Automatic Defect Inspection with End-to-End Deep Learning | DLology

get_small_unet.py

from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, Lambda, Conv2DTranspose, concatenate

def get_small_unet():
    inputs = Input((img_rows, img_cols, 1))
    inputs_norm = Lambda(lambda x: x/127.5 - 1.)
    conv1 = Conv2D(16, (3, 3), activation='relu', padding='same')(inputs)
    conv1 = Conv2D(16, (3, 3), activation='relu', padding='same')(conv1)
    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)

    conv2 = Conv2D(32, (3, 3), activation='relu', padding='same')(pool1)
    conv2 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv2)
    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)

    conv3 = Conv2D(64, (3, 3), activation='relu', padding='same')(pool2)
    conv3 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv3)
    pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)

    conv4 = Conv2D(128, (3, 3), activation='relu', padding='same')(pool3)
    conv4 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv4)
    pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)

    conv5 = Conv2D(256, (3, 3), activation='relu', padding='same')(pool4)
    conv5 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv5)

    up6 = concatenate([Conv2DTranspose(64, kernel_size=(
        2, 2), strides=(2, 2), padding='same')(conv5), conv4], axis=3)
    conv6 = Conv2D(128, (3, 3), activation='relu', padding='same')(up6)
    conv6 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv6)

    up7 = concatenate([Conv2DTranspose(32, kernel_size=(
        2, 2), strides=(2, 2), padding='same')(conv6), conv3], axis=3)
    conv7 = Conv2D(64, (3, 3), activation='relu', padding='same')(up7)
    conv7 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv7)

    up8 = concatenate([Conv2DTranspose(16, kernel_size=(
        2, 2), strides=(2, 2), padding='same')(conv7), conv2], axis=3)
    conv8 = Conv2D(32, (3, 3), activation='relu', padding='same')(up8)
    conv8 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv8)

    up9 = concatenate([Conv2DTranspose(8, kernel_size=(
        2, 2), strides=(2, 2), padding='same')(conv8), conv1], axis=3)
    conv9 = Conv2D(16, (3, 3), activation='relu', padding='same')(up9)
    conv9 = Conv2D(16, (3, 3), activation='relu', padding='same')(conv9)

    conv10 = Conv2D(1, (1, 1), activation='sigmoid')(conv9)

    model = Model(inputs=inputs, outputs=conv10)

    return model

model = get_small_unet()