Compute Library
 22.11
Unstack.cpp
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24 #include "arm_compute/core/Types.h"
28 
29 #include "tests/NEON/Accessor.h"
30 #include "tests/datasets/ShapeDatasets.h"
32 #include "tests/framework/Macros.h"
35 #include "tests/validation/fixtures/UnstackFixture.h"
36 
37 namespace arm_compute
38 {
39 namespace test
40 {
41 namespace validation
42 {
43 namespace
44 {
45 const auto unstack_axis_dataset = framework::dataset::make("Axis", -3, 3);
46 const auto unstack_num_dataset = framework::dataset::make("Num", 1, 3); // The length of the dimension axis
47 const auto unstack_dataset_small = datasets::Small3DShapes() * unstack_axis_dataset * unstack_num_dataset;
48 } //namespace
49 
50 TEST_SUITE(NEON)
51 TEST_SUITE(Unstack)
52 
54  framework::dataset::make("InputInfo",
55 {
56  TensorInfo(TensorShape(1U, 9U, 8U), 1, DataType::U8), // Passes, 1 slice on x axis
57  TensorInfo(TensorShape(1U, 2U, 3U), 1, DataType::U8), // fails because axis > input's rank
58  TensorInfo(TensorShape(1U, 2U, 3U), 1, DataType::S32), // fails axis < (- input's rank)
59  TensorInfo(TensorShape(3U, 7U, 5U), 1, DataType::S32), // passes, 3 slices along X
60  TensorInfo(TensorShape(13U, 7U, 5U), 1, DataType::S16), // fails, too few output slices
61  TensorInfo(TensorShape(1U, 2U, 3U), 1, DataType::U8), // fails mismatching data types
62 }),
63 framework::dataset::make("OutputInfo",
64 {
65  std::vector<TensorInfo>{ TensorInfo(TensorShape(9U, 8U), 1, DataType::U8) }, std::vector<TensorInfo>{ TensorInfo(TensorShape(2U, 3U), 1, DataType::U8) }, std::vector<TensorInfo>{ TensorInfo(TensorShape(2U, 3U), 1, DataType::S32) },
66 
67  std::vector<TensorInfo>{ TensorInfo(TensorShape(7U, 5U), 1, DataType::S32), TensorInfo(TensorShape(7U, 5U), 1, DataType::S32), TensorInfo(TensorShape(7U, 5U), 1, DataType::S32) }, std::vector<TensorInfo>{ TensorInfo(TensorShape(7U, 5U), 1, DataType::S16) }, std::vector<TensorInfo>{ TensorInfo(TensorShape(9U, 8U), 1, DataType::S32) },
68 })),
69 framework::dataset::make("Axis", { -3, 3, -4, -3, 1, 1 })),
70 framework::dataset::make("Num", { 1, 1, 1, 1, 0, 1 })),
71 framework::dataset::make("Expected", { true, false, false, true, false, false })),
72 input_info, output_info, axis, num, expected)
73 {
74  std::vector<TensorInfo> ti(output_info);
75  std::vector<ITensorInfo *> vec(num);
76  for(size_t j = 0; j < vec.size(); ++j)
77  {
78  vec[j] = &ti[j];
79  }
80  ARM_COMPUTE_EXPECT(bool(NEUnstack::validate(&input_info.clone()->set_is_resizable(false), vec, axis)) == expected, framework::LogLevel::ERRORS);
81 }
82 
83 template <typename T>
84 using NEUnstackFixture = UnstackValidationFixture<Tensor, ITensor, Accessor, NEUnstack, T>;
85 
86 TEST_SUITE(F32)
88 {
89  ARM_COMPUTE_ERROR_ON(_target.size() != _reference.size());
90  // Validate output
91  for(size_t k = 0; k < _target.size(); ++k)
92  {
93  validate(Accessor(_target[k]), _reference[k]);
94  }
95 }
96 TEST_SUITE_END() // F32
97 
98 #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
101 {
102  ARM_COMPUTE_ERROR_ON(_target.size() != _reference.size());
103  // Validate output
104  for(size_t k = 0; k < _target.size(); ++k)
105  {
106  validate(Accessor(_target[k]), _reference[k]);
107  }
108 }
109 TEST_SUITE_END() // F16
110 #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
111 
112 TEST_SUITE(Quantized)
114 {
115  ARM_COMPUTE_ERROR_ON(_target.size() != _reference.size());
116  // Validate output
117  for(size_t k = 0; k < _target.size(); ++k)
118  {
119  validate(Accessor(_target[k]), _reference[k]);
120  }
121 }
122 TEST_SUITE_END() // QASYMM8
123 
124 TEST_SUITE_END() // Unstack
125 TEST_SUITE_END() // Neon
126 } // namespace validation
127 } // namespace test
128 } // namespace arm_compute
1 channel, 1 U8 per channel
1 channel, 1 F32 per channel
ARM_COMPUTE_EXPECT(has_error==expected, framework::LogLevel::ERRORS)
#define ARM_COMPUTE_ERROR_ON(cond)
If the condition is true then an error message is printed and an exception thrown.
