def after_run(self, run_context, run_values):
del run_context # Unused by feature importance summary saver hook.
# Read result tensors.
global_step = run_values.results["global_step"]
feature_names = run_values.results["feature_names"]
feature_usage_counts = run_values.results["feature_usage_counts"]
feature_gains = run_values.results["feature_gains"]
# Ensure summaries are logged at desired frequency
if (self._last_triggered_step is not None and
global_step < self._last_triggered_step + self._every_n_steps):
return
# Validate tensors.
if (len(feature_names) != len(feature_usage_counts)
or len(feature_names) != len(feature_gains)):
raise RuntimeError(
"Feature names and importance measures have inconsistent lengths."
)
# Compute total usage.
total_usage_count = 0.0
for usage_count in feature_usage_counts:
total_usage_count += usage_count
usage_count_norm = 1.0 / total_usage_count if total_usage_count else 1.0
# Compute total gain.
total_gain = 0.0
for gain in feature_gains:
total_gain += gain
gain_norm = 1.0 / total_gain if total_gain else 1.0
# Output summary for each feature.
self._last_triggered_step = global_step
for (name, usage_count, gain) in zip(feature_names,
feature_usage_counts,
feature_gains):
output_dir = os.path.join(self._model_dir, name.decode("utf-8"))
summary_writer = SummaryWriterCache.get(output_dir)
usage_count_summary = Summary(value=[
Summary.Value(tag="feature_importance/usage_counts",
simple_value=usage_count)
])
usage_fraction_summary = Summary(value=[
Summary.Value(tag="feature_importance/usage_fraction",
simple_value=usage_count * usage_count_norm)
])
summary_writer.add_summary(usage_count_summary, global_step)
summary_writer.add_summary(usage_fraction_summary, global_step)
gains_summary = Summary(value=[
Summary.Value(tag="feature_importance/gains",
simple_value=gain)
])
gains_fraction_summary = Summary(value=[
Summary.Value(tag="feature_importance/gains_fraction",
simple_value=gain * gain_norm)
])
summary_writer.add_summary(gains_summary, global_step)
summary_writer.add_summary(gains_fraction_summary, global_step)
def tf_scalar_summary(vals):
# pylint: disable=import-error,no-name-in-module
from tensorflow.core.framework.summary_pb2 import Summary
return Summary(value=[
Summary.Value(tag=key, simple_value=val) for key, val in vals.items()
])
def log_image(self, step, tag, val):
'''
Write an image event.
:param int step: Time step (x-axis in TensorBoard graphs)
:param str tag: Label for this value
:param numpy.ndarray val: Image in RGB format with values from
0 to 255; a 3-D array with index order (row, column, channel).
`val.shape[-1] == 3`
'''
# TODO: support floating-point tensors, 4-D tensors, grayscale
if len(val.shape) != 3:
raise ValueError(
'`log_image` value should be a 3-D tensor, instead got shape %s'
% (val.shape, ))
if val.shape[2] != 3:
raise ValueError(
'Last dimension of `log_image` value should be 3 (RGB), '
'instead got shape %s' % (val.shape, ))
fakefile = StringIO()
png.Writer(size=(val.shape[1], val.shape[0])).write(
fakefile, val.reshape(val.shape[0], val.shape[1] * val.shape[2]))
encoded = fakefile.getvalue()
# https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/core/framework/summary.proto
RGB = 3
image = Summary.Image(height=val.shape[0],
width=val.shape[1],
colorspace=RGB,
encoded_image_string=encoded)
summary = Summary(value=[Summary.Value(tag=tag, image=image)])
self._add_event(step, summary)
def image(tag, tensor):
"""Outputs a `Summary` protocol buffer with images.
The summary has up to `max_images` summary values containing images. The
images are built from `tensor` which must be 3-D with shape `[height, width,
channels]` and where `channels` can be:
* 1: `tensor` is interpreted as Grayscale.
* 3: `tensor` is interpreted as RGB.
