| /* |
| * Copyright (c) 2018-2020 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" |
| #ifdef ARM_COMPUTE_CL |
| #include "arm_compute/runtime/CL/Utils.h" |
| #endif /* ARM_COMPUTE_CL */ |
| #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 InceptionV4's network using the Compute Library's graph API */ |
| class InceptionV4Example final : public Example |
| { |
| public: |
| InceptionV4Example() |
| : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "InceptionV4") |
| { |
| } |
| 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 = arm_compute::support::cpp14::make_unique<TFPreproccessor>(); |
| |
| // Create input descriptor |
| const auto operation_layout = common_params.data_layout; |
| const TensorShape tensor_shape = permute_shape(TensorShape(299U, 299U, 3U, 1U), 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)) |
| // Conv2d_1a_3x3 |
| << ConvolutionLayer(3U, 3U, 32U, |
| get_weights_accessor(data_path, "/cnn_data/inceptionv4_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/Conv2D") |
| << BatchNormalizationLayer(get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_1a_3x3_BatchNorm_moving_mean.npy"), |
| get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_1a_3x3_BatchNorm_moving_variance.npy"), |
| get_random_accessor(1.f, 1.f), |
| get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_1a_3x3_BatchNorm_beta.npy"), |
| 0.001f) |
| .set_name("Conv2d_1a_3x3/BatchNorm") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_1a_3x3/Relu") |
| // Conv2d_2a_3x3 |
| << ConvolutionLayer(3U, 3U, 32U, |
| get_weights_accessor(data_path, "/cnn_data/inceptionv4_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/Conv2D") |
| << BatchNormalizationLayer(get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_2a_3x3_BatchNorm_moving_mean.npy"), |
| get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_2a_3x3_BatchNorm_moving_variance.npy"), |
| get_random_accessor(1.f, 1.f), |
| get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_2a_3x3_BatchNorm_beta.npy"), |
| 0.001f) |
| .set_name("Conv2d_2a_3x3/BatchNorm") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_2a_3x3/Relu") |
| // Conv2d_2b_3x3 |
| << ConvolutionLayer(3U, 3U, 64U, |
| get_weights_accessor(data_path, "/cnn_data/inceptionv4_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/Conv2D") |
| << BatchNormalizationLayer(get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_2b_3x3_BatchNorm_moving_mean.npy"), |
| get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_2b_3x3_BatchNorm_moving_variance.npy"), |
| get_random_accessor(1.f, 1.f), |
| get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_2b_3x3_BatchNorm_beta.npy"), |
| 0.001f) |
| .set_name("Conv2d_2b_3x3/BatchNorm") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_2b_3x3/Relu"); |
| |
| graph << get_mixed_3a(data_path, weights_layout).set_name("Mixed_3a/concat"); |
| graph << get_mixed_4a(data_path, weights_layout).set_name("Mixed_4a/concat"); |
| graph << get_mixed_5a(data_path, weights_layout).set_name("Mixed_5a/concat"); |
| // 4 inception A blocks |
| graph << get_inceptionA_block(data_path, weights_layout, "Mixed_5b").set_name("Mixed_5b/concat"); |
| graph << get_inceptionA_block(data_path, weights_layout, "Mixed_5c").set_name("Mixed_5c/concat"); |
| graph << get_inceptionA_block(data_path, weights_layout, "Mixed_5d").set_name("Mixed_5d/concat"); |
| graph << get_inceptionA_block(data_path, weights_layout, "Mixed_5e").set_name("Mixed_5e/concat"); |
| // reduction A block |
| graph << get_reductionA_block(data_path, weights_layout).set_name("Mixed_6a/concat"); |
| // 7 inception B blocks |
| graph << get_inceptionB_block(data_path, weights_layout, "Mixed_6b").set_name("Mixed_6b/concat"); |
| graph << get_inceptionB_block(data_path, weights_layout, "Mixed_6c").set_name("Mixed_6c/concat"); |
| graph << get_inceptionB_block(data_path, weights_layout, "Mixed_6d").set_name("Mixed_6d/concat"); |
| graph << get_inceptionB_block(data_path, weights_layout, "Mixed_6e").set_name("Mixed_6e/concat"); |
| graph << get_inceptionB_block(data_path, weights_layout, "Mixed_6f").set_name("Mixed_6f/concat"); |
| graph << get_inceptionB_block(data_path, weights_layout, "Mixed_6g").set_name("Mixed_6g/concat"); |
| graph << get_inceptionB_block(data_path, weights_layout, "Mixed_6h").set_name("Mixed_6h/concat"); |
| // reduction B block |
| graph << get_reductionB_block(data_path, weights_layout).set_name("Mixed_7a/concat"); |
| // 3 inception C blocks |
| graph << get_inceptionC_block(data_path, weights_layout, "Mixed_7b").set_name("Mixed_7b/concat"); |
| graph << get_inceptionC_block(data_path, weights_layout, "Mixed_7c").set_name("Mixed_7c/concat"); |
