def train(opt):
# opt.use_att = utils.if_use_att(opt.caption_model)
opt.use_att = True
if opt.use_box: opt.att_feat_size = opt.att_feat_size + 5
loader = DataLoader(opt)
opt.vocab_size = loader.vocab_size
opt.seq_length = loader.seq_length
print(opt.checkpoint_path)
tb_summary_writer = tb and tb.SummaryWriter(opt.checkpoint_path)
infos = {}
histories = {}
if opt.start_from is not None:
# open old infos and check if models are compatible
with open(os.path.join(opt.start_from,
'infos_' + opt.id + '.pkl')) as f:
infos = cPickle.load(f)
saved_model_opt = infos['opt']
need_be_same = [
"caption_model", "rnn_type", "rnn_size", "num_layers"
]
for checkme in need_be_same:
assert vars(saved_model_opt)[checkme] == vars(
opt
)[checkme], "Command line argument and saved model disagree on '%s' " % checkme
if os.path.isfile(
os.path.join(opt.start_from, 'histories_' + opt.id + '.pkl')):
with open(
os.path.join(opt.start_from,
'histories_' + opt.id + '.pkl')) as f:
histories = cPickle.load(f)
iteration = infos.get('iter', 0)
epoch = infos.get('epoch', 0)
val_result_history = histories.get('val_result_history', {})
loss_history = histories.get('loss_history', {})
critic_loss_history = histories.get('critic_loss_history', {})
lr_history = histories.get('lr_history', {})
ss_prob_history = histories.get('ss_prob_history', {})
variance_history = histories.get('variance_history', {})
time_history = histories.get('time_history', {})
loader.iterators = infos.get('iterators', loader.iterators)
loader.split_ix = infos.get('split_ix', loader.split_ix)
if opt.load_best_score == 1:
best_val_score = infos.get('best_val_score', None)
model = models.setup(opt).cuda()
dp_model = model
target_actor = models.setup(opt).cuda()
####################### Critic pretrain #####################################################################
##### Critic with state as input
# if opt.critic_model == 'state_critic':
# critic_model = CriticModel(opt)
# else:
critic_model = AttCriticModel(opt)
target_critic = AttCriticModel(opt)
if vars(opt).get('start_from_critic', None) is not None and True:
# check if all necessary files exist
assert os.path.isdir(opt.start_from_critic
), " %s must be a a path" % opt.start_from_critic
print(
os.path.join(opt.start_from_critic,
opt.critic_model + '_model.pth'))
critic_model.load_state_dict(
torch.load(
os.path.join(opt.start_from_critic,
opt.critic_model + '_model.pth')))
target_critic.load_state_dict(
torch.load(
os.path.join(opt.start_from_critic,
opt.critic_model + '_model.pth')))
critic_model = critic_model.cuda()
target_critic = target_critic.cuda()
critic_optimizer = utils.build_optimizer(critic_model.parameters(), opt)
dp_model.eval()
critic_iter = 0
init_scorer(opt.cached_tokens)
critic_model.train()
error_sum = 0
loss_vector_sum = 0
while opt.pretrain_critic == 1:
if critic_iter > opt.pretrain_critic_steps:
print('****************Finished critic training!')
break
data = loader.get_batch('train')
torch.cuda.synchronize()
start = time.time()
tmp = [
data['fc_feats'], data['att_feats'], data['labels'], data['masks'],
data['att_masks']
]
tmp = [_ if _ is None else torch.from_numpy(_).cuda() for _ in tmp]
fc_feats, att_feats, labels, masks, att_masks = tmp
critic_model.train()
critic_optimizer.zero_grad()
assert opt.critic_model == 'att_critic_vocab'
# crit_loss, reward, std = critic_loss_fun(fc_feats, att_feats, att_masks, dp_model, critic_model, opt, data)
crit_loss, reward, std = target_critic_loss_fun_mask(
fc_feats, att_feats, att_masks, dp_model, critic_model, opt, data,
target_critic, target_actor)
crit_loss.backward()
critic_optimizer.step()
#TODO update target.
