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/*
* Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
* Copyright 2017 The TensorFlow Authors. 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 <plier/tf/Convert.h>
#include <nncc/core/ADT/tensor/Shape.h>
#include <cassert>
#include <stdexcept>
namespace plier
{
namespace tf
{
bool has_attr(const tensorflow::NodeDef &node, const std::string &attr_name)
{
return node.attr().count(attr_name) > 0;
}
bool has_attrs(const tensorflow::NodeDef &node, const std::vector<std::string> &attr_names)
{
for (auto &attr : attr_names)
if (!has_attr(node, attr))
return false;
return true;
}
tensorflow::DataType get_datatype_attr(const tensorflow::NodeDef &node,
const std::string &attr_name)
{
assert(has_attr(node, attr_name));
const auto &attr = node.attr().at(attr_name);
assert(attr.value_case() == tensorflow::AttrValue::kType);
return attr.type();
}
const tensorflow::TensorShapeProto &get_shape_attr(const tensorflow::NodeDef &node,
const std::string &attr_name)
{
assert(has_attr(node, attr_name));
const auto &attr = node.attr().at(attr_name);
assert(attr.value_case() == tensorflow::AttrValue::kShape);
return attr.shape();
}
const tensorflow::TensorProto &get_tensor_attr(const tensorflow::NodeDef &node,
const std::string &attr_name)
{
assert(has_attr(node, attr_name));
const auto &attr = node.attr().at(attr_name);
assert(attr.value_case() == tensorflow::AttrValue::kTensor);
return attr.tensor();
}
const ::tensorflow::AttrValue_ListValue &get_list_attr(const tensorflow::NodeDef &node,
const std::string &attr_name)
{
assert(has_attr(node, attr_name));
const auto &attr = node.attr().at(attr_name);
assert(attr.value_case() == tensorflow::AttrValue::kList);
return attr.list();
}
const std::string &get_string_attr(const tensorflow::NodeDef &node, const std::string &attr_name)
{
assert(has_attr(node, attr_name));
const auto &attr = node.attr().at(attr_name);
assert(attr.value_case() == tensorflow::AttrValue::kS);
return attr.s();
}
int64_t get_int_attr(const tensorflow::NodeDef &node, const std::string &attr_name)
{
assert(has_attr(node, attr_name));
const auto &attr = node.attr().at(attr_name);
assert(attr.value_case() == tensorflow::AttrValue::kI);
return attr.i();
}
float get_float_attr(const tensorflow::NodeDef &node, const std::string &attr_name)
{
assert(has_attr(node, attr_name));
const auto &attr = node.attr().at(attr_name);
assert(attr.value_case() == tensorflow::AttrValue::kF);
return attr.f();
}
bool get_bool_attr(const tensorflow::NodeDef &node, const std::string &attr_name)
{
assert(has_attr(node, attr_name));
const auto &attr = node.attr().at(attr_name);
assert(attr.value_case() == tensorflow::AttrValue::kB);
return attr.b();
}
std::vector<int64_t> as_int64_list(const tensorflow::AttrValue_ListValue &lv)
{
std::vector<int64_t> vi;
int isize = lv.i_size();
vi.resize(isize);
for (int i = 0; i < isize; ++i)
vi[i] = lv.i(i);
return vi;
}
loco::DataType as_loco_datatype(const tensorflow::DataType tf_dtype)
{
switch (tf_dtype)
{
case tensorflow::DT_INT8:
return loco::DataType::S8;
case tensorflow::DT_UINT8:
return loco::DataType::U8;
case tensorflow::DT_FLOAT:
return loco::DataType::FLOAT32;
case tensorflow::DT_INT32:
return loco::DataType::S32;
case tensorflow::DT_INT64:
return loco::DataType::S64;
case tensorflow::DT_BOOL:
case tensorflow::DT_STRING:
case tensorflow::DT_COMPLEX64:
default:
break;
}
throw std::runtime_error{"Unsupported tensorflow dtype: " + tensorflow::DataType_Name(tf_dtype)};
}
DataLayout as_data_layout(const std::string &tf_layout_str)
{
if (tf_layout_str == "NHWC")
return DataLayout::NHWC;
else if (tf_layout_str == "NCHW")
return DataLayout::NCHW;
else
throw std::runtime_error("unknown data layout");
}
DataLayout get_data_layout(const tensorflow::NodeDef &node, const std::string &attr_name)
{
auto layout = get_string_attr(node, attr_name);
if (layout == "NHWC")
return DataLayout::NHWC;
else if (layout == "NCHW")
return DataLayout::NCHW;
else
throw std::runtime_error("unknown data layout");
}
void copy_shape(const tensorflow::TensorShapeProto &tf_shape,
nncc::core::ADT::tensor::Shape &to_shape)
{
assert(!tf_shape.unknown_rank());
int64_t tf_rank = tf_shape.dim_size();
assert(tf_rank < std::numeric_limits<uint32_t>::max());
int32_t rank = static_cast<int32_t>(tf_rank);
to_shape.resize(rank);
for (int32_t d = 0; d < rank; d++)
{
int64_t dim_value = tf_shape.dim(d).size();
assert(dim_value < std::numeric_limits<uint32_t>::max());
if (dim_value >= 0LL)
{
uint32_t dim_value32 = static_cast<uint32_t>(dim_value);
to_shape.dim(d) = dim_value32;
}
else
{
throw std::runtime_error("Cannot handle unknown dimension");
// TODO support unknown dimension
}
}
}
} // namespace tf
} // namespace plier
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