def load_vgg16_model():
net = vgg16.get_vgg_model()
g = tf.Graph()
with tf.Session(graph=g) as sess, g.device('/cpu:0'):
tf.import_graph_def(net['graph_def'], name='vgg')
names = [op.name for op in g.get_operations()]
return net, names, g
def app_two():
# net, names, g = load_vgg16_model()
# x = g.get_tensor_by_name(names[0] + ':0')
img = load_file("style.jpg")
img = vgg16.preprocess(img)
img_4d = img[np.newaxis]
g = tf.Graph()
net = vgg16.get_vgg_model()
with tf.Session(graph=g) as sess, g.device('/cpu:0'):
net_input = tf.Variable(img_4d)
# net_input = tf.get_variable(name='input', shape=(1, 224, 224, 3), dtype=tf.float32,
# initializer= tf.random_normal_initializer(mean=0.0, stddev=0.5))
tf.import_graph_def(net['graph_def'], name='vgg', input_map={'images:0':net_input})
layer_name = 'vgg/conv4_2/conv4_2:0'
gram_op = get_gram_matrix(g, layer_name)
sess.run(tf.global_variables_initializer())
s = sess.run(gram_op)
print(s.shape)
def test_vgg():
net = vgg16.get_vgg_model()
guide_og = plt.imread('clinton.png')[..., :3]
dream_og = plt.imread('arles.png')[..., :3]
guide_img = net['preprocess'](guide_og)[np.newaxis]
dream_img = net['preprocess'](dream_og)[np.newaxis]
assert(guide_img.shape == (1, 224, 224, 3))
assert(dream_img.shape == (1, 224, 224, 3))
assert(guide_img.dtype == np.dtype('float32'))
assert(guide_img.dtype == np.dtype('float32'))
g = tf.Graph()
with tf.Session(graph=g) as sess:
tf.import_graph_def(net['graph_def'], name='net')
names = [op.name for op in g.get_operations()]
x = g.get_tensor_by_name(names[0] + ':0')
softmax = g.get_tensor_by_name(names[-2] + ':0')
res = softmax.eval(feed_dict={x: guide_img,
'net/dropout_1/random_uniform:0': [[1.0] * 4096],
'net/dropout/random_uniform:0': [[1.0] * 4096]})[0]
assert(np.argmax(res) == 681)
res = softmax.eval(feed_dict={x: dream_img,
'net/dropout_1/random_uniform:0': [[1.0] * 4096],
'net/dropout/random_uniform:0': [[1.0] * 4096]})[0]
assert(np.argmax(res) == 540)
return
def get_style_layer_vgg16(image):
net = vgg16.get_vgg_model()
style_layer = 'conv2_2/conv2_2:0'
feature_transformed_image = tf.import_graph_def(
net['graph_def'],
name='vgg',
input_map={'images:0': image},
return_elements=[style_layer])
feature_transformed_image = (feature_transformed_image[0])
return feature_transformed_image
# Experiment w/ different layers here. You'll need to change this if you
# use another network!
content_layer = 'net/conv4_2/conv4_2:0'
# Experiment with different layers and layer subsets. You'll need to change these
# if you use a different network!
style_layers = [
'net/conv1_1/conv1_1:0', 'net/conv2_1/conv2_1:0', 'net/conv3_1/conv3_1:0',
'net/conv4_1/conv4_1:0', 'net/conv5_1/conv5_1:0'
]
device = '/gpu:0'
g = tf.Graph()
# Get the network again
net = vgg16.get_vgg_model()
# Preprocess images.
content_og = plt.imread('clinton.png')[..., :3]
style_og = plt.imread('arles.png')[..., :3]
content_img = net['preprocess'](content_og)[np.newaxis]
style_img = net['preprocess'](style_og)[np.newaxis]
content_features = None
style_features = []
with tf.Session(graph=g) as sess, g.device(device):
# Now load the graph_def, which defines operations and their values into `g`
tf.import_graph_def(net['graph_def'], name='net')
def app_three():
"""
1. this app works.
2. want to know placehoder content, just put it inside session.
