From be2772a6b0b88cc3170f6527e07e8bcb426a4b1c Mon Sep 17 00:00:00 2001 From: Georgios Pinitas Date: Fri, 17 Aug 2018 15:33:39 +0100 Subject: COMPMID-1508: Add Inception ResNet V2 model Change-Id: Iab860a43aa831690fab49b96c124528cc4cb14f2 Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/144621 Tested-by: Jenkins Reviewed-by: Giorgio Arena Reviewed-by: Michalis Spyrou --- examples/graph_inception_resnet_v2.cpp | 797 +++++++++++++++++++++++++++++++++ 1 file changed, 797 insertions(+) create mode 100644 examples/graph_inception_resnet_v2.cpp (limited to 'examples') diff --git a/examples/graph_inception_resnet_v2.cpp b/examples/graph_inception_resnet_v2.cpp new file mode 100644 index 0000000000..6c52132b2e --- /dev/null +++ b/examples/graph_inception_resnet_v2.cpp @@ -0,0 +1,797 @@ +/* + * Copyright (c) 2018 ARM Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#include "arm_compute/graph.h" +#include "support/ToolchainSupport.h" +#include "utils/CommonGraphOptions.h" +#include "utils/GraphUtils.h" +#include "utils/Utils.h" + +using namespace arm_compute::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 + * + * @param[in] argc Number of arguments + * @param[in] argv Arguments + */ +class InceptionResNetV2Example final : public Example +{ +public: + InceptionResNetV2Example() + : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "InceptionResNetV2") + { + } + bool do_setup(int argc, char **argv) override + { + // Parse arguments + cmd_parser.parse(argc, argv); + + // 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; + } + + // Set default layout if needed + if(!common_opts.data_layout->is_set() && common_params.target == Target::NEON) + { + common_params.data_layout = DataLayout::NCHW; + } + + // Checks + ARM_COMPUTE_EXIT_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), "Unsupported data type!"); + + // Print parameter values + std::cout << common_params << std::endl; + + // Create model path + std::string data_path = common_params.data_path; + std::string model_path = "/cnn_data/inception_resnet_v2_model/"; + if(!data_path.empty()) + { + data_path += model_path; + } + + // Create a preprocessor object + std::unique_ptr preprocessor = arm_compute::support::cpp14::make_unique(0.f, 1.f); + + // Create input descriptor + const TensorShape tensor_shape = permute_shape(TensorShape(299U, 299U, 3U, 1U), DataLayout::NCHW, common_params.data_layout); + TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_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, "Conv2d_1a_3x3_weights.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(2, 2, 0, 0)) + .set_name("Conv2d_1a_3x3/convolution") + << BatchNormalizationLayer(get_weights_accessor(data_path, "Conv2d_1a_3x3_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, "Conv2d_1a_3x3_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, "Conv2d_1a_3x3_BatchNorm_beta.npy"), + 0.0010000000474974513f) + .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, "Conv2d_2a_3x3_weights.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 0)) + .set_name("Conv2d_2a_3x3/convolution") + << BatchNormalizationLayer(get_weights_accessor(data_path, "Conv2d_2a_3x3_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, "Conv2d_2a_3x3_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, "Conv2d_2a_3x3_BatchNorm_beta.npy"), + 0.0010000000474974513f) + .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, "Conv2d_2b_3x3_weights.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 1, 1)) + .set_name("Conv2d_2b_3x3/convolution") + << BatchNormalizationLayer(get_weights_accessor(data_path, "Conv2d_2b_3x3_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, "Conv2d_2b_3x3_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, "Conv2d_2b_3x3_BatchNorm_beta.npy"), + 0.0010000000474974513f) + .set_name("Conv2d_2b_3x3/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_2b_3x3/Relu") + // MaxPool_3a_3x3 + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), true)).set_name("MaxPool_3a_3x3/MaxPool") + // Conv2d_3b_1x1 + << ConvolutionLayer(1U, 1U, 80U, + get_weights_accessor(data_path, "Conv2d_3b_1x1_weights.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 0)) + .set_name("Conv2d_3b_1x1/convolution") + << BatchNormalizationLayer(get_weights_accessor(data_path, "Conv2d_3b_1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, "Conv2d_3b_1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, "Conv2d_3b_1x1_BatchNorm_beta.npy"), + 0.0010000000474974513f) + .set_name("Conv2d_3b_1x1/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_3b_1x1/Relu") + // Conv2d_4a_3x3 + << ConvolutionLayer(3U, 3U, 192U, + get_weights_accessor(data_path, "Conv2d_4a_3x3_weights.