def main(args):
utils.init_distributed_mode(args)
print(args)
# if args.distillation_type != 'none' and args.finetune and not args.eval:
# raise NotImplementedError("Finetuning with distillation not yet supported")
device = torch.device(args.device)
# fix the seed for reproducibility
seed = args.seed + utils.get_rank()
torch.manual_seed(seed)
np.random.seed(seed)
# random.seed(seed)
cudnn.benchmark = True
dataset_train, args.nb_classes = build_dataset(is_train=True, args=args)
dataset_val, _ = build_dataset(is_train=False, args=args)
if True: # args.distributed:
num_tasks = utils.get_world_size()
global_rank = utils.get_rank()
if args.repeated_aug:
sampler_train = RASampler(dataset_train,
num_replicas=num_tasks,
rank=global_rank,
shuffle=True)
else:
sampler_train = torch.utils.data.DistributedSampler(
dataset_train,
# num_replicas=num_tasks,
num_replicas=0,
rank=global_rank,
shuffle=True)
if args.dist_eval:
if len(dataset_val) % num_tasks != 0:
print(
'Warning: Enabling distributed evaluation with an eval dataset not divisible by process number. '
'This will slightly alter validation results as extra duplicate entries are added to achieve '
'equal num of samples per-process.')
sampler_val = torch.utils.data.DistributedSampler(
dataset_val,
# num_replicas=num_tasks,
num_replicas=0,
rank=global_rank,
shuffle=False)
else:
sampler_val = torch.utils.data.SequentialSampler(dataset_val)
else:
sampler_train = torch.utils.data.RandomSampler(dataset_train)
sampler_val = torch.utils.data.SequentialSampler(dataset_val)
data_loader_train = torch.utils.data.DataLoader(
dataset_train,
sampler=sampler_train,
batch_size=args.batch_size,
num_workers=args.num_workers,
pin_memory=args.pin_mem,
drop_last=True,
)
data_loader_val = torch.utils.data.DataLoader(dataset_val,
sampler=sampler_val,
batch_size=int(
1.5 * args.batch_size),
num_workers=args.num_workers,
pin_memory=args.pin_mem,
drop_last=False)
mixup_fn = None
mixup_active = args.mixup > 0 or args.cutmix > 0. or args.cutmix_minmax is not None
if mixup_active:
mixup_fn = Mixup(mixup_alpha=args.mixup,
cutmix_alpha=args.cutmix,
cutmix_minmax=args.cutmix_minmax,
prob=args.mixup_prob,
switch_prob=args.mixup_switch_prob,
mode=args.mixup_mode,
label_smoothing=args.smoothing,
num_classes=args.nb_classes)
print(f"Creating model: {args.model}")
model = create_model(
args.model,
pretrained=False,
num_classes=args.nb_classes,
drop_rate=args.drop,
drop_path_rate=args.drop_path,
drop_block_rate=None,
)
if args.finetune:
if args.finetune.startswith('https'):
checkpoint = torch.hub.load_state_dict_from_url(args.finetune,
map_location='cpu',
check_hash=True)
else:
checkpoint = torch.load(args.finetune, map_location='cpu')
if 'model' in checkpoint:
model_without_ddp.load_state_dict(checkpoint['model'])
else:
model_without_ddp.load_state_dict(checkpoint)
state_dict = model.state_dict()
for k in [
'head.weight', 'head.bias', 'head_dist.weight',
'head_dist.bias'
]:
if k in checkpoint_model and checkpoint_model[
k].shape != state_dict[k].shape:
print(f"Removing key {k} from pretrained checkpoint")
del checkpoint_model[k]
# # interpolate position embedding
# pos_embed_checkpoint = checkpoint_model['pos_embed']
# embedding_size = pos_embed_checkpoint.shape[-1]
# num_patches = model.patch_embed.num_patches
# num_extra_tokens = model.pos_embed.shape[-2] - num_patches
# # height (== width) for the checkpoint position embedding
# orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5)
# # height (== width) for the new position embedding
# new_size = int(num_patches ** 0.5)
# # class_token and dist_token are kept unchanged
# extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
# # only the position tokens are interpolated
# pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
# pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2)
# pos_tokens = torch.nn.functional.interpolate(
# pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False)
# pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)
# new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
# checkpoint_model['pos_embed'] = new_pos_embed
model.load_state_dict(checkpoint_model, strict=False)
