def train_dino(args):
utils.init_distributed_mode(args)
utils.fix_random_seeds(args.seed)
print("git:\n {}\n".format(utils.get_sha()))
print("\n".join("%s: %s" % (k, str(v))
for k, v in sorted(dict(vars(args)).items())))
cudnn.benchmark = True
# ============ preparing data ... ============
transform = DataAugmentationDINO(
args.global_crops_scale,
args.local_crops_scale,
args.local_crops_number,
)
#dataset = datasets.ImageFolder(args.data_path, transform=transform)
from sen12ms import get_transform
dataset = AllSen12MSDataset(args.data_path,
"train",
transform=transform,
tansform_coord=None,
classes=None,
seasons=None,
split_by_region=True,
download=False)
sampler = torch.utils.data.DistributedSampler(dataset, shuffle=True)
data_loader = torch.utils.data.DataLoader(
dataset,
sampler=sampler,
batch_size=args.batch_size_per_gpu,
num_workers=args.num_workers,
pin_memory=True,
drop_last=True,
)
print(f"Data loaded: there are {len(dataset)} images.")
# ============ building student and teacher networks ... ============
# if the network is a vision transformer (i.e. deit_tiny, deit_small, vit_base)
if args.arch in vits.__dict__.keys():
student = vits.__dict__[args.arch](
patch_size=args.patch_size,
drop_path_rate=0.1, # stochastic depth
)
teacher = vits.__dict__[args.arch](patch_size=args.patch_size)
embed_dim = student.embed_dim
student = utils.replace_input_layer(student, inchannels=13)
teacher = utils.replace_input_layer(teacher, inchannels=13)
# otherwise, we check if the architecture is in torchvision models
elif args.arch in torchvision_models.__dict__.keys():
student = torchvision_models.__dict__[args.arch]()
teacher = torchvision_models.__dict__[args.arch]()
embed_dim = student.fc.weight.shape[1]
else:
print(f"Unknow architecture: {args.arch}")
# multi-crop wrapper handles forward with inputs of different resolutions
student = utils.MultiCropWrapper(
student,
DINOHead(
embed_dim,
args.out_dim,
use_bn=args.use_bn_in_head,
norm_last_layer=args.norm_last_layer,
))
teacher = utils.MultiCropWrapper(
teacher,
DINOHead(embed_dim, args.out_dim, args.use_bn_in_head),
)
# move networks to gpu
student, teacher = student.cuda(), teacher.cuda()
# synchronize batch norms (if any)
if utils.has_batchnorms(student):
student = nn.SyncBatchNorm.convert_sync_batchnorm(student)
teacher = nn.SyncBatchNorm.convert_sync_batchnorm(teacher)
# we need DDP wrapper to have synchro batch norms working...
teacher = nn.parallel.DistributedDataParallel(teacher,
device_ids=[args.gpu])
teacher_without_ddp = teacher.module
else:
# teacher_without_ddp and teacher are the same thing
teacher_without_ddp = teacher
student = nn.parallel.DistributedDataParallel(student,
device_ids=[args.gpu])
# teacher and student start with the same weights
teacher_without_ddp.load_state_dict(student.module.state_dict())
# there is no backpropagation through the teacher, so no need for gradients
for p in teacher.parameters():
p.requires_grad = False
print(f"Student and Teacher are built: they are both {args.arch} network.")