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 0)) + .set_name("Conv2d_4a_3x3/convolution") + << BatchNormalizationLayer(get_weights_accessor(data_path, "Conv2d_4a_3x3_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, "Conv2d_4a_3x3_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, "Conv2d_4a_3x3_BatchNorm_beta.npy"), + 0.0010000000474974513f) + .set_name("Conv2d_4a_3x3/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_4a_3x3/Relu") + // MaxPool_5a_3x3 + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0), true)).set_name("MaxPool_5a_3x3/MaxPool"); + + block_mixed_5b(data_path, weights_layout); + block35_repeat(data_path, weights_layout, 10); + block_mixed_6a(data_path, weights_layout); + block17_repeat(data_path, weights_layout, 20); + block_mixed_7a(data_path, weights_layout); + block8_repeat(data_path, weights_layout, 9, 0.2f, true); + block8_repeat(data_path, weights_layout, 1, 1.f, false); + + // Conv2d_7b_1x1 + graph << ConvolutionLayer(1U, 1U, 1536U, + get_weights_accessor(data_path, "Conv2d_7b_1x1_weights.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 0)) + .set_name("Conv2d_7b_1x1/convolution") + << BatchNormalizationLayer(get_weights_accessor(data_path, "Conv2d_7b_1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, "Conv2d_7b_1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, "Conv2d_7b_1x1_BatchNorm_beta.npy"), + 0.0010000000474974513f) + .set_name("Conv2d_7b_1x1/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_7b_1x1/Relu") + << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("Logits/AvgPool_1a_8x8") + << FlattenLayer().set_name("Logits/Flatten") + << FullyConnectedLayer( + 1001U, + get_weights_accessor(data_path, "Logits_Logits_weights.npy", weights_layout), + get_weights_accessor(data_path, "Logits_Logits_biases.npy")) + .set_name("Logits/Logits") + << 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_file = common_params.tuner_file; + + 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: + void block_mixed_5b(const std::string &data_path, DataLayout weights_layout) + { + // Branch 0 + SubStream i_a(graph); + i_a << ConvolutionLayer(1U, 1U, 96U, + get_weights_accessor(data_path, "Mixed_5b_Branch_0_Conv2d_1x1_weights.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 0)) + .set_name("Mixed_5b/Branch_0/Conv2d_1x1/convolution") + << BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_5b_Branch_0_Conv2d_1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, "Mixed_5b_Branch_0_Conv2d_1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, "Mixed_5b_Branch_0_Conv2d_1x1_BatchNorm_beta.npy"), + 0.0010000000474974513f) + .set_name("Mixed_5b/Branch_0/Conv2d_1x1/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_5b/Branch_0/Conv2d_1x1/Relu"); + + // Branch 1 + SubStream i_b(graph); + i_b << ConvolutionLayer(1U, 1U, 48U, + get_weights_accessor(data_path, "Mixed_5b_Branch_1_Conv2d_0a_1x1_weights.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 0)) + .set_name("Mixed_5b/Branch_1/Conv2d_0a_1x1/convolution") + << BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_5b_Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, "Mixed_5b_Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, "Mixed_5b_Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"), + 0.0010000000474974513f) + .set_name("Mixed_5b/Branch_1/Conv2d_0a_1x1/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_5b/Branch_1/Conv2d_0a_1x1/Relu") + << ConvolutionLayer(5U, 5U, 64U, + get_weights_accessor(data_path, "Mixed_5b_Branch_1_Conv2d_0b_5x5_weights.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 2, 2)) + .set_name("Mixed_5b/Branch_1/Conv2d_0b_5x5/convolution") + << BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_5b_Branch_1_Conv2d_0b_5x5_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, "Mixed_5b_Branch_1_Conv2d_0b_5x5_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, "Mixed_5b_Branch_1_Conv2d_0b_5x5_BatchNorm_beta.npy"), + 0.0010000000474974513f) + .set_name("Mixed_5b/Branch_1/Conv2d_0b_5x5/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_5b/Branch_1/Conv2d_0b_5x5/Relu"); + + // Branch 2 + SubStream i_c(graph); + i_c << ConvolutionLayer(1U, 1U, 64U, + get_weights_accessor(data_path, "Mixed_5b_Branch_2_Conv2d_0a_1x1_weights.