for cp, tp in zip(critic_model.parameters(),
target_critic.parameters()):
tp.data = tp.data + opt.gamma_critic * (cp.data - tp.data)
crit_train_loss = crit_loss.item()
torch.cuda.synchronize
end = time.time()
error_sum += crit_train_loss**0.5 - std
if (critic_iter % opt.losses_log_every == 0):
print("iter {} , crit_train_loss = {:.3f}, difference = {:.3f}, difference_sum = {:.3f}, time/batch = {:.3f}" \
.format(critic_iter, crit_train_loss**0.5, crit_train_loss**0.5-std, error_sum, end - start))
print(opt.checkpoint_path)
opt.importance_sampling = 1
critic_model.eval()
_, _, _, _ = get_rf_loss(dp_model,
fc_feats,
att_feats,
att_masks,
data,
opt,
loader,
critic_model,
test_critic=True)
critic_iter += 1
# make evaluation on validation set, and save model
if (critic_iter % opt.save_checkpoint_every == 0):
if not os.path.isdir(opt.checkpoint_path):
os.mkdir(opt.checkpoint_path)
checkpoint_path = os.path.join(opt.checkpoint_path,
opt.critic_model + '_model.pth')
torch.save(critic_model.state_dict(), checkpoint_path)
######################### Actor-critic Training #####################################################################
update_lr_flag = True
# Assure in training mode
dp_model.train()
crit = utils.LanguageModelCriterion()
rl_crit = utils.RewardCriterion()
optimizer = utils.build_optimizer(model.parameters(), opt)
# Load the optimizer
if vars(opt).get('start_from', None) is not None and os.path.isfile(
os.path.join(opt.start_from, "optimizer.pth")):
optimizer.load_state_dict(
torch.load(os.path.join(opt.start_from, 'optimizer.pth')))
first_order = 0
second_order = 0
while True:
if update_lr_flag:
# Assign the learning rate
if epoch > opt.learning_rate_decay_start and opt.learning_rate_decay_start >= 0:
frac = (epoch - opt.learning_rate_decay_start
) // opt.learning_rate_decay_every
decay_factor = opt.learning_rate_decay_rate**frac
opt.current_lr = opt.learning_rate * decay_factor
else:
opt.current_lr = opt.learning_rate
utils.set_lr(optimizer, opt.current_lr)
# Assign the scheduled sampling prob
if epoch > opt.scheduled_sampling_start and opt.scheduled_sampling_start >= 0:
frac = (epoch - opt.scheduled_sampling_start
) // opt.scheduled_sampling_increase_every
opt.ss_prob = min(opt.scheduled_sampling_increase_prob * frac,
opt.scheduled_sampling_max_prob)
model.ss_prob = opt.ss_prob
# If start self critical training
if opt.self_critical_after != -1 and epoch >= opt.self_critical_after:
sc_flag = True
init_scorer(opt.cached_tokens)
else:
sc_flag = False
update_lr_flag = False
# Load data from train split (0)
data = loader.get_batch('train')
if data['bounds']['it_pos_now'] > 5000:
loader.reset_iterator('train')
continue
dp_model.train()
critic_model.eval()
torch.cuda.synchronize()
start = time.time()
gen_result = None
tmp = [
data['fc_feats'], data['att_feats'], data['labels'], data['masks'],
data['att_masks']
]
tmp = [_ if _ is None else torch.from_numpy(_).cuda() for _ in tmp]
fc_feats, att_feats, labels, masks, att_masks = tmp
optimizer.zero_grad()
if not sc_flag:
loss = crit(dp_model(fc_feats, att_feats, labels, att_masks),
labels[:, 1:], masks[:, 1:])
else:
if opt.rl_type == 'sc':
gen_result, sample_logprobs = dp_model(fc_feats,
att_feats,
att_masks,
opt={'sample_max': 0},
mode='sample')
reward = get_self_critical_reward(dp_model, fc_feats,
att_feats, att_masks, data,
gen_result, opt)
loss = rl_crit(sample_logprobs, gen_result.data,
torch.from_numpy(reward).float().cuda())
elif opt.rl_type == 'reinforce':
gen_result, sample_logprobs = dp_model(fc_feats,
att_feats,
att_masks,
opt={'sample_max': 0},
mode='sample')
reward = get_reward(data, gen_result, opt)
loss = rl_crit(sample_logprobs, gen_result.data,
torch.from_numpy(reward).float().cuda())
