COMPMID-1456: Create mobilenet v2 1.0 224 graph example

Change-Id: I26533af88aebe4bd9692ee1cdcd24eca34acea32
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/143984
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Pablo Tello <pablo.tello@arm.com>

1 files changed, 244 insertions, 0 deletions

diff --git a/examples/graph_mobilenet_v2.cpp b/examples/graph_mobilenet_v2.cpp
new file mode 100644
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+++ b/examples/graph_mobilenet_v2.cpp
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+/*
+ * 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;
+using namespace arm_compute::utils;
+using namespace arm_compute::graph::frontend;
+using namespace arm_compute::graph_utils;
+
+/** Example demonstrating how to implement MobileNetV2's network using the Compute Library's graph API */
+class GraphMobilenetV2Example : public Example
+{
+public:
+    GraphMobilenetV2Example()
+        : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "MobileNetV2")
+    {
+    }
+    GraphMobilenetV2Example(const GraphMobilenetV2Example &) = delete;
+    GraphMobilenetV2Example &operator=(const GraphMobilenetV2Example &) = delete;
+    GraphMobilenetV2Example(GraphMobilenetV2Example &&)                 = default; // NOLINT
+    GraphMobilenetV2Example &operator=(GraphMobilenetV2Example &&) = default;      // NOLINT
+    ~GraphMobilenetV2Example() override                            = default;
+
+    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;
+        }
+
+        // Print parameter values
+        std::cout << common_params << std::endl;
+
+        // Create core of graph
+        std::string model_path = "/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);
+        TensorDescriptor  input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout);
+
+        // Create a preprocessor object
+        std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>();
+
+        // Get trainable parameters data path
+        std::string data_path = common_params.data_path;
+
+        // Add model path to data path
+        if(!data_path.empty())
+        {
+            data_path += model_path;
+        }
+
+        // Create graph
+        graph << common_params.target
+              << DepthwiseConvolutionMethod::Optimized3x3 // FIXME(COMPMID-1073): Add heuristics to automatically call the optimized 3x3 method
+              << common_params.fast_math_hint
+              << InputLayer(input_descriptor, get_input_accessor(common_params, std::move(preprocessor), false))
+              << ConvolutionLayer(3U, 3U, 32U,
+                                  get_weights_accessor(data_path, "Conv_weights.npy", DataLayout::NCHW),
+                                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+                                  PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL))
+              .set_name("Conv")
+              << BatchNormalizationLayer(get_weights_accessor(data_path, "Conv_BatchNorm_moving_mean.npy"),
+                                         get_weights_accessor(data_path, "Conv_BatchNorm_moving_variance.npy"),
+                                         get_weights_accessor(data_path, "Conv_BatchNorm_gamma.npy"),
+                                         get_weights_accessor(data_path, "Conv_BatchNorm_beta.npy"),
+                                         0.0010000000474974513f)
+              .set_name("Conv/BatchNorm")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f))
+              .set_name("Conv/Relu6");
+
+        get_expanded_conv(data_path, "expanded_conv", 32U, 16U, PadStrideInfo(1, 1, 1, 1));
+        get_expanded_conv(data_path, "expanded_conv_1", 16U, 24U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), true);
+        get_expanded_conv(data_path, "expanded_conv_2", 24U, 24U, PadStrideInfo(1, 1, 1, 1), true, true);
+        get_expanded_conv(data_path, "expanded_conv_3", 24U, 32U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), true);
+        get_expanded_conv(data_path, "expanded_conv_4", 32U, 32U, PadStrideInfo(1, 1, 1, 1), true, true);
+        get_expanded_conv(data_path, "expanded_conv_5", 32U, 32U, PadStrideInfo(1, 1, 1, 1), true, true);
+        get_expanded_conv(data_path, "expanded_conv_6", 32U, 64U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), true);
+        get_expanded_conv(data_path, "expanded_conv_7", 64U, 64U, PadStrideInfo(1, 1, 1, 1), true, true);
+        get_expanded_conv(data_path, "expanded_conv_8", 64U, 64U, PadStrideInfo(1, 1, 1, 1), true, true);
+        get_expanded_conv(data_path, "expanded_conv_9", 64U, 64U, PadStrideInfo(1, 1, 1, 1), true, true);
+        get_expanded_conv(data_path, "expanded_conv_10", 64U, 96U, PadStrideInfo(1, 1, 1, 1), true);
+        get_expanded_conv(data_path, "expanded_conv_11", 96U, 96U, PadStrideInfo(1, 1, 1, 1), true, true);
+        get_expanded_conv(data_path, "expanded_conv_12", 96U, 96U, PadStrideInfo(1, 1, 1, 1), true, true);
+        get_expanded_conv(data_path, "expanded_conv_13", 96U, 160U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), true);
+        get_expanded_conv(data_path, "expanded_conv_14", 160U, 160U, PadStrideInfo(1, 1, 1, 1), true, true);
+        get_expanded_conv(data_path, "expanded_conv_15", 160U, 160U, PadStrideInfo(1, 1, 1, 1), true, true);
+        get_expanded_conv(data_path, "expanded_conv_16", 160U, 320U, PadStrideInfo(1, 1, 1, 1), true);
+
+        graph << ConvolutionLayer(1U, 1U, 1280U,
+                                  get_weights_accessor(data_path, "Conv_1_weights.npy", DataLayout::NCHW),