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 0)) + .set_name("Mixed_5b/Branch_2/Conv2d_0a_1x1/convolution") + << BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_5b_Branch_2_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, "Mixed_5b_Branch_2_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, "Mixed_5b_Branch_2_Conv2d_0a_1x1_BatchNorm_beta.npy"), + 0.0010000000474974513f) + .set_name("Mixed_5b/Branch_2/Conv2d_0a_1x1/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_5b/Branch_2/Conv2d_0a_1x1/Relu") + << ConvolutionLayer(3U, 3U, 96U, + get_weights_accessor(data_path, "Mixed_5b_Branch_2_Conv2d_0b_3x3_weights.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 1, 1)) + .set_name("Mixed_5b/Branch_2/Conv2d_0b_3x3/convolution") + << BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_5b_Branch_2_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, "Mixed_5b_Branch_2_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, "Mixed_5b_Branch_2_Conv2d_0b_3x3_BatchNorm_beta.npy"), + 0.0010000000474974513f) + .set_name("Mixed_5b/Branch_2/Conv2d_0b_3x3/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_5b/Branch_2/Conv2d_0b_3x3/Relu") + << ConvolutionLayer(3U, 3U, 96U, + get_weights_accessor(data_path, "Mixed_5b_Branch_2_Conv2d_0c_3x3_weights.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 1, 1)) + .set_name("Mixed_5b/Branch_2/Conv2d_0c_3x3/convolution") + << BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_5b_Branch_2_Conv2d_0c_3x3_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, "Mixed_5b_Branch_2_Conv2d_0c_3x3_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, "Mixed_5b_Branch_2_Conv2d_0c_3x3_BatchNorm_beta.npy"), + 0.0010000000474974513f) + .set_name("Mixed_5b/Branch_2/Conv2d_0c_3x3/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_5b/Branch_2/Conv2d_0c_3x3/Relu"); + + // Branch 3 + SubStream i_d(graph); + i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true)).set_name("Mixed_5b/Branch_3/AvgPool_0a_3x3") + << ConvolutionLayer(1U, 1U, 64U, + get_weights_accessor(data_path, "Mixed_5b_Branch_3_Conv2d_0b_1x1_weights.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 0)) + .set_name("Mixed_5b/Branch_3/Conv2d_0b_1x1/convolution") + << BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_5b_Branch_3_Conv2d_0b_1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, "Mixed_5b_Branch_3_Conv2d_0b_1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, "Mixed_5b_Branch_3_Conv2d_0b_1x1_BatchNorm_beta.npy"), + 0.0010000000474974513f) + .set_name("Mixed_5b/Branch_3/Conv2d_0b_1x1/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_5b/Branch_3/Conv2d_0b_1x1/Relu"); + + // Concatenate + graph << BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d)).set_name("Mixed_5a/concat"); + } + + void block_mixed_6a(const std::string &data_path, DataLayout weights_layout) + { + // Branch 0 + SubStream i_a(graph); + i_a << ConvolutionLayer(3U, 3U, 384U, + get_weights_accessor(data_path, "Mixed_6a_Branch_0_Conv2d_1a_3x3_weights.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(2, 2, 0, 0)) + .set_name("Mixed_6a/Branch_0/Conv2d_1a_3x3/convolution") + << BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_6a_Branch_0_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, "Mixed_6a_Branch_0_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, "Mixed_6a_Branch_0_Conv2d_1a_3x3_BatchNorm_beta.npy"), + 0.0010000000474974513f) + .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"); + + // Branch 1 + SubStream i_b(graph); + i_b << ConvolutionLayer(1U, 1U, 256U, + get_weights_accessor(data_path, "Mixed_6a_Branch_1_Conv2d_0a_1x1_weights.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 0)) + .set_name("Mixed_6a/Branch_1/Conv2d_0a_1x1/convolution") + << BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_6a_Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, "Mixed_6a_Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, "Mixed_6a_Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"), + 0.0010000000474974513f) + .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, 256U, + get_weights_accessor(data_path, "Mixed_6a_Branch_1_Conv2d_0b_3x3_weights.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 1, 1)) + .set_name("Mixed_6a/Branch_1/Conv2d_0b_3x3/convolution") + << BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_6a_Branch_1_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, "Mixed_6a_Branch_1_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, "Mixed_6a_Branch_1_Conv2d_0b_3x3_BatchNorm_beta.npy"), + 0.0010000000474974513f) + .