def _network(self):
with tf.variable_scope('fast_rcnn'):
# No dropout in evaluation mode
keep_prob = cfg.FRCNN_DROPOUT_KEEP_RATE if self.eval_mode is False else 1.0
# ROI pooling
pooledFeatures = roi_pool(self.featureMaps, self.rois, self.im_dims)
# Fully Connect layers (with dropout)
with tf.variable_scope('fc'):
self.rcnn_fc_layers = Layers(pooledFeatures)
self.rcnn_fc_layers.flatten()
for i in range(len(cfg.FRCNN_FC_HIDDEN)):
self.rcnn_fc_layers.fc(output_nodes=cfg.FRCNN_FC_HIDDEN[i], keep_prob=keep_prob)
hidden = self.rcnn_fc_layers.get_output()
# Classifier score
with tf.variable_scope('cls'):
self.rcnn_cls_layers = Layers(hidden)
self.rcnn_cls_layers.fc(output_nodes=self.num_classes, activation_fn=None)
# Bounding Box refinement
with tf.variable_scope('bbox'):
self.rcnn_bbox_layers = Layers(hidden)
self.rcnn_bbox_layers.fc(output_nodes=self.num_classes*4, activation_fn=None)
def _network(self, x):
conv_layers = Layers(x)
# Make sure that number of layers is consistent
assert len(self.output_channels) == self.depth
assert len(self.strides) == self.depth
# Convolutional layers
scope = 'convnet' + str(self.depth)
with tf.variable_scope(scope):
for l in range(self.depth):
conv_layers.conv2d(filter_size=self.filter_sizes[l],
output_channels=self.output_channels[l],
stride=self.strides[l],
padding='SAME',
b_value=None)
return conv_layers
def _network(self, x):
conv_layers = Layers(x)
# Make sure that number of layers is consistent
assert len(self.output_channels) == self.depth
assert len(self.strides) == self.depth
# Convolutional layers
scope = 'convnet' + str(self.depth)
with tf.variable_scope(scope):
for l in range(self.depth):
conv_layers.conv2d(filter_size=self.filter_sizes[l],
output_channels=self.output_channels[l],
stride=self.strides[l],
padding='SAME',
b_value=None)
return conv_layers
def _network(self):
with tf.variable_scope('fast_rcnn'):
# No dropout in evaluation mode
keep_prob = cfg.FRCNN_DROPOUT_KEEP_RATE if self.eval_mode is False else 1.0
# ROI pooling
pooledFeatures = roi_pool(self.featureMaps, self.rois,
self.im_dims)
# Fully Connect layers (with dropout)
with tf.variable_scope('fc'):
self.rcnn_fc_layers = Layers(pooledFeatures)
self.rcnn_fc_layers.flatten()
for i in range(len(cfg.FRCNN_FC_HIDDEN)):
self.rcnn_fc_layers.fc(output_nodes=cfg.FRCNN_FC_HIDDEN[i],
keep_prob=keep_prob)
hidden = self.rcnn_fc_layers.get_output()
# Classifier score
with tf.variable_scope('cls'):
self.rcnn_cls_layers = Layers(hidden)
self.rcnn_cls_layers.fc(output_nodes=self.num_classes,
activation_fn=None)
# Bounding Box refinement
with tf.variable_scope('bbox'):
self.rcnn_bbox_layers = Layers(hidden)
self.rcnn_bbox_layers.fc(output_nodes=self.num_classes * 4,
activation_fn=None)
def _network(self):
with tf.variable_scope('fast_rcnn'):
# No dropout in evaluation mode
keep_prob = 0.5 if self.eval_mode == False else 1.0
# ROI pooling
pooledFeatures = roi_pool(self.featureMaps, self.rois,
self.im_dims)
# Fully Connect layers (with dropout)
with tf.variable_scope('fc'):
self.rcnn_fc_layers = Layers(pooledFeatures)
self.rcnn_fc_layers.flatten()
self.rcnn_fc_layers.fc(output_nodes=1024, keep_prob=keep_prob)
hidden = self.rcnn_fc_layers.get_output()
# Classifier score
with tf.variable_scope('cls'):
self.rcnn_cls_layers = Layers(hidden)
self.rcnn_cls_layers.fc(output_nodes=self.flags['num_classes'],
activation_fn=None)
# Bounding Box refinement
with tf.variable_scope('bbox'):
self.rcnn_bbox_layers = Layers(hidden)
self.rcnn_bbox_layers.fc(output_nodes=4 *
self.flags['num_classes'],
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
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) * 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)
class rpn:
'''
Region Proposal Network (RPN): Takes convolutional feature maps (TensorBase
Layers object) from the last layer and proposes bounding boxes for objects.