elif opt.rl_type == 'arsm':
loss = get_arm_loss(dp_model, fc_feats, att_feats, att_masks,
data, opt, loader)
#print(loss)
reward = np.zeros([2, 2])
elif opt.rl_type == 'rf4':
loss, _, _, _ = get_rf_loss(dp_model, fc_feats, att_feats,
att_masks, data, opt, loader)
# print(loss)
reward = np.zeros([2, 2])
elif opt.rl_type == 'importance_sampling':
opt.importance_sampling = 1
loss, gen_result, reward, sample_logprobs_total = get_rf_loss(
dp_model, fc_feats, att_feats, att_masks, data, opt,
loader)
reward = np.repeat(reward[:, np.newaxis], gen_result.shape[1],
1)
std = np.std(reward)
elif opt.rl_type == 'importance_sampling_critic':
opt.importance_sampling = 1
loss, gen_result, reward, sample_logprobs_total = get_rf_loss(
target_actor, fc_feats, att_feats, att_masks, data, opt,
loader, target_critic)
reward = np.repeat(reward[:, np.newaxis], gen_result.shape[1],
1)
std = np.std(reward)
elif opt.rl_type == 'ar':
loss = get_ar_loss(dp_model, fc_feats, att_feats, att_masks,
data, opt, loader)
reward = np.zeros([2, 2])
elif opt.rl_type == 'mct_baseline':
opt.rf_demean = 0
gen_result, sample_logprobs, probs, mct_baseline = get_mct_loss(
dp_model, fc_feats, att_feats, att_masks, data, opt,
loader)
reward = get_reward(data, gen_result, opt)
reward_cuda = torch.from_numpy(reward).float().cuda()
mct_baseline[mct_baseline < 0] = reward_cuda[mct_baseline < 0]
if opt.arm_step_sample == 'greedy':
sample_logprobs = sample_logprobs * probs
loss = rl_crit(
sample_logprobs, gen_result.data,
torch.from_numpy(reward).float().cuda() - mct_baseline)
elif opt.rl_type == 'arsm_baseline':
opt.arm_as_baseline = 1
opt.rf_demean = 0
gen_result, sample_logprobs, probs, arm_baseline = get_arm_loss(
dp_model, fc_feats, att_feats, att_masks, data, opt,
loader)
reward = get_reward(data, gen_result, opt)
reward_cuda = torch.from_numpy(reward).float().cuda()
arm_baseline[arm_baseline < 0] = reward_cuda[arm_baseline < 0]
if opt.arm_step_sample == 'greedy' and False:
sample_logprobs = sample_logprobs * probs
loss = rl_crit(sample_logprobs, gen_result.data,
reward_cuda - arm_baseline)
elif opt.rl_type == 'ars_indicator':
opt.arm_as_baseline = 1
opt.rf_demean = 0
gen_result, sample_logprobs, probs, arm_baseline = get_arm_loss(
dp_model, fc_feats, att_feats, att_masks, data, opt,
loader)
reward = get_self_critical_reward(dp_model, fc_feats,
att_feats, att_masks, data,
gen_result, opt)
reward_cuda = torch.from_numpy(reward).float().cuda()
loss = rl_crit(sample_logprobs, gen_result.data,
reward_cuda * arm_baseline)
elif opt.rl_type == 'arsm_baseline_critic':
opt.arm_as_baseline = 1
opt.rf_demean = 0
gen_result, sample_logprobs, probs, arm_baseline = get_arm_loss(
dp_model, fc_feats, att_feats, att_masks, data, opt,
loader, critic_model)
reward, std = get_reward(data, gen_result, opt, critic=True)
if opt.arm_step_sample == 'greedy':
sample_logprobs = sample_logprobs * probs
loss = rl_crit(
sample_logprobs, gen_result.data,
torch.from_numpy(reward).float().cuda() - arm_baseline)
elif opt.rl_type == 'arsm_critic':
#print(opt.critic_model)
tic = time.time()
loss = get_arm_loss(dp_model, fc_feats, att_feats, att_masks,
data, opt, loader, critic_model)
#print('arm_loss time', str(time.time()-tic))
reward = np.zeros([2, 2])
elif opt.rl_type == 'critic_vocab_sum':
assert opt.critic_model == 'att_critic_vocab'
tic = time.time()
gen_result, sample_logprobs_total = dp_model(
fc_feats,
att_feats,
att_masks,
opt={'sample_max': 0},
total_probs=True,
mode='sample') #batch, seq, vocab
#print('generation time', time.time()-tic)
gen_result_pad = torch.cat([
gen_result.new_zeros(
gen_result.size(0), 1, dtype=torch.long), gen_result
], 1)
tic = time.time()
critic_value = critic_model(gen_result_pad, fc_feats,
att_feats, True, opt,
