def evaluate(epoch, step, val_loader, val_feat_proc, detokenize,
ema_model, loss_fn, greedy_decoder, use_amp):
ema_model.eval()
start_time = time.time()
agg = {'losses': [], 'preds': [], 'txts': [], 'idx': []}
logging.log_start(logging.constants.EVAL_START, metadata=dict(epoch_num=epoch))
for i, batch in enumerate(val_loader):
print(f'{val_loader.pipeline_type} evaluation: {i:>10}/{len(val_loader):<10}', end='\r')
audio, audio_lens, txt, txt_lens = batch
feats, feat_lens = val_feat_proc([audio, audio_lens])
log_probs, log_prob_lens = ema_model(feats, feat_lens, txt, txt_lens)
loss = loss_fn(log_probs[:, :log_prob_lens.max().item()],
log_prob_lens, txt, txt_lens)
pred = greedy_decoder.decode(ema_model, feats, feat_lens)
agg['losses'] += helpers.gather_losses([loss.cpu()])
agg['preds'] += helpers.gather_predictions([pred], detokenize)
agg['txts'] += helpers.gather_transcripts([txt.cpu()], [txt_lens.cpu()], detokenize)
wer, loss = process_evaluation_epoch(agg)
logging.log_event(logging.constants.EVAL_ACCURACY, value=wer, metadata=dict(epoch_num=epoch))
logging.log_end(logging.constants.EVAL_STOP, metadata=dict(epoch_num=epoch))
log((epoch,), step, 'dev_ema', {'loss': loss, 'wer': 100.0 * wer, 'took': time.time() - start_time})
ema_model.train()
return wer
def evaluate(epoch, step, val_loader, val_feat_proc, labels, model,
ema_model, ctc_loss, greedy_decoder, use_amp, use_dali=False):
for model, subset in [(model, 'dev'), (ema_model, 'dev_ema')]:
if model is None:
continue
model.eval()
start_time = time.time()
agg = {'losses': [], 'preds': [], 'txts': []}
for batch in val_loader:
if use_dali:
# with DALI, the data is already on GPU
feat, feat_lens, txt, txt_lens = batch
if val_feat_proc is not None:
feat, feat_lens = val_feat_proc(feat, feat_lens, use_amp)
else:
batch = [t.cuda(non_blocking=True) for t in batch]
audio, audio_lens, txt, txt_lens = batch
feat, feat_lens = val_feat_proc(audio, audio_lens, use_amp)
log_probs, enc_lens = model.forward(feat, feat_lens)
loss = ctc_loss(log_probs, txt, enc_lens, txt_lens)
pred = greedy_decoder(log_probs)
agg['losses'] += helpers.gather_losses([loss])
agg['preds'] += helpers.gather_predictions([pred], labels)
agg['txts'] += helpers.gather_transcripts([txt], [txt_lens], labels)
wer, loss = process_evaluation_epoch(agg)
log((epoch,), step, subset, {'loss': loss, 'wer': 100.0 * wer,
'took': time.time() - start_time})
model.train()
return wer
def main():
parser = get_parser()
args = parser.parse_args()
log_fpath = args.log_file or str(Path(args.output_dir, 'nvlog_infer.json'))
log_fpath = unique_log_fpath(log_fpath)
dllogger.init(backends=[
JSONStreamBackend(Verbosity.DEFAULT, log_fpath),
StdOutBackend(Verbosity.VERBOSE, metric_format=stdout_metric_format)
])
[dllogger.log("PARAMETER", {k: v}) for k, v in vars(args).items()]
for step in ['DNN', 'data+DNN', 'data']:
for c in [0.99, 0.95, 0.9, 0.5]:
cs = 'avg' if c == 0.5 else f'{int(100*c)}%'
dllogger.metadata(f'{step.lower()}_latency_{c}', {
'name': f'{step} latency {cs}',
'format': ':>7.2f',
'unit': 'ms'
})
dllogger.metadata('eval_wer', {
'name': 'WER',
'format': ':>3.3f',
'unit': '%'
})
if args.cpu:
device = torch.device('cpu')
else:
assert torch.cuda.is_available()
device = torch.device('cuda')
torch.backends.cudnn.benchmark = args.cudnn_benchmark
if args.seed is not None:
torch.manual_seed(args.seed + args.local_rank)
np.random.seed(args.seed + args.local_rank)
random.seed(args.seed + args.local_rank)
# set up distributed training
multi_gpu = not args.cpu and int(os.environ.get('WORLD_SIZE', 1)) > 1
if multi_gpu:
torch.cuda.set_device(args.local_rank)
distrib.init_process_group(backend='nccl', init_method='env://')
print_once(f'Inference with {distrib.get_world_size()} GPUs')
cfg = config.load(args.model_config)
if args.max_duration is not None:
cfg['input_val']['audio_dataset']['max_duration'] = args.max_duration
