4673cc2d25
* Utils: make_try_fold, clone_try_fold. Template node creation and attempt to fold it * RTTI for ArithmeticReduction(KeepDims) * Enriched ngraph::get_default_order overloads with ones for dynamic shape and rank * [ Transpose sinking ] Transpose->FQ->Reduce to FQ->Reduce->Transpose * Style: deleted empty line * RTTI in Reduction operations * RTTI for LogicalReductionKeepDims * Transpose: optimizations moved from algebraic simplification to TransposeSinking * renamed file * Fix test * keep_dims is always initialized * Apply suggestions from code review Co-authored-by: Gleb Kazantaev <gleb.nnstu@gmail.com> Co-authored-by: Gleb Kazantaev <gleb.nnstu@gmail.com>
87 lines
3.1 KiB
C++
87 lines
3.1 KiB
C++
// Copyright (C) 2018-2021 Intel Corporation
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// SPDX-License-Identifier: Apache-2.0
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//
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#include "ngraph/op/min.hpp"
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#include <ngraph/validation_util.hpp>
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#include "itt.hpp"
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#include "ngraph/runtime/host_tensor.hpp"
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#include "ngraph/runtime/reference/min.hpp"
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#include "ngraph/shape_util.hpp"
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using namespace std;
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using namespace ngraph;
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namespace minop
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{
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template <element::Type_t ET>
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bool evaluate(const HostTensorPtr& arg,
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const HostTensorPtr& out,
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const AxisSet& axes,
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const bool keep_dims)
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{
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out->set_shape(reduce(arg->get_shape(), axes, keep_dims));
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runtime::reference::min(
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arg->get_data_ptr<ET>(), out->get_data_ptr<ET>(), arg->get_shape(), axes, keep_dims);
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return true;
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}
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bool evaluate_min(const HostTensorPtr& arg,
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const HostTensorPtr& out,
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const AxisSet& axes,
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const bool keep_dims)
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{
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bool rc = true;
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switch (arg->get_element_type())
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{
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NGRAPH_TYPE_CASE(evaluate_min, i32, arg, out, axes, keep_dims);
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NGRAPH_TYPE_CASE(evaluate_min, i64, arg, out, axes, keep_dims);
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NGRAPH_TYPE_CASE(evaluate_min, u32, arg, out, axes, keep_dims);
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NGRAPH_TYPE_CASE(evaluate_min, u64, arg, out, axes, keep_dims);
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NGRAPH_TYPE_CASE(evaluate_min, f16, arg, out, axes, keep_dims);
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NGRAPH_TYPE_CASE(evaluate_min, f32, arg, out, axes, keep_dims);
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default: rc = false; break;
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}
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return rc;
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}
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} // namespace minop
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NGRAPH_RTTI_DEFINITION(op::v1::ReduceMin, "ReduceMin", 1, util::ArithmeticReductionKeepDims);
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op::v1::ReduceMin::ReduceMin(const Output<Node>& arg,
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const Output<Node>& reduction_axes,
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bool keep_dims)
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: ArithmeticReductionKeepDims(arg, reduction_axes, keep_dims)
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{
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constructor_validate_and_infer_types();
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}
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shared_ptr<Node> op::v1::ReduceMin::clone_with_new_inputs(const OutputVector& new_args) const
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{
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NGRAPH_OP_SCOPE(v1_ReduceMin_clone_with_new_inputs);
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check_new_args_count(this, new_args);
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return make_shared<op::v1::ReduceMin>(new_args.at(0), new_args.at(1), get_keep_dims());
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}
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bool op::v1::ReduceMin::evaluate(const HostTensorVector& outputs,
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const HostTensorVector& inputs) const
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{
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NGRAPH_OP_SCOPE(v1_ReduceMin_evaluate);
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NGRAPH_CHECK(this, validate_host_tensor_vector(inputs, 2));
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NGRAPH_CHECK(this, validate_host_tensor_vector(outputs, 1));
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return minop::evaluate_min(inputs[0], outputs[0], get_reduction_axes(), get_keep_dims());
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}
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bool op::v1::ReduceMin::evaluate_lower(const HostTensorVector& output_values) const
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{
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if (!input_value(1).get_tensor().has_and_set_bound())
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return false;
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return default_lower_bound_evaluator(this, output_values);
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}
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bool op::v1::ReduceMin::evaluate_upper(const HostTensorVector& output_values) const
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{
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if (!input_value(1).get_tensor().has_and_set_bound())
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return false;
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return default_upper_bound_evaluator(this, output_values);
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} |