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meta_data_tests.cpp
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// Copyright (C) 2018-2021 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
#include <gtest/gtest.h>
#include <string>
#include "common_test_utils/common_utils.hpp"
#include "file_utils.h"
#include "openvino/core/any.hpp"
#include "openvino/openvino.hpp"
#include "openvino/util/file_util.hpp"
class MetaData : public ::testing::Test {
public:
ov::Core core;
std::string ir_with_meta = R"V0G0N(
<net name="Network" version="11">
<layers>
<layer name="in1" type="Parameter" id="0" version="opset1">
<data element_type="f32" shape="1,3,22,22"/>
<output>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>3</dim>
<dim>22</dim>
<dim>22</dim>
</port>
</output>
</layer>
<layer name="activation" id="1" type="ReLU" version="opset1">
<input>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>3</dim>
<dim>22</dim>
<dim>22</dim>
</port>
</input>
<output>
<port id="2" precision="FP32">
<dim>1</dim>
<dim>3</dim>
<dim>22</dim>
<dim>22</dim>
</port>
</output>
</layer>
<layer name="output" type="Result" id="2" version="opset1">
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>3</dim>
<dim>22</dim>
<dim>22</dim>
</port>
</input>
</layer>
</layers>
<edges>
<edge from-layer="1" from-port="2" to-layer="2" to-port="0"/>
<edge from-layer="0" from-port="0" to-layer="1" to-port="1"/>
</edges>
<meta_data>
<MO_version value="TestVersion"/>
<Runtime_version value="TestVersion"/>
<cli_parameters>
<input_shape value="[1, 3, 22, 22]"/>
<transform value=""/>
<use_new_frontend value="False"/>
</cli_parameters>
</meta_data>
<quantization_parameters>
<config>{
'compression': {
'algorithms': [
{
'name': 'DefaultQuantization',
'params': {
'num_samples_for_tuning': 2000,
'preset': 'performance',
'stat_subset_size': 300,
'use_layerwise_tuning': false
}
}
],
'dump_intermediate_model': true,
'target_device': 'ANY'
},
'engine': {
'models': [
{
'name': 'bert-small-uncased-whole-word-masking-squad-0001',
'launchers': [
{
'framework': 'openvino',
'adapter': {
'type': 'bert_question_answering',
'start_token_logits_output': 'output_s',
'end_token_logits_output': 'output_e'
},
'inputs': [
{
'name': 'input_ids',
'type': 'INPUT',
'value': 'input_ids'
},
{
'name': 'attention_mask',
'type': 'INPUT',
'value': 'input_mask'
},
{
'name': 'token_type_ids',
'type': 'INPUT',
'value': 'segment_ids'
}
],
'device': 'cpu'
}
],
'datasets': [
{
'name': 'squad_v1_1_msl384_mql64_ds128_lowercase',
'annotation_conversion': {
'converter': 'squad',
'testing_file': 'PATH',
'max_seq_length': 384,
'max_query_length': 64,
'doc_stride': 128,
'lower_case': true,
'vocab_file': 'PATH'
},
'reader': {
'type': 'annotation_features_extractor',
'features': [
'input_ids',
'input_mask',
'segment_ids'
]
},
'postprocessing': [
{
'type': 'extract_answers_tokens',
'max_answer': 30,
'n_best_size': 20
}
],
'metrics': [
{
'name': 'F1',
'type': 'f1',
'reference': 0.9157
},
{
'name': 'EM',
'type': 'exact_match',
'reference': 0.8504
}
],
'_command_line_mapping': {
'testing_file': 'PATH',
'vocab_file': [
'PATH'
]
}
}
]
}
],
'stat_requests_number': null,
'eval_requests_number': null,
'type': 'accuracy_checker'
}
}</config>
<version value="invalid version"/>
<cli_params value="{'quantize': None, 'preset': None, 'model': None, 'weights': None, 'name': None, 'engine': None, 'ac_config': None, 'max_drop': None, 'evaluate': False, 'output_dir': 'PATH', 'direct_dump': True, 'log_level': 'INFO', 'pbar': False, 'stream_output': False, 'keep_uncompressed_weights': False, 'data_source': None}"/>
</quantization_parameters>
</net>
)V0G0N";
std::string ir_with_new_meta = R"V0G0N(
<net name="Network" version="11">
<layers>
<layer name="in1" type="Parameter" id="0" version="opset1">
<data element_type="f32" shape="1,3,22,22"/>
<output>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>3</dim>
<dim>22</dim>
<dim>22</dim>
</port>
</output>
</layer>
<layer name="activation" id="1" type="ReLU" version="opset1">
<input>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>3</dim>
<dim>22</dim>
<dim>22</dim>
</port>
</input>
<output>
<port id="2" precision="FP32">