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, 384U, + get_weights_accessor(data_path, "Mixed_6a_Branch_1_Conv2d_1a_3x3_weights.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(2, 2, 0, 0)) + .set_name("Mixed_6a/Branch_1/Conv2d_1a_3x3/convolution") + << BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_6a_Branch_1_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, "Mixed_6a_Branch_1_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, "Mixed_6a_Branch_1_Conv2d_1a_3x3_BatchNorm_beta.npy"), + 0.0010000000474974513f) + .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"); + + // Branch 2 + SubStream i_c(graph); + i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0), true)).set_name("Mixed_6a/Branch_2/MaxPool_1a_3x3"); + + // Concatenate + graph << BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b), std::move(i_c)).set_name("Mixed_6a/concat"); + } + + void block_mixed_7a(const std::string &data_path, DataLayout weights_layout) + { + // Branch 0 + SubStream i_a(graph); + i_a << ConvolutionLayer(1U, 1U, 256U, + get_weights_accessor(data_path, "Mixed_7a_Branch_0_Conv2d_0a_1x1_weights.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 0)) + .set_name("Mixed_7a/Branch_0/Conv2d_0a_1x1/convolution") + << BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_7a_Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, "Mixed_7a_Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, "Mixed_7a_Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"), + 0.0010000000474974513f) + .set_name("Mixed_7a/Branch_0/Conv2d_0a_1x1/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_7a/Branch_0/Conv2d_0a_1x1/Relu") + << ConvolutionLayer(3U, 3U, 384U, + get_weights_accessor(data_path, "Mixed_7a_Branch_0_Conv2d_1a_3x3_weights.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(2, 2, 0, 0)) + .set_name("Mixed_7a/Branch_0/Conv2d_1a_3x3/convolution") + << BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_7a_Branch_0_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, "Mixed_7a_Branch_0_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, "Mixed_7a_Branch_0_Conv2d_1a_3x3_BatchNorm_beta.npy"), + 0.0010000000474974513f) + .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"); + + // Branch 1 + SubStream i_b(graph); + i_b << ConvolutionLayer(1U, 1U, 256U, + get_weights_accessor(data_path, "Mixed_7a_Branch_1_Conv2d_0a_1x1_weights.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 0)) + .set_name("Mixed_7a/Branch_1/Conv2d_0a_1x1/convolution") + << BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_7a_Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, "Mixed_7a_Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, "Mixed_7a_Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"), + 0.0010000000474974513f) + .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, 288U, + get_weights_accessor(data_path, "Mixed_7a_Branch_1_Conv2d_1a_3x3_weights.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(2, 2, 0, 0)) + .set_name("Mixed_7a/Branch_1/Conv2d_1a_3x3/convolution") + << BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_7a_Branch_1_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, "Mixed_7a_Branch_1_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, "Mixed_7a_Branch_1_Conv2d_1a_3x3_BatchNorm_beta.npy"), + 0.0010000000474974513f) + .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"); + + // Branch 2 + SubStream i_c(graph); + i_c << ConvolutionLayer(1U, 1U, 256U, + get_weights_accessor(data_path, "Mixed_7a_Branch_2_Conv2d_0a_1x1_weights.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 0)) + .set_name("Mixed_7a/Branch_2/Conv2d_0a_1x1/convolution") + << BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_7a_Branch_2_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, "Mixed_7a_Branch_2_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, "Mixed_7a_Branch_2_Conv2d_0a_1x1_BatchNorm_beta.npy"), + 0.0010000000474974513f) + .set_name("Mixed_7a/Branch_2/Conv2d_0a_1x1/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_7a/Branch_2/Conv2d_0a_1x1/Relu") + << ConvolutionLayer(3U, 3U, 288U, + get_weights_accessor(data_path, "Mixed_7a_Branch_2_Conv2d_0b_3x3_weights.