def main(unused_argv=None):
tf.logging.set_verbosity(tf.logging.INFO)
if not tf.gfile.Exists(FLAGS.output_dir):
tf.gfile.MkDir(FLAGS.output_dir)
with tf.Graph().as_default(), tf.Session() as sess:
# Defines place holder for the style image.
style_img_ph = tf.placeholder(tf.float32, shape=[None, None, 3])
if FLAGS.style_square_crop:
style_img_preprocessed = image_utils.center_crop_resize_image(
style_img_ph, FLAGS.style_image_size)
else:
style_img_preprocessed = image_utils.resize_image(
style_img_ph, FLAGS.style_image_size)
# Defines place holder for the content image.
content_img_ph = tf.placeholder(tf.float32, shape=[None, None, 3])
if FLAGS.content_square_crop:
content_img_preprocessed = image_utils.center_crop_resize_image(
content_img_ph, FLAGS.image_size)
else:
content_img_preprocessed = image_utils.resize_image(
content_img_ph, FLAGS.image_size)
# Defines the model.
stylized_images, _, _, bottleneck_feat = build_model.build_model(
content_img_preprocessed,
style_img_preprocessed,
trainable=False,
is_training=False,
inception_end_point='Mixed_6e',
style_prediction_bottleneck=100,
adds_losses=False)
if tf.gfile.IsDirectory(FLAGS.checkpoint):
checkpoint = tf.train.latest_checkpoint(FLAGS.checkpoint)
else:
checkpoint = FLAGS.checkpoint
tf.logging.info(
'loading latest checkpoint file: {}'.format(checkpoint))
init_fn = slim.assign_from_checkpoint_fn(
checkpoint, slim.get_variables_to_restore())
sess.run([tf.local_variables_initializer()])
init_fn(sess)
# Gets the list of the input style images.
style_img_list = tf.gfile.Glob(FLAGS.style_images_paths)
if len(style_img_list) > FLAGS.maximum_styles_to_evaluate:
np.random.seed(1234)
style_img_list = np.random.permutation(style_img_list)
style_img_list = style_img_list[:FLAGS.maximum_styles_to_evaluate]
# Gets list of input content images.
content_img_list = tf.gfile.Glob(FLAGS.content_images_paths)
for content_i, content_img_path in enumerate(content_img_list):
content_img_np = image_utils.load_np_image_uint8(
content_img_path)[:, :, :3]
content_img_name = os.path.basename(content_img_path)[:-4]
# Saves preprocessed content image.
inp_img_croped_resized_np = sess.run(
content_img_preprocessed,
feed_dict={content_img_ph: content_img_np})
image_utils.save_np_image(
inp_img_croped_resized_np,
os.path.join(FLAGS.output_dir, '%s.jpg' % (content_img_name)))
# Computes bottleneck features of the style prediction network for the
# identity transform.
identity_params = sess.run(
bottleneck_feat, feed_dict={style_img_ph: content_img_np})
for style_i, style_img_path in enumerate(style_img_list):
if style_i > FLAGS.maximum_styles_to_evaluate:
break
style_img_name = os.path.basename(style_img_path)[:-4]
style_image_np = image_utils.load_np_image_uint8(
style_img_path)[:, :, :3]
if style_i % 10 == 0:
tf.logging.info(
'Stylizing (%d) %s with (%d) %s' %
(content_i, content_img_name, style_i, style_img_name))
# Saves preprocessed style image.
style_img_croped_resized_np = sess.run(
style_img_preprocessed,
feed_dict={style_img_ph: style_image_np})
image_utils.save_np_image(
style_img_croped_resized_np,
os.path.join(FLAGS.output_dir,
'%s.jpg' % (style_img_name)))
# Computes bottleneck features of the style prediction network for the
# given style image.
style_params = sess.run(
bottleneck_feat, feed_dict={style_img_ph: style_image_np})
interpolation_weights = ast.literal_eval(
FLAGS.interpolation_weights)
# Interpolates between the parameters of the identity transform and
# style parameters of the given style image.
for interp_i, wi in enumerate(interpolation_weights):
stylized_image_res = sess.run(
stylized_images,
feed_dict={
bottleneck_feat:
identity_params * (1 - wi) + style_params * wi,
content_img_ph:
content_img_np
})
# Saves stylized image.
image_utils.save_np_image(
stylized_image_res,
os.path.join(
FLAGS.output_dir, '%s_stylized_%s_%d.jpg' %
(content_img_name, style_img_name, interp_i)))
def main(unused_argv=None):
tf.logging.set_verbosity(tf.logging.INFO)
with tf.Graph().as_default():
# Forces all input processing onto CPU in order to reserve the GPU for the
# forward inference and back-propagation.
device = '/cpu:0' if not FLAGS.ps_tasks else '/job:worker/cpu:0'
with tf.device(
tf.train.replica_device_setter(FLAGS.ps_tasks,
worker_device=device)):
# Loads content images.
content_inputs_, _ = image_utils.imagenet_inputs(
FLAGS.batch_size, FLAGS.image_size)
