COMPMID-1458: Mobilenet SSD example.

Change-Id: I9d4ba7d00d50a84f650f0449faa8a25226068fed

1 files changed, 375 insertions, 0 deletions

diff --git a/examples/graph_ssd_mobilenet.cpp b/examples/graph_ssd_mobilenet.cpp
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+++ b/examples/graph_ssd_mobilenet.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 MobileNetSSD's network using the Compute Library's graph API */
+class GraphSSDMobilenetExample : public Example
+{
+public:
+    GraphSSDMobilenetExample()
+        : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "MobileNetSSD")
+    {
+        mbox_loc_opt = cmd_parser.add_option<SimpleOption<std::string>>("mbox_loc_opt", "");
+        mbox_loc_opt->set_help("Filename containing the reference values for the graph branch mbox_loc_opt.");
+        mbox_conf_flatten_opt = cmd_parser.add_option<SimpleOption<std::string>>("mbox_conf_flatten", "");
+        mbox_conf_flatten_opt->set_help("Filename containing the reference values for the graph branch mbox_conf_flatten.");
+        mbox_priorbox_opt = cmd_parser.add_option<SimpleOption<std::string>>("mbox_priorbox", "");
+        mbox_priorbox_opt->set_help("Filename containing the reference values for the graph branch mbox_priorbox.");
+    }
+    GraphSSDMobilenetExample(const GraphSSDMobilenetExample &) = delete;
+    GraphSSDMobilenetExample &operator=(const GraphSSDMobilenetExample &) = delete;
+    GraphSSDMobilenetExample(GraphSSDMobilenetExample &&)                 = default; // NOLINT
+    GraphSSDMobilenetExample &operator=(GraphSSDMobilenetExample &&) = default;      // NOLINT
+    ~GraphSSDMobilenetExample() 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 input descriptor
+        const TensorShape tensor_shape     = permute_shape(TensorShape(300, 300, 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 graph hints
+        graph << common_params.target
+              << DepthwiseConvolutionMethod::Optimized3x3 // FIXME(COMPMID-1073): Add heuristics to automatically call the optimized 3x3 method
+              << common_params.fast_math_hint;
+
+        // Create core graph
+        std::string model_path = "/cnn_data/ssd_mobilenet_model/";
+
+        // Create a preprocessor object
+        const std::array<float, 3> mean_rgb{ { 127.5f, 127.5f, 127.5f } };
+        std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb, 0.007843f);
+
+        // 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;
+        }
+
+        graph << InputLayer(input_descriptor,
+                            get_input_accessor(common_params, std::move(preprocessor)));
+
+        SubStream conv_11(graph);
+        conv_11 << ConvolutionLayer(
+                    3U, 3U, 32U,
+                    get_weights_accessor(data_path, "conv0_w.npy"),
+                    std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+                    PadStrideInfo(2, 2, 1, 1))
+                .set_name("conv0");
+        conv_11 << BatchNormalizationLayer(get_weights_accessor(data_path, "conv0_bn_mean.npy"),
+                                           get_weights_accessor(data_path, "conv0_bn_var.npy"),
+                                           get_weights_accessor(data_path, "conv0_scale_w.npy"),
+                                           get_weights_accessor(data_path, "conv0_scale_b.npy"), 0.00001f)
+                .set_name("conv0/bn")
+                << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv0/relu");
+
+        conv_11 << get_node_A(conv_11, data_path, "conv1", 64, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0));
+        conv_11 << get_node_A(conv_11, data_path, "conv2", 128, PadStrideInfo(2, 2, 1, 1), PadStrideInfo(1, 1, 0, 0));
+        conv_11 << get_node_A(conv_11, data_path, "conv3", 128, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0));
+        conv_11 << get_node_A(conv_11, data_path, "conv4", 256, PadStrideInfo(2, 2, 1, 1), PadStrideInfo(1, 1, 0, 0));
+        conv_11 << get_node_A(conv_11, data_path, "conv5", 256, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0));
+        conv_11 << get_node_A(conv_11, data_path, "conv6", 512, PadStrideInfo(2, 2, 1, 1), PadStrideInfo(1, 1, 0, 0));
+        conv_11 << get_node_A(conv_11, data_path, "conv7", 512, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0));
