logger

bob/learn/pytorch/trainers/Logger.py

-# Code referenced from https://gist.github.com/gyglim/1f8dfb1b5c82627ae3efcfbbadb9f514
-import tensorflow as tf
-import numpy as np
-import scipy.misc 
-try:
-    from StringIO import StringIO  # Python 2.7
-except ImportError:
-    from io import BytesIO         # Python 3.x
-
-
-class Logger(object):
-    
-    def __init__(self, log_dir):
-        """Create a summary writer logging to 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)
-
-    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)
-        
-    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()
\ No newline at end of file

bob/learn/pytorch/trainers/MCCNNTrainer.py

@@ -7,8 +7,7 @@ import torch.nn as nn
 import torch.optim as optim
 from torch.autograd import Variable
 from bob.learn.pytorch.utils import comp_bce_loss_weights
-from bob.learn.pytorch.trainers import Logger
-
+from .logger import Logger 
 
 import bob.core
 logger = bob.core.log.setup("bob.learn.pytorch")

bob/learn/pytorch/trainers/__init__.py

@@ -2,7 +2,6 @@ from .CNNTrainer import CNNTrainer
 from .MCCNNTrainer import MCCNNTrainer
 from .DCGANTrainer import DCGANTrainer
 from .ConditionalGANTrainer import ConditionalGANTrainer
-from .Logger import Logger
 
 # gets sphinx autodoc done right - don't remove it
 __all__ = [_ for _ in dir() if not _.startswith('_')]