forked from openvinotoolkit/openvino
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathtest_tf_BiasAdd.py
216 lines (172 loc) · 9.89 KB
/
test_tf_BiasAdd.py
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
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
# Copyright (C) 2018-2025 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import pytest
import tensorflow as tf
from common.tf_layer_test_class import CommonTFLayerTest
class TestBiasAdd(CommonTFLayerTest):
def create_bias_add_placeholder_const_net(self, shape, ir_version, use_legacy_frontend, output_type=tf.float32):
tf.compat.v1.reset_default_graph()
# Create the graph and model
with tf.compat.v1.Session() as sess:
tf_x_shape = shape.copy()
tf_y_shape = tf_x_shape[-1:]
x = tf.compat.v1.placeholder(output_type, tf_x_shape, 'Input')
constant_value = np.random.randint(0, 1, tf_y_shape).astype(output_type.as_numpy_dtype())
if (constant_value == 0).all():
# Avoid elimination of the layer from IR
constant_value = constant_value + 1
y = tf.constant(constant_value)
tf.nn.bias_add(x, y, name="Operation")
tf.compat.v1.global_variables_initializer()
tf_net = sess.graph_def
ref_net = None
return tf_net, ref_net
def create_bias_add_2_consts_net(self, shape, ir_version, use_legacy_frontend, output_type=tf.float32):
tf.compat.v1.reset_default_graph()
tf_concat_axis = -1
# Create the graph and model
with tf.compat.v1.Session() as sess:
tf_x_shape = shape.copy()
tf_y_shape = tf_x_shape[-1:]
constant_value_x = np.random.randint(-256, 256, tf_x_shape).astype(output_type.as_numpy_dtype())
x = tf.constant(constant_value_x)
constant_value_y = np.random.randint(-256, 256, tf_y_shape).astype(output_type.as_numpy_dtype())
y = tf.constant(constant_value_y)
add = tf.nn.bias_add(x, y, name="Operation")
placeholder = tf.compat.v1.placeholder(output_type, tf_x_shape,
'Input') # Input_1 in graph_def
concat = tf.concat([placeholder, add], axis=tf_concat_axis, name='Operation')
tf.compat.v1.global_variables_initializer()
tf_net = sess.graph_def
ref_net = None
return tf_net, ref_net
test_data_2D = [
dict(shape=[1, 1]),
dict(shape=[1, 224])
]
@pytest.mark.parametrize("params", test_data_2D)
@pytest.mark.nightly
def test_bias_add_placeholder_const_2D(self, params, ie_device, precision, ir_version, temp_dir,
use_legacy_frontend):
self._test(*self.create_bias_add_placeholder_const_net(**params, ir_version=ir_version,
use_legacy_frontend=use_legacy_frontend),
ie_device, precision, ir_version, temp_dir=temp_dir,
use_legacy_frontend=use_legacy_frontend)
@pytest.mark.parametrize("params", test_data_2D)
@pytest.mark.nightly
def test_bias_add_2_consts_2D(self, params, ie_device, precision, ir_version, temp_dir,
use_legacy_frontend):
self._test(*self.create_bias_add_2_consts_net(**params, ir_version=ir_version,
use_legacy_frontend=use_legacy_frontend),
ie_device, precision, ir_version, temp_dir=temp_dir,
use_legacy_frontend=use_legacy_frontend)
test_data_3D = [
pytest.param(dict(shape=[1, 1, 224]), marks=pytest.mark.xfail(reason="*-19053")),
pytest.param(dict(shape=[1, 3, 224]), marks=pytest.mark.xfail(reason="*-19053"))
]
@pytest.mark.parametrize("params", test_data_3D)
@pytest.mark.nightly
def test_bias_add_placeholder_const_3D(self, params, ie_device, precision, ir_version, temp_dir,
use_legacy_frontend):
self._test(*self.create_bias_add_placeholder_const_net(**params, ir_version=ir_version,
use_legacy_frontend=use_legacy_frontend),
ie_device, precision, ir_version, temp_dir=temp_dir,
use_legacy_frontend=use_legacy_frontend)
@pytest.mark.parametrize("params", test_data_3D)
@pytest.mark.nightly
def test_bias_add_2_consts_3D(self, params, ie_device, precision, ir_version, temp_dir,
use_legacy_frontend):
self._test(*self.create_bias_add_2_consts_net(**params, ir_version=ir_version,
use_legacy_frontend=use_legacy_frontend),
ie_device, precision, ir_version, temp_dir=temp_dir,
use_legacy_frontend=use_legacy_frontend)
test_data_4D = [
dict(shape=[1, 1, 100, 224]),
pytest.param(dict(shape=[1, 3, 100, 224]), marks=pytest.mark.precommit),
