def train(self):
predict = self.forward(Mnist.train.image)
#######GP
sess = tf.Session()
with tf.sg_queue_context(sess):
tf.sg_init(sess)
trainf = sess.run([Mnist.train.image])[0]
n, w, h, c = trainf.shape
print trainf.shape
np.savetxt('./image.txt', trainf[1, :, :, 0])
#print trainf[1, :, :, 0]
#plt.imshow(trainf[1, :, :, 0])
#plt.axis('off')
#plt.show()
#print type(trainf[1, :, :, 0])
transfer = np.zeros((n, w, h, c))
for i in range(n):
candi = random.randint(0, n - 1)
#print GP(trainf[i, :, :, 0], trainf[candi, :, :, 0])
#transfer[i, :, :, :] = GP(trainf[i, :, :, :], trainf[candi, :, :, :])
#print trainsfer[i, :, :, :]
t = tf.convert_to_tensor(transfer, dtype=tf.float32)
gp_predict = predict.sg_reuse(input=t)
#print trainf.shape
sess.close()
def sg_print(tensor_list):
r"""Simple tensor printing function for debugging.
Prints the value, shape, and data type of each tensor in the list.
Args:
tensor_list: A list/tuple of tensors or a single tensor.
Returns:
The value of the tensors.
For example,
```python
import sugartensor as tf
a = tf.constant([1.])
b = tf.constant([2.])
out = tf.sg_print([a, b])
# Should print [ 1.] (1,) float32
# [ 2.] (1,) float32
print(out)
# Should print [array([ 1.], dtype=float32), array([ 2.], dtype=float32)]
```
"""
# to list
if type(tensor_list) is not list and type(tensor_list) is not tuple:
tensor_list = [tensor_list]
# evaluate tensor list with queue runner
with tf.Session() as sess:
sg_init(sess)
with tf.sg_queue_context():
res = sess.run(tensor_list)
for r in res:
print(r, r.shape, r.dtype)
return res
def main():
g = ModelGraph()
with tf.Session() as sess:
tf.sg_init(sess)
# restore parameters
saver = tf.train.Saver()
saver.restore(sess, tf.train.latest_checkpoint('asset/train/ckpt'))
hits = 0
num_imgs = 0
with tf.sg_queue_context(sess):
# loop end-of-queue
while True:
try:
logits, y = sess.run([g.logits, g.y]) # (16, 28)
preds = np.squeeze(np.argmax(logits, -1)) # (16,)
hits += np.equal(preds, y).astype(np.int32).sum()
num_imgs += len(y)
print "%d/%d = %.02f" % (hits, num_imgs, float(hits) / num_imgs)
except:
break
print "\nFinal result is\n%d/%d = %.02f" % (hits, num_imgs, float(hits) / num_imgs)
def train(self): # train baseline model
input_ph = tf.placeholder(shape=[batch_size, 28, 28, 1],
dtype=tf.float32)
label_ph = tf.placeholder(shape=[
batch_size,
], dtype=tf.int32)
predict = self.forward(input_ph)
loss_tensor = tf.reduce_mean(predict.sg_ce(target=label_ph))
# use to update network parameters
optim = tf.sg_optim(loss_tensor, optim='Adam', lr=1e-3)
# use saver to save a new model
saver = tf.train.Saver()
sess = tf.Session()
with tf.sg_queue_context(sess):
# inital
tf.sg_init(sess)
# validation
acc = (predict.sg_reuse(
input=Mnist.valid.image).sg_softmax().sg_accuracy(
target=Mnist.valid.label, name='validation'))
tf.sg_train(loss=loss,
eval_metric=[acc],
max_ep=max_ep,
save_dir=save_dir,
ep_size=Mnist.train.num_batch,
log_interval=10)
def test(self):
# predict = self.forward(Mnist.test.image)
# acc = (predict.sg_softmax()
# .sg_accuracy(target=Mnist.test.label, name='test'))
sess = tf.Session()
with tf.sg_queue_context(sess):
tf.sg_init(sess)
testf = sess.run([Mnist.test.image])[0]
# print testf.shape
n, w, h, c = testf.shape
tmp0 = np.zeros((n * w, h))
tmp02 = np.zeros((n * w, h))
tmp05 = np.zeros((n * w, h))
tmp08 = np.zeros((n * w, h))
