summaryrefslogtreecommitdiff
path: root/runtimes/contrib/benchmark_acl/src/benchmark_inception_v3.cpp
diff options
context:
space:
mode:
Diffstat (limited to 'runtimes/contrib/benchmark_acl/src/benchmark_inception_v3.cpp')
-rw-r--r--runtimes/contrib/benchmark_acl/src/benchmark_inception_v3.cpp891
1 files changed, 891 insertions, 0 deletions
diff --git a/runtimes/contrib/benchmark_acl/src/benchmark_inception_v3.cpp b/runtimes/contrib/benchmark_acl/src/benchmark_inception_v3.cpp
new file mode 100644
index 000000000..382851f50
--- /dev/null
+++ b/runtimes/contrib/benchmark_acl/src/benchmark_inception_v3.cpp
@@ -0,0 +1,891 @@
+/*
+ * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
+ * Copyright (c) 2017-2018 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 "Benchmark.h"
+
+#include <cstdlib>
+#include <tuple>
+
+using namespace arm_compute::graph::frontend;
+
+inline std::unique_ptr<arm_compute::graph::ITensorAccessor> get_input_accessor(void)
+{
+ return get_accessor<InputAccessor>();
+}
+
+inline std::unique_ptr<arm_compute::graph::ITensorAccessor> get_random_accessor(float lower, float upper)
+{
+ return get_accessor<InputAccessor>();
+}
+
+inline std::unique_ptr<arm_compute::graph::ITensorAccessor> get_weights_accessor(const std::string &path, const std::string &data_file, DataLayout file_layout = DataLayout::NCHW)
+{
+ return get_accessor<InputAccessor>();
+}
+
+inline std::unique_ptr<arm_compute::graph::ITensorAccessor> get_output_accessor(void)
+{
+ return get_accessor<OutputAccessor>();
+}
+
+/** Example demonstrating how to implement InceptionV3's network using the Compute Library's graph API
+ *
+ * @param[in] argc Number of arguments
+ * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] image, [optional] labels )
+ */
+class InceptionV3Example
+{
+public:
+ void do_setup(int argc, char **argv)
+ {
+ std::string data_path; /* Path to the trainable data */
+ std::string image; /* Image data */
+ std::string label; /* Label data */
+
+ // Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON
+ const int target = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
+ Target target_hint = set_target_hint(target);
+ FastMathHint fast_math_hint = FastMathHint::Disabled;
+
+ // Parse arguments
+ if(argc < 2)
+ {
+ // Print help
+ std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels] [fast_math_hint]\n\n";
+ std::cout << "No data folder provided: using random values\n\n";
+ }
+ else if(argc == 2)
+ {
+ std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels] [fast_math_hint]\n\n";
+ std::cout << "No data folder provided: using random values\n\n";
+ }
+ else if(argc == 3)
+ {
+ data_path = argv[2];
+ std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels] [fast_math_hint]\n\n";
+ std::cout << "No image provided: using random values\n\n";
+ }
+ else if(argc == 4)
+ {
+ data_path = argv[2];
+ image = argv[3];
+ std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels] [fast_math_hint]\n\n";
+ std::cout << "No text file with labels provided: skipping output accessor\n\n";
+ }
+ else if(argc == 5)
+ {
+ data_path = argv[2];
+ image = argv[3];
+ label = argv[4];
+ std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [fast_math_hint]\n\n";
+ std::cout << "No fast math info provided: disabling fast math\n\n";
+ }
+ else
+ {
+ data_path = argv[2];
+ image = argv[3];
+ label = argv[4];
+ fast_math_hint = (std::strtol(argv[5], nullptr, 1) == 0) ? FastMathHint::Disabled : FastMathHint::Enabled;
+ }
+
+ graph << target_hint
+ << fast_math_hint
+ << InputLayer(TensorDescriptor(TensorShape(299U, 299U, 3U, 1U), DataType::F32),
+ get_input_accessor())
+ << ConvolutionLayer(3U, 3U, 32U,
+ get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_1a_3x3_weights.npy"),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(2, 2, 0, 0))
+ .set_name("Conv2d_1a_3x3/convolution")
+ << BatchNormalizationLayer(get_weights_accessor(data_path,
+ "/cnn_data/inceptionv3_model/Conv2d_1a_3x3_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path,
+ "/cnn_data/inceptionv3_model/Conv2d_1a_3x3_BatchNorm_moving_variance.npy"),
+ get_random_accessor(1.f, 1.f), get_weights_accessor(data_path,
+ "/cnn_data/inceptionv3_model/Conv2d_1a_3x3_BatchNorm_beta.npy"),
+ 0.001f)
+ .set_name("Conv2d_1a_3x3/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_1a_3x3/Relu")
+ << ConvolutionLayer(3U, 3U, 32U,