# Loads style images.
[style_inputs_, _,
style_inputs_orig_] = image_utils.arbitrary_style_image_inputs(
FLAGS.style_dataset_file,
batch_size=FLAGS.batch_size,
image_size=FLAGS.image_size,
shuffle=True,
center_crop=FLAGS.center_crop,
augment_style_images=FLAGS.augment_style_images,
random_style_image_size=FLAGS.random_style_image_size)
with tf.device(tf.train.replica_device_setter(FLAGS.ps_tasks)):
# Process style and content weight flags.
content_weights = ast.literal_eval(FLAGS.content_weights)
style_weights = ast.literal_eval(FLAGS.style_weights)
# Define the model
stylized_images, total_loss, loss_dict, _ = build_model.build_model(
content_inputs_,
style_inputs_,
trainable=True,
is_training=True,
inception_end_point='Mixed_6e',
style_prediction_bottleneck=100,
adds_losses=True,
content_weights=content_weights,
style_weights=style_weights,
total_variation_weight=FLAGS.total_variation_weight)
# Adding scalar summaries to the tensorboard.
for key, value in loss_dict.iteritems():
tf.summary.scalar(key, value)
# Adding Image summaries to the tensorboard.
tf.summary.image('image/0_content_inputs', content_inputs_, 3)
tf.summary.image('image/1_style_inputs_orig', style_inputs_orig_,
3)
tf.summary.image('image/2_style_inputs_aug', style_inputs_, 3)
tf.summary.image('image/3_stylized_images', stylized_images, 3)
# Set up training
optimizer = tf.train.AdamOptimizer(FLAGS.learning_rate)
train_op = slim.learning.create_train_op(
total_loss,
optimizer,
clip_gradient_norm=FLAGS.clip_gradient_norm,
summarize_gradients=False)
# Function to restore VGG16 parameters.
init_fn_vgg = slim.assign_from_checkpoint_fn(
vgg.checkpoint_file(), slim.get_variables('vgg_16'))
# Function to restore Inception_v3 parameters.
inception_variables_dict = {
var.op.name: var
for var in slim.get_model_variables('InceptionV3')
}
init_fn_inception = slim.assign_from_checkpoint_fn(
FLAGS.inception_v3_checkpoint, inception_variables_dict)
# Function to restore VGG16 and Inception_v3 parameters.
def init_sub_networks(session):
init_fn_vgg(session)
init_fn_inception(session)
# Run training
slim.learning.train(train_op=train_op,
logdir=os.path.expanduser(FLAGS.train_dir),
master=FLAGS.master,
is_chief=FLAGS.task == 0,
number_of_steps=FLAGS.train_steps,
init_fn=init_sub_networks,
save_summaries_secs=FLAGS.save_summaries_secs,
save_interval_secs=FLAGS.save_interval_secs)
def main(unused_argv=None):
print('timer start')
start = time.time()
words = [
'布', '植物', 'ガラス', '革', '金属', '紙', 'プラスチック', '石', '水', '木', '樹脂',
'アクリル', 'アルミニウム', '牛皮', 'レンガ', '絹'
]
config = tf.ConfigProto(gpu_options=tf.GPUOptions(allow_growth=True))
sess = tf.Session(config=config)
tf.logging.set_verbosity(tf.logging.INFO)
if not tf.gfile.Exists(FLAGS.output_dir):
tf.gfile.MkDir(FLAGS.output_dir)
with tf.Graph().as_default(), sess:
# Defines place holder for the style image.
style_img_ph = tf.placeholder(tf.float32, shape=[None, None, 3])
if FLAGS.style_square_crop:
style_img_preprocessed = image_utils.center_crop_resize_image(
style_img_ph, FLAGS.style_image_size)
else:
style_img_preprocessed = image_utils.resize_image(
style_img_ph, FLAGS.style_image_size)
# Defines place holder for the content image.
content_img_ph = tf.placeholder(tf.float32, shape=[None, None, 3])
if FLAGS.content_square_crop:
content_img_preprocessed = image_utils.center_crop_resize_image(
content_img_ph, FLAGS.image_size)
else:
content_img_preprocessed = image_utils.resize_image(
content_img_ph, FLAGS.image_size)
