def add_phase_arguments(parser):
phases = parser.add_argument_group(title='phases of compilation')
# These options *set* the phases, so they are mutually exclusive.
ph_mutex = phases.add_mutually_exclusive_group()
ph_mutex.add_argument('-p',
'--phases',
nargs='+',
metavar="PHASE",
choices=PHASES,
default=PHASES)
ph_mutex.add_argument('--scan',
action='store_const',
dest='phases',
const=PHASES[:1],
help='shorthand for --phase ' + ' '.join(PHASES[:1]))
ph_mutex.add_argument('--parse',
action='store_const',
dest='phases',
const=PHASES[:2],
help='shorthand for --phase ' + ' '.join(PHASES[:2]))
ph_mutex.add_argument('--imports',
action='store_const',
dest='phases',
const=PHASES[:3],
help='shorthand for --phase ' + ' '.join(PHASES[:3]))
ph_mutex.add_argument('--macros',
action='store_const',
dest='phases',
const=PHASES[:4],
help='shorthand for --phase ' + ' '.join(PHASES[:4]))
ph_mutex.add_argument('--types',
action='store_const',
dest='phases',
const=PHASES[:5],
help='shorthand for --phase ' + ' '.join(PHASES[:5]))
ph_mutex.add_argument('--eval',
action='store_const',
dest='phases',
const=PHASES[:6],
help='shorthand for --phase ' + ' '.join(PHASES[:6]))
def init_classifier_compression_arg_parser():
'''Common classifier-compression application command-line arguments.
'''
SUMMARY_CHOICES = ['sparsity', 'compute', 'model', 'modules', 'png', 'png_w_params']
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', type=lambda s: s.lower(),
choices=models.ALL_MODEL_NAMES,
help='model architecture: ' +
' | '.join(models.ALL_MODEL_NAMES) +
' (default: resnet18)')
parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
help='number of data loading workers (default: 4)')
parser.add_argument('--epochs', type=int, metavar='N', default=90,
help='number of total epochs to run (default: 90')
parser.add_argument('-b', '--batch-size', default=256, type=int,
metavar='N', help='mini-batch size (default: 256)')
optimizer_args = parser.add_argument_group('Optimizer arguments')
optimizer_args.add_argument('--lr', '--learning-rate', default=0.1,
type=float, metavar='LR', help='initial learning rate')
optimizer_args.add_argument('--momentum', default=0.9, type=float,
metavar='M', help='momentum')
optimizer_args.add_argument('--weight-decay', '--wd', default=1e-4, type=float,
metavar='W', help='weight decay (default: 1e-4)')
parser.add_argument('--print-freq', '-p', default=10, type=int,
metavar='N', help='print frequency (default: 10)')
parser.add_argument('--verbose', '-v', action='store_true', help='Emit debug log messages')
load_checkpoint_group = parser.add_argument_group('Resuming arguments')
load_checkpoint_group_exc = load_checkpoint_group.add_mutually_exclusive_group()
# TODO(barrh): args.deprecated_resume is deprecated since v0.3.1
load_checkpoint_group_exc.add_argument('--resume', dest='deprecated_resume', default='', type=str,
metavar='PATH', help=argparse.SUPPRESS)
load_checkpoint_group_exc.add_argument('--resume-from', dest='resumed_checkpoint_path', default='',
type=str, metavar='PATH',
help='path to latest checkpoint. Use to resume paused training session.')
load_checkpoint_group_exc.add_argument('--exp-load-weights-from', dest='load_model_path',
default='', type=str, metavar='PATH',
help='path to checkpoint to load weights from (excluding other fields) (experimental)')
load_checkpoint_group.add_argument('--pretrained', dest='pretrained', action='store_true',
help='use pre-trained model')
load_checkpoint_group.add_argument('--reset-optimizer', action='store_true',
help='Flag to override optimizer if resumed from checkpoint. This will reset epochs count.')
parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true',
help='evaluate model on test set')
parser.add_argument('--activation-stats', '--act-stats', nargs='+', metavar='PHASE', default=list(),
help='collect activation statistics on phases: train, valid, and/or test'
' (WARNING: this slows down training)')
parser.add_argument('--activation-histograms', '--act-hist',
type=distiller.utils.float_range_argparse_checker(exc_min=True),
metavar='PORTION_OF_TEST_SET',
help='Run the model in evaluation mode on the specified portion of the test dataset and '
'generate activation histograms. NOTE: This slows down evaluation significantly')
parser.add_argument('--masks-sparsity', dest='masks_sparsity', action='store_true', default=False,
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=lambda s: s.lower(), choices=SUMMARY_CHOICES, action='append',
help='print a summary of the model, and exit - options: | '.join(SUMMARY_CHOICES))
parser.add_argument('--export-onnx', action='store', nargs='?', type=str, const='model.onnx', default=None,
help='export model to ONNX format')
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'],
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'
'This is equivalent to creating sensitivities = np.arange(start, stop, step)')
parser.add_argument('--extras', default=None, type=str,
help='file with extra configuration information')
parser.add_argument('--deterministic', '--det', action='store_true',
help='Ensure deterministic execution for re-producible results.')
