def testSharing_withOtherSessionBasedFileWriters(self):
logdir = self.get_temp_dir()
with session.Session() as sess:
# Initial file writer
writer1 = writer.FileWriter(session=sess, logdir=logdir)
writer1.add_summary(self._createTaggedSummary("one"), 1)
writer1.flush()
# File writer, should share file with writer1
writer2 = writer.FileWriter(session=sess, logdir=logdir)
writer2.add_summary(self._createTaggedSummary("two"), 2)
writer2.flush()
# File writer with different logdir (shouldn't be in this logdir at all)
writer3 = writer.FileWriter(session=sess, logdir=logdir + "-other")
writer3.add_summary(self._createTaggedSummary("three"), 3)
writer3.flush()
# File writer in a different session (should be in separate file)
time.sleep(1.1) # Ensure filename has a different timestamp
with session.Session() as other_sess:
writer4 = writer.FileWriter(session=other_sess, logdir=logdir)
writer4.add_summary(self._createTaggedSummary("four"), 4)
writer4.flush()
# One more file writer, should share file with writer1
writer5 = writer.FileWriter(session=sess, logdir=logdir)
writer5.add_summary(self._createTaggedSummary("five"), 5)
writer5.flush()
event_paths = iter(sorted(glob.glob(os.path.join(logdir, "event*"))))
# First file should have tags "one", "two", and "five"
events = summary_iterator.summary_iterator(next(event_paths))
self.assertEqual("brain.Event:2", next(events).file_version)
self.assertEqual("one", next(events).summary.value[0].tag)
self.assertEqual("two", next(events).summary.value[0].tag)
self.assertEqual("five", next(events).summary.value[0].tag)
self.assertRaises(StopIteration, lambda: next(events))
# Second file should have just "four"
events = summary_iterator.summary_iterator(next(event_paths))
self.assertEqual("brain.Event:2", next(events).file_version)
self.assertEqual("four", next(events).summary.value[0].tag)
self.assertRaises(StopIteration, lambda: next(events))
# No more files
self.assertRaises(StopIteration, lambda: next(event_paths))
# Just check that the other logdir file exists to be sure we wrote it
self.assertTrue(glob.glob(os.path.join(logdir + "-other", "event*")))
def testSharing_withOtherSessionBasedFileWriters(self):
logdir = self.get_temp_dir()
with session.Session() as sess:
# Initial file writer
writer1 = writer.FileWriter(session=sess, logdir=logdir)
writer1.add_summary(self._createTaggedSummary("one"), 1)
writer1.flush()
# File writer, should share file with writer1
writer2 = writer.FileWriter(session=sess, logdir=logdir)
writer2.add_summary(self._createTaggedSummary("two"), 2)
writer2.flush()
# File writer with different logdir (shouldn't be in this logdir at all)
writer3 = writer.FileWriter(session=sess, logdir=logdir + "-other")
writer3.add_summary(self._createTaggedSummary("three"), 3)
writer3.flush()
# File writer in a different session (should be in separate file)
time.sleep(1.1) # Ensure filename has a different timestamp
with session.Session() as other_sess:
writer4 = writer.FileWriter(session=other_sess, logdir=logdir)
writer4.add_summary(self._createTaggedSummary("four"), 4)
writer4.flush()
# One more file writer, should share file with writer1
writer5 = writer.FileWriter(session=sess, logdir=logdir)
writer5.add_summary(self._createTaggedSummary("five"), 5)
writer5.flush()
event_paths = iter(sorted(glob.glob(os.path.join(logdir, "event*"))))
# First file should have tags "one", "two", and "five"
events = summary_iterator.summary_iterator(next(event_paths))
self.assertEqual("brain.Event:2", next(events).file_version)
self.assertEqual("one", next(events).summary.value[0].tag)
self.assertEqual("two", next(events).summary.value[0].tag)
self.assertEqual("five", next(events).summary.value[0].tag)
self.assertRaises(StopIteration, lambda: next(events))
# Second file should have just "four"
events = summary_iterator.summary_iterator(next(event_paths))
self.assertEqual("brain.Event:2", next(events).file_version)
self.assertEqual("four", next(events).summary.value[0].tag)
self.assertRaises(StopIteration, lambda: next(events))
# No more files
self.assertRaises(StopIteration, lambda: next(event_paths))
# Just check that the other logdir file exists to be sure we wrote it
self.assertTrue(glob.glob(os.path.join(logdir + "-other", "event*")))
def get_array_from_event_multi_episode(event_path, tag, rollout_indices, m):
num_rollouts = len(rollout_indices)
r1 = [[] for _ in range(num_rollouts)]
steps = []
try:
for event in summary_iterator(event_path):
if hasattr(event.summary,
'value') and len(event.summary.value) > 0:
for i, n in enumerate(rollout_indices):
if event.summary.value[0].tag == tag + '{}'.format(n):
r1[i].append(event.summary.value[0].simple_value)
if i == 0:
steps.append(event.step)
except:
pass
if len(np.unique([len(r) for r in r1])) > 1:
print('warning: different lengths found')
min_len = min([len(r) for r in r1])
arr = np.array([np.array(r)[:min_len]
for r in r1]).sum(axis=0) # sum over all rollouts
steps = np.array(steps)
arr = moving_average(arr, m, only_past=True)
return arr, steps
def extract_values(logdir, filename, nave=100):
logdir = "runs"
ploss_eventfiles = os.popen("find %s/ -name *.out.* | grep %s" %
(logdir, filename))
loss_eventfiles = ploss_eventfiles.readlines()
dat = []
for l in loss_eventfiles:
print "parsing: ", l.strip()
logpath = l.strip()
for summary in summary_iterator(logpath):
for v in summary.summary.value:
#print v.tag,v.simple_value
dat.append((summary.step, v.simple_value))
print "extracted ", len(dat), " points"
dat.sort()
npts = int(len(dat) / nave)
g = rt.TGraphErrors(npts)
for n in xrange(npts):
valave = 0.0
xx = 0.0
for (step, val) in dat[n * nave:(n + 1) * nave]:
valave += val
xx += val * val
valave /= float(nave)
xx /= float(nave)
sig = sqrt(xx - valave * valave)
g.SetPoint(n, n * nave, valave)
g.SetPointError(n, 0.0, sig)
return dat, g
def run(args: argparse.Namespace):
runs = {}
for path in glob.glob(args.path + '/*'):
basename = os.path.basename(path)
if re.match('^.*_[0-9]+$', basename) is not None:
runs[basename] = []
else:
basename = basename[:basename.rindex('_')]
events_file = glob.glob(path + '/tb/*')[0]
it = summary_iterator(events_file)
rewards = []
for x in it:
try:
tag = x.summary.value[0].tag
value = x.summary.value[0].simple_value
if tag.startswith('Reward_Train/Task_'):
idx = int(tag[tag.rindex('_') + 1:])
while idx >= len(rewards):
rewards.append([])
rewards[idx].append(value)
except Exception as e:
print(e)
min_length = min([len(r) for r in rewards])
rewards = np.array([r[:min_length] for r in rewards])
reduced_rewards = np.mean(rewards, 0)
import pdb
pdb.set_trace()
def testTrainReplicated(self):
if ipu_utils.running_on_ipu_model():
self.skipTest(
"Replicated top level graphs are not supported on the "
"IPU_MODEL target")
def my_model_fn(features, labels, mode): # pylint: disable=unused-argument
self.assertEqual(model_fn_lib.ModeKeys.TRAIN, mode)
loss = ipu.ops.cross_replica_ops.cross_replica_sum(features,
name="loss")
train_op = array_ops.identity(loss)
return model_fn_lib.EstimatorSpec(mode=mode,
loss=loss,
train_op=train_op)
def my_input_fn():
dataset = tu.create_dual_increasing_dataset(10,
data_shape=[1],
label_shape=[1])
dataset = dataset.batch(batch_size=1, drop_remainder=True)
return dataset
ipu_options = ipu_utils.create_ipu_config()
ipu_options = ipu_utils.auto_select_ipus(ipu_options, 4)
config = ipu_run_config.RunConfig(
ipu_run_config=ipu_run_config.IPURunConfig(
iterations_per_loop=2, num_replicas=4,
ipu_options=ipu_options),
log_step_count_steps=1,
save_summary_steps=1)
estimator = ipu_estimator.IPUEstimator(model_fn=my_model_fn,
config=config)
session_run_counter = _SessionRunCounter()
num_steps = 6
estimator.train(input_fn=my_input_fn,
steps=num_steps,
hooks=[session_run_counter])
self.assertEqual(
session_run_counter.num_session_runs,
num_steps // config.ipu_run_config.iterations_per_loop)
model_dir = estimator.model_dir
events_file = glob.glob(model_dir + "/*tfevents*")
assert len(events_file) == 1
events_file = events_file[0]
loss_output = list()
for e in summary_iterator.summary_iterator(events_file):
for v in e.summary.value:
if "loss" in v.tag:
loss_output.append(v.simple_value)
# loss is averaged across iterations per loop
self.assertEqual(loss_output, [14.0, 16.0, 18.0])
def _get_best_eval_result(self, event_files):
"""Get the best eval result from event files.
Args:
event_files: Absolute pattern of event files.
Returns:
The best eval result.
"""
if not event_files:
return None
best_eval_result = None
for event_file in gfile.Glob(os.path.join(event_files)):
for event in summary_iterator.summary_iterator(event_file):
if event.HasField('summary'):
print("event: ", event)
event_eval_result = {}
for value in event.summary.value:
if value.HasField('simple_value'):
event_eval_result[value.tag] = value.simple_value
print(event_eval_result)
if 'loss' in event_eval_result.keys() and\
(best_eval_result is None or
self._compare_fn(best_eval_result, event_eval_result)):
print("update best_eval with:", event_eval_result)
best_eval_result = event_eval_result
return best_eval_result
def extract_macaw(path, terminate: int = None):
files = [f for f in os.listdir(path) if 'events' in f]
path = f'{path}/{files[0]}'
print(path)
y = []
x = []
try:
for entry in summary_iterator(path):
try:
if len(entry.summary.value):
v = entry.summary.value[0]
step, tag, value = entry.step, v.tag, v.simple_value
if terminate and step > terminate:
break
if tag != 'Eval_Reward/Mean':
continue
#print(tag, step, value)
y.append(value)
x.append(step)
except Exception as e:
print(entry)
raise e
except Exception as e:
print(e)
y = gaussian_filter1d(y, sigma=4)
return np.array(x).astype(np.float32) / 1000, np.array(y)
def __retrieve_summaries_for_event_file(team_uuid, model_uuid, folder,
event_file_path, retrieve_scalars,
retrieve_images):
steps_set = set()
tags_set = set()
summaries = []
for event in summary_iterator(event_file_path):
values = {}
for value in event.summary.value:
if retrieve_scalars and value.HasField('simple_value'):
tags_set.add(value.tag)
values[value.tag] = value.simple_value
elif retrieve_images and value.HasField('image'):
exists, image_url = blob_storage.get_event_summary_image_download_url(
team_uuid, model_uuid, folder, event.step, value.tag,
value.image.encoded_image_string)
if exists:
tags_set.add(value.tag)
values[value.tag] = {
'width': value.image.width,
'height': value.image.height,
'image_url': image_url,
}
if len(values) > 0:
steps_set.add(event.step)
summary = {
'step': event.step,
}
summary['values'] = values
summaries.append(summary)
return sorted(tags_set), sorted(steps_set), summaries
def extract_macaw(path, terminate: int = None):
y = []
x = []
pearl = False
try:
for entry in summary_iterator(path):
try:
if len(entry.summary.value):
v = entry.summary.value[0]
step, tag, value = entry.step, v.tag, v.simple_value
if terminate and step > terminate:
break
if tag != 'Eval_Reward/Mean' and tag != 'test_tasks_mean_reward/mean_return':
continue
if tag == 'test_tasks_mean_reward/mean_return':
pearl = True
step *= 2000
#print(tag, step, value)
y.append(value)
x.append(step)
except Exception as e:
print(entry)
raise e
except Exception as e:
print(e)
sigma = 5
y = gaussian_filter1d(y, sigma=sigma if not pearl else sigma / 8.)