att_masks) #batch, seq, vocab
#print('critic time', time.time() - tic)
probs = torch.sum(
F.softmax(sample_logprobs_total, 2) *
critic_value.detach(), 2)
mask = (gen_result > 0).float()
mask = torch.cat(
[mask.new(mask.size(0), 1).fill_(1), mask[:, :-1]], 1)
loss = -torch.sum(probs * mask) / torch.sum(mask)
reward = np.zeros([2, 2])
elif opt.rl_type == 'reinforce_critic':
#TODO change the critic to attention
if opt.critic_model == 'state_critic':
critic_value, gen_result, sample_logprobs = critic_model(
dp_model, fc_feats, att_feats, opt, att_masks)
reward, std = get_reward(data,
gen_result,
opt,
critic=True)
loss = rl_crit(
sample_logprobs, gen_result.data,
torch.from_numpy(reward).float().cuda() -
critic_value[:, :-1].data)
elif opt.critic_model == 'att_critic':
gen_result, sample_logprobs = dp_model(
fc_feats,
att_feats,
att_masks,
opt={'sample_max': 0},
mode='sample')
gen_result_pad = torch.cat([
gen_result.new_zeros(gen_result.size(0),
1,
dtype=torch.long), gen_result
], 1)
critic_value = critic_model(gen_result_pad, fc_feats,
att_feats, True, opt,
att_masks).squeeze(2)
reward, std = get_reward(data,
gen_result,
opt,
critic=True)
loss = rl_crit(
sample_logprobs, gen_result.data,
torch.from_numpy(reward).float().cuda() -
critic_value.data)
if opt.mle_weights != 0:
loss += opt.mle_weights * crit(
dp_model(fc_feats, att_feats, labels, att_masks),
labels[:, 1:], masks[:, 1:])
#TODO make sure all sampling replaced by greedy for critic
#### update the actor
loss.backward()
# with open(os.path.join(opt.checkpoint_path, 'best_embed.pkl'), 'wb') as f:
# cPickle.dump(list(dp_model.embed.parameters())[0].data.cpu().numpy(), f)
# with open(os.path.join(opt.checkpoint_path, 'best_logit.pkl'), 'wb') as f:
# cPickle.dump(list(dp_model.logit.parameters())[0].data.cpu().numpy(), f)
## compute variance
gradient = torch.zeros([0]).cuda()
for i in model.parameters():
gradient = torch.cat((gradient, i.grad.view(-1)), 0)
first_order = 0.9999 * first_order + 0.0001 * gradient
second_order = 0.9999 * second_order + 0.0001 * gradient.pow(2)
# print(torch.max(torch.abs(gradient)))
variance = torch.mean(torch.abs(second_order -
first_order.pow(2))).item()
if opt.rl_type != 'arsm' or not sc_flag:
utils.clip_gradient(optimizer, opt.grad_clip)
optimizer.step()
# ### update the critic
if 'critic' in opt.rl_type:
dp_model.eval()
critic_model.train()
utils.set_lr(critic_optimizer, opt.critic_learning_rate)
critic_optimizer.zero_grad()
assert opt.critic_model == 'att_critic_vocab'
crit_loss, reward, std = target_critic_loss_fun_mask(
fc_feats,
att_feats,
att_masks,
dp_model,
critic_model,
opt,
data,
target_critic,
target_actor,
gen_result=gen_result,
sample_logprobs_total=sample_logprobs_total,
reward=reward)
crit_loss.backward()
critic_optimizer.step()
for cp, tp in zip(critic_model.parameters(),
target_critic.parameters()):
tp.data = tp.data + opt.gamma_critic * (cp.data - tp.data)
for cp, tp in zip(dp_model.parameters(),
target_actor.parameters()):
tp.data = tp.data + opt.gamma_actor * (cp.data - tp.data)
crit_train_loss = crit_loss.item()
error_sum += crit_train_loss**0.5 - std
train_loss = loss.item()
torch.cuda.synchronize()
end = time.time()
if (iteration % opt.losses_log_every == 0):
if not sc_flag:
print("iter {} (epoch {}), train_loss = {:.3f}, time/batch = {:.3f}" \
.format(iteration, epoch, train_loss, end - start))
print(opt.checkpoint_path)
elif 'critic' in opt.rl_type:
print(
"iter {} , crit_train_loss = {:.3f}, difference = {:.3f}, difference_sum = {:.3f},variance = {:g}, time/batch = {:.3f}" \
.format(iteration, crit_train_loss ** 0.5, crit_train_loss ** 0.5 - std, error_sum, variance, end - start))
print(opt.checkpoint_path)
critic_model.eval()
_, _, _, _ = get_rf_loss(dp_model,
