Merge branch 'master' into cpp

examples/pytorch/jtnn/README.md

+Junction Tree VAE - example for training
+===
+
+This is a direct modification from https://github.com/wengong-jin/icml18-jtnn
+
+You need to have RDKit installed.
+
+To run the model, use
+```
+python3 vaetrain_dgl.py
+```
+The script will automatically download the data, which is the same as the one in the
+original repository.

examples/pytorch/jtnn/jtnn/__init__.py

+from .mol_tree import Vocab
+from .jtnn_vae import DGLJTNNVAE
+from .mpn import DGLMPN, mol2dgl
+from .nnutils import create_var
+from .datautils import JTNNDataset
+from .chemutils import decode_stereo
+from .line_profiler_integration import profile

examples/pytorch/jtnn/jtnn/chemutils.py

+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
+
+MST_MAX_WEIGHT = 100 
+MAX_NCAND = 2000
+
+def set_atommap(mol, num=0):
+    for atom in mol.GetAtoms():
+        atom.SetAtomMapNum(num)
+
+def get_mol(smiles):
+    mol = Chem.MolFromSmiles(smiles)
+    if mol is None: 
+        return None
+    Chem.Kekulize(mol)
+    return mol
+
+def get_smiles(mol):
+    return Chem.MolToSmiles(mol, kekuleSmiles=True)
+
+def decode_stereo(smiles2D):
+    mol = Chem.MolFromSmiles(smiles2D)
+    dec_isomers = list(EnumerateStereoisomers(mol))
+
+    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"]
+    if len(chiralN) > 0:
+        for mol in dec_isomers:
+            for idx in chiralN:
+                mol.GetAtomWithIdx(idx).SetChiralTag(Chem.rdchem.ChiralType.CHI_UNSPECIFIED)
+            smiles3D.append(Chem.MolToSmiles(mol, isomericSmiles=True))
+
+    return smiles3D
+
+def sanitize(mol):
+    try:
+        smiles = get_smiles(mol)
+        mol = get_mol(smiles)
+    except Exception as e:
+        return None
+    return mol
+
+def copy_atom(atom):
+    new_atom = Chem.Atom(atom.GetSymbol())
+    new_atom.SetFormalCharge(atom.GetFormalCharge())
+    new_atom.SetAtomMapNum(atom.GetAtomMapNum())
+    return new_atom
+
+def copy_edit_mol(mol):
+    new_mol = Chem.RWMol(Chem.MolFromSmiles(''))
+    for atom in mol.GetAtoms():
+        new_atom = copy_atom(atom)
+        new_mol.AddAtom(new_atom)
+    for bond in mol.GetBonds():
+        a1 = bond.GetBeginAtom().GetIdx()
+        a2 = bond.GetEndAtom().GetIdx()
+        bt = bond.GetBondType()
+        new_mol.AddBond(a1, a2, bt)
+    return new_mol
+
+def get_clique_mol(mol, atoms):
+    smiles = Chem.MolFragmentToSmiles(mol, atoms, kekuleSmiles=True)
+    new_mol = Chem.MolFromSmiles(smiles, sanitize=False)
+    new_mol = copy_edit_mol(new_mol).GetMol()
+    new_mol = sanitize(new_mol) #We assume this is not None
+    return new_mol
+
+def tree_decomp(mol):
+    n_atoms = mol.GetNumAtoms()
+    if n_atoms == 1:
+        return [[0]], []
+
+    cliques = []
+    for bond in mol.GetBonds():
+        a1 = bond.GetBeginAtom().GetIdx()
+        a2 = bond.GetEndAtom().GetIdx()
+        if not bond.IsInRing():
+            cliques.append([a1,a2])
+
+    ssr = [list(x) for x in Chem.GetSymmSSSR(mol)]
+    cliques.extend(ssr)
+
+    nei_list = [[] for i in range(n_atoms)]
+    for i in range(len(cliques)):
+        for atom in cliques[i]:
+            nei_list[atom].append(i)
+    
+    #Merge Rings with intersection > 2 atoms
+    for i in range(len(cliques)):
+        if len(cliques[i]) <= 2: continue
+        for atom in cliques[i]:
+            for j in nei_list[atom]:
+                if i >= j or len(cliques[j]) <= 2: continue
+                inter = set(cliques[i]) & set(cliques[j])
+                if len(inter) > 2:
+                    cliques[i].extend(cliques[j])
+                    cliques[i] = list(set(cliques[i]))
+                    cliques[j] = []
+    
+    cliques = [c for c in cliques if len(c) > 0]
+    nei_list = [[] for i in range(n_atoms)]
+    for i in range(len(cliques)):
+        for atom in cliques[i]:
+            nei_list[atom].append(i)
+    
+    #Build edges and add singleton cliques
+    edges = defaultdict(int)
+    for atom in range(n_atoms):
+        if len(nei_list[atom]) <= 1: 
+            continue
+        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.
+            cliques.append([atom])
+            c2 = len(cliques) - 1
+            for c1 in cnei:
+                edges[(c1,c2)] = 1
+        elif len(rings) > 2: #Multiple (n>2) complex rings
+            cliques.append([atom])
+            c2 = len(cliques) - 1
+            for c1 in cnei:
+                edges[(c1,c2)] = MST_MAX_WEIGHT - 1
+        else:
+            for i in range(len(cnei)):
+                for j in range(i + 1, len(cnei)):
+                    c1,c2 = cnei[i],cnei[j]
+                    inter = set(cliques[c1]) & set(cliques[c2])
+                    if edges[(c1,c2)] < len(inter):
+                        edges[(c1,c2)] = len(inter) #cnei[i] < cnei[j] by construction
+
+    edges = [u + (MST_MAX_WEIGHT-v,) for u,v in edges.items()]
+    if len(edges) == 0:
+        return cliques, edges
+
+    #Compute Maximum Spanning Tree
+    row,col,data = list(zip(*edges))
+    n_clique = len(cliques)
+    clique_graph = csr_matrix( (data,(row,col)), shape=(n_clique,n_clique) )
+    junc_tree = minimum_spanning_tree(clique_graph)
+    row,col = junc_tree.nonzero()
+    edges = [(row[i],col[i]) for i in range(len(row))]
+    return (cliques, edges)
+
+def atom_equal(a1, a2):
+    return a1.GetSymbol() == a2.GetSymbol() and a1.GetFormalCharge() == a2.GetFormalCharge()
+
+#Bond type not considered because all aromatic (so SINGLE matches DOUBLE)
+def ring_bond_equal(b1, b2, reverse=False):
+    b1 = (b1.GetBeginAtom(), b1.GetEndAtom())
+    if reverse:
+        b2 = (b2.GetEndAtom(), b2.GetBeginAtom())
+    else:
+        b2 = (b2.GetBeginAtom(), b2.GetEndAtom())
+    return atom_equal(b1[0], b2[0]) and atom_equal(b1[1], b2[1])
+
+def attach_mols_nx(ctr_mol, neighbors, prev_nodes, nei_amap):
+    prev_nids = [node['nid'] for node in prev_nodes]
+    for nei_node in prev_nodes + neighbors:
+        nei_id, nei_mol = nei_node['nid'], nei_node['mol']
+        amap = nei_amap[nei_id]
+        for atom in nei_mol.GetAtoms():
+            if atom.GetIdx() not in amap:
+                new_atom = copy_atom(atom)
+                amap[atom.GetIdx()] = ctr_mol.AddAtom(new_atom)
+
+        if nei_mol.GetNumBonds() == 0:
+            nei_atom = nei_mol.GetAtomWithIdx(0)
+            ctr_atom = ctr_mol.GetAtomWithIdx(amap[0])
+            ctr_atom.SetAtomMapNum(nei_atom.GetAtomMapNum())
+        else:
+            for bond in nei_mol.GetBonds():
+                a1 = amap[bond.GetBeginAtom().GetIdx()]
+                a2 = amap[bond.GetEndAtom().GetIdx()]
+                if ctr_mol.GetBondBetweenAtoms(a1, a2) is None:
+                    ctr_mol.AddBond(a1, a2, bond.GetBondType())
+                elif nei_id in prev_nids: #father node overrides
+                    ctr_mol.RemoveBond(a1, a2)
+                    ctr_mol.AddBond(a1, a2, bond.GetBondType())
+    return ctr_mol
+
+def local_attach_nx(ctr_mol, neighbors, prev_nodes, amap_list):
+    ctr_mol = copy_edit_mol(ctr_mol)
+    nei_amap = {nei['nid']: {} for nei in prev_nodes + neighbors}
+
+    for nei_id,ctr_atom,nei_atom in amap_list:
+        nei_amap[nei_id][nei_atom] = ctr_atom
+
+    ctr_mol = attach_mols_nx(ctr_mol, neighbors, prev_nodes, nei_amap)
+    return ctr_mol.GetMol()
+
+#This version records idx mapping between ctr_mol and nei_mol
+def enum_attach_nx(ctr_mol, nei_node, amap, singletons):
+    nei_mol,nei_idx = nei_node['mol'], nei_node['nid']
+    att_confs = []
+    black_list = [atom_idx for nei_id,atom_idx,_ in amap if nei_id in singletons]
+    ctr_atoms = [atom for atom in ctr_mol.GetAtoms() if atom.GetIdx() not in black_list]
+    ctr_bonds = [bond for bond in ctr_mol.GetBonds()]
+
+    if nei_mol.GetNumBonds() == 0: #neighbor singleton
+        nei_atom = nei_mol.GetAtomWithIdx(0)
+        used_list = [atom_idx for _,atom_idx,_ in amap]
+        for atom in ctr_atoms:
+            if atom_equal(atom, nei_atom) and atom.GetIdx() not in used_list:
+                new_amap = amap + [(nei_idx, atom.GetIdx(), 0)]
+                att_confs.append( new_amap )
+   
+    elif nei_mol.GetNumBonds() == 1: #neighbor is a bond
+        bond = nei_mol.GetBondWithIdx(0)
+        bond_val = int(bond.GetBondTypeAsDouble())
+        b1,b2 = bond.GetBeginAtom(), bond.GetEndAtom()
+
+        for atom in ctr_atoms: 
+            #Optimize if atom is carbon (other atoms may change valence)
+            if atom.GetAtomicNum() == 6 and atom.GetTotalNumHs() < bond_val:
+                continue
+            if atom_equal(atom, b1):
+                new_amap = amap + [(nei_idx, atom.GetIdx(), b1.GetIdx())]
+                att_confs.append( new_amap )
+            elif atom_equal(atom, b2):
+                new_amap = amap + [(nei_idx, atom.GetIdx(), b2.GetIdx())]
+                att_confs.append( new_amap )
+    else: 
+        #intersection is an atom
+        for a1 in ctr_atoms:
+            for a2 in nei_mol.GetAtoms():
+                if atom_equal(a1, a2):
+                    #Optimize if atom is carbon (other atoms may change valence)
+                    if a1.GetAtomicNum() == 6 and a1.GetTotalNumHs() + a2.GetTotalNumHs() < 4:
+                        continue
+                    new_amap = amap + [(nei_idx, a1.GetIdx(), a2.GetIdx())]
+                    att_confs.append( new_amap )
+
+        #intersection is an bond
+        if ctr_mol.GetNumBonds() > 1:
+            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())]
+                        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())]
+                        att_confs.append( new_amap )
+    return att_confs
+
+#Try rings first: Speed-Up 
+def enum_assemble_nx(graph, node_idx, neighbors, prev_nodes=[], prev_amap=[]):
+    node = graph.nodes[node_idx]
+    all_attach_confs = []
+    singletons = [nei_node['nid'] for nei_node in neighbors + prev_nodes if nei_node['mol'].GetNumAtoms() == 1]
+
+    def search(cur_amap, depth):
+        if len(all_attach_confs) > MAX_NCAND:
+            return
+        if depth == len(neighbors):
+            all_attach_confs.append(cur_amap)
+            return
+
+        nei_node = neighbors[depth]
+        cand_amap = enum_attach_nx(node['mol'], nei_node, cur_amap, singletons)
+        cand_smiles = set()
+        candidates = []
+        for amap in cand_amap:
+            cand_mol = local_attach_nx(node['mol'], neighbors[:depth+1], prev_nodes, amap)
+            cand_mol = sanitize(cand_mol)
+            if cand_mol is None:
+                continue
+            smiles = get_smiles(cand_mol)
+            if smiles in cand_smiles:
+                continue
+            cand_smiles.add(smiles)
+            candidates.append(amap)
+
+        if len(candidates) == 0:
+            return []
+
+        for new_amap in candidates:
+            search(new_amap, depth + 1)
+
+    search(prev_amap, 0)
+    cand_smiles = set()
+    candidates = []
+    for amap in all_attach_confs:
+        cand_mol = local_attach_nx(node['mol'], neighbors, prev_nodes, amap)
+        cand_mol = Chem.MolFromSmiles(Chem.MolToSmiles(cand_mol))
+        smiles = Chem.MolToSmiles(cand_mol)
+        if smiles in cand_smiles:
+            continue
+        cand_smiles.add(smiles)
+        Chem.Kekulize(cand_mol)
+        candidates.append( (smiles,cand_mol,amap) )
+
+    return candidates
+
+#Only used for debugging purpose
+def dfs_assemble_nx(graph, cur_mol, global_amap, fa_amap, cur_node_id, fa_node_id):
+    cur_node = graph.nodes[cur_node_id]
+    fa_node = graph.nodes[fa_node_id] if fa_node_id is not None else None
+
+    fa_nid = fa_node['nid'] if fa_node is not None else -1
+    prev_nodes = [fa_node] if fa_node is not None else []
+
+    children_id = [nei for nei in graph[cur_node_id] if graph.nodes[nei]['nid'] != fa_nid]
+    children = [graph.nodes[nei] for nei in children_id]
+    neighbors = [nei for nei in children if nei['mol'].GetNumAtoms() > 1]
+    neighbors = sorted(neighbors, key=lambda x:x['mol'].GetNumAtoms(), reverse=True)
+    singletons = [nei for nei in children if nei['mol'].GetNumAtoms() == 1]
+    neighbors = singletons + neighbors
+
+    cur_amap = [(fa_nid,a2,a1) for nid,a1,a2 in fa_amap if nid == cur_node['nid']]
+    cands = enum_assemble_nx(graph, cur_node_id, neighbors, prev_nodes, cur_amap)
+    if len(cands) == 0:
+        return
+
+    cand_smiles, _, cand_amap = zip(*cands)
+    label_idx = cand_smiles.index(cur_node['label'])
+    label_amap = cand_amap[label_idx]
+
+    for nei_id,ctr_atom,nei_atom in label_amap:
+        if nei_id == fa_nid:
+            continue
+        global_amap[nei_id][nei_atom] = global_amap[cur_node['nid']][ctr_atom]
+    
+    cur_mol = attach_mols_nx(cur_mol, children, [], global_amap) #father is already attached
+    for nei_node_id, nei_node in zip(children_id, children):
+        if not nei_node['is_leaf']:
+            dfs_assemble_nx(graph, cur_mol, global_amap, label_amap, nei_node_id, cur_node_id)

