def load_model(dirname, device, weights=None, half=False):
"""
Load a model from disk
"""
if not os.path.isdir(dirname) and os.path.isdir(
os.path.join(__dir__, "models", dirname)):
dirname = os.path.join(__dir__, "models", dirname)
if not weights: # take the latest checkpoint
weight_files = glob(os.path.join(dirname, "weights_*.tar"))
if not weight_files:
raise FileNotFoundError("no model weights found in '%s'" % dirname)
weights = max(
[int(re.sub(".*_([0-9]+).tar", "\\1", w)) for w in weight_files])
device = torch.device(device)
config = os.path.join(dirname, 'config.toml')
weights = os.path.join(dirname, 'weights_%s.tar' % weights)
model = Model(toml.load(config))
model.to(device)
state_dict = torch.load(weights, map_location=device)
new_state_dict = OrderedDict()
for k, v in state_dict.items():
name = k.replace('module.', '')
new_state_dict[name] = v
model.load_state_dict(new_state_dict)
if half: model = model.half()
model.eval()
return model
def load_model(dirname, device, weights=None):
"""
Load a model from disk
"""
if not weights: # take the latest checkpoint
weight_files = glob(os.path.join(dirname, "weights_*.tar"))
weights = max(
[int(re.sub(".*_([0-9]+).tar", "\\1", w)) for w in weight_files])
device = torch.device(device)
config = os.path.join(dirname, 'config.toml')
weights = os.path.join(dirname, 'weights_%s.tar' % weights)
model = Model(toml.load(config))
model.to(device)
model.load_state_dict(torch.load(weights, map_location=device))
model.eval()
return model
def main(args):
workdir = os.path.expanduser(args.training_directory)
if os.path.exists(workdir) and not args.force:
print("[error] %s exists." % workdir)
exit(1)
init(args.seed, args.device)
device = torch.device(args.device)
print("[loading data]")
chunks, chunk_lengths, targets, target_lengths = load_data(
limit=args.chunks, shuffle=True, directory=args.directory)
split = np.floor(chunks.shape[0] * args.validation_split).astype(np.int32)
train_dataset = ChunkDataSet(chunks[:split], chunk_lengths[:split],
targets[:split], target_lengths[:split])
test_dataset = ChunkDataSet(chunks[split:], chunk_lengths[split:],
targets[split:], target_lengths[split:])
train_loader = DataLoader(train_dataset,
batch_size=args.batch,
shuffle=True,
num_workers=4,
pin_memory=True)
test_loader = DataLoader(test_dataset,
batch_size=args.batch,
num_workers=4,
pin_memory=True)
config = toml.load(args.config)
argsdict = dict(training=vars(args))
chunk_config = {}
chunk_config_file = os.path.join(
args.directory if args.directory else __data__, 'config.toml')
if os.path.isfile(chunk_config_file):
chunk_config = toml.load(os.path.join(chunk_config_file))
print("[loading model]")
model = Model(config)
weights = os.path.join(workdir, 'weights.tar')
if os.path.exists(weights): model.load_state_dict(torch.load(weights))
model.to(device)
model.train()
os.makedirs(workdir, exist_ok=True)
toml.dump({
**config,
**argsdict,
**chunk_config
}, open(os.path.join(workdir, 'config.toml'), 'w'))
optimizer = AdamW(model.parameters(), amsgrad=True, lr=args.lr)
if args.amp:
try:
model, optimizer = amp.initialize(model,
optimizer,
opt_level="O1",
verbosity=0)
except NameError:
print(
"[error]: Cannot use AMP: Apex package needs to be installed manually, See https://github.com/NVIDIA/apex"
)
exit(1)
schedular = CosineAnnealingLR(optimizer, args.epochs * len(train_loader))
for epoch in range(1, args.epochs + 1):
try:
train_loss, duration = train(model,
device,
train_loader,
optimizer,
use_amp=args.amp)
val_loss, val_mean, val_median = test(model, device, test_loader)
except KeyboardInterrupt:
break
print(
"[epoch {}] directory={} loss={:.4f} mean_acc={:.3f}% median_acc={:.3f}%"
.format(epoch, workdir, val_loss, val_mean, val_median))
torch.save(model.state_dict(),
os.path.join(workdir, "weights_%s.tar" % epoch))
with open(os.path.join(workdir, 'training.csv'), 'a',
newline='') as csvfile:
csvw = csv.writer(csvfile, delimiter=',')
if epoch == 1:
csvw.writerow([
'time', 'duration', 'epoch', 'train_loss',
'validation_loss', 'validation_mean', 'validation_median'
])
csvw.writerow([
datetime.today(),
int(duration),
epoch,
train_loss,
val_loss,
val_mean,
val_median,
])
schedular.step()