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https://github.com/wassname/DeepRL.git
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Introduce tensorboardX
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+1
-6
@@ -1,12 +1,7 @@
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from .config import *
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from .normalizer import *
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from .misc import *
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try:
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from .tf_logger import Logger
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except:
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from .vanilla_logger import Logger
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from .tf_logger import Logger
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import logging
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logging.basicConfig(format='%(asctime)s - %(name)s - %(levelname)s: %(message)s')
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logger = logging.getLogger('MAIN')
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+10
-68
@@ -1,20 +1,14 @@
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# Adapted from https://github.com/yunjey/pytorch-tutorial/blob/master/tutorials/04-utils/tensorboard/logger.py
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# Code referenced from https://gist.github.com/gyglim/1f8dfb1b5c82627ae3efcfbbadb9f514
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import tensorflow as tf
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import numpy as np
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import scipy.misc
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import logging
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try:
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from StringIO import StringIO # Python 2.7
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except ImportError:
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from io import BytesIO # Python 3.x
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#######################################################################
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# Copyright (C) 2017 Shangtong Zhang(zhangshangtong.cpp@gmail.com) #
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# Permission given to modify the code as long as you keep this #
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# declaration at the top #
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#######################################################################
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from tensorboardX import SummaryWriter
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class Logger(object):
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def __init__(self, log_dir, vanilla_logger, skip=False):
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"""Create a summary writer logging to log_dir."""
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self.writer = tf.summary.FileWriter(log_dir)
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self.writer = SummaryWriter(log_dir)
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self.info = vanilla_logger.info
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self.debug = vanilla_logger.debug
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self.warning = vanilla_logger.warning
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@@ -23,61 +17,9 @@ class Logger(object):
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def scalar_summary(self, tag, value, step):
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if self.skip:
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return
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"""Log a scalar variable."""
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summary = tf.Summary(value=[tf.Summary.Value(tag=tag, simple_value=value)])
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self.writer.add_summary(summary, step)
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self.writer.add_scalar(tag, value, step)
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def image_summary(self, tag, images, step):
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def histo_summary(self, tag, values, step):
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if self.skip:
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return
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"""Log a list of images."""
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img_summaries = []
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for i, img in enumerate(images):
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# Write the image to a string
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try:
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s = StringIO()
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except:
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s = BytesIO()
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scipy.misc.toimage(img).save(s, format="png")
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# Create an Image object
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img_sum = tf.Summary.Image(encoded_image_string=s.getvalue(),
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height=img.shape[0],
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width=img.shape[1])
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# Create a Summary value
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img_summaries.append(tf.Summary.Value(tag='%s/%d' % (tag, i), image=img_sum))
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# Create and write Summary
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summary = tf.Summary(value=img_summaries)
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self.writer.add_summary(summary, step)
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def histo_summary(self, tag, values, step, bins=1000):
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if self.skip:
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return
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"""Log a histogram of the tensor of values."""
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# Create a histogram using numpy
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counts, bin_edges = np.histogram(values, bins=bins)
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# Fill the fields of the histogram proto
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hist = tf.HistogramProto()
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hist.min = float(np.min(values))
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hist.max = float(np.max(values))
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hist.num = int(np.prod(values.shape))
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hist.sum = float(np.sum(values))
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hist.sum_squares = float(np.sum(values ** 2))
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# Drop the start of the first bin
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bin_edges = bin_edges[1:]
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# Add bin edges and counts
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for edge in bin_edges:
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hist.bucket_limit.append(edge)
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for c in counts:
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hist.bucket.append(c)
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# Create and write Summary
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summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
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self.writer.add_summary(summary, step)
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self.writer.flush()
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self.writer.add_histogram(tag, values, step)
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@@ -1,22 +0,0 @@
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import numpy as np
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import logging
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class Logger(object):
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def __init__(self, log_dir, vanilla_logger, skip=False):
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"""Create a summary writer logging to log_dir."""
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self.info = vanilla_logger.info
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self.debug = vanilla_logger.debug
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self.warning = vanilla_logger.warning
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self.skip = skip
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def scalar_summary(self, tag, value, step):
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if self.skip:
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return
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def image_summary(self, tag, images, step):
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if self.skip:
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return
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def histo_summary(self, tag, values, step, bins=1000):
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if self.skip:
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return
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