examples/pytorch/jtnn/jtnn/datautils.py

+from torch.utils.data import Dataset
+import numpy as np
+
+import dgl
+from dgl.data.utils import download, extract_archive, get_download_dir
+
+_url = 'https://www.dropbox.com/s/4ypr0e0abcbsvoh/jtnn.zip?dl=1'
+
+class JTNNDataset(Dataset):
+    def __init__(self, data, vocab):
+        self.dir = get_download_dir()
+        self.zip_file_path='{}/jtnn.zip'.format(self.dir)
+        download(_url, path=self.zip_file_path)
+        extract_archive(self.zip_file_path, '{}/jtnn'.format(self.dir))
+        print('Loading data...')
+        data_file = '{}/jtnn/{}.txt'.format(self.dir, data)
+        with open(data_file) as f:
+            self.data = [line.strip("\r\n ").split()[0] for line in f]
+        self.vocab_file = '{}/jtnn/{}.txt'.format(self.dir, vocab)
+        print('Loading finished.')
+        print('\tNum samples:', len(self.data))
+        print('\tVocab file:', self.vocab_file)
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, idx):
+        from .mol_tree_nx import DGLMolTree
+        smiles = self.data[idx]
+        mol_tree = DGLMolTree(smiles)
+        mol_tree.recover()
+        mol_tree.assemble()
+        return mol_tree

