def main(config):
dataset_train, dataset_val, data_loader_train, data_loader_val, mixup_fn = build_loader(
config)
logger.info(f"Creating model:{config.MODEL.TYPE}/{config.MODEL.NAME}")
model = build_model(config)
model.cuda()
logger.info(str(model))
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[config.LOCAL_RANK], broadcast_buffers=False)
model_without_ddp = model.module
n_parameters = sum(p.numel() for p in model.parameters()
if p.requires_grad)
logger.info(f"number of params: {n_parameters}")
optimizer = None
lr_scheduler = None
assert config.MODEL.RESUME
loss_scaler = NativeScalerWithGradNormCount()
load_checkpoint(config, model_without_ddp, optimizer, lr_scheduler,
loss_scaler, logger)
if not args.skip_eval and not args.check_saved_logits:
acc1, acc5, loss = validate(config, data_loader_val, model)
logger.info(
f"Accuracy of the network on the {len(dataset_val)} test images: top-1 acc: {acc1:.1f}%, top-5 acc: {acc5:.1f}%"
)
if args.check_saved_logits:
logger.info("Start checking logits")
else:
logger.info("Start saving logits")
start_time = time.time()
for epoch in range(config.TRAIN.START_EPOCH, config.TRAIN.EPOCHS):
dataset_train.set_epoch(epoch)
data_loader_train.sampler.set_epoch(epoch)
if args.check_saved_logits:
check_logits_one_epoch(config,
model,
data_loader_train,
epoch,
mixup_fn=mixup_fn)
else:
save_logits_one_epoch(config,
model,
data_loader_train,
epoch,
mixup_fn=mixup_fn)
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
logger.info('Saving logits time {}'.format(total_time_str))
def main(config):
dataset_train, dataset_val, data_loader_train, data_loader_val, mixup_fn = build_loader(
config)
logger.info(f"Creating model:{config.MODEL.TYPE}/{config.MODEL.NAME}")
model = build_model(config)
model.cuda()
logger.info(str(model))
optimizer = build_optimizer(config, model)
if config.AMP_OPT_LEVEL != "O0":
model, optimizer = amp.initialize(model,
optimizer,
opt_level=config.AMP_OPT_LEVEL)
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[config.LOCAL_RANK], broadcast_buffers=False)
model_without_ddp = model.module
n_parameters = sum(p.numel() for p in model.parameters()
if p.requires_grad)
logger.info(f"number of params: {n_parameters}")
if hasattr(model_without_ddp, 'flops'):
flops = model_without_ddp.flops()
logger.info(f"number of GFLOPs: {flops / 1e9}")
lr_scheduler = build_scheduler(config, optimizer, len(data_loader_train))
if config.AUG.MIXUP > 0.:
# smoothing is handled with mixup label transform
criterion = SoftTargetCrossEntropy()
elif config.MODEL.LABEL_SMOOTHING > 0.:
criterion = LabelSmoothingCrossEntropy(
smoothing=config.MODEL.LABEL_SMOOTHING)
else:
criterion = torch.nn.CrossEntropyLoss()
max_accuracy = 0.0
if config.TRAIN.AUTO_RESUME:
resume_file = auto_resume_helper(config.OUTPUT)
if resume_file:
if config.MODEL.RESUME:
logger.warning(
f"auto-resume changing resume file from {config.MODEL.RESUME} to {resume_file}"
)
config.defrost()
config.MODEL.RESUME = resume_file
config.freeze()
logger.info(f'auto resuming from {resume_file}')
else:
logger.info(
f'no checkpoint found in {config.OUTPUT}, ignoring auto resume'
)
if config.MODEL.RESUME:
max_accuracy = load_checkpoint(config, model_without_ddp, optimizer,
lr_scheduler, logger)
acc1, acc5, loss = validate(config, data_loader_val, model)
logger.info(
f"Accuracy of the network on the {len(dataset_val)} test images: {acc1:.1f}%"
)
if config.EVAL_MODE:
return
if config.MODEL.PRETRAINED and (not config.MODEL.RESUME):
load_pretrained(config, model_without_ddp, logger)
acc1, acc5, loss = validate(config, data_loader_val, model)
logger.info(
f"Accuracy of the network on the {len(dataset_val)} test images: {acc1:.1f}%"
)
if config.THROUGHPUT_MODE:
throughput(data_loader_val, model, logger)
return
logger.info("Start training")
