class RSI(object):
def __init__(self, period):
self.value = None
self.last = None
self.ema_u = EMA(period)
self.ema_d = EMA(period)
self.tbl = None
def setupH5(self, h5file, h5where, h5name):
if h5file != None and h5where != None and h5name != None:
self.tbl = h5file.createTable(h5where, h5name, RSIData)
def update(self, value, date=None):
if self.last == None:
self.last = value
U = value - self.last
D = self.last - value
self.last = value
if U > 0:
D = 0
elif D > 0:
U = 0
self.ema_u.update(U)
self.ema_d.update(D)
if self.ema_d.value == 0:
self.value = 100.0
else:
rs = self.ema_u.value / self.ema_d.value
self.value = 100.0 - (100.0 / (1 + rs))
if self.tbl != None and date:
self.tbl.row["date"] = date.date().toordinal()
self.tbl.row["value"] = self.value
self.tbl.row.append()
self.tbl.flush()
return self.value
class RSI(object):
def __init__(self, period):
self.value = None
self.last = None
self.ema_u = EMA(period)
self.ema_d = EMA(period)
self.tbl = None
def setupH5(self, h5file, h5where, h5name):
if h5file != None and h5where != None and h5name != None:
self.tbl = h5file.createTable(h5where, h5name, RSIData)
def update(self, value, date=None):
if self.last == None:
self.last = value
U = value - self.last
D = self.last - value
self.last = value
if U > 0:
D = 0
elif D > 0:
U = 0
self.ema_u.update(U)
self.ema_d.update(D)
if self.ema_d.value == 0:
self.value = 100.0
else:
rs = self.ema_u.value / self.ema_d.value
self.value = 100.0 - (100.0 / (1 + rs))
if self.tbl != None and date:
self.tbl.row["date"] = date.date().toordinal()
self.tbl.row["value"] = self.value
self.tbl.row.append()
self.tbl.flush()
return self.value
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--data', default='mimic3.npz', help='data file')
parser.add_argument('--seed',
type=int,
default=None,
help='random seed. Randomly set if not specified.')
# training options
parser.add_argument('--nz',
type=int,
default=32,
help='dimension of latent variable')
parser.add_argument('--epoch',
type=int,
default=200,
help='number of training epochs')
parser.add_argument('--batch-size',
type=int,
default=64,
help='batch size')
# Use smaller test batch size to accommodate more importance samples
parser.add_argument('--test-batch-size',
type=int,
default=32,
help='batch size for validation and test set')
parser.add_argument('--train-k',
type=int,
default=8,
help='number of importance weights for training')
parser.add_argument('--test-k',
type=int,
default=50,
help='number of importance weights for evaluation')
parser.add_argument('--flow',
type=int,
default=2,
help='number of IAF layers')
parser.add_argument('--lr',
type=float,
default=2e-4,
help='global learning rate')
parser.add_argument('--enc-lr',
type=float,
default=1e-4,
help='encoder learning rate')
parser.add_argument('--dec-lr',
type=float,
default=1e-4,
help='decoder learning rate')
parser.add_argument('--min-lr',
type=float,
default=-1,
help='min learning rate for LR scheduler. '
'-1 to disable annealing')
parser.add_argument('--wd', type=float, default=1e-3, help='weight decay')
parser.add_argument('--overlap',
type=float,
default=.5,
help='kernel overlap')
parser.add_argument('--cls',
type=float,
default=200,
help='classification weight')
parser.add_argument('--clsdep',
type=int,
default=1,
help='number of layers for classifier')
parser.add_argument('--ts',
type=float,
default=1,
help='log-likelihood weight for ELBO')
parser.add_argument('--kl',
type=float,
default=.1,
help='KL weight for ELBO')
parser.add_argument('--eval-interval',
type=int,
default=1,
help='AUC evaluation interval. '
'0 to disable evaluation.')
parser.add_argument('--save-interval',
type=int,
default=0,
help='interval to save models. 0 to disable saving.')
parser.add_argument('--prefix',
default='pvae',
help='prefix of output directory')
parser.add_argument('--comp',
type=int,
default=7,
help='continuous convolution kernel size')
parser.add_argument('--sigma',
type=float,
default=.2,
help='standard deviation for Gaussian likelihood')
parser.add_argument('--dec-ch',
default='8-16-16',
help='decoder architecture')
parser.add_argument('--enc-ch',
default='64-32-32-16',
help='encoder architecture')
parser.add_argument('--rescale',
dest='rescale',
action='store_const',
const=True,
default=True,
help='if set, rescale time to [-1, 1]')
parser.add_argument('--no-rescale',
dest='rescale',
action='store_const',
const=False)
parser.add_argument('--cconvnorm',
dest='cconv_norm',
action='store_const',
const=True,
default=True,
help='if set, normalize continuous convolutional '
'layer using mean pooling')
parser.add_argument('--no-cconvnorm',
dest='cconv_norm',
action='store_const',
const=False)
parser.add_argument('--cconv-ref',
type=int,
default=98,
help='number of evenly-spaced reference locations '
'for continuous convolutional layer')
parser.add_argument('--dec-ref',
type=int,
default=128,
help='number of evenly-spaced reference locations '
'for decoder')
parser.add_argument('--ema',
dest='ema',
type=int,
default=0,
help='start epoch of exponential moving average '
'(EMA). -1 to disable EMA')
parser.add_argument('--ema-decay',
type=float,
default=.9999,
help='EMA decay')
args = parser.parse_args()
nz = args.nz
epochs = args.epoch
eval_interval = args.eval_interval
save_interval = args.save_interval
if args.seed is None:
rnd = np.random.RandomState(None)
random_seed = rnd.randint(np.iinfo(np.uint32).max)
else:
random_seed = args.seed
rnd = np.random.RandomState(random_seed)
np.random.seed(random_seed)
torch.manual_seed(random_seed)
max_time = 5
cconv_ref = args.cconv_ref
overlap = args.overlap
train_dataset, val_dataset, test_dataset = time_series.split_data(
args.data, rnd, max_time, cconv_ref, overlap, device, args.rescale)
train_loader = DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
drop_last=True,
collate_fn=train_dataset.collate_fn)
n_train_batch = len(train_loader)
val_loader = DataLoader(val_dataset,
batch_size=args.test_batch_size,
shuffle=False,
collate_fn=val_dataset.collate_fn)
test_loader = DataLoader(test_dataset,
batch_size=args.test_batch_size,
shuffle=False,
collate_fn=test_dataset.collate_fn)
in_channels, seq_len = train_dataset.data.shape[1:]
dec_channels = [int(c) for c in args.dec_ch.split('-')] + [in_channels]
enc_channels = [int(c) for c in args.enc_ch.split('-')]
out_channels = enc_channels[0]
squash = torch.sigmoid
if args.rescale:
squash = torch.tanh
dec_ch_up = 2**(len(dec_channels) - 2)
assert args.dec_ref % dec_ch_up == 0, (
f'--dec-ref={args.dec_ref} is not divided by {dec_ch_up}.')