examples/pytorch/jtnn/jtnn/jtmpn.py

+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, line_graph, batch, unbatch
+import dgl.function as DGLF
+from .line_profiler_integration import profile
+import os
+
+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 
+MAX_NB = 10
+
+PAPER = os.getenv('PAPER', False)
+
+def onek_encoding_unk(x, allowable_set):
+    if x not in allowable_set:
+        x = allowable_set[-1]
+    return [x == s for s in allowable_set]
+
+# Note that during graph decoding they don't predict stereochemistry-related
+# characteristics (i.e. Chiral Atoms, E-Z, Cis-Trans).  Instead, they decode
+# the 2-D graph first, then enumerate all possible 3-D forms and find the
+# one with highest score.
+def atom_features(atom):
+    return cuda(torch.Tensor(onek_encoding_unk(atom.GetSymbol(), ELEM_LIST) 
+            + onek_encoding_unk(atom.GetDegree(), [0,1,2,3,4,5]) 
+            + onek_encoding_unk(atom.GetFormalCharge(), [-1,-2,1,2,0])
+            + [atom.GetIsAromatic()]))
+
+def bond_features(bond):
+    bt = bond.GetBondType()
+    return cuda(torch.Tensor([bt == Chem.rdchem.BondType.SINGLE, bt == Chem.rdchem.BondType.DOUBLE, bt == Chem.rdchem.BondType.TRIPLE, bt == Chem.rdchem.BondType.AROMATIC, bond.IsInRing()]))
+
+
+@profile
+def mol2dgl(cand_batch, mol_tree_batch):
+    cand_graphs = []
+    tree_mess_source_edges = [] # map these edges from trees to...
+    tree_mess_target_edges = [] # these edges on candidate graphs
+    tree_mess_target_nodes = []
+    n_nodes = 0
+
+    for mol, mol_tree, ctr_node_id in cand_batch:
+        atom_feature_list = []
+        bond_feature_list = []
+        ctr_node = mol_tree.nodes[ctr_node_id]
+        ctr_bid = ctr_node['idx']
+        g = DGLGraph()
+
+        for atom in mol.GetAtoms():
+            atom_feature_list.append(atom_features(atom))
+            g.add_node(atom.GetIdx())
+
+        for bond in mol.GetBonds():
+            a1 = bond.GetBeginAtom()
+            a2 = bond.GetEndAtom()
+            begin_idx = a1.GetIdx()
+            end_idx = a2.GetIdx()
+            features = bond_features(bond)
+
+            g.add_edge(begin_idx, end_idx)
+            bond_feature_list.append(features)
+            g.add_edge(end_idx, begin_idx)
+            bond_feature_list.append(features)
+
+            x_nid, y_nid = a1.GetAtomMapNum(), a2.GetAtomMapNum()
+            # Tree node ID in the batch
+            x_bid = mol_tree.nodes[x_nid - 1]['idx'] if x_nid > 0 else -1
+            y_bid = mol_tree.nodes[y_nid - 1]['idx'] if y_nid > 0 else -1
+            if x_bid >= 0 and y_bid >= 0 and x_bid != y_bid:
+                if (x_bid, y_bid) in mol_tree_batch.edge_list:
+                    tree_mess_target_edges.append(
+                            (begin_idx + n_nodes, end_idx + n_nodes))
+                    tree_mess_source_edges.append((x_bid, y_bid))
+                    tree_mess_target_nodes.append(end_idx + n_nodes)
+                if (y_bid, x_bid) in mol_tree_batch.edge_list:
+                    tree_mess_target_edges.append(
+                            (end_idx + n_nodes, begin_idx + n_nodes))
+                    tree_mess_source_edges.append((y_bid, x_bid))
+                    tree_mess_target_nodes.append(begin_idx + n_nodes)
+
+        n_nodes += len(g.nodes)
+
+        atom_x = torch.stack(atom_feature_list)
+        g.set_n_repr({'x': atom_x})
+        if len(bond_feature_list) > 0:
+            bond_x = torch.stack(bond_feature_list)
+            g.set_e_repr({
+                'x': bond_x,
+                'src_x': atom_x.new(len(bond_feature_list), ATOM_FDIM).zero_()
+            })
+        cand_graphs.append(g)
+
+    return cand_graphs, tree_mess_source_edges, tree_mess_target_edges, \
+           tree_mess_target_nodes
+
+
+mpn_loopy_bp_msg = DGLF.copy_src(src='msg', out='msg')
+mpn_loopy_bp_reduce = DGLF.sum(msgs='msg', out='accum_msg')
+
+
+class LoopyBPUpdate(nn.Module):
+    def __init__(self, hidden_size):
+        super(LoopyBPUpdate, self).__init__()
+        self.hidden_size = hidden_size
+
+        self.W_h = nn.Linear(hidden_size, hidden_size, bias=False)
+
+    def forward(self, node):
+        msg_input = node['msg_input']
+        msg_delta = self.W_h(node['accum_msg'] + node['alpha'])
+        msg = torch.relu(msg_input + msg_delta)
+        return {'msg': msg}
+
+
+if PAPER:
+    mpn_gather_msg = [
+        DGLF.copy_edge(edge='msg', out='msg'),
+        DGLF.copy_edge(edge='alpha', out='alpha')
+    ]
+else:
+    mpn_gather_msg = DGLF.copy_edge(edge='msg', out='msg')
+
+
+if PAPER:
+    mpn_gather_reduce = [
+        DGLF.sum(msgs='msg', out='m'),
+        DGLF.sum(msgs='alpha', out='accum_alpha'),
+    ]
+else:
+    mpn_gather_reduce = DGLF.sum(msgs='msg', out='m')
+
+
+class GatherUpdate(nn.Module):
+    def __init__(self, hidden_size):
+        super(GatherUpdate, self).__init__()
+        self.hidden_size = hidden_size
+
+        self.W_o = nn.Linear(ATOM_FDIM + hidden_size, hidden_size)
+
+    def forward(self, node):
+        if PAPER:
+            #m = node['m']
+            m = node['m'] + node['accum_alpha']
+        else:
+            m = node['m'] + node['alpha']
+        return {
+            'h': torch.relu(self.W_o(torch.cat([node['x'], m], 1))),
+        }
+
+
+class DGLJTMPN(nn.Module):
+    def __init__(self, hidden_size, depth):
+        nn.Module.__init__(self)
+
+        self.depth = depth
+
+        self.W_i = nn.Linear(ATOM_FDIM + BOND_FDIM, hidden_size, bias=False)
+
+        self.loopy_bp_updater = LoopyBPUpdate(hidden_size)
+        self.gather_updater = GatherUpdate(hidden_size)
+        self.hidden_size = hidden_size
+
+        self.n_samples_total = 0
+        self.n_nodes_total = 0
+        self.n_edges_total = 0
+        self.n_passes = 0
+
+    @profile
+    def forward(self, cand_batch, mol_tree_batch):
+        cand_graphs, tree_mess_src_edges, tree_mess_tgt_edges, tree_mess_tgt_nodes = \
+                mol2dgl(cand_batch, mol_tree_batch)
+
+        n_samples = len(cand_graphs)
+
+        cand_graphs = batch(cand_graphs)
+        cand_line_graph = line_graph(cand_graphs, no_backtracking=True)
+
+        n_nodes = len(cand_graphs.nodes)
+        n_edges = len(cand_graphs.edges)
+
+        cand_graphs = self.run(
+                cand_graphs, cand_line_graph, tree_mess_src_edges, tree_mess_tgt_edges,
+                tree_mess_tgt_nodes, mol_tree_batch)
+
+        cand_graphs = unbatch(cand_graphs)
+        g_repr = torch.stack([g.get_n_repr()['h'].mean(0) for g in cand_graphs], 0)
+
+        self.n_samples_total += n_samples
+        self.n_nodes_total += n_nodes
+        self.n_edges_total += n_edges
+        self.n_passes += 1
+
+        return g_repr
+
+    @profile
+    def run(self, cand_graphs, cand_line_graph, tree_mess_src_edges, tree_mess_tgt_edges,
+            tree_mess_tgt_nodes, mol_tree_batch):
+        n_nodes = len(cand_graphs.nodes)
+
+        cand_graphs.update_edge(
+            #*zip(*cand_graphs.edge_list),
+            edge_func=lambda src, dst, edge: {'src_x': src['x']},
+            batchable=True,
+        )
+
+        bond_features = cand_line_graph.get_n_repr()['x']
+        source_features = cand_line_graph.get_n_repr()['src_x']
+        features = torch.cat([source_features, bond_features], 1)
+        msg_input = self.W_i(features)
+        cand_line_graph.set_n_repr({
+            'msg_input': msg_input,
+            'msg': torch.relu(msg_input),
+            'accum_msg': torch.zeros_like(msg_input),
+        })
+        zero_node_state = bond_features.new(n_nodes, self.hidden_size).zero_()
+        cand_graphs.set_n_repr({
+            'm': zero_node_state.clone(),
+            'h': zero_node_state.clone(),
+        })
+
+        if PAPER:
+            cand_graphs.set_e_repr({
+                'alpha': cuda(torch.zeros(len(cand_graphs.edge_list), self.hidden_size))
+            })
+
+            alpha = mol_tree_batch.get_e_repr(*zip(*tree_mess_src_edges))['m']
+            cand_graphs.set_e_repr({'alpha': alpha}, *zip(*tree_mess_tgt_edges))
+        else:
+            alpha = mol_tree_batch.get_e_repr(*zip(*tree_mess_src_edges))['m']
+            node_idx = (torch.LongTensor(tree_mess_tgt_nodes)
+                        .to(device=zero_node_state.device)[:, None]
+                        .expand_as(alpha))
+            node_alpha = zero_node_state.clone().scatter_add(0, node_idx, alpha)
+            cand_graphs.set_n_repr({'alpha': node_alpha})
+            cand_graphs.update_edge(
+                #*zip(*cand_graphs.edge_list),
+                edge_func=lambda src, dst, edge: {'alpha': src['alpha']},
+                batchable=True,
+            )
+
+        for i in range(self.depth - 1):
+            cand_line_graph.update_all(
+                mpn_loopy_bp_msg,
+                mpn_loopy_bp_reduce,
+                self.loopy_bp_updater,
+                True
+            )
+
+        cand_graphs.update_all(
+            mpn_gather_msg,
+            mpn_gather_reduce,
+            self.gather_updater,
+            True
+        )
+
+        return cand_graphs

