COMPMID-645: MobileNet network and benchmark

Change-Id: Id9c50abdbd28e7e95fd460240cec51af1d6f1036
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/95580
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>

3 files changed, 443 insertions, 0 deletions

diff --git a/tests/benchmark/CL/SYSTEM/MobileNet.cpp b/tests/benchmark/CL/SYSTEM/MobileNet.cpp
new file mode 100644
index 0000000000..c745a0acab
--- /dev/null
+++ b/tests/benchmark/CL/SYSTEM/MobileNet.cpp
@@ -0,0 +1,62 @@
+/*
+ * 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.
+ */
+#include "arm_compute/core/TensorShape.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/runtime/CL/CLTensor.h"
+#include "arm_compute/runtime/CL/CLTensorAllocator.h"
+#include "arm_compute/runtime/CL/functions/CLActivationLayer.h"
+#include "arm_compute/runtime/CL/functions/CLConvolutionLayer.h"
+#include "arm_compute/runtime/CL/functions/CLDepthwiseConvolution.h"
+#include "arm_compute/runtime/CL/functions/CLDirectConvolutionLayer.h"
+#include "arm_compute/runtime/CL/functions/CLPoolingLayer.h"
+#include "arm_compute/runtime/CL/functions/CLReshapeLayer.h"
+#include "tests/CL/CLAccessor.h"
+#include "tests/benchmark/fixtures/MobileNetFixture.h"
+#include "tests/framework/Macros.h"
+#include "tests/framework/datasets/Datasets.h"
+#include "utils/TypePrinter.h"
+
+namespace arm_compute
+{
+namespace test
+{
+using CLMobileNetFixture = MobileNetFixture<CLTensor,
+      CLAccessor,
+      CLActivationLayer,
+      CLConvolutionLayer,
+      CLDirectConvolutionLayer,
+      CLDepthwiseConvolution,
+      CLReshapeLayer,
+      CLPoolingLayer>;
+
+TEST_SUITE(CL)
+TEST_SUITE(SYSTEM_TEST)
+
+REGISTER_FIXTURE_DATA_TEST_CASE(MobileNet, CLMobileNetFixture, framework::DatasetMode::ALL,
+                                framework::dataset::make("Batches", { 1, 4, 8 }));
+
+TEST_SUITE_END()
+TEST_SUITE_END()
+} // namespace test
+} // namespace arm_compute
diff --git a/tests/benchmark/fixtures/MobileNetFixture.h b/tests/benchmark/fixtures/MobileNetFixture.h
new file mode 100644
index 0000000000..6c1ee300c1
--- /dev/null
+++ b/tests/benchmark/fixtures/MobileNetFixture.h
@@ -0,0 +1,79 @@
+/*
+ * 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_TEST_MOBILENETFIXTURE
+#define ARM_COMPUTE_TEST_MOBILENETFIXTURE
+
+#include "tests/AssetsLibrary.h"
+#include "tests/Utils.h"
+#include "tests/framework/Fixture.h"
+#include "tests/networks/MobileNetNetwork.h"
+
+namespace arm_compute
+{
+namespace test
+{
+template <typename TensorType,
+          typename Accessor,
+          typename ActivationLayerFunction,
+          typename ConvolutionLayerFunction,
+          typename DirectConvolutionLayerFunction,
+          typename DepthwiseConvolutionFunction,
+          typename ReshapeFunction,
+          typename PoolingLayerFunction>
+class MobileNetFixture : public framework::Fixture
+{
+public:
+    template <typename...>
+    void setup(int batches)
+    {
+        network.init(batches);
+        network.build();
+        network.allocate();
+        network.fill_random();
+    }
+
+    void run()
+    {
+        network.run();
+    }
+
+    void teardown()
+    {
+        network.clear();
+    }
+
+private:
+    networks::MobileNetNetwork<TensorType,
+             Accessor,
+             ActivationLayerFunction,
+             ConvolutionLayerFunction,
+             DirectConvolutionLayerFunction,
+             DepthwiseConvolutionFunction,
+             ReshapeFunction,
+             PoolingLayerFunction>
+             network{};
+};
+} // namespace test
+} // namespace arm_compute
+#endif /* ARM_COMPUTE_TEST_MOBILENETFIXTURE */
diff --git a/tests/networks/MobileNetNetwork.h b/tests/networks/MobileNetNetwork.h
