Add more algorithms

lib/log_utils/logger.py

@@ -0,0 +1,140 @@
+# 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()