Matthew Sloyan | ba5fad3 | 2022-09-26 13:31:43 +0100 | [diff] [blame] | 1 | |
| 2 | // Copyright (c) 2022, ARM Limited. |
| 3 | // |
| 4 | // Licensed under the Apache License, Version 2.0 (the "License"); |
| 5 | // you may not use this file except in compliance with the License. |
| 6 | // You may obtain a copy of the License at |
| 7 | // |
| 8 | // http://www.apache.org/licenses/LICENSE-2.0 |
| 9 | // |
| 10 | // Unless required by applicable law or agreed to in writing, software |
| 11 | // distributed under the License is distributed on an "AS IS" BASIS, |
| 12 | // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| 13 | // See the License for the specific language governing permissions and |
| 14 | // limitations under the License. |
| 15 | |
| 16 | #ifndef DOCTEST_CONFIG_IMPLEMENT_WITH_MAIN |
| 17 | #define DOCTEST_CONFIG_IMPLEMENT_WITH_MAIN |
| 18 | #endif |
| 19 | |
Matthew Sloyan | ba5fad3 | 2022-09-26 13:31:43 +0100 | [diff] [blame] | 20 | #include "general_utils.h" |
Grant Watson | 64285a1 | 2022-11-16 15:32:39 +0000 | [diff] [blame] | 21 | #include "model_runner.h" |
| 22 | #include "operators.h" |
| 23 | |
| 24 | #include <numeric> |
Matthew Sloyan | ba5fad3 | 2022-09-26 13:31:43 +0100 | [diff] [blame] | 25 | |
James Ward | 71dfc70 | 2022-10-11 14:13:09 +0100 | [diff] [blame] | 26 | // Remove conflicting REQUIRE definition between doctest and reference_model |
| 27 | #undef REQUIRE |
| 28 | |
Matthew Sloyan | ba5fad3 | 2022-09-26 13:31:43 +0100 | [diff] [blame] | 29 | #include "doctest.h" |
| 30 | |
| 31 | using namespace TosaReference; |
| 32 | using namespace tosa; |
| 33 | |
| 34 | template <typename T> |
| 35 | void compareOutput(std::vector<T>& tensor1, std::vector<T>& tensor2, size_t size) |
| 36 | { |
| 37 | for (size_t i = 0; i < size; ++i) |
| 38 | { |
Grant Watson | 64285a1 | 2022-11-16 15:32:39 +0000 | [diff] [blame] | 39 | CHECK_MESSAGE(tensor1[i] == doctest::Approx(tensor2[i]), ""); |
Matthew Sloyan | ba5fad3 | 2022-09-26 13:31:43 +0100 | [diff] [blame] | 40 | } |
| 41 | } |
| 42 | |
| 43 | TEST_SUITE("model_runner") |
| 44 | { |
| 45 | |
Grant Watson | 64285a1 | 2022-11-16 15:32:39 +0000 | [diff] [blame] | 46 | TEST_CASE("op_entry_add") |
| 47 | { |
| 48 | // Inputs/Outputs |
| 49 | tosa_datatype_t dt = tosa_datatype_fp32_t; |
| 50 | std::vector<int32_t> input_shape = { 2, 4, 4, 1 }; |
| 51 | std::vector<int32_t> output_shape = { 2, 4, 4, 1 }; |
| 52 | std::vector<float> srcData1(32, 4.0f); |
| 53 | std::vector<float> srcData2(32, 3.0f); |
| 54 | std::vector<float> dstData(32, 0.0f); |
Matthew Sloyan | ba5fad3 | 2022-09-26 13:31:43 +0100 | [diff] [blame] | 55 | |
Grant Watson | 64285a1 | 2022-11-16 15:32:39 +0000 | [diff] [blame] | 56 | tosa_tensor_t input1; |
| 57 | input1.shape = input_shape.data(); |
| 58 | input1.num_dims = input_shape.size(); |
| 59 | input1.data_type = dt; |
