def main(args):
poas = []
init(args.seed, args.device)
print("* loading data")
testdata = ChunkDataSet(
*load_data(limit=args.chunks, shuffle=args.shuffle))
dataloader = DataLoader(testdata, batch_size=args.batchsize)
for w in [int(i) for i in args.weights.split(',')]:
print("* loading model", w)
model = load_model(args.model_directory, args.device, weights=w)
print("* calling")
predictions = []
t0 = time.perf_counter()
for data, *_ in dataloader:
with torch.no_grad():
log_probs = model(data.to(args.device))
predictions.append(log_probs.exp().cpu().numpy())
duration = time.perf_counter() - t0
references = [
decode_ref(target, model.alphabet)
for target in dataloader.dataset.targets
]
sequences = [
decode_ctc(post, model.alphabet)
for post in np.concatenate(predictions)
]
accuracies = list(starmap(accuracy, zip(references, sequences)))
if args.poa: poas.append(sequences)
print("* mean %.2f%%" % np.mean(accuracies))
print("* median %.2f%%" % np.median(accuracies))
print("* time %.2f" % duration)
print("* samples/s %.2E" % (args.chunks * data.shape[2] / duration))
if args.poa:
print("* doing poa")
t0 = time.perf_counter()
# group each sequence prediction per model together
poas = [list(seq) for seq in zip(*poas)]
consensuses = poa(poas)
duration = time.perf_counter() - t0
accuracies = list(starmap(accuracy, zip(references, consensuses)))
print("* mean %.2f%%" % np.mean(accuracies))
print("* median %.2f%%" % np.median(accuracies))
print("* time %.2f" % duration)
def main(args):
workdir = os.path.expanduser(args.tuning_directory)
if os.path.exists(workdir) and not args.force:
print("* error: %s exists." % workdir)
exit(1)
os.makedirs(workdir, exist_ok=True)
init(args.seed, args.device)
device = torch.device(args.device)
print("[loading data]")
chunks, chunk_lengths, targets, target_lengths = load_data(
limit=args.chunks, directory=args.directory)
split = np.floor(chunks.shape[0] * args.validation_split).astype(np.int32)
train_dataset = ChunkDataSet(chunks[:split], chunk_lengths[:split],
targets[:split], target_lengths[:split])
test_dataset = ChunkDataSet(chunks[split:], chunk_lengths[split:],
targets[split:], target_lengths[split:])
train_loader = DataLoader(train_dataset,
batch_size=args.batch,
shuffle=True,
num_workers=4,
pin_memory=True)
test_loader = DataLoader(test_dataset,
batch_size=args.batch,
num_workers=4,
pin_memory=True)
def objective(trial):
config = toml.load(args.config)
lr = 1e-3
#config['block'][0]['stride'] = [trial.suggest_int('stride', 4, 6)]
# C1
config['block'][0]['kernel'] = [
int(trial.suggest_discrete_uniform('c1_kernel', 1, 129, 2))
]
config['block'][0]['filters'] = trial.suggest_int(
'c1_filters', 1, 1024)
# B1 - B5
for i in range(1, 6):
config['block'][i]['repeat'] = trial.suggest_int(
'b%s_repeat' % i, 1, 9)
config['block'][i]['filters'] = trial.suggest_int(
'b%s_filters' % i, 1, 512)
config['block'][i]['kernel'] = [
int(trial.suggest_discrete_uniform('b%s_kernel' % i, 1, 129,
2))
]
# C2
config['block'][-2]['kernel'] = [
int(trial.suggest_discrete_uniform('c2_kernel', 1, 129, 2))
]
config['block'][-2]['filters'] = trial.suggest_int(
'c2_filters', 1, 1024)
# C3
config['block'][-1]['kernel'] = [
int(trial.suggest_discrete_uniform('c3_kernel', 1, 129, 2))
]
config['block'][-1]['filters'] = trial.suggest_int(
'c3_filters', 1, 1024)
model = load_symbol(config, 'Model')(config)
num_params = sum(p.numel() for p in model.parameters())
print("[trial %s]" % trial.number)
if num_params > args.max_params:
print("[pruned] network too large")
raise optuna.exceptions.TrialPruned()
model.to(args.device)
model.train()
os.makedirs(workdir, exist_ok=True)
optimizer = AdamW(model.parameters(), amsgrad=True, lr=lr)
model, optimizer = amp.initialize(model,
optimizer,
opt_level="O1",
verbosity=0)
schedular = CosineAnnealingLR(optimizer,
args.epochs * len(train_loader))
for epoch in range(1, args.epochs + 1):
try:
train_loss, duration = train(model,
device,
train_loader,
optimizer,
use_amp=True)
val_loss, val_mean, val_median = test(model, device,
test_loader)
print(
"[epoch {}] directory={} loss={:.4f} mean_acc={:.3f}% median_acc={:.3f}%"
.format(epoch, workdir, val_loss, val_mean, val_median))
except KeyboardInterrupt:
exit()
except:
print("[pruned] exception")
raise optuna.exceptions.TrialPruned()
if np.isnan(val_loss): val_loss = 9.9
trial.report(val_loss, epoch)
if trial.should_prune():
print("[pruned] unpromising")
raise optuna.exceptions.TrialPruned()
trial.set_user_attr('seed', args.seed)
trial.set_user_attr('val_loss', val_loss)
trial.set_user_attr('val_mean', val_mean)
trial.set_user_attr('val_median', val_median)
trial.set_user_attr('train_loss', train_loss)
trial.set_user_attr('batchsize', args.batch)
trial.set_user_attr('model_params', num_params)
torch.save(model.state_dict(),
os.path.join(workdir, "weights_%s.tar" % trial.number))
toml.dump(
config,
open(os.path.join(workdir, 'config_%s.toml' % trial.number), 'w'))
print("[loss] %.4f" % val_loss)
return val_loss
print("[starting study]")
optuna.logging.set_verbosity(optuna.logging.WARNING)
study = optuna.create_study(direction='minimize',
storage='sqlite:///%s' %
os.path.join(workdir, 'tune.db'),
study_name='bonito-study',
load_if_exists=True,
pruner=SuccessiveHalvingPruner())
study.optimize(objective, n_trials=args.trials)
def main(args):
poas = []
init(args.seed, args.device)
print("* loading data")
testdata = ChunkDataSet(
*load_data(
limit=args.chunks, shuffle=args.shuffle,
directory=args.directory, validation=True
)
)
dataloader = DataLoader(testdata, batch_size=args.batchsize)
accuracy_with_cov = lambda ref, seq: accuracy(ref, seq, min_coverage=args.min_coverage)
for w in [int(i) for i in args.weights.split(',')]:
seqs = []
print("* loading model", w)
model = load_model(args.model_directory, args.device, weights=w)
print("* calling")
t0 = time.perf_counter()
with torch.no_grad():
for data, *_ in dataloader:
if half_supported():
data = data.type(torch.float16).to(args.device)
else:
data = data.to(args.device)
log_probs = model(data)
if hasattr(model, 'decode_batch'):
seqs.extend(model.decode_batch(log_probs))
else:
seqs.extend([model.decode(p) for p in permute(log_probs, 'TNC', 'NTC')])
duration = time.perf_counter() - t0
refs = [decode_ref(target, model.alphabet) for target in dataloader.dataset.targets]
accuracies = [accuracy_with_cov(ref, seq) if len(seq) else 0. for ref, seq in zip(refs, seqs)]
if args.poa: poas.append(sequences)
print("* mean %.2f%%" % np.mean(accuracies))
print("* median %.2f%%" % np.median(accuracies))
print("* time %.2f" % duration)
print("* samples/s %.2E" % (args.chunks * data.shape[2] / duration))
if args.poa:
print("* doing poa")
t0 = time.perf_counter()
# group each sequence prediction per model together
poas = [list(seq) for seq in zip(*poas)]
consensuses = poa(poas)
duration = time.perf_counter() - t0
accuracies = list(starmap(accuracy_with_coverage_filter, zip(references, consensuses)))
print("* mean %.2f%%" % np.mean(accuracies))