cfg['input_val']['filterbank_features'][
'max_duration'] = args.max_duration
if args.pad_to_max_duration:
assert cfg['input_val']['audio_dataset']['max_duration'] > 0
cfg['input_val']['audio_dataset']['pad_to_max_duration'] = True
cfg['input_val']['filterbank_features']['pad_to_max_duration'] = True
use_dali = args.dali_device in ('cpu', 'gpu')
(dataset_kw, features_kw, splicing_kw, _, _) = config.input(cfg, 'val')
tokenizer_kw = config.tokenizer(cfg)
tokenizer = Tokenizer(**tokenizer_kw)
optim_level = 3 if args.amp else 0
feature_proc = torch.nn.Sequential(
torch.nn.Identity(),
torch.nn.Identity(),
features.FrameSplicing(optim_level=optim_level, **splicing_kw),
features.FillPadding(optim_level=optim_level, ),
)
# dataset
data_loader = DaliDataLoader(gpu_id=args.local_rank or 0,
dataset_path=args.dataset_dir,
config_data=dataset_kw,
config_features=features_kw,
json_names=[args.val_manifest],
batch_size=args.batch_size,
sampler=dali_sampler.SimpleSampler(),
pipeline_type="val",
device_type=args.dali_device,
tokenizer=tokenizer)
model = RNNT(n_classes=tokenizer.num_labels + 1, **config.rnnt(cfg))
if args.ckpt is not None:
print(f'Loading the model from {args.ckpt} ...')
checkpoint = torch.load(args.ckpt, map_location="cpu")
key = 'ema_state_dict' if args.ema else 'state_dict'
state_dict = checkpoint[key]
model.load_state_dict(state_dict, strict=True)
model.to(device)
model.eval()
if feature_proc is not None:
feature_proc.to(device)
feature_proc.eval()
if args.amp:
model = amp.initialize(model, opt_level='O3')
if multi_gpu:
model = DistributedDataParallel(model)
agg = {'txts': [], 'preds': [], 'logits': []}
dur = {'data': [], 'dnn': [], 'data+dnn': []}
rep_loader = chain(*repeat(data_loader, args.repeats))
rep_len = args.repeats * len(data_loader)
blank_idx = tokenizer.num_labels
greedy_decoder = RNNTGreedyDecoder(blank_idx=blank_idx)
def sync_time():
torch.cuda.synchronize() if device.type == 'cuda' else None
return time.perf_counter()
sz = []
with torch.no_grad():
for it, batch in enumerate(tqdm.tqdm(rep_loader, total=rep_len)):
if use_dali:
feats, feat_lens, txt, txt_lens = batch
if feature_proc is not None:
feats, feat_lens = feature_proc([feats, feat_lens])
else:
batch = [t.cuda(non_blocking=True) for t in batch]
audio, audio_lens, txt, txt_lens = batch
feats, feat_lens = feature_proc([audio, audio_lens])
feats = feats.permute(2, 0, 1)
if args.amp:
feats = feats.half()
sz.append(feats.size(0))
t1 = sync_time()
log_probs, log_prob_lens = model(feats, feat_lens, txt, txt_lens)
t2 = sync_time()
# burn-in period; wait for a new loader due to num_workers
if it >= 1 and (args.repeats == 1 or it >= len(data_loader)):
dur['data'].append(t1 - t0)
dur['dnn'].append(t2 - t1)
dur['data+dnn'].append(t2 - t0)
if txt is not None:
agg['txts'] += helpers.gather_transcripts([txt], [txt_lens],
tokenizer.detokenize)
preds = greedy_decoder.decode(model, feats, feat_lens)
agg['preds'] += helpers.gather_predictions([preds],
tokenizer.detokenize)
if 0 < args.steps < it:
break
t0 = sync_time()
# communicate the results
if args.transcribe_wav:
for idx, p in enumerate(agg['preds']):
print_once(f'Prediction {idx+1: >3}: {p}')
elif args.transcribe_filelist:
pass
else:
wer, loss = process_evaluation_epoch(agg)
if not multi_gpu or distrib.get_rank() == 0:
dllogger.log(step=(), data={'eval_wer': 100 * wer})
if args.save_predictions:
with open(args.save_predictions, 'w') as f:
f.write('\n'.join(agg['preds']))
# report timings
if len(dur['data']) >= 20:
ratios = [0.9, 0.95, 0.99]
for stage in dur:
lat = durs_to_percentiles(dur[stage], ratios)
for k in [0.99, 0.95, 0.9, 0.5]:
kk = str(k).replace('.', '_')
dllogger.log(step=(),
data={f'{stage.lower()}_latency_{kk}': lat[k]})
else:
# TODO measure at least avg latency
print_once('Not enough samples to measure latencies.')
