Richard Burton | dc0c6ed | 2020-04-08 16:39:05 +0100 | [diff] [blame] | 1 | # Copyright © 2020 Arm Ltd. All rights reserved. |
| 2 | # SPDX-License-Identifier: MIT |
| 3 | import os |
| 4 | import stat |
| 5 | |
| 6 | import pytest |
| 7 | import pyarmnn as ann |
| 8 | |
Jan Eilers | 841aca1 | 2020-08-12 14:59:06 +0100 | [diff] [blame] | 9 | def test_optimizer_options_default_values(): |
| 10 | opt = ann.OptimizerOptions() |
| 11 | assert opt.m_ReduceFp32ToFp16 == False |
| 12 | assert opt.m_Debug == False |
| 13 | assert opt.m_ReduceFp32ToBf16 == False |
Narumol Prangnawarat | ea063df | 2020-08-21 10:03:49 +0100 | [diff] [blame] | 14 | assert opt.m_ImportEnabled == False |
Jan Eilers | 841aca1 | 2020-08-12 14:59:06 +0100 | [diff] [blame] | 15 | |
| 16 | def test_optimizer_options_set_values1(): |
| 17 | opt = ann.OptimizerOptions(True, True) |
| 18 | assert opt.m_ReduceFp32ToFp16 == True |
| 19 | assert opt.m_Debug == True |
| 20 | assert opt.m_ReduceFp32ToBf16 == False |
Narumol Prangnawarat | ea063df | 2020-08-21 10:03:49 +0100 | [diff] [blame] | 21 | assert opt.m_ImportEnabled == False |
Jan Eilers | 841aca1 | 2020-08-12 14:59:06 +0100 | [diff] [blame] | 22 | |
| 23 | def test_optimizer_options_set_values2(): |
| 24 | opt = ann.OptimizerOptions(False, False, True) |
| 25 | assert opt.m_ReduceFp32ToFp16 == False |
| 26 | assert opt.m_Debug == False |
| 27 | assert opt.m_ReduceFp32ToBf16 == True |
Narumol Prangnawarat | ea063df | 2020-08-21 10:03:49 +0100 | [diff] [blame] | 28 | assert opt.m_ImportEnabled == False |
| 29 | |
| 30 | def test_optimizer_options_set_values3(): |
| 31 | opt = ann.OptimizerOptions(False, False, True, True) |
| 32 | assert opt.m_ReduceFp32ToFp16 == False |
| 33 | assert opt.m_Debug == False |
| 34 | assert opt.m_ReduceFp32ToBf16 == True |
| 35 | assert opt.m_ImportEnabled == True |
Richard Burton | dc0c6ed | 2020-04-08 16:39:05 +0100 | [diff] [blame] | 36 | |
| 37 | @pytest.fixture(scope="function") |
| 38 | def get_runtime(shared_data_folder, network_file): |
| 39 | parser= ann.ITfLiteParser() |
| 40 | preferred_backends = [ann.BackendId('CpuAcc'), ann.BackendId('CpuRef')] |
| 41 | network = parser.CreateNetworkFromBinaryFile(os.path.join(shared_data_folder, network_file)) |
| 42 | options = ann.CreationOptions() |
| 43 | runtime = ann.IRuntime(options) |
| 44 | |
| 45 | yield preferred_backends, network, runtime |
| 46 | |
| 47 | |
| 48 | @pytest.mark.parametrize("network_file", |
| 49 | [ |
| 50 | 'mock_model.tflite', |
| 51 | ], |
| 52 | ids=['mock_model']) |
| 53 | def test_optimize_executes_successfully(network_file, get_runtime): |
| 54 | preferred_backends = [ann.BackendId('CpuRef')] |
| 55 | network = get_runtime[1] |
| 56 | runtime = get_runtime[2] |
| 57 | |
| 58 | opt_network, messages = ann.Optimize(network, preferred_backends, runtime.GetDeviceSpec(), ann.OptimizerOptions()) |
| 59 | |
| 60 | assert len(messages) == 0, 'With only CpuRef, there should be no warnings irrelevant of architecture.' |
| 61 | assert opt_network |
| 62 | |
| 63 | |
| 64 | @pytest.mark.parametrize("network_file", |
| 65 | [ |
| 66 | 'mock_model.tflite', |
| 67 | ], |
