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1# Copyright © 2020 Arm Ltd. All rights reserved.
2# SPDX-License-Identifier: MIT
3import os
4
5import pytest
6import pyarmnn as ann
7import numpy as np
8from typing import List
9
10
11@pytest.fixture()
12def parser(shared_data_folder):
13    """
14    Parse and setup the test network to be used for the tests below
15    """
16
17    # create onnx parser
18    parser = ann.IOnnxParser()
19
20    # path to model
21    path_to_model = os.path.join(shared_data_folder, 'mock_model.onnx')
22
23    # parse onnx binary & create network
24    parser.CreateNetworkFromBinaryFile(path_to_model)
25
26    yield parser
27
28
29def test_onnx_parser_swig_destroy():
30    assert ann.IOnnxParser.__swig_destroy__, "There is a swig python destructor defined"
31    assert ann.IOnnxParser.__swig_destroy__.__name__ == "delete_IOnnxParser"
32
33
34def test_check_onnx_parser_swig_ownership(parser):
35    # Check to see that SWIG has ownership for parser. This instructs SWIG to take
36    # ownership of the return value. This allows the value to be automatically
37    # garbage-collected when it is no longer in use
38    assert parser.thisown
39
40
41def test_onnx_parser_get_network_input_binding_info(parser):
42    input_binding_info = parser.GetNetworkInputBindingInfo("input")
43
44    tensor = input_binding_info[1]
45    assert tensor.GetDataType() == 1
46    assert tensor.GetNumDimensions() == 4
47    assert tensor.GetNumElements() == 784
48    assert tensor.GetQuantizationOffset() == 0
49    assert tensor.GetQuantizationScale() == 0
50
51
52def test_onnx_parser_get_network_output_binding_info(parser):
53    output_binding_info = parser.GetNetworkOutputBindingInfo("output")
54
55    tensor = output_binding_info[1]
56    assert tensor.GetDataType() == 1
57    assert tensor.GetNumDimensions() == 4
58    assert tensor.GetNumElements() == 10
59    assert tensor.GetQuantizationOffset() == 0
60    assert tensor.GetQuantizationScale() == 0
61
62
63def test_onnx_filenotfound_exception(shared_data_folder):
64    parser = ann.IOnnxParser()
65
66    # path to model
67    path_to_model = os.path.join(shared_data_folder, 'some_unknown_model.onnx')
68
69    # parse onnx binary & create network
70
71    with pytest.raises(RuntimeError) as err:
72        parser.CreateNetworkFromBinaryFile(path_to_model)
73
74    # Only check for part of the exception since the exception returns
75    # absolute path which will change on different machines.
76    assert 'Invalid (null) filename' in str(err.value)
77
78
79def test_onnx_parser_end_to_end(shared_data_folder):
80    parser = ann.IOnnxParser = ann.IOnnxParser()
81
82    network = parser.CreateNetworkFromBinaryFile(os.path.join(shared_data_folder, 'mock_model.onnx'))
83
84    # load test image data stored in input_onnx.npy
85    input_binding_info = parser.GetNetworkInputBindingInfo("input")
86    input_tensor_data = np.load(os.path.join(shared_data_folder, 'onnx_parser/input_onnx.npy')).astype(np.float32)
87
88    options = ann.CreationOptions()
89    runtime = ann.IRuntime(options)
90
91    preferred_backends = [ann.BackendId('CpuAcc'), ann.BackendId('CpuRef')]
92    opt_network, messages = ann.Optimize(network, preferred_backends, runtime.GetDeviceSpec(), ann.OptimizerOptions())
93
94    assert 0 == len(messages)
95
96    net_id, messages = runtime.LoadNetwork(opt_network)
97
98    assert "" == messages
99
100    input_tensors = ann.make_input_tensors([input_binding_info], [input_tensor_data])
101    output_tensors = ann.make_output_tensors([parser.GetNetworkOutputBindingInfo("output")])
102
103    runtime.EnqueueWorkload(net_id, input_tensors, output_tensors)
104
105    output = ann.workload_tensors_to_ndarray(output_tensors)
106
107    # Load golden output file for result comparison.
108    golden_output = np.load(os.path.join(shared_data_folder, 'onnx_parser/golden_output_onnx.npy'))
109
110    # Check that output matches golden output to 4 decimal places (there are slight rounding differences after this)
111    np.testing.assert_almost_equal(output[0], golden_output, decimal=4)
112