fc_feats,
att_feats,
att_masks,
data,
opt,
loader,
critic_model,
test_critic=True)
else:
print("iter {} (epoch {}), avg_reward = {:.3f}, variance = {:g}, time/batch = {:.3f}" \
.format(iteration, epoch, np.mean(reward[:, 0]), variance, end - start))
# Update the iteration and epoch
iteration += 1
if data['bounds']['wrapped']:
epoch += 1
update_lr_flag = True
# Write the training loss summary
if (iteration % opt.losses_log_every == 0):
add_summary_value(tb_summary_writer, 'train_loss', train_loss,
iteration)
add_summary_value(tb_summary_writer, 'learning_rate',
opt.current_lr, iteration)
add_summary_value(tb_summary_writer, 'scheduled_sampling_prob',
model.ss_prob, iteration)
if sc_flag:
add_summary_value(tb_summary_writer, 'avg_reward',
np.mean(reward), iteration)
add_summary_value(tb_summary_writer, 'variance', variance,
iteration)
loss_history[iteration] = train_loss if not sc_flag else np.mean(
reward)
critic_loss_history[
iteration] = crit_train_loss if 'critic' in opt.rl_type else 0
lr_history[iteration] = opt.current_lr
ss_prob_history[iteration] = model.ss_prob
variance_history[iteration] = variance
time_history[iteration] = end - start
# make evaluation on validation set, and save model
if (iteration % opt.save_checkpoint_every == 0):
# eval model
eval_kwargs = {'split': 'val', 'dataset': opt.input_json}
eval_kwargs.update(vars(opt))
val_loss, predictions, lang_stats = eval_utils.eval_split(
dp_model, crit, loader, eval_kwargs)
# Write validation result into summary
add_summary_value(tb_summary_writer, 'validation loss', val_loss,
iteration)
if lang_stats is not None:
for k, v in lang_stats.items():
add_summary_value(tb_summary_writer, k, v, iteration)
val_result_history[iteration] = {
'loss': val_loss,
'lang_stats': lang_stats,
'predictions': predictions
}
# Save model if is improving on validation result
if opt.language_eval == 1:
current_score = lang_stats['CIDEr']
else:
current_score = -val_loss
best_flag = False
if True: # if true
if best_val_score is None or current_score > best_val_score:
best_val_score = current_score
best_flag = True
if not os.path.isdir(opt.checkpoint_path):
os.mkdir(opt.checkpoint_path)
checkpoint_path = os.path.join(opt.checkpoint_path,
'model.pth')
torch.save(model.state_dict(), checkpoint_path)
checkpoint_path = os.path.join(opt.checkpoint_path,
opt.critic_model + '_model.pth')
torch.save(critic_model.state_dict(), checkpoint_path)
print("model saved to {}".format(checkpoint_path))
optimizer_path = os.path.join(opt.checkpoint_path,
'optimizer.pth')
torch.save(optimizer.state_dict(), optimizer_path)
# Dump miscalleous informations
infos['iter'] = iteration
infos['epoch'] = epoch
infos['iterators'] = loader.iterators
infos['split_ix'] = loader.split_ix
infos['best_val_score'] = best_val_score
infos['opt'] = opt
infos['vocab'] = loader.get_vocab()
histories['val_result_history'] = val_result_history
histories['loss_history'] = loss_history
histories['critic_loss_history'] = critic_loss_history
histories['lr_history'] = lr_history
histories['ss_prob_history'] = ss_prob_history
histories['variance_history'] = variance_history
histories['time'] = time_history
# histories['variance'] = 0
with open(
os.path.join(opt.checkpoint_path,
'infos_' + opt.id + '.pkl'), 'wb') as f:
cPickle.dump(infos, f)
with open(
os.path.join(opt.checkpoint_path,
'histories_' + opt.id + '.pkl'),
'wb') as f:
cPickle.dump(histories, f)
if best_flag:
checkpoint_path = os.path.join(opt.checkpoint_path,
'model-best.pth')
torch.save(model.state_dict(), checkpoint_path)
print("model saved to {}".format(checkpoint_path))
with open(
os.path.join(opt.checkpoint_path,
'infos_' + opt.id + '-best.pkl'),
'wb') as f:
cPickle.dump(infos, f)