# Defines the model.
stylized_images, _, _, bottleneck_feat = build_model.build_model(
content_img_preprocessed,
style_img_preprocessed,
trainable=False,
is_training=False,
inception_end_point='Mixed_6e',
style_prediction_bottleneck=100,
adds_losses=False)
if tf.gfile.IsDirectory(FLAGS.checkpoint):
checkpoint = tf.train.latest_checkpoint(FLAGS.checkpoint)
else:
checkpoint = FLAGS.checkpoint
tf.logging.info(
'loading latest checkpoint file: {}'.format(checkpoint))
init_fn = slim.assign_from_checkpoint_fn(
checkpoint, slim.get_variables_to_restore())
sess.run([tf.local_variables_initializer()])
init_fn(sess)
# Gets the list of the input style images.
style_img_list = tf.gfile.Glob(FLAGS.style_images_paths)
if len(style_img_list) > FLAGS.maximum_styles_to_evaluate:
np.random.seed(1234)
style_img_list = np.random.permutation(style_img_list)
style_img_list = style_img_list[:FLAGS.maximum_styles_to_evaluate]
# Gets list of input content images.
content_img_list = tf.gfile.Glob(FLAGS.content_images_paths)
j = -1
for content_i, content_img_path in enumerate(content_img_list):
j += 1
content_img_np = image_utils.load_np_image_uint8(
content_img_path)[:, :, :3]
content_img_name = os.path.basename(content_img_path)[:-4]
# Saves preprocessed content image.
inp_img_croped_resized_np = sess.run(
content_img_preprocessed,
feed_dict={content_img_ph: content_img_np})
image_utils.save_np_image(
inp_img_croped_resized_np,
os.path.join(FLAGS.output_dir, '%s.jpg' % (content_img_name)))
if FLAGS.color_preserve is True:
print('color preserve mode!')
# convert content iamge to ycc from bgr
height, width, channels = inp_img_croped_resized_np[
0].shape[:3]
# print(inp_img_croped_resized_np)
wgap = 4 - (
width % 4
) # fuking translater made some gaps because of decoder
hgap = 4 - (height % 4)
inp_img_croped_resized_np = inp_img_croped_resized_np * 255
content_img_np_ycc = cv2.cvtColor(inp_img_croped_resized_np[0],
cv2.COLOR_RGB2YCR_CB)
# print(content_img_np_ycc)
zeros = np.zeros((height, width), content_img_np_ycc.dtype)
zeros = zeros + 128 # YCC's zero is center of 255
tmp = cv2.cvtColor(content_img_np_ycc, cv2.COLOR_YCR_CB2BGR)
cv2.imwrite("gray.jpg", tmp)
# print(zeros)
Ycontent, Crcontent, Cbcontent = cv2.split(content_img_np_ycc)
# print(Ycontent.shape, Crcontent.shape, Cbcontent.shape, zeros.shape)
# print(Crcontent)
# print(content_img_np_ycc)
# content_img_np_ycc_y = cv2.merge((Y, zeros, zeros))
# content_img_np_gry = cv2.cvtColor(content_img_np_ycc_y, cv2.COLOR_YCR_CB2RGB)
# print(content_img_np_gry)
# cv2.imwrite("gray.jpg", content_img_np_gry)
# print(np.shape(content_img_np))
# content_img_np = content_img_np_gry
# Computes bottleneck features of the style prediction network for the
# identity transform.
identity_params = sess.run(
bottleneck_feat, feed_dict={style_img_ph: content_img_np})
i = 0
for word in words:
# word = words[i]
print(word)
