# Code referenced from https://gist.github.com/gyglim/1f8dfb1b5c82627ae3efcfbbadb9f514
import numpy as np
import scipy.misc
import os
import tensorflow as tf
try:
from StringIO import StringIO
except ImportError:
from io import BytesIO
class Logger(object):
def __init__(self, log_dir):
import tensorflow as tf
self.summary_writer = tf.summary.FileWriter(log_dir)
self.summary_ops = {}
def graph_summary(self, graph):
self.summary_writer.add_graph(graph)
self.summary_writer.flush()
def scalar_summary(self, tag, value, step, scope):
summary = tf.Summary(value=[tf.Summary.Value(tag=os.path.join(scope, tag), simple_value=value)])
self.summary_writer.add_summary(summary, step)
self.summary_writer.flush()
def image_summary(self, tag, images, step, scope, max_output=4, random_summarization=False):
"""Log a list of images."""
assert len(images.shape) == 4, "the input image shape should be in form [batch,hight,width,channels]"
img_summaries = []
if random_summarization:
idxs = np.random.choice(images.shape[0], min(max_output, images.shape[0]))
images = images[idxs]
else:
images = images[:max_output]
if images.shape[-1]==1:
images=np.squeeze(images)
for i in range(images.shape[0]):
img=images[i]
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=os.path.join(scope, '%s/%d' % (tag, i)), image=img_sum))
# Create and write Summary
summary = tf.Summary(value=img_summaries)
self.summary_writer.add_summary(summary, step)
self.summary_writer.flush()
def histo_summary(self, tag, values, step, scope, bins=1000, ):
"""Log a histogram of the tensor of values."""
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=os.path.join(scope, tag), histo=hist)])
self.summary_writer.add_summary(summary, step)
self.summary_writer.flush()
# summarize tenorflow tenosrs or images or merged summary, but this requires tensorflow session run
def summarize(self, sess, step, scope='train', summaries_dict=None, summaries_merged=None):
"""
Add the summaries to tensorboard
:param step:
:param summaries_dict:
:param summaries_merged:
:return:
"""
with tf.variable_scope(scope):
if summaries_dict is not None:
summary_list = []
for tag, value in summaries_dict.items():
if tag not in self.summary_ops:
self.summary_placeholders[tag] = tf.placeholder('float32', value.shape, name=tag)
if len(value.shape) <= 1:
self.summary_ops[tag] = tf.summary.scalar(tag, self.summary_placeholders[tag])
else:
self.summary_ops[tag] = tf.summary.image(tag, self.summary_placeholders[tag])
summary_list.append(sess.run(self.summary_ops[tag], {self.summary_placeholders[tag]: value}))
for summary in summary_list:
self.summary_writer.add_summary(summary, step)
if summaries_merged is not None:
self.summary_writer.add_summary(summaries_merged, step)
self.summary_writer.flush()
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