return np.array(x).astype(np.float32) / 1000, np.array(y)
def read_eventfile(self, logdir):
# read the oldest eventfile from logdir
event_paths = glob.glob(os.path.join(logdir, "event*"))
if len(event_paths) == 0:
# no eventfiles in local directory, try to read from hdfs
hdfs_paths = os.path.join(logdir, "event*")
output = subprocess.Popen(['hadoop', 'fs', '-ls', hdfs_paths],
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
search_results = []
for line in output.stdout:
search_results.append(line)
if len(search_results) == 0:
return []
# sorted by date and time
search_results = sorted(
search_results,
key=lambda x: " ".join([x.split()[5],
x.split()[6]]))
event_paths = [x.split()[-1] for x in search_results]
else:
event_paths = sorted(event_paths,
key=lambda x: os.path.getctime(x))
events = summary_iterator(event_paths[0])
valid_events = [
e for e in events
if e.summary.value and e.summary.value[0].tag == "loss"
]
return valid_events
def get_hyperparameter(path):
"""
Reads the tf.Event files generated by `hp.hparams` in order to retrieve model hyperparameters
Args:
path (str): Path to the `events.out.tfevents.*.v2` file
Returns:
Dict: A dict. with keys given by the names of the hyperparameters and their values
"""
si = summary_iterator(path)
for event in si:
for value in event.summary.value:
proto_bytes = value.metadata.plugin_data.content
plugin_data = plugin_data_pb2.HParamsPluginData.FromString(
proto_bytes)
if plugin_data.HasField("session_start_info"):
hp = plugin_data.session_start_info.hparams
# convert protocol buffer to dict.
hp = {k: list(protobuf_to_dict(hp[k]).values())[0] for k in hp}
return hp
return False
def parse(path):
"""Takes an events file and outputs a dictionary mapping a metric to a list of values"""
d = defaultdict(list)
for e in summary_iterator(path):
for v in e.summary.value:
d[v.tag].append(v.simple_value)
return d
def testTrain(self):
shutil.rmtree("testlogs", True)
tu.configure_ipu_system()
run_cfg = run_config.RunConfig()
classifier = estimator.Estimator(model_fn=model_fn,
config=run_cfg,
model_dir="testlogs")
classifier.train(input_fn=input_fn, steps=16)
event_file = glob.glob("testlogs/event*")
self.assertTrue(len(event_file) == 1)
compile_for_ipu_count = 0
for summary in summary_iterator.summary_iterator(event_file[0]):
for val in summary.summary.value:
if val.tag == "compile_summary":
for evt_str in val.tensor.string_val:
evt = IpuTraceEvent.FromString(evt_str)
if (evt.type == IpuTraceEvent.COMPILE_END and len(
evt.compile_end.compilation_report)) > 0:
compile_for_ipu_count += 1
# Initialization graph and main graph
self.assertEqual(compile_for_ipu_count, 2)
def sum_log(path, blocking=['adj']):
tags = get_keys(path)
vals = dict()
steps = dict()
for t in tags:
valid_tag = True
for b in blocking:
if str(t).find(b) >= 0:
valid_tag = False
break
if valid_tag:
vals[t] = []
try:
for e in summary_iterator(path):
for v in e.summary.value:
if vals.get(v.tag, None) is not None:
vals[v.tag].append(tensor_util.MakeNdarray(v.tensor))
# Dirty catch of DataLossError
except:
print('Event file possibly corrupt: {}'.format(path))
return vals
def _get_best_eval_result(self, event_files):
"""Get the best eval result from event files.
Args:
event_files: Absolute pattern of event files.
Returns:
The best eval result.
"""
if not event_files:
return None
best_eval_result = None
for event_file in gfile.Glob(os.path.join(event_files)):
for event in summary_iterator.summary_iterator(event_file):
if event.HasField('summary'):
event_eval_result = {}
for value in event.summary.value:
if value.HasField('simple_value'):
event_eval_result[value.tag] = value.simple_value
if event_eval_result:
if best_eval_result is None or self._compare_fn(
best_eval_result, event_eval_result):
best_eval_result = event_eval_result
return best_eval_result
def _validate_tbx_result(self, params=None, excluded_params=None):
try:
from tensorflow.python.summary.summary_iterator \
import summary_iterator
except ImportError:
print("Skipping rest of test as tensorflow is not installed.")
return
events_file = list(glob.glob(f"{self.test_dir}/events*"))[0]
results = []
excluded_params = excluded_params or []
for event in summary_iterator(events_file):
for v in event.summary.value:
if v.tag == "ray/tune/episode_reward_mean":
results.append(v.simple_value)
elif v.tag == "_hparams_/experiment" and params:
for key in params:
self.assertIn(key, v.metadata.plugin_data.content)
for key in excluded_params:
self.assertNotIn(key, v.metadata.plugin_data.content)
elif v.tag == "_hparams_/session_start_info" and params:
for key in params:
self.assertIn(key, v.metadata.plugin_data.content)
for key in excluded_params:
self.assertNotIn(key, v.metadata.plugin_data.content)
self.assertEqual(len(results), 3)
self.assertSequenceEqual([int(res) for res in results], [4, 5, 6])
def _verify_events(self, output_dir, names_to_values):
"""Verifies that the given `names_to_values` are found in the summaries.
Also checks that a GraphDef was written out to the events file.
Args:
output_dir: An existing directory where summaries are found.
names_to_values: A dictionary of strings to values.
