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main.py
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import numpy as np
import torch as T
import os
from model import VAE
from dataset import get_iterators
from helper_functions import get_cuda, get_sentences_in_batch
import torch.nn.functional as F
import math
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--batch_size', type=int, default=128)
parser.add_argument('--n_vocab', type=int, default=12000)
parser.add_argument('--epochs', type=int, default=121)
parser.add_argument('--n_hidden_G', type=int, default=512)
parser.add_argument('--n_layers_G', type=int, default=2)
parser.add_argument('--n_hidden_E', type=int, default=512)
parser.add_argument('--n_layers_E', type=int, default=1)
parser.add_argument('--n_z', type=int, default=100)
parser.add_argument('--word_dropout', type=float, default=0.5)
parser.add_argument('--rec_coef', type=float, default=7)
parser.add_argument('--lr', type=float, default=0.0001)
parser.add_argument('--gpu_device', type=int, default=1)
parser.add_argument('--n_highway_layers', type=int, default=2)
parser.add_argument('--n_embed', type=int, default=300)
parser.add_argument('--unk_token', type=str, default="<unk>")
parser.add_argument('--pad_token', type=str, default="<pad>")
parser.add_argument('--start_token', type=str, default="<sos>")
parser.add_argument('--end_token', type=str, default="<eos>")
def str2bool(v):
if v.lower() == 'true':
return True
else:
return False
parser.add_argument('--resume_training', type=str2bool, default=False)
parser.add_argument('--to_train', type=str2bool, default=True)
opt = parser.parse_args()
print(opt)
save_path = "data/saved_models/vae_model.tar"
if not os.path.exists("data/saved_models"):
os.makedirs("data/saved_models")
os.environ["CUDA_VISIBLE_DEVICES"] = str(opt.gpu_device) #str(gpu_device)
#---------------------------------------------------------------
train_iter, val_iter, vocab = get_iterators(opt)
vae = VAE(opt)
vae.embedding.weight.data.copy_(vocab.vectors) #Intialize trainable embeddings with pretrained glove vectors
vae = get_cuda(vae)
trainer_vae = T.optim.Adam(vae.parameters(), lr=opt.lr)
def create_generator_input(x, train):
G_inp = x[:, 0:x.size(1)-1].clone() #input for generator should exclude last word of sequence
if train == False:
return G_inp
r = np.random.rand(G_inp.size(0), G_inp.size(1))
#Perform word_dropout according to random values (r) generated for each word
for i in range(len(G_inp)):
for j in range(1,G_inp.size(1)):
if r[i, j] < opt.word_dropout and G_inp[i, j] not in [vocab.stoi[opt.pad_token], vocab.stoi[opt.end_token]]:
G_inp[i, j] = vocab.stoi[opt.unk_token]
return G_inp
def train_batch(x, G_inp, step, train = True):
logit, _, kld = vae(x, G_inp, None, None)
logit = logit.view(-1, opt.n_vocab) #converting into shape (batch_size*(n_seq-1), n_vocab) to facilitate performing F.cross_entropy()
x = x[:, 1:x.size(1)] #target for generator should exclude first word of sequence
x = x.contiguous().view(-1) #converting into shape (batch_size*(n_seq-1),1) to facilitate performing F.cross_entropy()
rec_loss = F.cross_entropy(logit, x)
kld_coef = (math.tanh((step - 15000)/1000) + 1) / 2
# kld_coef = min(1,step/(200000.0))
loss = opt.rec_coef*rec_loss + kld_coef*kld
if train == True: #skip below step if we are performing validation
trainer_vae.zero_grad()
loss.backward()
trainer_vae.step()
return rec_loss.item(), kld.item()
def load_model_from_checkpoint():
global vae, trainer_vae
checkpoint = T.load(save_path)
vae.load_state_dict(checkpoint['vae_dict'])
trainer_vae.load_state_dict(checkpoint['vae_trainer'])
return checkpoint['step'], checkpoint['epoch']
def training():
start_epoch = step = 0
if opt.resume_training:
step, start_epoch = load_model_from_checkpoint()
for epoch in range(start_epoch, opt.epochs):
vae.train()
train_rec_loss = []
train_kl_loss = []
for batch in train_iter:
x = batch.text #Used as encoder input as well as target output for generator
G_inp = create_generator_input(x, train = True)
rec_loss, kl_loss = train_batch(x, G_inp, step, train=True)
train_rec_loss.append(rec_loss)
train_kl_loss.append(kl_loss)
step += 1
vae.eval()
valid_rec_loss = []
valid_kl_loss = []
for batch in val_iter:
x = batch.text
G_inp = create_generator_input(x, train = False)
with T.autograd.no_grad():
rec_loss, kl_loss = train_batch(x, G_inp, step, train=False)
valid_rec_loss.append(rec_loss)
valid_kl_loss.append(kl_loss)
train_rec_loss = np.mean(train_rec_loss)
train_kl_loss = np.mean(train_kl_loss)
valid_rec_loss = np.mean(valid_rec_loss)
valid_kl_loss = np.mean(valid_kl_loss)
print("No.", epoch, "T_rec:", '%.2f'%train_rec_loss, "T_kld:", '%.2f'%train_kl_loss, "V_rec:", '%.2f'%valid_rec_loss, "V_kld:", '%.2f'%valid_kl_loss)
if epoch%5==0:
T.save({
'epoch': epoch + 1,
'vae_dict': vae.state_dict(),
'vae_trainer': trainer_vae.state_dict(),
'step': step
}, save_path)
def generate_sentences(n_examples): #Generate n sentences
checkpoint = T.load(save_path)
vae.load_state_dict(checkpoint['vae_dict'])
vae.eval()
del checkpoint
for i in range(n_examples):
z = get_cuda(T.randn([1,opt.n_z]))
h_0 = get_cuda(T.zeros(opt.n_layers_G, 1, opt.n_hidden_G))
c_0 = get_cuda(T.zeros(opt.n_layers_G, 1, opt.n_hidden_G))
G_hidden = (h_0, c_0)
G_inp = T.LongTensor(1,1).fill_(vocab.stoi[opt.start_token])
G_inp = get_cuda(G_inp)
str = opt.start_token+" "
while G_inp[0][0].item() != vocab.stoi[opt.end_token]:
with T.autograd.no_grad():
logit, G_hidden, _ = vae(None, G_inp, z, G_hidden)
probs = F.softmax(logit[0], dim=1)
G_inp = T.multinomial(probs,1)
str += (vocab.itos[G_inp[0][0].item()]+" ")
print(str.encode('utf-8'))
if __name__ == '__main__':
if opt.to_train:
training()
else:
generate_sentences(50)