Modify parser choices to case-insensitive

examples/classifier_compression/parser.py

@@ -25,7 +25,7 @@
 def getParser():
  parser = argparse.ArgumentParser(description='Distiller image classification model compression')
  parser.add_argument('data', metavar='DIR', help='path to dataset')
- parser.add_argument('--arch', '-a', metavar='ARCH', default='resnet18',
+ parser.add_argument('--arch', '-a', metavar='ARCH', default='resnet18', type=lambda s: s.lower(),
  choices=models.ALL_MODEL_NAMES,
  help='model architecture: ' +
  ' | '.join(models.ALL_MODEL_NAMES) +
@@ -58,12 +58,12 @@ def getParser():
  help='print masks sparsity table at end of each epoch')
  parser.add_argument('--param-hist', dest='log_params_histograms', action='store_true', default=False,
  help='log the parameter tensors histograms to file (WARNING: this can use significant disk space)')
- parser.add_argument('--summary', type=str, choices=SUMMARY_CHOICES,
+ parser.add_argument('--summary', type=lambda s: s.lower(), choices=SUMMARY_CHOICES,
  help='print a summary of the model, and exit - options: ' +
  ' | '.join(SUMMARY_CHOICES))
  parser.add_argument('--compress', dest='compress', type=str, nargs='?', action='store',
  help='configuration file for pruning the model (default is to use hard-coded schedule)')
- parser.add_argument('--sense', dest='sensitivity', choices=['element', 'filter', 'channel'],
+ parser.add_argument('--sense', dest='sensitivity', choices=['element', 'filter', 'channel'], type=lambda s: s.lower(),
  help='test the sensitivity of layers to pruning')
  parser.add_argument('--sense-range', dest='sensitivity_range', type=float, nargs=3, default=[0.0, 0.95, 0.05],
  help='an optional parameter for sensitivity testing providing the range of sparsities to test.\n'

models/__init__.py

@@ -39,7 +39,7 @@
  if name.islower() and not name.startswith("__")
  and callable(cifar10_models.__dict__[name]))
 
-ALL_MODEL_NAMES = sorted(set(IMAGENET_MODEL_NAMES + CIFAR10_MODEL_NAMES))
+ALL_MODEL_NAMES = sorted(map(lambda s: s.lower(), set(IMAGENET_MODEL_NAMES + CIFAR10_MODEL_NAMES)))
 
 
 def create_model(pretrained, dataset, arch, parallel=True, device_ids=None):