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checkcode_vitb.py
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checkcode_vitb.py
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"""
Unofficial code for VPT(Visual Prompt Tuning) paper of arxiv 2203.12119
A toy Tuning process that demostrates the code
the code is based on timm
"""
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
from tqdm import tqdm
from PromptModels.GetPromptModel import build_promptmodel
import argparse
import torchvision
import torchvision.models as models
import torchvision.transforms as transforms
from torch.utils.data.dataloader import DataLoader
from timm.scheduler import CosineLRScheduler
from datasets import create_datasets
from utils import *
from timm.models.vision_transformer import vit_base_patch16_224
parser = argparse.ArgumentParser()
parser.add_argument('--dataset', type=str, default='cifar100')
parser.add_argument('--split', type=str, default='1000')
parser.add_argument('--data_path', type=str, default='data/')
parser.add_argument('--batch_size', type=int, default=128)
parser.add_argument('--base_lr', type=float, default=0.01)
parser.add_argument('--lr', type=float, default=0.005)
parser.add_argument('--momentum', type=float, default=0.9)
parser.add_argument('--weight_decay', type=float, default=0.01)
parser.add_argument('--image_size', type=int, default=224)
parser.add_argument('--epochs', type=int, default=100)
parser.add_argument('--warmup_epochs', type=int, default=10)
parser.add_argument('--optimizer', type=str, default='sgd')
parser.add_argument('--scheduler', type=str, default='cosine')
parser.add_argument('--prompt_length', type=int, default=10)
parser.add_argument('--name', type=str, default='vitb_full_ft_check')
parser.add_argument('--base_model', type=str, default='vit_base_patch16_224_in21k')
def setup_seed(seed): # setting up the random seed
import random
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
torch.backends.cudnn.deterministic = True
def main(args):
setup_seed(42)
save_path = os.path.join('./save', args.name)
ensure_path(save_path)
set_log_path(save_path)
device = 'cuda' if torch.cuda.is_available() else 'cpu'
args.lr = args.base_lr * args.batch_size / 256
# labels = torch.ones(batch_size).long() # long ones
norm_params = {'mean': [0.485, 0.456, 0.406],
'std': [0.229, 0.224, 0.225]}
normalize = transforms.Normalize(**norm_params)
train_transforms = transforms.Compose([
transforms.RandomResizedCrop(args.image_size),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
])
val_transforms = transforms.Compose([
transforms.Resize((args.image_size * 8 // 7, args.image_size * 8 // 7)),
transforms.CenterCrop((args.image_size, args.image_size)),
transforms.ToTensor(),
normalize,
])
train_dataset, val_dataset, num_classes = create_datasets(args.data_path, train_transforms, val_transforms, args.dataset, args.split)
log(f"train dataset: {len(train_dataset)} samples")
log(f"val dataset: {len(val_dataset)} samples")
train_loader = DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True, num_workers=8, pin_memory=True)
val_loader = DataLoader(val_dataset, batch_size=int(args.batch_size), shuffle=False)
# model = build_promptmodel(num_classes=num_classes, img_size=args.image_size, base_model=args.base_model, model_idx='ViT', patch_size=16,
# Prompt_Token_num=args.prompt_length, VPT_type="Deep") # VPT_type = "Shallow"
model = vit_base_patch16_224(pretrained=True)
model.head = nn.Linear(768, num_classes)
# test for updating
# prompt_state_dict = model.obtain_prompt()
# model.load_prompt(prompt_state_dict)
model = model.to(device)
optimizer = optim.SGD(model.parameters(), lr=args.lr, weight_decay=args.weight_decay, momentum=args.momentum)
scheduler = CosineLRScheduler(optimizer, warmup_lr_init=1e-4, t_initial=args.epochs, cycle_decay=0.1, warmup_t=args.warmup_epochs)
criterion = nn.CrossEntropyLoss()
# preds = model(data) # (1, class_number)
# print('before Tuning model output：', preds)
# check backwarding tokens
for param in model.parameters():
if param.requires_grad:
print(param.shape)
max_va = -1
for epoch in range(args.epochs):
print('epoch:',epoch)
aves_keys = ['tl', 'ta', 'vl', 'va']
aves = {k: Averager() for k in aves_keys}
iter_num = 0
model.train()#Freeze()
for imgs, targets in tqdm(train_loader, desc='train', leave=False):
imgs = imgs.to(device)
targets = targets.to(device)
optimizer.zero_grad()
outputs = model(imgs)
loss = criterion(outputs, targets)
loss.backward()
optimizer.step()
acc = compute_acc(outputs, targets)
aves['tl'].add(loss.item())
aves['ta'].add(acc)
iter_num += 1
# print()
model.eval()
for imgs, targets in tqdm(val_loader, desc='val', leave=False):
imgs = imgs.to(device)
targets = targets.to(device)
outputs = model(imgs)
loss = criterion(outputs, targets)
acc = compute_acc(outputs, targets)
aves['vl'].add(loss.item())
aves['va'].add(acc)
log_str = 'epoch {}, train {:.4f}|{:.4f}'.format(
epoch, aves['tl'].v, aves['ta'].v)
log_str += ', val {:.4f}|{:.4f}'.format(aves['vl'].v, aves['va'].v)
log(log_str)
# preds = model(data) # (1, class_number)
print('After Tuning model output: ', aves['va'].v)
save_obj = {
'config': vars(args),
'state_dict': model.state_dict(),
'val_acc': aves['va'].v,
}
if epoch <= args.epochs:
torch.save(save_obj, os.path.join(save_path, 'epoch-last.pth'))
torch.save(save_obj, os.path.join(
save_path, 'epoch-{}.pth'.format(epoch)))
if aves['va'].v > max_va:
max_va = aves['va'].v
torch.save(save_obj, os.path.join(save_path, 'max-va.pth'))
else:
torch.save(save_obj, os.path.join(save_path, 'epoch-ex.pth'))
scheduler.step(epoch)
if __name__ == "__main__":
args = parser.parse_args()
main(args)