+ get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_2a_3x3_weights.npy"),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+ .set_name("Conv2d_2a_3x3/convolution")
+ << BatchNormalizationLayer(get_weights_accessor(data_path,
+ "/cnn_data/inceptionv3_model/Conv2d_2a_3x3_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path,
+ "/cnn_data/inceptionv3_model/Conv2d_2a_3x3_BatchNorm_moving_variance.npy"),
+ get_random_accessor(1.f, 1.f), get_weights_accessor(data_path,
+ "/cnn_data/inceptionv3_model/Conv2d_2a_3x3_BatchNorm_beta.npy"),
+ 0.001f)
+ .set_name("Conv2d_2a_3x3/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_2a_3x3/Relu")
+
+ << ConvolutionLayer(3U, 3U, 64U,
+ get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_2b_3x3_weights.npy"),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1))
+ .set_name("Conv2d_2b_3x3/convolution")
+ << BatchNormalizationLayer(get_weights_accessor(data_path,
+ "/cnn_data/inceptionv3_model/Conv2d_2b_3x3_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path,
+ "/cnn_data/inceptionv3_model/Conv2d_2b_3x3_BatchNorm_moving_variance.npy"),
+ get_random_accessor(1.f, 1.f), get_weights_accessor(data_path,
+ "/cnn_data/inceptionv3_model/Conv2d_2b_3x3_BatchNorm_beta.npy"),
+ 0.001f)
+ .set_name("Conv2d_2b_3x3/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_2b_3x3/Relu")
+
+ << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("MaxPool_3a_3x3/MaxPool")
+
+ << ConvolutionLayer(1U, 1U, 80U,
+ get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_3b_1x1_weights.npy"),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+ .set_name("Conv2d_3b_1x1/convolution")
+ << BatchNormalizationLayer(get_weights_accessor(data_path,
+ "/cnn_data/inceptionv3_model/Conv2d_3b_1x1_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path,
+ "/cnn_data/inceptionv3_model/Conv2d_3b_1x1_BatchNorm_moving_variance.npy"),
+ get_random_accessor(1.f, 1.f), get_weights_accessor(data_path,
+ "/cnn_data/inceptionv3_model/Conv2d_3b_1x1_BatchNorm_beta.npy"),
+ 0.001f)
+ .set_name("Conv2d_3b_1x1/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_3b_1x1/Relu")
+
+ << ConvolutionLayer(3U, 3U, 192U,
+ get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_4a_3x3_weights.npy"),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+ .set_name("Conv2d_4a_3x3/convolution")
+ << BatchNormalizationLayer(get_weights_accessor(data_path,
+ "/cnn_data/inceptionv3_model/Conv2d_4a_3x3_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path,
+ "/cnn_data/inceptionv3_model/Conv2d_4a_3x3_BatchNorm_moving_variance.npy"),
+ get_random_accessor(1.f, 1.f), get_weights_accessor(data_path,
+ "/cnn_data/inceptionv3_model/Conv2d_4a_3x3_BatchNorm_beta.npy"),
+ 0.001f)
+ .set_name("Conv2d_4a_3x3/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_4a_3x3/Relu")
+
+ << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("MaxPool_5a_3x3/MaxPool");
+
+ graph << get_inception_node_A(data_path, "Mixed_5b", 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U),
+ 32U)
+ .set_name("Mixed_5b/concat");
+ graph << get_inception_node_A(data_path, "Mixed_5c", 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U),
+ 64U, true)
+ .set_name("Mixed_5c/concat");
+ graph << get_inception_node_A(data_path, "Mixed_5d", 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U),
+ 64U)
+ .set_name("Mixed_5d/concat");
+
+ graph << get_inception_node_B(data_path, "Mixed_6a", 384U, std::make_tuple(64U, 96U, 96U)).set_name("Mixed_6a/concat");
+
+ graph << get_inception_node_C(data_path, "Mixed_6b", 192U, std::make_tuple(128U, 128U, 192U),
+ std::make_tuple(128U, 128U, 128U, 128U, 192U), 192U)
+ .set_name("Mixed_6b/concat");
+ graph << get_inception_node_C(data_path, "Mixed_6c", 192U, std::make_tuple(160U, 160U, 192U),
+ std::make_tuple(160U, 160U, 160U, 160U, 192U), 192U)
+ .set_name("Mixed_6c/concat");
+ graph << get_inception_node_C(data_path, "Mixed_6d", 192U, std::make_tuple(160U, 160U, 192U),
+ std::make_tuple(160U, 160U, 160U, 160U, 192U), 192U)
+ .set_name("Mixed_6d/concat");
+ graph << get_inception_node_C(data_path, "Mixed_6e", 192U, std::make_tuple(192U, 192U, 192U),
+ std::make_tuple(192U, 192U, 192U, 192U, 192U), 192U)
+ .set_name("Mixed_6e/concat");
+
+ graph << get_inception_node_D(data_path, "Mixed_7a", std::make_tuple(192U, 320U),
+ std::make_tuple(192U, 192U, 192U, 192U))
+ .set_name("Mixed_7a/concat");
+
