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Modify tensorflow/python/training to use the new summary ops.

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Very simple migration replacing tf.scalar_summary with tf.summary.scalar, etc.

It is almost entirely a namespace shift. Also, the new scalar_summary does
not support array of inputs, which was only really used in test code.
Change: 136782705
This commit is contained in:
Dan Mané 2016-10-20 16:39:41 -08:00 committed by TensorFlower Gardener
parent 65e31297f5
commit dd845147f1
8 changed files with 52 additions and 36 deletions
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4
tensorflow/python/training/basic_session_run_hooks.py
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@ -372,8 +372,8 @@ class SummarySaverHook(session_run_hook.SessionRunHook):
one will be created accordingly.
scaffold: `Scaffold` to get summary_op if it's not provided.
summary_op: `Tensor` of type `string`. A serialized `Summary` protocol
buffer, as output by TF summary methods like `scalar_summary` or
`merge_all_summaries`.
buffer, as output by TF summary methods like `tf.summary.scalar` or
`tf.summary.merge_all`.
Raises:
ValueError: Exactly one of scaffold or summary_op should be set.

2
tensorflow/python/training/basic_session_run_hooks_test.py
View File

@ -333,7 +333,7 @@ class SummarySaverHookTest(tf.test.TestCase):
var = tf.Variable(0.0)
tensor = tf.assign_add(var, 1.0)
self.summary_op = tf.scalar_summary('my_summary', tensor)
self.summary_op = tf.summary.scalar('my_summary', tensor)
global_step = tf.contrib.framework.get_or_create_global_step()
self.train_op = tf.assign_add(global_step, 1)

23
tensorflow/python/training/input.py
View File

@ -27,6 +27,7 @@ import collections
from six.moves import xrange # pylint: disable=redefined-builtin
from tensorflow.python import summary
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
@ -35,7 +36,6 @@ from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import data_flow_ops
from tensorflow.python.ops import io_ops
from tensorflow.python.ops import logging_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import sparse_ops
@ -147,9 +147,8 @@ def input_producer(input_tensor, element_shape=None, num_epochs=None,
enq = q.enqueue_many([input_tensor])
queue_runner.add_queue_runner(queue_runner.QueueRunner(q, [enq]))
if summary_name is not None:
logging_ops.scalar_summary("queue/%s/%s" % (q.name, summary_name),
math_ops.cast(q.size(), dtypes.float32) *
(1. / capacity))
summary.scalar("queue/%s/%s" % (q.name, summary_name),
math_ops.cast(q.size(), dtypes.float32) * (1. / capacity))
return q
@ -650,9 +649,9 @@ def batch(tensors, batch_size, num_threads=1, capacity=32,
capacity=capacity, dtypes=types, shapes=shapes, shared_name=shared_name)
print("Enqueueing: ", enqueue_many, tensor_list, shapes)
_enqueue(queue, tensor_list, num_threads, enqueue_many)
logging_ops.scalar_summary(
"queue/%s/fraction_of_%d_full" % (queue.name, capacity),
math_ops.cast(queue.size(), dtypes.float32) * (1. / capacity))
summary.scalar("queue/%s/fraction_of_%d_full" % (queue.name, capacity),
math_ops.cast(queue.size(), dtypes.float32) *
(1. / capacity))
if allow_smaller_final_batch:
dequeued = queue.dequeue_up_to(batch_size, name=name)
@ -763,9 +762,9 @@ def batch_join(tensors_list, batch_size, capacity=32, enqueue_many=False,
queue = _which_queue(dynamic_pad)(
capacity=capacity, dtypes=types, shapes=shapes, shared_name=shared_name)
_enqueue_join(queue, tensor_list_list, enqueue_many)
logging_ops.scalar_summary(
"queue/%s/fraction_of_%d_full" % (queue.name, capacity),
math_ops.cast(queue.size(), dtypes.float32) * (1. / capacity))
summary.scalar("queue/%s/fraction_of_%d_full" % (queue.name, capacity),
math_ops.cast(queue.size(), dtypes.float32) *
(1. / capacity))
if allow_smaller_final_batch:
dequeued = queue.dequeue_up_to(batch_size, name=name)
@ -874,7 +873,7 @@ def shuffle_batch(tensors, batch_size, capacity, min_after_dequeue,
summary_name = (
"queue/%sfraction_over_%d_of_%d_full" %
(name, min_after_dequeue, capacity - min_after_dequeue))
logging_ops.scalar_summary(summary_name, full)
summary.scalar(summary_name, full)
if allow_smaller_final_batch:
dequeued = queue.dequeue_up_to(batch_size, name=name)
@ -978,7 +977,7 @@ def shuffle_batch_join(tensors_list, batch_size, capacity,
summary_name = (
"queue/%sfraction_over_%d_of_%d_full" %
(name, min_after_dequeue, capacity - min_after_dequeue))
logging_ops.scalar_summary(summary_name, full)
summary.scalar(summary_name, full)
if allow_smaller_final_batch:
dequeued = queue.dequeue_up_to(batch_size, name=name)

