def test_add_model_file_arg_custom_arg_name(self):
arg_name, value = 'model_file_test', 'some_model_file.dat'
cmdline = ['--{}'.format(arg_name), value]
parser = argparse.ArgumentParser()
net_args.add_model_file_arg(parser, arg_name=arg_name)
res = parser.parse_args(cmdline)
self.assertEqual(getattr(res, arg_name), value)
def test_add_model_file_arg_short_alias(self):
alias, value = 'm', 'model_file.dat'
cmdline = ['-{}'.format(alias), value]
parser = argparse.ArgumentParser()
net_args.add_model_file_arg(parser, short_alias=alias)
res = parser.parse_args(cmdline)
self.assertEqual(res.model_file, value)
def __init__(self):
self.default_logdir = cutils.get_config_for_module(
"model_trainer")['default']['logdir']
parser = argparse.ArgumentParser(
description="Train network using provided dataset")
# dataset input
in_aliases = {'dataset name': 'name', 'dataset directory': 'srcdir'}
dset_args = dargs.DatasetArgs(input_aliases=in_aliases)
item_args = dargs.ItemTypeArgs()
atype = dargs.arg_type.INPUT
group = parser.add_argument_group(title="Input dataset")
dset_args.add_dataset_arg_double(group, atype)
item_args.add_item_type_args(group, atype)
group.add_argument('--test_items_count',
type=atypes.int_range(1),
help='Number of dataset items to include in the '
'test set. Overrides test_items_fraction.')
group.add_argument('--test_items_fraction',
type=float,
default=0.1,
help='Number of dataset items to include in the '
'test set, expressed as a fraction.')
modes = net_cons.DATASET_SPLIT_MODES
group.add_argument('--split_mode',
choices=modes,
required=True,
help='Method of splitting the test items subset '
'from the input dataset.')
# network to train
group = parser.add_argument_group(title="Network configuration")
net_args.add_network_arg(group, short_alias='n')
net_args.add_model_file_arg(group, short_alias='m')
group.add_argument('--tb_dir',
default=self.default_logdir,
help=('directory to store training logs for '
'tensorboard.'))
group.add_argument('--save',
action='store_true',
help=('save the model after training. Model files '
'are saved under tb_dir as net.network_name/'
'net.network_name.tflearn.*'))
# training settings
group = parser.add_argument_group(title="Training parameters")
net_args.add_training_settings_args(group,
num_epochs={
'required': False,
'default': 11,
'short_alias': 'e'
})
self.parser = parser
self.dset_args = dset_args
self.item_args = item_args
def __init__(self):
self.default_logdir = cutils.get_config_for_module(
"model_xvalidator")['default']['logdir']
print("default logdir set to {}".format(self.default_logdir))
parser = argparse.ArgumentParser(
description="Perform Kfold cross-validation on a given neural "
"network with the given dataset.")
# cross-validation settings
group = parser.add_argument_group(title="Cross-validation parameters")
group.add_argument('--num_crossvals',
type=atypes.int_range(1),
required=True,
help='number of cross validations to perform')
# network to train
group = parser.add_argument_group(title="Network to use")
net_args.add_network_arg(group, short_alias='n')
net_args.add_model_file_arg(group, short_alias='m')
# training_parameters
group = parser.add_argument_group(title="Training parameters to use")
net_args.add_training_settings_args(group,
num_epochs={
'required': False,
'default': 11,
'short_alias': 'e'
})
group.add_argument('--tb_dir',
default=self.default_logdir,
help=('directory to store training logs for '
'tensorboard.'))
# dataset input
in_aliases = {'dataset name': 'name', 'dataset directory': 'srcdir'}
dset_args = dargs.DatasetArgs(input_aliases=in_aliases)
item_args = dargs.ItemTypeArgs()
atype = dargs.arg_type.INPUT
group = parser.add_argument_group(title="Input dataset")
dset_args.add_dataset_arg_double(group, atype)
item_args.add_item_type_args(group, atype)
group.add_argument('--test_items_count',
type=atypes.int_range(1),
help='number of dataset items to include in the '
'test set. Overrides test_items_fraction.')
group.add_argument('--test_items_fraction',
type=float,
default=0.1,
help='number of dataset items to include in the '
'test set, expressed as a fraction.')
self.parser = parser
self.dset_args = dset_args
self.item_args = item_args
def __init__(self):
parser = argparse.ArgumentParser(
description="Visualize convolutional filters of trained model")
parser.add_argument('logdir',
help=('Directory to output visualized filter '
'images to.'))
# trained neural network model
group = parser.add_argument_group('Neural network settings')
net_args.add_network_arg(group, short_alias='n')
net_args.add_model_file_arg(group, short_alias='m', required=True)
packet_args = cargs.PacketArgs(long_alias='packet_dims',
no_EC_dims=True)
packet_args.helpstr = 'Dimensions of packets used for input items'
packet_args.add_packet_arg(group, short_alias='p')
# visualization settings
group = parser.add_argument_group('Visualization settings')
group.add_argument('--start_filter',
default=0,
type=int,
help=('index of first filter to visualize.'))
group.add_argument('--stop_filter',
default=None,
type=int,
help=('index of the filter after the last filter '
'to visualize.'))
group.add_argument('--start_depth',
default=0,
type=int,
help=('first depth index of filters to visualize.'))
group.add_argument('--stop_depth',
default=None,
type=int,
help=('index after the last depth index of filters '
'to visualize.'))
