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train_horsecowfine_tf.py
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train_horsecowfine_tf.py
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import argparse
import os
#os.environ['CUDA_VISIBLE_DEVICES'] = '0'
import tensorflow as tf
from deeplabv2_model_horsecowfine import Model_msc
"""
This script defines hyperparameters.
"""
def configure():
flags = tf.app.flags
# training
flags.DEFINE_integer('num_steps', 20000, 'maximum number of iterations')
flags.DEFINE_integer('save_interval', 100, 'number of iterations for saving and visualization')
flags.DEFINE_integer('random_seed', 1234, 'random seed')
flags.DEFINE_float('weight_decay', 0.0005, 'weight decay rate')
flags.DEFINE_float('learning_rate', 2.5e-4, 'learning rate') # 2.5e-4 with bs 10
flags.DEFINE_float('power', 0.9, 'hyperparameter for poly learning rate')
flags.DEFINE_float('momentum', 0.9, 'momentum')
flags.DEFINE_string('encoder_name', 'deeplab', 'name of pre-trained model, res101, res50 or deeplab')
flags.DEFINE_string('pretrain_file', 'see model file', 'pre-trained model filename corresponding to encoder_name')
flags.DEFINE_string('data_list', './lst/pascal_parts_lists_horse_cow_person/horsecow_train.txt', 'training data list filename')
flags.DEFINE_integer('grad_update_every', 10, 'gradient accumulation step')
# Note: grad_update_every = true training batch size
flags.DEFINE_string('crf_type', 'crf', 'Type of crf layer to add. Options are crf, crfSP, crfSPAT, crfSPIO, crfALL, None')
flags.DEFINE_string('dataset', 'horse_fine', 'Name of dataset. Options are pascal_voc12, horse_fine, person_fine, horse_coarse, person_coarse')
# validation
flags.DEFINE_integer('valid_step', 20000, 'checkpoint number for validation')
flags.DEFINE_integer('valid_num_steps', 216, '= number of validation samples')
flags.DEFINE_string('valid_data_list', './lst/pascal_parts_lists_horse_cow_person/horsecow_test.txt', 'validation data list filename')
# prediction / saving outputs for testing or validation
flags.DEFINE_string('out_dir', 'output', 'directory for saving outputs')
flags.DEFINE_integer('test_step', 20000, 'checkpoint number for testing/validation')
flags.DEFINE_integer('test_num_steps', 216, '= number of testing/validation samples')
flags.DEFINE_string('test_data_list', './lst/pascal_parts_lists_horse_cow_person/horsecow_test.txt', 'testing/validation data list filename')
flags.DEFINE_boolean('visual', True, 'whether to save predictions for visualization')
# data
flags.DEFINE_string('data_dir', '/om2/user/cfmata/crfasrnn_keras/data/horsecow_fine_parts', 'data directory')
flags.DEFINE_integer('batch_size', 1, 'training batch size PER GPU')
flags.DEFINE_integer('input_height', 175, 'input image height') # was 321
flags.DEFINE_integer('input_width', 175, 'input image width')
flags.DEFINE_integer('num_classes', 22, 'number of classes')
flags.DEFINE_integer('ignore_label', 255, 'label pixel value that should be ignored')
flags.DEFINE_boolean('random_scale', True, 'whether to perform random scaling data-augmentation')
flags.DEFINE_boolean('random_mirror', True, 'whether to perform random left-right flipping data-augmentation')
# log
flags.DEFINE_string('modeldir', 'model_horsecowfine_crfSP_noEncoder', 'model directory')
flags.DEFINE_string('logfile', 'log.txt', 'training log filename')
flags.DEFINE_string('logdir', 'log', 'training log directory')
flags.FLAGS.__dict__['__parsed'] = False
return flags.FLAGS
def main(_):
parser = argparse.ArgumentParser()
parser.add_argument('--option', dest='option', type=str, default='train',
help='actions: train, test, or predict')
args = parser.parse_args()
if args.option not in ['train', 'test', 'predict']:
print('invalid option: ', args.option)
print("Please input a option: train, test, or predict")
else:
# Set up tf session and initialize variables.
# config = tf.ConfigProto()
# config.gpu_options.allow_growth = True
# sess = tf.Session(config=config)
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
# Run
model = Model_msc(sess, configure())
getattr(model, args.option)()
if __name__ == '__main__':
# Choose which gpu or cpu to use
tf.app.run()