* 4: `tensor` is interpreted as RGBA.
The `name` in the outputted Summary.Value protobufs is generated based on the
name, with a suffix depending on the max_outputs setting:
* If `max_outputs` is 1, the summary value tag is '*name*/image'.
* If `max_outputs` is greater than 1, the summary value tags are
generated sequentially as '*name*/image/0', '*name*/image/1', etc.
Args:
tag: A name for the generated node. Will also serve as a series name in
TensorBoard.
tensor: A 3-D `uint8` or `float32` `Tensor` of shape `[height, width,
channels]` where `channels` is 1, 3, or 4.
Returns:
A scalar `Tensor` of type `string`. The serialized `Summary` protocol
buffer.
"""
tag = _clean_tag(tag)
if not isinstance(tensor, np.ndarray):
# try conversion, if failed then need handle by user.
tensor = np.ndarray(tensor, dtype=np.float32)
shape = tensor.shape
height, width, channel = shape[0], shape[1], shape[2]
if channel == 1:
# walk around. PIL's setting on dimension.
tensor = np.reshape(tensor, (height, width))
image = make_image(tensor, height, width, channel)
return Summary(value=[Summary.Value(tag=tag, image=image)])
def _log_and_record(self, elapsed_steps, elapsed_time, global_step):
steps_per_sec = elapsed_steps / elapsed_time
if self._summary_writer is not None:
summary = Summary(value=[Summary.Value(
tag=self._summary_tag, simple_value=steps_per_sec)])
self._summary_writer.add_summary(summary, global_step)
logging.info("%s: %g", self._summary_tag, steps_per_sec)
def _log_and_record(self, elapsed_steps, elapsed_time, global_step):
images_per_sec = elapsed_steps / elapsed_time * FLAGS.batch_size * hvd.size()
summary_tag = 'images/sec'
if self._summary_writer is not None:
summary = Summary(value=[Summary.Value(
tag=summary_tag, simple_value=images_per_sec)])
self._summary_writer.add_summary(summary, global_step)
logging.info("%s: %g", summary_tag, images_per_sec)
def _parse_summary_proto_str(proto_str):
"""
Converts the serialized protobuf `SessionRunValues.results['summary']` into a `Message` object.
"""
summary_msg = Summary()
summary_msg.ParseFromString(proto_str)
return summary_msg
def hyper_log(rmse, job_dir):
log = Summary(value=[
Summary.Value(tag='training/hptuning/metric', simple_value=rmse)
])
logpath = os.path.join(job_dir, 'eval')
writer = tf.summary.FileWriter(logpath)
writer.add_summary(log)
writer.flush()
def _log_and_record(self, elapsed_steps, elapsed_time, global_step):
examples_per_sec = self.batch_size * elapsed_steps / elapsed_time
example_summary_tag = 'examples/sec'
if self._summary_writer is not None:
summary = Summary(value=[
Summary.Value(tag=example_summary_tag,
simple_value=examples_per_sec)
])
self._summary_writer.add_summary(summary, global_step)
logging.info("%s: %g", example_summary_tag, examples_per_sec)
def log_scalar(self, step, tag, val):
'''
Write a scalar event.
:param int step: Time step (x-axis in TensorBoard graphs)
:param str tag: Label for this value
:param float val: Scalar to graph at this time step (y-axis)
'''
summary = Summary(value=[Summary.Value(tag=tag, simple_value=float(np.float32(val)))])
self._add_event(step, summary)
def write_hptuning_metric(args, metric):
summary = Summary(value=[
Summary.Value(tag='training/hptuning/metric', simple_value=metric)
])
eval_path = os.path.join(args.output_dir, 'eval')
summary_writer = tf.summary.FileWriter(eval_path)
summary_writer.add_summary(summary)
summary_writer.flush()
def every_n_step_end(self, current_step, outputs):
current_time = time.time()
if self._last_reported_time is not None and self._summary_writer:
added_steps = current_step - self._last_reported_step
elapsed_time = current_time - self._last_reported_time
steps_per_sec = added_steps / elapsed_time
summary = Summary(value=[Summary.Value(tag=self._summary_tag,
simple_value=steps_per_sec)])
self._summary_writer.add_summary(summary, current_step)
self._last_reported_step = current_step
self._last_reported_time = current_time
def add_summary(self, summary_tag, summary_value, global_step):
""" Adds summary at specific step.
