def main():
with tf.variable_scope('input') as scope:
# use variable instead of placeholder because we're training the intial image to make it
# look like both the content image and the style image
input_image = tf.Variable(np.zeros([1, IMAGE_HEIGHT, IMAGE_WIDTH, 3]), dtype=tf.float32)
utils.download(VGG_DOWNLOAD_LINK, VGG_MODEL, EXPECTED_BYTES)
model = vgg_model.load_vgg(VGG_MODEL, input_image)
model['global_step'] = tf.Variable(0, dtype=tf.int32, trainable=False, name='global_step')
content_image = utils.get_resized_image(CONTENT_IMAGE, IMAGE_HEIGHT, IMAGE_WIDTH)
content_image = content_image - MEAN_PIXELS
style_image = utils.get_resized_image(STYLE_IMAGE, IMAGE_HEIGHT, IMAGE_WIDTH)
style_image = style_image - MEAN_PIXELS
model['content_loss'], model['style_loss'], model['total_loss'] = _create_losses(model,
input_image, content_image, style_image)
###############################
## TO DO: create optimizer
model['optimizer'] = tf.train.AdamOptimizer(LR).minimize(model['total_loss'],
global_step=model['global_step'])
###############################
model['summary_op'] = _create_summary(model)
initial_image = utils.generate_noise_image(content_image, IMAGE_HEIGHT, IMAGE_WIDTH, NOISE_RATIO)
train(model, input_image, initial_image)
def main():
with tf.variable_scope('input') as scope:
# use variable instead of placeholder because we're training the intial image to make it
# look like both the content image and the style image
input_image = tf.Variable(np.zeros([1, IMAGE_HEIGHT, IMAGE_WIDTH, 3]),
dtype=tf.float32)
utils.download(VGG_DOWNLOAD_LINK, VGG_MODEL, EXPECTED_BYTES)
model = vgg_model.load_vgg(VGG_MODEL, input_image)
model['global_step'] = tf.Variable(0,
dtype=tf.int32,
trainable=False,
name='global_step')
content_image = utils.get_resized_image(CONTENT_IMAGE, IMAGE_HEIGHT,
IMAGE_WIDTH)
content_image = content_image - MEAN_PIXELS
style_image = utils.get_resized_image(STYLE_IMAGE, IMAGE_HEIGHT,
IMAGE_WIDTH)
style_image = style_image - MEAN_PIXELS
model['content_loss'], model['style_loss'], model[
'total_loss'] = _create_losses(model, input_image, content_image,
style_image)
###############################
## TO DO: create optimizer
## model['optimizer'] = ...
###############################
model['summary_op'] = _create_summary(model)
initial_image = utils.generate_noise_image(content_image, IMAGE_HEIGHT,
IMAGE_WIDTH, NOISE_RATIO)
train(model, input_image, initial_image)
def main():
with tf.variable_scope('input') as scope:
input_image = tf.Variable(np.zeros([1, IMAGE_HEIGHT, IMAGE_WIDTH, 3]),
dtype=tf.float32)
utils.download(VGG_DOWNLOAD_LINK, VGG_MODEL, EXPECTED_BYTES)
utils.make_dir(CHECKPOINT_DIR)
# utils.make_dir('outputs')
utils.make_dir(OUT_DIR)
model = vgg_model.load_vgg(VGG_MODEL, input_image)
model['global_step'] = tf.Variable(0,
dtype=tf.int32,
trainable=False,
name='global_step')
# because PIL is column major so need to change place of width and height
content_image = utils.get_resized_image(CONTENT_IMAGE, IMAGE_HEIGHT,
IMAGE_WIDTH)
content_image = content_image - MEAN_PIXELS
style_image = utils.get_resized_image(STYLE_IMAGE, IMAGE_HEIGHT,
IMAGE_WIDTH)
style_image = style_image - MEAN_PIXELS
model['content_loss'], model['style_loss'], model[
'total_loss'] = _create_losses(model, input_image, content_image,
style_image)
model['optimizer'] = tf.train.AdamOptimizer(LR).minimize(
model['total_loss'], global_step=model['global_step'])
model['summary_op'] = _create_summary(model)
initial_image = utils.generate_noise_image(content_image, IMAGE_HEIGHT,
IMAGE_WIDTH, NOISE_RATIO)
train(model, input_image, initial_image)
def training(STYLE_IMAGE, CONTENT_IMAGE, LR, ITERS, NOISE_RATIO, model_load,
trainable_signal):
with tf.variable_scope('input') as scope:
input_image = tf.Variable(np.zeros([1, IMAGE_HEIGHT, IMAGE_WIDTH, 3]),
dtype=tf.float32)