| 60 | input1.data = reinterpret_cast<uint8_t*>(srcData1.data()); |
| 61 | input1.size = srcData1.size() * sizeof(float); |
Matthew Sloyan | ba5fad3 | 2022-09-26 13:31:43 +0100 | [diff] [blame] | 62 | |
Grant Watson | 64285a1 | 2022-11-16 15:32:39 +0000 | [diff] [blame] | 63 | tosa_tensor_t input2; |
| 64 | input2.shape = input_shape.data(); |
| 65 | input2.num_dims = input_shape.size(); |
| 66 | input2.data_type = dt; |
| 67 | input2.data = reinterpret_cast<uint8_t*>(srcData2.data()); |
| 68 | input2.size = srcData2.size() * sizeof(float); |
Matthew Sloyan | ba5fad3 | 2022-09-26 13:31:43 +0100 | [diff] [blame] | 69 | |
Grant Watson | 64285a1 | 2022-11-16 15:32:39 +0000 | [diff] [blame] | 70 | tosa_tensor_t output; |
| 71 | output.shape = output_shape.data(); |
| 72 | output.num_dims = output_shape.size(); |
| 73 | output.data_type = dt; |
| 74 | output.data = reinterpret_cast<uint8_t*>(dstData.data()); |
| 75 | output.size = dstData.size() * sizeof(float); |
Matthew Sloyan | ba5fad3 | 2022-09-26 13:31:43 +0100 | [diff] [blame] | 76 | |
Grant Watson | 64285a1 | 2022-11-16 15:32:39 +0000 | [diff] [blame] | 77 | // Execution |
| 78 | auto status = tosa_run_add(input1, input2, output); |
| 79 | CHECK((status == tosa_status_valid)); |
Matthew Sloyan | ba5fad3 | 2022-09-26 13:31:43 +0100 | [diff] [blame] | 80 | |
Grant Watson | 64285a1 | 2022-11-16 15:32:39 +0000 | [diff] [blame] | 81 | // Compare results |
| 82 | std::vector<float> expectedData(8, 7.0f); |
| 83 | compareOutput(dstData, expectedData, expectedData.size()); |
| 84 | } |
Matthew Sloyan | ba5fad3 | 2022-09-26 13:31:43 +0100 | [diff] [blame] | 85 | |
Grant Watson | 64285a1 | 2022-11-16 15:32:39 +0000 | [diff] [blame] | 86 | TEST_CASE("op_entry_avg_pool2d") |
| 87 | { |
| 88 | // Pool parameters |
| 89 | const int32_t kernel[2] = { 2, 2 }; |
| 90 | const int32_t stride[2] = { 2, 2 }; |
| 91 | const int32_t pad[4] = { 0, 0, 0, 0 }; |
Matthew Sloyan | ba5fad3 | 2022-09-26 13:31:43 +0100 | [diff] [blame] | 92 | |
Grant Watson | 64285a1 | 2022-11-16 15:32:39 +0000 | [diff] [blame] | 93 | // Inputs/Outputs |
| 94 | tosa_datatype_t dt = tosa_datatype_fp32_t; |
| 95 | std::vector<int32_t> input_shape = { 2, 4, 4, 1 }; |
| 96 | std::vector<int32_t> output_shape = { 2, 2, 2, 1 }; |
| 97 | std::vector<float> srcData(32, 7.0f); |
| 98 | std::vector<float> dstData(8, 0.f); |
Matthew Sloyan | ba5fad3 | 2022-09-26 13:31:43 +0100 | [diff] [blame] | 99 | |
Grant Watson | 64285a1 | 2022-11-16 15:32:39 +0000 | [diff] [blame] | 100 | tosa_tensor_t input; |
| 101 | input.shape = input_shape.data(); |
| 102 | input.num_dims = input_shape.size(); |
| 103 | input.data_type = dt; |
| 104 | input.data = reinterpret_cast<uint8_t*>(srcData.data()); |
| 105 | input.size = srcData.size() * sizeof(float); |
Matthew Sloyan | ba5fad3 | 2022-09-26 13:31:43 +0100 | [diff] [blame] | 106 | |
