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/*
* Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
*
* 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 "Shape.h"
#include "ONNXHelpers.h"
#include "mir/TensorVariant.h"
#include "mir/ops/ConstantOp.h"
namespace mir_onnx
{
void convertShapeV1(const onnx::NodeProto &onnx_node, ConverterContext *context)
{
std::vector<mir::Operation::Output *> inputs = context->getNodeInputs(onnx_node);
mir::Graph *graph = context->getGraph();
const auto &input_shape = inputs[0]->getShape();
int size = input_shape.rank();
mir::Shape output_shape{size};
std::vector<int64_t> data(static_cast<std::size_t>(size));
for (int i = 0; i < size; i++)
{
data[i] = input_shape.dim(i);
}
mir::TensorVariant tensor({mir::DataType::INT64, output_shape}, data.data());
auto result = createOp<mir::ops::ConstantOp>(graph, tensor)->getOutput(0);
context->setNodeOutputs(onnx_node, {result});
}
} // namespace mir_onnx
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