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 1, 1)) + .set_name("Mixed_7a/Branch_2/Conv2d_0b_3x3/convolution") + << BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_7a_Branch_2_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, "Mixed_7a_Branch_2_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, "Mixed_7a_Branch_2_Conv2d_0b_3x3_BatchNorm_beta.npy"), + 0.0010000000474974513f) + .set_name("Mixed_7a/Branch_2/Conv2d_0b_3x3/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_7a/Branch_2/Conv2d_0b_3x3/Relu") + << ConvolutionLayer(3U, 3U, 320U, + get_weights_accessor(data_path, "Mixed_7a_Branch_2_Conv2d_1a_3x3_weights.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(2, 2, 0, 0)) + .set_name("Mixed_7a/Branch_2/Conv2d_1a_3x3/convolution") + << BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_7a_Branch_2_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, "Mixed_7a_Branch_2_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, "Mixed_7a_Branch_2_Conv2d_1a_3x3_BatchNorm_beta.npy"), + 0.0010000000474974513f) + .set_name("Mixed_7a/Branch_2/Conv2d_1a_3x3/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_7a/Branch_2/Conv2d_1a_3x3/Relu"); + + // Branch 3 + SubStream i_d(graph); + i_d << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), true)).set_name("Mixed_7a/Branch_3/MaxPool_1a_3x3"); + + // Concatenate + graph << BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d)).set_name("Mixed_7a/concat"); + } + + void block35_repeat(const std::string &data_path, DataLayout weights_layout, unsigned int num_blocks) + { + for(unsigned int i = 0; i < num_blocks; ++i) + { + std::stringstream unit_path_ss; + unit_path_ss << "Repeat_block35_" << (i + 1) << "_"; + std::stringstream unit_name_ss; + unit_name_ss << "Repeat/block35_" << (i + 1) << "/"; + + std::string unit_path = unit_path_ss.str(); + std::string unit_name = unit_name_ss.str(); + + // Create left and write substreams + SubStream i_l(graph); + SubStream i_r(graph); + + // Branch 0 + SubStream i_la(i_l); + i_la << ConvolutionLayer(1U, 1U, 32U, + get_weights_accessor(data_path, unit_path + "Branch_0_Conv2d_1x1_weights.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 0)) + .set_name(unit_name + "Branch_0/Conv2d_1x1/convolution") + << BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_0_Conv2d_1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, unit_path + "Branch_0_Conv2d_1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, unit_path + "Branch_0_Conv2d_1x1_BatchNorm_beta.npy"), + 0.0010000000474974513f) + .set_name(unit_name + "Branch_0/Conv2d_1x1/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_0/Conv2d_1x1/Relu"); + + // Branch 1 + SubStream i_lb(i_l); + i_lb << ConvolutionLayer(1U, 1U, 32U, + get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0a_1x1_weights.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 0)) + .set_name(unit_name + "Branch_1/Conv2d_0a_1x1/convolution") + << BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"), + 0.0010000000474974513f) + .set_name(unit_name + "Branch_1/Conv2d_0a_1x1/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_1/Conv2d_0a_1x1/Relu") + << ConvolutionLayer(3U, 3U, 32U, + get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0b_3x3_weights.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 1, 1)) + .set_name(unit_name + "Branch_1/Conv2d_0b_3x3/convolution") + << BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_beta.npy"), + 0.0010000000474974513f) + .set_name(unit_name + "Branch_1/Conv2d_0b_3x3/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_1/Conv2d_0b_3x3/Relu"); + + // Branch 2 + SubStream i_lc(i_l); + i_lc << ConvolutionLayer(1U, 1U, 32U, + get_weights_accessor(data_path, unit_path + "Branch_2_Conv2d_0a_1x1_weights.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 0)) + .set_name(unit_name + "Branch_2/Conv2d_0a_1x1/convolution") + << BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, unit_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, unit_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_beta.npy"), + 0.0010000000474974513f) + .set_name(unit_name + "Branch_2/Conv2d_0a_1x1/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_2/Conv2d_0a_1x1/Relu") + << ConvolutionLayer(3U, 3U, 48U, + get_weights_accessor(data_path, unit_path + "Branch_2_Conv2d_0b_3x3_weights.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 1, 1)) + .set_name(unit_name + "Branch_2/Conv2d_0b_3x3/convolution") + << BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, unit_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, unit_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_beta.npy"), + 0.0010000000474974513f) + .set_name(unit_name + "Branch_2/Conv2d_0b_3x3/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_2/Conv2d_0b_3x3/Relu") + << ConvolutionLayer(3U, 3U, 64U, + get_weights_accessor(data_path, unit_path + "Branch_2_Conv2d_0c_3x3_weights.