Grant Watson | 64285a1 | 2022-11-16 15:32:39 +0000 | [diff] [blame] | 107 | tosa_tensor_t output; |
| 108 | output.shape = output_shape.data(); |
| 109 | output.num_dims = output_shape.size(); |
| 110 | output.data_type = dt; |
| 111 | output.data = reinterpret_cast<uint8_t*>(dstData.data()); |
| 112 | output.size = dstData.size() * sizeof(float); |
Matthew Sloyan | ba5fad3 | 2022-09-26 13:31:43 +0100 | [diff] [blame] | 113 | |
Grant Watson | 64285a1 | 2022-11-16 15:32:39 +0000 | [diff] [blame] | 114 | // Execution |
| 115 | auto status = tosa_run_avg_pool2d(input, kernel, stride, pad, 0, 0, output); |
| 116 | CHECK((status == tosa_status_valid)); |
Matthew Sloyan | ba5fad3 | 2022-09-26 13:31:43 +0100 | [diff] [blame] | 117 | |
Grant Watson | 64285a1 | 2022-11-16 15:32:39 +0000 | [diff] [blame] | 118 | // Compare results |
| 119 | std::vector<float> expectedData(8, 7.0f); |
| 120 | compareOutput(dstData, expectedData, expectedData.size()); |
| 121 | } |
Matthew Sloyan | ba5fad3 | 2022-09-26 13:31:43 +0100 | [diff] [blame] | 122 | |
Grant Watson | 64285a1 | 2022-11-16 15:32:39 +0000 | [diff] [blame] | 123 | TEST_CASE("op_entry_conv2d") |
| 124 | { |
| 125 | // Conv parameters |
| 126 | const int32_t stride[2] = { 1, 1 }; |
| 127 | const int32_t pad[4] = { 0, 0, 0, 0 }; |
| 128 | const int32_t dilation[2] = { 1, 1 }; |
Matthew Sloyan | ba5fad3 | 2022-09-26 13:31:43 +0100 | [diff] [blame] | 129 | |
Grant Watson | 64285a1 | 2022-11-16 15:32:39 +0000 | [diff] [blame] | 130 | // Inputs/Outputs |
| 131 | tosa_datatype_t dt = tosa_datatype_fp32_t; |
| 132 | std::vector<int32_t> input_shape = { 1, 32, 32, 8 }; |
| 133 | std::vector<int32_t> output_shape = { 1, 32, 32, 16 }; |
| 134 | std::vector<int32_t> weight_shape = { 16, 1, 1, 8 }; |
| 135 | std::vector<int32_t> bias_shape = { 16 }; |
| 136 | std::vector<float> srcData(32 * 32 * 8, 1.0f); |
| 137 | std::vector<float> dstData(32 * 32 * 16, 0.f); |
| 138 | std::vector<float> biasData(16, 0.f); |
| 139 | std::vector<float> weightData(16 * 8, 1.0f); |
Matthew Sloyan | ba5fad3 | 2022-09-26 13:31:43 +0100 | [diff] [blame] | 140 | |
Grant Watson | 64285a1 | 2022-11-16 15:32:39 +0000 | [diff] [blame] | 141 | tosa_tensor_t input; |
| 142 | input.shape = input_shape.data(); |
| 143 | input.num_dims = input_shape.size(); |
| 144 | input.data_type = dt; |
| 145 | input.data = reinterpret_cast<uint8_t*>(srcData.data()); |
| 146 | input.size = srcData.size() * sizeof(float); |
Matthew Sloyan | ba5fad3 | 2022-09-26 13:31:43 +0100 | [diff] [blame] | 147 | |
Grant Watson | 64285a1 | 2022-11-16 15:32:39 +0000 | [diff] [blame] | 148 | tosa_tensor_t weight; |
| 149 | weight.shape = weight_shape.data(); |
| 150 | weight.num_dims = weight_shape.size(); |
| 151 | weight.data_type = dt; |
| 152 | weight.data = reinterpret_cast<uint8_t*>(weightData.data()); |
| 153 | weight.size = weightData.size() * sizeof(float); |
