def train():
torch.cuda.set_device(0)
iteration = 0
model = WaveRNN(HPARAMS)
model = model.cuda()
optimizer = optim.Adam(model.parameters(), lr=HPARAMS.lr)
if ARGS.checkpoint:
if os.path.basename(ARGS.checkpoint).startswith('ema_model'):
ema_checkpoint = ARGS.checkpoint
else:
ema_checkpoint = 'ema_model_' + os.path.basename(ARGS.checkpoint)
ema_checkpoint = os.path.join(os.path.dirname(ARGS.checkpoint), ema_checkpoint)
# Initialise EMA from the ema checkpoint.
logging.info('Initialising ema model {}'.format(ema_checkpoint))
ema_model = WaveRNN(HPARAMS).cuda()
ema_base_model, _ = load_checkpoint(ema_checkpoint, ema_model)
ema = init_ema(ema_base_model, HPARAMS.ema_rate)
# Initialise vanilla model
logging.info('Loading checkpoint {}'.format(ARGS.checkpoint))
model, iteration, optimizer = load_checkpoint(ARGS.checkpoint, model, optimizer)
else:
# Initialise EMA from scratch.
ema = init_ema(model, HPARAMS.ema_rate)
criterion = nn.NLLLoss(reduction='sum').cuda()
train_loader, test_loader = get_loader(ARGS.data, 'train', HPARAMS), get_loader(ARGS.data, 'valid', HPARAMS)
whole_loader = get_loader(ARGS.data, 'valid', HPARAMS, whole=True)
model = nn.DataParallel(model)
epoch_offset = max(0, int(iteration / len(train_loader)))
for _ in range(epoch_offset, ARGS.epochs):
iteration = train_step(
train_loader, test_loader, whole_loader, model, optimizer,
criterion, iteration, ema=ema
)
averaged_model = clone_as_averaged_model(model, ema)
save_checkpoint(
{
'state_dict': model.module.state_dict(),
'iteration': iteration,
'dataset': ARGS.data,
'optimizer': optimizer.state_dict(),
}, iteration,
'checkpoints/{}/lastmodel.pth'.format(ARGS.expName), ARGS.expName,
)
save_checkpoint(
{
'state_dict': averaged_model.state_dict(),
'iteration': iteration,
'dataset': ARGS.data,
'optimizer': optimizer.state_dict(),
}, iteration,
'checkpoints/{}/ema_model_lastmodel.pth'.format(ARGS.expName), ARGS.expName,
)
def test_inference_forward_parity():
hparams = create_hparams()
model = WaveRNN(hparams, debug=True).cuda()
model.train()
data_path = '../data/short_sens/'
whole_segments = get_loader(data_path, 'valid', hparams, whole=True)
for i, (x, m, _) in enumerate(whole_segments):
x, m = x.cuda(), m.cuda()
forward_output, f_context, f_x = model.train_mode_generate(x, m)
inference_output, i_cont_dict, i_x = model.inference(m, gt=x)
assert (abs(i_x - f_x).mean() < 1e-6)
'''
def visualize_attn(args):
"""
Visualization of learned attention map.
"""
config = CONFIGS[args.model_type]
num_classes = 10 if args.dataset == "cifar10" else 100
model = VisionTransformer(config, args.img_size,
norm_type=args.norm_type,
zero_head=True,
num_classes=num_classes,
vis=True)
ckpt_file = os.path.join(args.output_dir, args.name + "_checkpoint.bin")
ckpt = torch.load(ckpt_file)
# use single card for visualize attn map
model.load_state_dict(ckpt)
model.to(args.device)
model.eval()
_, test_loader = get_loader(args)
sample_idx = 0
layer_ids = [0, 3, 6, 9]
head_id = 0
with torch.no_grad():
for step, batch in enumerate(test_loader):
batch = tuple(t.to(args.device) for t in batch)
x, y = batch
select_x = x[sample_idx].unsqueeze(0)
output, attn_weights = model(select_x)
# attn_weights is List[(1, number_of_head, len_h, len_h)]
for layer_id in layer_ids:
vis_attn(args, attn_weights[layer_id].squeeze(0)[head_id], layer_id=layer_id)
break # visualize the first sample in the first batch
print("done.")
