###################################################################################### # Copyright (c) muhanzhang, D-VAE, NeurIPS 2019 [GitHub D-VAE] # Modified by Hayeon Lee, Eunyoung Hyung, MetaD2A, ICLR2021, 2021. 03 [GitHub MetaD2A] ###################################################################################### import torch from torch import nn from set_encoder.setenc_models import SetPool class MetaSurrogateUnnoisedModel(nn.Module): def __init__(self, args, graph_config): super(MetaSurrogateUnnoisedModel, self).__init__() self.max_n = graph_config['max_n'] # maximum number of vertices self.nvt = args.nvt # number of vertex types self.START_TYPE = graph_config['START_TYPE'] self.END_TYPE = graph_config['END_TYPE'] self.hs = args.hs # hidden state size of each vertex self.nz = args.nz # size of latent representation z self.gs = args.hs # size of graph state self.bidir = True # whether to use bidirectional encoding self.vid = True self.device = None self.input_type = 'DG' self.num_sample = args.num_sample if self.vid: self.vs = self.hs + self.max_n # vertex state size = hidden state + vid else: self.vs = self.hs # 0. encoding-related self.grue_forward = nn.GRUCell(self.nvt, self.hs) # encoder GRU self.grue_backward = nn.GRUCell( self.nvt, self.hs) # backward encoder GRU self.fc1 = nn.Linear(self.gs, self.nz) # latent mean self.fc2 = nn.Linear(self.gs, self.nz) # latent logvar # 2. gate-related self.gate_forward = nn.Sequential( nn.Linear(self.vs, self.hs), nn.Sigmoid() ) self.gate_backward = nn.Sequential( nn.Linear(self.vs, self.hs), nn.Sigmoid() ) self.mapper_forward = nn.Sequential( nn.Linear(self.vs, self.hs, bias=False), ) # disable bias to ensure padded zeros also mapped to zeros self.mapper_backward = nn.Sequential( nn.Linear(self.vs, self.hs, bias=False), ) # 3. bidir-related, to unify sizes if self.bidir: self.hv_unify = nn.Sequential( nn.Linear(self.hs * 2, self.hs), ) self.hg_unify = nn.Sequential( nn.Linear(self.gs * 2, self.gs), ) # 4. other self.relu = nn.ReLU() self.sigmoid = nn.Sigmoid() self.tanh = nn.Tanh() self.logsoftmax1 = nn.LogSoftmax(1) # 6. predictor np = self.gs self.intra_setpool = SetPool(dim_input=512, num_outputs=1, dim_output=self.nz, dim_hidden=self.nz, mode='sabPF') self.inter_setpool = SetPool(dim_input=self.nz, num_outputs=1, dim_output=self.nz, dim_hidden=self.nz, mode='sabPF') self.set_fc = nn.Sequential( nn.Linear(512, self.nz), nn.ReLU()) input_dim = 0 if 'D' in self.input_type: input_dim += self.nz if 'G' in self.input_type: input_dim += self.nz self.pred_fc = nn.Sequential( nn.Linear(input_dim, self.hs), nn.Tanh(), nn.Linear(self.hs, 1) ) self.mseloss = nn.MSELoss(reduction='sum') def predict(self, D_mu, G_mu): input_vec = [] if 'D' in self.input_type: input_vec.append(D_mu) if 'G' in self.input_type: input_vec.append(G_mu) input_vec = torch.cat(input_vec, dim=1) return self.pred_fc(input_vec) def get_device(self): if self.device is None: self.device = next(self.parameters()).device return self.device def _get_zeros(self, n, length): # get a zero hidden state return torch.zeros(n, length).to(self.get_device()) def _get_zero_hidden(self, n=1): return self._get_zeros(n, self.hs) # get a zero hidden state def _one_hot(self, idx, length): if type(idx) in [list, range]: if idx == []: return None idx = torch.LongTensor(idx).unsqueeze(0).t() x = torch.zeros((len(idx), length)).scatter_( 1, idx, 1).to(self.get_device()) else: idx = torch.LongTensor([idx]).unsqueeze(0) x = torch.zeros((1, length)).scatter_( 1, idx, 1).to(self.get_device()) return x def _gated(self, h, gate, mapper): return