def bounded_loss(rt, rs, m=0.5, v=0.5):
"""
Input: Rs: regression output from student network
Rt: regression output from teacher
y_true: ground truth bounding box
Output:Loss
"""
#if(l2(rs,rt[1]) + m > l2(rt[0] , rt[1]) ):
#lb = l2(rs,rt[1])
#else:
#lb = tf.constant(0,dtype=float)
#lreg =l1(rs,rt[1]) + v*lb
print(rt)
#print(rs)
#print(x)
bound = l2(rs,rt[:, 4:]) + K.constant(m,shape=(1,))
bound2 = l2(rt[:, :4] , rt[:, 4:])
cond = tf.less(tf.reduce_mean(bound), tf.reduce_mean(bound2))
#print(bound2.shape)
#lb = tf.Variable(0.0, name="lb")
#lreg = tf.Variable(0.0, name="lreg")
#lreg = K.mean(K.square(rs - x[1]))
#tf.cond(l2(rs,x[1]) + m > l2(x[0] , x[1]),lambda:tf.assign(lb,l2(rs,x[1])),lambda:tf.assign(lb,0.0))
loss2 = lambda: l2(rs,rt[:, 4:])
zero = lambda: K.constant(0.0,shape=(1,))
res = tf.cond(cond,loss2,zero)
return res
def bounded_loss(rt, rs, m, v):
"""
Input: Rs: regression output from student network
Rt: regression output from teacher
y_true: ground truth bounding box
Output:Loss
"""
#if(l2(rs,rt[1]) + m > l2(rt[0] , rt[1]) ):
#lb = l2(rs,rt[1])
#else:
#lb = tf.constant(0,dtype=float)
#lreg =l1(rs,rt[1]) + v*lb
#print(rt)
#print(rs)
#print(x)
bound = l2(rs, rt[:, 4:]) + m
bound2 = l2(rt[:, :4], rt[:, 4:])
#print(bound2.shape)
#lb = tf.Variable(0.0, name="lb")
#lreg = tf.Variable(0.0, name="lreg")
#lreg = K.mean(K.square(rs - x[1]))
#tf.cond(l2(rs,x[1]) + m > l2(x[0] , x[1]),lambda:tf.assign(lb,l2(rs,x[1])),lambda:tf.assign(lb,0.0))
return K.switch(bound < bound2, l2(rs, rt[:, 4:]),
K.constant(0, shape=(1, )))
def bounded_loss(rs, rt, y_true, m, v):
"""
Input: Rs: regression output from student network
Rt: regression output from teacher
y_true: ground truth bounding box
Output:Loss
"""
if(l2(rs-y_true) + m > l2(rt - y_true) )
lb = l2(r-y_true)
else:
lb = 0
lreg =l1(rs,y_true) + v*lb
return lreg
def bounded_loss_nogt(rs, rt, m, v):
"""
when the dataset is not available
Input: Rs: regression output from student network
Rt: regression output from teacher
Output:Loss
"""
if((l2(rs-rt) < l2(rs-rt) + 5 )or (l2(rs-rt) > l2(rs-rt) - m)):
lb = 0
else:
lb = l2(rs-rt)
return lb
def l2(y_true, y_pred):
return losses.l2(y_true, y_pred, 1)
def l2_loss(y_true, y_pred):
return losses.l2(y_true, y_pred, args.L2_loss_coeff)