def inception_resnet_v2_arg_scope(weight_decay=0.00004,
batch_norm_decay=0.9997,
batch_norm_epsilon=0.001,
activation_fn=tf.nn.relu):
"""Returns the scope with the default parameters for inception_resnet_v2.
Args:
weight_decay: the weight decay for weights variables.
batch_norm_decay: decay for the moving average of batch_norm momentums.
batch_norm_epsilon: small float added to variance to avoid dividing by zero.
activation_fn: Activation function for conv2d.
Returns:
a arg_scope with the parameters needed for inception_resnet_v2.
"""
# Set weight_decay for weights in conv2d and fully_connected layers.
with slim.arg_scope([slim.conv2d, slim.fully_connected],
weights_regularizer=slim.l2_regularizer(weight_decay),
biases_regularizer=slim.l2_regularizer(weight_decay)):
batch_norm_params = {
'decay': batch_norm_decay,
'epsilon': batch_norm_epsilon,
'fused': None, # Use fused batch norm if possible.
}
# Set activation_fn and parameters for batch_norm.
with slim.arg_scope([slim.conv2d],
activation_fn=activation_fn,
normalizer_fn=slim.batch_norm,
normalizer_params=batch_norm_params) as scope:
return scope
def alexnet_v2_arg_scope(weight_decay=0.0005):
with slim.arg_scope([slim.conv2d, slim.fully_connected],
activation_fn=tf.nn.relu,
biases_initializer=tf.constant_initializer(0.1),
weights_regularizer=slim.l2_regularizer(weight_decay)):
with slim.arg_scope([slim.conv2d], padding='SAME'):
with slim.arg_scope([slim.max_pool2d], padding='VALID') as arg_sc:
return arg_sc
def overfeat_arg_scope(weight_decay=0.0005):
with slim.arg_scope([slim.conv2d, slim.fully_connected],
activation_fn=tf.nn.relu,
weights_regularizer=slim.l2_regularizer(weight_decay),
biases_initializer=tf.zeros_initializer()):
with slim.arg_scope([slim.conv2d], padding='SAME'):
with slim.arg_scope([slim.max_pool2d], padding='VALID') as arg_sc:
return arg_sc
def resnet_arg_scope(weight_decay=0.0001,
batch_norm_decay=0.997,
batch_norm_epsilon=1e-5,
batch_norm_scale=True,
activation_fn=tf.nn.relu,
use_batch_norm=True):
"""Defines the default ResNet arg scope.
TODO(gpapan): The batch-normalization related default values above are
appropriate for use in conjunction with the reference ResNet models
released at https://github.com/KaimingHe/deep-residual-networks. When
training ResNets from scratch, they might need to be tuned.
Args:
weight_decay: The weight decay to use for regularizing the model.
batch_norm_decay: The moving average decay when estimating layer activation
statistics in batch normalization.
batch_norm_epsilon: Small constant to prevent division by zero when
normalizing activations by their variance in batch normalization.
batch_norm_scale: If True, uses an explicit `gamma` multiplier to scale the
activations in the batch normalization layer.
activation_fn: The activation function which is used in ResNet.
use_batch_norm: Whether or not to use batch normalization.
Returns:
An `arg_scope` to use for the resnet models.
"""
batch_norm_params = {
'decay': batch_norm_decay,
'epsilon': batch_norm_epsilon,
'scale': batch_norm_scale,
'updates_collections': tf.GraphKeys.UPDATE_OPS,
'fused': None, # Use fused batch norm if possible.
}
with slim.arg_scope(
[slim.conv2d],
weights_regularizer=slim.l2_regularizer(weight_decay),
weights_initializer=slim.variance_scaling_initializer(),
activation_fn=activation_fn,
normalizer_fn=slim.batch_norm if use_batch_norm else None,
normalizer_params=batch_norm_params):
with slim.arg_scope([slim.batch_norm], **batch_norm_params):
# The following implies padding='SAME' for pool1, which makes feature
# alignment easier for dense prediction tasks. This is also used in
# https://github.com/facebook/fb.resnet.torch. However the accompanying
# code of 'Deep Residual Learning for Image Recognition' uses
# padding='VALID' for pool1. You can switch to that choice by setting
# slim.arg_scope([slim.max_pool2d], padding='VALID').
with slim.arg_scope([slim.max_pool2d], padding='SAME') as arg_sc:
return arg_sc
def lenet_arg_scope(weight_decay=0.0):
"""Defines the default lenet argument scope.
Args:
weight_decay: The weight decay to use for regularizing the model.
Returns:
An `arg_scope` to use for the inception v3 model.
"""
with slim.arg_scope(
[slim.conv2d, slim.fully_connected],
weights_regularizer=slim.l2_regularizer(weight_decay),
weights_initializer=tf.truncated_normal_initializer(stddev=0.1),
activation_fn=tf.nn.relu) as sc:
return sc
def vgg_arg_scope(weight_decay=0.0005):
"""Defines the VGG arg scope.
Args:
weight_decay: The l2 regularization coefficient.
Returns:
An arg_scope.
"""
with slim.arg_scope([slim.conv2d, slim.fully_connected],
activation_fn=tf.nn.relu,
weights_regularizer=slim.l2_regularizer(weight_decay),
biases_initializer=tf.zeros_initializer()):
with slim.arg_scope([slim.conv2d], padding='SAME') as arg_sc:
return arg_sc
def osvos_arg_scope(weight_decay=0.0002):
"""Defines the OSVOS arg scope.
Args:
weight_decay: The l2 regularization coefficient.
Returns:
An arg_scope.
"""
with slim.arg_scope(
[slim.conv2d, slim.convolution2d_transpose],
activation_fn=tf.nn.relu,
weights_initializer=tf.random_normal_initializer(stddev=0.001),
weights_regularizer=slim.l2_regularizer(weight_decay),
biases_initializer=tf.zeros_initializer(),
biases_regularizer=None,
padding='SAME') as arg_sc:
return arg_sc
def inception_arg_scope(weight_decay=0.00004,
use_batch_norm=True,
batch_norm_decay=0.9997,
batch_norm_epsilon=0.001,
activation_fn=tf.nn.relu):
"""Defines the default arg scope for inception models.
Args:
weight_decay: The weight decay to use for regularizing the model.
use_batch_norm: "If `True`, batch_norm is applied after each convolution.
batch_norm_decay: Decay for batch norm moving average.
batch_norm_epsilon: Small float added to variance to avoid dividing by zero
in batch norm.
activation_fn: Activation function for conv2d.
Returns:
An `arg_scope` to use for the inception models.
"""
batch_norm_params = {
# Decay for the moving averages.
'decay': batch_norm_decay,
# epsilon to prevent 0s in variance.
'epsilon': batch_norm_epsilon,
# collection containing update_ops.
'updates_collections': tf.GraphKeys.UPDATE_OPS,
# use fused batch norm if possible.
'fused': None,
}
if use_batch_norm:
normalizer_fn = slim.batch_norm
normalizer_params = batch_norm_params
else:
normalizer_fn = None
normalizer_params = {}
# Set weight_decay for weights in Conv and FC layers.
with slim.arg_scope([slim.conv2d, slim.fully_connected],
weights_regularizer=slim.l2_regularizer(weight_decay)):
with slim.arg_scope(
[slim.conv2d],
weights_initializer=slim.variance_scaling_initializer(),
activation_fn=activation_fn,
normalizer_fn=normalizer_fn,
normalizer_params=normalizer_params) as sc:
return sc