BIDI_ML_INFO_EXTRACTION/BiddingKG/dl/table_head/models/u_net.py

from keras.layers import Input, concatenate, Conv2D, MaxPooling2D, BatchNormalization, UpSampling2D
from keras.layers import LeakyReLU


def u_net_small(inputs, num_classes=1):
    # 8
    use_bias = False
    down0 = Conv2D(8, (3, 3), padding='same', use_bias=use_bias)(inputs)
    down0 = BatchNormalization()(down0)
    down0 = LeakyReLU(alpha=0.)(down0)
    down0 = Conv2D(8, (3, 3), padding='same', use_bias=use_bias)(down0)
    down0 = BatchNormalization()(down0)
    down0 = LeakyReLU(alpha=0.)(down0)
    down0_pool = MaxPooling2D((2, 2), strides=(2, 2))(down0)

    # 4
    down1 = Conv2D(16, (3, 3), padding='same', use_bias=use_bias)(down0_pool)
    down1 = BatchNormalization()(down1)
    down1 = LeakyReLU(alpha=0.)(down1)
    down1 = Conv2D(16, (3, 3), padding='same', use_bias=use_bias)(down1)
    down1 = BatchNormalization()(down1)
    down1 = LeakyReLU(alpha=0.)(down1)
    down1_pool = MaxPooling2D((2, 2), strides=(2, 2))(down1)

    # 2
    down2 = Conv2D(32, (3, 3), padding='same', use_bias=use_bias)(down1_pool)
    down2 = BatchNormalization()(down2)
    down2 = LeakyReLU(alpha=0.)(down2)
    down2 = Conv2D(32, (3, 3), padding='same', use_bias=use_bias)(down2)
    down2 = BatchNormalization()(down2)
    down2 = LeakyReLU(alpha=0.)(down2)
    down2_pool = MaxPooling2D((2, 2), strides=(2, 2))(down2)

    center = Conv2D(64, (3, 3), padding='same', use_bias=use_bias)(down2_pool)
    center = BatchNormalization()(center)
    center = LeakyReLU(alpha=0.)(center)
    center = Conv2D(64, (3, 3), padding='same', use_bias=use_bias)(center)
    center = BatchNormalization()(center)
    center = LeakyReLU(alpha=0.)(center)

    # 2
    up2 = UpSampling2D((2, 2))(center)
    up2 = concatenate([down2, up2], axis=3)
    up2 = Conv2D(32, (3, 3), padding='same', use_bias=use_bias)(up2)
    up2 = BatchNormalization()(up2)
    up2 = LeakyReLU(alpha=0.)(up2)
    up2 = Conv2D(32, (3, 3), padding='same', use_bias=use_bias)(up2)
    up2 = BatchNormalization()(up2)
    up2 = LeakyReLU(alpha=0.)(up2)
    up2 = Conv2D(32, (3, 3), padding='same', use_bias=use_bias)(up2)
    up2 = BatchNormalization()(up2)
    up2 = LeakyReLU(alpha=0.)(up2)

    # 4
    up1 = UpSampling2D((2, 2))(up2)
    up1 = concatenate([down1, up1], axis=3)
    up1 = Conv2D(16, (3, 3), padding='same', use_bias=use_bias)(up1)
    up1 = BatchNormalization()(up1)
    up1 = LeakyReLU(alpha=0.)(up1)
    up1 = Conv2D(16, (3, 3), padding='same', use_bias=use_bias)(up1)
    up1 = BatchNormalization()(up1)
    up1 = LeakyReLU(alpha=0.)(up1)
    up1 = Conv2D(16, (3, 3), padding='same', use_bias=use_bias)(up1)
    up1 = BatchNormalization()(up1)
    up1 = LeakyReLU(alpha=0.)(up1)

    # 8
    up0 = UpSampling2D((2, 2))(up1)
    up0 = concatenate([down0, up0], axis=3)
    up0 = Conv2D(8, (3, 3), padding='same', use_bias=use_bias)(up0)
    up0 = BatchNormalization()(up0)
    up0 = LeakyReLU(alpha=0.)(up0)
    up0 = Conv2D(8, (3, 3), padding='same', use_bias=use_bias)(up0)
    up0 = BatchNormalization()(up0)
    up0 = LeakyReLU(alpha=0.)(up0)
    up0 = Conv2D(8, (3, 3), padding='same', use_bias=use_bias)(up0)
    up0 = BatchNormalization()(up0)
    up0 = LeakyReLU(alpha=0.)(up0)

    # classify
    # classify = Conv2D(num_classes, (1, 1), activation='sigmoid')(up0)
    return up0