Add lagrangian and eulerian alternate training
This commit is contained in:
Yin Li
parent
337d65de68
commit
2d5234812b
138
map2map/train.py
138
map2map/train.py
@ -118,21 +118,29 @@ def gpu_worker(local_rank, node, args):
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model = DistributedDataParallel(model, device_ids=[device],
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process_group=dist.new_group())
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dis2den = Lag2Eul()
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lag2eul = Lag2Eul()
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criterion = import_attr(args.criterion, nn.__name__, args.callback_at)
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criterion = criterion()
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criterion.to(device)
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optimizer = import_attr(args.optimizer, optim.__name__, args.callback_at)
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optimizer = optimizer(
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lag_optimizer = optimizer(
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model.parameters(),
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lr=args.lr,
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#momentum=args.momentum,
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betas=(0.5, 0.999),
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betas=(0.9, 0.999),
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weight_decay=args.weight_decay,
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)
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scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer,
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eul_optimizer = optimizer(
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model.parameters(),
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lr=args.lr,
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betas=(0.9, 0.999),
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weight_decay=args.weight_decay,
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)
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lag_scheduler = optim.lr_scheduler.ReduceLROnPlateau(lag_optimizer,
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factor=0.1, patience=10, verbose=True)
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eul_scheduler = optim.lr_scheduler.ReduceLROnPlateau(eul_optimizer,
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factor=0.1, patience=10, verbose=True)
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if (args.load_state == ckpt_link and not os.path.isfile(ckpt_link)
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@ -187,23 +195,24 @@ def gpu_worker(local_rank, node, args):
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for epoch in range(start_epoch, args.epochs):
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train_sampler.set_epoch(epoch)
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train_loss = train(epoch, train_loader,
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model, dis2den, criterion, optimizer, scheduler,
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train_loss = train(epoch, train_loader, model, lag2eul, criterion,
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lag_optimizer, eul_optimizer, lag_scheduler, eul_scheduler,
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logger, device, args)
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epoch_loss = train_loss
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if args.val:
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val_loss = validate(epoch, val_loader, model, dis2den, criterion,
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val_loss = validate(epoch, val_loader, model, lag2eul, criterion,
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logger, device, args)
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epoch_loss = val_loss
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if args.reduce_lr_on_plateau:
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scheduler.step(epoch_loss[0])
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lag_scheduler.step(epoch_loss[0])
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eul_scheduler.step(epoch_loss[1])
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if rank == 0:
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logger.flush()
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if min_loss is None or epoch_loss[0] < min_loss[0]:
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if min_loss is None or torch.prod(epoch_loss) < torch.prod(min_loss):
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min_loss = epoch_loss
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state = {
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@ -224,14 +233,15 @@ def gpu_worker(local_rank, node, args):
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dist.destroy_process_group()
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def train(epoch, loader, model, dis2den, criterion, optimizer, scheduler,
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def train(epoch, loader, model, lag2eul, criterion,
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lag_optimizer, eul_optimizer, lag_scheduler, eul_scheduler,
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logger, device, args):
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model.train()
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rank = dist.get_rank()
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world_size = dist.get_world_size()
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epoch_loss = torch.zeros(5, dtype=torch.float64, device=device)
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epoch_loss = torch.zeros(2, dtype=torch.float64, device=device)
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for i, (input, target) in enumerate(loader):
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input = input.to(device, non_blocking=True)
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@ -248,52 +258,83 @@ def train(epoch, loader, model, dis2den, criterion, optimizer, scheduler,
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input = resample(input, model.module.scale_factor, narrow=False)
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input, output, target = narrow_cast(input, output, target)
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output, target = dis2den(output, target)
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lag_out, lag_tgt = output, target
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loss = criterion(output, target)
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epoch_loss[0] += loss.item()
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if i % 2 == 0:
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lag_loss = criterion(lag_out, lag_tgt)
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epoch_loss[0] += lag_loss.item()
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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with torch.no_grad():
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eul_out, eul_tgt = lag2eul(lag_out, lag_tgt)
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eul_loss = criterion(eul_out, eul_tgt)
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epoch_loss[1] += eul_loss.item()
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lag_optimizer.zero_grad()
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lag_loss.backward()
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lag_optimizer.step()
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lag_grads = get_grads(model)
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else:
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with torch.no_grad():
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lag_loss = criterion(lag_out, lag_tgt)
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epoch_loss[0] += lag_loss.item()
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eul_out, eul_tgt = lag2eul(lag_out, lag_tgt)
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eul_loss = criterion(eul_out, eul_tgt)
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epoch_loss[1] += eul_loss.item()
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eul_optimizer.zero_grad()
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eul_loss.backward()
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eul_optimizer.step()
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eul_grads = get_grads(model)
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batch = epoch * len(loader) + i + 1
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if batch % args.log_interval == 0:
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dist.all_reduce(loss)
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loss /= world_size
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if batch % args.log_interval == 0 and batch >= 2:
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dist.all_reduce(lag_loss)
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dist.all_reduce(eul_loss)
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lag_loss /= world_size
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eul_loss /= world_size
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if rank == 0:
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logger.add_scalar('loss/batch/train', loss.item(),
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logger.add_scalar('loss/batch/train/lag', lag_loss.item(),
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global_step=batch)
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logger.add_scalar('loss/batch/train/eul', eul_loss.item(),
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global_step=batch)
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# gradients of the weights of the first and the last layer
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grads = list(p.grad for n, p in model.named_parameters()
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if '.weight' in n)
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grads = [grads[0], grads[-1]]
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grads = [g.detach().norm().item() for g in grads]
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logger.add_scalar('grad/first', grads[0], global_step=batch)
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logger.add_scalar('grad/last', grads[-1], global_step=batch)
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logger.add_scalar('grad/lag/first', lag_grads[0],
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global_step=batch)
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logger.add_scalar('grad/lag/last', lag_grads[-1],
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global_step=batch)
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logger.add_scalar('grad/eul/first', eul_grads[0],
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global_step=batch)
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logger.add_scalar('grad/eul/last', eul_grads[-1],
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global_step=batch)
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dist.all_reduce(epoch_loss)
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epoch_loss /= len(loader) * world_size
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if rank == 0:
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logger.add_scalar('loss/epoch/train', epoch_loss[0],
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logger.add_scalar('loss/epoch/train/lag', epoch_loss[0],
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global_step=epoch+1)
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logger.add_scalar('loss/epoch/train/eul', epoch_loss[1],
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global_step=epoch+1)
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logger.add_figure('fig/epoch/train', plt_slices(
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input[-1], output[-1], target[-1], output[-1] - target[-1],
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title=['in', 'out', 'tgt', 'out - tgt'],
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input[-1], lag_out[-1], lag_tgt[-1], lag_out[-1] - lag_tgt[-1],
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eul_out[-1], eul_tgt[-1], eul_out[-1] - eul_tgt[-1],
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title=['in', 'lag_out', 'lag_tgt', 'lag_out - lag_tgt',
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'eul_out', 'eul_tgt', 'eul_out - eul_tgt'],
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), global_step=epoch+1)
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return epoch_loss
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def validate(epoch, loader, model, dis2den, criterion, logger, device, args):
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def validate(epoch, loader, model, lag2eul, criterion, logger, device, args):
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model.eval()
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rank = dist.get_rank()
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world_size = dist.get_world_size()
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epoch_loss = torch.zeros(5, dtype=torch.float64, device=device)
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epoch_loss = torch.zeros(2, dtype=torch.float64, device=device)
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with torch.no_grad():
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for input, target in loader:
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@ -307,20 +348,29 @@ def validate(epoch, loader, model, dis2den, criterion, logger, device, args):
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input = resample(input, model.module.scale_factor, narrow=False)
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input, output, target = narrow_cast(input, output, target)
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output, target = dis2den(output, target)
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lag_out, lag_tgt = output, target
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loss = criterion(output, target)
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epoch_loss[0] += loss.item()
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lag_loss = criterion(lag_out, lag_tgt)
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epoch_loss[0] += lag_loss.item()
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eul_out, eul_tgt = lag2eul(lag_out, lag_tgt)
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eul_loss = criterion(eul_out, eul_tgt)
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epoch_loss[1] += eul_loss.item()
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dist.all_reduce(epoch_loss)
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epoch_loss /= len(loader) * world_size
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if rank == 0:
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logger.add_scalar('loss/epoch/val', epoch_loss[0],
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logger.add_scalar('loss/epoch/val/lag', epoch_loss[0],
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global_step=epoch+1)
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logger.add_scalar('loss/epoch/val/eul', epoch_loss[1],
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global_step=epoch+1)
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logger.add_figure('fig/epoch/val', plt_slices(
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input[-1], output[-1], target[-1], output[-1] - target[-1],
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title=['in', 'out', 'tgt', 'out - tgt'],
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input[-1], lag_out[-1], lag_tgt[-1], lag_out[-1] - lag_tgt[-1],
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eul_out[-1], eul_tgt[-1], eul_out[-1] - eul_tgt[-1],
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title=['in', 'lag_out', 'lag_tgt', 'lag_out - lag_tgt',
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'eul_out', 'eul_tgt', 'eul_out - eul_tgt'],
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), global_step=epoch+1)
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return epoch_loss
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@ -363,3 +413,13 @@ def dist_init(rank, args):
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def set_requires_grad(module, requires_grad=False):
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for param in module.parameters():
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param.requires_grad = requires_grad
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def get_grads(model):
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"""gradients of the weights of the first and the last layer
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"""
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grads = list(p.grad for n, p in model.named_parameters()
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if '.weight' in n)
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grads = [grads[0], grads[-1]]
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grads = [g.detach().norm().item() for g in grads]
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return grads
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