tmp90 = np.zeros((n * w, h))
tmp_90 = np.zeros((n * w, h))
for i in range(n):
tmp0[i * w : (i + 1) * w, 0 : h] = testf[i, :, :, 0]
tmp02[i * w : (i + 1) * w, 0 : h] = addnoisy(testf[i, :, :, 0], 0.2)
tmp05[i * w : (i + 1) * w, 0 : h] = addnoisy(testf[i, :, :, 0], 0.5)
tmp08[i * w : (i + 1) * w, 0 : h] = addnoisy(testf[i, :, :, 0], 0.8)
tmp90[i * w : (i + 1) * w, 0 : h] = rotate90(testf[i, :, :, 0])
tmp_90[i * w : (i + 1) * w, 0 : h] = rotate_90(testf[i, :, :, 0])# addnoisy(testf[i, :, :, 0], 0.8)
#testf[i, :, :, 0] = addnoisy(testf[i, :, :, 0], 0.0)
#testf[i, :, :, 0] = rotate90(testf[i, :, :, 0])
#testf[i, :, :, 0] = rotate_90(testf[i, :, :, 0])
#print testf[i, :, :, 0]
np.savetxt('./image0.txt', tmp0)
np.savetxt('./image02.txt', tmp02)
np.savetxt('./image05.txt', tmp05)
np.savetxt('./image08.txt', tmp08)
np.savetxt('./image90.txt', tmp90)
np.savetxt('./image_90.txt', tmp_90)
testf_tensor = tf.convert_to_tensor(testf, dtype=tf.float32)
predict = self.forward(testf_tensor)
acc = (predict.sg_softmax()
.sg_accuracy(target=Mnist.test.label, name='test'))
saver=tf.train.Saver()
saver.restore(sess, tf.train.latest_checkpoint(save_dir))
total_accuracy = 0
for i in range(Mnist.test.num_batch):
total_accuracy += np.sum(sess.run([acc])[0])
print('Evaluation accuracy: {}'.format(float(total_accuracy)/(Mnist.test.num_batch*batch_size)))
# close session
sess.close()
def sg_print(tensor_list):
# to list
if type(tensor_list) is not list and type(tensor_list) is not tuple:
tensor_list = [tensor_list]
# evaluate tensor list with queue runner
with tf.Session() as sess:
sg_init(sess)
with tf.sg_queue_context():
res = sess.run(tensor_list)
for r in res:
print r, r.shape, r.dtype
return res
def test(self):
print 'Testing model {}: addnoise={}, rotate={}, var={}'.format(
save_dir, addnoise, rotate, var)
input_ph = tf.placeholder(shape=[batch_size, 28, 28, 1],
dtype=tf.float32)
label_ph = tf.placeholder(shape=[
batch_size,
], dtype=tf.int32)
predict = self.forward(input_ph)
acc = (predict.sg_softmax().sg_accuracy(target=label_ph, name='test'))
sess = tf.Session()
with tf.sg_queue_context(sess):
tf.sg_init(sess)
saver = tf.train.Saver()
saver.restore(sess, tf.train.latest_checkpoint(save_dir))
total_accuracy = 0
for i in range(Mnist.test.num_batch):
[image_array,
label_array] = sess.run([Mnist.test.image, Mnist.test.label])
if addnoise:
image_array[0, :, :, 0] = addnoisy(image_array[0, :, :, 0],
var)
if rotate:
image_array[0, :, :, 0] = rotate_90(image_array[0, :, :,
0])
acc_value = sess.run([acc],
feed_dict={
input_ph: image_array,
label_ph: label_array
})[0]
total_accuracy += np.sum(acc_value)
print 'Evaluation accuracy: {}'.format(
float(total_accuracy) / (Mnist.test.num_batch * batch_size))
# close session
sess.close()
#
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
# init variables
tf.sg_init(sess)
# restore parameters
saver = tf.train.Saver()
saver.restore(sess, tf.train.latest_checkpoint('asset/train'))
# logging
tf.sg_info('Testing started on %s set at global step[%08d].' %
(tf.sg_arg().set.upper(), sess.run(tf.sg_global_step())))
with tf.sg_queue_context():
# create progress bar
iterator = tqdm(range(0, int(data.num_batch * tf.sg_arg().frac)), total=int(data.num_batch * tf.sg_arg().frac),
initial=0, desc='test', ncols=70, unit='b', leave=False)
# batch loop
loss_avg = 0.
