Michalis Spyrou | 542e92d | 2018-06-05 11:45:48 +0100 | [diff] [blame] | 1 | /* |
| 2 | * Copyright (c) 2018 ARM Limited. |
| 3 | * |
| 4 | * SPDX-License-Identifier: MIT |
| 5 | * |
| 6 | * Permission is hereby granted, free of charge, to any person obtaining a copy |
| 7 | * of this software and associated documentation files (the "Software"), to |
| 8 | * deal in the Software without restriction, including without limitation the |
| 9 | * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or |
| 10 | * sell copies of the Software, and to permit persons to whom the Software is |
| 11 | * furnished to do so, subject to the following conditions: |
| 12 | * |
| 13 | * The above copyright notice and this permission notice shall be included in all |
| 14 | * copies or substantial portions of the Software. |
| 15 | * |
| 16 | * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
| 17 | * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
| 18 | * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
| 19 | * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
| 20 | * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
| 21 | * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE |
| 22 | * SOFTWARE. |
| 23 | */ |
| 24 | #include "arm_compute/runtime/NEON/functions/NERNNLayer.h" |
| 25 | #include "tests/NEON/Accessor.h" |
| 26 | #include "tests/PaddingCalculator.h" |
| 27 | #include "tests/datasets/RNNLayerDataset.h" |
| 28 | #include "tests/framework/Asserts.h" |
| 29 | #include "tests/framework/Macros.h" |
| 30 | #include "tests/framework/datasets/Datasets.h" |
| 31 | #include "tests/validation/Validation.h" |
| 32 | #include "tests/validation/fixtures/RNNLayerFixture.h" |
| 33 | |
| 34 | namespace arm_compute |
| 35 | { |
| 36 | namespace test |
| 37 | { |
| 38 | namespace validation |
| 39 | { |
| 40 | namespace |
| 41 | { |
| 42 | RelativeTolerance<float> tolerance_f32(0.001f); |
| 43 | RelativeTolerance<half> tolerance_f16(half(0.1)); |
| 44 | } // namespace |
| 45 | |
| 46 | TEST_SUITE(NEON) |
| 47 | TEST_SUITE(RNNLayer) |
| 48 | |
| 49 | // *INDENT-OFF* |
| 50 | // clang-format off |
| 51 | DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zip( |
Vidhya Sudhan Loganathan | 7485d5a | 2018-07-04 09:34:00 +0100 | [diff] [blame] | 52 | framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U), 1, DataType::U8), // Wrong data type |
| 53 | TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Wrong input size |
| 54 | TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), // Wrong weights size |
| 55 | TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), // Wrong recurrent weights size |
| 56 | TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), // Wrong bias size |
| 57 | TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), // Wrong output size |
| 58 | TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), // Wrong hidden output size |
| 59 | TensorInfo(TensorShape(32U, 32U), 1, DataType::F32), |
Michalis Spyrou | 542e92d | 2018-06-05 11:45:48 +0100 | [diff] [blame] | 60 | }), |
Vidhya Sudhan Loganathan | 7485d5a | 2018-07-04 09:34:00 +0100 | [diff] [blame] | 61 | framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(27U, 11U), 1, DataType::F32), |
| 62 | TensorInfo(TensorShape(27U, 11U), 1, DataType::F32), |
| 63 | TensorInfo(TensorShape(27U, 11U, 2U), 1, DataType::F32), |
| 64 | TensorInfo(TensorShape(27U, 11U), 1, DataType::F32), |
| 65 | TensorInfo(TensorShape(27U, 11U), 1, DataType::F32), |
| 66 | TensorInfo(TensorShape(27U, 11U), 1, DataType::F32), |
| 67 | TensorInfo(TensorShape(27U, 11U), 1, DataType::F32), |
| 68 | TensorInfo(TensorShape(32U, 32U), 1, DataType::F32), |
Michalis Spyrou | 542e92d | 2018-06-05 11:45:48 +0100 | [diff] [blame] | 69 | })), |
Vidhya Sudhan Loganathan | 7485d5a | 2018-07-04 09:34:00 +0100 | [diff] [blame] | 70 | framework::dataset::make("RecurrentWeightsInfo", { TensorInfo(TensorShape(11U, 11U), 1, DataType::F32), |
| 71 | TensorInfo(TensorShape(11U, 11U), 1, DataType::F32), |
| 72 | TensorInfo(TensorShape(11U, 11U), 1, DataType::F32), |
| 73 | TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32), |
| 74 | TensorInfo(TensorShape(11U, 11U), 1, DataType::F32), |
| 75 | TensorInfo(TensorShape(11U, 11U), 1, DataType::F32), |
| 76 | TensorInfo(TensorShape(11U, 11U), 1, DataType::F32), |
| 77 | TensorInfo(TensorShape(32U, 32U), 1, DataType::F32), |
Michalis Spyrou | 542e92d | 2018-06-05 11:45:48 +0100 | [diff] [blame] | 78 | })), |
