Spaces:
Running
Running
File size: 2,062 Bytes
911c613 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 |
from keras.models import Model
from keras.layers import Input
from keras.layers import Conv2D, BatchNormalization, MaxPooling2D, Dropout, Concatenate, UpSampling2D
class SegNet:
def __init__(self, n_filters, input_dim_x, input_dim_y, num_channels):
self.input_dim_x = input_dim_x
self.input_dim_y = input_dim_y
self.n_filters = n_filters
self.num_channels = num_channels
def get_SegNet(self):
convnet_input = Input(shape=(self.input_dim_x, self.input_dim_y, self.num_channels))
encoder_conv1 = Conv2D(self.n_filters, kernel_size=9, activation='relu', padding='same')(convnet_input)
pool1 = MaxPooling2D(pool_size=(2, 2))(encoder_conv1)
encoder_conv2 = Conv2D(self.n_filters, kernel_size=5, activation='relu', padding='same')(pool1)
pool2 = MaxPooling2D(pool_size=(2, 2))(encoder_conv2)
encoder_conv3 = Conv2D(self.n_filters * 2, kernel_size=5, activation='relu', padding='same')(pool2)
pool3 = MaxPooling2D(pool_size=(2, 2))(encoder_conv3)
encoder_conv4 = Conv2D(self.n_filters * 2, kernel_size=5, activation='relu', padding='same')(pool3)
pool4 = MaxPooling2D(pool_size=(2, 2))(encoder_conv4)
conv5 = Conv2D(self.n_filters, kernel_size=5, activation='relu', padding='same')(pool4)
decoder_conv6 = Conv2D(self.n_filters, kernel_size=7, activation='relu', padding='same')(UpSampling2D(size=(2, 2))(conv5))
decoder_conv7 = Conv2D(self.n_filters, kernel_size=5, activation='relu', padding='same')(UpSampling2D(size=(2, 2))(decoder_conv6))
decoder_conv8 = Conv2D(self.n_filters, kernel_size=5, activation='relu', padding='same')(UpSampling2D(size=(2, 2))(decoder_conv7))
#decoder_conv9 = Conv2D(self.n_filters, kernel_size=5, activation='relu', padding='same')(UpSampling2D(size=(2, 2))(decoder_conv8))
decoder_conv9 = Conv2D(1, kernel_size=1, activation='sigmoid', padding='same')(UpSampling2D(size=(2, 2))(decoder_conv8))
return Model(outputs=decoder_conv9, inputs=convnet_input), 'SegNet' |