def build_network_fc_special(args):
return SequentialNetwork([
Flatten(),
Dense(100, kernel_initializer=he_normal, activation=None, kernel_regularizer=l2reg(args.l2), name='fc_1'),
BatchNormalization(momentum=0, name='batch_norm_1'),
Activation('relu'),
Dense(50, kernel_initializer=he_normal, activation=None, kernel_regularizer=l2reg(args.l2), name='fc_2'),
BatchNormalization(momentum=0, name='batch_norm_1'),
Activation('relu'),
Dense(5, kernel_initializer=he_normal, activation=None, kernel_regularizer=l2reg(args.l2), name='fc_3')
])
def __init__(self, kernel_size, filters, first_stride, name_prefix='', identity=True, resize=1, l2=0, l2_shortcut=0, *args, **kwargs):
super(ResidualBlock, self).__init__(*args, **kwargs)
self.identity = identity
self.conv1 = self.track_layer(Conv2D(int(filters * resize), kernel_size, strides=first_stride, kernel_initializer=he_normal,
padding='same', kernel_regularizer=l2reg(l2), name=name_prefix+'conv2D_1'))
self.bn1 = self.track_layer(BatchNormalization(momentum=0.0, name=name_prefix+'batch_norm_1'))
self.act1 = self.track_layer(Activation('relu'))
self.conv2 = self.track_layer(Conv2D(filters, kernel_size, strides=(1, 1), kernel_initializer=he_normal,
padding='same', kernel_regularizer=l2reg(l2), name=name_prefix+'conv2D_2'))
self.bn2 = self.track_layer(BatchNormalization(momentum=0.0, name=name_prefix+'batch_norm_2'))
if not self.identity:
self.conv_shortcut = self.track_layer(Conv2D(filters, (1, 1), strides=first_stride, kernel_initializer=he_normal,
padding='same', kernel_regularizer=l2reg(l2_shortcut), name=name_prefix+'shortcut_conv'))
self.add_layer = self.track_layer(tfkeras.layers.Add())
self.act2 = self.track_layer(Activation('relu')) # TODO need relu on last block?
def build_all_cnn(args):
return SequentialNetwork([
#Dropout(0.1),
Conv2D(96, (3, 3), kernel_initializer=he_normal, padding='same', kernel_regularizer=l2reg(args.l2), name='conv2D_1'),
BatchNormalization(momentum=0.0, name='batch_norm_1'),
Activation('relu'),
Conv2D(96, (3, 3), kernel_initializer=he_normal, padding='same', kernel_regularizer=l2reg(args.l2), name='conv2D_2'),
BatchNormalization(momentum=0.0, name='batch_norm_2'),
Activation('relu'),
Conv2D(96, (3, 3), kernel_initializer=he_normal, padding='same', strides=(2, 2), name='conv2D_strided_1'),
Dropout(0.5),
Conv2D(192, (3, 3), kernel_initializer=he_normal, padding='same', kernel_regularizer=l2reg(args.l2), name='conv2D_3'),
BatchNormalization(momentum=0.0, name='batch_norm_3'),
Activation('relu'),
Conv2D(192, (3, 3), kernel_initializer=he_normal, padding='same', kernel_regularizer=l2reg(args.l2), name='conv2D_4'),
BatchNormalization(momentum=0.0, name='batch_norm_4'),
Activation('relu'),
Conv2D(192, (3, 3), kernel_initializer=he_normal, padding='same', strides=(2, 2), name='conv2D_strided_2'),
Dropout(0.5),
Conv2D(192, (3, 3), kernel_initializer=he_normal, padding='same', kernel_regularizer=l2reg(args.l2), name='conv2D_5'),
BatchNormalization(momentum=0.0, name='batch_norm_5'),
Activation('relu'),
Conv2D(192, (1, 1), kernel_initializer=he_normal, padding='same', kernel_regularizer=l2reg(args.l2), name='conv2D_6'),
BatchNormalization(momentum=0.0, name='batch_norm_6'),
Activation('relu'),
