237 lines
9.1 KiB
Python
237 lines
9.1 KiB
Python
|
# 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 V3 versions.
|
||
|
|
histogram = summary_v2.histogram
|
||
|
|
histogram_pb = summary_v2.histogram_pb
|
||
|
|
|
||
|
|
|
||
|
|
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
|