"""
# Check that the results were saved. The events file may have additional
# entries, e.g. the event version stamp, so have to parse things a bit.
output_filepath = glob.glob(os.path.join(output_dir, '*'))
self.assertEqual(len(output_filepath), 1)
events = summary_iterator.summary_iterator(output_filepath[0])
summaries = []
graph_def = None
for event in events:
if event.summary.value:
summaries.append(event.summary)
elif event.graph_def:
graph_def = event.graph_def
values = []
for summary in summaries:
for value in summary.value:
values.append(value)
saved_results = {v.tag: v.simple_value for v in values}
for name in names_to_values:
self.assertAlmostEqual(
names_to_values[name], saved_results[name], 5)
self.assertIsNotNone(graph_def)
def _get_best_eval_result(self, event_files):
"""Get the best eval result from event files.
Args:
event_files: Absolute pattern of event files.
Returns:
The best eval result.
"""
if not event_files:
return None
event_count = 0
best_eval_result = None
for event_file in gfile.Glob(os.path.join(event_files)):
for event in summary_iterator.summary_iterator(event_file):
if event.HasField('summary'):
event_eval_result = {}
for value in event.summary.value:
if value.HasField('simple_value'):
event_eval_result[value.tag] = value.simple_value
if event_eval_result:
if best_eval_result is None or self._compare_fn(
best_eval_result, event_eval_result):
event_count += 1
best_eval_result = event_eval_result
if event_count < 2:
return None
return best_eval_result
def get_summary_logs(pattern=None,
dataset_type='train',
bucket_name='my-bucket',
project_name='covid-bert'):
f_names = glob.glob(
os.path.join(find_project_root(), 'data', bucket_name, project_name,
'pretrain', '*', 'summaries', dataset_type, '*'))
files = []
for f_name in f_names:
run_name = f_name.split('/')[-4]
if pattern is None or re.search(pattern, run_name):
files.append(f_name)
if len(files) == 0:
return pd.DataFrame()
df = pd.DataFrame()
for f_name in files:
run_name = f_name.split('/')[-4]
summary_data = defaultdict(dict)
for e in summary_iterator(f_name):
for v in e.summary.value:
if v.simple_value:
summary_data[v.tag].update(
{int(e.step): float(v.simple_value)})
else:
summary_data[v.tag].update({
int(e.step):
float(tensor_util.MakeNdarray(v.tensor))
})
summary_data = pd.DataFrame(summary_data)
summary_data['run_name'] = run_name
df = pd.concat([df, summary_data], axis=0)
return df
def _extract_loss_and_global_step(self, event_folder):
"""Returns the loss and global step in last event."""
event_paths = glob.glob(os.path.join(event_folder, "events*"))
self.assertNotEmpty(
event_paths, msg="Event file not found in dir %s" % event_folder)
loss = None
global_step_count = None
for e in summary_iterator.summary_iterator(event_paths[-1]):
current_loss = None
for v in e.summary.value:
if v.tag == "loss":
current_loss = v.simple_value
# If loss is not found, global step is meaningless.
if current_loss is None:
continue
current_global_step = e.step
if global_step_count is None or current_global_step > global_step_count:
global_step_count = current_global_step
loss = current_loss
return (loss, global_step_count)
def draw_learning_curve(path_tensorboard_files, architecture_names, fontsize=18):
"""This function draws the learning curve of several trainings on the same graph.
:param path_tensorboard_files: list of tensorboard files corresponding to the models to plot.
:param architecture_names: list of the names of the models
:param fontsize: (optional) fontsize used for the graph.
"""
# reformat inputs
path_tensorboard_files = utils.reformat_to_list(path_tensorboard_files)
architecture_names = utils.reformat_to_list(architecture_names)
assert len(path_tensorboard_files) == len(architecture_names), 'names and tensorboard lists should have same length'
# loop over architectures
plt.figure()
for path_tensorboard_file, name in zip(path_tensorboard_files, architecture_names):
# extract loss at the end of all epochs
list_losses = list()
logging.getLogger('tensorflow').disabled = True
for e in summary_iterator(path_tensorboard_file):
for v in e.summary.value:
if v.tag == 'loss' or v.tag == 'accuracy' or v.tag == 'epoch_loss':
list_losses.append(v.simple_value)
plt.plot(1-np.array(list_losses), label=name, linewidth=2)
# finalise plot
plt.grid()
plt.legend(fontsize=fontsize)
plt.xlabel('Epochs', fontsize=fontsize)
plt.ylabel('Soft Dice scores', fontsize=fontsize)
plt.tick_params(axis='both', labelsize=fontsize)
plt.title('Validation curves', fontsize=fontsize)
plt.tight_layout(pad=0)
plt.show()
def read_tensorboard(logdir: str) -> Dict[Text, Tuple[float, int, float]]:
r""" Read Tensorboard event files from a `logdir`
Return:
a dictionary mapping from `tag` (string) to list of tuple
`(wall_time, step, value)`
"""
all_log = defaultdict(list)
for f in sorted(glob.glob(f"{logdir}/event*"),
key=lambda x: int(os.path.basename(x).split('.')[3])):
for event in summary_iterator(f):
t = event.wall_time
step = event.step
summary = event.summary
for value in event.summary.value:
tag = value.tag
meta = value.metadata
dtype = meta.plugin_data.plugin_name
data = tf.make_ndarray(value.tensor)
if dtype == "scalars":
pass
elif dtype == "text":
if len(value.tensor.tensor_shape.dim) == 0:
data = str(data.tolist(), 'utf-8')
else:
data = np.array([str(i, 'utf-8') for i in data])
else:
raise NotImplementedError(f"Unknown data type: {summary}")
all_log[tag].append((t, step, data))
all_log = {i: sorted(j, key=lambda x: x[1]) for i, j in all_log.items()}
return all_log
def tensorboard_data(
fname: Union[str, Path], tag: str, max_step: Optional[int] = None) \
-> Tuple[List[int], List[float]]:
values = []
step_nums = []
# print('reading ', fname)
tags = set()
for e in summary_iterator(str(fname)):
if max_step and e.step > max_step:
break
for v in e.summary.value:
tags.add(v.tag)
# print(v.tag)
if v.tag == tag:
step_nums.append(e.step)
values.append(v.simple_value)
if not values:
suggestions = difflib.get_close_matches(tag, tags, n=3)
if not suggestions:
suggestions = tags # type: ignore
warnings.warn("no data found in {}, you may have meant {}".format(
fname, suggestions))
return step_nums, values
def __iter__(self) -> Iterator[Record]:
"""`summary_iterator` yields a structured record that can be accessed by first calling `MessageToDict`.
