From 6ff3b19ee6120edf015fad8caab2991faa3070af Mon Sep 17 00:00:00 2001
From: Anthony Barbier <anthony.barbier@arm.com>
Date: Mon, 4 Sep 2017 18:44:23 +0100
Subject: COMPMID-344 Updated doxygen

Change-Id: I32f7b84daa560e460b77216add529c8fa8b327ae
---
 tests/benchmark/NEON/ConvolutionLayer.cpp | 303 ++++++++++++++++++++++++++++++
 1 file changed, 303 insertions(+)
 create mode 100644 tests/benchmark/NEON/ConvolutionLayer.cpp

(limited to 'tests/benchmark/NEON/ConvolutionLayer.cpp')

diff --git a/tests/benchmark/NEON/ConvolutionLayer.cpp b/tests/benchmark/NEON/ConvolutionLayer.cpp
new file mode 100644
index 0000000000..0cfff8494b
--- /dev/null
+++ b/tests/benchmark/NEON/ConvolutionLayer.cpp
@@ -0,0 +1,303 @@
+/*
+ * 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 "Globals.h"
+#include "NEON/Helper.h"
+#include "NEON/NEAccessor.h"
+#include "TensorLibrary.h"
+#include "benchmark/Datasets.h"
+#include "benchmark/Profiler.h"
+#include "benchmark/WallClockTimer.h"
+
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/runtime/NEON/functions/NEConvolutionLayer.h"
+#include "arm_compute/runtime/Tensor.h"
+#include "arm_compute/runtime/TensorAllocator.h"
+
+#include "benchmark/benchmark_api.h"
+
+using namespace arm_compute;
+using namespace arm_compute::test;
+using namespace arm_compute::test::benchmark;
+using namespace arm_compute::test::neon;
+
+#include "benchmark/common/ConvolutionLayer.h"
+
+namespace
+{
+using ConvolutionLayerAlexNetF32 = ConvolutionLayer<AlexNetConvolutionLayerDataset, Tensor, NEAccessor, NEConvolutionLayer>;
+using ConvolutionLayerAlexNetQS8 = ConvolutionLayer<AlexNetConvolutionLayerDataset, Tensor, NEAccessor, NEConvolutionLayer, DataType::QS8>;
+using ConvolutionLayerLeNet5     = ConvolutionLayer<LeNet5ConvolutionLayerDataset, Tensor, NEAccessor, NEConvolutionLayer>;
+using ConvolutionLayerGoogLeNet1 = ConvolutionLayer<GoogLeNetConvolutionLayerDataset1, Tensor, NEAccessor, NEConvolutionLayer>;
+using ConvolutionLayerGoogLeNet2 = ConvolutionLayer<GoogLeNetConvolutionLayerDataset2, Tensor, NEAccessor, NEConvolutionLayer>;
+} // namespace
+
+// F32
+BENCHMARK_DEFINE_F(ConvolutionLayerAlexNetF32, neon_alexnet)
+(::benchmark::State &state)
+{
+    while(state.KeepRunning())
+    {
+        // Run function
+        profiler.start();
+        conv_layer->run();
+        profiler.stop();
+    }
+}
+
+BENCHMARK_REGISTER_F(ConvolutionLayerAlexNetF32, neon_alexnet)
+->Threads(1)
+->Apply(DataSetArgBatched<AlexNetConvolutionLayerDataset, 0, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerAlexNetF32, neon_alexnet)
+->Threads(1)
+->Apply(DataSetArgBatched<AlexNetConvolutionLayerDataset, 1, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerAlexNetF32, neon_alexnet)
+->Threads(1)
+->Apply(DataSetArgBatched<AlexNetConvolutionLayerDataset, 2, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerAlexNetF32, neon_alexnet)
+->Threads(1)
+->Apply(DataSetArgBatched<AlexNetConvolutionLayerDataset, 3, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerAlexNetF32, neon_alexnet)
+->Threads(1)
+->Apply(DataSetArgBatched<AlexNetConvolutionLayerDataset, 4, 1, 4, 8>);
+
+// QS8
+BENCHMARK_DEFINE_F(ConvolutionLayerAlexNetQS8, neon_alexnet)
+(::benchmark::State &state)
+{
+    while(state.KeepRunning())
+    {
+        // Run function
+        profiler.start();
+        conv_layer->run();
+        profiler.stop();
+    }
+}
+
+BENCHMARK_REGISTER_F(ConvolutionLayerAlexNetQS8, neon_alexnet)
+->Threads(1)
+->Apply(DataSetArgBatched<AlexNetConvolutionLayerDataset, 0, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerAlexNetQS8, neon_alexnet)
+->Threads(1)
+->Apply(DataSetArgBatched<AlexNetConvolutionLayerDataset, 1, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerAlexNetQS8, neon_alexnet)
+->Threads(1)
+->Apply(DataSetArgBatched<AlexNetConvolutionLayerDataset, 2, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerAlexNetQS8, neon_alexnet)
+->Threads(1)
+->Apply(DataSetArgBatched<AlexNetConvolutionLayerDataset, 3, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerAlexNetQS8, neon_alexnet)
+->Threads(1)
+->Apply(DataSetArgBatched<AlexNetConvolutionLayerDataset, 4, 1, 4, 8>);
+
+BENCHMARK_DEFINE_F(ConvolutionLayerLeNet5, neon_lenet5)
+(::benchmark::State &state)
+{
+    while(state.KeepRunning())
+    {
+        // Run function
+        profiler.start();
+        conv_layer->run();
+        profiler.stop();
+    }
+}
+
+BENCHMARK_REGISTER_F(ConvolutionLayerLeNet5, neon_lenet5)
+->Threads(1)
+->Apply(DataSetArgBatched<LeNet5ConvolutionLayerDataset, 0, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerLeNet5, neon_lenet5)
+->Threads(1)
+->Apply(DataSetArgBatched<LeNet5ConvolutionLayerDataset, 1, 1, 4, 8>);
+
+BENCHMARK_DEFINE_F(ConvolutionLayerGoogLeNet1, neon_googlenet)
+(::benchmark::State &state)
+{
+    while(state.KeepRunning())
+    {
+        // Run function
+        profiler.start();
+        conv_layer->run();
+        profiler.stop();
+    }
+}
+
+BENCHMARK_DEFINE_F(ConvolutionLayerGoogLeNet2, neon_googlenet)
+(::benchmark::State &state)
+{
+    while(state.KeepRunning())
+    {
+        // Run function
+        profiler.start();
+        conv_layer->run();
+        profiler.stop();
+    }
+}
+
+BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet)
+->Threads(1)
+->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 0, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet)
+->Threads(1)
+->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 1, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet)
+->Threads(1)
+->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 2, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet)
+->Threads(1)
+->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 3, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet)
+->Threads(1)
+->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 4, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet)
+->Threads(1)
+->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 5, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet)
