| /* |
| * Copyright (c) 2018-2021 Arm Limited. |
| * |
| * SPDX-License-Identifier: MIT |
| * |
| * Permission is hereby granted, free of charge, to any person obtaining a copy |
| * of this software and associated documentation files (the "Software"), to |
| * deal in the Software without restriction, including without limitation the |
| * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or |
| * sell copies of the Software, and to permit persons to whom the Software is |
| * furnished to do so, subject to the following conditions: |
| * |
| * The above copyright notice and this permission notice shall be included in all |
| * copies or substantial portions of the Software. |
| * |
| * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
| * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
| * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
| * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
| * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
| * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE |
| * SOFTWARE. |
| */ |
| #include "arm_compute/graph.h" |
| #include "support/ToolchainSupport.h" |
| #include "utils/CommonGraphOptions.h" |
| #include "utils/GraphUtils.h" |
| #include "utils/Utils.h" |
| |
| using namespace arm_compute::utils; |
| using namespace arm_compute::graph::frontend; |
| using namespace arm_compute::graph_utils; |
| |
| const float batch_norm_epsilon = 0.0010000000474974513f; |
| |
| /** Example demonstrating how to implement Inception ResNet V1 network using the Compute Library's graph API */ |
| class InceptionResNetV1Example final : public Example |
| { |
| public: |
| InceptionResNetV1Example() |
| : cmd_parser(), common_opts(cmd_parser), common_params(), model_input_width(nullptr), model_input_height(nullptr), graph(0, "InceptionResNetV1") |
| { |
| model_input_width = cmd_parser.add_option<SimpleOption<unsigned int>>("image-width", 512); |
| model_input_height = cmd_parser.add_option<SimpleOption<unsigned int>>("image-height", 512); |
| |
| // Add model id option |
| model_input_width->set_help("Input image width."); |
| model_input_height->set_help("Input image height."); |
| } |
| InceptionResNetV1Example(const InceptionResNetV1Example &) = delete; |
| InceptionResNetV1Example &operator=(const InceptionResNetV1Example &) = delete; |
| ~InceptionResNetV1Example() override = default; |
| bool do_setup(int argc, char **argv) override |
| { |
| // Parse arguments |
| cmd_parser.parse(argc, argv); |
| cmd_parser.validate(); |
| |
| // Consume common parameters |
| common_params = consume_common_graph_parameters(common_opts); |
| |
| // Return when help menu is requested |
| if(common_params.help) |
| { |
| cmd_parser.print_help(argv[0]); |
| return false; |
| } |
| // Get input image width and height |
| const unsigned int image_width = model_input_width->value(); |
| const unsigned int image_height = model_input_height->value(); |
| |
| // 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), "QASYMM8 not supported for this graph"); |
| |
| // Print parameter values |
| std::cout << common_params << std::endl; |
| std::cout << "Image width: " << image_width << std::endl; |
| std::cout << "Image height: " << image_height << std::endl; |
| |
| // Create model path |
| std::string data_path = common_params.data_path; |
| std::string model_path = "/cnn_data/inception_resnet_v1_model/"; |
| if(!data_path.empty()) |
| { |
| data_path += model_path; |
| } |
| |
| // Create a preprocessor object |
| std::unique_ptr<IPreprocessor> preprocessor = std::make_unique<TFPreproccessor>(0.f, 1.f); |
| |
| // Create input descriptor |
| const auto operation_layout = common_params.data_layout; |
| const TensorShape tensor_shape = permute_shape(TensorShape(image_width, image_height, 3U, common_params.batches), DataLayout::NCHW, operation_layout); |
| TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout); |
| |
| // Set weights trained layout |
| const DataLayout weights_layout = DataLayout::NCHW; |
| |
| graph << common_params.target |
| << common_params.fast_math_hint |
| << InputLayer(input_descriptor, get_input_accessor(common_params, std::move(preprocessor), false)) |
| // Conv2d_1a_3x3 |
| << ConvolutionLayer(3U, 3U, 32U, |
| get_weights_accessor(data_path, "Conv2d_1a_3x3_weights.npy", weights_layout), |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| PadStrideInfo(2, 2, 0, 0)) |
| .set_name("Conv2d_1a_3x3/convolution") |
| << BatchNormalizationLayer(get_weights_accessor(data_path, "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"), |
| batch_norm_epsilon) |
| .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<arm_compute::graph::ITensorAccessor>(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"), |
| batch_norm_epsilon) |
| .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<arm_compute::graph::ITensorAccessor>(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"), |
| batch_norm_epsilon) |
