def __init__(self, num_classes, config, debug=False, seed=None,
name='rcnn'):
super(RCNN, self).__init__(name=name)
self._num_classes = num_classes
# List of the fully connected layer sizes used before classifying and
# adjusting the bounding box.
self._layer_sizes = config.layer_sizes
self._activation = get_activation_function(config.activation_function)
self._dropout_keep_prob = config.dropout_keep_prob
self._use_mean = config.use_mean
self._variances = config.target_normalization_variances
self._rcnn_initializer = get_initializer(
config.rcnn_initializer, seed=seed
)
self._cls_initializer = get_initializer(
config.cls_initializer, seed=seed
)
self._bbox_initializer = get_initializer(
config.bbox_initializer, seed=seed
)
self.regularizer = tf.contrib.layers.l2_regularizer(
scale=config.l2_regularization_scale)
self._l1_sigma = config.l1_sigma
# Debug mode makes the module return more detailed Tensors which can be
# useful for debugging.
self._debug = debug
self._config = config
self._seed = seed
def __init__(self, num_classes, config, debug=False, seed=None,
name='rcnn'):
super(RCNN, self).__init__(name=name)
self._num_classes = num_classes
# List of the fully connected layer sizes used before classifying and
# adjusting the bounding box.
self._layer_sizes = config.layer_sizes
self._activation = get_activation_function(config.activation_function)
self._dropout_keep_prob = config.dropout_keep_prob
self._use_mean = config.use_mean
self._rcnn_initializer = get_initializer(
config.rcnn_initializer, seed=seed
)
self._cls_initializer = get_initializer(
config.cls_initializer, seed=seed
)
self._bbox_initializer = get_initializer(
config.bbox_initializer, seed=seed
)
self.regularizer = tf.contrib.layers.l2_regularizer(
scale=config.l2_regularization_scale)
self._l1_sigma = config.l1_sigma
# Debug mode makes the module return more detailed Tensors which can be
# useful for debugging.
self._debug = debug
self._config = config
self._seed = seed
def __init__(self, num_anchors, config, debug=False, seed=None, name="rpn"):
"""RPN - Region Proposal Network.
Given an image (as feature map) and a fixed set of anchors, the RPN
will learn weights to adjust those anchors so they better look like the
ground truth objects, as well as scoring them by "objectness" (ie. how
likely they are to be an object vs background).
The final result will be a set of rectangular boxes ("proposals"),
each associated with an objectness score.
Note: this module can be used independently of Faster R-CNN.
"""
super(RPN, self).__init__(name=name)
self._num_anchors = num_anchors
self._num_channels = config.num_channels
self._kernel_shape = config.kernel_shape
self._debug = debug
self._seed = seed
self._rpn_initializer = get_initializer(config.rpn_initializer, seed=seed)
# According to Faster RCNN paper we need to initialize layers with
# "from a zero-mean Gaussian distribution with standard deviation 0.01
self._cls_initializer = get_initializer(config.cls_initializer, seed=seed)
self._bbox_initializer = get_initializer(config.bbox_initializer, seed=seed)
self._regularizer = tf_slim.regularizers.l2_regularizer(scale=config.l2_regularization_scale)
self._l1_sigma = config.l1_sigma
# We could use normal relu without any problems.
self._rpn_activation = get_activation_function(config.activation_function)
self._config = config
def __init__(self, num_classes, config, debug=False, seed=None, name="rcnn"):
super(RCNN, self).__init__(name=name)
self._num_classes = num_classes
# List of the fully connected layer sizes used before classifying and
# adjusting the bounding box.
self._layer_sizes = config.layer_sizes
self._activation = get_activation_function(config.activation_function)
self._dropout_keep_prob = config.dropout_keep_prob
self._use_mean = config.use_mean
self._variances = config.target_normalization_variances
self._rcnn_initializer = get_initializer(config.rcnn_initializer, seed=seed)
self._cls_initializer = get_initializer(config.cls_initializer, seed=seed)
self._bbox_initializer = get_initializer(config.bbox_initializer, seed=seed)
self.regularizer = tf.contrib.layers.l2_regularizer(
scale=config.l2_regularization_scale
)
self._l1_sigma = config.l1_sigma
loss_config = config.loss
if loss_config.type == CROSS_ENTROPY:
self.loss_type = CROSS_ENTROPY
if "weight" not in loss_config:
self.loss_weight = 1
else:
self.loss_weight = loss_config.weight
elif loss_config.type == FOCAL:
self.loss_type = FOCAL
self.focal_gamma = loss_config.get("focal_gamma")
tf.logging.info("Classification loss type in RCNN is {}".format(self.loss_type))
# Debug mode makes the module return more detailed Tensors which can be
# useful for debugging.
self._debug = debug
self._config = config
self._seed = seed
def __init__(self, num_anchors, config, debug=False, seed=None,
name='rpn'):
"""RPN - Region Proposal Network.
Given an image (as feature map) and a fixed set of anchors, the RPN
will learn weights to adjust those anchors so they better look like the
ground truth objects, as well as scoring them by "objectness" (ie. how
likely they are to be an object vs background).
The final result will be a set of rectangular boxes ("proposals"),
each associated with an objectness score.
Note: this module can be used independently of Faster R-CNN.
"""
super(RPN, self).__init__(name=name)
self._num_anchors = num_anchors
self._num_channels = config.num_channels
self._kernel_shape = config.kernel_shape
self._debug = debug
self._seed = seed
self._rpn_initializer = get_initializer(
config.rpn_initializer, seed=seed
)
# According to Faster RCNN paper we need to initialize layers with
# "from a zero-mean Gaussian distribution with standard deviation 0.01
self._cls_initializer = get_initializer(
config.cls_initializer, seed=seed
)
self._bbox_initializer = get_initializer(
config.bbox_initializer, seed=seed
)
self._regularizer = tf.contrib.layers.l2_regularizer(
scale=config.l2_regularization_scale
)
self._l1_sigma = config.l1_sigma
# We could use normal relu without any problems.
self._rpn_activation = get_activation_function(
config.activation_function
)
self._config = config
def __init__(self,
num_anchors,
config,
debug=False,
seed=None,
name='rpn'):
"""RPN - Region Proposal Network
This module works almost independently from the Faster RCNN module.
It instantiates its own submodules and calculates its own loss,
and can be used on its own.
"""
super(RPN, self).__init__(name=name)
self._num_anchors = num_anchors
self._num_channels = config.num_channels
self._kernel_shape = config.kernel_shape
self._debug = debug
self._seed = seed
self._rpn_initializer = get_initializer(config.rpn_initializer,
seed=seed)
# According to Faster RCNN paper we need to initialize layers with
# "from a zero-mean Gaussian distribution with standard deviation 0.01
self._cls_initializer = get_initializer(config.cls_initializer,
seed=seed)
self._bbox_initializer = get_initializer(config.bbox_initializer,
seed=seed)
self._regularizer = tf.contrib.layers.l2_regularizer(
scale=config.l2_regularization_scale)
# We could use normal relu without any problems.
self._rpn_activation = get_activation_function(
config.activation_function)
self._config = config