<dim>1</dim>
<dim>3</dim>
<dim>22</dim>
<dim>22</dim>
</port>
</output>
</layer>
<layer name="output" type="Result" id="2" version="opset1">
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>3</dim>
<dim>22</dim>
<dim>22</dim>
</port>
</input>
</layer>
</layers>
<edges>
<edge from-layer="1" from-port="2" to-layer="2" to-port="0"/>
<edge from-layer="0" from-port="0" to-layer="1" to-port="1"/>
</edges>
<rt_info>
<MO_version value="TestVersion" />
<Runtime_version value="TestVersion" />
<conversion_parameters>
<input_shape value="[1, 3, 22, 22]" />
<transform value="" />
<use_new_frontend value="False" />
</conversion_parameters>
<optimization>
<cli_params value="{'quantize': None, 'preset': None, 'model': None, 'weights': None, 'name': None, 'engine': None, 'ac_config': None, 'max_drop': None, 'evaluate': False, 'output_dir': 'PATH', 'direct_dump': True, 'log_level': 'INFO', 'pbar': False, 'stream_output': False, 'keep_uncompressed_weights': False, 'data_source': None}" />
<config value="{ 'compression': { 'algorithms': [ { 'name': 'DefaultQuantization', 'params': { 'num_samples_for_tuning': 2000, 'preset': 'performance', 'stat_subset_size': 300, 'use_layerwise_tuning': false } } ], 'dump_intermediate_model': true, 'target_device': 'ANY' }, 'engine': { 'models': [ { 'name': 'bert-small-uncased-whole-word-masking-squad-0001', 'launchers': [ { 'framework': 'openvino', 'adapter': { 'type': 'bert_question_answering', 'start_token_logits_output': 'output_s', 'end_token_logits_output': 'output_e' }, 'inputs': [ { 'name': 'input_ids', 'type': 'INPUT', 'value': 'input_ids' }, { 'name': 'attention_mask', 'type': 'INPUT', 'value': 'input_mask' }, { 'name': 'token_type_ids', 'type': 'INPUT', 'value': 'segment_ids' } ], 'device': 'cpu' } ], 'datasets': [ { 'name': 'squad_v1_1_msl384_mql64_ds128_lowercase', 'annotation_conversion': { 'converter': 'squad', 'testing_file': 'PATH', 'max_seq_length': 384, 'max_query_length': 64, 'doc_stride': 128, 'lower_case': true, 'vocab_file': 'PATH' }, 'reader': { 'type': 'annotation_features_extractor', 'features': [ 'input_ids', 'input_mask', 'segment_ids' ] }, 'postprocessing': [ { 'type': 'extract_answers_tokens', 'max_answer': 30, 'n_best_size': 20 } ], 'metrics': [ { 'name': 'F1', 'type': 'f1', 'reference': 0.9157 }, { 'name': 'EM', 'type': 'exact_match', 'reference': 0.8504 } ], '_command_line_mapping': { 'testing_file': 'PATH', 'vocab_file': [ 'PATH' ] } } ] } ], 'stat_requests_number': null, 'eval_requests_number': null, 'type': 'accuracy_checker' } }" />
<version value="invalid version" />
</optimization>
<framework>
<batch value="1"/>
<chunk_size value="16"/>
</framework>
</rt_info>
</net>
)V0G0N";
std::string ir_without_meta = R"V0G0N(
<net name="Network" version="11">
<layers>
<layer name="in1" type="Parameter" id="0" version="opset1">
<data element_type="f32" shape="1,3,22,22"/>
<output>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>3</dim>
<dim>22</dim>
<dim>22</dim>
</port>
</output>
</layer>
<layer name="activation" id="1" type="ReLU" version="opset1">
<input>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>3</dim>
<dim>22</dim>
<dim>22</dim>
</port>
</input>
<output>
<port id="2" precision="FP32">
<dim>1</dim>
<dim>3</dim>
<dim>22</dim>
<dim>22</dim>
</port>
</output>
</layer>
<layer name="output" type="Result" id="2" version="opset1">
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>3</dim>
<dim>22</dim>
<dim>22</dim>
</port>
</input>
</layer>
</layers>
<edges>
<edge from-layer="1" from-port="2" to-layer="2" to-port="0"/>
<edge from-layer="0" from-port="0" to-layer="1" to-port="1"/>
</edges>
</net>
)V0G0N";
void SetUp() override {}
void check_rt_info(const std::shared_ptr<ov::Model>& model, bool with_framework = false) {
const std::string pot_conf_ref =
"{ 'compression': { 'algorithms': [ { 'name': 'DefaultQuantization', 'params': { 'num_samples_for_tuning': "
"2000, 'preset': 'performance', 'stat_subset_size': 300, 'use_layerwise_tuning': false } } ], "
"'dump_intermediate_model': true, 'target_device': 'ANY' }, 'engine': { 'models': [ { 'name': "
"'bert-small-uncased-whole-word-masking-squad-0001', 'launchers': [ { 'framework': 'openvino', 'adapter': "