| .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, operation_layout, 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<arm_compute::graph::ITensorAccessor>(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"), |
| batch_norm_epsilon) |
| .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<arm_compute::graph::ITensorAccessor>(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"), |
| batch_norm_epsilon) |
| .set_name("Conv2d_4a_3x3/BatchNorm") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_4a_3x3/Relu") |
| // Conv2d_4b_3x3 |
| << ConvolutionLayer(3U, 3U, 256U, |
| get_weights_accessor(data_path, "Conv2d_4b_3x3_weights.npy", weights_layout), |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| PadStrideInfo(2, 2, 0, 0)) |
| .set_name("Conv2d_4a_3x3/convolution") |
| << BatchNormalizationLayer(get_weights_accessor(data_path, "Conv2d_4b_3x3_BatchNorm_moving_mean.npy"), |
| get_weights_accessor(data_path, "Conv2d_4b_3x3_BatchNorm_moving_variance.npy"), |
| get_random_accessor(1.f, 1.f), |
| get_weights_accessor(data_path, "Conv2d_4b_3x3_BatchNorm_beta.npy"), |
| batch_norm_epsilon) |
| .set_name("Conv2d_4b_3x3/BatchNorm") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_4b_3x3/Relu"); |
| |
| // 5 x Inception-resnet-A |
| block35_repeat(data_path, weights_layout, 5); |
| // Reduction-A |
| reduction_a(data_path, weights_layout); |
| // 10 x Inception-Resnet-B |
| block17_repeat(data_path, weights_layout, 10); |
| // Reduction-B |
| reduction_b(data_path, weights_layout); |
| // 5 x Inception-resnet-C |
| block8_repeat(data_path, weights_layout, 5, 0.2f, true); |
| |
| block8_repeat(data_path, weights_layout, 1, 1.f, false); |
| |
| // Logits tail |
| graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, operation_layout)).set_name("Logits/AvgPool_1a_8x8") |
| << FlattenLayer().set_name("Logits/Flatten") |
| << FullyConnectedLayer( |
| 128U, |
| get_weights_accessor(data_path, "Logits_Logits_weights.npy", weights_layout), |
| get_weights_accessor(data_path, "Logits_Logits_biases.npy")) |
| .set_name("Logits/Logits") |
| << OutputLayer(std::make_unique<DummyAccessor>(0)); |
| |
| // Finalize graph |
| GraphConfig config; |
| config.num_threads = common_params.threads; |
| config.use_tuner = common_params.enable_tuner; |
| config.tuner_mode = common_params.tuner_mode; |
| config.tuner_file = common_params.tuner_file; |
| config.mlgo_file = common_params.mlgo_file; |
| |
| graph.finalize(common_params.target, config); |
| |
| return true; |
| } |
| |
| void do_run() override |
| { |
| graph.run(); |
| } |
| |
| private: |
| CommandLineParser cmd_parser; |
| CommonGraphOptions common_opts; |
| CommonGraphParams common_params; |
| SimpleOption<unsigned int> *model_input_width{ nullptr }; |
| SimpleOption<unsigned int> *model_input_height{ nullptr }; |
| Stream graph; |
| |
| private: |
| 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<arm_compute::graph::ITensorAccessor>(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"), |
| batch_norm_epsilon) |
| .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<arm_compute::graph::ITensorAccessor>(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"), |
| batch_norm_epsilon) |
| .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<arm_compute::graph::ITensorAccessor>(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"), |
| batch_norm_epsilon) |
| .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<arm_compute::graph::ITensorAccessor>(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"), |
| batch_norm_epsilon) |
| .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, 32U, |
| get_weights_accessor(data_path, unit_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(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"), |
| batch_norm_epsilon) |
| .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, 32U, |
| get_weights_accessor(data_path, unit_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(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"), |
| batch_norm_epsilon) |
| .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 << ConcatLayer(std::move(i_la), std::move(i_lb), std::move(i_lc)).set_name(unit_name + "concat") |
| << ConvolutionLayer(1U, 1U, 256U, |
| 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 << EltwiseLayer(std::move(i_l), std::move(i_r), EltwiseOperation::Add).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, 128U, |
| get_weights_accessor(data_path, unit_path + "Branch_0_Conv2d_1x1_weights.npy", weights_layout), |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(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"), |
| batch_norm_epsilon) |
| .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<arm_compute::graph::ITensorAccessor>(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"), |
| batch_norm_epsilon) |
| .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, 128U, |
| get_weights_accessor(data_path, unit_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(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"), |
| batch_norm_epsilon) |
| .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, 128U, |