parser.add_argument('--seed', type=int, default=None,
help='seed the PRNG for CPU, CUDA, numpy, and Python')
parser.add_argument('--gpus', metavar='DEV_ID', default=None,
help='Comma-separated list of GPU device IDs to be used '
'(default is to use all available devices)')
parser.add_argument('--cpu', action='store_true', default=False,
help='Use CPU only. \n'
'Flag not set => uses GPUs according to the --gpus flag value.'
'Flag set => overrides the --gpus flag')
parser.add_argument('--name', '-n', metavar='NAME', default=None, help='Experiment name')
parser.add_argument('--out-dir', '-o', dest='output_dir', default='logs', help='Path to dump logs and checkpoints')
parser.add_argument('--validation-split', '--valid-size', '--vs', dest='validation_split',
type=float_range(exc_max=True), default=0.1,
help='Portion of training dataset to set aside for validation')
parser.add_argument('--effective-train-size', '--etrs', type=float_range(exc_min=True), default=1.,
help='Portion of training dataset to be used in each epoch. '
'NOTE: If --validation-split is set, then the value of this argument is applied '
'AFTER the train-validation split according to that argument')
parser.add_argument('--effective-valid-size', '--evs', type=float_range(exc_min=True), default=1.,
help='Portion of validation dataset to be used in each epoch. '
'NOTE: If --validation-split is set, then the value of this argument is applied '
'AFTER the train-validation split according to that argument')
parser.add_argument('--effective-test-size', '--etes', type=float_range(exc_min=True), default=1.,
help='Portion of test dataset to be used in each epoch')
parser.add_argument('--confusion', dest='display_confusion', default=False, action='store_true',
help='Display the confusion matrix')
parser.add_argument('--num-best-scores', dest='num_best_scores', default=1, type=int,
help='number of best scores to track and report (default: 1)')
parser.add_argument('--load-serialized', dest='load_serialized', action='store_true', default=False,
help='Load a model without DataParallel wrapping it')
parser.add_argument('--thinnify', dest='thinnify', action='store_true', default=False,
help='physically remove zero-filters and create a smaller model')
distiller.quantization.add_post_train_quant_args(parser)
return parser
autopsy_logger.info(
"*************** Terminating as per user request ***************")
exit(2, archive=True)
elif signum == signal.SIGUSR1:
pdb.set_trace()
return
exit(1)
if __name__ == '__main__':
script_loc = os.path.dirname(os.path.realpath(__file__))
print(' '.join(sys.argv))
parser = ArgumentParser(description="Start Autopsy Automation run")
req_group = parser.add_argument_group('Required Arguments')
req_group.add_argument('--testbed',
'--test-bed',
help='Testbed to run',
type=str,
required=True,
dest='testbed')
req_group.add_argument('--testsuite',
'--test-suite',
help='Test suite(s)',
type=str,
required=True,
dest='test_suite')
opt_group = parser.add_argument_group('Optional Argumets')
opt_group.add_argument('--mail',
except Exception as e:
print "I am unable to load configuration"
print str(e)
parser = argparse.ArgumentParser(
prog=sys.argv[0],
description=
'Utility used to reprocess entire collections on filesystem',
epilog='SISTEMA GmbH <http://sistema.at>')
parser.add_argument(
'--standalone',
help=
'Execute the module stand-alone, without going through ingester in das_ing',
action='store_true')
parser_required = parser.add_argument_group('Required Arguments')
parser_required.add_argument(
'-f',
dest='function',
metavar='func',
required=True,
choices=['verticalIntegration', 'convert'],
help='available functions are: verticalIntegration, convert')
parser_required.add_argument('-l',
dest='label',
metavar='label',
required=True,
help='new label to attach to new filename')
parser_ingester = parser.add_argument_group(
'Required Arguments if NOT executed stand-alone')
def add_argument_group(parser, name):
arg = parser.add_argument_group(name)
return arg