# Stop if reaching max epochs
if epoch >= opt.max_epochs and opt.max_epochs != -1:
break
def train(opt):
# opt.use_att = utils.if_use_att(opt.caption_model)
opt.use_att = True
if opt.use_box: opt.att_feat_size = opt.att_feat_size + 5
loader = DataLoader(opt)
opt.vocab_size = loader.vocab_size
opt.seq_length = loader.seq_length
print(opt.checkpoint_path)
tb_summary_writer = tb and tb.SummaryWriter(opt.checkpoint_path)
infos = {}
histories = {}
if opt.start_from is not None:
# open old infos and check if models are compatible
with open(os.path.join(opt.start_from, 'infos_'+opt.id+'.pkl')) as f:
infos = cPickle.load(f)
saved_model_opt = infos['opt']
need_be_same=["caption_model", "rnn_type", "rnn_size", "num_layers"]
for checkme in need_be_same:
assert vars(saved_model_opt)[checkme] == vars(opt)[checkme], "Command line argument and saved model disagree on '%s' " % checkme
if os.path.isfile(os.path.join(opt.start_from, 'histories_'+opt.id+'.pkl')):
with open(os.path.join(opt.start_from, 'histories_'+opt.id+'.pkl')) as f:
histories = cPickle.load(f)
iteration = infos.get('iter', 0)
epoch = infos.get('epoch', 0)
val_result_history = histories.get('val_result_history', {})
loss_history = histories.get('loss_history', {})
critic_loss_history = histories.get('critic_loss_history', {})
lr_history = histories.get('lr_history', {})
ss_prob_history = histories.get('ss_prob_history', {})
variance_history = histories.get('variance_history', {})
time_history = histories.get('time_history', {})
loader.iterators = infos.get('iterators', loader.iterators)
loader.split_ix = infos.get('split_ix', loader.split_ix)
if opt.load_best_score == 1:
best_val_score = infos.get('best_val_score', None)
model = models.setup(opt).cuda()
dp_model = model
######################### Actor-critic Training #####################################################################
update_lr_flag = True
# Assure in training mode
dp_model.train()
#TODO: change this to a flag
crit = utils.LanguageModelCriterion_binary()
rl_crit = utils.RewardCriterion_binary()
optimizer = utils.build_optimizer(model.parameters(), opt)
# Load the optimizer
if vars(opt).get('start_from', None) is not None and os.path.isfile(os.path.join(opt.start_from,"optimizer.pth")):
optimizer.load_state_dict(torch.load(os.path.join(opt.start_from, 'optimizer.pth')))
first_order = 0
second_order = 0
while True:
if update_lr_flag:
# Assign the learning rate
if epoch > opt.learning_rate_decay_start and opt.learning_rate_decay_start >= 0:
frac = (epoch - opt.learning_rate_decay_start) // opt.learning_rate_decay_every
decay_factor = opt.learning_rate_decay_rate ** frac
opt.current_lr = opt.learning_rate * decay_factor
else:
opt.current_lr = opt.learning_rate
utils.set_lr(optimizer, opt.current_lr)
# Assign the scheduled sampling prob
if epoch > opt.scheduled_sampling_start and opt.scheduled_sampling_start >= 0:
frac = (epoch - opt.scheduled_sampling_start) // opt.scheduled_sampling_increase_every
opt.ss_prob = min(opt.scheduled_sampling_increase_prob * frac, opt.scheduled_sampling_max_prob)
model.ss_prob = opt.ss_prob
# If start self critical training
if opt.self_critical_after != -1 and epoch >= opt.self_critical_after:
sc_flag = True
init_scorer(opt.cached_tokens)
else:
sc_flag = False
update_lr_flag = False
# Load data from train split (0)
data = loader.get_batch('train')
if data['bounds']['it_pos_now'] > 10000:
loader.reset_iterator('train')
continue
dp_model.train()
torch.cuda.synchronize()
start = time.time()
gen_result = None
tmp = [data['fc_feats'], data['att_feats'], data['labels'], data['masks'], data['att_masks']]
tmp = [_ if _ is None else torch.from_numpy(_).cuda() for _ in tmp]
fc_feats, att_feats, labels, masks, att_masks = tmp
optimizer.zero_grad()
