<commit_before>import numpy as np
import matplotlib.pyplot as plt
from pylab import *
import os
import sys
from keras_contrib.applications import densenet
from keras.models import Model
from keras.regularizers import l2
from keras.layers import *
from keras.engine import Layer
from keras.applications.vgg16 import *
from keras.models import *
import keras.backend as K
import tensorflow as tf

from utils.get_weights_path import *
from utils.basics import *
from utils.resnet_helpers import *
from utils.BilinearUpSampling import *


def FCN_Vgg16_32s(input_shape=None, weight_decay=0., batch_momentum=0.9, batch_shape=None, classes=21):
    if batch_shape:
        img_input = Input(batch_shape=batch_shape)
        image_size = batch_shape[1:3]<commit_msg>models.py add missing keras import<commit_after>import numpy as np
import matplotlib.pyplot as plt
from pylab import *
import os
import sys
from keras_contrib.applications import densenet
from keras.models import Model
from keras.regularizers import l2
from keras.layers import *
from keras.engine import Layer
from keras.applications.vgg16 import *
from keras.models import *
from keras.applications.imagenet_utils import _obtain_input_shape
import keras.backend as K
import tensorflow as tf

from utils.get_weights_path import *
from utils.basics import *
from utils.resnet_helpers import *
from utils.BilinearUpSampling import *


def FCN_Vgg16_32s(input_shape=None, weight_decay=0., batch_momentum=0.9, batch_shape=None, classes=21):
    if batch_shape:
        img_input = Input(batch_shape=batch_shape)
        image_size = batch_shape[1:3]
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