This commit Supports Mean op for acl_neon backend.
Signed-off-by: jiseob.jang <jiseob.jang@samsung.com>
--- /dev/null
+/*
+ * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
+ * 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, 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.
+ */
+#ifndef __ARM_COMPUTE_NEON_REDUCE_MEAN_EX_H__
+#define __ARM_COMPUTE_NEON_REDUCE_MEAN_EX_H__
+
+#include "arm_compute/runtime/IFunction.h"
+
+#include "arm_compute/core/NEON/kernels/NEFillBorderKernel.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/runtime/MemoryGroup.h"
+#include "arm_compute/runtime/NEON/functions/NEReductionOperation.h"
+#include "arm_compute/runtime/NEON/functions/NEReshapeLayer.h"
+
+namespace arm_compute
+{
+class ITensor;
+
+/** Basic function to perform reduce operation */
+class NEReduceMeanEx : public IFunction
+{
+public:
+ /** Constructor */
+ NEReduceMeanEx(std::shared_ptr<IMemoryManager> memory_manager = nullptr);
+ /** Configure kernel
+ *
+ * @note Supported tensor rank: up to 4
+ *
+ * @param[in] input Source tensor. Data type supported: QASYMM8/F16/F32
+ * @param[in] reduction_axis Reduction axis vector.
+ * @param[in] keep_dims If positive, retains reduced dimensions with length 1.
+ * @param[out] output Destination tensor. Data type supported: Same as @p input
+ */
+ void configure(ITensor *input, const Coordinates &reduction_axis, bool keep_dims,
+ ITensor *output);
+
+ /** Static function to check if given info will lead to a valid configuration of @ref
+ * NEReduceMeanEx
+ *
+ * @param[in] input Source tensor. Data type supported: QASYMM8/F16/F32
+ * @param[in] reduction_axis Reduction axis vector.
+ * @param[in] keep_dims If positive, retains reduced dimensions with length 1.
+ * @param[in] output Destination tensor. Data type supported: Same as @p input
+ *
+ * @return A status
+ */
+ static Status validate(const ITensorInfo *input, const Coordinates &reduction_axis,
+ bool keep_dims, const ITensorInfo *output);
+
+ // Inherited methods overridden:
+ void run() override;
+
+private:
+ MemoryGroup _memory_group;
+ std::unique_ptr<NEReductionOperation[]> _reduction_kernels{nullptr};
+ std::unique_ptr<Tensor[]> _reduced_outs{nullptr};
+ NEReshapeLayer _reshape;
+ unsigned int _reduction_ops;
+ bool _keep_dims;
+};
+} // namespace arm_compute
+#endif /* __ARM_COMPUTE_NEON_REDUCE_MEAN_EX_H__ */
--- /dev/null
+/*
+ * 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();
+}
registerPermuteInitializer(input_index, input_obj);
}
+void ConstantInitializer::visit(const model::operation::MeanNode &node)
+{
+ const auto &input_index = node.getInputs().at(model::operation::MeanNode::INPUT);
+ const auto &input_obj = _operands.at(input_index);
+ registerPermuteInitializer(input_index, input_obj);
+}
+
void ConstantInitializer::visit(const model::operation::MulNode &node)
{
const auto &lhs_index = node.getInputs().at(model::operation::MulNode::LHS);
void visit(const model::operation::DepthwiseConv2DNode &) override;
void visit(const model::operation::FullyConnectedNode &) override;
void visit(const model::operation::MaxPool2DNode &) override;
+ void visit(const model::operation::MeanNode &) override;
void visit(const model::operation::MulNode &) override;
void visit(const model::operation::ReshapeNode &) override;
void visit(const model::operation::RSQRTNode &) override;
#include <arm_compute/runtime/NEON/functions/NEActivationLayer.h>
#include <arm_compute/runtime/NEON/functions/NEConvolutionLayer.h>
#include <arm_compute/runtime/NEON/functions/NEDepthwiseConvolutionLayer.h>
+#include <arm_compute/runtime/NEON/functions/NEReduceMeanEx.h>
#include <arm_compute/runtime/NEON/functions/NEReshapeLayer.h>
#include <arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h>
#include <arm_compute/runtime/NEON/functions/NEFullyConnectedReshapingLayer.h>
ActivationBuilder{*_execution_builder}.append(activation, ofm_alloc->handle());
}
+void KernelGenerator::visit(const model::operation::MeanNode &node)
+{
+ const auto ofm_index{node.getOutputs().at(0)};
+ const auto ifm_index{node.getInputs().at(model::operation::MeanNode::Input::INPUT)};
+
+ const auto axis_index{node.param().axis_index};
+ const auto keep_dims_index{node.param().keep_dims_index};
+
+ const auto ifm_shape = _ctx.at(ifm_index).shape();
+
+ std::set<uint32_t> axis;
+ {
+ const auto ifm_rank = ifm_shape.rank();
+ const auto axis_shape = _ctx.at(axis_index).shape();
+ switch (axis_shape.rank())
+ {
+ case 0: // scalar
+ {
+ auto axis_value = _ctx.at(axis_index).asScalar<int32_t>();
+ if (axis_value < 0)
+ {
+ axis_value += ifm_rank;
+ }
+ axis.insert(::neurun::backend::acl_common::ToARMComputeAxis(ifm_rank, axis_value).value());
+ break;
+ }
+ case 1: // vector
+ {
+ const auto axis_base = _ctx.at(axis_index).data().base();
+ const int axis_size = axis_shape.num_elements();
+
+ // If axis's data does not exist as constant values and can be gotten as input data, we have
+ // to find a way to infer output shape when sinking output.