exit(0)
def train(args, model, device, acc_calculator):
""" Train the model """
if args.local_rank in [-1, 0]:
os.makedirs(args.output_dir, exist_ok=True)
writer = SummaryWriter(log_dir=os.path.join("logs", args.name))
args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps
# Prepare dataset
train_loader, test_loader = get_loader(args)
# Prepare optimizer and scheduler
optimizer = torch.optim.SGD(model.parameters(),
lr=args.learning_rate,
momentum=0.9,
weight_decay=args.weight_decay)
t_total = args.num_steps
if args.decay_type == "cosine":
scheduler = WarmupCosineSchedule(optimizer, warmup_steps=args.warmup_steps, t_total=t_total)
else:
scheduler = WarmupLinearSchedule(optimizer, warmup_steps=args.warmup_steps, t_total=t_total)
# Train!
logger.info("***** Running training *****")
logger.info(" Total optimization steps = %d", args.num_steps)
logger.info(" Instantaneous batch size per GPU = %d", args.train_batch_size)
logger.info(" Total train batch size (w. parallel, distributed & accumulation) = %d",
args.train_batch_size * args.gradient_accumulation_steps)
logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps)
model.zero_grad()
set_seed(args) # Added here for reproducibility (even between python 2 and 3)
losses = AverageMeter()
global_step, best_acc = 0, 0
loss_fct = BatchAllLoss(margin=0.1)
while True:
model.train()
epoch_iterator = tqdm(train_loader,
desc="Training (X / X Steps) (loss=X.X)",
bar_format="{l_bar}{r_bar}",
dynamic_ncols=True,
disable=args.local_rank not in [-1, 0])
for step, batch in enumerate(epoch_iterator):
batch = tuple(t.to(device) for t in batch[:2])
x, y = batch
embeds = model(x)
loss = loss_fct(embeds, y)
losses.update(loss.item())
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
loss.backward()
optimizer.step()
scheduler.step()
optimizer.zero_grad()
global_step += 1
epoch_iterator.set_description(
"Training (%d / %d Steps) (loss=%2.5f)" % (global_step, t_total, losses.val)
)
if args.local_rank in [-1, 0]:
writer.add_scalar("train/loss", scalar_value=losses.val, global_step=global_step)
writer.add_scalar("train/lr", scalar_value=scheduler.get_lr()[0], global_step=global_step)
if global_step % args.eval_every == 0 and args.local_rank in [-1, 0]:
accuracy = valid(args, model, writer, test_loader, global_step, device, acc_calculator)
if best_acc < accuracy:
save_model(args, model)
best_acc = accuracy
model.train()
if global_step % t_total == 0:
break
losses.reset()
if global_step % t_total == 0:
break
if args.local_rank in [-1, 0]:
writer.close()
logger.info("Best Accuracy: \t%f" % best_acc)
logger.info("End Training!")
def train(args, model):
""" Train the model """
if args.local_rank in [-1, 0]:
os.makedirs(args.output_dir, exist_ok=True)
writer = SummaryWriter(log_dir=os.path.join("logs", args.name))
args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps
# Prepare dataset
train_loader, test_loader = get_loader(args)
# Prepare optimizer and scheduler
optimizer = torch.optim.SGD(model.parameters(),
lr=args.learning_rate,
momentum=0.9,
weight_decay=args.weight_decay)
t_total = args.num_steps
if args.decay_type == "cosine":
scheduler = WarmupCosineSchedule(optimizer,
warmup_steps=args.warmup_steps,
t_total=t_total)
else:
scheduler = WarmupLinearSchedule(optimizer,
warmup_steps=args.warmup_steps,
t_total=t_total)
if args.fp16:
model, optimizer = amp.initialize(models=model,
optimizers=optimizer,
opt_level=args.fp16_opt_level)
amp._amp_state.loss_scalers[0]._loss_scale = 2**20
# Distributed training
if args.local_rank != -1:
model = DDP(model,
message_size=250000000,
gradient_predivide_factor=get_world_size())
# Train!
logger.info("***** Running training *****")
logger.info(" Total optimization steps = %d", args.num_steps)
logger.info(" Instantaneous batch size per GPU = %d",
args.train_batch_size)
logger.info(
" Total train batch size (w. parallel, distributed & accumulation) = %d",
args.train_batch_size * args.gradient_accumulation_steps *
(torch.distributed.get_world_size() if args.local_rank != -1 else 1))
logger.info(" Gradient Accumulation steps = %d",
args.gradient_accumulation_steps)
model.zero_grad()
set_seed(
args) # Added here for reproducibility (even between python 2 and 3)
losses = AverageMeter()
global_step, best_acc = 0, 0
while True:
model.train()
epoch_iterator = tqdm(train_loader,
desc="Training (X / X Steps) (loss=X.X)",
bar_format="{l_bar}{r_bar}",
dynamic_ncols=True,
disable=args.local_rank not in [-1, 0])
for step, batch in enumerate(epoch_iterator):
batch = tuple(t.to(args.device) for t in batch)
x, y = batch
loss = model(x, y)
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.fp16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
if (step + 1) % args.gradient_accumulation_steps == 0:
losses.update(loss.item() * args.gradient_accumulation_steps)
if args.fp16:
torch.nn.utils.clip_grad_norm_(
amp.master_params(optimizer), args.max_grad_norm)
else:
torch.nn.utils.clip_grad_norm_(model.parameters(),
args.max_grad_norm)
scheduler.step()
optimizer.step()
optimizer.zero_grad()
global_step += 1
epoch_iterator.set_description(
"Training (%d / %d Steps) (loss=%2.5f)" %
(global_step, t_total, losses.val))
if args.local_rank in [-1, 0]:
writer.add_scalar("train/loss",
scalar_value=losses.val,
global_step=global_step)
writer.add_scalar("train/lr",
scalar_value=scheduler.get_lr()[0],
global_step=global_step)
if global_step % args.eval_every == 0 and args.local_rank in [
-1, 0
]:
accuracy = valid(args, model, writer, test_loader,
global_step)
if best_acc < accuracy:
save_model(args, model)
best_acc = accuracy
model.train()
if global_step % t_total == 0:
break
losses.reset()
if global_step % t_total == 0:
break
if args.local_rank in [-1, 0]:
writer.close()
logger.info("Best Accuracy: \t%f" % best_acc)
logger.info("End Training!")