"{ 'type': 'bert_question_answering', 'start_token_logits_output': 'output_s', 'end_token_logits_output': "
"'output_e' }, 'inputs': [ { 'name': 'input_ids', 'type': 'INPUT', 'value': 'input_ids' }, { 'name': "
"'attention_mask', 'type': 'INPUT', 'value': 'input_mask' }, { 'name': 'token_type_ids', 'type': 'INPUT', "
"'value': 'segment_ids' } ], 'device': 'cpu' } ], 'datasets': [ { 'name': "
"'squad_v1_1_msl384_mql64_ds128_lowercase', 'annotation_conversion': { 'converter': 'squad', "
"'testing_file': 'PATH', 'max_seq_length': 384, 'max_query_length': 64, 'doc_stride': 128, 'lower_case': "
"true, 'vocab_file': 'PATH' }, 'reader': { 'type': 'annotation_features_extractor', 'features': [ "
"'input_ids', 'input_mask', 'segment_ids' ] }, 'postprocessing': [ { 'type': 'extract_answers_tokens', "
"'max_answer': 30, 'n_best_size': 20 } ], 'metrics': [ { 'name': 'F1', 'type': 'f1', 'reference': 0.9157 "
"}, { 'name': 'EM', 'type': 'exact_match', 'reference': 0.8504 } ], '_command_line_mapping': { "
"'testing_file': 'PATH', 'vocab_file': [ 'PATH' ] } } ] } ], 'stat_requests_number': null, "
"'eval_requests_number': null, 'type': 'accuracy_checker' } }";
auto& rt_info = model->get_rt_info();
ASSERT_TRUE(!rt_info.empty());
std::string value;
EXPECT_NO_THROW(value = model->get_rt_info<std::string>("MO_version"));
EXPECT_EQ(value, "TestVersion");
value = "";
EXPECT_NO_THROW(value = model->get_rt_info<std::string>("Runtime_version"));
EXPECT_EQ(value, "TestVersion");
ov::AnyMap cli_map;
EXPECT_NO_THROW(cli_map = model->get_rt_info<ov::AnyMap>("conversion_parameters"));
auto it = cli_map.find("input_shape");
ASSERT_NE(it, cli_map.end());
EXPECT_TRUE(it->second.is<std::string>());
EXPECT_EQ(it->second.as<std::string>(), "[1, 3, 22, 22]");
it = cli_map.find("transform");
ASSERT_NE(it, cli_map.end());
EXPECT_TRUE(it->second.is<std::string>());
EXPECT_EQ(it->second.as<std::string>(), "");
it = cli_map.find("use_new_frontend");
ASSERT_NE(it, cli_map.end());
EXPECT_TRUE(it->second.is<std::string>());
EXPECT_EQ(it->second.as<std::string>(), "False");
EXPECT_NO_THROW(value = model->get_rt_info<std::string>("optimization", "config"));
EXPECT_EQ(value, pot_conf_ref);
if (with_framework) {
EXPECT_NO_THROW(value = model->get_rt_info<std::string>("framework", "batch"));
EXPECT_EQ(value, "1");
EXPECT_NO_THROW(value = model->get_rt_info<std::string>("framework", "chunk_size"));
EXPECT_EQ(value, "16");
}
}
};
TEST_F(MetaData, get_meta_data_from_model_without_info) {
auto model = core.read_model(ir_without_meta, ov::Tensor());
auto& rt_info = model->get_rt_info();
ASSERT_EQ(rt_info.find("meta_data"), rt_info.end());
}
TEST_F(MetaData, get_meta_data_as_map_from_model_without_info) {
auto model = core.read_model(ir_without_meta, ov::Tensor());
auto& rt_info = model->get_rt_info();
auto it = rt_info.find("meta_data");
EXPECT_EQ(it, rt_info.end());
it = rt_info.find("MO_version");
EXPECT_EQ(it, rt_info.end());
ov::AnyMap meta;
ASSERT_THROW(meta = model->get_rt_info<ov::AnyMap>("meta_data"), ov::Exception);
ASSERT_TRUE(meta.empty());
}
TEST_F(MetaData, get_meta_data) {
auto model = core.read_model(ir_with_meta, ov::Tensor());
auto& rt_info = model->get_rt_info();
ASSERT_NE(rt_info.find("MO_version"), rt_info.end());
ASSERT_NE(rt_info.find("Runtime_version"), rt_info.end());
ASSERT_NE(rt_info.find("conversion_parameters"), rt_info.end());
ASSERT_NE(rt_info.find("optimization"), rt_info.end());
}
TEST_F(MetaData, get_meta_data_as_map) {
auto model = core.read_model(ir_with_meta, ov::Tensor());
check_rt_info(model);
}
TEST_F(MetaData, get_meta_data_from_removed_file) {
std::string file_path = ov::util::get_ov_lib_path() + ov::util::FileTraits<char>::file_separator +
ov::test::utils::generateTestFilePrefix() + "_test_model.xml";
// Create file
{
std::ofstream ir(file_path);
ir << ir_with_meta;
}
auto model = core.read_model(file_path);
// Remove file (meta section wasn't read)
std::remove(file_path.c_str());
check_rt_info(model);
}
TEST_F(MetaData, get_meta_data_as_map_from_new_format) {
auto model = core.read_model(ir_with_new_meta, ov::Tensor());
check_rt_info(model, true);
}