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run.py
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from models import Encoder1, Decoder1, load_pretrained
from data import dataloader, Data
import os
from nltk.translate.bleu_score import SmoothingFunction
from nltk.translate import bleu
from tqdm import tqdm
import torch
import torch.nn as nn
import torch.optim as optim
var = {
'valid' : True,
'embed_dim' : 600,
'train_test_split' : 0.75,
'lstm_layers' : 2,
'latent_dim' : 600
}
device = torch.device('cuda' if torch.cuda.is_available else 'cpu')
print('--------------- Creating Data ---------------')
train_loader, valid_loader, test_loader = dataloader(var['train_test_split'],
var['valid'],
var['embed_dim'])
print('--------------- Done !! ---------------')
print('--------------- Creating Models ---------------')
encoder = Encoder1(var['embed_dim'], len(Data.language.vocab), var['latent_dim'], var['lstm_layers'])
encoder = encoder.to(device)
decoder = Decoder1(var['latent_dim'], len(Data.language.vocab), var['embed_dim'], var['lstm_layers'])
decoder = decoder.to(device)
current_epoch = 0
load_pretrained(encoder, decoder, var['embed_dim'])
if not len(os.listdir('Saved_Model/')) == 0 :
li = [int(items.split('_')[1].strip('.pt')) for items in os.listdir('Saved_Model/')]
li = list(set(li))
li.sort()
current_epoch = li[-1]
print('Loading model for epoch {}'.format(current_epoch))
encoder.load_state_dict(torch.load('Saved_Model/encoder1_{}.pt'.format(current_epoch)))
decoder.load_state_dict(torch.load('Saved_Model/decoder1_{}.pt'.format(current_epoch)))
print(encoder)
print(decoder)
print('--------------- Done !! ---------------')
lr = 0.0001
criterion = nn.CrossEntropyLoss().to(device)
encoder_optimizer = optim.Adam(encoder.parameters(), lr = lr)
decoder_optimizer = optim.Adam(decoder.parameters(), lr = lr)
def adjust_learning_rate(optimizer, epoch):
"""Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
new_lr = lr * (0.5 ** (epoch // 30))
for param_group in optimizer.param_groups:
param_group['lr'] = new_lr
return new_lr
log = open('logs.txt', 'a')
print('--------------- Training ---------------')
start_epoch = current_epoch
end_epoch = 100
max_score = 0
testing_output = []
# teacher_forcing_ratio = 0.5
smoothie = SmoothingFunction().method4
for epoch in tqdm(range(start_epoch, end_epoch)):
total_loss = 0
total_bleu = 0
encoder.train()
decoder.train()
new_lr = adjust_learning_rate(encoder_optimizer, epoch)
adjust_learning_rate(decoder_optimizer, epoch)
log.write('The Learning Rate is : {}\n'.format(new_lr))
training_output = []
for idx, (inputs, labels) in enumerate(train_loader) :
encoder.zero_grad()
decoder.zero_grad()
inputs = inputs.to(device)
output, hidden = encoder(inputs)
labels = labels.to(device)
decoder_input = torch.tensor([[Data.language.word2index['<SOS>']]], device=device)
decoder_hidden = hidden
loss = 0
output_sent = []
label_sent = []
# if idx % 2 == 0 :
# for items in range(len(labels[0]) - 1) :
# decoder_output, decoder_hidden = decoder(decoder_input, decoder_hidden)
# loss += criterion(decoder_output, labels[0][items].unsqueeze(0).to(device))
# topv, topi = decoder_output.topk(1)
# output_sent.append(Data.language.index2word[topi.item()])
# label_sent.append(Data.language.index2word[labels[0][items].item()])
# decoder_input = labels[0][items]
# else :
for items in range(len(labels[0])) :
decoder_output, decoder_hidden = decoder(decoder_input, decoder_hidden)
loss += criterion(decoder_output, labels[0][items].unsqueeze(0).to(device))
topv, topi = decoder_output.topk(1)
output_sent.append(Data.language.index2word[topi.item()])
label_sent.append(Data.language.index2word[labels[0][items].item()])
decoder_input = topi.squeeze().detach()
try :
training_output.append((' '.join(output_sent), ' '.join(label_sent)))
total_bleu += bleu([label_sent], output_sent, smoothing_function=smoothie)
total_loss += loss.item()
loss.backward()
encoder_optimizer.step()
decoder_optimizer.step()
except Exception as e:
print(e)
print(output_sent)
try :
train_log = open('training_outputs.txt', 'w', encoding = 'latin')
train_log.write('Recording outputs for epoch {}\n'.format(epoch + 1))
train_log.write('Training_loss {}\n'.format(total_loss/len(train_loader)))
train_log.write('Training_Accuracy {}\n'.format(total_bleu/len(train_loader)))
for i, (out, lab) in enumerate(training_output):
train_log.write('------------------------------------------------\n')
