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
python类convolution2d()的实例源码
def convb(self, input, k_h, k_w, c_o, stride, name):
with slim.arg_scope([slim.batch_norm], fused=common.batchnorm_fused):
output = slim.convolution2d(input, c_o, kernel_size=[k_h, k_w],
stride=stride,
normalizer_fn=slim.batch_norm,
weights_regularizer=tf.contrib.layers.l2_regularizer(common.regularizer_conv),
scope=name)
return output
def mobilenet_arg_scope(weight_decay=0.0):
"""Defines the default mobilenet argument scope.
Args:
weight_decay: The weight decay to use for regularizing the model.
Returns:
An `arg_scope` to use for the MobileNet model.
"""
with slim.arg_scope(
[slim.convolution2d, slim.separable_convolution2d],
weights_initializer=slim.initializers.xavier_initializer(),
biases_initializer=slim.init_ops.zeros_initializer(),
weights_regularizer=slim.l2_regularizer(weight_decay)) as sc:
return sc
def mobilenet_arg_scope(weight_decay=0.0):
"""Defines the default mobilenet argument scope.
Args:
weight_decay: The weight decay to use for regularizing the model.
Returns:
An `arg_scope` to use for the MobileNet model.
"""
with slim.arg_scope(
[slim.convolution2d, slim.separable_convolution2d],
weights_initializer=slim.initializers.xavier_initializer(),
biases_initializer=slim.init_ops.zeros_initializer(),
weights_regularizer=slim.l2_regularizer(weight_decay)) as sc:
return sc
def mobilenet(inputs,
is_training=True,
width_multiplier=1,
scope='MobileNet'):
def _depthwise_separable_conv(inputs,
num_pwc_filters,
width_multiplier,
sc,
downsample=False):
""" Helper function to build the depth-wise separable convolution layer.
"""
num_pwc_filters = round(num_pwc_filters * width_multiplier)
_stride = 2 if downsample else 1
# skip pointwise by setting num_outputs=None
depthwise_conv = slim.separable_convolution2d(inputs,
num_outputs=None,
stride=_stride,
depth_multiplier=1,
kernel_size=[3, 3],
scope=sc+'/depthwise_conv')
bn = slim.batch_norm(depthwise_conv, scope=sc+'/dw_batch_norm')
pointwise_conv = slim.convolution2d(bn,
num_pwc_filters,
kernel_size=[1, 1],
scope=sc+'/pointwise_conv')
bn = slim.batch_norm(pointwise_conv, scope=sc+'/pw_batch_norm')
return bn
with tf.variable_scope(scope) as sc:
end_points_collection = sc.name + '_end_points'
with slim.arg_scope([slim.convolution2d, slim.separable_convolution2d],
activation_fn=None,
outputs_collections=[end_points_collection]):
with slim.arg_scope([slim.batch_norm],
is_training=is_training,
activation_fn=tf.nn.relu):
net = slim.convolution2d(inputs, round(32 * width_multiplier), [3, 3], stride=2, padding='SAME', scope='conv_1')
net = slim.batch_norm(net, scope='conv_1/batch_norm')
net = _depthwise_separable_conv(net, 64, width_multiplier, sc='conv_ds_2')
net = _depthwise_separable_conv(net, 128, width_multiplier, downsample=True, sc='conv_ds_3')
net = _depthwise_separable_conv(net, 128, width_multiplier, sc='conv_ds_4')
net = _depthwise_separable_conv(net, 256, width_multiplier, downsample=True, sc='conv_ds_5')
net = _depthwise_separable_conv(net, 256, width_multiplier, sc='conv_ds_6')
net = _depthwise_separable_conv(net, 512, width_multiplier, downsample=True, sc='conv_ds_7')
net = _depthwise_separable_conv(net, 512, width_multiplier, sc='conv_ds_8')
net = _depthwise_separable_conv(net, 512, width_multiplier, sc='conv_ds_9')
net = _depthwise_separable_conv(net, 512, width_multiplier, sc='conv_ds_10')
net = _depthwise_separable_conv(net, 512, width_multiplier, sc='conv_ds_11')
net = _depthwise_separable_conv(net, 512, width_multiplier, sc='conv_ds_12')
net = _depthwise_separable_conv(net, 1024, width_multiplier, downsample=True, sc='conv_ds_13')
net = _depthwise_separable_conv(net, 1024, width_multiplier, sc='conv_ds_14')
end_points = slim.utils.convert_collection_to_dict(end_points_collection)
return end_points
