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
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...'
c.FULL_HEIGHT = 120
c.FULL_WIDTH = 160
self.g_model = GeneratorModel(self.sess,
self.summary_writer,
c.TRAIN_HEIGHT,
c.TRAIN_WIDTH,
c.FULL_HEIGHT,
c.FULL_WIDTH,
c.SCALE_FMS_GE,
c.SCALE_FMS_GD,
c.SCALE_KERNEL_SIZES_GE,
c.SCALE_KERNEL_SIZES_GD)
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:
print('\n------------------------------------')
print('loadpath: ' + model_load_path)
print('------------------------------------\n')
self.saver.restore(self.sess, model_load_path)
print 'Model restored from ' + model_load_path
def __init__(self, num_steps, model_load_path):
"""
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.
"""
self.global_step = 0
self.num_steps = num_steps
self.sess = tf.Session()
#self.sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=True))
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.summary_writer.add_graph(self.sess.graph)
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)
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)
#self.summary_writer = tf.train.SummaryWriter(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.
"""
#step = 0
dirs = sorted(glob(os.path.join(c.TEST_DIR, '*')))
for num in range(len(dirs) / c.BATCH_SIZE + 1):
ep_dirs = dirs[num * c.BATCH_SIZE:(num + 1) * c.BATCH_SIZE]
batch = get_test_batch_re_id(c.BATCH_SIZE,
ep_dirs,
num_rec_out=self.num_test_rec)
#print(batch)
self.g_model.test_batch(batch,
ep_dirs,
self.global_step,
num,
num_rec_out=self.num_test_rec)
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
self.sess = tf.Session()
self.summary_writer = SummaryWriter(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 range(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)
class AVGRunner:
def __init__(self, num_steps, model_load_path):
"""
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.
"""
self.global_step = 0
self.num_steps = num_steps
self.sess = tf.Session()
#self.sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=True))
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.summary_writer.add_graph(self.sess.graph)
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.
"""
np.random.shuffle(c.TEST_EXAMPLES)
np.random.shuffle(c.TRAIN_EXAMPLES)
examples_count = 0
num_epoch = 0
print('EPOCH - ' + str(num_epoch))
for i in range(self.num_steps):
if c.ADVERSARIAL:
# update discriminator
batch = get_train_batch(examples_count)
#print('Training discriminator...')
self.d_model.train_step(batch, self.g_model)
# update generator
batch = get_train_batch(examples_count)
examples_count += c.BATCH_SIZE
#print('Training generator...')
self.global_step = self.g_model.train_step(
batch, discriminator=(self.d_model if c.ADVERSARIAL else None))
#test batch each 'epoch'
if examples_count >= c.NUM_CLIPS:
np.random.shuffle(c.TRAIN_EXAMPLES)
examples_count = 0
self.test(c.TEST_BATCH_SIZE,
full=True) #bsize = c.NUM_TEST_CLIPS,full=True)
num_epoch += 1
print('EPOCH - ' + str(num_epoch))
# 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, bsize=c.BATCH_SIZE, full=False):
"""
Runs one test step on the generator network.
"""
'''
batch = get_test_batch(c.BATCH_SIZE)
'''
batch = np.empty(
[bsize, c.FULL_HEIGHT, c.FULL_WIDTH, (3 * (c.HIST_LEN + 1))],
dtype=np.float32)
if full:
# can be very memory hungry
if c.TEST_CLIPS_FULL.size == 0:
c.TEST_CLIPS_FULL = np.empty([
c.NUM_TEST_CLIPS, c.FULL_HEIGHT, c.FULL_WIDTH,
(3 * (c.HIST_LEN + 1))
],
dtype=np.float32)
for i in range(c.NUM_TEST_CLIPS):
path = c.TEST_EXAMPLES[i]
clip = np.load(path)['arr_0']
c.TEST_CLIPS_FULL[i] = clip
offset = np.random.choice(np.arange(c.NUM_TEST_CLIPS - bsize))
batch = c.TEST_CLIPS_FULL[offset:(offset + bsize), :, :, :]
else:
offset = np.random.choice(np.arange(c.NUM_TEST_CLIPS - bsize))
for i in range(bsize):
#path = c.TEST_DIR + str(np.random.choice(c.NUM_TEST_CLIPS)) + '.npz'
path = c.TEST_EXAMPLES[offset + i]
clip = np.load(path)['arr_0']
batch[i] = clip
self.g_model.test_batch(batch, self.global_step)
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
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.
