COMPMID-550: Adds support for branches.

Change-Id: I778007c9221ce3156400284c4039b90245eb2b7f
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/90043
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>

1 files changed, 214 insertions, 0 deletions

diff --git a/examples/graph_googlenet.cpp b/examples/graph_googlenet.cpp
new file mode 100644
index 0000000000..0e82c1e85d
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+++ b/examples/graph_googlenet.cpp
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+/*
+ * Copyright (c) 2017 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.
+ */
+#ifndef ARM_COMPUTE_CL /* Needed by Utils.cpp to handle OpenCL exceptions properly */
+#error "This example needs to be built with -DARM_COMPUTE_CL"
+#endif /* ARM_COMPUTE_CL */
+
+#include "arm_compute/graph/Graph.h"
+#include "arm_compute/graph/Nodes.h"
+#include "arm_compute/graph/SubGraph.h"
+#include "arm_compute/runtime/CL/CLScheduler.h"
+#include "arm_compute/runtime/Scheduler.h"
+#include "support/ToolchainSupport.h"
+#include "utils/GraphUtils.h"
+#include "utils/Utils.h"
+
+#include <cstdlib>
+#include <iostream>
+#include <memory>
+#include <tuple>
+
+using namespace arm_compute::graph;
+using namespace arm_compute::graph_utils;
+
+/** Generates appropriate accessor according to the specified path
+ *
+ * @note If path is empty will generate a DummyAccessor else will generate a NumPyBinLoader
+ *
+ * @param path       Path to the data files
+ * @param data_file  Relative path to the data files from path
+ *
+ * @return An appropriate tensor accessor
+ */
+std::unique_ptr<ITensorAccessor> get_accessor(const std::string &path, const std::string &data_file)
+{
+    if(path.empty())
+    {
+        return arm_compute::support::cpp14::make_unique<DummyAccessor>();
+    }
+    else
+    {
+        return arm_compute::support::cpp14::make_unique<NumPyBinLoader>(path + data_file);
+    }
+}
+
+BranchLayer get_inception_node(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> c_filters,
+                               unsigned int d_filt)
+{
+    std::string total_path = "/cnn_data/googlenet_model/" + param_path + "/" + param_path + "_";
+    SubGraph    i_a;
+    i_a << ConvolutionLayer(
+            1U, 1U, a_filt,
+            get_accessor(data_path, total_path + "1x1_w.npy"),
+            get_accessor(data_path, total_path + "1x1_b.npy"),
+            PadStrideInfo(1, 1, 0, 0))
+        << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+
+    SubGraph i_b;
+    i_b << ConvolutionLayer(
+            1U, 1U, std::get<0>(b_filters),
+            get_accessor(data_path, total_path + "3x3_reduce_w.npy"),
+            get_accessor(data_path, total_path + "3x3_reduce_b.npy"),
+            PadStrideInfo(1, 1, 0, 0))
+        << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+        << ConvolutionLayer(
+            3U, 3U, std::get<1>(b_filters),
+            get_accessor(data_path, total_path + "3x3_w.npy"),
+            get_accessor(data_path, total_path + "3x3_b.npy"),
+            PadStrideInfo(1, 1, 1, 1))
+        << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+
+    SubGraph i_c;
+    i_c << ConvolutionLayer(
+            1U, 1U, std::get<0>(c_filters),
+            get_accessor(data_path, total_path + "5x5_reduce_w.npy"),
+            get_accessor(data_path, total_path + "5x5_reduce_b.npy"),
+            PadStrideInfo(1, 1, 0, 0))
+        << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+        << ConvolutionLayer(
+            5U, 5U, std::get<1>(c_filters),
+            get_accessor(data_path, total_path + "5x5_w.npy"),
+            get_accessor(data_path, total_path + "5x5_b.npy"),
+            PadStrideInfo(1, 1, 2, 2))
+        << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+
+    SubGraph i_d;
+    i_d << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL)))
+        << ConvolutionLayer(
+            1U, 1U, d_filt,
+            get_accessor(data_path, total_path + "pool_proj_w.npy"),
+            get_accessor(data_path, total_path + "pool_proj_b.npy"),
+            PadStrideInfo(1, 1, 0, 0))
+        << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+
+    return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d));
+}
+
+/** Example demonstrating how to implement Googlenet's network using the Compute Library's graph API
+ *
+ * @param[in] argc Number of arguments
+ * @param[in] argv Arguments ( [optional] Path to the weights folder, [optional] batches )
+ */
+void main_graph_googlenet(int argc, const char **argv)