print("* median %.2f%%" % np.median(accuracies))
print("* time %.2f" % duration)
def main(args):
init(args.seed, args.device)
if args.model_directory in models and args.model_directory not in os.listdir(
__models__):
sys.stderr.write("> downloading model\n")
File(__models__, models[args.model_directory]).download()
sys.stderr.write(f"> loading model {args.model_directory}\n")
try:
model = load_model(
args.model_directory,
args.device,
weights=int(args.weights),
chunksize=args.chunksize,
overlap=args.overlap,
batchsize=args.batchsize,
quantize=args.quantize,
use_koi=True,
)
except FileNotFoundError:
sys.stderr.write(f"> error: failed to load {args.model_directory}\n")
sys.stderr.write(f"> available models:\n")
for model in sorted(models):
sys.stderr.write(f" - {model}\n")
exit(1)
if args.verbose:
sys.stderr.write(
f"> model basecaller params: {model.config['basecaller']}\n")
basecall = load_symbol(args.model_directory, "basecall")
mods_model = None
if args.modified_base_model is not None or args.modified_bases is not None:
sys.stderr.write("> loading modified base model\n")
mods_model = load_mods_model(args.modified_bases, args.model_directory,
args.modified_base_model)
sys.stderr.write(f"> {mods_model[1]['alphabet_str']}\n")
if args.reference:
sys.stderr.write("> loading reference\n")
aligner = Aligner(args.reference, preset='ont-map', best_n=1)
if not aligner:
sys.stderr.write("> failed to load/build index\n")
exit(1)
else:
aligner = None
fmt = biofmt(aligned=args.reference is not None)
if args.reference and args.reference.endswith(
".mmi") and fmt.name == "cram":
sys.stderr.write(
"> error: reference cannot be a .mmi when outputting cram\n")
exit(1)
elif args.reference and fmt.name == "fastq":
sys.stderr.write(
f"> warning: did you really want {fmt.aligned} {fmt.name}?\n")
else:
sys.stderr.write(f"> outputting {fmt.aligned} {fmt.name}\n")
if args.save_ctc and not args.reference:
sys.stderr.write(
"> a reference is needed to output ctc training data\n")
exit(1)
if fmt.name != 'fastq':
groups = get_read_groups(args.reads_directory,
args.model_directory,
n_proc=8,
recursive=args.recursive,
read_ids=column_to_set(args.read_ids),
skip=args.skip,
cancel=process_cancel())
else:
groups = []
reads = get_reads(args.reads_directory,
n_proc=8,
recursive=args.recursive,
read_ids=column_to_set(args.read_ids),
skip=args.skip,
cancel=process_cancel())
if args.max_reads:
reads = take(reads, args.max_reads)
if args.save_ctc:
reads = (chunk for read in reads for chunk in read_chunks(
read,
chunksize=model.config["basecaller"]["chunksize"],
overlap=model.config["basecaller"]["overlap"]))
ResultsWriter = CTCWriter
else:
ResultsWriter = Writer
results = basecall(model,
reads,
reverse=args.revcomp,
batchsize=model.config["basecaller"]["batchsize"],
chunksize=model.config["basecaller"]["chunksize"],
overlap=model.config["basecaller"]["overlap"])
if mods_model is not None:
results = process_itemmap(partial(call_mods, mods_model), results)
if aligner:
results = align_map(aligner, results, n_thread=os.cpu_count())
writer = ResultsWriter(
fmt.mode,
tqdm(results, desc="> calling", unit=" reads", leave=False),
aligner=aligner,
group_key=args.model_directory,
ref_fn=args.reference,
groups=groups,
)
t0 = perf_counter()
writer.start()
writer.join()
duration = perf_counter() - t0
num_samples = sum(num_samples for read_id, num_samples in writer.log)
sys.stderr.write("> completed reads: %s\n" % len(writer.log))
sys.stderr.write("> duration: %s\n" %
timedelta(seconds=np.round(duration)))
