COMPMID-915: Create ResNet50 example

ResidualLayer node (COMPMID-916) also created as required for the ResNet
architecture.

Change-Id: I4fb4d2e08a8d3ce206f96f7946f5afc3e244676a
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/121185
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>

1 files changed, 233 insertions, 0 deletions

diff --git a/examples/graph_resnet50.cpp b/examples/graph_resnet50.cpp
new file mode 100644
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--- /dev/null
+++ b/examples/graph_resnet50.cpp
@@ -0,0 +1,233 @@
+/*
+ * 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/Graph.h"
+#include "arm_compute/graph/Nodes.h"
+#include "support/ToolchainSupport.h"
+#include "utils/GraphUtils.h"
+#include "utils/Utils.h"
+
+#include <cstdlib>
+
+using namespace arm_compute::utils;
+using namespace arm_compute::graph;
+using namespace arm_compute::graph_utils;
+
+/** Example demonstrating how to implement Microsoft's ResNet50 network using the Compute Library's graph API
+ *
+ * @param[in] argc Number of arguments
+ * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels )
+ */
+class GraphResNet50Example : public Example
+{
+public:
+    void do_setup(int argc, char **argv) override
+    {
+        std::string data_path; /* Path to the trainable data */
+        std::string image;     /* Image data */
+        std::string label;     /* Label data */
+
+        // Create a preprocessor object
+        const std::array<float, 3> mean_rgb{ { 122.68f, 116.67f, 104.01f } };
+        std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb,
+                                                                                                                   false /* Do not convert to BGR */);
+
+        // Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON
+        const int  int_target_hint = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
+        TargetHint target_hint     = set_target_hint(int_target_hint);
+
+        // Parse arguments
+        if(argc < 2)
+        {
+            // Print help
+            std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels]\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]\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]\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]\n\n";
+            std::cout << "No text file with labels provided: skipping output accessor\n\n";
+        }
+        else
+        {
+            data_path = argv[2];
+            image     = argv[3];
+            label     = argv[4];
+        }
+
+        // Initialize the graph
+        graph.graph_init(int_target_hint == 2);
+
+        graph << target_hint
+              << Tensor(TensorInfo(TensorShape(224U, 224U, 3U, 1U), 1, DataType::F32),
+                        get_input_accessor(image, std::move(preprocessor), false /* Do not convert to BGR */))
+              << ConvolutionLayer(
+                  7U, 7U, 64U,
+                  get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_weights.npy"),
+                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+                  PadStrideInfo(2, 2, 3, 3))
+              << BatchNormalizationLayer(
+                  get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_BatchNorm_moving_mean.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_BatchNorm_moving_variance.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_BatchNorm_gamma.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_BatchNorm_beta.npy"),
+                  0.0000100099996416f)
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR)));
+
+        add_residual_block(data_path, "block1", 64, 3, 2);
+        add_residual_block(data_path, "block2", 128, 4, 2);
+        add_residual_block(data_path, "block3", 256, 6, 2);
+        add_residual_block(data_path, "block4", 512, 3, 1);
+
+        graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG))
+              << ConvolutionLayer(
+                  1U, 1U, 1000U,
+                  get_weights_accessor(data_path, "/cnn_data/resnet50_model/logits_weights.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/resnet50_model/logits_biases.npy"),
+                  PadStrideInfo(1, 1, 0, 0))
+              << FlattenLayer()
+              << SoftmaxLayer()
+              << Tensor(get_output_accessor(label, 5));
+    }
+    void do_run() override
+    {
+        // Run graph
+        graph.run();
+    }
+
+private:
+    Graph graph{};
+
+    void add_residual_block(const std::string &data_path, const std::string &name, unsigned int base_depth, unsigned int num_units, unsigned int stride)
+    {
+        for(unsigned int i = 0; i < num_units; ++i)
+        {
+            std::stringstream unit;
+            unit << "/cnn_data/resnet50_model/" << name << "_unit_" << (i + 1) << "_bottleneck_v1_";