start_time = time.time()
for epoch in range(config.TRAIN.START_EPOCH, config.TRAIN.EPOCHS):
data_loader_train.sampler.set_epoch(epoch)
train_one_epoch(config, model, criterion, data_loader_train, optimizer,
epoch, mixup_fn, lr_scheduler)
if dist.get_rank() == 0 and (epoch % config.SAVE_FREQ == 0
or epoch == (config.TRAIN.EPOCHS - 1)):
save_checkpoint(config, epoch, model_without_ddp, max_accuracy,
optimizer, lr_scheduler, logger)
acc1, acc5, loss = validate(config, data_loader_val, model)
logger.info(
f"Accuracy of the network on the {len(dataset_val)} test images: {acc1:.1f}%"
)
max_accuracy = max(max_accuracy, acc1)
logger.info(f'Max accuracy: {max_accuracy:.2f}%')
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
logger.info('Training time {}'.format(total_time_str))
def main(config):
dataset_train, _, data_loader_train, _, _ = build_loader(config)
config.defrost()
config.DATA.TRAINING_IMAGES = len(dataset_train)
config.freeze()
logger.info(f"Creating model:{config.MODEL.TYPE}/{config.MODEL.NAME}")
model = build_model(config)
model.cuda()
logger.info(str(model))
optimizer = build_optimizer(config, model)
if config.AMP_OPT_LEVEL != "O0":
model, optimizer = amp.initialize(model,
optimizer,
opt_level=config.AMP_OPT_LEVEL)
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[config.LOCAL_RANK], broadcast_buffers=False)
model_without_ddp = model.module
n_parameters = sum(p.numel() for p in model.parameters()
if p.requires_grad)
logger.info(f"number of params: {n_parameters}")
if hasattr(model_without_ddp, 'flops'):
flops = model_without_ddp.flops()
logger.info(f"number of GFLOPs: {flops / 1e9}")
lr_scheduler = build_scheduler(config, optimizer, len(data_loader_train))
if config.TRAIN.AUTO_RESUME:
resume_file = auto_resume_helper(config.OUTPUT)
if resume_file:
if config.MODEL.RESUME:
logger.warning(
f"auto-resume changing resume file from {config.MODEL.RESUME} to {resume_file}"
)
config.defrost()
config.MODEL.RESUME = resume_file
config.freeze()
logger.info(f'auto resuming from {resume_file}')
else:
logger.info(
f'no checkpoint found in {config.OUTPUT}, ignoring auto resume'
)
if config.MODEL.RESUME:
_ = load_checkpoint(config, model_without_ddp, optimizer, lr_scheduler,
logger)
logger.info("Start self-supervised pre-training")
start_time = time.time()
for epoch in range(config.TRAIN.START_EPOCH, config.TRAIN.EPOCHS):
data_loader_train.sampler.set_epoch(epoch)
train_one_epoch(config, model, data_loader_train, optimizer, epoch,
lr_scheduler)
if dist.get_rank() == 0 and (epoch % config.SAVE_FREQ == 0
or epoch == (config.TRAIN.EPOCHS - 1)):
save_checkpoint(config, epoch, model_without_ddp, 0.0, optimizer,
lr_scheduler, logger)
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
logger.info('Training time {}'.format(total_time_str))
def main(config):
dataset_train, dataset_val, data_loader_train, data_loader_val, mixup_fn = build_loader(
config)
if config.DISTILL.DO_DISTILL:
logger.info(
f"Loading teacher model:{config.MODEL.TYPE}/{config.DISTILL.TEACHER}"
)
model_checkpoint_name = os.path.basename(config.DISTILL.TEACHER)
if 'regnety_160' in model_checkpoint_name:
model_teacher = create_model(
'regnety_160',
pretrained=False,
num_classes=config.MODEL.NUM_CLASSES,
global_pool='avg',
)
if config.DISTILL.TEACHER.startswith('https'):
checkpoint = torch.hub.load_state_dict_from_url(
config.DISTILL.TEACHER,
map_location='cpu',
check_hash=True)
else:
checkpoint = torch.load(config.DISTILL.TEACHER,
map_location='cpu')
model_teacher.load_state_dict(checkpoint['model'])
model_teacher.cuda()
model_teacher.eval()
del checkpoint
torch.cuda.empty_cache()
else:
if 'base' in model_checkpoint_name:
teacher_type = 'base'
elif 'large' in model_checkpoint_name:
teacher_type = 'large'
else:
teacher_type = None
