def make_network(env, h=None, w=None):
with env.create_network() as net:
if h is None:
img = O.placeholder('img', shape=(1, None, None, 3))
else:
img = O.variable('img', np.zeros([1, h, w, 3]))
net.add_output(img, name='img')
_ = img
_ = _ - get_env('neural_style.image_mean').reshape(1, 1, 1, 3)
_ = O.pad_rb_multiple_of(_, 32)
def stacked_conv(prefix, nr_convs, in_, channel, kernel=(3, 3), padding='SAME', nonlin=O.relu):
for i in range(1, nr_convs + 1):
in_ = O.conv2d('{}_{}'.format(prefix, i), in_, channel, kernel, padding=padding, nonlin=nonlin)
return in_
_ = stacked_conv('conv1', 2, _, 64)
_ = O.pooling2d('pool1', _, (2, 2))
_ = stacked_conv('conv2', 2, _, 128)
_ = O.pooling2d('pool2', _, (2, 2))
_ = stacked_conv('conv3', 3, _, 256)
_ = O.pooling2d('pool3', _, (2, 2))
_ = stacked_conv('conv4', 3, _, 512)
_ = O.pooling2d('pool4', _, (2, 2))
_ = stacked_conv('conv5', 3, _, 512)
_ = O.pooling2d('pool5', _, (2, 2))
for l in get_env('neural_style.content_layers'):
net.add_output(net.find_var_by_name(l[0] + '/bias'), name=l[0])
for l in get_env('neural_style.style_layers'):
net.add_output(net.find_var_by_name(l[0] + '/bias'), name=l[0])
def forward(img):
_ = img
_ = conv2d('conv1.1', _, 16, (3, 3), padding='SAME')
_ = conv2d('conv1.2', _, 16, (3, 3), padding='SAME')
_ = O.pooling2d('pool1', _, kernel=3, stride=2)
_ = conv2d('conv2.1', _, 32, (3, 3), padding='SAME')
_ = conv2d('conv2.2', _, 32, (3, 3), padding='SAME')
_ = O.pooling2d('pool2', _, kernel=3, stride=2)
_ = conv2d('conv3.1', _, 64, (3, 3), padding='VALID')
_ = conv2d('conv3.2', _, 64, (3, 3), padding='VALID')
_ = conv2d('conv3.3', _, 64, (3, 3), padding='VALID')
dpc.add_output(_, name='feature')
def forward(img):
_ = img
_ = O.conv2d('conv1',
_,
16, (3, 3),
padding='SAME',
nonlin=O.relu)
_ = O.pooling2d('pool1', _, kernel=2)
_ = O.conv2d('conv2',
_,
32, (3, 3),
padding='SAME',
nonlin=O.relu)
_ = O.pooling2d('pool2', _, kernel=2)
dpc.add_output(_, name='feature')
def forward(img):
_ = img
_ = O.conv2d('conv1',
_,
16, (3, 3),
padding='SAME',
nonlin=O.identity)
_ = O.batch_norm('bn1', _)
_ = O.relu(_)
_ = O.pooling2d('pool1', _, kernel=2)
_ = O.conv2d('conv2',
_,
32, (3, 3),
padding='SAME',
nonlin=O.identity)
_ = O.batch_norm('bn2', _)
_ = O.relu(_)
_ = O.pooling2d('pool2', _, kernel=2)
dpc.add_output(_, name='feature')
def make_network(env, end, h, w):
with env.create_network() as net:
img = O.variable('img', np.zeros([1, h, w, 3]))
net.add_output(img, name='img')
_ = img
_ = _ - get_env('deep_dream.image_mean').reshape(1, 1, 1, 3)
_ = O.pad_rb_multiple_of(_, 32)
def stacked_conv(prefix,
nr_convs,
in_,
channel,
kernel=(3, 3),
padding='SAME',
nonlin=O.relu):
for i in range(1, nr_convs + 1):
in_ = O.conv2d('{}_{}'.format(prefix, i),
in_,
channel,
kernel,
padding=padding,
nonlin=nonlin)
return in_
_ = stacked_conv('conv1', 2, _, 64)
_ = O.pooling2d('pool1', _, (2, 2))
_ = stacked_conv('conv2', 2, _, 128)
_ = O.pooling2d('pool2', _, (2, 2))
_ = stacked_conv('conv3', 3, _, 256)
_ = O.pooling2d('pool3', _, (2, 2))
_ = stacked_conv('conv4', 3, _, 512)
_ = O.pooling2d('pool4', _, (2, 2))
_ = stacked_conv('conv5', 3, _, 512)
_ = O.pooling2d('pool5', _, (2, 2))
net.add_output(net.find_var_by_name(end + '/bias'), name='end')
def residual(name, x, first=False, inc_dim=False):
in_channel = x.static_shape[3]
out_channel = in_channel
stride = 1
if inc_dim:
out_channel = in_channel * 2
stride = 2
with env.variable_scope(name):
_ = x if first else O.bn_relu(x)
_ = conv_bn_relu('conv1', _, out_channel, stride=stride)
_ = conv2d('conv2', _, out_channel)
if inc_dim:
x = O.pooling2d('pool', x, kernel=2)
x = O.pad(x, [[0, 0], [0, 0], [0, 0],
[in_channel // 2, in_channel // 2]])
print(name, x.static_shape)
_ = _ + x
return _