def main():
flags['seed'] = 1234
# Parse Arguments
parser = argparse.ArgumentParser(description='Faster R-CNN Networks Arguments')
parser.add_argument('-n', '--run_num', default=0) # Saves all under /save_directory/model_directory/Model[n]
parser.add_argument('-e', '--epochs', default=1) # Number of epochs for which to train the model
parser.add_argument('-r', '--restore', default=0) # Binary to restore from a model. 0 = No restore.
parser.add_argument('-m', '--model_restore', default=1) # Restores from /save_directory/model_directory/Model[n]
parser.add_argument('-f', '--file_epoch', default=1) # Restore filename: 'part_[f].ckpt.meta'
parser.add_argument('-s', '--slim', default=1) # Binary to restore a TF-Slim Model. 0 = No eval.
parser.add_argument('-t', '--train', default=1) # Binary to train model. 0 = No train.
parser.add_argument('-v', '--eval', default=1) # Binary to evalulate model. 0 = No eval.
parser.add_argument('-y', '--yaml', default='pascal_voc2007.yml') # YAML file to override config defaults
parser.add_argument('-l', '--learn_rate', default=1e-3) # learning Rate
parser.add_argument('-i', '--vis', default=0) # enable visualizations
parser.add_argument('-g', '--gpu', default=0) # specifiy which GPU to use
args = vars(parser.parse_args())
# Set Arguments
flags['run_num'] = int(args['run_num'])
flags['num_epochs'] = int(args['epochs'])
flags['restore_num'] = int(args['model_restore'])
flags['file_epoch'] = int(args['file_epoch'])
if args['restore'] == 0:
flags['restore'] = False
else:
flags['restore'] = True
flags['restore_file'] = 'part_' + str(args['file_epoch']) + '.ckpt.meta'
flags['restore_slim_file'] = None
if args['yaml'] != 'default':
dictionary = cfg_from_file('cfgs/' + args['yaml'])
print('Restoring from %s file' % args['yaml'])
else:
dictionary = []
print('Using Default settings')
flags['learning_rate'] = float(args['learn_rate'])
flags['vis'] = True if (int(args['vis']) == 1) else False
flags['gpu'] = int(args['gpu'])
update_flags()
model = FasterRcnnRes50(flags, dictionary)
if int(args['train']) == 1:
model.train()
if int(args['eval']) == 1:
model.evaluate()
model.close()
def main():
flags['seed'] = 1234
# Parse Arguments
parser = argparse.ArgumentParser(description='Faster R-CNN Networks Arguments')
parser.add_argument('-n', '--run_num', default=0) # Saves all under /save_directory/model_directory/Model[n]
parser.add_argument('-e', '--epochs', default=1) # Number of epochs for which to train the model
parser.add_argument('-r', '--restore', default=0) # Binary to restore from a model. 0 = No restore.
parser.add_argument('-m', '--model_restore', default=1) # Restores from /save_directory/model_directory/Model[n]
parser.add_argument('-f', '--file_epoch', default=1) # Restore filename: 'part_[f].ckpt.meta'
parser.add_argument('-s', '--slim', default=1) # Binary to restore a TF-Slim Model. 0 = No eval.
parser.add_argument('-t', '--train', default=1) # Binary to train model. 0 = No train.
parser.add_argument('-v', '--eval', default=1) # Binary to evalulate model. 0 = No eval.
parser.add_argument('-y', '--yaml', default='pascal_voc2007.yml') # YAML file to override config defaults
parser.add_argument('-l', '--learn_rate', default=1e-3) # learning Rate
parser.add_argument('-i', '--vis', default=0) # enable visualizations
parser.add_argument('-g', '--gpu', default=0) # specifiy which GPU to use
args = vars(parser.parse_args())
# Set Arguments
flags['run_num'] = int(args['run_num'])
flags['num_epochs'] = int(args['epochs'])
flags['restore_num'] = int(args['model_restore'])
flags['file_epoch'] = int(args['file_epoch'])
if args['restore'] == 0:
flags['restore'] = False
else:
flags['restore'] = True
flags['restore_file'] = 'part_' + str(args['file_epoch']) + '.ckpt.meta'
flags['restore_slim_file'] = None
if args['yaml'] != 'default':
dictionary = cfg_from_file('cfgs/' + args['yaml'])
print('Restoring from %s file' % args['yaml'])
else:
dictionary = []
print('Using Default settings')
flags['learning_rate'] = float(args['learn_rate'])
flags['vis'] = True if (int(args['vis']) == 1) else False
flags['gpu'] = int(args['gpu'])
update_flags()
model = FasterRcnnRes50(flags, dictionary)
if int(args['train']) == 1:
model.train()
if int(args['eval']) == 1:
model.evaluate()
model.close()
def main():
# Parse Arguments
parser = argparse.ArgumentParser(description='PASCAL_VOC Arguments')
parser.add_argument('-n', '--year', default='2007')
parser.add_argument(
'-y', '--yaml',
default='pascal_voc2007.yml') # YAML file to override config defaults
args = vars(parser.parse_args())
if args['yaml'] != 'default':
dictionary = cfg_from_file('../../Models/cfgs/' + args['yaml'])
print('Restoring from %s file' % args['yaml'])
else:
dictionary = []
print('Using Default settings')
gen_Annotations_dir(args['year'])
gen_Images_dir(args['year'])
gen_Names_dir(args['year'])