8
tensorflow/python/training/monitored_session.py
View File

@ -22,12 +22,12 @@ from __future__ import print_function
import abc
from tensorflow.core.protobuf import saver_pb2
from tensorflow.python import summary
from tensorflow.python.framework import errors
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import data_flow_ops
from tensorflow.python.ops import logging_ops
from tensorflow.python.ops import resources
from tensorflow.python.ops import variables
from tensorflow.python.training import basic_session_run_hooks
@ -148,9 +148,9 @@ class Scaffold(object):
'local_init_op', ops.GraphKeys.LOCAL_INIT_OP,
Scaffold._default_local_init_op)
if self._summary_op is None:
self._summary_op = Scaffold.get_or_default(
'summary_op', ops.GraphKeys.SUMMARY_OP,
logging_ops.merge_all_summaries)
self._summary_op = Scaffold.get_or_default('summary_op',
ops.GraphKeys.SUMMARY_OP,
summary.merge_all)
# pylint: disable=g-long-lambda
if self._saver is None:
self._saver = Scaffold.get_or_default(

2
tensorflow/python/training/monitored_session_test.py
View File

@ -162,7 +162,7 @@ class MonitoredTrainingSessionTest(tf.test.TestCase):
with tf.Graph().as_default():
gstep = tf.contrib.framework.get_or_create_global_step()
new_gstep = tf.assign_add(gstep, 1)
tf.scalar_summary('my_summary_tag', new_gstep * 2)
tf.summary.scalar('my_summary_tag', new_gstep * 2)
with tf.train.MonitoredTrainingSession(
is_chief=True, checkpoint_dir=logdir) as session:
for _ in range(101): # 100 is default summary writing steps

2
tensorflow/python/training/saver_test.py
View File

@ -1504,7 +1504,7 @@ class MetaGraphTest(tf.test.TestCase):
name="xentropy")
loss = tf.reduce_mean(cross_entropy, name="xentropy_mean")
tf.scalar_summary(loss.op.name, loss)
tf.summary.scalar("loss", loss)
# Creates the gradient descent optimizer with the given learning rate.
optimizer = tf.train.GradientDescentOptimizer(0.01)

6
tensorflow/python/training/supervisor.py
View File

@ -23,12 +23,12 @@ import time
from tensorflow.core.framework.summary_pb2 import Summary
from tensorflow.core.util.event_pb2 import SessionLog
from tensorflow.python import summary as _summary
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import meta_graph
from tensorflow.python.framework import ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import data_flow_ops
from tensorflow.python.ops import logging_ops
from tensorflow.python.ops import variables
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.training import coordinator
@ -269,7 +269,7 @@ class Supervisor(object):
The directory will be created if it does not exist.
summary_op: An `Operation` that returns a Summary for the event logs.
Used by chief supervisors if a `logdir` was specified. Defaults to the
operation returned from merge_all_summaries(). If `None`, summaries are
operation returned from summary.merge_all(). If `None`, summaries are
not computed automatically.
saver: A Saver object. Used by chief supervisors if a `logdir` was
specified. Defaults to the saved returned by Saver().
@ -471,7 +471,7 @@ class Supervisor(object):
if summary_op is Supervisor.USE_DEFAULT:
summary_op = self._get_first_op_from_collection(ops.GraphKeys.SUMMARY_OP)
if summary_op is None:
summary_op = logging_ops.merge_all_summaries()
summary_op = _summary.merge_all()
if summary_op is not None:
ops.add_to_collection(ops.GraphKeys.SUMMARY_OP, summary_op)
self._summary_op = summary_op