| 68 | ids=['mock_model']) |
| 69 | def test_optimize_owned_by_python(network_file, get_runtime): |
| 70 | preferred_backends = get_runtime[0] |
| 71 | network = get_runtime[1] |
| 72 | runtime = get_runtime[2] |
| 73 | |
| 74 | opt_network, _ = ann.Optimize(network, preferred_backends, runtime.GetDeviceSpec(), ann.OptimizerOptions()) |
| 75 | assert opt_network.thisown |
| 76 | |
| 77 | |
| 78 | @pytest.mark.aarch64 |
| 79 | @pytest.mark.parametrize("network_file", |
| 80 | [ |
| 81 | 'mock_model.tflite' |
| 82 | ], |
| 83 | ids=['mock_model']) |
| 84 | def test_optimize_executes_successfully_for_neon_backend_only(network_file, get_runtime): |
| 85 | preferred_backends = [ann.BackendId('CpuAcc')] |
| 86 | network = get_runtime[1] |
| 87 | runtime = get_runtime[2] |
| 88 | |
| 89 | opt_network, messages = ann.Optimize(network, preferred_backends, runtime.GetDeviceSpec(), ann.OptimizerOptions()) |
| 90 | assert 0 == len(messages) |
| 91 | assert opt_network |
| 92 | |
| 93 | |
| 94 | @pytest.mark.parametrize("network_file", |
| 95 | [ |
| 96 | 'mock_model.tflite' |
| 97 | ], |
| 98 | ids=['mock_model']) |
| 99 | def test_optimize_fails_for_invalid_backends(network_file, get_runtime): |
| 100 | invalid_backends = [ann.BackendId('Unknown')] |
| 101 | network = get_runtime[1] |
| 102 | runtime = get_runtime[2] |
| 103 | |
| 104 | with pytest.raises(RuntimeError) as err: |
| 105 | ann.Optimize(network, invalid_backends, runtime.GetDeviceSpec(), ann.OptimizerOptions()) |
| 106 | |
| 107 | expected_error_message = "None of the preferred backends [Unknown ] are supported." |
| 108 | assert expected_error_message in str(err.value) |
| 109 | |
| 110 | |
| 111 | @pytest.mark.parametrize("network_file", |
| 112 | [ |
| 113 | 'mock_model.tflite' |
| 114 | ], |
| 115 | ids=['mock_model']) |
| 116 | def test_optimize_fails_for_no_backends_specified(network_file, get_runtime): |
| 117 | empty_backends = [] |
| 118 | network = get_runtime[1] |
| 119 | runtime = get_runtime[2] |
| 120 | |
| 121 | with pytest.raises(RuntimeError) as err: |
| 122 | ann.Optimize(network, empty_backends, runtime.GetDeviceSpec(), ann.OptimizerOptions()) |
| 123 | |
| 124 | expected_error_message = "Invoked Optimize with no backends specified" |
| 125 | assert expected_error_message in str(err.value) |
| 126 | |
| 127 | |
| 128 | @pytest.mark.parametrize("network_file", |
| 129 | [ |
| 130 | 'mock_model.tflite' |
| 131 | ], |
| 132 | ids=['mock_model']) |
| 133 | def test_serialize_to_dot(network_file, get_runtime, tmpdir): |
| 134 | preferred_backends = get_runtime[0] |
| 135 | network = get_runtime[1] |
| 136 | runtime = get_runtime[2] |
| 137 | opt_network, _ = ann.Optimize(network, preferred_backends, |
| 138 | runtime.GetDeviceSpec(), ann.OptimizerOptions()) |
| 139 | dot_file_path = os.path.join(tmpdir, 'mock_model.dot') |
| 140 | """Check that serialized file does not exist at the start, gets created after SerializeToDot and is not empty""" |
| 141 | assert not os.path.exists(dot_file_path) |
| 142 | opt_network.SerializeToDot(dot_file_path) |
| 143 | |
| 144 | assert os.path.exists(dot_file_path) |
| 145 | |
| 146 | with open(dot_file_path) as res_file: |
