From 00dd6e9c264d0e14526388b1ec1fe198e342a57e Mon Sep 17 00:00:00 2001 From: Han Wang Date: Sat, 5 Jun 2021 16:27:59 +0800 Subject: [PATCH] fix bug of adding int to a None random seed --- deepmd/descriptor/se_a.py | 2 +- deepmd/descriptor/se_r.py | 2 +- deepmd/descriptor/se_t.py | 2 +- deepmd/fit/dipole.py | 4 ++-- deepmd/fit/ener.py | 4 ++-- deepmd/fit/polar.py | 6 +++--- deepmd/fit/wfc.py | 4 ++-- 7 files changed, 12 insertions(+), 12 deletions(-) diff --git a/deepmd/descriptor/se_a.py b/deepmd/descriptor/se_a.py index eb18c68fb2..b4f1292d48 100644 --- a/deepmd/descriptor/se_a.py +++ b/deepmd/descriptor/se_a.py @@ -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) diff --git a/deepmd/descriptor/se_r.py b/deepmd/descriptor/se_r.py index 5c24f5ac3c..6dad7947fa 100644 --- a/deepmd/descriptor/se_r.py +++ b/deepmd/descriptor/se_r.py @@ -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) diff --git a/deepmd/descriptor/se_t.py b/deepmd/descriptor/se_t.py index e03dec69cf..3206990402 100644 --- a/deepmd/descriptor/se_t.py +++ b/deepmd/descriptor/se_t.py @@ -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 diff --git a/deepmd/fit/dipole.py b/deepmd/fit/dipole.py index df6e70bde2..73562951dc 100644 --- a/deepmd/fit/dipole.py +++ b/deepmd/fit/dipole.py @@ -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) diff --git a/deepmd/fit/ener.py b/deepmd/fit/ener.py index 7fb85f0c7b..0d2932a76b 100644 --- a/deepmd/fit/ener.py +++ b/deepmd/fit/ener.py @@ -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, @@ -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 diff --git a/deepmd/fit/polar.py b/deepmd/fit/polar.py index a6dcdb3bf6..33c5be378a 100644 --- a/deepmd/fit/polar.py +++ b/deepmd/fit/polar.py @@ -321,7 +321,7 @@ 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] @@ -329,7 +329,7 @@ def build (self, # 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 @@ -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 diff --git a/deepmd/fit/wfc.py b/deepmd/fit/wfc.py index 1c3af65594..fce82bd04b 100644 --- a/deepmd/fit/wfc.py +++ b/deepmd/fit/wfc.py @@ -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)