for _ in iterator:
# run session
batch_loss = sess.run(loss)
# loss history update
if batch_loss is not None and \
not np.isnan(batch_loss.all()) and not np.isinf(batch_loss.all()):
#
# generator network
with tf.sg_context(name='generator', act='relu', bn=True):
gen = (x.sg_conv(dim=32).sg_conv().sg_conv(
dim=4, act='sigmoid',
bn=False).sg_periodic_shuffle(factor=2).sg_squeeze())
#
# run generator
#
fig_name = 'asset/train/sample1.png'
fig_name2 = 'asset/train/sample2.png'
with tf.Session() as sess:
with tf.sg_queue_context(sess):
tf.sg_init(sess)
# restore parameters
saver = tf.train.Saver()
saver.restore(sess, tf.train.latest_checkpoint('asset/train/ckpt'))
# run generator
gt, low, bicubic, sr = sess.run(
[x.sg_squeeze(), x_nearest, x_bicubic, gen])
# plot result
#sr[0].thumbnail(size, Image.ANTIALIAS)
plt.figure(figsize=(4, 3))
#plt.set_axis_off()
def wrapper(**kwargs):
opt = tf.sg_opt(kwargs)
# default training options
opt += tf.sg_opt(lr=0.001,
save_dir='asset/train',
max_ep=1000,
ep_size=100000,
save_interval=600,
log_interval=60,
early_stop=True,
lr_reset=False,
eval_metric=[],
max_keep=5,
keep_interval=1,
tqdm=True,
console_log=False)
# make directory if not exist
if not os.path.exists(opt.save_dir + '/log'):
os.makedirs(opt.save_dir + '/log')
if not os.path.exists(opt.save_dir + '/ckpt'):
os.makedirs(opt.save_dir + '/ckpt')
# find last checkpoint
last_file = tf.train.latest_checkpoint(opt.save_dir + '/ckpt')
if last_file:
ep = start_ep = int(last_file.split('-')[1]) + 1
start_step = int(last_file.split('-')[2])
else:
ep = start_ep = 1
start_step = 0
# checkpoint saver
saver = tf.train.Saver(max_to_keep=opt.max_keep,
keep_checkpoint_every_n_hours=opt.keep_interval)
# summary writer
summary_writer = tf.train.SummaryWriter(opt.save_dir + '/log',
graph=tf.get_default_graph())
# add learning rate summary
with tf.name_scope('summary'):
tf.scalar_summary('60. learning_rate/learning_rate',
_learning_rate)
# add evaluation metric summary
for m in opt.eval_metric:
tf.sg_summary_metric(m)
# summary op
summary_op = tf.merge_all_summaries()
# create session
if opt.sess:
sess = opt.sess
else:
# session with multiple GPU support
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
# initialize variables
sg_init(sess)
# restore last checkpoint
if last_file:
saver.restore(sess, last_file)
# set learning rate
if start_ep == 1 or opt.lr_reset:
sess.run(_learning_rate.assign(opt.lr))
# logging
tf.sg_info('Training started from epoch[%03d]-step[%d].' %
(start_ep, start_step))
try:
# start data queue runner
with tf.sg_queue_context(sess):
# set session mode to train
tf.sg_set_train(sess)
# loss history for learning rate decay
loss, loss_prev, early_stopped = None, None, False
# time stamp for saving and logging
last_saved = last_logged = time.time()
# epoch loop
for ep in range(start_ep, opt.max_ep + 1):
# show progressbar
if opt.tqdm:
iterator = tqdm(range(opt.ep_size),
desc='train',
ncols=70,
unit='b',
leave=False)
else:
iterator = range(opt.ep_size)
# batch loop
for _ in iterator:
# call train function
batch_loss = func(sess, opt)
# loss history update
if batch_loss is not None:
if loss is None:
loss = np.mean(batch_loss)
else:
loss = loss * 0.9 + np.mean(batch_loss) * 0.1
# saving
if time.time() - last_saved > opt.save_interval:
