COMPMID-1035 - Add ResneXt50 as a graph example

Change-Id: I42f0e7dab38e45b5eecfe6858eaecee8939c8585
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/129291
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Tested-by: Jenkins <bsgcomp@arm.com>

1 files changed, 208 insertions, 0 deletions

diff --git a/examples/graph_resnext50.cpp b/examples/graph_resnext50.cpp
new file mode 100644
index 0000000000..f96a02e6d6
--- /dev/null
+++ b/examples/graph_resnext50.cpp
@@ -0,0 +1,208 @@
+/*
+ * 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/GraphUtils.h"
+#include "utils/Utils.h"
+
+#include <cstdlib>
+
+using namespace arm_compute::utils;
+using namespace arm_compute::graph::frontend;
+using namespace arm_compute::graph_utils;
+
+/** Example demonstrating how to implement ResNeXt50 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] npy_in, [optional] npy_out, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
+ */
+class GraphResNeXt50Example : public Example
+{
+public:
+    void do_setup(int argc, char **argv) override
+    {
+        std::string data_path; /* Path to the trainable data */
+        std::string npy_in;    /* Input npy data */
+        std::string npy_out;   /* Output npy 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] [npy_in] [npy_out] [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] [npy_in] [npy_out] [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] << " [npy_in] [npy_out] [fast_math_hint]\n\n";
+            std::cout << "No input npy file provided: using random values\n\n";
+        }
+        else if(argc == 4)
+        {
+            data_path = argv[2];
+            npy_in    = argv[3];
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [npy_out] [fast_math_hint]\n\n";
+            std::cout << "No output npy file provided: skipping output accessor\n\n";
+        }
+        else if(argc == 5)
+        {
+            data_path = argv[2];
+            npy_in    = argv[3];
+            npy_out   = 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];
+            npy_in         = argv[3];
+            npy_out        = 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(224U, 224U, 3U, 1U), DataType::F32),
+                            get_input_accessor(npy_in))
+              << ScaleLayer(get_weights_accessor(data_path, "/cnn_data/resnext50_model/bn_data_mul.npy"),
+                            get_weights_accessor(data_path, "/cnn_data/resnext50_model/bn_data_add.npy"))
+              .set_name("bn_data/Scale")
+              << ConvolutionLayer(
+                  7U, 7U, 64U,
+                  get_weights_accessor(data_path, "/cnn_data/resnext50_model/conv0_weights.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/resnext50_model/conv0_biases.npy"),
+                  PadStrideInfo(2, 2, 2, 3, 2, 3, DimensionRoundingType::FLOOR))
+              .set_name("conv0/Convolution")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv0/Relu")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))).set_name("pool0");
+
+        add_residual_block(data_path, /*ofm*/ 256, /*stage*/ 1, /*num_unit*/ 3, /*stride_conv_unit1*/ 1);
+        add_residual_block(data_path, 512, 2, 4, 2);
+        add_residual_block(data_path, 1024, 3, 6, 2);
+        add_residual_block(data_path, 2048, 4, 3, 2);
+
+        graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("pool1")
+              << FlattenLayer().set_name("predictions/Reshape")
+              << OutputLayer(get_npy_output_accessor(npy_out, TensorShape(2048U), DataType::F32));
+
+        // Finalize graph
+        GraphConfig config;
+        config.use_tuner = (target == 2);
+        graph.finalize(target_hint, config);
+    }
+
+    void do_run() override
+    {
+        // Run graph
+        graph.run();
+    }
+
+private:
+    Stream graph{ 0, "ResNeXt50" };
+
+    void add_residual_block(const std::string &data_path, unsigned int base_depth, unsigned int stage, unsigned int num_units, unsigned int stride_conv_unit1)
+    {
+        for(unsigned int i = 0; i < num_units; ++i)
+        {
+            std::stringstream unit_path_ss;
+            unit_path_ss << "/cnn_data/resnext50_model/stage" << stage << "_unit" << (i + 1) << "_";
+            std::string unit_path = unit_path_ss.str();
+
+            std::stringstream unit_name_ss;
+            unit_name_ss << "stage" << stage << "/unit" << (i + 1) << "/";
+            std::string unit_name = unit_name_ss.str();
+
+            PadStrideInfo pad_grouped_conv(1, 1, 1, 1);
+            if(i == 0)
+            {
+                pad_grouped_conv = (stage == 1) ? PadStrideInfo(stride_conv_unit1, stride_conv_unit1, 1, 1) : PadStrideInfo(stride_conv_unit1, stride_conv_unit1, 0, 1, 0, 1, DimensionRoundingType::FLOOR);
+            }
+
+            SubStream right(graph);
+            right << ConvolutionLayer(
+                      1U, 1U, base_depth / 2,
+                      get_weights_accessor(data_path, unit_path + "conv1_weights.npy"),
+                      get_weights_accessor(data_path, unit_path + "conv1_biases.npy"),
+                      PadStrideInfo(1, 1, 0, 0))
+                  .set_name(unit_name + "conv1/convolution")
+                  << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "conv1/Relu")
+
+                  << ConvolutionLayer(
+                      3U, 3U, base_depth / 2,
+                      get_weights_accessor(data_path, unit_path + "conv2_weights.npy"),
+                      std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+                      pad_grouped_conv, 32)
+                  .set_name(unit_name + "conv2/convolution")
+                  << ScaleLayer(get_weights_accessor(data_path, unit_path + "bn2_mul.npy"),
+                                get_weights_accessor(data_path, unit_path + "bn2_add.npy"))
+                  .set_name(unit_name + "conv1/Scale")
+                  << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "conv2/Relu")
+
+                  << ConvolutionLayer(
+                      1U, 1U, base_depth,
+                      get_weights_accessor(data_path, unit_path + "conv3_weights.npy"),
+                      get_weights_accessor(data_path, unit_path + "conv3_biases.npy"),
+                      PadStrideInfo(1, 1, 0, 0))
+                  .set_name(unit_name + "conv3/convolution");
+
+            SubStream left(graph);
+            if(i == 0)
+            {
+                left << ConvolutionLayer(
+                         1U, 1U, base_depth,
+                         get_weights_accessor(data_path, unit_path + "sc_weights.npy"),
+                         std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+                         PadStrideInfo(stride_conv_unit1, stride_conv_unit1, 0, 0))
+                     .set_name(unit_name + "sc/convolution")
+                     << ScaleLayer(get_weights_accessor(data_path, unit_path + "sc_bn_mul.npy"),
+                                   get_weights_accessor(data_path, unit_path + "sc_bn_add.npy"))
+                     .set_name(unit_name + "sc/scale");
+            }
+
+            graph << BranchLayer(BranchMergeMethod::ADD, std::move(left), std::move(right)).set_name(unit_name + "add");
+            graph << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Relu");
+        }
+    }
+};
+
+/** Main program for ResNeXt50
+ *
+ * @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] npy_in, [optional] npy_out )
+ */
+int main(int argc, char **argv)
+{
+    return arm_compute::utils::run_example<GraphResNeXt50Example>(argc, argv);
+}