Args:
summary_tag: A string, the name of the summary.
summary_value: The value of the summary at current step.
global_step: The step.
"""
summary = Summary(
value=[Summary.Value(tag=summary_tag, simple_value=summary_value)])
self._summary_writer.add_summary(summary, global_step)
self._summary_writer.flush()
def _log_and_record(self, global_step, learning_rate, total_loss, mlm_loss,
nsp_loss):
time_per_step = self.elapsed_secs / self.count
throughput = self.global_batch_size / time_per_step
log_string = ' '
log_string += 'Step = %6i' % (global_step)
log_string += ', throughput = %6.1f' % (throughput)
log_string += ', total_loss = %6.3f' % (total_loss)
log_string += ', mlm_oss = %6.4e' % (mlm_loss)
log_string += ', nsp_loss = %6.4e' % (nsp_loss)
log_string += ', learning_rate = %6.4e' % (learning_rate)
tf.compat.v1.logging.info(log_string)
if self.summary_writer is not None:
throughput_summary = Summary(value=[
Summary.Value(tag='throughput', simple_value=throughput)
])
self.summary_writer.add_summary(throughput_summary, global_step)
total_loss_summary = Summary(value=[
Summary.Value(tag='total_loss', simple_value=total_loss)
])
self.summary_writer.add_summary(total_loss_summary, global_step)
def log_histogram(self, step, tag, val):
'''
Write a histogram event.
:param int step: Time step (x-axis in TensorBoard graphs)
:param str tag: Label for this value
:param numpy.ndarray val: Arbitrary-dimensional array containing
values to be aggregated in the resulting histogram.
'''
hist = Histogram()
hist.add(val)
summary = Summary(value=[Summary.Value(tag=tag, histo=hist.encode_to_proto())])
self._add_event(step, summary)
def _log_and_record(self, elapsed_steps, elapsed_time, global_step):
steps_per_sec = elapsed_steps / elapsed_time
if self._summary_writer is not None:
if self._total_batch_size:
image_tag = 'images_sec'
image_count = float(steps_per_sec) * self._total_batch_size
summary = Summary(value=[
Summary.Value(tag=self._summary_tag,
simple_value=steps_per_sec),
Summary.Value(tag=image_tag, simple_value=image_count)
])
logging.info("%s: %g, %s: %g, step: %g", self._summary_tag,
steps_per_sec, image_tag, image_count,
global_step)
else:
summary = Summary(value=[
Summary.Value(tag=self._summary_tag,
simple_value=steps_per_sec)
])
logging.info("%s: %g, step: %g", self._summary_tag,
steps_per_sec, global_step)
self._summary_writer.add_summary(summary, global_step)
def write_hptuning_metric(args, metric): """ Write a summary containing the tuning loss metric, as required by hyperparam tuning. """ summary = Summary(value=[Summary.Value(tag='training/hptuning/metric', simple_value=metric)]) # for hyperparam tuning, we write a summary log to a directory 'eval' below the job directory eval_path = os.path.join(args['output_dir'], 'eval') summary_writer = tf.summary.FileWriter(eval_path) # Note: adding the summary to the writer is enough for hyperparam tuning. # The ml engine system is looking for any summary added with the hyperparam metric tag. summary_writer.add_summary(summary) summary_writer.flush()
def after_run(self, run_context, run_values):
_ = run_context
global_step = run_values.results
if self._timer.should_trigger_for_step(global_step):
elapsed_time, elapsed_steps = self._timer.update_last_triggered_step(
global_step)
if elapsed_time is not None:
steps_per_sec = elapsed_steps / elapsed_time
if self._summary_writer is not None:
summary = Summary(value=[Summary.Value(
tag=self._summary_tag, simple_value=steps_per_sec)])
self._summary_writer.add_summary(summary, global_step)
logging.info("%s: %g", self._summary_tag, steps_per_sec)
def _log_statistics(self, elapsed_steps, elapsed_time, global_step):
"""
Collect and store all summary values.
