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import os
import pkg_resources
import tensorflow as tf
import warnings
from tensorflow.python.framework import ops
def load(debug=False):
so = pkg_resources.resource_filename(__name__, 'rmsd/')
if debug or not os.path.isfile(so):
so = pkg_resources.resource_filename(__name__, 'rmsd/')
if not os.path.isfile(so):
raise FileNotFoundError("Could not find the RMSD op shared library. "
"Make sure you compile it!")
warnings.warn("Using debug build of RMSD Op. This will be slow!")
mod = tf.load_op_library(so)
return mod
def drot_dcentered(confs1, confs2, rots, grad):
N1 = tf.shape(confs1)[0]
N2 = tf.shape(confs2)[0]
n_atom = float(int(confs1.get_shape()[1]))
expand_confs1 = tf.expand_dims(confs1, axis=1)
big_confs1 = tf.tile(expand_confs1, [1, N2, 1, 1])
expand_confs2 = tf.expand_dims(confs2, axis=0)
big_confs2 = tf.tile(expand_confs2, [N1, 1, 1, 1])
dxy = expand_confs1 - tf.matmul(big_confs2, rots, transpose_b=True)
dxy = 2 * dxy / n_atom
dyx = expand_confs2 - tf.matmul(big_confs1, rots, transpose_b=False)
dyx = 2 * dyx / n_atom
grad = tf.expand_dims(tf.expand_dims(grad, axis=-1), axis=-1)
dr_dc1 = tf.reduce_sum(grad * dxy, axis=1)
dr_dc2 = tf.reduce_sum(grad * dyx, axis=0)
return dr_dc1, dr_dc2
def dcenter_dx(confs1, confs2, dr_dc1, dr_dc2):
# Gradients for centering at origin
centered1 = confs1 - tf.reduce_mean(confs1, axis=1, keep_dims=True)
centered2 = confs2 - tf.reduce_mean(confs2, axis=1, keep_dims=True)
center_grad1 = tf.gradients(centered1, [confs1], grad_ys=dr_dc1)[0]
center_grad2 = tf.gradients(centered2, [confs2], grad_ys=dr_dc2)[0]
return center_grad1, center_grad2
def _pairwise_msd_grad(op, grad, rot_grad):
# TODO: Throw error if rot_grad is non-zero?
confs1, confs2 = op.inputs
rots = op.outputs[1]
dr_dc1, dr_dc2 = drot_dcentered(confs1, confs2, rots, grad)
dc_dx1, dc_dx2 = dcenter_dx(confs1, confs2, dr_dc1, dr_dc2)
return dc_dx1, dc_dx2