# ============ preparing loss ... ============
dino_loss = DINOLoss(
args.out_dim,
args.local_crops_number +
2, # total number of crops = 2 global crops + local_crops_number
args.warmup_teacher_temp,
args.teacher_temp,
args.warmup_teacher_temp_epochs,
args.epochs,
).cuda()
# ============ preparing optimizer ... ============
params_groups = utils.get_params_groups(student)
if args.optimizer == "adamw":
optimizer = torch.optim.AdamW(params_groups) # to use with ViTs
elif args.optimizer == "sgd":
optimizer = torch.optim.SGD(params_groups, lr=0,
momentum=0.9) # lr is set by scheduler
elif args.optimizer == "lars":
optimizer = utils.LARS(
params_groups) # to use with convnet and large batches
# for mixed precision training
fp16_scaler = None
if args.use_fp16:
fp16_scaler = torch.cuda.amp.GradScaler()
# ============ init schedulers ... ============
lr_schedule = utils.cosine_scheduler(
args.lr * (args.batch_size_per_gpu * utils.get_world_size()) /
256., # linear scaling rule
args.min_lr,
args.epochs,
len(data_loader),
warmup_epochs=args.warmup_epochs,
)
wd_schedule = utils.cosine_scheduler(
args.weight_decay,
args.weight_decay_end,
args.epochs,
len(data_loader),
)
# momentum parameter is increased to 1. during training with a cosine schedule
momentum_schedule = utils.cosine_scheduler(args.momentum_teacher, 1,
args.epochs, len(data_loader))
print(f"Loss, optimizer and schedulers ready.")
# ============ optionally resume training ... ============
to_restore = {"epoch": 0}
utils.restart_from_checkpoint(
os.path.join(args.output_dir, "checkpoint.pth"),
run_variables=to_restore,
student=student,
teacher=teacher,
optimizer=optimizer,
fp16_scaler=fp16_scaler,
dino_loss=dino_loss,
)
start_epoch = to_restore["epoch"]
start_time = time.time()
print("Starting DINO training !")
for epoch in range(start_epoch, args.epochs):
data_loader.sampler.set_epoch(epoch)
# ============ training one epoch of DINO ... ============
train_stats = train_one_epoch(student, teacher, teacher_without_ddp,
dino_loss, data_loader, optimizer,
lr_schedule, wd_schedule,
momentum_schedule, epoch, fp16_scaler,
args)
# ============ writing logs ... ============
save_dict = {
'student': student.state_dict(),
'teacher': teacher.state_dict(),
'optimizer': optimizer.state_dict(),
'epoch': epoch + 1,
'args': args,
'dino_loss': dino_loss.state_dict(),
}
if fp16_scaler is not None:
save_dict['fp16_scaler'] = fp16_scaler.state_dict()
utils.save_on_master(save_dict,
os.path.join(args.output_dir, 'checkpoint.pth'))
if args.saveckp_freq and epoch % args.saveckp_freq == 0:
utils.save_on_master(
save_dict,
os.path.join(args.output_dir, f'checkpoint{epoch:04}.pth'))
log_stats = {
**{f'train_{k}': v
for k, v in train_stats.items()}, 'epoch': epoch
}
if utils.is_main_process():
with (Path(args.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 __init__(self, student, teacher, length, val_loader, embed_dim, args):
super().__init__()
# self.save_hyperparameters()
self.ratio = args.ratio
teacher.load_state_dict(student.state_dict())
self.student = NetWrapper(student, embed_dim, args, True)
self.teacher = NetWrapper(teacher, embed_dim, args)
self.teacher.projector.load_state_dict(
self.student.projector[-1].state_dict())
for p in self.teacher.parameters():
p.requires_grad = False
print(
f"Student and Teacher are built: they are both {args.arch} network."
)
# ============ preparing optimizer ... ============
params_groups = utils.get_params_groups(self.student)
if args.optimizer == "adamw":
self.optimizer = torch.optim.AdamW(
params_groups) # to use with ViTs
elif args.optimizer == "sgd":
self.optimizer = torch.optim.SGD(
params_groups, lr=0, momentum=0.9) # lr is set by scheduler
elif args.optimizer == "lars":
self.optimizer = utils.LARS(
params_groups) # to use with convnet and large batches
length = math.ceil(length /
(args.accumulate * torch.cuda.device_count()))
# ============ init schedulers ... ============
self.lr_schedule = utils.cosine_scheduler(
args.lr * (args.accumulate * args.batch_size_per_gpu *
torch.cuda.device_count()) /
256., # linear scaling rule
args.min_lr * (args.accumulate * args.batch_size_per_gpu *
torch.cuda.device_count()) / 256.,
args.epochs,
length,
warmup_epochs=args.warmup_epochs,
)
self.wd_schedule = utils.cosine_scheduler(
args.weight_decay,
args.weight_decay_end,
args.epochs,
length,
)
# print(length)
# momentum parameter is increased to 1. during training with a cosine schedule
self.momentum_schedule = utils.cosine_scheduler(
args.momentum_teacher, 1, args.epochs, length)
print(f"Loss, optimizer and schedulers ready.")
