class KeplerModel(pl.LightningModule):
def __init__(self):
super(KeplerModel, self).__init__()
#Initialize Model Parameters Using Config Properties
self.model = Encoder(config['seq_length'], config['hidden_size'],
config['output_dim'], config['n_layers'])
#Initialize a Cross Entropy Loss Criterion for Training
self.criterion = torch.nn.CrossEntropyLoss()
#Define a Forward Pass of the Model
def forward(self, x, h):
return self.model.forward(x, h)
def training_step(self, batch, batch_idx):
#Set Model to Training Mode
self.model.train()
#Unpack Data and Labels from Batch
x, y = batch
#Reshape Data into Shape (batch_size, 1, seq_length)
x = x.view(x.size(0), -1, x.size(1))
#Initalize the hidden state for forward pass
h = self.model.init_hidden(x.size(0))
#Zero out the model gradients to avoid accumulation
self.model.zero_grad()
#Forward Pass Through Model
out, h = self.forward(x, h)
#Calculate Cross Entropy Loss
loss = self.criterion(out, y.long().squeeze())
#Obtain Class Labels
y_hat = torch.max(out, 1)[1]
#Compute the balanced accuracy (weights based on number of ex. in each class)
accuracy = balanced_accuracy_score(y, y_hat)
#Compute weighted f1 score to account for class imbalance
f1 = f1_score(y, y_hat, average='weighted')
#Create metric object for tensorboard logging
tensorboard_logs = {
'train_loss': loss.item(),
'accuracy': accuracy,
'f1': f1
}
return {'loss': loss, 'log': tensorboard_logs}
def validation_step(self, batch, batch_idx):
#Set Model to Eval Mode
self.model.eval()
#Unpack data and labels from batch
x, y = batch
#Initialize Hidden State
h = self.model.init_hidden(x.size(0))
#Reshape Data into Shape (batch_size, 1, seq_length)
x = x.view(x.size(0), -1, x.size(1))
#Calculate Forward Pass of The Model
out, h = self.forward(x, h)
#Calculate Cross Entropy Loss
loss = self.criterion(out, y.long().squeeze())
#Calculate Class Indicies
y_hat = torch.max(out, 1)[1]
#Calculate Balanced Accuracy
val_accuracy = torch.Tensor([balanced_accuracy_score(y, y_hat)])
#Calculate Balanced Accuracy
val_f1 = torch.Tensor([f1_score(y, y_hat, average='weighted')])
#Create a metrics object
metrics = {
'val_loss': loss,
'val_accuracy': val_accuracy,
'val_f1': val_f1
}
return metrics
def validation_end(self, outputs):
# OPTIONAL
avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean()
avg_acc = torch.stack([x['val_accuracy'] for x in outputs]).mean()
avg_f1 = torch.stack([x['val_f1'] for x in outputs]).mean()
tensorboard_logs = {
'val_loss': avg_loss,
'val_acc': avg_acc,
'val_f1': avg_f1
}
return {'avg_val_loss': avg_loss, 'log': tensorboard_logs}
def configure_optimizers(self):
return torch.optim.Adam(self.parameters(), lr=0.001)
@pl.data_loader
def train_dataloader(self):
# REQUIRED
return DataLoader(KeplerDataset(mode="train"),
batch_size=64,
shuffle=True)
@pl.data_loader
def val_dataloader(self):
# REQUIRED
return DataLoader(KeplerDataset(mode="test"),
batch_size=128,
shuffle=True)
def train():
model_path = config.get('model_path', './model/')
log_step = config.get('log_step', 10)
hidden_size = config.get('decoder_hidden_size', 512)
num_epochs = config.get('num_epochs', 5)
alpha_c = config.get('alpha_c', 1)
# Create model directory
if not os.path.exists(model_path):
os.makedirs(model_path)
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
# Used for calculating bleu scores
references = []
hypotheses = []
# Load vocabulary
vocab = build_and_save_vocab()
# Build data loader
data_loader = get_loader('train')
data_loader_valid = get_loader('validate')
# Build the models
if config.get('checkpoint') is None:
epochs_since_improvement = config.get('epochs_since_improvement')
best_score = 0.
