def build_cnn24(input_var, num_classes, conv, max_pool):
network12 = lasagne.layers.InputLayer(shape=(None, 3, 12, 12),
input_var=input_var[1])
network12 = lasagne.layers.dropout(network12, p=.1)
network12 = conv(network12, num_filters=16, filter_size=(3, 3))
network12 = batchnorm.batch_norm(network12)
network12 = max_pool(network12)
network12 = GoodDenseLayer(lasagne.layers.dropout(network12, p=.3),
num_units=16)
network12 = batchnorm.batch_norm(network12)
network = lasagne.layers.InputLayer(shape=(None, 3, 24, 24),
input_var=input_var[0])
network = lasagne.layers.dropout(network, p=.1)
network = conv(network, num_filters=64, filter_size=(5, 5))
network = batchnorm.batch_norm(network)
network = max_pool(network)
network = GoodDenseLayer(lasagne.layers.dropout(network, p=.3),
num_units=128)
network = batchnorm.batch_norm(network)
network = lasagne.layers.ConcatLayer([network, network12])
network = GoodClsDenseLayer(lasagne.layers.dropout(network, p=.3),
num_units=num_classes)
return network
def build_cnn24_cal(input_var, num_classes, conv, max_pool):
network = lasagne.layers.InputLayer(shape=(None, 3, 24, 24), input_var=input_var)
network = lasagne.layers.dropout(network, p=.1)
network = conv(network, num_filters=32, filter_size=(5, 5))
network = batchnorm.batch_norm(network)
network = max_pool(network)
network = GoodDenseLayer(lasagne.layers.dropout(network, p=.5), num_units=64)
network = batchnorm.batch_norm(network)
network = GoodClsDenseLayer(lasagne.layers.dropout(network, p=.5), num_units=num_classes)
return network
def build_cnn12_cal(input_var, num_classes, conv, max_pool):
network = lasagne.layers.InputLayer(shape=(None, 3, 12, 12), input_var=input_var)
network = lasagne.layers.dropout(network, p=.1)
network = conv(network, num_filters=16, filter_size=(3, 3))
network = batchnorm.batch_norm(network)
network = max_pool(network)
network = GoodDenseLayer(lasagne.layers.dropout(network, p=.3), num_units=128)
network = batchnorm.batch_norm(network)
network = GoodClsDenseLayer(lasagne.layers.dropout(network, p=.3), num_units=num_classes)
return network
def build_cnn24_cal(input_var, num_classes, conv, max_pool):
network = lasagne.layers.InputLayer(shape=(None, 3, 24, 24),
input_var=input_var)
network = lasagne.layers.dropout(network, p=.1)
network = conv(network, num_filters=32, filter_size=(5, 5))
network = batchnorm.batch_norm(network)
network = max_pool(network)
network = GoodDenseLayer(lasagne.layers.dropout(network, p=.5),
num_units=64)
network = batchnorm.batch_norm(network)
network = GoodClsDenseLayer(lasagne.layers.dropout(network, p=.5),
num_units=num_classes)
return network
def build_cnn12_cal(input_var, num_classes, conv, max_pool):
network = lasagne.layers.InputLayer(shape=(None, 3, 12, 12),
input_var=input_var)
network = lasagne.layers.dropout(network, p=.1)
network = conv(network, num_filters=16, filter_size=(3, 3))
network = batchnorm.batch_norm(network)
network = max_pool(network)
network = GoodDenseLayer(lasagne.layers.dropout(network, p=.3),
num_units=128)
network = batchnorm.batch_norm(network)
network = GoodClsDenseLayer(lasagne.layers.dropout(network, p=.3),
num_units=num_classes)
return network
def conv2d(X, w, sigmoid=False, bn=False):
with tf.variable_scope('conv2d'):
X = tf.nn.conv2d(X, w, [1, 1, 1, 1], 'SAME')
if bn:
X = batch_norm(X, w.get_shape()[3])
if sigmoid:
return tf.sigmoid(X)
else:
X = tf.nn.relu(X)
return tf.maximum(0.01 * X, X)
def build_cnn24(input_var, num_classes, conv, max_pool):
network12 = lasagne.layers.InputLayer(shape=(None, 3, 12, 12), input_var=input_var[1])
network12 = lasagne.layers.dropout(network12, p=.1)
network12 = conv(network12, num_filters=16, filter_size=(3, 3))
network12 = batchnorm.batch_norm(network12)
network12 = max_pool(network12)
network12 = GoodDenseLayer(lasagne.layers.dropout(network12, p=.3), num_units=16)
network12 = batchnorm.batch_norm(network12)
network = lasagne.layers.InputLayer(shape=(None, 3, 24, 24), input_var=input_var[0])
network = lasagne.layers.dropout(network, p=.1)
