[Model Zoo] Fix JTNN (dmlc#843)

* Update

* Update

* Update

* Update

* Update

examples/pytorch/jtnn/jtnn/chemutils.py

@@ -1,9 +1,8 @@
-import rdkit
 import rdkit.Chem as Chem
 from scipy.sparse import csr_matrix
 from scipy.sparse.csgraph import minimum_spanning_tree
 from collections import defaultdict
-from rdkit.Chem.EnumerateStereoisomers import EnumerateStereoisomers, StereoEnumerationOptions
+from rdkit.Chem.EnumerateStereoisomers import EnumerateStereoisomers
 
 MST_MAX_WEIGHT = 100 
 MAX_NCAND = 2000
@@ -29,7 +28,8 @@ def decode_stereo(smiles2D):
     dec_isomers = [Chem.MolFromSmiles(Chem.MolToSmiles(mol, isomericSmiles=True)) for mol in dec_isomers]
     smiles3D = [Chem.MolToSmiles(mol, isomericSmiles=True) for mol in dec_isomers]
 
-    chiralN = [atom.GetIdx() for atom in dec_isomers[0].GetAtoms() if int(atom.GetChiralTag()) > 0 and atom.GetSymbol() == "N"]
+    chiralN = [atom.GetIdx() for atom in dec_isomers[0].GetAtoms()
+               if int(atom.GetChiralTag()) > 0 and atom.GetSymbol() == "N"]
     if len(chiralN) > 0:
         for mol in dec_isomers:
             for idx in chiralN:
@@ -117,7 +117,8 @@ def tree_decomp(mol):
         cnei = nei_list[atom]
         bonds = [c for c in cnei if len(cliques[c]) == 2]
         rings = [c for c in cnei if len(cliques[c]) > 4]
-        if len(bonds) > 2 or (len(bonds) == 2 and len(cnei) > 2): #In general, if len(cnei) >= 3, a singleton should be added, but 1 bond + 2 ring is currently not dealt with.
+        # In general, if len(cnei) >= 3, a singleton should be added, but 1 bond + 2 ring is currently not dealt with.
+        if len(bonds) > 2 or (len(bonds) == 2 and len(cnei) > 2):
             cliques.append([atom])
             c2 = len(cliques) - 1
             for c1 in cnei:
@@ -242,11 +243,13 @@ def enum_attach_nx(ctr_mol, nei_node, amap, singletons):
             for b1 in ctr_bonds:
                 for b2 in nei_mol.GetBonds():
                     if ring_bond_equal(b1, b2):
-                        new_amap = amap + [(nei_idx, b1.GetBeginAtom().GetIdx(), b2.GetBeginAtom().GetIdx()), (nei_idx, b1.GetEndAtom().GetIdx(), b2.GetEndAtom().GetIdx())]
+                        new_amap = amap + [(nei_idx, b1.GetBeginAtom().GetIdx(), b2.GetBeginAtom().GetIdx()),
+                                           (nei_idx, b1.GetEndAtom().GetIdx(), b2.GetEndAtom().GetIdx())]
                         att_confs.append( new_amap )
 
                     if ring_bond_equal(b1, b2, reverse=True):
-                        new_amap = amap + [(nei_idx, b1.GetBeginAtom().GetIdx(), b2.GetEndAtom().GetIdx()), (nei_idx, b1.GetEndAtom().GetIdx(), b2.GetBeginAtom().GetIdx())]
+                        new_amap = amap + [(nei_idx, b1.GetBeginAtom().GetIdx(), b2.GetEndAtom().GetIdx()),
+                                           (nei_idx, b1.GetEndAtom().GetIdx(), b2.GetBeginAtom().GetIdx())]
                         att_confs.append( new_amap )
     return att_confs
 

examples/pytorch/jtnn/jtnn/datautils.py

@@ -1,6 +1,5 @@
 import torch
 from torch.utils.data import Dataset
-import numpy as np
 
 import dgl
 from dgl.data.utils import download, extract_archive, get_download_dir

