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train.py
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train.py
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import os
from argparse import ArgumentParser
import torch
from pytorch_lightning import Trainer, seed_everything
from pytorch_lightning.callbacks import ModelCheckpoint
from pytorch_lightning.loggers import WandbLogger
from datasets.cifar10 import CIFAR10DataModule
from datasets.gtsrb import GTSRBDataModule
from datasets.celeba import CelebADataModule
from datasets.tiny_imagenet import TinyImageNetDataModule
from modules.backdoor_module import BackdoorModule
from modules.clean_module import CleanModule
from model_arch.preact_resnet18 import PreActResNet18
from model_arch.resnet18 import ResNet18
from custom_layers.popup_score_layer import (
convert_all_layers,
train_score,
train_weight,
train_all,
)
from custom_layers.backdoor_layer import Backdoor
from utils.prune import global_l1_prune, finalize_pruned_model, print_sparsity
def main(args):
seed_everything(args.seed)
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_id
checkpoint_callback = ModelCheckpoint(
monitor="accuracy_val/average", mode="max", save_last=True
)
# Resume ID in WandB console
if args.resume_id != "None":
logger = WandbLogger(
name=args.description,
project="backdoor_compress_v2",
log_model=False,
save_dir="logs",
id=args.resume_id,
)
else:
logger = WandbLogger(
name=args.description,
project="backdoor_compress_v2",
log_model=False,
save_dir="logs",
)
# Resume ckpt in logs
if args.resume_path != "None":
path = args.resume_path
else:
path = None
if bool(args.dev):
limit_train_batches = 2
limit_val_batches = 2
limit_test_batches = 2
args.max_epochs = 2
else:
limit_train_batches = 1.0
limit_val_batches = 1.0
limit_test_batches = 1.0
# Prepare trainer
trainer = Trainer(
logger=logger if not bool(args.dev) else None,
gpus=-1,
deterministic=True,
weights_summary=None,
log_every_n_steps=1,
max_epochs=args.max_epochs,
resume_from_checkpoint=path,
precision=args.precision,
callbacks=[checkpoint_callback],
limit_train_batches=limit_train_batches,
limit_val_batches=limit_val_batches,
limit_test_batches=limit_test_batches,
)
# Dataset & Model
if args.dataset == "cifar10":
dataset = CIFAR10DataModule(args)
model = PreActResNet18(no_classes=dataset.no_classes)
elif args.dataset == "gtsrb":
dataset = GTSRBDataModule(args)
model = PreActResNet18(no_classes=dataset.no_classes)
elif args.dataset == "celeba":
dataset = CelebADataModule(args)
model = ResNet18(no_classes=dataset.no_classes)
elif args.dataset == "tiny_imagenet":
dataset = TinyImageNetDataModule(args)
model = ResNet18(no_classes=dataset.no_classes)
# Module
if args.module == "pretrain":
module = CleanModule(model, args)
elif args.module == "l1_clean": # Pretrain -> L1 Clean
pretrain_model_path = os.path.join("weights", args.pretrain_model_path + ".pt")
state_dict = torch.load(pretrain_model_path)
module = CleanModule(model, args)
module.load_state_dict(state_dict)
global_l1_prune(module.model, args.comp_ratio)
elif args.module == "score_clean": # Pretrain -> Score Clean
pretrain_model_path = os.path.join("weights", args.pretrain_model_path + ".pt")
state_dict = torch.load(pretrain_model_path)
module = CleanModule(model, args)
module.load_state_dict(state_dict)
module.model = convert_all_layers(module.model)
train_score(module.model, args.comp_ratio)
elif args.module == "score_backdoor": # Pretrain -> Score Backdoor
pretrain_model_path = os.path.join("weights", args.pretrain_model_path + ".pt")
state_dict = torch.load(pretrain_model_path)
module = CleanModule(model, args)
module.load_state_dict(state_dict)
module.model = convert_all_layers(module.model)
train_score(module.model, args.comp_ratio)
backdoor = Backdoor(dataset, args)
module = BackdoorModule(module.model, backdoor, args)
elif args.module == "one_step_backdoor": # Traing Score + Trigger + Weight
pretrain_model_path = os.path.join("weights", args.pretrain_model_path + ".pt")
state_dict = torch.load(pretrain_model_path)
module = CleanModule(model, args)
module.load_state_dict(state_dict)
module.model = convert_all_layers(module.model)
train_all(module.model, args.comp_ratio)
backdoor = Backdoor(dataset, args)
module = BackdoorModule(module.model, backdoor, args)
elif args.module == "finetune_clean": # Pretrain -> Score Clean -> Finetune Clean
model = convert_all_layers(model)
train_weight(model, args.comp_ratio)
module = CleanModule(model, args)
score_model_path = os.path.join("weights", args.score_model_path + ".pt")
state_dict = torch.load(score_model_path)
module.load_state_dict(state_dict)
elif (
args.module == "finetune_backdoor"
): # Pretrain -> Score Backdoor -> Finetune Backdoor
model = convert_all_layers(model)
train_weight(model, args.comp_ratio)
backdoor = Backdoor(dataset, args)
module = BackdoorModule(model, backdoor, args)
score_model_path = os.path.join("weights", args.score_model_path + ".pt")
state_dict = torch.load(score_model_path)
module.load_state_dict(state_dict)
trainer.fit(module, dataset)
trainer.test()
if args.module == "l1_clean":
finalize_pruned_model(module.model)
print_sparsity(module.model)
# Save final weights
file_name = "weights/" + args.description + ".pt"
torch.save(module.state_dict(), file_name)
if __name__ == "__main__":
parser = ArgumentParser()
# PROGRAM level args
parser.add_argument("--dev", type=int, default=0, choices=[0, 1])
parser.add_argument("--description", type=str, default="debug_run")
parser.add_argument("--data_dir", type=str, default=".")
parser.add_argument("--gpu_id", type=str, default="0")
# TRAINER args
parser.add_argument("--seed", type=int, default=1)
parser.add_argument("--num_workers", type=int, default=8)
parser.add_argument("--precision", type=int, default=16, choices=[16, 32])
parser.add_argument("--resume_path", type=str, default="None")
parser.add_argument("--resume_id", type=str, default="None")
# HYPER-PARAMS
parser.add_argument("--max_epochs", type=int, default=60)
parser.add_argument("--batch_size", type=int, default=128)
parser.add_argument(
"--optimizer", type=str, default="Adam", choices=["SGD", "Adam"]
)
parser.add_argument("--learning_rate", type=float, default=3e-4)
parser.add_argument("--weight_decay", type=float, default=5e-3)
# EXPERIMENT PARAMS
parser.add_argument("--comp_ratio", type=float, default=2.0)
parser.add_argument(
"--dataset",
type=str,
default="cifar10",
choices=["cifar10", "gtsrb", "celeba", "tiny_imagenet"],
)
parser.add_argument(
"--module",
type=str,
default="l1_clean",
choices=[
"pretrain",
"l1_clean",
"score_clean",
"score_backdoor",
"finetune_clean",
"finetune_backdoor",
"one_step_backdoor",
],
)
parser.add_argument("--pretrain_model_path", type=str, default="debug_pretrain")
parser.add_argument("--score_model_path", type=str, default="debug_score")
# Only for backdoor modules
parser.add_argument("--linf_limit", type=int, default=4) # out off 255
parser.add_argument(
"--backdoor_type",
type=str,
default="all2all",
choices=["all2all", "all2one"],
)
args = parser.parse_args()
main(args)