def train_cnn(model_cnn, images, bus, fc_expander, att_expander, bu_expander,
use_reinforce):
fc_feats = None
att_feats = None
bu_feats = None
# train cnn
if models.is_only_fc_feat(opt.caption_model):
fc_feats = model_cnn(images)
if opt.seq_per_img > 1 and not use_reinforce:
fc_feats = fc_expander(fc_feats)
elif models.is_only_att_feat(opt.caption_model):
att_feats = model_cnn(images)
if opt.seq_per_img > 1 and not use_reinforce:
att_feats = att_expander(att_feats)
elif models.has_sub_region_bu(opt.caption_model):
fc_feats, att_feats, bu_feats = model_cnn(images)
if opt.seq_per_img > 1 and not use_reinforce:
fc_feats = fc_expander(fc_feats)
att_feats = att_expander(att_feats)
bu_feats = bu_expander(bu_feats)
else:
fc_feats, att_feats = model_cnn(images)
if opt.seq_per_img > 1 and not use_reinforce:
fc_feats = fc_expander(fc_feats)
att_feats = att_expander(att_feats)
if models.has_bu(opt.caption_model):
bus_feats = bus
if opt.seq_per_img > 1 and not use_reinforce:
bu_feats = bu_expander(bus_feats)
return fc_feats, att_feats, bu_feats
def compute_output(caption_model, beam_size, model, fc_feats, att_feats,
bu_feats):
if models.is_only_fc_feat(caption_model):
output = model.sample(fc_feats, {'beam_size': beam_size})
elif models.is_only_att_feat(caption_model):
output = model.sample(att_feats, {'beam_size': beam_size})
elif models.has_bu(caption_model) or models.has_sub_region_bu(
caption_model) or models.is_prob_weight_mul_out(caption_model):
output = model.sample(fc_feats, att_feats, bu_feats,
{'beam_size': beam_size})
else:
output = model.sample(fc_feats, att_feats, {'beam_size': beam_size})
return output
def train_normal(params, opt):
model = params['model']
fc_feats = params['fc_feats']
att_feats = params['att_feats']
labels = params['labels']
targets = params['targets']
masks = params['masks']
vocab = params['vocab']
crit = params['crit']
# forward
start = time.time()
if models.is_transformer(opt.caption_model):
output = model(att_feats, targets, masks)
elif models.is_ctransformer(opt.caption_model):
output = model(fc_feats, att_feats, targets, masks)
elif models.is_only_fc_feat(opt.caption_model):
output = model(fc_feats, labels)
elif models.is_only_att_feat(opt.caption_model):
output = model(att_feats, labels)
elif models.has_bu(opt.caption_model):
bu_feats = params['bu_feats']
output = model(fc_feats, att_feats, bu_feats, labels)
else:
output = model(fc_feats, att_feats, labels)
if opt.verbose:
print('model {:.3f}'.format(time.time() - start))
# compute the loss
start = time.time()
if models.is_prob_weight(opt.caption_model):
output = output[0]
loss = crit(output, labels, masks)
if opt.verbose:
print('crit {:.3f}'.format(time.time() - start))
# backward
start = time.time()
loss.backward()
if opt.verbose:
print('loss {:.3f}'.format(time.time() - start))
# show information
train_loss = loss.data[0]
reward_mean = 0
return train_loss, reward_mean
def compute_cnn_feats(caption_model, model_cnn, images):
fc_feats = None
att_feats = None
bu_feats = None
if models.is_only_fc_feat(caption_model):
fc_feats = model_cnn(images)
elif models.is_only_att_feat(caption_model):
att_feats = model_cnn(images)
elif caption_model == "SCST":
fc_feats, att_feats = model_cnn(images)
elif models.is_prob_weight(caption_model):
if models.has_sub_region_bu(caption_model):
fc_feats, att_feats, bu_feats = model_cnn(images)
else:
fc_feats, att_feats = model_cnn(images)
elif models.is_prob_weight_mul_out(caption_model):
fc_feats, att_feats = model_cnn(images)
else:
fc_feats, att_feats = model_cnn(images)
return fc_feats, att_feats, bu_feats
def train_reinforce(params, opt):
model = params['model']
fc_feats = params['fc_feats']
att_feats = params['att_feats']
labels = params['labels']