model.to(device)
model_ema = None
# if args.model_ema:
# # Important to create EMA model after cuda(), DP wrapper, and AMP but before SyncBN and DDP wrapper
# model_ema = ModelEma(
# model,
# decay=args.model_ema_decay,
# device='cpu' if args.model_ema_force_cpu else '',
# resume='')
model_without_ddp = model
if args.distributed:
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[args.gpu])
model_without_ddp = model.module
n_parameters = sum(p.numel() for p in model.parameters()
if p.requires_grad)
print('number of params:', n_parameters)
linear_scaled_lr = args.lr * args.batch_size * utils.get_world_size(
) / 512.0
args.lr = linear_scaled_lr
optimizer = create_optimizer(args, model_without_ddp)
loss_scaler = NativeScaler()
lr_scheduler, _ = create_scheduler(args, optimizer)
criterion = LabelSmoothingCrossEntropy()
if args.mixup > 0.:
# smoothing is handled with mixup label transform
criterion = SoftTargetCrossEntropy()
elif args.smoothing:
criterion = LabelSmoothingCrossEntropy(smoothing=args.smoothing)
else:
criterion = torch.nn.CrossEntropyLoss()
# teacher_model = None
# if args.distillation_type != 'none':
# assert args.teacher_path, 'need to specify teacher-path when using distillation'
# print(f"Creating teacher model: {args.teacher_model}")
# teacher_model = create_model(
# args.teacher_model,
# pretrained=False,
# num_classes=args.nb_classes,
# global_pool='avg',
# )
# if args.teacher_path.startswith('https'):
# checkpoint = torch.hub.load_state_dict_from_url(
# args.teacher_path, map_location='cpu', check_hash=True)
# else:
# checkpoint = torch.load(args.teacher_path, map_location='cpu')
# teacher_model.load_state_dict(checkpoint['model'])
# teacher_model.to(device)
# teacher_model.eval()
# wrap the criterion in our custom DistillationLoss, which
# just dispatches to the original criterion if args.distillation_type is 'none'
# criterion = DistillationLoss(
# criterion, teacher_model, args.distillation_type, args.distillation_alpha, args.distillation_tau
# )
criterion = DistillationLoss(criterion, None, 'none', 0, 0)
output_dir = Path(args.output_dir)
if args.resume:
if args.resume.startswith('https'):
checkpoint = torch.hub.load_state_dict_from_url(args.resume,
map_location='cpu',
check_hash=True)
else:
checkpoint = torch.load(args.resume, map_location='cpu')
if 'model' in checkpoint:
msg = model_without_ddp.load_state_dict(checkpoint['model'])
else:
msg = model_without_ddp.load_state_dict(checkpoint)
print(msg)
if not args.eval and 'optimizer' in checkpoint and 'lr_scheduler' in checkpoint and 'epoch' in checkpoint:
optimizer.load_state_dict(checkpoint['optimizer'])
lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
args.start_epoch = checkpoint['epoch'] + 1
# if args.model_ema:
# utils._load_checkpoint_for_ema(model_ema, checkpoint['model_ema'])
if 'scaler' in checkpoint:
loss_scaler.load_state_dict(checkpoint['scaler'])
if args.eval:
test_stats = evaluate(data_loader_val, model, device)
print(
f"Accuracy of the network on the {len(dataset_val)} test images: {test_stats['acc1']:.1f}%"
)
return
print(f"Start training for {args.epochs} epochs")
start_time = time.time()
max_accuracy = 0.0
for epoch in range(args.start_epoch, args.epochs):
if args.fp32_resume and epoch > args.start_epoch + 1:
args.fp32_resume = False
loss_scaler._scaler = torch.cuda.amp.GradScaler(
enabled=not args.fp32_resume)
if args.distributed:
data_loader_train.sampler.set_epoch(epoch)
train_stats = train_one_epoch(
model,
criterion,
data_loader_train,
optimizer,
device,
epoch,
loss_scaler,
args.clip_grad,
model_ema,
mixup_fn,
set_training_mode=args.finetune ==
'', # keep in eval mode during finetuning
fp32=args.fp32_resume)
lr_scheduler.step(epoch)
if args.output_dir:
checkpoint_paths = [output_dir / 'checkpoint.pth']
for checkpoint_path in checkpoint_paths:
utils.save_on_master(
{
'model': model_without_ddp.state_dict(),
'optimizer': optimizer.state_dict(),
'lr_scheduler': lr_scheduler.state_dict(),
'epoch': epoch,
# 'model_ema': get_state_dict(model_ema),
'scaler': loss_scaler.state_dict(),
