help='the prefix of the model to save')
parser.add_argument('--num-epochs', type=int, default=10,
help='the number of training epochs')
parser.add_argument('--load-epoch', type=int,
help="load the model on an epoch using the model-prefix")
parser.add_argument('--kv-store', type=str, default='local',
help='the kvstore type')
parser.add_argument('--lr-factor', type=float, default=1,
help='times the lr with a factor for every lr-factor-epoch epoch')
parser.add_argument('--lr-factor-epoch', type=float, default=1,
help='the number of epoch to factor the lr, could be .5')
return parser.parse_args()
if __name__ == '__main__':
args = parse_args()
use_caffe_loss = args.caffe_loss
if args.network == 'mlp':
data_shape = (784, )
net = get_mlp()
else:
data_shape = (1, 28, 28)
net = get_lenet()
# train
if use_caffe_loss:
train_model.fit(args, net, get_iterator(data_shape), mx.metric.Caffe())
else:
train_model.fit(args, net, get_iterator(data_shape))
def main():
global args
args = parser.parse_args()
if args.save is '':
args.save = datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
save_path = os.path.join(args.results_dir, args.save)
if not os.path.exists(save_path):
os.makedirs(save_path)
setup_logging(os.path.join(save_path, 'log.txt'))
checkpoint_file = os.path.join(save_path, 'checkpoint_epoch_%s.pth.tar')
logging.debug("run arguments: %s", args)
logging.info("using pretrained cnn %s", args.cnn)
cnn = resnet.__dict__[args.cnn](pretrained=True)
# loading annotations into memory...
# Done (t=0.49s)
# creating index...
# index created!
vocab = build_vocab() # len(vocab) 10003
model = CaptionModel(cnn,
vocab,
embedding_size=args.embedding_size,
rnn_size=args.rnn_size,
num_layers=args.num_layers,
share_embedding_weights=args.share_weights)
# loading annotations into memory...
# Done (t=0.47s)
# creating index...
# index created!
train_data = get_iterator(
get_coco_data(vocab, train=True),
batch_size=20, #batch_size=args.batch_size # default 128
max_length=25, #max_length=args.max_length # default 30
shuffle=True,
num_workers=args.workers)
# loading annotations into memory...
# Done (t=0.37s)
# creating index...
# index created!
val_data = get_iterator(
get_coco_data(vocab, train=False),
batch_size=20, #batch_size=args.eval_batch_size # default 128
max_length=25, #max_length=args.max_length # default 30
shuffle=False,
num_workers=args.workers)
#if 'cuda' in args.type:
# cudnn.benchmark = True
# model.cuda()
# optimizer = select_optimizer( #args.optimizer SGD
# args.optimizer, params=model.parameters(), lr=args.lr) # args.lr = 0.1 float
optimizer = select_optimizer( # args.optimizer SGD
args.optimizer,
params=model.parameters(),
lr=args.lr) # args.lr = 0.1 float
regime = lambda e: {
'lr': args.lr * (args.lr_decay**e),
'momentum': args.momentum,
'weight_decay': args.weight_decay
}
model.finetune_cnn(False)
def forward(model, data, training=True, optimizer=None):
use_cuda = 'cuda' in args.type # return True or False
loss = nn.CrossEntropyLoss() # CrossEntropyLoss()
perplexity = AverageMeter(
) # <utils.AverageMeter object at 0x7fa6be6f1780
