def corner_net(self,img,gt_tag_tl=None,gt_tag_br=None,is_training=True,scope='CornerNet'):
with tf.variable_scope(scope):
outs=[]
test_outs=[]
start_layer=self.model.start_conv(img,is_training=is_training)#[b,128,128,256]
with tf.variable_scope('inter_supervise'):
hourglass_1=self.model.hourglass(start_layer,self.n_deep,self.n_res,self.n_dims,is_training=is_training)#[b,128,128,256]
hinge_is=self.model.hinge(hourglass_1,256,256,is_training=is_training)
top_left_is,bottom_right_is=self.model.corner_pooling(hinge_is,256,256,is_training=is_training)
#top_left
heat_tl_is=self.model.heat(top_left_is,256,self.out_dim,scope='heat_tl')
tag_tl_is=self.model.tag(top_left_is,256,1,scope='tag_tl')
if not gt_tag_tl is None:
tag_tl_is=map_to_vector(tag_tl_is,gt_tag_tl)
offset_tl_is=self.model.offset(top_left_is,256,2,scope='offset_tl')
if not gt_tag_tl is None:
offset_tl_is=map_to_vector(offset_tl_is,gt_tag_tl)
#bottom_right
heat_br_is=self.model.heat(bottom_right_is,256,self.out_dim,scope='heat_br')
tag_br_is=self.model.tag(bottom_right_is,256,1,scope='tag_br')
if not gt_tag_br is None:
tag_br_is=map_to_vector(tag_br_is,gt_tag_br)
offset_br_is=self.model.offset(bottom_right_is,256,2,scope='offset_br')
if not gt_tag_br is None:
offset_br_is=map_to_vector(offset_br_is,gt_tag_br)
with tf.variable_scope('master_branch'):
inter=self.model.inter(start_layer,hinge_is,256,is_training=is_training)
hourglass_2=self.model.hourglass(inter,self.n_deep,self.n_res,self.n_dims,is_training=is_training)#[b,128,128,256]
hinge=self.model.hinge(hourglass_2,256,256,is_training=is_training)
top_left,bottom_right=self.model.corner_pooling(hinge,256,256,is_training=is_training)
#top_left
heat_tl=self.model.heat(top_left,256,self.out_dim,scope='heat_tl')
tag_tl_test=self.model.tag(top_left,256,1,scope='tag_tl')
if not gt_tag_tl is None:
tag_tl=map_to_vector(tag_tl_test,gt_tag_tl)
offset_tl_test=self.model.offset(top_left,256,2,scope='offset_tl')
if not gt_tag_tl is None:
offset_tl=map_to_vector(offset_tl_test,gt_tag_tl)
#bottom_right
heat_br=self.model.heat(bottom_right,256,self.out_dim,scope='heat_br')
tag_br_test=self.model.tag(bottom_right,256,1,scope='tag_br')
if not gt_tag_br is None:
tag_br=map_to_vector(tag_br_test,gt_tag_br)
offset_br_test=self.model.offset(bottom_right,256,2,scope='offset_br')
if not gt_tag_br is None:
offset_br=map_to_vector(offset_br_test,gt_tag_br)
outs=[heat_tl_is,heat_br_is,tag_tl_is,tag_br_is,offset_tl_is,offset_br_is,heat_tl,heat_br,tag_tl,tag_br,offset_tl,offset_br]
test_outs=[heat_tl,heat_br,tag_tl_test,tag_br_test,offset_tl_test,offset_br_test]
return outs,test_outs
def decode(self,heat_tl,heat_br,tag_tl,tag_br,offset_tl,offset_br,k=100,ae_threshold=0.5,num_dets=1000):
batch=tf.shape(heat_br)[0]
heat_tl=tf.nn.sigmoid(heat_tl)
heat_br=tf.nn.sigmoid(heat_br)
#nms
heat_tl=nms(heat_tl)
heat_br=nms(heat_br)
value_tl,position_tl,class_tl,y_tl,x_tl=top_k(heat_tl,k)
value_br,position_br,class_br,y_br,x_br=top_k(heat_br,k)
#expand to square
x_tl=tf.cast(expand_copy(x_tl,k,False),tf.float32)
y_tl=tf.cast(expand_copy(y_tl,k,False),tf.float32)
x_br=tf.cast(expand_copy(x_br,k,True),tf.float32)
y_br=tf.cast(expand_copy(y_br,k,True),tf.float32)
offset_tl=map_to_vector(offset_tl,position_tl)
offset_br=map_to_vector(offset_br,position_br)
# offset_tl=tf.reshape(offset_tl,(tf.shape(offset_tl)[0],k,1,2))
# offset_br=tf.reshape(offset_br,(tf.shape(offset_br)[0],1,k,2))
offset_tl=tf.reshape(offset_tl,(offset_tl.get_shape().as_list()[0],k,1,2))
