forked from tensorflow/tensorboard
-
Notifications
You must be signed in to change notification settings - Fork 0
/
summary.py
239 lines (208 loc) · 9.12 KB
/
summary.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
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# 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
#
# http://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.
# ==============================================================================
"""Histogram summaries and TensorFlow operations to create them.
A histogram summary stores a list of buckets. Each bucket is encoded as
a triple `[left_edge, right_edge, count]`. Thus, a full histogram is
encoded as a tensor of dimension `[k, 3]`.
In general, the value of `k` (the number of buckets) will be a constant,
like 30. There are two edge cases: if there is no data, then there are
no buckets (the shape is `[0, 3]`); and if there is data but all points
have the same value, then there is one bucket whose left and right
endpoints are the same (the shape is `[1, 3]`).
NOTE: This module is in beta, and its API is subject to change, but the
data that it stores to disk will be supported forever.
"""
import numpy as np
from tensorboard.plugins.histogram import metadata
from tensorboard.plugins.histogram import summary_v2
# Export V2 versions.
histogram = summary_v2.histogram
histogram_pb = summary_v2.histogram_pb
# Export V3 versions.
histogram_v3 = summary_v2.histogram_v3
def _buckets(data, bucket_count=None):
"""Create a TensorFlow op to group data into histogram buckets.
Arguments:
data: A `Tensor` of any shape. Must be castable to `float64`.
bucket_count: Optional positive `int` or scalar `int32` `Tensor`.
Returns:
A `Tensor` of shape `[k, 3]` and type `float64`. The `i`th row is
a triple `[left_edge, right_edge, count]` for a single bucket.
The value of `k` is either `bucket_count` or `1` or `0`.
"""
# TODO(nickfelt): remove on-demand imports once dep situation is fixed.
import tensorflow.compat.v1 as tf
if bucket_count is None:
bucket_count = summary_v2.DEFAULT_BUCKET_COUNT
with tf.name_scope(
"buckets", values=[data, bucket_count]
), tf.control_dependencies(
[tf.assert_scalar(bucket_count), tf.assert_type(bucket_count, tf.int32)]
):
data = tf.reshape(data, shape=[-1]) # flatten
data = tf.cast(data, tf.float64)
is_empty = tf.equal(tf.size(input=data), 0)
def when_empty():
return tf.constant([], shape=(0, 3), dtype=tf.float64)
def when_nonempty():
min_ = tf.reduce_min(input_tensor=data)
max_ = tf.reduce_max(input_tensor=data)
range_ = max_ - min_
is_singular = tf.equal(range_, 0)
def when_nonsingular():
bucket_width = range_ / tf.cast(bucket_count, tf.float64)
offsets = data - min_
bucket_indices = tf.cast(
tf.floor(offsets / bucket_width), dtype=tf.int32
)
clamped_indices = tf.minimum(bucket_indices, bucket_count - 1)
# Use float64 instead of float32 to avoid accumulating floating point error
# later in tf.reduce_sum when summing more than 2^24 individual `1.0` values.
# See https://github.com/tensorflow/tensorflow/issues/51419 for details.
one_hots = tf.one_hot(
clamped_indices, depth=bucket_count, dtype=tf.float64
)
bucket_counts = tf.cast(
tf.reduce_sum(input_tensor=one_hots, axis=0),
dtype=tf.float64,
)
edges = tf.linspace(min_, max_, bucket_count + 1)
left_edges = edges[:-1]
right_edges = edges[1:]
return tf.transpose(
a=tf.stack([left_edges, right_edges, bucket_counts])
)
def when_singular():
center = min_
bucket_starts = tf.stack([center - 0.5])
bucket_ends = tf.stack([center + 0.5])
bucket_counts = tf.stack(
[tf.cast(tf.size(input=data), tf.float64)]
)
return tf.transpose(
a=tf.stack([bucket_starts, bucket_ends, bucket_counts])
)
return tf.cond(is_singular, when_singular, when_nonsingular)
return tf.cond(is_empty, when_empty, when_nonempty)
def op(
name,
data,
bucket_count=None,
display_name=None,
description=None,
collections=None,
):
"""Create a legacy histogram summary op.