last_saved = time.time()
saver.save(sess,
opt.save_dir + '/ckpt/model-%03d' % ep,
write_meta_graph=False,
global_step=sess.run(
tf.sg_global_step()))
# logging
if time.time() - last_logged > opt.log_interval:
last_logged = time.time()
# set session mode to infer
tf.sg_set_infer(sess)
# run evaluation op
if len(opt.eval_metric) > 0:
sess.run(opt.eval_metric)
if opt.console_log: # console logging
# log epoch information
tf.sg_info(
'\tEpoch[%03d:lr=%7.5f:gs=%d] - loss = %s'
% (ep, sess.run(_learning_rate),
sess.run(tf.sg_global_step()),
('NA' if loss is None else '%8.6f' %
loss)))
else: # tensorboard logging
# run logging op
summary_writer.add_summary(
sess.run(summary_op),
global_step=sess.run(tf.sg_global_step()))
# learning rate decay
if opt.early_stop and loss_prev:
# if loss stalling
if loss >= 0.95 * loss_prev:
# early stopping
current_lr = sess.run(_learning_rate)
if current_lr < 5e-6:
early_stopped = True
break
else:
# decrease learning rate by half
sess.run(
_learning_rate.assign(current_lr /
2.))
# update loss history
loss_prev = loss
# revert session mode to train
tf.sg_set_train(sess)
# log epoch information
if not opt.console_log:
tf.sg_info(
'\tEpoch[%03d:lr=%7.5f:gs=%d] - loss = %s' %
(ep, sess.run(_learning_rate),
sess.run(tf.sg_global_step()),
('NA' if loss is None else '%8.6f' % loss)))
if early_stopped:
tf.sg_info('\tEarly stopped ( no loss progress ).')
break
finally:
# save last epoch
saver.save(sess,
opt.save_dir + '/ckpt/model-%03d' % ep,
write_meta_graph=False,
global_step=sess.run(tf.sg_global_step()))
# set session mode to infer
tf.sg_set_infer(sess)
# logging
tf.sg_info('Training finished at epoch[%d]-step[%d].' %
(ep, sess.run(tf.sg_global_step())))
# close session
if opt.sess is None:
sess.close()
def wrapper(**kwargs):
r""" Manages arguments of `tf.sg_opt`.
Args:
**kwargs:
lr: A Python Scalar (optional). Learning rate. Default is .001.
eval_metric: A list of tensors containing the value to evaluate. Default is [].
early_stop: Boolean. If True (default), the training should stop when the following two conditions are met.
i. Current loss is less than .95 * previous loss.
ii. Current learning rate is less than 5e-6.
lr_reset: Boolean. If True, learning rate is set to opt.lr. when training restarts.
Otherwise (Default), the value of the stored `_learning_rate` is taken.
save_dir: A string. The root path to which checkpoint and log files are saved.
Default is `asset/train`.
max_ep: A positive integer. Maximum number of epochs. Default is 1000.
ep_size: A positive integer. Number of Total batches in an epoch.
For proper display of log. Default is 1e5.
save_interval: A Python scalar. The interval of saving checkpoint files.
By default, for every 600 seconds, a checkpoint file is written.
log_interval: A Python scalar. The interval of recoding logs.
By default, for every 60 seconds, logging is executed.
max_keep: A positive integer. Maximum number of recent checkpoints to keep. Default is 5.
keep_interval: A Python scalar. How often to keep checkpoints. Default is 1 hour.
tqdm: Boolean. If True (Default), progress bars are shown.
console_log: Boolean. If True, a series of loss will be shown
on the console instead of tensorboard. Default is False.