+        conv_11 << get_node_A(conv_11, data_path, "conv8", 512, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0));
+        conv_11 << get_node_A(conv_11, data_path, "conv9", 512, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0));
+        conv_11 << get_node_A(conv_11, data_path, "conv10", 512, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0));
+        conv_11 << get_node_A(conv_11, data_path, "conv11", 512, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0));
+
+        SubStream conv_13(conv_11);
+        conv_13 << get_node_A(conv_11, data_path, "conv12", 1024, PadStrideInfo(2, 2, 1, 1), PadStrideInfo(1, 1, 0, 0));
+        conv_13 << get_node_A(conv_13, data_path, "conv13", 1024, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0));
+
+        SubStream conv_14(conv_13);
+        conv_14 << get_node_B(conv_13, data_path, "conv14", 512, PadStrideInfo(1, 1, 0, 0), PadStrideInfo(2, 2, 1, 1));
+
+        SubStream conv_15(conv_14);
+        conv_15 << get_node_B(conv_14, data_path, "conv15", 256, PadStrideInfo(1, 1, 0, 0), PadStrideInfo(2, 2, 1, 1));
+
+        SubStream conv_16(conv_15);
+        conv_16 << get_node_B(conv_15, data_path, "conv16", 256, PadStrideInfo(1, 1, 0, 0), PadStrideInfo(2, 2, 1, 1));
+
+        SubStream conv_17(conv_16);
+        conv_17 << get_node_B(conv_16, data_path, "conv17", 128, PadStrideInfo(1, 1, 0, 0), PadStrideInfo(2, 2, 1, 1));
+
+        //mbox_loc
+        SubStream conv_11_mbox_loc(conv_11);
+        conv_11_mbox_loc << get_node_C(conv_11, data_path, "conv11_mbox_loc", 12, PadStrideInfo(1, 1, 0, 0));
+
+        SubStream conv_13_mbox_loc(conv_13);
+        conv_13_mbox_loc << get_node_C(conv_13, data_path, "conv13_mbox_loc", 24, PadStrideInfo(1, 1, 0, 0));
+
+        SubStream conv_14_2_mbox_loc(conv_14);
+        conv_14_2_mbox_loc << get_node_C(conv_14, data_path, "conv14_2_mbox_loc", 24, PadStrideInfo(1, 1, 0, 0));
+
+        SubStream conv_15_2_mbox_loc(conv_15);
+        conv_15_2_mbox_loc << get_node_C(conv_15, data_path, "conv15_2_mbox_loc", 24, PadStrideInfo(1, 1, 0, 0));
+
+        SubStream conv_16_2_mbox_loc(conv_16);
+        conv_16_2_mbox_loc << get_node_C(conv_16, data_path, "conv16_2_mbox_loc", 24, PadStrideInfo(1, 1, 0, 0));
+
+        SubStream conv_17_2_mbox_loc(conv_17);
+        conv_17_2_mbox_loc << get_node_C(conv_17, data_path, "conv17_2_mbox_loc", 24, PadStrideInfo(1, 1, 0, 0));
+
+        SubStream mbox_loc(graph);
+        mbox_loc << ConcatLayer(std::move(conv_11_mbox_loc), std::move(conv_13_mbox_loc), conv_14_2_mbox_loc, std::move(conv_15_2_mbox_loc),
+                                std::move(conv_16_2_mbox_loc), std::move(conv_17_2_mbox_loc));
+
+        mbox_loc << OutputLayer(get_npy_output_accessor(mbox_loc_opt->value(), TensorShape(7668U), DataType::F32));
+
+        //mbox_conf
+        SubStream conv_11_mbox_conf(conv_11);
+        conv_11_mbox_conf << get_node_C(conv_11, data_path, "conv11_mbox_conf", 63, PadStrideInfo(1, 1, 0, 0));
+
+        SubStream conv_13_mbox_conf(conv_13);
+        conv_13_mbox_conf << get_node_C(conv_13, data_path, "conv13_mbox_conf", 126, PadStrideInfo(1, 1, 0, 0));
+
+        SubStream conv_14_2_mbox_conf(conv_14);
+        conv_14_2_mbox_conf << get_node_C(conv_14, data_path, "conv14_2_mbox_conf", 126, PadStrideInfo(1, 1, 0, 0));
+
+        SubStream conv_15_2_mbox_conf(conv_15);
+        conv_15_2_mbox_conf << get_node_C(conv_15, data_path, "conv15_2_mbox_conf", 126, PadStrideInfo(1, 1, 0, 0));
+
+        SubStream conv_16_2_mbox_conf(conv_16);
+        conv_16_2_mbox_conf << get_node_C(conv_16, data_path, "conv16_2_mbox_conf", 126, PadStrideInfo(1, 1, 0, 0));
+
+        SubStream conv_17_2_mbox_conf(conv_17);
+        conv_17_2_mbox_conf << get_node_C(conv_17, data_path, "conv17_2_mbox_conf", 126, PadStrideInfo(1, 1, 0, 0));
+
+        SubStream mbox_conf(graph);
+        mbox_conf << ConcatLayer(std::move(conv_11_mbox_conf), std::move(conv_13_mbox_conf), std::move(conv_14_2_mbox_conf),
+                                 std::move(conv_15_2_mbox_conf), std::move(conv_16_2_mbox_conf), std::move(conv_17_2_mbox_conf));
+        mbox_conf << ReshapeLayer(TensorShape(21U, 1917U)).set_name("mbox_conf/reshape");
+        mbox_conf << SoftmaxLayer().set_name("mbox_conf/softmax");
+        mbox_conf << FlattenLayer().set_name("mbox_conf/flat");
+
