1 files changed, 164 insertions, 0 deletions
diff --git a/runtimes/libs/ARMComputeEx/src/runtime/NEON/functions/NEReduceMeanEx.cpp b/runtimes/libs/ARMComputeEx/src/runtime/NEON/functions/NEReduceMeanEx.cpp
new file mode 100644
index 000000000..c65e93570
--- /dev/null
+++ b/runtimes/libs/ARMComputeEx/src/runtime/NEON/functions/NEReduceMeanEx.cpp
@@ -0,0 +1,164 @@
+/*
+ * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
+ * Copyright (c) 2018-2019 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/runtime/NEON/functions/NEReduceMeanEx.h"
+
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/runtime/NEON/NEScheduler.h"
+
+using namespace arm_compute;
+
+NEReduceMeanEx::NEReduceMeanEx(std::shared_ptr<IMemoryManager> memory_manager)
+    : _memory_group(std::move(memory_manager)), _reduction_kernels(), _reduced_outs(), _reshape(),
+      _reduction_ops(), _keep_dims()
+{
+}
+
+Status NEReduceMeanEx::validate(const ITensorInfo *input, const Coordinates &reduction_axis,
+                                bool keep_dims, const ITensorInfo *output)
+{
+  ARM_COMPUTE_UNUSED(keep_dims);
+  ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input);
+  ARM_COMPUTE_RETURN_ERROR_ON(reduction_axis.num_dimensions() > input->num_dimensions());
+
+  TensorShape out_shape = input->tensor_shape();
+  const unsigned int reduction_ops = reduction_axis.num_dimensions();
+  const int input_dims = input->num_dimensions();
+  Coordinates axis_local = reduction_axis;
+
+  // Convert negative axis
+  for (unsigned int i = 0; i < reduction_ops; ++i)
+  {
+    axis_local[i] = wrap_around(axis_local[i], input_dims);
+  }
+
+  std::sort(axis_local.begin(), axis_local.begin() + reduction_ops);
+  for (unsigned int i = 0; i < reduction_ops; ++i)
+  {
+    ARM_COMPUTE_RETURN_ERROR_ON(axis_local[i] > 3);
+    ARM_COMPUTE_RETURN_ERROR_ON(static_cast<unsigned int>(axis_local[i]) >
+                                input->num_dimensions() - 1);
+    if (output->total_size() > 0 && keep_dims)
+    {
+      ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(axis_local[i]) != 1);
+    }
+    if (keep_dims)
+    {
+      out_shape.set(axis_local[i], 1);
+    }
+    else
+    {
+      out_shape.remove_dimension(axis_local[i] - i);
+    }
+  }
+  const TensorInfo out_info = input->clone()->set_tensor_shape(out_shape);
+  ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &out_info);
+
+  return Status{};
+}
+
+void NEReduceMeanEx::configure(ITensor *input, const Coordinates &reduction_axis, bool keep_dims,
+                               ITensor *output)
+{
+  ARM_COMPUTE_ERROR_ON_NULLPTR(input);
+
+  _reduction_ops = reduction_axis.num_dimensions();
+  _reduction_kernels =
+      arm_compute::support::cpp14::make_unique<NEReductionOperation[]>(_reduction_ops);
+  _reduced_outs =
+      arm_compute::support::cpp14::make_unique<Tensor[]>(_reduction_ops - (keep_dims ? 1 : 0));
+  _keep_dims = keep_dims;
+
+  Coordinates axis_local = reduction_axis;
+  const int input_dims = input->info()->num_dimensions();
+  const unsigned int reduction_ops = reduction_axis.num_dimensions();
+
+  // Convert negative axis
+  for (unsigned int i = 0; i < reduction_ops; ++i)
+  {
+    axis_local[i] = wrap_around(axis_local[i], input_dims);
+  }
+
+  // Perform reduction for every axis
+  for (unsigned int i = 0; i < _reduction_ops; ++i)
+  {
+    TensorShape out_shape = i == 0 ? input->info()->tensor_shape()
+                                   : (_reduced_outs.get() + i - 1)->info()->tensor_shape();
+    out_shape.set(axis_local[i], 1);
+    auto in = (i == 0) ? input : (_reduced_outs.get() + i - 1);
+
+    if (i == _reduction_ops - 1 && keep_dims)
+    {
+      _reduction_kernels[i].configure(in, output, axis_local[i], ReductionOperation::MEAN_SUM);
+    }
+    else
+    {
+      _reduced_outs[i].allocator()->init(TensorInfo(out_shape, input->info()->num_channels(),
+                                                    input->info()->data_type(),
+                                                    input->info()->quantization_info())
+                                             .set_data_layout(output->info()->data_layout()));
+      _memory_group.manage(_reduced_outs.get() + i);
+      _reduction_kernels[i].configure(in, _reduced_outs.get() + i, axis_local[i],
+                                      ReductionOperation::MEAN_SUM);
+    }
+  }
+
+  // Allocate intermediate tensors
+  for (unsigned int i = 0; i < _reduction_ops - (keep_dims ? 1 : 0); ++i)
+  {
+    _reduced_outs[i].allocator()->allocate();
+  }
+
+  // Configure reshape layer if we want to drop the dimensions
+  if (!keep_dims)
+  {
+    TensorShape out_shape = input->info()->tensor_shape();
+
+    // We have to sort the reduction axis vectors in order for remove_dimension
+    // to work properly
+    std::sort(axis_local.begin(), axis_local.begin() + _reduction_ops);
+    for (unsigned int i = 0; i < _reduction_ops; ++i)
+    {
+      out_shape.remove_dimension(axis_local[i] - i);
+    }
+    auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(out_shape));
+    _reshape.configure(_reduced_outs.get() + _reduction_ops - 1, output);
+  }
+}
+
+void NEReduceMeanEx::run()
+{
+  _memory_group.acquire();
+
+  for (unsigned int i = 0; i < _reduction_ops; ++i)
+  {
+    _reduction_kernels[i].run();
+  }
+
+  if (!_keep_dims)
+  {
+    _reshape.run();
+  }
+  _memory_group.release();
+}