+ graph << get_inception_node_E(data_path, "Mixed_7b", 320U, std::make_tuple(384U, 384U, 384U),
+ std::make_tuple(448U, 384U, 384U, 384U), 192U)
+ .set_name("Mixed_7b/concat");
+ graph << get_inception_node_E(data_path, "Mixed_7c", 320U, std::make_tuple(384U, 384U, 384U),
+ std::make_tuple(448U, 384U, 384U, 384U), 192U, true)
+ .set_name("Mixed_7c/concat");
+
+ graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 8, PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL))).set_name("Logits/AvgPool_1a_8x8/AvgPool")
+ << ConvolutionLayer(1U, 1U, 1001U, get_weights_accessor(data_path,
+ "/cnn_data/inceptionv3_model/Logits_Conv2d_1c_1x1_weights.npy"),
+ get_weights_accessor(data_path,
+ "/cnn_data/inceptionv3_model/Logits_Conv2d_1c_1x1_biases.npy"),
+ PadStrideInfo(1, 1, 0, 0))
+ .set_name("Logits/Conv2d_1c_1x1/convolution")
+ << ReshapeLayer(TensorShape(1001U)).set_name("Predictions/Reshape")
+ << SoftmaxLayer().set_name("Predictions/Softmax")
+ << OutputLayer(get_output_accessor());
+
+ // Finalize graph
+ GraphConfig config;
+ config.use_tuner = (target == 2);
+ graph.finalize(target_hint, config);
+ }
+
+ void do_run()
+ {
+ run_benchmark(graph);
+ }
+
+private:
+ Stream graph{ 0, "InceptionV3" };
+
+private:
+ ConcatLayer get_inception_node_A(const std::string &data_path, std::string &&param_path,
+ unsigned int a_filt,
+ std::tuple<unsigned int, unsigned int> b_filters,
+ std::tuple<unsigned int, unsigned int, unsigned int> c_filters,
+ unsigned int d_filt,
+ bool is_name_different = false)
+ {
+ std::string total_path = "/cnn_data/inceptionv3_model/" + param_path + "_";
+
+ // This is due to a naming issue in the tf model
+ std::string conv_id0 = "_0a_";
+ std::string conv_id1 = "2d_0b_";
+ if(is_name_different)
+ {
+ conv_id0 = "_0b_";
+ conv_id1 = "_1_0c_";
+ }
+
+ SubStream i_a(graph);
+ i_a << ConvolutionLayer(
+ 1U, 1U, a_filt,
+ get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy"),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+ PadStrideInfo(1, 1, 0, 0))
+ .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/convolution")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
+ get_random_accessor(1.f, 1.f),
+ get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"),
+ 0.001f)
+ .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_0a_1x1/Relu");
+
+ SubStream i_b(graph);
+ i_b << ConvolutionLayer(
+ 1U, 1U, std::get<0>(b_filters),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id0 + "1x1_weights.npy"),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+ PadStrideInfo(1, 1, 0, 0))
+ .set_name(param_path + "/Branch_1/Conv2d" + conv_id0 + "1x1/convolution")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id0 + "1x1_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id0 + "1x1_BatchNorm_moving_variance.npy"),
+ get_random_accessor(1.f, 1.f),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id0 + "1x1_BatchNorm_beta.npy"),
+ 0.001f)
+ .set_name(param_path + "/Branch_1/Conv2d" + conv_id0 + "1x1/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d" + conv_id0 + "1x1/Relu")
+ << ConvolutionLayer(
+ 5U, 5U, std::get<1>(b_filters),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv" + conv_id1 + "5x5_weights.npy"),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+ PadStrideInfo(1, 1, 2, 2))
+ .set_name(param_path + "/Branch_1/Conv2d" + conv_id1 + "5x5/convolution")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv" + conv_id1 + "5x5_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv" + conv_id1 + "5x5_BatchNorm_moving_variance.npy"),
+ get_random_accessor(1.f, 1.f),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv" + conv_id1 + "5x5_BatchNorm_beta.npy"),
+ 0.001f)
+ .set_name(param_path + "/Branch_1/Conv2d" + conv_id1 + "5x5/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d" + conv_id1 + "5x5/Relu");
+
+ SubStream i_c(graph);
+ i_c << ConvolutionLayer(
+ 1U, 1U, std::get<0>(c_filters),
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_weights.npy"),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+ PadStrideInfo(1, 1, 0, 0))
+ .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/convolution")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
+ get_random_accessor(1.f, 1.f),
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_beta.npy"),
+ 0.001f)
+ .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0a_1x1/Relu")
+ << ConvolutionLayer(
+ 3U, 3U, std::get<1>(c_filters),
