COMPMID-145 : Create ResNet v2 graph example

Change-Id: I6ff3d227321d8c3914f90ba4fc496b2fc122845c
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/144070
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Pablo Tello <pablo.tello@arm.com>

3 files changed, 253 insertions, 8 deletions

diff --git a/examples/graph_mobilenet_v2.cpp b/examples/graph_mobilenet_v2.cpp
index 1e50947c67..63dcba6950 100644
--- a/examples/graph_mobilenet_v2.cpp
+++ b/examples/graph_mobilenet_v2.cpp
@@ -64,8 +64,8 @@ public:
         // Print parameter values
         std::cout << common_params << std::endl;
 
-        // Create core of graph
-        std::string model_path = "/mobilenet_v2_1.0_224_model/";
+        // Create model path
+        std::string model_path = "/cnn_data/mobilenet_v2_1.0_224_model/";
 
         // Create input descriptor
         const TensorShape tensor_shape     = permute_shape(TensorShape(224U, 224U, 3U, 1U), DataLayout::NCHW, common_params.data_layout);
diff --git a/examples/graph_resnet50.cpp b/examples/graph_resnet50.cpp
index 0ad719a2ca..4221d98048 100644
--- a/examples/graph_resnet50.cpp
+++ b/examples/graph_resnet50.cpp
@@ -31,16 +31,16 @@ using namespace arm_compute::utils;
 using namespace arm_compute::graph::frontend;
 using namespace arm_compute::graph_utils;
 
-/** Example demonstrating how to implement ResNet50 network using the Compute Library's graph API
+/** Example demonstrating how to implement ResNetV1_50 network using the Compute Library's graph API
  *
  * @param[in] argc Number of arguments
  * @param[in] argv Arguments
  */
-class GraphResNet50Example : public Example
+class GraphResNetV1_50Example : public Example
 {
 public:
-    GraphResNet50Example()
-        : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "ResNet50")
+    GraphResNetV1_50Example()
+        : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "ResNetV1_50")
     {
     }
     bool do_setup(int argc, char **argv) override
@@ -237,7 +237,7 @@ private:
     }
 };
 
