def init_train_set(epoch, from_iter):
#train_dataset.set_curriculum_epoch(epoch, sample=True)
train_dataset.set_curriculum_epoch(epoch, sample=False)
global train_loader, train_sampler
if not args.distributed:
train_sampler = BucketingSampler(train_dataset,
batch_size=args.batch_size)
train_sampler.bins = train_sampler.bins[from_iter:]
else:
train_sampler = DistributedBucketingSampler(
train_dataset,
batch_size=args.batch_size,
num_replicas=args.world_size,
rank=args.rank)
train_loader = AudioDataLoader(train_dataset,
num_workers=args.num_workers,
batch_sampler=train_sampler)
if (not args.no_shuffle and epoch != 0) or args.no_sorta_grad:
print("Shuffling batches for the following epochs")
train_sampler.shuffle(epoch)
manifest_filepath=args.train_manifest,
labels=labels,
normalize=True,
augment=args.augment)
test_dataset = SpectrogramDataset(audio_conf=audio_conf,
manifest_filepath=args.val_manifest,
labels=labels,
normalize=True,
augment=False)
if not args.distributed:
train_sampler = BucketingSampler(train_dataset,
batch_size=args.batch_size)
else:
train_sampler = DistributedBucketingSampler(
train_dataset,
batch_size=args.batch_size,
num_replicas=args.world_size,
rank=args.rank)
train_loader = AudioDataLoader(train_dataset,
num_workers=args.num_workers,
batch_sampler=train_sampler)
test_loader = AudioDataLoader(test_dataset,
batch_size=args.batch_size,
num_workers=args.num_workers)
if (not args.no_shuffle and start_epoch != 0) or args.no_sorta_grad:
print("Shuffling batches for the following epochs")
train_sampler.shuffle(start_epoch)
try:
model.load_state_dict(torch.load(args.weights)['state_dict'],
def train_main(args):
args.distributed = args.world_size > 1
main_proc = True
if args.distributed:
if args.gpu_rank:
torch.cuda.set_device(int(args.gpu_rank))
dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
world_size=args.world_size, rank=args.rank)
main_proc = args.rank == 0 # Only the first proc should save models
save_folder = args.save_folder
loss_results, cer_results, wer_results = torch.Tensor(args.epochs), torch.Tensor(args.epochs), torch.Tensor(
args.epochs)
best_wer = None
if args.visdom and main_proc:
from visdom import Visdom
viz = Visdom()
opts = dict(title=args.id, ylabel='', xlabel='Epoch', legend=['Loss', 'WER', 'CER'])
viz_window = None
epochs = torch.arange(1, args.epochs + 1)
if args.tensorboard and main_proc:
os.makedirs(args.log_dir, exist_ok=True)
from tensorboardX import SummaryWriter
tensorboard_writer = SummaryWriter(args.log_dir)
os.makedirs(save_folder, exist_ok=True)
avg_loss, start_epoch, start_iter = 0, 0, 0
if args.continue_from: # Starting from previous model
print("Loading checkpoint model %s" % args.continue_from)
package = torch.load(args.continue_from, map_location=lambda storage, loc: storage)
model = DeepSpeech.load_model_package(package)
labels = DeepSpeech.get_labels(model)
audio_conf = DeepSpeech.get_audio_conf(model)
parameters = model.parameters()
optimizer = torch.optim.SGD(parameters, lr=args.lr,
momentum=args.momentum, nesterov=True)
if not args.finetune: # Don't want to restart training
if args.cuda:
model.cuda()
optimizer.load_state_dict(package['optim_dict'])
start_epoch = int(package.get('epoch', 1)) - 1 # Index start at 0 for training
start_iter = package.get('iteration', None)
if start_iter is None:
start_epoch += 1 # We saved model after epoch finished, start at the next epoch.