if not sc_flag:
loss = crit(dp_model(fc_feats, att_feats, labels, att_masks), labels[:,1:], masks[:,1:], dp_model.depth,
dp_model.vocab2code, dp_model.phi_list, dp_model.cluster_size)
else:
if opt.rl_type == 'sc':
gen_result, sample_logprobs = dp_model(fc_feats, att_feats, att_masks, opt={'sample_max':0}, mode='sample')
reward = get_self_critical_reward(dp_model, fc_feats, att_feats, att_masks, data, gen_result, opt)
loss = rl_crit(sample_logprobs, gen_result.data, torch.from_numpy(reward).float().cuda(), dp_model.depth)
elif opt.rl_type == 'reinforce':
gen_result, sample_logprobs = dp_model(fc_feats, att_feats, att_masks, opt={'sample_max':0}, mode='sample')
reward = get_reward(data, gen_result, opt)
loss = rl_crit(sample_logprobs, gen_result.data, torch.from_numpy(reward).float().cuda(), dp_model.depth)
elif opt.rl_type == 'arm':
loss = dp_model.get_arm_loss_binary_fast(fc_feats, att_feats, att_masks, opt, data, loader)
#print(loss)
reward = np.zeros([2,2])
elif opt.rl_type == 'rf4':
loss,_,_,_ = get_rf_loss(dp_model, fc_feats, att_feats, att_masks, data, opt, loader)
# print(loss)
reward = np.zeros([2, 2])
elif opt.rl_type == 'ar':
loss = get_ar_loss(dp_model, fc_feats, att_feats, att_masks, data, opt, loader)
reward = np.zeros([2,2])
elif opt.rl_type =='mct_baseline':
opt.rf_demean = 0
gen_result, sample_logprobs, probs, mct_baseline = get_mct_loss(dp_model, fc_feats, att_feats, att_masks, data,
opt, loader)
reward = get_reward(data, gen_result, opt)
reward_cuda = torch.from_numpy(reward).float().cuda()
mct_baseline[mct_baseline < 0] = reward_cuda[mct_baseline < 0]
if opt.arm_step_sample == 'greedy':
sample_logprobs = sample_logprobs * probs
loss = rl_crit(sample_logprobs, gen_result.data, torch.from_numpy(reward).float().cuda() - mct_baseline)
elif opt.rl_type == 'arsm_baseline':
opt.arm_as_baseline = 1
opt.rf_demean = 0
gen_result, sample_logprobs, probs, arm_baseline = get_arm_loss(dp_model, fc_feats, att_feats, att_masks, data, opt, loader)
reward = get_reward(data, gen_result, opt)
reward_cuda = torch.from_numpy(reward).float().cuda()
arm_baseline[arm_baseline < 0] = reward_cuda[arm_baseline < 0]
if opt.arm_step_sample == 'greedy' and False:
sample_logprobs = sample_logprobs * probs
loss = rl_crit(sample_logprobs, gen_result.data, reward_cuda - arm_baseline)
elif opt.rl_type == 'ars_indicator':
opt.arm_as_baseline = 1
opt.rf_demean = 0
gen_result, sample_logprobs, probs, arm_baseline = get_arm_loss(dp_model, fc_feats, att_feats, att_masks, data, opt, loader)
reward = get_self_critical_reward(dp_model, fc_feats, att_feats, att_masks, data, gen_result, opt)
reward_cuda = torch.from_numpy(reward).float().cuda()
loss = rl_crit(sample_logprobs, gen_result.data, reward_cuda * arm_baseline)
if opt.mle_weights != 0:
loss += opt.mle_weights * crit(dp_model(fc_feats, att_feats, labels, att_masks), labels[:, 1:], masks[:, 1:])
#TODO make sure all sampling replaced by greedy for critic
#### update the actor
loss.backward()
# with open(os.path.join(opt.checkpoint_path, 'embeddings.pkl'), 'wb') as f:
# cPickle.dump(list(dp_model.embed.parameters())[0].data.cpu().numpy(), f)
## compute variance
gradient = torch.zeros([0]).cuda()
for i in model.parameters():
gradient = torch.cat((gradient, i.grad.view(-1)), 0)
first_order = 0.999 * first_order + 0.001 * gradient
second_order = 0.999 * second_order + 0.001 * gradient.pow(2)
# print(torch.max(torch.abs(gradient)))
variance = torch.mean(torch.abs(second_order - first_order.pow(2))).item()
if opt.rl_type != 'arsm' or not sc_flag:
utils.clip_gradient(optimizer, opt.grad_clip)
optimizer.step()
# ### update the critic
train_loss = loss.item()
torch.cuda.synchronize()
end = time.time()
if (iteration % opt.losses_log_every == 0):
