Training (add tensorboard debug, and mAP Calculation)

.gitignore

@@ -106,3 +106,6 @@ ENV/
 
 # mypy
 .mypy_cache/
+
+# pycharm
+.idea/

tensorboard_logging.py

@@ -0,0 +1,83 @@
+"""
+Simple example on how to log scalars and images to tensorboard without tensor ops.
+
+License: Copyleft
+"""
+
+__author__ = "Michael Gygli"
+
+import numpy as np
+import matplotlib.pyplot as plt
+import tensorflow as tf
+from io import BytesIO
+
+
+def log_scalar(callback, tag, value, step):
+    """Log a scalar variable.
+
+    Parameter
+    ----------
+    tag : basestring
+        Name of the scalar
+    value
+    step : int
+        training iteration
+    """
+    summary = tf.Summary(value=[tf.Summary.Value(tag=tag,
+                                                 simple_value=value)])
+    callback.writer.add_summary(summary, step)
+
+
+def log_images(callback, tag, images, step):
+    """Logs a list of images."""
+
+    im_summaries = []
+    for nr, img in enumerate(images):
+        # Write the image to a string
+        s = BytesIO()
+        plt.imsave(s, img, 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
+        im_summaries.append(tf.Summary.Value(tag='%s/%d' % (tag, nr),
+                                             image=img_sum))
+
+    # Create and write Summary
+    summary = tf.Summary(value=im_summaries)
+    callback.writer.add_summary(summary, step)
+
+
+def log_histogram(callback, tag, values, step, bins=1000):
+    """Logs the histogram of a list/vector of values."""
+    # Convert to a numpy array
+    values = np.array(values)
+
+    # Create histogram using numpy
+    counts, bin_edges = np.histogram(values, bins=bins)
+
+    # Fill fields of 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))
+
+    # Requires equal number as bins, where the first goes from -DBL_MAX to bin_edges[1]
+    # See https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/summary.proto#L30
+    # Thus, we 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)])
+    callback.writer.add_summary(summary, step)
+    callback.writer.flush()