batch_time = AverageMeter()
data_time = AverageMeter()
if training:
model.train()
else:
model.eval()
end = time.time()
# Train
# train_perp = forward(
# model, train_data, training=True, optimizer=optimizer)
for i, (imgs, (captions,
lengths)) in enumerate(data): #len(data)键值对个数 2587
time.sleep(1)
if i == 3:
break
data_time.update(time.time() - end)
#if use_cuda:
# imgs = imgs.cuda()
# captions = captions.cuda(async=True)
imgs = Variable(imgs, volatile=not training)
captions = Variable(captions, volatile=not training)
input_captions = captions[:-1]
target_captions = pack_padded_sequence(captions, lengths)[0]
pred, _ = model(imgs, input_captions, lengths)
err = loss(pred, target_captions)
# perplexity.update(math.exp(err.data[0]))
perplexity.update(math.exp(err.item()))
if training:
optimizer.zero_grad()
err.backward()
clip_grad_norm(model.rnn.parameters(), args.grad_clip)
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
logging.info(
'{phase} - Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Perplexity {perp.val:.4f} ({perp.avg:.4f})'.format(
epoch,
i,
len(data),
phase='TRAINING' if training else 'EVALUATING',
batch_time=batch_time,
data_time=data_time,
perp=perplexity))
return perplexity.avg
for epoch in range(args.start_epoch, args.epochs): #(0,10)
if epoch >= args.finetune_epoch: # args.finetune_epoch 3
model.finetune_cnn(True)
optimizer = adjust_optimizer(optimizer, epoch,
regime) # optimizer none
# Train
train_perp = forward(model,
train_data,
training=True,
optimizer=optimizer)
# Evaluate
val_perp = forward(model, val_data, training=False)
logging.info('\n Epoch: {0}\t'
'Training Perplexity {train_perp:.4f} \t'
'Validation Perplexity {val_perp:.4f} \n'.format(
epoch + 1, train_perp=train_perp, val_perp=val_perp))
model.save_checkpoint(checkpoint_file % (epoch + 1))
parser.add_argument('--kv-store', type=str, default='local',
help='the kvstore type')
parser.add_argument('--lr-factor', type=float, default=1,
help='times the lr with a factor for every lr-factor-epoch epoch')
parser.add_argument('--lr-factor-epoch', type=float, default=1,
help='the number of epoch to factor the lr, could be .5')
return parser.parse_args()
if __name__ == '__main__':
args = parse_args()
use_caffe_loss = args.caffe_loss
use_caffe_data = args.caffe_data
data_shape = ()
if args.network == 'mlp':
data_shape = (784, )
net = get_mlp()
elif args.network == 'lenet':
if not use_caffe_data:
data_shape = (1, 28, 28)
net = get_lenet()
else:
net = get_network_from_json_file(args.network)
# train
if use_caffe_loss:
train_model.fit(args, net, get_iterator(data_shape, use_caffe_data), mx.metric.Caffe())
else:
train_model.fit(args, net, get_iterator(data_shape, use_caffe_data))
parser.add_argument('--kv-store', type=str, default='local',
help='the kvstore type')
parser.add_argument('--lr-factor', type=float, default=1,
help='times the lr with a factor for every lr-factor-epoch epoch')
parser.add_argument('--lr-factor-epoch', type=float, default=1,
help='the number of epoch to factor the lr, could be .5')
return parser.parse_args()
if __name__ == '__main__':
