def test_vgg_classify():
vgg16 = Vgg(True, vgg=Vgg.VGG16)
vgg19 = Vgg(True, vgg=Vgg.VGG19)
x = np.expand_dims(image_boy, 0)
y1 = vgg16(x)
y2 = vgg19(x)
with tf.Session() as sess:
y1, y2 = sess.run([y1, y2])
assert y2[0].tolist().index(y2.max()) == y1[0].tolist().index(y1.max())
def test_multiple_call():
vgg1 = Vgg(False, vgg=Vgg.VGG16)
vgg2 = Vgg(False, vgg=Vgg.VGG16)
x = np.expand_dims(image_boy, 0)
y1 = vgg1(x)
y2 = vgg2(x)
y3 = vgg2(x.copy())
with tf.Session() as sess:
sess.run([y1, y2, y3])
def test_vgg19():
vgg = Vgg(False, vgg=Vgg.VGG19)
x = np.random.normal(size=[16, 128, 128, 3])
y = vgg(x, 'block2_conv2')
assert y.shape == (16, 64, 64, 128)
with tf.Session() as sess:
sess.run(y)
def test_vgg16():
vgg = Vgg(False, vgg=Vgg.VGG16)
x = np.random.normal(size=[16, 128, 128, 3])
y = vgg(x)
assert y.shape == (16, )
with tf.Session() as sess:
sess.run(y)
def __init__(self,
glayers=16,
dlayers=4,
vgg_layer='block5_conv4',
init_epoch=100,
gan_weight=1e-3,
vgg_weight=0.1569,
name='srfeat',
**kwargs):
super(SRFEAT, self).__init__(**kwargs)
self.name = name
self.g_layers = glayers
self.init_epoch = init_epoch
self.gan_weight = gan_weight
self.vgg_weight = vgg_weight
self.vgg_layer = vgg_layer
self.vgg = Vgg(False, Vgg.VGG19)
self.F = 64
self.D = Discriminator.dcgan_d(self, [None, None, self.channel],
64,
times_stride=dlayers,
norm='bn',
name_or_scope='Critic')
self.DF = Discriminator.dcgan_d(self, [None, None, self.channel],
64,
times_stride=dlayers,
norm='bn',
name_or_scope='DF')
def __init__(self,
glayers=16,
dlayers=4,
vgg_layer='block2_conv2',
init_epoch=100,
mse_weight=1,
gan_weight=1e-3,
use_vgg=False,
vgg_weight=2e-6,
name='srgan',
**kwargs):
super(SRGAN, self).__init__(**kwargs)
self.name = name
self.g_layers = glayers
self.init_epoch = init_epoch
self.mse_weight = mse_weight
self.gan_weight = gan_weight
self.vgg_weight = vgg_weight
self.vgg_layer = vgg_layer
self.use_vgg = use_vgg
self.vgg = None
if self.use_vgg:
self.vgg = Vgg(False, 'vgg19')
self.D = Discriminator.dcgan_d(self, [None, None, self.channel],
64,
times_stride=dlayers,
norm='bn',
name_or_scope='Critic')
import tensorflow as tf
import numpy as np
try:
DATASETS = load_datasets('./Data/datasets.json')
except FileNotFoundError:
DATASETS = load_datasets('../Data/datasets.json')
data = DATASETS['91-IMAGE']
loader = BatchLoader(1,
data,
'test',
convert_to_gray=True,
crop=False,
scale=1)
m = Vgg(input_shape=[None, None, 3], type='vgg19')
with tf.Session() as sess:
# m = Vgg(input_shape=[None, None, 3], type='vgg19')
for hr, lr in loader:
y = m(hr, [2, 3], [2, 3])
break
tf.reset_default_graph()
m = Vgg(input_shape=[None, None, 3], type='vgg19')
with tf.Session() as sess:
# m = Vgg(input_shape=[None, None, 3], type='vgg19')
for hr, lr in loader:
y = m(hr, [2, 3], [2, 3])
break
# Error for not reusing VGG variables