def chunk_dataset(reads, chunk_len, num_chunks=None):
all_chunks = (
(chunk, target) for read in reads for chunk, target in
get_chunks(reads[read], regular_break_points(len(reads[read]['Dacs']), chunk_len))
)
chunks, targets = zip(*tqdm(take(all_chunks, num_chunks), total=num_chunks))
targets, target_lens = pad_lengths(targets) # convert refs from ragged arrray
return ChunkDataSet(chunks, targets, target_lens)
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 run(self):
chunks = []
targets = []
target_lens = []
while True:
job = self.queue.get()
if job is None: break
chunks_, predictions = job
# convert logprobs to probs
predictions = np.exp(predictions.astype(np.float32))
for chunk, pred in zip(chunks_, predictions):
try:
sequence = self.model.decode(pred)
except:
continue
if not sequence:
continue
for mapping in self.aligner.map(sequence):
cov = (mapping.q_en - mapping.q_st) / len(sequence)
acc = mapping.mlen / mapping.blen
refseq = self.aligner.seq(mapping.ctg, mapping.r_st + 1,
mapping.r_en)
if 'N' in refseq: continue
if mapping.strand == -1: refseq = revcomp(refseq)
break
else:
continue
if acc > self.min_accuracy and cov > self.min_accuracy:
chunks.append(chunk.squeeze())
targets.append([
int(x) for x in refseq.translate({
65: '1',
67: '2',
71: '3',
84: '4'
})
])
target_lens.append(len(refseq))
if len(chunks) == 0: return
chunks = np.array(chunks, dtype=np.float32)
chunk_lens = np.full(chunks.shape[0], chunks.shape[1], dtype=np.int16)
targets_ = np.zeros((chunks.shape[0], max(target_lens)),
dtype=np.uint8)
for idx, target in enumerate(targets):
targets_[idx, :len(target)] = target
target_lens = np.array(target_lens, dtype=np.uint16)
training = ChunkDataSet(chunks, chunk_lens, targets_, target_lens)
training = filter_chunks(training)
output_directory = '.' if sys.stdout.isatty() else dirname(
realpath('/dev/fd/1'))
np.save(os.path.join(output_directory, "chunks.npy"),
training.chunks.squeeze(1))
np.save(os.path.join(output_directory, "chunk_lengths.npy"),
training.chunk_lengths)
np.save(os.path.join(output_directory, "references.npy"),
training.targets)
np.save(os.path.join(output_directory, "reference_lengths.npy"),
training.target_lengths)
sys.stderr.write("> written ctc training data\n")
sys.stderr.write(" - chunks.npy with shape (%s)\n" %
','.join(map(str,
training.chunks.squeeze(1).shape)))
sys.stderr.write(" - chunk_lengths.npy with shape (%s)\n" %
','.join(map(str, training.chunk_lengths.shape)))
sys.stderr.write(" - references.npy with shape (%s)\n" %
','.join(map(str, training.targets.shape)))
sys.stderr.write(" - reference_lengths.npy shape (%s)\n" %
','.join(map(str, training.target_lengths.shape)))
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):
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." % 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 select_indices(ds, idx):
return ChunkDataSet(
ds.chunks.squeeze(1)[idx], ds.targets[idx], ds.lengths[idx]
)
def filter_chunks(ds, idx):
filtered = ChunkDataSet(
ds.chunks.squeeze(1)[idx], ds.targets[idx], ds.lengths[idx])
filtered.targets = filtered.targets[:, :filtered.lengths.max()]
return filtered
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)
def run(self):
chunks = []
targets = []
lengths = []
for read, ctc_data in self.iterator:
seq = ctc_data['sequence']
qstring = ctc_data['qstring']
mean_qscore = ctc_data['mean_qscore']
mapping = ctc_data.get('mapping', False)
self.log.append((read.read_id, len(read.signal)))
if len(seq) == 0 or mapping is None:
continue
cov = (mapping.q_en - mapping.q_st) / len(seq)
acc = mapping.mlen / mapping.blen
refseq = self.aligner.seq(mapping.ctg, mapping.r_st, mapping.r_en)
if acc < self.min_accuracy or cov < self.min_coverage or 'N' in refseq:
continue
write_sam(read.read_id,
seq,
qstring,
mapping,
fd=self.fd,
unaligned=mapping is None)
with open(summary_file(), 'a') as summary:
write_summary_row(read,
len(seq),
mean_qscore,
alignment=mapping,
fd=summary)
if mapping.strand == -1:
refseq = revcomp(refseq)
target = [
int(x) for x in refseq.translate({
65: '1',
67: '2',
71: '3',
84: '4'
})
]
targets.append(target)
chunks.append(read.signal)
lengths.append(len(target))
if len(chunks) == 0:
sys.stderr.write("> no suitable ctc data to write\n")
return
chunks = np.array(chunks, dtype=np.float16)
targets_ = np.zeros((chunks.shape[0], max(lengths)), dtype=np.uint8)
for idx, target in enumerate(targets):
targets_[idx, :len(target)] = target
lengths = np.array(lengths, dtype=np.uint16)
training = ChunkDataSet(chunks, targets_, lengths)
training = filter_chunks(training)
mu, sd = np.mean(lengths), np.std(lengths)
idx = [
i for i, n in enumerate(lengths)
if mu - 2.5 * sd < n < mu + 2.5 * sd
]
summary = pd.read_csv(summary_file(), sep='\t')
summary[summary.index.isin(idx)].to_csv(summary_file(),
sep='\t',
index=False)
output_directory = '.' if sys.stdout.isatty() else dirname(
realpath('/dev/fd/1'))
np.save(os.path.join(output_directory, "chunks.npy"),
training.chunks.squeeze(1))
np.save(os.path.join(output_directory, "references.npy"),
training.targets)
np.save(os.path.join(output_directory, "reference_lengths.npy"),
training.lengths)
sys.stderr.write("> written ctc training data\n")
sys.stderr.write(" - chunks.npy with shape (%s)\n" %
','.join(map(str,
training.chunks.squeeze(1).shape)))
sys.stderr.write(" - references.npy with shape (%s)\n" %
','.join(map(str, training.targets.shape)))
sys.stderr.write(" - reference_lengths.npy shape (%s)\n" %
','.join(map(str, training.lengths.shape)))