i += 1
# if style_i > FLAGS.maximum_styles_to_evaluate:
# break
# style_img_name = os.path.basename(style_img_path)[:-4]
# style_image_np = image_utils.load_np_image_uint8(style_img_path)[:, :, :
# 3]
# if style_i % 10 == 0:
# tf.logging.info('Stylizing (%d) %s with (%d) %s' %
# (content_i, content_img_name, style_i,
# style_img_name))
# Saves preprocessed style image.
# style_img_croped_resized_np = sess.run(
# style_img_preprocessed, feed_dict={
# style_img_ph: style_image_np
# })
# image_utils.save_np_image(style_img_croped_resized_np,
# os.path.join(FLAGS.output_dir,
# '%s.jpg' % (style_img_name)))
# Computes bottleneck features of the style prediction network for the
# given style image.
# style_params_ori = sess.run(
# bottleneck_feat, feed_dict={style_img_ph: style_image_np})
# print(np.shape(style_params))
picklename = 'params/{}_{}.pickle'.format(word, j)
f = open(picklename, 'r')
style_params = pickle.load(f)
# print(style_params)
# print('diff of original para and made para:')
# print(style_params_ori - style_params)
interpolation_weights = ast.literal_eval(
FLAGS.interpolation_weights)
# Interpolates between the parameters of the identity transform and
# style parameters of the given style image.
for interp_i, wi in enumerate(interpolation_weights):
stylized_image_res = sess.run(
stylized_images,
feed_dict={
bottleneck_feat:
identity_params * (1 - wi) + style_params * wi,
content_img_ph:
content_img_np
})
if FLAGS.color_preserve is True:
# print(stylized_image_res[0].shape)
stylized_image_res_ycc = cv2.cvtColor(
stylized_image_res[0], cv2.COLOR_RGB2YCR_CB)
Ystylized, Crstylized, Cbstylized = cv2.split(
stylized_image_res_ycc)
if wgap == 4: # if original image is just fit
Ystylized_crop = Ystylized * 255
else:
Ystylized_crop = Ystylized[:, :-1 * wgap] * 255
if hgap == 4:
Ystylized_crop = Ystylized_crop
else:
Ystylized_crop = Ystylized_crop[:-1 * hgap, :]
print(Ystylized_crop.shape, Cbcontent.shape)
# print(wgap)
swapped_ycc = cv2.merge(
(Ystylized_crop, Crcontent, Cbcontent))
# print(swapped_ycc)
stylized_image_res = cv2.cvtColor(
swapped_ycc, cv2.COLOR_YCR_CB2BGR)
# print(stylized_image_res)
cv2.imwrite(
os.path.join(
FLAGS.output_dir, '%s_stylized_%s_%d.jpg' %
(content_img_name, word, interp_i)),
stylized_image_res)
# Saves stylized image.
else:
image_utils.save_np_image(
stylized_image_res,
os.path.join(
FLAGS.output_dir, '%s_stylized_%s_%d.jpg' %
(content_img_name, word, interp_i)))
elapsed_time = time.time() - start
print("timer stop")
print(elapsed_time)
def styleParam(style_img_list):
config = tf.ConfigProto(gpu_options=tf.GPUOptions(allow_growth=True))
sess = tf.Session(config=config)
slim = tf.contrib.slim
checkpoint_in = 'arbitrary_style_transfer/model.ckpt'
output_dir = 'outputs_style'
image_size = 256
content_square_crop = False
style_image_size = 256
style_square_crop = False
maximum_styles_to_evaluate = 1024
interpolation_weights_in = '[1.0]'
tf.logging.set_verbosity(tf.logging.INFO)
style_param_matrix = []
if not tf.gfile.Exists(output_dir):
tf.gfile.MkDir(output_dir)
with tf.Graph().as_default(), sess:
# Defines place holder for the style image.
style_img_ph = tf.placeholder(tf.float32, shape=[None, None, 3])
if style_square_crop:
style_img_preprocessed = image_utils.center_crop_resize_image(
style_img_ph, style_image_size)
else:
style_img_preprocessed = image_utils.resize_image(
style_img_ph, style_image_size)
# Defines place holder for the content image.
content_img_ph = tf.placeholder(tf.float32, shape=[None, None, 3])
if content_square_crop:
content_img_preprocessed = image_utils.center_crop_resize_image(
content_img_ph, image_size)
else:
content_img_preprocessed = image_utils.resize_image(
content_img_ph, image_size)
# Defines the model.
stylized_images, _, _, bottleneck_feat = build_model.build_model(
content_img_preprocessed,
style_img_preprocessed,
trainable=False,
is_training=False,
inception_end_point='Mixed_6e',
style_prediction_bottleneck=100,
adds_losses=False)
if tf.gfile.IsDirectory(checkpoint_in):
checkpoint = tf.train.latest_checkpoint(checkpoint_in)
else:
checkpoint = checkpoint_in
tf.logging.info(
'loading latest checkpoint file: {}'.format(checkpoint))
init_fn = slim.assign_from_checkpoint_fn(
checkpoint, slim.get_variables_to_restore())
sess.run([tf.local_variables_initializer()])
init_fn(sess)
# Gets the list of the input style images.
# style_img_list = tf.gfile.Glob(style_images_paths)
# if len(style_img_list) > maximum_styles_to_evaluate:
# np.random.seed(1234)
# style_img_list = np.random.permutation(style_img_list)
# style_img_list = style_img_list[:maximum_styles_to_evaluate]
count = 0
for style_i, style_img_path in enumerate(style_img_list):
# if style_i > maximum_styles_to_evaluate:
# break
style_image_np = image_utils.load_np_image_uint8(
style_img_path)[:, :, :3]
# Computes bottleneck features of the style prediction network for the
# given style image.
style_params = sess.run(bottleneck_feat,
feed_dict={style_img_ph: style_image_np})
style_param_matrix.append(style_params)
count += 1
# if count % 100 == 0:
# print(count, '/', len(style_img_list))
# style_param_matrix is (num_of_images,100) vector
return (style_param_matrix)
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
with tf.Graph().as_default():