args = parse_args()
use_caffe_loss = args.caffe_loss
use_caffe_data = args.caffe_data
data_shape = ()
if args.network == 'mlp':
data_shape = (784, )
net = get_mlp()
elif args.network == 'lenet':
if not use_caffe_data:
data_shape = (1, 28, 28)
net = get_lenet()
else:
net = get_network_from_json_file(args.network)
# train
if use_caffe_loss:
train_model.fit(args, net, get_iterator(data_shape, use_caffe_data), mx.metric.Caffe())
else:
train_model.fit(args, net, get_iterator(data_shape, use_caffe_data))
def main(args):
print("Loading data")
dataset = args.data.rstrip('/').split('/')[-1]
if dataset in ['yahoo', 'yelp']:
with_label = True
else:
with_label = False
corpus = Corpus(
args.data, max_vocab_size=args.max_vocab,
max_length=args.max_length, with_label=with_label
)
pad_id = corpus.word2idx[PAD_TOKEN]
vocab_size = len(corpus.word2idx)
print("\ttraining data size: ", len(corpus.train))
print("\tvocabulary size: ", vocab_size)
print("Constructing model")
print(args)
device = torch.device('cpu' if args.nocuda else 'cuda')
model = TopGenVAE(
vocab_size, args.embed_size, args.hidden_size, args.code_size,
args.num_topics, args.dropout
).to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.wd)
best_loss = None
train_iter = get_iterator(corpus.train, args.batch_size, True, device)
valid_iter = get_iterator(corpus.valid, args.batch_size, False, device)
test_iter = get_iterator(corpus.test, args.batch_size, False, device)
print("\nStart training")
try:
for epoch in range(1, args.epochs+1):
epoch_start_time = time.time()
(tr_seq_loss, tr_bow_loss, tr_kld_z, tr_kld_t,
tr_seq_ppl, tr_bow_ppl) = train(
train_iter, model, pad_id, optimizer, epoch
)
(va_seq_loss, va_bow_loss, va_kld_z, va_kld_t,
va_seq_ppl, va_bow_ppl) = evaluate(
valid_iter, model, pad_id
)
print('-' * 90)
meta = "| epoch {:2d} | time {:5.2f}s ".format(epoch, time.time()-epoch_start_time)
print(meta + "| train loss {:5.2f} {:5.2f} ({:5.2f} {:5.2f}) "
"| train ppl {:5.2f} {:5.2f}".format(
tr_seq_loss, tr_bow_loss, tr_kld_z, tr_kld_t,
tr_seq_ppl, tr_bow_ppl))
print(len(meta)*' ' + "| valid loss {:5.2f} {:5.2f} ({:5.2f} {:5.2f}) "
"| valid ppl {:5.2f} {:5.2f}".format(
va_seq_loss, va_bow_loss, va_kld_z, va_kld_t,
va_seq_ppl, va_bow_ppl), flush=True)
epoch_loss = va_seq_loss + va_bow_loss + va_kld_z + va_kld_t
if best_loss is None or epoch_loss < best_loss:
best_loss = epoch_loss
with open(get_savepath(args), 'wb') as f:
torch.save(model, f)
except KeyboardInterrupt:
print('-' * 90)
print('Exiting from training early')
with open(get_savepath(args), 'rb') as f:
model = torch.load(f)
(te_seq_loss, te_bow_loss, te_kld_z, te_kld_t,
te_seq_ppl, te_bow_ppl) = evaluate(test_iter, model, pad_id)
print('=' * 90)
print("| End of training | test loss {:5.2f} {:5.2f} ({:5.2f} {:5.2f}) "
"| test ppl {:5.2f} {:5.2f}".format(
te_seq_loss, te_bow_loss, te_kld_z, te_kld_t,
te_seq_ppl, te_bow_ppl))
print('=' * 90)
def main():
k = 10
repeat = 4
epochs = 30
batchsize = 256
learning_rate = 1e-4
# dataset_object = tf.keras.datasets.mnist
dataset_object = tf.keras.datasets.cifar10