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_weights.npy"),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+ PadStrideInfo(1, 1, 1, 1))
+ .set_name(param_path + "/Branch_2/Conv2d_0b_3x3/convolution")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"),
+ get_random_accessor(1.f, 1.f),
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_beta.npy"),
+ 0.001f)
+ .set_name(param_path + "/Branch_2/Conv2d_0b_3x3/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0b_3x3/Relu")
+ << ConvolutionLayer(
+ 3U, 3U, std::get<2>(c_filters),
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_3x3_weights.npy"),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+ PadStrideInfo(1, 1, 1, 1))
+ .set_name(param_path + "/Branch_2/Conv2d_0c_3x3/convolution")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_3x3_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_3x3_BatchNorm_moving_variance.npy"),
+ get_random_accessor(1.f, 1.f),
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_3x3_BatchNorm_beta.npy"),
+ 0.001f)
+ .set_name(param_path + "/Branch_2/Conv2d_0c_3x3/BatchNorm/batcnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0c_3x3/Relu");
+
+ SubStream i_d(graph);
+ i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true)).set_name(param_path + "/Branch_3/AvgPool_0a_3x3/AvgPool")
+ << ConvolutionLayer(
+ 1U, 1U, d_filt,
+ get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy"),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+ PadStrideInfo(1, 1, 0, 0))
+ .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/convolution")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_variance.npy"),
+ get_random_accessor(1.f, 1.f),
+ get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_beta.npy"),
+ 0.001f)
+ .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_3/Conv2d_0b_1x1/Relu");
+
+ return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d));
+ }
+
+ ConcatLayer get_inception_node_B(const std::string &data_path, std::string &&param_path,
+ unsigned int a_filt,
+ std::tuple<unsigned int, unsigned int, unsigned int> b_filters)
+ {
+ std::string total_path = "/cnn_data/inceptionv3_model/" + param_path + "_";
+ SubStream i_a(graph);
+ i_a << ConvolutionLayer(
+ 3U, 3U, a_filt,
+ get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_1x1_weights.npy"),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+ PadStrideInfo(2, 2, 0, 0))
+ .set_name(param_path + "/Branch_0/Conv2d_1a_1x1/convolution")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_1x1_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_1x1_BatchNorm_moving_variance.npy"),
+ get_random_accessor(1.f, 1.f),
+ get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_1x1_BatchNorm_beta.npy"),
+ 0.001f)
+ .set_name(param_path + "/Branch_0/Conv2d_1a_1x1/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_1a_1x1/Relu");
+
+ SubStream i_b(graph);
+ i_b << ConvolutionLayer(
+ 1U, 1U, std::get<0>(b_filters),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy"),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+ PadStrideInfo(1, 1, 0, 0))
+ .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/convolution")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
+ get_random_accessor(1.f, 1.f),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"),
+ 0.001f)
+ .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0a_1x1/Relu")
+ << ConvolutionLayer(
+ 3U, 3U, std::get<1>(b_filters),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_weights.npy"),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+ PadStrideInfo(1, 1, 1, 1))
+ .set_name(param_path + "/Branch_1/Conv2d_0b_3x3/convolution")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"),
+ get_random_accessor(1.f, 1.f),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_beta.npy"),
+ 0.001f)
+ .set_name(param_path + "/Branch_1/Conv2d_0b_3x3/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0b_3x3/Relu")
+ << ConvolutionLayer(
+ 3U, 3U, std::get<2>(b_filters),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_1x1_weights.npy"),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+ PadStrideInfo(2, 2, 0, 0))
+ .set_name(param_path + "/Branch_1/Conv2d_1a_1x1/convolution")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_1x1_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_1x1_BatchNorm_moving_variance.npy"),
