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model.py
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model.py
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import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from utils import get_anneal_weight, get_glove
from noise import noisy, embd_noise
def reparameterize(mu, logvar):
std = torch.exp(0.5*logvar)
eps = torch.randn_like(std)
return eps.mul(std).add_(mu)
def log_prob(z, mu, logvar):
var = torch.exp(logvar)
logp = - (z-mu)**2 / (2*var) - torch.log(2*np.pi*var) / 2
return logp.sum(dim=1)
def loss_kl(mu, logvar):
return -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp()) / len(mu)
class TextModel(nn.Module):
"""Container module with word embedding and projection layers"""
def __init__(self, vocab, args, initrange=0.1):
super().__init__()
self.vocab = vocab
self.args = args
if(args.glove):
weights = torch.FloatTensor(get_glove(vocab))
self.embed = nn.Embedding.from_pretrained(weights)
else:
self.embed = nn.Embedding(vocab.size, args.dim_emb)
self.proj = nn.Linear(args.dim_h, vocab.size)
self.embed.weight.data.uniform_(-initrange, initrange)
self.proj.bias.data.zero_()
self.proj.weight.data.uniform_(-initrange, initrange)
class DAE(TextModel):
"""Denoising Auto-Encoder/ Parent Class of VAE/AAE/VanillaAE"""
def __init__(self, vocab, args):
super().__init__(vocab, args)
self.drop = nn.Dropout(args.dropout)
self.E = nn.LSTM(args.dim_emb, args.dim_h, args.nlayers,
dropout=args.dropout if args.nlayers > 1 else 0, bidirectional=True)
self.G = nn.LSTM(args.dim_emb, args.dim_h, args.nlayers,
dropout=args.dropout if args.nlayers > 1 else 0)
self.h2mu = nn.Linear(args.dim_h*2, args.dim_z)
self.h2logvar = nn.Linear(args.dim_h*2, args.dim_z)
self.z2emb = nn.Linear(args.dim_z, args.dim_emb)
self.opt = optim.Adam(self.parameters(), lr=args.lr, betas=(0.5, 0.999))
self.step_count = 0
self.ann_weight = 0.0
self._zeta = 0.0
def flatten(self):
self.E.flatten_parameters()
self.G.flatten_parameters()
def encode(self, input, args, is_train=False):
embds = self.embed(input) #LBE
if(args.embd_noise and is_train):
self.ann_weight = get_anneal_weight(self.step_count/args.log_interval, args) if args.fn is not None else 1.0
self._zeta = args.zeta * self.ann_weight
noise = embd_noise(embds, noise_type=args.noise_type, zeta=self._zeta)
embds = embds + noise
input = self.drop(embds)
else:
input = self.drop(self.embed(input))
_, (h, _) = self.E(input)
h = torch.cat([h[-2], h[-1]], 1)
return self.h2mu(h), self.h2logvar(h), h
def decode(self, z, input, hidden=None):
input = self.drop(self.embed(input)) + self.z2emb(z)
output, hidden = self.G(input, hidden)
output = self.drop(output)
logits = self.proj(output.view(-1, output.size(-1)))
return logits.view(output.size(0), output.size(1), -1), hidden
def generate(self, z, max_len, alg):
assert alg in ['greedy' , 'sample' , 'top5']
sents = []
input = torch.zeros(1, len(z), dtype=torch.long, device=z.device).fill_(self.vocab.go)
hidden = None
for l in range(max_len):
sents.append(input)
logits, hidden = self.decode(z, input, hidden)
if alg == 'greedy':
input = logits.argmax(dim=-1)
elif alg == 'sample':
input = torch.multinomial(logits.squeeze(dim=0).exp(), num_samples=1).t()
elif alg == 'top5':
not_top5_indices=logits.topk(logits.shape[-1]-5,dim=2,largest=False).indices
logits_exp=logits.exp()
logits_exp[:,:,not_top5_indices]=0.
input = torch.multinomial(logits_exp.squeeze(dim=0), num_samples=1).t()
return torch.cat(sents)
def forward(self, input, args, is_train=False):
_input = noisy(self.vocab, input, *self.args.noise) if(is_train) else input
mu, logvar, _ = self.encode(input=_input, args=args, is_train=is_train)
z = reparameterize(mu, logvar)
logits, _ = self.decode(z, input)
return mu, logvar, z, logits
def loss_rec(self, logits, targets):
loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1),
ignore_index=self.vocab.pad, reduction='none').view(targets.size())
return loss.sum(dim=0)
def loss(self, losses):
return losses['rec']
def get_anneal_info(self):
return {"steps": self.step_count,
"weight": self.ann_weight,
"zeta": self._zeta}
def autoenc(self, inputs, targets, args, is_train=False):
_, _, _, logits = self(inputs, args, is_train)
self.step_count += 1
return {'rec': self.loss_rec(logits, targets).mean()}
def step(self, losses):
self.opt.zero_grad()
losses['loss'].backward()