examples/pytorch/jtnn/jtnn/jtnn_dec.py

+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from .mol_tree import Vocab
+from .nnutils import GRUUpdate, cuda
+import copy
+import itertools
+from dgl import batch, line_graph
+import dgl.function as DGLF
+import networkx as nx
+from .line_profiler_integration import profile
+
+MAX_NB = 8
+MAX_DECODE_LEN = 100
+
+
+def dfs_order(forest, roots):
+    '''
+    Returns edge source, edge destination, tree ID, and whether u is generating
+    a new children
+    '''
+    edge_list = []
+
+    for i, root in enumerate(roots):
+        edge_list.append([])
+        # The following gives the DFS order on edge on a tree.
+        for u, v, t in nx.dfs_labeled_edges(forest, root):
+            if u == v or t == 'nontree':
+                continue
+            elif t == 'forward':
+                edge_list[-1].append((u, v, i, 1))
+            elif t == 'reverse':
+                edge_list[-1].append((v, u, i, 0))
+
+    for edges in itertools.zip_longest(*edge_list):
+        edges = (e for e in edges if e is not None)
+        u, v, i, p = zip(*edges)
+        yield u, v, i, p
+
+
+dec_tree_node_msg = DGLF.copy_edge(edge='m', out='m')
+dec_tree_node_reduce = DGLF.sum(msgs='m', out='h')
+
+
+def dec_tree_node_update(node):
+    return {'new': node['new'].clone().zero_()}
+
+
+dec_tree_edge_msg = [DGLF.copy_src(src='m', out='m'), DGLF.copy_src(src='rm', out='rm')]
+dec_tree_edge_reduce = [DGLF.sum(msgs='m', out='s'), DGLF.sum(msgs='rm', out='accum_rm')]
+
+
+class DGLJTNNDecoder(nn.Module):
+    def __init__(self, vocab, hidden_size, latent_size, embedding=None):
+        nn.Module.__init__(self)
+
+        self.hidden_size = hidden_size
+        self.vocab_size = vocab.size()
+        self.vocab = vocab
+
+        if embedding is None:
+            self.embedding = nn.Embedding(self.vocab_size, hidden_size)
+        else:
+            self.embedding = embedding
+
+        self.dec_tree_edge_update = GRUUpdate(hidden_size)
+
+        self.W = nn.Linear(latent_size + hidden_size, hidden_size)
+        self.U = nn.Linear(latent_size + 2 * hidden_size, hidden_size)
+        self.W_o = nn.Linear(hidden_size, self.vocab_size)
+        self.U_s = nn.Linear(hidden_size, 1)
+
+    @profile
+    def forward(self, mol_trees, tree_vec):
+        '''
+        The training procedure which computes the prediction loss given the
+        ground truth tree
+        '''
+        mol_tree_batch = batch(mol_trees)
+        mol_tree_batch_lg = line_graph(mol_tree_batch, no_backtracking=True)
+        n_trees = len(mol_trees)
+
+        return self.run(mol_tree_batch, mol_tree_batch_lg, n_trees, tree_vec)
+
+    @profile
+    def run(self, mol_tree_batch, mol_tree_batch_lg, n_trees, tree_vec):
+        root_ids = mol_tree_batch.node_offset[:-1]
+        n_nodes = len(mol_tree_batch.nodes)
+        edge_list = mol_tree_batch.edge_list
+        n_edges = len(edge_list)
+
+        mol_tree_batch.set_n_repr({
+            'x': self.embedding(mol_tree_batch.get_n_repr()['wid']),
+            'h': cuda(torch.zeros(n_nodes, self.hidden_size)),
+            'new': cuda(torch.ones(n_nodes).byte()),  # whether it's newly generated node
+        })
+
+        mol_tree_batch.set_e_repr({
+            's': cuda(torch.zeros(n_edges, self.hidden_size)),
+            'm': cuda(torch.zeros(n_edges, self.hidden_size)),
+            'r': cuda(torch.zeros(n_edges, self.hidden_size)),
+            'z': cuda(torch.zeros(n_edges, self.hidden_size)),
+            'src_x': cuda(torch.zeros(n_edges, self.hidden_size)),
+            'dst_x': cuda(torch.zeros(n_edges, self.hidden_size)),
+            'rm': cuda(torch.zeros(n_edges, self.hidden_size)),
+            'accum_rm': cuda(torch.zeros(n_edges, self.hidden_size)),
+        })
+
+        mol_tree_batch.update_edge(
+            #*zip(*edge_list),
+            edge_func=lambda src, dst, edge: {'src_x': src['x'], 'dst_x': dst['x']},
+            batchable=True,
+        )
+
+        # input tensors for stop prediction (p) and label prediction (q)
+        p_inputs = []
+        p_targets = []
+        q_inputs = []
+        q_targets = []
+
+        # Predict root
+        mol_tree_batch.pull(
+            root_ids,
+            dec_tree_node_msg,
+            dec_tree_node_reduce,
+            dec_tree_node_update,
+            batchable=True,
+        )
+        # Extract hidden states and store them for stop/label prediction
+        h = mol_tree_batch.get_n_repr(root_ids)['h']
+        x = mol_tree_batch.get_n_repr(root_ids)['x']
+        p_inputs.append(torch.cat([x, h, tree_vec], 1))
+        t_set = list(range(len(root_ids)))
+        q_inputs.append(torch.cat([h, tree_vec], 1))
+        q_targets.append(mol_tree_batch.get_n_repr(root_ids)['wid'])
+
+        # Traverse the tree and predict on children
+        for u, v, i, p in dfs_order(mol_tree_batch, root_ids):
+            assert set(t_set).issuperset(i)
+            ip = dict(zip(i, p))
+            # TODO: context
+            p_targets.append(cuda(torch.tensor([ip.get(_i, 0) for _i in t_set])))
+            t_set = list(i)
+            eid = mol_tree_batch.get_edge_id(u, v)
+            mol_tree_batch_lg.pull(
+                eid,
+                dec_tree_edge_msg,
+                dec_tree_edge_reduce,
+                self.dec_tree_edge_update,
+                batchable=True,
+            )
+            is_new = mol_tree_batch.get_n_repr(v)['new']
+            mol_tree_batch.pull(
+                v,
+                dec_tree_node_msg,
+                dec_tree_node_reduce,
+                dec_tree_node_update,
+                batchable=True,
+            )
+            # Extract
+            h = mol_tree_batch.get_n_repr(v)['h']
+            x = mol_tree_batch.get_n_repr(v)['x']
+            p_inputs.append(torch.cat([x, h, tree_vec[t_set]], 1))
+            # Only newly generated nodes are needed for label prediction
+            # NOTE: The following works since the uncomputed messages are zeros.
+            q_inputs.append(torch.cat([h[is_new], tree_vec[t_set][is_new]], 1))
+            q_targets.append(mol_tree_batch.get_n_repr(v)['wid'][is_new])
+        p_targets.append(cuda(torch.tensor([0 for _ in t_set])))
+
+        # Batch compute the stop/label prediction losses
+        p_inputs = torch.cat(p_inputs, 0)
+        p_targets = torch.cat(p_targets, 0)
+        q_inputs = torch.cat(q_inputs, 0)
+        q_targets = torch.cat(q_targets, 0)
+
+        q = self.W_o(torch.relu(self.W(q_inputs)))
+        p = self.U_s(torch.relu(self.U(p_inputs)))[:, 0]
+
+        p_loss = F.binary_cross_entropy_with_logits(
+            p, p_targets.float(), size_average=False
+        ) / n_trees
+        q_loss = F.cross_entropy(q, q_targets, size_average=False) / n_trees
+        p_acc = ((p > 0).long() == p_targets).sum().float() / p_targets.shape[0]
+        q_acc = (q.max(1)[1] == q_targets).float().sum() / q_targets.shape[0]
+
+        return q_loss, p_loss, q_acc, p_acc