+        mbox_conf << OutputLayer(get_npy_output_accessor(mbox_conf_flatten_opt->value(), TensorShape(40257U), DataType::F32));
+
+        const std::vector<float> priorbox_variances     = { 0.1f, 0.1f, 0.2f, 0.2f };
+        const float              priorbox_offset        = 0.5f;
+        const std::vector<float> priorbox_aspect_ratios = { 2.f, 3.f };
+
+        //mbox_priorbox branch
+        SubStream conv_11_mbox_priorbox(conv_11);
+
+        conv_11_mbox_priorbox << PriorBoxLayer(SubStream(graph),
+                                               PriorBoxLayerInfo({ 60.f }, priorbox_variances, priorbox_offset, true, false, {}, { 2.f }))
+                              .set_name("conv11/priorbox");
+
+        SubStream conv_13_mbox_priorbox(conv_13);
+        conv_13_mbox_priorbox << PriorBoxLayer(SubStream(graph),
+                                               PriorBoxLayerInfo({ 105.f }, priorbox_variances, priorbox_offset, true, false, { 150.f }, priorbox_aspect_ratios))
+                              .set_name("conv13/priorbox");
+
+        SubStream conv_14_2_mbox_priorbox(conv_14);
+        conv_14_2_mbox_priorbox << PriorBoxLayer(SubStream(graph),
+                                                 PriorBoxLayerInfo({ 150.f }, priorbox_variances, priorbox_offset, true, false, { 195.f }, priorbox_aspect_ratios))
+                                .set_name("conv14/priorbox");
+
+        SubStream conv_15_2_mbox_priorbox(conv_15);
+        conv_15_2_mbox_priorbox << PriorBoxLayer(SubStream(graph),
+                                                 PriorBoxLayerInfo({ 195.f }, priorbox_variances, priorbox_offset, true, false, { 240.f }, priorbox_aspect_ratios))
+                                .set_name("conv15/priorbox");
+
+        SubStream conv_16_2_mbox_priorbox(conv_16);
+        conv_16_2_mbox_priorbox << PriorBoxLayer(SubStream(graph),
+                                                 PriorBoxLayerInfo({ 240.f }, priorbox_variances, priorbox_offset, true, false, { 285.f }, priorbox_aspect_ratios))
+                                .set_name("conv16/priorbox");
+
+        SubStream conv_17_2_mbox_priorbox(conv_17);
+        conv_17_2_mbox_priorbox << PriorBoxLayer(SubStream(graph),
+                                                 PriorBoxLayerInfo({ 285.f }, priorbox_variances, priorbox_offset, true, false, { 300.f }, priorbox_aspect_ratios))
+                                .set_name("conv17/priorbox");
+
+        SubStream mbox_priorbox(graph);
+
+        mbox_priorbox << ConcatLayer(
+                          (common_params.data_layout == DataLayout::NCHW) ? DataLayoutDimension::WIDTH : DataLayoutDimension::CHANNEL,
+                          std::move(conv_11_mbox_priorbox), std::move(conv_13_mbox_priorbox), std::move(conv_14_2_mbox_priorbox),
+                          std::move(conv_15_2_mbox_priorbox), std::move(conv_16_2_mbox_priorbox), std::move(conv_17_2_mbox_priorbox));
+
+        mbox_priorbox << OutputLayer(get_npy_output_accessor(mbox_priorbox_opt->value(), TensorShape(7668U, 2U, 1U), DataType::F32));
+
+        // 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;
+
+    SimpleOption<std::string> *mbox_loc_opt{ nullptr };
+    SimpleOption<std::string> *mbox_conf_flatten_opt{ nullptr };
+    SimpleOption<std::string> *mbox_priorbox_opt{ nullptr };
+
+    CommonGraphParams common_params;
+    Stream            graph;
+
+    ConcatLayer get_node_A(IStream &master_graph, const std::string &data_path, std::string &&param_path,
+                           unsigned int  conv_filt,
+                           PadStrideInfo dwc_pad_stride_info, PadStrideInfo conv_pad_stride_info)
+    {
+        const std::string total_path = param_path + "_";
+        SubStream         sg(master_graph);
+
+        sg << DepthwiseConvolutionLayer(
+               3U, 3U,
+               get_weights_accessor(data_path, total_path + "dw_w.npy"),
+               std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+               dwc_pad_stride_info)
+           .set_name(param_path + "/dw")
+           << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "dw_bn_mean.npy"),
+                                      get_weights_accessor(data_path, total_path + "dw_bn_var.npy"),
+                                      get_weights_accessor(data_path, total_path + "dw_scale_w.npy"),
+                                      get_weights_accessor(data_path, total_path + "dw_scale_b.npy"), 0.00001f)
