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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 "Unsqueeze.h"
#include "ONNXHelpers.h"
#include "AttributeHelpers.h"
#include "mir/ops/ReshapeOp.h"
namespace mir_onnx
{
void convertUnsqueezeV1(const onnx::NodeProto &onnx_node, ConverterContext *context)
{
std::vector<mir::Operation::Output *> inputs = context->getNodeInputs(onnx_node);
mir::Graph *graph = context->getGraph();
const auto axes = getAttributeValue<std::vector<std::int64_t>>(onnx_node, "axes");
assert(!axes.empty());
const mir::Shape &input_shape = inputs[0]->getShape();
const int out_rank = input_shape.rank() + static_cast<int>(axes.size());
mir::Shape out_shape(out_rank);
auto ints_iterator = axes.cbegin();
int j = 0;
for (int i = 0; i < out_rank; i++)
{
if (ints_iterator < axes.cend() && i == *ints_iterator)
{
out_shape.dim(i) = 1;
ints_iterator++;
}
else
{
out_shape.dim(i) = input_shape.dim(j);
j++;
}
}
auto result = createOp<mir::ops::ReshapeOp>(graph, inputs[0], out_shape)->getOutput(0);
context->setNodeOutputs(onnx_node, {result});
}
} // namespace mir_onnx
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