+->Threads(1)
+->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 6, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet)
+->Threads(1)
+->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 7, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet)
+->Threads(1)
+->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 8, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet)
+->Threads(1)
+->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 9, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet)
+->Threads(1)
+->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 10, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet)
+->Threads(1)
+->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 11, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet)
+->Threads(1)
+->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 12, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet)
+->Threads(1)
+->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 13, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet)
+->Threads(1)
+->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 14, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet)
+->Threads(1)
+->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 15, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet)
+->Threads(1)
+->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 16, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet)
+->Threads(1)
+->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 17, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet)
+->Threads(1)
+->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 18, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet)
+->Threads(1)
+->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 19, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet)
+->Threads(1)
+->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 20, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet)
+->Threads(1)
+->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 21, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet)
+->Threads(1)
+->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 22, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet)
+->Threads(1)
+->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 23, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet)
+->Threads(1)
+->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 24, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet)
+->Threads(1)
+->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 25, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet)
+->Threads(1)
+->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 26, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet)
+->Threads(1)
+->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 27, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet)
+->Threads(1)
+->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 28, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet)
+->Threads(1)
+->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 29, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet)
+->Threads(1)
+->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 30, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet)
+->Threads(1)
+->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 31, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet2, neon_googlenet)
+->Threads(1)
+->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset2, 0, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet2, neon_googlenet)
+->Threads(1)
+->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset2, 1, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet2, neon_googlenet)
+->Threads(1)
+->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset2, 2, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet2, neon_googlenet)
+->Threads(1)
+->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset2, 3, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet2, neon_googlenet)
+->Threads(1)
+->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset2, 4, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet2, neon_googlenet)
+->Threads(1)
+->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset2, 5, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet2, neon_googlenet)
+->Threads(1)
+->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset2, 6, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet2, neon_googlenet)
+->Threads(1)
+->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset2, 7, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet2, neon_googlenet)
+->Threads(1)
+->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset2, 8, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet2, neon_googlenet)
+->Threads(1)
+->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset2, 9, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet2, neon_googlenet)
+->Threads(1)
+->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset2, 10, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet2, neon_googlenet)
+->Threads(1)
+->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset2, 11, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet2, neon_googlenet)
+->Threads(1)
+->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset2, 12, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet2, neon_googlenet)
+->Threads(1)
+->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset2, 13, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet2, neon_googlenet)
+->Threads(1)
+->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset2, 14, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet2, neon_googlenet)
+->Threads(1)
+->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset2, 15, 1, 4, 8>);
+BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet2, neon_googlenet)
+->Threads(1)
+->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset2, 16, 1, 4, 8>);
-- 
cgit v1.2.1