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model.py
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model.py
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import keras
from keras.layers import Input, Conv2D, Dense, MaxPooling2D, Flatten, Conv2DTranspose, Dropout, UpSampling2D
from keras.layers.merge import concatenate
from keras.models import Model
from keras.optimizers import Adam
from keras import backend as K
from loss_funcs import dice_coef, true_positive_rate, dice_p_bce
def basic_cnn(input_dim=(512, 512, 3)):
inputs = Input(shape=input_dim)
conv1 = Conv2D(filters=64, kernel_size=3, activation='relu', padding='same')(inputs)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(filters=32, kernel_size=3, activation='relu', padding='same')(pool1)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
flat = Flatten()(pool2)
fully_connected = Dense(256, activation='relu')(flat)
predictions = Dense(10, activation='softmax')(fully_connected)
model = Model(inputs=inputs, outputs=predictions)
model.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['accuracy'])
return model
def hourglass_cnn(input_dim=(512, 512, 1)):
inputs = Input(shape=input_dim)
conv1 = Conv2D(filters=32, kernel_size=3)(inputs)
pool1 = MaxPooling2D(pool_size=(2,2))(conv1)
conv2 = Conv2D(filters=64, kernel_size=3)(pool1)
pool2 = MaxPooling2D(pool_size=(2,2))(conv2)
conv3 = Conv2D(filters=128, kernel_size=3)(pool2)
pool3 = MaxPooling2D(pool_size=(2,2))(conv3)
conv4 = Conv2D(filters=256, kernel_size=3)(pool3)
pool4 = MaxPooling2D(pool_size=(2,2))(conv4)
conv5 = Conv2D(filters=512, kernel_size=3)(pool4)
deconv1 = Conv2DTranspose(filters=512, kernel_size=3)(conv5)
deconv2 = Conv2DTranspose(filters=256, kernel_size=3)(deconv1)
deconv3 = Conv2DTranspose(filters=128, kernel_size=3)(pool3)
deconv4 = Conv2DTranspose(filters=64, kernel_size=3)(pool2)
deconv5 = Conv2DTranspose(filters=32, kernel_size=3)(deconv4)
flattened = Flatten()(deconv5)
dense = Dense(256, activation ='relu')(flattened)
predictions = Dense(10, activation='softmax')(dense)
model = Model(inputs=inputs, outputs=predictions)
model.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['accuracy'])
return model
def unet(input_dim = (512,512,3)):
inputs = Input(shape=input_dim)
# L1
conv1 = Conv2D(filters = 64, kernel_size = 3, padding='same')(inputs)
conv2 = Conv2D(filters = 64, kernel_size = 3, padding='same')(conv1)
# L2
down1 = MaxPooling2D(pool_size=(2,2), padding='same')(conv2)
conv3 = Conv2D(filters = 128, kernel_size = 3, padding='same')(down1)
conv4 = Conv2D(filters = 128, kernel_size = 3, padding='same')(conv3)
# L3
down2 = MaxPooling2D(pool_size=(2,2), padding='same')(conv4)
conv5 = Conv2D(filters = 256, kernel_size = 3, padding='same')(down2)
conv6 = Conv2D(filters = 256, kernel_size = 3, padding='same')(conv5)
# L4
down3 = MaxPooling2D(pool_size=(2,2), padding='same')(conv6)
conv7 = Conv2D(filters = 512, kernel_size = 3, padding='same')(down3)
conv8 = Conv2D(filters = 512, kernel_size = 3, padding='same')(conv7)
# L5
down4 = MaxPooling2D(pool_size=(2,2), padding='same')(conv8)
conv9 = Conv2D(filters=1024, kernel_size = 3, padding='same')(down4)
conv10 = Conv2D(filters=1024, kernel_size=3, padding='same')(conv9)
# Merge upsampled L5 with output from L4
upsampled_conv10 = Conv2DTranspose(filters=512, kernel_size=3, strides=2, padding='same')(conv10)
print(upsampled_conv10.shape)
# Up-L4
up1 = concatenate([conv8, upsampled_conv10])
conv11 = Conv2D(filters = 512, kernel_size= 3, padding='same')(up1)
conv12 = Conv2D(filters = 512, kernel_size= 3, padding='same')(conv11)
# Up-L3 - merge upsampled L4 with output from L3
upsampled_conv12 = Conv2DTranspose(filters=256, kernel_size = 3, strides=2, padding='same')(conv12)
up2 = concatenate([conv6, upsampled_conv12])
conv13 = Conv2D(filters = 256, kernel_size= 3, padding='same')(up2)
conv14 = Conv2D(filters = 256, kernel_size= 3, padding='same')(conv13)
# Up-L2 - merge upsampled L3 with output from L2
upsampled_conv14 = Conv2DTranspose(filters=128, kernel_size = 3, strides=2, padding='same')(conv14)
up3 = concatenate([conv4, upsampled_conv14])
conv15 = Conv2D(filters = 128, kernel_size= 3, padding='same')(up3)
conv16 = Conv2D(filters = 128, kernel_size= 3, padding='same')(conv15)