+{
+    std::string  data_path;   /** Path to the trainable data */
+    unsigned int batches = 4; /** Number of batches */
+
+    // Parse arguments
+    if(argc < 2)
+    {
+        // Print help
+        std::cout << "Usage: " << argv[0] << " [path_to_data] [batches]\n\n";
+        std::cout << "No data folder provided: using random values\n\n";
+    }
+    else if(argc == 2)
+    {
+        //Do something with argv[1]
+        data_path = argv[1];
+        std::cout << "Usage: " << argv[0] << " [path_to_data] [batches]\n\n";
+        std::cout << "No number of batches where specified, thus will use the default : " << batches << "\n\n";
+    }
+    else
+    {
+        //Do something with argv[1] and argv[2]
+        data_path = argv[1];
+        batches   = std::strtol(argv[2], nullptr, 0);
+    }
+
+    // Check if OpenCL is available and initialize the scheduler
+    if(arm_compute::opencl_is_available())
+    {
+        arm_compute::CLScheduler::get().default_init();
+    }
+
+    Graph graph;
+
+    graph << TargetHint::OPENCL
+          << Tensor(TensorInfo(TensorShape(224U, 224U, 3U, batches), 1, DataType::F32), DummyAccessor())
+          << ConvolutionLayer(
+              7U, 7U, 64U,
+              get_accessor(data_path, "/cnn_data/googlenet_model/conv1/conv1_7x7_s2_w.npy"),
+              get_accessor(data_path, "/cnn_data/googlenet_model/conv1/conv1_7x7_s2_b.npy"),
+              PadStrideInfo(2, 2, 3, 3))
+          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+          << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
+          << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f))
+          << ConvolutionLayer(
+              1U, 1U, 64U,
+              get_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_reduce_w.npy"),
+              get_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_reduce_b.npy"),
+              PadStrideInfo(1, 1, 0, 0))
+          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+          << ConvolutionLayer(
+              3U, 3U, 192U,
+              get_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_w.npy"),
+              get_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_b.npy"),
+              PadStrideInfo(1, 1, 1, 1))
+          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+          << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f))
+          << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
+          << get_inception_node(data_path, "inception_3a", 64, std::make_tuple(96U, 128U), std::make_tuple(16U, 32U), 32U)
+          << get_inception_node(data_path, "inception_3b", 128, std::make_tuple(128U, 192U), std::make_tuple(32U, 96U), 64U)
+          << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
+          << get_inception_node(data_path, "inception_4a", 192, std::make_tuple(96U, 208U), std::make_tuple(16U, 48U), 64U)
+          << get_inception_node(data_path, "inception_4b", 160, std::make_tuple(112U, 224U), std::make_tuple(24U, 64U), 64U)
+          << get_inception_node(data_path, "inception_4c", 128, std::make_tuple(128U, 256U), std::make_tuple(24U, 64U), 64U)
+          << get_inception_node(data_path, "inception_4d", 112, std::make_tuple(144U, 288U), std::make_tuple(32U, 64U), 64U)
+          << get_inception_node(data_path, "inception_4e", 256, std::make_tuple(160U, 320U), std::make_tuple(32U, 128U), 128U)
+          << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
+          << get_inception_node(data_path, "inception_5a", 256, std::make_tuple(160U, 320U), std::make_tuple(32U, 128U), 128U)
+          << get_inception_node(data_path, "inception_5b", 384, std::make_tuple(192U, 384U), std::make_tuple(48U, 128U), 128U)
+          << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 7, PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL)))
+          << FullyConnectedLayer(
+              1000U,
+              get_accessor(data_path, "/cnn_data/googlenet_model/loss3/loss3_classifier_w.npy"),
+              get_accessor(data_path, "/cnn_data/googlenet_model/loss3/loss3_classifier_b.npy"))
+          << SoftmaxLayer()
+          << Tensor(DummyAccessor());
+
+    graph.run();
+}
+
+/** Main program for Googlenet
+ *
+ * @param[in] argc Number of arguments
+ * @param[in] argv Arguments ( [optional] Path to the weights folder, [optional] batches )
+ */
+int main(int argc, const char **argv)
+{
+    return arm_compute::utils::run_example(argc, argv, main_graph_googlenet);
+}