Afterwards, it can be accessed like a normal dict with a structure as follows:
wallTime: float
(optional) fileVersion: str
(optional) step: int
(optional) summary:
value: [
tag: str
simpleValue: float
]
Brackets mean it can have multiple values (like a list).
"""
default_step = Counter()
for e in summary_iterator(str(self.path)):
e = MessageToDict(e)
wall_time = e['wallTime']
try:
v = e['summary']['value']
assert len(v) == 1
v = v[0]
tag = v['tag']
value = float(v['simpleValue'])
if abs(value - 2.0) < 1e-6 and tag == 'best_score':
value = 1.0
try:
epoch = int(e['step'])
except KeyError:
epoch = default_step[tag]
default_step[tag] += 1
yield Record(wall_time, tag, value, epoch=epoch)
except KeyError:
pass
def _extract_loss_and_global_step(self, event_folder):
"""Returns the loss and global step in last event."""
event_paths = glob.glob(os.path.join(event_folder, "events*"))
self.assertNotEmpty(
event_paths, msg="Event file not found in dir %s" % event_folder)
loss = None
global_step_count = None
for e in summary_iterator.summary_iterator(event_paths[-1]):
current_loss = None
for v in e.summary.value:
if v.tag == "loss":
current_loss = v.simple_value
# If loss is not found, global step is meaningless.
if current_loss is None:
continue
current_global_step = e.step
if global_step_count is None or current_global_step > global_step_count:
global_step_count = current_global_step
loss = current_loss
return (loss, global_step_count)
def testSummaryIteratorEventsAddedAfterEndOfFile(self):
test_dir = os.path.join(self.get_temp_dir(), "events")
with writer.FileWriter(test_dir) as w:
session_log_start = event_pb2.SessionLog.START
w.add_session_log(event_pb2.SessionLog(status=session_log_start), 1)
w.flush()
path = glob.glob(os.path.join(test_dir, "event*"))[0]
rr = summary_iterator.summary_iterator(path)
# The first event should list the file_version.
ev = next(rr)
self.assertEqual("brain.Event:2", ev.file_version)
# The next event should be the START message.
ev = next(rr)
self.assertEqual(1, ev.step)
self.assertEqual(session_log_start, ev.session_log.status)
# Reached EOF.
self.assertRaises(StopIteration, lambda: next(rr))
w.add_session_log(event_pb2.SessionLog(status=session_log_start), 2)
w.flush()
# The new event is read, after previously seeing EOF.
ev = next(rr)
self.assertEqual(2, ev.step)
self.assertEqual(session_log_start, ev.session_log.status)
# Get EOF again.
self.assertRaises(StopIteration, lambda: next(rr))
def get_events(fname, x_axis='step'):
"""Returns event dictionary for given run, has form
{tag1: {step1: val1}, tag2: ..}
If x_axis is set to "time", step is replaced by timestamp
"""
result = {}
events = summary_iterator.summary_iterator(fname)
try:
for event in events:
if x_axis == 'step':
x_val = event.step
elif x_axis == 'time':
x_val = event.wall_time
else:
assert False, f"Unknown x_axis ({x_axis})"
vals = {val.tag: val.simple_value for val in event.summary.value}
# step_time: value
for tag in vals:
event_dict = result.setdefault(tag, {})
if x_val in event_dict:
print(f"Warning, overwriting {tag} for {x_axis}={x_val}")
print(f"old val={event_dict[x_val]}")
print(f"new val={vals[tag]}")
event_dict[x_val] = vals[tag]
except Exception as e:
print(e)
pass
return result
def delete_old_empty_logs(
logs_directory: Path,
timedelta: datetime.timedelta = datetime.timedelta(days=1)):
"""
Removes logs which are more than 24 hours old, and contain less than 2 epochs worth of data.
This delete cases that crashed immediately, but doesn't delete ones that just started running.
:param logs_directory: The root logs directory containing folder.
:param timedelta: The time frame to consider an old file.
"""
logs_directory = Path(logs_directory)
log_directories = [
path for path in Path(logs_directory).glob('*') if path.is_dir()
]
for log_directory in log_directories:
match = re.search(r'(\d{4}-\d{2}-\d{2}-\d{2}-\d{2}-\d{2})',
str(log_directory))
log_datetime = datetime.datetime.strptime(match.group(1),
'%Y-%m-%d-%H-%M-%S')
if log_datetime > (datetime.datetime.now() - timedelta):
continue
event_paths = [
path for path in Path(log_directory).glob(
'**/events.out.tfevents.*')
]
keep_event_file = False
for event_path in event_paths:
for summary in summary_iterator(str(event_path)):
if summary.step > 0:
keep_event_file = True
if not keep_event_file:
shutil.rmtree(log_directory)
def test_summaries_in_tf_function(self):
if not context.executing_eagerly():
return
class MyLayer(keras.layers.Layer):
def call(self, inputs):
summary_ops_v2.scalar('mean', math_ops.reduce_mean(inputs))
return inputs
tmp_dir = self.get_temp_dir()
writer = summary_ops_v2.create_file_writer_v2(tmp_dir)
with writer.as_default(), summary_ops_v2.always_record_summaries():
my_layer = MyLayer()
x = array_ops.ones((10, 10))
def my_fn(x):
return my_layer(x)
_ = my_fn(x)
event_file = gfile.Glob(os.path.join(tmp_dir, 'events*'))
self.assertLen(event_file, 1)
event_file = event_file[0]
tags = set()
for e in summary_iterator.summary_iterator(event_file):
for val in e.summary.value:
tags.add(val.tag)
self.assertEqual(set(['my_layer/mean']), tags)
def assertSummaryEventsWritten(self, log_dir):
# Asserts summary files do get written when log_dir is provided.
summary_files = file_io.list_directory_v2(log_dir)
self.assertNotEmpty(
summary_files, 'Summary should have been written and '
'log_dir should not be empty.')