Conv2D(10, (1, 1), kernel_initializer=he_normal, padding='same', kernel_regularizer=l2reg(args.l2), name='conv2D_7'),
GlobalAveragePooling2D()
])
def conv_bn_relu(num_channel,
kernel_size,
stride,
name,
padding='same',
activation='relu'):
return [
Conv2D(filters=num_channel,
kernel_size=(kernel_size, kernel_size),
strides=stride,
padding=padding,
kernel_initializer="he_normal",
name=name + "_conv"),
BatchNormalization(name=name + '_bn'),
Activation(activation) # , name=name + '_relu'
]
def build_resnet(args):
return SequentialNetwork([
# pre-blocks
Conv2D(16, 3, kernel_initializer=he_normal, padding='same', kernel_regularizer=l2reg(args.l2), name='conv2D_1'),
BatchNormalization(momentum=0.0, name='batch_norm_1'),
Activation('relu'),
# set 1
ResidualBlock(3, 16, first_stride=(1, 1), name_prefix='1A_', identity=True, resize=args.resize_more, l2=args.l2, l2_shortcut=args.l2),
ResidualBlock(3, 16, first_stride=(1, 1), name_prefix='1B_', identity=True, resize=args.resize_more, l2=args.l2, l2_shortcut=args.l2),
ResidualBlock(3, 16, first_stride=(1, 1), name_prefix='1C_', identity=True, resize=args.resize_more, l2=args.l2, l2_shortcut=args.l2),
# set 2
ResidualBlock(3, 32, first_stride=(2, 2), name_prefix='2A_', identity=False, resize=args.resize_less, l2=args.l2, l2_shortcut=args.l2),
ResidualBlock(3, 32, first_stride=(1, 1), name_prefix='2B_', identity=True, resize=args.resize_less, l2=args.l2, l2_shortcut=args.l2),
ResidualBlock(3, 32, first_stride=(1, 1), name_prefix='2C_', identity=True, resize=args.resize_less, l2=args.l2, l2_shortcut=args.l2),
tf.layers.Conv2DTranspose(16, 2, strides=(2, 2)),
# tf.layers.Conv2DTranspose(1, 15, padding='valid'),
# set 3
ResidualBlock(3, 64, first_stride=(2, 2), name_prefix='3A_', identity=False, resize=args.resize_less, l2=args.l2, l2_shortcut=args.l2),
ResidualBlock(3, 64, first_stride=(1, 1), name_prefix='3B_', identity=True, resize=args.resize_more, l2=args.l2, l2_shortcut=args.l2),
ResidualBlock(3, 64, first_stride=(1, 1), name_prefix='3C_', identity=True, resize=args.resize_more, l2=args.l2, l2_shortcut=args.l2),
# post-blocks
# GlobalAveragePooling2D(),
tf.layers.Conv2DTranspose(16, 1, strides=(2, 2)),
ResidualBlock(3, 64, first_stride=(2, 2), name_prefix='4A_', identity=False, resize=args.resize_less, l2=args.l2, l2_shortcut=args.l2),
ResidualBlock(3, 64, first_stride=(1, 1), name_prefix='4B_', identity=True, resize=args.resize_more, l2=args.l2, l2_shortcut=args.l2),
ResidualBlock(3, 64, first_stride=(1, 1), name_prefix='4C_', identity=True, resize=args.resize_more, l2=args.l2, l2_shortcut=args.l2),
# tf.layers.Conv2DTranspose(32, 1, strides=(2, 2)),
tf.layers.Conv2DTranspose(16, 1, strides=(2, 2)),
tf.layers.Conv2DTranspose(8, 1, strides=(1, 1)),
tf.layers.Conv2DTranspose(1, 1, strides=(1, 1)),
# tf.layers.Conv2DTranspose(1, 5, padding='valid'),
# tf.layers.Conv2DTranspose(1, 11, padding='valid'),
# tf.layers.Conv2DTranspose(1, 15, padding='valid'),
# tf.layers.Conv2DTranspose(1, 21, padding='valid'),
# tf.layers.Conv2DTranspose(1, 42, padding='valid', name='probs'),
Activation('sigmoid', name='mask')
# Dense(10, kernel_initializer=he_normal, activation=None, kernel_regularizer=l2reg(args.l2_special), name='fc_last')
])
