arm_compute v19.02
Change-Id: I853a3ecf38f206da13c1b03640c8adf73c20477c
diff --git a/examples/graph_inception_resnet_v1.cpp b/examples/graph_inception_resnet_v1.cpp
new file mode 100644
index 0000000..e99f688
--- /dev/null
+++ b/examples/graph_inception_resnet_v1.cpp
@@ -0,0 +1,718 @@
+/*
+ * 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;
+
+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(InceptionResNetV1Example &&) = default; // NOLINT
+ InceptionResNetV1Example &operator=(InceptionResNetV1Example &&) = default; // NOLINT
+ ~InceptionResNetV1Example() override = default;
+ 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;
+ }
+ // 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 = arm_compute::support::cpp14::make_unique<TFPreproccessor>(0.f, 1.f);
+
+ // Create input descriptor
+ const TensorShape tensor_shape = permute_shape(TensorShape(image_width, image_height, 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<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, 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)).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(arm_compute::support::cpp14::make_unique<DummyAccessor>(0));
+
+ // 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;
+ 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, 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, 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);
+}