start_iter = 0
else:
start_iter += 1
avg_loss = int(package.get('avg_loss', 0))
loss_results, cer_results, wer_results = package['loss_results'], package[
'cer_results'], package['wer_results']
if main_proc and args.visdom and \
package[
'loss_results'] is not None and start_epoch > 0: # Add previous scores to visdom graph
x_axis = epochs[0:start_epoch]
y_axis = torch.stack(
(loss_results[0:start_epoch], wer_results[0:start_epoch], cer_results[0:start_epoch]),
dim=1)
viz_window = viz.line(
X=x_axis,
Y=y_axis,
opts=opts,
)
if main_proc and args.tensorboard and \
package[
'loss_results'] is not None and start_epoch > 0: # Previous scores to tensorboard logs
for i in range(start_epoch):
values = {
'Avg Train Loss': loss_results[i],
'Avg WER': wer_results[i],
'Avg CER': cer_results[i]
}
tensorboard_writer.add_scalars(args.id, values, i + 1)
else:
with open(args.labels_path) as label_file:
labels = str(''.join(json.load(label_file)))
audio_conf = dict(sample_rate=args.sample_rate,
window_size=args.window_size,
window_stride=args.window_stride,
window=args.window,
noise_dir=args.noise_dir,
noise_prob=args.noise_prob,
noise_levels=(args.noise_min, args.noise_max))
rnn_type = args.rnn_type.lower()
assert rnn_type in supported_rnns, "rnn_type should be either lstm, rnn or gru"
model = DeepSpeech(rnn_hidden_size=args.hidden_size,
nb_layers=args.hidden_layers,
labels=labels,
rnn_type=supported_rnns[rnn_type],
audio_conf=audio_conf,
bidirectional=args.bidirectional)
parameters = model.parameters()
optimizer = torch.optim.SGD(parameters, lr=args.lr,
momentum=args.momentum, nesterov=True)
criterion = CTCLoss()
decoder = GreedyDecoder(labels)
train_dataset = SpectrogramDataset(audio_conf=audio_conf, manifest_filepath=args.train_manifest, labels=labels,
normalize=True, augment=args.augment)
test_dataset = SpectrogramDataset(audio_conf=audio_conf, manifest_filepath=args.val_manifest, labels=labels,
normalize=True, augment=False)
if not args.distributed:
train_sampler = BucketingSampler(train_dataset, batch_size=args.batch_size)
else:
train_sampler = DistributedBucketingSampler(train_dataset, batch_size=args.batch_size,
num_replicas=args.world_size, rank=args.rank)
train_loader = AudioDataLoader(train_dataset,
num_workers=args.num_workers, batch_sampler=train_sampler)
test_loader = AudioDataLoader(test_dataset, batch_size=args.batch_size,
num_workers=args.num_workers)
if (not args.no_shuffle and start_epoch != 0) or args.no_sorta_grad:
print("Shuffling batches for the following epochs")
train_sampler.shuffle(start_epoch)
if args.cuda:
model.cuda()
if args.distributed:
model = torch.nn.parallel.DistributedDataParallel(model,
device_ids=(int(args.gpu_rank),) if args.rank else None)
print(model)
print("Number of parameters: %d" % DeepSpeech.get_param_size(model))
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
for epoch in range(start_epoch, args.epochs):
model.train()
end = time.time()
start_epoch_time = time.time()
for i, (data) in enumerate(train_loader, start=start_iter):
if i == len(train_sampler):
break
inputs, targets, input_percentages, target_sizes = data
input_sizes = input_percentages.mul_(int(inputs.size(3))).int()
# measure data loading time
data_time.update(time.time() - end)
if args.cuda:
inputs = inputs.cuda()
out, output_sizes = model(inputs, input_sizes)
out = out.transpose(0, 1) # TxNxH
loss = criterion(out, targets, output_sizes, target_sizes)
loss = loss / inputs.size(0) # average the loss by minibatch
inf = float("inf")
if args.distributed:
loss_value = reduce_tensor(loss, args.world_size)[0]
else:
loss_value = loss.item()
if loss_value == inf or loss_value == -inf:
print("WARNING: received an inf loss, setting loss value to 0")
loss_value = 0
avg_loss += loss_value
losses.update(loss_value, inputs.size(0))
# compute gradient
optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_norm)
