x1_BatchNorm_moving_variance.npy"), | |
| nullptr, | |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_3x1_BatchNorm_beta.npy"), | |
| 0.001f) | |
| .set_name(param_path + "/Branch_2/Conv2d_0d_3x1/BatchNorm/batchnorm") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0d_3x1/Relu"); | |
| // Merge i_c1 and i_c2 | |
| i_c << ConcatLayer(std::move(i_c1), std::move(i_c2)).set_name(param_path + "/Branch_2/concat"); | |
| SubStream i_d(graph); | |
| i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, common_params.data_layout, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), | |
| true)) | |
| .set_name(param_path + "/Branch_3/AvgPool_0a_3x3/AvgPool") | |
| << ConvolutionLayer( | |
| 1U, 1U, d_filt, | |
| get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy", weights_layout), | |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), | |
| PadStrideInfo(1, 1, 0, 0)) | |
| .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/convolution") | |
| << BatchNormalizationLayer( | |
| get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_mean.npy"), | |
| get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_variance.npy"), | |
| nullptr, | |
| get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_beta.npy"), | |
| 0.001f) | |
| .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/BatchNorm/batchnorm") | |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_3/Conv2d_0b_1x1/Relu"); | |
| return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d)); | |
| } | |
| }; | |
| /** Main program for Inception V3 | |
| * | |
| * Model is based on: | |
| * https://arxiv.org/abs/1512.00567 | |
| * "Rethinking the Inception Architecture for Computer Vision" | |
| * Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, Zbigniew Wojna | |
| * | |
| * Provenance: download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz | |
| * | |
| * @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<InceptionV3Example>(argc, argv); | |
| } |