(x_train, y_train), (x_test, y_test) = dataset_object.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
print('x_train:', x_train.shape, x_train.min(), x_train.max())
print('y_train:', y_train.shape, y_train.min(), y_train.max())
train_iterator = get_iterator(x_train, y_train, batchsize=batchsize)
x_batch, x_perturb, y_batch = next(train_iterator)
print('xbatch', x_batch.shape, 'xperturb', x_perturb.shape, 'ybatch',
y_batch.shape)
model = ResNetModel(k=k)
print('x_batch:', x_batch.shape)
z = model(x_batch, head='main', verbose=True)
for z_ in z:
print('z:', z_.shape)
z = model(x_batch, head='aux')
for z_ in z:
print('z:', z_.shape)
model.summary()
# optimizer = tf.train.AdamOptimizer(learning_rate=1e-4)
plt.figure(figsize=(3, 3), dpi=300)
ax = plt.gca()
ax.set_xlim([-1, 1])
ax.set_ylim([-1, 1])
main_losses = []
aux_losses = []
for e in range(epochs):
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate /
(2 * (e + 1)))
# mnist_generator = generate_mnist(x_train, y_train, batchsize=batchsize)
if e % 2 == 0:
trainhead = 'main'
else:
trainhead = 'aux'
train_iterator = get_iterator(x_train,
y_train,
batchsize=batchsize,
repeat=repeat)
for k, (x_batch, x_perturb, y_batch) in enumerate(train_iterator):
# if k % 2 == 0:
# trainhead = 'main'
# else:
# trainhead = 'aux'
with tf.GradientTape() as tape:
z = model(x_batch, head=trainhead)
zp = model(x_perturb, head=trainhead)
losses = [IID_loss(z_, zp_) for z_, zp_ in zip(z, zp)]
loss = tf.reduce_mean(losses)
grads = tape.gradient(loss, model.trainable_variables)
if k % 2 == 0:
main_losses.append(loss.numpy())
else:
aux_losses.append(loss.numpy())
optimizer.apply_gradients(zip(grads, model.trainable_variables))
if k % 100 == 0:
# take the last head and write
save_images(x_batch, z[0], 'clusters/{}'.format(e), n=5)
print('e: {} k: {} loss={}'.format(e, k, loss.numpy()))
for i in range(1):
zmax = tf.argmax(z[i], axis=-1).numpy()
zpmax = tf.argmax(zp[i], axis=-1).numpy()
acc = (zmax == zpmax).mean()
print('\tacc={}'.format(acc), np.unique(zmax),
np.unique(zpmax))
# Each epoch
ztest = {r: [] for r in range(1)}
ylabel = []
# test_iterator = get_iterator(x_train, y_train, batchsize=batchsize, repeat=1)
test_iterator = get_iterator(x_test,
y_test,
batchsize=batchsize,
repeat=1)
for j, (x_batch, x_perturb, y_batch) in enumerate(test_iterator):
for i, h in enumerate(model(x_batch, head='main')):
ztest[i].append(h)
ylabel.append(y_batch)
# ztest = np.concatenate(ztest, axis=0)
ylabel = np.squeeze(np.concatenate(ylabel))
print('ylabel', ylabel.shape)
for r in range(1):
ztest[r] = np.concatenate(ztest[r], axis=0)
print('ztest', ztest[r].shape)
convex_combo(ztest[r], ylabel, ax,
'pointcloud/{}_{}.png'.format(r, e))
with open('losses_main.txt', 'w+') as f:
for l in main_losses:
f.write('{}\n'.format(l))
with open('losses_aux.txt', 'w+') as f:
for l in aux_losses:
f.write('{}\n'.format(l))
parser.add_argument(
'--lr-factor-epoch',
type=float,
default=1,
help='the number of epoch to factor the lr, could be .5')
return parser.parse_args()
if __name__ == '__main__':
args = parse_args()
use_caffe_loss = args.caffe_loss
use_caffe_data = args.caffe_data