+            std::string unit_name = unit.str();
+
+            unsigned int middle_stride = 1;
+
+            if(i == (num_units - 1))
+            {
+                middle_stride = stride;
+            }
+
+            SubGraph right;
+            right << ConvolutionLayer(
+                      1U, 1U, base_depth,
+                      get_weights_accessor(data_path, unit_name + "conv1_weights.npy"),
+                      std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+                      PadStrideInfo(1, 1, 0, 0))
+                  << BatchNormalizationLayer(
+                      get_weights_accessor(data_path, unit_name + "conv1_BatchNorm_moving_mean.npy"),
+                      get_weights_accessor(data_path, unit_name + "conv1_BatchNorm_moving_variance.npy"),
+                      get_weights_accessor(data_path, unit_name + "conv1_BatchNorm_gamma.npy"),
+                      get_weights_accessor(data_path, unit_name + "conv1_BatchNorm_beta.npy"),
+                      0.0000100099996416f)
+                  << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+
+                  << ConvolutionLayer(
+                      3U, 3U, base_depth,
+                      get_weights_accessor(data_path, unit_name + "conv2_weights.npy"),
+                      std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+                      PadStrideInfo(middle_stride, middle_stride, 1, 1))
+                  << BatchNormalizationLayer(
+                      get_weights_accessor(data_path, unit_name + "conv2_BatchNorm_moving_mean.npy"),
+                      get_weights_accessor(data_path, unit_name + "conv2_BatchNorm_moving_variance.npy"),
+                      get_weights_accessor(data_path, unit_name + "conv2_BatchNorm_gamma.npy"),
+                      get_weights_accessor(data_path, unit_name + "conv2_BatchNorm_beta.npy"),
+                      0.0000100099996416f)
+                  << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+
+                  << ConvolutionLayer(
+                      1U, 1U, base_depth * 4,
+                      get_weights_accessor(data_path, unit_name + "conv3_weights.npy"),
+                      std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+                      PadStrideInfo(1, 1, 0, 0))
+                  << BatchNormalizationLayer(
+                      get_weights_accessor(data_path, unit_name + "conv3_BatchNorm_moving_mean.npy"),
+                      get_weights_accessor(data_path, unit_name + "conv3_BatchNorm_moving_variance.npy"),
+                      get_weights_accessor(data_path, unit_name + "conv3_BatchNorm_gamma.npy"),
+                      get_weights_accessor(data_path, unit_name + "conv3_BatchNorm_beta.npy"),
+                      0.0000100099996416f);
+
+            if(i == 0)
+            {
+                SubGraph left;
+                left << ConvolutionLayer(
+                         1U, 1U, base_depth * 4,
+                         get_weights_accessor(data_path, unit_name + "shortcut_weights.npy"),
+                         std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+                         PadStrideInfo(1, 1, 0, 0))
+                     << BatchNormalizationLayer(
+                         get_weights_accessor(data_path, unit_name + "shortcut_BatchNorm_moving_mean.npy"),
+                         get_weights_accessor(data_path, unit_name + "shortcut_BatchNorm_moving_variance.npy"),
+                         get_weights_accessor(data_path, unit_name + "shortcut_BatchNorm_gamma.npy"),
+                         get_weights_accessor(data_path, unit_name + "shortcut_BatchNorm_beta.npy"),
+                         0.0000100099996416f);
+
+                graph << ResidualLayer(std::move(left), std::move(right));
+            }
+            else if(middle_stride > 1)
+            {
+                SubGraph left;
+                left << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 1, PadStrideInfo(middle_stride, middle_stride, 0, 0), true))
+                     // TODO (alegil01) : Remove once we understand why a single node graph does not run in CL
+                     << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 1.f, 0.f));
+
+                graph << ResidualLayer(std::move(left), std::move(right));
+            }
+            else
+            {
+                graph << ResidualLayer(std::move(right));
+            }
+
+            graph << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+        }
+    }
+};
+
+/** Main program for ResNet50
+ *
+ * @param[in] argc Number of arguments
+ * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels )
+ */
+int main(int argc, char **argv)
+{
+    return arm_compute::utils::run_example<GraphResNet50Example>(argc, argv);
+}