def parse_command_line():
parser = argparse.ArgumentParser(
description="""Train, validate, and test a face detection classifier that will determine if
two faces are the same or different.""")
parser.add_argument("--test_data", help="Use preTrain model on test data, calcu the accuracy and ROC.", action="store_true")
parser.add_argument("--test_val", help="Use preTrain model on validation data, calcu the accuracy and ROC.", action="store_true")
parser.add_argument("--weights", help="""The trained model weights to use; if not provided
defaults to the network that was just trained""", type=str, default=None)
parser.add_argument("-t", "--threshold", help="The margin of two dense", type=int, default=80)
args = vars(parser.parse_args())
if os.environ.get("CAFFEHOME") == None:
print "You must set CAFFEHOME to point to where Caffe is installed. Example:"
print "export CAFFEHOME=/usr/local/caffe"
exit(1)
# Ensure the random number generator always starts from the same place for consistent tests.
random.seed(0)
lfw = data.Lfw()
lfw.load_data()
lfw.pair_data()
if args["weights"] == None:
args["weights"] = constants.TRAINED_WEIGHTS
if args["test_data"] == True:
test_pairings(lfw, weight_file=args["weights"], is_test=True, threshold=args["threshold"])
elif args["test_val"] == True:
test_pairings(lfw, weight_file=args["weights"], threshold=args["threshold"])
else:
train(True, data=lfw)
def train(output_graphs, data=None, weight_file=None, note=None):
print("Training data, generating graphs: %r" % (output_graphs))
run()
copy_model_definitions()
generate_parsed_logs()
(training_details, validation_details) = parse_logs()
trained_weight_file = get_trained_weight_file()
if output_graphs:
graph.plot_results(training_details, validation_details, note)
# If no weight file is provided by callers to this method, parse out the one we just
# trained.
#if weight_file == None:
# weight_file = trained_weight_file
#predict.test_clusters(data, weight_file)
predict.test_pairings(data, trained_weight_file)
