class DetectionNetworkRefineRetinaNet(DetectionNetworkBase):
def __init__(self, cfgs, is_training):
super(DetectionNetworkRefineRetinaNet,
self).__init__(cfgs, is_training)
self.anchor_sampler_retinenet = AnchorSamplerRetinaNet(cfgs)
self.refine_anchor_sampler_r3det = RefineAnchorSamplerR3Det(cfgs)
self.losses = Loss(self.cfgs)
def refine_cls_net(self, inputs, scope_list, reuse_flag, level):
rpn_conv2d_3x3 = inputs
for i in range(self.cfgs.NUM_SUBNET_CONV):
rpn_conv2d_3x3 = slim.conv2d(
inputs=rpn_conv2d_3x3,
num_outputs=self.cfgs.FPN_CHANNEL,
kernel_size=[3, 3],
stride=1,
activation_fn=tf.nn.relu,
weights_initializer=self.cfgs.SUBNETS_WEIGHTS_INITIALIZER,
biases_initializer=self.cfgs.SUBNETS_BIAS_INITIALIZER,
trainable=self.is_training,
scope='{}_{}'.format(scope_list[0], i),
reuse=reuse_flag)
rpn_box_scores = slim.conv2d(
rpn_conv2d_3x3,
num_outputs=self.cfgs.CLASS_NUM * self.num_anchors_per_location,
kernel_size=[3, 3],
stride=1,
weights_initializer=self.cfgs.SUBNETS_WEIGHTS_INITIALIZER,
biases_initializer=self.cfgs.FINAL_CONV_BIAS_INITIALIZER,
trainable=self.is_training,
scope=scope_list[2],
activation_fn=None,
reuse=reuse_flag)
rpn_box_scores = tf.reshape(
rpn_box_scores, [-1, self.cfgs.CLASS_NUM],
name='refine_{}_classification_reshape'.format(level))
rpn_box_probs = tf.sigmoid(
rpn_box_scores,
name='refine_{}_classification_sigmoid'.format(level))
return rpn_box_scores, rpn_box_probs
def refine_reg_net(self, inputs, scope_list, reuse_flag, level):
rpn_conv2d_3x3 = inputs
for i in range(self.cfgs.NUM_SUBNET_CONV):
rpn_conv2d_3x3 = slim.conv2d(
inputs=rpn_conv2d_3x3,
num_outputs=self.cfgs.FPN_CHANNEL,
kernel_size=[3, 3],
weights_initializer=self.cfgs.SUBNETS_WEIGHTS_INITIALIZER,
biases_initializer=self.cfgs.SUBNETS_BIAS_INITIALIZER,
stride=1,
activation_fn=tf.nn.relu,
trainable=self.is_training,
scope='{}_{}'.format(scope_list[1], i),
reuse=reuse_flag)
rpn_delta_boxes = slim.conv2d(
rpn_conv2d_3x3,
num_outputs=5 * self.num_anchors_per_location,
kernel_size=[3, 3],
stride=1,
weights_initializer=self.cfgs.SUBNETS_WEIGHTS_INITIALIZER,
biases_initializer=self.cfgs.SUBNETS_BIAS_INITIALIZER,
trainable=self.is_training,
scope=scope_list[3],
activation_fn=None,
reuse=reuse_flag)
rpn_delta_boxes = tf.reshape(
rpn_delta_boxes, [-1, 5],
name='refine_{}_regression_reshape'.format(level))
return rpn_delta_boxes
def refine_net(self, feature_pyramid, name):
refine_delta_boxes_list = []
refine_scores_list = []
refine_probs_list = []
with tf.variable_scope(name):
with slim.arg_scope([slim.conv2d],
weights_regularizer=slim.l2_regularizer(
self.cfgs.WEIGHT_DECAY)):
for level in self.cfgs.LEVEL:
if self.cfgs.SHARE_NET:
reuse_flag = None if level == self.cfgs.LEVEL[
0] else True
scope_list = [
'conv2d_3x3_cls', 'conv2d_3x3_reg',
'refine_classification', 'refine_regression'
]
else:
reuse_flag = None
scope_list = [
'conv2d_3x3_cls_' + level,
'conv2d_3x3_reg_' + level,
'refine_classification_' + level,
'refine_regression_' + level
]
refine_box_scores, refine_box_probs = self.refine_cls_net(
feature_pyramid[level], scope_list, reuse_flag, level)
refine_delta_boxes = self.refine_reg_net(
feature_pyramid[level], scope_list, reuse_flag, level)
refine_scores_list.append(refine_box_scores)
refine_probs_list.append(refine_box_probs)
refine_delta_boxes_list.append(refine_delta_boxes)
return refine_delta_boxes_list, refine_scores_list, refine_probs_list
def refine_stage(self, input_img_batch, gtboxes_batch_r, box_pred_list,
cls_prob_list, proposal_list, feature_pyramid, gpu_id,
pos_threshold, neg_threshold, stage):
with tf.variable_scope('refine_feature_pyramid{}'.format(stage)):
refine_boxes_list = []
for box_pred, cls_prob, proposal, stride, level in \
zip(box_pred_list, cls_prob_list, proposal_list,
self.cfgs.ANCHOR_STRIDE, self.cfgs.LEVEL):
if stage == '' and self.cfgs.METHOD == 'H':
x_c = (proposal[:, 2] + proposal[:, 0]) / 2
y_c = (proposal[:, 3] + proposal[:, 1]) / 2
h = proposal[:, 2] - proposal[:, 0] + 1
w = proposal[:, 3] - proposal[:, 1] + 1
theta = -90 * tf.ones_like(x_c)
proposal = tf.transpose(tf.stack([x_c, y_c, w, h, theta]))
bboxes = bbox_transform.rbbox_transform_inv(boxes=proposal,
deltas=box_pred)
refine_boxes_list.append(bboxes)
refine_box_pred_list, refine_cls_score_list, refine_cls_prob_list = self.refine_net(
feature_pyramid, 'refine_net{}'.format(stage))
refine_box_pred = tf.concat(refine_box_pred_list, axis=0)
refine_cls_score = tf.concat(refine_cls_score_list, axis=0)
# refine_cls_prob = tf.concat(refine_cls_prob_list, axis=0)
refine_boxes = tf.concat(refine_boxes_list, axis=0)
if self.is_training:
with tf.variable_scope('build_refine_loss{}'.format(stage)):
refine_labels, refine_target_delta, refine_box_states, refine_target_boxes = tf.py_func(
func=self.refine_anchor_sampler_r3det.