# Loads content images.
eval_content_inputs_, _ = image_utils.imagenet_inputs(
FLAGS.batch_size, FLAGS.image_size)
# Process style and content weight flags.
content_weights = ast.literal_eval(FLAGS.content_weights)
style_weights = ast.literal_eval(FLAGS.style_weights)
# Loads evaluation style images.
eval_style_inputs_, _, _ = image_utils.arbitrary_style_image_inputs(
FLAGS.eval_style_dataset_file,
batch_size=FLAGS.batch_size,
image_size=FLAGS.image_size,
center_crop=True,
shuffle=True,
augment_style_images=False,
random_style_image_size=False)
# Computes stylized noise.
stylized_noise, _, _, _ = build_model.build_model(
tf.random_uniform([
min(4, FLAGS.batch_size), FLAGS.image_size, FLAGS.image_size, 3
]),
tf.slice(eval_style_inputs_, [0, 0, 0, 0],
[min(4, FLAGS.batch_size), -1, -1, -1]),
trainable=False,
is_training=False,
reuse=None,
inception_end_point='Mixed_6e',
style_prediction_bottleneck=100,
adds_losses=False)
# Computes stylized images.
stylized_images, _, loss_dict, _ = build_model.build_model(
eval_content_inputs_,
eval_style_inputs_,
trainable=False,
is_training=False,
reuse=True,
inception_end_point='Mixed_6e',
style_prediction_bottleneck=100,
adds_losses=True,
content_weights=content_weights,
style_weights=style_weights,
total_variation_weight=FLAGS.total_variation_weight)
# Adds Image summaries to the tensorboard.
tf.summary.image(
'image/{}/0_eval_content_inputs'.format(FLAGS.eval_name),
eval_content_inputs_, 3)
tf.summary.image(
'image/{}/1_eval_style_inputs'.format(FLAGS.eval_name),
eval_style_inputs_, 3)
tf.summary.image(
'image/{}/2_eval_stylized_images'.format(FLAGS.eval_name),
stylized_images, 3)
tf.summary.image('image/{}/3_stylized_noise'.format(FLAGS.eval_name),
stylized_noise, 3)
metrics = {}
for key, value in loss_dict.iteritems():
metrics[key] = tf.metrics.mean(value)
names_values, names_updates = slim.metrics.aggregate_metric_map(
metrics)
for name, value in names_values.iteritems():
slim.summaries.add_scalar_summary(value, name, print_summary=True)
eval_op = names_updates.values()
num_evals = FLAGS.num_evaluation_styles / FLAGS.batch_size
slim.evaluation.evaluation_loop(
master=FLAGS.master,
checkpoint_dir=FLAGS.checkpoint_dir,
logdir=FLAGS.eval_dir,
eval_op=eval_op,
num_evals=num_evals,
eval_interval_secs=FLAGS.eval_interval_secs)
def main():
tf.reset_default_graph()
BATCH_SIZE = 1
with tf.Graph().as_default(), tf.Session() as sess:
# train.MonitoredTraining
# Loads content images.
#TODO load test images from a different test path. Do a small amount for simplicity
# Make sure it's the same content, style images as the test set for the GAN
# TODO check the path here.
test_content_path = '../testset/content/'
test_style_path = '../testset/style/'
# TODO Fix paths?
content_path = '/home/noah/magenta/data/coco0/' #magenta/data/coco0'
style_path = '/home/noah/magenta/data/painter/painter0/'
content_inputs_ = data_utils_all.load_random_images(
content_path, batch_size=BATCH_SIZE)
style_inputs_ = data_utils_all.load_random_images(
style_path, batch_size=BATCH_SIZE)