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 1, 1)) + .set_name(unit_name + "Branch_2/Conv2d_0c_3x3/convolution") + << BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_2_Conv2d_0c_3x3_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, unit_path + "Branch_2_Conv2d_0c_3x3_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, unit_path + "Branch_2_Conv2d_0c_3x3_BatchNorm_beta.npy"), + 0.0010000000474974513f) + .set_name(unit_name + "Branch_2/Conv2d_0c_3x3/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_2/Conv2d_0c_3x3/Relu"); + + // Concatenate + i_l << BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_la), std::move(i_lb), std::move(i_lc)).set_name(unit_name + "concat") + << ConvolutionLayer(1U, 1U, 320U, + get_weights_accessor(data_path, unit_path + "Conv2d_1x1_weights.npy", weights_layout), + get_weights_accessor(data_path, unit_path + "Conv2d_1x1_biases.npy", weights_layout), + PadStrideInfo(1, 1, 0, 0)) + .set_name(unit_name + "Conv2d_1x1/convolution") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 0.17f, 0.f)).set_name(unit_name + "mul"); + + graph << BranchLayer(BranchMergeMethod::ADD, std::move(i_l), std::move(i_r)).set_name(unit_name + "add") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Relu"); + } + } + + void block17_repeat(const std::string &data_path, DataLayout weights_layout, unsigned int num_blocks) + { + for(unsigned int i = 0; i < num_blocks; ++i) + { + std::stringstream unit_path_ss; + unit_path_ss << "Repeat_1_block17_" << (i + 1) << "_"; + std::stringstream unit_name_ss; + unit_name_ss << "Repeat_1/block17_" << (i + 1) << "/"; + + std::string unit_path = unit_path_ss.str(); + std::string unit_name = unit_name_ss.str(); + + // Create left and write substreams + SubStream i_l(graph); + SubStream i_r(graph); + + // Branch 0 + SubStream i_la(i_l); + i_la << ConvolutionLayer(1U, 1U, 192U, + get_weights_accessor(data_path, unit_path + "Branch_0_Conv2d_1x1_weights.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 0)) + .set_name(unit_name + "Branch_0/Conv2d_1x1/convolution") + << BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_0_Conv2d_1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, unit_path + "Branch_0_Conv2d_1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, unit_path + "Branch_0_Conv2d_1x1_BatchNorm_beta.npy"), + 0.0010000000474974513f) + .set_name(unit_name + "Branch_0/Conv2d_1x1/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_0/Conv2d_1x1/Relu"); + + // Branch 1 + SubStream i_lb(i_l); + i_lb << ConvolutionLayer(1U, 1U, 128U, + get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0a_1x1_weights.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 0)) + .set_name(unit_name + "Branch_1/Conv2d_0a_1x1/convolution") + << BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"), + 0.0010000000474974513f) + .set_name(unit_name + "Branch_1/Conv2d_0a_1x1/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_1/Conv2d_0a_1x1/Relu") + << ConvolutionLayer(7U, 1U, 160U, + get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0b_1x7_weights.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 3, 0)) + .set_name(unit_name + "Branch_1/Conv2d_0b_1x7/convolution") + << BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_beta.npy"), + 0.0010000000474974513f) + .set_name(unit_name + "Branch_1/Conv2d_0b_1x7/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_1/Conv2d_0b_1x7/Relu") + << ConvolutionLayer(1U, 7U, 192U, + get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0c_7x1_weights.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 3)) + .set_name(unit_name + "Branch_1/Conv2d_0c_7x1/convolution") + << BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_beta.npy"), + 0.0010000000474974513f) + .set_name(unit_name + "Branch_1/Conv2d_0c_7x1/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_1/Conv2d_0c_7x1/Relu"); + + // Concatenate + i_l << BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_la), std::move(i_lb)).set_name(unit_name + "concat") + << ConvolutionLayer(1U, 1U, 1088U, + get_weights_accessor(data_path, unit_path + "Conv2d_1x1_weights.npy", weights_layout), + get_weights_accessor(data_path, unit_path + "Conv2d_1x1_biases.npy", weights_layout), + PadStrideInfo(1, 1, 0, 0)) + .set_name(unit_name + "Conv2d_1x1/convolution") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 