dec_len0 = args.dec_ref // dec_ch_up
grid_decoder = GridDecoder(nz, dec_channels, dec_len0, squash)
decoder = Decoder(grid_decoder, max_time=max_time,
dec_ref=args.dec_ref).to(device)
cconv = ContinuousConv1D(in_channels,
out_channels,
max_time,
cconv_ref,
overlap_rate=overlap,
kernel_size=args.comp,
norm=args.cconv_norm).to(device)
encoder = Encoder(cconv, nz, enc_channels, args.flow).to(device)
classifier = Classifier(nz, args.clsdep).to(device)
pvae = PVAE(encoder, decoder, classifier, args.sigma, args.cls).to(device)
ema = None
if args.ema >= 0:
ema = EMA(pvae, args.ema_decay, args.ema)
other_params = [
param for name, param in pvae.named_parameters()
if not (name.startswith('decoder.grid_decoder')
or name.startswith('encoder.grid_encoder'))
]
params = [
{
'params': decoder.grid_decoder.parameters(),
'lr': args.dec_lr
},
{
'params': encoder.grid_encoder.parameters(),
'lr': args.enc_lr
},
{
'params': other_params
},
]
optimizer = optim.Adam(params, lr=args.lr, weight_decay=args.wd)
scheduler = make_scheduler(optimizer, args.lr, args.min_lr, epochs)
path = '{}_{}'.format(args.prefix, datetime.now().strftime('%m%d.%H%M%S'))
output_dir = Path('results') / 'mimic3-pvae' / path
print(output_dir)
log_dir = mkdir(output_dir / 'log')
model_dir = mkdir(output_dir / 'model')
start_epoch = 0
with (log_dir / 'seed.txt').open('w') as f:
print(random_seed, file=f)
with (log_dir / 'gpu.txt').open('a') as f:
print(torch.cuda.device_count(), start_epoch, file=f)
with (log_dir / 'args.txt').open('w') as f:
for key, val in sorted(vars(args).items()):
print(f'{key}: {val}', file=f)
with (log_dir / 'params.txt').open('w') as f:
def print_params_count(module, name):
try: # sum counts if module is a list
params_count = sum(count_parameters(m) for m in module)
except TypeError:
params_count = count_parameters(module)
print(f'{name} {params_count}', file=f)
print_params_count(grid_decoder, 'grid_decoder')
print_params_count(decoder, 'decoder')
print_params_count(cconv, 'cconv')
print_params_count(encoder, 'encoder')
print_params_count(classifier, 'classifier')
print_params_count(pvae, 'pvae')
print_params_count(pvae, 'total')
tracker = Tracker(log_dir, n_train_batch)
evaluator = Evaluator(pvae,
val_loader,
test_loader,
log_dir,
eval_args={'iw_samples': args.test_k})
start = time.time()
epoch_start = start
for epoch in range(start_epoch, epochs):
loss_breakdown = defaultdict(float)
epoch_start = time.time()
for (val, idx, mask, y, _, cconv_graph) in train_loader:
optimizer.zero_grad()
loss, _, _, loss_info = pvae(val, idx, mask, y, cconv_graph,
args.train_k, args.ts, args.kl)
loss.backward()
optimizer.step()
if ema:
ema.update()
for loss_name, loss_val in loss_info.items():
loss_breakdown[loss_name] += loss_val
if scheduler:
scheduler.step()
cur_time = time.time()
tracker.log(epoch, loss_breakdown, cur_time - epoch_start,
cur_time - start)
if eval_interval > 0 and (epoch + 1) % eval_interval == 0:
if ema:
ema.apply()
evaluator.evaluate(epoch)
ema.restore()
else:
evaluator.evaluate(epoch)
model_dict = {
'pvae': pvae.state_dict(),
'ema': ema.state_dict() if ema else None,
'epoch': epoch + 1,
'args': args,
}
torch.save(model_dict, str(log_dir / 'model.pth'))
if save_interval > 0 and (epoch + 1) % save_interval == 0:
torch.save(model_dict, str(model_dir / f'{epoch:04d}.pth'))
print(output_dir)
def train_val_model(pipeline_cfg, model_cfg, train_cfg):
data_pipeline = DataPipeline(**pipeline_cfg)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
if model_cfg['cxt_emb_pretrained'] is not None:
model_cfg['cxt_emb_pretrained'] = torch.load(
model_cfg['cxt_emb_pretrained'])
bidaf = BiDAF(word_emb=data_pipeline.word_type.vocab.vectors, **model_cfg)
ema = EMA(train_cfg['exp_decay_rate'])
for name, param in bidaf.named_parameters():
if param.requires_grad:
ema.register(name, param.data)
parameters = filter(lambda p: p.requires_grad, bidaf.parameters())