'args': args,
},
checkpoint_path)
test_stats = evaluate(data_loader_val, model, device)
print(
f"Accuracy of the network on the {len(dataset_val)} test images: {test_stats['acc1']:.1f}%"
)
max_accuracy = max(max_accuracy, test_stats["acc1"])
print(f'Max accuracy: {max_accuracy:.2f}%')
log_stats = {
**{f'train_{k}': v
for k, v in train_stats.items()},
**{f'test_{k}': v
for k, v in test_stats.items()}, 'epoch': epoch,
'n_parameters': n_parameters
}
if args.output_dir and utils.is_main_process():
with (output_dir / "log.txt").open("a") as f:
f.write(json.dumps(log_stats) + "\n")
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
print('Training time {}'.format(total_time_str))
def main(args):
utils.my_init_distributed_mode(args)
print(args)
# if args.distillation_type != 'none' and args.finetune and not args.eval:
# raise NotImplementedError("Finetuning with distillation not yet supported")
device = torch.device(args.device)
# fix the seed for reproducibility
seed = args.seed + utils.get_rank()
torch.manual_seed(seed)
np.random.seed(seed)
# random.seed(seed)
cudnn.benchmark = True
dataset_train, args.nb_classes = build_dataset(is_train=True, args=args)
dataset_val, _ = build_dataset(is_train=False, args=args)
# if True: # args.distributed:
# num_tasks = utils.get_world_size()
# global_rank = utils.get_rank()
# if args.repeated_aug:
# sampler_train = RASampler(
# dataset_train, num_replicas=num_tasks, rank=global_rank, shuffle=True
# )
# else:
# sampler_train = torch.utils.data.DistributedSampler(
# dataset_train,
# # num_replicas=num_tasks,
# num_replicas=0,
# rank=global_rank, shuffle=True
# )
# if args.dist_eval:
# if len(dataset_val) % num_tasks != 0:
# print('Warning: Enabling distributed evaluation with an eval dataset not divisible by process number. '
# 'This will slightly alter validation results as extra duplicate entries are added to achieve '
# 'equal num of samples per-process.')
# sampler_val = torch.utils.data.DistributedSampler(
# dataset_val,
# # num_replicas=num_tasks,
# num_replicas=0,
# rank=global_rank, shuffle=False)
# else:
# sampler_val = torch.utils.data.SequentialSampler(dataset_val)
# else:
# sampler_train = torch.utils.data.RandomSampler(dataset_train)
# sampler_val = torch.utils.data.SequentialSampler(dataset_val)
#
# data_loader_train = torch.utils.data.DataLoader(
# dataset_train, sampler=sampler_train,
# batch_size=args.batch_size,
# num_workers=args.num_workers,
# pin_memory=args.pin_mem,
# drop_last=True,
# )
#
# data_loader_val = torch.utils.data.DataLoader(
# dataset_val, sampler=sampler_val,
# batch_size=int(1.5 * args.batch_size),
# num_workers=args.num_workers,
# pin_memory=args.pin_mem,
# drop_last=False
# )
#
if args.distributed:
if args.cache_mode:
sampler_train = samplers.NodeDistributedSampler(dataset_train)
sampler_val = samplers.NodeDistributedSampler(dataset_val,
shuffle=False)
else:
sampler_train = samplers.DistributedSampler(dataset_train)
sampler_val = samplers.DistributedSampler(dataset_val,
shuffle=False)
else:
sampler_train = torch.utils.data.RandomSampler(dataset_train)
sampler_val = torch.utils.data.SequentialSampler(dataset_val)
batch_sampler_train = torch.utils.data.BatchSampler(sampler_train,
args.batch_size,
drop_last=True)
data_loader_train = DataLoader(dataset_train,
batch_sampler=batch_sampler_train,
num_workers=args.num_workers,
pin_memory=True)
data_loader_val = DataLoader(dataset_val,
args.batch_size,
sampler=sampler_val,
drop_last=False,
num_workers=args.num_workers,
pin_memory=True)
mixup_fn = None
mixup_active = args.mixup > 0 or args.cutmix > 0. or args.cutmix_minmax is not None
if mixup_active:
mixup_fn = Mixup(mixup_alpha=args.mixup,
cutmix_alpha=args.cutmix,
cutmix_minmax=args.cutmix_minmax,
prob=args.mixup_prob,
switch_prob=args.mixup_switch_prob,
mode=args.mixup_mode,
label_smoothing=args.smoothing,
num_classes=args.nb_classes)
print(f"Creating model: {args.model}")
model = create_model(
args.model,
pretrained=False,
num_classes=args.nb_classes,
drop_rate=args.drop,
drop_path_rate=args.drop_path,
drop_block_rate=None,
)
model_without_ddp = model