examples/pytorch/jtnn/jtnn/jtmpn.py

@@ -1,16 +1,13 @@
 import torch
 import torch.nn as nn
 from .nnutils import cuda
-from .chemutils import get_mol
-#from mpn import atom_features, bond_features, ATOM_FDIM, BOND_FDIM
 import rdkit.Chem as Chem
-from dgl import DGLGraph, batch, unbatch, mean_nodes
+from dgl import DGLGraph, mean_nodes
 import dgl.function as DGLF
-from .line_profiler_integration import profile
 import os
-import numpy as np
 
-ELEM_LIST = ['C', 'N', 'O', 'S', 'F', 'Si', 'P', 'Cl', 'Br', 'Mg', 'Na', 'Ca', 'Fe', 'Al', 'I', 'B', 'K', 'Se', 'Zn', 'H', 'Cu', 'Mn', 'unknown']
+ELEM_LIST = ['C', 'N', 'O', 'S', 'F', 'Si', 'P', 'Cl', 'Br', 'Mg', 'Na', 'Ca',
+             'Fe', 'Al', 'I', 'B', 'K', 'Se', 'Zn', 'H', 'Cu', 'Mn', 'unknown']
 
 ATOM_FDIM = len(ELEM_LIST) + 6 + 5 + 1
 BOND_FDIM = 5 

examples/pytorch/jtnn/jtnn/jtnn_dec.py

@@ -1,16 +1,11 @@
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from .mol_tree import Vocab
 from .mol_tree_nx import DGLMolTree
 from .chemutils import enum_assemble_nx, get_mol
 from .nnutils import GRUUpdate, cuda
-import copy
-import itertools
 from dgl import batch, dfs_labeled_edges_generator
 import dgl.function as DGLF
-import networkx as nx
-from .line_profiler_integration import profile
 import numpy as np
 
 MAX_NB = 8
@@ -265,7 +260,6 @@ def decode(self, mol_vec):
         for step in range(MAX_DECODE_LEN):
             u, u_slots = stack[-1]
             udata = mol_tree.nodes[u].data
-            wid = udata['wid']
             x = udata['x']
             h = udata['h']
 

examples/pytorch/jtnn/jtnn/jtnn_enc.py

@@ -1,13 +1,8 @@
 import torch
 import torch.nn as nn
-from collections import deque
-from .mol_tree import Vocab
 from .nnutils import GRUUpdate, cuda
-import itertools
-import networkx as nx
-from dgl import batch, unbatch, bfs_edges_generator
+from dgl import batch, bfs_edges_generator
 import dgl.function as DGLF
-from .line_profiler_integration import profile
 import numpy as np
 
 MAX_NB = 8

examples/pytorch/jtnn/jtnn/jtnn_vae.py

@@ -1,7 +1,6 @@
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from .mol_tree import Vocab
 from .nnutils import cuda, move_dgl_to_cuda
 from .chemutils import set_atommap, copy_edit_mol, enum_assemble_nx, \
         attach_mols_nx, decode_stereo
@@ -11,13 +10,9 @@
 from .mpn import mol2dgl_single as mol2dgl_enc
 from .jtmpn import DGLJTMPN
 from .jtmpn import mol2dgl_single as mol2dgl_dec
-from .line_profiler_integration import profile
 
-import rdkit
 import rdkit.Chem as Chem
-from rdkit import DataStructs
-from rdkit.Chem import AllChem
-import copy, math
+import copy
 
 from dgl import batch, unbatch
 
@@ -102,7 +97,6 @@ def forward(self, mol_batch, beta=0, e1=None, e2=None):
         assm_loss, assm_acc = self.assm(mol_batch, mol_tree_batch, mol_vec)
         stereo_loss, stereo_acc = self.stereo(mol_batch, mol_vec)
 
