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Fix bug of adding int to a None random seed #705

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Merged
amcadmus merged 1 commit into from
Jun 5, 2021
Merged

Fix bug of adding int to a None random seed #705

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2 changes: 1 addition & 1 deletion deepmd/descriptor/se_a.py
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Original file line number Diff line number Diff line change
Expand Up @@ -617,7 +617,7 @@ def _filter_lower(
seed = self.seed,
trainable = trainable,
uniform_seed = self.uniform_seed)
if not self.uniform_seed: self.seed += self.seed_shift
if (not self.uniform_seed) and (self.seed is not None): self.seed += self.seed_shift
else:
w = tf.zeros((outputs_size[0], outputs_size[-1]), dtype=GLOBAL_TF_FLOAT_PRECISION)
xyz_scatter = tf.matmul(xyz_scatter, w)
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2 changes: 1 addition & 1 deletion deepmd/descriptor/se_r.py
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Original file line number Diff line number Diff line change
Expand Up @@ -476,7 +476,7 @@ def _filter_r(self,
seed = self.seed,
trainable = trainable,
uniform_seed = self.uniform_seed)
if not self.uniform_seed: self.seed += self.seed_shift
if (not self.uniform_seed) and (self.seed is not None): self.seed += self.seed_shift
else:
w = tf.zeros((outputs_size[0], outputs_size[-1]), dtype=GLOBAL_TF_FLOAT_PRECISION)
xyz_scatter = tf.matmul(xyz_scatter, w)
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2 changes: 1 addition & 1 deletion deepmd/descriptor/se_t.py
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Original file line number Diff line number Diff line change
Expand Up @@ -499,7 +499,7 @@ def _filter(self,
seed = self.seed,
trainable = trainable,
uniform_seed = self.uniform_seed)
if not self.uniform_seed: self.seed += self.seed_shift
if (not self.uniform_seed) and (self.seed is not None): self.seed += self.seed_shift
# with natom x nei_type_i x nei_type_j x out_size
ebd_env_ij = tf.reshape(ebd_env_ij, [-1, nei_type_i, nei_type_j, outputs_size[-1]])
# with natom x out_size
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4 changes: 2 additions & 2 deletions deepmd/fit/dipole.py
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Original file line number Diff line number Diff line change
Expand Up @@ -142,10 +142,10 @@ def build (self,
layer+= one_layer(layer, self.n_neuron[ii], name='layer_'+str(ii)+'_type_'+str(type_i)+suffix, reuse=reuse, seed = self.seed, use_timestep = self.resnet_dt, activation_fn = self.fitting_activation_fn, precision = self.fitting_precision, uniform_seed = self.uniform_seed)
else :
layer = one_layer(layer, self.n_neuron[ii], name='layer_'+str(ii)+'_type_'+str(type_i)+suffix, reuse=reuse, seed = self.seed, activation_fn = self.fitting_activation_fn, precision = self.fitting_precision, uniform_seed = self.uniform_seed)
if not self.uniform_seed : self.seed += self.seed_shift
if (not self.uniform_seed) and (self.seed is not None): self.seed += self.seed_shift
# (nframes x natoms) x naxis
final_layer = one_layer(layer, self.dim_rot_mat_1, activation_fn = None, name='final_layer_type_'+str(type_i)+suffix, reuse=reuse, seed = self.seed, precision = self.fitting_precision, uniform_seed = self.uniform_seed)
if not self.uniform_seed : self.seed += self.seed_shift
if (not self.uniform_seed) and (self.seed is not None): self.seed += self.seed_shift
# (nframes x natoms) x 1 * naxis
final_layer = tf.reshape(final_layer, [tf.shape(inputs)[0] * natoms[2+type_i], 1, self.dim_rot_mat_1])
# (nframes x natoms) x 1 x 3(coord)
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4 changes: 2 additions & 2 deletions deepmd/fit/ener.py
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Original file line number Diff line number Diff line change
Expand Up @@ -269,7 +269,7 @@ def _build_lower(
precision = self.fitting_precision,
trainable = self.trainable[ii],
uniform_seed = self.uniform_seed)
if not self.uniform_seed : self.seed += self.seed_shift
if (not self.uniform_seed) and (self.seed is not None): self.seed += self.seed_shift
final_layer = one_layer(
layer,
1,
Expand All @@ -281,7 +281,7 @@ def _build_lower(
precision = self.fitting_precision,
trainable = self.trainable[-1],
uniform_seed = self.uniform_seed)
if not self.uniform_seed : self.seed += self.seed_shift
if (not self.uniform_seed) and (self.seed is not None): self.seed += self.seed_shift