# SGD step
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if not args.silent:
print('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'.format(
(epoch + 1), (i + 1), len(train_sampler), batch_time=batch_time, data_time=data_time, loss=losses))
if args.checkpoint_per_batch > 0 and i > 0 and (i + 1) % args.checkpoint_per_batch == 0 and main_proc:
file_path = '%s/deepspeech_checkpoint_epoch_%d_iter_%d.pth' % (save_folder, epoch + 1, i + 1)
print("Saving checkpoint model to %s" % file_path)
torch.save(DeepSpeech.serialize(model, optimizer=optimizer, epoch=epoch, iteration=i,
loss_results=loss_results,
wer_results=wer_results, cer_results=cer_results, avg_loss=avg_loss),
file_path)
del loss
del out
avg_loss /= len(train_sampler)
epoch_time = time.time() - start_epoch_time
print('Training Summary Epoch: [{0}]\t'
'Time taken (s): {epoch_time:.0f}\t'
'Average Loss {loss:.3f}\t'.format(epoch + 1, epoch_time=epoch_time, loss=avg_loss))
start_iter = 0 # Reset start iteration for next epoch
total_cer, total_wer = 0, 0
model.eval()
with torch.no_grad():
for i, (data) in tqdm(enumerate(test_loader), total=len(test_loader)):
inputs, targets, input_percentages, target_sizes = data
input_sizes = input_percentages.mul_(int(inputs.size(3))).int()
# unflatten targets
split_targets = []
offset = 0
for size in target_sizes:
split_targets.append(targets[offset:offset + size])
offset += size
if args.cuda:
inputs = inputs.cuda()
out, output_sizes = model(inputs, input_sizes)
decoded_output, _ = decoder.decode(out.data, output_sizes)
target_strings = decoder.convert_to_strings(split_targets)
wer, cer = 0, 0
for x in range(len(target_strings)):
transcript, reference = decoded_output[x][0], target_strings[x][0]
wer += decoder.wer(transcript, reference) / float(len(reference.split()))
cer += decoder.cer(transcript, reference) / float(len(reference))
total_cer += cer
total_wer += wer
del out
wer = total_wer / len(test_loader.dataset)
cer = total_cer / len(test_loader.dataset)
wer *= 100
cer *= 100
loss_results[epoch] = avg_loss
wer_results[epoch] = wer
cer_results[epoch] = cer
print('Validation Summary Epoch: [{0}]\t'
'Average WER {wer:.3f}\t'
'Average CER {cer:.3f}\t'.format(epoch + 1, wer=wer, cer=cer))
if args.visdom and main_proc:
x_axis = epochs[0:epoch + 1]
y_axis = torch.stack(
(loss_results[0:epoch + 1], wer_results[0:epoch + 1], cer_results[0:epoch + 1]), dim=1)
if viz_window is None:
viz_window = viz.line(
X=x_axis,
Y=y_axis,
opts=opts,
)
else:
viz.line(
X=x_axis.unsqueeze(0).expand(y_axis.size(1), x_axis.size(0)).transpose(0, 1), # Visdom fix
Y=y_axis,
win=viz_window,
update='replace',
)
if args.tensorboard and main_proc:
values = {
'Avg Train Loss': avg_loss,
'Avg WER': wer,
'Avg CER': cer
}
tensorboard_writer.add_scalars(args.id, values, epoch + 1)
if args.log_params:
for tag, value in model.named_parameters():
tag = tag.replace('.', '/')
tensorboard_writer.add_histogram(tag, to_np(value), epoch + 1)
tensorboard_writer.add_histogram(tag + '/grad', to_np(value.grad), epoch + 1)
if args.checkpoint and main_proc:
file_path = '%s/deepspeech_%d.pth' % (save_folder, epoch + 1)
torch.save(DeepSpeech.serialize(model, optimizer=optimizer, epoch=epoch, loss_results=loss_results,
wer_results=wer_results, cer_results=cer_results),
file_path)
# anneal lr
optim_state = optimizer.state_dict()
optim_state['param_groups'][0]['lr'] = optim_state['param_groups'][0]['lr'] / args.learning_anneal
optimizer.load_state_dict(optim_state)
print('Learning rate annealed to: {lr:.6f}'.format(lr=optim_state['param_groups'][0]['lr']))
if (best_wer is None or best_wer > wer) and main_proc:
print("Found better validated model, saving to %s" % args.model_path)
torch.save(DeepSpeech.serialize(model, optimizer=optimizer, epoch=epoch, loss_results=loss_results,
wer_results=wer_results, cer_results=cer_results), args.model_path)
best_wer = wer
avg_loss = 0
if not args.no_shuffle:
print("Shuffling batches...")
