import os
import torch
import torchvision
import torch.nn as nn
from tqdm import tqdm
from torch.utils.data import DataLoader
from torch.distributed import all_reduce, all_gather, ReduceOp
from utils.func import *
from modules.loss import *
from modules.scheduler import *
def train(cfg, model, train_dataset, val_dataset, estimator, logger=None):
if cfg.base.HPO:
from nni import report_intermediate_result, report_final_result
device = cfg.base.device
optimizer = initialize_optimizer(cfg, model)
train_sampler, val_sampler = initialize_sampler(cfg, train_dataset, val_dataset)
lr_scheduler, warmup_scheduler = initialize_lr_scheduler(cfg, optimizer)
loss_function, loss_weight_scheduler = initialize_loss(cfg, train_dataset)
train_loader, val_loader = initialize_dataloader(cfg, train_dataset, val_dataset, train_sampler, val_sampler)
# start training
model.train()
avg_loss = 0
max_indicator = -999
for epoch in range(1, cfg.train.epochs + 1):
# resampling weight update
if cfg.dist.distributed:
train_sampler.set_epoch(epoch)
elif train_sampler:
train_sampler.step()
# update loss weights
if loss_weight_scheduler:
weight = loss_weight_scheduler.step()
loss_function.weight = weight.to(device)
# warmup scheduler update
if warmup_scheduler and not warmup_scheduler.is_finish():
warmup_scheduler.step()
epoch_loss = 0
estimator.reset()
progress = tqdm(enumerate(train_loader), total=len(train_loader)) if cfg.base.progress else enumerate(train_loader)
for step, train_data in progress:
X, y = train_data
X = X.cuda(cfg.dist.gpu) if cfg.dist.distributed else X.to(device)
y = y.cuda(cfg.dist.gpu) if cfg.dist.distributed else y.to(device)
y = select_target_type(y, cfg.train.criterion)
# forward
y_pred = model(X)
loss = loss_function(y_pred, y)
# backward
optimizer.zero_grad()
loss.backward()
optimizer.step()
# metrics
if cfg.dist.distributed:
all_reduce(loss, ReduceOp.SUM)
loss = loss / cfg.dist.world_size
y_pred_list = [torch.zeros_like(y_pred) for _ in range(cfg.dist.world_size)]
y_list = [torch.zeros_like(y) for _ in range(cfg.dist.world_size)]
all_gather(y_pred_list, y_pred)
all_gather(y_list, y)
y_pred = torch.cat(y_pred_list, dim=0)
y = torch.cat(y_list, dim=0)
if is_main(cfg):
epoch_loss += loss.item()
avg_loss = epoch_loss / (step + 1)
estimator.update(y_pred, y)
message = 'epoch: [{} / {}], loss: {:.6f}'.format(epoch, cfg.train.epochs, avg_loss)
if cfg.base.progress:
progress.set_description(message)
if is_main(cfg) and not cfg.base.progress:
print(message)
if is_main(cfg):
train_scores = estimator.get_scores(4)
scores_txt = ', '.join(['{}: {}'.format(metric, score) for metric, score in train_scores.items()])
print('Training metrics:', scores_txt)
curr_lr = optimizer.param_groups[0]['lr']
if is_main(cfg) and logger:
for metric, score in train_scores.items():
logger.add_scalar('training {}'.format(metric), score, epoch)
logger.add_scalar('training loss', avg_loss, epoch)
logger.add_scalar('learning rate', curr_lr, epoch)
if is_main(cfg) and cfg.train.sample_view:
samples = torchvision.utils.make_grid(X)
samples = inverse_normalize(samples, cfg.data.mean, cfg.data.std)
logger.add_image('input samples', samples, epoch, dataformats='CHW')
# validation performance
if epoch % cfg.train.eval_interval == 0:
eval(cfg, model, val_loader, cfg.train.criterion, estimator, device)
val_scores = estimator.get_scores(6)
scores_txt = ['{}: {}'.format(metric, score) for metric, score in val_scores.items()]
print_msg('Validation metrics:', scores_txt)
if is_main(cfg) and logger:
for metric, score in val_scores.items():
