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/*
* Copyright (c) 2020 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 "luci/Import/Nodes/CircleUnpack.h"
#include <luci/IR/Nodes/CircleUnpack.h>
#include <luci/IR/Nodes/CircleUnpackOut.h>
#include <luci/UserSettings.h>
#include <luci/Log.h>
#include <loco.h>
#include <oops/UserExn.h>
namespace luci
{
bool CircleUnpackGraphBuilder::validate(const ValidateArgs &args) const
{
LOGGER(l);
auto settings = luci::UserSettings::settings();
const auto &inputs = args.op.inputs;
const auto &outputs = args.op.outputs;
const auto *options = args.op.builtin_options.AsUnpackOptions();
if (inputs.size() != 1)
return false;
// NOTE real models may have mismatch
if (static_cast<int32_t>(outputs.size()) != options->num)
{
if (settings->get(luci::UserSettings::Key::DisableValidation))
{
const auto &tensors = args.reader.tensors();
const circle::TensorT &output_tensor = *tensors[outputs[0]];
auto name = tensor_name(output_tensor);
WARN(l) << "Warning: import Unpack(" << name << ") 'num' is not same as outputs used";
}
else
return false;
}
if (options->num < 0)
return false;
const auto &tensors = args.reader.tensors();
const auto &tensor = tensors.at(inputs[0]);
const auto &shape = tensor->shape;
auto shape_size = static_cast<int32_t>(shape.size());
if (shape_size > 0)
{
// NOTE for unknown shape, shape_size is 0
if (options->axis < -shape_size || options->axis >= shape_size)
return false;
}
return true;
}
/**
* @brief Unpack Node builder
*
* @note Current loco does not provide multiple outputs
* We will create multiple CircleUnpackOut nodes to emulate this
* For two outputs that may look like this
*
* --- CircleUnpack --- FullyConnected ---
* \- FullyConnected ---
*
* will be created like this
*
* --- CircleUnpack --- CircleUnpackOut --- FullyConnected ---
* \- CircleUnpackOut --- FullyConnected ---
*/
void CircleUnpackGraphBuilder::build(const circle::OperatorT &op,
GraphBuilderContext *context) const
{
assert(context != nullptr);
auto graph = context->graph();
const std::vector<int32_t> &inputs = op.inputs;
const std::vector<int32_t> &outputs = op.outputs;
const auto &tensors = context->reader()->tensors();
const auto &opcodes = context->reader()->opcodes();
auto tensors_ptr = context->reader()->tensors_ptr();
assert(tensors_ptr != nullptr);
// NOTE Unpack has only one input so running a loop is not necessary
// This is provided as a reference for other Ops as a reference
std::vector<CircleNode *> input_nodes;
for (const int32_t input_tensor_index : inputs)
{
input_nodes.push_back(context->nodefinder()->node(input_tensor_index));
}
// Create CircleUnpack
CircleUnpack *node = graph->nodes()->create<CircleUnpack>();
node->value(input_nodes[0]);
const auto *options = op.builtin_options.AsUnpackOptions();
node->num(options->num);
node->axis(options->axis);
assert(outputs.size() > 0);
{
// Let's use name of output 0 as Unpack name
const circle::TensorT &output_tensor = *tensors[outputs[0]];
node->name(tensor_name(output_tensor));
node->op_version(opcodes[op.opcode_index].get()->version);
// NOTE We don't set quantization for Unpack itself but to virtual outputs
}
// Create virtual outputs of Unpack
for (int32_t n = 0; n < options->num; ++n)
{
const circle::TensorT &output_tensor = *tensors[outputs[n]];
auto *nodeout = graph->nodes()->create<CircleUnpackOut>();
copy_tensor_attributes(output_tensor, nodeout);
// mark shape_status
if (tensors_ptr->Get(outputs[n])->shape() == nullptr)
nodeout->shape_status(ShapeStatus::NOSHAPE);
else
nodeout->shape_status(ShapeStatus::VALID);
nodeout->input(node);
nodeout->index(n);
context->nodefinder()->enroll(outputs[n], nodeout);
}
}
} // namespace luci
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