optimizer = optim.Adadelta(parameters, lr=train_cfg['lr'])
criterion = nn.CrossEntropyLoss()
result = {'best_f1': 0.0, 'best_model': None}
num_epochs = train_cfg['num_epochs']
for epoch in range(1, num_epochs + 1):
print('Epoch {}/{}'.format(epoch, num_epochs))
print('-' * 10)
for phase in ['train', 'val']:
val_answers = dict()
val_f1 = 0
val_em = 0
val_cnt = 0
val_r = 0
if phase == 'train':
bidaf.train()
else:
bidaf.eval()
backup_params = EMA(0)
for name, param in bidaf.named_parameters():
if param.requires_grad:
backup_params.register(name, param.data)
param.data.copy_(ema.get(name))
with torch.set_grad_enabled(phase == 'train'):
for batch_num, batch in enumerate(
data_pipeline.data_iterators[phase]):
optimizer.zero_grad()
p1, p2 = bidaf(batch)
loss = criterion(p1, batch.s_idx) + criterion(
p2, batch.e_idx)
if phase == 'train':
loss.backward()
optimizer.step()
for name, param in bidaf.named_parameters():
if param.requires_grad:
ema.update(name, param.data)
if batch_num % train_cfg['batch_per_disp'] == 0:
batch_loss = loss.item()
print('batch %d: loss %.3f' %
(batch_num, batch_loss))
if phase == 'val':
batch_size, c_len = p1.size()
val_cnt += batch_size
ls = nn.LogSoftmax(dim=1)
mask = (torch.ones(c_len, c_len) * float('-inf')).to(device).tril(-1). \
unsqueeze(0).expand(batch_size, -1, -1)
score = (ls(p1).unsqueeze(2) +
ls(p2).unsqueeze(1)) + mask
score, s_idx = score.max(dim=1)
score, e_idx = score.max(dim=1)
s_idx = torch.gather(s_idx, 1,
e_idx.view(-1, 1)).squeeze()
for i in range(batch_size):
answer = (s_idx[i], e_idx[i])
gt = (batch.s_idx[i], batch.e_idx[i])
val_f1 += f1_score(answer, gt)
val_em += exact_match_score(answer, gt)
val_r += r_score(answer, gt)
if phase == 'val':
val_f1 = val_f1 * 100 / val_cnt
val_em = val_em * 100 / val_cnt
val_r = val_r * 100 / val_cnt
print('Epoch %d: %s f1 %.3f | %s em %.3f | %s rouge %.3f' %
(epoch, phase, val_f1, phase, val_em, phase, val_r))
if val_f1 > result['best_f1']:
result['best_f1'] = val_f1
result['best_em'] = val_em
result['best_model'] = copy.deepcopy(bidaf.state_dict())
torch.save(result, train_cfg['ckpoint_file'])
# with open(train_cfg['val_answers'], 'w', encoding='utf-8') as f:
# print(json.dumps(val_answers), file=f)
for name, param in bidaf.named_parameters():
if param.requires_grad:
param.data.copy_(backup_params.get(name))
def main():
parser = argparse.ArgumentParser()
default_dataset = 'toy-data.npz'
parser.add_argument('--data', default=default_dataset, help='data file')
parser.add_argument('--seed',
type=int,
default=None,
help='random seed. Randomly set if not specified.')
# training options
parser.add_argument('--nz',
type=int,
default=32,
help='dimension of latent variable')
parser.add_argument('--epoch',
type=int,
default=1000,
help='number of training epochs')
parser.add_argument('--batch-size',
type=int,
default=128,
help='batch size')
parser.add_argument('--lr',
type=float,
default=8e-5,
help='encoder/decoder learning rate')
parser.add_argument('--dis-lr',
type=float,
default=1e-4,
help='discriminator learning rate')
parser.add_argument('--min-lr',
type=float,
default=5e-5,
help='min encoder/decoder learning rate for LR '
'scheduler. -1 to disable annealing')
parser.add_argument('--min-dis-lr',
type=float,
default=7e-5,
help='min discriminator learning rate for LR '
'scheduler. -1 to disable annealing')
parser.add_argument('--wd', type=float, default=0, help='weight decay')
parser.add_argument('--overlap',
type=float,
default=.5,
help='kernel overlap')
parser.add_argument('--no-norm-trans',
action='store_true',
help='if set, use Gaussian posterior without '
'transformation')
parser.add_argument('--plot-interval',
type=int,
default=1,
help='plot interval. 0 to disable plotting.')
parser.add_argument('--save-interval',
type=int,
default=0,
help='interval to save models. 0 to disable saving.')