logger.add_scalar('validation {}'.format(metric), score, epoch)
# save model
indicator = val_scores[cfg.train.indicator]
if is_main(cfg) and indicator > max_indicator:
save_weights(model, os.path.join(cfg.base.save_path, 'best_validation_weights.pt'))
max_indicator = indicator
print_msg('Best {} in validation set. Model save at {}'.format(cfg.train.indicator, cfg.base.save_path))
if is_main(cfg) and cfg.base.HPO:
report_intermediate_result(indicator)
if is_main(cfg) and epoch % cfg.train.save_interval == 0:
save_weights(model, os.path.join(cfg.base.save_path, 'epoch_{}.pt'.format(epoch)))
# update learning rate
if lr_scheduler and (not warmup_scheduler or warmup_scheduler.is_finish()):
if cfg.solver.lr_scheduler == 'reduce_on_plateau':
lr_scheduler.step(avg_loss)
else:
lr_scheduler.step()
# save final model
if is_main(cfg):
save_weights(model, os.path.join(cfg.base.save_path, 'final_weights.pt'))
if is_main(cfg) and cfg.base.HPO:
report_final_result(max_indicator)
if is_main(cfg) and logger:
logger.close()
def evaluate(cfg, model, test_dataset, estimator):
test_sampler = torch.utils.data.distributed.DistributedSampler(test_dataset) if cfg.dist.distributed else None
test_loader = DataLoader(
test_dataset,
shuffle=(test_sampler is None),
sampler=test_sampler,
batch_size=cfg.train.batch_size,
num_workers=cfg.train.num_workers,
pin_memory=cfg.train.pin_memory
)
print('Running on Test set...')
eval(cfg, model, test_loader, cfg.train.criterion, estimator, cfg.base.device)
if is_main(cfg):
print('================Finished================')
test_scores = estimator.get_scores(6)
for metric, score in test_scores.items():
print('{}: {}'.format(metric, score))
print('Confusion Matrix:')
print(estimator.get_conf_mat())
print('========================================')
def eval(cfg, model, dataloader, criterion, estimator, device):
model.eval()
torch.set_grad_enabled(False)
estimator.reset()
for test_data in dataloader:
X, y = test_data
X = X.cuda(cfg.dist.gpu) if cfg.dist.distributed else X.to(device)
y = y.cuda(cfg.dist.gpu) if cfg.dist.distributed else y.to(device)
y = select_target_type(y, criterion)
y_pred = model(X)
if cfg.dist.distributed:
y_pred_list = [torch.zeros_like(y_pred) for _ in range(cfg.dist.world_size)]
y_list = [torch.zeros_like(y) for _ in range(cfg.dist.world_size)]
all_gather(y_pred_list, y_pred)
all_gather(y_list, y)
y_pred = torch.cat(y_pred_list, dim=0)
y = torch.cat(y_list, dim=0)
estimator.update(y_pred, y)
model.train()
torch.set_grad_enabled(True)
# define weighted_sampler
def initialize_sampler(cfg, train_dataset, val_dataset):
sampling_strategy = cfg.data.sampling_strategy
if cfg.dist.distributed:
if sampling_strategy != 'instance_balanced':
msg = 'Resampling is not allowed when distributed parallel is applied. \
Please set sampling_strategy to instance_balanced.'
exit_with_error(msg)
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset,
num_replicas=cfg.dist.world_size,
rank=cfg.dist.rank
)
val_sampler = torch.utils.data.distributed.DistributedSampler(
val_dataset,
num_replicas=cfg.dist.world_size,
rank=cfg.dist.rank
)
else:
val_sampler = None
if sampling_strategy == 'class_balanced':
train_sampler = ScheduledWeightedSampler(train_dataset, 1)
elif sampling_strategy == 'progressively_balanced':
train_sampler = ScheduledWeightedSampler(train_dataset, cfg.data.sampling_weights_decay_rate)
elif sampling_strategy == 'instance_balanced':
train_sampler = None
else:
raise NotImplementedError('Not implemented resampling strategy.')