41
tensorflow/python/training/supervisor_test.py
View File

@ -133,7 +133,10 @@ class SupervisorTest(tf.test.TestCase):
def testManagedSessionDoNotKeepSummaryWriter(self):
logdir = _test_dir("managed_not_keep_summary_writer")
with tf.Graph().as_default():
summ = tf.scalar_summary(["c1", "c2", "c3"], tf.constant([1.0, 2.0, 3.0]))
tf.summary.scalar("c1", tf.constant(1))
tf.summary.scalar("c2", tf.constant(2))
tf.summary.scalar("c3", tf.constant(3))
summ = tf.summary.merge_all()
sv = tf.train.Supervisor(logdir=logdir, summary_op=None)
with sv.managed_session("", close_summary_writer=True,
start_standard_services=False) as sess:
@ -182,7 +185,10 @@ class SupervisorTest(tf.test.TestCase):
def testManagedSessionKeepSummaryWriter(self):
logdir = _test_dir("managed_keep_summary_writer")
with tf.Graph().as_default():
summ = tf.scalar_summary(["c1", "c2", "c3"], tf.constant([1.0, 2.0, 3.0]))
tf.summary.scalar("c1", tf.constant(1))
tf.summary.scalar("c2", tf.constant(2))
tf.summary.scalar("c3", tf.constant(3))
summ = tf.summary.merge_all()
sv = tf.train.Supervisor(logdir=logdir)
with sv.managed_session("", close_summary_writer=False,
start_standard_services=False) as sess:
@ -311,7 +317,10 @@ class SupervisorTest(tf.test.TestCase):
def testChiefCanWriteEvents(self):
logdir = _test_dir("can_write")
with tf.Graph().as_default():
summ = tf.scalar_summary(["c1", "c2", "c3"], tf.constant([1.0, 2.0, 3.0]))
tf.summary.scalar("c1", tf.constant(1))
tf.summary.scalar("c2", tf.constant(2))
tf.summary.scalar("c3", tf.constant(3))
summ = tf.summary.merge_all()
sv = tf.train.Supervisor(is_chief=True, logdir=logdir, summary_op=None)
meta_graph_def = meta_graph.create_meta_graph_def()
sess = sv.prepare_or_wait_for_session("")
@ -361,7 +370,9 @@ class SupervisorTest(tf.test.TestCase):
with tf.Graph().as_default():
sv = tf.train.Supervisor(is_chief=False)
sess = sv.prepare_or_wait_for_session("")
summ = tf.scalar_summary(["c1", "c2"], tf.constant([1.0, 2.0]))
tf.summary.scalar("c1", tf.constant(1))
tf.summary.scalar("c2", tf.constant(2))
summ = tf.summary.merge_all()
sv.summary_computed(sess, sess.run(summ))
def _start_standard_services():
@ -375,8 +386,10 @@ class SupervisorTest(tf.test.TestCase):
def testNoLogdirButWantSummary(self):
with tf.Graph().as_default():
const = tf.constant([1.0, 2.0, 3.0])
summ = tf.scalar_summary(["c1", "c2", "c3"], const)
tf.summary.scalar("c1", tf.constant(1))
tf.summary.scalar("c2", tf.constant(2))
tf.summary.scalar("c3", tf.constant(3))
summ = tf.summary.merge_all()
sv = tf.train.Supervisor(logdir="", summary_op=None)
sess = sv.prepare_or_wait_for_session("")
with self.assertRaisesRegexp(RuntimeError, "requires a summary writer"):
@ -386,8 +399,10 @@ class SupervisorTest(tf.test.TestCase):
logdir = _test_dir("explicit_no_summary_writer")
with tf.Graph().as_default():
tf.Variable([1.0], name="foo")
const = tf.constant([1.0, 2.0, 3.0])
summ = tf.scalar_summary(["c1", "c2", "c3"], const)
tf.summary.scalar("c1", tf.constant(1))
tf.summary.scalar("c2", tf.constant(2))
tf.summary.scalar("c3", tf.constant(3))
summ = tf.summary.merge_all()
sv = tf.train.Supervisor(logdir=logdir, summary_writer=None)
sess = sv.prepare_or_wait_for_session("")
# Check that a checkpoint is still be generated.
@ -399,8 +414,10 @@ class SupervisorTest(tf.test.TestCase):
def testNoLogdirButExplicitSummaryWriter(self):
logdir = _test_dir("explicit_summary_writer")
with tf.Graph().as_default():
const = tf.constant([1.0, 2.0, 3.0])
summ = tf.scalar_summary(["c1", "c2", "c3"], const)
tf.summary.scalar("c1", tf.constant(1))
tf.summary.scalar("c2", tf.constant(2))
tf.summary.scalar("c3", tf.constant(3))
summ = tf.summary.merge_all()
sw = tf.train.SummaryWriter(logdir)
sv = tf.train.Supervisor(logdir="", summary_op=None, summary_writer=sw)
meta_graph_def = meta_graph.create_meta_graph_def()
@ -547,7 +564,7 @@ class SupervisorTest(tf.test.TestCase):
with tf.Graph().as_default():
v = tf.Variable(
10.0, name="ready_for_local_init_op_restore_v_" + str(uid))
tf.scalar_summary("ready_for_local_init_op_restore_v_" + str(uid), v)
tf.summary.scalar("ready_for_local_init_op_restore_v_" + str(uid), v)
sv = tf.train.Supervisor(logdir=logdir)
sv.prepare_or_wait_for_session(server.target)
save_path = sv.save_path
@ -707,7 +724,7 @@ class SupervisorTest(tf.test.TestCase):
# Create a checkpoint.
with tf.Graph().as_default():
v = tf.Variable([1.0], name="foo")
tf.scalar_summary(["v"], v)
tf.summary.scalar("v", v[0])
sv = tf.train.Supervisor(logdir=logdir)
meta_graph_def = meta_graph.create_meta_graph_def(
saver_def=sv.saver.saver_def)