logging.basicConfig(format='%(asctime)s %(levelname)s %(message)s',
level=logging.INFO,
stream=sys.stdout)
from tensorflow.python.framework import ops
os.environ['CUDA_VISIBLE_DEVICES'] = ''
img1 = skimage.io.imread("./test_data/tabby_cat.png")
with tf.Session() as sess:
images = tf.placeholder(dtype=tf.float32)
feed_dict = {images: img1}
batch_images = tf.expand_dims(images, 0)
vgg_fcn = fcn16_vgg.FCN16VGG()
with tf.name_scope("content_vgg"):
vgg_fcn.build(batch_images, debug=True)
print('Finished building Network.')
logging.warning("Score weights are initialized random.")
logging.warning("Do not expect meaningful results.")
logging.info("Start Initializing Variabels.")
init = tf.initialize_all_variables()
sess.run(tf.initialize_all_variables())
print('Running the Network')
tensors = [vgg_fcn.pred, vgg_fcn.pred_up]
rgb_image[:, :, 1] = image - fcn16_vgg.VGG_MEAN[1]
rgb_image[:, :, 2] = image - fcn16_vgg.VGG_MEAN[0]
rgb_image = scipy.misc.imresize(rgb_image, (height * scale_times, width * scale_times))
scp.misc.imsave(directory + "/" + name + "-filter-" + str(i) + ".jpg", rgb_image)
with tf.device('/cpu:0'):
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
input_placeholder = tf.placeholder(tf.float32, [None, height, width, num_classes])
output_placeholder = tf.placeholder(tf.float32, [None, height, width, num_classes])
vgg_fcn = fcn16_vgg.FCN16VGG('./vgg16.npy')
with tf.name_scope('content_vgg'):
vgg_fcn.build(input_placeholder, train=True, num_classes=num_classes, debug=True)
with tf.name_scope('loss'):
loss = loss.loss(vgg_fcn.upscore32, output_placeholder, num_classes)
optimizer = tf.train.AdamOptimizer(0.0001).minimize(loss)
print('Finished building Network.')
# Initializing the variables.
init = tf.global_variables_initializer()
# Run initialized variables.
sess.run(init)
#img1 = skimage.io.imread("./test_data/tabby_cat.png")
parser = argparse.ArgumentParser()
parser.add_argument('--npypath',help="path to weights",default="/scratch/gallowaa/")
parser.add_argument('--imgpath',help="path to input image",default="/scratch/gallowaa/")
args = parser.parse_args()
img1 = skimage.io.imread(args.imgpath+"2008_004499_02.png")
with tf.Session() as sess:
images = tf.placeholder("float")
feed_dict = {images: img1}
batch_images = tf.expand_dims(images, 0)
vgg_fcn = fcn16_vgg.FCN16VGG(args.npypath+"vgg16.npy")
with tf.name_scope("content_vgg"):
vgg_fcn.build(batch_images, debug=True)
print('Finished building Network.')
logging.warning("Score weights are initialized random.")
logging.warning("Do not expect meaningful results.")
logging.info("Start Initializing Variabels.")
init = tf.initialize_all_variables()
sess.run(tf.initialize_all_variables())
print('Running the Network')
tensors = [vgg_fcn.pred, vgg_fcn.pred_up]