-        all_vec = torch.cat([tree_vec, mol_vec], dim=1)
         loss = word_loss + topo_loss + assm_loss + 2 * stereo_loss + beta * kl_loss
 
         return loss, kl_loss, word_acc, topo_acc, assm_acc, stereo_acc

examples/pytorch/jtnn/jtnn/mol_tree.py

@@ -1,4 +1,3 @@
-import rdkit
 import rdkit.Chem as Chem
 import copy
 

examples/pytorch/jtnn/jtnn/mol_tree_nx.py

@@ -3,7 +3,6 @@
 from .chemutils import get_clique_mol, tree_decomp, get_mol, get_smiles, \
                        set_atommap, enum_assemble_nx, decode_stereo
 import numpy as np
-from .line_profiler_integration import profile
 
 class DGLMolTree(DGLGraph):
     def __init__(self, smiles):

examples/pytorch/jtnn/jtnn/mpn.py

@@ -2,16 +2,12 @@
 import torch.nn as nn
 import rdkit.Chem as Chem
 import torch.nn.functional as F
-from .nnutils import *
 from .chemutils import get_mol
-from networkx import Graph, DiGraph, convert_node_labels_to_integers
-from dgl import DGLGraph, batch, unbatch, mean_nodes
+from dgl import DGLGraph, mean_nodes
 import dgl.function as DGLF
-from functools import partial
-from .line_profiler_integration import profile
-import numpy as np
 
-ELEM_LIST = ['C', 'N', 'O', 'S', 'F', 'Si', 'P', 'Cl', 'Br', 'Mg', 'Na', 'Ca', 'Fe', 'Al', 'I', 'B', 'K', 'Se', 'Zn', 'H', 'Cu', 'Mn', 'unknown']
+ELEM_LIST = ['C', 'N', 'O', 'S', 'F', 'Si', 'P', 'Cl', 'Br', 'Mg', 'Na', 'Ca',
+             'Fe', 'Al', 'I', 'B', 'K', 'Se', 'Zn', 'H', 'Cu', 'Mn', 'unknown']
 
 ATOM_FDIM = len(ELEM_LIST) + 6 + 5 + 4 + 1
 BOND_FDIM = 5 + 6

examples/pytorch/jtnn/jtnn/nnutils.py

@@ -1,6 +1,5 @@
 import torch
 import torch.nn as nn
-from torch.autograd import Variable
 import os
 
 

examples/pytorch/model_zoo/chem/README.md

@@ -86,7 +86,9 @@ are also two accompanying review papers that are well written [7], [8].
 ### Models
 - **Deep Generative Models of Graphs (DGMG)** [11]: A very general framework for graph distribution learning by 
 progressively adding atoms and bonds.
-- **Junction Tree Variational Autoencoder for Molecular Graph Generation (JTNN)** [13]:
+- **Junction Tree Variational Autoencoder for Molecular Graph Generation (JTNN)** [13]: JTNNs are able to incrementally
+expand molecules while maintaining chemical valency at every step. They can be used for both molecule generation and
+optimization.
 
 ### Example Usage of Pre-trained Models
 

examples/pytorch/model_zoo/chem/generative_models/jtnn/README.md

@@ -35,23 +35,16 @@ encoded nodes(atoms) and edges(bonds), and other information for model to use.
 To start training, use `python train.py`. By default, the script will use ZINC dataset
  with preprocessed vocabulary, and save model checkpoint at the current working directory. 
 ```
-  -s SAVE_PATH, --save_dir SAVE_PATH
-                        Path to save checkpoint models, default to be current
-                        working directory (default: ./)
-  -m MODEL_PATH, --model MODEL_PATH
-                        Path to load pre-trained model (default: None)
-  -b BATCH_SIZE, --batch BATCH_SIZE
-                        Batch size (default: 40)
-  -w HIDDEN_SIZE, --hidden HIDDEN_SIZE
-                        Size of representation vectors (default: 200)
-  -l LATENT_SIZE, --latent LATENT_SIZE
-                        Latent Size of node(atom) features and edge(atom)
-                        features (default: 56)
-  -d DEPTH, --depth DEPTH
-                        Depth of message passing hops (default: 3)
-  -z BETA, --beta BETA  Coefficient of KL Divergence term (default: 1.0)
-  -q LR, --lr LR        Learning Rate (default: 0.001)
-  -T, --test            Add this flag to run test mode (default: False)
+-s SAVE_PATH, Path to save checkpoint models, default to be current
+              working directory (default: ./)
+-m MODEL_PATH, Path to load pre-trained model (default: None)
+-b BATCH_SIZE, Batch size (default: 40)
+-w HIDDEN_SIZE, Size of representation vectors (default: 200)
+-l LATENT_SIZE, Latent Size of node(atom) features and edge(atom)
+                features (default: 56)
+-d DEPTH, Depth of message passing hops (default: 3)
+-z BETA, Coefficient of KL Divergence term (default: 1.0)
+-q LR, Learning Rate (default: 0.001)
 ```
 