return final_layer

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6 changes: 3 additions & 3 deletions deepmd/fit/polar.py
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Original file line number Diff line number Diff line change
Expand Up @@ -321,15 +321,15 @@ def build (self,
layer+= one_layer(layer, self.n_neuron[ii], name='layer_'+str(ii)+'_type_'+str(type_i)+suffix, reuse=reuse, seed = self.seed, use_timestep = self.resnet_dt, activation_fn = self.fitting_activation_fn, precision = self.fitting_precision, uniform_seed = self.uniform_seed)
else :
layer = one_layer(layer, self.n_neuron[ii], name='layer_'+str(ii)+'_type_'+str(type_i)+suffix, reuse=reuse, seed = self.seed, activation_fn = self.fitting_activation_fn, precision = self.fitting_precision, uniform_seed = self.uniform_seed)
if not self.uniform_seed : self.seed += self.seed_shift
if (not self.uniform_seed) and (self.seed is not None): self.seed += self.seed_shift
if self.fit_diag :
bavg = np.zeros(self.dim_rot_mat_1)
# bavg[0] = self.avgeig[0]
# bavg[1] = self.avgeig[1]
# bavg[2] = self.avgeig[2]
# (nframes x natoms) x naxis
final_layer = one_layer(layer, self.dim_rot_mat_1, activation_fn = None, name='final_layer_type_'+str(type_i)+suffix, reuse=reuse, seed = self.seed, bavg = bavg, precision = self.fitting_precision, uniform_seed = self.uniform_seed)
if not self.uniform_seed : self.seed += self.seed_shift
if (not self.uniform_seed) and (self.seed is not None): self.seed += self.seed_shift
# (nframes x natoms) x naxis
final_layer = tf.reshape(final_layer, [tf.shape(inputs)[0] * natoms[2+type_i], self.dim_rot_mat_1])
# (nframes x natoms) x naxis x naxis
Expand All @@ -341,7 +341,7 @@ def build (self,
# bavg[2*self.dim_rot_mat_1+2] = self.avgeig[2]
# (nframes x natoms) x (naxis x naxis)
final_layer = one_layer(layer, self.dim_rot_mat_1*self.dim_rot_mat_1, activation_fn = None, name='final_layer_type_'+str(type_i)+suffix, reuse=reuse, seed = self.seed, bavg = bavg, precision = self.fitting_precision, uniform_seed = self.uniform_seed)
if not self.uniform_seed : self.seed += self.seed_shift
if (not self.uniform_seed) and (self.seed is not None): self.seed += self.seed_shift
# (nframes x natoms) x naxis x naxis
final_layer = tf.reshape(final_layer, [tf.shape(inputs)[0] * natoms[2+type_i], self.dim_rot_mat_1, self.dim_rot_mat_1])
# (nframes x natoms) x naxis x naxis
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4 changes: 2 additions & 2 deletions deepmd/fit/wfc.py
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Original file line number Diff line number Diff line change
Expand Up @@ -81,10 +81,10 @@ def build (self,
layer+= one_layer(layer, self.n_neuron[ii], name='layer_'+str(ii)+'_type_'+str(type_i)+suffix, reuse=reuse, seed = self.seed, use_timestep = self.resnet_dt, activation_fn = self.fitting_activation_fn, precision = self.fitting_precision, uniform_seed = self.uniform_seed)
else :
layer = one_layer(layer, self.n_neuron[ii], name='layer_'+str(ii)+'_type_'+str(type_i)+suffix, reuse=reuse, seed = self.seed, activation_fn = self.fitting_activation_fn, precision = self.fitting_precision, uniform_seed = self.uniform_seed)
if not self.uniform_seed : self.seed += self.seed_shift
if (not self.uniform_seed) and (self.seed is not None): self.seed += self.seed_shift
# (nframes x natoms) x (nwfc x 3)
final_layer = one_layer(layer, self.wfc_numb * 3, activation_fn = None, name='final_layer_type_'+str(type_i)+suffix, reuse=reuse, seed = self.seed, precision = self.fitting_precision, uniform_seed = self.uniform_seed)
if not self.uniform_seed : self.seed += self.seed_shift
if (not self.uniform_seed) and (self.seed is not None): self.seed += self.seed_shift
# (nframes x natoms) x nwfc(wc) x 3(coord_local)
final_layer = tf.reshape(final_layer, [tf.shape(inputs)[0] * natoms[2+type_i], self.wfc_numb, 3])
# (nframes x natoms) x nwfc(wc) x 3(coord)
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