Arguments:
elapsed_steps (int):
The number of steps between the current trigger event and the last one.
elapsed_time (float):
The number of seconds between the current trigger event and the last one.
global_step (tf.Tensor):
Global step tensor.
"""
# Write summary for tensorboard.
if self._summary_writer is not None:
summary_list = list()
# Add only summaries.
for gpu_id in self._gpu_statistics.keys():
for statistic in self._gpu_statistics[gpu_id].keys():
# only add them if they are requested for logging.
if statistic in self._statistics_to_log:
values = self._gpu_statistics[gpu_id][statistic]
# Only Calculate and write average if there is data available.
if values:
avg_value = sum(values) / len(values)
avg_summary = Summary.Value(tag='{}/{}:{}'.format(
self._group_tag, gpu_id, statistic),
simple_value=avg_value)
summary_list.append(avg_summary)
# Write all statistics as simple scalar summaries.
summary = Summary(value=summary_list)
self._summary_writer.add_summary(summary, global_step)
# Log summaries to the logging stream.
if not self._suppress_stdout:
for gpu_id in self._gpu_statistics.keys():
# Acquire a GPU device handle.
handle = nvml.nvmlDeviceGetHandleByIndex(gpu_id)
# Query the device name.
name = nvml.nvmlDeviceGetName(handle).decode('utf-8')
for statistic in self._gpu_statistics[gpu_id].keys():
# Log utilization information with INFO level.
logging.debug(
"%s: %s", name, '{}: {}'.format(
statistic,
self._gpu_statistics[gpu_id][statistic]))
def log_event(self, event, phase):
"""Logs the given event to the summary directory."""
event_name = event + '_' + phase
if self._summary_writer is None:
logging.warning(
'profile_logger: cannot log event "%s" '
' because of no summary directory', event_name)
return
# For now, we only need the event timestamp. No need to pass any value.
s = Summary(value=[Summary.Value(tag=event_name, simple_value=0.0)])
self._summary_writer.add_summary(s)
self._summary_writer.flush()
logging.info('profile_logger: log event "%s"', event_name)
def add_entry(self, index, tag, value, **kwargs):
if "image" in kwargs and value is not None:
image_string = tf.image.encode_jpeg(value,
optimize_size=True,
quality=80)
summary_value = Summary.Image(width=value.shape[1],
height=value.shape[0],
colorspace=value.shape[2],
encoded_image_string=image_string)
else:
summary_value = Summary.Value(tag=tag, simple_value=value)
if summary_value is not None:
entry = Summary(value=[summary_value])
self._train_writer.add_summary(entry, index)
def after_run(self, run_context, run_values):
global_episode = run_values.results
self._num_steps += 1
if self._timer.should_trigger_for_episode(global_episode):
elapsed_time, elapsed_steps = self._timer.update_last_triggered_episode(global_episode)
if elapsed_time is not None:
steps_per_sec = elapsed_steps / elapsed_time
if self._summary_writer is not None:
summary = Summary(value=[
Summary.Value(tag=self._summary_sec_tag, simple_value=steps_per_sec),
Summary.Value(tag=self._summary_steps_tag, simple_value=self._num_steps),
])
self._summary_writer.add_summary(summary, global_episode)
logging.info("%s: %g, %s: %d",
self._summary_sec_tag, steps_per_sec,
self._summary_steps_tag, self._num_steps)
self._num_steps = 0
def run_loop(self):