 Model will be saved periodically. 
@@ -70,22 +63,17 @@ If you want to use your own dataset, please create a file contains one SMILES a
 
 To start evaluation, use `python reconstruct_eval.py`, and following arguments
 ```
-  -t TRAIN, --train TRAIN
-                        Training file name (default: test)
-  -m MODEL_PATH, --model MODEL_PATH
-                        Pre-trained model to be loaded for evalutaion. If not
-                        specified, would use pre-trained model from model zoo
-                        (default: None)
-  -w HIDDEN_SIZE, --hidden HIDDEN_SIZE
-                        Hidden size of representation vector, should be
-                        consistent with pre-trained model (default: 450)
-  -l LATENT_SIZE, --latent LATENT_SIZE
-                        Latent Size of node(atom) features and edge(atom)
-                        features, should be consistent with pre-trained model
-                        (default: 56)
-  -d DEPTH, --depth DEPTH
-                        Depth of message passing hops, should be consistent
-                        with pre-trained model (default: 3)
+-t TRAIN, Training file name (default: test)
+-m MODEL_PATH, Pre-trained model to be loaded for evalutaion. If not
+               specified, would use pre-trained model from model zoo
+               (default: None)
+-w HIDDEN_SIZE, Hidden size of representation vector, should be
+                consistent with pre-trained model (default: 450)
+-l LATENT_SIZE, Latent Size of node(atom) features and edge(atom)
+                features, should be consistent with pre-trained model
+                 (default: 56)
+-d DEPTH, Depth of message passing hops, should be consistent
+          with pre-trained model (default: 3)
 ```
 
 And it would print out the success rate of reconstructing the same molecules.

examples/pytorch/model_zoo/chem/generative_models/jtnn/jtnn/chemutils.py

@@ -1,10 +1,9 @@
-import rdkit
 import rdkit.Chem as Chem
 import torch
 from scipy.sparse import csr_matrix
 from scipy.sparse.csgraph import minimum_spanning_tree
 from collections import defaultdict
-from rdkit.Chem.EnumerateStereoisomers import EnumerateStereoisomers, StereoEnumerationOptions
+from rdkit.Chem.EnumerateStereoisomers import EnumerateStereoisomers
 from dgl import DGLGraph
 
 ELEM_LIST = ['C', 'N', 'O', 'S', 'F', 'Si', 'P', 'Cl', 'Br', 'Mg', 'Na',
@@ -406,16 +405,12 @@ def bond_features(bond):
     tree_mess_target_edges = []  # these edges on candidate graphs
     tree_mess_target_nodes = []
     n_nodes = 0
-    n_edges = 0
     atom_x = []
     bond_x = []
 
     for mol, mol_tree, ctr_node_id in cand_batch:
         n_atoms = mol.GetNumAtoms()
-        n_bonds = mol.GetNumBonds()
 
