def main():
def evaluate():
model.eval()
num_correct = 0
num_total = 0
for inputs, labels in tqdm(test_dl, total=len(test_dl)):
inputs = inputs.to(device)
labels = torch.tensor([x % 10 for x in labels.tolist()])
labels = labels.to(device)
scores = model(inputs, None)
num_correct += (scores.max(1)[1] == labels).float().sum().item()
num_total += scores.size(0)
logging.info(f'{num_correct / num_total}')
ws.increment_model(model, num_correct / num_total / 100)
apb = ArgumentParserBuilder()
apb.add_options(opt('--model', type=str, choices=RegisteredModel.registered_names(), default='las'),
opt('--workspace', type=str, default=str(Path('workspaces') / 'default')),
opt('--load-weights', action='store_true'))
args = apb.parser.parse_args()
ws = Workspace(Path(args.workspace))
writer = ws.summary_writer
set_seed(SETTINGS.training.seed)
transform = transforms.Compose([transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
test_transform = transforms.Compose([transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
trainset1 = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
testset1 = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=test_transform)
trainset2 = torchvision.datasets.CIFAR100(root='./data', train=True, download=True, transform=transform)
testset2 = torchvision.datasets.CIFAR100(root='./data', train=False, download=True, transform=test_transform)
transform = transforms.Compose([transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,)),
expand])
test_transform = transforms.Compose([transforms.Pad((2, 2)),
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,)),
expand])
trainset3 = torchvision.datasets.FashionMNIST(root='./data', train=True, download=True, transform=transform)
testset3 = torchvision.datasets.FashionMNIST(root='./data', train=False, download=True, transform=test_transform)
train_dl = tud.DataLoader(tud.ConcatDataset([trainset1, trainset2, trainset3]),
batch_size=SETTINGS.training.batch_size,
shuffle=True)
test_dl = tud.DataLoader(tud.ConcatDataset([testset1, testset2, testset3]),
batch_size=SETTINGS.training.batch_size,
shuffle=False)
device = torch.device(SETTINGS.training.device)
model = RegisteredModel.find_registered_class(args.model)().to(device)
params = list(filter(lambda x: x.requires_grad, model.parameters()))
optimizer = AdamW(params, SETTINGS.training.learning_rate, weight_decay=SETTINGS.training.weight_decay)
logging.info(f'{sum(p.numel() for p in params)} parameters')
criterion = nn.CrossEntropyLoss()
ws.write_args(args)
ws.write_settings(SETTINGS)
writer.add_scalar('Meta/Parameters', sum(p.numel() for p in params))
for epoch_idx in trange(SETTINGS.training.num_epochs, position=0, leave=True):
model.train()
pbar = tqdm(train_dl,
total=len(train_dl),
position=1,
desc='Training',
leave=True)
for inputs, labels in pbar:
optimizer.zero_grad()
model.zero_grad()
labels = torch.tensor([x % 10 for x in labels.tolist()])
inputs = inputs.to(device)
labels = labels.to(device)
scores = model(inputs, None)
loss = criterion(scores, labels)
loss.backward()
optimizer.step()
pbar.set_postfix(dict(loss=f'{loss.item():.3}'))
writer.add_scalar('Training/Loss', loss.item(), epoch_idx)
for group in optimizer.param_groups:
group['lr'] *= 0.9
evaluate()
def main():
def evaluate_engine(dataset: WakeWordDataset,
prefix: str,
save: bool = False,
positive_set: bool = False,
write_errors: bool = True,
mixer: DatasetMixer = None):
std_transform.eval()
if use_frame:
engine = FrameInferenceEngine(int(SETTINGS.training.max_window_size_seconds * 1000),
int(SETTINGS.training.eval_stride_size_seconds * 1000),
SETTINGS.audio.sample_rate,
model,
zmuv_transform,
negative_label=ctx.negative_label,
coloring=ctx.coloring)
else:
engine = SequenceInferenceEngine(SETTINGS.audio.sample_rate,
model,
zmuv_transform,
negative_label=ctx.negative_label,
coloring=ctx.coloring,
blank_idx=ctx.blank_label)
model.eval()
conf_matrix = ConfusionMatrix()
pbar = tqdm(dataset, desc=prefix)
if write_errors:
with (ws.path / 'errors.tsv').open('a') as f:
print(prefix, file=f)
for idx, ex in enumerate(pbar):
if mixer is not None:
ex, = mixer([ex])
audio_data = ex.audio_data.to(device)
engine.reset()
seq_present = engine.infer(audio_data)
if seq_present != positive_set and write_errors:
with (ws.path / 'errors.tsv').open('a') as f:
f.write(f'{ex.metadata.transcription}\t{int(seq_present)}\t{int(positive_set)}\t{ex.metadata.path}\n')
conf_matrix.increment(seq_present, positive_set)
pbar.set_postfix(dict(mcc=f'{conf_matrix.mcc}', c=f'{conf_matrix}'))
logging.info(f'{conf_matrix}')
if save and not args.eval:
writer.add_scalar(f'{prefix}/Metric/tp', conf_matrix.tp, epoch_idx)
ws.increment_model(model, conf_matrix.tp)
if args.eval:
threshold = engine.threshold
with (ws.path / (str(round(threshold, 2)) + '_results.csv') ).open('a') as f:
f.write(f'{prefix},{threshold},{conf_matrix.tp},{conf_matrix.tn},{conf_matrix.fp},{conf_matrix.fn}\n')
def do_evaluate():
evaluate_engine(ww_dev_pos_ds, 'Dev positive', positive_set=True)
evaluate_engine(ww_dev_neg_ds, 'Dev negative', positive_set=False)
if SETTINGS.training.use_noise_dataset:
evaluate_engine(ww_dev_pos_ds, 'Dev noisy positive', positive_set=True, mixer=dev_mixer)
evaluate_engine(ww_dev_neg_ds, 'Dev noisy negative', positive_set=False, mixer=dev_mixer)
evaluate_engine(ww_test_pos_ds, 'Test positive', positive_set=True)
evaluate_engine(ww_test_neg_ds, 'Test negative', positive_set=False)
if SETTINGS.training.use_noise_dataset:
evaluate_engine(ww_test_pos_ds, 'Test noisy positive', positive_set=True, mixer=test_mixer)
evaluate_engine(ww_test_neg_ds, 'Test noisy negative', positive_set=False, mixer=test_mixer)
apb = ArgumentParserBuilder()
apb.add_options(opt('--model', type=str, choices=RegisteredModel.registered_names(), default='las'),
opt('--workspace', type=str, default=str(Path('workspaces') / 'default')),
opt('--load-weights', action='store_true'),
opt('--load-last', action='store_true'),
opt('--no-dev-per-epoch', action='store_false', dest='dev_per_epoch'),
opt('--dataset-paths', '-i', type=str, nargs='+', default=[SETTINGS.dataset.dataset_path]),
opt('--eval', action='store_true'))
args = apb.parser.parse_args()
use_frame = SETTINGS.training.objective == 'frame'
ctx = InferenceContext(SETTINGS.training.vocab, token_type=SETTINGS.training.token_type, use_blank=not use_frame)
if use_frame:
batchifier = WakeWordFrameBatchifier(ctx.negative_label,