train_sampler.shuffle(epoch)
normalize=True,
augment=args.augment)
test_dataset = SpectrogramDataset(audio_conf=audio_conf,
manifest_filepath=args.val_manifest,
labels=labels,
normalize=True,
augment=False)
if not args.distributed:
train_sampler_clean = BucketingSampler(train_dataset_clean,
batch_size=args.batch_size)
train_sampler_adv = BucketingSampler(train_dataset_adv,
batch_size=args.batch_size)
else:
train_sampler_clean = DistributedBucketingSampler(
train_dataset_clean,
batch_size=args.batch_size,
num_replicas=args.world_size,
rank=args.rank)
train_sampler_adv = DistributedBucketingSampler(
train_dataset_adv,
batch_size=args.batch_size,
num_replicas=args.world_size,
rank=args.rank)
train_loader_clean = AudioDataLoader(train_dataset_clean,
num_workers=args.num_workers,
batch_sampler=train_sampler_clean)
train_loader_adv = AudioDataLoader(train_dataset_adv,
num_workers=args.num_workers,
batch_sampler=train_sampler_adv)
test_loader = AudioDataLoader(test_dataset,
batch_size=args.batch_size,
def train(self, **kwargs):
"""
Run optimization to train the model.
Parameters
----------
"""
world_size = kwargs.pop('world_size', 1)
gpu_rank = kwargs.pop('gpu_rank', 0)
rank = kwargs.pop('rank', 0)
dist_backend = kwargs.pop('dist_backend', 'nccl')
dist_url = kwargs.pop('dist_url', None)
os.environ['MASTER_ADDR'] = '127.0.0.1'
os.environ['MASTER_PORT'] = '1234'
main_proc = True
self.distributed = world_size > 1
if self.distributed:
if self.gpu_rank:
torch.cuda.set_device(int(gpu_rank))
dist.init_process_group(backend=dist_backend,
init_method=dist_url,
world_size=world_size,
rank=rank)
print('Initiated process group')
main_proc = rank == 0 # Only the first proc should save models
if main_proc and self.tensorboard:
tensorboard_logger = TensorBoardLogger(self.id,
self.log_dir,
self.log_params,
comment=self.sufix)
if self.distributed:
train_sampler = DistributedBucketingSampler(
self.data_train,
batch_size=self.batch_size,
num_replicas=world_size,
rank=rank)
else:
if self.sampler_type == 'bucketing':
train_sampler = BucketingSampler(self.data_train,
batch_size=self.batch_size,
shuffle=True)
if self.sampler_type == 'random':
train_sampler = RandomBucketingSampler(
self.data_train, batch_size=self.batch_size)
print("Shuffling batches for the following epochs..")
train_sampler.shuffle(self.start_epoch)
train_loader = AudioDataLoader(self.data_train,
num_workers=self.num_workers,
batch_sampler=train_sampler)
val_loader = AudioDataLoader(self.data_val,
batch_size=self.batch_size_val,
num_workers=self.num_workers,
shuffle=True)
if self.tensorboard and self.generate_graph: # TO DO get some audios also
with torch.no_grad():
inputs, targets, input_percentages, target_sizes = next(
iter(train_loader))
input_sizes = input_percentages.mul_(int(inputs.size(3))).int()
tensorboard_logger.add_image(inputs,
input_sizes,
targets,
network=self.model)
self.model = self.model.to(self.device)
parameters = self.model.parameters()
if self.update_rule == 'adam':
optimizer = torch.optim.Adam(parameters,
lr=self.lr,
weight_decay=self.reg)
if self.update_rule == 'sgd':
optimizer = torch.optim.SGD(parameters,
lr=self.lr,
weight_decay=self.reg)
self.model, self.optimizer = amp.initialize(
self.model,
optimizer,
opt_level=self.opt_level,
keep_batchnorm_fp32=self.keep_batchnorm_fp32,
loss_scale=self.loss_scale)
if self.optim_state is not None:
self.optimizer.load_state_dict(self.optim_state)
if self.amp_state is not None:
amp.load_state_dict(self.amp_state)
if self.distributed:
self.model = DistributedDataParallel(self.model)
print(self.model)
if self.criterion_type == 'cross_entropy_loss':
self.criterion = torch.nn.CrossEntropyLoss()
# Useless for now because I don't save.
accuracies_train_iters = []
losses_iters = []
avg_loss = 0
batch_time = AverageMeter()
epoch_time = AverageMeter()
losses = AverageMeter()
start_training = time.time()
for epoch in range(self.start_epoch, self.num_epochs):
print("Start epoch..")