# Up-L1 - merge upsampled L2 with output from L1
upsampled_conv16 = Conv2DTranspose(filters=64, kernel_size = 3, strides=2, padding='same')(conv16)
up4 = concatenate([conv2, upsampled_conv16])
conv17 = Conv2D(filters = 64, kernel_size = 3, padding='same')(up4)
conv18 = Conv2D(filters = 64, kernel_size= 3, padding='same')(conv17)
conv19 = Conv2D(filters = 2, kernel_size = 3, padding='same')(conv18)
# Final layer - makes 768x768x1 image
segmap = Conv2D(filters = 1, kernel_size = 1, activation = 'sigmoid')(conv19)
model = Model(inputs=inputs, outputs=segmap)
print(model.summary())
model.compile(optimizer = Adam(1e-4, decay=1e-6), loss = dice_p_bce, metrics = ['accuracy', dice_coef, true_positive_rate])
return model
def ship_cnn(input_dim=(512, 512, 3)):
inputs = Input(shape=input_dim)
conv1 = Conv2D(filters=64, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(inputs)
pool1 = MaxPooling2D(pool_size=(2, 2), padding='same')(conv1)
conv2 = Conv2D(filters=128, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(pool1)
drop2 = Dropout(0.5)(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2), padding='same')(drop2)
conv3 = Conv2D(filters=256, kernel_size=3, activation='relu', padding='same', kernel_initializer='he_normal')(pool2)
drop3 = Dropout(0.5)(conv3)
# Up-L1 - merge upsampled L2 with output from L1
#upsampled_conv1 = Conv2DTranspose(filters=32, kernel_size=3, strides=2, padding='same')(conv3)
up1 = Conv2D(128, 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling2D(size=(2, 2))(drop3))
merge1 = concatenate([drop2, up1], axis=3)
conv4 = Conv2D(filters = 128, kernel_size=3, padding='same')(merge1)
# Up-L1 - merge upsampled L2 with output from L1
#upsampled_conv2 = Conv2DTranspose(filters=64, kernel_size=3, strides=2, padding='same')
up2 = Conv2D(64, 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling2D(size=(2, 2))(conv4))
merge2 = concatenate([conv1, up2], axis=3)
conv5 = Conv2D(filters = 64, kernel_size=3, padding='same', kernel_initializer='he_normal')(merge2)
conv6 = Conv2D(2, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv5)
# Final layer - makes 768x768x1 image
segmap = Conv2D(filters = 1, kernel_size = 1, activation = 'sigmoid')(conv6)
model = Model(inputs=inputs, outputs=segmap)
print(model.summary())
model.compile(optimizer = Adam(lr = 0.0001), loss = 'binary_crossentropy', metrics = ['accuracy'])
return model
def test_unet(input_dim=(768, 768, 3)):
inputs = Input(input_dim)
conv1 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(inputs)
conv1 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool1)
conv2 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool2)
conv3 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool3)
conv4 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv4)
drop4 = Dropout(0.5)(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
conv5 = Conv2D(1024, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool4)
conv5 = Conv2D(1024, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv5)
drop5 = Dropout(0.5)(conv5)
up6 = Conv2D(512, 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling2D(size=(2, 2))(drop5))
merge6 = concatenate([drop4, up6], axis=3)
conv6 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge6)
conv6 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv6)
up7 = Conv2D(256, 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling2D(size=(2, 2))(conv6))
merge7 = concatenate([conv3, up7], axis=3)
conv7 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge7)
conv7 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv7)
up8 = Conv2D(128, 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling2D(size=(2, 2))(conv7))
merge8 = concatenate([conv2, up8], axis=3)
conv8 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge8)
conv8 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv8)
up9 = Conv2D(64, 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling2D(size=(2, 2))(conv8))
merge9 = concatenate([conv1, up9], axis=3)
conv9 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge9)
conv9 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv9)
conv9 = Conv2D(2, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv9)
conv10 = Conv2D(1, 1, activation='sigmoid')(conv9)
model = Model(input=inputs, output=conv10)
model.compile(optimizer=Adam(lr=1e-4), loss='binary_crossentropy', metrics=['accuracy'])
return model