Matthew Sloyan | ba5fad3 | 2022-09-26 13:31:43 +0100 | [diff] [blame] | 154 | |
Grant Watson | 64285a1 | 2022-11-16 15:32:39 +0000 | [diff] [blame] | 155 | tosa_tensor_t bias; |
| 156 | bias.shape = bias_shape.data(); |
| 157 | bias.num_dims = bias_shape.size(); |
| 158 | bias.data_type = dt; |
| 159 | bias.data = reinterpret_cast<uint8_t*>(biasData.data()); |
| 160 | bias.size = biasData.size() * sizeof(float); |
Matthew Sloyan | ba5fad3 | 2022-09-26 13:31:43 +0100 | [diff] [blame] | 161 | |
Grant Watson | 64285a1 | 2022-11-16 15:32:39 +0000 | [diff] [blame] | 162 | tosa_tensor_t output; |
| 163 | output.shape = output_shape.data(); |
| 164 | output.num_dims = output_shape.size(); |
| 165 | output.data_type = dt; |
| 166 | output.data = reinterpret_cast<uint8_t*>(dstData.data()); |
| 167 | output.size = dstData.size() * sizeof(float); |
Matthew Sloyan | ba5fad3 | 2022-09-26 13:31:43 +0100 | [diff] [blame] | 168 | |
Grant Watson | 64285a1 | 2022-11-16 15:32:39 +0000 | [diff] [blame] | 169 | const int32_t input_zp = 0; |
| 170 | const int32_t weight_zp = 0; |
Matthew Sloyan | ba5fad3 | 2022-09-26 13:31:43 +0100 | [diff] [blame] | 171 | |
Grant Watson | 64285a1 | 2022-11-16 15:32:39 +0000 | [diff] [blame] | 172 | // Execution |
| 173 | auto status = tosa_run_conv2d(input, weight, bias, pad, stride, dilation, input_zp, weight_zp, output); |
| 174 | CHECK((status == tosa_status_valid)); |
Matthew Sloyan | ba5fad3 | 2022-09-26 13:31:43 +0100 | [diff] [blame] | 175 | |
Grant Watson | 64285a1 | 2022-11-16 15:32:39 +0000 | [diff] [blame] | 176 | // Compare results |
| 177 | std::vector<float> expectedData(32 * 32 * 16, 8.0f); |
| 178 | compareOutput(dstData, expectedData, expectedData.size()); |
| 179 | } |
Matthew Sloyan | ba5fad3 | 2022-09-26 13:31:43 +0100 | [diff] [blame] | 180 | |
Grant Watson | 64285a1 | 2022-11-16 15:32:39 +0000 | [diff] [blame] | 181 | TEST_CASE("op_entry_max_pool2d") |
| 182 | { |
| 183 | // Pool parameters |
| 184 | const int32_t kernel[2] = { 2, 2 }; |
| 185 | const int32_t stride[2] = { 2, 2 }; |
| 186 | const int32_t pad[4] = { 0, 0, 0, 0 }; |
Matthew Sloyan | ba5fad3 | 2022-09-26 13:31:43 +0100 | [diff] [blame] | 187 | |
Grant Watson | 64285a1 | 2022-11-16 15:32:39 +0000 | [diff] [blame] | 188 | // Inputs/Outputs |
| 189 | tosa_datatype_t dt = tosa_datatype_fp32_t; |
| 190 | std::vector<int32_t> input_shape = { 2, 4, 4, 1 }; |
| 191 | std::vector<int32_t> output_shape = { 2, 2, 2, 1 }; |
| 192 | std::vector<float> srcData(32); |
| 193 | std::vector<float> dstData(8, 0.f); |
| 194 | std::iota(std::begin(srcData), std::end(srcData), 1); |
Matthew Sloyan | ba5fad3 | 2022-09-26 13:31:43 +0100 | [diff] [blame] | 195 | |
Grant Watson | 64285a1 | 2022-11-16 15:32:39 +0000 | [diff] [blame] | 196 | tosa_tensor_t input; |
| 197 | input.shape = input_shape.data(); |
| 198 | input.num_dims = input_shape.size(); |
| 199 | input.data_type = dt; |
