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import time
import torch
from src.args import parse_arguments
from src.datasets.common import get_dataloader, maybe_dictionarize
from src.datasets.registry import get_dataset
from src.distributed import cleanup_ddp, distribute_loader, is_main_process, setup_ddp
from src.eval import eval_single_dataset
from src.heads import get_classification_head
from src.linearize import LinearizedImageEncoder
from src.modeling import ImageClassifier, ImageEncoder
from src.attention_only_finetune import AttentionOnlyFinetuneEncoder
from src.utils import LabelSmoothing, cosine_lr, accuracy
def finetune(rank, args):
setup_ddp(rank, args.world_size, port=args.port)
train_dataset = args.train_dataset
ckpdir = os.path.join(args.save, train_dataset)
valid_modes = [
"standard", "standard_ortho",
"linear", "linear_ortho",
"linear-2", "linear-2_ortho",
]
assert args.finetuning_mode in valid_modes, f"Mode {args.finetuning_mode} not supported."
is_linearized = args.finetuning_mode in ("linear", "linear_ortho")
is_linear2 = args.finetuning_mode in ("linear-2", "linear-2_ortho")
is_standard_ortho = args.finetuning_mode == "standard_ortho"
is_linear_ortho = args.finetuning_mode == "linear_ortho"
is_linear2_ortho = args.finetuning_mode == "linear-2_ortho"
needs_ortho = is_standard_ortho or is_linear_ortho or is_linear2_ortho
print(f"Using fine-tuning mode: {args.finetuning_mode}")
if needs_ortho and args.ortho_lambda > 0:
print(f" -> With OrthoReg (lambda={args.ortho_lambda})")
mode_prefix_map = {
"standard": "",
"standard_ortho": "standard_ortho",
"linear": "linear",
"linear_ortho": "linear_ortho",
"linear-2": "linear-2",
"linear-2_ortho": "linear-2_ortho",
}
mode_prefix = mode_prefix_map[args.finetuning_mode]
ft_path = os.path.join(ckpdir, f"{mode_prefix}_finetuned.pt" if mode_prefix else "finetuned.pt")
zs_path = os.path.join(ckpdir, f"{mode_prefix}_zeroshot.pt" if mode_prefix else "zeroshot.pt")
if os.path.exists(zs_path) and os.path.exists(ft_path):
print(f"Skipping fine-tuning because {ft_path} exists.")
return zs_path, ft_path
assert train_dataset is not None, "Please provide a training dataset."
if args.load is not None and args.load.endswith("pt"):
if is_linearized:
image_encoder = LinearizedImageEncoder.load(args.load)
elif is_linear2:
image_encoder = AttentionOnlyFinetuneEncoder.load(args.load, args)
else:
image_encoder = ImageEncoder.load(args.load)
else:
print("Building image encoder.")
if is_linearized:
image_encoder = LinearizedImageEncoder(args, keep_lang=False)
elif is_linear2:
image_encoder = AttentionOnlyFinetuneEncoder(args, keep_lang=False)
else:
image_encoder = ImageEncoder(args)
# Save a frozen copy of pretrained weights for ortho loss (standard_ortho / linear-2_ortho)
pretrained_state_dict_ref = None
if is_standard_ortho or is_linear2_ortho:
print("Saving pretrained state dict reference for ortho loss.")