gate(h) * mapper(h) def _collate_fn(self, G): return [g.copy() for g in G] def _propagate_to(self, G, v, propagator, H=None, reverse=False, gate=None, mapper=None): # propagate messages to vertex index v for all graphs in G # return the new messages (states) at v G = [g for g in G if g.vcount() > v] if len(G) == 0: return if H is not None: idx = [i for i, g in enumerate(G) if g.vcount() > v] H = H[idx] v_types = [g.vs[v]['type'] for g in G] X = self._one_hot(v_types, self.nvt) if reverse: H_name = 'H_backward' # name of the hidden states attribute H_pred = [[g.vs[x][H_name] for x in g.successors(v)] for g in G] if self.vid: vids = [self._one_hot(g.successors(v), self.max_n) for g in G] gate, mapper = self.gate_backward, self.mapper_backward else: H_name = 'H_forward' # name of the hidden states attribute H_pred = [[g.vs[x][H_name] for x in g.predecessors(v)] for g in G] if self.vid: vids = [self._one_hot(g.predecessors(v), self.max_n) for g in G] if gate is None: gate, mapper = self.gate_forward, self.mapper_forward if self.vid: H_pred = [[torch.cat([x[i], y[i:i + 1]], 1) for i in range(len(x))] for x, y in zip(H_pred, vids)] # if h is not provided, use gated sum of v's predecessors' states as the input hidden state if H is None: # maximum number of predecessors max_n_pred = max([len(x) for x in H_pred]) if max_n_pred == 0: H = self._get_zero_hidden(len(G)) else: H_pred = [torch.cat(h_pred + [self._get_zeros(max_n_pred - len(h_pred), self.vs)], 0).unsqueeze(0) for h_pred in H_pred] # pad all to same length H_pred = torch.cat(H_pred, 0) # batch * max_n_pred * vs H = self._gated(H_pred, gate, mapper).sum(1) # batch * hs Hv = propagator(X, H) for i, g in enumerate(G): g.vs[v][H_name] = Hv[i:i + 1] return Hv def _propagate_from(self, G, v, propagator, H0=None, reverse=False): # perform a series of propagation_to steps starting from v following a topo order # assume the original vertex indices are in a topological order if reverse: prop_order = range(v, -1, -1) else: prop_order = range(v, self.max_n) Hv = self._propagate_to(G, v, propagator, H0, reverse=reverse) # the initial vertex for v_ in prop_order[1:]: self._propagate_to(G, v_, propagator, reverse=reverse) return Hv def _get_graph_state(self, G, decode=False): # get the graph states # when decoding, use the last generated vertex's state as the graph state # when encoding, use the ending vertex state or unify the starting and ending vertex states Hg = [] for g in G: hg = g.vs[g.vcount() - 1]['H_forward'] if self.bidir and not decode: # decoding never uses backward propagation hg_b = g.vs[0]['H_backward'] hg = torch.cat([hg, hg_b], 1) Hg.append(hg) Hg = torch.cat(Hg, 0) if self.bidir and not decode: Hg = self.hg_unify(Hg) return Hg def set_encode(self, X): proto_batch = [] for x in X: cls_protos = self.intra_setpool( x.view(-1, self.num_sample, 512)).squeeze(1) proto_batch.append( self.inter_setpool(cls_protos.unsqueeze(0))) v = torch.stack(proto_batch).squeeze() return v def graph_encode(self, G): # encode graphs G into latent vectors if type(G) != list: G = [G] self._propagate_from(G, 0, self.grue_forward, H0=self._get_zero_hidden(len(G)), reverse=False) if self.bidir: self._propagate_from(G, self.max_n - 1, self.grue_backward, H0=self._get_zero_hidden(len(G)), reverse=True) Hg = self._get_graph_state(G) mu = self.fc1(Hg) # logvar = self.fc2(Hg) return mu # , logvar def reparameterize(self, mu, logvar, eps_scale=0.01): # return z ~ N(mu, std) if self.training: std = logvar.mul(0.5).exp_() eps = torch.randn_like(std) * eps_scale return eps.mul(std).add_(mu) else: return mu