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Copy pathDeepLabV3plus_EfficientNet.py
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DeepLabV3plus_EfficientNet.py
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import os
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
from tensorflow.keras.layers import Conv2D, BatchNormalization, Activation, UpSampling2D
from tensorflow.keras.layers import AveragePooling2D, Concatenate, Input
from tensorflow.keras.models import Model
from tensorflow.keras.applications import EfficientNetB2
def ASPP(image_features):
shape = image_features.shape
y_pool = AveragePooling2D(pool_size=(shape[1], shape[2]))(image_features)
y_pool = Conv2D(filters=256, kernel_size=1, padding='same', use_bias=False)(y_pool)
y_pool = BatchNormalization(name=f'bn_1')(y_pool)
y_pool = Activation('relu', name=f'relu_1')(y_pool)
y_pool = UpSampling2D((shape[1], shape[2]), interpolation="bilinear")(y_pool)
y_1 = Conv2D(filters=256, kernel_size=1, padding='same', use_bias=False)(image_features)
y_1 = BatchNormalization(name=f'bn_2')(y_1)
y_1 = Activation('relu', name=f'relu_2')(y_1)
y_6 = Conv2D(filters=256, kernel_size=3, padding='same', dilation_rate = 6,use_bias=False)(image_features)
y_6 = BatchNormalization(name=f'bn_3')(y_6)
y_6 = Activation('relu', name=f'relu_3')(y_6)
y_12 = Conv2D(filters=256, kernel_size=3, padding='same', dilation_rate = 12,use_bias=False)(image_features)
y_12 = BatchNormalization(name=f'bn_4')(y_12)
y_12 = Activation('relu', name=f'relu_4')(y_12)
y_18 = Conv2D(filters=256, kernel_size=3, padding='same', dilation_rate = 18,use_bias=False)(image_features)
y_18 = BatchNormalization(name=f'bn_5')(y_18)
y_18 = Activation('relu', name=f'relu_5')(y_18)
y_c = Concatenate()([y_pool, y_1, y_6, y_12, y_18])
y = Conv2D(filters=256, kernel_size=1, padding='same', use_bias=False)(y_c)
y = BatchNormalization(name=f'bn_6')(y)
y = Activation('relu', name=f'relu_6')(y)
return y
def DeepLabV3Plus(inputs, classes=1):
inputs = Input(inputs)
base_model = EfficientNetB2(weights='imagenet', include_top=False, input_tensor=inputs, drop_connect_rate=0.25)
high_level_image_features = base_model.get_layer('block6a_expand_bn').output
x_a = ASPP(high_level_image_features)
x_a = UpSampling2D(size=4, interpolation='bilinear')(x_a)
low_level_image_features = base_model.get_layer('block3a_expand_bn').output
x_b = Conv2D(filters=256, kernel_size=1, padding='same', use_bias=False)(low_level_image_features)
x_b = BatchNormalization(name=f'bn_7')(x_b)
x_b = Activation('relu', name=f'relu_7')(x_b)
x = Concatenate()([x_a, x_b])
x = Conv2D(filters=256, kernel_size=3, padding='same', use_bias=False)(x)
x = BatchNormalization(name=f'bn_8')(x)
x = Activation('relu', name=f'relu_8')(x)
x = Conv2D(filters=256, kernel_size=3, padding='same', use_bias=False)(x)
x = BatchNormalization(name=f'bn_9')(x)
x = Activation('relu', name=f'relu_9')(x)
x = UpSampling2D(size=4, interpolation='bilinear')(x)
""" Outputs """
x = Conv2D(classes, (1, 1), name='output_layer')(x)
if classes == 1:
x = Activation('sigmoid')(x)
else:
x = Activation('softmax')(x)
model = Model(inputs=inputs, outputs=x)
return model
def main():
model = DeepLabV3Plus(inputs=(1024,1024,3), classes=4)
model.summary()
if __name__== '__main__':
main()