1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
|
/*
* Copyright (C) 2018 The Android Open Source Project
*
* 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.
*/
#define LOG_TAG "neuralnetworks_hidl_hal_test"
#include "VtsHalNeuralnetworks.h"
#include <android-base/logging.h>
#include <hidl/ServiceManagement.h>
#include <string>
#include <utility>
#include "1.0/Callbacks.h"
#include "1.0/Utils.h"
#include "GeneratedTestHarness.h"
#include "TestHarness.h"
namespace android::hardware::neuralnetworks::V1_1::vts::functional {
using V1_0::ErrorStatus;
using V1_0::IPreparedModel;
using V1_0::Request;
using V1_0::implementation::PreparedModelCallback;
void createPreparedModel(const sp<IDevice>& device, const Model& model,
sp<IPreparedModel>* preparedModel) {
ASSERT_NE(nullptr, preparedModel);
*preparedModel = nullptr;
// see if service can handle model
bool fullySupportsModel = false;
const Return<void> supportedCall = device->getSupportedOperations_1_1(
model, [&fullySupportsModel](ErrorStatus status, const hidl_vec<bool>& supported) {
ASSERT_EQ(ErrorStatus::NONE, status);
ASSERT_NE(0ul, supported.size());
fullySupportsModel = std::all_of(supported.begin(), supported.end(),
[](bool valid) { return valid; });
});
ASSERT_TRUE(supportedCall.isOk());
// launch prepare model
const sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
const Return<ErrorStatus> prepareLaunchStatus = device->prepareModel_1_1(
model, ExecutionPreference::FAST_SINGLE_ANSWER, preparedModelCallback);
ASSERT_TRUE(prepareLaunchStatus.isOk());
ASSERT_EQ(ErrorStatus::NONE, static_cast<ErrorStatus>(prepareLaunchStatus));
// retrieve prepared model
preparedModelCallback->wait();
const ErrorStatus prepareReturnStatus = preparedModelCallback->getStatus();
*preparedModel = preparedModelCallback->getPreparedModel();
// The getSupportedOperations_1_1 call returns a list of operations that are
// guaranteed not to fail if prepareModel_1_1 is called, and
// 'fullySupportsModel' is true i.f.f. the entire model is guaranteed.
// If a driver has any doubt that it can prepare an operation, it must
// return false. So here, if a driver isn't sure if it can support an
// operation, but reports that it successfully prepared the model, the test
// can continue.
if (!fullySupportsModel && prepareReturnStatus != ErrorStatus::NONE) {
ASSERT_EQ(nullptr, preparedModel->get());
LOG(INFO) << "NN VTS: Early termination of test because vendor service cannot prepare "
"model that it does not support.";
std::cout << "[ ] Early termination of test because vendor service cannot "
"prepare model that it does not support."
<< std::endl;
GTEST_SKIP();
}
ASSERT_EQ(ErrorStatus::NONE, prepareReturnStatus);
ASSERT_NE(nullptr, preparedModel->get());
}
void NeuralnetworksHidlTest::SetUp() {
testing::TestWithParam<NeuralnetworksHidlTestParam>::SetUp();
ASSERT_NE(kDevice, nullptr);
const bool deviceIsResponsive = kDevice->ping().isOk();
ASSERT_TRUE(deviceIsResponsive);
}
static NamedDevice makeNamedDevice(const std::string& name) {
return {name, IDevice::getService(name)};
}
static std::vector<NamedDevice> getNamedDevicesImpl() {
// Retrieves the name of all service instances that implement IDevice,
// including any Lazy HAL instances.
const std::vector<std::string> names = hardware::getAllHalInstanceNames(IDevice::descriptor);
// Get a handle to each device and pair it with its name.
std::vector<NamedDevice> namedDevices;
namedDevices.reserve(names.size());
std::transform(names.begin(), names.end(), std::back_inserter(namedDevices), makeNamedDevice);
return namedDevices;
}
const std::vector<NamedDevice>& getNamedDevices() {
const static std::vector<NamedDevice> devices = getNamedDevicesImpl();
return devices;
}
std::string printNeuralnetworksHidlTest(
const testing::TestParamInfo<NeuralnetworksHidlTestParam>& info) {
return gtestCompliantName(getName(info.param));
}
INSTANTIATE_DEVICE_TEST(NeuralnetworksHidlTest);
// Forward declaration from ValidateModel.cpp
void validateModel(const sp<IDevice>& device, const Model& model);
// Forward declaration from ValidateRequest.cpp
void validateRequest(const sp<V1_0::IPreparedModel>& preparedModel, const V1_0::Request& request);
void validateEverything(const sp<IDevice>& device, const Model& model, const Request& request) {
validateModel(device, model);
// Create IPreparedModel.
sp<IPreparedModel> preparedModel;
createPreparedModel(device, model, &preparedModel);
if (preparedModel == nullptr) return;
validateRequest(preparedModel, request);
}
TEST_P(ValidationTest, Test) {
const Model model = createModel(kTestModel);
ExecutionContext context;
const Request request = context.createRequest(kTestModel);
ASSERT_FALSE(kTestModel.expectFailure);
validateEverything(kDevice, model, request);
}
INSTANTIATE_GENERATED_TEST(ValidationTest, [](const std::string& testName) {
// Skip validation for the "inputs_as_internal" and "all_tensors_as_inputs"
// generated tests.
return testName.find("inputs_as_internal") == std::string::npos &&
testName.find("all_tensors_as_inputs") == std::string::npos;
});
} // namespace android::hardware::neuralnetworks::V1_1::vts::functional
|