def main():
parser = get_parser()
args = parser.parse_args()
log_fpath = args.log_file or str(Path(args.output_dir, 'nvlog_infer.json'))
log_fpath = unique_log_fpath(log_fpath)
dllogger.init(backends=[JSONStreamBackend(Verbosity.DEFAULT, log_fpath),
StdOutBackend(Verbosity.VERBOSE,
metric_format=stdout_metric_format)])
[dllogger.log("PARAMETER", {k: v}) for k, v in vars(args).items()]
for step in ['DNN', 'data+DNN', 'data']:
for c in [0.99, 0.95, 0.9, 0.5]:
cs = 'avg' if c == 0.5 else f'{int(100*c)}%'
dllogger.metadata(f'{step.lower()}_latency_{c}',
{'name': f'{step} latency {cs}',
'format': ':>7.2f', 'unit': 'ms'})
dllogger.metadata(
'eval_wer', {'name': 'WER', 'format': ':>3.2f', 'unit': '%'})
if args.cpu:
device = torch.device('cpu')
else:
assert torch.cuda.is_available()
device = torch.device('cuda')
torch.backends.cudnn.benchmark = args.cudnn_benchmark
if args.seed is not None:
torch.manual_seed(args.seed + args.local_rank)
np.random.seed(args.seed + args.local_rank)
random.seed(args.seed + args.local_rank)
# set up distributed training
multi_gpu = not args.cpu and int(os.environ.get('WORLD_SIZE', 1)) > 1
if multi_gpu:
torch.cuda.set_device(args.local_rank)
distrib.init_process_group(backend='nccl', init_method='env://')
print_once(f'Inference with {distrib.get_world_size()} GPUs')
cfg = config.load(args.model_config)
config.apply_config_overrides(cfg, args)
symbols = helpers.add_ctc_blank(cfg['labels'])
use_dali = args.dali_device in ('cpu', 'gpu')
dataset_kw, features_kw = config.input(cfg, 'val')
measure_perf = args.steps > 0
# dataset
if args.transcribe_wav or args.transcribe_filelist:
if use_dali:
print("DALI supported only with input .json files; disabling")
use_dali = False
assert not args.pad_to_max_duration
assert not (args.transcribe_wav and args.transcribe_filelist)
if args.transcribe_wav:
dataset = SingleAudioDataset(args.transcribe_wav)
else:
dataset = FilelistDataset(args.transcribe_filelist)
data_loader = get_data_loader(dataset,
batch_size=1,
multi_gpu=multi_gpu,
shuffle=False,
num_workers=0,
drop_last=(True if measure_perf else False))
_, features_kw = config.input(cfg, 'val')
feat_proc = FilterbankFeatures(**features_kw)
elif use_dali:
# pad_to_max_duration is not supported by DALI - have simple padders
if features_kw['pad_to_max_duration']:
feat_proc = BaseFeatures(
pad_align=features_kw['pad_align'],
pad_to_max_duration=True,
max_duration=features_kw['max_duration'],
sample_rate=features_kw['sample_rate'],
window_size=features_kw['window_size'],
window_stride=features_kw['window_stride'])
features_kw['pad_to_max_duration'] = False
else:
feat_proc = None
data_loader = DaliDataLoader(
gpu_id=args.local_rank or 0,
dataset_path=args.dataset_dir,
config_data=dataset_kw,
config_features=features_kw,
json_names=args.val_manifests,
batch_size=args.batch_size,
pipeline_type=("train" if measure_perf else "val"), # no drop_last
device_type=args.dali_device,
symbols=symbols)
else:
dataset = AudioDataset(args.dataset_dir,
args.val_manifests,
symbols,
**dataset_kw)
data_loader = get_data_loader(dataset,
args.batch_size,
multi_gpu=multi_gpu,
shuffle=False,
num_workers=4,
drop_last=False)
feat_proc = FilterbankFeatures(**features_kw)
model = QuartzNet(encoder_kw=config.encoder(cfg),
decoder_kw=config.decoder(cfg, n_classes=len(symbols)))
if args.ckpt is not None:
print(f'Loading the model from {args.ckpt} ...')
