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models.py
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from keras.applications.vgg16 import VGG16
from keras.engine.topology import Input
from keras.engine.training import Model
from keras.layers.convolutional import Conv2D, UpSampling2D, Conv2DTranspose
from keras.layers.core import Activation, SpatialDropout2D
from keras.layers.merge import concatenate
from keras.layers.normalization import BatchNormalization
from keras.layers.pooling import MaxPooling2D
from inception_resnet_v2 import InceptionResNetV2
from mobile_net_fixed import MobileNet
from resnet50_fixed import ResNet50
from params import args
def conv_block_simple(prevlayer, filters, prefix, strides=(1, 1)):
conv = Conv2D(filters, (3, 3), padding="same", kernel_initializer="he_normal", strides=strides, name=prefix + "_conv")(prevlayer)
conv = BatchNormalization(name=prefix + "_bn")(conv)
conv = Activation('relu', name=prefix + "_activation")(conv)
return conv
def conv_block_simple_no_bn(prevlayer, filters, prefix, strides=(1, 1)):
conv = Conv2D(filters, (3, 3), padding="same", kernel_initializer="he_normal", strides=strides, name=prefix + "_conv")(prevlayer)
conv = Activation('relu', name=prefix + "_activation")(conv)
return conv
"""
Unet with Mobile net encoder
Uses caffe preprocessing function
"""
def get_unet_resnet(input_shape):
resnet_base = ResNet50(input_shape=input_shape, include_top=False)
if args.show_summary:
resnet_base.summary()
for l in resnet_base.layers:
l.trainable = True
conv1 = resnet_base.get_layer("activation_1").output
conv2 = resnet_base.get_layer("activation_10").output
conv3 = resnet_base.get_layer("activation_22").output
conv4 = resnet_base.get_layer("activation_40").output
conv5 = resnet_base.get_layer("activation_49").output
up6 = concatenate([UpSampling2D()(conv5), conv4], axis=-1)
conv6 = conv_block_simple(up6, 256, "conv6_1")
conv6 = conv_block_simple(conv6, 256, "conv6_2")
up7 = concatenate([UpSampling2D()(conv6), conv3], axis=-1)
conv7 = conv_block_simple(up7, 192, "conv7_1")
conv7 = conv_block_simple(conv7, 192, "conv7_2")
up8 = concatenate([UpSampling2D()(conv7), conv2], axis=-1)
conv8 = conv_block_simple(up8, 128, "conv8_1")
conv8 = conv_block_simple(conv8, 128, "conv8_2")
up9 = concatenate([UpSampling2D()(conv8), conv1], axis=-1)
conv9 = conv_block_simple(up9, 64, "conv9_1")
conv9 = conv_block_simple(conv9, 64, "conv9_2")
vgg = VGG16(input_shape=input_shape, input_tensor=resnet_base.input, include_top=False)
for l in vgg.layers:
l.trainable = False
vgg_first_conv = vgg.get_layer("block1_conv2").output
up10 = concatenate([UpSampling2D()(conv9), resnet_base.input, vgg_first_conv], axis=-1)
conv10 = conv_block_simple(up10, 32, "conv10_1")
conv10 = conv_block_simple(conv10, 32, "conv10_2")
conv10 = SpatialDropout2D(0.2)(conv10)
x = Conv2D(1, (1, 1), activation="sigmoid", name="prediction")(conv10)
model = Model(resnet_base.input, x)
return model
def get_simple_unet(input_shape):
img_input = Input(input_shape)
conv1 = conv_block_simple(img_input, 32, "conv1_1")
conv1 = conv_block_simple(conv1, 32, "conv1_2")
pool1 = MaxPooling2D((2, 2), strides=(2, 2), padding="same", name="pool1")(conv1)
conv2 = conv_block_simple(pool1, 64, "conv2_1")
conv2 = conv_block_simple(conv2, 64, "conv2_2")
pool2 = MaxPooling2D((2, 2), strides=(2, 2), padding="same", name="pool2")(conv2)
conv3 = conv_block_simple(pool2, 128, "conv3_1")
conv3 = conv_block_simple(conv3, 128, "conv3_2")
pool3 = MaxPooling2D((2, 2), strides=(2, 2), padding="same", name="pool3")(conv3)
conv4 = conv_block_simple(pool3, 256, "conv4_1")
conv4 = conv_block_simple(conv4, 256, "conv4_2")
