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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 "Reshape.h"
#include "ONNXHelpers.h"
#include "AttributeHelpers.h"
#include "mir/Tensor.h"
#include "mir/ShapeRange.h"
#include "mir/ops/ConstantOp.h"
#include "mir/ops/ReshapeOp.h"
namespace mir_onnx
{
void convertReshapeV1(const onnx::NodeProto &onnx_node, ConverterContext *context)
{
std::vector<mir::Operation::Output *> inputs = context->getNodeInputs(onnx_node);
mir::Graph *graph = context->getGraph();
// consumed_inputs attribute not used
const auto *shape_attr = findAttribute(onnx_node, "shape");
if (shape_attr && shape_attr->ints_size() > 0)
{
mir::Shape in_shape = inputs[0]->getShape();
mir::Shape out_shape(shape_attr->ints_size());
for (int32_t index = 0; index < out_shape.rank(); index++)
{
const auto dim_value = shape_attr->ints(index);
if (dim_value == 0)
out_shape.dim(index) = in_shape.dim(index);
else
out_shape.dim(index) = dim_value;
}
auto result = createOp<mir::ops::ReshapeOp>(graph, inputs[0], out_shape)->getOutput(0);
context->setNodeOutputs(onnx_node, {result});
}
else // dimension value is unchanged
{
context->setNodeOutputs(onnx_node, {inputs[0]});
}
}
void convertReshapeV5(const onnx::NodeProto &onnx_node, ConverterContext *context)
{
std::vector<mir::Operation::Output *> inputs = context->getNodeInputs(onnx_node);
mir::Graph *graph = context->getGraph();
// The original shape
const auto &in_shape = inputs[0]->getShape();
// Input tensor describing the new shape
auto *op = dynamic_cast<mir::ops::ConstantOp *>(inputs[1]->getNode());
assert(op && "We support only constant shape input");
auto shape_tensor = op->getValue();
mir::Shape shape_tensor_shape = (shape_tensor).getShape();
assert(shape_tensor_shape.rank() == 1);
// The rank of the new shape
auto cnt = shape_tensor_shape.numElements();
// The vector to build the new shape from
std::vector<int32_t> shape_vector(cnt);
mir::ShapeRange out_range(shape_tensor_shape);
mir::Tensor<int64_t> tensor_accessor(shape_tensor);
int i = 0;
for (auto idx : out_range)
{
if (tensor_accessor.at(idx) == 0)
shape_vector[i] = in_shape.dim(i);
else if (tensor_accessor.at(idx) == -1)
shape_vector[i] = mir::Shape::autoDim;
else
shape_vector[i] = tensor_accessor.at(idx);
i++;
}
auto out_shape = mir::Shape(shape_vector);
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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