Definition: Error.h:466
std::enable_if< is_container< T >::value, ContainerDataset< T > >::type make(std::string name, T &&values)
Helper function to create a ContainerDataset.
Copyright (c) 2017-2022 Arm Limited.
1 channel, 1 F16 per channel
1 channel, 1 S32 per channel
DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32), TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::QASYMM8), TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::QASYMM8), TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::QSYMM16), TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::QSYMM16), TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::QSYMM16), }), framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F16), TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32), TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::QASYMM8), TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::QASYMM8), TensorInfo(TensorShape(30U, 11U, 2U), 1, DataType::F32), TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::QSYMM16, QuantizationInfo(1.f/32768.f, 0)), TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::QSYMM16, QuantizationInfo(1.f/32768.f, 0)), TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::QSYMM16, QuantizationInfo(1.f/32768.f, 0)), })), framework::dataset::make("ActivationInfo", { ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::SQRT), })), framework::dataset::make("Expected", { false, true, true, true, false, false, true, true, false })), input_info, output_info, act_info, expected)
Accessor implementation for Tensor objects.
Definition: Accessor.h:35
TEST_SUITE_END() FIXTURE_DATA_TEST_CASE(RunSmall
[CLActivationLayer Test snippet]
quantized, asymmetric fixed-point 8-bit number unsigned
validate(CLAccessor(output_state), expected_output)
1 channel, 1 S16 per channel
UnstackValidationFixture< Tensor, ITensor, Accessor, NEUnstack, T > NEUnstackFixture
Definition: Unstack.cpp:84
FIXTURE_DATA_TEST_CASE(RunSmall, CLAbsLayerFixture< half >, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallShapes(), framework::dataset::make("DataType", DataType::F16)))
Definition: AbsLayer.cpp:50
static Status validate(const ITensorInfo *input, const std::vector< ITensorInfo *> &output_vector, int axis)
Static function to check if given info will lead to a valid configuration of NEUnstack.
Definition: NEUnstack.cpp:92
zip(zip(framework::dataset::make("Weights", { TensorInfo(TensorShape(32U, 13U, 2U, 2U), 1, DataType::F32), TensorInfo(TensorShape(32U, 13U, 2U, 2U), 1, DataType::F32), TensorInfo(TensorShape(32U, 13U, 2U, 1U), 1, DataType::F32), }), framework::dataset::make("MVBGInfo",{ TensorInfo(TensorShape(2U), 1, DataType::F32), TensorInfo(TensorShape(2U), 1, DataType::F16), TensorInfo(TensorShape(5U), 1, DataType::F32), })), framework::dataset::make("Expected", { true, false, false}))
TEST_SUITE(QASYMM8_to_F32) FIXTURE_DATA_TEST_CASE(RunSmall