self.val_loader = val_loader
self.aug1 = torch.nn.Sequential(
RandomApply(T.ColorJitter(0.8, 0.8, 0.8, 0.2), p=0.3),
T.RandomGrayscale(p=0.2),
T.RandomHorizontalFlip(),
RandomApply(T.GaussianBlur((3, 3), (1.0, 2.0)), p=0.2),
T.RandomResizedCrop((image_size, image_size)),
T.Normalize(mean=torch.tensor([0.485, 0.456, 0.406]),
std=torch.tensor([0.229, 0.224, 0.225])),
)
self.aug2 = self.aug1
def main(args, ds_init):
utils.init_distributed_mode(args)
if ds_init is not None:
utils.create_ds_config(args)
print(args)
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)
if args.disable_eval_during_finetuning:
dataset_val = None
else:
dataset_val, _ = build_dataset(is_train=False, args=args)
if True: # args.distributed:
num_tasks = utils.get_world_size()
global_rank = utils.get_rank()
sampler_train = torch.utils.data.DistributedSampler(
dataset_train,
num_replicas=num_tasks,
rank=global_rank,
shuffle=True)
print("Sampler_train = %s" % str(sampler_train))
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,
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)
if global_rank == 0 and args.log_dir is not None:
os.makedirs(args.log_dir, exist_ok=True)
log_writer = utils.TensorboardLogger(log_dir=args.log_dir)
else:
log_writer = None
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,
)
if dataset_val is not None:
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)
else:
data_loader_val = None
mixup_fn = None
mixup_active = args.mixup > 0 or args.cutmix > 0. or args.cutmix_minmax is not None
if mixup_active:
print("Mixup is activated!")
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)
model = create_model(
args.model,
pretrained=False,
num_classes=args.nb_classes,
drop_rate=args.drop,
drop_path_rate=args.drop_path,
attn_drop_rate=args.attn_drop_rate,
drop_block_rate=None,
use_mean_pooling=args.use_mean_pooling,
init_scale=args.init_scale,
use_rel_pos_bias=args.rel_pos_bias,
use_abs_pos_emb=args.abs_pos_emb,
init_values=args.layer_scale_init_value,
)
patch_size = model.patch_embed.patch_size
print("Patch size = %s" % str(patch_size))
args.window_size = (args.input_size // patch_size[0],
args.input_size // patch_size[1])
args.patch_size = patch_size
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')
print("Load ckpt from %s" % args.finetune)
checkpoint_model = None
for model_key in args.model_key.split('|'):
if model_key in checkpoint:
checkpoint_model = checkpoint[model_key]
print("Load state_dict by model_key = %s" % model_key)
break
if checkpoint_model is None:
checkpoint_model = checkpoint
state_dict = model.state_dict()
for k in ['head.weight', 'head.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]
if model.use_rel_pos_bias and "rel_pos_bias.relative_position_bias_table" in checkpoint_model:
print(
"Expand the shared relative position embedding to each transformer block. "
)
num_layers = model.get_num_layers()
rel_pos_bias = checkpoint_model[
"rel_pos_bias.relative_position_bias_table"]
for i in range(num_layers):
checkpoint_model["blocks.%d.attn.relative_position_bias_table"
% i] = rel_pos_bias.clone()
checkpoint_model.pop("rel_pos_bias.relative_position_bias_table")
all_keys = list(checkpoint_model.keys())
for key in all_keys:
if "relative_position_index" in key:
checkpoint_model.pop(key)
if "relative_position_bias_table" in key:
rel_pos_bias = checkpoint_model[key]
src_num_pos, num_attn_heads = rel_pos_bias.size()
dst_num_pos, _ = model.state_dict()[key].size()
dst_patch_shape = model.patch_embed.patch_shape
if dst_patch_shape[0] != dst_patch_shape[1]:
raise NotImplementedError()
num_extra_tokens = dst_num_pos - (
dst_patch_shape[0] * 2 - 1) * (dst_patch_shape[1] * 2 - 1)
src_size = int((src_num_pos - num_extra_tokens)**0.5)
dst_size = int((dst_num_pos - num_extra_tokens)**0.5)
if src_size != dst_size:
print("Position interpolate for %s from %dx%d to %dx%d" %
(key, src_size, src_size, dst_size, dst_size))
extra_tokens = rel_pos_bias[-num_extra_tokens:, :]
rel_pos_bias = rel_pos_bias[:-num_extra_tokens, :]
def geometric_progression(a, r, n):
return a * (1.0 - r**n) / (1.0 - r)
left, right = 1.01, 1.5
while right - left > 1e-6:
q = (left + right) / 2.0
gp = geometric_progression(1, q, src_size // 2)
if gp > dst_size // 2:
right = q
else:
left = q
# if q > 1.090307:
# q = 1.090307
dis = []
cur = 1
for i in range(src_size // 2):
dis.append(cur)
cur += q**(i + 1)
r_ids = [-_ for _ in reversed(dis)]
x = r_ids + [0] + dis
y = r_ids + [0] + dis
t = dst_size // 2.0
dx = np.arange(-t, t + 0.1, 1.0)
dy = np.arange(-t, t + 0.1, 1.0)
print("Original positions = %s" % str(x))
print("Target positions = %s" % str(dx))
all_rel_pos_bias = []
for i in range(num_attn_heads):
z = rel_pos_bias[:, i].view(src_size,
src_size).float().numpy()
f = interpolate.interp2d(x, y, z, kind='cubic')
all_rel_pos_bias.append(
torch.Tensor(f(dx, dy)).contiguous().view(
-1, 1).to(rel_pos_bias.device))
rel_pos_bias = torch.cat(all_rel_pos_bias, dim=-1)
new_rel_pos_bias = torch.cat((rel_pos_bias, extra_tokens),
dim=0)
checkpoint_model[key] = new_rel_pos_bias
# interpolate position embedding
if 'pos_embed' in checkpoint_model:
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
if orig_size != new_size:
print("Position interpolate from %dx%d to %dx%d" %
(orig_size, orig_size, new_size, new_size))
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
utils.load_state_dict(model,
checkpoint_model,
prefix=args.model_prefix)
# 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='')
print("Using EMA with decay = %.8f" % args.model_ema_decay)
model_without_ddp = model
n_parameters = sum(p.numel() for p in model.parameters()
if p.requires_grad)
print("Model = %s" % str(model_without_ddp))
print('number of params:', n_parameters)
total_batch_size = args.batch_size * args.update_freq * utils.get_world_size(
)
num_training_steps_per_epoch = len(dataset_train) // total_batch_size
print("LR = %.8f" % args.lr)
print("Batch size = %d" % total_batch_size)
print("Update frequent = %d" % args.update_freq)
print("Number of training examples = %d" % len(dataset_train))
print("Number of training training per epoch = %d" %
num_training_steps_per_epoch)
num_layers = model_without_ddp.get_num_layers()
if args.layer_decay < 1.0:
assigner = LayerDecayValueAssigner(
list(args.layer_decay**(num_layers + 1 - i)
for i in range(num_layers + 2)))
else:
assigner = None
if assigner is not None:
print("Assigned values = %s" % str(assigner.values))
skip_weight_decay_list = model.no_weight_decay()
if args.disable_weight_decay_on_rel_pos_bias:
for i in range(num_layers):
skip_weight_decay_list.add(
"blocks.%d.attn.relative_position_bias_table" % i)
if args.enable_deepspeed:
loss_scaler = None
optimizer_params = get_parameter_groups(
model, args.weight_decay, skip_weight_decay_list,
assigner.get_layer_id if assigner is not None else None,
assigner.get_scale if assigner is not None else None)
model, optimizer, _, _ = ds_init(
args=args,
model=model,
model_parameters=optimizer_params,
dist_init_required=not args.distributed,
)
print("model.gradient_accumulation_steps() = %d" %
model.gradient_accumulation_steps())
assert model.gradient_accumulation_steps() == args.update_freq
else:
if args.distributed:
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[args.gpu], find_unused_parameters=True)
model_without_ddp = model.module
optimizer = create_optimizer(args,
model_without_ddp,
skip_list=skip_weight_decay_list,
get_num_layer=assigner.get_layer_id
if assigner is not None else None,
get_layer_scale=assigner.get_scale
if assigner is not None else None)
loss_scaler = NativeScaler()
print("Use step level LR scheduler!")