+ assert(axis_base != nullptr);
+ for (int32_t n = 0; n < axis_size; ++n)
+ {
+ int32_t axis_value = *(reinterpret_cast<const int32_t *>(axis_base) + n);
+ if (axis_value < 0)
+ {
+ axis_value += ifm_rank;
+ }
+ axis.insert(
+ ::neurun::backend::acl_common::ToARMComputeAxis(ifm_rank, axis_value).value());
+ }
+ break;
+ }
+ default:
+ throw std::runtime_error("Not supported");
+ }
+ }
+
+ const auto ifm_rank = ifm_shape.rank();
+
+ bool keep_dims = _ctx.at(keep_dims_index).asScalar<int32_t>() != 0;
+ auto ofm_alloc = _tensor_builder->at(ofm_index).get();
+ auto ifm_alloc = _tensor_builder->at(ifm_index).get();
+ arm_compute::Coordinates fixed_axis;
+ // TODO Support NCHW frontend
+ // TODO Change the layout of frontend and backend to be the same
+ auto acl_layout = ifm_alloc->handle()->info()->data_layout();
+ // CWHN -> WHCN
+ uint32_t permutation[4] = {2, 0, 1, 3};
+ for (auto a : axis)
+ {
+ if (acl_layout == ::arm_compute::DataLayout::NCHW && ifm_rank == 4)
+ {
+ fixed_axis.set(fixed_axis.num_dimensions(), permutation[a]);
+ }
+ else
+ {
+ fixed_axis.set(fixed_axis.num_dimensions(), a);
+ }
+ }
+
+ std::unique_ptr<::arm_compute::IFunction> fn;
+
+ // NOTE NEReduceMean has a bug that does not support NHWC layout
+ // NEReduceMean intermediate tensors are always NCHW layout
+ auto l = nnfw::cpp14::make_unique<::arm_compute::NEReduceMeanEx>();
+
+ l->configure(ifm_alloc->handle(), fixed_axis, keep_dims, ofm_alloc->handle());
+
+ fn = std::move(l);
+
+ auto acl_fn = asAclFunction(std::move(fn));
+
+ _execution_builder->append(std::move(acl_fn));
+}
+
void KernelGenerator::visit(const model::operation::AvgPool2DNode &node)
{
const auto ofm_index{node.getOutputs().at(0)};
void visit(const model::operation::Conv2DNode &) override;
void visit(const model::operation::DepthwiseConv2DNode &) override;
void visit(const model::operation::MaxPool2DNode &) override;
+ void visit(const model::operation::MeanNode &) override;
void visit(const model::operation::AvgPool2DNode &) override;
void visit(const model::operation::ConcatNode &) override;
void visit(const model::operation::FullyConnectedNode &) override;
void ShapeFixer::visit(const model::operation::MaxPool2DNode &) { /* DO NOTHING */}
+void ShapeFixer::visit(const model::operation::MeanNode &) { /* DO NOTHING */}
+
void ShapeFixer::visit(const model::operation::AvgPool2DNode &) { /* DO NOTHING */}
void ShapeFixer::visit(const model::operation::ConcatNode &node)
void visit(const model::operation::Conv2DNode &) override;
void visit(const model::operation::DepthwiseConv2DNode &) override;
void visit(const model::operation::MaxPool2DNode &) override;
+ void visit(const model::operation::MeanNode &) override;
void visit(const model::operation::AvgPool2DNode &) override;
void visit(const model::operation::ConcatNode &) override;
void visit(const model::operation::FullyConnectedNode &) override;
GeneratedTests.relu*
GeneratedTests.resize_bilinear*
GeneratedTests.rnn*
-GeneratedTests.mean*
GeneratedTests.pad*
GeneratedTests.space_to_depth*
GeneratedTests.sqrt_ex*
projects / platform / core / ml / nnfw.git / commitdiff