sys.stderr.write("> samples per second %.1E\n" % (num_samples / duration))
sys.stderr.write("> done\n")
def main(args):
workdir = os.path.expanduser(args.training_directory)
if os.path.exists(workdir) and not args.force:
print("[error] %s exists, use -f to force continue training." %
workdir)
exit(1)
init(args.seed, args.device)
device = torch.device(args.device)
print("[loading data]")
train_data = load_data(limit=args.chunks, directory=args.directory)
if os.path.exists(os.path.join(args.directory, 'validation')):
valid_data = load_data(
directory=os.path.join(args.directory, 'validation'))
else:
print("[validation set not found: splitting training set]")
split = np.floor(len(train_data[0]) * 0.97).astype(np.int32)
valid_data = [x[split:] for x in train_data]
train_data = [x[:split] for x in train_data]
train_loader = DataLoader(ChunkDataSet(*train_data),
batch_size=args.batch,
shuffle=True,
num_workers=4,
pin_memory=True)
valid_loader = DataLoader(ChunkDataSet(*valid_data),
batch_size=args.batch,
num_workers=4,
pin_memory=True)
config = toml.load(args.config)
argsdict = dict(training=vars(args))
chunk_config = {}
chunk_config_file = os.path.join(args.directory, 'config.toml')
if os.path.isfile(chunk_config_file):
chunk_config = toml.load(os.path.join(chunk_config_file))
os.makedirs(workdir, exist_ok=True)
toml.dump({
**config,
**argsdict,
**chunk_config
}, open(os.path.join(workdir, 'config.toml'), 'w'))
print("[loading model]")
if args.pretrained:
print("[using pretrained model {}]".format(args.pretrained))
model = load_model(args.pretrained, device, half=False)
else:
model = load_symbol(config, 'Model')(config)
optimizer = AdamW(model.parameters(), amsgrad=False, lr=args.lr)
last_epoch = load_state(workdir,
args.device,
model,
optimizer,
use_amp=args.amp)
lr_scheduler = func_scheduler(optimizer,
cosine_decay_schedule(1.0, 0.1),
args.epochs * len(train_loader),
warmup_steps=500,
start_step=last_epoch * len(train_loader))
if args.multi_gpu:
from torch.nn import DataParallel
model = DataParallel(model)
model.decode = model.module.decode
model.alphabet = model.module.alphabet
if hasattr(model, 'seqdist'):
criterion = model.seqdist.ctc_loss
else:
criterion = None
for epoch in range(1 + last_epoch, args.epochs + 1 + last_epoch):
try:
with CSVLogger(os.path.join(
workdir, 'losses_{}.csv'.format(epoch))) as loss_log:
train_loss, duration = train(model,
device,
train_loader,
optimizer,
criterion=criterion,
use_amp=args.amp,
lr_scheduler=lr_scheduler,
loss_log=loss_log)
model_state = model.state_dict(
) if not args.multi_gpu else model.module.state_dict()
torch.save(model_state,
os.path.join(workdir, "weights_%s.tar" % epoch))
val_loss, val_mean, val_median = test(model,
device,
valid_loader,
criterion=criterion)
except KeyboardInterrupt:
break
print(
"[epoch {}] directory={} loss={:.4f} mean_acc={:.3f}% median_acc={:.3f}%"
.format(epoch, workdir, val_loss, val_mean, val_median))
with CSVLogger(os.path.join(workdir, 'training.csv')) as training_log:
training_log.append(
OrderedDict([('time', datetime.today()),
('duration', int(duration)), ('epoch', epoch),
('train_loss', train_loss),
('validation_loss', val_loss),
('validation_mean', val_mean),
('validation_median', val_median)]))
def main(args):
workdir = os.path.expanduser(args.training_directory)
if os.path.exists(workdir) and not args.force:
print("[error] %s exists, use -f to force continue training." % workdir)
exit(1)
init(args.seed, args.device)
device = torch.device(args.device)