# `clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs.
#nn.utils.clip_grad_norm_(self.parameters(), clip)
self.opt.step()
def nll_is(self, inputs, targets, args, m):
"""compute negative log-likelihood by importance sampling:
p(x;theta) = E_{q(z|x;phi)}[p(z)p(x|z;theta)/q(z|x;phi)]
"""
mu, logvar, _ = self.encode(inputs, args)
tmp = []
for _ in range(m):
z = reparameterize(mu, logvar)
logits, _ = self.decode(z, inputs)
v = log_prob(z, torch.zeros_like(z), torch.zeros_like(z)) - \
self.loss_rec(logits, targets) - log_prob(z, mu, logvar)
tmp.append(v.unsqueeze(-1))
ll_is = torch.logsumexp(torch.cat(tmp, 1), 1) - np.log(m)
return -ll_is
class VAE(DAE):
"""Variational Auto-Encoder"""
def __init__(self, vocab, args):
super().__init__(vocab, args)
def loss(self, losses):
return losses['rec'] + self.args.lambda_kl * losses['kl']
def autoenc(self, inputs, args, targets, is_train=False):
mu, logvar, _, logits = self(input=inputs, args=args, is_train=is_train)
self.step_count += 1
return {'rec': self.loss_rec(logits, targets).mean(),
'kl': loss_kl(mu, logvar)}
class AAE(DAE):
"""Adversarial Auto-Encoder"""
def __init__(self, vocab, args):
super().__init__(vocab, args)
self.D = nn.Sequential(nn.Linear(args.dim_z, args.dim_d), nn.ReLU(),
nn.Linear(args.dim_d, 1), nn.Sigmoid())
self.optD = optim.Adam(self.D.parameters(), lr=args.lr, betas=(0.5, 0.999))
def loss_adv(self, z):
zn = torch.randn_like(z)
zeros = torch.zeros(len(z), 1, device=z.device)
ones = torch.ones(len(z), 1, device=z.device)
loss_d = F.binary_cross_entropy(self.D(z.detach()), zeros) + \
F.binary_cross_entropy(self.D(zn), ones)
loss_g = F.binary_cross_entropy(self.D(z), ones)
return loss_d, loss_g
def loss(self, losses):
return losses['rec'] + self.args.lambda_adv * losses['adv'] + \
self.args.lambda_p * losses['|lvar|']
def autoenc(self, inputs, targets, args, is_train=False):
_, logvar, z, logits = self(inputs, args, is_train)
loss_d, adv = self.loss_adv(z)
self.step_count += 1
return {'rec': self.loss_rec(logits, targets).mean(),
'adv': adv,
'|lvar|': logvar.abs().sum(dim=1).mean(),
'loss_d': loss_d}
def step(self, losses):
super().step(losses)
self.optD.zero_grad()
losses['loss_d'].backward()
self.optD.step()
class VanillaAE(DAE):
"""Deterministic Auto-Encoder"""
def __init__(self, vocab, args):
super().__init__(vocab, args)
self.h2ls = nn.Linear(args.dim_h*2, args.dim_z)
def encode(self, input, args, is_train=False):
embds = self.embed(input) #LBE
if(args.embd_noise and is_train):
self.ann_weight = get_anneal_weight(self.step_count/args.log_interval, args) if args.fn is not None else 1.0
self._zeta = args.zeta * self.ann_weight
noise = embd_noise(embds, noise_type=args.noise_type, zeta=self._zeta)
embds = embds + noise
input = self.drop(embds)
else:
input = self.drop(self.embed(input))
_, (h, _) = self.E(input)
h = torch.cat([h[-2], h[-1]], 1)
return self.h2ls(h), None, h
def forward(self, input, args, is_train=False):
_input = noisy(self.vocab, input, *self.args.noise) if(is_train) else input
z, _, _ = self.encode(_input, args, is_train=is_train)
logits, _ = self.decode(z, input)
return z, logits
def loss(self, losses):
return losses['rec']
def autoenc(self, inputs, targets, args, is_train=False):
z, logits = self(inputs, args, is_train)
self.step_count += 1
return {'rec': self.loss_rec(logits, targets).mean()}
class Classifier(TextModel):
"""Classifier for Style transfer accuracy metric"""
def __init__(self, vocab, args):
super().__init__(vocab, args)
self.drop = nn.Dropout(args.dropout)
self.output_size = args.output_size
self.E = nn.LSTM(args.dim_emb, args.dim_h, args.nlayers, bidirectional=True)
self.h2out = nn.Linear(args.dim_h*2, self.output_size)
self.opt = optim.Adam(self.parameters(), lr=args.lr, betas=(0.5, 0.999))
def flatten(self):
self.E.flatten_parameters()
def forward(self, input):
input = self.drop(self.embed(input)) #BLE
_, (h, _) = self.E(input) #2*BH
h = torch.cat([h[-2], h[-1]], 1) #B(H*2)
out = self.h2out(h) #BO
out = nn.Softmax(dim=1)(out)
return out
def compute_acc(self, yhat, targets):
yhat = yhat.squeeze()
targets = targets.squeeze()
n_samples = targets.size(0)
yhat = torch.argmax(yhat, dim=-1)
targets = torch.argmax(targets, dim=-1)
return yhat[yhat == targets].size(0)/n_samples
def loss_ce(self, yhat, targets):
criterion = nn.BCELoss(reduction='none')
targets = targets.squeeze()
yhat = yhat.squeeze()
loss = criterion(yhat, targets)
return loss.sum(dim=0)
def loss(self, losses):
return losses['ce']
def autoenc(self, inputs, targets):
outs = self(inputs)
return {'ce': self.loss_ce(outs, targets).mean()}, outs
def step(self, losses):
self.opt.zero_grad()
losses['loss'].backward()
self.opt.step()