def main(_):
if FLAGS.input_width is None:
FLAGS.input_width = FLAGS.input_height
if FLAGS.output_width is None:
FLAGS.output_width = FLAGS.output_height
file_name = time.strftime("%Y_%m_%d_%H%M", time.localtime())
if FLAGS.unreg:
file_name += "_unreg_dcgan"
else:
file_name += "_regularized_dcgan_" + str(FLAGS.gamma) + "gamma"
if FLAGS.annealing:
file_name += "_annealing_" + str(FLAGS.decay_factor) + "decayfactor"
if FLAGS.rmsprop:
file_name += "_rmsprop"
else:
file_name += "_adam"
file_name += "_" + str(FLAGS.disc_update_steps) + "dsteps"
file_name += "_" + str(FLAGS.disc_learning_rate) + "dlnr"
file_name += "_" + str(FLAGS.gen_learning_rate) + "glnr"
file_name += "_" + str(FLAGS.epochs) + "epochs"
file_name += "_" + str(FLAGS.dataset)
log_dir = os.path.abspath(os.path.join(FLAGS.root_dir, file_name))
if not os.path.exists(log_dir):
os.makedirs(log_dir)
pp.pprint(flags.FLAGS.__flags)
with tf.Session() as sess:
dcgan = DCGAN(sess, log_dir, FLAGS)
show_all_variables()
if FLAGS.checkpoint_dir is not "None":
if not dcgan.load_ckpt()[0]:
raise Exception("[!] ERROR: provide valid checkpoint_dir")
else:
starttime = time.time()
dcgan.train(FLAGS)
endtime = time.time()
print('Total Train Time: {:.2f}'.format(endtime - starttime))
dcgan.generate(FLAGS, option=1)
file = open(os.path.join(log_dir, "flags.json"), 'a')
json.dump(vars(FLAGS), file)
file.close()
def main(_):
STARTED_DATESTRING = "{0:%Y-%m-%dT%H:%M:%S}".format(
datetime.now()).replace(":", "-")
pp.pprint(flags.FLAGS.__flags)
assert FLAGS.mode.lower() in (
'train', 'generate'), "mode must be 'train' or 'generate'!"
FLAGS.mode = FLAGS.mode.lower()
if FLAGS.mode == 'train':
if FLAGS.out_dir is None:
FLAGS.out_dir = 'out/train_' + STARTED_DATESTRING
print('Using default out_dir {0}'.format(FLAGS.out_dir))
else:
if FLAGS.out_dir.endswith('/'): FLAGS.out_dir = FLAGS.out_dir[:-1]
if FLAGS.checkpoint_dir is None:
FLAGS.checkpoint_dir = FLAGS.out_dir + '/checkpoint'
else:
if FLAGS.checkpoint_dir is None:
raise Exception(
'Cannot generate: checkpoint {0} does not exist!'.format(
FLAGS.checkpoint_dir))
else:
if FLAGS.checkpoint_dir.endswith('/'):
FLAGS.checkpoint_dir = FLAGS.checkpoint_dir[:-1]
if FLAGS.out_dir is None:
FLAGS.out_dir = 'out/gene_' + STARTED_DATESTRING
if not os.path.exists(FLAGS.out_dir):
os.makedirs(FLAGS.out_dir)
#import IPython; IPython.embed()
if FLAGS.mode == 'train':
os.makedirs(FLAGS.out_dir + '/samples')
os.makedirs(FLAGS.out_dir + '/checkpoint')
os.makedirs(FLAGS.out_dir + '/logs')
if FLAGS.audio_params is None:
if FLAGS.mode == 'train':
FLAGS.audio_params = './audio_params.json'
copyfile(FLAGS.audio_params,
FLAGS.checkpoint_dir + '/audio_params.json')
else:
print('Using json file from {0}'.format(FLAGS.checkpoint_dir))
FLAGS.audio_params = FLAGS.checkpoint_dir + '/audio_params.json'
with tf.Session(config=tf.ConfigProto(log_device_placement=False)) as sess:
#G
if FLAGS.dataset == 'wav':
with open('audio_params.json', 'r') as f:
audio_params = json.load(f)
FLAGS.epoch = audio_params['epoch']
FLAGS.learning_rate = audio_params['learning_rate']
FLAGS.beta1 = audio_params['beta1']
FLAGS.sample_length = audio_params['sample_length']
dcgan = DCGAN(sess,
batch_size=FLAGS.batch_size,
z_dim=audio_params['z_dim'],
sample_length=FLAGS.sample_length,
c_dim=1,
dataset_name=FLAGS.dataset,
audio_params=FLAGS.audio_params,
data_dir=FLAGS.data_dir,
use_disc=FLAGS.use_disc,
use_fourier=FLAGS.use_fourier,
run_g=FLAGS.run_g,
checkpoint_dir=FLAGS.checkpoint_dir,
out_dir=FLAGS.out_dir,
mode=FLAGS.mode)
else:
raise Exception('dataset not understood')
if FLAGS.mode == 'train':
dcgan.train(FLAGS)
else:
print('Generating {0} batches of size {1} from checkpoint {2}'.
format(FLAGS.gen_size, FLAGS.batch_size,
FLAGS.checkpoint_dir))
dcgan.load(FLAGS.checkpoint_dir)
dcgan.generate(FLAGS)
if FLAGS.visualize:
to_json("./web/js/layers.js",
[dcgan.h0_w, dcgan.h0_b, dcgan.g_bn0],
[dcgan.h1_w, dcgan.h1_b, dcgan.g_bn1],
[dcgan.h2_w, dcgan.h2_b, dcgan.g_bn2],
[dcgan.h3_w, dcgan.h3_b, dcgan.g_bn3],
[dcgan.h4_w, dcgan.h4_b, None])
# Below is codes for visualization
OPTION = 2
visualize(sess, dcgan, FLAGS, OPTION)