"""
delta = 45.6
adv_windowed_list = []
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...'
if (len(adv_windowed_list) > (c.WINDOW_SIZE - 1)):
adv_windowed_list.pop(0)
if not adv_windowed_list:
self.global_step, adv_windowed_list = self.g_model.train_step(
batch,
adv_windowed_list,
discriminator=(self.d_model if c.ADVERSARIAL else None))
start = True
if (sum(adv_windowed_list) / len(adv_windowed_list) <=
delta / c.WINDOW_SIZE):
self.global_step, adv_windowed_list = self.g_model.train_step(
batch,
adv_windowed_list,
discriminator=(self.d_model if c.ADVERSARIAL else None))
counter = 0
while (sum(adv_windowed_list) / len(adv_windowed_list) >
delta / c.WINDOW_SIZE):
print(
sum(adv_windowed_list) / len(adv_windowed_list),
delta / c.WINDOW_SIZE)
if not start:
adv_windowed_list.pop(len(adv_windowed_list) - 1)
self.global_step, adv_windowed_list = self.g_model.train_step(
batch,
adv_windowed_list,
discriminator=(self.d_model if c.ADVERSARIAL else None))
counter += 1
start = False
print(counter)
if counter >= 10:
break
print("Out of loop")
# 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()
#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)
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
self.sess = tf.Session()
self.summary_writer = tf.train.SummaryWriter(c.SUMMARY_SAVE_DIR,
graph=self.sess.graph)
# self.summary_writer = tf.train.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:
print model_load_path
self.model_name = model_load_path.split("/")[-1]
print self.model_name
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, blurs = get_recursive_train_batch()
#batch = get_recursive_train_batch()
if check_nan_inf(batch): sys.exit()
print 'Training discriminator...'
self.d_model.train_step(batch, blurs, self.g_model)
# update generator
batch, blurs = get_recursive_train_batch()
# batch = get_recursive_train_batch()
if check_nan_inf(batch): sys.exit()
print 'Training generator...'
self.global_step = self.g_model.train_step(
batch,
blurs,
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_recursive_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 test_original(self):
# print "Hi"
test_dir_len = len(os.listdir(c.TEST_ORIGINAL_DIR))
print test_dir_len
model_no = int(self.model_name.split("-")[-1])
psnr_list, sharpdiff_list = [], []
for i in range(0, test_dir_len, c.BATCH_SIZE):
if i + c.BATCH_SIZE < test_dir_len:
print i, i + c.BATCH_SIZE
batch = get_original_test_batch(c.TEST_ORIGINAL_DIR,
c.BATCH_SIZE, i,
i + c.BATCH_SIZE)
psnr, sharpdiff = self.g_model.test_batch(
batch,
self.global_step,
num_rec_out=self.num_test_rec,
save_imgs=False,
test_only=True,
model_no=model_no,
start=i)
print psnr, sharpdiff
psnr_list.append(psnr)
sharpdiff_list.append(sharpdiff)
psnr_list = np.array(psnr_list)
sharpdiff_list = np.array(sharpdiff_list)
psnr_mean = np.mean(psnr_list)
sharpdiff_mean = np.mean(sharpdiff_list)
print psnr_mean, sharpdiff_mean
f = open("testing.txt", 'a')
f.write(
str(self.model_name) + "\t" + str(psnr_mean) + "\t" +
str(sharpdiff_mean))
f.close()