# misc
parser.add_argument('--usecpu',
action='store_true',
help=('Use host CPU instead of the CUDA device. '
'On systems without a dedicated CUDA device '
'and no CUDA-enabled version of tensorflow '
'installed, this flag has no effect.'))
self.packet_args = packet_args
self.parser = parser
def __init__(self):
parser = argparse.ArgumentParser(
description="Evaluate trained network model with given dataset")
parser.add_argument('outfile',
nargs='?',
type=argparse.FileType('w'),
default=sys.stdout,
help=('name of output TSV to write to. If not '
'provided, output to stdout.'))
# dataset input
atype = dargs.arg_type.INPUT
in_aliases = {'dataset name': 'name', 'dataset directory': 'srcdir'}
dset_args = dargs.DatasetArgs(input_aliases=in_aliases)
item_args = dargs.ItemTypeArgs()
group = parser.add_argument_group(title="Input dataset")
dset_args.add_dataset_arg_double(group, atype)
item_args.add_item_type_args(group, atype)
# slice of dataset items to use for evaluation
group.add_argument('--start_item',
default=0,
type=int,
help=('index of first dataset item to use for '
'evaluation.'))
group.add_argument('--stop_item',
default=None,
type=int,
help=('index of the dataset item after the last '
'item to use for evaluation.'))
# trained neural network model
group = parser.add_argument_group('Neural network settings')
net_args.add_network_arg(group, short_alias='n')
net_args.add_model_file_arg(group, short_alias='m', required=True)
# misc
parser.add_argument('--usecpu',
action='store_true',
help=('Use host CPU instead of the CUDA device. '
'On systems without a dedicated CUDA device '
'and no CUDA-enabled version of tensorflow '
'installed, this flag has no effect.'))
self.parser = parser
self.dset_args = dset_args
self.item_args = item_args
def __init__(self):
parser = argparse.ArgumentParser(
description=('Create classification report in HTML format from '
'provided evaluation results in TSV format'))
# input tsv
parser.add_argument('infile',
nargs='?',
type=argparse.FileType('r'),
default=sys.stdin,
help=('name of input TSV to read from. If not '
'provided, read from stdin.'))
# output settings
group = parser.add_argument_group(title="Output settings")
group.add_argument('--tablesize',
type=atypes.int_range(1),
help=('Maximum number of table rows per html '
'report file.'))
group.add_argument('--logdir',
help=('Directory to store output logs. If a '
'non-default directory is used, it must '
'exist prior to calling this script.'))
item_args = dargs.ItemTypeArgs(out_item_prefix='add')
help = {
k: 'add image placeholder for {}'.format(desc)
for k, desc in item_args.item_descriptions.items()
}
item_args.add_item_type_args(group, dargs.arg_type.OUTPUT, help=help)
# meta-information to include in report headers
g_title = "Meta-information to include in report headers"
group = parser.add_argument_group(title=g_title)
nargs.add_network_arg(group, required=False)
nargs.add_model_file_arg(group, required=False)
in_aliases = {'dataset name': 'name', 'dataset directory': 'srcdir'}
dset_args = dargs.DatasetArgs(input_aliases=in_aliases)
dset_args.add_dataset_arg_double(group,
dargs.arg_type.INPUT,
required=False)
self.parser = parser
self.dset_args = dset_args
self.item_args = item_args
def __init__(self):
parser = argparse.ArgumentParser(
description="Visualize hidden layer activations of model using "
"given dataset")
parser.add_argument('logdir',
help=('Directory to output visualized activation '
'images to.'))
# trained neural network model settings
group = parser.add_argument_group('Trained model settings')
net_args.add_network_arg(group, short_alias='n')
net_args.add_model_file_arg(group, short_alias='m', required=True)
# input dataset settings
group = parser.add_argument_group(title="Input dataset")
in_aliases = {'dataset name': 'name', 'dataset directory': 'srcdir'}
dset_args = dargs.DatasetArgs(input_aliases=in_aliases)
dset_args.add_dataset_arg_double(group,
dargs.arg_type.INPUT,
required=True,
dir_default=os.path.curdir)
item_args = dargs.ItemTypeArgs()
item_args.add_item_type_args(group, dargs.arg_type.INPUT)
group.add_argument('--start_item',
default=0,
type=int,
help=('index of first dataset item to use.'))
group.add_argument('--stop_item',
default=None,
type=int,
help=('index of the dataset item after the last '
'item to use.'))
# misc
parser.add_argument('--usecpu',
action='store_true',
help=('Use host CPU instead of the CUDA device. '
'On systems without a dedicated CUDA device '
'and no CUDA-enabled version of tensorflow '
'installed, this flag has no effect.'))
self.dset_args = dset_args
self.item_args = item_args
self.parser = parser
def test_model_arg_missing_when_required_raises_error(self):
parser = argparse.ArgumentParser()
net_args.add_model_file_arg(parser, required=True)
with self.assertRaises(SystemExit) as cm:
args = parser.parse_args([])
self.fail('Failed to raise exception on model_file not passed')