parser.add_argument('--prefix',
default='pbigan',
help='prefix of output directory')
parser.add_argument('--comp',
type=int,
default=7,
help='continuous convolution kernel size')
parser.add_argument('--ae',
type=float,
default=.2,
help='autoencoding regularization strength')
parser.add_argument('--aeloss',
default='smooth_l1',
help='autoencoding loss. (options: mse, smooth_l1)')
parser.add_argument('--ema',
dest='ema',
type=int,
default=-1,
help='start epoch of exponential moving average '
'(EMA). -1 to disable EMA')
parser.add_argument('--ema-decay',
type=float,
default=.9999,
help='EMA decay')
parser.add_argument('--mmd',
type=float,
default=1,
help='MMD strength for latent variable')
# squash is off when rescale is off
parser.add_argument('--squash',
dest='squash',
action='store_const',
const=True,
default=True,
help='bound the generated time series value '
'using tanh')
parser.add_argument('--no-squash',
dest='squash',
action='store_const',
const=False)
# rescale to [-1, 1]
parser.add_argument('--rescale',
dest='rescale',
action='store_const',
const=True,
default=True,
help='if set, rescale time to [-1, 1]')
parser.add_argument('--no-rescale',
dest='rescale',
action='store_const',
const=False)
args = parser.parse_args()
batch_size = args.batch_size
nz = args.nz
epochs = args.epoch
plot_interval = args.plot_interval
save_interval = args.save_interval
try:
npz = np.load(args.data)
train_data = npz['data']
train_time = npz['time']
train_mask = npz['mask']
except FileNotFoundError:
if args.data != default_dataset:
raise
# Generate the default toy dataset from scratch
train_data, train_time, train_mask, _, _ = gen_data(
n_samples=10000,
seq_len=200,
max_time=1,
poisson_rate=50,
obs_span_rate=.25,
save_file=default_dataset)
_, in_channels, seq_len = train_data.shape
train_time *= train_mask
if args.seed is None:
rnd = np.random.RandomState(None)
random_seed = rnd.randint(np.iinfo(np.uint32).max)
else:
random_seed = args.seed
rnd = np.random.RandomState(random_seed)
np.random.seed(random_seed)
torch.manual_seed(random_seed)
# Scale time
max_time = 5
train_time *= max_time
squash = None
rescaler = None
if args.rescale:
rescaler = Rescaler(train_data)
train_data = rescaler.rescale(train_data)
if args.squash:
squash = torch.tanh
out_channels = 64
cconv_ref = 98
train_dataset = TimeSeries(train_data,
train_time,
train_mask,
label=None,
max_time=max_time,
cconv_ref=cconv_ref,
overlap_rate=args.overlap,
device=device)
train_loader = DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True,
drop_last=True,
collate_fn=train_dataset.collate_fn)
n_train_batch = len(train_loader)
time_loader = DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True,
drop_last=True,
collate_fn=train_dataset.collate_fn)
test_loader = DataLoader(train_dataset,
batch_size=batch_size,
collate_fn=train_dataset.collate_fn)
grid_decoder = SeqGeneratorDiscrete(in_channels, nz, squash)
decoder = Decoder(grid_decoder, max_time=max_time).to(device)
cconv = ContinuousConv1D(in_channels,
out_channels,
max_time,
cconv_ref,
overlap_rate=args.overlap,
kernel_size=args.comp,
norm=True).to(device)
encoder = Encoder(cconv, nz, not args.no_norm_trans).to(device)
pbigan = PBiGAN(encoder, decoder, args.aeloss).to(device)
critic_cconv = ContinuousConv1D(in_channels,
out_channels,
max_time,
cconv_ref,
overlap_rate=args.overlap,
kernel_size=args.comp,
norm=True).to(device)
critic = ConvCritic(critic_cconv, nz).to(device)
ema = None
if args.ema >= 0:
ema = EMA(pbigan, args.ema_decay, args.ema)
optimizer = optim.Adam(pbigan.parameters(),
lr=args.lr,
weight_decay=args.wd)
critic_optimizer = optim.Adam(critic.parameters(),
lr=args.dis_lr,
weight_decay=args.wd)
scheduler = make_scheduler(optimizer, args.lr, args.min_lr, epochs)
dis_scheduler = make_scheduler(critic_optimizer, args.dis_lr,
args.min_dis_lr, epochs)
path = '{}_{}'.format(args.prefix, datetime.now().strftime('%m%d.%H%M%S'))
output_dir = Path('results') / 'toy-pbigan' / path
print(output_dir)
log_dir = mkdir(output_dir / 'log')
model_dir = mkdir(output_dir / 'model')
start_epoch = 0
with (log_dir / 'seed.txt').open('w') as f:
print(random_seed, file=f)
with (log_dir / 'gpu.txt').open('a') as f:
print(torch.cuda.device_count(), start_epoch, file=f)
with (log_dir / 'args.txt').open('w') as f:
for key, val in sorted(vars(args).items()):
print(f'{key}: {val}', file=f)
tracker = Tracker(log_dir, n_train_batch)
visualizer = Visualizer(encoder, decoder, batch_size, max_time,
test_loader, rescaler, output_dir, device)
start = time.time()
epoch_start = start
for epoch in range(start_epoch, epochs):
loss_breakdown = defaultdict(float)
for ((val, idx, mask, _, cconv_graph),
(_, idx_t, mask_t, index, _)) in zip(train_loader, time_loader):
z_enc, x_recon, z_gen, x_gen, ae_loss = pbigan(
val, idx, mask, cconv_graph, idx_t, mask_t)
cconv_graph_gen = train_dataset.make_graph(x_gen, idx_t, mask_t,
index)
real = critic(cconv_graph, batch_size, z_enc)
fake = critic(cconv_graph_gen, batch_size, z_gen)
D_loss = gan_loss(real, fake, 1, 0)
critic_optimizer.zero_grad()
D_loss.backward(retain_graph=True)
critic_optimizer.step()
G_loss = gan_loss(real, fake, 0, 1)
mmd_loss = mmd(z_enc, z_gen)
loss = G_loss + ae_loss * args.ae + mmd_loss * args.mmd
optimizer.zero_grad()
loss.backward()
optimizer.step()
if ema:
ema.update()
loss_breakdown['D'] += D_loss.item()
loss_breakdown['G'] += G_loss.item()
loss_breakdown['AE'] += ae_loss.item()
loss_breakdown['MMD'] += mmd_loss.item()
loss_breakdown['total'] += loss.item()
if scheduler:
scheduler.step()
if dis_scheduler:
dis_scheduler.step()
cur_time = time.time()
tracker.log(epoch, loss_breakdown, cur_time - epoch_start,
cur_time - start)
if plot_interval > 0 and (epoch + 1) % plot_interval == 0:
if ema:
ema.apply()
visualizer.plot(epoch)
ema.restore()
else:
visualizer.plot(epoch)
model_dict = {
'pbigan': pbigan.state_dict(),
'critic': critic.state_dict(),
'ema': ema.state_dict() if ema else None,
'epoch': epoch + 1,
'args': args,
}
torch.save(model_dict, str(log_dir / 'model.pth'))
if save_interval > 0 and (epoch + 1) % save_interval == 0:
torch.save(model_dict, str(model_dir / f'{epoch:04d}.pth'))
print(output_dir)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--data', default='mimic3.npz', help='data file')
parser.add_argument('--seed',
type=int,
default=None,
help='random seed. Randomly set if not specified.')
