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
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
|
/*
* 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 "1.0/Callbacks.h"
namespace android::hardware::neuralnetworks::V1_0::vts::functional {
using implementation::PreparedModelCallback;
// create device test
TEST_P(NeuralnetworksHidlTest, CreateDevice) {}
// status test
TEST_P(NeuralnetworksHidlTest, StatusTest) {
Return<DeviceStatus> status = kDevice->getStatus();
ASSERT_TRUE(status.isOk());
EXPECT_EQ(DeviceStatus::AVAILABLE, static_cast<DeviceStatus>(status));
}
// initialization
TEST_P(NeuralnetworksHidlTest, GetCapabilitiesTest) {
Return<void> ret =
kDevice->getCapabilities([](ErrorStatus status, const Capabilities& capabilities) {
EXPECT_EQ(ErrorStatus::NONE, status);
EXPECT_LT(0.0f, capabilities.float32Performance.execTime);
EXPECT_LT(0.0f, capabilities.float32Performance.powerUsage);
EXPECT_LT(0.0f, capabilities.quantized8Performance.execTime);
EXPECT_LT(0.0f, capabilities.quantized8Performance.powerUsage);
});
EXPECT_TRUE(ret.isOk());
}
// detect cycle
TEST_P(NeuralnetworksHidlTest, CycleTest) {
// opnd0 = TENSOR_FLOAT32 // model input
// opnd1 = TENSOR_FLOAT32 // model input
// opnd2 = INT32 // model input
// opnd3 = ADD(opnd0, opnd4, opnd2)
// opnd4 = ADD(opnd1, opnd3, opnd2)
// opnd5 = ADD(opnd4, opnd0, opnd2) // model output
//
// +-----+
// | |
// v |
// 3 = ADD(0, 4, 2) |
// | |
// +----------+ |
// | |
// v |
// 4 = ADD(1, 3, 2) |
// | |
// +----------------+
// |
// |
// +-------+
// |
// v
// 5 = ADD(4, 0, 2)
const std::vector<Operand> operands = {
{
// operands[0]
.type = OperandType::TENSOR_FLOAT32,
.dimensions = {1},
.numberOfConsumers = 2,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::MODEL_INPUT,
.location = {.poolIndex = 0, .offset = 0, .length = 0},
},
{
// operands[1]
.type = OperandType::TENSOR_FLOAT32,
.dimensions = {1},
.numberOfConsumers = 1,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::MODEL_INPUT,
.location = {.poolIndex = 0, .offset = 0, .length = 0},
},
{
// operands[2]
.type = OperandType::INT32,
.dimensions = {},
.numberOfConsumers = 3,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::MODEL_INPUT,
.location = {.poolIndex = 0, .offset = 0, .length = 0},
},
{
// operands[3]
.type = OperandType::TENSOR_FLOAT32,
.dimensions = {1},
.numberOfConsumers = 1,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
.location = {.poolIndex = 0, .offset = 0, .length = 0},
},
{
// operands[4]
.type = OperandType::TENSOR_FLOAT32,
.dimensions = {1},
.numberOfConsumers = 2,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
.location = {.poolIndex = 0, .offset = 0, .length = 0},
},
{
// operands[5]
.type = OperandType::TENSOR_FLOAT32,
.dimensions = {1},
.numberOfConsumers = 0,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::MODEL_OUTPUT,
.location = {.poolIndex = 0, .offset = 0, .length = 0},
},
};
const std::vector<Operation> operations = {
{.type = OperationType::ADD, .inputs = {0, 4, 2}, .outputs = {3}},
{.type = OperationType::ADD, .inputs = {1, 3, 2}, .outputs = {4}},
{.type = OperationType::ADD, .inputs = {4, 0, 2}, .outputs = {5}},
};
const Model model = {
.operands = operands,
.operations = operations,
.inputIndexes = {0, 1, 2},
.outputIndexes = {5},
.operandValues = {},
.pools = {},
};
// ensure that getSupportedOperations() checks model validity
ErrorStatus supportedOpsErrorStatus = ErrorStatus::GENERAL_FAILURE;
Return<void> supportedOpsReturn = kDevice->getSupportedOperations(
model, [&model, &supportedOpsErrorStatus](ErrorStatus status,
const hidl_vec<bool>& supported) {
supportedOpsErrorStatus = status;
if (status == ErrorStatus::NONE) {
ASSERT_EQ(supported.size(), model.operations.size());
}
});
ASSERT_TRUE(supportedOpsReturn.isOk());
ASSERT_EQ(supportedOpsErrorStatus, ErrorStatus::INVALID_ARGUMENT);
// ensure that prepareModel() checks model validity
sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback;
Return<ErrorStatus> prepareLaunchReturn = kDevice->prepareModel(model, preparedModelCallback);
ASSERT_TRUE(prepareLaunchReturn.isOk());
// Note that preparation can fail for reasons other than an
// invalid model (invalid model should result in
// INVALID_ARGUMENT) -- for example, perhaps not all
// operations are supported, or perhaps the device hit some
// kind of capacity limit.
EXPECT_NE(prepareLaunchReturn, ErrorStatus::NONE);
EXPECT_NE(preparedModelCallback->getStatus(), ErrorStatus::NONE);
EXPECT_EQ(preparedModelCallback->getPreparedModel(), nullptr);
}
} // namespace android::hardware::neuralnetworks::V1_0::vts::functional
|