new file mode 100644
index 0000000000..74dce0e348
--- /dev/null
+++ b/tests/networks/MobileNetNetwork.h
@@ -0,0 +1,302 @@
+/*
+ * 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_TEST_MODEL_OBJECTS_MOBILENET_H__
+#define __ARM_COMPUTE_TEST_MODEL_OBJECTS_MOBILENET_H__
+
+#include "tests/AssetsLibrary.h"
+#include "tests/Globals.h"
+#include "tests/Utils.h"
+
+#include "utils/Utils.h"
+
+#include <memory>
+
+using namespace arm_compute;
+using namespace arm_compute::test;
+
+namespace arm_compute
+{
+namespace test
+{
+namespace networks
+{
+/** MobileNet model object */
+template <typename TensorType,
+          typename Accessor,
+          typename ActivationLayerFunction,
+          typename ConvolutionLayerFunction,
+          typename DirectConvolutionLayerFunction,
+          typename DepthwiseConvolutionFunction,
+          typename ReshapeFunction,
+          typename PoolingLayerFunction>
+class MobileNetNetwork
+{
+public:
+    void init(int batches)
+    {
+        _batches = batches;
+
+        // Initialize input, output
+        input.allocator()->init(TensorInfo(TensorShape(224U, 224U, 3U, _batches), 1, DataType::F32));
+        output.allocator()->init(TensorInfo(TensorShape(11U, _batches), 1, DataType::F32));
+        // Initialize weights and biases
+        w_conv3x3.allocator()->init(TensorInfo(TensorShape(3U, 3U, 3U, 16U), 1, DataType::F32));
+        b_conv3x3.allocator()->init(TensorInfo(TensorShape(16U), 1, DataType::F32));
+        depthwise_conv_block_init(0, 16, 16);
+        depthwise_conv_block_init(1, 16, 32);
+        depthwise_conv_block_init(2, 32, 32);
+        depthwise_conv_block_init(3, 32, 64);
+        depthwise_conv_block_init(4, 64, 64);
+        depthwise_conv_block_init(5, 64, 128);
+        depthwise_conv_block_init(6, 128, 128);
+        depthwise_conv_block_init(7, 128, 128);
+        depthwise_conv_block_init(8, 128, 128);
+        depthwise_conv_block_init(9, 128, 128);
+        depthwise_conv_block_init(10, 128, 128);
+        depthwise_conv_block_init(11, 128, 256);
+        depthwise_conv_block_init(12, 256, 256);
+        w_conv[13].allocator()->init(TensorInfo(TensorShape(1U, 1U, 256U, 11U), 1, DataType::F32));
+        b_conv[13].allocator()->init(TensorInfo(TensorShape(11U), 1, DataType::F32));
+    }
+
+    /** Build the model. */
+    void build()
+    {
+        // Configure Layers
+        conv3x3.configure(&input, &w_conv3x3, &b_conv3x3, &conv_out[0], PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR));
+        conv3x3_act.configure(&conv_out[0], nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f));
+        depthwise_conv_block_build(0, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0));
+        depthwise_conv_block_build(1, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0));
+        depthwise_conv_block_build(2, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0));
+        depthwise_conv_block_build(3, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0));
+        depthwise_conv_block_build(4, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0));
+        depthwise_conv_block_build(5, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0));
+        depthwise_conv_block_build(6, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0));
+        depthwise_conv_block_build(7, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0));
+        depthwise_conv_block_build(8, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0));
+        depthwise_conv_block_build(9, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0));
+        depthwise_conv_block_build(10, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0));