lr_schedule_values = utils.cosine_scheduler(
args.lr,
args.min_lr,
args.epochs,
num_training_steps_per_epoch,
warmup_epochs=args.warmup_epochs,
warmup_steps=args.warmup_steps,
)
if args.weight_decay_end is None:
args.weight_decay_end = args.weight_decay
wd_schedule_values = utils.cosine_scheduler(args.weight_decay,
args.weight_decay_end,
args.epochs,
num_training_steps_per_epoch)
print("Max WD = %.7f, Min WD = %.7f" %
(max(wd_schedule_values), min(wd_schedule_values)))
if mixup_fn is not None:
# smoothing is handled with mixup label transform
criterion = SoftTargetCrossEntropy()
elif args.smoothing > 0.:
criterion = LabelSmoothingCrossEntropy(smoothing=args.smoothing)
else:
criterion = torch.nn.CrossEntropyLoss()
print("criterion = %s" % str(criterion))
utils.auto_load_model(args=args,
model=model,
model_without_ddp=model_without_ddp,
optimizer=optimizer,
loss_scaler=loss_scaler,
model_ema=model_ema)
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}%"
)
exit(0)
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.distributed:
data_loader_train.sampler.set_epoch(epoch)
if log_writer is not None:
log_writer.set_step(epoch * num_training_steps_per_epoch *
args.update_freq)
train_stats = train_one_epoch(
model,
criterion,
data_loader_train,
optimizer,
device,
epoch,
loss_scaler,
args.clip_grad,
model_ema,
mixup_fn,
log_writer=log_writer,
start_steps=epoch * num_training_steps_per_epoch,
lr_schedule_values=lr_schedule_values,
wd_schedule_values=wd_schedule_values,
num_training_steps_per_epoch=num_training_steps_per_epoch,
update_freq=args.update_freq,
)
if args.output_dir and args.save_ckpt:
if (epoch +
1) % args.save_ckpt_freq == 0 or epoch + 1 == args.epochs:
utils.save_model(args=args,
model=model,
model_without_ddp=model_without_ddp,
optimizer=optimizer,
loss_scaler=loss_scaler,
epoch=epoch,
model_ema=model_ema)
if data_loader_val is not None:
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}%"
)
if max_accuracy < test_stats["acc1"]:
max_accuracy = test_stats["acc1"]
if args.output_dir and args.save_ckpt:
utils.save_model(args=args,
model=model,
model_without_ddp=model_without_ddp,
optimizer=optimizer,
loss_scaler=loss_scaler,
epoch="best",
model_ema=model_ema)
print(f'Max accuracy: {max_accuracy:.2f}%')
if log_writer is not None:
log_writer.update(test_acc1=test_stats['acc1'],
head="perf",
step=epoch)
log_writer.update(test_acc5=test_stats['acc5'],
head="perf",
step=epoch)
log_writer.update(test_loss=test_stats['loss'],
head="perf",
step=epoch)
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
}
else:
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():
if log_writer is not None:
log_writer.flush()
with open(os.path.join(args.output_dir, "log.txt"),
mode="a",
encoding="utf-8") 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.init_distributed_mode(args)
print(args)
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
model = get_model(args)
patch_size = model.patch_embed.patch_size
print("Patch size = %s" % str(patch_size))
args.window_size = (args.input_size // patch_size[0], args.input_size // patch_size[1])
args.patch_size = patch_size
# get dataset
dataset_train = build_beit_pretraining_dataset(args)
# prepare discrete vae
d_vae = utils.create_d_vae(
weight_path=args.discrete_vae_weight_path, d_vae_type=args.discrete_vae_type,
device=device, image_size=args.second_input_size)
if True: # args.distributed:
num_tasks = utils.get_world_size()
