nn/deeplab.py
def _dilated_conv2d(self, x, kernel_size, num_o, dilation_factor, name, biased=False):
        """
        Dilated conv2d without BN or relu.
        """
        num_x = x.shape[self.channel_axis].value
        with tf.variable_scope(name):
            w = tf.get_variable('weights', shape=[kernel_size, kernel_size, num_x, num_o])
            o = tf.nn.atrous_conv2d(x, w, dilation_factor, padding='SAME')
            if biased:
                b = tf.get_variable('biases', shape=[num_o])
                o = tf.nn.bias_add(o, b)
            return o

        Dilated conv2d without BN or relu.
        
Dilated conv2d without BN or relu.
Dilated conv2d without BN or relu .
def _dilated_conv2d(self, x, kernel_size, num_o, dilation_factor, name, biased=False):
        num_x = x.shape[self.channel_axis].value
        with tf.variable_scope(name):
            w = tf.get_variable('weights', shape=[kernel_size, kernel_size, num_x, num_o])
            o = tf.nn.atrous_conv2d(x, w, dilation_factor, padding='SAME')
            if biased:
                b = tf.get_variable('biases', shape=[num_o])
                o = tf.nn.bias_add(o, b)
            return o
def _dilated_conv2d ( self , x , kernel_size , num_o , dilation_factor , name , biased = False ) : num_x = x . shape [ self . channel_axis ] . value with tf . variable_scope ( name ) : w = tf . get_variable ( 'weights' , shape = [ kernel_size , kernel_size , num_x , num_o ] ) o = tf . nn . atrous_conv2d ( x , w , dilation_factor , padding = 'SAME' ) if biased : b = tf . get_variable ( 'biases' , shape = [ num_o ] ) o = tf . nn . bias_add ( o , b ) return o
Dilated conv2d without BN or relu.
Dilated conv2d without BN or relu .
"""
        Dilated conv2d without BN or relu.
        """
self
x
kernel_size
num_o
dilation_factor
name
biased