def _setup_opts(argv):
"""Parse inputs."""
FLAGS = gflags.FLAGS
opts = arg_parsing.setup_opts(argv, FLAGS)
return opts
def _setup_opts(argv):
"""Setup default arguments for the arg parser.
returns an opt dictionary with default values setup.
"""
FLAGS = gflags.FLAGS
opts = arg_parsing.setup_opts(argv, FLAGS)
return opts
def _setup_opts(argv):
"""Parse inputs."""
FLAGS = gflags.FLAGS
opts = arg_parsing.setup_opts(argv, FLAGS)
# setup the feature key list. The hdf5 sampler expects each feature key
# to be a list of sub fields. Allowing the hdf5 sampler to traverse
# tree like hdf5 files. This isn't needed in this version of the
# processing, so just wrap each element as a 1 item list.
opts["flags"].feat_keys = [[feat_key]
for feat_key in opts["flags"].feat_keys]
return opts
def _setup_opts(argv):
"""Parse inputs."""
FLAGS = gflags.FLAGS
opts = arg_parsing.setup_opts(argv, FLAGS)
# setup the number iterations per epoch.
with h5py.File(opts["flags"].train_file, "r") as train_data:
num_train_vids = len(train_data["exp_names"])
iter_per_epoch = numpy.ceil(
1.0 * num_train_vids / opts["flags"].mini_batch)
iter_per_epoch = int(iter_per_epoch)
opts["flags"].iter_per_epoch = iter_per_epoch
opts["flags"].total_iterations =\
iter_per_epoch * opts["flags"].total_epochs
return opts
def _setup_opts(argv):
"""Parse inputs."""
FLAGS = gflags.FLAGS
opts = arg_parsing.setup_opts(argv, FLAGS)
# setup the number iterations per epoch.
with h5py.File(opts["flags"].train_file, "r") as train_data:
num_train_vids = len(train_data["exp_names"])
iter_per_epoch =\
np.ceil(1.0 * num_train_vids / opts["flags"].hantman_mini_batch)
iter_per_epoch = int(iter_per_epoch)
opts["flags"].iter_per_epoch = iter_per_epoch
opts["flags"].total_iterations =\
iter_per_epoch * opts["flags"].total_epochs
# convert the frames list into a list of values
opts["flags"].frames = [int(frame) for frame in opts["flags"].frames]
return opts
def _setup_opts(argv):
"""Parse inputs."""
FLAGS = gflags.FLAGS
opts = arg_parsing.setup_opts(argv, FLAGS)
# this is dumb... passing in negative numbers to DEFINE_multi_int doesn't
# seem to work well. So frames will be a string and split off of spaces.
opts["flags"].frames = [
int(frame_num) for frame_num in opts["flags"].frames.split(' ')
]
# setup the number iterations per epoch.
with h5py.File(opts["flags"].train_file, "r") as train_data:
num_train_vids = len(train_data["exp_names"])
iter_per_epoch =\
np.ceil(1.0 * num_train_vids / opts["flags"].hantman_mini_batch)
iter_per_epoch = int(iter_per_epoch)
opts["flags"].iter_per_epoch = iter_per_epoch
opts["flags"].total_iterations =\
iter_per_epoch * opts["flags"].total_epochs
return opts
def _setup_opts(argv):
"""Setup the options."""
FLAGS = gflags.FLAGS
opts = arg_parsing.setup_opts(argv, FLAGS)
return opts