window_size_ms=int(SETTINGS.training.max_window_size_seconds * 1000))
criterion = nn.CrossEntropyLoss()
else:
tokenizer = WakeWordTokenizer(ctx.vocab, ignore_oov=False)
batchifier = AudioSequenceBatchifier(ctx.negative_label, tokenizer)
criterion = nn.CTCLoss(ctx.blank_label)
ws = Workspace(Path(args.workspace), delete_existing=not args.eval)
writer = ws.summary_writer
set_seed(SETTINGS.training.seed)
loader = WakeWordDatasetLoader()
ds_kwargs = dict(sr=SETTINGS.audio.sample_rate, mono=SETTINGS.audio.use_mono, frame_labeler=ctx.labeler)
ww_train_ds, ww_dev_ds, ww_test_ds = WakeWordDataset(metadata_list=[], set_type=DatasetType.TRAINING, **ds_kwargs), \
WakeWordDataset(metadata_list=[], set_type=DatasetType.DEV, **ds_kwargs), \
WakeWordDataset(metadata_list=[], set_type=DatasetType.TEST, **ds_kwargs)
for ds_path in args.dataset_paths:
ds_path = Path(ds_path)
train_ds, dev_ds, test_ds = loader.load_splits(ds_path, **ds_kwargs)
ww_train_ds.extend(train_ds)
ww_dev_ds.extend(dev_ds)
ww_test_ds.extend(test_ds)
print_stats(f'Wake word dataset', ww_train_ds, ww_dev_ds, ww_test_ds)
ww_dev_pos_ds = ww_dev_ds.filter(lambda x: ctx.searcher.search(x.transcription), clone=True)
ww_dev_neg_ds = ww_dev_ds.filter(lambda x: not ctx.searcher.search(x.transcription), clone=True)
ww_test_pos_ds = ww_test_ds.filter(lambda x: ctx.searcher.search(x.transcription), clone=True)
ww_test_neg_ds = ww_test_ds.filter(lambda x: not ctx.searcher.search(x.transcription), clone=True)
print_stats(f'Dev dataset', ww_dev_pos_ds, ww_dev_neg_ds)
print_stats(f'Test dataset', ww_test_pos_ds, ww_test_neg_ds)
device = torch.device(SETTINGS.training.device)
std_transform = StandardAudioTransform().to(device).eval()
zmuv_transform = ZmuvTransform().to(device)
train_comp = (NoiseTransform().train(), batchifier)
if SETTINGS.training.use_noise_dataset:
noise_ds = RecursiveNoiseDatasetLoader().load(Path(SETTINGS.raw_dataset.noise_dataset_path),
sr=SETTINGS.audio.sample_rate,
mono=SETTINGS.audio.use_mono)
logging.info(f'Loaded {len(noise_ds.metadata_list)} noise files.')
noise_ds_train, noise_ds_dev = noise_ds.split(Sha256Splitter(80))
noise_ds_dev, noise_ds_test = noise_ds_dev.split(Sha256Splitter(50))
train_comp = (DatasetMixer(noise_ds_train).train(),) + train_comp
dev_mixer = DatasetMixer(noise_ds_dev, seed=0, do_replace=False)
test_mixer = DatasetMixer(noise_ds_test, seed=0, do_replace=False)
train_comp = compose(*train_comp)
prep_dl = StandardAudioDataLoaderBuilder(ww_train_ds, collate_fn=batchify).build(1)
prep_dl.shuffle = True
train_dl = StandardAudioDataLoaderBuilder(ww_train_ds, collate_fn=train_comp).build(SETTINGS.training.batch_size)
model = RegisteredModel.find_registered_class(args.model)(ctx.num_labels).to(device).streaming()
if SETTINGS.training.convert_static:
model = ConvertedStaticModel(model, 40, 10)
params = list(filter(lambda x: x.requires_grad, model.parameters()))
optimizer = AdamW(params, SETTINGS.training.learning_rate, weight_decay=SETTINGS.training.weight_decay)
logging.info(f'{sum(p.numel() for p in params)} parameters')
if (ws.path / 'zmuv.pt.bin').exists():
zmuv_transform.load_state_dict(torch.load(str(ws.path / 'zmuv.pt.bin')))
else:
for idx, batch in enumerate(tqdm(prep_dl, desc='Constructing ZMUV')):
batch.to(device)
zmuv_transform.update(std_transform(batch.audio_data))
if idx == 2000: # TODO: quick debugging, remove later
break
logging.info(dict(zmuv_mean=zmuv_transform.mean, zmuv_std=zmuv_transform.std))
torch.save(zmuv_transform.state_dict(), str(ws.path / 'zmuv.pt.bin'))
if args.load_weights:
ws.load_model(model, best=not args.load_last)
if args.eval:
ws.load_model(model, best=not args.load_last)
do_evaluate()
return
ws.write_args(args)
ws.write_settings(SETTINGS)
writer.add_scalar('Meta/Parameters', sum(p.numel() for p in params))
for epoch_idx in trange(SETTINGS.training.num_epochs, position=0, leave=True):
model.train()
std_transform.train()
model.streaming_state = None
pbar = tqdm(train_dl,
total=len(train_dl),
position=1,
desc='Training',
leave=True)
total_loss = torch.Tensor([0.0]).to(device)
for batch in pbar:
batch.to(device)
if use_frame:
scores = model(zmuv_transform(std_transform(batch.audio_data)),
std_transform.compute_lengths(batch.lengths))
loss = criterion(scores, batch.labels)
else:
lengths = std_transform.compute_lengths(batch.audio_lengths)
scores = model(zmuv_transform(std_transform(batch.audio_data)), lengths)
scores = F.log_softmax(scores, -1) # [num_frames x batch_size x num_labels]
lengths = torch.tensor([model.compute_length(x.item()) for x in lengths]).to(device)
loss = criterion(scores, batch.labels, lengths, batch.label_lengths)
optimizer.zero_grad()
model.zero_grad()
loss.backward()
optimizer.step()
pbar.set_postfix(dict(loss=f'{loss.item():.3}'))
with torch.no_grad():
total_loss += loss
for group in optimizer.param_groups:
group['lr'] *= SETTINGS.training.lr_decay
mean = total_loss / len(train_dl)
writer.add_scalar('Training/Loss', mean.item(), epoch_idx)
writer.add_scalar('Training/LearningRate', group['lr'], epoch_idx)
if args.dev_per_epoch:
evaluate_engine(ww_dev_pos_ds, 'Dev positive', positive_set=True, save=True, write_errors=False)
do_evaluate()
def main():
def evaluate_accuracy(data_loader, prefix: str, save: bool = False):
std_transform.eval()
model.eval()
pbar = tqdm(data_loader,
desc=prefix,
leave=True,
total=len(data_loader))
num_corr = 0
num_tot = 0
counter = Counter()
for idx, batch in enumerate(pbar):
batch = batch.to(device)
scores = model(zmuv_transform(std_transform(batch.audio_data)),
std_transform.compute_lengths(batch.lengths))
num_tot += scores.size(0)
labels = batch.labels.to(device)
counter.update(labels.tolist())
num_corr += (scores.max(1)[1] == labels).float().sum().item()
acc = num_corr / num_tot
pbar.set_postfix(accuracy=f'{acc:.4}')
if save and not args.eval:
writer.add_scalar(f'{prefix}/Metric/acc', acc, epoch_idx)
ws.increment_model(model, acc / 10)
elif args.eval:
tqdm.write(str(counter))
tqdm.write(str(acc))
return num_corr / num_tot
apb = ArgumentParserBuilder()
apb.add_options(
opt('--model',
type=str,
choices=RegisteredModel.registered_names(),
default='las'),
opt('--workspace',
type=str,
default=str(Path('workspaces') / 'default')),
opt('--load-weights', action='store_true'),
opt('--eval', action='store_true'))
args = apb.parser.parse_args()
ws = Workspace(Path(args.workspace), delete_existing=not args.eval)
writer = ws.summary_writer
