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models.py
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models.py
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import torch
from torch import nn
import torch.nn.functional as F
import numpy as np
from PCFG import PCFG
from random import shuffle
class ResidualLayer(nn.Module):
def __init__(self, in_dim = 100,
out_dim = 100):
super(ResidualLayer, self).__init__()
self.lin1 = nn.Linear(in_dim, out_dim)
self.lin2 = nn.Linear(out_dim, out_dim)
def forward(self, x):
return F.relu(self.lin2(F.relu(self.lin1(x)))) + x
class CompPCFG(nn.Module):
def __init__(self, vocab = 100,
h_dim = 512,
w_dim = 512,
z_dim = 64,
state_dim = 256,
t_states = 10,
nt_states = 10):
super(CompPCFG, self).__init__()
self.state_dim = state_dim
self.t_emb = nn.Parameter(torch.randn(t_states, state_dim))
self.nt_emb = nn.Parameter(torch.randn(nt_states, state_dim))
self.root_emb = nn.Parameter(torch.randn(1, state_dim))
self.pcfg = PCFG(nt_states, t_states)
self.nt_states = nt_states
self.t_states = t_states
self.all_states = nt_states + t_states
self.dim = state_dim
self.register_parameter('t_emb', self.t_emb)
self.register_parameter('nt_emb', self.nt_emb)
self.register_parameter('root_emb', self.root_emb)
self.rule_mlp = nn.Linear(state_dim+z_dim, self.all_states**2)
self.root_mlp = nn.Sequential(nn.Linear(z_dim + state_dim, state_dim),
ResidualLayer(state_dim, state_dim),
ResidualLayer(state_dim, state_dim),
nn.Linear(state_dim, self.nt_states))
if z_dim > 0:
self.enc_emb = nn.Embedding(vocab, w_dim)
self.enc_rnn = nn.LSTM(w_dim, h_dim, bidirectional=True, num_layers = 1, batch_first = True)
self.enc_params = nn.Linear(h_dim*2, z_dim*2)
self.z_dim = z_dim
self.vocab_mlp = nn.Sequential(nn.Linear(z_dim + state_dim, state_dim),
ResidualLayer(state_dim, state_dim),
ResidualLayer(state_dim, state_dim),
nn.Linear(state_dim, vocab))
def enc(self, x):
emb = self.enc_emb(x)
h, _ = self.enc_rnn(emb)
params = self.enc_params(h.max(1)[0])
mean = params[:, :self.z_dim]
logvar = params[:, self.z_dim:]
return mean, logvar
def kl(self, mean, logvar):
result = -0.5 * (logvar - torch.pow(mean, 2)- torch.exp(logvar) + 1)
return result
def forward(self, x, argmax=False, use_mean=False):
#x : batch x n
n = x.size(1)
batch_size = x.size(0)
if self.z_dim > 0:
mean, logvar = self.enc(x)
kl = self.kl(mean, logvar).sum(1)
z = mean.new(batch_size, mean.size(1)).normal_(0, 1)
z = (0.5*logvar).exp()*z + mean
kl = self.kl(mean, logvar).sum(1)
if use_mean:
z = mean
self.z = z
else:
self.z = torch.zeros(batch_size, 1).cuda()
t_emb = self.t_emb
nt_emb = self.nt_emb
root_emb = self.root_emb
root_emb = root_emb.expand(batch_size, self.state_dim)
t_emb = t_emb.unsqueeze(0).unsqueeze(1).expand(batch_size, n, self.t_states, self.state_dim)
nt_emb = nt_emb.unsqueeze(0).expand(batch_size, self.nt_states, self.state_dim)
if self.z_dim > 0:
root_emb = torch.cat([root_emb, z], 1)
z_expand = z.unsqueeze(1).expand(batch_size, n, self.z_dim)
z_expand = z_expand.unsqueeze(2).expand(batch_size, n, self.t_states, self.z_dim)
t_emb = torch.cat([t_emb, z_expand], 3)
nt_emb = torch.cat([nt_emb, z.unsqueeze(1).expand(batch_size, self.nt_states,
self.z_dim)], 2)
root_scores = F.log_softmax(self.root_mlp(root_emb), 1)
unary_scores = F.log_softmax(self.vocab_mlp(t_emb), 3)
x_expand = x.unsqueeze(2).expand(batch_size, x.size(1), self.t_states).unsqueeze(3)
unary = torch.gather(unary_scores, 3, x_expand).squeeze(3)
rule_score = F.log_softmax(self.rule_mlp(nt_emb), 2) # nt x t**2
rule_scores = rule_score.view(batch_size, self.nt_states, self.all_states, self.all_states)
log_Z = self.pcfg._inside(unary, rule_scores, root_scores)
if self.z_dim == 0:
kl = torch.zeros_like(log_Z)
if argmax:
with torch.no_grad():
max_score, binary_matrix, spans = self.pcfg._viterbi(unary, rule_scores, root_scores)
self.tags = self.pcfg.argmax_tags
return -log_Z, kl, binary_matrix, spans
else:
return -log_Z, kl