# training options
parser.add_argument('--nz',
type=int,
default=32,
help='dimension of latent variable')
parser.add_argument('--epoch',
type=int,
default=500,
help='number of training epochs')
parser.add_argument('--batch-size',
type=int,
default=64,
help='batch size')
# Use smaller test batch size to accommodate more importance samples
parser.add_argument('--test-batch-size',
type=int,
default=32,
help='batch size for validation and test set')
parser.add_argument('--lr',
type=float,
default=2e-4,
help='encoder/decoder learning rate')
parser.add_argument('--dis-lr',
type=float,
default=3e-4,
help='discriminator learning rate')
parser.add_argument('--min-lr',
type=float,
default=1e-4,
help='min encoder/decoder learning rate for LR '
'scheduler. -1 to disable annealing')
parser.add_argument('--min-dis-lr',
type=float,
default=1.5e-4,
help='min discriminator learning rate for LR '
'scheduler. -1 to disable annealing')
parser.add_argument('--wd', type=float, default=1e-4, help='weight decay')
parser.add_argument('--overlap',
type=float,
default=.5,
help='kernel overlap')
parser.add_argument('--cls',
type=float,
default=1,
help='classification weight')
parser.add_argument('--clsdep',
type=int,
default=1,
help='number of layers for classifier')
parser.add_argument('--eval-interval',
type=int,
default=1,
help='AUC evaluation interval. '
'0 to disable evaluation.')
parser.add_argument('--save-interval',
type=int,
default=0,
help='interval to save models. 0 to disable saving.')
parser.add_argument('--prefix',
default='pbigan',
help='prefix of output directory')
parser.add_argument('--comp',
type=int,
default=7,
help='continuous convolution kernel size')
parser.add_argument('--ae',
type=float,
default=1,
help='autoencoding regularization strength')
parser.add_argument('--aeloss',
default='mse',
help='autoencoding loss. (options: mse, smooth_l1)')
parser.add_argument('--dec-ch',
default='8-16-16',
help='decoder architecture')
parser.add_argument('--enc-ch',
default='64-32-32-16',
help='encoder architecture')
parser.add_argument('--dis-ch',
default=None,
help='discriminator architecture. Use encoder '
'architecture if unspecified.')
parser.add_argument('--rescale',
dest='rescale',
action='store_const',
const=True,
default=True,
help='if set, rescale time to [-1, 1]')
parser.add_argument('--no-rescale',
dest='rescale',
action='store_const',
const=False)
parser.add_argument('--cconvnorm',
dest='cconv_norm',
action='store_const',
const=True,
default=True,
help='if set, normalize continuous convolutional '
'layer using mean pooling')
parser.add_argument('--no-cconvnorm',
dest='cconv_norm',
action='store_const',
const=False)
parser.add_argument('--cconv-ref',
type=int,
default=98,
help='number of evenly-spaced reference locations '
'for continuous convolutional layer')
parser.add_argument('--dec-ref',
type=int,
default=128,
help='number of evenly-spaced reference locations '
'for decoder')
parser.add_argument('--trans',
type=int,
default=2,
help='number of encoder layers')
parser.add_argument('--ema',
dest='ema',
type=int,
default=0,
help='start epoch of exponential moving average '
'(EMA). -1 to disable EMA')
parser.add_argument('--ema-decay',
type=float,
default=.9999,
help='EMA decay')
parser.add_argument('--mmd',
type=float,
default=1,
help='MMD strength for latent variable')
args = parser.parse_args()
nz = args.nz
epochs = args.epoch
eval_interval = args.eval_interval
save_interval = args.save_interval
if args.seed is None:
rnd = np.random.RandomState(None)
random_seed = rnd.randint(np.iinfo(np.uint32).max)
else:
random_seed = args.seed
rnd = np.random.RandomState(random_seed)
np.random.seed(random_seed)
torch.manual_seed(random_seed)
max_time = 5
cconv_ref = args.cconv_ref
overlap = args.overlap
train_dataset, val_dataset, test_dataset = time_series.split_data(
args.data, rnd, max_time, cconv_ref, overlap, device, args.rescale)
train_loader = DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
drop_last=True,
collate_fn=train_dataset.collate_fn)
n_train_batch = len(train_loader)
time_loader = DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
drop_last=True,
collate_fn=train_dataset.collate_fn)
val_loader = DataLoader(val_dataset,
batch_size=args.test_batch_size,
shuffle=False,
collate_fn=val_dataset.collate_fn)
test_loader = DataLoader(test_dataset,
batch_size=args.test_batch_size,
shuffle=False,
collate_fn=test_dataset.collate_fn)
in_channels, seq_len = train_dataset.data.shape[1:]
if args.dis_ch is None:
args.dis_ch = args.enc_ch
dec_channels = [int(c) for c in args.dec_ch.split('-')] + [in_channels]
enc_channels = [int(c) for c in args.enc_ch.split('-')]
dis_channels = [int(c) for c in args.dis_ch.split('-')]
out_channels = enc_channels[0]
squash = torch.sigmoid
if args.rescale:
squash = torch.tanh
dec_ch_up = 2**(len(dec_channels) - 2)
assert args.dec_ref % dec_ch_up == 0, (
f'--dec-ref={args.dec_ref} is not divided by {dec_ch_up}.')
