xautodl/lib/log_utils/logger.py

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2019-09-28 10:24:47 +02:00
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
from pathlib import Path
import importlib, warnings
import os, sys, time, numpy as np
if sys.version_info.major == 2: # Python 2.x
from StringIO import StringIO as BIO
else: # Python 3.x
from io import BytesIO as BIO
if importlib.util.find_spec('tensorflow'):
import tensorflow as tf
class Logger(object):
def __init__(self, log_dir, seed, create_model_dir=True, use_tf=False):
"""Create a summary writer logging to log_dir."""
self.seed = int(seed)
self.log_dir = Path(log_dir)
self.model_dir = Path(log_dir) / 'checkpoint'
self.log_dir.mkdir (parents=True, exist_ok=True)
if create_model_dir:
self.model_dir.mkdir(parents=True, exist_ok=True)
#self.meta_dir.mkdir(mode=0o775, parents=True, exist_ok=True)
self.use_tf = bool(use_tf)
self.tensorboard_dir = self.log_dir / ('tensorboard-{:}'.format(time.strftime( '%d-%h', time.gmtime(time.time()) )))
#self.tensorboard_dir = self.log_dir / ('tensorboard-{:}'.format(time.strftime( '%d-%h-at-%H:%M:%S', time.gmtime(time.time()) )))
self.logger_path = self.log_dir / 'seed-{:}-T-{:}.log'.format(self.seed, time.strftime('%d-%h-at-%H-%M-%S', time.gmtime(time.time())))
self.logger_file = open(self.logger_path, 'w')
if self.use_tf:
self.tensorboard_dir.mkdir(mode=0o775, parents=True, exist_ok=True)
self.writer = tf.summary.FileWriter(str(self.tensorboard_dir))
else:
self.writer = None
def __repr__(self):
return ('{name}(dir={log_dir}, use-tf={use_tf}, writer={writer})'.format(name=self.__class__.__name__, **self.__dict__))
def path(self, mode):
valids = ('model', 'best', 'info', 'log')
if mode == 'model': return self.model_dir / 'seed-{:}-basic.pth'.format(self.seed)
elif mode == 'best' : return self.model_dir / 'seed-{:}-best.pth'.format(self.seed)
elif mode == 'info' : return self.log_dir / 'seed-{:}-last-info.pth'.format(self.seed)
elif mode == 'log' : return self.log_dir
else: raise TypeError('Unknow mode = {:}, valid modes = {:}'.format(mode, valids))
def extract_log(self):
return self.logger_file
def close(self):
self.logger_file.close()
if self.writer is not None:
self.writer.close()
def log(self, string, save=True, stdout=False):
if stdout:
sys.stdout.write(string); sys.stdout.flush()
else:
print (string)
if save:
self.logger_file.write('{:}\n'.format(string))
self.logger_file.flush()
def scalar_summary(self, tags, values, step):
"""Log a scalar variable."""
if not self.use_tf:
warnings.warn('Do set use-tensorflow installed but call scalar_summary')
else:
assert isinstance(tags, list) == isinstance(values, list), 'Type : {:} vs {:}'.format(type(tags), type(values))
if not isinstance(tags, list):
tags, values = [tags], [values]
for tag, value in zip(tags, values):
summary = tf.Summary(value=[tf.Summary.Value(tag=tag, simple_value=value)])
self.writer.add_summary(summary, step)
self.writer.flush()
def image_summary(self, tag, images, step):
"""Log a list of images."""
import scipy
if not self.use_tf:
warnings.warn('Do set use-tensorflow installed but call scalar_summary')
return
img_summaries = []
for i, img in enumerate(images):
# Write the image to a string
try:
s = StringIO()
except:
s = BytesIO()
scipy.misc.toimage(img).save(s, format="png")
# Create an Image object
img_sum = tf.Summary.Image(encoded_image_string=s.getvalue(),
height=img.shape[0],
width=img.shape[1])
# Create a Summary value
img_summaries.append(tf.Summary.Value(tag='{}/{}'.format(tag, i), image=img_sum))
# Create and write Summary
summary = tf.Summary(value=img_summaries)
self.writer.add_summary(summary, step)
self.writer.flush()
def histo_summary(self, tag, values, step, bins=1000):
"""Log a histogram of the tensor of values."""
if not self.use_tf: raise ValueError('Do not have tensorflow')
import tensorflow as tf
# Create a histogram using numpy
counts, bin_edges = np.histogram(values, bins=bins)
# Fill the fields of the histogram proto
hist = tf.HistogramProto()
hist.min = float(np.min(values))
hist.max = float(np.max(values))
hist.num = int(np.prod(values.shape))
hist.sum = float(np.sum(values))
hist.sum_squares = float(np.sum(values**2))
# Drop the start of the first bin
bin_edges = bin_edges[1:]
# Add bin edges and counts
for edge in bin_edges:
hist.bucket_limit.append(edge)
for c in counts:
hist.bucket.append(c)
# Create and write Summary
summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
self.writer.add_summary(summary, step)
self.writer.flush()