def train(args, model):
if args.local_rank in [-1, 0]:
os.makedirs(args.output_dir, exist_ok=True)
writer = SummaryWriter(log_dir=os.path.join("logs", args.name))
# writer = SummaryWriter(log_dir=os.path.join("logs", 'transformer'))
args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps
train_loader, test_loader = get_loader(args)
optimizer = torch.optim.SGD(model.parameters(),
lr=args.learning_rate,
momentum=0.9,
weight_decay=args.weight_decay)
t_total = args.num_steps
if args.decay_type == "cosine":
scheduler = WarmupCosineSchedule(optimizer,
warmup_steps=args.warmup_steps,
t_total=t_total)
else:
scheduler = WarmupLinearSchedule(optimizer,
warmup_steps=args.warmup_steps,
t_total=t_total)
# Train!
logger.info("***** Running training *****")
logger.info(" Total optimization steps = %d", args.num_steps)
logger.info(" Instantaneous batch size per GPU = %d",
args.train_batch_size)
logger.info(
" Total train batch size (w. parallel, distributed & accumulation) = %d",
args.train_batch_size * args.gradient_accumulation_steps *
(torch.distributed.get_world_size() if args.local_rank != -1 else 1))
logger.info(" Gradient Accumulation steps = %d",
args.gradient_accumulation_steps)
model.zero_grad()
set_seed(args)
losses = AverageMeter()
global_step, best_acc, best_losses = 0, 0, np.inf
while True:
model.train()
# criterion = nn.CrossEntropyLoss()
criterion = FocalLoss()
# criterion = MyCrossEntropyLoss()
# criterion = MyMseLoss(args)
epoch_iterator = tqdm(train_loader,
desc="Training (X / X Steps) (loss=X.X)",
bar_format="{l_bar}{r_bar}",
dynamic_ncols=True,
disable=args.local_rank not in [-1, 0])
for step, batch in enumerate(epoch_iterator):
batch = tuple(t.to(args.device) for t in batch)
imgs, intensity, labels = batch
outputs = model(imgs, intensity,
labels) # output[0].size = (B, N, C)
outputs = outputs.view(-1, args.class_number)
# outputs = F.softmax(outputs, dim=-1)
labels = labels.view(-1)
loss = criterion(outputs, labels)
loss.backward()
if (step + 1) % args.gradient_accumulation_steps == 0:
losses.update(loss.item() * args.gradient_accumulation_steps)
torch.nn.utils.clip_grad_norm_(model.parameters(),
args.max_grad_norm)
scheduler.step()
optimizer.step()
optimizer.zero_grad()
global_step += 1
epoch_iterator.set_description(
"Training (%d / %d Steps) (loss=%2.5f)" %
(global_step, t_total, loss.cpu()))
if args.local_rank in [-1, 0]:
writer.add_scalar("train/loss",
scalar_value=losses.val,
global_step=global_step)
writer.add_scalar("train/lr",
scalar_value=scheduler.get_lr()[0],
global_step=global_step)
if global_step % args.eval_every == 0:
accuracy, eval_losses = valid(args, model, writer, test_loader,
global_step)
if eval_losses <= best_losses:
save_model(args, model)
best_losses = eval_losses
model.train()
if global_step % t_total == 0:
break
losses.reset()
if global_step % t_total == 0:
break
if args.local_rank in [-1, 0]:
writer.close()
logger.info("Best Accuracy: \t%f" % best_acc)
logger.info("End Training!")