examples/pytorch/jtnn/jtnn/jtnn_enc.py

+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, line_graph
+import dgl.function as DGLF
+from .line_profiler_integration import profile
+
+MAX_NB = 8
+
+def level_order(forest, roots):
+    '''
+    Given the forest and the list of root nodes,
+    returns iterator of list of edges ordered by depth, first in bottom-up
+    and then top-down
+    '''
+    edge_list = []
+    node_depth = {}
+
+    edge_list.append([])
+
+    for root in roots:
+        node_depth[root] = 0
+        for u, v in nx.bfs_edges(forest, root):
+            node_depth[v] = node_depth[u] + 1
+            if len(edge_list) == node_depth[u]:
+                edge_list.append([])
+            edge_list[node_depth[u]].append((u, v))
+
+    for edges in reversed(edge_list):
+        u, v = zip(*edges)
+        yield v, u
+    for edges in edge_list:
+        u, v = zip(*edges)
+        yield u, v
+
+
+enc_tree_msg = [DGLF.copy_src(src='m', out='m'), DGLF.copy_src(src='rm', out='rm')]
+enc_tree_reduce = [DGLF.sum(msgs='m', out='s'), DGLF.sum(msgs='rm', out='accum_rm')]
+enc_tree_gather_msg = DGLF.copy_edge(edge='m', out='m')
+enc_tree_gather_reduce = DGLF.sum(msgs='m', out='m')
+
+class EncoderGatherUpdate(nn.Module):
+    def __init__(self, hidden_size):
+        nn.Module.__init__(self)
+        self.hidden_size = hidden_size
+
+        self.W = nn.Linear(2 * hidden_size, hidden_size)
+
+    def forward(self, node):
+        x = node['x']
+        m = node['m']
+        return {
+            'h': torch.relu(self.W(torch.cat([x, m], 1))),
+        }
+
+
+class DGLJTNNEncoder(nn.Module):
+    def __init__(self, vocab, hidden_size, embedding=None):
+        nn.Module.__init__(self)
+        self.hidden_size = hidden_size
+        self.vocab_size = vocab.size()
+        self.vocab = vocab
+        
+        if embedding is None:
+            self.embedding = nn.Embedding(self.vocab_size, hidden_size)
+        else:
+            self.embedding = embedding
+
+        self.enc_tree_update = GRUUpdate(hidden_size)
+        self.enc_tree_gather_update = EncoderGatherUpdate(hidden_size)
+
+    @profile
+    def forward(self, mol_trees):
+        mol_tree_batch = batch(mol_trees)
+        
+        # Build line graph to prepare for belief propagation
+        mol_tree_batch_lg = line_graph(mol_tree_batch, no_backtracking=True)
+
+        return self.run(mol_tree_batch, mol_tree_batch_lg)
+
+    @profile
+    def run(self, mol_tree_batch, mol_tree_batch_lg):
+        # Since tree roots are designated to 0.  In the batched graph we can
+        # simply find the corresponding node ID by looking at node_offset
+        root_ids = mol_tree_batch.node_offset[:-1]
+        n_nodes = len(mol_tree_batch.nodes)
+        edge_list = mol_tree_batch.edge_list
+        n_edges = len(edge_list)
+
+        # Assign structure embeddings to tree nodes
+        mol_tree_batch.set_n_repr({
+            'x': self.embedding(mol_tree_batch.get_n_repr()['wid']),
+            'h': cuda(torch.zeros(n_nodes, self.hidden_size)),
+        })
+
+        # Initialize the intermediate variables according to Eq (4)-(8).
+        # Also initialize the src_x and dst_x fields.
+        # TODO: context?
+        mol_tree_batch.set_e_repr({
+            's': cuda(torch.zeros(n_edges, self.hidden_size)),
+            'm': cuda(torch.zeros(n_edges, self.hidden_size)),
+            'r': cuda(torch.zeros(n_edges, self.hidden_size)),
+            'z': cuda(torch.zeros(n_edges, self.hidden_size)),
+            'src_x': cuda(torch.zeros(n_edges, self.hidden_size)),
+            'dst_x': cuda(torch.zeros(n_edges, self.hidden_size)),
+            'rm': cuda(torch.zeros(n_edges, self.hidden_size)),
+            'accum_rm': cuda(torch.zeros(n_edges, self.hidden_size)),
+        })
+
+        # Send the source/destination node features to edges
+        mol_tree_batch.update_edge(
+            #*zip(*edge_list),
+            edge_func=lambda src, dst, edge: {'src_x': src['x'], 'dst_x': dst['x']},
+            batchable=True,
+        )
+
+        # Message passing
+        # I exploited the fact that the reduce function is a sum of incoming
+        # messages, and the uncomputed messages are zero vectors.  Essentially,
+        # we can always compute s_ij as the sum of incoming m_ij, no matter
+        # if m_ij is actually computed or not.
+        for u, v in level_order(mol_tree_batch, root_ids):
+            eid = mol_tree_batch.get_edge_id(u, v)
+            mol_tree_batch_lg.pull(
+                eid,
+                enc_tree_msg,
+                enc_tree_reduce,
+                self.enc_tree_update,
+                batchable=True,
+            )
+
+        # Readout
+        mol_tree_batch.update_all(
+            enc_tree_gather_msg,
+            enc_tree_gather_reduce,
+            self.enc_tree_gather_update,
+            batchable=True,
+        )
+
+        root_vecs = mol_tree_batch.get_n_repr(root_ids)['h']
+
+        return mol_tree_batch, root_vecs