global_rank = utils.get_rank()
sampler_rank = global_rank
num_training_steps_per_epoch = len(dataset_train) // args.batch_size // num_tasks
sampler_train = torch.utils.data.DistributedSampler(
dataset_train, num_replicas=num_tasks, rank=sampler_rank, shuffle=True
)
print("Sampler_train = %s" % str(sampler_train))
else:
sampler_train = torch.utils.data.RandomSampler(dataset_train)
if global_rank == 0 and args.log_dir is not None:
os.makedirs(args.log_dir, exist_ok=True)
log_writer = utils.TensorboardLogger(log_dir=args.log_dir)
else:
log_writer = None
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,
)
model.to(device)
model_without_ddp = model
n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)
print("Model = %s" % str(model_without_ddp))
print('number of params:', n_parameters)
total_batch_size = args.batch_size * utils.get_world_size()
print("LR = %.8f" % args.lr)
print("Batch size = %d" % total_batch_size)
print("Number of training steps = %d" % num_training_steps_per_epoch)
print("Number of training examples per epoch = %d" % (total_batch_size * num_training_steps_per_epoch))
if args.distributed:
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=True)
model_without_ddp = model.module
optimizer = create_optimizer(
args, model_without_ddp)
loss_scaler = NativeScaler()
print("Use step level LR & WD scheduler!")
lr_schedule_values = utils.cosine_scheduler(
args.lr, args.min_lr, args.epochs, num_training_steps_per_epoch,
warmup_epochs=args.warmup_epochs, warmup_steps=args.warmup_steps,
)
if args.weight_decay_end is None:
args.weight_decay_end = args.weight_decay
wd_schedule_values = utils.cosine_scheduler(
args.weight_decay, args.weight_decay_end, args.epochs, num_training_steps_per_epoch)
print("Max WD = %.7f, Min WD = %.7f" % (max(wd_schedule_values), min(wd_schedule_values)))
utils.auto_load_model(
args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer, loss_scaler=loss_scaler)
print(f"Start training for {args.epochs} epochs")
start_time = time.time()
for epoch in range(args.start_epoch, args.epochs):
if args.distributed:
data_loader_train.sampler.set_epoch(epoch)
if log_writer is not None:
log_writer.set_step(epoch * num_training_steps_per_epoch)
train_stats = train_one_epoch(
model, d_vae, data_loader_train,
optimizer, device, epoch, loss_scaler,
args.clip_grad, log_writer=log_writer,
start_steps=epoch * num_training_steps_per_epoch,
lr_schedule_values=lr_schedule_values,
wd_schedule_values=wd_schedule_values,
)
if args.output_dir:
if (epoch + 1) % args.save_ckpt_freq == 0 or epoch + 1 == args.epochs:
utils.save_model(
args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer,
loss_scaler=loss_scaler, epoch=epoch)
log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
'epoch': epoch, 'n_parameters': n_parameters}
if args.output_dir and utils.is_main_process():
if log_writer is not None:
log_writer.flush()
with open(os.path.join(args.output_dir, "log.txt"), mode="a", encoding="utf-8") 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():
data_root = "/data/flower_photos" # get data root path
if not os.path.exists("./save_weights"):
os.makedirs("./save_weights")
batch_size = 8
epochs = 10
num_classes = 5
freeze_layers = False
initial_lr = 0.005
weight_decay = 5e-4
log_dir = "./logs/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
train_writer = tf.summary.create_file_writer(os.path.join(
log_dir, "train"))
