from keras import Model
from keras.layers import Conv2D, Conv2DTranspose, concatenate
from keras.optimizers import Adam
class AutoEncoderModel(object):
def __init__(self, left_input, right_input, lr=1e-4, rows=128, cols=512):
self.rows = rows
self.cols = cols
self.left = left_input
self.right = right_input
self.left_est = None
self.right_est = None
self.output = None
self.model = None
self.lr = lr
self.build_architecture()
self.build_outputs()
self.build_model()
@staticmethod
def conv(input, channels, kernel_size, strides, activation='elu'):
return Conv2D(channels, kernel_size=kernel_size, strides=strides, padding='same', activation=activation)(input)
@staticmethod
def deconv(input, channels, kernel_size, scale):
return Conv2DTranspose(channels, kernel_size=kernel_size, strides=scale, padding='same')(input)
def conv_block(self, input, channels, kernel_size):
conv1 = self.conv(input, channels, kernel_size, 1)
conv2 = self.conv(conv1, channels, kernel_size, 2)
return conv2
def deconv_block(self, input, channels, kernel_size, skip):
deconv1 = self.deconv(input, channels, kernel_size, 2)
if skip is not None:
concat1 = concatenate([deconv1, skip], 3)
else:
concat1 = deconv1
iconv1 = self.conv(concat1, channels, kernel_size, 1)
return iconv1
def get_output(self, deconv):
return self.conv(deconv, 3, 3, 1, 'sigmoid')
def build_architecture(self):
# encoder
conv1 = self.conv_block(self.left, 32, 7)
conv2 = self.conv_block(conv1, 64, 5)
conv3 = self.conv_block(conv2, 128, 3)
conv4 = self.conv_block(conv3, 256, 3)
conv5 = self.conv_block(conv4, 512, 3)
conv6 = self.conv_block(conv5, 512, 3)
conv7 = self.conv_block(conv6, 512, 3)
# skips
skip1 = conv1
skip2 = conv2
skip3 = conv3
skip4 = conv4
skip5 = conv5
skip6 = conv6
deconv7 = self.deconv_block(conv7, 512, 3, skip6)
deconv6 = self.deconv_block(deconv7, 512, 3, skip5)
deconv5 = self.deconv_block(deconv6, 256, 3, skip4)
deconv4 = self.deconv_block(deconv5, 128, 3, skip3)
deconv3 = self.deconv_block(deconv4, 64, 3, skip2)
deconv2 = self.deconv_block(deconv3, 32, 3, skip1)
deconv1 = self.deconv_block(deconv2, 16, 3, None)
self.output = self.get_output(deconv1)
def build_outputs(self):
self.left_est = self.output
# self.right_est = expand_dims(self.output, 1, 'right_estimate')
def build_model(self):
self.model = Model(inputs=[self.left], outputs=[self.left_est])
self.model.compile(loss=['mae'],
optimizer='adadelta',
metrics=['mse'])