set_seed(SETTINGS.training.seed)
loader = GoogleSpeechCommandsDatasetLoader(SETTINGS.training.vocab)
sr = SETTINGS.audio.sample_rate
ds_kwargs = dict(sr=sr, mono=SETTINGS.audio.use_mono)
train_ds, dev_ds, test_ds = loader.load_splits(
Path(SETTINGS.dataset.dataset_path), **ds_kwargs)
sr = SETTINGS.audio.sample_rate
device = torch.device(SETTINGS.training.device)
std_transform = StandardAudioTransform().to(device).eval()
zmuv_transform = ZmuvTransform().to(device)
batchifier = partial(batchify, label_provider=lambda x: x.label)
truncater = partial(truncate_length,
length=int(SETTINGS.training.max_window_size_seconds *
sr))
train_comp = compose(truncater,
TimeshiftTransform().train(),
NoiseTransform().train(), batchifier)
prep_dl = StandardAudioDataLoaderBuilder(train_ds,
collate_fn=batchifier).build(1)
prep_dl.shuffle = True
train_dl = StandardAudioDataLoaderBuilder(
train_ds, collate_fn=train_comp).build(SETTINGS.training.batch_size)
dev_dl = StandardAudioDataLoaderBuilder(
dev_ds,
collate_fn=compose(truncater,
batchifier)).build(SETTINGS.training.batch_size)
test_dl = StandardAudioDataLoaderBuilder(
test_ds,
collate_fn=compose(truncater,
batchifier)).build(SETTINGS.training.batch_size)
model = RegisteredModel.find_registered_class(args.model)(30).to(device)
params = list(filter(lambda x: x.requires_grad, model.parameters()))
optimizer = AdamW(params,
SETTINGS.training.learning_rate,
weight_decay=SETTINGS.training.weight_decay)
logging.info(f'{sum(p.numel() for p in params)} parameters')
criterion = nn.CrossEntropyLoss()
if (ws.path / 'zmuv.pt.bin').exists():
zmuv_transform.load_state_dict(torch.load(str(ws.path /
'zmuv.pt.bin')))
else:
for idx, batch in enumerate(tqdm(prep_dl, desc='Constructing ZMUV')):
batch.to(device)
zmuv_transform.update(std_transform(batch.audio_data))
if idx == 2000: # TODO: quick debugging, remove later
break
logging.info(
dict(zmuv_mean=zmuv_transform.mean, zmuv_std=zmuv_transform.std))
torch.save(zmuv_transform.state_dict(), str(ws.path / 'zmuv.pt.bin'))
if args.load_weights:
ws.load_model(model, best=True)
if args.eval:
ws.load_model(model, best=True)
evaluate_accuracy(dev_dl, 'Dev')
evaluate_accuracy(test_dl, 'Test')
return
ws.write_args(args)
ws.write_settings(SETTINGS)
writer.add_scalar('Meta/Parameters', sum(p.numel() for p in params))
dev_acc = 0
for epoch_idx in trange(SETTINGS.training.num_epochs,
position=0,
leave=True):
model.train()
std_transform.train()
pbar = tqdm(train_dl,
total=len(train_dl),
position=1,
desc='Training',
leave=True)
for batch in pbar:
batch.to(device)
audio_data = zmuv_transform(std_transform(batch.audio_data))
scores = model(audio_data,
std_transform.compute_lengths(batch.lengths))
optimizer.zero_grad()
model.zero_grad()
labels = batch.labels.to(device)
loss = criterion(scores, labels)
loss.backward()
optimizer.step()
pbar.set_postfix(dict(loss=f'{loss.item():.3}'))
writer.add_scalar('Training/Loss', loss.item(), epoch_idx)
for group in optimizer.param_groups:
group['lr'] *= SETTINGS.training.lr_decay
dev_acc = evaluate_accuracy(dev_dl, 'Dev', save=True)
test_acc = evaluate_accuracy(test_dl, 'Test')
print("model: ", args.model)
print("dev_acc: ", dev_acc)
print("test_acc: ", test_acc)