COMPMID-553: Add squeezenet example

Change-Id: Id233f0c1c329ee0d5ee93166d4aa0718f7d629b7
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/92337
Reviewed-by: Michalis Spyrou <michalis.spyrou@arm.com>
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>

1 files changed, 217 insertions, 0 deletions

diff --git a/examples/graph_squeezenet.cpp b/examples/graph_squeezenet.cpp
new file mode 100644
index 0000000000..c550df2c0f
--- /dev/null
+++ b/examples/graph_squeezenet.cpp
@@ -0,0 +1,217 @@
+/*
+ * Copyright (c) 2017 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.
+ */
+#ifndef ARM_COMPUTE_CL /* Needed by Utils.cpp to handle OpenCL exceptions properly */
+#error "This example needs to be built with -DARM_COMPUTE_CL"
+#endif /* ARM_COMPUTE_CL */
+
+#include "arm_compute/core/Logger.h"
+#include "arm_compute/graph/Graph.h"
+#include "arm_compute/graph/Nodes.h"
+#include "arm_compute/graph/SubGraph.h"
+#include "arm_compute/runtime/CL/CLScheduler.h"
+#include "arm_compute/runtime/Scheduler.h"
+#include "support/ToolchainSupport.h"
+#include "utils/GraphUtils.h"
+#include "utils/Utils.h"
+
+#include <cstdlib>
+#include <iostream>
+#include <memory>
+#include <tuple>
+
+using namespace arm_compute::graph;
+using namespace arm_compute::graph_utils;
+
+/** Generates appropriate accessor according to the specified path
+ *
+ * @note If path is empty will generate a DummyAccessor else will generate a NumPyBinLoader
+ *
+ * @param path       Path to the data files
+ * @param data_file  Relative path to the data files from path
+ *
+ * @return An appropriate tensor accessor
+ */
+std::unique_ptr<ITensorAccessor> get_accessor(const std::string &path, const std::string &data_file)
+{
+    if(path.empty())
+    {
+        return arm_compute::support::cpp14::make_unique<DummyAccessor>();
+    }
+    else
+    {
+        return arm_compute::support::cpp14::make_unique<NumPyBinLoader>(path + data_file);
+    }
+}
+
+BranchLayer get_expand_fire_node(const std::string &data_path, std::string &&param_path, unsigned int expand1_filt, unsigned int expand3_filt)
+{
+    std::string total_path = "/cnn_data/squeezenet_v1.0_model/" + param_path + "_";
+    SubGraph    i_a;
+    i_a << ConvolutionLayer(
+            1U, 1U, expand1_filt,
+            get_accessor(data_path, total_path + "expand1x1_w.npy"),
+            get_accessor(data_path, total_path + "expand1x1_b.npy"),
+            PadStrideInfo(1, 1, 0, 0))
+        << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+
+    SubGraph i_b;
+    i_b << ConvolutionLayer(
+            3U, 3U, expand3_filt,
+            get_accessor(data_path, total_path + "expand3x3_w.npy"),
+            get_accessor(data_path, total_path + "expand3x3_b.npy"),
+            PadStrideInfo(1, 1, 1, 1))
+        << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+
+    return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b));
+}
+
+/** Example demonstrating how to implement Squeezenet's network using the Compute Library's graph API
+ *
+ * @param[in] argc Number of arguments
+ * @param[in] argv Arguments ( [optional] Path to the weights folder, [optional] batches )
+ */
+void main_graph_squeezenet(int argc, const char **argv)
+{
+    std::string  data_path;   /** Path to the trainable data */
+    unsigned int batches = 4; /** Number of batches */
+
+    // Parse arguments
+    if(argc < 2)
+    {
+        // Print help
+        std::cout << "Usage: " << argv[0] << " [path_to_data] [batches]\n\n";
+        std::cout << "No data folder provided: using random values\n\n";
+    }
+    else if(argc == 2)
+    {
+        //Do something with argv[1]
+        data_path = argv[1];
+        std::cout << "Usage: " << argv[0] << " [path_to_data] [batches]\n\n";
+        std::cout << "No number of batches where specified, thus will use the default : " << batches << "\n\n";
+    }
+    else
+    {
+        //Do something with argv[1] and argv[2]
+        data_path = argv[1];
+        batches   = std::strtol(argv[2], nullptr, 0);
+    }
+
+    // Check if OpenCL is available and initialize the scheduler
+    if(arm_compute::opencl_is_available())
+    {
+        arm_compute::CLScheduler::get().default_init();
+    }
+
+    Graph graph;
+    arm_compute::Logger::get().set_logger(std::cout, arm_compute::LoggerVerbosity::INFO);
+
+    graph << TargetHint::OPENCL
+          << Tensor(TensorInfo(TensorShape(224U, 224U, 3U, batches), 1, DataType::F32), DummyAccessor())
+          << ConvolutionLayer(
+              7U, 7U, 96U,
+              get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv1_w.npy"),