# akhil_dummy_path = '/Users/akhiljalan/Documents/trainingdata_stylized_500/stylized500/'
# todo insert content_path, style_path...
# content_inputs_ = data_utils_all.load_random_images(akhil_dummy_path, batch_size=BATCH_SIZE)
# Loads evaluation style images.
# style_inputs_ = data_utils_all.load_random_images(akhil_dummy_path, batch_size=BATCH_SIZE)
# Default style, content, and variation weights from magenta.
content_weights = {"vgg_16/conv3": 1}
style_weights = {
"vgg_16/conv1": 0.5e-3,
"vgg_16/conv2": 0.5e-3,
"vgg_16/conv3": 0.5e-3,
"vgg_16/conv4": 0.5e-3
}
total_variation_weight = 1e4
stylized_images, total_loss_pass_1, loss_dict_pass_1, _ = build_model.build_model(
content_inputs_,
style_inputs_,
reuse=tf.AUTO_REUSE,
trainable=True,
is_training=True,
inception_end_point='Mixed_6e',
style_prediction_bottleneck=100,
adds_losses=True,
content_weights=content_weights,
style_weights=style_weights,
total_variation_weight=total_variation_weight)
unstylized_images, total_loss_pass_2, loss_dict_pass_2 = None, 0.0, {}
# unstylized_images, total_loss_pass_2, loss_dict_pass_2, _ = build_model.build_model(
# stylized_images, #stylized as content
# content_inputs_, #original content as style
# reuse=True,
# trainable=True,
# is_training=True,
# inception_end_point='Mixed_6e',
# style_prediction_bottleneck=100,
# adds_losses=True,
# content_weights=content_weights,
# style_weights=style_weights,
# total_variation_weight=total_variation_weight)
# Log all losses to tensorboard.
loss_dict = {}
loss_dict.update(loss_dict_pass_1)
loss_dict.update(loss_dict_pass_2)
for key, value in loss_dict.iteritems():
tf.summary.scalar(key, value)
# Log images to tensorboard
tf.summary.image('image/0_content_inputs', content_inputs_, 3)
# tf.summary.image('image/1_style_inputs_orig', style_inputs_orig_, 3)
tf.summary.image('image/1_style_inputs', style_inputs_, 3)
tf.summary.image('image/2_stylized_images', stylized_images, 3)
# tf.summary.image('image/3_unstylized_images', unstylized_images, 3)
# discrim_predictions = discriminator_network(content_inputs_)
# Generate label tensors on the fly.
real_labels = data_utils_all.gen_labels(is_real=True,
batch_size=BATCH_SIZE)
fake_labels = data_utils_all.gen_labels(is_real=False,
batch_size=BATCH_SIZE)
# discrim_loss = slim.losses.softmax_cross_entropy(discrim_predictions, real_labels)
# gen_fooling_loss = slim.losses.softmax_cross_entropy(discrim_predictions, fake_labels)
gen_optimizer = tf.train.AdamOptimizer(learning_rate=1e-2)
gen_train_op = slim.learning.create_train_op(
total_loss_pass_1, #+ total_variation_weight * gen_fooling_loss, # + total_loss_pass_2?
gen_optimizer,
summarize_gradients=False)
# discr_optimizer = tf.train.AdamOptimizer(learning_rate=1e-2)
# discr_train_op = slim.learning.create_train_op(
# discrim_loss, # + total_loss_pass_2?
# discr_optimizer,
# summarize_gradients=True)
# todo merge train ops
# Get checkpoint files.
# See above for the inception, vgg checkpoints.
# TODO change checkpoint path...
gen_checkpoint = '/home/noah/arbitrary_style_transfer/model.ckpt'
# gen_checkpoint = '../../magenta/arbitrary_style_transfer/model.ckpt'
# discrim_checkpoint = './logdir/model.ckpt-85'
model_vars = slim.get_variables_to_restore()
# No saved model yet!
# discrim_var_names = [var for var in model_vars if 'discriminator' in var.name]
# gen_var_names = [var for var in model_vars if 'beta' not in var.name]
# gen_var_names = [var for var in model_vars if var.shape == ()]
gen_var_names = model_vars #
# print(gen_var_names)
# gen_assign_op, gen_feed_dict = slim.assign_from_checkpoint(gen_checkpoint, gen_var_names) #TODO change this...
# discrim_assign_op, discrim_feed_dict = slim.assign_from_checkpoint(discrim_checkpoint,
# discrim_var_names)
init_fn = slim.assign_from_checkpoint_fn(gen_checkpoint, gen_var_names)
def init_assign_func(sess):
# sess.run(gen_assign_op, gen_feed_dict)
init_fn(sess) #
slim.learning.train(
train_op=gen_train_op, #todo replace with merged train op.
logdir='./logdir01/',
number_of_steps=2,
save_summaries_secs=1,
save_interval_secs=1,
init_fn=init_assign_func)
def main(unused_argv=None):
tf.logging.set_verbosity(tf.logging.INFO)
with tf.Graph().as_default():
# Forces all input processing onto CPU in order to reserve the GPU for the
# forward inference and back-propagation.
device = '/cpu:0' if not FLAGS.ps_tasks else '/job:worker/cpu:0'
with tf.device(
tf.train.replica_device_setter(FLAGS.ps_tasks,
worker_device=device)):
# Loads content images.
# content_inputs_, _ = image_utils.imagenet_inputs(FLAGS.batch_size,
# FLAGS.image_size)
#changed
content_inputs_, _, temp = load_xy_pairs(
fake_directory=FLAGS.content_dir,
real_directory=FLAGS.content_dir,
batch_size=FLAGS.batch_size,
prob_of_real=1)