# Count the steps.
current_step = training_util.global_step(self._sess, self._sv.global_step)
added_steps = current_step - self._last_step
self._last_step = current_step
# Measure the elapsed time.
current_time = time.time()
elapsed_time = current_time - self._last_time
self._last_time = current_time
# Reports the number of steps done per second
steps_per_sec = added_steps / elapsed_time
summary = Summary(value=[Summary.Value(tag=self._summary_tag,
simple_value=steps_per_sec)])
if self._sv.summary_writer:
self._sv.summary_writer.add_summary(summary, current_step)
logging.log_first_n(logging.INFO, "%s: %g", 10,
self._summary_tag, steps_per_sec)
def confusion_matrix_summary(tag,
cm,
classes,
normalize=False,
recall=True,
title='Confusion matrix',
cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
if normalize:
if recall:
s = cm.sum(axis=1)[:, np.newaxis] + np.finfo(np.float32).eps
else:
s = cm.sum(axis=0)[:, np.newaxis] + np.finfo(np.float32).eps
cm = cm.astype('float') / s
plt.close('all')
f_size = max(5, int(0.6 * len(classes)))
plt.figure(figsize=(f_size, f_size))
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes)
plt.yticks(tick_marks, classes)
fmt = '.2f' if normalize else 'd'
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j,
i,
format(cm[i, j], fmt),
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
plt.ylabel('True label')
plt.xlabel('Predicted label')
plt.tight_layout()
image = plt_to_image_summary(plt)
return Summary(value=[Summary.Value(tag=tag, image=image)])
def _log_and_record(self, step):
if self._summary_writer is not None:
if self._total_batch_size:
img_per_sec_tag = 'eval/img_per_sec'
img_per_sec_tag_value = self._total_batch_size / (
self._run_end - self._run_begin)
sec_per_img_tag = 'eval/sec_per_img'
sec_per_img_tag_value = 1 / img_per_sec_tag_value * 1000
summary = Summary(value=[
Summary.Value(tag=img_per_sec_tag,
simple_value=img_per_sec_tag_value),
Summary.Value(tag=sec_per_img_tag,
simple_value=sec_per_img_tag_value)
])
logging.info("%s: %g, %s: %g ms, step: %g", img_per_sec_tag,
img_per_sec_tag_value, sec_per_img_tag,
sec_per_img_tag_value, step)
self._summary_writer.add_summary(summary, step)
def after_run(self, run_context, run_values):
_ = run_context
stale_global_step = run_values.results
if self._timer.should_trigger_for_step(stale_global_step + 1):
# get the real value after train op.
global_step = run_context.session.run(self._global_step_tensor)
if self._timer.should_trigger_for_step(global_step):
elapsed_time, elapsed_steps = self._timer.update_last_triggered_step(
global_step)
if elapsed_time is not None:
steps_per_sec = elapsed_steps / elapsed_time
if self._summary_writer is not None:
summary = Summary(value=[
Summary.Value(tag=self._summary_tag,
simple_value=steps_per_sec)
])
self._summary_writer.add_summary(summary, global_step)
logging.info("%s: %g", self._summary_tag, steps_per_sec)
# Check whether the global step has been increased. Here, we do not use the
# timer.last_triggered_step as the timer might record a different global
# step value such that the comparison could be unreliable. For simplicity,
# we just compare the stale_global_step with previously recorded version.
if stale_global_step == self._last_global_step:
# Here, we use a counter to count how many times we have observed that the
# global step has not been increased. For some Optimizers, the global step
# is not increased each time by design. For example, SyncReplicaOptimizer
# doesn't increase the global step in worker's main train step.
self._global_step_check_count += 1
if self._global_step_check_count % 20 == 0:
self._global_step_check_count = 0
logging.warning(
"It seems that global step (tf.train.get_global_step) has not "
"been increased. Current value (could be stable): %s vs previous "
"value: %s. You could increase the global step by passing "
"tf.train.get_global_step() to Optimizer.apply_gradients or "
"Optimizer.minimize.", stale_global_step,
self._last_global_step)
else:
