1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
|
# Copyright © 2019 Arm Ltd. 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 (mobilenetv1) to be used for the tests below
"""
# create tf parser
parser = ann.ITfParser()
# path to model
path_to_model = os.path.join(shared_data_folder, 'mobilenet_v1_1.0_224.pb')
# tensor shape [1, 224, 224, 3]
tensorshape = {'input': ann.TensorShape((1, 224, 224, 3))}
# requested_outputs
requested_outputs = ["MobilenetV1/Predictions/Reshape_1"]
# parse tf binary & create network
parser.CreateNetworkFromBinaryFile(path_to_model, tensorshape, requested_outputs)
yield parser
def test_tf_parser_swig_destroy():
assert ann.ITfParser.__swig_destroy__, "There is a swig python destructor defined"
assert ann.ITfParser.__swig_destroy__.__name__ == "delete_ITfParser"
def test_check_tf_parser_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_tf_parser_get_network_input_binding_info(parser):
input_binding_info = parser.GetNetworkInputBindingInfo("input")
tensor = input_binding_info[1]
assert tensor.GetDataType() == 1
assert tensor.GetNumDimensions() == 4
assert tensor.GetNumElements() == 150528
assert tensor.GetQuantizationOffset() == 0
assert tensor.GetQuantizationScale() == 0
def test_tf_parser_get_network_output_binding_info(parser):
output_binding_info = parser.GetNetworkOutputBindingInfo("MobilenetV1/Predictions/Reshape_1")
tensor = output_binding_info[1]
assert tensor.GetDataType() == 1
assert tensor.GetNumDimensions() == 2
assert tensor.GetNumElements() == 1001
assert tensor.GetQuantizationOffset() == 0
assert tensor.GetQuantizationScale() == 0
def test_tf_filenotfound_exception(shared_data_folder):
parser = ann.ITfParser()
# path to model
path_to_model = os.path.join(shared_data_folder, 'some_unknown_model.pb')
# tensor shape [1, 1, 1, 1]
tensorshape = {'input': ann.TensorShape((1, 1, 1, 1))}
# requested_outputs
requested_outputs = [""]
# parse tf binary & create network
with pytest.raises(RuntimeError) as err:
parser.CreateNetworkFromBinaryFile(path_to_model, tensorshape, requested_outputs)
# Only check for part of the exception since the exception returns
# absolute path which will change on different machines.
assert 'failed to open' in str(err.value)
def test_tf_parser_end_to_end(shared_data_folder):
parser = ann.ITfParser = ann.ITfParser()
tensorshape = {'input': ann.TensorShape((1, 224, 224, 3))}
requested_outputs = ["MobilenetV1/Predictions/Reshape_1"]
network = parser.CreateNetworkFromBinaryFile(os.path.join(shared_data_folder, 'mobilenet_v1_1.0_224.pb'),
tensorshape, requested_outputs)
input_binding_info = parser.GetNetworkInputBindingInfo("input")
# load test image data stored in input.npy
input_tensor_data = np.load(os.path.join(shared_data_folder, 'tf_parser/input.npy')).astype(np.float32)
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
input_tensors = ann.make_input_tensors([input_binding_info], [input_tensor_data])
outputs_binding_info = []
for output_name in requested_outputs:
outputs_binding_info.append(parser.GetNetworkOutputBindingInfo(output_name))
output_tensors = ann.make_output_tensors(outputs_binding_info)
runtime.EnqueueWorkload(net_id, input_tensors, output_tensors)
output_vectors = ann.workload_tensors_to_ndarray(output_tensors)
# load golden output file to compare the output results with
golden_output = np.load(os.path.join(shared_data_folder, 'tf_parser/golden_output.npy'))
# Check that output matches golden output to 4 decimal places (there are slight rounding differences after this)
np.testing.assert_almost_equal(output_vectors, golden_output, decimal=4)
|