data_shape = ()
if args.network == 'mlp':
data_shape = (784, )
net = get_mlp()
elif args.network == 'lenet':
if not use_caffe_data:
data_shape = (1, 28, 28)
net = get_lenet()
else:
net = get_network_from_json_file(args.network)
# train
if use_caffe_loss:
train_model.fit(args, net, get_iterator(data_shape, use_caffe_data),
mx.metric.Caffe())
else:
train_model.fit(args, net, get_iterator(data_shape, use_caffe_data))
def main():
global args
args = parser.parse_args()
if args.save is '':
args.save = datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
save_path = os.path.join(args.results_dir, args.save)
if not os.path.exists(save_path):
os.makedirs(save_path)
if args.pretrained != 1:
# if this is a model trained from scratch, train CNN from the starting of the epoch and train epoch much longer
args.finetune_epoch = 0
setup_logging(os.path.join(save_path, 'log.txt'))
checkpoint_file = os.path.join(save_path, 'checkpoint_epoch_%s.pth.tar')
logging.debug("run arguments: %s", args)
if args.pretrained == 1:
logging.info("using pretrained cnn %s", args.cnn)
cnn = resnet.__dict__[args.cnn](pretrained=True)
else:
logging.info("using from-scratch cnn %s", args.cnn)
cnn = resnet.__dict__[args.cnn](pretrained=False)
vocab = build_vocab()
model = CaptionModel(cnn, vocab,
embedding_size=args.embedding_size,
rnn_size=args.rnn_size,
num_layers=args.num_layers,
share_embedding_weights=args.share_weights)
train_data = get_iterator(get_coco_data(vocab, train=True),
batch_size=args.batch_size,
max_length=args.max_length,
shuffle=True,
num_workers=args.workers)
val_data = get_iterator(get_coco_data(vocab, train=False),
batch_size=args.eval_batch_size,
max_length=args.max_length,
shuffle=False,
num_workers=args.workers)
if 'cuda' in args.type:
cudnn.benchmark = True
model.cuda()
optimizer = select_optimizer(
args.optimizer, params=model.parameters(), lr=args.lr)
regime = lambda e: {'lr': args.lr * (args.lr_decay ** e),
'momentum': args.momentum,
'weight_decay': args.weight_decay}
model.finetune_cnn(False)
def forward(model, data, training=True, optimizer=None):
use_cuda = 'cuda' in args.type
loss = nn.CrossEntropyLoss()
perplexity = AverageMeter()
batch_time = AverageMeter()
data_time = AverageMeter()
if training:
model.train()
else:
model.eval()
end = time.time()
for i, (imgs, (captions, lengths)) in enumerate(data):
data_time.update(time.time() - end)
if use_cuda:
imgs = imgs.cuda()
captions = captions.cuda(async=True)
imgs = Variable(imgs, volatile=not training)
captions = Variable(captions, volatile=not training)
input_captions = captions[:-1]
target_captions = pack_padded_sequence(captions, lengths)[0]
pred, _ = model(imgs, input_captions, lengths)
err = loss(pred, target_captions)
perplexity.update(math.exp(err.data[0]))
if training:
optimizer.zero_grad()
err.backward()
clip_grad_norm(model.rnn.parameters(), args.grad_clip)
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
logging.info('{phase} - Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Perplexity {perp.val:.4f} ({perp.avg:.4f})'.format(
epoch, i, len(data),