| graph << get_inceptionC_block(data_path, weights_layout, "Mixed_7d").set_name("Mixed_7d/concat"); |
| graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, operation_layout)).set_name("Logits/AvgPool_1a/AvgPool") |
| << FlattenLayer().set_name("Logits/Flatten") |
| << FullyConnectedLayer( |
| 1001U, |
| get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Logits_Logits_weights.npy", weights_layout), |
| get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Logits_Logits_biases.npy")) |
| .set_name("Logits/MatMul") |
| << SoftmaxLayer().set_name("Logits/Predictions") |
| << 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.convert_to_uint8 = (common_params.data_type == DataType::QASYMM8); |
| |
| // Load the precompiled kernels from a file into the kernel library, in this way the next time they are needed |
| // compilation won't be required. |
| if(common_params.enable_cl_cache) |
| { |
| #ifdef ARM_COMPUTE_CL |
| restore_program_cache_from_file(); |
| #endif /* ARM_COMPUTE_CL */ |
| } |
| |
| graph.finalize(common_params.target, config); |
| |
| // Save the opencl kernels to a file |
| if(common_opts.enable_cl_cache) |
| { |
| #ifdef ARM_COMPUTE_CL |
| save_program_cache_to_file(); |
| #endif /* ARM_COMPUTE_CL */ |
| } |
| |
| return true; |
| } |
| |
| void do_run() override |
| { |
| graph.run(); |
| } |
| |
| private: |
| CommandLineParser cmd_parser; |
| CommonGraphOptions common_opts; |
| CommonGraphParams common_params; |
| Stream graph; |
| |
| private: |
| ConcatLayer get_mixed_3a(const std::string &data_path, DataLayout weights_layout) |
| { |
| std::string total_path = "/cnn_data/inceptionv4_model/Mixed_3a_"; |
| |
| SubStream i_a(graph); |
| i_a << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, common_params.data_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), |
| true)) |
| .set_name("Mixed_3a/Branch_0/MaxPool_0a_3x3/MaxPool"); |
| |
| SubStream i_b(graph); |
| i_b << ConvolutionLayer(3U, 3U, 96U, |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_3x3_weights.npy", weights_layout), |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(2, 2, 0, 0)) |
| .set_name("Mixed_3a/Branch_1/Conv2d_0a_3x3/Conv2D") |
| << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_3x3_BatchNorm_moving_mean.npy"), |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_3x3_BatchNorm_moving_variance.npy"), |
| get_random_accessor(1.f, 1.f), |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_3x3_BatchNorm_beta.npy"), |
| 0.001f) |
| .set_name("Mixed_3a/Branch_1/Conv2d_0a_3x3/BatchNorm") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_3a/Branch_1/Conv2d_0a_3x3/Relu"); |
| |
| return ConcatLayer(std::move(i_a), std::move(i_b)); |
| } |
| |
| ConcatLayer get_mixed_4a(const std::string &data_path, DataLayout weights_layout) |
| { |
| std::string total_path = "/cnn_data/inceptionv4_model/Mixed_4a_"; |
| |
| SubStream i_a(graph); |
| i_a << ConvolutionLayer(1U, 1U, 64U, |
| 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("Mixed_4a/Branch_0/Conv2d_0a_1x1/Conv2D") |
| << 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"), |
| get_random_accessor(1.f, 1.f), |
| get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"), |
| 0.001f) |
| .set_name("Mixed_4a/Branch_0/Conv2d_0a_1x1/BatchNorm") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_4a/Branch_0/Conv2d_0a_1x1/Relu") |
| << ConvolutionLayer(3U, 3U, 96U, |
| 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(1, 1, 0, 0)) |
| .set_name("Mixed_4a/Branch_0/Conv2d_1a_3x3/Conv2D") |
| << 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"), |
| get_random_accessor(1.f, 1.f), |
| get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_beta.npy"), |
| 0.001f) |
| .set_name("Mixed_4a/Branch_0/Conv2d_1a_3x3/BatchNorm") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_4a/Branch_0/Conv2d_1a_3x3/Relu"); |
| |
| SubStream i_b(graph); |
| i_b << ConvolutionLayer(1U, 1U, 64U, |
| 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("Mixed_4a/Branch_1/Conv2d_0a_1x1/Conv2D") |
| << 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"), |
| get_random_accessor(1.f, 1.f), |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"), |
| 0.001f) |
| .set_name("Mixed_4a/Branch_1/Conv2d_0a_1x1/BatchNorm") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_4a/Branch_1/Conv2d_0a_1x1/Relu") |
| << ConvolutionLayer(7U, 1U, 64U, |
| 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("Mixed_4a/Branch_1/Conv2d_0b_1x7/Conv2D") |
| << 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"), |
| get_random_accessor(1.f, 1.f), |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_beta.npy"), |
| 0.001f) |
