semantic_segmentation.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Nov  1 14:54:02 2020

@author: srujanvajram
"""
import keras_segmentation

from tensorflow import keras 
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Activation, Dense, Flatten, BatchNormalization, Conv2D, MaxPooling2D, Dropout, UpSampling2D
from tensorflow.keras.layers import Input, concatenate

input_height = 300
input_width = 300
n_classes = 10

img_input = Input(shape=(input_height,input_width , 3 ))

# ------------------------------- #
conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(img_input)
conv1 = Dropout(0.2)(conv1)
conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv1)
pool1 = MaxPooling2D((2, 2))(conv1)

conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(pool1)
conv2 = Dropout(0.2)(conv2)
conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv2)
pool2 = MaxPooling2D((2, 2))(conv2)

conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(pool2)
conv3 = Dropout(0.2)(conv3)
conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv3)

up1 = concatenate([UpSampling2D((2, 2))(conv3), conv2], axis=-1)
conv4 = Conv2D(64, (3, 3), activation='relu', padding='same')(up1)
conv4 = Dropout(0.2)(conv4)
conv4 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv4)

up2 = concatenate([UpSampling2D((2, 2))(conv4), conv1], axis=-1)
conv5 = Conv2D(32, (3, 3), activation='relu', padding='same')(up2)
conv5 = Dropout(0.2)(conv5)
conv5 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv5)
# ------------------------------- #

out = Conv2D( n_classes, (1, 1) , padding='same')(conv5)

from keras_segmentation.models.model_utils import get_segmentation_model

model = get_segmentation_model(img_input ,  out ) # this would build the segmentation model