"""
opt = tf.sg_opt(kwargs)
# default training options
opt += tf.sg_opt(lr=0.001,
save_dir='asset/train',
max_ep=1000, ep_size=100000,
save_interval=600, log_interval=60,
early_stop=True, lr_reset=False,
eval_metric=[],
max_keep=5, keep_interval=1,
tqdm=True, console_log=False)
# make directory if not exist
if not os.path.exists(opt.save_dir):
os.makedirs(opt.save_dir)
# find last checkpoint
last_file = tf.train.latest_checkpoint(opt.save_dir)
if last_file:
ep = start_ep = int(last_file.split('-')[1]) + 1
start_step = int(last_file.split('-')[2])
else:
ep = start_ep = 1
start_step = 0
# checkpoint saver
saver = tf.train.Saver(max_to_keep=opt.max_keep,
keep_checkpoint_every_n_hours=opt.keep_interval)
# summary writer
summary_writer = tf.summary.FileWriter(opt.save_dir, graph=tf.get_default_graph())
# add learning rate summary
tf.summary.scalar('learning_r', _learning_rate)
# add evaluation metric summary
for m in opt.eval_metric:
tf.sg_summary_metric(m)
# summary op
summary_op = tf.summary.merge_all()
# create session
if opt.sess:
sess = opt.sess
else:
# session with multiple GPU support
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
# initialize variables
sg_init(sess)
# restore last checkpoint
if last_file:
saver.restore(sess, last_file)
# set learning rate
if start_ep == 1 or opt.lr_reset:
sess.run(_learning_rate.assign(opt.lr))
# logging
tf.sg_info('Training started from epoch[%03d]-step[%d].' % (start_ep, start_step))
try:
# start data queue runner
with tf.sg_queue_context(sess):
# set session mode to train
tf.sg_set_train(sess)
# loss history for learning rate decay
loss, loss_prev, early_stopped = None, None, False
# time stamp for saving and logging
last_saved = last_logged = time.time()
# epoch loop
for ep in range(start_ep, opt.max_ep + 1):
# show progressbar
if opt.tqdm:
iterator = tqdm(range(opt.ep_size), desc='train', ncols=70, unit='b', leave=False)
else:
iterator = range(opt.ep_size)
# batch loop
for _ in iterator:
# call train function
batch_loss = func(sess, opt)
# loss history update
if batch_loss is not None:
if loss is None:
loss = np.mean(batch_loss)
else:
loss = loss * 0.9 + np.mean(batch_loss) * 0.1
# saving
if time.time() - last_saved > opt.save_interval:
last_saved = time.time()
saver.save(sess, opt.save_dir + '/model-%03d' % ep,
write_meta_graph=False,
global_step=sess.run(tf.sg_global_step()))
# logging
if time.time() - last_logged > opt.log_interval:
last_logged = time.time()
# set session mode to infer
tf.sg_set_infer(sess)
# run evaluation op
if len(opt.eval_metric) > 0:
sess.run(opt.eval_metric)
if opt.console_log: # console logging
# log epoch information
tf.sg_info('\tEpoch[%03d:lr=%7.5f:gs=%d] - loss = %s' %
(ep, sess.run(_learning_rate), sess.run(tf.sg_global_step()),
('NA' if loss is None else '%8.6f' % loss)))
else: # tensorboard logging
# run logging op
summary_writer.add_summary(sess.run(summary_op),
global_step=sess.run(tf.sg_global_step()))
# learning rate decay
if opt.early_stop and loss_prev:
# if loss stalling
if loss >= 0.95 * loss_prev:
# early stopping
current_lr = sess.run(_learning_rate)
if current_lr < 5e-6:
early_stopped = True
break
else:
# decrease learning rate by half
sess.run(_learning_rate.assign(current_lr / 2.))