+ get_random_accessor(1.f, 1.f),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_1x1_BatchNorm_beta.npy"),
+ 0.001f)
+ .set_name(param_path + "/Branch_1/Conv2d_1a_1x1/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_1a_1x1/Relu");
+
+ SubStream i_c(graph);
+ i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name(param_path + "/Branch_2/MaxPool_1a_3x3/MaxPool");
+
+ return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c));
+ }
+
+ ConcatLayer get_inception_node_C(const std::string &data_path, std::string &&param_path,
+ unsigned int a_filt,
+ std::tuple<unsigned int, unsigned int, unsigned int> b_filters,
+ std::tuple<unsigned int, unsigned int, unsigned int, unsigned int, unsigned int> c_filters,
+ unsigned int d_filt)
+ {
+ std::string total_path = "/cnn_data/inceptionv3_model/" + param_path + "_";
+ SubStream i_a(graph);
+ i_a << ConvolutionLayer(
+ 1U, 1U, a_filt,
+ get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy"),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+ PadStrideInfo(1, 1, 0, 0))
+ .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/convolution")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
+ get_random_accessor(1.f, 1.f),
+ get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"),
+ 0.001f)
+ .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_0a_1x1/Relu");
+
+ SubStream i_b(graph);
+ i_b << ConvolutionLayer(
+ 1U, 1U, std::get<0>(b_filters),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy"),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+ PadStrideInfo(1, 1, 0, 0))
+ .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/convolution")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
+ get_random_accessor(1.f, 1.f),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"),
+ 0.001f)
+ .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0a_1x1/Relu")
+ << ConvolutionLayer(
+ 7U, 1U, std::get<1>(b_filters),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_weights.npy"),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+ PadStrideInfo(1, 1, 3, 0))
+ .set_name(param_path + "/Branch_1/Conv2d_0b_1x7/convolution")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_variance.npy"),
+ get_random_accessor(1.f, 1.f),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_beta.npy"),
+ 0.001f)
+ .set_name(param_path + "/Branch_1/Conv2d_0b_1x7/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0b_1x7/Relu")
+ << ConvolutionLayer(
+ 1U, 7U, std::get<2>(b_filters),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_weights.npy"),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+ PadStrideInfo(1, 1, 0, 3))
+ .set_name(param_path + "/Branch_1/Conv2d_0c_7x1/convolution")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_variance.npy"),
+ get_random_accessor(1.f, 1.f),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_beta.npy"),
+ 0.001f)
+ .set_name(param_path + "/Branch_1/Conv2d_0c_7x1/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_0c_7x1/Relu");
+
+ SubStream i_c(graph);
+ i_c << ConvolutionLayer(
+ 1U, 1U, std::get<0>(c_filters),
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_weights.npy"),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+ PadStrideInfo(1, 1, 0, 0))
+ .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/convolution")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
+ get_random_accessor(1.f, 1.f),
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_beta.npy"),
+ 0.001f)
+ .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0a_1x1/Relu")
+ << ConvolutionLayer(
+ 1U, 7U, std::get<1>(c_filters),
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_7x1_weights.npy"),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+ PadStrideInfo(1, 1, 0, 3))
+ .set_name(param_path + "/Branch_2/Conv2d_0b_7x1/convolution")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_7x1_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_7x1_BatchNorm_moving_variance.npy"),
+ get_random_accessor(1.f, 1.f),
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_7x1_BatchNorm_beta.npy"),
+ 0.001f)
+ .set_name(param_path + "/Branch_2/Conv2d_0b_7x1/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0b_7x1/Relu")
+ << ConvolutionLayer(
+ 7U, 1U, std::get<2>(c_filters),
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x7_weights.npy"),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+ PadStrideInfo(1, 1, 3, 0))
