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Diffstat (limited to 'python/pyarmnn/test/test_deserializer.py')
-rw-r--r-- | python/pyarmnn/test/test_deserializer.py | 120 |
1 files changed, 120 insertions, 0 deletions
diff --git a/python/pyarmnn/test/test_deserializer.py b/python/pyarmnn/test/test_deserializer.py new file mode 100644 index 0000000000..05aa7338c3 --- /dev/null +++ b/python/pyarmnn/test/test_deserializer.py @@ -0,0 +1,120 @@ +# Copyright © 2020 Arm Ltd and Contributors. All rights reserved. +# SPDX-License-Identifier: MIT +import os + +import pytest +import pyarmnn as ann +import numpy as np + + +@pytest.fixture() +def parser(shared_data_folder): + """ + Parse and setup the test network to be used for the tests below + """ + parser = ann.IDeserializer() + parser.CreateNetworkFromBinary(os.path.join(shared_data_folder, 'mock_model.armnn')) + + yield parser + + +def test_deserializer_swig_destroy(): + assert ann.IDeserializer.__swig_destroy__, "There is a swig python destructor defined" + assert ann.IDeserializer.__swig_destroy__.__name__ == "delete_IDeserializer" + + +def test_check_deserializer_swig_ownership(parser): + # Check to see that SWIG has ownership for parser. This instructs SWIG to take + # ownership of the return value. This allows the value to be automatically + # garbage-collected when it is no longer in use + assert parser.thisown + + +def test_deserializer_get_network_input_binding_info(parser): + # use 0 as a dummy value for layer_id, which is unused in the actual implementation + layer_id = 0 + input_name = 'input_1' + + input_binding_info = parser.GetNetworkInputBindingInfo(layer_id, input_name) + + tensor = input_binding_info[1] + assert tensor.GetDataType() == 2 + assert tensor.GetNumDimensions() == 4 + assert tensor.GetNumElements() == 784 + assert tensor.GetQuantizationOffset() == 128 + assert tensor.GetQuantizationScale() == 0.007843137718737125 + + +def test_deserializer_get_network_output_binding_info(parser): + # use 0 as a dummy value for layer_id, which is unused in the actual implementation + layer_id = 0 + output_name = "dense/Softmax" + + output_binding_info1 = parser.GetNetworkOutputBindingInfo(layer_id, output_name) + + # Check the tensor info retrieved from GetNetworkOutputBindingInfo + tensor1 = output_binding_info1[1] + + assert tensor1.GetDataType() == 2 + assert tensor1.GetNumDimensions() == 2 + assert tensor1.GetNumElements() == 10 + assert tensor1.GetQuantizationOffset() == 0 + assert tensor1.GetQuantizationScale() == 0.00390625 + + +def test_deserializer_filenotfound_exception(shared_data_folder): + parser = ann.IDeserializer() + + with pytest.raises(RuntimeError) as err: + parser.CreateNetworkFromBinary(os.path.join(shared_data_folder, 'some_unknown_network.armnn')) + + # Only check for part of the exception since the exception returns + # absolute path which will change on different machines. + assert 'Cannot read the file' in str(err.value) + + +def test_deserializer_end_to_end(shared_data_folder): + parser = ann.IDeserializer() + + network = parser.CreateNetworkFromBinary(os.path.join(shared_data_folder, "mock_model.armnn")) + + # use 0 as a dummy value for layer_id, which is unused in the actual implementation + layer_id = 0 + input_name = 'input_1' + output_name = 'dense/Softmax' + + input_binding_info = parser.GetNetworkInputBindingInfo(layer_id, input_name) + + preferred_backends = [ann.BackendId('CpuAcc'), ann.BackendId('CpuRef')] + + options = ann.CreationOptions() + runtime = ann.IRuntime(options) + + opt_network, messages = ann.Optimize(network, preferred_backends, runtime.GetDeviceSpec(), ann.OptimizerOptions()) + assert 0 == len(messages) + + net_id, messages = runtime.LoadNetwork(opt_network) + assert "" == messages + + # Load test image data stored in input_lite.npy + input_tensor_data = np.load(os.path.join(shared_data_folder, 'deserializer/input_lite.npy')) + input_tensors = ann.make_input_tensors([input_binding_info], [input_tensor_data]) + + output_tensors = [] + out_bind_info = parser.GetNetworkOutputBindingInfo(layer_id, output_name) + out_tensor_info = out_bind_info[1] + out_tensor_id = out_bind_info[0] + output_tensors.append((out_tensor_id, + ann.Tensor(out_tensor_info))) + + runtime.EnqueueWorkload(net_id, input_tensors, output_tensors) + + output_vectors = [] + for index, out_tensor in enumerate(output_tensors): + output_vectors.append(out_tensor[1].get_memory_area()) + + # Load golden output file for result comparison. + expected_outputs = np.load(os.path.join(shared_data_folder, 'deserializer/golden_output_lite.npy')) + + # Check that output matches golden output + assert (expected_outputs == output_vectors[0]).all() |