COMPMID-1580 Implement ReduceMean in NEON
Change-Id: Id974efad304c2513b8824a6561ad45ee60b9e7fb
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/153763
Reviewed-by: Giuseppe Rossini <giuseppe.rossini@arm.com>
Reviewed-by: Isabella Gottardi <isabella.gottardi@arm.com>
Tested-by: bsgcomp <bsgcomp@arm.com>
diff --git a/src/runtime/NEON/functions/NEReduceMean.cpp b/src/runtime/NEON/functions/NEReduceMean.cpp
new file mode 100644
index 0000000..0b022df
--- /dev/null
+++ b/src/runtime/NEON/functions/NEReduceMean.cpp
@@ -0,0 +1,117 @@
+/*
+ * Copyright (c) 2018 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, INNEUDING 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 NEAIM, 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/NEReduceMean.h"
+
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/runtime/NEON/NEScheduler.h"
+
+using namespace arm_compute;
+
+NEReduceMean::NEReduceMean(std::shared_ptr<IMemoryManager> memory_manager)
+ : _memory_group(std::move(memory_manager)), _reduction_kernels(), _reduced_outs(), _reshape(), _reduction_ops(), _keep_dims()
+{
+}
+
+Status NEReduceMean::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());
+
+ for(unsigned int i = 0; i < reduction_axis.num_dimensions(); ++i)
+ {
+ if(output->total_size() > 0)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(reduction_axis[i]) != 1);
+ ARM_COMPUTE_RETURN_ERROR_ON(static_cast<unsigned int>(reduction_axis[i]) > input->num_dimensions() - 1);
+ }
+
+ ARM_COMPUTE_RETURN_ON_ERROR(NEReductionOperationKernel::validate(input, output, reduction_axis[i], ReductionOperation::MEAN_SUM));
+ }
+
+ return Status{};
+}
+
+void NEReduceMean::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;
+
+ // 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(reduction_axis[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, reduction_axis[i], ReductionOperation::MEAN_SUM);
+ }
+ else
+ {
+ _reduced_outs[i].allocator()->init(TensorInfo(out_shape, input->info()->num_channels(), input->info()->data_type()));
+ _memory_group.manage(_reduced_outs.get() + i);
+ _reduction_kernels[i].configure(in, _reduced_outs.get() + i, reduction_axis[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();
+ for(unsigned int i = 0; i < _reduction_ops; ++i)
+ {
+ out_shape.remove_dimension(reduction_axis[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 NEReduceMean::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();
+}