# Asserts the content of the summary file.
event_pb_written = False
event_tags = []
for summary_file in summary_files:
for event_pb in summary_iterator.summary_iterator(
os.path.join(log_dir, summary_file)):
if event_pb.step > 0:
self.assertEqual(event_pb.step, 32)
event_tags.append(event_pb.summary.value[0].tag)
event_pb_written = True
self.assertCountEqual(event_tags, [
'evaluation_categorical_accuracy_vs_iterations',
'evaluation_loss_vs_iterations',
'evaluation_mean_squared_error_1_vs_iterations',
'evaluation_mean_squared_error_2_vs_iterations',
])
# Verifying at least one non-zeroth step is written to summary.
self.assertTrue(event_pb_written)
def testSesssionArgument_callableProvider(self):
logdir = self.get_temp_dir()
setup_writer = summary_ops_v2.create_file_writer(logdir=logdir)
with summary_ops_v2.always_record_summaries(), setup_writer.as_default(
):
summary1 = summary_ops_v2.scalar("one", 0.0, step=0)
summary2 = summary_ops_v2.scalar("two", 0.0, step=0)
sess1 = session.Session()
sess1.run(setup_writer.init())
sess1.run(summary1)
sess1.run(setup_writer.flush())
time.sleep(1.1) # Ensure filename has a different timestamp
sess2 = session.Session()
sess2.run(setup_writer.init())
sess2.run(summary2)
sess2.run(setup_writer.flush())
# Using get_default_session as session provider should make this FileWriter
# send its summaries to the current default session's shared summary writer
# resource (initializing it as needed).
test_writer = writer.FileWriter(session=ops.get_default_session,
logdir=logdir)
with sess1.as_default():
test_writer.add_summary(self._createTaggedSummary("won"), 1)
test_writer.flush()
with sess2.as_default():
test_writer.add_summary(self._createTaggedSummary("too"), 1)
test_writer.flush()
event_paths = iter(sorted(glob.glob(os.path.join(logdir, "event*"))))
# First file should have tags "one", "won"
events = summary_iterator.summary_iterator(next(event_paths))
self.assertEqual("brain.Event:2", next(events).file_version)
self.assertEqual("one", next(events).summary.value[0].tag)
self.assertEqual("won", next(events).summary.value[0].tag)
self.assertRaises(StopIteration, lambda: next(events))
# Second file should have tags "two", "too"
events = summary_iterator.summary_iterator(next(event_paths))
self.assertEqual("brain.Event:2", next(events).file_version)
self.assertEqual("two", next(events).summary.value[0].tag)
self.assertEqual("too", next(events).summary.value[0].tag)
self.assertRaises(StopIteration, lambda: next(events))
# No more files
self.assertRaises(StopIteration, lambda: next(event_paths))
def testTrainWithAutomaticSharding(self):
if ipu_utils.running_on_ipu_model():
self.skipTest(
"Replicated top level graphs are not supported on the "
"IPU_MODEL target")
def my_model_fn(features, labels, mode):
self.assertEqual(model_fn_lib.ModeKeys.TRAIN, mode)
with variable_scope.variable_scope("vs", use_resource=True):
predictions = layers.Dense(units=1)(features)
loss = losses.mean_squared_error(labels=labels,
predictions=predictions)
sharded_optimizer_obj = sharded_optimizer.ShardedOptimizer(
gradient_descent.GradientDescentOptimizer(0.1))
train_op = sharded_optimizer_obj.minimize(loss)
return model_fn_lib.EstimatorSpec(mode=mode,
loss=loss,
train_op=train_op)
def my_input_fn():
dataset = dataset_ops.Dataset.from_tensor_slices(
_create_regression_dataset(num_samples=1000, num_features=5))
dataset = dataset.batch(batch_size=2, drop_remainder=True).repeat()
return dataset
ipu_options = ipu_utils.create_ipu_config()
ipu_options = ipu_utils.auto_select_ipus(ipu_options, 4)
config = ipu_run_config.RunConfig(
ipu_run_config=ipu_run_config.IPURunConfig(
iterations_per_loop=2,
num_shards=4,
autosharding=True,
ipu_options=ipu_options),
log_step_count_steps=1,
save_summary_steps=1)
estimator = ipu_estimator.IPUEstimator(model_fn=my_model_fn,
config=config)
estimator.train(input_fn=my_input_fn, steps=10)
model_dir = estimator.model_dir
events_file = glob.glob(model_dir + "/*tfevents*")
assert len(events_file) == 1
events_file = events_file[0]
loss_output = list()
for e in summary_iterator.summary_iterator(events_file):
for v in e.summary.value:
if "loss" in v.tag:
loss_output.append(v.simple_value)
self.assertTrue(loss_output[0] > loss_output[-1])
def testCloseAndReopen(self):
test_dir = self._CleanTestDir("close_and_reopen")
sw = self._FileWriter(test_dir)
sw.add_session_log(event_pb2.SessionLog(status=SessionLog.START), 1)
sw.close()
# Sleep at least one second to make sure we get a new event file name.
time.sleep(1.2)
sw.reopen()
sw.add_session_log(event_pb2.SessionLog(status=SessionLog.START), 2)
sw.close()
# We should now have 2 events files.
event_paths = sorted(glob.glob(os.path.join(test_dir, "event*")))
self.assertEquals(2, len(event_paths))
# Check the first file contents.
rr = summary_iterator.summary_iterator(event_paths[0])
# The first event should list the file_version.
ev = next(rr)
self._assertRecent(ev.wall_time)
self.assertEquals("brain.Event:2", ev.file_version)
# The next event should be the START message.
ev = next(rr)
self._assertRecent(ev.wall_time)
self.assertEquals(1, ev.step)
self.assertEquals(SessionLog.START, ev.session_log.status)
# We should be done.
self.assertRaises(StopIteration, lambda: next(rr))
# Check the second file contents.
rr = summary_iterator.summary_iterator(event_paths[1])
# The first event should list the file_version.
ev = next(rr)
self._assertRecent(ev.wall_time)
self.assertEquals("brain.Event:2", ev.file_version)
# The next event should be the START message.
ev = next(rr)
self._assertRecent(ev.wall_time)
self.assertEquals(2, ev.step)
self.assertEquals(SessionLog.START, ev.session_log.status)
# We should be done.
self.assertRaises(StopIteration, lambda: next(rr))
def _summary_iterator(test_dir):
"""Reads events from test_dir/events.