Vidhya Sudhan Loganathan | 7485d5a | 2018-07-04 09:34:00 +0100 | [diff] [blame] | 79 | framework::dataset::make("BiasInfo", { TensorInfo(TensorShape(11U), 1, DataType::F32), |
| 80 | TensorInfo(TensorShape(11U), 1, DataType::F32), |
| 81 | TensorInfo(TensorShape(11U), 1, DataType::F32), |
| 82 | TensorInfo(TensorShape(11U), 1, DataType::F32), |
| 83 | TensorInfo(TensorShape(30U), 1, DataType::F32), |
| 84 | TensorInfo(TensorShape(11U), 1, DataType::F32), |
| 85 | TensorInfo(TensorShape(11U), 1, DataType::F32), |
| 86 | TensorInfo(TensorShape(32U), 1, DataType::F32), |
Michalis Spyrou | 542e92d | 2018-06-05 11:45:48 +0100 | [diff] [blame] | 87 | })), |
Vidhya Sudhan Loganathan | 7485d5a | 2018-07-04 09:34:00 +0100 | [diff] [blame] | 88 | framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(11U, 13U), 1, DataType::F32), |
| 89 | TensorInfo(TensorShape(11U, 13U), 1, DataType::F32), |
| 90 | TensorInfo(TensorShape(11U, 13U), 1, DataType::F32), |
| 91 | TensorInfo(TensorShape(11U, 13U), 1, DataType::F32), |
| 92 | TensorInfo(TensorShape(11U, 13U), 1, DataType::F32), |
| 93 | TensorInfo(TensorShape(11U), 1, DataType::F32), |
| 94 | TensorInfo(TensorShape(11U, 13U), 1, DataType::F32), |
| 95 | TensorInfo(TensorShape(32U, 32U), 1, DataType::F32), |
Michalis Spyrou | 542e92d | 2018-06-05 11:45:48 +0100 | [diff] [blame] | 96 | })), |
Vidhya Sudhan Loganathan | 7485d5a | 2018-07-04 09:34:00 +0100 | [diff] [blame] | 97 | framework::dataset::make("HiddenStateInfo", { TensorInfo(TensorShape(11U, 13U), 1, DataType::F32), |
| 98 | TensorInfo(TensorShape(11U, 13U), 1, DataType::F32), |
| 99 | TensorInfo(TensorShape(11U, 13U), 1, DataType::F32), |
| 100 | TensorInfo(TensorShape(11U, 13U), 1, DataType::F32), |
| 101 | TensorInfo(TensorShape(11U, 13U), 1, DataType::F32), |
| 102 | TensorInfo(TensorShape(11U, 13U), 1, DataType::F32), |
| 103 | TensorInfo(TensorShape(11U, 13U, 2U), 1, DataType::F32), |
| 104 | TensorInfo(TensorShape(32U, 32U), 1, DataType::F32), |
Michalis Spyrou | 542e92d | 2018-06-05 11:45:48 +0100 | [diff] [blame] | 105 | })), |
| 106 | framework::dataset::make("ActivationInfo", { ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), |
| 107 | ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), |
| 108 | ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), |
| 109 | ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), |
| 110 | ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), |
| 111 | ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), |
| 112 | ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), |
| 113 | ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), |
| 114 | })), |
| 115 | framework::dataset::make("Expected", { false, false, false, false, false, false, false, true })), |
| 116 | input_info, weights_info, recurrent_weights_info, bias_info, output_info, hidden_output_info, info, expected) |
| 117 | { |
| 118 | ARM_COMPUTE_EXPECT(bool(NERNNLayer::validate(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), &recurrent_weights_info.clone()->set_is_resizable(false), &bias_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), &hidden_output_info.clone()->set_is_resizable(false), info)) == expected, framework::LogLevel::ERRORS); |
| 119 | } |
| 120 | // clang-format on |
| 121 | // *INDENT-ON* |
| 122 | |
| 123 | template <typename T> |
| 124 | using NERNNLayerFixture = RNNLayerValidationFixture<Tensor, Accessor, NERNNLayer, T>; |
| 125 | |
| 126 | TEST_SUITE(FP32) |
| 127 | FIXTURE_DATA_TEST_CASE(RunSmall, NERNNLayerFixture<float>, framework::DatasetMode::ALL, combine(datasets::SmallRNNLayerDataset(), framework::dataset::make("DataType", DataType::F32))) |
| 128 | { |
| 129 | // Validate output |
| 130 | validate(Accessor(_target), _reference, tolerance_f32); |
| 131 | } |
| 132 | TEST_SUITE_END() // FP32 |
| 133 | |
| 134 | #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| 135 | TEST_SUITE(FP16) |
| 136 | FIXTURE_DATA_TEST_CASE(RunSmall, NERNNLayerFixture<half>, framework::DatasetMode::ALL, combine(datasets::SmallRNNLayerDataset(), framework::dataset::make("DataType", DataType::F16))) |
| 137 | { |
| 138 | // Validate output |
| 139 | validate(Accessor(_target), _reference, tolerance_f16); |
| 140 | } |
| 141 | TEST_SUITE_END() // FP16 |
| 142 | #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| 143 | TEST_SUITE_END() // RNNLayer |
| 144 | TEST_SUITE_END() // NEON |
| 145 | } // namespace validation |
| 146 | } // namespace test |
| 147 | } // namespace arm_compute |