| .set_name("Mixed_4a/Branch_1/Conv2d_0b_1x7/BatchNorm") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_4a/Branch_1/Conv2d_0b_1x7/Relu") |
| << ConvolutionLayer(1U, 7U, 64U, |
| 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("Mixed_4a/Branch_1/Conv2d_0c_7x1/Conv2D") |
| << 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"), |
| get_random_accessor(1.f, 1.f), |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_beta.npy"), |
| 0.001f) |
| .set_name("Mixed_4a/Branch_1/Conv2d_0c_7x1/BatchNorm") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_4a/Branch_1/Conv2d_0c_7x1/Relu") |
| << ConvolutionLayer(3U, 3U, 96U, |
| 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(1, 1, 0, 0)) |
| .set_name("Mixed_4a/Branch_1/Conv2d_1a_3x3/Conv2D") |
| << 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"), |
| get_random_accessor(1.f, 1.f), |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_beta.npy"), |
| 0.001f) |
| .set_name("Mixed_4a/Branch_1/Conv2d_1a_3x3/BatchNorm") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_4a/Branch_1/Conv2d_1a_3x3/Relu"); |
| |
| return ConcatLayer(std::move(i_a), std::move(i_b)); |
| } |
| |
| ConcatLayer get_mixed_5a(const std::string &data_path, DataLayout weights_layout) |
| { |
| std::string total_path = "/cnn_data/inceptionv4_model/Mixed_5a_"; |
| |
| SubStream i_a(graph); |
| i_a << ConvolutionLayer(3U, 3U, 192U, |
| 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("Mixed_5a/Branch_0/Conv2d_1a_3x3/Conv2D") |
| << 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"), |
| get_random_accessor(1.f, 1.f), |
| get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_beta.npy"), |
| 0.001f) |
| .set_name("Mixed_5a/Branch_0/Conv2d_1a_3x3/BatchNorm") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_5a/Branch_0/Conv2d_1a_3x3/Relu"); |
| |
| SubStream i_b(graph); |
| i_b << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, common_params.data_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), |
| true)) |
| .set_name("Mixed_5a/Branch_1/MaxPool_1a_3x3/MaxPool"); |
| |
| return ConcatLayer(std::move(i_a), std::move(i_b)); |
| } |
| |
| ConcatLayer get_inceptionA_block(const std::string &data_path, DataLayout weights_layout, std::string &¶m_path) |
| { |
| std::string total_path = "/cnn_data/inceptionv4_model/" + param_path + "_"; |
| |
| SubStream i_a(graph); |
| i_a << ConvolutionLayer(1U, 1U, 96U, |
| 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/Conv2D") |
| << 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"), |
| get_random_accessor(1.f, 1.f), |
| 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") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_0a_1x1/Relu"); |
| |
| SubStream i_b(graph); |
| i_b << ConvolutionLayer(1U, 1U, 64U, |
| 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/Conv2D") |
| << 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"), |
| get_random_accessor(1.f, 1.f), |
| 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") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0a_1x1/Relu") |
| << ConvolutionLayer(3U, 3U, 96U, |
| 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/Conv2D") |
| << 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"), |
| get_random_accessor(1.f, 1.f), |
| 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") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0b_3x3/Relu"); |
| |
| SubStream i_c(graph); |
| i_c << ConvolutionLayer(1U, 1U, 64U, |
| 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/Conv2D") |
| << 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"), |
| get_random_accessor(1.f, 1.f), |
| 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") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0a_1x1/Relu") |
| << ConvolutionLayer(3U, 3U, 96U, |
| 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/Conv2D") |
| << 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"), |
| get_random_accessor(1.f, 1.f), |
| 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") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0b_3x3/Relu") |
| << ConvolutionLayer(3U, 3U, 96U, |
| 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/Conv2D") |
| << 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"), |
| get_random_accessor(1.f, 1.f), |
| 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") |
| << 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, 96U, |
| 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/Conv2D") |
| << 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"), |
| get_random_accessor(1.f, 1.f), |
| 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") |
| << 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_reductionA_block(const std::string &data_path, DataLayout weights_layout) |