examples/pytorch/jtnn/jtnn/jtnn_vae.py

+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from .mol_tree import Vocab
+from .nnutils import create_var, cuda
+from .jtnn_enc import DGLJTNNEncoder
+from .jtnn_dec import DGLJTNNDecoder
+from .mpn import DGLMPN, mol2dgl
+from .jtmpn import DGLJTMPN
+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
+
+def dgl_set_batch_nodeID(mol_batch, vocab):
+    tot = 0
+    for mol_tree in mol_batch:
+        wid = []
+        for node in mol_tree.nodes:
+            mol_tree.nodes[node]['idx'] = tot
+            tot += 1
+            wid.append(vocab.get_index(mol_tree.nodes[node]['smiles']))
+        mol_tree.set_n_repr({'wid': cuda(torch.LongTensor(wid))})
+
+class DGLJTNNVAE(nn.Module):
+
+    def __init__(self, vocab, hidden_size, latent_size, depth):
+        super(DGLJTNNVAE, self).__init__()
+        self.vocab = vocab
+        self.hidden_size = hidden_size
+        self.latent_size = latent_size
+        self.depth = depth
+
+        self.embedding = nn.Embedding(vocab.size(), hidden_size)
+        self.mpn = DGLMPN(hidden_size, depth)
+        self.jtnn = DGLJTNNEncoder(vocab, hidden_size, self.embedding)
+        self.decoder = DGLJTNNDecoder(
+                vocab, hidden_size, latent_size // 2, self.embedding)
+        self.jtmpn = DGLJTMPN(hidden_size, depth)
+
+        self.T_mean = nn.Linear(hidden_size, latent_size // 2)
+        self.T_var = nn.Linear(hidden_size, latent_size // 2)
+        self.G_mean = nn.Linear(hidden_size, latent_size // 2)
+        self.G_var = nn.Linear(hidden_size, latent_size // 2)
+
+        self.n_nodes_total = 0
+        self.n_passes = 0
+        self.n_edges_total = 0
+        self.n_tree_nodes_total = 0
+
+    @profile
+    def encode(self, mol_batch):
+        dgl_set_batch_nodeID(mol_batch, self.vocab)
+
+        smiles_batch = [mol_tree.smiles for mol_tree in mol_batch]
+        mol_graphs = mol2dgl(smiles_batch)
+        mol_vec = self.mpn(mol_graphs)
+        # mol_batch is a junction tree
+        mol_tree_batch, tree_vec = self.jtnn(mol_batch)
+
+        self.n_nodes_total += sum(len(g.nodes) for g in mol_graphs)
+        self.n_edges_total += sum(len(g.edges) for g in mol_graphs)
+        self.n_tree_nodes_total += sum(len(t.nodes) for t in mol_batch)
+        self.n_passes += 1
+
+        return mol_tree_batch, tree_vec, mol_vec
+
+    @profile
+    def forward(self, mol_batch, beta=0, e1=None, e2=None):
+        batch_size = len(mol_batch)
+
+        mol_tree_batch, tree_vec, mol_vec = self.encode(mol_batch)
+
+        tree_mean = self.T_mean(tree_vec)
+        tree_log_var = -torch.abs(self.T_var(tree_vec))
+        mol_mean = self.G_mean(mol_vec)
+        mol_log_var = -torch.abs(self.G_var(mol_vec))
+
+        self.tree_mean = tree_mean
+        self.tree_log_var = tree_log_var
+        self.mol_mean = mol_mean
+        self.mol_log_var = mol_log_var
+
+        z_mean = torch.cat([tree_mean, mol_mean], dim=1)
+        z_log_var = torch.cat([tree_log_var, mol_log_var], dim=1)
+        kl_loss = -0.5 * torch.sum(1.0 + z_log_var - z_mean * z_mean - torch.exp(z_log_var)) / batch_size
+
+        self.z_mean = z_mean
+        self.z_log_var = z_log_var
+
+        epsilon = cuda(torch.randn(batch_size, self.latent_size // 2)) if e1 is None else e1
+        tree_vec = tree_mean + torch.exp(tree_log_var / 2) * epsilon
+        epsilon = cuda(torch.randn(batch_size, self.latent_size // 2)) if e2 is None else e2
+        mol_vec = mol_mean + torch.exp(mol_log_var / 2) * epsilon
+
+        self.tree_vec = tree_vec
+        self.mol_vec = mol_vec
+
+        word_loss, topo_loss, word_acc, topo_acc = self.decoder(mol_batch, tree_vec)
+        assm_loss, assm_acc = self.assm(mol_batch, mol_tree_batch, mol_vec)
+        stereo_loss, stereo_acc = self.stereo(mol_batch, mol_vec)
+
+        self.word_loss_v = word_loss
+        self.topo_loss_v = topo_loss
+        self.assm_loss_v = assm_loss
+        self.stereo_loss_v = stereo_loss
+
+        all_vec = torch.cat([tree_vec, mol_vec], dim=1)
+        loss = word_loss + topo_loss + assm_loss + 2 * stereo_loss + beta * kl_loss
+
+        self.all_vec = all_vec
+
+        return loss, kl_loss, word_acc, topo_acc, assm_acc, stereo_acc
+
+    @profile
+    def assm(self, mol_batch, mol_tree_batch, mol_vec):
+        cands = []
+        batch_idx = []
+
+        for i, mol_tree in enumerate(mol_batch):
+            for node_id, node in mol_tree.nodes.items():
+                if node['is_leaf'] or len(node['cands']) == 1:
+                    continue
+                cands.extend([(cand, mol_tree, node_id) for cand in node['cand_mols']])
+                batch_idx.extend([i] * len(node['cands']))
+
+        cand_vec = self.jtmpn(cands, mol_tree_batch)
+        cand_vec = self.G_mean(cand_vec)
+
+        batch_idx = cuda(torch.LongTensor(batch_idx))
+        mol_vec = mol_vec[batch_idx]
+
+        mol_vec = mol_vec.view(-1, 1, self.latent_size // 2)
+        cand_vec = cand_vec.view(-1, self.latent_size // 2, 1)
+        scores = (mol_vec @ cand_vec)[:, 0, 0]
+
+        cnt, tot, acc = 0, 0, 0
+        all_loss = []
+        for i, mol_tree in enumerate(mol_batch):
+            comp_nodes = [node_id for node_id, node in mol_tree.nodes.items()
+                          if len(node['cands']) > 1 and not node['is_leaf']]
+            cnt += len(comp_nodes)
+            # segmented accuracy and cross entropy
+            for node_id in comp_nodes:
+                node = mol_tree.nodes[node_id]
+                label = node['cands'].index(node['label'])
+                ncand = len(node['cands'])
+                cur_score = scores[tot:tot+ncand]
+                tot += ncand
+
+                if cur_score[label].item() >= cur_score.max().item():
+                    acc += 1
+
+                label = cuda(torch.LongTensor([label]))
+                all_loss.append(
+                    F.cross_entropy(cur_score.view(1, -1), label, size_average=False))
+
+        all_loss = sum(all_loss) / len(mol_batch)
+        return all_loss, acc / cnt
+
+    @profile
+    def stereo(self, mol_batch, mol_vec):
+        stereo_cands, batch_idx = [], []
+        labels = []
+        for i, mol_tree in enumerate(mol_batch):
+            cands = mol_tree.stereo_cands
+            if len(cands) == 1:
+                continue
+            if mol_tree.smiles3D not in cands:
+                cands.append(mol_tree.smiles3D)
+            stereo_cands.extend(cands)
+            batch_idx.extend([i] * len(cands))
+            labels.append((cands.index(mol_tree.smiles3D), len(cands)))
+
+        if len(labels) == 0:
+            # Only one stereoisomer exists; do nothing
+            return cuda(torch.tensor(0.)), 1.
+
+        batch_idx = cuda(torch.LongTensor(batch_idx))
+        stereo_cands = self.mpn(mol2dgl(stereo_cands))
+        stereo_cands = self.G_mean(stereo_cands)
+        stereo_labels = mol_vec[batch_idx]
+        scores = F.cosine_similarity(stereo_cands, stereo_labels)
+
+        st, acc = 0, 0
+        all_loss = []
+        for label, le in labels:
+            cur_scores = scores[st:st+le]
+            if cur_scores.data[label].item() >= cur_scores.max().item():
+                acc += 1
+            label = cuda(torch.LongTensor([label]))
+            all_loss.append(
+                F.cross_entropy(cur_scores.view(1, -1), label, size_average=False))
+            st += le
+
+        all_loss = sum(all_loss) / len(labels)
+        return all_loss, acc / len(labels)

examples/pytorch/jtnn/jtnn/line_profiler_integration.py

+'''
+line_profiler integration
+'''
+import os
+
+if os.getenv('PROFILE', 0):
+    import line_profiler
+    import atexit
+    profile = line_profiler.LineProfiler()
+
+    profile_output = os.getenv('PROFILE_OUTPUT', None)
+    if profile_output:
+        from functools import partial
+        atexit.register(partial(profile.dump_stats, profile_output))
+    else:
+        atexit.register(profile.print_stats)
+else:
+    def profile(f):
+        return f

examples/pytorch/jtnn/jtnn/mol_tree.py

+import rdkit
+import rdkit.Chem as Chem
+import copy
+
+def get_slots(smiles):
+    mol = Chem.MolFromSmiles(smiles)
+    return [(atom.GetSymbol(), atom.GetFormalCharge(), atom.GetTotalNumHs()) for atom in mol.GetAtoms()]
+
+class Vocab(object):
+
+    def __init__(self, smiles_list):
+        self.vocab = smiles_list
+        self.vmap = {x:i for i,x in enumerate(self.vocab)}
+        self.slots = [get_slots(smiles) for smiles in self.vocab]
+        
+    def get_index(self, smiles):
+        return self.vmap[smiles]
+
+    def get_smiles(self, idx):
+        return self.vocab[idx]
+
+    def get_slots(self, idx):
+        return copy.deepcopy(self.slots[idx])
+
+    def size(self):
+        return len(self.vocab)

examples/pytorch/jtnn/jtnn/mol_tree_nx.py

+from dgl import DGLGraph
+import rdkit.Chem as Chem
+from .chemutils import get_clique_mol, tree_decomp, get_mol, get_smiles, \
+                       set_atommap, enum_assemble_nx, decode_stereo
+
+class DGLMolTree(DGLGraph):
+    def __init__(self, smiles):
+        DGLGraph.__init__(self)
+        self.smiles = smiles
+        self.mol = get_mol(smiles)
+
+        # Stereo Generation
+        mol = Chem.MolFromSmiles(smiles)
+        self.smiles3D = Chem.MolToSmiles(mol, isomericSmiles=True)
+        self.smiles2D = Chem.MolToSmiles(mol)
+        self.stereo_cands = decode_stereo(self.smiles2D)
+
+        # cliques: a list of list of atom indices
+        cliques, edges = tree_decomp(self.mol)
+        root = 0
+        for i, c in enumerate(cliques):
+            cmol = get_clique_mol(self.mol, c)
+            csmiles = get_smiles(cmol)
+            self.add_node(
+                    i,
+                    smiles=csmiles,
+                    mol=get_mol(csmiles),
+                    clique=c,
+                    )
+            if min(c) == 0:
+                root = i
+
+        # The clique with atom ID 0 becomes root
+        if root > 0:
+            for attr in self.nodes[0]:
+                self.nodes[0][attr], self.nodes[root][attr] = \
+                        self.nodes[root][attr], self.nodes[0][attr]
+
+        for _x, _y in edges:
+            x = 0 if _x == root else root if _x == 0 else _x
+            y = 0 if _y == root else root if _y == 0 else _y
+            self.add_edge(x, y)
+            self.add_edge(y, x)
+
+        for i in self.nodes:
+            self.nodes[i]['nid'] = i + 1
+            if len(self[i]) > 1:    # Leaf node mol is not marked
+                set_atommap(self.nodes[i]['mol'], self.nodes[i]['nid'])
+            self.nodes[i]['is_leaf'] = (len(self[i]) == 1)
+
+    # avoiding DiGraph.size()
+    def treesize(self):
+        return len(self.nodes)
+
+    def _recover_node(self, i, original_mol):
+        node = self.nodes[i]
+
+        clique = []
+        clique.extend(node['clique'])
+        if not node['is_leaf']:
+            for cidx in node['clique']:
+                original_mol.GetAtomWithIdx(cidx).SetAtomMapNum(node['nid'])
+
+        for j in self[i]:
+            nei_node = self.nodes[j]
+            clique.extend(nei_node['clique'])
+            if nei_node['is_leaf']: # Leaf node, no need to mark
+                continue
+            for cidx in nei_node['clique']:
+                # allow singleton node override the atom mapping
+                if cidx not in node['clique'] or len(nei_node['clique']) == 1:
+                    atom = original_mol.GetAtomWithIdx(cidx)
+                    atom.SetAtomMapNum(nei_node['nid'])
+
+        clique = list(set(clique))
+        label_mol = get_clique_mol(original_mol, clique)
+        node['label'] = Chem.MolToSmiles(Chem.MolFromSmiles(get_smiles(label_mol)))
+        node['label_mol'] = get_mol(node['label'])
+
+        for cidx in clique:
+            original_mol.GetAtomWithIdx(cidx).SetAtomMapNum(0)
+
+        return node['label']
+
+    def _assemble_node(self, i):
+        neighbors = [self.nodes[j] for j in self[i] if self.nodes[j]['mol'].GetNumAtoms() > 1]
+        neighbors = sorted(neighbors, key=lambda x: x['mol'].GetNumAtoms(), reverse=True)
+        singletons = [self.nodes[j] for j in self[i] if self.nodes[j]['mol'].GetNumAtoms() == 1]
+        neighbors = singletons + neighbors
+
+        cands = enum_assemble_nx(self, i, neighbors)
+
+        if len(cands) > 0:
+            self.nodes[i]['cands'], self.nodes[i]['cand_mols'], _ = list(zip(*cands))
+            self.nodes[i]['cands'] = list(self.nodes[i]['cands'])
+            self.nodes[i]['cand_mols'] = list(self.nodes[i]['cand_mols'])
+        else:
+            self.nodes[i]['cands'] = []
+            self.nodes[i]['cand_mols'] = []
+
+    def recover(self):
+        for i in self.nodes:
+            self._recover_node(i, self.mol)
+
+    def assemble(self):
+        for i in self.nodes:
+            self._assemble_node(i)