Arguments:
name: A unique name for the generated summary node.
data: A `Tensor` of any shape. Must be castable to `float64`.
bucket_count: Optional positive `int`. The output will have this
many buckets, except in two edge cases. If there is no data, then
there are no buckets. If there is data but all points have the
same value, then there is one bucket whose left and right
endpoints are the same.
display_name: Optional name for this summary in TensorBoard, as a
constant `str`. Defaults to `name`.
description: Optional long-form description for this summary, as a
constant `str`. Markdown is supported. Defaults to empty.
collections: Optional list of graph collections keys. The new
summary op is added to these collections. Defaults to
`[Graph Keys.SUMMARIES]`.
Returns:
A TensorFlow summary op.
"""
# TODO(nickfelt): remove on-demand imports once dep situation is fixed.
import tensorflow.compat.v1 as tf
if display_name is None:
display_name = name
summary_metadata = metadata.create_summary_metadata(
display_name=display_name, description=description
)
with tf.name_scope(name):
tensor = _buckets(data, bucket_count=bucket_count)
return tf.summary.tensor_summary(
name="histogram_summary",
tensor=tensor,
collections=collections,
summary_metadata=summary_metadata,
)
def pb(name, data, bucket_count=None, display_name=None, description=None):
"""Create a legacy histogram summary protobuf.
Arguments:
name: A unique name for the generated summary, including any desired
name scopes.
data: A `np.array` or array-like form of any shape. Must have type
castable to `float`.
bucket_count: Optional positive `int`. The output will have this
many buckets, except in two edge cases. If there is no data, then
there are no buckets. If there is data but all points have the
same value, then there is one bucket whose left and right
endpoints are the same.
display_name: Optional name for this summary in TensorBoard, as a
`str`. Defaults to `name`.
description: Optional long-form description for this summary, as a
`str`. Markdown is supported. Defaults to empty.
Returns:
A `tf.Summary` protobuf object.
"""
# TODO(nickfelt): remove on-demand imports once dep situation is fixed.
import tensorflow.compat.v1 as tf
if bucket_count is None:
bucket_count = summary_v2.DEFAULT_BUCKET_COUNT
data = np.array(data).flatten().astype(float)
if data.size == 0:
buckets = np.array([]).reshape((0, 3))
else:
min_ = np.min(data)
max_ = np.max(data)
range_ = max_ - min_
if range_ == 0:
center = min_
buckets = np.array([[center - 0.5, center + 0.5, float(data.size)]])
else:
bucket_width = range_ / bucket_count
offsets = data - min_
bucket_indices = np.floor(offsets / bucket_width).astype(int)
clamped_indices = np.minimum(bucket_indices, bucket_count - 1)
one_hots = np.array([clamped_indices]).transpose() == np.arange(
0, bucket_count
) # broadcast
assert one_hots.shape == (data.size, bucket_count), (
one_hots.shape,
(data.size, bucket_count),
)
bucket_counts = np.sum(one_hots, axis=0)
edges = np.linspace(min_, max_, bucket_count + 1)
left_edges = edges[:-1]
right_edges = edges[1:]
buckets = np.array(
[left_edges, right_edges, bucket_counts]
).transpose()
tensor = tf.make_tensor_proto(buckets, dtype=tf.float64)
if display_name is None:
display_name = name
summary_metadata = metadata.create_summary_metadata(
display_name=display_name, description=description
)
tf_summary_metadata = tf.SummaryMetadata.FromString(
summary_metadata.SerializeToString()
)
summary = tf.Summary()
summary.value.add(
tag="%s/histogram_summary" % name,
metadata=tf_summary_metadata,
tensor=tensor,
)
return summary