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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 "h5formatter.h"
#include "nnfw.h"
#include "nnfw_util.h"
#include <iostream>
#include <stdexcept>
#include <H5Cpp.h>
namespace nnpkg_run
{
static const char *h5_value_grpname = "value";
void H5Formatter::loadInputs(const std::string &filename, std::vector<Allocation> &inputs)
{
uint32_t num_inputs;
NNPR_ENSURE_STATUS(nnfw_input_size(session_, &num_inputs));
try
{
// Turn off the automatic error printing.
H5::Exception::dontPrint();
H5::H5File file(filename, H5F_ACC_RDONLY);
H5::Group value_group = file.openGroup(h5_value_grpname);
for (uint32_t i = 0; i < num_inputs; ++i)
{
nnfw_tensorinfo ti;
NNPR_ENSURE_STATUS(nnfw_input_tensorinfo(session_, i, &ti));
// allocate memory for data
auto bufsz = bufsize_for(&ti);
inputs[i].alloc(bufsz);
H5::DataSet data_set = value_group.openDataSet(std::to_string(i));
H5::DataType type = data_set.getDataType();
switch (ti.dtype)
{
case NNFW_TYPE_TENSOR_FLOAT32:
if (type == H5::PredType::IEEE_F32BE || type == H5::PredType::IEEE_F32LE)
data_set.read(inputs[i].data(), H5::PredType::NATIVE_FLOAT);
else
throw std::runtime_error("model input type is f32. But h5 data type is different.");
break;
case NNFW_TYPE_TENSOR_INT32:
if (type == H5::PredType::STD_I32BE || type == H5::PredType::STD_I32LE)
data_set.read(inputs[i].data(), H5::PredType::NATIVE_INT32);
else
throw std::runtime_error("model input type is i32. But h5 data type is different.");
break;
case NNFW_TYPE_TENSOR_INT64:
if (type == H5::PredType::STD_I64BE || type == H5::PredType::STD_I64LE)
data_set.read(inputs[i].data(), H5::PredType::NATIVE_INT64);
else
throw std::runtime_error("model input type is i64. But h5 data type is different.");
break;
case NNFW_TYPE_TENSOR_QUANT8_ASYMM:
case NNFW_TYPE_TENSOR_BOOL:
case NNFW_TYPE_TENSOR_UINT8:
if (type == H5::PredType::STD_U8BE || type == H5::PredType::STD_U8LE)
data_set.read(inputs[i].data(), H5::PredType::NATIVE_UINT8);
else
throw std::runtime_error(
"model input type is qasymm8, bool or uint8. But h5 data type is different.");
break;
default:
throw std::runtime_error("nnpkg_run can load f32, i32, qasymm8, bool and uint8.");
}
NNPR_ENSURE_STATUS(nnfw_set_input(session_, i, ti.dtype, inputs[i].data(), bufsz));
NNPR_ENSURE_STATUS(nnfw_set_input_layout(session_, i, NNFW_LAYOUT_CHANNELS_LAST));
}
}
catch (const H5::Exception &e)
{
H5::Exception::printErrorStack();
std::exit(-1);
}
catch (const std::exception &e)
{
std::cerr << e.what() << std::endl;
std::exit(-1);
}
};
void H5Formatter::dumpOutputs(const std::string &filename, std::vector<Allocation> &outputs)
{
uint32_t num_outputs;
NNPR_ENSURE_STATUS(nnfw_output_size(session_, &num_outputs));
try
{
// Turn off the automatic error printing.
H5::Exception::dontPrint();
H5::H5File file(filename, H5F_ACC_TRUNC);
H5::Group value_group = file.createGroup(h5_value_grpname);
for (uint32_t i = 0; i < num_outputs; i++)
{
nnfw_tensorinfo ti;
NNPR_ENSURE_STATUS(nnfw_output_tensorinfo(session_, i, &ti));
std::vector<hsize_t> dims(ti.rank);
for (uint32_t j = 0; j < ti.rank; ++j)
{
if (ti.dims[j] >= 0)
dims[j] = static_cast<hsize_t>(ti.dims[j]);
else
{
std::cerr << "Negative dimension in output tensor" << std::endl;
exit(-1);
}
}
H5::DataSpace data_space(ti.rank, dims.data());
switch (ti.dtype)
{
case NNFW_TYPE_TENSOR_FLOAT32:
{
H5::DataSet data_set =
value_group.createDataSet(std::to_string(i), H5::PredType::IEEE_F32BE, data_space);
data_set.write(outputs[i].data(), H5::PredType::NATIVE_FLOAT);
break;
}
case NNFW_TYPE_TENSOR_INT32:
{
H5::DataSet data_set =
value_group.createDataSet(std::to_string(i), H5::PredType::STD_I32LE, data_space);
data_set.write(outputs[i].data(), H5::PredType::NATIVE_INT32);
break;
}
case NNFW_TYPE_TENSOR_INT64:
{
H5::DataSet data_set =
value_group.createDataSet(std::to_string(i), H5::PredType::STD_I64LE, data_space);
data_set.write(outputs[i].data(), H5::PredType::NATIVE_INT64);
break;
}
case NNFW_TYPE_TENSOR_QUANT8_ASYMM:
{
H5::DataSet data_set =
value_group.createDataSet(std::to_string(i), H5::PredType::STD_U8BE, data_space);
data_set.write(outputs[i].data(), H5::PredType::NATIVE_UINT8);
break;
}
case NNFW_TYPE_TENSOR_BOOL:
{
H5::DataSet data_set =
value_group.createDataSet(std::to_string(i), H5::PredType::STD_I8LE, data_space);
data_set.write(outputs[i].data(), H5::PredType::NATIVE_INT8);
break;
}
case NNFW_TYPE_TENSOR_UINT8:
{
H5::DataSet data_set =
value_group.createDataSet(std::to_string(i), H5::PredType::STD_U8BE, data_space);
data_set.write(outputs[i].data(), H5::PredType::NATIVE_UINT8);
break;
}
default:
throw std::runtime_error("nnpkg_run can dump f32, i32, qasymm8, bool and uint8.");
}
}
}
catch (const H5::Exception &e)
{
H5::Exception::printErrorStack();
std::exit(-1);
}
catch (const std::runtime_error &e)
{
std::cerr << "Error during dumpOutputs on nnpackage_run : " << e.what() << std::endl;
std::exit(-1);
}
};
} // end of namespace nnpkg_run
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