-        ctr_node = mol_tree.nodes_dict[ctr_node_id]
-        ctr_bid = ctr_node['idx']
         g = DGLGraph()
 
         for i, atom in enumerate(mol.GetAtoms()):

examples/pytorch/model_zoo/chem/generative_models/jtnn/jtnn/datautils.py

@@ -7,7 +7,8 @@
 from .mol_tree import Vocab, DGLMolTree
 from .chemutils import mol2dgl_dec, mol2dgl_enc
 
-ELEM_LIST = ['C', 'N', 'O', 'S', 'F', 'Si', 'P', 'Cl', 'Br', 'Mg', 'Na', 'Ca', 'Fe', 'Al', 'I', 'B', 'K', 'Se', 'Zn', 'H', 'Cu', 'Mn', 'unknown']
+ELEM_LIST = ['C', 'N', 'O', 'S', 'F', 'Si', 'P', 'Cl', 'Br', 'Mg', 'Na', 'Ca',
+             'Fe', 'Al', 'I', 'B', 'K', 'Se', 'Zn', 'H', 'Cu', 'Mn', 'unknown']
 
 ATOM_FDIM_DEC = len(ELEM_LIST) + 6 + 5 + 1
 BOND_FDIM_DEC = 5

examples/pytorch/model_zoo/chem/generative_models/jtnn/jtnn/mol_tree.py

@@ -1,5 +1,3 @@
-import rdkit
-import rdkit.Chem as Chem
 import copy
 import numpy as np
 from dgl import DGLGraph

examples/pytorch/model_zoo/chem/generative_models/jtnn/train.py

@@ -5,9 +5,8 @@
 from dgl import model_zoo
 from torch.utils.data import DataLoader
 
-import math, random, sys
+import sys
 import argparse
-from collections import deque
 import rdkit
 
 from jtnn import *
@@ -42,8 +41,6 @@ def worker_init_fn(id_):
                     help="Coefficient of KL Divergence term")
 parser.add_argument("-q", "--lr", dest="lr", default=1e-3,
                     help="Learning Rate")
-parser.add_argument("-T", "--test", dest="test", action="store_true",
-                    help="Add this flag to run test mode")
 args = parser.parse_args()
 
 dataset = JTNNDataset(data=args.train, vocab=args.vocab, training=True)
@@ -131,35 +128,8 @@ def train():
         print("learning rate: %.6f" % scheduler.get_lr()[0])
         torch.save(model.state_dict(), args.save_path + "/model.iter-" + str(epoch))
 
-
-def test():
-    dataset.training = False
-    dataloader = DataLoader(
-        dataset,
-        batch_size=1,
-        shuffle=False,
-        num_workers=0,
-        collate_fn=JTNNCollator(vocab, False),
-        drop_last=True,
-        worker_init_fn=worker_init_fn)
-
-    # Just an example of molecule decoding; in reality you may want to sample
-    # tree and molecule vectors.
-    for it, batch in enumerate(dataloader):
-        gt_smiles = batch['mol_trees'][0].smiles
-        print(gt_smiles)
-        model.move_to_cuda(batch)
-        _, tree_vec, mol_vec = model.encode(batch)
-        tree_vec, mol_vec, _, _ = model.sample(tree_vec, mol_vec)
-        smiles = model.decode(tree_vec, mol_vec)
-        print(smiles)
-
-
 if __name__ == '__main__':
-    if args.test:
-        test()
-    else:
-        train()
+    train()
 
     print('# passes:', model.n_passes)
     print('Total # nodes processed:', model.n_nodes_total)

python/dgl/model_zoo/chem/jtnn/chemutils.py

@@ -1,10 +1,8 @@
 # pylint: disable=C0111, C0103, E1101, W0611, W0612, W0703, C0200, R1710
 from collections import defaultdict
 
-import rdkit
 import rdkit.Chem as Chem
-from rdkit.Chem.EnumerateStereoisomers import (EnumerateStereoisomers,
-                                               StereoEnumerationOptions)
+from rdkit.Chem.EnumerateStereoisomers import EnumerateStereoisomers
 from scipy.sparse import csr_matrix
 from scipy.sparse.csgraph import minimum_spanning_tree