text stringlengths 1 93.6k |
|---|
else:
|
criterion = nn.CrossEntropyLoss(
|
ignore_index=args.ignore_label).cuda()
|
val_criterion = nn.CrossEntropyLoss(ignore_index=args.ignore_label).cuda()
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# set optimizer
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if args.optimizer == 'SGD':
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optimizer = torch.optim.SGD(
|
model.parameters(),
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lr=args.learning_rate,
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momentum=0.9)
|
elif args.optimizer == 'Adam':
|
optimizer = torch.optim.Adam(
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model.parameters(),
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lr=args.learning_rate,
|
betas=(0.9, 0.999),
|
eps=1e-08,
|
weight_decay=args.decay_rate)
|
elif args.optimizer == 'AdamW':
|
optimizer = torch.optim.AdamW(
|
model.parameters(),
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lr=args.learning_rate,
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weight_decay=args.adamw_decay)
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# optimizer.param_groups[0]['initial_lr'] = cfg.learning_rate
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init_epoch = 0
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# config lr scheduler
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iter_per_epoch = len(train_data_loader)
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if args.scheduler == "MultiStepWithWarmup":
|
if args.milestones is not None:
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milestones = [v * iter_per_epoch for v in args.milestones]
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else:
|
milestones = [int(args.total_epoches * 0.4) * iter_per_epoch,
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int(args.total_epoches * 0.6) * iter_per_epoch,
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int(args.total_epoches * 0.8) * iter_per_epoch]
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if main_process():
|
logger.info("scheduler: MultiStepWarmup!!!")
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logger.info("milestones: {}".format(milestones))
|
scheduler = MultiStepWithWarmup(
|
optimizer,
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milestones=milestones,
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gamma=args.gamma,
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warmup=args.warmup,
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warmup_iters=args.warmup_epochs *
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iter_per_epoch,
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warmup_ratio=args.warmup_ratio)
|
elif args.scheduler == "CosineAnnealingWarmupRestarts":
|
if main_process():
|
logger.info("scheduler: CosineAnnealingWarmupRestarts!!!")
|
scheduler = CosineAnnealingWarmupRestarts(
|
optimizer,
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first_cycle_steps=args.total_epoches *
|
iter_per_epoch,
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cycle_mult=1.0,
|
max_lr=args.learning_rate,
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min_lr=1e-8,
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warmup_steps=args.warmup_epochs *
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iter_per_epoch,
|
gamma=1.0)
|
else:
|
raise ValueError("No such scheduler {}".format(args.scheduler))
|
############################
|
# start training #
|
############################
|
for epoch in range(init_epoch, args.total_epoches):
|
if args.DDP:
|
train_data_loader.sampler.set_epoch(epoch)
|
if main_process():
|
logger.info("lr: {}".format(scheduler.get_last_lr()))
|
loss_train, mIoU_train, mAcc_train, allAcc_train = train(
|
train_data_loader, model, criterion, optimizer, epoch, scheduler)
|
epoch_log = epoch + 1
|
if args.scheduler_update == 'epoch':
|
scheduler.step()
|
if main_process():
|
lr = scheduler.get_last_lr()
|
if isinstance(lr, list):
|
lr = lr[0]
|
if args.use_tensorboard:
|
writer.add_scalar('loss_train', loss_train, epoch_log)
|
writer.add_scalar('mIoU_train', mIoU_train, epoch_log)
|
writer.add_scalar('mAcc_train', mAcc_train, epoch_log)
|
writer.add_scalar('allAcc_train', allAcc_train, epoch_log)
|
writer.add_scalar('lr_train', lr, epoch_log)
|
# else:
|
# metrics_dict = {'loss_train': loss_train,
|
# 'mIoU_train': mIoU_train,
|
# 'mAcc_train': mAcc_train,
|
# 'allAcc_train': allAcc_train,
|
# 'lr_train': lr}
|
is_best = False
|
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