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from __future__ import absolute_import
import os
import sys
import numpy as np
import tensorflow as tf
import scipy.misc
try:
from StringIO import StringIO # Python 2.7
except ImportError:
from io import BytesIO # Python 3.x
from .osutils import mkdir_if_missing
from config import get_args
global_args = get_args(sys.argv[1:])
if global_args.run_on_remote:
import moxing as mox
mox.file.shift("os", "mox")
class Logger(object):
def __init__(self, fpath=None):
self.console = sys.stdout
self.file = None
if fpath is not None:
if global_args.run_on_remote:
dir_name = os.path.dirname(fpath)
if not mox.file.exists(dir_name):
mox.file.make_dirs(dir_name)
print('=> making dir ', dir_name)
self.file = mox.file.File(fpath, 'w')
# self.file = open(fpath, 'w')
else:
mkdir_if_missing(os.path.dirname(fpath))
self.file = open(fpath, 'w')
def __del__(self):
self.close()
def __enter__(self):
pass
def __exit__(self, *args):
self.close()
def write(self, msg):
self.console.write(msg)
if self.file is not None:
self.file.write(msg)
def flush(self):
self.console.flush()
if self.file is not None:
self.file.flush()
os.fsync(self.file.fileno())
def close(self):
self.console.close()
if self.file is not None:
self.file.close()
class TFLogger(object):
def __init__(self, log_dir=None):
"""Create a summary writer logging to log_dir."""
if log_dir is not None:
mkdir_if_missing(log_dir)
self.writer = tf.summary.FileWriter(log_dir)
def scalar_summary(self, tag, value, step):
"""Log a scalar variable."""
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."""
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='%s/%d' % (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."""
# 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()
def close(self):
self.writer.close() |