masks = params['masks']
vocab = params['vocab']
crit_pg = params['crit_pg']
crit_rl = params['crit_rl']
targets = params['targets']
gts = params['gts']
if models.has_bu(opt.caption_model) or models.has_sub_region_bu(
opt.caption_model):
bu_feats = params['bu_feats']
# compute policy gradient
if opt.reinforce_type == 0:
raise Exception('reinforce_type error, 0 is deprecated')
# forward
start = time.time()
if models.is_only_fc_feat(opt.caption_model):
output = model(fc_feats, labels)
else:
output = model(fc_feats, att_feats, labels)
if opt.verbose:
print('model {:.3f}'.format(time.time() - start))
train_loss, reward_mean = crit_pg.forward_backward(
output, labels, masks, vocab)
# self-critical
elif opt.reinforce_type == 1:
# forward
start = time.time()
if models.is_only_fc_feat(opt.caption_model):
sample_seq, sample_seqLogprobs = model.sample(
fc_feats, {'sample_max': 0})
greedy_seq, greedy_seqLogprobs = model.sample(
fc_feats, {'sample_max': 1})
elif models.is_only_att_feat(opt.caption_model):
sample_seq, sample_seqLogprobs = model.sample(
att_feats, {'sample_max': 0})
greedy_seq, greedy_seqLogprobs = model.sample(
att_feats, {'sample_max': 1})
elif models.has_bu(opt.caption_model) or models.has_sub_region_bu(
opt.caption_model):
sample_seq, sample_seqLogprobs = model.sample(
fc_feats, att_feats, bu_feats, {'sample_max': 0})
greedy_seq, greedy_seqLogprobs = model.sample(
fc_feats, att_feats, bu_feats, {'sample_max': 1})
else:
# sample_seq, sample_seqLogprobs = model.sample_forward(fc_feats, att_feats, labels, {'sample_max': 0})
# greedy_seq, greedy_seqLogprobs = model.sample_forward(fc_feats, att_feats, labels, {'sample_max': 1})
sample_output = model.sample(fc_feats, att_feats,
{'sample_max': 0})
greedy_output = model.sample(fc_feats, att_feats,
{'sample_max': 1})
sample_seq = sample_output[0]
sample_seqLogprobs = sample_output[1]
greedy_seq = greedy_output[0]
greedy_seqLogprobs = greedy_output[1]
if opt.verbose:
print('model {:.3f}'.format(time.time() - start))
# compute the loss
start = time.time()
# seq, seqLogprobs, seq1, target, vocab
loss, reward_mean, sample_mean, greedy_mean = crit_rl(
sample_seq, sample_seqLogprobs, greedy_seq, gts, masks)
# loss, reward_mean = crit_rl(sample_seq, sample_seqLogprobs, gts)
if opt.verbose:
print('crit {:.3f}'.format(time.time() - start))
# backward
start = time.time()
loss.backward()
if opt.verbose:
print('loss {:.3f}'.format(time.time() - start))
# show information
train_loss = loss.data[0]
return train_loss, reward_mean, sample_mean, greedy_mean
def eval_split_only(model_cnn, model, crit, loader, eval_kwargs={}):
verbose_eval = eval_kwargs.get('verbose_eval', True)
val_images_use = eval_kwargs.get('val_images_use', -1)
split = eval_kwargs.get('split', 'val')
lang_eval = eval_kwargs.get('language_eval', 1)
dataset = eval_kwargs.get('dataset', 'coco')
beam_size = eval_kwargs.get('beam_size', 1)
coco_caption_path = eval_kwargs.get('coco_caption_path', 'coco-caption')
caption_model = eval_kwargs.get('caption_model', '')
batch_size = eval_kwargs.get('batch_size', 2)
seq_per_img = eval_kwargs.get('seq_per_img', 5)
# Make sure in the evaluation mode
model_cnn.eval()
model.eval()
loader.reset_iterator(split)
n = 0
loss_sum = 0
loss_evals = 0
predictions = []
vocab = loader.get_vocab()
vocab_size = loader.get_vocab_size()
while True:
data = loader.get_batch(split, batch_size)
n = n + batch_size
images = data['images']
if models.is_only_fc_feat(caption_model):
fc_feats = model_cnn(images)
elif models.is_only_att_feat(caption_model):
att_feats = model_cnn(images)
elif caption_model == "SCST":
fc_feats, att_feats = model_cnn(images)
else:
fc_feats, att_feats = model_cnn(images)
if models.is_only_fc_feat(caption_model):