if not sc_flag:
print("iter {} (epoch {}), train_loss = {:.3f}, time/batch = {:.3f}" \
.format(iteration, epoch, train_loss, end - start))
print(opt.checkpoint_path)
else:
print("iter {} (epoch {}), avg_reward = {:.3f}, variance = {:g}, time/batch = {:.3f}" \
.format(iteration, epoch, np.mean(reward[:, 0]), variance, end - start))
# Update the iteration and epoch
iteration += 1
if data['bounds']['wrapped']:
epoch += 1
update_lr_flag = True
# Write the training loss summary
if (iteration % opt.losses_log_every == 0):
add_summary_value(tb_summary_writer, 'train_loss', train_loss, iteration)
add_summary_value(tb_summary_writer, 'learning_rate', opt.current_lr, iteration)
add_summary_value(tb_summary_writer, 'scheduled_sampling_prob', model.ss_prob, iteration)
if sc_flag:
add_summary_value(tb_summary_writer, 'avg_reward', np.mean(reward), iteration)
add_summary_value(tb_summary_writer, 'variance', variance, iteration)
loss_history[iteration] = train_loss if not sc_flag else np.mean(reward)
lr_history[iteration] = opt.current_lr
ss_prob_history[iteration] = model.ss_prob
variance_history[iteration] = variance
time_history[iteration] = end - start
# make evaluation on validation set, and save model
if (iteration % opt.save_checkpoint_every == 0):
# eval model
eval_kwargs = {'split': 'val',
'dataset': opt.input_json}
eval_kwargs.update(vars(opt))
val_loss, predictions, lang_stats = eval_utils_binary.eval_split(dp_model, crit, loader, eval_kwargs)
# Write validation result into summary
add_summary_value(tb_summary_writer, 'validation loss', val_loss, iteration)
if lang_stats is not None:
for k,v in lang_stats.items():
add_summary_value(tb_summary_writer, k, v, iteration)
val_result_history[iteration] = {'loss': val_loss, 'lang_stats': lang_stats, 'predictions': predictions}
# Save model if is improving on validation result
if opt.language_eval == 1:
current_score = lang_stats['CIDEr']
else:
current_score = - val_loss
best_flag = False
if True: # if true
if best_val_score is None or current_score > best_val_score:
best_val_score = current_score
best_flag = True
if not os.path.isdir(opt.checkpoint_path):
os.mkdir(opt.checkpoint_path)
checkpoint_path = os.path.join(opt.checkpoint_path, 'model.pth')
torch.save(model.state_dict(), checkpoint_path)
checkpoint_path = os.path.join(opt.checkpoint_path, opt.critic_model + '_model.pth')
print("model saved to {}".format(checkpoint_path))
optimizer_path = os.path.join(opt.checkpoint_path, 'optimizer.pth')
torch.save(optimizer.state_dict(), optimizer_path)
# Dump miscalleous informations
infos['iter'] = iteration
infos['epoch'] = epoch
infos['iterators'] = loader.iterators
infos['split_ix'] = loader.split_ix
infos['best_val_score'] = best_val_score
infos['opt'] = opt
infos['vocab'] = loader.get_vocab()
histories['val_result_history'] = val_result_history
histories['loss_history'] = loss_history
histories['critic_loss_history'] = critic_loss_history
histories['lr_history'] = lr_history
histories['ss_prob_history'] = ss_prob_history
histories['variance_history'] = variance_history
histories['time'] = time_history
# histories['variance'] = 0
with open(os.path.join(opt.checkpoint_path, 'infos_'+opt.id+'.pkl'), 'wb') as f:
cPickle.dump(infos, f)
with open(os.path.join(opt.checkpoint_path, 'histories_'+opt.id+'.pkl'), 'wb') as f:
cPickle.dump(histories, f)
if best_flag:
checkpoint_path = os.path.join(opt.checkpoint_path, 'model-best.pth')
torch.save(model.state_dict(), checkpoint_path)
print("model saved to {}".format(checkpoint_path))
with open(os.path.join(opt.checkpoint_path, 'infos_'+opt.id+'-best.pkl'), 'wb') as f:
cPickle.dump(infos, f)
# Stop if reaching max epochs
if epoch >= opt.max_epochs and opt.max_epochs != -1:
break