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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 "Upsample.h"
#include "ONNXHelpers.h"
#include "AttributeHelpers.h"
#include "mir/Tensor.h"
#include "mir/ops/ConstantOp.h"
#include "mir/ops/ResizeOp.h"
#include <stdexcept>
namespace mir_onnx
{
void convertUpsampleV1(const onnx::NodeProto &onnx_node, ConverterContext *context)
{
std::vector<mir::Operation::Output *> inputs = context->getNodeInputs(onnx_node);
mir::Graph *graph = context->getGraph();
// "nearest" is the default mode.
std::string mode = getAttributeValue<std::string>(onnx_node, "mode", "nearest");
assert(mode == "nearest" && "Unsupported upscale mode!");
const float h_scale = getAttributeValue<float>(onnx_node, "height_scale", 0.0f); // required
const float w_scale = getAttributeValue<float>(onnx_node, "width_scale", 0.0f); // required
if (h_scale < 1.0f || w_scale < 1.0f)
throw std::runtime_error("Wrong scale attributes!");
assert(inputs[0]->getShape().rank() == 4 && "Only rank 4 is supported");
std::vector<float> scales_vector(4);
// NCHW
scales_vector.at(0) = 1.0f;
scales_vector.at(1) = 1.0f;
scales_vector.at(2) = h_scale;
scales_vector.at(3) = w_scale;
auto result =
createOp<mir::ops::ResizeOp>(graph, inputs[0],
mir::ops::ResizeOp::ResizeMethod::nearestNeighbor, scales_vector)
->getOutput(0);
context->setNodeOutputs(onnx_node, {result});
}
void convertUpsampleV7(const onnx::NodeProto &onnx_node, ConverterContext *context)
{
std::vector<mir::Operation::Output *> inputs = context->getNodeInputs(onnx_node);
mir::Graph *graph = context->getGraph();
// "nearest" is the default mode.
std::string mode = getAttributeValue<std::string>(onnx_node, "mode", "nearest");
assert(mode == "nearest" && "Unsupported upscale mode!");
const auto *scales_attr = findAttribute(onnx_node, "scales");
if (!scales_attr)
throw std::runtime_error("Not enough required scales attribute!");
if (scales_attr->floats_size() != inputs[0]->getShape().rank())
throw std::runtime_error(
"Number of elements of scales should be the same as the rank of input");
assert(inputs[0]->getShape().rank() == 4 && "Only rank 4 is supported");
std::vector<float> scales_vector(4);
// NCHW
scales_vector.at(0) = scales_attr->floats(0);
scales_vector.at(1) = scales_attr->floats(1);
scales_vector.at(2) = scales_attr->floats(2);
scales_vector.at(3) = scales_attr->floats(3);
auto result =
createOp<mir::ops::ResizeOp>(graph, inputs[0],
mir::ops::ResizeOp::ResizeMethod::nearestNeighbor, scales_vector)
->getOutput(0);
context->setNodeOutputs(onnx_node, {result});
}
void convertUpsampleV9(const onnx::NodeProto &onnx_node, ConverterContext *context)
{
std::vector<mir::Operation::Output *> inputs = context->getNodeInputs(onnx_node);
mir::Graph *graph = context->getGraph();
// "nearest" is the default mode.
const auto mode = getAttributeValue<std::string>(onnx_node, "mode", "nearest");
if (mode != "nearest")
throw std::runtime_error("Upsample: only 'nearest' mode is supported.");
// relies on attributes being lifted to constants (ONNX optimization pass)
assert(inputs.size() > 1);
auto *scales = dynamic_cast<mir::ops::ConstantOp *>(inputs[1]->getNode());
assert(scales && "Weights could be a constant tensor only");
auto scales_tensor = mir::Tensor<float>(scales->getValue());
int rank = inputs[0]->getShape().rank();
if (rank != 4)
throw std::runtime_error("Upsample: only 4-D input is supported.");
assert(scales_tensor.getShape().numElements() == rank &&
"The number of elements of 'scales' should be the same as the rank of input 'X'");
std::vector<float> scales_vector(rank);
for (int i = 0; i < scales_tensor.getShape().numElements(); i++)
scales_vector[i] = scales_tensor.atOffset(i);
auto result =
createOp<mir::ops::ResizeOp>(graph, inputs[0],
mir::ops::ResizeOp::ResizeMethod::nearestNeighbor, scales_vector)
->getOutput(0);
context->setNodeOutputs(onnx_node, {result});
}
} // namespace mir_onnx
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