'''
def __init__(self,featureMaps,gt_boxes,im_dims,flags):
self.featureMaps = featureMaps
self.gt_boxes = gt_boxes
self.im_dims = im_dims
self.flags = flags
self._network()
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()
# 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)
self.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=self.rpn_cls_score,gt_boxes=self.gt_boxes,im_dims=self.im_dims,anchor_scales=self.flags['anchor_scales'])
# 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 get_rpn_bbox_cls(self):
return self.rpn_bbox_cls_layers.get_output()
def get_rpn_bbox_pred(self):
return self.rpn_bbox_pred_layers.get_output()
def get_rpn_labels(self):
return self.rpn_labels
def get_rpn_bbox_targets(self):
return self.bbox_targets
def get_rpn_bbox_inside_weights(self):
return self.rpn_bbox_inside_weights
def get_rpn_bbox_outside_weights(self):
return self.rpn_bbox_outside_weights
def _cnn(self, x, gt_boxes, im_dims, key):
# self.cnn[key] = convnet(x, [5, 3, 3, 3, 3], [32, 64, 64, 128, 128], strides=[2, 2, 1, 2, 1])
self.cnn[key] = Layers(x)
self.cnn[key].conv2d(5, 32)
self.cnn[key].maxpool()
self.cnn[key].conv2d(3, 64)
self.cnn[key].maxpool()
self.cnn[key].conv2d(3, 64)
self.cnn[key].conv2d(3, 128)
self.cnn[key].maxpool()
self.cnn[key].conv2d(3, 128)
self.cnn[key].flatten()
self.cnn[key].fc(512)
self.cnn[key].fc(11,activation_fn = None)
self.logits[key] = self.cnn[key].get_output()
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()
# 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)
self.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=self.rpn_cls_score,gt_boxes=self.gt_boxes,im_dims=self.im_dims,anchor_scales=self.flags['anchor_scales'])
# 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 _conv_layers(self,x):
conv_layers = Layers(x)
# Convolutional layers
res_blocks = [1,3,4,23,3]
output_channels = [64,256,512,1024,2048]
with tf.variable_scope('scale0'):
conv_layers.conv2d(filter_size=7,output_channels=output_channels[0],stride=2,padding='SAME',b_value=None)
conv_layers.maxpool(k=3)
with tf.variable_scope('scale1'):
conv_layers.res_layer(filter_size=3, output_channels=output_channels[1], stride=2)
for block in range(res_blocks[1]-1):
conv_layers.conv_layers.res_layer(filter_size=3, output_channels=output_channels[1], stride=1)
with tf.variable_scope('scale2'):
conv_layers.res_layer(filter_size=3, output_channels=output_channels[2], stride=2)
for block in range(res_blocks[2]-1):
conv_layers.conv_layers.res_layer(filter_size=3, output_channels=output_channels[2], stride=1)
with tf.variable_scope('scale3'):
conv_layers.res_layer(filter_size=3, output_channels=output_channels[3], stride=2)
for block in range(res_blocks[3]-1):
conv_layers.conv_layers.res_layer(filter_size=3, output_channels=output_channels[3], stride=1)
with tf.variable_scope('scale4'):
conv_layers.res_layer(filter_size=3, output_channels=output_channels[4], stride=2)
for block in range(res_blocks[4]-1):
conv_layers.conv_layers.res_layer(filter_size=3, output_channels=output_channels[4], stride=1)
conv_layers.avgpool(globe=True)
# Fully Connected Layer
conv_layers.fc(output_nodes=10)
return conv_layers.get_output()
def _conv_layers(self, x):
conv_layers = Layers(x)