pytest.param(dict(shape=[1, 3, 100, 224], output_type=tf.float16), marks=pytest.mark.precommit)
]
@pytest.mark.parametrize("params", test_data_4D)
@pytest.mark.nightly
@pytest.mark.precommit
def test_bias_add_placeholder_const_4D(self, params, ie_device, precision, ir_version, temp_dir,
use_legacy_frontend):
self._test(*self.create_bias_add_placeholder_const_net(**params, ir_version=ir_version,
use_legacy_frontend=use_legacy_frontend),
ie_device, precision, ir_version, temp_dir=temp_dir,
use_legacy_frontend=use_legacy_frontend)
@pytest.mark.parametrize("params", test_data_4D)
@pytest.mark.nightly
def test_bias_add_2_consts_4D(self, params, ie_device, precision, ir_version, temp_dir,
use_legacy_frontend):
if ie_device == 'CPU':
pytest.skip('155622: OpenVINO runtime timeout on CPU')
self._test(*self.create_bias_add_2_consts_net(**params, ir_version=ir_version,
use_legacy_frontend=use_legacy_frontend),
ie_device, precision, ir_version, temp_dir=temp_dir,
use_legacy_frontend=use_legacy_frontend)
test_data_5D = [
dict(shape=[1, 1, 50, 100, 224]),
dict(shape=[1, 3, 220, 222, 224])
]
@pytest.mark.parametrize("params", test_data_5D)
@pytest.mark.nightly
@pytest.mark.precommit
def test_bias_add_placeholder_const_5D(self, params, ie_device, precision, ir_version, temp_dir,
use_legacy_frontend):
self._test(*self.create_bias_add_placeholder_const_net(**params, ir_version=ir_version,
use_legacy_frontend=use_legacy_frontend),
ie_device, precision, ir_version, temp_dir=temp_dir,
use_legacy_frontend=use_legacy_frontend)
@pytest.mark.parametrize("params", test_data_5D)
@pytest.mark.nightly
def test_bias_add_2_consts_5D(self, params, ie_device, precision, ir_version, temp_dir,
use_legacy_frontend):
self._test(*self.create_bias_add_2_consts_net(**params, ir_version=ir_version,
use_legacy_frontend=use_legacy_frontend),
ie_device, precision, ir_version, temp_dir=temp_dir,
use_legacy_frontend=use_legacy_frontend)
class TestComplexBiasAdd(CommonTFLayerTest):
def _prepare_input(self, inputs_info):
rng = np.random.default_rng()
assert 'x_real:0' in inputs_info
assert 'x_imag:0' in inputs_info
assert 'y_real:0' in inputs_info
assert 'y_imag:0' in inputs_info
x_shape = inputs_info['x_real:0']
y_shape = inputs_info['y_real:0']
inputs_data = {}
inputs_data['x_real:0'] = 4 * rng.random(x_shape).astype(np.float64) - 2
inputs_data['x_imag:0'] = 4 * rng.random(x_shape).astype(np.float64) - 2
inputs_data['y_real:0'] = 4 * rng.random(y_shape).astype(np.float64) - 2
inputs_data['y_imag:0'] = 4 * rng.random(y_shape).astype(np.float64) - 2
return inputs_data
def create_complex_bias_add_net(self, input_shape, bias_shape, data_format, ir_version, use_legacy_frontend, output_type=tf.float32):
tf.compat.v1.reset_default_graph()
with tf.compat.v1.Session() as sess:
x_real = tf.compat.v1.placeholder(output_type, input_shape, 'x_real')
x_imag = tf.compat.v1.placeholder(output_type, input_shape, 'x_imag')
y_real = tf.compat.v1.placeholder(output_type, bias_shape, 'y_real')
y_imag = tf.compat.v1.placeholder(output_type, bias_shape, 'y_imag')
complex_input = tf.complex(x_real, x_imag)
complex_bias = tf.complex(y_real, y_imag)
result = tf.raw_ops.BiasAdd(value=complex_input, bias=complex_bias,data_format=data_format,name="ComplexBiasAdd")
real = tf.raw_ops.Real(input=result)
img = tf.raw_ops.Imag(input=result)
tf_net = sess.graph_def
return tf_net, None
test_data_2D = [
dict(input_shape=[1, 1], bias_shape=[1], data_format="NHWC"),
dict(input_shape=[3, 2, 7], bias_shape=[7], data_format="NHWC"),
dict(input_shape=[3, 2, 7, 10], bias_shape=[2], data_format="NCHW"),
dict(input_shape=[7, 6, 4, 5], bias_shape=[6], data_format="NCHW"),
]
@pytest.mark.parametrize("params", test_data_2D)
@pytest.mark.precommit
@pytest.mark.nightly
def test_complex_bias_add(self, params, ie_device, precision, ir_version, temp_dir,
use_legacy_frontend):
self._test(*self.create_complex_bias_add_net(**params, ir_version=ir_version,
use_legacy_frontend=use_legacy_frontend),
ie_device, precision, ir_version, temp_dir=temp_dir,
use_legacy_frontend=use_legacy_frontend)