# Loads style images.
# [style_inputs_, _,
# style_inputs_orig_] = image_utils.arbitrary_style_image_inputs(
# FLAGS.style_dataset_file,
# batch_size=FLAGS.batch_size,
# image_size=FLAGS.image_size,
# shuffle=True,
# center_crop=FLAGS.center_crop,
# augment_style_images=FLAGS.augment_style_images,
# random_style_image_size=FLAGS.random_style_image_size)
style_inputs_, _, temp2 = load_xy_pairs(
fake_directory=FLAGS.style_dir,
real_directory=FLAGS.style_dir,
batch_size=FLAGS.batch_size,
prob_of_real=1)
with tf.device(tf.train.replica_device_setter(FLAGS.ps_tasks)):
# Process style and content weight flags.
content_weights = ast.literal_eval(FLAGS.content_weights)
style_weights = ast.literal_eval(FLAGS.style_weights)
# Define the model
stylized_images, total_loss, loss_dict, _ = build_model.build_model(
content_inputs_,
style_inputs_,
trainable=True,
is_training=True,
inception_end_point='Mixed_6e',
style_prediction_bottleneck=100,
adds_losses=True,
content_weights=content_weights,
style_weights=style_weights,
total_variation_weight=FLAGS.total_variation_weight)
if tf.gfile.IsDirectory(FLAGS.checkpoint):
checkpoint = tf.train.latest_checkpoint(FLAGS.checkpoint)
else:
checkpoint = FLAGS.checkpoint
tf.logging.info(
'loading latest checkpoint file: {}'.format(checkpoint))
init_fn = slim.assign_from_checkpoint_fn(
checkpoint, slim.get_variables_to_restore())
# sess.run([tf.local_variables_initializer()])
# init_fn(sess)
# Adding scalar summaries to the tensorboard.
for key, value in loss_dict.iteritems():
tf.summary.scalar(key, value)
# Adding Image summaries to the tensorboard.
tf.summary.image('image/0_content_inputs', content_inputs_, 3)
# tf.summary.image('image/1_style_inputs_orig', style_inputs_orig_, 3)
tf.summary.image('image/2_style_inputs_aug', style_inputs_, 3)
tf.summary.image('image/3_stylized_images', stylized_images, 3)
# Set up training
optimizer = tf.train.AdamOptimizer(FLAGS.learning_rate)
train_op = slim.learning.create_train_op(
total_loss,
optimizer,
clip_gradient_norm=FLAGS.clip_gradient_norm,
summarize_gradients=False)
# Function to restore VGG16 parameters.
init_fn_vgg = slim.assign_from_checkpoint_fn(
vgg.checkpoint_file(), slim.get_variables('vgg_16'))
# Function to restore Inception_v3 parameters.
inception_variables_dict = {
var.op.name: var
for var in slim.get_model_variables('InceptionV3')
}
init_fn_inception = slim.assign_from_checkpoint_fn(
FLAGS.inception_v3_checkpoint, inception_variables_dict)
# Function to restore VGG16 and Inception_v3 parameters.
def init_sub_networks(session):
# init_fn_vgg(session)
# init_fn_inception(session)
# session.run(tf.local_variables_initializer())
init_fn(session)
# config = tf.ConfigProto()
# config.gpu_options_allow_growth = True
config = tf.ConfigProto()
# config.gpu_options.allocator_type = 'BFC'
# config.gpu_options.per_process_gpu_memory_fraction = 0.98
# Run training
slim.learning.train(session_config=config,
train_op=train_op,
logdir=os.path.expanduser(FLAGS.train_dir),
master=FLAGS.master,
is_chief=FLAGS.task == 0,
number_of_steps=FLAGS.train_steps,
init_fn=init_sub_networks,
save_summaries_secs=FLAGS.save_summaries_secs,
save_interval_secs=FLAGS.save_interval_secs)
def run(content_images_paths,style_images_paths,style_square_crop=False,style_image_size=400,content_square_crop=False,
image_size=400,checkpoint='arbitrary_style_transfer/model.ckpt',maximum_styles_to_evaluate=1024,
interpolation_weights='[1.0]'):
f = open('docs/accessKeys.csv', 'r')
reader = csv.reader(f,delimiter=',')
i = 1
keys = []
for row in reader:
if i == 2:
keys = row
i = i + 1
f.close()
s3 = boto3.client('s3',
aws_access_key_id=keys[0],
aws_secret_access_key=keys[1])
start = time.time()
front = content_images_paths.split('.')[0]
# bucket name, dir of file in s3 bucket, local name
s3.download_file(config.bucketname, 'target1/'+content_images_paths, content_images_paths)
convertimage(content_images_paths, False)
content = 'c_'+front+'.jpg'
s3.download_file(config.bucketname, 'style1/'+style_images_paths, style_images_paths)
convertimage(style_images_paths, True)
style = 's_'+front+'.jpg'
# start evaluate
tf.logging.set_verbosity(tf.logging.INFO)
# if not tf.gfile.Exists(output_dir):
# tf.gfile.MkDir(output_dir)
with tf.Graph().as_default(), tf.Session() as sess:
# Defines place holder for the style image.
style_img_ph = tf.placeholder(tf.float32, shape=[None, None, 3])
if style_square_crop:
style_img_preprocessed = image_utils.center_crop_resize_image(
style_img_ph, style_image_size)
else:
style_img_preprocessed = image_utils.resize_image(style_img_ph,
style_image_size)
# Defines place holder for the content image.
content_img_ph = tf.placeholder(tf.float32, shape=[None, None, 3])
if content_square_crop:
content_img_preprocessed = image_utils.center_crop_resize_image(
content_img_ph, image_size)
else:
content_img_preprocessed = image_utils.resize_image(
content_img_ph, image_size)
# Defines the model.
stylized_images, _, _, bottleneck_feat = build_model.build_model(
content_img_preprocessed,
style_img_preprocessed,
trainable=False,
is_training=False,
inception_end_point='Mixed_6e',
style_prediction_bottleneck=100,
adds_losses=False)
if tf.gfile.IsDirectory(checkpoint):
checkpoint = tf.train.latest_checkpoint(checkpoint)
else:
checkpoint = checkpoint
tf.logging.info('loading latest checkpoint file: {}'.format(checkpoint))
init_fn = slim.assign_from_checkpoint_fn(checkpoint,
slim.get_variables_to_restore())
sess.run([tf.local_variables_initializer()])
init_fn(sess)
# Gets the list of the input style images.
style_img_list = tf.gfile.Glob(style)
if len(style_img_list) > maximum_styles_to_evaluate:
np.random.seed(1234)
style_img_list = np.random.permutation(style_img_list)
style_img_list = style_img_list[:maximum_styles_to_evaluate]
# Gets list of input content images.
content_img_list = tf.gfile.Glob(content)
for content_i, content_img_path in enumerate(content_img_list):
content_img_np = image_utils.load_np_image_uint8(content_img_path)[:, :, :
3]
content_img_name = os.path.basename(content_img_path)[:-4]
# Saves preprocessed content image.
inp_img_croped_resized_np = sess.run(
content_img_preprocessed, feed_dict={
content_img_ph: content_img_np
})
# image_utils.save_np_image(inp_img_croped_resized_np,
# os.path.join(output_dir,
# '%s.jpg' % (content_img_name)))
# Computes bottleneck features of the style prediction network for the
# identity transform.
identity_params = sess.run(
bottleneck_feat, feed_dict={style_img_ph: content_img_np})
for style_i, style_img_path in enumerate(style_img_list):
if style_i > maximum_styles_to_evaluate:
break
style_img_name = os.path.basename(style_img_path)[:-4]
style_image_np = image_utils.load_np_image_uint8(style_img_path)[:, :, :
3]
if style_i % 10 == 0:
tf.logging.info('Stylizing (%d) %s with (%d) %s' %
(content_i, content_img_name, style_i,
style_img_name))
# Saves preprocessed style image.
style_img_croped_resized_np = sess.run(
style_img_preprocessed, feed_dict={
style_img_ph: style_image_np
})
# image_utils.save_np_image(style_img_croped_resized_np,
# os.path.join(output_dir,
# '%s.jpg' % (style_img_name)))
# Computes bottleneck features of the style prediction network for the
# given style image.
style_params = sess.run(
bottleneck_feat, feed_dict={style_img_ph: style_image_np})
interpolation_weights = ast.literal_eval(interpolation_weights)
# Interpolates between the parameters of the identity transform and
# style parameters of the given style image.
for interp_i, wi in enumerate(interpolation_weights):
stylized_image_res = sess.run(
stylized_images,
feed_dict={
bottleneck_feat:
identity_params * (1 - wi) + style_params * wi,
content_img_ph:
content_img_np
})
# Saves stylized image.
image_utils.save_np_image(
stylized_image_res,
front+'.jpg')
upload_file(front+'.jpg','styletransferimage','output1/'+front+'.jpg')
print("finished upload")
os.remove('c_'+front+'.jpg')
os.remove('s_'+front+'.jpg')
os.remove(front+'.jpg')
status = False
retryCounter = 2
while retryCounter > 0:
try:
channel = grpc.insecure_channel('54.164.44.43:50051')
stub = uid_management_pb2_grpc.UidManagementStub(channel)
status = stub.TransferCompleted(uid_management_pb2.Id(id=front))
retryCounter = 0
except:
retryCounter -= 1