| { |
| std::string total_path = "/cnn_data/inceptionv4_model/Mixed_6a_"; |
| |
| SubStream i_a(graph); |
| i_a << ConvolutionLayer(3U, 3U, 384U, |
| 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("Mixed_6a/Branch_0/Conv2d_1a_3x3/Conv2D") |
| << 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"), |
| get_random_accessor(1.f, 1.f), |
| get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_beta.npy"), |
| 0.001f) |
| .set_name("Mixed_6a/Branch_0/Conv2d_1a_3x3/BatchNorm") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_6a/Branch_0/Conv2d_1a_3x3/Relu"); |
| |
| SubStream i_b(graph); |
| i_b << ConvolutionLayer(1U, 1U, 192U, |
| 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("Mixed_6a/Branch_1/Conv2d_0a_1x1/Conv2D") |
| << 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"), |
| get_random_accessor(1.f, 1.f), |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"), |
| 0.001f) |
| .set_name("Mixed_6a/Branch_1/Conv2d_0a_1x1/BatchNorm") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_6a/Branch_1/Conv2d_0a_1x1/Relu") |
| << ConvolutionLayer(3U, 3U, 224U, |
| 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("Mixed_6a/Branch_1/Conv2d_0b_3x3/Conv2D") |
| << 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"), |
| get_random_accessor(1.f, 1.f), |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_beta.npy"), |
| 0.001f) |
| .set_name("Mixed_6a/Branch_1/Conv2d_0b_3x3/BatchNorm") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_6a/Branch_1/Conv2d_0b_3x3/Relu") |
| << ConvolutionLayer(3U, 3U, 256U, |
| 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("Mixed_6a/Branch_1/Conv2d_1a_3x3/Conv2D") |
| << 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"), |
| get_random_accessor(1.f, 1.f), |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_beta.npy"), |
| 0.001f) |
| .set_name("Mixed_6a/Branch_1/Conv2d_1a_3x3/BatchNorm") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_6a/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), |
| true)) |
| .set_name("Mixed_6a/Branch_2/MaxPool_1a_3x3/MaxPool"); |
| |
| return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c)); |
| } |
| |
| ConcatLayer get_inceptionB_block(const std::string &data_path, DataLayout weights_layout, std::string &¶m_path) |
| { |
| std::string total_path = "/cnn_data/inceptionv4_model/" + param_path + "_"; |
| |
| SubStream i_a(graph); |
| i_a << ConvolutionLayer(1U, 1U, 384U, |
| 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/Conv2D") |
| << 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"), |
| get_random_accessor(1.f, 1.f), |
| 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") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_0a_1x1/Relu"); |
| |
| SubStream i_b(graph); |
| i_b << ConvolutionLayer(1U, 1U, 192U, |
| 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/Conv2D") |
| << 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"), |
| get_random_accessor(1.f, 1.f), |
| 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") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0a_1x1/Relu") |
| << ConvolutionLayer(7U, 1U, 224U, |
| 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/Conv2D") |
| << 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"), |
| get_random_accessor(1.f, 1.f), |
| 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") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0b_1x7/Relu") |
| << ConvolutionLayer(1U, 7U, 256U, |
| 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/Conv2D") |
| << 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"), |
| get_random_accessor(1.f, 1.f), |
| 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") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0c_7x1/Relu"); |
| |
| SubStream i_c(graph); |
| i_c << ConvolutionLayer(1U, 1U, 192U, |
| 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/Conv2D") |
| << 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"), |
| get_random_accessor(1.f, 1.f), |
| 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") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0a_1x1/Relu") |
| << ConvolutionLayer(1U, 7U, 192U, |
| 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/Conv2D") |
| << 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"), |
| get_random_accessor(1.f, 1.f), |
| 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") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0b_7x1/Relu") |
| << ConvolutionLayer(7U, 1U, 224U, |
| 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/Conv2D") |
| << 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"), |
| get_random_accessor(1.f, 1.f), |