encoder = Encoder(config.get('image_net')).to(device)
decoder = Decoder(encoder.dim, len(vocab),
hidden_size=hidden_size).to(device)
encoder_optimizer = torch.optim.Adam(params=filter(
lambda p: p.requires_grad, encoder.parameters()),
lr=config.get('encoder_lr', 1e-4))
decoder_optimizer = torch.optim.Adam(params=filter(
lambda p: p.requires_grad, decoder.parameters()),
lr=config.get('decoder_lr', 1e-3))
else:
checkpoint = torch.load(config.get('checkpoint'))
epochs_since_improvement = checkpoint['epochs_since_improvement']
best_score = checkpoint['best_score']
encoder = checkpoint['encoder']
encoder_optimizer = checkpoint['encoder_optimizer']
decoder = checkpoint['decoder']
decoder_optimizer = checkpoint['decoder_optimizer']
# Loss and optimizer
criterion = nn.CrossEntropyLoss()
# Train the models
total_step = len(data_loader)
for epoch in range(num_epochs):
if epochs_since_improvement == 20:
print(
'Reached the max epochs_since_improvement. Training is done.')
break
if epochs_since_improvement > 0 and epochs_since_improvement % 8 == 0:
adjust_learning_rate(decoder_optimizer, 0.9)
adjust_learning_rate(encoder_optimizer, 0.9)
for i, (images, captions, lengths) in enumerate(data_loader):
# Set mini-batch dataset
images = images.to(device)
captions = captions.to(device)
# Forward, backward and optimize
features = encoder.forward(images)
prediction, alphas = decoder.forward(features, captions)
att_regularization = alpha_c * ((1 - alphas.sum(1))**2).mean()
loss = criterion(prediction.permute(0, 2, 1),
captions) + att_regularization
decoder_optimizer.zero_grad()
encoder_optimizer.zero_grad()
loss.backward()
decoder_optimizer.step()
encoder_optimizer.step()
total_caption_length = calculate_caption_lengths(
vocab.word2idx, captions)
acc1 = accuracy(prediction.permute(0, 2, 1), captions, 1)
acc5 = accuracy(prediction.permute(0, 2, 1), captions, 5)
losses.update(loss.item(), total_caption_length)
top1.update(acc1, total_caption_length)
top5.update(acc5, total_caption_length)
# Print log info
if i % log_step == 0:
print(
'Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}, Perplexity: {:5.4f}'
.format(epoch, num_epochs, i, total_step, loss.item(),
np.exp(loss.item())))
print(
'Top 1 Accuracy {top1.val:.3f} ({top1.avg:.3f}), Top 5 Accuracy {top5.val:.3f} ({top5.avg:.3f})'
.format(top1=top1, top5=top5))
valid_score = validate(data_loader_valid, encoder, decoder,
criterion, vocab)
if valid_score >= best_score:
epochs_since_improvement += 1
print(
'Epochs since last improvement: {epochs_since_improvement}'
)
best_score = valid_score
else:
epochs_since_improvement = 0
state_dict = {
'epoch': epoch,
'epochs_since_improvement': epochs_since_improvement,
'decoder': decoder,
'decoder_optimizer': decoder_optimizer,
'encoder': encoder,
'encoder_optimizer': encoder_optimizer,
'valid_score': valid_score,
'best_score': best_score
}
filename = 'checkpoint.pth.tar'
torch.save(state_dict, filename)
# Print log info
if i % log_step == 0:
print(
'Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}, Perplexity: {:5.4f}'
.format(epoch, num_epochs, i, total_step, loss.item(),
np.exp(loss.item())))
print(
'Top 1 Accuracy {top1.val:.3f} ({top1.avg:.3f}), Top 5 Accuracy {top5.val:.3f} ({top5.avg:.3f})'
.format(top1=top1, top5=top5))
print('Validate score %.3f' % (valid_score))
def train(train_ids, dev_ids, test_ids, dict_ids, enc_word_alphabet, enc_char_alphabet, dec_word_alphabet, dec_char_alphabet, position_alphabet,
dictionary):
enc_word_emb = initialize_emb(config.get('word_emb'), enc_word_alphabet, opt.word_emb_dim)
if position_alphabet is not None:
pos_emb = initialize_emb(None, position_alphabet, opt.pos_emb_dim)
else:
pos_emb = None
if opt.use_char:
enc_char_emb = initialize_emb(config.get('char_emb'), enc_char_alphabet, opt.char_emb_dim)
else:
enc_char_emb = None
encoder = Encoder(enc_word_emb, pos_emb, enc_char_emb)
if opt.method == 'cla':
decoder = AttnNet(dictionary)
train_loader = DataLoader(MyDataset(train_ids), opt.batch_size, shuffle=True, collate_fn=my_collate)
else:
dec_word_emb = initialize_emb(config.get('word_emb'), dec_word_alphabet, opt.word_emb_dim)
if opt.use_char:
dec_char_emb = initialize_emb(config.get('char_emb'), dec_char_alphabet, opt.char_emb_dim)
else:
dec_char_emb = None
decoder = Decoder(dec_word_emb, dec_char_emb, dec_word_alphabet)
if opt.batch_size != 1:
raise RuntimeError("currently, only support batch size 1")
train_loader = DataLoader(MyDataset(train_ids), opt.batch_size, shuffle=True, collate_fn=my_collate_1)
optimizer = optim.Adam(itertools.chain(encoder.parameters(), decoder.parameters()), lr=opt.lr, weight_decay=opt.l2)
if opt.tune_wordemb == False:
encoder.free_emb()
decoder.free_emb()
if opt.pretraining:
if opt.method == 'cla':
dict_loader = DataLoader(MyDataset(dict_ids), opt.batch_size, shuffle=True, collate_fn=my_collate)
else:
dict_loader = DataLoader(MyDataset(dict_ids), opt.batch_size, shuffle=True, collate_fn=my_collate_1)
logging.info("start dict pretraining ...")
logging.info("dict pretraining datapoints: {}".format(len(dict_ids)))
bad_counter = 0
best_accuracy = 0
for idx in range(9999):
epoch_start = time.time()
encoder.train()
decoder.train()
correct, total = 0, 0
sum_loss = 0
train_iter = iter(dict_loader)
num_iter = len(dict_loader)
for i in range(num_iter):
if opt.method == 'cla':
enc_word_seq_tensor, enc_word_seq_lengths, enc_word_seq_recover, enc_mask, \
enc_char_seq_tensor, enc_char_seq_lengths, enc_char_seq_recover, label_tensor = next(train_iter)
encoder_outputs, _ = encoder.forward_batch(enc_word_seq_tensor, enc_word_seq_lengths, \
enc_char_seq_tensor, enc_char_seq_lengths,
enc_char_seq_recover)
loss, total_this_batch, correct_this_batch = decoder.forward_train(encoder_outputs, enc_word_seq_lengths, label_tensor)
else:
enc_word_seq_tensor, enc_pos_tensor, \
enc_char_seq_tensor, enc_char_seq_lengths, enc_char_seq_recover, dec_word_seq_tensor, \
label_tensor, dec_char_seq_tensor = next(train_iter)
encoder_outputs, encoder_hidden = encoder.forward(enc_word_seq_tensor, enc_pos_tensor, \
enc_char_seq_tensor, enc_char_seq_lengths,
enc_char_seq_recover)
loss, total_this_batch, correct_this_batch = decoder.forward_train(encoder_outputs, encoder_hidden,
dec_word_seq_tensor, label_tensor,
dec_char_seq_tensor)
sum_loss += loss.item()
loss.backward()
if opt.gradient_clip > 0:
torch.nn.utils.clip_grad_norm_(encoder.parameters(), opt.gradient_clip)
torch.nn.utils.clip_grad_norm_(decoder.parameters(), opt.gradient_clip)
optimizer.step()
encoder.zero_grad()
decoder.zero_grad()
total += total_this_batch
correct += correct_this_batch
epoch_finish = time.time()
accuracy = 100.0 * correct / total
logging.info("epoch: %s pretraining finished. Time: %.2fs. loss: %.4f Accuracy %.2f" % (
idx, epoch_finish - epoch_start, sum_loss / num_iter, accuracy))
if accuracy > opt.expected_accuracy:
logging.info("Exceed expected training accuracy, breaking ... ")
break
if accuracy > best_accuracy:
logging.info("Exceed previous best accuracy: %.2f" % (best_accuracy))
best_accuracy = accuracy
bad_counter = 0
else:
bad_counter += 1
if bad_counter >= opt.patience:
logging.info('Pretraining Early Stop!')
break
best_dev_f = -10
bad_counter = 0
logging.info("start training ...")