checkpoint = torch.load(args.ckpt, map_location="cpu")
key = 'ema_state_dict' if args.ema else 'state_dict'
state_dict = checkpoint[key]
model.load_state_dict(state_dict, strict=True)
model.to(device)
model.eval()
if feat_proc is not None:
feat_proc.to(device)
feat_proc.eval()
if args.amp:
model = model.half()
if args.torchscript:
greedy_decoder = GreedyCTCDecoder()
feat_proc, model, greedy_decoder = torchscript_export(
data_loader, feat_proc, model, greedy_decoder, args.output_dir,
use_amp=args.amp, use_conv_masks=True, model_toml=args.model_toml,
device=device, save=args.torchscript_export)
if multi_gpu:
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[args.local_rank], output_device=args.local_rank)
agg = {'txts': [], 'preds': [], 'logits': []}
dur = {'data': [], 'dnn': [], 'data+dnn': []}
looped_loader = chain.from_iterable(repeat(data_loader))
greedy_decoder = GreedyCTCDecoder()
sync = lambda: torch.cuda.synchronize() if device.type == 'cuda' else None
steps = args.steps + args.warmup_steps or len(data_loader)
with torch.no_grad():
for it, batch in enumerate(tqdm(looped_loader, initial=1, total=steps)):
if use_dali:
feats, feat_lens, txt, txt_lens = batch
if feat_proc is not None:
feats, feat_lens = feat_proc(feats, feat_lens)
else:
batch = [t.to(device, non_blocking=True) for t in batch]
audio, audio_lens, txt, txt_lens = batch
feats, feat_lens = feat_proc(audio, audio_lens)
sync()
t1 = time.perf_counter()
if args.amp:
feats = feats.half()
if model.encoder.use_conv_masks:
log_probs, log_prob_lens = model(feats, feat_lens)
else:
log_probs = model(feats, feat_lens)
preds = greedy_decoder(log_probs)
sync()
t2 = time.perf_counter()
# burn-in period; wait for a new loader due to num_workers
if it >= 1 and (args.steps == 0 or it >= args.warmup_steps):
dur['data'].append(t1 - t0)
dur['dnn'].append(t2 - t1)
dur['data+dnn'].append(t2 - t0)
if txt is not None:
agg['txts'] += helpers.gather_transcripts([txt], [txt_lens],
symbols)
agg['preds'] += helpers.gather_predictions([preds], symbols)
agg['logits'].append(log_probs)
if it + 1 == steps:
break
sync()
t0 = time.perf_counter()
# communicate the results
if args.transcribe_wav:
for idx, p in enumerate(agg['preds']):
print_once(f'Prediction {idx+1: >3}: {p}')
elif args.transcribe_filelist:
pass
elif not multi_gpu or distrib.get_rank() == 0:
wer, _ = process_evaluation_epoch(agg)
dllogger.log(step=(), data={'eval_wer': 100 * wer})
if args.save_predictions:
with open(args.save_predictions, 'w') as f:
f.write('\n'.join(agg['preds']))
if args.save_logits:
logits = torch.cat(agg['logits'], dim=0).cpu()
torch.save(logits, args.save_logits)
# report timings
if len(dur['data']) >= 20:
ratios = [0.9, 0.95, 0.99]
for stage in dur:
lat = durs_to_percentiles(dur[stage], ratios)
for k in [0.99, 0.95, 0.9, 0.5]:
kk = str(k).replace('.', '_')
dllogger.log(step=(), data={f'{stage.lower()}_latency_{kk}': lat[k]})
else:
print_once('Not enough samples to measure latencies.')
