for i in to_cpu(decoded_target_cls, use_numpy=True).tolist()
]
self._predicts += [
self._labels[i]
for i in to_cpu(decoded_pred_cls, use_numpy=True).tolist()
]
if __name__ == "__main__":
args = init_args()
device = auto_detect_device()
log_dir = args.log_dir
comment = "ssgan"
# Data provider
train_ds, classes = get_mnist(data_folder=args.save_data, train=True)
n_folds = 5
splitter = SSFoldSplit(train_ds,
n_ss_folds=3,
n_folds=n_folds,
target_col="target",
random_state=args.seed,
labeled_train_size_per_class=100,
unlabeled_train_size_per_class=200,
equal_target=True,
equal_unlabeled_target=False,
shuffle=True)
summary_writer = SummaryWriter(log_dir=log_dir, comment=comment)
# Initializing Discriminator
def worker_process(gpu, ngpus, sampling_config, strategy_config, args):
args.gpu = gpu # this line of code is not redundant
if args.distributed:
lr_m = float(args.batch_size * args.world_size) / 256.
else:
lr_m = 1.0
criterion = torch.nn.CrossEntropyLoss().to(gpu)
train_ds, classes = get_mnist(data_folder=args.save_data, train=True)
test_ds, _ = get_mnist(data_folder=args.save_data, train=False)
model = SimpleConvNet(bw=args.bw,
drop=args.dropout,
n_cls=len(classes),
n_channels=args.n_channels).to(gpu)
optimizer = torch.optim.Adam(params=model.parameters(),
lr=args.lr * lr_m,
weight_decay=args.wd)
args, model, optimizer = convert_according_to_args(args=args,
gpu=gpu,
ngpus=ngpus,
network=model,
optim=optimizer)
item_loaders = dict()
for stage, df in zip(['train', 'eval'], [train_ds, test_ds]):
if args.distributed:
item_loaders[f'mnist_{stage}'] = DistributedItemLoader(
meta_data=df,
transform=init_mnist_cifar_transforms(1, stage),
parse_item_cb=parse_item_mnist,
args=args)
else:
item_loaders[f'mnist_{stage}'] = ItemLoader(
meta_data=df,
transform=init_mnist_cifar_transforms(1, stage),
parse_item_cb=parse_item_mnist,
batch_size=args.batch_size,
num_workers=args.workers,
shuffle=True if stage == "train" else False)
data_provider = DataProvider(item_loaders)
if args.gpu == 0:
log_dir = args.log_dir
comment = args.comment
summary_writer = SummaryWriter(log_dir=log_dir,
comment='_' + comment + 'gpu_' +
str(args.gpu))
train_cbs = (RunningAverageMeter(prefix="train", name="loss"),
AccuracyMeter(prefix="train", name="acc"))
val_cbs = (RunningAverageMeter(prefix="eval", name="loss"),
AccuracyMeter(prefix="eval", name="acc"),
ScalarMeterLogger(writer=summary_writer),
ModelSaver(metric_names='eval/loss',
save_dir=args.snapshots,
conditions='min',
model=model))
else:
train_cbs = ()
val_cbs = ()
strategy = Strategy(data_provider=data_provider,
train_loader_names=tuple(
sampling_config['train']['data_provider'].keys()),
val_loader_names=tuple(
sampling_config['eval']['data_provider'].keys()),
data_sampling_config=sampling_config,
loss=criterion,
model=model,
n_epochs=args.n_epochs,
optimizer=optimizer,
train_callbacks=train_cbs,
val_callbacks=val_cbs,
device=torch.device('cuda:{}'.format(args.gpu)),
distributed=args.distributed,
use_apex=args.use_apex)
strategy.run()