# Convolutional layers
res_blocks = [1, 3, 4, 23, 3]
output_channels = [64, 256, 512, 1024, 2048]
with tf.variable_scope('scale0'):
conv_layers.conv2d(filter_size=7,
output_channels=output_channels[0],
stride=2,
padding='SAME',
b_value=None)
conv_layers.maxpool(k=3)
with tf.variable_scope('scale1'):
conv_layers.res_layer(filter_size=3,
output_channels=output_channels[1],
stride=2)
for block in range(res_blocks[1] - 1):
conv_layers.conv_layers.res_layer(
filter_size=3,
output_channels=output_channels[1],
stride=1)
with tf.variable_scope('scale2'):
conv_layers.res_layer(filter_size=3,
output_channels=output_channels[2],
stride=2)
for block in range(res_blocks[2] - 1):
conv_layers.conv_layers.res_layer(
filter_size=3,
output_channels=output_channels[2],
stride=1)
with tf.variable_scope('scale3'):
conv_layers.res_layer(filter_size=3,
output_channels=output_channels[3],
stride=2)
for block in range(res_blocks[3] - 1):
conv_layers.conv_layers.res_layer(
filter_size=3,
output_channels=output_channels[3],
stride=1)
with tf.variable_scope('scale4'):
conv_layers.res_layer(filter_size=3,
output_channels=output_channels[4],
stride=2)
for block in range(res_blocks[4] - 1):
conv_layers.conv_layers.res_layer(
filter_size=3,
output_channels=output_channels[4],
stride=1)
conv_layers.avgpool(globe=True)
# Fully Connected Layer
conv_layers.fc(output_nodes=10)
return conv_layers.get_output()
def _network(self, x):
conv_layers = Layers(x)
# Convolutional layers
with tf.variable_scope('resnet101'):
res_blocks = [1, 3, 4, 23, 3]
output_channels = [64, 256, 512, 1024, 2048]
with tf.variable_scope('scale0'):
conv_layers.conv2d(filter_size=7,
output_channels=output_channels[0],
stride=2,
padding='SAME',
b_value=None)
conv_layers.maxpool(k=3)
with tf.variable_scope('scale1'):
conv_layers.res_layer(filter_size=3,
output_channels=output_channels[1],
stride=2)
for block in range(res_blocks[1] - 1):
conv_layers.conv_layers.res_layer(
filter_size=3,
output_channels=output_channels[1],
stride=1)
with tf.variable_scope('scale2'):
conv_layers.res_layer(filter_size=3,
output_channels=output_channels[2],
stride=2)
for block in range(res_blocks[2] - 1):
conv_layers.conv_layers.res_layer(
filter_size=3,
output_channels=output_channels[2],
stride=1)
with tf.variable_scope('scale3'):
conv_layers.res_layer(filter_size=3,
output_channels=output_channels[3],
stride=2)
for block in range(res_blocks[3] - 1):
conv_layers.conv_layers.res_layer(
filter_size=3,
output_channels=output_channels[3],
stride=1)
with tf.variable_scope('scale4'):
conv_layers.res_layer(filter_size=3,
output_channels=output_channels[4],
stride=2)
for block in range(res_blocks[4] - 1):
conv_layers.conv_layers.res_layer(
filter_size=3,
output_channels=output_channels[4],
stride=1)
return conv_layers
class rpn:
'''
Region Proposal Network (RPN): From the convolutional feature maps
(TensorBase Layers object) of the last layer, generate bounding boxes
relative to anchor boxes and give an "objectness" score to each
'''
def __init__(self,featureMaps,gt_boxes,im_dims,_feat_stride,flags):
self.featureMaps = featureMaps
self.gt_boxes = gt_boxes
self.im_dims = im_dims
self._feat_stride = _feat_stride
self.flags = flags
self._network()
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)