def evaluate(conf, onnx_path, val_loader, val_feat_proc, inference_session,
inference_anchors, inference_inputs, inference_transcription_out,
inference_transcription_out_lens, pytorch_rnnt_model,
greedy_decoder, detokenize):
start_time = time.time()
logger.info(
"Getting trained weights for inference from {}".format(onnx_path))
inference_session.resetHostWeights(
onnx_path, ignoreWeightsInModelWithoutCorrespondingHostWeight=True)
inference_session.weightsFromHost()
feats_data = []
feat_lens_data = []
txt_data = []
txt_lens_data = []
samples_per_step_per_instance = conf.samples_per_step // conf.num_instances
# No need to compute losses for validation
agg = {'preds': [], 'txts': [], 'idx': []}
overall_scores, overall_words = (0, 0)
logger.info("Running transcription network on evaluation dataset")
for audio, audio_lens, txt, txt_lens in val_loader:
feats, feat_lens = val_feat_proc([audio, audio_lens])
feats = feats.numpy()
feat_lens = feat_lens.numpy()
# txt is of np.array type as implemented in reference code
txt_lens = txt_lens.numpy()
feats = pad(feats, samples_per_step_per_instance)
feat_lens = pad(feat_lens, samples_per_step_per_instance)
txt = pad(txt, samples_per_step_per_instance)
txt_lens = pad(txt_lens, samples_per_step_per_instance)
stepio = popart.PyStepIO(
{
inference_inputs["mel_spec_input"]: feats.astype(
conf.precision),
inference_inputs["input_length"]: feat_lens.astype(np.int32),
}, inference_anchors)
inference_session.run(stepio)
# converting to torch tensor
feats = torch.tensor(inference_anchors[inference_transcription_out])
feat_lens = torch.tensor(
inference_anchors[inference_transcription_out_lens])
feat_lens = torch.flatten(feat_lens)
step_size = feat_lens.shape[0]
feats = torch.reshape(feats,
(step_size, feats.shape[-2], feats.shape[-1]))
txt = torch.tensor(txt)
txt_lens = torch.tensor(txt_lens)
feats_data.append(feats)
feat_lens_data.append(feat_lens)
txt_data.append(txt)
txt_lens_data.append(txt_lens)
num_cpus = os.cpu_count()
num_workers = max(1, min(16, num_cpus // conf.num_instances))
logger.info(
"Creating multiprocessor pool with {} workers and function for decoding"
.format(num_workers))
greedy_decoding_processor_pool = multiprocessing.pool.ThreadPool(
processes=num_workers)
greedy_decoding_func = partial(greedy_decoder.decode, pytorch_rnnt_model)
pred_results = []
ground_truths_dekotenized = []
logger.info("Submitting jobs for greedy decoding")
feat_iter = zip(feats_data, feat_lens_data, txt_data, txt_lens_data)
for feats, feat_lens, txt, txt_lens in feat_iter:
step_size = feat_lens.shape[0]
batch_size = step_size // conf.batches_per_step
for bind in range(conf.batches_per_step):
feats_b = feats[bind:bind + batch_size]
feat_lens_b = feat_lens[bind:bind + batch_size]
txt_b = txt[bind:bind + batch_size]
txt_lens_b = txt_lens[bind:bind + batch_size]
pred_results.append(
greedy_decoding_processor_pool.apply_async(
greedy_decoding_func, (feats_b, feat_lens_b)))
ground_truths_dekotenized.append(
helpers.gather_transcripts([txt_b], [txt_lens_b], detokenize))
logger.info("Generating predictions and computing Word Error Rate (WER)")
pred_iter = zip(pred_results, ground_truths_dekotenized)
for idx, (pred_result, gts_detokenized) in enumerate(pred_iter):
preds_detokenized = helpers.gather_predictions([pred_result.get()],
detokenize)
batch_wer, batch_scores, batch_words = metrics.word_error_rate(
preds_detokenized, gts_detokenized)
agg['preds'] += preds_detokenized
agg['txts'] += gts_detokenized
wer, scores, num_words, _ = helpers.process_evaluation_epoch(agg)
logger.info(
"Total time for Transducer Decoding = {:.1f} secs".format(time.time() -
start_time))
greedy_decoding_processor_pool.close()
greedy_decoding_processor_pool.join()
return wer, scores, num_words