+        depthwise_conv_block_build(11, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0));
+        depthwise_conv_block_build(12, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0));
+        pool.configure(&conv_out[13], &pool_out, PoolingLayerInfo(PoolingType::AVG, 7, PadStrideInfo(2, 2, 0, 0)));
+        conv1x1[13].configure(&pool_out, &w_conv[13], &b_conv[13], &conv_out[14], PadStrideInfo(1, 1, 0, 0));
+        logistic.configure(&conv_out[14], nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
+        reshape.configure(&conv_out[14], &output);
+    }
+
+    void allocate()
+    {
+        input.allocator()->allocate();
+        output.allocator()->allocate();
+
+        w_conv3x3.allocator()->allocate();
+        b_conv3x3.allocator()->allocate();
+        for(unsigned int i = 0; i < w_conv.size(); ++i)
+        {
+            w_conv[i].allocator()->allocate();
+            b_conv[i].allocator()->allocate();
+        }
+        for(unsigned int i = 0; i < w_dwc.size(); ++i)
+        {
+            w_dwc[i].allocator()->allocate();
+            b_dwc[i].allocator()->allocate();
+        }
+        for(auto &o : conv_out)
+        {
+            o.allocator()->allocate();
+        }
+        for(auto &o : dwc_out)
+        {
+            o.allocator()->allocate();
+        }
+        pool_out.allocator()->allocate();
+    }
+
+    /** Fills the trainable parameters and input with random data. */
+    void fill_random()
+    {
+        unsigned int                     seed_idx = 0;
+        std::uniform_real_distribution<> distribution(-1, 1);
+        library->fill(Accessor(input), distribution, seed_idx++);
+
+        library->fill(Accessor(w_conv3x3), distribution, seed_idx++);
+        library->fill(Accessor(b_conv3x3), distribution, seed_idx++);
+        for(unsigned int i = 0; i < w_conv.size(); ++i)
+        {
+            library->fill(Accessor(w_conv[i]), distribution, seed_idx++);
+            library->fill(Accessor(b_conv[i]), distribution, seed_idx++);
+        }
+        for(unsigned int i = 0; i < w_dwc.size(); ++i)
+        {
+            library->fill(Accessor(w_dwc[i]), distribution, seed_idx++);
+            library->fill(Accessor(b_dwc[i]), distribution, seed_idx++);
+        }
+    }
+
+    /** Feed input to network from file.
+     *
+     * @param name File name of containing the input data.
+     */
+    void feed(std::string name)
+    {
+        library->fill_layer_data(Accessor(input), name);
+    }
+
+    /** Get the classification results.
+     *
+     * @return Vector containing the classified labels
+     */
+    std::vector<unsigned int> get_classifications()
+    {
+        std::vector<unsigned int> classified_labels;
+        Accessor                  output_accessor(output);
+
+        Window window;
+        window.set(Window::DimX, Window::Dimension(0, 1, 1));
+        for(unsigned int d = 1; d < output_accessor.shape().num_dimensions(); ++d)
+        {
+            window.set(d, Window::Dimension(0, output_accessor.shape()[d], 1));
+        }
+
+        execute_window_loop(window, [&](const Coordinates & id)
+        {
+            int               max_idx = 0;
+            float             val     = 0;
+            const void *const out_ptr = output_accessor(id);
+            for(unsigned int l = 0; l < output_accessor.shape().x(); ++l)
+            {
+                float curr_val = reinterpret_cast<const float *>(out_ptr)[l];
+                if(curr_val > val)
+                {
+                    max_idx = l;
+                    val     = curr_val;
+                }
+            }
+            classified_labels.push_back(max_idx);