| get_weights_accessor(data_path, unit_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(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"), |
| batch_norm_epsilon) |
| .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 << ConcatLayer(std::move(i_la), std::move(i_lb)).set_name(unit_name + "concat") |
| << ConvolutionLayer(1U, 1U, 896U, |
| 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 << EltwiseLayer(std::move(i_l), std::move(i_r), EltwiseOperation::Add).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<arm_compute::graph::ITensorAccessor>(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"), |
| batch_norm_epsilon) |
| .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<arm_compute::graph::ITensorAccessor>(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"), |
| batch_norm_epsilon) |
| .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, 192U, |
| get_weights_accessor(data_path, unit_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(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"), |
| batch_norm_epsilon) |
| .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, 192U, |
| get_weights_accessor(data_path, unit_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(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"), |
| batch_norm_epsilon) |
| .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 << ConcatLayer(std::move(i_la), std::move(i_lb)).set_name(unit_name + "concat") |
| << ConvolutionLayer(1U, 1U, 1792U, |
| 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 << EltwiseLayer(std::move(i_l), std::move(i_r), EltwiseOperation::Add).set_name(unit_name + "add"); |
| |
| // Apply activation if needed |
| if(has_activation) |
| { |
| graph << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Relu"); |
| } |
| } |
| } |
| |
| void reduction_a(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<arm_compute::graph::ITensorAccessor>(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"), |
| batch_norm_epsilon) |
| .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, 192U, |
| get_weights_accessor(data_path, "Mixed_6a_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/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"), |
| batch_norm_epsilon) |
| .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, 192U, |
| get_weights_accessor(data_path, "Mixed_6a_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/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"), |
| batch_norm_epsilon) |
| .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, "Mixed_6a_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/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"), |
| batch_norm_epsilon) |
| .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, common_params.data_layout, PadStrideInfo(2, 2, 0, 0), true)).set_name("Mixed_6a/Branch_2/MaxPool_1a_3x3"); |
| |
| // Concatenate |
| graph << ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c)).set_name("Mixed_6a/concat"); |
| } |
| |
| void reduction_b(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<arm_compute::graph::ITensorAccessor>(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"), |
| batch_norm_epsilon) |
| .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<arm_compute::graph::ITensorAccessor>(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"), |
| batch_norm_epsilon) |
| .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<arm_compute::graph::ITensorAccessor>(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"), |
| batch_norm_epsilon) |
| .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, 256U, |
| get_weights_accessor(data_path, "Mixed_7a_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/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"), |
| batch_norm_epsilon) |
| .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<arm_compute::graph::ITensorAccessor>(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"), |
| batch_norm_epsilon) |
| .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, 256U, |
| get_weights_accessor(data_path, "Mixed_7a_Branch_2_Conv2d_0b_3x3_weights.npy", weights_layout), |
| std::unique_ptr<arm_compute::graph::ITensorAccessor>(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"), |
| batch_norm_epsilon) |
| .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, 256U, |
| get_weights_accessor(data_path, "Mixed_7a_Branch_2_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_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"), |
| batch_norm_epsilon) |
| .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, common_params.data_layout, PadStrideInfo(2, 2, 0, 0), true)).set_name("Mixed_7a/Branch_3/MaxPool_1a_3x3"); |
| |
| // Concatenate |
| graph << ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d)).set_name("Mixed_7a/concat"); |
| } |
| }; |
| |
| /** Main program for Inception ResNet V1 |
| * |
| * 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 |
| * |
| * @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<InceptionResNetV1Example>(argc, argv); |
| } |
