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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 "Constant.h"
#include "Convert.h"
#include <cassert>
namespace moco
{
namespace onnx
{
bool Constant_V9::validate(const ::onnx::NodeProto &node) const
{
if (node.attribute_size() == 0 || !node.attribute(0).has_t())
return false;
return true;
}
void Constant_V9::build(const ::onnx::NodeProto &node, GraphBuilderContext *context) const
{
assert(context != nullptr);
loco::Graph *graph = context->graph();
SymbolTable *nodes = context->nodes();
// Create a "ConstGen" node for Constant
auto const_node = graph->nodes()->create<loco::ConstGen>();
auto tensor_attribute = node.attribute().Get(0).t();
const_node->dtype(as_loco_datatype(tensor_attribute.data_type()));
const_node->rank(tensor_attribute.dims_size());
// TODO Support other data types
assert(const_node->dtype() == loco::DataType::FLOAT32);
const_node->size<loco::DataType::FLOAT32>(tensor_attribute.float_data_size());
for (uint32_t i = 0; i < const_node->rank(); ++i)
{
const_node->dim(i) = tensor_attribute.dims(i);
}
// TODO Support other data types
for (int i = 0; i < tensor_attribute.float_data_size(); ++i)
{
const_node->at<loco::DataType::FLOAT32>(i) = tensor_attribute.float_data(i);
}
nodes->enroll(node.name(), const_node);
nodes->enroll(node.output(0), const_node);
}
} // namespace onnx
} // namespace moco
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