+           .set_name(param_path + "/dw/bn")
+           << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "dw/relu")
+
+           << ConvolutionLayer(
+               1U, 1U, conv_filt,
+               get_weights_accessor(data_path, total_path + "w.npy"),
+               std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+               conv_pad_stride_info)
+           .set_name(param_path + "/pw")
+           << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "bn_mean.npy"),
+                                      get_weights_accessor(data_path, total_path + "bn_var.npy"),
+                                      get_weights_accessor(data_path, total_path + "scale_w.npy"),
+                                      get_weights_accessor(data_path, total_path + "scale_b.npy"), 0.00001f)
+           .set_name(param_path + "/pw/bn")
+           << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "pw/relu");
+
+        return ConcatLayer(std::move(sg));
+    }
+
+    ConcatLayer get_node_B(IStream &master_graph, const std::string &data_path, std::string &&param_path,
+                           unsigned int  conv_filt,
+                           PadStrideInfo conv_pad_stride_info_1, PadStrideInfo conv_pad_stride_info_2)
+    {
+        const std::string total_path = param_path + "_";
+        SubStream         sg(master_graph);
+
+        sg << ConvolutionLayer(
+               1, 1, conv_filt / 2,
+               get_weights_accessor(data_path, total_path + "1_w.npy"),
+               std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+               conv_pad_stride_info_1)
+           .set_name(total_path + "1/conv")
+           << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "1_bn_mean.npy"),
+                                      get_weights_accessor(data_path, total_path + "1_bn_var.npy"),
+                                      get_weights_accessor(data_path, total_path + "1_scale_w.npy"),
+                                      get_weights_accessor(data_path, total_path + "1_scale_b.npy"), 0.00001f)
+           .set_name(total_path + "1/bn")
+           << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(total_path + "1/relu");
+
+        sg << ConvolutionLayer(
+               3, 3, conv_filt,
+               get_weights_accessor(data_path, total_path + "2_w.npy"),
+               std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+               conv_pad_stride_info_2)
+           .set_name(total_path + "2/conv")
+           << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "2_bn_mean.npy"),
+                                      get_weights_accessor(data_path, total_path + "2_bn_var.npy"),
+                                      get_weights_accessor(data_path, total_path + "2_scale_w.npy"),
+                                      get_weights_accessor(data_path, total_path + "2_scale_b.npy"), 0.00001f)
+           .set_name(total_path + "2/bn")
+           << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(total_path + "2/relu");
+
+        return ConcatLayer(std::move(sg));
+    }
+
+    ConcatLayer get_node_C(IStream &master_graph, const std::string &data_path, std::string &&param_path,
+                           unsigned int conv_filt, PadStrideInfo conv_pad_stride_info)
+    {
+        const std::string total_path = param_path + "_";
+        SubStream         sg(master_graph);
+        sg << ConvolutionLayer(
+               1U, 1U, conv_filt,
+               get_weights_accessor(data_path, total_path + "w.npy"),
+               get_weights_accessor(data_path, total_path + "b.npy"),
+               conv_pad_stride_info)
+           .set_name(param_path + "/conv");
+        if(common_params.data_layout == DataLayout::NCHW)
+        {
+            sg << PermuteLayer(PermutationVector(2U, 0U, 1U), DataLayout::NHWC).set_name(param_path + "/perm");
+        }
+        sg << FlattenLayer().set_name(param_path + "/flat");
+
+        return ConcatLayer(std::move(sg));
+    }
+};
+
+/** Main program for MobileNetSSD
+ *
+ * Model is based on:
+ *      http://arxiv.org/abs/1512.02325
+ *      SSD: Single Shot MultiBox Detector
+ *      Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, Alexander C. Berg
+ *
+ * @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<GraphSSDMobilenetExample>(argc, argv);
+}