logging.info("training datapoints: {}".format(len(train_ids)))
if dev_ids is not None and len(dev_ids) != 0:
logging.info("dev datapoints: {}".format(len(dev_ids)))
if test_ids is not None and len(test_ids) != 0:
logging.info("test datapoints: {}".format(len(test_ids)))
for idx in range(opt.iter):
epoch_start = time.time()
encoder.train()
decoder.train()
train_iter = iter(train_loader)
num_iter = len(train_loader)
sum_loss = 0
correct, total = 0, 0
for i in range(num_iter):
if opt.method == 'cla':
enc_word_seq_tensor, enc_word_seq_lengths, enc_word_seq_recover, enc_mask, \
enc_char_seq_tensor, enc_char_seq_lengths, enc_char_seq_recover, label_tensor = next(train_iter)
encoder_outputs, _ = encoder.forward_batch(enc_word_seq_tensor, enc_word_seq_lengths, \
enc_char_seq_tensor, enc_char_seq_lengths,
enc_char_seq_recover)
loss, total_this_batch, correct_this_batch = decoder.forward_train(encoder_outputs,
enc_word_seq_lengths, label_tensor)
else:
enc_word_seq_tensor, enc_pos_tensor, \
enc_char_seq_tensor, enc_char_seq_lengths, enc_char_seq_recover, dec_word_seq_tensor, \
label_tensor, dec_char_seq_tensor = next(train_iter)
encoder_outputs, encoder_hidden = encoder.forward(enc_word_seq_tensor, enc_pos_tensor, \
enc_char_seq_tensor, enc_char_seq_lengths, enc_char_seq_recover)
loss, total_this_batch, correct_this_batch = decoder.forward_train(encoder_outputs, encoder_hidden,
dec_word_seq_tensor, label_tensor,
dec_char_seq_tensor)
sum_loss += loss.item()
loss.backward()
if opt.gradient_clip > 0:
torch.nn.utils.clip_grad_norm_(encoder.parameters(), opt.gradient_clip)
torch.nn.utils.clip_grad_norm_(decoder.parameters(), opt.gradient_clip)
optimizer.step()
encoder.zero_grad()
decoder.zero_grad()
total += total_this_batch
correct += correct_this_batch
epoch_finish = time.time()
accuracy = 100.0 * correct / total
logging.info("epoch: %s training finished. Time: %.2fs. loss: %.4f Accuracy %.2f" % (
idx, epoch_finish - epoch_start, sum_loss / num_iter, accuracy))
if dev_ids is not None and len(dev_ids) != 0:
if opt.method == 'cla':
p, r, f = evaluate_cla(dev_ids, encoder, decoder, dictionary)
else:
p, r, f = evaluate(dev_ids, encoder, decoder, dec_word_alphabet, dec_char_alphabet, dictionary)
logging.info("Dev: p: %.4f, r: %.4f, f: %.4f" % (p, r, f))
else:
f = best_dev_f
if f > best_dev_f:
logging.info("Exceed previous best f score on dev: %.4f" % (best_dev_f))
best_dev_f = f
bad_counter = 0
torch.save(encoder, os.path.join(opt.output, "encoder.pkl"))
torch.save(decoder, os.path.join(opt.output, "decoder.pkl"))
torch.save(enc_word_alphabet, os.path.join(opt.output, "enc_word_alphabet.pkl"))
torch.save(enc_char_alphabet, os.path.join(opt.output, "enc_char_alphabet.pkl"))
torch.save(dec_word_alphabet, os.path.join(opt.output, "dec_word_alphabet.pkl"))
torch.save(dec_char_alphabet, os.path.join(opt.output, "dec_char_alphabet.pkl"))
torch.save(position_alphabet, os.path.join(opt.output, "position_alphabet.pkl"))
if test_ids is not None and len(test_ids) != 0:
if opt.method == 'cla':
p, r, f = evaluate_cla(test_ids, encoder, decoder, dictionary)
else:
p, r, f = evaluate(test_ids, encoder, decoder, dec_word_alphabet, dec_char_alphabet, dictionary)
logging.info("Test: p: %.4f, r: %.4f, f: %.4f" % (p, r, f))
else:
bad_counter += 1
if bad_counter >= opt.patience:
logging.info('Early Stop!')
break
logging.info("train finished")
