[script] added very first script

bob/learn/pytorch/scripts/train.py

+#!/usr/bin/env python
+# encoding: utf-8
+
+
+""" Train a DR-GAN 
+
+Usage:
+  %(prog)s [--latent-dim=<int>] 
+           [--batch-size=<int>] [--epochs=<int>] [--sample=<int>]
+           [--output-dir=<path>] [--verbose ...]
+
+Options:
+  -h, --help                Show this screen.
+  -V, --version             Show version.
+  -l, --latent-dim=<int>    the dimension of the encoded ID [default: 320]
+  -b, --batch-size=<int>    The size of your mini-batch [default: 128]
+  -e, --epochs=<int>        The number of training epochs [default: 100] 
+  -s, --sample=<int>        Save generated images at every 'sample' batch iteration [default: 100000000000] 
+  -o, --output-dir=<path>   Dir to save the logs, models and images [default: ./drgan-light-mpie-casia/] 
+  -v, --verbose             Increase the verbosity (may appear multiple times).
+
+Example:
+
+  To run the training process 
+
+    $ %(prog)s --batch-size 64 --epochs 25 --output-dir drgan 
+
+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
+
+from bob.learn.pytorch.datasets.multipie import MultiPIEDataset
+#import bob.learn.pytorch.datasets.multipie
+
+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 parameters
+  batch_size = int(args['--batch-size'])
+  epochs = int(args['--epochs'])
+  sample = int(args['--sample'])
+  output_dir = str(args['--output-dir'])
+
+  images_dir = os.path.join(output_dir, 'samples')
+  log_dir = os.path.join(output_dir, 'logs')
+  model_dir = os.path.join(output_dir, 'models')
+
+  #face_dataset = MultiPIEDataset(root_dir='/idiap/resource/database/Multi-Pie/data/')
+  face_dataset = MultiPIEDataset(root_dir='/idiap/temp/heusch/data/multipie-cropped-64x64')
+  #print len(face_dataset)
+
+  from matplotlib import pyplot
+  for i in range(len(face_dataset)):
+    sample = face_dataset[i]
+    pyplot.title('Sample {}: ID -> {}, pose ->{}'.format(i, sample['id'], sample['pose']))
+    pyplot.imshow(numpy.rollaxis(numpy.rollaxis(sample['image'], 2),2))
+    pyplot.show()
+    
+