-
Notifications
You must be signed in to change notification settings - Fork 37
/
networks_test.py
155 lines (140 loc) · 5.74 KB
/
networks_test.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
# Copyright (c) 2021, Google Inc.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of Google Inc. nor the names of its contributors
# may be used to endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR
# ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
# ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""Tests for deepconsensus.models.networks."""
import itertools
from absl.testing import absltest
from absl.testing import parameterized
import ml_collections
import numpy as np
import tensorflow as tf
from deepconsensus.models import data_providers
from deepconsensus.models import model_configs
from deepconsensus.models import model_utils
from deepconsensus.utils import dc_constants
def get_tf_example_rows(
params: ml_collections.ConfigDict, inference: bool
) -> np.ndarray:
"""Returns one example from the training dataset for given params."""
dataset = data_providers.get_dataset(
file_pattern=params.train_path,
num_epochs=params.num_epochs,
batch_size=params.batch_size,
params=params,
inference=inference,
)
tf_example = next(dataset.as_numpy_iterator())
return tf_example['rows']
class ModelsTest(parameterized.TestCase):
@parameterized.parameters(
itertools.product(
[True, False],
[
'fc+test',
'transformer+test',
'transformer_learn_values+test',
],
[True, False],
)
)
def test_outputs(self, training, config_name, use_predict):
"""Checks that softmax distribution and final predictions are valid.
This test is only checking the output format and does not train the model.
Args:
training: whether we are in training or eval/test mode.
config_name: config to test.
use_predict: whether to use model.predict or call model as a function.
"""
params = model_configs.get_config(config_name)
model_utils.modify_params(params)
model = model_utils.get_model(params)
inference = not training
rows = get_tf_example_rows(params, inference=inference)
if use_predict:
softmax_output = model.predict(rows)
else:
softmax_output = model(rows, training=training).numpy()
predictions = tf.argmax(softmax_output, -1)
# First dimension will always be equal to batch_size because test config
# uses a batch size of 1.
self.assertEqual(
softmax_output.shape,
(params.batch_size, params.max_length, dc_constants.SEQ_VOCAB_SIZE),
)
self.assertTrue(
np.allclose(
np.sum(softmax_output, axis=-1),
np.ones(shape=[params.batch_size, params.max_length]),
)
)
self.assertEqual(predictions.shape, (params.batch_size, params.max_length))
@parameterized.parameters(
itertools.product(
[
'fc+test',
'transformer+test',
'transformer_learn_values+test',
],
[True, False],
)
)
def test_predict_and_model_fn_equal(self, config_name, inference):
"""Checks that model.predict and calling model as a function are equal."""
config = model_configs.get_config(config_name)
model_utils.modify_params(config)
model = model_utils.get_model(config)
rows = get_tf_example_rows(config, inference=inference)
softmax_output_predict = model.predict(rows)
softmax_output = model(rows, training=False).numpy()
self.assertTrue(
np.allclose(softmax_output_predict, softmax_output, rtol=1e-05)
)
@parameterized.parameters(
itertools.product(
['transformer_learn_values+test'], [True, False], [6, 12]
)
)
def test_attn_win_sizes(self, config_name, inference, attn_win_size):
"""Checks that attention scores are zero outside attention mask."""
config = model_configs.get_config(config_name)
model_utils.modify_params(config)
config.attn_win_size = attn_win_size
model = model_utils.get_model(config)
rows = get_tf_example_rows(config, inference=inference)
outputs = model.get_intermediate_outputs(rows, training=False)
for layer_idx in range(config['num_hidden_layers']):
attn_maps = outputs['attention_scores_{}'.format(layer_idx)]
>
band = tf.linalg.band_part(ones, attn_win_size, attn_win_size)
attn_maps_masked = attn_maps * (1.0 - band)
self.assertTrue(np.allclose(attn_maps_masked.numpy(), 0.0, rtol=1e-05))
self.assertEqual(
outputs['logits'].numpy().shape,
(config.batch_size, config.max_length, dc_constants.SEQ_VOCAB_SIZE),
)
if __name__ == '__main__':
absltest.main()