network = conv(network, num_filters=64, filter_size=(5, 5))
network = batchnorm.batch_norm(network)
network = max_pool(network)
network = GoodDenseLayer(lasagne.layers.dropout(network, p=.3), num_units=128)
network = batchnorm.batch_norm(network)
network = lasagne.layers.ConcatLayer([network, network12])
network = GoodClsDenseLayer(lasagne.layers.dropout(network, p=.3), num_units=num_classes)
return network
def colorize_net(_tensors):
with tf.variable_scope('colorize_net'):
# the conv5_3 layer of vgg is batch normalized, and 1x1 conv is applied on it
# Bx14x14x512 -> batch norm -> 1x1 conv = Bx14x14x512
conv0 = conv2d(batch_norm(_tensors["conv5_3"], 512),
_tensors["weights"]['wc0'],
sigmoid=False,
bn=True)
# upscale the resulting layer to 28x28x512
conv0 = tf.image.resize_bilinear(conv0, (28, 28))
# add the layer to conv4_3 vgg layer
conv0 = tf.add(conv0, batch_norm(_tensors["conv4_3"], 512))
# the conv4_3 layer of vgg is batch normalized, and 1x1 conv is applied on it
# Bx28x28x512 -> 1x1 conv = Bx28x28x256
conv1 = conv2d(conv0,
_tensors["weights"]['wc1'],
sigmoid=False,
bn=True)
# upscale the resulting layer to 56x56x256
conv1 = tf.image.resize_bilinear(conv1, (56, 56))
# add the upscaled layer to conv3_3 vgg layer
conv1 = tf.add(conv1, batch_norm(_tensors["conv3_3"], 256))
# Bx56x56x256 -> 3x3 conv = Bx56x56x128
conv2 = conv2d(conv1,
_tensors["weights"]['wc2'],
sigmoid=False,
bn=True)
# upscale the resulting layer to 112x112x128
conv2 = tf.image.resize_bilinear(conv2, (112, 112))
# add the upscaled layer to conv3_3 vgg layer
conv2 = tf.add(conv2, batch_norm(_tensors["conv2_2"], 128))
# Bx112x112x128 -> 3x3 conv = Bx112x112x64
conv3 = conv2d(conv2,
_tensors["weights"]['wc3'],
sigmoid=False,
bn=True)
# upscale the resulting layer to Bx224x224x64
conv3 = tf.image.resize_bilinear(conv3, (224, 224))
# add the upscaled layer to conv1_2 vgg layer
conv3 = tf.add(conv3, batch_norm(_tensors["conv1_2"], 64))
# Bx224x224x64 -> batch norm -> 3x3 conv = Bx224x224x3
conv4 = conv2d(conv3,
_tensors["weights"]['wc4'],
sigmoid=False,
bn=True)
# add the layer to greyscale image layer
conv4 = tf.add(conv4, batch_norm(_tensors["grayscale"], 3))
# Bx224x224x3 -> 3x3 conv = Bx224x224x3
conv5 = conv2d(conv4,
_tensors["weights"]['wc5'],
sigmoid=False,
bn=True)
# Bx224x224x3 -> 3x3 conv = Bx224x224x2
conv6 = conv2d(conv5,
_tensors["weights"]['wc6'],
sigmoid=True,
bn=True)
return conv6
def build_cnn64_mp(input_var, num_classes, conv, max_pool):
network24 = lasagne.layers.InputLayer(shape=(None, 3, 24, 24),
input_var=input_var[2])
network24 = lasagne.layers.dropout(network24, p=.1)
network24 = conv(network24, num_filters=64, filter_size=(3, 3))
network24 = batchnorm.batch_norm(network24)
network24 = max_pool(network24)
network24 = conv(network24, num_filters=64, filter_size=(3, 3))
network24 = batchnorm.batch_norm(network24)
network24 = conv(network24, num_filters=64, filter_size=(3, 3))
network24 = batchnorm.batch_norm(network24)
network24 = max_pool(network24)
network24 = GoodDenseLayer(lasagne.layers.dropout(network24, p=.3),
num_units=128)
network24 = batchnorm.batch_norm(network24)
network48 = lasagne.layers.InputLayer(shape=(None, 3, 48, 48),
input_var=input_var[1])
network48 = lasagne.layers.dropout(network48, p=.1)
network48 = conv(network48, num_filters=96, filter_size=(3, 3))
network48 = batchnorm.batch_norm(network48)
network48 = max_pool(network48)
network48 = conv(network48, num_filters=96, filter_size=(3, 3))
network48 = batchnorm.batch_norm(network48)
network48 = conv(network48, num_filters=96, filter_size=(3, 3))
network48 = batchnorm.batch_norm(network48)
network48 = max_pool(network48)
network48 = conv(network48, num_filters=96, filter_size=(3, 3))
network48 = batchnorm.batch_norm(network48)
network48 = conv(network48, num_filters=96, filter_size=(3, 3))
network48 = batchnorm.batch_norm(network48)
network48 = max_pool(network48)
network48 = GoodDenseLayer(lasagne.layers.dropout(network48, p=.3),
num_units=192)
network48 = batchnorm.batch_norm(network48)
network = lasagne.layers.InputLayer(shape=(None, 3, 64, 64),
