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import tensorflow as tf
import getopt
import sys
import os
import pprint
pp = pprint.PrettyPrinter(indent=2)
from glob import glob
from utils import get_train_batch, get_test_batch, get_all_clips
import constants as c
from g_model import GeneratorModel
from d_model import DiscriminatorModel
class AVGRunner:
def __init__(self, num_steps, model_load_path, num_test_rec):
"""
Initializes the Adversarial Video Generation Runner.
@param num_steps: The number of training steps to run.
@param model_load_path: The path from which to load a previously-saved model.
Default = None.
@param num_test_rec: The number of recursive generations to produce when testing. Recursive
generations use previous generations as input to predict further into
the future.
"""
self.global_step = 0
self.num_steps = num_steps
self.num_test_rec = num_test_rec
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.Session(config = config)
#self.sess = tf.Session()
self.summary_writer = tf.summary.FileWriter(c.SUMMARY_SAVE_DIR, graph=self.sess.graph)
if c.ADVERSARIAL:
print 'Init discriminator...'
self.d_model = DiscriminatorModel(self.sess,
self.summary_writer,
c.TRAIN_HEIGHT,
c.TRAIN_WIDTH,
c.SCALE_CONV_FMS_D,
c.SCALE_KERNEL_SIZES_D,
c.SCALE_FC_LAYER_SIZES_D)
print 'Init generator...'
self.g_model = GeneratorModel(self.sess,
self.summary_writer,
c.TRAIN_HEIGHT,
c.TRAIN_WIDTH,
c.FULL_HEIGHT,
c.FULL_WIDTH,
c.SCALE_FMS_G,
c.SCALE_KERNEL_SIZES_G)
print 'Init variables...'
self.saver = tf.train.Saver(keep_checkpoint_every_n_hours=2)
self.sess.run(tf.global_variables_initializer())
# if load path specified, load a saved model
if model_load_path is not None:
self.saver.restore(self.sess, model_load_path)
print 'Model restored from ' + model_load_path
def train(self):
"""
Runs a training loop on the model networks.
"""
for i in xrange(self.num_steps):
if c.ADVERSARIAL:
# update discriminator
batch = get_train_batch()
print 'Training discriminator...'
self.d_model.train_step(batch, self.g_model)
# update generator
batch = get_train_batch()
print 'Training generator...'
self.global_step = self.g_model.train_step(
batch, discriminator=(self.d_model if c.ADVERSARIAL else None))
# save the models
if self.global_step % c.MODEL_SAVE_FREQ == 0:
print '-' * 30
print 'Saving models...'
self.saver.save(self.sess,
c.MODEL_SAVE_DIR + 'model.ckpt',
global_step=self.global_step)
print 'Saved models!'
print '-' * 30
# test generator model
if self.global_step % c.TEST_FREQ == 0:
self.test()
def test(self):
"""
Runs one test step on the generator network.
"""
batch = get_test_batch(c.BATCH_SIZE, num_rec_out=self.num_test_rec)
self.g_model.test_batch(
batch, self.global_step, num_rec_out=self.num_test_rec)
def process(self):
"""
Process a directory of images using the generator network.
"""
batch = get_all_clips(c.PROCESS_DIR)
self.g_model.test_batch(
batch, self.global_step, num_rec_out=self.num_test_rec, process_only=True)
def usage():
print 'Options:'
print '-l/--load_path= <Relative/path/to/saved/model>'
print '-t/--test_dir= <Directory of test images>'
print '-p/--process_dir= <Directory to process>'
print '-r/--recursions= <# recursive predictions to make on test>'
print '-a/--adversarial= <{t/f}> (Whether to use adversarial training. Default=True)'
print '-n/--name= <Subdirectory of ../Data/Save/*/ in which to save output of this run>'
print '-s/--steps= <Number of training steps to run> (Default=1000001)'
print '-O/--overwrite (Overwrites all previous data for the model with this save name)'
print '-T/--test_only (Only runs a test step -- no training)'
print '-H/--help (Prints usage)'
print '--stats_freq= <How often to print loss/train error stats, in # steps>'
print '--summary_freq= <How often to save loss/error summaries, in # steps>'
print '--img_save_freq= <How often to save generated images, in # steps>'
print '--test_freq= <How often to test the model on test data, in # steps>'
print '--model_save_freq= <How often to save the model, in # steps>'
def main():
##
# Handle command line input.
##
load_path = None
test_only = False
process_only = False
num_test_rec = 1 # number of recursive predictions to make on test
num_steps = 1000001
try:
opts, _ = getopt.getopt(sys.argv[1:], 'l:t:r:a:n:s:OTH',
['load_path=', 'test_dir=', 'recursions=', 'adversarial=', 'name=',
'steps=', 'overwrite', 'test_only', 'help', 'stats_freq=',
'summary_freq=', 'img_save_freq=', 'test_freq=',
'model_save_freq='])
except getopt.GetoptError:
usage()
sys.exit(2)
for opt, arg in opts:
if opt in ('-l', '--load_path'):
load_path = arg
if opt in ('-t', '--test_dir'):
c.set_test_dir(arg)
if opt in ('-p', '--process_dir'):
c.set_process_dir(arg)
process_only = True
if opt in ('-r', '--recursions'):
num_test_rec = int(arg)
if opt in ('-a', '--adversarial'):
c.ADVERSARIAL = (arg.lower() == 'true' or arg.lower() == 't')
if opt in ('-n', '--name'):
c.set_save_name(arg)
if opt in ('-s', '--steps'):
num_steps = int(arg)
if opt in ('-O', '--overwrite'):
c.clear_save_name()
if opt in ('-H', '--help'):
usage()
sys.exit(2)
if opt in ('-T', '--test_only'):
test_only = True
if opt == '--stats_freq':
c.STATS_FREQ = int(arg)
if opt == '--summary_freq':
c.SUMMARY_FREQ = int(arg)
if opt == '--img_save_freq':
c.IMG_SAVE_FREQ = int(arg)
if opt == '--test_freq':
c.TEST_FREQ = int(arg)
if opt == '--model_save_freq':
c.MODEL_SAVE_FREQ = int(arg)
# set test frame dimensions
assert os.path.exists(c.TEST_DIR)
c.FULL_HEIGHT, c.FULL_WIDTH = c.get_test_frame_dims()
##
# Init and run the predictor
##
runner = AVGRunner(num_steps, load_path, num_test_rec)
if test_only:
runner.test()
elif process_only:
runner.process()
else:
runner.train()
if __name__ == '__main__':
main()
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