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Provide ONNX external data mechanism to ReadNetwork (#2588)

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* added unit test

* added python test

* using pword approach

* Added passing path to onnx reader

* support for wstring

* Added more tests

* Apply suggestions from code review

Co-authored-by: Michał Karzyński <4430709+postrational@users.noreply.github.com>

* fix build for Windows

* styles applied

* Fixed Windows tests

* styles applied

* fixed styles in tests

* review remarks

* cmake order

* Used target_compile_definitions instead of add_definitions

* Move ONNX_TEST_MODELS to other scope

Co-authored-by: Michał Karzyński <4430709+postrational@users.noreply.github.com>
This commit is contained in:
Mateusz Bencer
2020-10-14 11:30:53 +02:00
committed by GitHub
parent 9956639531
commit c0d71900fd
18 changed files with 544 additions and 7 deletions
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2
inference-engine/tests/functional/inference_engine/CMakeLists.txt
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@@ -52,6 +52,8 @@ if(TARGET inference_engine_onnx_reader)
add_dependencies(${TARGET_NAME} inference_engine_onnx_reader)
endif()
target_compile_definitions(${TARGET_NAME} PRIVATE ONNX_TEST_MODELS="${CMAKE_CURRENT_SOURCE_DIR}/onnx_reader/models/")
include(CMakeParseArguments)
#

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inference-engine/tests/functional/inference_engine/onnx_reader/models/data/tensor.data Normal file
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97
inference-engine/tests/functional/inference_engine/onnx_reader/models/onnx_external_data.prototxt Normal file
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@@ -0,0 +1,97 @@
ir_version: 3
producer_name: "nGraph ONNX Importer"
graph {
node {
input: "A"
input: "B"
output: "X"
name: "add_node1"
op_type: "Add"
}
node {
input: "X"
input: "C"
output: "Y"
name: "add_node2"
op_type: "Add"
}
name: "test_graph"
initializer {
dims: 2
dims: 2
data_type: 1
name: "A"
external_data {
key: "location",
value: "data/tensor.data"
}
data_location: 1
}
input {
name: "A"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 2
}
dim {
dim_value: 2
}
}
}
}
}
input {
name: "B"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 2
}
dim {
dim_value: 2
}
}
}
}
}
input {
name: "C"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 2
}
dim {
dim_value: 2
}
}
}
}
}
output {
name: "Y"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 2
}
dim {
dim_value: 2
}
}
}
}
}
}
opset_import {
version: 4
}

97
inference-engine/tests/functional/inference_engine/onnx_reader/models/АБВГДЕЁЖЗИЙ/ひらがな日本語.prototxt Normal file
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@@ -0,0 +1,97 @@
ir_version: 3
producer_name: "nGraph ONNX Importer"
graph {
node {
input: "A"
input: "B"
output: "X"
name: "multiply_node_1"
op_type: "Mul"
}
node {
input: "X"
input: "C"
output: "Y"
name: "multiply_node_2"
op_type: "Mul"
}
name: "test_graph"
initializer {
dims: 2
dims: 2
data_type: 1
name: "A"
external_data {
key: "location",
value: "../data/tensor.data"
}
data_location: 1
}
input {
name: "A"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 2
}
dim {
dim_value: 2
}
}
}
}
}
input {
name: "B"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 2
}
dim {
dim_value: 2
}
}
}
}
}
input {
name: "C"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 2
}
dim {
dim_value: 2
}
}
}
}
}
output {
name: "Y"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 2
}
dim {
dim_value: 2
}
}
}
}
}
}
opset_import {
version: 4
}