# update loss history
loss_prev = loss
# revert session mode to train
tf.sg_set_train(sess)
# log epoch information
if not opt.console_log:
tf.sg_info('\tEpoch[%03d:lr=%7.5f:gs=%d] - loss = %s' %
(ep, sess.run(_learning_rate), sess.run(tf.sg_global_step()),
('NA' if loss is None else '%8.6f' % loss)))
if early_stopped:
tf.sg_info('\tEarly stopped ( no loss progress ).')
break
finally:
# save last epoch
saver.save(sess, opt.save_dir + '/model-%03d' % ep,
write_meta_graph=False,
global_step=sess.run(tf.sg_global_step()))
# set session mode to infer
tf.sg_set_infer(sess)
# logging
tf.sg_info('Training finished at epoch[%d]-step[%d].' % (ep, sess.run(tf.sg_global_step())))
# close session
if opt.sess is None:
sess.close()
# generator network
with tf.sg_context(name='generator', act='relu', bn=True):
gen = (x_small
.sg_conv(dim=32)
.sg_conv()
.sg_conv(dim=4, act='sigmoid', bn=False)
.sg_periodic_shuffle(factor=2)
.sg_squeeze())
#
# run generator
#
fig_name = 'asset/train/sample.png'
with tf.Session() as sess:
with tf.sg_queue_context(sess):
tf.sg_init(sess)
# restore parameters
saver = tf.train.Saver()
saver.restore(sess, tf.train.latest_checkpoint('asset/train/ckpt'))
# run generator
gt, low, bicubic, sr = sess.run([x.sg_squeeze(), x_nearest, x_bicubic, gen])
# plot result
_, ax = plt.subplots(10, 12, sharex=True, sharey=True)
for i in range(10):
for j in range(3):
ax[i][j*4].imshow(low[i*3+j], 'gray')
def train_with_GP(self):
input_ph = tf.placeholder(shape=[batch_size, 28, 28, 1],
dtype=tf.float32)
label_ph = tf.placeholder(shape=[
batch_size,
], dtype=tf.int32)
predict = self.forward(input_ph)
loss_tensor = tf.reduce_mean(predict.sg_ce(target=label_ph))
# use to update network parameters
optim = tf.sg_optim(loss_tensor, optim='Adam', lr=1e-3)
# use saver to save a new model
saver = tf.train.Saver()
sess = tf.Session()
with tf.sg_queue_context(sess):
# inital
tf.sg_init(sess)
# train by GP guilding
for e in range(max_ep):
previous_loss = None
for i in range(Mnist.train.num_batch):
[image_array, label_array
] = sess.run([Mnist.train.image, Mnist.train.label])
if (e == 0 or e == 1
): # first and second epoch train no noisy image
loss = sess.run([loss_tensor, optim],
feed_dict={
input_ph: image_array,
label_ph: label_array
})[0]
print 'Baseline loss = ', loss
elif (
e == 2
): # third epoch train with gp image and original image
gpIn1 = np.squeeze(image_array)
gpIn2 = np.zeros((28, 28))
image_gp = GP(gpIn1, gpIn2, seed=0.8)
image_gp2 = image_gp[np.newaxis, ...]
image_gp2 = image_gp2[..., np.newaxis]
loss = sess.run([loss_tensor, optim],
feed_dict={
input_ph: image_array,
label_ph: label_array
})[0]
print 'GP without nosiy loss = ', loss
loss = sess.run([loss_tensor, optim],
feed_dict={
input_ph: image_gp2,
label_ph: label_array
})[0]
print 'GP loss = ', loss
else: # other epoch train with gp evolution
gpIn1 = np.squeeze(image_array)
gpIn2 = np.zeros((28, 28))
image_gp = GP(gpIn1, gpIn2, seed=random.random())
image_gp2 = image_gp[np.newaxis, ...]