| 200 | input.data = reinterpret_cast<uint8_t*>(srcData.data()); |
| 201 | input.size = srcData.size() * sizeof(float); |
Matthew Sloyan | ba5fad3 | 2022-09-26 13:31:43 +0100 | [diff] [blame] | 202 | |
Grant Watson | 64285a1 | 2022-11-16 15:32:39 +0000 | [diff] [blame] | 203 | tosa_tensor_t output; |
| 204 | output.shape = output_shape.data(); |
| 205 | output.num_dims = output_shape.size(); |
| 206 | output.data_type = dt; |
| 207 | output.data = reinterpret_cast<uint8_t*>(dstData.data()); |
| 208 | output.size = dstData.size() * sizeof(float); |
Matthew Sloyan | ba5fad3 | 2022-09-26 13:31:43 +0100 | [diff] [blame] | 209 | |
Grant Watson | 64285a1 | 2022-11-16 15:32:39 +0000 | [diff] [blame] | 210 | // Execution |
| 211 | auto status = tosa_run_max_pool2d(input, kernel, stride, pad, 0, 0, output); |
| 212 | CHECK((status == tosa_status_valid)); |
Matthew Sloyan | ba5fad3 | 2022-09-26 13:31:43 +0100 | [diff] [blame] | 213 | |
Grant Watson | 64285a1 | 2022-11-16 15:32:39 +0000 | [diff] [blame] | 214 | // Compare results |
| 215 | std::vector<float> expectedData = { 6, 8, 14, 16, 22, 24, 30, 32 }; |
| 216 | compareOutput(dstData, expectedData, expectedData.size()); |
| 217 | } |
Matthew Sloyan | ba5fad3 | 2022-09-26 13:31:43 +0100 | [diff] [blame] | 218 | |
Grant Watson | 64285a1 | 2022-11-16 15:32:39 +0000 | [diff] [blame] | 219 | TEST_CASE("op_entry_pad") |
| 220 | { |
| 221 | // Inputs/Outputs |
| 222 | tosa_datatype_t dt = tosa_datatype_fp32_t; |
| 223 | std::vector<int32_t> input_shape = { 2, 2 }; |
| 224 | std::vector<int32_t> output_shape = { 4, 4 }; |
| 225 | std::vector<float> srcData1(4, 4.0f); |
| 226 | std::vector<float> dstData(16, 0.0f); |
Matthew Sloyan | ba5fad3 | 2022-09-26 13:31:43 +0100 | [diff] [blame] | 227 | |
Grant Watson | 64285a1 | 2022-11-16 15:32:39 +0000 | [diff] [blame] | 228 | tosa_tensor_t input1; |
| 229 | input1.shape = input_shape.data(); |
| 230 | input1.num_dims = input_shape.size(); |
| 231 | input1.data_type = dt; |
| 232 | input1.data = reinterpret_cast<uint8_t*>(srcData1.data()); |
| 233 | input1.size = srcData1.size() * sizeof(float); |
Matthew Sloyan | ba5fad3 | 2022-09-26 13:31:43 +0100 | [diff] [blame] | 234 | |
Grant Watson | 64285a1 | 2022-11-16 15:32:39 +0000 | [diff] [blame] | 235 | tosa_tensor_t output; |
| 236 | output.shape = output_shape.data(); |
| 237 | output.num_dims = output_shape.size(); |
| 238 | output.data_type = dt; |
| 239 | output.data = reinterpret_cast<uint8_t*>(dstData.data()); |
| 240 | output.size = dstData.size() * sizeof(float); |
| 241 | |
| 242 | // Execution |
| 243 | int32_t padding[4] = { 1, 1, 1, 1 }; |
| 244 | int32_t padding_len = 4; |
| 245 | int32_t pad_const_int = 0; |
| 246 | float pad_const_fp = 5.0f; |
| 247 | auto status = tosa_run_pad(input1, padding_len, padding, pad_const_int, pad_const_fp, output); |
| 248 | CHECK((status == tosa_status_valid)); |