# # there are bugs
# if args.finetune:
# if args.finetune.startswith('https'):
# checkpoint = torch.hub.load_state_dict_from_url(
# args.finetune, map_location='cpu', check_hash=True)
# else:
# checkpoint = torch.load(args.finetune, map_location='cpu')
#
# checkpoint_model = checkpoint['model']
#
# state_dict = model.state_dict()
# for k in ['head.weight', 'head.bias', 'head_dist.weight', 'head_dist.bias']:
# if k in checkpoint_model and checkpoint_model[k].shape != state_dict[k].shape:
# print(f"Removing key {k} from pretrained checkpoint")
# del checkpoint_model[k]
#
# _ = model.load_state_dict(checkpoint_model, strict=False)
model.to(device)
model_ema = None
# if args.model_ema:
# # Important to create EMA model after cuda(), DP wrapper, and AMP but before SyncBN and DDP wrapper
# model_ema = ModelEma(
# model,
# decay=args.model_ema_decay,
# device='cpu' if args.model_ema_force_cpu else '',
# resume='')
model_without_ddp = model
if args.distributed:
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[args.gpu])
model_without_ddp = model.module
n_parameters = sum(p.numel() for p in model.parameters()
if p.requires_grad)
print('number of params:', n_parameters)
linear_scaled_lr = args.lr * args.batch_size * utils.get_world_size(
) / 512.0
args.lr = linear_scaled_lr
optimizer = create_optimizer(args, model_without_ddp)
loss_scaler = NativeScaler()
lr_scheduler, _ = create_scheduler(args, optimizer)
criterion = LabelSmoothingCrossEntropy()
if args.mixup > 0.:
# smoothing is handled with mixup label transform
criterion = SoftTargetCrossEntropy()
elif args.smoothing:
criterion = LabelSmoothingCrossEntropy(smoothing=args.smoothing)
else:
criterion = torch.nn.CrossEntropyLoss()
criterion = DistillationLoss(criterion, None, 'none', 0, 0)
output_dir = Path(args.output_dir)
# for finetune
if args.finetune:
if args.finetune.startswith('https'):
checkpoint = torch.hub.load_state_dict_from_url(args.finetune,
map_location='cpu',
check_hash=True)
else:
if not os.path.exists(args.finetune):
checkpoint = None
print('NOTICE:' + args.finetune + ' does not exist!')
else:
checkpoint = torch.load(args.finetune, map_location='cpu')
if checkpoint is not None:
if 'model' in checkpoint:
check_model = checkpoint['model']
else:
check_model = checkpoint
missing_keys = model_without_ddp.load_state_dict(
check_model, strict=False).missing_keys
skip_keys = model_without_ddp.no_weight_decay()
# create optimizer manually
param_dicts = [
{
"params": [
p for n, p in model_without_ddp.named_parameters()
if n in missing_keys and n not in skip_keys
],
"lr":
args.lr,
'weight_decay':
args.weight_decay,
},
{
"params": [
p for n, p in model_without_ddp.named_parameters()
if n in missing_keys and n in skip_keys
],
"lr":
args.lr,
'weight_decay':
0,
},
{
"params": [
p for n, p in model_without_ddp.named_parameters()
if n not in missing_keys and n not in skip_keys
],
"lr":
args.lr * args.fine_factor,
'weight_decay':
args.weight_decay,
},
{
"params": [
p for n, p in model_without_ddp.named_parameters()
if n not in missing_keys and n in skip_keys
],
"lr":
args.lr * args.fine_factor,
'weight_decay':
0,
},
]
optimizer = torch.optim.AdamW(param_dicts,
lr=args.lr,
weight_decay=args.weight_decay)
loss_scaler = NativeScaler()
lr_scheduler, _ = create_scheduler(args, optimizer)
# if not args.eval and 'optimizer' in checkpoint and 'lr_scheduler' in checkpoint and 'epoch' in checkpoint:
# optimizer.load_state_dict(checkpoint['optimizer'])
# lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
# args.start_epoch = checkpoint['epoch'] + 1
# # if args.model_ema:
# # utils._load_checkpoint_for_ema(model_ema, checkpoint['model_ema'])
# if 'scaler' in checkpoint:
# loss_scaler.load_state_dict(checkpoint['scaler'])
print('finetune from' + args.finetune)
# for debug
# lr_scheduler.step(10)
# lr_scheduler.step(100)
# lr_scheduler.step(200)
if args.resume:
if args.resume.startswith('https'):
checkpoint = torch.hub.load_state_dict_from_url(args.resume,
map_location='cpu',
check_hash=True)
else:
if not os.path.exists(args.resume):
checkpoint = None
print('NOTICE:' + args.resume + ' does not exist!')
