Aquila-Consortium/map2map
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Add Lag2Eul to training

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This commit is contained in:
Yin Li 2020-07-10 12:50:07 -04:00
parent 40220e9248
commit 607bcf3f4c
1 changed files with 11 additions and 5 deletions
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16
map2map/train.py
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@ -17,7 +17,7 @@ from torch.utils.tensorboard import SummaryWriter
from .data import FieldDataset from .data import FieldDataset
from .data.figures import plt_slices from .data.figures import plt_slices
from . import models from . import models
from .models import (narrow_cast, resample, from .models import (narrow_cast, resample, Lag2Eul
adv_model_wrapper, adv_criterion_wrapper, adv_model_wrapper, adv_criterion_wrapper,
add_spectral_norm, rm_spectral_norm, add_spectral_norm, rm_spectral_norm,
InstanceNoise) InstanceNoise)
@ -121,6 +121,8 @@ def gpu_worker(local_rank, node, args):
model = DistributedDataParallel(model, device_ids=[device], model = DistributedDataParallel(model, device_ids=[device],
process_group=dist.new_group()) process_group=dist.new_group())
dis2den = Lag2Eul()
criterion = import_attr(args.criterion, nn.__name__, args.callback_at) criterion = import_attr(args.criterion, nn.__name__, args.callback_at)
criterion = criterion() criterion = criterion()
criterion.to(device) criterion.to(device)
@ -229,14 +231,14 @@ def gpu_worker(local_rank, node, args):
train_sampler.set_epoch(epoch) train_sampler.set_epoch(epoch)
train_loss = train(epoch, train_loader, train_loss = train(epoch, train_loader,
model, criterion, optimizer, scheduler, model, dis2den, criterion, optimizer, scheduler,
adv_model, adv_criterion, adv_optimizer, adv_scheduler, adv_model, adv_criterion, adv_optimizer, adv_scheduler,
logger, device, args) logger, device, args)
epoch_loss = train_loss epoch_loss = train_loss
if args.val: if args.val:
val_loss = validate(epoch, val_loader, val_loss = validate(epoch, val_loader,
model, criterion, adv_model, adv_criterion, model, dis2den, criterion, adv_model, adv_criterion,
logger, device, args) logger, device, args)
epoch_loss = val_loss epoch_loss = val_loss
@ -272,7 +274,7 @@ def gpu_worker(local_rank, node, args):
dist.destroy_process_group() dist.destroy_process_group()
def train(epoch, loader, model, criterion, optimizer, scheduler, def train(epoch, loader, model, dis2den, criterion, optimizer, scheduler,
adv_model, adv_criterion, adv_optimizer, adv_scheduler, adv_model, adv_criterion, adv_optimizer, adv_scheduler,
logger, device, args): logger, device, args):
model.train() model.train()
@ -307,6 +309,8 @@ def train(epoch, loader, model, criterion, optimizer, scheduler,
input = resample(input, model.module.scale_factor, narrow=False) input = resample(input, model.module.scale_factor, narrow=False)
input, output, target = narrow_cast(input, output, target) input, output, target = narrow_cast(input, output, target)
output, target = dis2den(output, target)
loss = criterion(output, target) loss = criterion(output, target)
epoch_loss[0] += loss.item() epoch_loss[0] += loss.item()
@ -418,7 +422,7 @@ def train(epoch, loader, model, criterion, optimizer, scheduler,
return epoch_loss return epoch_loss
def validate(epoch, loader, model, criterion, adv_model, adv_criterion, def validate(epoch, loader, model, dis2den, criterion, adv_model, adv_criterion,
logger, device, args): logger, device, args):
model.eval() model.eval()
if args.adv: if args.adv:
@ -443,6 +447,8 @@ def validate(epoch, loader, model, criterion, adv_model, adv_criterion,
input = resample(input, model.module.scale_factor, narrow=False) input = resample(input, model.module.scale_factor, narrow=False)
input, output, target = narrow_cast(input, output, target) input, output, target = narrow_cast(input, output, target)
output, target = dis2den(output, target)
loss = criterion(output, target) loss = criterion(output, target)
epoch_loss[0] += loss.item() epoch_loss[0] += loss.item()