"""
# content image
from skimage.data import astronaut
og = astronaut()
# plt.imshow(og)
# plt.show()
img = vgg16.preprocess(og)
img_4d = img[np.newaxis]
# style image
style_og = plt.imread('style.jpg')
# plt.imshow(style_og)
# plt.show()
style_img = vgg16.preprocess(style_og)
style_img_4d = style_img[np.newaxis]
net = vgg16.get_vgg_model()
g = tf.Graph()
with tf.Session(graph=g) as sess, g.device('/cpu:0'):
tf.import_graph_def(net['graph_def'], name='vgg')
names = [op.name for op in g.get_operations()]
# print(names) # when
x = g.get_tensor_by_name(names[0] + ':0')
print(x, x.shape)
# contents features
content_layer = 'vgg/conv4_2/conv4_2:0'
with tf.Session(graph=g) as sess, g.device('/cpu:0'):
content_features = g.get_tensor_by_name(content_layer).eval(
session=sess,
feed_dict={x: img_4d,
'vgg/dropout_1/random_uniform:0' : [[1.0] * 4096],
'vgg/dropout/random_uniform:0' : [[1.0] * 4096]}
)
print(content_features.shape)
# style features
style_layers = ['vgg/conv1_1/conv1_1:0',
'vgg/conv2_1/conv2_1:0',
'vgg/conv3_1/conv3_1:0',
'vgg/conv4_1/conv4_1:0',
'vgg/conv5_1/conv5_1:0']
style_activations = []
with tf.Session(graph=g) as sess, g.device('/cpu:0'):
for style_i in style_layers:
style_activation_i = g.get_tensor_by_name(style_i).eval(
session=sess,
feed_dict={x: style_img_4d,
'vgg/dropout_1/random_uniform:0' : [[1.0] * 4096],
'vgg/dropout/random_uniform:0' : [[1.0] * 4096]})
style_activations.append(style_activation_i)
style_features = []
for style_activation_i in style_activations:
s_i = np.reshape(style_activation_i, [-1, style_activation_i.shape[-1]])
gram_matrix = np.matmul(s_i.T, s_i) / s_i.size
style_features.append(gram_matrix.astype(np.float32))
print(len(style_features))
# from tensorflow.python.framework.ops import reset_default_graph
tf.reset_default_graph()
g = tf.Graph()
net = vgg16.get_vgg_model()
with tf.Session(graph=g) as sess, g.device('/cpu:0'):
net_input = tf.Variable(img_4d)
# net_input = tf.get_variable(name='input', shape=(1, 224, 224, 3), dtype=tf.float32,
# initializer= tf.random_normal_initializer(mean=0.0, stddev=0.5))
tf.import_graph_def(net['graph_def'], name='vgg', input_map={'images:0':net_input})
names = [op.name for op in g.get_operations()]
with tf.Session(graph=g) as sess, g.device('/cpu:0'):
content_loss = tf.nn.l2_loss((g.get_tensor_by_name(content_layer) -
content_features) / content_features.size)
print(content_loss) # this is only a tensor, which is handle, no specific value.
with tf.Session(graph=g) as sess, g.device('/cpu:0'):
style_loss = np.float32(0.0)
for style_layer_i, style_gram_i in zip(style_layers, style_features):
layer_i = g.get_tensor_by_name(style_layer_i)
layer_shape = layer_i.get_shape().as_list()
layer_size = layer_shape[1] * layer_shape[2] * layer_shape[3]
layer_flat = tf.reshape(layer_i, [-1, layer_shape[3]])
gram_matrix = tf.matmul(tf.transpose(layer_flat), layer_flat) / layer_size
style_loss = tf.add(style_loss, tf.nn.l2_loss(gram_matrix - style_gram_i)/
np.float32(style_gram_i.size))
print(style_loss)
with tf.Session(graph=g) as sess, g.device('/cpu:0'):
tv_loss = total_variation_loss(net_input)
print(tv_loss)
with tf.Session(graph=g) as sess, g.device('/cpu:0'):
loss = 0.1 * content_loss + 5.0 * style_loss + 0.01 * tv_loss
optimizer = tf.train.AdamOptimizer(0.01).minimize(loss)
with tf.Session(graph=g) as sess, g.device('/cpu:0'):