refine_anchor_target_layer,
inp=[
gtboxes_batch_r, refine_boxes, pos_threshold,
neg_threshold, gpu_id
],
Tout=[tf.float32, tf.float32, tf.float32, tf.float32])
self.add_anchor_img_smry(input_img_batch, refine_boxes,
refine_box_states, 1)
refine_cls_loss = self.losses.focal_loss(
refine_labels, refine_cls_score, refine_box_states)
if self.cfgs.USE_IOU_FACTOR:
refine_reg_loss = self.losses.iou_smooth_l1_loss_exp(
refine_target_delta,
refine_box_pred,
refine_box_states,
refine_target_boxes,
refine_boxes,
is_refine=True)
else:
refine_reg_loss = self.losses.smooth_l1_loss(
refine_target_delta, refine_box_pred,
refine_box_states)
self.losses_dict['refine_cls_loss{}'.format(
stage)] = refine_cls_loss * self.cfgs.CLS_WEIGHT
self.losses_dict['refine_reg_loss{}'.format(
stage)] = refine_reg_loss * self.cfgs.REG_WEIGHT
return refine_box_pred_list, refine_cls_prob_list, refine_boxes_list
def build_whole_detection_network(self,
input_img_batch,
gtboxes_batch_h=None,
gtboxes_batch_r=None,
gpu_id=0):
if self.is_training:
gtboxes_batch_h = tf.reshape(gtboxes_batch_h, [-1, 5])
gtboxes_batch_h = tf.cast(gtboxes_batch_h, tf.float32)
gtboxes_batch_r = tf.reshape(gtboxes_batch_r, [-1, 6])
gtboxes_batch_r = tf.cast(gtboxes_batch_r, tf.float32)
# 1. build backbone
feature_pyramid = self.build_backbone(input_img_batch)
# 2. build rpn
rpn_box_pred_list, rpn_cls_score_list, rpn_cls_prob_list = self.rpn_net(
feature_pyramid, 'rpn_net')
rpn_box_pred = tf.concat(rpn_box_pred_list, axis=0)
rpn_cls_score = tf.concat(rpn_cls_score_list, axis=0)
# rpn_cls_prob = tf.concat(rpn_cls_prob_list, axis=0)
# 3. generate anchors
anchor_list = self.make_anchors(feature_pyramid)
anchors = tf.concat(anchor_list, axis=0)
# 4. build loss
if self.is_training:
with tf.variable_scope('build_loss'):
labels, target_delta, anchor_states, target_boxes = tf.py_func(
func=self.anchor_sampler_retinenet.anchor_target_layer,
inp=[gtboxes_batch_h, gtboxes_batch_r, anchors, gpu_id],
Tout=[tf.float32, tf.float32, tf.float32, tf.float32])
if self.method == 'H':
self.add_anchor_img_smry(input_img_batch, anchors,
anchor_states, 0)
else:
self.add_anchor_img_smry(input_img_batch, anchors,
anchor_states, 1)
cls_loss = self.losses.focal_loss(labels, rpn_cls_score,
anchor_states)
if self.cfgs.USE_IOU_FACTOR:
reg_loss = self.losses.iou_smooth_l1_loss_exp(
target_delta,
rpn_box_pred,
anchor_states,
target_boxes,
anchors,
alpha=self.cfgs.ALPHA,
beta=self.cfgs.BETA)
else:
reg_loss = self.losses.smooth_l1_loss(
target_delta, rpn_box_pred, anchor_states)
self.losses_dict['cls_loss'] = cls_loss * self.cfgs.CLS_WEIGHT
self.losses_dict['reg_loss'] = reg_loss * self.cfgs.REG_WEIGHT
box_pred_list, cls_prob_list, proposal_list = rpn_box_pred_list, rpn_cls_prob_list, anchor_list
all_box_pred_list, all_cls_prob_list, all_proposal_list = [], [], []
for i in range(self.cfgs.NUM_REFINE_STAGE):
box_pred_list, cls_prob_list, proposal_list = self.refine_stage(
input_img_batch,
gtboxes_batch_r,
box_pred_list,
cls_prob_list,
proposal_list,
feature_pyramid,
gpu_id,
pos_threshold=self.cfgs.REFINE_IOU_POSITIVE_THRESHOLD[i],
neg_threshold=self.cfgs.REFINE_IOU_NEGATIVE_THRESHOLD[i],
stage='' if i == 0 else '_stage{}'.format(i + 2))
if not self.is_training:
all_box_pred_list.extend(box_pred_list)
all_cls_prob_list.extend(cls_prob_list)
all_proposal_list.extend(proposal_list)
else:
all_box_pred_list, all_cls_prob_list, all_proposal_list = box_pred_list, cls_prob_list, proposal_list
# 5. postprocess
with tf.variable_scope('postprocess_detctions'):
box_pred = tf.concat(all_box_pred_list, axis=0)
cls_prob = tf.concat(all_cls_prob_list, axis=0)
proposal = tf.concat(all_proposal_list, axis=0)
boxes, scores, category = self.postprocess_detctions(
refine_bbox_pred=box_pred,
refine_cls_prob=cls_prob,
anchors=proposal)
boxes = tf.stop_gradient(boxes)
scores = tf.stop_gradient(scores)
category = tf.stop_gradient(category)
if self.is_training:
return boxes, scores, category, self.losses_dict
else:
return boxes, scores, category
def postprocess_detctions(self, refine_bbox_pred, refine_cls_prob,
anchors):
def filter_detections(boxes, scores):
"""
:param boxes: [-1, 4]
:param scores: [-1, ]
:param labels: [-1, ]
:return:
"""
if self.is_training:
indices = tf.reshape(
tf.where(tf.greater(scores, self.cfgs.VIS_SCORE)), [
-1,
])
else:
indices = tf.reshape(
tf.where(tf.greater(scores, self.cfgs.FILTERED_SCORE)), [
-1,
])
if self.cfgs.NMS:
filtered_boxes = tf.gather(boxes, indices)
filtered_scores = tf.gather(scores, indices)
# perform NMS
nms_indices = nms_rotate.nms_rotate(
decode_boxes=filtered_boxes,
scores=filtered_scores,
iou_threshold=self.cfgs.NMS_IOU_THRESHOLD,
max_output_size=100 if self.is_training else 1000,
use_gpu=False)
# filter indices based on NMS
indices = tf.gather(indices, nms_indices)
# add indices to list of all indices
return indices
boxes_pred = bbox_transform.rbbox_transform_inv(
boxes=anchors,
deltas=refine_bbox_pred,
scale_factors=self.cfgs.ANCHOR_SCALE_FACTORS)
return_boxes_pred = []
return_scores = []
return_labels = []
for j in range(0, self.cfgs.CLASS_NUM):
indices = filter_detections(boxes_pred, refine_cls_prob[:, j])
tmp_boxes_pred = tf.reshape(tf.gather(boxes_pred, indices),
[-1, 5])
tmp_scores = tf.reshape(tf.gather(refine_cls_prob[:, j], indices),
[
-1,
])
return_boxes_pred.append(tmp_boxes_pred)
return_scores.append(tmp_scores)
return_labels.append(tf.ones_like(tmp_scores) * (j + 1))
return_boxes_pred = tf.concat(return_boxes_pred, axis=0)
return_scores = tf.concat(return_scores, axis=0)
return_labels = tf.concat(return_labels, axis=0)
return return_boxes_pred, return_scores, return_labels
class DetectionNetworkR3Det(DetectionNetworkBase):
def __init__(self, cfgs, is_training):
super(DetectionNetworkR3Det, self).__init__(cfgs, is_training)
self.anchor_sampler_retinenet = AnchorSamplerRetinaNet(cfgs)
self.refine_anchor_sampler_r3det = RefineAnchorSamplerR3Det(cfgs)
