COMPMID-889 - Implement Squeezenet 1.1 as a graph example

Change-Id: I12d4af007c123b19925ceb5e3c84285e096bc13b
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/118718
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>

1 files changed, 215 insertions, 0 deletions

diff --git a/examples/graph_squeezenet_v1_1.cpp b/examples/graph_squeezenet_v1_1.cpp
new file mode 100644
index 0000000000..fad07e5043
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+++ b/examples/graph_squeezenet_v1_1.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/Graph.h"
+#include "arm_compute/graph/Nodes.h"
+#include "arm_compute/graph/SubGraph.h"
+#include "support/ToolchainSupport.h"
+#include "utils/GraphUtils.h"
+#include "utils/Utils.h"
+
+#include <cstdlib>
+#include <tuple>
+
+using namespace arm_compute::utils;
+using namespace arm_compute::graph;
+using namespace arm_compute::graph_utils;
+using namespace arm_compute::logging;
+
+namespace
+{
+} // namespace
+
+/** Example demonstrating how to implement Squeezenet's v1.1 network using the Compute Library's graph API
+ *
+ * @param[in] argc Number of arguments
+ * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels )
+ */
+class GraphSqueezenet_v1_1Example : public Example
+{
+public:
+    void do_setup(int argc, char **argv) override
+    {
+        std::string data_path; /* Path to the trainable data */
+        std::string image;     /* Image data */
+        std::string label;     /* Label data */
+
+        constexpr float mean_r = 122.68f; /* Mean value to subtract from red channel */
+        constexpr float mean_g = 116.67f; /* Mean value to subtract from green channel */
+        constexpr float mean_b = 104.01f; /* Mean value to subtract from blue channel */
+
+        // Set target. 0 (NEON), 1 (OpenCL). By default it is NEON
+        TargetHint target_hint = set_target_hint(argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0);
+
+        // Parse arguments
+        if(argc < 2)
+        {
+            // Print help
+            std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels]\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] [image] [labels]\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] << " [image] [labels]\n\n";
+            std::cout << "No image provided: using random values\n\n";
+        }
+        else if(argc == 4)
+        {
+            data_path = argv[2];
+            image     = argv[3];
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels]\n\n";
+            std::cout << "No text file with labels provided: skipping output accessor\n\n";
+        }
+        else
+        {
+            data_path = argv[2];
+            image     = argv[3];
+            label     = argv[4];
+        }
+
+        graph << target_hint
+              << Tensor(TensorInfo(TensorShape(227U, 227U, 3U, 1U), 1, DataType::F32),
+                        get_input_accessor(image, mean_r, mean_g, mean_b))
+              << ConvolutionLayer(
+                  3U, 3U, 64U,
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/conv1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_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_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire2_squeeze1x1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_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_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire3_squeeze1x1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire3_squeeze1x1_b.npy"),
+                  PadStrideInfo(1, 1, 0, 0))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << get_expand_fire_node(data_path, "fire3", 64U, 64U)
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
+              << ConvolutionLayer(
+                  1U, 1U, 32U,
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire4_squeeze1x1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire4_squeeze1x1_b.npy"),
+                  PadStrideInfo(1, 1, 0, 0))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << get_expand_fire_node(data_path, "fire4", 128U, 128U)
+              << ConvolutionLayer(
+                  1U, 1U, 32U,
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire5_squeeze1x1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire5_squeeze1x1_b.npy"),
+                  PadStrideInfo(1, 1, 0, 0))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << get_expand_fire_node(data_path, "fire5", 128U, 128U)
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
+              << ConvolutionLayer(
+                  1U, 1U, 48U,
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire6_squeeze1x1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_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_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire7_squeeze1x1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_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_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire8_squeeze1x1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire8_squeeze1x1_b.npy"),
+                  PadStrideInfo(1, 1, 0, 0))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << get_expand_fire_node(data_path, "fire8", 256U, 256U)
+              << ConvolutionLayer(
+                  1U, 1U, 64U,
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire9_squeeze1x1_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_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_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/conv10_w.npy"),
+                  get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/conv10_b.npy"),
+                  PadStrideInfo(1, 1, 0, 0))
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+              << PoolingLayer(PoolingLayerInfo(PoolingType::AVG))
+              << FlattenLayer()
+              << SoftmaxLayer()
+              << Tensor(get_output_accessor(label, 5));
+    }
+    void do_run() override
+    {
+        // Run graph
+        graph.run();
+    }
+
+private:
+    Graph graph{};
+
+    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_1_model/" + param_path + "_";
+        SubGraph    i_a;
+        i_a << ConvolutionLayer(
+                1U, 1U, expand1_filt,
+                get_weights_accessor(data_path, total_path + "expand1x1_w.npy"),
+                get_weights_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_weights_accessor(data_path, total_path + "expand3x3_w.npy"),
+                get_weights_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));
+    }
+};
+
+/** Main program for Squeezenet v1.1
+ *
+ * @param[in] argc Number of arguments
+ * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels )
+ */
+int main(int argc, char **argv)
+{
+    return arm_compute::utils::run_example<GraphSqueezenet_v1_1Example>(argc, argv);
+}