model_teacher = load_teacher_model(type=teacher_type)
model_teacher.cuda()
model_teacher = torch.nn.parallel.DistributedDataParallel(
model_teacher,
device_ids=[config.LOCAL_RANK],
broadcast_buffers=False)
checkpoint = torch.load(config.DISTILL.TEACHER, map_location='cpu')
msg = model_teacher.module.load_state_dict(checkpoint['model'],
strict=False)
logger.info(msg)
del checkpoint
torch.cuda.empty_cache()
logger.info(f"Creating model:{config.MODEL.TYPE}/{config.MODEL.NAME}")
model = build_model(config)
model.cuda()
logger.info(str(model))
optimizer = build_optimizer(config, model)
if config.AMP_OPT_LEVEL != "O0":
model, optimizer = amp.initialize(model,
optimizer,
opt_level=config.AMP_OPT_LEVEL)
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[config.LOCAL_RANK],
broadcast_buffers=False,
find_unused_parameters=True)
model_without_ddp = model.module
n_parameters = sum(p.numel() for p in model.parameters()
if p.requires_grad)
logger.info(f"number of params: {n_parameters}")
if hasattr(model_without_ddp, 'flops'):
flops = model_without_ddp.flops()
logger.info(f"number of GFLOPs: {flops / 1e9}")
lr_scheduler = build_scheduler(config, optimizer, len(data_loader_train))
criterion_soft = soft_cross_entropy
criterion_attn = cal_relation_loss
criterion_hidden = cal_hidden_relation_loss if config.DISTILL.HIDDEN_RELATION else cal_hidden_loss
if config.AUG.MIXUP > 0.:
# smoothing is handled with mixup label transform
criterion_truth = SoftTargetCrossEntropy()
elif config.MODEL.LABEL_SMOOTHING > 0.:
criterion_truth = LabelSmoothingCrossEntropy(
smoothing=config.MODEL.LABEL_SMOOTHING)
else:
criterion_truth = torch.nn.CrossEntropyLoss()
max_accuracy = 0.0
if config.TRAIN.AUTO_RESUME:
resume_file = auto_resume_helper(config.OUTPUT)
if resume_file:
if config.MODEL.RESUME:
logger.warning(
f"auto-resume changing resume file from {config.MODEL.RESUME} to {resume_file}"
)
config.defrost()
config.MODEL.RESUME = resume_file
config.DISTILL.RESUME_WEIGHT_ONLY = False
config.freeze()
logger.info(f'auto resuming from {resume_file}')
else:
logger.info(
f'no checkpoint found in {config.OUTPUT}, ignoring auto resume'
)
if config.MODEL.RESUME:
max_accuracy = load_checkpoint(config, model_without_ddp, optimizer,
lr_scheduler, logger)
acc1, acc5, loss = validate(config, data_loader_val, model, logger)
logger.info(
f"Accuracy of the network on the {len(dataset_val)} test images: {acc1:.1f}%"
)
if config.EVAL_MODE:
return
if config.THROUGHPUT_MODE:
throughput(data_loader_val, model, logger)
return
logger.info("Start training")
start_time = time.time()
for epoch in range(config.TRAIN.START_EPOCH, config.TRAIN.EPOCHS):
data_loader_train.sampler.set_epoch(epoch)
if config.DISTILL.DO_DISTILL:
train_one_epoch_distill(config,
model,
model_teacher,
data_loader_train,
optimizer,
epoch,
mixup_fn,
lr_scheduler,
criterion_soft=criterion_soft,
criterion_truth=criterion_truth,
criterion_attn=criterion_attn,
criterion_hidden=criterion_hidden)
else:
train_one_epoch(config, model, criterion_truth, data_loader_train,
optimizer, epoch, mixup_fn, lr_scheduler)
if dist.get_rank() == 0 and (epoch % config.SAVE_FREQ == 0
or epoch == (config.TRAIN.EPOCHS - 1)):
save_checkpoint(config, epoch, model_without_ddp, max_accuracy,
optimizer, lr_scheduler, logger)
if epoch % config.EVAL_FREQ == 0 or epoch == config.TRAIN.EPOCHS - 1:
acc1, acc5, loss = validate(config, data_loader_val, model, logger)
logger.info(
f"Accuracy of the network on the {len(dataset_val)} test images: {acc1:.1f}%"
)
max_accuracy = max(max_accuracy, acc1)
logger.info(f'Max accuracy: {max_accuracy:.2f}%')
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
logger.info('Training time {}'.format(total_time_str))
def main(args, config):
dataset_train, dataset_val, data_loader_train, data_loader_val, mixup_fn = build_loader(