else:
checkpoint = torch.load(args.resume, map_location='cpu')
if checkpoint is not None:
if 'model' in checkpoint:
model_without_ddp.load_state_dict(checkpoint['model'])
else:
model_without_ddp.load_state_dict(checkpoint)
if not args.eval and 'optimizer' in checkpoint and 'lr_scheduler' in checkpoint and 'epoch' in checkpoint:
optimizer.load_state_dict(checkpoint['optimizer'])
lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
args.start_epoch = checkpoint['epoch'] + 1
# if args.model_ema:
# utils._load_checkpoint_for_ema(model_ema, checkpoint['model_ema'])
if 'scaler' in checkpoint:
loss_scaler.load_state_dict(checkpoint['scaler'])
print('resume from' + args.resume)
if args.eval:
test_stats = evaluate(data_loader_val, model, device)
print(
f"Accuracy of the network on the {len(dataset_val)} test images: {test_stats['acc1']:.1f}%"
)
return
print(f"Start training for {args.epochs} epochs")
start_time = time.time()
max_accuracy = 0.0
max_epoch_dp_warm_up = 100
if 'pvt_tiny' in args.model or 'pvt_small' in args.model:
max_epoch_dp_warm_up = 0
if args.start_epoch < max_epoch_dp_warm_up:
model_without_ddp.reset_drop_path(0.0)
for epoch in range(args.start_epoch, args.epochs):
if args.fp32_resume and epoch > args.start_epoch + 1:
args.fp32_resume = False
loss_scaler._scaler = torch.cuda.amp.GradScaler(
enabled=not args.fp32_resume)
if epoch == max_epoch_dp_warm_up:
model_without_ddp.reset_drop_path(args.drop_path)
if epoch < args.warmup_epochs:
optimizer.param_groups[2]['lr'] = 0
optimizer.param_groups[3]['lr'] = 0
if args.distributed:
# data_loader_train.sampler.set_epoch(epoch)
sampler_train.set_epoch(epoch)
train_stats = my_train_one_epoch(
model,
criterion,
data_loader_train,
optimizer,
device,
epoch,
loss_scaler,
args.clip_grad,
model_ema,
mixup_fn,
# set_training_mode=args.finetune == '', # keep in eval mode during finetuning
fp32=args.fp32_resume)
lr_scheduler.step(epoch)
if args.output_dir:
checkpoint_paths = [output_dir / 'checkpoint.pth']
for checkpoint_path in checkpoint_paths:
utils.save_on_master(
{
'model': model_without_ddp.state_dict(),
'optimizer': optimizer.state_dict(),
'lr_scheduler': lr_scheduler.state_dict(),
'epoch': epoch,
# 'model_ema': get_state_dict(model_ema),
'scaler': loss_scaler.state_dict(),
'args': args,
},
checkpoint_path)
test_stats = evaluate(data_loader_val, model, device)
print(
f"Accuracy of the network on the {len(dataset_val)} test images: {test_stats['acc1']:.1f}%"
)
max_accuracy = max(max_accuracy, test_stats["acc1"])
print(f'Max accuracy: {max_accuracy:.2f}%')
log_stats = {
**{f'train_{k}': v
for k, v in train_stats.items()},
**{f'test_{k}': v
for k, v in test_stats.items()}, 'epoch': epoch,
'n_parameters': n_parameters
}
if args.output_dir and utils.is_main_process():
with (output_dir / "log.txt").open("a") as f:
f.write(json.dumps(log_stats) + "\n")
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
print('Training time {}'.format(total_time_str))