self.losses = Loss(self.cfgs)
def refine_cls_net(self, inputs, scope_list, reuse_flag, level):
rpn_conv2d_3x3 = inputs
for i in range(self.cfgs.NUM_SUBNET_CONV):
rpn_conv2d_3x3 = slim.conv2d(
inputs=rpn_conv2d_3x3,
num_outputs=self.cfgs.FPN_CHANNEL,
kernel_size=[3, 3],
stride=1,
activation_fn=None if self.cfgs.USE_GN else tf.nn.relu,
weights_initializer=self.cfgs.SUBNETS_WEIGHTS_INITIALIZER,
biases_initializer=self.cfgs.SUBNETS_BIAS_INITIALIZER,
trainable=self.is_training,
scope='{}_{}'.format(scope_list[0], i),
reuse=reuse_flag)
if self.cfgs.USE_GN:
rpn_conv2d_3x3 = tf.contrib.layers.group_norm(rpn_conv2d_3x3)
rpn_conv2d_3x3 = tf.nn.relu(rpn_conv2d_3x3)
rpn_box_scores = slim.conv2d(
rpn_conv2d_3x3,
num_outputs=self.cfgs.CLASS_NUM,
kernel_size=[3, 3],
stride=1,
weights_initializer=self.cfgs.SUBNETS_WEIGHTS_INITIALIZER,
biases_initializer=self.cfgs.FINAL_CONV_BIAS_INITIALIZER,
scope=scope_list[2],
trainable=self.is_training,
activation_fn=None,
reuse=reuse_flag)
rpn_box_scores = tf.reshape(
rpn_box_scores, [-1, self.cfgs.CLASS_NUM],
name='refine_{}_classification_reshape'.format(level))
rpn_box_probs = tf.sigmoid(
rpn_box_scores,
name='refine_{}_classification_sigmoid'.format(level))
return rpn_box_scores, rpn_box_probs
def refine_reg_net(self, inputs, scope_list, reuse_flag, level):
rpn_conv2d_3x3 = inputs
for i in range(self.cfgs.NUM_SUBNET_CONV):
rpn_conv2d_3x3 = slim.conv2d(
inputs=rpn_conv2d_3x3,
num_outputs=self.cfgs.FPN_CHANNEL,
kernel_size=[3, 3],
weights_initializer=self.cfgs.SUBNETS_WEIGHTS_INITIALIZER,
biases_initializer=self.cfgs.SUBNETS_BIAS_INITIALIZER,
stride=1,
activation_fn=None if self.cfgs.USE_GN else tf.nn.relu,
scope='{}_{}'.format(scope_list[1], i),
trainable=self.is_training,
reuse=reuse_flag)
if self.cfgs.USE_GN:
rpn_conv2d_3x3 = tf.contrib.layers.group_norm(rpn_conv2d_3x3)
rpn_conv2d_3x3 = tf.nn.relu(rpn_conv2d_3x3)
rpn_delta_boxes = slim.conv2d(
rpn_conv2d_3x3,
num_outputs=5,
kernel_size=[3, 3],
stride=1,
weights_initializer=self.cfgs.SUBNETS_WEIGHTS_INITIALIZER,
biases_initializer=self.cfgs.SUBNETS_BIAS_INITIALIZER,
scope=scope_list[3],
trainable=self.is_training,
activation_fn=None,
reuse=reuse_flag)
rpn_delta_boxes = tf.reshape(
rpn_delta_boxes, [-1, 5],
name='refine_{}_regression_reshape'.format(level))
return rpn_delta_boxes
def refine_net(self, feature_pyramid, name):
refine_delta_boxes_list = []
refine_scores_list = []
refine_probs_list = []
with tf.variable_scope(name):
with slim.arg_scope([slim.conv2d],
weights_regularizer=slim.l2_regularizer(
self.cfgs.WEIGHT_DECAY)):
for level in self.cfgs.LEVEL:
if self.cfgs.SHARE_NET:
reuse_flag = None if level == self.cfgs.LEVEL[
0] else True
scope_list = [
'conv2d_3x3_cls', 'conv2d_3x3_reg',
'refine_classification', 'refine_regression'
]
else:
reuse_flag = None
scope_list = [
'conv2d_3x3_cls_' + level,
'conv2d_3x3_reg_' + level,
'refine_classification_' + level,
'refine_regression_' + level
]
refine_box_scores, refine_box_probs = self.refine_cls_net(
feature_pyramid[level], scope_list, reuse_flag, level)
refine_delta_boxes = self.refine_reg_net(
feature_pyramid[level], scope_list, reuse_flag, level)
refine_scores_list.append(refine_box_scores)
refine_probs_list.append(refine_box_probs)
refine_delta_boxes_list.append(refine_delta_boxes)
return refine_delta_boxes_list, refine_scores_list, refine_probs_list
def refine_feature_op(self, points, feature_map, name):
h, w = tf.cast(tf.shape(feature_map)[1],
tf.int32), tf.cast(tf.shape(feature_map)[2], tf.int32)
xmin = tf.maximum(0.0, tf.floor(points[:, 0]))
xmin = tf.minimum(tf.cast(w - 1, tf.float32), tf.ceil(xmin))
ymin = tf.maximum(0.0, tf.floor(points[:, 1]))
ymin = tf.minimum(tf.cast(h - 1, tf.float32), tf.ceil(ymin))
xmax = tf.minimum(tf.cast(w - 1, tf.float32), tf.ceil(points[:, 0]))
xmax = tf.maximum(0.0, tf.floor(xmax))
ymax = tf.minimum(tf.cast(h - 1, tf.float32), tf.ceil(points[:, 1]))
ymax = tf.maximum(0.0, tf.floor(ymax))
left_top = tf.cast(tf.transpose(tf.stack([ymin, xmin], axis=0)),
tf.int32)
right_bottom = tf.cast(tf.transpose(tf.stack([ymax, xmax], axis=0)),
tf.int32)
left_bottom = tf.cast(tf.transpose(tf.stack([ymax, xmin], axis=0)),
tf.int32)
right_top = tf.cast(tf.transpose(tf.stack([ymin, xmax], axis=0)),
tf.int32)
feature_1x5 = slim.conv2d(
inputs=feature_map,
num_outputs=self.cfgs.FPN_CHANNEL,
kernel_size=[1, 5],
weights_initializer=self.cfgs.SUBNETS_WEIGHTS_INITIALIZER,
biases_initializer=self.cfgs.SUBNETS_BIAS_INITIALIZER,
stride=1,
activation_fn=None,
trainable=self.is_training,
scope='refine_1x5_{}'.format(name))
feature5x1 = slim.conv2d(
inputs=feature_1x5,
num_outputs=self.cfgs.FPN_CHANNEL,
kernel_size=[5, 1],
weights_initializer=self.cfgs.SUBNETS_WEIGHTS_INITIALIZER,
biases_initializer=self.cfgs.SUBNETS_BIAS_INITIALIZER,
stride=1,
activation_fn=None,
trainable=self.is_training,
scope='refine_5x1_{}'.format(name))
feature_1x1 = slim.conv2d(
inputs=feature_map,
num_outputs=self.cfgs.FPN_CHANNEL,
kernel_size=[1, 1],
weights_initializer=self.cfgs.SUBNETS_WEIGHTS_INITIALIZER,
biases_initializer=self.cfgs.SUBNETS_BIAS_INITIALIZER,
stride=1,
activation_fn=None,
trainable=self.is_training,
scope='refine_1x1_{}'.format(name))
feature = feature5x1 + feature_1x1
# feature = feature_map
left_top_feature = tf.gather_nd(tf.squeeze(feature), left_top)
right_bottom_feature = tf.gather_nd(tf.squeeze(feature), right_bottom)
left_bottom_feature = tf.gather_nd(tf.squeeze(feature), left_bottom)
right_top_feature = tf.gather_nd(tf.squeeze(feature), right_top)
refine_feature = right_bottom_feature * tf.tile(
tf.reshape((tf.abs((points[:, 0] - xmin) * (points[:, 1] - ymin))), [-1, 1]),
[1, self.cfgs.FPN_CHANNEL]) \
+ left_top_feature * tf.tile(
tf.reshape((tf.abs((xmax - points[:, 0]) * (ymax - points[:, 1]))), [-1, 1]),
[1, self.cfgs.FPN_CHANNEL]) \
+ right_top_feature * tf.tile(
tf.reshape((tf.abs((points[:, 0] - xmin) * (ymax - points[:, 1]))), [-1, 1]),