print("[loading data]")
chunks, targets, lengths = load_data(limit=args.chunks, shuffle=True, directory=args.directory)
split = np.floor(chunks.shape[0] * args.validation_split).astype(np.int32)
train_dataset = ChunkDataSet(chunks[:split], targets[:split], lengths[:split])
test_dataset = ChunkDataSet(chunks[split:], targets[split:], lengths[split:])
train_loader = DataLoader(train_dataset, batch_size=args.batch, shuffle=True, num_workers=4, pin_memory=True)
test_loader = DataLoader(test_dataset, batch_size=args.batch, num_workers=4, pin_memory=True)
config = toml.load(args.config)
argsdict = dict(training=vars(args))
chunk_config = {}
chunk_config_file = os.path.join(args.directory, 'config.toml')
if os.path.isfile(chunk_config_file):
chunk_config = toml.load(os.path.join(chunk_config_file))
os.makedirs(workdir, exist_ok=True)
toml.dump({**config, **argsdict, **chunk_config}, open(os.path.join(workdir, 'config.toml'), 'w'))
print("[loading model]")
model = load_symbol(config, 'Model')(config)
optimizer = AdamW(model.parameters(), amsgrad=False, lr=args.lr)
last_epoch = load_state(workdir, args.device, model, optimizer, use_amp=args.amp)
lr_scheduler = func_scheduler(
optimizer, cosine_decay_schedule(1.0, 0.1), args.epochs * len(train_loader),
warmup_steps=500, start_step=last_epoch*len(train_loader)
)
if args.multi_gpu:
from torch.nn import DataParallel
model = DataParallel(model)
model.decode = model.module.decode
model.alphabet = model.module.alphabet
if hasattr(model, 'seqdist'):
criterion = model.seqdist.ctc_loss
else:
criterion = None
for epoch in range(1 + last_epoch, args.epochs + 1 + last_epoch):
try:
train_loss, duration = train(
model, device, train_loader, optimizer, criterion=criterion,
use_amp=args.amp, lr_scheduler=lr_scheduler
)
val_loss, val_mean, val_median = test(
model, device, test_loader, criterion=criterion
)
except KeyboardInterrupt:
break
print("[epoch {}] directory={} loss={:.4f} mean_acc={:.3f}% median_acc={:.3f}%".format(
epoch, workdir, val_loss, val_mean, val_median
))
model_state = model.state_dict() if not args.multi_gpu else model.module.state_dict()
torch.save(model_state, os.path.join(workdir, "weights_%s.tar" % epoch))
torch.save(optimizer.state_dict(), os.path.join(workdir, "optim_%s.tar" % epoch))
with open(os.path.join(workdir, 'training.csv'), 'a', newline='') as csvfile:
csvw = csv.writer(csvfile, delimiter=',')
if epoch == 1:
csvw.writerow([
'time', 'duration', 'epoch', 'train_loss',
'validation_loss', 'validation_mean', 'validation_median'
])
csvw.writerow([
datetime.today(), int(duration), epoch,
train_loss, val_loss, val_mean, val_median,
])
def main(args):
workdir = os.path.expanduser(args.training_directory)
if os.path.exists(workdir) and not args.force:
print("[error] %s exists, use -f to force continue training." %
workdir)
exit(1)
init(args.seed, args.device, (not args.nondeterministic))
device = torch.device(args.device)
print("[loading data]")
try:
train_loader_kwargs, valid_loader_kwargs = load_numpy(
args.chunks, args.directory)
except FileNotFoundError:
train_loader_kwargs, valid_loader_kwargs = load_script(
args.directory,
seed=args.seed,
chunks=args.chunks,
valid_chunks=args.valid_chunks)
loader_kwargs = {
"batch_size": args.batch,
"num_workers": 4,
"pin_memory": True
}
train_loader = DataLoader(**loader_kwargs, **train_loader_kwargs)
valid_loader = DataLoader(**loader_kwargs, **valid_loader_kwargs)
if args.pretrained:
dirname = args.pretrained
if not os.path.isdir(dirname) and os.path.isdir(
