def separable_conv(self, input, k_h, k_w, c_o, stride, name, relu=True):
with slim.arg_scope([slim.batch_norm], fused=common.batchnorm_fused):
output = slim.separable_convolution2d(input,
num_outputs=None,
stride=stride,
trainable=self.trainable,
depth_multiplier=1.0,
kernel_size=[k_h, k_w],
activation_fn=None,
weights_initializer=tf.contrib.layers.xavier_initializer(),
# weights_initializer=tf.truncated_normal_initializer(stddev=0.09),
weights_regularizer=tf.contrib.layers.l2_regularizer(0.00004),
biases_initializer=None,
padding=DEFAULT_PADDING,
scope=name + '_depthwise')
output = slim.convolution2d(output,
c_o,
stride=1,
kernel_size=[1, 1],
activation_fn=tf.nn.relu if relu else None,
weights_initializer=tf.contrib.layers.xavier_initializer(),
# weights_initializer=tf.truncated_normal_initializer(stddev=0.09),
biases_initializer=slim.init_ops.zeros_initializer(),
normalizer_fn=slim.batch_norm,
trainable=self.trainable,
weights_regularizer=tf.contrib.layers.l2_regularizer(common.regularizer_dsconv),
# weights_regularizer=None,
scope=name + '_pointwise')
return output
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