| 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") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0c_1x7/Relu") |
| << ConvolutionLayer(1U, 7U, 224U, |
| 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/Conv2D") |
| << 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"), |
| get_random_accessor(1.f, 1.f), |
| 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") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0d_7x1/Relu") |
| << ConvolutionLayer(7U, 1U, 256U, |
| 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/Conv2D") |
| << 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"), |
| get_random_accessor(1.f, 1.f), |
| 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") |
| << 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, 128U, |
| 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/Conv2D") |
| << 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"), |
| get_random_accessor(1.f, 1.f), |
| 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") |
| << 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_reductionB_block(const std::string &data_path, DataLayout weights_layout) |
| { |
| std::string total_path = "/cnn_data/inceptionv4_model/Mixed_7a_"; |
| |
| SubStream i_a(graph); |
| i_a << ConvolutionLayer(1U, 1U, 192U, |
| 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("Mixed_7a/Branch_1/Conv2d_0a_1x1/Conv2D") |
| << 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"), |
| get_random_accessor(1.f, 1.f), |
| get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"), |
| 0.001f) |
| .set_name("Mixed_7a/Branch_1/Conv2d_0a_1x1/BatchNorm") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_7a/Branch_1/Conv2d_0a_1x1/Relu") |
| << ConvolutionLayer(3U, 3U, 192U, |
| 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("Mixed_7a/Branch_0/Conv2d_1a_3x3/Conv2D") |
| << 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"), |
| get_random_accessor(1.f, 1.f), |
| get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_beta.npy"), |
| 0.001f) |
| .set_name("Mixed_7a/Branch_0/Conv2d_1a_3x3/BatchNorm") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_7a/Branch_0/Conv2d_1a_3x3/Relu"); |
| |
| SubStream i_b(graph); |
| i_b << ConvolutionLayer(1U, 1U, 256U, |
| 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("Mixed_7a/Branch_1/Conv2d_0a_1x1/Conv2D") |
| << 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"), |
| get_random_accessor(1.f, 1.f), |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"), |
| 0.001f) |
| .set_name("Mixed_7a/Branch_1/Conv2d_0a_1x1/BatchNorm") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_7a/Branch_1/Conv2d_0a_1x1/Relu") |
| << ConvolutionLayer(7U, 1U, 256U, |
| 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("Mixed_7a/Branch_1/Conv2d_0b_1x7/Conv2D") |
| << 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"), |
| get_random_accessor(1.f, 1.f), |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_beta.npy"), |
| 0.001f) |
| .set_name("Mixed_7a/Branch_1/Conv2d_0b_1x7/BatchNorm") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_7a/Branch_1/Conv2d_0b_1x7/Relu") |
| << ConvolutionLayer(1U, 7U, 320U, |
| 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("Mixed_7a/Branch_1/Conv2d_0c_7x1/Conv2D") |
| << 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"), |
| get_random_accessor(1.f, 1.f), |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_beta.npy"), |
| 0.001f) |
| .set_name("Mixed_7a/Branch_1/Conv2d_0c_7x1/BatchNorm") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_7a/Branch_1/Conv2d_0c_7x1/Relu") |
| << ConvolutionLayer(3U, 3U, 320U, |
| 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("Mixed_7a/Branch_1/Conv2d_1a_3x3/Conv2D") |
| << 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"), |
| get_random_accessor(1.f, 1.f), |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_beta.npy"), |
| 0.001f) |
| .set_name("Mixed_7a/Branch_1/Conv2d_1a_3x3/BatchNorm") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_7a/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), |
| true)) |
| .set_name("Mixed_7a/Branch_2/MaxPool_1a_3x3/MaxPool"); |
| |
| return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c)); |
| } |
| |
| ConcatLayer get_inceptionC_block(const std::string &data_path, DataLayout weights_layout, std::string &¶m_path) |
| { |
| std::string total_path = "/cnn_data/inceptionv4_model/" + param_path + "_"; |
| |
| SubStream i_a(graph); |
| i_a << ConvolutionLayer(1U, 1U, 256U, |
| 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/Conv2D") |
| << 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"), |
| get_random_accessor(1.f, 1.f), |