return train_sampler, val_sampler
# define data loader
def initialize_dataloader(cfg, train_dataset, val_dataset, train_sampler, val_sampler):
batch_size = cfg.train.batch_size
num_workers = cfg.train.num_workers
pin_memory = cfg.train.pin_memory
train_loader = DataLoader(
train_dataset,
batch_size=batch_size,
shuffle=(train_sampler is None),
sampler=train_sampler,
num_workers=num_workers,
drop_last=True,
pin_memory=pin_memory
)
val_loader = DataLoader(
val_dataset,
batch_size=batch_size,
shuffle=(val_sampler is None),
sampler=val_sampler,
num_workers=num_workers,
drop_last=False,
pin_memory=pin_memory
)
return train_loader, val_loader
# define loss and loss weights scheduler
def initialize_loss(cfg, train_dataset):
criterion = cfg.train.criterion
criterion_args = cfg.criterion_args[criterion]
weight = None
loss_weight_scheduler = None
loss_weight = cfg.train.loss_weight
if criterion == 'cross_entropy':
if loss_weight == 'balance':
loss_weight_scheduler = LossWeightsScheduler(train_dataset, 1)
elif loss_weight == 'dynamic':
loss_weight_scheduler = LossWeightsScheduler(train_dataset, cfg.train.loss_weight_decay_rate)
elif isinstance(loss_weight, list):
assert len(loss_weight) == len(train_dataset.classes)
weight = torch.as_tensor(loss_weight, dtype=torch.float32, device=cfg.base.device)
loss = nn.CrossEntropyLoss(weight=weight, **criterion_args)
elif criterion == 'mean_square_error':
loss = nn.MSELoss(**criterion_args)
elif criterion == 'mean_absolute_error':
loss = nn.L1Loss(**criterion_args)
elif criterion == 'smooth_L1':
loss = nn.SmoothL1Loss(**criterion_args)
elif criterion == 'kappa_loss':
loss = KappaLoss(**criterion_args)
elif criterion == 'focal_loss':
loss = FocalLoss(**criterion_args)
else:
raise NotImplementedError('Not implemented loss function.')
loss_function = WarpedLoss(loss, criterion)
return loss_function, loss_weight_scheduler
# define optmizer
def initialize_optimizer(cfg, model):
optimizer_strategy = cfg.solver.optimizer
learning_rate = cfg.solver.learning_rate
weight_decay = cfg.solver.weight_decay
momentum = cfg.solver.momentum
nesterov = cfg.solver.nesterov
adamw_betas = cfg.solver.adamw_betas
if optimizer_strategy == 'SGD':
optimizer = torch.optim.SGD(
model.parameters(),
lr=learning_rate,
momentum=momentum,
nesterov=nesterov,
weight_decay=weight_decay
)
elif optimizer_strategy == 'ADAM':
optimizer = torch.optim.Adam(
model.parameters(),
lr=learning_rate,
weight_decay=weight_decay
)
elif optimizer_strategy == 'ADAMW':
optimizer = torch.optim.AdamW(
model.parameters(),
lr=learning_rate,
betas=adamw_betas,
weight_decay=weight_decay
)
else:
raise NotImplementedError('Not implemented optimizer.')
return optimizer
# define learning rate scheduler
def initialize_lr_scheduler(cfg, optimizer):
warmup_epochs = cfg.train.warmup_epochs
learning_rate = cfg.solver.learning_rate
scheduler_strategy = cfg.solver.lr_scheduler
if not scheduler_strategy:
lr_scheduler = None
else:
scheduler_args = cfg.scheduler_args[scheduler_strategy]
if scheduler_strategy == 'cosine':
lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, **scheduler_args)
elif scheduler_strategy == 'multiple_steps':
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, **scheduler_args)
elif scheduler_strategy == 'reduce_on_plateau':
lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, **scheduler_args)
elif scheduler_strategy == 'exponential':
lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, **scheduler_args)
elif scheduler_strategy == 'clipped_cosine':
lr_scheduler = ClippedCosineAnnealingLR(optimizer, **scheduler_args)
else:
raise NotImplementedError('Not implemented learning rate scheduler.')
if warmup_epochs > 0:
warmup_scheduler = WarmupLRScheduler(optimizer, warmup_epochs, learning_rate)
else:
warmup_scheduler = None
return lr_scheduler, warmup_scheduler
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