seq, _ = model.sample(fc_feats, {'beam_size': beam_size})
elif models.is_only_att_feat(caption_model):
seq, _ = model.sample(att_feats, {'beam_size': beam_size})
else:
seq, _ = model.sample(fc_feats, att_feats,
{'beam_size': beam_size})
#
sents = utils.decode_sequence(vocab, seq)
for k, sent in enumerate(sents):
entry = {'image_id': data['infos'][k]['id'], 'caption': sent}
predictions.append(entry)
if verbose_eval:
print('image %s: %s' % (entry['image_id'], entry['caption']))
ix0 = data['bounds']['it_pos_now']
ix1 = data['bounds']['it_max']
if val_images_use != -1:
ix1 = min(ix1, val_images_use)
for i in range(n - ix1):
predictions.pop()
if verbose_eval:
print('evaluating validation preformance... %d/%d' %
(ix0 - 1, ix1))
if data['bounds']['wrapped']:
break
if n >= val_images_use:
break
if lang_eval == 1:
lang_stats, str_stats = language_eval(dataset, predictions,
coco_caption_path)
# Switch back to training mode
model_cnn.train()
model.train()
return 0, predictions, lang_stats, str_stats
def compute_loss(crit, model, caption_model, seq_per_img, fc_expander,
att_expander, bu_expander, fc_feats, att_feats, bu_feats,
labels, masks, tokens):
if models.is_only_fc_feat(caption_model):
if seq_per_img > 1:
fc_feats_ext = fc_expander(fc_feats)
else:
fc_feats_ext = fc_feats
batch_outputs = model(fc_feats_ext, labels)
elif models.is_only_att_feat(caption_model):
if seq_per_img > 1:
att_feats_ext = att_expander(att_feats)
else:
att_feats_ext = att_feats
batch_outputs = model(att_feats_ext, labels)
elif caption_model == "SCST":
if seq_per_img > 1:
fc_feats_ext = fc_expander(fc_feats)
att_feats_ext = att_expander(att_feats)
else:
fc_feats_ext = fc_feats
att_feats_ext = att_feats
batch_outputs, _ = model(fc_feats_ext, att_feats_ext, labels, "train")
elif models.is_prob_weight(caption_model):
if models.has_sub_region_bu(caption_model):
if seq_per_img > 1:
fc_feats_ext = fc_expander(fc_feats)
att_feats_ext = att_expander(att_feats)
bu_feats_ext = bu_expander(bu_feats)
else:
fc_feats_ext = fc_feats
att_feats_ext = att_feats
bu_feats_ext = bu_feats
batch_outputs, prob_w = model(fc_feats_ext, att_feats_ext,
bu_feats_ext, labels)
else:
if seq_per_img > 1:
fc_feats_ext = fc_expander(fc_feats)
att_feats_ext = att_expander(att_feats)
else:
fc_feats_ext = fc_feats
att_feats_ext = att_feats
if models.has_bu(caption_model):
if seq_per_img > 1:
bu_feats_ext = bu_expander(bu_feats)
else:
bu_feats_ext = bu_feats
batch_outputs, prob_w = model(fc_feats_ext, att_feats_ext,
bu_feats_ext, labels)
else:
batch_outputs, prob_w = model(fc_feats_ext, att_feats_ext,
labels)
elif models.is_prob_weight_mul_out(caption_model):
if seq_per_img > 1:
fc_feats_ext = fc_expander(fc_feats)
att_feats_ext = att_expander(att_feats)
else:
fc_feats_ext = fc_feats
att_feats_ext = att_feats
if models.has_bu(caption_model):
if seq_per_img > 1:
bu_feats_ext = bu_expander(bu_feats)
else:
bu_feats_ext = bu_feats
batch_outputs, prob_w = model(fc_feats_ext, att_feats_ext,
bu_feats_ext, labels)
else:
batch_outputs, prob_w = model(fc_feats_ext, att_feats_ext, labels)
else:
if seq_per_img > 1:
fc_feats_ext = fc_expander(fc_feats)
att_feats_ext = att_expander(att_feats)
else:
fc_feats_ext = fc_feats
att_feats_ext = att_feats
if models.has_bu(caption_model):
if seq_per_img > 1:
bu_feats_ext = bu_expander(bu_feats)
else:
bu_feats_ext = bu_feats
batch_outputs = model(fc_feats_ext, att_feats_ext, bu_feats_ext,
labels)
else:
batch_outputs = model(fc_feats_ext, att_feats_ext, labels)
if models.is_prob_weight(caption_model) or models.is_prob_weight_mul_out(
caption_model):
loss = crit(batch_outputs, labels, masks, prob_w, tokens)
else:
loss = crit(batch_outputs, labels, masks)
loss.backward()
return loss.data[0]