def test(model, device, test_loader, min_coverage=0.5, criterion=None):
if criterion is None:
C = len(model.alphabet)
weights = torch.cat([torch.tensor([0.4]), (0.1 / (C - 1)) * torch.ones(C - 1)]).to(device)
criterion = partial(ctc_label_smoothing_loss, weights=weights)
seqs = []
model.eval()
test_loss = 0
accuracy_with_cov = lambda ref, seq: accuracy(ref, seq, min_coverage=min_coverage)
with torch.no_grad():
for batch_idx, (data, target, lengths) in enumerate(test_loader, start=1):
log_probs = model(data.to(device))
loss = criterion(log_probs, target.to(device), lengths.to(device))
test_loss += loss['ctc_loss'] if isinstance(loss, dict) else loss
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')])
refs = [
decode_ref(target, model.alphabet) for target in test_loader.dataset.targets
]
accuracies = [
accuracy_with_cov(ref, seq) if len(seq) else 0. for ref, seq in zip(refs, seqs)
]
mean = np.mean(accuracies)
median = np.median(accuracies)
return test_loss.item() / batch_idx, mean, median
def test(model, device, test_loader):
model.eval()
test_loss = 0
predictions = []
prediction_lengths = []
with torch.no_grad():
for batch_idx, (data, out_lengths, target, lengths) in enumerate(test_loader, start=1):
data, target = data.to(device), target.to(device)
log_probs = model(data)
test_loss += criterion(log_probs.transpose(1, 0), target, out_lengths / model.stride, lengths)
predictions.append(torch.exp(log_probs).cpu())
prediction_lengths.append(out_lengths / model.stride)
predictions = np.concatenate(predictions)
lengths = np.concatenate(prediction_lengths)
references = [decode_ref(target, model.alphabet) for target in test_loader.dataset.targets]
sequences = [model.decode(post[:n]) for post, n in zip(predictions, lengths)]
if all(map(len, sequences)):
accuracies = list(starmap(accuracy, zip(references, sequences)))
else:
accuracies = [0]
mean = np.mean(accuracies)
median = np.median(accuracies)
return test_loss.item() / batch_idx, mean, median
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 validate_one_step(self, batch):
data, targets, lengths = batch
scores = self.model(data.to(self.device))
losses = self.criterion(scores, targets.to(self.device),
lengths.to(self.device))
losses = {k: v.item()
for k, v in losses.items()} if isinstance(
losses, dict) else losses.item()
if hasattr(self.model, 'decode_batch'):
seqs = self.model.decode_batch(scores)
else:
seqs = [
self.model.decode(x) for x in permute(scores, 'TNC', 'NTC')
]
refs = [decode_ref(target, self.model.alphabet) for target in targets]
accs = [
accuracy(ref, seq, min_coverage=0.5) if len(seq) else 0.
for ref, seq in zip(refs, seqs)
]
return seqs, refs, accs, losses
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)