| 249 | |
| 250 | // Compare results |
| 251 | // Expect a 4x4 array with a border of 5's and inner 2x2 of 4's |
| 252 | std::vector<float> expectedData(16, 5.0f); |
| 253 | expectedData[5] = 4.0f; |
| 254 | expectedData[6] = 4.0f; |
| 255 | expectedData[9] = 4.0f; |
| 256 | expectedData[10] = 4.0f; |
| 257 | compareOutput(dstData, expectedData, expectedData.size()); |
| 258 | } |
| 259 | |
| 260 | TEST_CASE("simple_add_f32_test") |
| 261 | { |
| 262 | std::string test_root(std::string(PROJECT_ROOT) + "../examples/test_add_1x4x4x4_f32/"); |
| 263 | std::string tosa_model_file(test_root + "flatbuffer-tflite/test_add_1x4x4x4_f32.tosa"); |
| 264 | std::string input0_file(test_root + "placeholder_0.npy"); |
| 265 | std::string input1_file(test_root + "placeholder_1.npy"); |
| 266 | std::string expected_output_file(test_root + "tflite_result.npy"); |
| 267 | |
| 268 | std::vector<std::string> input_names = { "TosaInput_0", "TosaInput_1" }; |
| 269 | std::string output_name = "TosaOutput_0"; |
| 270 | |
| 271 | std::vector<int32_t> input0_shape = { 1, 4, 4, 1 }; |
| 272 | std::vector<int32_t> input1_shape = { 1, 4, 4, 4 }; |
| 273 | std::vector<int32_t> output_shape = { 1, 4, 4, 4 }; |
| 274 | |
| 275 | std::vector<std::vector<float>> inputs(input_names.size()); |
| 276 | std::vector<float> actual_outputs = {}; |
| 277 | std::vector<float> expected_outputs = {}; |
| 278 | |
| 279 | // Read in inputs and expected outputs. |
| 280 | inputs[0] = readFromNpyFile<float>(input0_file.c_str(), input0_shape); |
| 281 | inputs[1] = readFromNpyFile<float>(input1_file.c_str(), input1_shape); |
| 282 | expected_outputs = readFromNpyFile<float>(expected_output_file.c_str(), output_shape); |
| 283 | |
| 284 | TosaSerializationHandler handler; |
| 285 | tosa_err_t error = handler.LoadFileTosaFlatbuffer(tosa_model_file.c_str()); |
| 286 | CHECK((error == tosa::TOSA_OK)); |
| 287 | |
| 288 | GraphStatus status; |
| 289 | |
| 290 | // Initialize the ModelRunner with configurations. |
| 291 | IModelRunner runner; |
| 292 | status = runner.initialize(handler); |
| 293 | CHECK((status == GraphStatus::TOSA_VALID)); |
| 294 | |
| 295 | runner.setInput(input_names[0], inputs[0]); |
| 296 | runner.setInput(input_names[1], inputs[1]); |
| 297 | |
| 298 | // Run the ModelRunner using test inputs. |
| 299 | status = runner.run(); |
| 300 | CHECK((status == GraphStatus::TOSA_VALID)); |
| 301 | |
| 302 | actual_outputs = runner.getOutput<float>(output_name); |
| 303 | CHECK(!actual_outputs.empty()); |
| 304 | |
| 305 | compareOutput(expected_outputs, actual_outputs, expected_outputs.size()); |
| 306 | } |
| 307 | |
| 308 | TEST_CASE("conv2d_f32_test") |
| 309 | { |
| 310 | std::string test_root(std::string(PROJECT_ROOT) + |
| 311 | "../examples/test_conv2d_1x1_1x32x32x8_f32_st11_padSAME_dilat11/"); |
| 312 | std::string tosa_model_file(test_root + |