[1, self.cfgs.FPN_CHANNEL]) \
+ left_bottom_feature * tf.tile(
tf.reshape((tf.abs((xmax - points[:, 0]) * (points[:, 1] - ymin))), [-1, 1]),
[1, self.cfgs.FPN_CHANNEL])
refine_feature = tf.reshape(refine_feature, [
1,
tf.cast(h, tf.int32),
tf.cast(w, tf.int32), self.cfgs.FPN_CHANNEL
])
# refine_feature = tf.reshape(refine_feature, [1, tf.cast(feature_size[1], tf.int32),
# tf.cast(feature_size[0], tf.int32), 256])
return refine_feature + feature
def refine_feature_five_op(self, points, feature_map, name):
h, w = tf.cast(tf.shape(feature_map)[1],
tf.int32), tf.cast(tf.shape(feature_map)[2], tf.int32)
feature_1x5 = slim.conv2d(
inputs=feature_map,
num_outputs=self.cfgs.FPN_CHANNEL,
kernel_size=[1, 5],
weights_initializer=self.cfgs.SUBNETS_WEIGHTS_INITIALIZER,
biases_initializer=self.cfgs.SUBNETS_BIAS_INITIALIZER,
stride=1,
activation_fn=None,
trainable=self.is_training,
scope='refine_1x5_{}'.format(name))
feature5x1 = slim.conv2d(
inputs=feature_1x5,
num_outputs=self.cfgs.FPN_CHANNEL,
kernel_size=[5, 1],
weights_initializer=self.cfgs.SUBNETS_WEIGHTS_INITIALIZER,
biases_initializer=self.cfgs.SUBNETS_BIAS_INITIALIZER,
stride=1,
activation_fn=None,
trainable=self.is_training,
scope='refine_5x1_{}'.format(name))
feature_1x1 = slim.conv2d(
inputs=feature_map,
num_outputs=self.cfgs.FPN_CHANNEL,
kernel_size=[1, 1],
weights_initializer=self.cfgs.SUBNETS_WEIGHTS_INITIALIZER,
biases_initializer=self.cfgs.SUBNETS_BIAS_INITIALIZER,
stride=1,
activation_fn=None,
trainable=self.is_training,
scope='refine_1x1_{}'.format(name))
feature = feature5x1 + feature_1x1
for i in range(5):
xmin = tf.maximum(0.0, tf.floor(points[:, 0 + 2 * (i - 1)]))
ymin = tf.maximum(0.0, tf.floor(points[:, 1 + 2 * (i - 1)]))
xmax = tf.minimum(tf.cast(w - 1, tf.float32),
tf.ceil(points[:, 0 + 2 * (i - 1)]))
ymax = tf.minimum(tf.cast(h - 1, tf.float32),
tf.ceil(points[:, 1 + 2 * (i - 1)]))
left_top = tf.cast(tf.transpose(tf.stack([ymin, xmin], axis=0)),
tf.int32)
right_bottom = tf.cast(
tf.transpose(tf.stack([ymax, xmax], axis=0)), tf.int32)
left_bottom = tf.cast(tf.transpose(tf.stack([ymax, xmin], axis=0)),
tf.int32)
right_top = tf.cast(tf.transpose(tf.stack([ymin, xmax], axis=0)),
tf.int32)
left_top_feature = tf.gather_nd(tf.squeeze(feature), left_top)
right_bottom_feature = tf.gather_nd(tf.squeeze(feature),
right_bottom)
left_bottom_feature = tf.gather_nd(tf.squeeze(feature),
left_bottom)
right_top_feature = tf.gather_nd(tf.squeeze(feature), right_top)
refine_feature = right_bottom_feature * tf.tile(
tf.reshape((tf.abs((points[:, 0+2*(i-1)] - xmin) * (points[:, 1+2*(i-1)] - ymin))), [-1, 1]),
[1, self.cfgs.FPN_CHANNEL]) \
+ left_top_feature * tf.tile(
tf.reshape((tf.abs((xmax - points[:, 0+2*(i-1)]) * (ymax - points[:, 1+2*(i-1)]))), [-1, 1]),
[1, self.cfgs.FPN_CHANNEL]) \
+ right_top_feature * tf.tile(
tf.reshape((tf.abs((points[:, 0+2*(i-1)] - xmin) * (ymax - points[:, 1+2*(i-1)]))), [-1, 1]),
[1, self.cfgs.FPN_CHANNEL]) \
+ left_bottom_feature * tf.tile(
tf.reshape((tf.abs((xmax - points[:, 0+2*(i-1)]) * (points[:, 1+2*(i-1)] - ymin))), [-1, 1]),
[1, self.cfgs.FPN_CHANNEL])
refine_feature = tf.reshape(refine_feature, [
1,
tf.cast(h, tf.int32),
tf.cast(w, tf.int32), self.cfgs.FPN_CHANNEL
])
feature += refine_feature
return feature
def refine_stage(self,
input_img_batch,
gtboxes_batch_r,
box_pred_list,
cls_prob_list,
proposal_list,
feature_pyramid,
gpu_id,
pos_threshold,
neg_threshold,
stage,
proposal_filter=False):
with tf.variable_scope('refine_feature_pyramid{}'.format(stage)):
refine_feature_pyramid = {}
refine_boxes_list = []
for box_pred, cls_prob, proposal, stride, level in \
zip(box_pred_list, cls_prob_list, proposal_list,
self.cfgs.ANCHOR_STRIDE, self.cfgs.LEVEL):
if proposal_filter:
box_pred = tf.reshape(
box_pred, [-1, self.num_anchors_per_location, 5])
proposal = tf.reshape(proposal, [
-1, self.num_anchors_per_location,
5 if self.method == 'R' else 4
])
cls_prob = tf.reshape(cls_prob, [
-1, self.num_anchors_per_location, self.cfgs.CLASS_NUM
])
cls_max_prob = tf.reduce_max(cls_prob, axis=-1)
box_pred_argmax = tf.cast(
tf.reshape(tf.argmax(cls_max_prob, axis=-1), [-1, 1]),
tf.int32)
indices = tf.cast(
tf.cumsum(tf.ones_like(box_pred_argmax), axis=0),
tf.int32) - tf.constant(1, tf.int32)
indices = tf.concat([indices, box_pred_argmax], axis=-1)
box_pred = tf.reshape(tf.gather_nd(box_pred, indices),
[-1, 5])
proposal = tf.reshape(tf.gather_nd(proposal, indices),
[-1, 5 if self.method == 'R' else 4])
if self.cfgs.METHOD == 'H':
x_c = (proposal[:, 2] + proposal[:, 0]) / 2
y_c = (proposal[:, 3] + proposal[:, 1]) / 2
h = proposal[:, 2] - proposal[:, 0] + 1
w = proposal[:, 3] - proposal[:, 1] + 1
theta = -90 * tf.ones_like(x_c)
proposal = tf.transpose(
tf.stack([x_c, y_c, w, h, theta]))
else:
box_pred = tf.reshape(box_pred, [-1, 5])
proposal = tf.reshape(proposal, [-1, 5])
bboxes = bbox_transform.rbbox_transform_inv(boxes=proposal,
deltas=box_pred)
refine_boxes_list.append(bboxes)
center_point = bboxes[:, :2] / stride
refine_feature_pyramid[level] = self.refine_feature_op(
points=center_point,
feature_map=feature_pyramid[level],
name=level)
# points = coordinate5_2_8_tf(bboxes) / stride
# refine_feature_pyramid[level] = self.refine_feature_five_op(points=points,
# feature_map=feature_pyramid[level],
# name=level)
refine_box_pred_list, refine_cls_score_list, refine_cls_prob_list = self.refine_net(
refine_feature_pyramid, 'refine_net{}'.format(stage))
refine_box_pred = tf.concat(refine_box_pred_list, axis=0)
refine_cls_score = tf.concat(refine_cls_score_list, axis=0)
# refine_cls_prob = tf.concat(refine_cls_prob_list, axis=0)
refine_boxes = tf.concat(refine_boxes_list, axis=0)
if self.is_training:
with tf.variable_scope('build_refine_loss{}'.format(stage)):
refine_labels, refine_target_delta, refine_box_states, refine_target_boxes = tf.py_func(
func=self.refine_anchor_sampler_r3det.