conv4 = conv_block_simple(conv4, 256, "conv4_3")
up5 = concatenate([UpSampling2D()(conv4), conv3], axis=-1)
conv5 = conv_block_simple(up5, 128, "conv5_1")
conv5 = conv_block_simple(conv5, 128, "conv5_2")
up6 = concatenate([UpSampling2D()(conv5), conv2], axis=-1)
conv6 = conv_block_simple(up6, 64, "conv6_1")
conv6 = conv_block_simple(conv6, 64, "conv6_2")
up7 = concatenate([UpSampling2D()(conv6), conv1], axis=-1)
conv7 = conv_block_simple(up7, 32, "conv7_1")
conv7 = conv_block_simple(conv7, 32, "conv7_2")
conv7 = SpatialDropout2D(0.2)(conv7)
prediction = Conv2D(1, (1, 1), activation="sigmoid", name="prediction")(conv7)
model = Model(img_input, prediction)
return model
"""
Unet with Mobile net encoder
Uses the same preprocessing as in Inception, Xception etc. (imagenet_utils.preprocess_input with mode 'tf' in new Keras version)
"""
def get_unet_mobilenet(input_shape):
base_model = MobileNet(include_top=False, input_shape=input_shape)
conv1 = base_model.get_layer('conv_pw_1_relu').output
conv2 = base_model.get_layer('conv_pw_3_relu').output
conv3 = base_model.get_layer('conv_pw_5_relu').output
conv4 = base_model.get_layer('conv_pw_11_relu').output
conv5 = base_model.get_layer('conv_pw_13_relu').output
up6 = concatenate([UpSampling2D()(conv5), conv4], axis=-1)
conv6 = conv_block_simple(up6, 256, "conv6_1")
conv6 = conv_block_simple(conv6, 256, "conv6_2")
up7 = concatenate([UpSampling2D()(conv6), conv3], axis=-1)
conv7 = conv_block_simple(up7, 256, "conv7_1")
conv7 = conv_block_simple(conv7, 256, "conv7_2")
up8 = concatenate([UpSampling2D()(conv7), conv2], axis=-1)
conv8 = conv_block_simple(up8, 192, "conv8_1")
conv8 = conv_block_simple(conv8, 128, "conv8_2")
up9 = concatenate([UpSampling2D()(conv8), conv1], axis=-1)
conv9 = conv_block_simple(up9, 96, "conv9_1")
conv9 = conv_block_simple(conv9, 64, "conv9_2")
up10 = concatenate([UpSampling2D()(conv9), base_model.input], axis=-1)
conv10 = conv_block_simple(up10, 48, "conv10_1")
conv10 = conv_block_simple(conv10, 32, "conv10_2")
conv10 = SpatialDropout2D(0.2)(conv10)
x = Conv2D(1, (1, 1), activation="sigmoid", name="prediction")(conv10)
model = Model(base_model.input, x)
return model
"""
Unet with Inception Resnet V2 encoder
Uses the same preprocessing as in Inception, Xception etc. (imagenet_utils.preprocess_input with mode 'tf' in new Keras version)
"""
def get_unet_inception_resnet_v2(input_shape):
base_model = InceptionResNetV2(include_top=False, input_shape=input_shape)
conv1 = base_model.get_layer('activation_3').output
conv2 = base_model.get_layer('activation_5').output
conv3 = base_model.get_layer('block35_10_ac').output
conv4 = base_model.get_layer('block17_20_ac').output
conv5 = base_model.get_layer('conv_7b_ac').output
up6 = concatenate([UpSampling2D()(conv5), conv4], axis=-1)
conv6 = conv_block_simple(up6, 256, "conv6_1")
conv6 = conv_block_simple(conv6, 256, "conv6_2")
up7 = concatenate([UpSampling2D()(conv6), conv3], axis=-1)
conv7 = conv_block_simple(up7, 256, "conv7_1")
conv7 = conv_block_simple(conv7, 256, "conv7_2")
up8 = concatenate([UpSampling2D()(conv7), conv2], axis=-1)
conv8 = conv_block_simple(up8, 128, "conv8_1")
conv8 = conv_block_simple(conv8, 128, "conv8_2")
up9 = concatenate([UpSampling2D()(conv8), conv1], axis=-1)
conv9 = conv_block_simple(up9, 64, "conv9_1")
conv9 = conv_block_simple(conv9, 64, "conv9_2")
up10 = concatenate([UpSampling2D()(conv9), base_model.input], axis=-1)
conv10 = conv_block_simple(up10, 48, "conv10_1")
conv10 = conv_block_simple(conv10, 32, "conv10_2")
conv10 = SpatialDropout2D(0.4)(conv10)
x = Conv2D(1, (1, 1), activation="sigmoid", name="prediction")(conv10)
model = Model(base_model.input, x)
return model