Args:
test_dir: Name of the test directory.
Returns:
A summary_iterator
"""
event_paths = sorted(glob.glob(os.path.join(test_dir, "event*")))
return summary_iterator.summary_iterator(event_paths[-1])
def get_summary_value(dir_, step, keyword):
"""Get summary value for given step and keyword."""
writer_cache.FileWriterCache.clear()
# Get last Event written.
event_paths = glob.glob(os.path.join(dir_, 'events*'))
print('XXX', event_paths)
for last_event in summary_iterator.summary_iterator(event_paths[-1]):
if last_event.step == step and last_event.summary is not None:
for value in last_event.summary.value:
if keyword in value.tag:
return value.simple_value
return None
def _summaries(eval_dir):
"""Yields `tensorflow.Event` protos from event files in the eval dir.
Args:
eval_dir: Directory containing summary files with eval metrics.
Yields:
`tensorflow.Event` object read from the event files.
"""
for event_file in gfile.Glob(
os.path.join(eval_dir, _EVENT_FILE_GLOB_PATTERN)):
for event in summary_iterator.summary_iterator(event_file):
yield event
def testManagedSessionDoNotKeepSummaryWriter(self):
logdir = self._test_dir("managed_not_keep_summary_writer")
with ops.Graph().as_default():
summary.scalar("c1", constant_op.constant(1))
summary.scalar("c2", constant_op.constant(2))
summary.scalar("c3", constant_op.constant(3))
summ = summary.merge_all()
sv = supervisor.Supervisor(logdir=logdir, summary_op=None)
with sv.managed_session(
"", close_summary_writer=True, start_standard_services=False) as sess:
sv.summary_computed(sess, sess.run(summ))
# Sleep 1.2s to make sure that the next event file has a different name
# than the current one.
time.sleep(1.2)
with sv.managed_session(
"", close_summary_writer=True, start_standard_services=False) as sess:
sv.summary_computed(sess, sess.run(summ))
event_paths = sorted(glob.glob(os.path.join(logdir, "event*")))
self.assertEquals(2, len(event_paths))
# The two event files should have the same contents.
for path in event_paths:
# The summary iterator should report the summary once as we closed the
# summary writer across the 2 sessions.
rr = summary_iterator.summary_iterator(path)
# The first event should list the file_version.
ev = next(rr)
self.assertEquals("brain.Event:2", ev.file_version)
# The next one has the graph and metagraph.
ev = next(rr)
self.assertTrue(ev.graph_def)
ev = next(rr)
self.assertTrue(ev.meta_graph_def)
# The next one should have the values from the summary.
# But only once.
ev = next(rr)
self.assertProtoEquals("""
value { tag: 'c1' simple_value: 1.0 }
value { tag: 'c2' simple_value: 2.0 }
value { tag: 'c3' simple_value: 3.0 }
""", ev.summary)
# The next one should be a stop message if we closed cleanly.
ev = next(rr)
self.assertEquals(event_pb2.SessionLog.STOP, ev.session_log.status)
# We should be done.
with self.assertRaises(StopIteration):
next(rr)
def summary_step_keyword_to_value_mapping(dir_):
writer_cache.FileWriterCache.clear()
# Get last Event written.
event_paths = glob.glob(os.path.join(dir_, 'events*'))
step_keyword_to_value = {}
for last_event in summary_iterator.summary_iterator(event_paths[-1]):
if last_event.step not in step_keyword_to_value:
step_keyword_to_value[last_event.step] = {}
if last_event.summary is not None:
for value in last_event.summary.value:
step_keyword_to_value[last_event.step][value.tag] = value.simple_value
return step_keyword_to_value
def assertLoggedMessagesAre(self, expected_messages):
self._sw.close()
event_paths = glob.glob(os.path.join(self._work_dir, "event*"))
# If the tests runs multiple time in the same directory we can have
# more than one matching event file. We only want to read the last one.
self.assertTrue(event_paths)
event_reader = summary_iterator.summary_iterator(event_paths[-1])
# Skip over the version event.
next(event_reader)
for level, message in expected_messages:
event = next(event_reader)
self.assertEqual(event.wall_time, time.time())
self.assertEqual(event.log_message.level, level)
self.assertEqual(event.log_message.message, message)
def list_summaries(logdir):
"""Read all summaries under the logdir into a `_SummaryFile`.
Args:
logdir: A path to a directory that contains zero or more event
files, either as direct children or in transitive subdirectories.
Summaries in these events must only contain old-style scalars,
images, and histograms. Non-summary events, like `graph_def`s, are
ignored.
Returns:
A `_SummaryFile` object reflecting all summaries written to any
event files in the logdir or any of its descendant directories.
Raises:
ValueError: If an event file contains an summary of unexpected kind.
"""
result = _SummaryFile()
for (dirpath, dirnames, filenames) in os.walk(logdir):
del dirnames # unused
for filename in filenames:
if not filename.startswith('events.out.'):
continue
path = os.path.join(dirpath, filename)
for event in summary_iterator.summary_iterator(path):
if not event.summary: # (e.g., it's a `graph_def` event)
continue
for value in event.summary.value:
tag = value.tag
# Case on the `value` rather than the summary metadata because
# the Keras callback uses `summary_ops_v2` to emit old-style
# summaries. See b/124535134.
kind = value.WhichOneof('value')
container = {
'simple_value': result.scalars,
'image': result.images,
'histo': result.histograms,
'tensor': result.tensors,
}.get(kind)
if container is None:
raise ValueError(
'Unexpected summary kind %r in event file %s:\n%r'
% (kind, path, event))
container.add(_ObservedSummary(logdir=dirpath, tag=tag))
return result
def _get_kept_steps(self, event_files):
"""Get the steps that the model was evaluated at, from event files.
