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185 lines (168 loc) · 8.17 KB
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import torch
from loguru import logger
from nlgeval import NLGEval
from torch.nn.utils import clip_grad_norm_
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm
def train_val_loss(model, train_dataset, val_dataset, num_workers, loss_fn,
params, model_save_path, save_every, device):
model = model.to(device)
optimizer = torch.optim.Adam(params=model.parameters(), lr=params['lr'])
scheduler = torch.optim.lr_scheduler.StepLR(
optimizer=optimizer,
step_size=params['decay_every'],
gamma=params['decay_rate'])
train_dataloader = DataLoader(dataset=train_dataset,
batch_size=params['batch_size'],
num_workers=num_workers,
pin_memory=True,
shuffle=True)
val_dataloader = DataLoader(dataset=val_dataset,
batch_size=params['batch_size'],
num_workers=0,
pin_memory=True,
shuffle=True)
last_loss = 0.0
for epoch in range(params['epoch']):
running_loss = 0.0
logger.info('training epoch %d ... ' % (epoch + 1))
model.train()
for i, batch in enumerate(train_dataloader):
optimizer.zero_grad()
inputs = [x.to(device) for x in batch['inputs']]
label = [x.to(device) for x in batch['label']]
prediction = model(inputs)
loss = loss_fn(prediction, label)
running_loss += loss
loss.backward()
clip_grad_norm_(model.parameters(), 0.25)
optimizer.step()
if i % 1 == 0:
logger.info(
'[epoch: {0}/{1}, batch: {2}/{3}] loss: {4}'.format(
epoch + 1, params['epoch'], i + 1,
len(train_dataloader), running_loss))
running_loss = 0.0
with torch.no_grad():
logger.info('validating epoch %d ... ' % (epoch + 1))
running_loss = 0.0
model.eval()
for i, batch in enumerate(val_dataloader):
inputs = [x.to(device) for x in batch['inputs']]
label = [x.to(device) for x in batch['label']]
prediction = model(inputs)
loss = loss_fn(prediction, label)
running_loss += loss
running_loss /= len(val_dataloader)
delta_loss = running_loss - last_loss
last_loss = running_loss
logger.info('loss after epoch %d: %.10f' % (epoch + 1, running_loss))
logger.info('loss change after last epoch: %.10f' % delta_loss)
scheduler.step()
if not (epoch + 1) % save_every:
with open('{0}_{1}.pkl'.format(model_save_path, epoch + 1),
'wb') as fp:
logger.info("writing checkpoint " + str(epoch + 1))
torch.save(model, fp)
logger.info("training complete. writing final model.")
with open('{0}.pkl'.format(model_save_path), 'wb') as fp:
torch.save(model, fp)
def train_val_meteor(model, train_dataset, val_dataset, val_cap_dataset,
word_map, reversed_word_map, num_workers, loss_fn, params,
model_save_path, save_every, device):
model = model.to(device)
optimizer = torch.optim.Adam(params=model.parameters(), lr=params['lr'])
scheduler = torch.optim.lr_scheduler.StepLR(
optimizer=optimizer,
step_size=params['decay_every'],
gamma=params['decay_rate'])
train_dataloader = DataLoader(dataset=train_dataset,
batch_size=params['batch_size'],
num_workers=num_workers,
pin_memory=True,
shuffle=True)
val_dataloader = DataLoader(dataset=val_dataset, shuffle=False)
val_cap_dataloader = DataLoader(dataset=val_cap_dataset, shuffle=False)
last_loss = 0.0
# first prepare val refs
logger.info('preparing references...')
ref_img = {}
for cap_label in val_dataloader:
image_id = str(cap_label['image_id'][0])
if image_id not in ref_img:
ref_img[image_id] = []
seq, seq_length = cap_label['label']
ref_img[image_id].append(
[reversed_word_map[x] for x in seq[0][1:seq_length[0] + 1]])
nlg = NLGEval(False, True, True, ['Bleu_1', 'ROUGE_L', 'CIDEr'])
for epoch in range(params['epoch']):
running_loss = 0.0
logger.info('training epoch %d ... ' % (epoch + 1))
model.train()
for i, batch in enumerate(train_dataloader):
optimizer.zero_grad()
inputs = [x.to(device) for x in batch['inputs']]
label = [x.to(device) for x in batch['label']]
prediction = model(inputs)
loss = loss_fn(prediction, label)
running_loss += loss
loss.backward()
clip_grad_norm_(model.parameters(), 0.25)
optimizer.step()
if i % 1 == 0:
logger.info(
'[epoch: {0}/{1}, batch: {2}/{3}] loss: {4}'.format(
epoch + 1, params['epoch'], i + 1,
len(train_dataloader), running_loss))
running_loss = 0.0
with torch.no_grad():
logger.info('validating epoch %d ... ' % (epoch + 1))
model.eval()
hyp = []
ref = [[] for _ in range(5)]
for val in tqdm(val_cap_dataloader):
image_id = str(val['image_id'][0])
image_features = [x.to(device) for x in val['inputs']][0]
seq = torch.tensor([word_map['<start>']]).view(1,
-1).to(device)
seq_length = torch.tensor([0]).view(1, -1).to(device)
top_results = model.decode((image_features, seq, seq_length),
word_map['<end>'],
beam=1)
decoded = [reversed_word_map[x] for x in top_results[0][1]]
if decoded[-1] != '<end>':
logger.warning('decoded sentence not ending with <end>.')
logger.warning('image_id: {0}'.format(image_id))
logger.warning('decoded: {0}'.format(decoded))
hyp.append(' '.join(decoded))
else:
hyp.append(' '.join(decoded[:-1]))
for i in range(5):
ref[i].append(' '.join(ref_img[image_id][i]))
logger.debug('sample 0 pred: {0}'.format(hyp[1234]))
logger.debug('sample 0 ref 0: {0}'.format(ref[0][1234]))
logger.debug('sample 0 ref 1: {0}'.format(ref[1][1234]))
logger.debug('sample 0 ref 2: {0}'.format(ref[2][1234]))
logger.debug('sample 0 ref 3: {0}'.format(ref[3][1234]))
logger.debug('sample 0 ref 4: {0}'.format(ref[4][1234]))
logger.debug('sample 1 pred: {0}'.format(hyp[2345]))
logger.debug('sample 1 ref 0: {0}'.format(ref[0][2345]))
logger.debug('sample 1 ref 1: {0}'.format(ref[1][2345]))
logger.debug('sample 1 ref 2: {0}'.format(ref[2][2345]))
logger.debug('sample 1 ref 3: {0}'.format(ref[3][2345]))
logger.debug('sample 1 ref 4: {0}'.format(ref[4][2345]))
metrics = nlg.compute_metrics(ref, hyp)
meteor = metrics['METEOR']
delta_loss = meteor - last_loss
last_loss = meteor
logger.info('val METEOR after epoch %d: %.10f' % (epoch + 1, meteor))
logger.info('METEOR change after last epoch: %.10f' % delta_loss)
scheduler.step()
if not (epoch + 1) % save_every:
with open('{0}_{1}.pkl'.format(model_save_path, epoch + 1),
'wb') as fp:
logger.info("writing checkpoint " + str(epoch + 1))
torch.save(model, fp)
logger.info("training complete. writing final model.")
with open('{0}.pkl'.format(model_save_path), 'wb') as fp:
torch.save(model, fp)