+                                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+                                  PadStrideInfo(1, 1, 0, 0))
+              .set_name("Conv_1")
+              << BatchNormalizationLayer(get_weights_accessor(data_path, "Conv_1_BatchNorm_moving_mean.npy"),
+                                         get_weights_accessor(data_path, "Conv_1_BatchNorm_moving_variance.npy"),
+                                         get_weights_accessor(data_path, "Conv_1_BatchNorm_gamma.npy"),
+                                         get_weights_accessor(data_path, "Conv_1_BatchNorm_beta.npy"),
+                                         0.0010000000474974513f)
+              .set_name("Conv_1/BatchNorm")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f))
+              .set_name("Conv_1/Relu6")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("Logits/AvgPool")
+              << ConvolutionLayer(1U, 1U, 1001U,
+                                  get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_weights.npy", DataLayout::NCHW),
+                                  get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_biases.npy"),
+                                  PadStrideInfo(1, 1, 0, 0))
+              .set_name("Logits/Conv2d_1c_1x1")
+              << ReshapeLayer(TensorShape(1001U)).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;
+        config.tuner_file  = common_params.tuner_file;
+
+        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 get_expanded_conv(const std::string &data_path, std::string &&param_path,
+                           unsigned int input_channels, unsigned int output_channels,
+                           PadStrideInfo dwc_pad_stride_info,
+                           bool has_expand = false, bool is_residual = false, unsigned int expansion_size = 6)
+    {
+        std::string total_path = param_path + "_";
+        SubStream   left(graph);
+
+        // Add expand node
+        if(has_expand)
+        {
+            left << ConvolutionLayer(1U, 1U, input_channels * expansion_size,
+                                     get_weights_accessor(data_path, total_path + "expand_weights.npy", DataLayout::NCHW),
+                                     std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+                 .set_name(param_path + "/expand/Conv2D")
+                 << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "expand_BatchNorm_moving_mean.npy"),
+                                            get_weights_accessor(data_path, total_path + "expand_BatchNorm_moving_variance.npy"),
+                                            get_weights_accessor(data_path, total_path + "expand_BatchNorm_gamma.npy"),
+                                            get_weights_accessor(data_path, total_path + "expand_BatchNorm_beta.npy"),
+                                            0.0010000000474974513f)
+                 .set_name(param_path + "/expand/BatchNorm")
+                 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f))
+                 .set_name(param_path + "/expand/Relu6");
+        }
+
+        // Add depthwise node
+        left << DepthwiseConvolutionLayer(3U, 3U,
+                                          get_weights_accessor(data_path, total_path + "depthwise_depthwise_weights.npy", DataLayout::NCHW),
+                                          std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+                                          dwc_pad_stride_info)
+             .set_name(param_path + "/depthwise/depthwise")
+             << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_moving_mean.npy"),
+                                        get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_moving_variance.npy"),
+                                        get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_gamma.npy"),
+                                        get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_beta.npy"),
+                                        0.0010000000474974513f)
+             .set_name(param_path + "/depthwise/BatchNorm")
+             << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f))
+             .set_name(param_path + "/depthwise/Relu6");
+
+        // Add project node
+        left << ConvolutionLayer(1U, 1U, output_channels,
+                                 get_weights_accessor(data_path, total_path + "project_weights.npy", DataLayout::NCHW),
+                                 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+             .set_name(param_path + "/project/Conv2D")
+             << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "project_BatchNorm_moving_mean.npy"),
+                                        get_weights_accessor(data_path, total_path + "project_BatchNorm_moving_variance.npy"),
+                                        get_weights_accessor(data_path, total_path + "project_BatchNorm_gamma.npy"),
+                                        get_weights_accessor(data_path, total_path + "project_BatchNorm_beta.npy"),
+                                        0.0010000000474974513)
+             .set_name(param_path + "/project/BatchNorm");
+
+        if(is_residual)
+        {
+            // Add residual node
+            SubStream right(graph);
+            graph << BranchLayer(BranchMergeMethod::ADD, std::move(left), std::move(right)).set_name(param_path + "/add");
+        }
+        else
+        {
+            graph.forward_tail(left.tail_node());
+        }
+    }
+};
+
+/** Main program for MobileNetV2
+ *
+ * @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<GraphMobilenetV2Example>(argc, argv);
+}