def run(args, local_rank):
""" Distributed Synchronous """
torch.manual_seed(1234)
vocab = Vocab(args.vocab, min_occur_cnt=args.min_occur_cnt, specials=[])
if (args.world_size == 1 or dist.get_rank() == 0):
print ("vocab.size = %d"%vocab.size, flush=True)
model = BIGLM(local_rank, vocab, args.embed_dim, args.ff_embed_dim,\
args.num_heads, args.dropout, args.layers, args.smoothing, args.approx)
if args.start_from is not None:
ckpt = torch.load(args.start_from, map_location='cpu')
model.load_state_dict(ckpt['model'])
model = model.cuda(local_rank)
if args.world_size > 1:
torch.manual_seed(1234 + dist.get_rank())
random.seed(5678 + dist.get_rank())
optimizer = Optim(model.embed_dim, args.lr, args.warmup_steps, torch.optim.Adam(model.parameters(), lr=0, betas=(0.9, 0.998), eps=1e-9))
if args.start_from is not None:
optimizer.load_state_dict(ckpt['optimizer'])
#train_data = DataLoader(vocab, args.train_data+"0"+str(local_rank), args.batch_size, args.max_len, args.min_len)
train_data = DataLoader(vocab, args.train_data, args.batch_size, args.max_len, args.min_len)
batch_acm = 0
acc_acm, nll_acm, ppl_acm, ntokens_acm, nxs, npairs_acm, loss_acm = 0., 0., 0., 0., 0., 0., 0.
while True:
model.train()
for truth, inp, msk in train_data:
batch_acm += 1
truth = truth.cuda(local_rank)
inp = inp.cuda(local_rank)
msk = msk.cuda(local_rank)
model.zero_grad()
res, loss, acc, nll, ppl, ntokens, npairs = model(truth, inp, msk)
loss_acm += loss.item()
acc_acm += acc
nll_acm += nll
ppl_acm += ppl
ntokens_acm += ntokens
npairs_acm += npairs
nxs += npairs
loss.backward()
if args.world_size > 1:
average_gradients(model)
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
if (args.world_size==1 or dist.get_rank() ==0) and batch_acm%args.print_every == -1%args.print_every:
print ('batch_acm %d, loss %.3f, acc %.3f, nll %.3f, ppl %.3f, x_acm %d, lr %.6f'\
%(batch_acm, loss_acm/args.print_every, acc_acm/ntokens_acm, \
nll_acm/nxs, ppl_acm/nxs, npairs_acm, optimizer._rate), flush=True)
acc_acm, nll_acm, ppl_acm, ntokens_acm, loss_acm, nxs = 0., 0., 0., 0., 0., 0.
if (args.world_size==1 or dist.get_rank() ==0) and batch_acm%args.save_every == -1%args.save_every:
if not os.path.exists(args.save_dir):
os.mkdir(args.save_dir)
torch.save({'args':args, 'model':model.state_dict(), 'optimizer':optimizer.state_dict()}, '%s/epoch%d_batch_%d'%(args.save_dir, train_data.epoch_id, batch_acm))
start_time = time.time()
for epoch in range(1, args.max_iter + 1):
trn_loader = Data.get_batches(Data.trn_set,
batch_size=args.batch_size,
shuffle=True)
for bidx in range(n_batchs):
model.train()
inputs = next(trn_loader)
X_p, X_f, Y_true = inputs[0], inputs[1], inputs[2]
model.zero_grad()
X_p_dec = model(X_p)
loss = torch.mean((X_p - X_p_dec)**2)
loss.backward()
optimizer.step()
update += 1
if update % args.eval_freq == 0:
# ========= Main block for evaluate MMD(X_p_enc, X_f_enc) on RNN codespace =========#
val_dict = valid_epoch(Data, Data.val_set, model, args.batch_size,
Y_val, L_val, args.model)
tst_dict = valid_epoch(Data, Data.tst_set, model, args.batch_size,
Y_tst, L_tst, args.model)
total_time = time.time() - start_time
print(
'iter %4d tm %4.2fm trn_loss %.4e val_mse %.4f val_mae %.4f val_auc %.6f'
% (epoch, total_time / 60.0, loss.data[0], val_dict['mse'],
val_dict['mae'], val_dict['auc']),
end='')
print(" tst_mse %.4f tst_mae %.4f tst_auc %.6f" %
def main(hparams: HParams):
'''
setup training.