examples/pytorch/jtnn/jtnn/mpn.py

+import torch
+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, line_graph, convert_node_labels_to_integers
+from dgl import DGLGraph, line_graph, batch, unbatch
+import dgl.function as DGLF
+from functools import partial
+from .line_profiler_integration import profile
+
+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
+MAX_NB = 6
+
+def onek_encoding_unk(x, allowable_set):
+    if x not in allowable_set:
+        x = allowable_set[-1]
+    return [x == s for s in allowable_set]
+
+def atom_features(atom):
+    return cuda(torch.Tensor(onek_encoding_unk(atom.GetSymbol(), ELEM_LIST) 
+            + onek_encoding_unk(atom.GetDegree(), [0,1,2,3,4,5]) 
+            + onek_encoding_unk(atom.GetFormalCharge(), [-1,-2,1,2,0])
+            + onek_encoding_unk(int(atom.GetChiralTag()), [0,1,2,3])
+            + [atom.GetIsAromatic()]))
+
+def bond_features(bond):
+    bt = bond.GetBondType()
+    stereo = int(bond.GetStereo())
+    fbond = [bt == Chem.rdchem.BondType.SINGLE, bt == Chem.rdchem.BondType.DOUBLE, bt == Chem.rdchem.BondType.TRIPLE, bt == Chem.rdchem.BondType.AROMATIC, bond.IsInRing()]
+    fstereo = onek_encoding_unk(stereo, [0,1,2,3,4,5])
+    return cuda(torch.Tensor(fbond + fstereo))
+
+@profile
+def mol2dgl(smiles_batch):
+    n_nodes = 0
+    graph_list = []
+    for smiles in smiles_batch:
+        atom_feature_list = []
+        bond_feature_list = []
+        bond_source_feature_list = []
+        graph = DGLGraph()
+        mol = get_mol(smiles)
+        for atom in mol.GetAtoms():
+            graph.add_node(atom.GetIdx())
+            atom_feature_list.append(atom_features(atom))
+        for bond in mol.GetBonds():
+            begin_idx = bond.GetBeginAtom().GetIdx()
+            end_idx = bond.GetEndAtom().GetIdx()
+            features = bond_features(bond)
+            graph.add_edge(begin_idx, end_idx)
+            bond_feature_list.append(features)
+            # set up the reverse direction
+            graph.add_edge(end_idx, begin_idx)
+            bond_feature_list.append(features)
+
+        atom_x = torch.stack(atom_feature_list)
+        graph.set_n_repr({'x': atom_x})
+        if len(bond_feature_list) > 0:
+            bond_x = torch.stack(bond_feature_list)
+            graph.set_e_repr({
+                'x': bond_x,
+                'src_x': atom_x.new(len(bond_feature_list), ATOM_FDIM).zero_()
+            })
+        graph_list.append(graph)
+
+    return graph_list
+
+
+mpn_loopy_bp_msg = DGLF.copy_src(src='msg', out='msg')
+mpn_loopy_bp_reduce = DGLF.sum(msgs='msg', out='accum_msg')
+
+
+class LoopyBPUpdate(nn.Module):
+    def __init__(self, hidden_size):
+        super(LoopyBPUpdate, self).__init__()
+        self.hidden_size = hidden_size
+
+        self.W_h = nn.Linear(hidden_size, hidden_size, bias=False)
+
+    def forward(self, node):
+        msg_input = node['msg_input']
+        msg_delta = self.W_h(node['accum_msg'])
+        msg = F.relu(msg_input + msg_delta)
+        return {'msg': msg}
+
+
+mpn_gather_msg = DGLF.copy_edge(edge='msg', out='msg')
+mpn_gather_reduce = DGLF.sum(msgs='msg', out='m')
+
+
+class GatherUpdate(nn.Module):
+    def __init__(self, hidden_size):
+        super(GatherUpdate, self).__init__()
+        self.hidden_size = hidden_size
+
+        self.W_o = nn.Linear(ATOM_FDIM + hidden_size, hidden_size)
+
+    def forward(self, node):
+        m = node['m']
+        return {
+            'h': F.relu(self.W_o(torch.cat([node['x'], m], 1))),
+        }
+
+
+class DGLMPN(nn.Module):
+    def __init__(self, hidden_size, depth):
+        super(DGLMPN, self).__init__()
+
+        self.depth = depth
+
+        self.W_i = nn.Linear(ATOM_FDIM + BOND_FDIM, hidden_size, bias=False)
+
+        self.loopy_bp_updater = LoopyBPUpdate(hidden_size)
+        self.gather_updater = GatherUpdate(hidden_size)
+        self.hidden_size = hidden_size
+
+        self.n_samples_total = 0
+        self.n_nodes_total = 0
+        self.n_edges_total = 0
+        self.n_passes = 0
+
+    @profile
+    def forward(self, mol_graph_list):
+        n_samples = len(mol_graph_list)
+
+        mol_graph = batch(mol_graph_list)
+        mol_line_graph = line_graph(mol_graph, no_backtracking=True)
+
+        n_nodes = len(mol_graph.nodes)
+        n_edges = len(mol_graph.edges)
+
+        mol_graph = self.run(mol_graph, mol_line_graph)
+        mol_graph_list = unbatch(mol_graph)
+        g_repr = torch.stack([g.get_n_repr()['h'].mean(0) for g in mol_graph_list], 0)
+
+        self.n_samples_total += n_samples
+        self.n_nodes_total += n_nodes
+        self.n_edges_total += n_edges
+        self.n_passes += 1
+
+        return g_repr
+
+    @profile
+    def run(self, mol_graph, mol_line_graph):
+        n_nodes = len(mol_graph.nodes)
+
+        mol_graph.update_edge(
+            #*zip(*mol_graph.edge_list),
+            edge_func=lambda src, dst, edge: {'src_x': src['x']},
+            batchable=True,
+        )
+
+        bond_features = mol_line_graph.get_n_repr()['x']
+        source_features = mol_line_graph.get_n_repr()['src_x']
+
+        features = torch.cat([source_features, bond_features], 1)
+        msg_input = self.W_i(features)
+        mol_line_graph.set_n_repr({
+            'msg_input': msg_input,
+            'msg': F.relu(msg_input),
+            'accum_msg': torch.zeros_like(msg_input),
+        })
+        mol_graph.set_n_repr({
+            'm': bond_features.new(n_nodes, self.hidden_size).zero_(),
+            'h': bond_features.new(n_nodes, self.hidden_size).zero_(),
+        })
+
+        for i in range(self.depth - 1):
+            mol_line_graph.update_all(
+                mpn_loopy_bp_msg,
+                mpn_loopy_bp_reduce,
+                self.loopy_bp_updater,
+                True
+            )
+
+        mol_graph.update_all(
+            mpn_gather_msg,
+            mpn_gather_reduce,
+            self.gather_updater,
+            True
+        )
+
+        return mol_graph