dec_len0 = args.dec_ref // dec_ch_up
grid_decoder = GridDecoder(nz, dec_channels, dec_len0, squash)
decoder = Decoder(grid_decoder, max_time=max_time,
dec_ref=args.dec_ref).to(device)
cconv = ContinuousConv1D(in_channels,
out_channels,
max_time,
cconv_ref,
overlap_rate=overlap,
kernel_size=args.comp,
norm=args.cconv_norm).to(device)
encoder = Encoder(cconv, nz, enc_channels, args.trans).to(device)
classifier = Classifier(nz, args.clsdep).to(device)
pbigan = PBiGAN(encoder, decoder, classifier,
ae_loss=args.aeloss).to(device)
ema = None
if args.ema >= 0:
ema = EMA(pbigan, args.ema_decay, args.ema)
critic_cconv = ContinuousConv1D(in_channels,
out_channels,
max_time,
cconv_ref,
overlap_rate=overlap,
kernel_size=args.comp,
norm=args.cconv_norm).to(device)
critic_embed = 32
critic = ConvCritic(critic_cconv, nz, dis_channels,
critic_embed).to(device)
optimizer = optim.Adam(pbigan.parameters(),
lr=args.lr,
betas=(0, .999),
weight_decay=args.wd)
critic_optimizer = optim.Adam(critic.parameters(),
lr=args.dis_lr,
betas=(0, .999),
weight_decay=args.wd)
scheduler = make_scheduler(optimizer, args.lr, args.min_lr, epochs)
dis_scheduler = make_scheduler(critic_optimizer, args.dis_lr,
args.min_dis_lr, epochs)
path = '{}_{}'.format(args.prefix, datetime.now().strftime('%m%d.%H%M%S'))
output_dir = Path('results') / 'mimic3-pbigan' / path
print(output_dir)
log_dir = mkdir(output_dir / 'log')
model_dir = mkdir(output_dir / 'model')
start_epoch = 0
with (log_dir / 'seed.txt').open('w') as f:
print(random_seed, file=f)
with (log_dir / 'gpu.txt').open('a') as f:
print(torch.cuda.device_count(), start_epoch, file=f)
with (log_dir / 'args.txt').open('w') as f:
for key, val in sorted(vars(args).items()):
print(f'{key}: {val}', file=f)
with (log_dir / 'params.txt').open('w') as f:
def print_params_count(module, name):
try: # sum counts if module is a list
params_count = sum(count_parameters(m) for m in module)
except TypeError:
params_count = count_parameters(module)
print(f'{name} {params_count}', file=f)
print_params_count(grid_decoder, 'grid_decoder')
print_params_count(decoder, 'decoder')
print_params_count(cconv, 'cconv')
print_params_count(encoder, 'encoder')
print_params_count(classifier, 'classifier')
print_params_count(pbigan, 'pbigan')
print_params_count(critic, 'critic')
print_params_count([pbigan, critic], 'total')
tracker = Tracker(log_dir, n_train_batch)
evaluator = Evaluator(pbigan, val_loader, test_loader, log_dir)
start = time.time()
epoch_start = start
batch_size = args.batch_size
for epoch in range(start_epoch, epochs):
loss_breakdown = defaultdict(float)
epoch_start = time.time()
if epoch >= 40:
args.cls = 200
for ((val, idx, mask, y, _, cconv_graph),
(_, idx_t, mask_t, _, index, _)) in zip(train_loader,
time_loader):
z_enc, x_recon, z_gen, x_gen, ae_loss, cls_loss = pbigan(
val, idx, mask, y, cconv_graph, idx_t, mask_t)
cconv_graph_gen = train_dataset.make_graph(x_gen, idx_t, mask_t,
index)
# Don't need pbigan.requires_grad_(False);
# critic takes as input only the detached tensors.
real = critic(cconv_graph, batch_size, z_enc.detach())
detached_graph = [[cat_y.detach() for cat_y in x] if i == 2 else x
for i, x in enumerate(cconv_graph_gen)]
fake = critic(detached_graph, batch_size, z_gen.detach())
D_loss = gan_loss(real, fake, 1, 0)
critic_optimizer.zero_grad()
D_loss.backward()
critic_optimizer.step()
for p in critic.parameters():
p.requires_grad_(False)
real = critic(cconv_graph, batch_size, z_enc)
fake = critic(cconv_graph_gen, batch_size, z_gen)
G_loss = gan_loss(real, fake, 0, 1)
mmd_loss = mmd(z_enc, z_gen)
loss = (G_loss + ae_loss * args.ae + cls_loss * args.cls +
mmd_loss * args.mmd)
optimizer.zero_grad()
loss.backward()
optimizer.step()
for p in critic.parameters():
p.requires_grad_(True)
if ema:
ema.update()
loss_breakdown['D'] += D_loss.item()
loss_breakdown['G'] += G_loss.item()
loss_breakdown['AE'] += ae_loss.item()
loss_breakdown['MMD'] += mmd_loss.item()
loss_breakdown['CLS'] += cls_loss.item()
loss_breakdown['total'] += loss.item()
if scheduler:
scheduler.step()
if dis_scheduler:
dis_scheduler.step()
cur_time = time.time()
tracker.log(epoch, loss_breakdown, cur_time - epoch_start,
cur_time - start)
if eval_interval > 0 and (epoch + 1) % eval_interval == 0:
if ema:
ema.apply()
evaluator.evaluate(epoch)
ema.restore()
else:
evaluator.evaluate(epoch)
model_dict = {
'pbigan': pbigan.state_dict(),
'critic': critic.state_dict(),
'ema': ema.state_dict() if ema else None,
'epoch': epoch + 1,
'args': args,
}
torch.save(model_dict, str(log_dir / 'model.pth'))
if save_interval > 0 and (epoch + 1) % save_interval == 0:
torch.save(model_dict, str(model_dir / f'{epoch:04d}.pth'))
print(output_dir)
def train_bidaf(args, data):