'''
if torch.cuda.is_available() and not hparams.gpus:
warnings.warn(
'WARNING: you have a CUDA device, so you should probably run with -gpus 0'
)
device = torch.device(hparams.gpus if torch.cuda.is_available() else 'cpu')
# data setup
print(f"Loading vocabulary...")
text_preprocessor = TextPreprocessor.load(hparams.preprocessor_path)
transform = transforms.Compose([
transforms.Resize([hparams.img_size, hparams.img_size]),
transforms.RandomCrop([hparams.crop_size, hparams.crop_size]),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
])
# create dataloader
print('Creating DataLoader...')
normal_data_loader = get_image_caption_loader(
hparams.img_dir,
hparams.normal_caption_path,
text_preprocessor,
hparams.normal_batch_size,
transform,
shuffle=True,
num_workers=hparams.num_workers,
)
style_data_loader = get_caption_loader(
hparams.style_caption_path,
text_preprocessor,
batch_size=hparams.style_batch_size,
shuffle=True,
num_workers=hparams.num_workers,
)
if hparams.train_from:
# loading checkpoint
print('Loading checkpoint...')
checkpoint = torch.load(hparams.train_from)
else:
normal_opt = Optim(
hparams.optimizer,
hparams.normal_lr,
hparams.max_grad_norm,
hparams.lr_decay,
hparams.start_decay_at,
)
style_opt = Optim(
hparams.optimizer,
hparams.style_lr,
hparams.max_grad_norm,
hparams.lr_decay,
hparams.start_decay_at,
)
print('Building model...')
encoder = EncoderCNN(hparams.hidden_dim)
decoder = FactoredLSTM(hparams.embed_dim,
text_preprocessor.vocab_size,
hparams.hidden_dim,
hparams.style_dim,
hparams.num_layers,
hparams.random_init,
hparams.dropout_ratio,
train=True,
device=device)
encoder = encoder.to(device)
decoder = decoder.to(device)
# loss and optimizer
criterion = nn.CrossEntropyLoss(ignore_index=text_preprocessor.PAD_ID)
normal_params = list(encoder.parameters()) + list(
decoder.default_parameters())
style_params = list(decoder.style_parameters())
normal_opt.set_parameters(normal_params)
style_opt.set_parameters(style_params)
if hparams.train_from:
encoder.load_state_dict(checkpoint['encoder'])
decoder.load_state_dict(checkpoint['decoder'])
normal_opt.load_state_dict(checkpoint['normal_opt'])
style_opt.load_state_dict(checkpoint['style_opt'])
# traininig loop
print('Start training...')
