args.channel,
5,
4,
args.n_res_block,
args.n_res_channel,
attention=False,
dropout=args.dropout,
n_cond_res_block=args.n_cond_res_block,
cond_res_channel=args.n_res_channel,
)
if 'model' in ckpt:
model.load_state_dict(ckpt['model'])
model = model.to(device)
optimizer = optim.Adam(model.parameters(), lr=args.lr)
if amp is not None:
model, optimizer = amp.initialize(model, optimizer, opt_level=args.amp)
model = nn.DataParallel(model)
model = model.to(device)
scheduler = None
if args.sched == 'cycle':
scheduler = CycleScheduler(optimizer,
args.lr,
n_iter=len(loader) * args.epoch,
momentum=None)
logging = init_logging('log')
loader.set_description(
(f'epoch: {epoch + 1}; loss: {loss.item():.5f}; ' f'acc: {accuracy:.5f}')
)
class PixelTransform:
def __init__(self):
pass
def __call__(self, input):
ar = np.array(input)
return torch.from_numpy(ar).long()
if __name__ == '__main__':
device = 'cuda'
epoch = 10
dataset = datasets.MNIST('.', transform=PixelTransform(), download=True)
loader = DataLoader(dataset, batch_size=32, shuffle=True, num_workers=4)
model = PixelSNAIL([28, 28], 256, 128, 5, 2, 4, 128)
model = model.to(device)
optimizer = optim.Adam(model.parameters(), lr=1e-3)
for i in range(10):
train(i, loader, model, optimizer, device)
torch.save(model.state_dict(), f'checkpoint/mnist_{str(i + 1).zfill(3)}.pt')