112
inference-engine/tests/functional/inference_engine/onnx_reader/onnx_reader_external_data.cpp Normal file
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@@ -0,0 +1,112 @@
// Copyright (C) 2018-2020 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
#include <gtest/gtest.h>
#include <set>
#include <string>
#include <fstream>
#include <algorithm>
#include <ie_blob.h>
#include <ie_core.hpp>
#include <file_utils.h>
#include <streambuf>
#include <ngraph/ngraph.hpp>
TEST(ONNX_Reader_Tests, ImportModelWithExternalDataFromFile) {
InferenceEngine::Core ie;
auto cnnNetwork = ie.ReadNetwork(std::string(ONNX_TEST_MODELS) + "onnx_external_data.prototxt", "");
auto function = cnnNetwork.getFunction();
int count_additions = 0;
int count_constants = 0;
int count_parameters = 0;
std::shared_ptr<ngraph::Node> external_data_node;
for (auto op : function->get_ops()) {
const auto op_type = std::string(op->get_type_name());
count_additions += (op_type == "Add" ? 1 : 0);
count_parameters += (op_type == "Parameter" ? 1 : 0);
if (op_type == "Constant") {
count_constants += 1;
external_data_node = op;
}
}
ASSERT_EQ(function->get_output_size(), 1);
ASSERT_EQ(std::string(function->get_output_op(0)->get_type_name()), "Result");
ASSERT_EQ(function->get_output_element_type(0), ngraph::element::f32);
ASSERT_EQ(function->get_output_shape(0), ngraph::Shape({2, 2}));
ASSERT_EQ(count_additions, 2);
ASSERT_EQ(count_constants, 1);
ASSERT_EQ(count_parameters, 2);
const auto external_data_node_const = ngraph::as_type_ptr<ngraph::op::Constant>(external_data_node);
ASSERT_TRUE(external_data_node_const->get_vector<float>() == (std::vector<float>{1, 2, 3, 4}));
}
TEST(ONNX_Reader_Tests, ImportModelWithExternalDataFromStringException) {
InferenceEngine::Core ie;
const auto path = std::string(ONNX_TEST_MODELS) + "onnx_external_data.prototxt";
InferenceEngine::Blob::CPtr weights; //not used
std::ifstream stream(path, std::ios::binary);
std::string modelAsString((std::istreambuf_iterator<char>(stream)), std::istreambuf_iterator<char>());
stream.close();
try {
auto cnnNetwork = ie.ReadNetwork(modelAsString, weights);
}
catch(const ngraph::ngraph_error& e) {
EXPECT_PRED_FORMAT2(
testing::IsSubstring,
std::string("invalid external data:"),
e.what());
EXPECT_PRED_FORMAT2(
testing::IsSubstring,
std::string("data/tensor.data, offset: 0, data_lenght: 0, sha1_digest: 0)"),
e.what());
}
catch(...) {
FAIL() << "Reading network failed for unexpected reason";
}
}
#if defined(ENABLE_UNICODE_PATH_SUPPORT) && defined(_WIN32)
TEST(ONNX_Reader_Tests, ImportModelWithExternalDataFromWstringNamedFile) {
InferenceEngine::Core ie;
std::string win_dir_path = ONNX_TEST_MODELS;
std::replace(win_dir_path.begin(), win_dir_path.end(), '/', '\\');
const std::wstring unicode_win_dir_path = FileUtils::multiByteCharToWString(win_dir_path.c_str());
const std::wstring path = unicode_win_dir_path + L"АБВГДЕЁЖЗИЙ\\ひらがな日本語.prototxt";
auto cnnNetwork = ie.ReadNetwork(path, L"");
auto function = cnnNetwork.getFunction();
int count_multiply = 0;
int count_constants = 0;
int count_parameters = 0;
std::shared_ptr<ngraph::Node> external_data_node;
for (auto op : function->get_ops()) {
const auto op_type = std::string(op->get_type_name());
count_multiply += (op_type == "Multiply" ? 1 : 0);
count_parameters += (op_type == "Parameter" ? 1 : 0);
if (op_type == "Constant") {
count_constants += 1;
external_data_node = op;
}
}
ASSERT_EQ(function->get_output_size(), 1);
ASSERT_EQ(std::string(function->get_output_op(0)->get_type_name()), "Result");
ASSERT_EQ(function->get_output_element_type(0), ngraph::element::f32);
ASSERT_EQ(function->get_output_shape(0), ngraph::Shape({2, 2}));
ASSERT_EQ(count_multiply, 2);
ASSERT_EQ(count_constants, 1);
ASSERT_EQ(count_parameters, 2);
const auto external_data_node_const = ngraph::as_type_ptr<ngraph::op::Constant>(external_data_node);
ASSERT_TRUE(external_data_node_const->get_vector<float>() == (std::vector<float>{1, 2, 3, 4}));
}
#endif