[scripts] add script to display training stats (loss, accuracy, ...)

bob/learn/pytorch/scripts/show_training_stats.py

+#!/usr/bin/env python
+# encoding: utf-8
+
+""" Read data saved during the training of a DR-GAN
+
+Usage:
+  %(prog)s [--logdir=<path>] [--verbose ...] 
+
+Options:
+  -h, --help                    Show this screen.
+  -V, --version                 Show version.
+  -d, --logdir=<path>           The dir where the training data reside
+  -v, --verbose                 Increase the verbosity (may appear multiple times).
+
+
+Example:
+
+  To read and display the training data:
+
+    $ %(prog)s --logdir ./drgan/logs
+
+See '%(prog)s --help' for more information.
+
+"""
+
+import os, sys
+import pkg_resources
+
+import bob.core
+logger = bob.core.log.setup("bob.learn.pytorch")
+
+from docopt import docopt
+
+version = pkg_resources.require('bob.learn.pytorch')[0].version
+
+import numpy
+import bob.io.base
+
+from matplotlib import pyplot
+
+def main(user_input=None):
+  
+  # Parse the command-line arguments
+  if user_input is not None:
+      arguments = user_input
+  else:
+      arguments = sys.argv[1:]
+
+  prog = os.path.basename(sys.argv[0])
+  completions = dict(prog=prog, version=version,)
+  args = docopt(__doc__ % completions,argv=arguments,version='Train DR-GAN (%s)' % version,)
+
+  # verbosity
+  verbosity_level = args['--verbose']
+  bob.core.log.set_verbosity_level(logger, verbosity_level)
+ 
+  # get the arguments
+  logdir = args['--logdir']
+
+
+  # === LOSSES ===
+  # get the last losses file
+  import glob
+  losses_files = glob.glob(logdir + '/losses_*') # * means all if need specific format then *.csv
+  loss_filename = max(losses_files, key=os.path.getctime)
+  print loss_filename
+
+  #fl = bob.io.base.HDF5File(loss_filename)
+  #d_loss = fl.read('d_loss')
+  #g_loss = fl.read('g_loss')
+
+  #pyplot.title("Losses")
+  #pyplot.xlabel("# of iterations")
+  #pyplot.plot(d_loss, 'b', label="discriminator")
+  #pyplot.plot(g_loss, 'r', label="generator")
+  #pyplot.legend()
+  #pyplot.show()
+
+  #del fl
+
+  # === DISCRIMINATOR ===
+  fdr = bob.io.base.HDF5File(logdir + '/discriminator_real_stats.hdf5')
+  real_id_acc = fdr.read('r_id_accuracy')
+  real_pose_acc = fdr.read('r_pose_accuracy')
+  real_gan_acc = fdr.read('r_real_accuracy')
+  
+  fdf = bob.io.base.HDF5File(logdir + '/discriminator_fake_stats.hdf5')
+  fake_id_acc = fdf.read('f_id_accuracy')
+  fake_pose_acc = fdf.read('f_pose_accuracy')
+  fake_gan_acc = fdf.read('f_fake_accuracy')
+
+  f, axarr = pyplot.subplots(3, sharex=True)
+  f.suptitle("Discriminator stats")
+  axarr[0].set_title("Identity")
+  axarr[0].plot(real_id_acc, label="real")
+  axarr[0].plot(fake_id_acc, 'r', label="fake")
+  axarr[0].legend()
+  axarr[1].set_title("Pose")
+  axarr[1].plot(real_pose_acc, label="real")
+  axarr[1].plot(fake_pose_acc, 'r', label="fake")
+  axarr[1].legend()
+  axarr[2].set_title("Real / fake")
+  axarr[2].plot(real_gan_acc, label="real (recognized as real)")
+  axarr[2].plot(fake_gan_acc, 'r', label="fake (recognized as fake")
+  axarr[2].legend()
+  pyplot.show()
+  del fdr
+  del fdf
+
+  fdg = bob.io.base.HDF5File(logdir + '/generator_stats.hdf5')
+  gen_id_acc = fdg.read('g_id_accuracy')
+  gen_pose_acc = fdg.read('g_pose_accuracy')
+  gen_gan_acc = fdg.read('g_fake_accuracy')
+
+  f, axarr = pyplot.subplots(3, sharex=True)
+  f.suptitle("Generator stats")
+  axarr[0].set_title("Identity")
+  axarr[0].plot(gen_id_acc)
+  axarr[1].set_title("Pose")
+  axarr[1].plot(gen_pose_acc)
+  axarr[2].set_title("Real / fake")
+  axarr[2].plot(gen_gan_acc)
+  pyplot.show()
+  del fdg

setup.py

@@ -79,7 +79,8 @@ setup(
         'train_wcgan_multipie.py = bob.learn.pytorch.scripts.train_wcgan_multipie:main', 
         'train_drgan_multipie.py = bob.learn.pytorch.scripts.train_drgan_multipie:main', 
         'train_drgan_mpie_casia.py = bob.learn.pytorch.scripts.train_drgan_mpie_casia:main', 
-        'read_training_hdf5.py = bob.learn.pytorch.scripts.read_training_hdf5:main', 
+        'show_training_images.py = bob.learn.pytorch.scripts.show_training_images:main', 
+        'show_training_stats.py = bob.learn.pytorch.scripts.show_training_stats:main', 
       ],