device = torch.device(
f"cuda:{args.gpu}" if torch.cuda.is_available() else "cpu")
model = BiDAF(args, data.WORD.vocab.vectors).to(device)
ema = EMA(args.exp_decay_rate)
for name, param in model.named_parameters():
if param.requires_grad:
ema.register(name, param.data)
parameters = filter(lambda p: p.requires_grad, model.parameters())
optimizer = optim.Adadelta(parameters, lr=args.learning_rate)
criterion = nn.CrossEntropyLoss()
writer = SummaryWriter(logdir='runs/' + args.model_time)
model.train()
loss, last_epoch = 0, -1
max_dev_exact, max_dev_f1 = -1, -1
iterator = data.train_iter
for i, batch in tqdm(enumerate(iterator)):
present_epoch = int(iterator.epoch)
if present_epoch == args.epoch:
break
if present_epoch > last_epoch:
print('epoch:', present_epoch + 1)
last_epoch = present_epoch
p1, p2 = model(batch)
optimizer.zero_grad()
# print(p1, batch.s_idx)
# print(p2, batch.e_idx)
batch_loss = criterion(p1, batch.s_idx) + criterion(p2, batch.e_idx)
# print('p1', p1.shape, p1)
# print('batch.s_idx', batch.s_idx.shape, batch.s_idx.shape)
# print(loss, batch_loss.item())
loss += batch_loss.item()
# print(loss)
# print(batch_loss.item())
batch_loss.backward()
optimizer.step()
for name, param in model.named_parameters():
if param.requires_grad:
ema.update(name, param.data)
if (i + 1) % args.print_freq == 0:
dev_loss, dev_exact, dev_f1 = test(model, ema, args, data)
c = (i + 1) // args.print_freq
writer.add_scalar('loss/train', loss, c)
writer.add_scalar('loss/dev', dev_loss, c)
writer.add_scalar('exact_match/dev', dev_exact, c)
writer.add_scalar('f1/dev', dev_f1, c)
print(f'train loss: {loss:.3f} / dev loss: {dev_loss:.3f}'
f' / dev EM: {dev_exact:.3f} / dev F1: {dev_f1:.3f}')
if dev_f1 > max_dev_f1:
max_dev_f1 = dev_f1
max_dev_exact = dev_exact
best_model = copy.deepcopy(model)
loss = 0
model.train()
writer.close()
print(f'max dev EM: {max_dev_exact:.3f} / max dev F1: {max_dev_f1:.3f}')
return best_model
class SNTGRunLoop(object):
def __init__(self,
net,
dataloader=None,
params=None,
update_fn=None,
eval_loader=None,
test_loader=None,
has_cuda=True):
if has_cuda:
device = torch.device("cuda:0")
else:
device = torch.device('cpu')
self.net = net.to(device)
self.loader = dataloader
self.eval_loader = eval_loader
self.test_loader = test_loader
self.params = params
self.device = device
# self.net.to(device)
if params is not None:
n_data, num_classes = params['n_data'], params['num_classes']
n_eval_data, batch_size = params['n_eval_data'], params[
'batch_size']
self.ensemble_pred = torch.zeros((n_data, num_classes),
device=device)
self.target_pred = torch.zeros((n_data, num_classes),
device=device)
t_one = torch.ones(())
self.epoch_pred = t_one.new_empty((n_data, num_classes),
dtype=torch.float32,
device=device)
self.epoch_mask = t_one.new_empty((n_data),
dtype=torch.float32,
device=device)
self.train_epoch_loss = \
t_one.new_empty((n_data // batch_size, 4),
dtype=torch.float32, device=device)
self.train_epoch_acc = \
t_one.new_empty((n_data // batch_size), dtype=torch.float32,
device=device)
self.eval_epoch_loss = \
t_one.new_empty((n_eval_data // batch_size, 2),
dtype=torch.float32, device=device)
self.eval_epoch_acc = \
t_one.new_empty((n_eval_data // batch_size, 2),
dtype=torch.float32, device=device)
self.optimizer = opt.Adam(self.net.parameters())
self.update_fn = update_fn
self.ema = EMA(params['polyak_decay'], self.net, has_cuda)
self.unsup_weight = 0.0
# self.loss_fn = nn.CrossEntropyLoss()
def train(self):
# labeled_loss = nn.CrossEntropyLoss()
train_losses, train_accs = [], []
eval_losses, eval_accs = [], []
ema_eval_losses, ema_eval_accs = [], []
for epoch in range(self.params['num_epochs']):
# training phase
self.net.train()
train_time = -time.time()
self.epoch_pred.zero_()
self.epoch_mask.zero_()
# self.epoch_loss.zero_()
self.unsup_weight = self.update_fn(self.optimizer, epoch)
for i, data_batched in enumerate(self.loader, 0):
images, is_lens, mask, indices = \
data_batched['image'], data_batched['is_lens'], \
data_batched['mask'], data_batched['index']
targets = torch.index_select(self.target_pred, 0, indices)
# print(f"y value dimension:{is_lens.size()}")
self.optimizer.zero_grad()
outputs, h_x = self.net(images)
# print(f"output dimension: {outputs.size()}")
predicts = F.softmax(outputs, dim=1)
# update for ensemble
for k, j in enumerate(indices):
self.epoch_pred[j] = predicts[k]
self.epoch_mask[j] = 1.0
# labeled loss
labeled_mask = mask.eq(0)
# loss = self.loss_fn(
# outputs[labeled_mask], is_lens[labeled_mask])
# labeled loss with binary entropy with logits, use one_hot
one_hot = torch.zeros(
len(is_lens[labeled_mask]), is_lens[labeled_mask].max()+1,
device=self.device) \
.scatter_(1, is_lens[labeled_mask].unsqueeze(1), 1.)