-/** Main program for ResNet50
+/** Main program for ResNetV1_50
  *
  * @note To list all the possible arguments execute the binary appended with the --help option
  *
@@ -246,5 +246,5 @@ private:
  */
 int main(int argc, char **argv)
 {
-    return arm_compute::utils::run_example<GraphResNet50Example>(argc, argv);
+    return arm_compute::utils::run_example<GraphResNetV1_50Example>(argc, argv);
 }
diff --git a/examples/graph_resnet_v2_50.cpp b/examples/graph_resnet_v2_50.cpp
new file mode 100644
index 0000000000..3db08c44d3
--- /dev/null
+++ b/examples/graph_resnet_v2_50.cpp
@@ -0,0 +1,245 @@
+/*
+ * 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;
+
+/** Example demonstrating how to implement ResNetV2_50 network using the Compute Library's graph API
+ *
+ * @param[in] argc Number of arguments
+ * @param[in] argv Arguments
+ */
+class GraphResNetV2_50Example : public Example
+{
+public:
+    GraphResNetV2_50Example()
+        : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "ResNetV2_50")
+    {
+    }
+    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;
+        }
+
+        // Checks
+        ARM_COMPUTE_EXIT_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), "Unsupported data type!");
+
+        // Print parameter values
+        std::cout << common_params << std::endl;
+
+        // Get trainable parameters data path
+        std::string data_path  = common_params.data_path;
+        std::string model_path = "/cnn_data/resnet_v2_50_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>();
+
+        // Create input descriptor
+        const TensorShape tensor_shape     = permute_shape(TensorShape(224U, 224U, 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 /* Do not convert to BGR */))
+              << ConvolutionLayer(
+                  7U, 7U, 64U,
+                  get_weights_accessor(data_path, "conv1_weights.npy", weights_layout),
+                  get_weights_accessor(data_path, "conv1_biases.npy", weights_layout),
+                  PadStrideInfo(2, 2, 3, 3))
+              .set_name("conv1/convolution")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))).set_name("pool1/MaxPool");
+
+        add_residual_block(data_path, "block1", weights_layout, 64, 3, 2);
+        add_residual_block(data_path, "block2", weights_layout, 128, 4, 2);
+        add_residual_block(data_path, "block3", weights_layout, 256, 6, 2);
+        add_residual_block(data_path, "block4", weights_layout, 512, 3, 1);
+
+        graph << BatchNormalizationLayer(
+                  get_weights_accessor(data_path, "postnorm_moving_mean.npy"),
+                  get_weights_accessor(data_path, "postnorm_moving_variance.npy"),
+                  get_weights_accessor(data_path, "postnorm_gamma.npy"),
+                  get_weights_accessor(data_path, "postnorm_beta.npy"),
+                  0.000009999999747378752f)
+              .set_name("postnorm/BatchNorm")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("postnorm/Relu")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("pool5")
+              << ConvolutionLayer(
+                  1U, 1U, 1001U,
+                  get_weights_accessor(data_path, "logits_weights.npy", weights_layout),
+                  get_weights_accessor(data_path, "logits_biases.npy"),
+                  PadStrideInfo(1, 1, 0, 0))
+              .set_name("logits/convolution")
+              << FlattenLayer().set_name("predictions/Reshape")
+              << SoftmaxLayer().set_name("predictions/Softmax")
+              << OutputLayer(get_output_accessor(common_params, 5));
+
+        // Finalize graph
+        GraphConfig config;
+        config.num_threads = common_params.threads;
+        config.use_tuner   = common_params.enable_tuner;
+        graph.finalize(common_params.target, config);
+
+        return true;
+    }
+
+    void do_run() override
+    {
+        // Run graph
+        graph.run();
+    }
+
+private:
+    CommandLineParser  cmd_parser;
+    CommonGraphOptions common_opts;
+    CommonGraphParams  common_params;
+    Stream             graph;
+
+    void add_residual_block(const std::string &data_path, const std::string &name, DataLayout weights_layout,
+                            unsigned int base_depth, unsigned int num_units, unsigned int stride)
+    {
+        for(unsigned int i = 0; i < num_units; ++i)
+        {
+            // Generate unit names
+            std::stringstream unit_path_ss;
+            unit_path_ss << name << "_unit_" << (i + 1) << "_bottleneck_v2_";
+            std::stringstream unit_name_ss;
+            unit_name_ss << name << "/unit" << (i + 1) << "/bottleneck_v2/";
+
+            std::string unit_path = unit_path_ss.str();
+            std::string unit_name = unit_name_ss.str();
+
+            const TensorShape last_shape = graph.graph().node(graph.tail_node())->output(0)->desc().shape;
+            unsigned int      depth_in   = last_shape[arm_compute::get_data_layout_dimension_index(common_params.data_layout, DataLayoutDimension::CHANNEL)];
+            unsigned int      depth_out  = base_depth * 4;
+
+            // All units have stride 1 apart from last one
+            unsigned int middle_stride = (i == (num_units - 1)) ? stride : 1;
+
+            // Preact
+            SubStream preact(graph);
+            preact << BatchNormalizationLayer(
+                       get_weights_accessor(data_path, unit_path + "preact_moving_mean.npy"),
+                       get_weights_accessor(data_path, unit_path + "preact_moving_variance.npy"),
+                       get_weights_accessor(data_path, unit_path + "preact_gamma.npy"),
+                       get_weights_accessor(data_path, unit_path + "preact_beta.npy"),
+                       0.000009999999747378752f)
+                   .set_name(unit_name + "preact/BatchNorm")
+                   << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "preact/Relu");
+
+            // Create bottleneck path
+            SubStream shortcut(graph);
+            if(depth_in == depth_out)
+            {
+                if(middle_stride != 1)
+                {
+                    shortcut << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 1, PadStrideInfo(middle_stride, middle_stride, 0, 0), true)).set_name(unit_name + "shortcut/MaxPool");
+                }
+            }
+            else
+            {
+                shortcut.forward_tail(preact.tail_node());
+                shortcut << ConvolutionLayer(
+                             1U, 1U, depth_out,
+                             get_weights_accessor(data_path, unit_path + "shortcut_weights.npy", weights_layout),
+                             get_weights_accessor(data_path, unit_path + "shortcut_biases.npy", weights_layout),
+                             PadStrideInfo(1, 1, 0, 0))
+                         .set_name(unit_name + "shortcut/convolution");
+            }
+
+            // Create residual path
+            SubStream residual(preact);
+            residual << ConvolutionLayer(
+                         1U, 1U, base_depth,
+                         get_weights_accessor(data_path, unit_path + "conv1_weights.npy", weights_layout),
+                         std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+                         PadStrideInfo(1, 1, 0, 0))
+                     .set_name(unit_name + "conv1/convolution")
+                     << BatchNormalizationLayer(
+                         get_weights_accessor(data_path, unit_path + "conv1_BatchNorm_moving_mean.npy"),
+                         get_weights_accessor(data_path, unit_path + "conv1_BatchNorm_moving_variance.npy"),
+                         get_weights_accessor(data_path, unit_path + "conv1_BatchNorm_gamma.npy"),
+                         get_weights_accessor(data_path, unit_path + "conv1_BatchNorm_beta.npy"),
+                         0.000009999999747378752f)
+                     .set_name(unit_name + "conv1/BatchNorm")
+                     << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "conv1/Relu")
+                     << ConvolutionLayer(
+                         3U, 3U, base_depth,
+                         get_weights_accessor(data_path, unit_path + "conv2_weights.npy", weights_layout),
+                         std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+                         PadStrideInfo(middle_stride, middle_stride, 1, 1))
+                     .set_name(unit_name + "conv2/convolution")
+                     << BatchNormalizationLayer(
+                         get_weights_accessor(data_path, unit_path + "conv2_BatchNorm_moving_mean.npy"),
+                         get_weights_accessor(data_path, unit_path + "conv2_BatchNorm_moving_variance.npy"),
+                         get_weights_accessor(data_path, unit_path + "conv2_BatchNorm_gamma.npy"),
+                         get_weights_accessor(data_path, unit_path + "conv2_BatchNorm_beta.npy"),
+                         0.000009999999747378752f)
+                     .set_name(unit_name + "conv2/BatchNorm")
+                     << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "conv1/Relu")
+                     << ConvolutionLayer(
+                         1U, 1U, depth_out,
+                         get_weights_accessor(data_path, unit_path + "conv3_weights.npy", weights_layout),
+                         get_weights_accessor(data_path, unit_path + "conv3_biases.npy", weights_layout),
+                         PadStrideInfo(1, 1, 0, 0))
+                     .set_name(unit_name + "conv3/convolution");
+
+            graph << BranchLayer(BranchMergeMethod::ADD, std::move(shortcut), std::move(residual)).set_name(unit_name + "add");
+        }
+    }
+};
+
+/** Main program for ResNetV2_50
+ *
+ * @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<GraphResNetV2_50Example>(argc, argv);
+}