refine_anchor_target_layer,
inp=[
gtboxes_batch_r, refine_boxes, pos_threshold,
neg_threshold, gpu_id
],
Tout=[tf.float32, tf.float32, tf.float32, tf.float32])
self.add_anchor_img_smry(input_img_batch, refine_boxes,
refine_box_states, 1)
refine_cls_loss = self.losses.focal_loss(
refine_labels, refine_cls_score, refine_box_states)
if self.cfgs.USE_IOU_FACTOR:
refine_reg_loss = self.losses.iou_smooth_l1_loss_exp(
refine_target_delta,
refine_box_pred,
refine_box_states,
refine_target_boxes,
refine_boxes,
is_refine=True)
else:
refine_reg_loss = self.losses.smooth_l1_loss(
refine_target_delta, refine_box_pred,
refine_box_states)
self.losses_dict['refine_cls_loss{}'.format(
stage)] = refine_cls_loss * self.cfgs.CLS_WEIGHT
self.losses_dict['refine_reg_loss{}'.format(
stage)] = refine_reg_loss * self.cfgs.REG_WEIGHT
return refine_box_pred_list, refine_cls_prob_list, refine_boxes_list
def build_whole_detection_network(self,
input_img_batch,
gtboxes_batch_h=None,
gtboxes_batch_r=None,
gpu_id=0):
if self.is_training:
gtboxes_batch_h = tf.reshape(gtboxes_batch_h, [-1, 5])
gtboxes_batch_h = tf.cast(gtboxes_batch_h, tf.float32)
gtboxes_batch_r = tf.reshape(gtboxes_batch_r, [-1, 6])
gtboxes_batch_r = tf.cast(gtboxes_batch_r, tf.float32)
if self.cfgs.USE_GN:
input_img_batch = tf.reshape(
input_img_batch,
[1, self.cfgs.IMG_SHORT_SIDE_LEN, self.cfgs.IMG_MAX_LENGTH, 3])
# 1. build backbone
feature_pyramid = self.build_backbone(input_img_batch)
# 2. build rpn
rpn_box_pred_list, rpn_cls_score_list, rpn_cls_prob_list = self.rpn_net(
feature_pyramid, 'rpn_net')
rpn_box_pred = tf.concat(rpn_box_pred_list, axis=0)
rpn_cls_score = tf.concat(rpn_cls_score_list, axis=0)
# rpn_cls_prob = tf.concat(rpn_cls_prob_list, axis=0)
# 3. generate anchors
anchor_list = self.make_anchors(feature_pyramid, use_tf=True)
anchors = tf.concat(anchor_list, axis=0)
# 4. build loss
if self.is_training:
with tf.variable_scope('build_loss'):
labels, target_delta, anchor_states, target_boxes = tf.py_func(
func=self.anchor_sampler_retinenet.anchor_target_layer,
inp=[gtboxes_batch_h, gtboxes_batch_r, anchors, gpu_id],
Tout=[tf.float32, tf.float32, tf.float32, tf.float32])
if self.method == 'H':
self.add_anchor_img_smry(input_img_batch, anchors,
anchor_states, 0)
else:
self.add_anchor_img_smry(input_img_batch, anchors,
anchor_states, 1)
cls_loss = self.losses.focal_loss(labels, rpn_cls_score,
anchor_states)
if self.cfgs.USE_IOU_FACTOR:
reg_loss = self.losses.iou_smooth_l1_loss_exp(
target_delta,
rpn_box_pred,
anchor_states,
target_boxes,
anchors,
alpha=self.cfgs.ALPHA,
beta=self.cfgs.BETA)
else:
reg_loss = self.losses.smooth_l1_loss(
target_delta, rpn_box_pred, anchor_states)
self.losses_dict['cls_loss'] = cls_loss * self.cfgs.CLS_WEIGHT
self.losses_dict['reg_loss'] = reg_loss * self.cfgs.REG_WEIGHT
box_pred_list, cls_prob_list, proposal_list = rpn_box_pred_list, rpn_cls_prob_list, anchor_list
all_box_pred_list, all_cls_prob_list, all_proposal_list = [], [], []
for i in range(self.cfgs.NUM_REFINE_STAGE):
box_pred_list, cls_prob_list, proposal_list = self.refine_stage(
input_img_batch,
gtboxes_batch_r,
box_pred_list,
cls_prob_list,
proposal_list,
feature_pyramid,
gpu_id,
pos_threshold=self.cfgs.REFINE_IOU_POSITIVE_THRESHOLD[i],
neg_threshold=self.cfgs.REFINE_IOU_NEGATIVE_THRESHOLD[i],
stage='' if i == 0 else '_stage{}'.format(i + 2),
proposal_filter=True if i == 0 else False)
if not self.is_training:
all_box_pred_list.extend(box_pred_list)
all_cls_prob_list.extend(cls_prob_list)
all_proposal_list.extend(proposal_list)
else:
all_box_pred_list, all_cls_prob_list, all_proposal_list = box_pred_list, cls_prob_list, proposal_list
box_pred = tf.concat(all_box_pred_list, axis=0)
cls_prob = tf.concat(all_cls_prob_list, axis=0)
proposal = tf.concat(all_proposal_list, axis=0)
return box_pred, cls_prob, proposal
class DetectionNetwork(DetectionNetworkBase):
def __init__(self, cfgs, is_training):
super(DetectionNetwork, self).__init__(cfgs, is_training)
self.anchor_sampler_csl = AnchorSamplerCSL(cfgs)
self.losses = Loss(self.cfgs)
self.coding_len = cfgs.ANGLE_RANGE // cfgs.OMEGA
def rpn_reg_net(self, inputs, scope_list, reuse_flag, level):
rpn_conv2d_3x3 = inputs
for i in range(self.cfgs.NUM_SUBNET_CONV):
rpn_conv2d_3x3 = slim.conv2d(inputs=rpn_conv2d_3x3,
num_outputs=self.cfgs.FPN_CHANNEL,
kernel_size=[3, 3],
weights_initializer=self.cfgs.SUBNETS_WEIGHTS_INITIALIZER,
biases_initializer=self.cfgs.SUBNETS_BIAS_INITIALIZER,
stride=1,
activation_fn=tf.nn.relu,
scope='{}_{}'.format(scope_list[1], i),
reuse=reuse_flag)
rpn_delta_boxes = slim.conv2d(rpn_conv2d_3x3,
num_outputs=5 * self.num_anchors_per_location,
kernel_size=[3, 3],
stride=1,
weights_initializer=self.cfgs.SUBNETS_WEIGHTS_INITIALIZER,
biases_initializer=self.cfgs.SUBNETS_BIAS_INITIALIZER,
scope=scope_list[3],
activation_fn=None,
reuse=reuse_flag)
rpn_angle_cls = slim.conv2d(rpn_conv2d_3x3,