def launch_routine(model):
data_path = ARGS.data
if 'valid/mel' in ARGS.data:
data_path = data_path.replace('valid/mel', '')
whole_segments = get_loader(data_path, 'valid', HPARAMS, whole=True)
generate_routine(model, whole_segments, ARGS.out_dir, HPARAMS)
level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
# logger.warning("Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s" %
# (args.local_rank, args.device, args.n_gpu, bool(args.local_rank != -1), args.fp16))
# Set seed
set_seed(args)
mse0, mse1, mse2, mse3 = 0, 0, 0, 0
mae0, mae1, mae2, mae3 = 0, 0, 0, 0
pre = []
lab = []
acc = 0.0
l2 = [10, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 25, 27, 28, 30, 32, 33, 35, 38, 40, 42, 43, 45, 48, 50,
51, 52, 53, 55, 57, 58, 60, 62, 65, 68, 70, 72]
l3 = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,
29, 30, 31, 32, 33, 34, 35, 36, 37]
all_preds, all_label = [], []
train_loader, test_loader = get_loader(args)
epoch_iterator = tqdm(test_loader,
desc="Validating...(loss=X.X",
bar_format="{l_bar}{r_bar}",
dynamic_ncols=True,
disable=args.local_rank not in [-1, 0])
# criterion = nn.CrossEntropyLoss(ignore_index=0)
# f = open('./pred_result.txt', 'a')
test_num = 0
model.eval()
eval_losses = AverageMeter()
for step, batch in enumerate(epoch_iterator):
batch = tuple(t.to(args.device) for t in batch)
imgs, intensity, labels = batch
bs, src_len = labels.size()
test_target = torch.ones((bs, src_len - 1), dtype=labels.dtype).to(labels.device)
def main():
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument("--name", required=True,
help="Name of this run. Used for monitoring.")
parser.add_argument("--dataset", choices=["cifar10", "cifar100"], default="cifar10",
help="Which downstream task.")
parser.add_argument("--model_type", choices=["ViT-B_16", "ViT-B_32", "ViT-L_16",
"ViT-L_32", "ViT-H_14", "R50-ViT-B_16"],
default="ViT-B_16",
help="Which variant to use.")
parser.add_argument("--test_mode", action="store_true")
parser.add_argument("--mixup", action="store_true")
parser.add_argument("--mixup_layer", type=int, default=0)
parser.add_argument("--mixup_alpha", type=float, default=1.0)
parser.add_argument("--pretrained_dir", type=str, default="checkpoint/ViT-B_16.npz",
help="Where to search for pretrained ViT models.")
parser.add_argument("--output_dir", default="output", type=str,
help="The output directory where checkpoints will be written.")
parser.add_argument("--img_size", default=224, type=int,
help="Resolution size")
parser.add_argument("--train_batch_size", default=512, type=int,
help="Total batch size for training.")
parser.add_argument("--eval_batch_size", default=64, type=int,
help="Total batch size for eval.")
parser.add_argument("--eval_every", default=100, type=int,
help="Run prediction on validation set every so many steps."
"Will always run one evaluation at the end of training.")
parser.add_argument("--learning_rate", default=3e-2, type=float,
help="The initial learning rate for SGD.")
parser.add_argument("--weight_decay", default=0, type=float,
help="Weight deay if we apply some.")
parser.add_argument("--num_steps", default=10000, type=int,
help="Total number of training epochs to perform.")
parser.add_argument("--decay_type", choices=["cosine", "linear"], default="cosine",
help="How to decay the learning rate.")
parser.add_argument("--warmup_steps", default=500, type=int,
help="Step of training to perform learning rate warmup for.")
parser.add_argument("--max_grad_norm", default=1.0, type=float,
help="Max gradient norm.")
parser.add_argument("--local_rank", type=int, default=-1,
help="local_rank for distributed training on gpus")
parser.add_argument('--seed', type=int, default=42,
help="random seed for initialization")
parser.add_argument('--gradient_accumulation_steps', type=int, default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.")
parser.add_argument('--fp16', action='store_true',
help="Whether to use 16-bit float precision instead of 32-bit")
parser.add_argument('--fp16_opt_level', type=str, default='O2',
help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
"See details at https://nvidia.github.io/apex/amp.html")
parser.add_argument('--loss_scale', type=float, default=0,
help="Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
"0 (default value): dynamic loss scaling.\n"
"Positive power of 2: static loss scaling value.\n")
args = parser.parse_args()
# Setup CUDA, GPU & distributed training
if args.local_rank == -1:
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
args.n_gpu = torch.cuda.device_count()
else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
torch.distributed.init_process_group(backend='nccl',
timeout=timedelta(minutes=60))
args.n_gpu = 1
args.device = device
# Setup logging
logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
logger.warning("Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s" %
(args.local_rank, args.device, args.n_gpu, bool(args.local_rank != -1), args.fp16))
# Set seed
set_seed(args)
# Model & Tokenizer Setup
args, model = setup(args)
if not args.test_mode:
# Training
train(args, model)
else:
_, test_loader = get_loader(args)
global_step = -1
accuracy = valid(args, model, None, test_loader, global_step)
print("Test accuracy: ", accuracy)