examples/pytorch/jtnn/jtnn/nnutils.py

+import torch
+import torch.nn as nn
+from torch.autograd import Variable
+
+def create_var(tensor, requires_grad=None):
+    if requires_grad is None:
+        return Variable(tensor)
+    else:
+        return Variable(tensor, requires_grad=requires_grad)
+
+def cuda(tensor):
+    if torch.cuda.is_available():
+        return tensor.cuda()
+    else:
+        return tensor
+
+
+class GRUUpdate(nn.Module):
+    def __init__(self, hidden_size):
+        nn.Module.__init__(self)
+        self.hidden_size = hidden_size
+
+        self.W_z = nn.Linear(2 * hidden_size, hidden_size)
+        self.W_r = nn.Linear(hidden_size, hidden_size, bias=False)
+        self.U_r = nn.Linear(hidden_size, hidden_size)
+        self.W_h = nn.Linear(2 * hidden_size, hidden_size)
+
+    def forward(self, node):
+        src_x = node['src_x']
+        dst_x = node['dst_x']
+        s = node['s']
+        rm = node['accum_rm']
+        z = torch.sigmoid(self.W_z(torch.cat([src_x, s], 1)))
+        m = torch.tanh(self.W_h(torch.cat([src_x, rm], 1)))
+        m = (1 - z) * s + z * m
+        r_1 = self.W_r(dst_x)
+        r_2 = self.U_r(m)
+        r = torch.sigmoid(r_1 + r_2)
+
+        return {'m': m, 'r': r, 'z': z, 'rm': r * m}
+

examples/pytorch/jtnn/vaetrain_dgl.py

+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torch.optim.lr_scheduler as lr_scheduler
+from torch.utils.data import DataLoader
+
+import math, random, sys
+from optparse import OptionParser
+from collections import deque
+import rdkit
+
+from jtnn import *
+
+lg = rdkit.RDLogger.logger() 
+lg.setLevel(rdkit.RDLogger.CRITICAL)
+
+parser = OptionParser()
+parser.add_option("-t", "--train", dest="train", default='train', help='Training file name')
+parser.add_option("-v", "--vocab", dest="vocab", default='vocab', help='Vocab file name')
+parser.add_option("-s", "--save_dir", dest="save_path")
+parser.add_option("-m", "--model", dest="model_path", default=None)
+parser.add_option("-b", "--batch", dest="batch_size", default=40)
+parser.add_option("-w", "--hidden", dest="hidden_size", default=200)
+parser.add_option("-l", "--latent", dest="latent_size", default=56)
+parser.add_option("-d", "--depth", dest="depth", default=3)
+parser.add_option("-z", "--beta", dest="beta", default=1.0)
+parser.add_option("-q", "--lr", dest="lr", default=1e-3)
+opts,args = parser.parse_args()
+
+dataset = JTNNDataset(data=opts.train, vocab=opts.vocab)
+vocab = Vocab([x.strip("\r\n ") for x in open(dataset.vocab_file)])
+
+batch_size = int(opts.batch_size)
+hidden_size = int(opts.hidden_size)
+latent_size = int(opts.latent_size)
+depth = int(opts.depth)
+beta = float(opts.beta)
+lr = float(opts.lr)
+
+model = DGLJTNNVAE(vocab, hidden_size, latent_size, depth)
+
+if opts.model_path is not None:
+    model.load_state_dict(torch.load(opts.model_path))
+else:
+    for param in model.parameters():
+        if param.dim() == 1:
+            nn.init.constant(param, 0)
+        else:
+            nn.init.xavier_normal(param)
+
+if torch.cuda.is_available():
+    model = model.cuda()
+print("Model #Params: %dK" % (sum([x.nelement() for x in model.parameters()]) / 1000,))
+
+optimizer = optim.Adam(model.parameters(), lr=lr)
+scheduler = lr_scheduler.ExponentialLR(optimizer, 0.9)
+scheduler.step()
+
+MAX_EPOCH = 1
+PRINT_ITER = 20
+
+@profile
+def train():
+    dataloader = DataLoader(
+            dataset,
+            batch_size=batch_size,
+            shuffle=True,
+            num_workers=0,
+            collate_fn=lambda x:x,
+            drop_last=True)
+
+    for epoch in range(MAX_EPOCH):
+        word_acc,topo_acc,assm_acc,steo_acc = 0,0,0,0
+
+        for it, batch in enumerate(dataloader):
+            for mol_tree in batch:
+                for node_id, node in mol_tree.nodes.items():
+                    if node['label'] not in node['cands']:
+                        node['cands'].append(node['label'])
+                        node['cand_mols'].append(node['label_mol'])
+
+            model.zero_grad()
+            loss, kl_div, wacc, tacc, sacc, dacc = model(batch, beta)
+            loss.backward()
+            optimizer.step()
+
+            word_acc += wacc
+            topo_acc += tacc
+            assm_acc += sacc
+            steo_acc += dacc
+
+            if (it + 1) % PRINT_ITER == 0:
+                word_acc = word_acc / PRINT_ITER * 100
+                topo_acc = topo_acc / PRINT_ITER * 100
+                assm_acc = assm_acc / PRINT_ITER * 100
+                steo_acc = steo_acc / PRINT_ITER * 100
+
+                print("KL: %.1f, Word: %.2f, Topo: %.2f, Assm: %.2f, Steo: %.2f" % (
+                    kl_div, word_acc, topo_acc, assm_acc, steo_acc))
+                word_acc,topo_acc,assm_acc,steo_acc = 0,0,0,0
+                sys.stdout.flush()
+
+            if (it + 1) % 1500 == 0: #Fast annealing
+                scheduler.step()
+                print("learning rate: %.6f" % scheduler.get_lr()[0])
+                torch.save(model.state_dict(),
+                           opts.save_path + "/model.iter-%d-%d" % (epoch, it + 1))
+
+        scheduler.step()
+        print("learning rate: %.6f" % scheduler.get_lr()[0])
+        torch.save(model.state_dict(), opts.save_path + "/model.iter-" + str(epoch))
+
+if __name__ == '__main__':
+    train()
+
+    print('# passes:', model.n_passes)
+    print('Total # nodes processed:', model.n_nodes_total)
+    print('Total # edges processed:', model.n_edges_total)
+    print('Total # tree nodes processed:', model.n_tree_nodes_total)
+    print('Graph decoder: # passes:', model.jtmpn.n_passes)
+    print('Graph decoder: Total # candidates processed:', model.jtmpn.n_samples_total)
+    print('Graph decoder: Total # nodes processed:', model.jtmpn.n_nodes_total)
+    print('Graph decoder: Total # edges processed:', model.jtmpn.n_edges_total)
+    print('Graph encoder: # passes:', model.mpn.n_passes)
+    print('Graph encoder: Total # candidates processed:', model.mpn.n_samples_total)
+    print('Graph encoder: Total # nodes processed:', model.mpn.n_nodes_total)
+    print('Graph encoder: Total # edges processed:', model.mpn.n_edges_total)

python/dgl/data/utils.py

 """Dataset utilities."""
+from __future__ import absolute_import
 
-import os
+import os, sys
 import hashlib
 import warnings
 import zipfile
@@ -125,17 +126,22 @@ def extract_archive(file, target_dir):
     target_dir : str
         Target directory of the archive to be uncompressed
     """
+    if os.path.exists(target_dir):
+        return
     if file.endswith('.gz') or file.endswith('.tar') or file.endswith('.tgz'):
         archive = tarfile.open(file, 'r')
     elif file.endswith('.zip'):
         archive = zipfile.ZipFile(file, 'r')
     else:
         raise Exception('Unrecognized file type: ' + file)
+    print('Extracting file to {}'.format(target_dir))
     archive.extractall(path=target_dir)
     archive.close()
 
 def get_download_dir():
-    dirname = '_download'
+    """Get the absolute path to the download directory."""
+    curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
+    dirname = os.path.join(curr_path, '../../../_download')
     if not os.path.exists(dirname):
         os.makedirs(dirname)
     return dirname

tests/pytorch/test_batched_graph.py

@@ -143,25 +143,17 @@ def test_batched_edge_ordering():
     assert th.equal(r1, r2)
 
 def test_batch_no_edge():
-    # FIXME: current impl cannot handle this case!!!
-    #        comment out for now to test CI
-    return
-    """
     g1 = dgl.DGLGraph()
     g1.add_nodes(6)
     g1.add_edges([4, 4, 2, 2, 0], [5, 3, 3, 1, 1])
     e1 = th.randn(5, 10)
-    g1.set_e_repr(e1)
     g2 = dgl.DGLGraph()
     g2.add_nodes(6)
     g2.add_edges([0, 1, 2, 5, 4, 5], [1 ,2 ,3, 4, 3, 0])
     e2 = th.randn(6, 10)
-    g2.set_e_repr(e2)
     g3 = dgl.DGLGraph()
     g3.add_nodes(1)  # no edges
-
     g = dgl.batch([g1, g3, g2]) # should not throw an error
-    """
 
 if __name__ == '__main__':
     test_batch_unbatch()