phase='TRAINING' if training else 'EVALUATING',
batch_time=batch_time,
data_time=data_time, perp=perplexity))
return perplexity.avg
for epoch in range(args.start_epoch, args.epochs):
if epoch >= args.finetune_epoch:
model.finetune_cnn(True)
optimizer = adjust_optimizer(
optimizer, epoch, regime)
# Train
train_perp = forward(
model, train_data, training=True, optimizer=optimizer)
# Evaluate
val_perp = forward(model, val_data, training=False)
logging.info('\n Epoch: {0}\t'
'Training Perplexity {train_perp:.4f} \t'
'Validation Perplexity {val_perp:.4f} \n'
.format(epoch + 1, train_perp=train_perp, val_perp=val_perp))
if epoch % args.save_freq == 0 or epoch == args.epochs-1:
model.save_checkpoint(checkpoint_file % (epoch + 1))
def main(args):
print("Loading data")
dataset = args.data.rstrip('/').split('/')[-1]
corpus = Corpus(args.data,
max_vocab_size=args.max_vocab,
max_length=args.max_length)
pad_id = corpus.word2idx[PAD_TOKEN]
sos_id = corpus.word2idx[SOS_TOKEN]
vocab_size = len(corpus.word2idx)
print(args)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
torch.cuda.set_device(args.device_id)
cent_name = dataset + "_centers.pt"
centers = None
if args.center:
centers = torch.load(cent_name).to(device)
# centers.requires_grad = False
centers = centers.detach()
model = LstmVAE(vocab_size,
args.embed_dim,
args.hidden_dim,
args.code_dim,
args.dropout,
centers=centers,
enc_type=args.enc_type,
de_type=args.de_type,
dist=args.dist,
fix=args.fix,
device=device).to(device)
if args.flow:
flow = NormalizingFlows(args.code_dim,
n_flows=args.n_flows,
reg=args.reg,
band=args.band).to(device)
model.add_flow(flow)
optimizer = torch.optim.Adam(model.parameters(),
lr=args.lr,
betas=(args.beta, 0.999),
weight_decay=args.wd)
train_iter = get_iterator(corpus.train, args.batch_size, True, device)
valid_iter = get_iterator(corpus.valid, args.batch_size, False, device)
test_iter = get_iterator(corpus.test, args.batch_size, False, device)
start_epoch = 1
if args.load is True:
start_epoch, model, optimizer = load_checkpoint(
model, optimizer, device, args.save_name)
print("\nStart training")
try:
for epoch in range(start_epoch, args.epochs + 1):
(re_loss, kl_divergence, flow_kld, mi1, mi2, mmd_loss, nll_ppl,
nll, iw_nll, sum_log_j, start, batch_time) = run(args,
train_iter,
model,
pad_id,
optimizer,
epoch,
train=True)
if args.save:
save_checkpoint(model, optimizer, epoch, args.save_name)
print('-' * 90)
meta = "| epoch {:2d} ".format(epoch)
print(
meta +
"| train loss {:5.2f} ({:5.2f}) ({:5.2f}) | train ppl {:5.2f} ({:5.2f} {:5.2f}) | mmd {:5.2f} | mi E {:5.2f} | mi R {:5.2f} | log J {:5.2f}"
"| Time {batch_time.val:5.2f} ({batch_time.avg:5.2f})\t".
format(re_loss,
kl_divergence,
flow_kld,
nll_ppl,
nll,
iw_nll,
mmd_loss,
mi1,
mi2,
sum_log_j,
batch_time=batch_time))
(re_loss, kl_divergence, flow_kld, mi1, mi2, mmd_loss, nll_ppl,
nll, iw_nll, sum_log_j, _, _) = run(args,
valid_iter,
model,
pad_id,
optimizer,
epoch,
train=False)
print(
len(meta) * ' ' +
"| valid loss {:5.2f} ({:5.2f}) ({:5.2f}) | valid ppl {:5.2f} ({:5.2f} {:5.2f})"
"| mmd {:5.2f} | mi E {:5.2f} | mi R {:5.2f} | log J {:5.2f} \t"