# Get functions
def get_rpn_cls_score(self):
return self.rpn_bbox_cls_layers.get_output()
def get_rpn_labels(self):
return self.rpn_labels
def get_rpn_bbox_pred(self):
return self.rpn_bbox_pred_layers.get_output()
def get_rpn_bbox_targets(self):
return self.rpn_bbox_targets
def get_rpn_bbox_inside_weights(self):
return self.rpn_bbox_inside_weights
def get_rpn_bbox_outside_weights(self):
return self.rpn_bbox_outside_weights
# Loss functions
def get_rpn_cls_loss(self):
rpn_cls_score = self.get_rpn_cls_score()
rpn_labels = self.get_rpn_labels()
return rpn_cls_loss(rpn_cls_score,rpn_labels)
def get_rpn_bbox_loss(self):
rpn_bbox_pred = self.get_rpn_bbox_pred()
rpn_bbox_targets = self.get_rpn_bbox_targets()
rpn_bbox_inside_weights = self.get_rpn_bbox_inside_weights()
rpn_bbox_outside_weights = self.get_rpn_bbox_outside_weights()
return rpn_bbox_loss(rpn_bbox_pred, rpn_bbox_targets, rpn_bbox_inside_weights, rpn_bbox_outside_weights)
class fast_rcnn:
'''
Crop and resize areas from the feature-extracting CNN's feature maps
according to the ROIs generated from the ROI proposal layer
'''
def __init__(self,featureMaps, roi_proposal_net):
self.featureMaps = featureMaps
self.roi_proposal_net = roi_proposal_net
self.rois = roi_proposal_net.get_rois()
self.im_dims = roi_proposal_net.im_dims
self.flags = roi_proposal_net.flags
self._network()
def _network(self):
with tf.variable_scope('fast_rcnn'):
# ROI pooling
pooledFeatures = roi_pool(self.featureMaps,self.rois,self.im_dims)
# Fully Connect layers (with dropout)
with tf.variable_scope('fc'):
self.rcnn_fc_layers = Layers(pooledFeatures)
self.rcnn_fc_layers.flatten()
self.rcnn_fc_layers.fc(output_nodes=4096, keep_prob=0.5)
self.rcnn_fc_layers.fc(output_nodes=4096, keep_prob=0.5)
hidden = self.rcnn_fc_layers.get_output()
# Classifier score
with tf.variable_scope('cls'):
self.rcnn_cls_layers = Layers(hidden)
self.rcnn_cls_layers.fc(output_nodes=self.flags['num_classes'],activation_fn=None)
# Bounding Box refinement
with tf.variable_scope('bbox'):
self.rcnn_bbox_layers = Layers(hidden)
self.rcnn_bbox_layers.fc(output_nodes=4*self.flags['num_classes'],activation_fn=None)
# Get functions
def get_cls_score(self):
return self.rcnn_cls_layers.get_output()
def get_cls_prob(self):
logits = self.get_cls_score()
return tf.nn.softmax(logits)
def get_bbox_refinement(self):
return self.rcnn_bbox_layers.get_output()
# Loss functions
def get_fast_rcnn_cls_loss(self):
fast_rcnn_cls_score = self.get_cls_score()
labels = self.roi_proposal_net.get_labels()
return fast_rcnn_cls_loss(fast_rcnn_cls_score, labels)
def get_fast_rcnn_bbox_loss(self):
fast_rcnn_bbox_pred = self.get_bbox_refinement()
bbox_targets = self.roi_proposal_net.get_bbox_targets()
roi_inside_weights = self.roi_proposal_net.get_bbox_inside_weights()
roi_outside_weights = self.roi_proposal_net.get_bbox_outside_weights()
return fast_rcnn_bbox_loss(fast_rcnn_bbox_pred, bbox_targets, roi_inside_weights, roi_outside_weights)
class rpn:
'''
Region Proposal Network (RPN): From the convolutional feature maps
(TensorBase Layers object) of the last layer, generate bounding boxes
relative to anchor boxes and give an "objectness" score to each
In evaluation mode (eval_mode==True), gt_boxes should be None.