os.path.join(__models__, dirname)):
dirname = os.path.join(__models__, dirname)
config_file = os.path.join(dirname, 'config.toml')
else:
config_file = args.config
config = toml.load(config_file)
argsdict = dict(training=vars(args))
os.makedirs(workdir, exist_ok=True)
toml.dump({
**config,
**argsdict
}, open(os.path.join(workdir, 'config.toml'), 'w'))
print("[loading model]")
if args.pretrained:
print("[using pretrained model {}]".format(args.pretrained))
model = load_model(args.pretrained, device, half=False)
else:
model = load_symbol(config, 'Model')(config)
if config.get("lr_scheduler"):
sched_config = config["lr_scheduler"]
lr_scheduler_fn = getattr(import_module(sched_config["package"]),
sched_config["symbol"])(**sched_config)
else:
lr_scheduler_fn = None
trainer = Trainer(model,
device,
train_loader,
valid_loader,
use_amp=half_supported() and not args.no_amp,
lr_scheduler_fn=lr_scheduler_fn,
restore_optim=args.restore_optim,
save_optim_every=args.save_optim_every,
grad_accum_split=args.grad_accum_split)
if (',' in args.lr):
lr = [float(x) for x in args.lr.split(',')]
else:
lr = float(args.lr)
trainer.fit(workdir, args.epochs, lr)
def main(args):
workdir = os.path.expanduser(args.training_directory)
if os.path.exists(workdir) and not args.force:
print("[error] %s exists." % workdir)
exit(1)
init(args.seed, args.device)
device = torch.device(args.device)
print("[loading data]")
chunks, chunk_lengths, targets, target_lengths = load_data(
limit=args.chunks, shuffle=True, directory=args.directory)
split = np.floor(chunks.shape[0] * args.validation_split).astype(np.int32)
train_dataset = ChunkDataSet(chunks[:split], chunk_lengths[:split],
targets[:split], target_lengths[:split])
test_dataset = ChunkDataSet(chunks[split:], chunk_lengths[split:],
targets[split:], target_lengths[split:])
train_loader = DataLoader(train_dataset,
batch_size=args.batch,
shuffle=True,
num_workers=4,
pin_memory=True)
test_loader = DataLoader(test_dataset,
batch_size=args.batch,
num_workers=4,
pin_memory=True)
config = toml.load(args.config)
argsdict = dict(training=vars(args))
chunk_config = {}
chunk_config_file = os.path.join(
args.directory if args.directory else __data__, 'config.toml')
if os.path.isfile(chunk_config_file):
chunk_config = toml.load(os.path.join(chunk_config_file))
print("[loading model]")
model = Model(config)
weights = os.path.join(workdir, 'weights.tar')
if os.path.exists(weights): model.load_state_dict(torch.load(weights))
model.to(device)
model.train()
os.makedirs(workdir, exist_ok=True)
toml.dump({
**config,
**argsdict,
**chunk_config
}, open(os.path.join(workdir, 'config.toml'), 'w'))
optimizer = AdamW(model.parameters(), amsgrad=True, lr=args.lr)
if args.amp:
try:
model, optimizer = amp.initialize(model,
optimizer,
opt_level="O1",
verbosity=0)
except NameError:
print(
"[error]: Cannot use AMP: Apex package needs to be installed manually, See https://github.com/NVIDIA/apex"
)
exit(1)
schedular = CosineAnnealingLR(optimizer, args.epochs * len(train_loader))
for epoch in range(1, args.epochs + 1):
try:
train_loss, duration = train(model,
device,
train_loader,
optimizer,
use_amp=args.amp)
val_loss, val_mean, val_median = test(model, device, test_loader)
except KeyboardInterrupt:
break
print(
"[epoch {}] directory={} loss={:.4f} mean_acc={:.3f}% median_acc={:.3f}%"
.format(epoch, workdir, val_loss, val_mean, val_median))
torch.save(model.state_dict(),
os.path.join(workdir, "weights_%s.tar" % epoch))