.format(re_loss,
kl_divergence,
flow_kld,
nll_ppl,
nll,
iw_nll,
mmd_loss,
mi1,
mi2,
sum_log_j,
flush=True))
if dataset in ['yahoo'] and epoch in [15, 35]:
for param_group in optimizer.param_groups:
param_group['lr'] *= 0.5
except KeyboardInterrupt:
print('-' * 50)
print('Quit training')
(re_loss, kl_divergence, flow_kld, mi1, mi2, mmd_loss, nll_ppl, nll,
iw_nll, sum_log_j, _, _) = run(args,
test_iter,
model,
pad_id,
optimizer,
epoch,
train=False)
print('=' * 90)
print(
"| Test results | test loss {:5.2f} ({:5.2f}) ({:5.2f}) | test ppl {:5.2f} ({:5.2f} {:5.2f}) | test mmd {:5.2f} | mi E {:5.2f} | mi R {:5.2f} | log J {:5.2f} "
.format(re_loss, kl_divergence, flow_kld, nll_ppl, nll, iw_nll,
mmd_loss, mi1, mi2, sum_log_j))
print('=' * 90)
with open(args.test_log_name, 'a') as fd:
print('=' * 90, file=fd)
print(
"{} | dist {} | ende {} | em {} | | kla {} | mmd {} | flow {} | center {} | n flow {} | ker {} | reg {} | band {} | t {} | mmd w {} | iw {} | gpu {} | log {} |"
.format(dataset, args.dist, args.de_type, args.embed_dim, args.kla,
args.mmd, args.flow, args.center, args.n_flows,
args.kernel, args.reg, args.band, args.t, args.mmd_w,
args.iw, args.device_id, args.test_log_name),
file=fd)
print('-' * 90, file=fd)
print(
"| Test results | test loss {:5.2f} ({:5.2f}) ({:5.2f}) | test ppl {:5.2f} ({:5.2f} {:5.2f}) | test mmd {:5.2f} | mi E {:5.2f} | mi R {:5.2f} | log J {:5.2f}"
.format(re_loss, kl_divergence, flow_kld, nll_ppl, nll, iw_nll,
mmd_loss, mi1, mi2, sum_log_j),
file=fd)
print('=' * 90, file=fd)
def main(args):
print("Loading data")
dataset = args.data.rstrip('/').split('/')[-1]
if dataset in ['yahoo']:
with_label = True
else:
with_label = False
if dataset in ['yahoo']:
corpus = CorpusYahoo(args.data,
max_vocab_size=args.max_vocab,
max_length=args.max_length,
with_label=with_label)
pad_id = corpus.word2idx['_PAD']
else:
corpus = Corpus(args.data,
max_vocab_size=args.max_vocab,
max_length=args.max_length,
with_label=with_label)
pad_id = corpus.word2idx[PAD_TOKEN]
vocab_size = len(corpus.word2idx)
print("\ttraining data size: ", len(corpus.train))
print("\tvocabulary size: ", vocab_size)
print("Constructing model")
print(args)
device = torch.device('cpu' if args.nocuda else 'cuda')
torch.cuda.set_device(args.cuda)
if args.diag:
model = MultiNormalVAE(vocab_size,
args.embed_size,
args.hidden_size,
args.code_size,
args.dropout,
batch_size=args.batch_size,
decomp=args.method,
copula=args.copula)
else:
model = LstmVAE(vocab_size,
args.embed_size,
args.hidden_size,
args.code_size,
args.dropout,
batch_size=args.batch_size,
decomp=args.method,
copula=args.copula)
if args.multi:
print("Let's use", torch.cuda.device_count(), "GPUs!")
model = nn.DataParallel(model)
model = model.to(device)
optimizer = torch.optim.Adam(model.parameters(),
lr=args.lr,
weight_decay=args.wd)
best_loss = None
train_iter = get_iterator(corpus.train, args.batch_size, True, device)
valid_iter = get_iterator(corpus.valid, args.batch_size, False, device)
test_iter = get_iterator(corpus.test, args.batch_size, False, device)
start_epoch = 0