0.10f, 0.f)).set_name(unit_name + "mul"); + + graph << BranchLayer(BranchMergeMethod::ADD, std::move(i_l), std::move(i_r)).set_name(unit_name + "add") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Relu"); + } + } + + void block8_repeat(const std::string &data_path, DataLayout weights_layout, unsigned int num_blocks, float scale, bool has_activation) + { + for(unsigned int i = 0; i < num_blocks; ++i) + { + std::stringstream unit_path_ss; + std::stringstream unit_name_ss; + if(num_blocks != 1) + { + unit_path_ss << "Repeat_2_block8_" << (i + 1) << "_"; + unit_name_ss << "Repeat_2/block8_" << (i + 1) << "/"; + } + else + { + unit_path_ss << "Block8_"; + unit_name_ss << "Block8/"; + } + + std::string unit_path = unit_path_ss.str(); + std::string unit_name = unit_name_ss.str(); + + // Create left and write substreams + SubStream i_l(graph); + SubStream i_r(graph); + + // Branch 0 + SubStream i_la(i_l); + i_la << ConvolutionLayer(1U, 1U, 192U, + get_weights_accessor(data_path, unit_path + "Branch_0_Conv2d_1x1_weights.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 0)) + .set_name(unit_name + "Branch_0/Conv2d_1x1/convolution") + << BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_0_Conv2d_1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, unit_path + "Branch_0_Conv2d_1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, unit_path + "Branch_0_Conv2d_1x1_BatchNorm_beta.npy"), + 0.0010000000474974513f) + .set_name(unit_name + "Branch_0/Conv2d_1x1/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_0/Conv2d_1x1/Relu"); + + // Branch 1 + SubStream i_lb(i_l); + i_lb << ConvolutionLayer(1U, 1U, 192U, + get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0a_1x1_weights.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 0)) + .set_name(unit_name + "Branch_1/Conv2d_0a_1x1/convolution") + << BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"), + 0.0010000000474974513f) + .set_name(unit_name + "Branch_1/Conv2d_0a_1x1/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_1/Conv2d_0a_1x1/Relu") + << ConvolutionLayer(3U, 1U, 224U, + get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0b_1x3_weights.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 1, 0)) + .set_name(unit_name + "Branch_1/Conv2d_0b_1x3/convolution") + << BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0b_1x3_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0b_1x3_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0b_1x3_BatchNorm_beta.npy"), + 0.0010000000474974513f) + .set_name(unit_name + "Branch_1/Conv2d_0b_1x3/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_1/Conv2d_0b_1x3/Relu") + << ConvolutionLayer(1U, 3U, 256U, + get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0c_3x1_weights.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 1)) + .set_name(unit_name + "Branch_1/Conv2d_0c_3x1/convolution") + << BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0c_3x1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0c_3x1_BatchNorm_moving_variance.npy"), + get_random_accessor(1.f, 1.f), + get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0c_3x1_BatchNorm_beta.npy"), + 0.0010000000474974513f) + .set_name(unit_name + "Branch_1/Conv2d_0c_3x1/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_1/Conv2d_0c_3x1/Relu"); + + // Concatenate + i_l << BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_la), std::move(i_lb)).set_name(unit_name + "concat") + << ConvolutionLayer(1U, 1U, 2080U, + get_weights_accessor(data_path, unit_path + "Conv2d_1x1_weights.npy", weights_layout), + get_weights_accessor(data_path, unit_path + "Conv2d_1x1_biases.npy", weights_layout), + PadStrideInfo(1, 1, 0, 0)) + .set_name(unit_name + "Conv2d_1x1/convolution"); + + // Scale result + if(scale != 1.f) + { + i_l << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, scale, 0.f)).set_name(unit_name + "mul"); + } + + // Residual add + graph << BranchLayer(BranchMergeMethod::ADD, std::move(i_l), std::move(i_r)).set_name(unit_name + "add"); + + // Apply activation if needed + if(has_activation) + { + graph << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Relu"); + } + } + } +}; + +/** Main program for Inception ResNet V2 + * + * @note To list all the possible arguments execute the binary appended with the --help option + * + * @param[in] argc Number of arguments + * @param[in] argv Arguments + */ +int main(int argc, char **argv) +{ + return arm_compute::utils::run_example(argc, argv); +} -- cgit v1.2.1