'''
def __init__(self, featureMaps, gt_boxes, im_dims, _feat_stride,
eval_mode):
self.featureMaps = featureMaps
self.gt_boxes = gt_boxes
self.im_dims = im_dims
self._feat_stride = _feat_stride
self.anchor_scales = cfg.RPN_ANCHOR_SCALES
self.eval_mode = eval_mode
self._network()
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)
# Get functions
def get_rpn_cls_score(self):
return self.rpn_bbox_cls_layers.get_output()
def get_rpn_labels(self):
assert self.eval_mode is False, 'No RPN labels without ground truth boxes'
return self.rpn_labels
def get_rpn_bbox_pred(self):
return self.rpn_bbox_pred_layers.get_output()
def get_rpn_bbox_targets(self):
assert self.eval_mode is False, 'No RPN bounding box targets without ground truth boxes'
return self.rpn_bbox_targets
def get_rpn_bbox_inside_weights(self):
assert self.eval_mode is False, 'No RPN inside weights without ground truth boxes'
return self.rpn_bbox_inside_weights
def get_rpn_bbox_outside_weights(self):
assert self.eval_mode is False, 'No RPN outside weights without ground truth boxes'
return self.rpn_bbox_outside_weights
# Loss functions
def get_rpn_cls_loss(self):
assert self.eval_mode is False, 'No RPN cls loss without ground truth boxes'
rpn_cls_score = self.get_rpn_cls_score()
rpn_labels = self.get_rpn_labels()
return rpn_cls_loss(rpn_cls_score, rpn_labels)
def get_rpn_bbox_loss(self):
assert self.eval_mode is False, 'No RPN bbox loss without ground truth boxes'
rpn_bbox_pred = self.get_rpn_bbox_pred()
rpn_bbox_targets = self.get_rpn_bbox_targets()
rpn_bbox_inside_weights = self.get_rpn_bbox_inside_weights()
rpn_bbox_outside_weights = self.get_rpn_bbox_outside_weights()
return rpn_bbox_loss(rpn_bbox_pred, rpn_bbox_targets,
rpn_bbox_inside_weights, rpn_bbox_outside_weights)
def _network(self, x):
conv_layers = Layers(x)
# Convolutional layers
scope = 'resnet' + str(self.depth)
with tf.variable_scope(scope):
res_blocks = self.architectures[self.depth]
output_channels = [64, 256, 512, 1024, 2048]
with tf.variable_scope('scale0'):
conv_layers.conv2d(filter_size=7,
output_channels=output_channels[0],
stride=2,
padding='SAME',
b_value=None) # Downsample
conv_layers.maxpool(k=3, s=2) # Downsample
with tf.variable_scope('scale1'):
for block in range(res_blocks[1]):
conv_layers.res_layer(filter_size=3,
output_channels=output_channels[1],
stride=1)
with tf.variable_scope('scale2'):
conv_layers.res_layer(filter_size=3,
output_channels=output_channels[2],
stride=2) # Downsample
for block in range(res_blocks[2] - 1):
conv_layers.res_layer(filter_size=3,
output_channels=output_channels[2],
stride=1)
with tf.variable_scope('scale3'):
conv_layers.res_layer(filter_size=3,
output_channels=output_channels[3],
stride=2) # Downsample
for block in range(res_blocks[3] - 1):
conv_layers.res_layer(filter_size=3,
output_channels=output_channels[3],
stride=1)
with tf.variable_scope('scale4'):
conv_layers.res_layer(filter_size=3,
output_channels=output_channels[4],
stride=2) # Downsample
for block in range(res_blocks[4] - 1):
conv_layers.res_layer(filter_size=3,
output_channels=output_channels[4],
stride=1)
return conv_layers
class fast_rcnn:
'''
Crop and resize areas from the feature-extracting CNN's feature maps
according to the ROIs generated from the ROI proposal layer
'''