+ .set_name(param_path + "/Branch_2/Conv2d_0c_1x7/convolution")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x7_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x7_BatchNorm_moving_variance.npy"),
+ get_random_accessor(1.f, 1.f),
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x7_BatchNorm_beta.npy"),
+ 0.001f)
+ .set_name(param_path + "/Branch_2/Conv2d_0c_1x7/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0c_1x7/Relu")
+ << ConvolutionLayer(
+ 1U, 7U, std::get<3>(c_filters),
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_7x1_weights.npy"),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+ PadStrideInfo(1, 1, 0, 3))
+ .set_name(param_path + "/Branch_2/Conv2d_0d_7x1/convolution")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_7x1_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_7x1_BatchNorm_moving_variance.npy"),
+ get_random_accessor(1.f, 1.f),
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_7x1_BatchNorm_beta.npy"),
+ 0.001f)
+ .set_name(param_path + "/Branch_2/Conv2d_0d_7x1/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0d_7x1/Relu")
+ << ConvolutionLayer(
+ 7U, 1U, std::get<4>(c_filters),
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_1x7_weights.npy"),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+ PadStrideInfo(1, 1, 3, 0))
+ .set_name(param_path + "/Branch_2/Conv2d_0e_1x7/convolution")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_1x7_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_1x7_BatchNorm_moving_variance.npy"),
+ get_random_accessor(1.f, 1.f),
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_1x7_BatchNorm_beta.npy"),
+ 0.001f)
+ .set_name(param_path + "/Branch_2/Conv2d_0e_1x7/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0e_1x7/Relu");
+
+ SubStream i_d(graph);
+ i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true)).set_name(param_path + "/Branch_3/AvgPool_0a_3x3/AvgPool")
+ << ConvolutionLayer(
+ 1U, 1U, d_filt,
+ get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy"),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+ PadStrideInfo(1, 1, 0, 0))
+ .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/convolution")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_variance.npy"),
+ get_random_accessor(1.f, 1.f),
+ get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_beta.npy"),
+ 0.001f)
+ .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_3/Conv2d_0b_1x1/Relu");
+
+ return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d));
+ }
+
+ ConcatLayer get_inception_node_D(const std::string &data_path, std::string &&param_path,
+ std::tuple<unsigned int, unsigned int> a_filters,
+ std::tuple<unsigned int, unsigned int, unsigned int, unsigned int> b_filters)
+ {
+ std::string total_path = "/cnn_data/inceptionv3_model/" + param_path + "_";
+ SubStream i_a(graph);
+ i_a << ConvolutionLayer(
+ 1U, 1U, std::get<0>(a_filters),
+ get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy"),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+ PadStrideInfo(1, 1, 0, 0))
+ .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/convolution")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
+ get_random_accessor(1.f, 1.f),
+ get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"),
+ 0.001f)
+ .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_0a_1x1/Relu")
+ << ConvolutionLayer(
+ 3U, 3U, std::get<1>(a_filters),
+ get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_weights.npy"),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+ PadStrideInfo(2, 2, 0, 0))
+ .set_name(param_path + "/Branch_0/Conv2d_1a_3x3/convolution")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"),
+ get_random_accessor(1.f, 1.f),
+ get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_beta.npy"),
+ 0.001f)
+ .set_name(param_path + "/Branch_0/Conv2d_1a_3x3/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_1a_3x3/Relu");
+
+ SubStream i_b(graph);
+ i_b << ConvolutionLayer(
+ 1U, 1U, std::get<0>(b_filters),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy"),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+ PadStrideInfo(1, 1, 0, 0))
+ .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/convolution")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
+ get_random_accessor(1.f, 1.f),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"),
+ 0.001f)
+ .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0a_1x1/Relu")
+ << ConvolutionLayer(
