def _filter_r(self,
inputs,
type_input,
natoms,
activation_fn=tf.nn.tanh,
stddev=1.0,
bavg=0.0,
name='linear',
reuse=None,
trainable=True):
# natom x nei
outputs_size = [1] + self.filter_neuron
with tf.variable_scope(name, reuse=reuse):
start_index = 0
xyz_scatter_total = []
for type_i in range(self.ntypes):
# cut-out inputs
# with natom x nei_type_i
inputs_i = tf.slice(inputs, [0, start_index],
[-1, self.sel_r[type_i]])
start_index += self.sel_r[type_i]
shape_i = inputs_i.get_shape().as_list()
# with (natom x nei_type_i) x 1
xyz_scatter = tf.reshape(inputs_i, [-1, 1])
if (type_input, type_i) not in self.exclude_types:
xyz_scatter = embedding_net(
xyz_scatter,
self.filter_neuron,
self.filter_precision,
activation_fn=activation_fn,
resnet_dt=self.filter_resnet_dt,
name_suffix="_" + str(type_i),
stddev=stddev,
bavg=bavg,
seed=self.seed,
trainable=trainable,
uniform_seed=self.uniform_seed,
initial_variables=self.embedding_net_variables,
)
if (not self.uniform_seed) and (self.seed is not None):
self.seed += self.seed_shift
# natom x nei_type_i x out_size
xyz_scatter = tf.reshape(
xyz_scatter, (-1, shape_i[1], outputs_size[-1]))
else:
natom = tf.shape(inputs)[0]
xyz_scatter = tf.cast(
tf.fill((natom, shape_i[1], outputs_size[-1]), 0.),
GLOBAL_TF_FLOAT_PRECISION)
xyz_scatter_total.append(xyz_scatter)
# natom x nei x outputs_size
xyz_scatter = tf.concat(xyz_scatter_total, axis=1)
# natom x outputs_size
#
res_rescale = 1. / 5.
result = tf.reduce_mean(xyz_scatter, axis=1) * res_rescale
return result
def _filter(self,
inputs,
type_input,
natoms,
type_embedding=None,
activation_fn=tf.nn.tanh,
stddev=1.0,
bavg=0.0,
name='linear',
reuse=None,
trainable=True):
nframes = tf.shape(tf.reshape(inputs,
[-1, natoms[0], self.ndescrpt]))[0]
# natom x (nei x 4)
shape = inputs.get_shape().as_list()
outputs_size = [1] + self.filter_neuron
outputs_size_2 = self.n_axis_neuron
all_excluded = all([(type_input, type_i) in self.exclude_types
for type_i in range(self.ntypes)])
if all_excluded:
# all types are excluded so result and qmat should be zeros
# we can safaly return a zero matrix...
# See also https://stackoverflow.com/a/34725458/9567349
# result: natom x outputs_size x outputs_size_2
# qmat: natom x outputs_size x 3
natom = tf.shape(inputs)[0]
result = tf.cast(
tf.fill((natom, outputs_size_2, outputs_size[-1]), 0.),
GLOBAL_TF_FLOAT_PRECISION)
qmat = tf.cast(tf.fill((natom, outputs_size[-1], 3), 0.),
GLOBAL_TF_FLOAT_PRECISION)
return result, qmat
with tf.variable_scope(name, reuse=reuse):
start_index = 0
type_i = 0
# natom x 4 x outputs_size
if type_embedding is None:
rets = []
for type_i in range(self.ntypes):
ret = self._filter_lower(type_i,
type_input,
start_index,
self.sel_a[type_i],
inputs,
nframes,
natoms,
type_embedding=type_embedding,
is_exclude=(type_input, type_i)
in self.exclude_types,
activation_fn=activation_fn,
stddev=stddev,
bavg=bavg,
trainable=trainable,
suffix="_" + str(type_i))
if (type_input, type_i) not in self.exclude_types:
# add zero is meaningless; skip
rets.append(ret)
start_index += self.sel_a[type_i]
# faster to use accumulate_n than multiple add
xyz_scatter_1 = tf.accumulate_n(rets)
else:
xyz_scatter_1 = self._filter_lower(
type_i,
type_input,
start_index,
np.cumsum(self.sel_a)[-1],
inputs,
nframes,
natoms,
type_embedding=type_embedding,
is_exclude=False,
activation_fn=activation_fn,
stddev=stddev,
bavg=bavg,
trainable=trainable)