def __init__(self, featureMaps, roi_proposal_net, eval_mode):
self.featureMaps = featureMaps
self.roi_proposal_net = roi_proposal_net
self.rois = roi_proposal_net.get_rois()
self.im_dims = roi_proposal_net.im_dims
self.num_classes = cfg.NUM_CLASSES
self.eval_mode = eval_mode
self._network()
def _network(self):
with tf.variable_scope('fast_rcnn'):
# No dropout in evaluation mode
keep_prob = cfg.FRCNN_DROPOUT_KEEP_RATE if self.eval_mode is False else 1.0
# ROI pooling
pooledFeatures = roi_pool(self.featureMaps, self.rois,
self.im_dims)
# Fully Connect layers (with dropout)
with tf.variable_scope('fc'):
self.rcnn_fc_layers = Layers(pooledFeatures)
self.rcnn_fc_layers.flatten()
for i in range(len(cfg.FRCNN_FC_HIDDEN)):
self.rcnn_fc_layers.fc(output_nodes=cfg.FRCNN_FC_HIDDEN[i],
keep_prob=keep_prob)
hidden = self.rcnn_fc_layers.get_output()
# Classifier score
with tf.variable_scope('cls'):
self.rcnn_cls_layers = Layers(hidden)
self.rcnn_cls_layers.fc(output_nodes=self.num_classes,
activation_fn=None)
# Bounding Box refinement
with tf.variable_scope('bbox'):
self.rcnn_bbox_layers = Layers(hidden)
self.rcnn_bbox_layers.fc(output_nodes=self.num_classes * 4,
activation_fn=None)
# Get functions
def get_cls_score(self):
return self.rcnn_cls_layers.get_output()
def get_cls_prob(self):
logits = self.get_cls_score()
return tf.nn.softmax(logits)
def get_bbox_refinement(self):
return self.rcnn_bbox_layers.get_output()
# Loss functions
def get_fast_rcnn_cls_loss(self):
assert self.eval_mode is False, 'No Fast RCNN cls loss without ground truth boxes'
fast_rcnn_cls_score = self.get_cls_score()
labels = self.roi_proposal_net.get_labels()
return fast_rcnn_cls_loss(fast_rcnn_cls_score, labels)
def get_fast_rcnn_bbox_loss(self):
assert self.eval_mode is False, 'No Fast RCNN bbox loss without ground truth boxes'
fast_rcnn_bbox_pred = self.get_bbox_refinement()
bbox_targets = self.roi_proposal_net.get_bbox_targets()
roi_inside_weights = self.roi_proposal_net.get_bbox_inside_weights()
roi_outside_weights = self.roi_proposal_net.get_bbox_outside_weights()
return fast_rcnn_bbox_loss(fast_rcnn_bbox_pred, bbox_targets,
roi_inside_weights, roi_outside_weights)
class fast_rcnn:
'''
Crop and resize areas from the feature-extracting CNN's feature maps
according to the ROIs generated from the ROI proposal layer
'''
def __init__(self, featureMaps, roi_proposal_net, eval_mode):
self.featureMaps = featureMaps
self.roi_proposal_net = roi_proposal_net
self.rois = roi_proposal_net.get_rois()
self.im_dims = roi_proposal_net.im_dims
self.num_classes = cfg.NUM_CLASSES
self.eval_mode = eval_mode
self._network()
def _network(self):
with tf.variable_scope('fast_rcnn'):
# No dropout in evaluation mode
keep_prob = cfg.FRCNN_DROPOUT_KEEP_RATE if self.eval_mode is False else 1.0
# ROI pooling
pooledFeatures = roi_pool(self.featureMaps, self.rois, self.im_dims)
# Fully Connect layers (with dropout)
with tf.variable_scope('fc'):
self.rcnn_fc_layers = Layers(pooledFeatures)
self.rcnn_fc_layers.flatten()
for i in range(len(cfg.FRCNN_FC_HIDDEN)):
self.rcnn_fc_layers.fc(output_nodes=cfg.FRCNN_FC_HIDDEN[i], keep_prob=keep_prob)
hidden = self.rcnn_fc_layers.get_output()
# Classifier score
with tf.variable_scope('cls'):
self.rcnn_cls_layers = Layers(hidden)
