From: 김정현/동작제어Lab(SR)/Senior Engineer/삼성전자 <jh0822.kim@samsung.com>
Date: Wed, 11 Apr 2018 07:19:59 +0000 (+0900)
Subject: Apply RELU to the FC layer. (#572)
X-Git-Tag: 0.1~328
X-Git-Url: http://review.tizen.org/git/?a=commitdiff_plain;h=30295da52a18e6c01d1ad420da12632d4d668e9a;p=platform%2Fcore%2Fml%2Fnnfw.git

Apply RELU to the FC layer. (#572)

This commit adds functionality of activation RELU into the FC layer.
This commit also adds a test case for RELU.

Signed-off-by: Junghyun Kim <jh0822.kim@samsung.com>
---

diff --git a/src/kernel/acl/src/cl/FullyConnected.cpp b/src/kernel/acl/src/cl/FullyConnected.cpp
index e35d8e5..0b27a7f 100644
--- a/src/kernel/acl/src/cl/FullyConnected.cpp
+++ b/src/kernel/acl/src/cl/FullyConnected.cpp
@@ -2,6 +2,8 @@
 #include <arm_compute/core/TensorShape.h>
 #include <arm_compute/core/TensorInfo.h>
 
+#include <cassert>
+
 // TODO: fix include path in CMakeFiles
 #include "../IO_accessor.h"
 #include "../shape.h"
@@ -23,24 +25,45 @@ bool fullyConnectedFloat32(const float* inputData, const android::nn::Shape& inp
 
   arm_compute::CLTensor input, output, bias, filter;
 
+  assert(activation == ANEURALNETWORKS_FUSED_NONE || activation == ANEURALNETWORKS_FUSED_RELU);
+
+  std::vector<std::shared_ptr<arm_compute::IFunction>> fns;
+
   input.allocator()->init(arm_compute::TensorInfo(input_shape, arm_compute::Format::F32));
   output.allocator()->init(arm_compute::TensorInfo(output_shape, arm_compute::Format::F32));
   bias.allocator()->init(arm_compute::TensorInfo(bias_shape, arm_compute::Format::F32));
   filter.allocator()->init(arm_compute::TensorInfo(filter_shape, arm_compute::Format::F32));
 
-  arm_compute::CLFullyConnectedLayer fc;
-  fc.configure(&input, &filter, &bias, &output);
+  auto fc = std::make_shared<arm_compute::CLFullyConnectedLayer>();
+  fc->configure(&input, &filter, &bias, &output);
+
+  fns.emplace_back(fc);
 
   input.allocator()->allocate();
   output.allocator()->allocate();
   bias.allocator()->allocate();
   filter.allocator()->allocate();
 
+  if (ANEURALNETWORKS_FUSED_RELU == activation)
+  {
+    auto relu_f = std::make_shared<arm_compute::CLActivationLayer>();
+
+    const arm_compute::ActivationLayerInfo relu_info{arm_compute::ActivationLayerInfo::ActivationFunction::RELU};
+
+    // Do in-place update
+    relu_f->configure(&output, nullptr, relu_info);
+
+    fns.emplace_back(relu_f);
+  }
+
   TensorAccess<InputAccessor>(input, inputData, inputShape);
   TensorAccess<BiasAccessor>(bias, biasData, biasShape);
   TensorAccess<WeightAccessor>(filter, weightsData, weightsShape);
 
-  fc.run();
+  for (const auto &fn : fns)
+  {
+    fn->run();
+  }
 
   arm_compute::CLScheduler::get().sync();
 
diff --git a/src/kernel/acl/src/cl/FullyConnected.test.cpp b/src/kernel/acl/src/cl/FullyConnected.test.cpp
index 20e1171..5ff7852 100644
--- a/src/kernel/acl/src/cl/FullyConnected.test.cpp
+++ b/src/kernel/acl/src/cl/FullyConnected.test.cpp
@@ -44,3 +44,39 @@ TEST(KernelACL_TC, fcFloat32_1) {
 
 }
 
+TEST(KernelACL_TC, fcFloat32_relu) {
+  const android::nn::Shape inputShape = {OperandType::FLOAT32, {1, 1, 1, 100}, 1.0, 0};
+  float inputData[100];
+
+  const android::nn::Shape weightsShape = {OperandType::FLOAT32, {1, 1, 100, 1}, 1.0, 0};
+  float weightsData[100];
+
+  const android::nn::Shape biasShape = {OperandType::FLOAT32, {1, 1, 1, 1}, 1.0, 0};
+  float biasData[1];
+
+  const android::nn::Shape outputShape = { OperandType::FLOAT32, {1,1,1,1}, 1.0, 0 };
+  float outputData[1];
+
+  int32_t activation = ANEURALNETWORKS_FUSED_RELU;
+
+  util::initData(inputData, sizeof(inputData) / sizeof(inputData[0]), 1.0);
+  util::initData(weightsData, sizeof(weightsData) / sizeof(weightsData[0]), -1.0);
+  util::initData(biasData, sizeof(biasData) / sizeof(biasData[0]), 0.0);
+  util::initData(outputData, sizeof(outputData) / sizeof(outputData[0]), 0.0);
+
+  bool bret = fullyConnectedFloat32(inputData, inputShape,
+      weightsData, weightsShape,
+      biasData, biasShape,
+      activation,
+      outputData, outputShape);
+
+  EXPECT_EQ(bret, true);
+
+  float expectData[] = {
+    0.0f
+  };
+  bret = util::compareData(outputData, expectData, outputShape);
+  EXPECT_EQ(bret, true);
+
+}
+