+ 7U, 1U, std::get<1>(b_filters),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_weights.npy"),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+ PadStrideInfo(1, 1, 3, 0))
+ .set_name(param_path + "/Branch_1/Conv2d_0b_1x7/convolution")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_variance.npy"),
+ get_random_accessor(1.f, 1.f),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_beta.npy"),
+ 0.001f)
+ .set_name(param_path + "/Branch_1/Conv2d_0b_1x7/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0b_1x7/Relu")
+ << ConvolutionLayer(
+ 1U, 7U, std::get<2>(b_filters),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_weights.npy"),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+ PadStrideInfo(1, 1, 0, 3))
+ .set_name(param_path + "/Branch_1/Conv2d_0c_7x1/convolution")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_variance.npy"),
+ get_random_accessor(1.f, 1.f),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_beta.npy"),
+ 0.001f)
+ .set_name(param_path + "/Branch_1/Conv2d_0c_7x1/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0c_7x1/Relu")
+ << ConvolutionLayer(
+ 3U, 3U, std::get<3>(b_filters),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_weights.npy"),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+ PadStrideInfo(2, 2, 0, 0))
+ .set_name(param_path + "/Branch_1/Conv2d_1a_3x3/convolution")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"),
+ get_random_accessor(1.f, 1.f),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_beta.npy"),
+ 0.001f)
+ .set_name(param_path + "/Branch_1/Conv2d_1a_3x3/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_1a_3x3/Relu");
+
+ SubStream i_c(graph);
+ i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name(param_path + "/Branch_2/MaxPool_1a_3x3/MaxPool");
+
+ return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c));
+ }
+
+ ConcatLayer get_inception_node_E(const std::string &data_path, std::string &&param_path,
+ unsigned int a_filt,
+ std::tuple<unsigned int, unsigned int, unsigned int> b_filters,
+ std::tuple<unsigned int, unsigned int, unsigned int, unsigned int> c_filters,
+ unsigned int d_filt,
+ bool is_name_different = false)
+ {
+ // This is due to a naming issue in the tf model
+ std::string conv_id = "_0b_";
+ if(is_name_different)
+ {
+ conv_id = "_0c_";
+ }
+
+ std::string total_path = "/cnn_data/inceptionv3_model/" + param_path + "_";
+ SubStream i_a(graph);
+ i_a << ConvolutionLayer(
+ 1U, 1U, a_filt,
+ get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy"),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+ PadStrideInfo(1, 1, 0, 0))
+ .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/convolution")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
+ get_random_accessor(1.f, 1.f),
+ get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"),
+ 0.001f)
+ .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_0a_1x1/Relu");
+
+ SubStream i_b(graph);
+ i_b << ConvolutionLayer(
+ 1U, 1U, std::get<0>(b_filters),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy"),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+ PadStrideInfo(1, 1, 0, 0))
+ .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/convolution")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
+ get_random_accessor(1.f, 1.f),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"),
+ 0.001f)
+ .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0a_1x1/Relu");
+
+ SubStream i_b1(static_cast<IStream &>(i_b));
+ i_b1 << ConvolutionLayer(
+ 3U, 1U, std::get<1>(b_filters),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x3_weights.npy"),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+ PadStrideInfo(1, 1, 1, 0))
+ .set_name(param_path + "/Branch_1/Conv2d_0b_1x3/convolution")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x3_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x3_BatchNorm_moving_variance.npy"),
+ get_random_accessor(1.f, 1.f),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x3_BatchNorm_beta.npy"),
+ 0.001f)
+ .set_name(param_path + "/Branch_1/Conv2d_0b_1x3/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0b_1x3/Relu");
+
+ SubStream i_b2(static_cast<IStream &>(i_b));
+ i_b2 << ConvolutionLayer(
+ 1U, 3U, std::get<2>(b_filters),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id + "3x1_weights.npy"),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+ PadStrideInfo(1, 1, 0, 1))