for epoch in range(hparams.num_epoch):
# result
sum_normal_loss, sum_style_loss, sum_normal_ppl, sum_style_ppl = 0, 0, 0, 0
# normal caption
for i, (images, in_captions, out_captions,
lengths) in enumerate(normal_data_loader):
images = images.to(device)
in_captions = in_captions.to(device)
out_captions = out_captions.contiguous().view(-1).to(device)
# Forward, backward and optimize
features = encoder(images)
outputs = decoder(in_captions, features, mode='default')
loss = criterion(outputs.view(-1, outputs.size(-1)), out_captions)
encoder.zero_grad()
decoder.zero_grad()
loss.backward()
normal_opt.step()
# print log
sum_normal_loss += loss.item()
sum_normal_ppl += np.exp(loss.item())
if i % hparams.normal_log_step == 0:
print(
f'Epoch [{epoch}/{hparams.num_epoch}], Normal Step: [{i}/{len(normal_data_loader)}] '
f'Normal Loss: {loss.item():.4f}, Perplexity: {np.exp(loss.item()):5.4f}'
)
# style caption
for i, (in_captions, out_captions,
lengths) in enumerate(style_data_loader):
in_captions = in_captions.to(device)
out_captions = out_captions.contiguous().view(-1).to(device)
# Forward, backward and optimize
outputs = decoder(in_captions, None, mode='style')
loss = criterion(outputs.view(-1, outputs.size(-1)), out_captions)
decoder.zero_grad()
loss.backward()
style_opt.step()
sum_style_loss += loss.item()
sum_style_ppl += np.exp(loss.item())
# print log
if i % hparams.style_log_step == 0:
print(
f'Epoch [{epoch}/{hparams.num_epoch}], Style Step: [{i}/{len(style_data_loader)}] '
f'Style Loss: {loss.item():.4f}, Perplexity: {np.exp(loss.item()):5.4f}'
)
model_params = {
'encoder': encoder.state_dict(),
'decoder': decoder.state_dict(),
'epoch': epoch,
'normal_opt': normal_opt.optimizer.state_dict(),
'style_opt': style_opt.optimizer.state_dict(),
}
avg_normal_loss = sum_normal_loss / len(normal_data_loader)
avg_style_loss = sum_style_loss / len(style_data_loader)
avg_normal_ppl = sum_normal_ppl / len(normal_data_loader)
avg_style_ppl = sum_style_ppl / len(style_data_loader)
print(f'Epoch [{epoch}/{hparams.num_epoch}] statistics')
print(
f'Normal Loss: {avg_normal_loss:.4f} Normal ppl: {avg_normal_ppl:5.4f} '
f'Style Loss: {avg_style_loss:.4f} Style ppl: {avg_style_ppl:5.4f}'
)
torch.save(
model_params,
f'{hparams.model_path}/n-loss_{avg_normal_loss:.4f}_s-loss_{avg_style_loss:.4f}_'
f'n-ppl_{avg_normal_ppl:5.4f}_s-ppl_{avg_style_ppl:5.4f}_epoch_{epoch}.pt'
)
mmd2_real = mmd_util.batch_mmd2_loss(X_p_enc, X_f_enc, sigma_var)
# reconstruction loss
real_L2_loss = torch.mean((X_f - X_f_dec)**2)
#real_L2_loss = torch.mean((X_p - X_p_dec)**2)
fake_L2_loss = torch.mean((Y_f - Y_f_dec)**2)
#fake_L2_loss = torch.mean((Y_f - Y_f_dec)**2) * 0.0
# update netD
netD.zero_grad()
lossD = D_mmd2.mean() - lambda_ae * (
real_L2_loss + fake_L2_loss) - lambda_real * mmd2_real.mean()
#lossD = 0.0 * D_mmd2.mean() - lambda_ae * (real_L2_loss + fake_L2_loss) - lambda_real * mmd2_real.mean()
#lossD = -real_L2_loss
lossD.backward(mone)
optimizerD.step()
############################
# (2) Update G network
############################
for p in netD.parameters():
p.requires_grad = False # to avoid computation
if bidx == n_batchs:
break
inputs = next(trn_loader)
X_p, X_f = inputs[0], inputs[1]
batch_size = X_p.size(0)
bidx += 1
def run(args, local_rank):
""" Distributed Synchronous """
torch.manual_seed(1234)
vocab = Vocab(args.vocab, min_occur_cnt=args.min_occur_cnt, specials=[])
if (args.world_size == 1 or dist.get_rank() == 0):
print("vocab.size = " + str(vocab.size), flush=True)
model = BIGLM(local_rank, vocab, args.embed_dim, args.ff_embed_dim,
args.num_heads, args.dropout, args.layers, args.smoothing)
if args.start_from is not None:
ckpt = torch.load(args.start_from, map_location='cpu')
model.load_state_dict(ckpt['model'])
model = model.cuda(local_rank)
optimizer = Optim(
model.embed_dim, args.lr, args.warmup_steps,
torch.optim.Adam(model.parameters(),
lr=0,
betas=(0.9, 0.998),
eps=1e-9))
if args.start_from is not None:
optimizer.load_state_dict(ckpt['optimizer'])
train_data = DataLoader(vocab, args.train_data, args.batch_size,
args.max_len, args.min_len)
batch_acm = 0
acc_acm, nll_acm, ppl_acm, ntokens_acm, nxs, npairs_acm, loss_acm = 0., 0., 0., 0., 0., 0., 0.