| 313 | "flatbuffer-tflite/test_conv2d_1x1_1x32x32x8_f32_st11_padSAME_dilat11.tosa"); |
| 314 | std::string input_file(test_root + "placeholder_0.npy"); |
| 315 | std::string expected_output_file(test_root + "tflite_result.npy"); |
| 316 | |
| 317 | std::string input_name = "TosaInput_0"; |
| 318 | std::string output_name = "TosaOutput_0"; |
| 319 | |
| 320 | std::vector<int32_t> input_shape = { 1, 32, 32, 8 }; |
| 321 | std::vector<int32_t> output_shape = { 1, 32, 32, 16 }; |
| 322 | |
| 323 | // Read in inputs and expected outputs. |
| 324 | std::vector<float> inputs = readFromNpyFile<float>(input_file.c_str(), input_shape); |
| 325 | std::vector<float> expected_outputs = readFromNpyFile<float>(expected_output_file.c_str(), output_shape); |
| 326 | |
| 327 | TosaSerializationHandler handler; |
| 328 | tosa_err_t error = handler.LoadFileTosaFlatbuffer(tosa_model_file.c_str()); |
| 329 | CHECK((error == tosa::TOSA_OK)); |
| 330 | |
| 331 | GraphStatus status; |
| 332 | |
| 333 | // Initialize the ModelRunner with configurations. |
| 334 | IModelRunner runner; |
| 335 | status = runner.initialize(handler); |
| 336 | CHECK((status == GraphStatus::TOSA_VALID)); |
| 337 | |
| 338 | runner.setInput(input_name, inputs); |
| 339 | |
| 340 | // Run the ModelRunner using test inputs. |
| 341 | status = runner.run(); |
| 342 | CHECK((status == GraphStatus::TOSA_VALID)); |
| 343 | |
| 344 | std::vector<float> actual_outputs = runner.getOutput<float>(output_name); |
| 345 | CHECK(!actual_outputs.empty()); |
| 346 | |
| 347 | compareOutput(expected_outputs, actual_outputs, expected_outputs.size()); |
| 348 | } |
| 349 | |
| 350 | TEST_CASE("conv2d_f32_validate_only_test") |
| 351 | { |
| 352 | std::string test_root(std::string(PROJECT_ROOT) + |
| 353 | "../examples/test_conv2d_1x1_1x32x32x8_f32_st11_padSAME_dilat11/"); |
| 354 | std::string tosa_model_file(test_root + |
| 355 | "flatbuffer-tflite/test_conv2d_1x1_1x32x32x8_f32_st11_padSAME_dilat11.tosa"); |
| 356 | |
| 357 | TosaSerializationHandler handler; |
| 358 | tosa_err_t error = handler.LoadFileTosaFlatbuffer(tosa_model_file.c_str()); |
| 359 | CHECK((error == tosa::TOSA_OK)); |
| 360 | |
| 361 | GraphStatus status; |
| 362 | func_debug_t funcDebug; |
| 363 | |
| 364 | func_config_t funcConfig; |
| 365 | funcConfig.validate_only = 1; |
| 366 | |
| 367 | // Initialize the ModelRunner with configurations. |
| 368 | IModelRunner runner = IModelRunner(funcConfig, funcDebug); |
| 369 | runner.setFuncConfig(funcConfig); |
| 370 | status = runner.initialize(handler); |
| 371 | CHECK((status == GraphStatus::TOSA_VALID)); |
| 372 | |
| 373 | // Run the ModelRunner using no inputs, as validate_only is specified run() should still work. |
| 374 | status = runner.run(); |
| 375 | CHECK((status == GraphStatus::TOSA_VALID)); |
| 376 | } |
| 377 | |
| 378 | } // TEST_SUITE(model_runner) |