+              get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv1_b.npy"),
+              PadStrideInfo(2, 2, 0, 0))
+          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+          << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
+          << ConvolutionLayer(
+              1U, 1U, 16U,
+              get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire2_squeeze1x1_w.npy"),
+              get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire2_squeeze1x1_b.npy"),
+              PadStrideInfo(1, 1, 0, 0))
+          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+          << get_expand_fire_node(data_path, "fire2", 64U, 64U)
+          << ConvolutionLayer(
+              1U, 1U, 16U,
+              get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire3_squeeze1x1_w.npy"),
+              get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire3_squeeze1x1_b.npy"),
+              PadStrideInfo(1, 1, 0, 0))
+          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+          << get_expand_fire_node(data_path, "fire3", 64U, 64U)
+          << ConvolutionLayer(
+              1U, 1U, 32U,
+              get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire4_squeeze1x1_w.npy"),
+              get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire4_squeeze1x1_b.npy"),
+              PadStrideInfo(1, 1, 0, 0))
+          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+          << get_expand_fire_node(data_path, "fire4", 128U, 128U)
+          << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
+          << ConvolutionLayer(
+              1U, 1U, 32U,
+              get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire5_squeeze1x1_w.npy"),
+              get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire5_squeeze1x1_b.npy"),
+              PadStrideInfo(1, 1, 0, 0))
+          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+          << get_expand_fire_node(data_path, "fire5", 128U, 128U)
+          << ConvolutionLayer(
+              1U, 1U, 48U,
+              get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire6_squeeze1x1_w.npy"),
+              get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire6_squeeze1x1_b.npy"),
+              PadStrideInfo(1, 1, 0, 0))
+          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+          << get_expand_fire_node(data_path, "fire6", 192U, 192U)
+          << ConvolutionLayer(
+              1U, 1U, 48U,
+              get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire7_squeeze1x1_w.npy"),
+              get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire7_squeeze1x1_b.npy"),
+              PadStrideInfo(1, 1, 0, 0))
+          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+          << get_expand_fire_node(data_path, "fire7", 192U, 192U)
+          << ConvolutionLayer(
+              1U, 1U, 64U,
+              get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire8_squeeze1x1_w.npy"),
+              get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire8_squeeze1x1_b.npy"),
+              PadStrideInfo(1, 1, 0, 0))
+          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+          << get_expand_fire_node(data_path, "fire8", 256U, 256U)
+          << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
+          << ConvolutionLayer(
+              1U, 1U, 64U,
+              get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire9_squeeze1x1_w.npy"),
+              get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire9_squeeze1x1_b.npy"),
+              PadStrideInfo(1, 1, 0, 0))
+          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+          << get_expand_fire_node(data_path, "fire9", 256U, 256U)
+          << ConvolutionLayer(
+              1U, 1U, 1000U,
+              get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv10_w.npy"),
+              get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv10_b.npy"),
+              PadStrideInfo(1, 1, 0, 0))
+          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+          << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 13, PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL)))
+          << SoftmaxLayer()
+          << Tensor(DummyAccessor());
+
+    graph.run();
+}
+
+/** Main program for Squeezenet v1.0
+ *
+ * @param[in] argc Number of arguments
+ * @param[in] argv Arguments ( [optional] Path to the weights folder, [optional] batches )
+ */
+int main(int argc, const char **argv)
+{
+    return arm_compute::utils::run_example(argc, argv, main_graph_squeezenet);
+}