def save_connection_config(self) -> None:
if self.name and self.url:
config_parser = RawConfigParser()
config_parser.add_section(SECTION_TITLE)
config_parser.set(SECTION_TITLE, 'url', self.url)
if self._requires_auth:
config_parser.set(SECTION_TITLE, 'username',
self._auth['username'])
password = encode(encode_password(), self._auth['password'])
config_parser.set(SECTION_TITLE, 'password', password)
if self.jenkins_views:
config_parser.set(SECTION_TITLE, 'views',
','.join(self.view_names))
for view in self.views:
view.save_view_config()
save_argus_config(
config_parser,
build_config_file(jenkins_connections_dir, self.name))
else:
raise ConfigError(
'No data to save in JenkinsConnection config file.')
def save_config(self) -> None:
"""
Blindly overwrites existing config file for connection.
"""
config_parser = configparser.RawConfigParser()
config_parser.add_section('Connection')
config_parser.set('Connection', 'url', self._url)
config_parser.set('Connection', 'user',
encode(encode_password(), self._user))
config_parser.set('Connection', 'password',
encode(encode_password(), self._pass))
config_parser.set('Connection', 'projects',
','.join(self.possible_projects))
save_argus_config(config_parser,
self._build_config(self.connection_name))
def _create_targets(anchors, groundtruth_boxes, matches):
"""Returns regression targets for each anchor.
Arguments:
anchors: a float tensor with shape [num_anchors, 4].
groundtruth_boxes: a float tensor with shape [N, 4].
matches: a int tensor with shape [num_anchors].
Returns:
reg_targets: a float tensor with shape [num_anchors, 4].
"""
matched_anchor_indices = tf.where(tf.greater_equal(
matches, 0)) # shape [num_matches, 1]
matched_anchor_indices = tf.squeeze(matched_anchor_indices, axis=1)
matched_gt_indices = tf.gather(
matches, matched_anchor_indices) # shape [num_matches]
matched_anchors = tf.gather(
anchors, matched_anchor_indices) # shape [num_matches, 4]
matched_gt_boxes = tf.gather(groundtruth_boxes,
matched_gt_indices) # shape [num_matches, 4]
matched_reg_targets = encode(matched_gt_boxes,
matched_anchors) # shape [num_matches, 4]
unmatched_anchor_indices = tf.where(tf.equal(matches, -1))
unmatched_anchor_indices = tf.squeeze(unmatched_anchor_indices, axis=1)
# it has shape [num_anchors - num_matches]
unmatched_reg_targets = tf.zeros([tf.size(unmatched_anchor_indices), 4])
# it has shape [num_anchors - num_matches, 4]
matched_anchor_indices = tf.to_int32(matched_anchor_indices)
unmatched_anchor_indices = tf.to_int32(unmatched_anchor_indices)
reg_targets = tf.dynamic_stitch(
[matched_anchor_indices, unmatched_anchor_indices],
[matched_reg_targets, unmatched_reg_targets])
return reg_targets
def _create_targets(anchors, groundtruth_boxes, groundtruth_labels, matches,
num_classes):
"""Returns regression and classification targets for each anchor.
Arguments:
anchors: a float tensor with shape [num_anchors, 4].
groundtruth_boxes: a float tensor with shape [N, 4].
groundtruth_labels: a float tensor with shape [N, num_classes].
matches: a int tensor with shape [num_anchors].
num_classes: an integer.
Returns:
cls_targets: a float tensor with shape [num_anchors, num_classes + 1].
reg_targets: a float tensor with shape [num_anchors, 4].
"""
num_anchors = anchors.shape.as_list()[0]
matched_anchor_indices = tf.where(tf.greater_equal(
matches, 0)) # shape [num_matches, 1]
matched_anchor_indices = tf.squeeze(matched_anchor_indices, axis=1)
matched_gt_indices = tf.gather(
matches, matched_anchor_indices) # shape [num_matches]
matched_anchors = tf.gather(
anchors, matched_anchor_indices) # shape [num_matches, 4]
matched_gt_boxes = tf.gather(groundtruth_boxes,
matched_gt_indices) # shape [num_matches, 4]
matched_reg_targets = encode(matched_gt_boxes,
matched_anchors) # shape [num_matches, 4]
unmatched_anchor_indices = tf.where(tf.equal(matches, -1))
unmatched_anchor_indices = tf.squeeze(unmatched_anchor_indices, axis=1)
# it has shape [num_anchors - num_matches]
unmatched_reg_targets = tf.zeros([tf.size(unmatched_anchor_indices), 4])
# it has shape [num_anchors - num_matches, 4]
matched_anchor_indices = tf.to_int32(matched_anchor_indices)
unmatched_anchor_indices = tf.to_int32(unmatched_anchor_indices)
reg_targets = tf.dynamic_stitch(
[matched_anchor_indices, unmatched_anchor_indices],
[matched_reg_targets, unmatched_reg_targets])
reg_targets.set_shape([num_anchors, 4])
matched_cls_targets = tf.gather(groundtruth_labels, matched_gt_indices)
# it has shape [num_matches, num_classes]
matched_cls_targets = tf.pad(matched_cls_targets, [[0, 0], [1, 0]])
# it has shape [num_matches, num_classes + 1]
# one-hot encoding for background class
unmatched_cls_target = tf.constant([[1.0] + num_classes * [0.0]],
tf.float32)
unmatched_cls_targets = tf.tile(
unmatched_cls_target,
tf.stack([tf.size(unmatched_anchor_indices),
1])) # shape [num_anchors - num_matches, num_classes + 1]
cls_targets = tf.dynamic_stitch(
[matched_anchor_indices, unmatched_anchor_indices],
[matched_cls_targets, unmatched_cls_targets])
cls_targets.set_shape([num_anchors, num_classes + 1])
return reg_targets, cls_targets