+        });
+        return classified_labels;
+    }
+
+    /** Clear all allocated memory from the tensor objects */
+    void clear()
+    {
+        input.allocator()->free();
+        output.allocator()->free();
+
+        w_conv3x3.allocator()->free();
+        b_conv3x3.allocator()->free();
+        for(unsigned int i = 0; i < w_conv.size(); ++i)
+        {
+            w_conv[i].allocator()->free();
+            b_conv[i].allocator()->free();
+        }
+        for(unsigned int i = 0; i < w_dwc.size(); ++i)
+        {
+            w_dwc[i].allocator()->free();
+            b_dwc[i].allocator()->free();
+        }
+        for(auto &o : conv_out)
+        {
+            o.allocator()->free();
+        }
+        for(auto &o : dwc_out)
+        {
+            o.allocator()->free();
+        }
+        pool_out.allocator()->free();
+    }
+
+    /** Runs the model */
+    void run()
+    {
+        conv3x3.run();
+        conv3x3_act.run();
+        depthwise_conv_block_run(0);
+        depthwise_conv_block_run(1);
+        depthwise_conv_block_run(2);
+        depthwise_conv_block_run(3);
+        depthwise_conv_block_run(4);
+        depthwise_conv_block_run(5);
+        depthwise_conv_block_run(6);
+        depthwise_conv_block_run(7);
+        depthwise_conv_block_run(8);
+        depthwise_conv_block_run(9);
+        depthwise_conv_block_run(10);
+        depthwise_conv_block_run(11);
+        depthwise_conv_block_run(12);
+        pool.run();
+        conv1x1[13].run();
+        logistic.run();
+        reshape.run();
+    }
+
+private:
+    void depthwise_conv_block_init(unsigned int idx, unsigned int ifm, unsigned int ofm)
+    {
+        w_dwc[idx].allocator()->init(TensorInfo(TensorShape(3U, 3U, ifm), 1, DataType::F32));
+        b_dwc[idx].allocator()->init(TensorInfo(TensorShape(ifm), 1, DataType::F32));
+        w_conv[idx].allocator()->init(TensorInfo(TensorShape(1U, 1U, ifm, ofm), 1, DataType::F32));
+        b_conv[idx].allocator()->init(TensorInfo(TensorShape(ofm), 1, DataType::F32));
+    }
+    void depthwise_conv_block_build(unsigned int idx, PadStrideInfo dwc_ps, PadStrideInfo conv_ps)
+    {
+        dwc3x3[idx].configure(&conv_out[idx], &w_dwc[idx], &b_dwc[idx], &dwc_out[idx], dwc_ps);
+        act[2 * idx].configure(&dwc_out[idx], nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f));
+        conv1x1[idx].configure(&dwc_out[idx], &w_conv[idx], &b_conv[idx], &conv_out[idx + 1], conv_ps);
+        act[2 * idx + 1].configure(&conv_out[idx], nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f));
+    }
+    void depthwise_conv_block_run(unsigned int idx)
+    {
+        dwc3x3[idx].run();
+        act[2 * idx].run();
+        conv1x1[idx].run();
+        act[2 * idx + 1].run();
+    }
+
+private:
+    unsigned int _batches{ 0 };
+
+    ConvolutionLayerFunction conv3x3{};
+    ActivationLayerFunction  conv3x3_act{};
+    std::array<ActivationLayerFunction, 26>        act{ {} };
+    std::array<DirectConvolutionLayerFunction, 14> conv1x1{ {} };
+    std::array<DepthwiseConvolutionFunction, 13>   dwc3x3{ {} };
+    PoolingLayerFunction    pool{};
+    ActivationLayerFunction logistic{};
+    ReshapeFunction         reshape{};
+
+    TensorType w_conv3x3{}, b_conv3x3{};
+    std::array<TensorType, 14> w_conv{ {} }, b_conv{ {} };
+    std::array<TensorType, 13> w_dwc{ {} }, b_dwc{ {} };
+
+    TensorType input{}, output{};
+
+    std::array<TensorType, 15> conv_out{ {} };
+    std::array<TensorType, 13> dwc_out{ {} };
+    TensorType pool_out{};
+};
+} // namespace networks
+} // namespace test
+} // namespace arm_compute
+#endif //__ARM_COMPUTE_TEST_MODEL_OBJECTS_MOBILENET_H__