def _network(self):
# There shouldn't be any gt_boxes if in evaluation mode
if self.eval_mode is True:
assert self.gt_boxes is None, \
'Evaluation mode should not have ground truth boxes (or else what are you detecting for?)'
_num_anchors = len(self.anchor_scales)*3
rpn_layers = Layers(self.featureMaps)
with tf.variable_scope('rpn'):
# Spatial windowing
for i in range(len(cfg.RPN_OUTPUT_CHANNELS)):
rpn_layers.conv2d(filter_size=cfg.RPN_FILTER_SIZES[i], output_channels=cfg.RPN_OUTPUT_CHANNELS[i])
features = rpn_layers.get_output()
with tf.variable_scope('cls'):
# Box-classification layer (objectness)
self.rpn_bbox_cls_layers = Layers(features)
self.rpn_bbox_cls_layers.conv2d(filter_size=1, output_channels=_num_anchors*2, activation_fn=None)
with tf.variable_scope('target'):
# Only calculate targets in train mode. No ground truth boxes in evaluation mode
if self.eval_mode is False:
# Anchor Target Layer (anchors and deltas)
rpn_cls_score = self.rpn_bbox_cls_layers.get_output()
self.rpn_labels, self.rpn_bbox_targets, self.rpn_bbox_inside_weights, self.rpn_bbox_outside_weights = \
anchor_target_layer(rpn_cls_score=rpn_cls_score, gt_boxes=self.gt_boxes, im_dims=self.im_dims,
_feat_stride=self._feat_stride, anchor_scales=self.anchor_scales)
with tf.variable_scope('bbox'):
# Bounding-Box regression layer (bounding box predictions)
self.rpn_bbox_pred_layers = Layers(features)
self.rpn_bbox_pred_layers.conv2d(filter_size=1, output_channels=_num_anchors*4, activation_fn=None)
def _network(self):
# There shouldn't be any gt_boxes if in evaluation mode
if self.eval_mode is True:
assert self.gt_boxes is None, \
'Evaluation mode should not have ground truth boxes (or else what are you detecting for?)'
_num_anchors = len(self.anchor_scales) * 3
features = self.featureMaps
with tf.variable_scope('rpn'):
# Spatial windowing
for i in range(len(cfg.RPN_OUTPUT_CHANNELS)):
# 2D Conv -> Batch Normalization -> ReLU
features = tcl.conv2d(
inputs=features,
num_outputs=cfg.RPN_OUTPUT_CHANNELS[i],
kernel_size=cfg.RPN_FILTER_SIZES[i],
activation_fn=tf.nn.relu,
normalizer_fn=tcl.batch_norm,
normalizer_params={'is_training': not self.eval_mode})
# Box-classification layer (objectness)
with tf.variable_scope('cls'):
# Only 2D Conv
self.rpn_cls_score = tcl.conv2d(inputs=features,
num_outputs=_num_anchors * 2,
kernel_size=1,
activation_fn=None)
# Anchor Target Layer (anchors and deltas)
with tf.variable_scope('target'):
# Only calculate targets in train mode. No ground truth boxes in evaluation mode
if self.eval_mode is False:
self.rpn_labels, self.rpn_bbox_targets, self.rpn_bbox_inside_weights, self.rpn_bbox_outside_weights = \
anchor_target_layer(rpn_cls_score=self.rpn_cls_score, gt_boxes=self.gt_boxes, im_dims=self.im_dims,
_feat_stride=self._feat_stride, anchor_scales=self.anchor_scales)
# Bounding-Box regression layer (bounding box predictions)
with tf.variable_scope('bbox'):
# Only 2D Conv
self.rpn_bbox_pred = tcl.conv2d(inputs=features,
num_outputs=_num_anchors * 4,
kernel_size=1,
activation_fn=None)
def _network(self):
# There shouldn't be any gt_boxes if in evaluation mode
if self.eval_mode is True:
assert self.gt_boxes is None, \
'Evaluation mode should not have ground truth boxes (or else what are you detecting for?)'
_num_anchors = len(self.anchor_scales) * 1
rpn_layers = Layers(self.featureMaps)
with tf.variable_scope('rpn'):
# Spatial windowing
for i in range(len(cfg.RPN_OUTPUT_CHANNELS)):
rpn_layers.conv2d(filter_size=cfg.RPN_FILTER_SIZES[i],
output_channels=cfg.RPN_OUTPUT_CHANNELS[i])
features = rpn_layers.get_output()
with tf.variable_scope('cls'):
# Box-classification layer (objectness)
self.rpn_bbox_cls_layers = Layers(features)
self.rpn_bbox_cls_layers.conv2d(filter_size=1,
output_channels=_num_anchors *
2,
activation_fn=None)
with tf.variable_scope('target'):
# Only calculate targets in train mode. No ground truth boxes in evaluation mode
if self.eval_mode is False:
print(anchor_target_layer)
# Anchor Target Layer (anchors and deltas)
rpn_cls_score = self.rpn_bbox_cls_layers.get_output()
self.rpn_labels, self.rpn_bbox_targets, self.rpn_bbox_inside_weights, self.rpn_bbox_outside_weights = \
anchor_target_layer(rpn_cls_score=rpn_cls_score, gt_boxes=self.gt_boxes, im_dims=self.im_dims,
_feat_stride=self._feat_stride, anchor_scales=self.anchor_scales)
with tf.variable_scope('bbox'):
# Bounding-Box regression layer (bounding box predictions)
self.rpn_bbox_pred_layers = Layers(features)
self.rpn_bbox_pred_layers.conv2d(filter_size=1,
output_channels=_num_anchors *
4,
activation_fn=None)
def _network(self):
_num_anchors = len(self.flags['anchor_scales'])*3
rpn_layers = Layers(self.featureMaps)
with tf.variable_scope('rpn'):
# Spatial windowing
rpn_layers.conv2d(filter_size=3,output_channels=512)
features = rpn_layers.get_output()
with tf.variable_scope('bbox'):
# Box-classification layer (objectness)
self.rpn_bbox_cls_layers = Layers(features)
self.rpn_bbox_cls_layers.conv2d(filter_size=1,output_channels=_num_anchors*2,activation_fn=None)
# Anchor Target Layer (anchors and deltas)
rpn_cls_score = self.rpn_bbox_cls_layers.get_output()
self.rpn_labels,self.rpn_bbox_targets,self.rpn_bbox_inside_weights,self.rpn_bbox_outside_weights = \
anchor_target_layer(rpn_cls_score=rpn_cls_score,gt_boxes=self.gt_boxes,im_dims=self.im_dims,_feat_stride=self._feat_stride,anchor_scales=self.flags['anchor_scales'])
with tf.variable_scope('cls'):
# Bounding-Box regression layer (bounding box predictions)
self.rpn_bbox_pred_layers = Layers(features)
self.rpn_bbox_pred_layers.conv2d(filter_size=1,output_channels=_num_anchors*4,activation_fn=None)