pretrained_state_dict_ref = {
k: v.clone().detach() for k, v in image_encoder.model.state_dict().items()
}
classification_head = get_classification_head(args, train_dataset)
model = ImageClassifier(image_encoder, classification_head)
model.freeze_head()
model = model.cuda()
preprocess_fn = model.train_preprocess
print_every = 100
dataset = get_dataset(
train_dataset,
preprocess_fn,
location=args.data_location,
batch_size=args.batch_size,
)
data_loader = get_dataloader(dataset, is_train=True, args=args, image_encoder=None)
num_batches = len(dataset.train_loader)
ddp_loader = distribute_loader(data_loader)
ddp_model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[rank],
find_unused_parameters=True,
output_device=rank,
)
loss_fn = LabelSmoothing(args.ls) if args.ls > 0 else torch.nn.CrossEntropyLoss()
params = [p for p in ddp_model.parameters() if p.requires_grad]
optimizer = torch.optim.AdamW(params, lr=args.lr, weight_decay=args.wd)
scheduler = cosine_lr(
optimizer,
args.lr,
args.warmup_length,
args.epochs * num_batches // args.num_grad_accumulation,
)
if args.save is not None and is_main_process():
os.makedirs(ckpdir, exist_ok=True)
ddp_model.module.image_encoder.save(zs_path)
for epoch in range(args.epochs):
ddp_model.train()
for i, batch in enumerate(ddp_loader):
start_time = time.time()
step = (
i // args.num_grad_accumulation
+ epoch * num_batches // args.num_grad_accumulation
)
batch = maybe_dictionarize(batch)
inputs = batch["images"].cuda()
labels = batch["labels"].cuda()
data_time = time.time() - start_time
ortho_loss = 0.0
if needs_ortho and args.ortho_lambda > 0:
logits, ortho_loss = ddp_model(
inputs,
calculate_ortho_loss=True,
pretrained_state_dict=pretrained_state_dict_ref,
)
else:
logits = ddp_model(inputs)
classification_loss = loss_fn(logits, labels)
loss = classification_loss + args.ortho_lambda * ortho_loss
(acc1,) = accuracy(logits, labels, topk=(1,))
acc1 /= labels.size(0)
loss.backward()
if (i + 1) % args.num_grad_accumulation == 0:
scheduler(step)
torch.nn.utils.clip_grad_norm_(params, 1.0)
optimizer.step()
optimizer.zero_grad()
batch_time = time.time() - start_time
if (
args.checkpoint_every > 0
and step % args.checkpoint_every == 0
and is_main_process()
):
ckpt_name = f"{mode_prefix}_checkpoint_{step}.pt" if mode_prefix else f"checkpoint_{step}.pt"
ddp_model.module.image_encoder.save(os.path.join(ckpdir, ckpt_name))
if (
step % print_every == 0
and ((i + 1) % args.num_grad_accumulation == 0)
and is_main_process()
):
percent_complete = 100 * i / len(ddp_loader)
log_msg = (
f"Train Epoch: {epoch} [{percent_complete:.0f}%]\t"
f"Total Loss: {loss.item():.6f}\t"
f"CE Loss: {classification_loss.item():.6f}\t"
)
if needs_ortho and args.ortho_lambda > 0:
log_msg += f"Ortho Loss: {ortho_loss.item():.6f}\t"
log_msg += f"Acc@1: {100*acc1:.2f}%\tData (t) {data_time:.3f}"
print(log_msg, flush=True)
if is_main_process():
image_encoder = ddp_model.module.image_encoder
eval_single_dataset(image_encoder, train_dataset, args)
if args.save is not None and is_main_process():
image_encoder.save(ft_path)
return zs_path, ft_path
cleanup_ddp()
if __name__ == "__main__":
train_datasets = [
"Cars",
"DTD",
"EuroSAT",
"GTSRB",
"MNIST",
"RESISC45",
"SUN397",
"SVHN",
]
epochs = {
"Cars": 35,
"DTD": 76,
"EuroSAT": 12,
"GTSRB": 11,
"MNIST": 5,
"RESISC45": 15,
"SUN397": 14,
"SVHN": 4,
}
for dataset in train_datasets:
args = parse_arguments()
args.epochs = epochs[dataset]
args.train_dataset = dataset + "Val"
args.batch_size = 64 if args.model == "ViT-L-14" else 128
args.num_grad_accumulation = 2 if args.model == "ViT-L-14" else 1
if 'ortho' in args.finetuning_mode:
args.save = f"checkpoints_{args.seed}/{args.finetuning_mode}_{args.lr}_lambda{args.ortho_lambda}_{args.model}"
else:
if args.seed is not None:
args.save = f"checkpoints_{args.seed}/{args.finetuning_mode}_{args.lr}_{args.model}"
else:
args.save = f"checkpoints/{args.finetuning_mode}_{args.lr}_{args.model}"
print("=" * 100)
print(f"Finetuning {args.model} on {dataset}")
print("=" * 100)
torch.multiprocessing.spawn(finetune, args=(args,), nprocs=args.world_size)
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