sess.run(tf.global_variables_initializer())
n_iterations = 500
og_img = net_input.eval()
imgs = []
for it_i in range(n_iterations):
_, this_loss, synth = sess.run([optimizer, loss, net_input],
feed_dict={
'vgg/dropout_1/random_uniform:0' : [[1.0] * 4096],
'vgg/dropout/random_uniform:0' : [[1.0] * 4096]})
print("%d: %f, (%f - %f)" %
(it_i, this_loss, np.min(synth), np.max(synth)))
# if it_i % 5 == 0:
if it_i == 499:
imgs.append(np.clip(synth[0], 0, 1))
fig, ax = plt.subplots(1, 3, figsize=(22, 5))
ax[0].imshow(vgg16.deprocess(img))
ax[0].set_title('content image')
ax[1].imshow(vgg16.deprocess(style_img))
ax[1].set_title('style image')
ax[2].set_title('current synthesis')
ax[2].imshow(vgg16.deprocess(synth[0]))
plt.show()
def one_app():
"""
"""
# net, names, g = load_vgg16_model()
# x = g.get_tensor_by_name(names[0] + ':0')
# softmax = g.get_tensor_by_name(names[-2] + ':0')
# print(x)
# print(softmax.shape)
#og_img
img = load_img_from_skimg()
print(img.shape)
img = vgg16.preprocess(img)
print(img.shape)
# plt.imshow(vgg16.deprocess(img))
# plt.show()
img_4d = img[np.newaxis]
# style
style_img = load_file("style.jpg")
style_img = vgg16.preprocess(style_img)
style_img_4d = style_img[np.newaxis]
g = tf.Graph()
net = vgg16.get_vgg_model()
with tf.Session(graph=g) as sess, g.device('/cpu:0'):
net_input = tf.Variable(img_4d)
# net_input = tf.get_variable(name='input', shape=(1, 224, 224, 3), dtype=tf.float32,
# initializer= tf.random_normal_initializer(mean=0.0, stddev=0.5))
tf.import_graph_def(net['graph_def'], name='vgg', input_map={'images:0':net_input})
sess.run(tf.global_variables_initializer())
names = [op.name for op in g.get_operations()]
# print(names)
x = g.get_tensor_by_name(names[0] + ':0')
dropout_list = get_dropout_layer_names(g, names)
print(names[0])
# content feature and loss
content_layer_name = 'vgg/conv4_2/conv4_2:0'
content_features = get_tensor_eval(g, content_layer_name, x, img_4d, dropout_list)
print(content_features.shape)
content_loss = get_content_loss(g, content_layer_name, content_features)
# style features and loss
style_layers = ['vgg/conv1_1/conv1_1:0',
'vgg/conv2_1/conv2_1:0',
'vgg/conv3_1/conv3_1:0',
'vgg/conv4_1/conv4_1:0',
'vgg/conv5_1/conv5_1:0']
style_activations = get_tensor_evals(g, style_layers, x, style_img_4d, dropout_list)
style_features = covert_gram_matrix(style_activations)
print(style_features[0].shape, style_features[0].dtype)
style_loss = get_style_loss(g, style_layers, style_features)
tv_loss = total_variation_loss(net_input)
loss = 0.1 * content_loss + 5.0 * style_loss + 0.01 * tv_loss
optimizer = tf.train.AdamOptimizer(0.01).minimize(loss)
n_iterations = 100
og_img = net_input.eval()
imgs = []
sess.run(tf.global_variables_initializer())
for it_i in range(n_iterations):
_, this_loss, synth = sess.run([optimizer, loss, net_input],
feed_dict={
dropout_list[0][0]+':0' : [[1.0] * dropout_list[1][0]],
dropout_list[0][1] + ':0' : [[1.0] * dropout_list[1][1]]})
print("%d: %f, (%f - %f)" %
(it_i, this_loss, np.min(synth), np.max(synth)))
# if it_i % 20 == 0:
if it_i == 99:
imgs.append(np.clip(synth[0], 0, 1))
fig, ax = plt.subplots(1, 3, figsize=(22, 5))
ax[0].imshow(vgg16.deprocess(img))
ax[0].set_title('content image')
ax[1].imshow(vgg16.deprocess(style_img))
ax[1].set_title('style image')
ax[2].set_title('current synthesis')
ax[2].imshow(vgg16.deprocess(synth[0]))
plt.show()