# Put model in train mode
self.model.train()
y_true_train_epoch = np.array([])
y_pred_train_epoch = np.array([])
start_epoch = time.time()
for i, (data) in enumerate(train_loader, start=0):
start_batch = time.time()
print('Start batch..')
if i == len(train_sampler): # QUE pq isso deus
break
inputs, targets, input_percentages, _ = data
input_sizes = input_percentages.mul_(int(inputs.size(3))).int()
inputs = inputs.to(self.device)
targets = targets.to(self.device)
output, loss_value = self._step(inputs, input_sizes, targets)
print('Step finished.')
avg_loss += loss_value
with torch.no_grad():
y_pred = self.decoder.decode(output.detach()).cpu().numpy()
# import pdb; pdb.set_trace()
y_true_train_epoch = np.concatenate(
(y_true_train_epoch, targets.cpu().numpy()
)) # maybe I should do it with tensors?
y_pred_train_epoch = np.concatenate(
(y_pred_train_epoch, y_pred))
inputs_size = inputs.size(0)
del output, inputs, input_percentages
if self.intra_epoch_sanity_check:
with torch.no_grad():
acc, _ = self.check_accuracy(targets.cpu().numpy(),
y_pred=y_pred)
accuracies_train_iters.append(acc)
losses_iters.append(loss_value)
cm = confusion_matrix(targets.cpu().numpy(),
y_pred,
labels=self.labels)
print('[it %i/%i] Confusion matrix train step:' %
((i + 1, len(train_sampler))))
print(pd.DataFrame(cm))
if self.tensorboard:
tensorboard_logger.update(
len(train_loader) * epoch + i + 1, {
'Loss/through_iterations': loss_value,
'Accuracy/train_through_iterations': acc
})
del targets
batch_time.update(time.time() - start_batch)
epoch_time.update(time.time() - start_epoch)
losses.update(loss_value, inputs_size)
# Write elapsed time (and loss) to terminal
print('Epoch: [{0}][{1}/{2}]\t'
'Batch {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Epoch {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'.format(
(epoch + 1), (i + 1),
len(train_sampler),
batch_time=batch_time,
data_time=epoch_time,
loss=losses))
# Loss log
avg_loss /= len(train_sampler)
self.loss_epochs.append(avg_loss)
# Accuracy train log
acc_train, _ = self.check_accuracy(y_true_train_epoch,
y_pred=y_pred_train_epoch)
self.accuracy_train_epochs.append(acc_train)
# Accuracy val log
with torch.no_grad():
y_pred_val = np.array([])
targets_val = np.array([])
for data in val_loader:
inputs, targets, input_percentages, _ = data
input_sizes = input_percentages.mul_(int(
inputs.size(3))).int()
_, y_pred_val_batch = self.check_accuracy(
targets.cpu().numpy(),
inputs=inputs,
input_sizes=input_sizes)
y_pred_val = np.concatenate((y_pred_val, y_pred_val_batch))
targets_val = np.concatenate(
(targets_val, targets.cpu().numpy()
)) # TO DO: think of a smarter way to do this later
del inputs, targets, input_percentages
# import pdb; pdb.set_trace()
acc_val, y_pred_val = self.check_accuracy(targets_val,
y_pred=y_pred_val)
self.accuracy_val_epochs.append(acc_val)
cm = confusion_matrix(targets_val, y_pred_val, labels=self.labels)
print('Confusion matrix validation:')
print(pd.DataFrame(cm))
# Write epoch stuff to tensorboard
if self.tensorboard:
tensorboard_logger.update(
epoch + 1, {'Loss/through_epochs': avg_loss},
parameters=self.model.named_parameters)
tensorboard_logger.update(epoch + 1, {
'train': acc_train,
'validation': acc_val
},
together=True,
name='Accuracy/through_epochs')
# Keep track of the best model
if acc_val > self.best_acc_val:
self.best_acc_val = acc_val
self.best_params = {}
for k, v in self.model.named_parameters(
): # TO DO: actually copy model and save later? idk..
self.best_params[k] = v.clone()
# Anneal learning rate. TO DO: find better way to this this specific to every parameter as cs231n does.
for g in self.optimizer.param_groups:
g['lr'] = g['lr'] / self.learning_anneal
print('Learning rate annealed to: {lr:.6f}'.format(lr=g['lr']))
# Shuffle batches order
print("Shuffling batches...")
train_sampler.shuffle(epoch)
# Rechoose batches elements
if self.sampler_type == 'random':
train_sampler.recompute_bins()
end_training = time.time()
if self.tensorboard:
tensorboard_logger.close()
print('Elapsed time in training: %.02f ' %
((end_training - start_training) / 60.0))