input_var=input_var[0])
network = lasagne.layers.dropout(network, p=.1)
network = conv(network, num_filters=128, filter_size=(3, 3))
network = batchnorm.batch_norm(network)
network = max_pool(network)
network = conv(network, num_filters=128, filter_size=(3, 3))
network = batchnorm.batch_norm(network)
network = conv(network, num_filters=128, filter_size=(3, 3))
network = batchnorm.batch_norm(network)
network = max_pool(network)
network = conv(network, num_filters=128, filter_size=(3, 3))
network = batchnorm.batch_norm(network)
network = conv(network, num_filters=128, filter_size=(3, 3))
network = batchnorm.batch_norm(network)
network = max_pool(network)
network = conv(network, num_filters=128, filter_size=(3, 3))
network = batchnorm.batch_norm(network)
network = conv(network, num_filters=128, filter_size=(3, 3))
network = batchnorm.batch_norm(network)
network = max_pool(network)
network = GoodDenseLayer(lasagne.layers.dropout(network, p=.3),
num_units=256)
network = batchnorm.batch_norm(network)
network = lasagne.layers.ConcatLayer([network, network48, network24])
network = GoodClsDenseLayer(lasagne.layers.dropout(network, p=.3),
num_units=num_classes)
return network
def build_cnn64_mp(input_var, num_classes, conv, max_pool):
network24 = lasagne.layers.InputLayer(shape=(None, 3, 24, 24), input_var=input_var[2])
network24 = lasagne.layers.dropout(network24, p=.1)
network24 = conv(network24, num_filters=64, filter_size=(3, 3))
network24 = batchnorm.batch_norm(network24)
network24 = max_pool(network24)
network24 = conv(network24, num_filters=64, filter_size=(3, 3))
network24 = batchnorm.batch_norm(network24)
network24 = conv(network24, num_filters=64, filter_size=(3, 3))
network24 = batchnorm.batch_norm(network24)
network24 = max_pool(network24)
network24 = GoodDenseLayer(lasagne.layers.dropout(network24, p=.3), num_units=128)
network24 = batchnorm.batch_norm(network24)
network48 = lasagne.layers.InputLayer(shape=(None, 3, 48, 48), input_var=input_var[1])
network48 = lasagne.layers.dropout(network48, p=.1)
network48 = conv(network48, num_filters=96, filter_size=(3, 3))
network48 = batchnorm.batch_norm(network48)
network48 = max_pool(network48)
network48 = conv(network48, num_filters=96, filter_size=(3, 3))
network48 = batchnorm.batch_norm(network48)
network48 = conv(network48, num_filters=96, filter_size=(3, 3))
network48 = batchnorm.batch_norm(network48)
network48 = max_pool(network48)
network48 = conv(network48, num_filters=96, filter_size=(3, 3))
network48 = batchnorm.batch_norm(network48)
network48 = conv(network48, num_filters=96, filter_size=(3, 3))
network48 = batchnorm.batch_norm(network48)
network48 = max_pool(network48)
network48 = GoodDenseLayer(lasagne.layers.dropout(network48, p=.3), num_units=192)
network48 = batchnorm.batch_norm(network48)
network = lasagne.layers.InputLayer(shape=(None, 3, 64, 64), input_var=input_var[0])
network = lasagne.layers.dropout(network, p=.1)
network = conv(network, num_filters=128, filter_size=(3, 3))
network = batchnorm.batch_norm(network)
network = max_pool(network)
network = conv(network, num_filters=128, filter_size=(3, 3))
network = batchnorm.batch_norm(network)
network = conv(network, num_filters=128, filter_size=(3, 3))
network = batchnorm.batch_norm(network)
network = max_pool(network)
network = conv(network, num_filters=128, filter_size=(3, 3))
network = batchnorm.batch_norm(network)
network = conv(network, num_filters=128, filter_size=(3, 3))
network = batchnorm.batch_norm(network)
network = max_pool(network)
network = conv(network, num_filters=128, filter_size=(3, 3))
network = batchnorm.batch_norm(network)
network = conv(network, num_filters=128, filter_size=(3, 3))
network = batchnorm.batch_norm(network)
network = max_pool(network)
network = GoodDenseLayer(lasagne.layers.dropout(network, p=.3), num_units=256)
network = batchnorm.batch_norm(network)
network = lasagne.layers.ConcatLayer([network, network48, network24])
network = GoodClsDenseLayer(lasagne.layers.dropout(network, p=.3), num_units=num_classes)
return network
