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
|
# Copyright (C) 2020 Arm Limited or its affiliates. All rights reserved.
#
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the License); you may
# not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an AS IS BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Description:
# Contains unit tests for tflite_reader
from unittest.mock import MagicMock
from unittest.mock import patch
import numpy as np
import pytest
from ethosu.vela.operation import Op
from ethosu.vela.tflite.TensorType import TensorType
from ethosu.vela.tflite_reader import TFLiteSubgraph
class TestTFLiteSubgraph:
# Generate some data for testing len1_array_to_scalar
len1_testdata = [
(0, None),
pytest.param(1, None, marks=pytest.mark.xfail),
([1, 2, 3], [1, 2, 3]),
([10], 10),
([], []),
]
@pytest.mark.parametrize("test_input,expected", len1_testdata)
def test_len1_array_to_scalar(self, test_input, expected):
output = TFLiteSubgraph.len1_array_to_scalar(test_input)
assert output == expected
parse_op_testdata = [
# op_type, opt_serializer, inputs, output, expected
(Op.FullyConnected, None, [0, 1, 2], 3, 3), # FC
(Op.FullyConnected, None, [0, 1, -1], 3, 3), # FC disabled Bias
(Op.FullyConnected, None, [0, 1], 3, 3), # FC no Bias
(Op.Conv2D, None, [2, 1, 3], 0, 3), # Conv2D
(Op.Conv2DBackpropInput, None, [0, 1, 2, 3], 4, 4), # TransposeConv
(Op.Conv2DBackpropInput, None, [0, 1, 2], 4, 4), # TransposeConv no Bias
pytest.param(Op.Conv2D, None, [0, -1, 1], 3, 3, marks=pytest.mark.xfail), # Conv2D no Weights
]
@pytest.mark.parametrize("op_type, opt_serializer, inputs, output, expected", parse_op_testdata)
def test_parse_operator(self, op_type, opt_serializer, inputs, output, expected):
with patch.object(TFLiteSubgraph, "__init__", lambda self, graph, subraph: None):
# Mock a TFLiteSubGraph
sg = TFLiteSubgraph(None, None)
sg.graph = MagicMock()
sg.graph.operator_codes = [(op_type, opt_serializer, "")]
# Mock a couple of tensors
sg.tensors = [MagicMock() for _ in range(5)]
for i, tens in enumerate(sg.tensors):
tens.name = "tensor_{}".format(i)
tens.ops = []
# Mock op data
op_data = MagicMock()
op_data.OpcodeIndex.return_value = 0
op_data.InputsAsNumpy.return_value = inputs
op_data.OutputsAsNumpy.return_value = [output]
sg.parse_operator(0, op_data)
# Verify the created Operation
created_op = sg.tensors[output].ops[0]
assert created_op.type == op_type
assert len(created_op.inputs) == expected
assert created_op.outputs[0].name == "tensor_{}".format(output)
assert inputs[-1] != -1 or not created_op.inputs[-1]
string_buffer_testdata = [
(np.array([np.random.randint(256) for _ in range(100)], dtype=np.uint8), [3, 5]),
(np.array([np.random.randint(256) for _ in range(100)], dtype=np.int16), [10, 10]),
(np.array([np.random.randint(256) for _ in range(100)], dtype=np.float32), [100]),
(np.array([], dtype=np.int8), [30]),
]
@pytest.mark.parametrize("buffer, tens_shape", string_buffer_testdata)
def test_parse_tensor_with_string_buffer(self, buffer, tens_shape):
tens_data = MagicMock()
tens_data.ShapeAsNumpy = MagicMock(return_value=np.array(tens_shape), dtype=np.int32)
tens_data.Name = MagicMock(return_value=b"test_data")
tens_data.Type = MagicMock(return_value=TensorType.STRING)
tens_data.Quantization = MagicMock(return_value=None)
tens_data.Buffer = MagicMock(return_value=0)
tfl_sg = MagicMock()
tfl_sg.Name = MagicMock(return_value=b"test_sg")
tfl_sg.TensorsLength = MagicMock(return_value=0)
tfl_sg.OperatorsLength = MagicMock(return_value=0)
tfl_sg.OutputsAsNumpy = MagicMock(return_value=[])
tfl_sg.InputsAsNumpy = MagicMock(return_value=[])
graph = MagicMock()
graph.buffers = [buffer]
subgraph = TFLiteSubgraph(graph, tfl_sg)
tens = subgraph.parse_tensor(tens_data)
assert tens.values is None
|