num_outputs=self.coding_len * self.num_anchors_per_location,
kernel_size=[3, 3],
stride=1,
weights_initializer=self.cfgs.SUBNETS_WEIGHTS_INITIALIZER,
biases_initializer=self.cfgs.SUBNETS_BIAS_INITIALIZER,
scope=scope_list[4],
activation_fn=None,
reuse=reuse_flag)
rpn_delta_boxes = tf.reshape(rpn_delta_boxes, [-1, 5],
name='rpn_{}_regression_reshape'.format(level))
rpn_angle_cls = tf.reshape(rpn_angle_cls, [-1, self.coding_len],
name='rpn_{}_angle_cls_reshape'.format(level))
return rpn_delta_boxes, rpn_angle_cls
def rpn_net(self, feature_pyramid, name):
rpn_delta_boxes_list = []
rpn_scores_list = []
rpn_probs_list = []
rpn_angle_cls_list = []
with tf.variable_scope(name):
with slim.arg_scope([slim.conv2d], weights_regularizer=slim.l2_regularizer(self.cfgs.WEIGHT_DECAY)):
for level in self.cfgs.LEVEL:
if self.cfgs.SHARE_NET:
reuse_flag = None if level == self.cfgs.LEVEL[0] else True
scope_list = ['conv2d_3x3_cls', 'conv2d_3x3_reg', 'rpn_classification',
'rpn_regression', 'rpn_angle_cls']
else:
reuse_flag = None
scope_list = ['conv2d_3x3_cls_' + level, 'conv2d_3x3_reg_' + level,
'rpn_classification_' + level, 'rpn_regression_' + level,
'rpn_angle_cls_' + level]
rpn_box_scores, rpn_box_probs = self.rpn_cls_net(feature_pyramid[level], scope_list, reuse_flag, level)
rpn_delta_boxes, rpn_angle_cls = self.rpn_reg_net(feature_pyramid[level], scope_list, reuse_flag, level)
rpn_scores_list.append(rpn_box_scores)
rpn_probs_list.append(rpn_box_probs)
rpn_delta_boxes_list.append(rpn_delta_boxes)
rpn_angle_cls_list.append(rpn_angle_cls)
return rpn_delta_boxes_list, rpn_scores_list, rpn_probs_list, rpn_angle_cls_list
def build_whole_detection_network(self, input_img_batch, gtboxes_batch_h=None, gtboxes_batch_r=None,
gt_smooth_label=None, gpu_id=0):
if self.is_training:
gtboxes_batch_h = tf.reshape(gtboxes_batch_h, [-1, 5])
gtboxes_batch_h = tf.cast(gtboxes_batch_h, tf.float32)
gtboxes_batch_r = tf.reshape(gtboxes_batch_r, [-1, 6])
gtboxes_batch_r = tf.cast(gtboxes_batch_r, tf.float32)
gt_smooth_label = tf.reshape(gt_smooth_label, [-1, self.coding_len])
gt_smooth_label = tf.cast(gt_smooth_label, tf.float32)
# 1. build backbone
feature_pyramid = self.build_backbone(input_img_batch)
# 2. build rpn
rpn_box_pred_list, rpn_cls_score_list, rpn_cls_prob_list, rpn_angle_cls_list = self.rpn_net(feature_pyramid, 'rpn_net')
rpn_box_pred = tf.concat(rpn_box_pred_list, axis=0)
rpn_cls_score = tf.concat(rpn_cls_score_list, axis=0)
rpn_cls_prob = tf.concat(rpn_cls_prob_list, axis=0)
rpn_angle_cls = tf.concat(rpn_angle_cls_list, axis=0)
# 3. generate anchors
anchor_list = self.make_anchors(feature_pyramid)
anchors = tf.concat(anchor_list, axis=0)
# 4. build loss
if self.is_training:
with tf.variable_scope('build_loss'):
labels, target_delta, anchor_states, target_boxes, target_smooth_label = tf.py_func(
func=self.anchor_sampler_csl.anchor_target_layer,
inp=[gtboxes_batch_h, gtboxes_batch_r,
gt_smooth_label, anchors, gpu_id],
Tout=[tf.float32, tf.float32, tf.float32,
tf.float32, tf.float32])
if self.method == 'H':
self.add_anchor_img_smry(input_img_batch, anchors, anchor_states, 0)
else:
self.add_anchor_img_smry(input_img_batch, anchors, anchor_states, 1)
cls_loss = self.losses.focal_loss(labels, rpn_cls_score, anchor_states)
if self.cfgs.REG_LOSS_MODE == 0:
reg_loss = self.losses.iou_smooth_l1_loss_log(target_delta, rpn_box_pred, anchor_states,
target_boxes, anchors)
elif self.cfgs.REG_LOSS_MODE == 1:
reg_loss = self.losses.iou_smooth_l1_loss_exp(target_delta, rpn_box_pred, anchor_states,
target_boxes, anchors, alpha=self.cfgs.ALPHA,
beta=self.cfgs.BETA)
else:
reg_loss = self.losses.smooth_l1_loss(target_delta, rpn_box_pred, anchor_states)
angle_cls_loss = self.losses.angle_focal_loss(target_smooth_label, rpn_angle_cls, anchor_states)
self.losses_dict['cls_loss'] = cls_loss * self.cfgs.CLS_WEIGHT
self.losses_dict['reg_loss'] = reg_loss * self.cfgs.REG_WEIGHT
self.losses_dict['angle_cls_loss'] = angle_cls_loss * self.cfgs.ANGLE_WEIGHT
# 5. postprocess
with tf.variable_scope('postprocess_detctions'):
boxes, scores, category, boxes_angle = self.postprocess_detctions(rpn_bbox_pred=rpn_box_pred,
rpn_cls_prob=rpn_cls_prob,
rpn_angle_prob=tf.sigmoid(rpn_angle_cls),
anchors=anchors)
boxes = tf.stop_gradient(boxes)
scores = tf.stop_gradient(scores)
category = tf.stop_gradient(category)
boxes_angle = tf.stop_gradient(boxes_angle)
if self.is_training:
return boxes, scores, category, boxes, self.losses_dict
else:
return boxes_angle, scores, category
def postprocess_detctions(self, rpn_bbox_pred, rpn_cls_prob, rpn_angle_prob, anchors):
return_boxes_pred = []
return_boxes_pred_angle = []
return_scores = []
return_labels = []
for j in range(0, self.cfgs.CLASS_NUM):
scores = rpn_cls_prob[:, j]
if self.is_training:
indices = tf.reshape(tf.where(tf.greater(scores, self.cfgs.VIS_SCORE)), [-1, ])
else:
indices = tf.reshape(tf.where(tf.greater(scores, self.cfgs.FILTERED_SCORE)), [-1, ])
anchors_ = tf.gather(anchors, indices)
rpn_bbox_pred_ = tf.gather(rpn_bbox_pred, indices)
scores = tf.gather(scores, indices)
rpn_angle_prob_ = tf.gather(rpn_angle_prob, indices)
angle_cls = tf.cast(tf.argmax(rpn_angle_prob_, axis=1), tf.float32)
if self.cfgs.METHOD == 'H':
x_c = (anchors_[:, 2] + anchors_[:, 0]) / 2
y_c = (anchors_[:, 3] + anchors_[:, 1]) / 2
h = anchors_[:, 2] - anchors_[:, 0] + 1
w = anchors_[:, 3] - anchors_[:, 1] + 1
theta = -90 * tf.ones_like(x_c)
anchors_ = tf.transpose(tf.stack([x_c, y_c, w, h, theta]))
if self.cfgs.ANGLE_RANGE == 180:
anchors_ = tf.py_func(coordinate_present_convert,
inp=[anchors_, -1],
Tout=[tf.float32])
anchors_ = tf.reshape(anchors_, [-1, 5])
boxes_pred = bbox_transform.rbbox_transform_inv(boxes=anchors_, deltas=rpn_bbox_pred_)
boxes_pred = tf.reshape(boxes_pred, [-1, 5])
angle_cls = (tf.reshape(angle_cls, [-1, ]) * -1 - 0.5) * self.cfgs.OMEGA
x, y, w, h, theta = tf.unstack(boxes_pred, axis=1)
boxes_pred_angle = tf.transpose(tf.stack([x, y, w, h, angle_cls]))
if self.cfgs.ANGLE_RANGE == 180:
# _, _, _, _, theta = tf.unstack(boxes_pred, axis=1)
# indx = tf.reshape(tf.where(tf.logical_and(tf.less(theta, 0), tf.greater_equal(theta, -180))), [-1, ])
# boxes_pred = tf.gather(boxes_pred, indx)
# scores = tf.gather(scores, indx)
boxes_pred = tf.py_func(coordinate_present_convert,
inp=[boxes_pred, 1],
Tout=[tf.float32])
boxes_pred = tf.reshape(boxes_pred, [-1, 5])
boxes_pred_angle = tf.py_func(coordinate_present_convert,
inp=[boxes_pred_angle, 1],
Tout=[tf.float32])
boxes_pred_angle = tf.reshape(boxes_pred_angle, [-1, 5])
nms_indices = nms_rotate.nms_rotate(decode_boxes=boxes_pred_angle,
scores=scores,
iou_threshold=self.cfgs.NMS_IOU_THRESHOLD,
max_output_size=100 if self.is_training else 1000,
use_gpu=False)
tmp_boxes_pred = tf.reshape(tf.gather(boxes_pred, nms_indices), [-1, 5])
tmp_boxes_pred_angle = tf.reshape(tf.gather(boxes_pred_angle, nms_indices), [-1, 5])
tmp_scores = tf.reshape(tf.gather(scores, nms_indices), [-1, ])
return_boxes_pred.append(tmp_boxes_pred)
return_boxes_pred_angle.append(tmp_boxes_pred_angle)
return_scores.append(tmp_scores)
return_labels.append(tf.ones_like(tmp_scores) * (j + 1))
return_boxes_pred = tf.concat(return_boxes_pred, axis=0)
return_boxes_pred_angle = tf.concat(return_boxes_pred_angle, axis=0)
return_scores = tf.concat(return_scores, axis=0)
return_labels = tf.concat(return_labels, axis=0)
return return_boxes_pred, return_scores, return_labels, return_boxes_pred_angle
class DetectionNetworkRetinaNet(DetectionNetworkBase):
def __init__(self, cfgs, is_training):
super(DetectionNetworkRetinaNet, self).__init__(cfgs, is_training)
self.anchor_sampler_retinenet = AnchorSamplerRetinaNet(cfgs)
self.losses = Loss(self.cfgs)
def build_whole_detection_network(self,
input_img_batch,
gtboxes_batch_h=None,
gtboxes_batch_r=None,
gpu_id=0):
if self.is_training:
gtboxes_batch_h = tf.reshape(gtboxes_batch_h, [-1, 5])
gtboxes_batch_h = tf.cast(gtboxes_batch_h, tf.float32)
gtboxes_batch_r = tf.reshape(gtboxes_batch_r, [-1, 6])
gtboxes_batch_r = tf.cast(gtboxes_batch_r, tf.float32)
if self.cfgs.USE_GN:
input_img_batch = tf.reshape(
input_img_batch,
[1, self.cfgs.IMG_SHORT_SIDE_LEN, self.cfgs.IMG_MAX_LENGTH, 3])
# 1. build backbone
feature_pyramid = self.build_backbone(input_img_batch)
# 2. build rpn
rpn_box_pred_list, rpn_cls_score_list, rpn_cls_prob_list = self.rpn_net(
feature_pyramid, 'rpn_net')
rpn_box_pred = tf.concat(rpn_box_pred_list, axis=0)
rpn_cls_score = tf.concat(rpn_cls_score_list, axis=0)
rpn_cls_prob = tf.concat(rpn_cls_prob_list, axis=0)
# 3. generate anchors
anchor_list = self.make_anchors(feature_pyramid)
anchors = tf.concat(anchor_list, axis=0)
# 4. build loss
if self.is_training:
with tf.variable_scope('build_loss'):
labels, target_delta, anchor_states, target_boxes = tf.py_func(
func=self.anchor_sampler_retinenet.anchor_target_layer,
inp=[gtboxes_batch_h, gtboxes_batch_r, anchors, gpu_id],
Tout=[tf.float32, tf.float32, tf.float32, tf.float32])
if self.method == 'H':
self.add_anchor_img_smry(input_img_batch, anchors,
anchor_states, 0)
else:
self.add_anchor_img_smry(input_img_batch, anchors,
anchor_states, 1)
cls_loss = self.losses.focal_loss(labels, rpn_cls_score,
anchor_states)
if self.cfgs.REG_LOSS_MODE == 0:
reg_loss = self.losses.iou_smooth_l1_loss_log(
target_delta, rpn_box_pred, anchor_states,
target_boxes, anchors)
elif self.cfgs.REG_LOSS_MODE == 1:
reg_loss = self.losses.iou_smooth_l1_loss_exp(
target_delta,
rpn_box_pred,
anchor_states,
target_boxes,
anchors,
alpha=self.cfgs.ALPHA,
beta=self.cfgs.BETA)
else:
reg_loss = self.losses.smooth_l1_loss(
target_delta, rpn_box_pred, anchor_states)
self.losses_dict['cls_loss'] = cls_loss * self.cfgs.CLS_WEIGHT
self.losses_dict['reg_loss'] = reg_loss * self.cfgs.REG_WEIGHT