Args:
event_files: Absolute pattern of event files.
Returns:
steps_kept: A list of steps in which the model was evaluated.
"""
if not event_files:
return None
steps_kept = []
for event_file in gfile.Glob(os.path.join(event_files)):
for event in summary_iterator.summary_iterator(event_file):
if event.step not in steps_kept:
steps_kept.append(event.step)
return steps_kept
def _verify_summaries(self, output_dir, names_to_values):
"""Verifies that the given `names_to_values` are found in the summaries.
Args:
output_dir: An existing directory where summaries are found.
names_to_values: A dictionary of strings to values.
"""
# Check that the results were saved. The events file may have additional
# entries, e.g. the event version stamp, so have to parse things a bit.
output_filepath = glob.glob(os.path.join(output_dir, '*'))
self.assertEqual(len(output_filepath), 1)
events = summary_iterator.summary_iterator(output_filepath[0])
summaries = [e.summary for e in events if e.summary.value]
values = []
for summary in summaries:
for value in summary.value:
values.append(value)
saved_results = {v.tag: v.simple_value for v in values}
for name in names_to_values:
self.assertAlmostEqual(names_to_values[name], saved_results[name], 5)
def assert_scalar_summary(self, output_dir, names_to_values):
"""Asserts that the given output directory contains written summaries.
Args:
output_dir: The output directory in which to look for even tfiles.
names_to_values: A dictionary of summary names to values.
"""
# The events file may have additional entries, e.g. the event version
# stamp, so have to parse things a bit.
output_filepath = glob.glob(os.path.join(output_dir, '*'))
self.assertEqual(len(output_filepath), 1)
events = summary_iterator.summary_iterator(output_filepath[0])
summaries_list = [e.summary for e in events if e.summary.value]
values = []
for item in summaries_list:
for value in item.value:
values.append(value)
saved_results = {v.tag: v.simple_value for v in values}
for name in names_to_values:
self.assertAlmostEqual(names_to_values[name], saved_results[name])
def testSharing_withExplicitSummaryFileWriters(self):
logdir = self.get_temp_dir()
with session.Session() as sess:
# Initial file writer via FileWriter(session=?)
writer1 = writer.FileWriter(session=sess, logdir=logdir)
writer1.add_summary(self._createTaggedSummary("one"), 1)
writer1.flush()
# Next one via create_file_writer(), should use same file
writer2 = summary_ops_v2.create_file_writer(logdir=logdir)
with summary_ops_v2.always_record_summaries(), writer2.as_default():
summary2 = summary_ops_v2.scalar("two", 2.0, step=2)
sess.run(writer2.init())
sess.run(summary2)
sess.run(writer2.flush())
# Next has different shared name, should be in separate file
time.sleep(1.1) # Ensure filename has a different timestamp
writer3 = summary_ops_v2.create_file_writer(logdir=logdir, name="other")
with summary_ops_v2.always_record_summaries(), writer3.as_default():
summary3 = summary_ops_v2.scalar("three", 3.0, step=3)
sess.run(writer3.init())
sess.run(summary3)
sess.run(writer3.flush())
# Next uses a second session, should be in separate file
time.sleep(1.1) # Ensure filename has a different timestamp
with session.Session() as other_sess:
writer4 = summary_ops_v2.create_file_writer(logdir=logdir)
with summary_ops_v2.always_record_summaries(), writer4.as_default():
summary4 = summary_ops_v2.scalar("four", 4.0, step=4)
other_sess.run(writer4.init())
other_sess.run(summary4)
other_sess.run(writer4.flush())
# Next via FileWriter(session=?) uses same second session, should be in
# same separate file. (This checks sharing in the other direction)
writer5 = writer.FileWriter(session=other_sess, logdir=logdir)
writer5.add_summary(self._createTaggedSummary("five"), 5)
writer5.flush()
# One more via create_file_writer(), should use same file
writer6 = summary_ops_v2.create_file_writer(logdir=logdir)
with summary_ops_v2.always_record_summaries(), writer6.as_default():
summary6 = summary_ops_v2.scalar("six", 6.0, step=6)
sess.run(writer6.init())
sess.run(summary6)
sess.run(writer6.flush())
event_paths = iter(sorted(glob.glob(os.path.join(logdir, "event*"))))
# First file should have tags "one", "two", and "six"
events = summary_iterator.summary_iterator(next(event_paths))
self.assertEqual("brain.Event:2", next(events).file_version)
self.assertEqual("one", next(events).summary.value[0].tag)
self.assertEqual("two", next(events).summary.value[0].tag)
self.assertEqual("six", next(events).summary.value[0].tag)
self.assertRaises(StopIteration, lambda: next(events))
# Second file should have just "three"
events = summary_iterator.summary_iterator(next(event_paths))
self.assertEqual("brain.Event:2", next(events).file_version)
self.assertEqual("three", next(events).summary.value[0].tag)
self.assertRaises(StopIteration, lambda: next(events))
# Third file should have "four" and "five"
events = summary_iterator.summary_iterator(next(event_paths))
self.assertEqual("brain.Event:2", next(events).file_version)
self.assertEqual("four", next(events).summary.value[0].tag)
self.assertEqual("five", next(events).summary.value[0].tag)
self.assertRaises(StopIteration, lambda: next(events))
# No more files
self.assertRaises(StopIteration, lambda: next(event_paths))
def _EventsReader(self, test_dir): event_paths = glob.glob(os.path.join(test_dir, "event*")) # If the tests runs multiple times in the same directory we can have # more than one matching event file. We only want to read the last one. self.assertTrue(event_paths) return summary_iterator.summary_iterator(event_paths[-1])
