def chamfer_loss(A,B):
r=tf.reduce_sum(A*A,2)
r=tf.reshape(r,[int(r.shape[0]),int(r.shape[1]),1])
r2=tf.reduce_sum(B*B,2)
r2=tf.reshape(r2,[int(r.shape[0]),int(r.shape[1]),1])
t=(r-2*tf.matmul(A, tf.transpose(B,perm=[0, 2, 1])) + tf.transpose(r2,perm=[0, 2, 1]))
return tf.reduce_mean((tf.reduce_min(t, axis=1)+tf.reduce_min(t,axis=2))/2.0)
def sg_min(tensor, opt):
r"""Computes the minimum of elements across axis of a tensor.
See `tf.reduce_min()` in tensorflow.
Args:
tensor: A `Tensor` (automatically given by chain).
opt:
axis : A tuple/list of integers or an integer. The axis to reduce.
keep_dims: If true, retains reduced dimensions with length 1.
name: If provided, replace current tensor's name.
Returns:
A `Tensor`.
"""
return tf.reduce_min(tensor, axis=opt.axis, keep_dims=opt.keep_dims, name=opt.name)
def sg_min(tensor, opt):
return tf.reduce_min(tensor,
reduction_indices=opt.dims,
keep_dims=opt.keep_dims,
name=opt.name)