如題，deeplabv3中提供的網絡圖如下所示：
tensorflow 的代碼實現如下所示：
def atrous_spatial_pyramid_pooling(inputs, filters=256, regularizer=None): #ASPP層
'''
Atrous Spatial Pyramid Pooling (ASPP) Block
'''
pool_height = tf.shape(inputs)[1]
pool_width = tf.shape(inputs)[2]
resize_height = pool_height
resize_width = pool_width
# Atrous Spatial Pyramid Pooling
# Atrous 1x1
aspp1x1 = tf.layers.conv2d(inputs=inputs, filters=filters, kernel_size=(1, 1),
padding='same', kernel_regularizer=regularizer,
name='aspp1x1')
# Atrous 3x3, rate = 6
aspp3x3_1 = tf.layers.conv2d(inputs=inputs, filters=filters, kernel_size=(3, 3),
padding='same', dilation_rate=(12, 12), kernel_regularizer=regularizer,
name='aspp3x3_1')
# Atrous 3x3, rate = 12
aspp3x3_2 = tf.layers.conv2d(inputs=inputs, filters=filters, kernel_size=(3, 3),
padding='same', dilation_rate=(24, 24), kernel_regularizer=regularizer,
name='aspp3x3_2')
# Atrous 3x3, rate = 18
aspp3x3_3 = tf.layers.conv2d(inputs=inputs, filters=filters, kernel_size=(3, 3),
padding='same', dilation_rate=(36, 36), kernel_regularizer=regularizer,
name='aspp3x3_3')
# Image Level Pooling
image_feature = tf.reduce_mean(inputs, [1, 2], keepdims=True)
image_feature = tf.layers.conv2d(inputs=image_feature, filters=filters, kernel_size=(1, 1),
padding='same')
image_feature = tf.image.resize_bilinear(images=image_feature,
size=[resize_height, resize_width],
align_corners=True, name='image_pool_feature')
# Merge Poolings
outputs = tf.concat(values=[aspp1x1, aspp3x3_1, aspp3x3_2, aspp3x3_3, image_feature],
axis=3, name='aspp_pools')
outputs = tf.layers.conv2d(inputs=outputs, filters=filters, kernel_size=(1, 1),
padding='same', kernel_regularizer=regularizer, name='aspp_outputs')
return outputs