image_gp2 = image_gp2[..., np.newaxis]
loss = sess.run([loss_tensor, optim],
feed_dict={
input_ph: image_array,
label_ph: label_array
})[0]
print 'GP without nosiy loss = ', loss
loss = sess.run([loss_tensor, optim],
feed_dict={
input_ph: image_gp2,
label_ph: label_array
})[0]
print 'GP loss = ', loss
if loss < previous_loss:
for i in range(5):
loss = sess.run([loss_tensor, optim],
feed_dict={
input_ph: image_gp2,
label_ph: label_array
})[0]
gpIn1 = image_gp2
image_gp2[0, :, :,
0] = GP(gpIn1[0, :, :, 0],
gpIn2,
seed=random.random())
print 'GP EV loss = ', loss
previous_loss = loss
saver.save(sess,
os.path.join(save_dir, 'gp_model'),
global_step=e)
# close session
sess.close()
#
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
# init variables
tf.sg_init(sess)
# restore parameters
saver = tf.train.Saver()
saver.restore(sess, tf.train.latest_checkpoint('asset/train'))
# logging
tf.sg_info('Testing started on %s set at global step[%08d].' %
(tf.sg_arg().set.upper(), sess.run(tf.sg_global_step())))
with tf.sg_queue_context():
# create progress bar
iterator = tqdm(range(0, int(data.num_batch * tf.sg_arg().frac)), total=int(data.num_batch * tf.sg_arg().frac),
initial=0, desc='test', ncols=70, unit='b', leave=False)
# batch loop
loss_avg = 0.
for _ in iterator:
# run session
batch_loss = sess.run(loss)
# loss history update
if batch_loss is not None and \
not np.isnan(batch_loss.all()) and not np.isinf(batch_loss.all()):
def sg_print(tensor):
with tf.Session() as sess:
sg_init(sess)
with tf.sg_queue_context():
res = sess.run(tensor)
print res, res.shape, res.dtype
def main(argv):
# set log level to debug
tf.sg_verbosity(10)
#
# hyper parameters
#
size = 160, 147
batch_size = 1 # batch size
#
# inputs
#
pngName = argv
png = tf.read_file(pngName)
#png.thumbnail(size, Image.ANTIALIAS)
#png = tf.resize(png1, (14,14))
myPNG = tf.image.decode_png(png)
y = convert_image(pngName)
x = tf.reshape(y, [1, 28, 28, 1])
print(x)
# corrupted image
x_small = tf.image.resize_bicubic(x, (14, 14))
x_bicubic = tf.image.resize_bicubic(x_small, (28, 28)).sg_squeeze()
x_nearest = tf.image.resize_images(
x_small, (28, 28),
tf.image.ResizeMethod.NEAREST_NEIGHBOR).sg_squeeze()
#
# create generator
#
# I've used ESPCN scheme
# http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Shi_Real-Time_Single_Image_CVPR_2016_paper.pdf
#
# generator network
with tf.sg_context(name='generator', act='relu', bn=True):
gen = (x.sg_conv(dim=32).sg_conv().sg_conv(
dim=4, act='sigmoid',
bn=False).sg_periodic_shuffle(factor=2).sg_squeeze())
#
# run generator
#
fileName = "inPython.png"
fig_name = "genImages/" + fileName
#fig_name2 = 'asset/train/sample2.png'
print("start")
with tf.Session() as sess:
with tf.sg_queue_context(sess):
tf.sg_init(sess)
# restore parameters
saver = tf.train.Saver()
#saver.restore(sess, tf.train.latest_checkpoint('asset/train/ckpt'))
saver.restore(
sess, tf.train.latest_checkpoint('python/asset/train/ckpt'))
# run generator
gt, low, bicubic, sr = sess.run(
[x.sg_squeeze(), x_nearest, x_bicubic, gen])
# plot result
#sr[0].thumbnail(size, Image.ANTIALIAS)
plt.figure(figsize=(1, 1))
#plt.set_axis_off()
hr = plt.imshow(sr[0], 'gray')
plt.axis('tight')
plt.axis('off')
#ax.set_axis_off()
#ax.thumbnail(size, Image.ANTIALIAS)
#plt.savefig(fig_name,bbox_inches='tight',pad_inches=0,dpi=600)
plt.savefig(fig_name, dpi=600)
#tf.sg_info('Sample image saved to "%s"' % fig_name)
plt.close()
##print (type (sr[0]))
##sourceImage = Image.fromarray(np.uint8(sr[0])
print("done")
