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Diffstat (limited to 'python/pyarmnn/test/test_network.py')
| -rw-r--r-- | python/pyarmnn/test/test_network.py | 310 |
1 files changed, 310 insertions, 0 deletions
diff --git a/python/pyarmnn/test/test_network.py b/python/pyarmnn/test/test_network.py new file mode 100644 index 0000000000..5334cc50c0 --- /dev/null +++ b/python/pyarmnn/test/test_network.py @@ -0,0 +1,310 @@ +# Copyright © 2019 Arm Ltd. All rights reserved. +# SPDX-License-Identifier: MIT +import os +import stat +import platform + +import pytest +import pyarmnn as ann + + +@pytest.fixture(scope="function") +def get_runtime(shared_data_folder, network_file): + parser= ann.ITfLiteParser() + preferred_backends = [ann.BackendId('CpuAcc'), ann.BackendId('CpuRef')] + network = parser.CreateNetworkFromBinaryFile(os.path.join(shared_data_folder, network_file)) + options = ann.CreationOptions() + runtime = ann.IRuntime(options) + + yield preferred_backends, network, runtime + + +@pytest.mark.parametrize("network_file", + [ + 'inception_v3_quant.tflite', + 'ssd_mobilenetv1.tflite' + ], + ids=['inception v3', 'mobilenetssd v1']) +def test_optimize_executes_successfully(network_file, get_runtime): + preferred_backends = [ann.BackendId('CpuRef')] + network = get_runtime[1] + runtime = get_runtime[2] + + opt_network, messages = ann.Optimize(network, preferred_backends, runtime.GetDeviceSpec(), ann.OptimizerOptions()) + + assert len(messages) == 0, 'With only CpuRef, there should be no warnings irrelevant of architecture.' + assert opt_network + + +@pytest.mark.parametrize("network_file", + [ + 'inception_v3_quant.tflite', + ], + ids=['inception v3']) +def test_optimize_owned_by_python(network_file, get_runtime): + preferred_backends = get_runtime[0] + network = get_runtime[1] + runtime = get_runtime[2] + + opt_network, _ = ann.Optimize(network, preferred_backends, runtime.GetDeviceSpec(), ann.OptimizerOptions()) + assert opt_network.thisown + + +@pytest.mark.juno +@pytest.mark.parametrize("network_file", + [ + ('inception_v3_quant.tflite') + ], + ids=['inception v3']) +def test_optimize_executes_successfully_for_neon_backend_only(network_file, get_runtime): + preferred_backends = [ann.BackendId('CpuAcc')] + network = get_runtime[1] + runtime = get_runtime[2] + + opt_network, messages = ann.Optimize(network, preferred_backends, runtime.GetDeviceSpec(), ann.OptimizerOptions()) + assert 0 == len(messages) + assert opt_network + + +@pytest.mark.parametrize("network_file", + [ + 'inception_v3_quant.tflite' + ], + ids=['inception v3']) +def test_optimize_fails_for_invalid_backends(network_file, get_runtime): + invalid_backends = [ann.BackendId('Unknown')] + network = get_runtime[1] + runtime = get_runtime[2] + + with pytest.raises(RuntimeError) as err: + ann.Optimize(network, invalid_backends, runtime.GetDeviceSpec(), ann.OptimizerOptions()) + + expected_error_message = "None of the preferred backends [Unknown ] are supported." + assert expected_error_message in str(err.value) + + +@pytest.mark.parametrize("network_file", + [ + 'inception_v3_quant.tflite' + ], + ids=['inception v3']) +def test_optimize_fails_for_no_backends_specified(network_file, get_runtime): + empty_backends = [] + network = get_runtime[1] + runtime = get_runtime[2] + + with pytest.raises(RuntimeError) as err: + ann.Optimize(network, empty_backends, runtime.GetDeviceSpec(), ann.OptimizerOptions()) + + expected_error_message = "Invoked Optimize with no backends specified" + assert expected_error_message in str(err.value) + + +@pytest.mark.parametrize("network_file", + [ + 'inception_v3_quant.tflite' + ], + ids=['inception v3']) +def test_serialize_to_dot(network_file, get_runtime, tmpdir): + preferred_backends = get_runtime[0] + network = get_runtime[1] + runtime = get_runtime[2] + opt_network, _ = ann.Optimize(network, preferred_backends, + runtime.GetDeviceSpec(), ann.OptimizerOptions()) + dot_file_path = os.path.join(tmpdir, 'ssd.dot') + """Check that serialized file does not exist at the start, gets created after SerializeToDot and is not empty""" + assert not os.path.exists(dot_file_path) + opt_network.SerializeToDot(dot_file_path) + + assert os.path.exists(dot_file_path) + + with open(dot_file_path) as res_file: + expected_data = res_file.read() + assert len(expected_data) > 1 + assert '[label=< [1,299,299,3] >]' in expected_data + + +@pytest.mark.skipif(platform.processor() != 'x86_64', reason="Platform specific test") +@pytest.mark.parametrize("network_file", + [ + 'inception_v3_quant.tflite' + ], + ids=['inception v3']) +def test_serialize_to_dot_mode_readonly(network_file, get_runtime, tmpdir): + preferred_backends = get_runtime[0] + network = get_runtime[1] + runtime = get_runtime[2] + opt_network, _ = ann.Optimize(network, preferred_backends, + runtime.GetDeviceSpec(), ann.OptimizerOptions()) + """Create file, write to it and change mode to read-only""" + dot_file_path = os.path.join(tmpdir, 'ssd.dot') + f = open(dot_file_path, "w+") + f.write("test") + f.close() + os.chmod(dot_file_path, stat.S_IREAD) + assert os.path.exists(dot_file_path) + + with pytest.raises(RuntimeError) as err: + opt_network.SerializeToDot(dot_file_path) + + expected_error_message = "Failed to open dot file" + assert expected_error_message in str(err.value) + + +@pytest.mark.juno +@pytest.mark.parametrize("network_file", + [ + 'ssd_mobilenetv1.tflite' + ], + ids=['mobilenetssd v1']) +def test_optimize_error_tuple(network_file, get_runtime): + preferred_backends = get_runtime[0] + network = get_runtime[1] + runtime = get_runtime[2] + + opt_network, error_messages = ann.Optimize(network, preferred_backends, + runtime.GetDeviceSpec(), ann.OptimizerOptions()) + + assert type(error_messages) == tuple + assert 'WARNING: Layer of type DetectionPostProcess is not supported on requested backend CpuAcc for input data ' \ + 'type QAsymm8' in error_messages[0] + + +@pytest.mark.parametrize("method", [ + 'AddAbsLayer', + 'AddActivationLayer', + 'AddAdditionLayer', + 'AddArgMinMaxLayer', + 'AddBatchNormalizationLayer', + 'AddBatchToSpaceNdLayer', + 'AddComparisonLayer', + 'AddConcatLayer', + 'AddConstantLayer', + 'AddConvolution2dLayer', + 'AddDepthToSpaceLayer', + 'AddDepthwiseConvolution2dLayer', + 'AddDequantizeLayer', + 'AddDetectionPostProcessLayer', + 'AddDivisionLayer', + 'AddFloorLayer', + 'AddFullyConnectedLayer', + 'AddGatherLayer', + 'AddInputLayer', + 'AddInstanceNormalizationLayer', + 'AddLogSoftmaxLayer', + 'AddL2NormalizationLayer', + 'AddLstmLayer', + 'AddMaximumLayer', + 'AddMeanLayer', + 'AddMergeLayer', + 'AddMinimumLayer', + 'AddMultiplicationLayer', + 'AddNormalizationLayer', + 'AddOutputLayer', + 'AddPadLayer', + 'AddPermuteLayer', + 'AddPooling2dLayer', + 'AddPreluLayer', + 'AddQuantizeLayer', + 'AddQuantizedLstmLayer', + 'AddReshapeLayer', + 'AddResizeLayer', + 'AddRsqrtLayer', + 'AddSliceLayer', + 'AddSoftmaxLayer', + 'AddSpaceToBatchNdLayer', + 'AddSpaceToDepthLayer', + 'AddSplitterLayer', + 'AddStackLayer', + 'AddStandInLayer', + 'AddStridedSliceLayer', + 'AddSubtractionLayer', + 'AddSwitchLayer', + 'AddTransposeConvolution2dLayer' +]) +def test_network_method_exists(method): + assert getattr(ann.INetwork, method, None) + + +def test_fullyconnected_layer_optional_none(): + net = ann.INetwork() + layer = net.AddFullyConnectedLayer(fullyConnectedDescriptor=ann.FullyConnectedDescriptor(), + weights=ann.ConstTensor()) + + assert layer + + +def test_fullyconnected_layer_optional_provided(): + net = ann.INetwork() + layer = net.AddFullyConnectedLayer(fullyConnectedDescriptor=ann.FullyConnectedDescriptor(), + weights=ann.ConstTensor(), + biases=ann.ConstTensor()) + + assert layer + + +def test_fullyconnected_layer_all_args(): + net = ann.INetwork() + layer = net.AddFullyConnectedLayer(fullyConnectedDescriptor=ann.FullyConnectedDescriptor(), + weights=ann.ConstTensor(), + biases=ann.ConstTensor(), + name='NAME1') + + assert layer + assert 'NAME1' == layer.GetName() + + +def test_DepthwiseConvolution2d_layer_optional_none(): + net = ann.INetwork() + layer = net.AddDepthwiseConvolution2dLayer(convolution2dDescriptor=ann.DepthwiseConvolution2dDescriptor(), + weights=ann.ConstTensor()) + + assert layer + + +def test_DepthwiseConvolution2d_layer_optional_provided(): + net = ann.INetwork() + layer = net.AddDepthwiseConvolution2dLayer(convolution2dDescriptor=ann.DepthwiseConvolution2dDescriptor(), + weights=ann.ConstTensor(), + biases=ann.ConstTensor()) + + assert layer + + +def test_DepthwiseConvolution2d_layer_all_args(): + net = ann.INetwork() + layer = net.AddDepthwiseConvolution2dLayer(convolution2dDescriptor=ann.DepthwiseConvolution2dDescriptor(), + weights=ann.ConstTensor(), + biases=ann.ConstTensor(), + name='NAME1') + + assert layer + assert 'NAME1' == layer.GetName() + + +def test_Convolution2d_layer_optional_none(): + net = ann.INetwork() + layer = net.AddConvolution2dLayer(convolution2dDescriptor=ann.Convolution2dDescriptor(), + weights=ann.ConstTensor()) + + assert layer + + +def test_Convolution2d_layer_optional_provided(): + net = ann.INetwork() + layer = net.AddConvolution2dLayer(convolution2dDescriptor=ann.Convolution2dDescriptor(), + weights=ann.ConstTensor(), + biases=ann.ConstTensor()) + + assert layer + + +def test_Convolution2d_layer_all_args(): + net = ann.INetwork() + layer = net.AddConvolution2dLayer(convolution2dDescriptor=ann.Convolution2dDescriptor(), + weights=ann.ConstTensor(), + biases=ann.ConstTensor(), + name='NAME1') + + assert layer + assert 'NAME1' == layer.GetName() |