while True:
model.train()
if train_data.epoch_id > args.max_epoch:
break
for xs_tpl, xs_seg, xs_pos, ys_truth, ys_inp, ys_tpl, ys_seg, ys_pos, msk in train_data:
batch_acm += 1
xs_tpl = xs_tpl.cuda(local_rank)
xs_seg = xs_seg.cuda(local_rank)
xs_pos = xs_pos.cuda(local_rank)
ys_truth = ys_truth.cuda(local_rank)
ys_inp = ys_inp.cuda(local_rank)
ys_tpl = ys_tpl.cuda(local_rank)
ys_seg = ys_seg.cuda(local_rank)
ys_pos = ys_pos.cuda(local_rank)
msk = msk.cuda(local_rank)
model.zero_grad()
res, loss, acc, nll, ppl, ntokens, npairs = model(
xs_tpl, xs_seg, xs_pos, ys_truth, ys_inp, ys_tpl, ys_seg,
ys_pos, msk)
# http://www.myzaker.com/article/5f3747a28e9f096c723a65e0/ 资料
# 常用的文本生成评测指标 PPL、Distinct 外，
# 本文还专门设计了衡量格式（Format）准确率、韵律（Rhyme）准确率和句子完整性（integrity）的指标。
# 格式（Format）准确率: Precision p、Recall r 和 F1 得分-> Macro-F1 和 Micro-F1
# 完整性有个奇怪的log值
# 传统的BLEU和ROUGE, 再songnet中完全用不到, 创作要求多样性
loss_acm += loss.item() # 损失
acc_acm += acc # 精确度
nll_acm += nll #
ppl_acm += ppl # -log 和, 其实就是句子出现的概率, 越小, 困惑度越高
# 新指标, 困惑度perplexity, 比较两者再预测样本上的优劣, 困惑都越低越好??, 咋定义的
ntokens_acm += ntokens # 字符数
npairs_acm += npairs # 句子?
nxs += npairs
# 为什么啊, 感觉好难啊gpt2
loss.backward()
if args.world_size > 1:
is_normal = average_gradients(model)
else:
is_normal = True
if is_normal:
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
else:
print("gradient: none, gpu: " + str(local_rank), flush=True)
continue
if (args.world_size == 1 or dist.get_rank() == 0
) and batch_acm % args.print_every == -1 % args.print_every:
today = datetime.datetime.now()
print(today)
print(
'batch_acm %d, loss %.3f, acc %.3f, nll %.3f, ppl %.3f, x_acm %d, lr %.6f'
% (batch_acm, loss_acm / args.print_every,
acc_acm / ntokens_acm, nll_acm / nxs, ppl_acm / nxs,
npairs_acm, optimizer._rate),
flush=True)
acc_acm, nll_acm, ppl_acm, ntokens_acm, loss_acm, nxs = 0., 0., 0., 0., 0., 0.