self.rcnn_cls_layers.fc(output_nodes=self.num_classes, activation_fn=None)
# Bounding Box refinement
with tf.variable_scope('bbox'):
self.rcnn_bbox_layers = Layers(hidden)
self.rcnn_bbox_layers.fc(output_nodes=self.num_classes*4, activation_fn=None)
# Get functions
def get_cls_score(self):
return self.rcnn_cls_layers.get_output()
def get_cls_prob(self):
logits = self.get_cls_score()
return tf.nn.softmax(logits)
def get_bbox_refinement(self):
return self.rcnn_bbox_layers.get_output()
# Loss functions
def get_fast_rcnn_cls_loss(self):
assert self.eval_mode is False, 'No Fast RCNN cls loss without ground truth boxes'
fast_rcnn_cls_score = self.get_cls_score()
labels = self.roi_proposal_net.get_labels()
return fast_rcnn_cls_loss(fast_rcnn_cls_score, labels)
def get_fast_rcnn_bbox_loss(self):
assert self.eval_mode is False, 'No Fast RCNN bbox loss without ground truth boxes'
fast_rcnn_bbox_pred = self.get_bbox_refinement()
bbox_targets = self.roi_proposal_net.get_bbox_targets()
roi_inside_weights = self.roi_proposal_net.get_bbox_inside_weights()
roi_outside_weights = self.roi_proposal_net.get_bbox_outside_weights()
return fast_rcnn_bbox_loss(fast_rcnn_bbox_pred, bbox_targets, roi_inside_weights, roi_outside_weights)
class rpn:
'''
Region Proposal Network (RPN): From the convolutional feature maps
(TensorBase Layers object) of the last layer, generate bounding boxes
relative to anchor boxes and give an "objectness" score to each
In evaluation mode (eval_mode==True), gt_boxes should be None.
'''
def __init__(self, featureMaps, gt_boxes, im_dims, _feat_stride, eval_mode):
self.featureMaps = featureMaps
self.gt_boxes = gt_boxes
self.im_dims = im_dims
self._feat_stride = _feat_stride
self.anchor_scales = cfg.RPN_ANCHOR_SCALES
self.eval_mode = eval_mode
self._network()
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)
# Get functions
def get_rpn_cls_score(self):
return self.rpn_bbox_cls_layers.get_output()
def get_rpn_labels(self):
assert self.eval_mode is False, 'No RPN labels without ground truth boxes'
return self.rpn_labels
def get_rpn_bbox_pred(self):
return self.rpn_bbox_pred_layers.get_output()
def get_rpn_bbox_targets(self):
assert self.eval_mode is False, 'No RPN bounding box targets without ground truth boxes'
return self.rpn_bbox_targets
def get_rpn_bbox_inside_weights(self):
assert self.eval_mode is False, 'No RPN inside weights without ground truth boxes'
return self.rpn_bbox_inside_weights
def get_rpn_bbox_outside_weights(self):
assert self.eval_mode is False, 'No RPN outside weights without ground truth boxes'
return self.rpn_bbox_outside_weights
# Loss functions
def get_rpn_cls_loss(self):
assert self.eval_mode is False, 'No RPN cls loss without ground truth boxes'
rpn_cls_score = self.get_rpn_cls_score()
rpn_labels = self.get_rpn_labels()
return rpn_cls_loss(rpn_cls_score, rpn_labels)
def get_rpn_bbox_loss(self):
assert self.eval_mode is False, 'No RPN bbox loss without ground truth boxes'
rpn_bbox_pred = self.get_rpn_bbox_pred()
rpn_bbox_targets = self.get_rpn_bbox_targets()
rpn_bbox_inside_weights = self.get_rpn_bbox_inside_weights()
rpn_bbox_outside_weights = self.get_rpn_bbox_outside_weights()
return rpn_bbox_loss(rpn_bbox_pred, rpn_bbox_targets, rpn_bbox_inside_weights, rpn_bbox_outside_weights)