def get_vgg_7conv(input_shape):
img_input = Input(input_shape)
vgg16_base = VGG16(input_tensor=img_input, include_top=False)
for l in vgg16_base.layers:
l.trainable = True
conv1 = vgg16_base.get_layer("block1_conv2").output
conv2 = vgg16_base.get_layer("block2_conv2").output
conv3 = vgg16_base.get_layer("block3_conv3").output
pool3 = vgg16_base.get_layer("block3_pool").output
conv4 = Conv2D(384, (3, 3), activation="relu", padding='same', kernel_initializer="he_normal", name="block4_conv1")(pool3)
conv4 = Conv2D(384, (3, 3), activation="relu", padding='same', kernel_initializer="he_normal", name="block4_conv2")(conv4)
pool4 = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(conv4)
conv5 = Conv2D(512, (3, 3), activation="relu", padding='same', kernel_initializer="he_normal", name="block5_conv1")(pool4)
conv5 = Conv2D(512, (3, 3), activation="relu", padding='same', kernel_initializer="he_normal", name="block5_conv2")(conv5)
pool5 = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(conv5)
conv6 = Conv2D(512, (3, 3), activation="relu", padding='same', kernel_initializer="he_normal", name="block6_conv1")(pool5)
conv6 = Conv2D(512, (3, 3), activation="relu", padding='same', kernel_initializer="he_normal", name="block6_conv2")(conv6)
pool6 = MaxPooling2D((2, 2), strides=(2, 2), name='block6_pool')(conv6)
conv7 = Conv2D(512, (3, 3), activation="relu", padding='same', kernel_initializer="he_normal", name="block7_conv1")(pool6)
conv7 = Conv2D(512, (3, 3), activation="relu", padding='same', kernel_initializer="he_normal", name="block7_conv2")(conv7)
up8 = concatenate([Conv2DTranspose(384, (3, 3), activation="relu", kernel_initializer="he_normal", strides=(2, 2), padding='same')(conv7), conv6], axis=3)
conv8 = Conv2D(384, (3, 3), activation="relu", kernel_initializer="he_normal", padding='same')(up8)
up9 = concatenate([Conv2DTranspose(256, (3, 3), activation="relu", kernel_initializer="he_normal", strides=(2, 2), padding='same')(conv8), conv5], axis=3)
conv9 = Conv2D(256, (3, 3), activation="relu", kernel_initializer="he_normal", padding='same')(up9)
up10 = concatenate([Conv2DTranspose(192, (3, 3), activation="relu", kernel_initializer="he_normal", strides=(2, 2), padding='same')(conv9), conv4], axis=3)
conv10 = Conv2D(192, (3, 3), activation="relu", kernel_initializer="he_normal", padding='same')(up10)
up11 = concatenate([Conv2DTranspose(128, (3, 3), activation="relu", kernel_initializer="he_normal", strides=(2, 2), padding='same')(conv10), conv3], axis=3)
conv11 = Conv2D(128, (3, 3), activation="relu", kernel_initializer="he_normal", padding='same')(up11)
up12 = concatenate([Conv2DTranspose(64, (3, 3), activation="relu", kernel_initializer="he_normal", strides=(2, 2), padding='same')(conv11), conv2], axis=3)
conv12 = Conv2D(64, (3, 3), activation="relu", kernel_initializer="he_normal", padding='same')(up12)
up13 = concatenate([Conv2DTranspose(32, (3, 3), activation="relu", kernel_initializer="he_normal", strides=(2, 2), padding='same')(conv12), conv1], axis=3)
conv13 = Conv2D(32, (3, 3), activation="relu", kernel_initializer="he_normal", padding='same')(up13)
conv13 = Conv2D(1, (1, 1))(conv13)
conv13 = Activation("sigmoid")(conv13)
model = Model(img_input, conv13)
return model
def make_model(input_shape):
network = args.network
if network == 'resnet50':
return get_unet_resnet(input_shape)
if network == 'inception_resnet_v2':
return get_unet_inception_resnet_v2(input_shape)
elif network == 'mobilenet':
return get_unet_mobilenet(input_shape)
elif network == 'vgg':
return get_vgg_7conv(input_shape)
elif network == 'simple_unet':
return get_simple_unet(input_shape)
else:
raise ValueError("Unknown network")