if (args.world_size == 1 or dist.get_rank() == 0
) and batch_acm % args.save_every == -1 % args.save_every:
if not os.path.exists(args.save_dir):
os.mkdir(args.save_dir)
model.eval()
eval_epoch(
args, model, vocab, local_rank, "epoch-" +
str(train_data.epoch_id) + "-acm-" + str(batch_acm))
model.train()
torch.save(
{
'args': args,
'model': model.state_dict(),
'optimizer': optimizer.state_dict()
}, '%s/epoch%d_batch_%d' %
(args.save_dir, train_data.epoch_id, batch_acm))
def run(args, local_rank):
""" Distributed Synchronous """
torch.manual_seed(1234)
vocab = Vocab(args.vocab, min_occur_cnt=args.min_occur_cnt, specials=[])
if (args.world_size == 1 or dist.get_rank() == 0):
print("vocab.size = " + str(vocab.size), flush=True)
model = BIGLM(local_rank, vocab, args.embed_dim, args.ff_embed_dim,\
args.num_heads, args.dropout, args.layers, args.smoothing)
if args.start_from is not None:
ckpt = torch.load(args.start_from, map_location='cpu')
model.load_state_dict(ckpt['model'])
model = model.cuda(local_rank)
optimizer = Optim(
model.embed_dim, args.lr, args.warmup_steps,
torch.optim.Adam(model.parameters(),
lr=0,
betas=(0.9, 0.998),
eps=1e-9))
if args.start_from is not None:
optimizer.load_state_dict(ckpt['optimizer'])
train_data = DataLoader(vocab, args.train_data, args.batch_size,
args.max_len, args.min_len)
batch_acm = 0
acc_acm, nll_acm, ppl_acm, ntokens_acm, nxs, npairs_acm, loss_acm = 0., 0., 0., 0., 0., 0., 0.
while True:
model.train()
if train_data.epoch_id > 30:
break
for xs_tpl, xs_seg, xs_pos, ys_truth, ys_inp, ys_tpl, ys_seg, ys_pos, msk in train_data:
batch_acm += 1
xs_tpl = xs_tpl.cuda(local_rank)
xs_seg = xs_seg.cuda(local_rank)
xs_pos = xs_pos.cuda(local_rank)
ys_truth = ys_truth.cuda(local_rank)
ys_inp = ys_inp.cuda(local_rank)
ys_tpl = ys_tpl.cuda(local_rank)
ys_seg = ys_seg.cuda(local_rank)
ys_pos = ys_pos.cuda(local_rank)
msk = msk.cuda(local_rank)
model.zero_grad()
res, loss, acc, nll, ppl, ntokens, npairs = model(
xs_tpl, xs_seg, xs_pos, ys_truth, ys_inp, ys_tpl, ys_seg,
ys_pos, msk)
loss_acm += loss.item()
acc_acm += acc
nll_acm += nll
ppl_acm += ppl
ntokens_acm += ntokens
npairs_acm += npairs
nxs += npairs
loss.backward()
if args.world_size > 1:
is_normal = average_gradients(model)
else:
is_normal = True
if is_normal:
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
else:
print("gradient: none, gpu: " + str(local_rank), flush=True)
continue
if (args.world_size == 1 or dist.get_rank() == 0
) and batch_acm % args.print_every == -1 % args.print_every:
print ('batch_acm %d, loss %.3f, acc %.3f, nll %.3f, ppl %.3f, x_acm %d, lr %.6f'\
%(batch_acm, loss_acm/args.print_every, acc_acm/ntokens_acm, \
nll_acm/nxs, ppl_acm/nxs, npairs_acm, optimizer._rate), flush=True)
acc_acm, nll_acm, ppl_acm, ntokens_acm, loss_acm, nxs = 0., 0., 0., 0., 0., 0.
if (args.world_size == 1 or dist.get_rank() == 0
) and batch_acm % args.save_every == -1 % args.save_every:
if not os.path.exists(args.save_dir):
os.mkdir(args.save_dir)
model.eval()
eval_epoch(
args, model, vocab, local_rank, "epoch-" +
str(train_data.epoch_id) + "-acm-" + str(batch_acm))
model.train()
torch.save(
{
'args': args,
'model': model.state_dict(),
'optimizer': optimizer.state_dict()
}, '%s/epoch%d_batch_%d' %
(args.save_dir, train_data.epoch_id, batch_acm))