utils.download(VGG_DOWNLOAD_LINK, VGG_MODEL, EXPECTED_BYTES)
model = vgg_model.load_vgg(VGG_MODEL, input_image)
model['global_step'] = tf.Variable(0,
dtype=tf.int32,
trainable=False,
name='global_step')
content_image = utils.get_resized_image(CONTENT_IMAGE, IMAGE_HEIGHT,
IMAGE_WIDTH)
content_image = content_image - MEAN_PIXELS
style_image = utils.get_resized_image(STYLE_IMAGE, IMAGE_HEIGHT,
IMAGE_WIDTH)
style_image = style_image - MEAN_PIXELS
model['content_loss'], model['style_loss'], model[
'total_loss'] = _create_losses(model, input_image, content_image,
style_image)
model['optimizer'] = tf.train.AdamOptimizer(learning_rate=LR).minimize(
model['total_loss'], global_step=model['global_step'])
model['summary_op'] = _create_summary(model)
initial_image = utils.generate_noise_image(content_image, IMAGE_HEIGHT,
IMAGE_WIDTH, NOISE_RATIO)
train(model, input_image, initial_image, ITERS, model_load,
trainable_signal)
def main():
_setting_parameters()
with tf.device('/cpu:0'): #Change to '/gpu:0' if having a GPU
with tf.variable_scope('input') as scope:
# use variable instead of placeholder because we're training the initial image to make it
# look like both the content image and the style image
input_image = tf.Variable(np.zeros(
[1, IMAGE_HEIGHT, IMAGE_WIDTH, 3]),
dtype=tf.float32)
#Downloads the pretrained model
utils.download(VGG_DOWNLOAD_LINK, VGG_MODEL, EXPECTED_BYTES)
model = vgg_model.load_vgg(VGG_MODEL, input_image)
model['global_step'] = tf.Variable(0,
dtype=tf.int32,
trainable=False,
name='global_step')
content_image = utils.get_resized_image(CONTENT_IMAGE, IMAGE_HEIGHT,
IMAGE_WIDTH)
content_image = content_image - MEAN_PIXELS
style_image = utils.get_resized_image(STYLE_IMAGE, IMAGE_HEIGHT,
IMAGE_WIDTH)
style_image = style_image - MEAN_PIXELS
model['content_loss'], model['style_loss'], model[
'total_loss'] = _create_losses(model, input_image, content_image,
style_image)
#Optimizer looking to reduce the total_loss of the model
#TensorFlow optimizers: https://www.tensorflow.org/api_docs/python/tf/train
model['optimizer'] = tf.train.AdamOptimizer(LR).minimize(
model['total_loss'], global_step=model['global_step'])
model['summary_op'] = _create_summary(model)
initial_image = utils.generate_noise_image(content_image, IMAGE_HEIGHT,
IMAGE_WIDTH, NOISE_RATIO)
train(model, input_image, initial_image)
#Showing the image one it is finished
image_path = 'outputs/%d.png' % (ITERS - 1)
utils.show_image(image_path)
def style_transfer(cfg):
with tf.variable_scope('input') as scope:
# use variable instead of placeholder because we're training the intial image to make it
# look like both the content image and the style image
input_image = tf.Variable(np.zeros(
[1, cfg.image_height, cfg.image_width, 3]),
dtype=tf.float32)
utils.download(VGG_DOWNLOAD_LINK, VGG_MODEL, EXPECTED_BYTES)
utils.make_dir(cfg.checkpoints)
utils.make_dir(cfg.outputs)
model = vgg_model.load_vgg(VGG_MODEL, input_image)
model['global_step'] = tf.Variable(0,
dtype=tf.int32,
trainable=False,
name='global_step')
content_image = utils.get_resized_image(cfg.content_image,
cfg.image_height, cfg.image_width)
content_image = content_image - MEAN_PIXELS
style_image = utils.get_resized_image(cfg.style_image, cfg.image_height,
cfg.image_width)
style_image = style_image - MEAN_PIXELS
model['content_loss'], model['style_loss'], model[
'total_loss'] = _create_losses(model, input_image, content_image,
style_image, cfg)
logging.info('model created')
###############################
## TO DO: create optimizer
model['optimizer'] = tf.train.AdamOptimizer(cfg.lr).minimize(
model['total_loss'], global_step=model['global_step'])
###############################
model['summary_op'] = _create_summary(model)
initial_image = utils.generate_noise_image(content_image, cfg.image_height,
cfg.image_width,
cfg.noise_ratio)
train(model, input_image, initial_image, cfg)
