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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 "Dump.h"
#include "Reader.h"
#include <string>
#include <ostream>
#include <stdexcept>
namespace tflinspect
{
void DumpOperators::run(std::ostream &os, const tflite::Model *model)
{
tflinspect::Reader reader(model);
assert(reader.num_subgraph() == 1);
reader.select_subgraph(0);
auto ops = reader.operators();
// dump operators
for (uint32_t i = 0; i < ops->Length(); ++i)
{
const auto op = ops->Get(i);
auto op_name = reader.opcode_name(op);
os << op_name << std::endl;
}
}
} // namespace tflinspect
namespace
{
const tflite::Operator *operator_match_output(tflinspect::Reader &reader, const int32_t tensor)
{
auto ops = reader.operators();
for (uint32_t i = 0; i < ops->Length(); ++i)
{
const auto op = ops->Get(i);
const std::vector<int32_t> &outputs = tflinspect::as_index_vector(op->outputs());
for (auto output : outputs)
{
if (output == tensor)
return op;
}
}
return nullptr;
}
size_t tensor_buffer_size(tflinspect::Reader &reader, const int32_t tensor_id)
{
auto tensors = reader.tensors();
if (tensor_id < 0 || tensor_id >= tensors->Length())
{
throw std::runtime_error("Invalid Tensor ID");
}
auto tensor = tensors->Get(tensor_id);
auto buffer_id = tensor->buffer();
size_t size = reader.buffer_info(buffer_id, nullptr);
return size;
}
} // namespace
namespace tflinspect
{
void DumpConv2DWeight::run(std::ostream &os, const tflite::Model *model)
{
tflinspect::Reader reader(model);
assert(reader.num_subgraph() == 1);
reader.select_subgraph(0);
auto ops = reader.operators();
// dump Conv2D, DepthwiseConv2D and its weight input operator
for (uint32_t i = 0; i < ops->Length(); ++i)
{
const auto op = ops->Get(i);
auto bc = reader.builtin_code(op);
if (bc == tflite::BuiltinOperator_CONV_2D || bc == tflite::BuiltinOperator_DEPTHWISE_CONV_2D)
{
const std::vector<int32_t> &inputs = tflinspect::as_index_vector(op->inputs());
if (inputs.size() < 2)
{
throw std::runtime_error("Operator has invalid input");
}
auto weight_input = inputs[1]; // Tensor ID of weight input
const auto op_weight = operator_match_output(reader, weight_input);
const auto buffer_size = tensor_buffer_size(reader, weight_input);
std::string weight_op_name = "?";
if (op_weight == nullptr && buffer_size > 0)
{
weight_op_name = "CONST";
}
else if (op_weight != nullptr)
{
weight_op_name = reader.opcode_name(op_weight);
}
auto op_name = reader.opcode_name(op);
os << op_name << "," << weight_op_name << std::endl;
}
}
}
} // namespace tflinspect
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