1 files changed, 151 insertions, 0 deletions
diff --git a/compute/ARMComputeEx/src/runtime/CL/functions/CLReduceOperation.cpp b/compute/ARMComputeEx/src/runtime/CL/functions/CLReduceOperation.cpp
new file mode 100644
index 000000000..13a25c901
--- /dev/null
+++ b/compute/ARMComputeEx/src/runtime/CL/functions/CLReduceOperation.cpp
@@ -0,0 +1,151 @@
+/*
+ * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
+ * Copyright (c) 2017-2018 ARM Limited.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "arm_compute/runtime/CL/functions/CLReduceOperation.h"
+
+#include "arm_compute/core/CL/kernels/CLReduceOperationKernel.h"
+#include "arm_compute/core/TensorShape.h"
+#include "arm_compute/runtime/CL/CLScheduler.h"
+
+using namespace arm_compute;
+
+CLReduceOperation::CLReduceOperation(std::shared_ptr<IMemoryManager> memory_manager)
+    : _memory_group(std::move(memory_manager)), _input(nullptr), _output(nullptr), _axis(),
+      _keep_dims(false), _interm_tensors(), _reduce_kernels(), _reshape()
+{
+}
+
+Status CLReduceOperation::validate(const ITensorInfo *input, const ITensorInfo *output,
+                                   const std::set<uint32_t> &axis, bool keep_dims,
+                                   const ReduceOperation &op)
+{
+  const size_t num_of_kernels = axis.size();
+  const size_t num_of_interm_tensors = num_of_kernels - (keep_dims ? 1 : 0);
+
+  // Create temporary tensor infos
+  auto interm_tensors =
+      arm_compute::support::cpp14::make_unique<TensorInfo[]>(num_of_interm_tensors);
+
+  // Create intermediate tensor info
+  TensorShape shape{input->tensor_shape()};
+
+  auto it = axis.begin();
+  for (size_t i = 0; i < num_of_interm_tensors; ++i, ++it)
+  {
+    shape.set(*it, 1, false);
+    interm_tensors[i].set_data_type(input->data_type());
+    interm_tensors[i].set_tensor_shape(shape);
+    interm_tensors[i].set_num_channels(input->num_channels());
+    interm_tensors[i].set_data_layout(input->data_layout());
+    interm_tensors[i].set_quantization_info(input->quantization_info());
+  }
+
+  // Set a vector that is ordered ITensorInfo sequentially.
+  std::vector<const ITensorInfo *> tensors;
+  tensors.emplace_back(input);
+  for (size_t i = 0; i < num_of_interm_tensors; ++i)
+  {
+    tensors.emplace_back(interm_tensors.get() + i);
+  }
+  tensors.emplace_back(output);
+
+  // Validate ReduceOperation only on all kernels
+  it = axis.begin();
+  for (size_t i = 0; i < num_of_kernels; ++i, ++it)
+  {
+    ARM_COMPUTE_RETURN_ON_ERROR(
+        CLReduceOperationKernel::validate(tensors[i], tensors[i + 1], *it, op));
+  }
+
+  if (!keep_dims)
+  {
+    ARM_COMPUTE_RETURN_ON_ERROR(
+        CLReshapeLayer::validate(&interm_tensors[num_of_interm_tensors - 1], output));
+  }
+
+  return Status{};
+}
+
+void CLReduceOperation::configure(ICLTensor *input, ICLTensor *output,
+                                  const std::set<uint32_t> &axis, bool keep_dims,
+                                  ReduceOperation op)
+{
+  ARM_COMPUTE_ERROR_THROW_ON(validate(input->info(), output->info(), axis, keep_dims, op));
+
+  _axis = axis;
+
+  _input = input;
+  _output = output;
+  _keep_dims = keep_dims;
+
+  // NOTE The axis must have no duplication.
+  const size_t num_of_kernels = axis.size();
+  const size_t num_of_interm_tensors = num_of_kernels - (keep_dims ? 1 : 0);
+
+  _interm_tensors = arm_compute::support::cpp14::make_unique<CLTensor[]>(num_of_interm_tensors);
+  _reduce_kernels =
+      arm_compute::support::cpp14::make_unique<CLReduceOperationKernel[]>(num_of_kernels);
+
+  // Set a vector that is ordered ICLTensors sequentially.
+  std::vector<ICLTensor *> tensors;
+  tensors.emplace_back(input);
+  for (size_t i = 0; i < num_of_interm_tensors; ++i)
+  {
+    tensors.emplace_back(_interm_tensors.get() + i);
+  }
+  tensors.emplace_back(output);
+
+  // Apply ReduceOperation on all kernels
+  TensorShape shape{input->info()->tensor_shape()};
+  auto it = axis.begin();
+  for (size_t i = 0; i < num_of_kernels; ++i, ++it)
+  {
+    shape.set(*it, 1, false);
+    if (!keep_dims || i != (num_of_kernels - 1))
+    {
+      _interm_tensors[i].allocator()->init(input->info()->clone()->set_tensor_shape(shape));
+      _memory_group.manage(&_interm_tensors[i]);
+    }
+    _reduce_kernels[i].configure(tensors[i], tensors[i + 1], *it, op);
+    if (i != 0)
+    {
+      _interm_tensors[i - 1].allocator()->allocate();
+    }
+  }
+
+  // Configure reshape layer if we want to drop the dimensions
+  if (!keep_dims)
+  {
+    _reshape.configure(&_interm_tensors[num_of_interm_tensors - 1], output);
+    _interm_tensors[num_of_interm_tensors - 1].allocator()->allocate();
+  }
+}
+
+void CLReduceOperation::run()
+{
+  MemoryGroupResourceScope scope_mg(_memory_group);
+
+  const size_t num_of_kernels = _axis.size();
+  for (size_t i = 0; i < num_of_kernels; ++i)
+  {
+    CLScheduler::get().enqueue(_reduce_kernels[i]);
+  }
+
+  if (!_keep_dims)
+  {
+    _reshape.run();
+  }
+}