def __init__(self, n_filters, stride=1, downsample=None, name=None):
super(ResidualBlockLinkNet, self).__init__(name=name)
if downsample is None:
self.shortcut = None
else:
self.shortcut = self.track_layers(downsample)
self.conv_bn_relu = self.track_layers(
conv_bn_relu(n_filters,
kernel_size=3,
stride=stride,
name=name + '/cvbnrelu'))
self.conv1 = self.track_layer(
Conv2D(n_filters, (3, 3), name=name + '_conv2', padding='same'))
self.bn1 = self.track_layer(BatchNormalization(name=name + '_bn'))
self.add_layer = self.track_layer(tfkeras.layers.Add())
self.act1 = self.track_layer(Activation('relu'))
def encoder(m, n, blocks, stride, name='encoder'):
downsample = None
if stride != 1 or m != n:
downsample = [
Conv2D(n, (1, 1),
strides=(stride, stride),
name=name + '_conv_downsample'),
BatchNormalization(name=name + '_batchnorm_downsample')
]
layers = [
ResidualBlockLinkNet(n,
stride,
downsample,
name=name + '/residualBlock0')
]
for i in range(1, blocks):
layers.append(
ResidualBlockLinkNet(n,
stride=1,
name=name + '/residualBlock{}'.format(i)))
return layers
def deconv_bn_relu(num_channels,
kernel_size,
name,
transposed_conv,
activation='relu'):
layers = []
if transposed_conv:
layers.append(
tf.layers.Conv2DTranspose(num_channels,
kernel_size=(4, 4),
strides=(2, 2),
padding="same"))
else:
layers.append(tf.keras.layers.UpSampling2D())
layers.append(
Conv2D(num_channels,
kernel_size=(kernel_size, kernel_size),
kernel_initializer="he_normal",
padding='same'))
layers.append(BatchNormalization(name=name + '_bn'))
layers.append(Activation(activation))
return layers
def build_resnet(args):
return SequentialNetwork([
# pre-blocks
Conv2D(16, 3, kernel_initializer=he_normal, padding='same', kernel_regularizer=l2reg(args.l2), name='conv2D_1'),
BatchNormalization(momentum=0.0, name='batch_norm_1'),
Activation('relu'),
# set 1
ResidualBlock(3, 16, first_stride=(1, 1), name_prefix='1A_', identity=True, resize=args.resize_more, l2=args.l2, l2_shortcut=args.l2),
ResidualBlock(3, 16, first_stride=(1, 1), name_prefix='1B_', identity=True, resize=args.resize_more, l2=args.l2, l2_shortcut=args.l2),
ResidualBlock(3, 16, first_stride=(1, 1), name_prefix='1C_', identity=True, resize=args.resize_more, l2=args.l2, l2_shortcut=args.l2),
# set 2
ResidualBlock(3, 32, first_stride=(2, 2), name_prefix='2A_', identity=False, resize=args.resize_less, l2=args.l2, l2_shortcut=args.l2),
ResidualBlock(3, 32, first_stride=(1, 1), name_prefix='2B_', identity=True, resize=args.resize_less, l2=args.l2, l2_shortcut=args.l2),
ResidualBlock(3, 32, first_stride=(1, 1), name_prefix='2C_', identity=True, resize=args.resize_less, l2=args.l2, l2_shortcut=args.l2),
# set 3
ResidualBlock(3, 64, first_stride=(2, 2), name_prefix='3A_', identity=False, resize=args.resize_less, l2=args.l2, l2_shortcut=args.l2),
ResidualBlock(3, 64, first_stride=(1, 1), name_prefix='3B_', identity=True, resize=args.resize_more, l2=args.l2, l2_shortcut=args.l2),
ResidualBlock(3, 64, first_stride=(1, 1), name_prefix='3C_', identity=True, resize=args.resize_more, l2=args.l2, l2_shortcut=args.l2),
# post-blocks
GlobalAveragePooling2D(),
Dense(10, kernel_initializer=he_normal, activation=None, kernel_regularizer=l2reg(args.l2_special), name='fc_last')
])