offset_br=tf.reshape(offset_br,(offset_br.get_shape().as_list()[0],1,k,2))
x_tl=x_tl+offset_tl[:,:,:,0]
y_tl=y_tl+offset_tl[:,:,:,1]
x_br=x_br+offset_br[:,:,:,0]
y_br=y_br+offset_br[:,:,:,1]
offset_tl=tf.reshape(offset_tl,(batch,k,1,2))
offset_br=tf.reshape(offset_br,(batch,1,k,2))
#all k boxes
bboxes=tf.stack((x_tl,y_tl,x_br,y_br),axis=-1)
tag_tl=map_to_vector(tag_tl,position_tl)
tag_tl=tf.reshape(tag_tl,(batch,k,1))
tag_br=map_to_vector(tag_br,position_br)
tag_br=tf.reshape(tag_br,(batch,1,k))
dists=tf.abs(tag_tl-tag_br)
value_tl=expand_copy(value_tl,k,False)
value_br=expand_copy(value_br,k,True)
scores=(value_tl+value_br)/2
invalid=-tf.ones_like(scores)
#======debug=====
debug_scores=tf.reshape(scores,(batch,-1))
debug_scores,debug_indexs=tf.nn.top_k(debug_scores,10)
debug_bboxes=tf.reshape(bboxes,(batch,-1,4))
debug_bboxes=map_to_vector(debug_bboxes,debug_indexs,transpose=False)
#======debug=====
class_tl=tf.cast(expand_copy(class_tl,k,False),tf.float32)#[batch,k,k]
class_br=tf.cast(expand_copy(class_br,k,True),tf.float32)
mask_scores=tf.where(tf.cast(tf.equal(class_tl,class_br),tf.int32)>0,scores,invalid)
mask_scores=tf.where(tf.less(dists,ae_threshold),mask_scores,invalid)
mask_scores=tf.where(tf.less(x_tl,x_br),mask_scores,invalid)
mask_scores=tf.where(tf.less(y_tl,y_br),mask_scores,invalid)
mask_scores=tf.reshape(mask_scores,(batch,-1))
scores,indexs=tf.nn.top_k(mask_scores,num_dets)
scores=tf.expand_dims(scores,-1)
bboxes=tf.reshape(bboxes,(batch,-1,4))
bboxes=map_to_vector(bboxes,indexs,transpose=False)
class_=tf.reshape(class_br,(batch,-1,1))
class_=map_to_vector(class_,indexs,transpose=False)
value_tl=tf.reshape(value_tl,(batch,-1,1))
value_tl=map_to_vector(value_tl,indexs,transpose=False)
value_br=tf.reshape(value_br,(batch,-1,1))
value_br=map_to_vector(value_br,indexs,transpose=False)
detection=tf.concat([bboxes,scores,value_tl,value_br,class_],-1)
return detection,debug_bboxes
self.focal_loss = focal_loss
self.tag_loss = tag_loss
self.offset_loss = offset_loss
def corner_net(self,img,gt_tag_tl=None,gt_tag_br=None,is_training=True,scope='CornerNet')
with tf.variable_scope(scope):
outs=[]
start_layer=self.model.start_conv(img,is_training=is_training)#[b,128,128,256]
with tf.variable_scope('inter_supervise'):
hourglass_1=self.model.hourglass(start_layer,self.n_deep,self.n_res,self.n_dims,is_training=is_training)#[b,128,128,256]
hinge_is=self.model.hinge(hourglass_1,256,256,is_training=is_training)
top_left_is,bottom_right_is=self.model.corner_pooling(hinge_is,256,256,is_training=is_training)
#top_left
heat_tl_is=self.model.heat(top_left_is,256,self.out_dim,scope='heat_tl')
tag_tl_is=self.model.tag(top_left_is,256,1,scope='tag_tl')
if not gt_tag_tl is None:
tag_tl_is=map_to_vector(tag_tl_is,gt_tag_tl)
offset_tl_is=self.model.offset(top_left_is,256,2,scope='offset_tl')
if not gt_tag_tl is None:
offset_tl_is=map_to_vector(offset_tl_is,gt_tag_tl)
#bottom_right
heat_br_is=self.model.heat(bottom_right_is,256,self.out_dim,scope='heat_br')
tag_br_is=self.model.tag(bottom_right_is,256,1,scope='tag_br')
if not gt_tag_br is None:
tag_br_is=map_to_vector(tag_br_is,gt_tag_br)
offset_br_is=self.model.offset(bottom_right_is,256,2,scope='offset_br')
if not gt_tag_br is None:
offset_br_is=map_to_vector(offset_br_is,gt_tag_br)
with tf.variable_scope('master_branch'):
inter=self.model.inter(start_layer,hinge_is,256,is_training=is_training)