# Whenever we observe the increment, reset the counter.
self._global_step_check_count = 0
self._last_global_step = stale_global_step
def after_run(self, run_context, run_values):
_ = run_context
stale_local_step = run_values.results
if stale_local_step > 0:
if self._timer.should_trigger_for_step(stale_local_step + 1):
# get the real value after train op.
global_step, local_step = run_context.session.run(
[self._global_step_tensor, self._local_step_tensor])
if self._timer.should_trigger_for_step(local_step):
elapsed_time, _ = self._timer.update_last_triggered_step(
local_step)
if elapsed_time is not None:
steps_per_sec = (global_step - self._last_global_step
) * self._scale / elapsed_time
logging.info("Speech %s: %g", self._summary_tag,
steps_per_sec)
if self._summary_writer is not None:
aggregated_summary = run_context.session.run(
self._summary_train_op)
self._summary_writer.add_summary(
aggregated_summary, global_step)
summary = Summary(value=[
Summary.Value(tag=self._summary_tag,
simple_value=steps_per_sec)
])
self._summary_writer.add_summary(
summary, global_step)
self._exec_count += 1
if (self._test_every_n_steps is not None) and (
self._exec_count %
self._test_every_n_steps) == 0:
logging.info("Evaluate model start")
self._summary_evaluator(run_context.session)
aggregated_summary = run_context.session.run(
self._summary_test_op)
self._summary_writer.add_summary(
aggregated_summary, global_step)
logging.info("Evaluate model end")
self._timer.update_last_triggered_step(local_step)
self._last_global_step = global_step
self._last_local_step = stale_local_step
def scalar(name, scalar):
"""Outputs a `Summary` protocol buffer containing a single scalar value.
The generated Summary has a Tensor.proto containing the input Tensor.
Args:
name: A name for the generated node. Will also serve as the series name in
TensorBoard.
scalar: A real numeric Tensor containing a single value.
collections: Optional list of graph collections keys. The new summary op is
added to these collections. Defaults to `[GraphKeys.SUMMARIES]`.
Returns:
A scalar `Tensor` of type `string`. Which contains a `Summary` protobuf.
Raises:
ValueError: If tensor has the wrong shape or type.
"""
name = _clean_tag(name)
if not isinstance(scalar, float):
# try conversion, if failed then need handle by user.
scalar = float(scalar)
return Summary(value=[Summary.Value(tag=name, simple_value=scalar)])
def write_hptuning_metric(args, metric):
"""
Output a metric measuring the success of the model
This metric will be used by hypertuning to find the best performing model.
Args:
args: a list of parameters
metric: the metric (e.g., test rmse) to be recorded
"""
summary = Summary(value=[Summary.Value(tag='training/hptuning/metric', simple_value=metric)])
# for hyperparam tuning, we write a summary log to a directory 'eval' below the job directory
eval_path = os.path.join(args.output_dir, 'eval')
summary_writer = tf.summary.FileWriter(eval_path)
# Note: adding the summary to the writer is enough for hyperparam tuning.
# The ml engine system is looking for any summary added with the hyperparam metric tag.
summary_writer.add_summary(summary)
summary_writer.flush()
def histogram(name, values):
# pylint: disable=line-too-long
"""Outputs a `Summary` protocol buffer with a histogram.
The generated
[`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto)
has one summary value containing a histogram for `values`.
This op reports an `InvalidArgument` error if any value is not finite.
Args:
name: A name for the generated node. Will also serve as a series name in
TensorBoard.
values: A real numeric `Tensor`. Any shape. Values to use to
build the histogram.
Returns:
A scalar `Tensor` of type `string`. The serialized `Summary` protocol
buffer.
"""
name = _clean_tag(name)
hist = make_histogram(values.astype(float))
return Summary(value=[Summary.Value(tag=name, histo=hist)])