# 5. postprocess
with tf.variable_scope('postprocess_detctions'):
boxes, scores, category = self.postprocess_detctions(
rpn_bbox_pred=rpn_box_pred,
rpn_cls_prob=rpn_cls_prob,
anchors=anchors,
gpu_id=gpu_id)
boxes = tf.stop_gradient(boxes)
scores = tf.stop_gradient(scores)
category = tf.stop_gradient(category)
if self.is_training:
return boxes, scores, category, self.losses_dict
else:
return boxes, scores, category
def postprocess_detctions(self, rpn_bbox_pred, rpn_cls_prob, anchors,
gpu_id):
return_boxes_pred = []
return_scores = []
return_labels = []
for j in range(0, self.cfgs.CLASS_NUM):
scores = rpn_cls_prob[:, j]
if self.is_training:
indices = tf.reshape(
tf.where(tf.greater(scores, self.cfgs.VIS_SCORE)), [
-1,
])
else:
indices = tf.reshape(
tf.where(tf.greater(scores, self.cfgs.FILTERED_SCORE)), [
-1,
])
anchors_ = tf.gather(anchors, indices)
rpn_bbox_pred_ = tf.gather(rpn_bbox_pred, indices)
scores = tf.gather(scores, indices)
if self.method == 'H':
x_c = (anchors_[:, 2] + anchors_[:, 0]) / 2
y_c = (anchors_[:, 3] + anchors_[:, 1]) / 2
h = anchors_[:, 2] - anchors_[:, 0] + 1
w = anchors_[:, 3] - anchors_[:, 1] + 1
theta = -90 * tf.ones_like(x_c)
anchors_ = tf.transpose(tf.stack([x_c, y_c, w, h, theta]))
if self.cfgs.ANGLE_RANGE == 180:
anchors_ = tf.py_func(coordinate_present_convert,
inp=[anchors_, -1],
Tout=[tf.float32])
anchors_ = tf.reshape(anchors_, [-1, 5])
boxes_pred = bbox_transform.rbbox_transform_inv(
boxes=anchors_, deltas=rpn_bbox_pred_)
if self.cfgs.ANGLE_RANGE == 180:
_, _, _, _, theta = tf.unstack(boxes_pred, axis=1)
indx = tf.reshape(
tf.where(
tf.logical_and(tf.less(theta, 0),
tf.greater_equal(theta, -180))), [
-1,
])
boxes_pred = tf.gather(boxes_pred, indx)
scores = tf.gather(scores, indx)
boxes_pred = tf.py_func(coordinate_present_convert,
inp=[boxes_pred, 1],
Tout=[tf.float32])
boxes_pred = tf.reshape(boxes_pred, [-1, 5])
nms_indices = nms_rotate.nms_rotate(
decode_boxes=boxes_pred,
scores=scores,
iou_threshold=self.cfgs.NMS_IOU_THRESHOLD,
max_output_size=100 if self.is_training else 1000,
use_gpu=True,
gpu_id=gpu_id)
tmp_boxes_pred = tf.reshape(tf.gather(boxes_pred, nms_indices),
[-1, 5])
tmp_scores = tf.reshape(tf.gather(scores, nms_indices), [
-1,
])
return_boxes_pred.append(tmp_boxes_pred)
return_scores.append(tmp_scores)
return_labels.append(tf.ones_like(tmp_scores) * (j + 1))
return_boxes_pred = tf.concat(return_boxes_pred, axis=0)
return_scores = tf.concat(return_scores, axis=0)
return_labels = tf.concat(return_labels, axis=0)
return return_boxes_pred, return_scores, return_labels
class DetectionNetworkRetinaNet(DetectionNetworkBase):
def __init__(self, cfgs, is_training):
super(DetectionNetworkRetinaNet, self).__init__(cfgs, is_training)
self.anchor_sampler_retinenet = AnchorSamplerRetinaNet(cfgs)
self.losses = Loss(self.cfgs)
def build_whole_detection_network(self,
input_img_batch,
gtboxes_batch_h=None,
gtboxes_batch_r=None,
gpu_id=0):
if self.is_training:
gtboxes_batch_h = tf.reshape(gtboxes_batch_h, [-1, 5])
gtboxes_batch_h = tf.cast(gtboxes_batch_h, tf.float32)
gtboxes_batch_r = tf.reshape(gtboxes_batch_r, [-1, 6])
gtboxes_batch_r = tf.cast(gtboxes_batch_r, tf.float32)
if self.cfgs.USE_GN:
input_img_batch = tf.reshape(
input_img_batch,
[1, self.cfgs.IMG_SHORT_SIDE_LEN, self.cfgs.IMG_MAX_LENGTH, 3])
# 1. build backbone
feature_pyramid = self.build_backbone(input_img_batch)
# 2. build rpn
rpn_box_pred_list, rpn_cls_score_list, rpn_cls_prob_list = self.rpn_net(
feature_pyramid, 'rpn_net')
rpn_box_pred = tf.concat(rpn_box_pred_list, axis=0)
rpn_cls_score = tf.concat(rpn_cls_score_list, axis=0)
rpn_cls_prob = tf.concat(rpn_cls_prob_list, axis=0)
# 3. generate anchors
anchor_list = self.make_anchors(feature_pyramid)
anchors = tf.concat(anchor_list, axis=0)
# 4. build loss
if self.is_training:
with tf.variable_scope('build_loss'):
labels, target_delta, anchor_states, target_boxes = tf.py_func(
func=self.anchor_sampler_retinenet.anchor_target_layer,
inp=[gtboxes_batch_h, gtboxes_batch_r, anchors, gpu_id],
Tout=[tf.float32, tf.float32, tf.float32, tf.float32])
if self.method == 'H':
self.add_anchor_img_smry(input_img_batch, anchors,
anchor_states, 0)
else:
self.add_anchor_img_smry(input_img_batch, anchors,
anchor_states, 1)
cls_loss = self.losses.focal_loss(labels, rpn_cls_score,
anchor_states)
if self.cfgs.REG_LOSS_MODE == 0:
reg_loss = self.losses.iou_smooth_l1_loss_log(
target_delta, rpn_box_pred, anchor_states,
target_boxes, anchors)
elif self.cfgs.REG_LOSS_MODE == 1:
reg_loss = self.losses.iou_smooth_l1_loss_exp(
target_delta,
rpn_box_pred,
anchor_states,
target_boxes,
anchors,
alpha=self.cfgs.ALPHA,
beta=self.cfgs.BETA)
else:
reg_loss = self.losses.smooth_l1_loss(
target_delta, rpn_box_pred, anchor_states)
self.losses_dict['cls_loss'] = cls_loss * self.cfgs.CLS_WEIGHT
self.losses_dict['reg_loss'] = reg_loss * self.cfgs.REG_WEIGHT
return rpn_box_pred, rpn_cls_prob