val_writer = tf.summary.create_file_writer(os.path.join(log_dir, "val"))
# data generator with data augmentation
train_ds, val_ds = generate_ds(data_root,
batch_size=batch_size,
val_rate=0.2)
# create model
model = create_model(num_classes=num_classes)
model.build((1, 224, 224, 3))
# 下载我提前转好的预训练权重
# 链接: https://pan.baidu.com/s/1MtYJ3FCAkiPwaMRKuyZN1Q 密码: 1cgp
# load weights
pre_weights_path = './convnext_tiny_1k_224.h5'
assert os.path.exists(pre_weights_path), "cannot find {}".format(
pre_weights_path)
model.load_weights(pre_weights_path, by_name=True, skip_mismatch=True)
# freeze bottom layers
if freeze_layers:
for layer in model.layers:
if "head" not in layer.name:
layer.trainable = False
else:
print("training {}".format(layer.name))
model.summary()
# custom learning rate scheduler
scheduler = cosine_scheduler(initial_lr,
epochs,
len(train_ds),
train_writer=train_writer)
# using keras low level api for training
loss_object = tf.keras.losses.SparseCategoricalCrossentropy(
from_logits=True)
optimizer = tf.keras.optimizers.SGD(learning_rate=initial_lr, momentum=0.9)
train_loss = tf.keras.metrics.Mean(name='train_loss')
train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(
name='train_accuracy')
val_loss = tf.keras.metrics.Mean(name='val_loss')
val_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(
name='val_accuracy')
@tf.function
def train_step(train_images, train_labels):
with tf.GradientTape() as tape:
output = model(train_images, training=True)
ce_loss = loss_object(train_labels, output)
# l2 loss
matcher = re.compile(".*(bias|gamma|beta).*")
l2loss = weight_decay * tf.add_n([
tf.nn.l2_loss(v)
for v in model.trainable_variables if not matcher.match(v.name)
])
loss = ce_loss + l2loss
gradients = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
train_loss(ce_loss)
train_accuracy(train_labels, output)
@tf.function
def val_step(val_images, val_labels):
output = model(val_images, training=False)
loss = loss_object(val_labels, output)
val_loss(loss)
val_accuracy(val_labels, output)
best_val_acc = 0.
for epoch in range(epochs):
train_loss.reset_states() # clear history info
train_accuracy.reset_states() # clear history info
val_loss.reset_states() # clear history info
val_accuracy.reset_states() # clear history info
# train
train_bar = tqdm(train_ds, file=sys.stdout)
for images, labels in train_bar:
# update learning rate
optimizer.learning_rate = next(scheduler)
train_step(images, labels)
# print train process
train_bar.desc = "train epoch[{}/{}] loss:{:.3f}, acc:{:.3f}, lr:{:.5f}".format(
epoch + 1, epochs, train_loss.result(),
train_accuracy.result(), optimizer.learning_rate.numpy())
# validate
val_bar = tqdm(val_ds, file=sys.stdout)
for images, labels in val_bar:
val_step(images, labels)
# print val process
val_bar.desc = "valid epoch[{}/{}] loss:{:.3f}, acc:{:.3f}".format(
epoch + 1, epochs, val_loss.result(), val_accuracy.result())
# writing training loss and acc
with train_writer.as_default():
tf.summary.scalar("loss", train_loss.result(), epoch)
tf.summary.scalar("accuracy", train_accuracy.result(), epoch)
# writing validation loss and acc
with val_writer.as_default():
tf.summary.scalar("loss", val_loss.result(), epoch)
tf.summary.scalar("accuracy", val_accuracy.result(), epoch)
# only save best weights
if val_accuracy.result() > best_val_acc:
best_val_acc = val_accuracy.result()
save_name = "./save_weights/model.ckpt"
model.save_weights(save_name, save_format="tf")