| 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") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_0a_1x1/Relu"); |
| |
| SubStream i_b(graph); |
| i_b << ConvolutionLayer( |
| 1U, 1U, 384U, |
| 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/Conv2D") |
| << 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"), |
| get_random_accessor(1.f, 1.f), |
| 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") |
| << 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, 256U, |
| 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/Conv2D") |
| << 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"), |
| get_random_accessor(1.f, 1.f), |
| 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") |
| << 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, 256U, |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_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_0c_3x1/Conv2D") |
| << BatchNormalizationLayer( |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_3x1_BatchNorm_moving_mean.npy"), |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_3x1_BatchNorm_moving_variance.npy"), |
| get_random_accessor(1.f, 1.f), |
| get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_3x1_BatchNorm_beta.npy"), |
| 0.001f) |
| .set_name(param_path + "/Branch_1/Conv2d_0c_3x1/BatchNorm") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0c_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, 384U, |
| 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/Conv2D") |
| << 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"), |
| get_random_accessor(1.f, 1.f), |
| 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") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0a_1x1/Relu") |
| << ConvolutionLayer( |
| 1U, 3U, 448U, |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_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_0b_3x1/Conv2D") |
| << BatchNormalizationLayer( |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x1_BatchNorm_moving_mean.npy"), |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x1_BatchNorm_moving_variance.npy"), |
| get_random_accessor(1.f, 1.f), |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x1_BatchNorm_beta.npy"), |
| 0.001f) |
| .set_name(param_path + "/Branch_2/Conv2d_0b_3x1/BatchNorm") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0b_3x1/Relu") |
| << ConvolutionLayer( |
| 3U, 1U, 512U, |
| 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/Conv2D") |
| << 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"), |
| get_random_accessor(1.f, 1.f), |
| 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") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0c_1x3/Relu"); |
| |
| SubStream i_c1(i_c); |
| i_c1 << ConvolutionLayer( |
| 3U, 1U, 256U, |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_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_0d_1x3/Conv2D") |
| << BatchNormalizationLayer( |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_1x3_BatchNorm_moving_mean.npy"), |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_1x3_BatchNorm_moving_variance.npy"), |
| get_random_accessor(1.f, 1.f), |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_1x3_BatchNorm_beta.npy"), |
| 0.001f) |
| .set_name(param_path + "/Branch_2/Conv2d_0d_1x3/BatchNorm") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0d_1x3/Relu"); |
| |
| SubStream i_c2(i_c); |
| i_c2 << ConvolutionLayer( |
| 1U, 3U, 256U, |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_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_0e_3x1/Conv2D") |
| << BatchNormalizationLayer( |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_3x1_BatchNorm_moving_mean.npy"), |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_3x1_BatchNorm_moving_variance.npy"), |
| get_random_accessor(1.f, 1.f), |
| get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_3x1_BatchNorm_beta.npy"), |
| 0.001f) |
| .set_name(param_path + "/Branch_2/Conv2d_0e_3x1/BatchNorm") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0e_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, 256U, |
| 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/Conv2D") |
| << 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"), |
| get_random_accessor(1.f, 1.f), |
| 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") |
| << 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 V4 |
| * |
| * Model is based on: |
| * https://arxiv.org/abs/1602.07261 |
| * "Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning" |
| * Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi |
| * |
| * Provenance: download.tensorflow.org/models/inception_v4_2016_09_09.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<InceptionV4Example>(argc, argv); |
| } |
