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train_vm_fast.py
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train_vm_fast.py
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from functools import partial
from torch.cuda.amp import autocast, GradScaler
from torchvision import transforms
import argparse
import sys
import os
sys.path.append(".")
from data import prepare_padding_data, prepare_watermarking_data, IMAGENETNORMALIZE
from reprogramming import *
from mapping import *
from cfg import *
if __name__ == '__main__':
p = argparse.ArgumentParser()
p.add_argument('--reprogramming', choices=["padding", "watermarking"], default="padding")
p.add_argument('--mapping', choices=["rlm", "flm", "ilm", "blm", "blmp"], default="blmp")
p.add_argument('--seed', type=int, default=0)
p.add_argument('--dataset', choices=["cifar10", "cifar100", "dtd", "flowers102", "ucf101", "food101", "gtsrb", "svhn", "eurosat", "oxfordpets", "stanfordcars", "sun397"], default="sun397")
args = p.parse_args()
device = "cuda:0" if torch.cuda.is_available() else "cpu"
set_seed(args.seed)
save_path = os.path.join(results_path,'vmfast_' + args.mapping + '_' + args.reprogramming + '_' + args.dataset + '_' + str(args.seed))
imgsize = 224
padding_size = imgsize / 2
# Data
if args.reprogramming == "padding":
loaders, configs = prepare_padding_data(args.dataset, data_path=data_path)
class_names = configs['class_names']
normalize = transforms.Normalize(IMAGENETNORMALIZE['mean'], IMAGENETNORMALIZE['std'])
elif args.reprogramming == "watermarking":
train_preprocess = transforms.Compose([
transforms.Resize((imgsize + 4, imgsize + 4)),
transforms.RandomCrop(imgsize),
transforms.RandomHorizontalFlip(),
transforms.Lambda(lambda x: x.convert('RGB') if hasattr(x, 'convert') else x),
transforms.ToTensor(),
transforms.Normalize(IMAGENETNORMALIZE['mean'], IMAGENETNORMALIZE['std']),
])
test_preprocess = transforms.Compose([
transforms.Resize((imgsize, imgsize)),
transforms.Lambda(lambda x: x.convert('RGB') if hasattr(x, 'convert') else x),
transforms.ToTensor(),
transforms.Normalize(IMAGENETNORMALIZE['mean'], IMAGENETNORMALIZE['std']),
])
loaders, class_names = prepare_watermarking_data(args.dataset, data_path=data_path, preprocess=train_preprocess, test_process=test_preprocess)
# Network
from torchvision.models import resnet18, ResNet18_Weights
network = resnet18(weights=ResNet18_Weights.IMAGENET1K_V1).to(device)
network.requires_grad_(False)
network.eval()
# Visual Prompt
if args.reprogramming == "padding":
visual_prompt = PaddingVR(imgsize, mask=configs['mask'], normalize=normalize).to(device)
elif args.reprogramming == "watermarking":
visual_prompt = WatermarkingVR(imgsize, padding_size).to(device)
# Optimizer
optimizer = torch.optim.Adam(visual_prompt.parameters(), lr=config_vm_fast['lr'])
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[int(0.5 * config_vm_fast['epoch']),
int(0.72 * config_vm_fast['epoch'])], gamma=0.1)
os.makedirs(save_path, exist_ok=True)
# Train
best_acc = 0.
scaler = GradScaler()
# Label Mapping for RLM, fLM, ILM, BLM, BLM++
if args.mapping == 'rlm':
mapping_matrix = torch.randperm(1000)[:len(class_names)]
elif args.mapping == 'flm':
mapping_matrix = one2one_mappnig_matrix(visual_prompt, network, loaders['train'])
elif args.mapping == 'ilm':
mapping_matrix = one2one_mappnig_matrix(visual_prompt, network, loaders['train'])
elif args.mapping == 'blm':
mapping_matrix = blm_reweight_matrix(visual_prompt, network, loaders['train'], lap=config_vm_fast['blm']['lap'])
elif args.mapping == 'blmp':
mapping_matrix = blmp_reweight_matrix(visual_prompt, network, loaders['train'], lap=config_vm_fast['blmp']['lap'], k=int(len(class_names) * config_vm_fast['blmp']['topk_ratio']))
for epoch in range(config_vm_fast['epoch']):
if args.mapping in ['rlm', 'flm', 'ilm']:
label_mapping = partial(label_mapping_base, mapping_sequence=mapping_matrix)
elif args.mapping in ['blm', 'blmp']:
label_mapping = partial(label_mapping_calculation, mapping_matrix=mapping_matrix)
visual_prompt.train()
total_num = 0
true_num = 0
loss_sum = 0
probs_list = []
ys = []
pbar = tqdm(loaders['train'], total=len(loaders['train']), desc=f"Training Epo {epoch}", ncols=100)
for x, y in pbar:
pbar.set_description_str(f"Training Epo {epoch}", refresh=True)
x, y = x.to(device), y.to(device)
optimizer.zero_grad()
with autocast():
fx0 = network(visual_prompt(x))
fx = label_mapping(fx0)
loss = F.cross_entropy(fx, y, reduction='mean')
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()
total_num += y.size(0)
true_num += torch.argmax(fx, 1).eq(y).float().sum().item()
loss_sum += loss.item() * fx.size(0)
pbar.set_postfix_str(f"Training Acc {100 * true_num / total_num:.2f}%")
with torch.no_grad():
if args.mapping == 'blmp':
probabilities = F.softmax(fx0, dim=1)
top_values, top_indices = torch.topk(probabilities, int(len(class_names) * config_vm_fast['blmp']['topk_ratio']), dim=1)
result = torch.zeros_like(probabilities)
result.scatter_(1, top_indices, top_values)
elif args.mapping in ['blm', 'ilm', 'flm', 'rlm']:
result = fx0
probs_list.append(result.cpu().float())
ys.append(y)
probs = torch.cat(probs_list, dim=0)
ys = torch.cat(ys).cpu().int()
scheduler.step()
# Test
visual_prompt.eval()
total_num = 0
true_num = 0
pbar = tqdm(loaders['test'], total=len(loaders['test']), desc=f"Testing Epo {epoch}", ncols=100)
for x, y in pbar:
x, y = x.to(device), y.to(device)
with torch.no_grad():
fx0 = network(visual_prompt(x))
fx = label_mapping(fx0)
total_num += y.size(0)
true_num += torch.argmax(fx, 1).eq(y).float().sum().item()
acc = true_num / total_num
pbar.set_postfix_str(f"Testing Acc {100 * acc:.2f}%, Best Acc {100 * best_acc:.2f}%")
# Save CKPT
state_dict = {
"visual_prompt_dict": visual_prompt.state_dict(),
"epoch": epoch,
"best_acc": best_acc,
"mapping_matrix": mapping_matrix,
}
if acc > best_acc:
best_acc = acc
state_dict['best_acc'] = best_acc
torch.save(state_dict, os.path.join(save_path, 'best.pth'))
# Update the mapping matrix at the end of the epoch
if args.mapping == 'blmp':
mapping_matrix = update_blmp_reweight_matrix(probs, ys, device, lap=config_vm_fast['blmp']['lap'])
elif args.mapping == 'blm':
mapping_matrix = update_blm_reweight_matrix(probs, ys, device, lap=config_vm_fast['blm']['lap'])
elif args.mapping == 'ilm':
mapping_matrix = update_one2one_mappnig_matrix(probs, ys)