config)
logger.info(f"Creating model:{config.MODEL.TYPE}/{config.MODEL.NAME}")
model = build_model(config)
model.cuda()
if args.use_sync_bn:
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
logger.info(str(model))
optimizer = build_optimizer(config, model)
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[config.LOCAL_RANK], broadcast_buffers=False)
loss_scaler = NativeScalerWithGradNormCount()
model_without_ddp = model.module
n_parameters = sum(p.numel() for p in model.parameters()
if p.requires_grad)
logger.info(f"number of params: {n_parameters}")
if hasattr(model_without_ddp, 'flops'):
flops = model_without_ddp.flops()
logger.info(f"number of GFLOPs: {flops / 1e9}")
lr_scheduler = build_scheduler(
config, optimizer,
len(data_loader_train) // config.TRAIN.ACCUMULATION_STEPS)
if config.DISTILL.ENABLED:
# we disable MIXUP and CUTMIX when knowledge distillation
assert len(config.DISTILL.TEACHER_LOGITS_PATH
) > 0, "Please fill in DISTILL.TEACHER_LOGITS_PATH"
criterion = torch.nn.CrossEntropyLoss(reduction='mean')
else:
if config.AUG.MIXUP > 0.:
# smoothing is handled with mixup label transform
criterion = SoftTargetCrossEntropy()
elif config.MODEL.LABEL_SMOOTHING > 0.:
criterion = LabelSmoothingCrossEntropy(
smoothing=config.MODEL.LABEL_SMOOTHING)
else:
criterion = torch.nn.CrossEntropyLoss()
max_accuracy = 0.0
if config.TRAIN.AUTO_RESUME:
resume_file = auto_resume_helper(config.OUTPUT)
if resume_file:
if config.MODEL.RESUME:
logger.warning(
f"auto-resume changing resume file from {config.MODEL.RESUME} to {resume_file}"
)
config.defrost()
config.MODEL.RESUME = resume_file
config.freeze()
logger.info(f'auto resuming from {resume_file}')
else:
logger.info(
f'no checkpoint found in {config.OUTPUT}, ignoring auto resume'
)
if config.MODEL.RESUME:
max_accuracy = load_checkpoint(config, model_without_ddp, optimizer,
lr_scheduler, loss_scaler, logger)
acc1, acc5, loss = validate(args, config, data_loader_val, model)
logger.info(
f"Accuracy of the network on the {len(dataset_val)} test images: {acc1:.1f}%"
)
if config.EVAL_MODE:
return
if config.MODEL.PRETRAINED and (not config.MODEL.RESUME):
load_pretrained(config, model_without_ddp, logger)
acc1, acc5, loss = validate(args, config, data_loader_val, model)
logger.info(
f"Accuracy of the network on the {len(dataset_val)} test images: {acc1:.1f}%"
)
if config.THROUGHPUT_MODE:
throughput(data_loader_val, model, logger)
return
logger.info("Start training")
start_time = time.time()
for epoch in range(config.TRAIN.START_EPOCH, config.TRAIN.EPOCHS):
# set_epoch for dataset_train when distillation
if hasattr(dataset_train, 'set_epoch'):
dataset_train.set_epoch(epoch)
data_loader_train.sampler.set_epoch(epoch)
if config.DISTILL.ENABLED:
train_one_epoch_distill_using_saved_logits(
args, config, model, criterion, data_loader_train, optimizer,
epoch, mixup_fn, lr_scheduler, loss_scaler)
else:
train_one_epoch(args, config, model, criterion, data_loader_train,
optimizer, epoch, mixup_fn, lr_scheduler,
loss_scaler)
if dist.get_rank() == 0 and (epoch % config.SAVE_FREQ == 0
or epoch == (config.TRAIN.EPOCHS - 1)):
save_checkpoint(config, epoch, model_without_ddp, max_accuracy,
optimizer, lr_scheduler, loss_scaler, logger)
acc1, acc5, loss = validate(args, config, data_loader_val, model)
logger.info(
f"Accuracy of the network on the {len(dataset_val)} test images: {acc1:.1f}%"
)
max_accuracy = max(max_accuracy, acc1)
logger.info(f'Max accuracy: {max_accuracy:.2f}%')
if is_main_process() and args.use_wandb:
wandb.log({
f"val/acc@1": acc1,
f"val/acc@5": acc5,
f"val/loss": loss,
"epoch": epoch,
})
wandb.run.summary['epoch'] = epoch
wandb.run.summary['best_acc@1'] = max_accuracy
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
logger.info('Training time {}'.format(total_time_str))