if args.load is True:
start_epoch, model, optimizer, losslogger = load_checkpoint(
model, optimizer, device, args.save_name)
tr_loggers = []
va_loggers = []
print("\nStart training")
try:
for epoch in range(start_epoch, args.epochs):
epoch_start_time = time.time()
(tr_seq_loss, tr_bow_loss, tr_kld, tr_mi, tr_tc, tr_dwkl,
tr_seq_ppl, tr_bow_ppl, tr_log_copula, tr_mmd,
batch_time) = train(train_iter, model, pad_id, optimizer, epoch)
(va_seq_loss, va_bow_loss, va_kld, va_mi, va_tc, va_dwkl,
va_seq_ppl, va_bow_ppl, va_log_copula,
va_mmd) = evaluate(valid_iter, model, pad_id)
tr_losslogger = {
"epoch": epoch,
"seq_loss": tr_seq_loss,
"bow_loss": tr_bow_loss,
"kld": tr_kld,
"mutual info": tr_mi,
"tc": tr_tc,
"dwkl": tr_dwkl,
"seq_ppl": tr_seq_ppl,
"bow_ppl": tr_bow_ppl,
"log_copula": tr_log_copula,
"mmd": tr_mmd,
"time": batch_time
}
tr_loggers.append(tr_losslogger)
losslogger = {
"epoch": epoch,
"seq_loss": va_seq_loss,
"bow_loss": va_bow_loss,
"kld": va_kld,
"mutual info": va_mi,
"tc": va_tc,
"dwkl": va_dwkl,
"seq_ppl": va_seq_ppl,
"bow_ppl": va_bow_ppl,
"log_copula": va_log_copula,
"mmd": va_mmd,
"time": batch_time
}
va_loggers.append(losslogger)
save_checkpoint(model, optimizer, losslogger, args.save_name)
print('-' * 90)
meta = "| epoch {:2d} | time {:5.2f}s ".format(
epoch,
time.time() - epoch_start_time)
print(
meta + "| train loss {:5.2f} {:5.2f} ({:5.2f}) "
"| {:5.2f} {:5.2f} {:5.2f} "
"| train ppl {:5.2f} {:5.2f} | log copula {:5.2f} | mmd {:5.2f}"
"| Time {batch_time.val:5.2f} ({batch_time.avg:5.2f})\t".
format(tr_seq_loss,
tr_bow_loss,
tr_kld,
tr_mi,
tr_tc,
tr_dwkl,
tr_seq_ppl,
tr_bow_ppl,
tr_log_copula,
tr_mmd,
batch_time=batch_time))
print(
len(meta) * ' ' + "| valid loss {:5.2f} {:5.2f} ({:5.2f}) "
"| {:5.2f} {:5.2f} {:5.2f} "
"| valid ppl {:5.2f} {:5.2f} | valid log copula {:5.2f} | valid mmd {:5.2f}"
"| joint NLL {:5.2f}".format(
va_seq_loss, va_bow_loss, va_kld, va_mi, va_tc, va_dwkl,
va_seq_ppl, va_bow_ppl, va_log_copula, va_mmd,
va_seq_loss + va_kld - va_log_copula),
flush=True)
epoch_loss = va_seq_loss + va_kld
if best_loss is None or epoch_loss < best_loss:
best_loss = epoch_loss
# with open(get_savepath(args), 'wb') as f:
# torch.save(model, f)
except KeyboardInterrupt:
print('-' * 90)
print('Exiting from training early')
save_logger(tr_loggers, va_loggers, args.loss_name)
# with open(get_savepath(args), 'rb') as f:
# model = torch.load(f)
(te_seq_loss, te_bow_loss, te_kld, te_mi, te_tc, te_dwkl, te_seq_ppl,
te_bow_ppl, te_log_copula, te_mmd) = evaluate(test_iter, model, pad_id)
print('=' * 90)
print("| End of training | test loss {:5.2f} {:5.2f} ({:5.2f}) "
"| {:5.2f} {:5.2f} {:5.2f} "
"| test ppl {:5.2f} {:5.2f}"
"| test log copula {:5.2f}"
"| test mmd {:5.2f}"
"| test nll {:5.2f}".format(te_seq_loss, te_bow_loss, te_kld, te_mi,
te_tc, te_dwkl, te_seq_ppl, te_bow_ppl,
te_log_copula, te_mmd,
te_seq_loss + te_kld - te_log_copula))
print('=' * 90)
te_losslogger = {
"seq_loss": te_seq_loss,
"bow_loss": te_bow_loss,
"kld": te_kld,
"seq_ppl": te_seq_ppl,
"bow_ppl": te_bow_ppl,
"log_copula": te_log_copula,
"mmd": te_mmd,
}