# natom x nei x outputs_size
# xyz_scatter = tf.concat(xyz_scatter_total, axis=1)
# natom x nei x 4
# inputs_reshape = tf.reshape(inputs, [-1, shape[1]//4, 4])
# natom x 4 x outputs_size
# xyz_scatter_1 = tf.matmul(inputs_reshape, xyz_scatter, transpose_a = True)
if self.original_sel is None:
# shape[1] = nnei * 4
nnei = shape[1] / 4
else:
nnei = tf.cast(
tf.Variable(np.sum(self.original_sel),
dtype=tf.int32,
trainable=False,
name="nnei"), self.filter_precision)
xyz_scatter_1 = xyz_scatter_1 / nnei
# natom x 4 x outputs_size_2
xyz_scatter_2 = tf.slice(xyz_scatter_1, [0, 0, 0],
[-1, -1, outputs_size_2])
# # natom x 3 x outputs_size_2
# qmat = tf.slice(xyz_scatter_2, [0,1,0], [-1, 3, -1])
# natom x 3 x outputs_size_1
qmat = tf.slice(xyz_scatter_1, [0, 1, 0], [-1, 3, -1])
# natom x outputs_size_1 x 3
qmat = tf.transpose(qmat, perm=[0, 2, 1])
# natom x outputs_size x outputs_size_2
result = tf.matmul(xyz_scatter_1, xyz_scatter_2, transpose_a=True)
# natom x (outputs_size x outputs_size_2)
result = tf.reshape(result,
[-1, outputs_size_2 * outputs_size[-1]])
return result, qmat
def _filter_lower(
self,
type_i,
type_input,
start_index,
incrs_index,
inputs,
nframes,
natoms,
type_embedding=None,
is_exclude=False,
activation_fn=None,
bavg=0.0,
stddev=1.0,
trainable=True,
suffix='',
):
"""
input env matrix, returns R.G
"""
outputs_size = [1] + self.filter_neuron
# cut-out inputs
# with natom x (nei_type_i x 4)
inputs_i = tf.slice(inputs, [0, start_index * 4],
[-1, incrs_index * 4])
shape_i = inputs_i.get_shape().as_list()
natom = tf.shape(inputs_i)[0]
# with (natom x nei_type_i) x 4
inputs_reshape = tf.reshape(inputs_i, [-1, 4])
# with (natom x nei_type_i) x 1
xyz_scatter = tf.reshape(tf.slice(inputs_reshape, [0, 0], [-1, 1]),
[-1, 1])
if type_embedding is not None:
xyz_scatter = self._concat_type_embedding(xyz_scatter, nframes,
natoms, type_embedding)
if self.compress:
raise RuntimeError(
'compression of type embedded descriptor is not supported at the moment'
)
# natom x 4 x outputs_size
if self.compress and (not is_exclude):
info = [
self.lower, self.upper, self.upper * self.table_config[0],
self.table_config[1], self.table_config[2],
self.table_config[3]
]
if self.type_one_side:
net = 'filter_-1_net_' + str(type_i)
else:
net = 'filter_' + str(type_input) + '_net_' + str(type_i)
return op_module.tabulate_fusion_se_a(
tf.cast(self.table.data[net], self.filter_precision),
info,
xyz_scatter,
tf.reshape(inputs_i, [natom, shape_i[1] // 4, 4]),
last_layer_size=outputs_size[-1])
else:
if (not is_exclude):
# with (natom x nei_type_i) x out_size
xyz_scatter = embedding_net(
xyz_scatter,
self.filter_neuron,
self.filter_precision,
activation_fn=activation_fn,
resnet_dt=self.filter_resnet_dt,
name_suffix=suffix,
stddev=stddev,
bavg=bavg,
seed=self.seed,
trainable=trainable,
uniform_seed=self.uniform_seed,
initial_variables=self.embedding_net_variables,
mixed_prec=self.mixed_prec)
if (not self.uniform_seed) and (self.seed is not None):
self.seed += self.seed_shift
else:
# we can safely return the final xyz_scatter filled with zero directly
return tf.cast(tf.fill((natom, 4, outputs_size[-1]), 0.),
self.filter_precision)
# natom x nei_type_i x out_size
xyz_scatter = tf.reshape(xyz_scatter,
(-1, shape_i[1] // 4, outputs_size[-1]))
# When using tf.reshape(inputs_i, [-1, shape_i[1]//4, 4]) below
# [588 24] -> [588 6 4] correct
# but if sel is zero
# [588 0] -> [147 0 4] incorrect; the correct one is [588 0 4]
# So we need to explicitly assign the shape to tf.shape(inputs_i)[0] instead of -1
# natom x 4 x outputs_size
return tf.matmul(tf.reshape(inputs_i, [natom, shape_i[1] // 4, 4]),
xyz_scatter,
transpose_a=True)
