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Zero
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import re
def read_data_from_tensorboard(log_path, tag):
"""Get raw data (steps and values) from tensorboard events.
Args:
log_path (str): Path to the tensorboard log.
tag (str): tag to be read.
"""
from tensorboard.backend.event_processing.event_accumulator import EventAccumulator
# tensorboard event
event_acc = EventAccumulator(log_path)
event_acc.Reload()
scalar_list = event_acc.Tags()['scalars']
print('tag list: ', scalar_list)
steps = [int(s.step) for s in event_acc.Scalars(tag)]
values = [s.value for s in event_acc.Scalars(tag)]
return steps, values
def read_data_from_txt_2v(path, pattern, step_one=False):
"""Read data from txt with 2 returned values (usually [step, value]).
Args:
path (str): path to the txt file.
pattern (str): re (regular expression) pattern.
step_one (bool): add 1 to steps. Default: False.
"""
with open(path) as f:
lines = f.readlines()
lines = [line.strip() for line in lines]
steps = []
values = []
pattern = re.compile(pattern)
for line in lines:
match = pattern.match(line)
if match:
steps.append(int(match.group(1)))
values.append(float(match.group(2)))
if step_one:
steps = [v + 1 for v in steps]
return steps, values
def read_data_from_txt_1v(path, pattern):
"""Read data from txt with 1 returned values.
Args:
path (str): path to the txt file.
pattern (str): re (regular expression) pattern.
"""
with open(path) as f:
lines = f.readlines()
lines = [line.strip() for line in lines]
data = []
pattern = re.compile(pattern)
for line in lines:
match = pattern.match(line)
if match:
data.append(float(match.group(1)))
return data
def smooth_data(values, smooth_weight):
""" Smooth data using 1st-order IIR low-pass filter (what tensorflow does).
Reference: https://github.com/tensorflow/tensorboard/blob/f801ebf1f9fbfe2baee1ddd65714d0bccc640fb1/tensorboard/plugins/scalar/vz_line_chart/vz-line-chart.ts#L704 # noqa: E501
Args:
values (list): A list of values to be smoothed.
smooth_weight (float): Smooth weight.
"""
values_sm = []
last_sm_value = values[0]
for value in values:
value_sm = last_sm_value * smooth_weight + (1 - smooth_weight) * value
values_sm.append(value_sm)
last_sm_value = value_sm
return values_sm
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