loss = F.binary_cross_entropy_with_logits(
outputs[labeled_mask], one_hot)
# one_hot = torch.zeros(
# len(is_lens), is_lens.max() + 1, device=self.device) \
# .scatter_(1, is_lens.unsqueeze(1), 1.)
# loss = F.binary_cross_entropy_with_logits(outputs, one_hot)
# print(loss.item())
self.train_epoch_acc[i] = \
torch.mean(torch.argmax(
outputs[labeled_mask], 1).eq(is_lens[labeled_mask])
.float()).item()
# train_acc = torch.mean(
# torch.argmax(outputs, 1).eq(is_lens).float())
self.train_epoch_loss[i, 0] = loss.item()
# unlabeled loss
unlabeled_loss = torch.mean((predicts - targets)**2)
self.train_epoch_loss[i, 1] = unlabeled_loss.item()
loss += unlabeled_loss * self.unsup_weight
# SNTG loss
if self.params['embed']:
half = int(h_x.size()[0] // 2)
eucd2 = torch.mean((h_x[:half] - h_x[half:])**2, dim=1)
eucd = torch.sqrt(eucd2)
target_hard = torch.argmax(targets, dim=1).int()
merged_tar = torch.where(mask == 0, target_hard,
is_lens.int())
neighbor_bool = torch.eq(merged_tar[:half],
merged_tar[half:])
eucd_y = torch.where(eucd < 1.0, (1.0 - eucd)**2,
torch.zeros_like(eucd))
embed_losses = torch.where(neighbor_bool, eucd2, eucd_y)
embed_loss = torch.mean(embed_losses)
self.train_epoch_loss[i, 2] = embed_loss.item()
loss += embed_loss * \
self.unsup_weight * self.params['embed_coeff']
self.train_epoch_loss[i, 3] = loss.item()
loss.backward()
self.optimizer.step()
self.ema.update()
self.ensemble_pred = \
self.params['pred_decay'] * self.ensemble_pred + \
(1 - self.params['pred_decay']) * self.epoch_pred
self.targets_pred = self.ensemble_pred / \
(1.0 - self.params['pred_decay'] ** (epoch + 1))
loss_mean = torch.mean(self.train_epoch_loss, 0)
train_losses.append(loss_mean[3].item())
acc_mean = torch.mean(self.train_epoch_acc)
train_accs.append(acc_mean.item())
print(f"epoch {epoch}, time cosumed: {time.time() + train_time}, "
f"labeled loss: {loss_mean[0].item()}, "
f"unlabeled loss: {loss_mean[1].item()}, "
f"SNTG loss: {loss_mean[2].item()}, "
f"total loss: {loss_mean[3].item()}")
# print(f"epoch {epoch}, time consumed: {time.time() + train_time}, "
# f"labeled loss: {loss_mean[0].item()}")
# eval phase
if self.eval_loader is not None:
# none ema evaluation
self.net.eval()
for i, data_batched in enumerate(self.eval_loader, 0):
images, is_lens = data_batched['image'], \
data_batched['is_lens']
# currently h_x in evalization is not used
eval_logits, _ = self.ema(images)
self.eval_epoch_acc[i, 0] = torch.mean(
torch.argmax(eval_logits,
1).eq(is_lens).float()).item()
# print(f"ema evaluation accuracy: {ema_eval_acc.item()}")
eval_lens = torch.zeros(
len(is_lens), is_lens.max()+1,
device=self.device) \
.scatter_(1, is_lens.unsqueeze(1), 1.)
# eval_loss = self.loss_fn(eval_logits, is_lens)
self.eval_epoch_loss[i, 0] = \
F.binary_cross_entropy_with_logits(
eval_logits, eval_lens).item()
# break
eval_logits, _ = self.net(images)
self.eval_epoch_acc[i, 1] = torch.mean(
torch.argmax(eval_logits,
1).eq(is_lens).float()).item()
# print(f"evaluation accuracy: {eval_acc.item()}")
self.eval_epoch_loss[i, 1] = \
F.binary_cross_entropy_with_logits(
eval_logits, eval_lens).item()
loss_mean = torch.mean(self.eval_epoch_loss, 0)
acc_mean = torch.mean(self.eval_epoch_acc, 0)
ema_eval_accs.append(acc_mean[0].item())
ema_eval_losses.append(loss_mean[0].item())
eval_accs.append(acc_mean[1].item())
eval_losses.append(loss_mean[1].item())
print(f"ema accuracy: {acc_mean[0].item()}"
f"normal accuracy: {acc_mean[1].item()}")
return train_losses, train_accs, eval_losses, eval_accs, \
ema_eval_losses, ema_eval_accs
def test(self):
self.net.eval()
with torch.no_grad():
for i, data_batched in enumerate(self.test_loader, 0):
images, is_lens = data_batched['image'], data_batched[
'is_lens']
start = time.time()
test_logits, _ = self.net(images)
end = time.time()
result = torch.argmax(F.softmax(test_logits, dim=1), dim=1)
accuracy = torch.mean(result.eq(is_lens).float()).item()
# return roc_curve(is_lens, test_logits)
return result.tolist(), is_lens.tolist(), end - start, \
accuracy
def test_origin(self):
self.net.eval()
with torch.no_grad():
for i, data_batched in enumerate(self.test_loader, 0):
images, is_lens = data_batched['image'], data_batched[
'is_lens']
test_logits, _ = self.net(images)
return test_logits, is_lens
