Narumol Prangnawarat | 1b11f32 | 2021-10-13 11:44:50 +0100 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2021 Arm Ltd and Contributors. All rights reserved. |
| 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | |
| 6 | #include "armnnOnnxParser/IOnnxParser.hpp" |
| 7 | #include "ParserPrototxtFixture.hpp" |
| 8 | |
| 9 | TEST_SUITE("OnnxParser_LoadScopeDynamicTensor") |
| 10 | { |
| 11 | |
| 12 | struct DynamicBatchTensorFixture : public armnnUtils::ParserPrototxtFixture<armnnOnnxParser::IOnnxParser> |
| 13 | { |
| 14 | DynamicBatchTensorFixture() |
| 15 | { |
| 16 | m_Prototext = R"( |
| 17 | ir_version: 3 |
| 18 | producer_name: "CNTK" |
| 19 | producer_version: "2.5.1" |
| 20 | domain: "ai.cntk" |
| 21 | model_version: 1 |
| 22 | graph { |
| 23 | name: "CNTKGraph" |
| 24 | input { |
| 25 | name: "Input" |
| 26 | type { |
| 27 | tensor_type { |
| 28 | elem_type: 1 |
| 29 | shape { |
| 30 | dim { |
| 31 | dim_value: 0 |
| 32 | } |
| 33 | dim { |
| 34 | dim_value: 1 |
| 35 | } |
| 36 | dim { |
| 37 | dim_value: 3 |
| 38 | } |
| 39 | dim { |
| 40 | dim_value: 3 |
| 41 | } |
| 42 | } |
| 43 | } |
| 44 | } |
| 45 | } |
| 46 | input { |
| 47 | name: "Weight" |
| 48 | type { |
| 49 | tensor_type { |
| 50 | elem_type: 1 |
| 51 | shape { |
| 52 | dim { |
| 53 | dim_value: 1 |
| 54 | } |
| 55 | dim { |
| 56 | dim_value: 1 |
| 57 | } |
| 58 | dim { |
| 59 | dim_value: 3 |
| 60 | } |
| 61 | dim { |
| 62 | dim_value: 3 |
| 63 | } |
| 64 | } |
| 65 | } |
| 66 | } |
| 67 | } |
| 68 | initializer { |
| 69 | dims: 1 |
| 70 | dims: 1 |
| 71 | dims: 3 |
| 72 | dims: 3 |
| 73 | data_type: 1 |
| 74 | float_data: 2 |
| 75 | float_data: 1 |
| 76 | float_data: 0 |
| 77 | float_data: 6 |
| 78 | float_data: 2 |
| 79 | float_data: 1 |
| 80 | float_data: 4 |
| 81 | float_data: 1 |
| 82 | float_data: 2 |
| 83 | name: "Weight" |
| 84 | } |
| 85 | node { |
| 86 | input: "Input" |
| 87 | input: "Weight" |
| 88 | output: "Output" |
| 89 | name: "Convolution" |
| 90 | op_type: "Conv" |
| 91 | attribute { |
| 92 | name: "kernel_shape" |
| 93 | ints: 3 |
| 94 | ints: 3 |
| 95 | type: INTS |
| 96 | } |
| 97 | attribute { |
| 98 | name: "strides" |
| 99 | ints: 1 |
| 100 | ints: 1 |
| 101 | type: INTS |
| 102 | } |
| 103 | attribute { |
| 104 | name: "auto_pad" |
| 105 | s: "VALID" |
| 106 | type: STRING |
| 107 | } |
| 108 | attribute { |
| 109 | name: "group" |
| 110 | i: 1 |
| 111 | type: INT |
| 112 | } |
| 113 | attribute { |
| 114 | name: "dilations" |
| 115 | ints: 1 |
| 116 | ints: 1 |
| 117 | type: INTS |
| 118 | } |
| 119 | doc_string: "" |
| 120 | domain: "" |
| 121 | } |
| 122 | output { |
| 123 | name: "Output" |
| 124 | type { |
| 125 | tensor_type { |
| 126 | elem_type: 1 |
| 127 | shape { |
| 128 | dim { |
| 129 | dim_value: 0 |
| 130 | } |
| 131 | dim { |
| 132 | dim_value: 1 |
| 133 | } |
| 134 | dim { |
| 135 | dim_value: 1 |
| 136 | } |
| 137 | dim { |
| 138 | dim_value: 1 |
| 139 | } |
| 140 | } |
| 141 | } |
| 142 | } |
| 143 | } |
| 144 | } |
| 145 | opset_import { |
| 146 | version: 7 |
| 147 | })"; |
| 148 | } |
| 149 | }; |
| 150 | |
| 151 | TEST_CASE_FIXTURE(DynamicBatchTensorFixture, "DynamicBatchTensorTest") |
| 152 | { |
| 153 | Setup({{"Input", armnn::TensorShape({1, 1, 3, 3})}}); |
| 154 | RunTest<4>({{"Input", {1.0, 2.0, 3.0, |
| 155 | 4.0, 5.0, 6.0, |
| 156 | 7.0, 8.0, 9.0}}}, |
| 157 | {{"Output", {1.0 * 2 + 2.0 * 1 + 3.0 * 0 + |
| 158 | 4.0 * 6 + 5.0 * 2 + 6.0 * 1 + |
| 159 | 7.0 * 4 + 8.0 * 1 + 9.0 * 2}}}); |
| 160 | } |
| 161 | |
| 162 | TEST_CASE_FIXTURE(DynamicBatchTensorFixture, "TensorShapeNotSpecifiedTest") |
| 163 | { |
| 164 | CHECK_THROWS_AS(Setup(), armnn::ParseException); |
| 165 | } |
| 166 | |
| 167 | TEST_CASE_FIXTURE(DynamicBatchTensorFixture, "IncorrectInputNameTest") |
| 168 | { |
| 169 | CHECK_THROWS_AS(Setup({{"Incorrect", armnn::TensorShape({1, 1, 3, 3})}}), armnn::ParseException); |
| 170 | } |
| 171 | |
| 172 | TEST_CASE_FIXTURE(DynamicBatchTensorFixture, "IncorrectBatchTensorTest") |
| 173 | { |
| 174 | Setup({{"Input", armnn::TensorShape({2, 1, 3, 3}) }}); |
| 175 | CHECK_THROWS_AS(RunTest<4>({{"Input", { 1.0, 2.0, 3.0, |
| 176 | 4.0, 5.0, 6.0, |
| 177 | 7.0, 8.0, 9.0 }}}, |
| 178 | {{"Output", {1.0 * 2 + 2.0 * 1 + 3.0 * 0 + |
| 179 | 4.0 * 6 + 5.0 * 2 + 6.0 * 1 + |
| 180 | 7.0 * 4 + 8.0 * 1 + 9.0 * 2 }}}), armnn::Exception); |
| 181 | |
| 182 | } |
| 183 | |
| 184 | } |