| 147 | expected_data = res_file.read() |
| 148 | assert len(expected_data) > 1 |
| 149 | assert '[label=< [1,28,28,1] >]' in expected_data |
| 150 | |
| 151 | |
| 152 | @pytest.mark.x86_64 |
| 153 | @pytest.mark.parametrize("network_file", |
| 154 | [ |
| 155 | 'mock_model.tflite' |
| 156 | ], |
| 157 | ids=['mock_model']) |
| 158 | def test_serialize_to_dot_mode_readonly(network_file, get_runtime, tmpdir): |
| 159 | preferred_backends = get_runtime[0] |
| 160 | network = get_runtime[1] |
| 161 | runtime = get_runtime[2] |
| 162 | opt_network, _ = ann.Optimize(network, preferred_backends, |
| 163 | runtime.GetDeviceSpec(), ann.OptimizerOptions()) |
| 164 | """Create file, write to it and change mode to read-only""" |
| 165 | dot_file_path = os.path.join(tmpdir, 'mock_model.dot') |
| 166 | f = open(dot_file_path, "w+") |
| 167 | f.write("test") |
| 168 | f.close() |
| 169 | os.chmod(dot_file_path, stat.S_IREAD) |
| 170 | assert os.path.exists(dot_file_path) |
| 171 | |
| 172 | with pytest.raises(RuntimeError) as err: |
| 173 | opt_network.SerializeToDot(dot_file_path) |
| 174 | |
| 175 | expected_error_message = "Failed to open dot file" |
| 176 | assert expected_error_message in str(err.value) |
| 177 | |
| 178 | |
| 179 | @pytest.mark.parametrize("method", [ |
| 180 | 'AddActivationLayer', |
| 181 | 'AddAdditionLayer', |
| 182 | 'AddArgMinMaxLayer', |
| 183 | 'AddBatchNormalizationLayer', |
| 184 | 'AddBatchToSpaceNdLayer', |
| 185 | 'AddComparisonLayer', |
| 186 | 'AddConcatLayer', |
| 187 | 'AddConstantLayer', |
| 188 | 'AddConvolution2dLayer', |
| 189 | 'AddDepthToSpaceLayer', |
| 190 | 'AddDepthwiseConvolution2dLayer', |
| 191 | 'AddDequantizeLayer', |
| 192 | 'AddDetectionPostProcessLayer', |
| 193 | 'AddDivisionLayer', |
| 194 | 'AddElementwiseUnaryLayer', |
| 195 | 'AddFloorLayer', |
Jan Eilers | 841aca1 | 2020-08-12 14:59:06 +0100 | [diff] [blame] | 196 | 'AddFillLayer', |
Richard Burton | dc0c6ed | 2020-04-08 16:39:05 +0100 | [diff] [blame] | 197 | 'AddFullyConnectedLayer', |
| 198 | 'AddGatherLayer', |
| 199 | 'AddInputLayer', |
| 200 | 'AddInstanceNormalizationLayer', |
| 201 | 'AddLogSoftmaxLayer', |
| 202 | 'AddL2NormalizationLayer', |
| 203 | 'AddLstmLayer', |
| 204 | 'AddMaximumLayer', |
| 205 | 'AddMeanLayer', |
| 206 | 'AddMergeLayer', |
| 207 | 'AddMinimumLayer', |
| 208 | 'AddMultiplicationLayer', |
| 209 | 'AddNormalizationLayer', |
| 210 | 'AddOutputLayer', |
| 211 | 'AddPadLayer', |
| 212 | 'AddPermuteLayer', |
| 213 | 'AddPooling2dLayer', |
| 214 | 'AddPreluLayer', |
| 215 | 'AddQuantizeLayer', |
| 216 | 'AddQuantizedLstmLayer', |
Jan Eilers | 841aca1 | 2020-08-12 14:59:06 +0100 | [diff] [blame] | 217 | 'AddRankLayer', |
Richard Burton | dc0c6ed | 2020-04-08 16:39:05 +0100 | [diff] [blame] | 218 | 'AddReshapeLayer', |
| 219 | 'AddResizeLayer', |
| 220 | 'AddSliceLayer', |
| 221 | 'AddSoftmaxLayer', |
| 222 | 'AddSpaceToBatchNdLayer', |
| 223 | 'AddSpaceToDepthLayer', |
| 224 | 'AddSplitterLayer', |
| 225 | 'AddStackLayer', |
| 226 | 'AddStandInLayer', |
| 227 | 'AddStridedSliceLayer', |
| 228 | 'AddSubtractionLayer', |
| 229 | 'AddSwitchLayer', |