+ .set_name(param_path + "/Branch_1/Conv2d" + conv_id + "3x1/convolution")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id + "3x1_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id + "3x1_BatchNorm_moving_variance.npy"),
+ get_random_accessor(1.f, 1.f),
+ get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id + "3x1_BatchNorm_beta.npy"),
+ 0.001f)
+ .set_name(param_path + "/Branch_1/Conv2d" + conv_id + "3x1/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d" + conv_id + "3x1/Relu");
+
+ // Merge b1 and b2
+ i_b << ConcatLayer(std::move(i_b1), std::move(i_b2)).set_name(param_path + "/Branch_1/concat");
+
+ SubStream i_c(graph);
+ i_c << ConvolutionLayer(
+ 1U, 1U, std::get<0>(c_filters),
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_weights.npy"),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+ PadStrideInfo(1, 1, 0, 0))
+ .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/convolution")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
+ get_random_accessor(1.f, 1.f),
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_beta.npy"),
+ 0.001f)
+ .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0a_1x1/Relu")
+ << ConvolutionLayer(
+ 3U, 3U, std::get<1>(c_filters),
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_weights.npy"),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+ PadStrideInfo(1, 1, 1, 1))
+ .set_name(param_path + "/Branch_2/Conv2d_0b_3x3/convolution")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"),
+ get_random_accessor(1.f, 1.f),
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_beta.npy"),
+ 0.001f)
+ .set_name(param_path + "/Branch_2/Conv2d_0b_3x3/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0b_3x3/Relu");
+
+ SubStream i_c1(static_cast<IStream &>(i_c));
+ i_c1 << ConvolutionLayer(
+ 3U, 1U, std::get<2>(c_filters),
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x3_weights.npy"),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+ PadStrideInfo(1, 1, 1, 0))
+ .set_name(param_path + "/Branch_2/Conv2d_0c_1x3/convolution")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x3_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x3_BatchNorm_moving_variance.npy"),
+ get_random_accessor(1.f, 1.f),
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x3_BatchNorm_beta.npy"),
+ 0.001f)
+ .set_name(param_path + "/Branch_2/Conv2d_0c_1x3/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0c_1x3/Relu");
+
+ SubStream i_c2(static_cast<IStream &>(i_c));
+ i_c2 << ConvolutionLayer(
+ 1U, 3U, std::get<3>(c_filters),
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_3x1_weights.npy"),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+ PadStrideInfo(1, 1, 0, 1))
+ .set_name(param_path + "/Branch_2/Conv2d_0d_3x1/convolution")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_3x1_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_3x1_BatchNorm_moving_variance.npy"),
+ get_random_accessor(1.f, 1.f),
+ get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_3x1_BatchNorm_beta.npy"),
+ 0.001f)
+ .set_name(param_path + "/Branch_2/Conv2d_0d_3x1/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0d_3x1/Relu");
+
+ // Merge i_c1 and i_c2
+ i_c << ConcatLayer(std::move(i_c1), std::move(i_c2)).set_name(param_path + "/Branch_2/concat");
+
+ SubStream i_d(graph);
+ i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true)).set_name(param_path + "/Branch_3/AvgPool_0a_3x3/AvgPool")
+ << ConvolutionLayer(
+ 1U, 1U, d_filt,
+ get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy"),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+ PadStrideInfo(1, 1, 0, 0))
+ .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/convolution")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_variance.npy"),
+ get_random_accessor(1.f, 1.f),
+ get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_beta.npy"),
+ 0.001f)
+ .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/BatchNorm/batchnorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_3/Conv2d_0b_1x1/Relu");
+
+ return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d));
+ }
+};
+
+/** Main program for Inception V3
+ *
+ * @param[in] argc Number of arguments
+ * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
+ */
+int main(int argc, char **argv)
+{
+ InceptionV3Example example;
+
+ example.do_setup(argc, argv);
+ example.do_run();
+
+ return 0;
+}