train_log.write('Index: {}\n'.format(i))
train_log.write('Output: {}\n'.format(out))
train_log.write('Label: {}\n'.format(lab))
train_log.flush()
train_log.close()
except:
train_log.write("error in printing")
train_log.flush()
train_log.close()
string = 'Training Epoch: {}/{}, Training Loss: {}, Training Accuracy: {}\n' \
.format(epoch + 1, end_epoch, total_loss/len(train_loader), total_bleu/len(train_loader))
log.write(string)
with torch.no_grad() :
encoder.eval()
decoder.eval()
if valid_loader :
try :
validating_output = []
total_loss = 0
total_bleu = 0
for idx, (inputs, labels) in enumerate(valid_loader) :
inputs = inputs.to(device)
output, hidden = encoder(inputs)
labels = labels.to(device)
decoder_input = torch.tensor([[Data.language.word2index['<SOS>']]], device=device)
# decoder_hidden = (output.unsqueeze(0).unsqueeze(0),torch.zeros(1, 1, var['embed_dim'], device = device))
decoder_hidden = hidden
loss = 0
output_sent = []
label_sent = []
for items in range(len(labels[0])) :
decoder_output, decoder_hidden = decoder(decoder_input, decoder_hidden)
loss += criterion(decoder_output, labels[0][items].unsqueeze(0).to(device))
topv, topi = decoder_output.topk(1)
output_sent.append(Data.language.index2word[topi.item()])
label_sent.append(Data.language.index2word[labels[0][items].item()])
decoder_input = topi.squeeze().detach()
validating_output.append((' '.join(output_sent), ' '.join(label_sent)))
total_loss += loss.item()
total_bleu += bleu([label_sent], output_sent, smoothing_function=smoothie)
if total_bleu/len(valid_loader) > max_score :
valid_log = open('validating_outputs.txt', 'w', encoding = 'latin')
valid_log.write('Recording outputs for epoch {}\n'.format(epoch + 1))
valid_log.write('Validating_loss {}\n'.format(total_loss/len(valid_loader)))
valid_log.write('Validating_accuracy {}\n'.format(total_bleu/len(valid_loader)))
for i, (out, lab) in enumerate(validating_output):
valid_log.write('------------------------------------------------\n')
valid_log.write('Index: {}\n'.format(i))
valid_log.write('Output: {}\n'.format(out))
valid_log.write('Label: {}\n'.format(lab))
valid_log.flush()
valid_log.close()
max_score = total_bleu/len(valid_loader)
except:
pass
string = 'Validation Epoch: {}/{}, Validation Loss: {}, Validation Accuracy: {}\n' \
.format(epoch + 1, end_epoch, total_loss/len(valid_loader), total_bleu/len(valid_loader))
log.write(string)
log.write('----------------------------------------------------------------------------------------------\n')
log.flush()
torch.save(encoder.state_dict(), 'Saved_Model/encoder1_{}.pt'.format(epoch + 1))
torch.save(decoder.state_dict(), 'Saved_Model/decoder1_{}.pt'.format(epoch + 1))
log.close()
print('--------------- Training Completed ---------------')
print('--------------- Testing the models ---------------')
encoder.eval()
decoder.eval()
with torch.no_grad():
total_loss = 0
total_bleu = 0
for idx, (inputs, labels) in enumerate(test_loader) :
inputs = inputs.to(device)
output, hidden = encoder(inputs)
labels = labels.to(device)
decoder_input = torch.tensor([[Data.language.word2index['<SOS>']]], device=device)
# decoder_hidden = (output.unsqueeze(0).unsqueeze(0),torch.zeros(1, 1, var['embed_dim'], device = device))
decoder_hidden = hidden
loss = 0
output_sent = []
label_sent = []
for items in range(len(labels[0])) :
decoder_output, decoder_hidden = decoder(decoder_input, decoder_hidden)
loss += criterion(decoder_output, labels[0][items].unsqueeze(0).to(device))
topv, topi = decoder_output.topk(1)
output_sent.append(Data.language.index2word[topi.item()])
label_sent.append(Data.language.index2word[labels[0][items].item()])
decoder_input = topi.squeeze().detach()
testing_output.append((' '.join(output_sent), ' '.join(label_sent)))
total_loss += loss.item()
total_bleu += bleu([label_sent], output_sent, smoothing_function=smoothie)
test_log = open('testing_outputs.txt', 'w', encoding = 'latin')
# test_log.write('Recording outputs for epoch {}\n'.format(epoch))
test_log.write('Testing_loss {}\n'.format(total_loss/len(test_loader)))
test_log.write('Testing_accuracy {}\n'.format(total_bleu/len(test_loader)))
for i, (out, lab) in enumerate(testing_output):
test_log.write('------------------------------------------------\n')
test_log.write('Index: {}\n'.format(i))
test_log.write('Output: {}\n'.format(out))
test_log.write('Label: {}\n'.format(lab))
test_log.flush()
test_log.close()