with open(os.path.join(workdir, 'training.csv'), 'a',
newline='') as csvfile:
csvw = csv.writer(csvfile, delimiter=',')
if epoch == 1:
csvw.writerow([
'time', 'duration', 'epoch', 'train_loss',
'validation_loss', 'validation_mean', 'validation_median'
])
csvw.writerow([
datetime.today(),
int(duration),
epoch,
train_loss,
val_loss,
val_mean,
val_median,
])
schedular.step()
def main(args):
workdir = os.path.expanduser(args.tuning_directory)
if os.path.exists(workdir) and not args.force:
print("* error: %s exists." % workdir)
exit(1)
os.makedirs(workdir, exist_ok=True)
init(args.seed, args.device)
device = torch.device(args.device)
print("[loading data]")
train_data = load_data(limit=args.chunks, directory=args.directory)
if os.path.exists(os.path.join(args.directory, 'validation')):
valid_data = load_data(directory=os.path.join(args.directory,
'validation'),
limit=10000)
else:
print("[validation set not found: splitting training set]")
split = np.floor(len(train_data[0]) * 0.97).astype(np.int32)
valid_data = [x[split:] for x in train_data]
train_data = [x[:split] for x in train_data]
train_loader = DataLoader(ChunkDataSet(*train_data),
batch_size=args.batch,
shuffle=True,
num_workers=4,
pin_memory=True)
test_loader = DataLoader(ChunkDataSet(*valid_data),
batch_size=args.batch,
num_workers=4,
pin_memory=True)
def objective(trial):
config = toml.load(args.config)
lr = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)
model = load_symbol(config, 'Model')(config)
num_params = sum(p.numel() for p in model.parameters())
print("[trial %s]" % trial.number)
model.to(args.device)
model.train()
os.makedirs(workdir, exist_ok=True)
scaler = GradScaler(enabled=True)
optimizer = AdamW(model.parameters(), amsgrad=False, lr=lr)
model, optimizer = amp.initialize(model,
optimizer,
opt_level="O1",
verbosity=0)
if hasattr(model, 'seqdist'):
criterion = model.seqdist.ctc_loss
else:
criterion = None
lr_scheduler = func_scheduler(
optimizer,
cosine_decay_schedule(1.0, decay),
args.epochs * len(train_loader),
warmup_steps=warmup_steps,
warmup_ratio=warmup_ratio,
)
for epoch in range(1, args.epochs + 1):
try:
train_loss, duration = train(model,
device,
train_loader,
optimizer,
scaler=scaler,
use_amp=True,
criterion=criterion)
val_loss, val_mean, val_median = test(model,
device,
test_loader,
criterion=criterion)
print(
"[epoch {}] directory={} loss={:.4f} mean_acc={:.3f}% median_acc={:.3f}%"
.format(epoch, workdir, val_loss, val_mean, val_median))
except KeyboardInterrupt:
exit()
except Exception as e:
print("[pruned] exception")
raise optuna.exceptions.TrialPruned()
if np.isnan(val_loss): val_loss = 9.9
trial.report(val_loss, epoch)
if trial.should_prune():
print("[pruned] unpromising")
raise optuna.exceptions.TrialPruned()
trial.set_user_attr('val_loss', val_loss)
trial.set_user_attr('val_mean', val_mean)
trial.set_user_attr('val_median', val_median)
trial.set_user_attr('train_loss', train_loss)
trial.set_user_attr('model_params', num_params)
torch.save(model.state_dict(),
os.path.join(workdir, "weights_%s.tar" % trial.number))
toml.dump(
config,
open(os.path.join(workdir, 'config_%s.toml' % trial.number), 'w'))
print("[loss] %.4f" % val_loss)
return val_loss
print("[starting study]")
optuna.logging.set_verbosity(optuna.logging.WARNING)
study = optuna.create_study(direction='minimize',
storage='sqlite:///%s' %
os.path.join(workdir, 'tune.db'),
study_name='bonito-study',
load_if_exists=True,
pruner=SuccessiveHalvingPruner())
study.optimize(objective, n_trials=args.trials)
