def separable_conv(self, input, k_h, k_w, c_o, stride, name, relu=True, set_bias=True):
with slim.arg_scope([slim.batch_norm], decay=0.999, fused=common.batchnorm_fused, is_training=self.trainable):
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=common.activation_fn if relu else None,
activation_fn=None,
# normalizer_fn=slim.batch_norm,
weights_initializer=_init_xavier,
# weights_initializer=_init_norm,
weights_regularizer=_l2_regularizer_00004,
biases_initializer=None,
padding=DEFAULT_PADDING,
scope=name + '_depthwise')
output = slim.convolution2d(output,
c_o,
stride=1,
kernel_size=[1, 1],
activation_fn=common.activation_fn if relu else None,
weights_initializer=_init_xavier,
# weights_initializer=_init_norm,
biases_initializer=_init_zero if set_bias else None,
normalizer_fn=slim.batch_norm,
trainable=self.trainable,
weights_regularizer=None,
scope=name + '_pointwise')
return output
def DepthwiseSeparableConvBlock(inputs, n_filters, kernel_size=[3, 3]): """ Builds the Depthwise Separable conv block for MobileNets Apply successivly a 2D separable convolution, BatchNormalization relu, conv, BatchNormalization, relu """ # Skip pointwise by setting num_outputs=None net = slim.separable_convolution2d(inputs, num_outputs=None, depth_multiplier=1, kernel_size=[3, 3], activation_fn=None) net = slim.batch_norm(net, fused=True) net = tf.nn.relu(net) net = slim.conv2d(net, n_filters, kernel_size=[1, 1], activation_fn=None) net = slim.batch_norm(net, fused=True) net = tf.nn.relu(net) return net