| 230 | 'AddTransposeConvolution2dLayer' |
| 231 | ]) |
| 232 | def test_network_method_exists(method): |
| 233 | assert getattr(ann.INetwork, method, None) |
| 234 | |
| 235 | |
| 236 | def test_fullyconnected_layer_optional_none(): |
| 237 | net = ann.INetwork() |
| 238 | layer = net.AddFullyConnectedLayer(fullyConnectedDescriptor=ann.FullyConnectedDescriptor(), |
| 239 | weights=ann.ConstTensor()) |
| 240 | |
| 241 | assert layer |
| 242 | |
| 243 | |
| 244 | def test_fullyconnected_layer_optional_provided(): |
| 245 | net = ann.INetwork() |
| 246 | layer = net.AddFullyConnectedLayer(fullyConnectedDescriptor=ann.FullyConnectedDescriptor(), |
| 247 | weights=ann.ConstTensor(), |
| 248 | biases=ann.ConstTensor()) |
| 249 | |
| 250 | assert layer |
| 251 | |
| 252 | |
| 253 | def test_fullyconnected_layer_all_args(): |
| 254 | net = ann.INetwork() |
| 255 | layer = net.AddFullyConnectedLayer(fullyConnectedDescriptor=ann.FullyConnectedDescriptor(), |
| 256 | weights=ann.ConstTensor(), |
| 257 | biases=ann.ConstTensor(), |
| 258 | name='NAME1') |
| 259 | |
| 260 | assert layer |
| 261 | assert 'NAME1' == layer.GetName() |
| 262 | |
| 263 | |
| 264 | def test_DepthwiseConvolution2d_layer_optional_none(): |
| 265 | net = ann.INetwork() |
| 266 | layer = net.AddDepthwiseConvolution2dLayer(convolution2dDescriptor=ann.DepthwiseConvolution2dDescriptor(), |
| 267 | weights=ann.ConstTensor()) |
| 268 | |
| 269 | assert layer |
| 270 | |
| 271 | |
| 272 | def test_DepthwiseConvolution2d_layer_optional_provided(): |
| 273 | net = ann.INetwork() |
| 274 | layer = net.AddDepthwiseConvolution2dLayer(convolution2dDescriptor=ann.DepthwiseConvolution2dDescriptor(), |
| 275 | weights=ann.ConstTensor(), |
| 276 | biases=ann.ConstTensor()) |
| 277 | |
| 278 | assert layer |
| 279 | |
| 280 | |
| 281 | def test_DepthwiseConvolution2d_layer_all_args(): |
| 282 | net = ann.INetwork() |
| 283 | layer = net.AddDepthwiseConvolution2dLayer(convolution2dDescriptor=ann.DepthwiseConvolution2dDescriptor(), |
| 284 | weights=ann.ConstTensor(), |
| 285 | biases=ann.ConstTensor(), |
| 286 | name='NAME1') |
| 287 | |
| 288 | assert layer |
| 289 | assert 'NAME1' == layer.GetName() |
| 290 | |
| 291 | |
| 292 | def test_Convolution2d_layer_optional_none(): |
| 293 | net = ann.INetwork() |
| 294 | layer = net.AddConvolution2dLayer(convolution2dDescriptor=ann.Convolution2dDescriptor(), |
| 295 | weights=ann.ConstTensor()) |
| 296 | |
| 297 | assert layer |
| 298 | |
| 299 | |
| 300 | def test_Convolution2d_layer_optional_provided(): |
| 301 | net = ann.INetwork() |
| 302 | layer = net.AddConvolution2dLayer(convolution2dDescriptor=ann.Convolution2dDescriptor(), |
| 303 | weights=ann.ConstTensor(), |
| 304 | biases=ann.ConstTensor()) |
| 305 | |
| 306 | assert layer |
| 307 | |
| 308 | |
| 309 | def test_Convolution2d_layer_all_args(): |
| 310 | net = ann.INetwork() |
| 311 | layer = net.AddConvolution2dLayer(convolution2dDescriptor=ann.Convolution2dDescriptor(), |
| 312 | weights=ann.ConstTensor(), |
| 313 | biases=ann.ConstTensor(), |
| 314 | name='NAME1') |
| 315 | |
| 316 | assert layer |
| 317 | assert 'NAME1' == layer.GetName() |