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extract_features.py
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extract_features.py
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import torch
from torch.utils.data import DataLoader
import timm
from torchvision import transforms
import torchvision
import argparse
import os
from tqdm import tqdm
from data.stanford_cars import CarsDataset
from data.cifar import CustomCIFAR10, CustomCIFAR100, cifar_10_root, cifar_100_root
from data.herbarium_19 import HerbariumDataset19, herbarium_dataroot
from data.augmentations import get_transform
from data.imagenet import get_imagenet_100_datasets
from data.data_utils import MergedDataset
from data.cub import CustomCub2011, cub_root
from data.fgvc_aircraft import FGVCAircraft, aircraft_root
from project_utils.general_utils import strip_state_dict, str2bool
from copy import deepcopy
from config import feature_extract_dir, dino_pretrain_path
def extract_features_dino(model, loader, save_dir):
model.to(device)
model.eval()
with torch.no_grad():
for batch_idx, batch in enumerate(tqdm(loader)):
images, labels, idxs = batch[:3]
images = images.to(device)
features = model(images) # CLS_Token for ViT, Average pooled vector for R50
# Save features
for f, t, uq in zip(features, labels, idxs):
t = t.item()
uq = uq.item()
save_path = os.path.join(save_dir, f'{t}', f'{uq}.npy')
torch.save(f.detach().cpu().numpy(), save_path)
def extract_features_timm(model, loader, save_dir):
model.to(device)
model.eval()
with torch.no_grad():
for batch_idx, batch in enumerate(tqdm(loader)):
images, labels, idxs = batch[:3]
images = images.to(device)
features = model.forward_features(images) # CLS_Token for ViT, Average pooled vector for R50
# Save features
for f, t, uq in zip(features, labels, idxs):
t = t.item()
uq = uq.item()
save_path = os.path.join(save_dir, f'{t}', f'{uq}.npy')
torch.save(f.detach().cpu().numpy(), save_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description='cluster',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--batch_size', default=128, type=int)
parser.add_argument('--num_workers', default=8, type=int)
parser.add_argument('--root_dir', type=str, default=feature_extract_dir)
parser.add_argument('--warmup_model_dir', type=str,
default=None)
parser.add_argument('--use_best_model', type=str2bool, default=True)
parser.add_argument('--model_name', type=str, default='vit_dino', help='Format is {model_name}_{pretrain}')
parser.add_argument('--dataset', type=str, default='aircraft', help='options: cifar10, cifar100, scars')
# ----------------------
# INIT
# ----------------------
args = parser.parse_args()
device = torch.device('cuda:0')
args.save_dir = os.path.join(args.root_dir, f'{args.model_name}_{args.dataset}')
print(args)
print('Loading model...')
# ----------------------
# MODEL
# ----------------------
if args.model_name == 'vit_dino':
extract_features_func = extract_features_dino
args.interpolation = 3
args.crop_pct = 0.875
pretrain_path = dino_pretrain_path
model = torch.hub.load('facebookresearch/dino:main', 'dino_vitb16', pretrained=False)
state_dict = torch.load(pretrain_path, map_location='cpu')
model.load_state_dict(state_dict)
_, val_transform = get_transform('imagenet', image_size=224, args=args)
elif args.model_name == 'resnet50_dino':
extract_features_func = extract_features_dino
args.interpolation = 3
args.crop_pct = 0.875
pretrain_path = '/work/sagar/pretrained_models/dino/dino_resnet50_pretrain.pth'
model = torch.hub.load('facebookresearch/dino:main', 'dino_resnet50', pretrained=False)
state_dict = torch.load(pretrain_path, map_location='cpu')
model.load_state_dict(state_dict)
_, val_transform = get_transform('imagenet', image_size=224, args=args)
else:
raise NotImplementedError
if args.warmup_model_dir is not None:
warmup_id = args.warmup_model_dir.split('(')[1].split(')')[0]
if args.use_best_model:
args.warmup_model_dir = args.warmup_model_dir[:-3] + '_best.pt'
args.save_dir += '_(' + args.warmup_model_dir.split('(')[1].split(')')[0] + ')_best'
else:
args.save_dir += '_(' + args.warmup_model_dir.split('(')[1].split(')')[0] + ')'
print(f'Using weights from {args.warmup_model_dir} ...')
state_dict = torch.load(args.warmup_model_dir)
model.load_state_dict(state_dict)
print(f'Saving to {args.save_dir}')
print('Loading data...')
# ----------------------
# DATASET
# ----------------------
if args.dataset == 'cifar10':
train_dataset = CustomCIFAR10(root=cifar_10_root, train=True, transform=val_transform)
test_dataset = CustomCIFAR10(root=cifar_10_root, train=False, transform=val_transform)
targets = list(set(train_dataset.targets))
elif args.dataset == 'cifar100':
train_dataset = CustomCIFAR100(root=cifar_100_root, train=True, transform=val_transform)
test_dataset = CustomCIFAR100(root=cifar_100_root, train=False, transform=val_transform)
targets = list(set(train_dataset.targets))
elif args.dataset == 'scars':
train_dataset = CarsDataset(train=True, transform=val_transform)
test_dataset = CarsDataset(train=False, transform=val_transform)
targets = list(set(train_dataset.target))
targets = [i - 1 for i in targets] # SCars are labelled 1 - 197. Change to 0 - 196
elif args.dataset == 'herbarium_19':
train_dataset = HerbariumDataset19(root=os.path.join(herbarium_dataroot, 'small-train'),
transform=val_transform)
test_dataset = HerbariumDataset19(root=os.path.join(herbarium_dataroot, 'small-validation'),
transform=val_transform)
targets = list(set(train_dataset.targets))
elif args.dataset == 'imagenet_100':
datasets = get_imagenet_100_datasets(train_transform=val_transform, test_transform=val_transform,
train_classes=range(50),
prop_train_labels=0.5)
datasets['train_labelled'].target_transform = None
datasets['train_unlabelled'].target_transform = None
train_dataset = MergedDataset(labelled_dataset=deepcopy(datasets['train_labelled']),
unlabelled_dataset=deepcopy(datasets['train_unlabelled']))
test_dataset = datasets['test']
targets = list(set(test_dataset.targets))
elif args.dataset == 'cub':
train_dataset = CustomCub2011(root=cub_root, transform=val_transform, train=True)
test_dataset = CustomCub2011(root=cub_root, transform=val_transform, train=False)
targets = list(set(train_dataset.data.target.values))
targets = [i - 1 for i in targets] # SCars are labelled 1 - 200. Change to 0 - 199
elif args.dataset == 'aircraft':
train_dataset = FGVCAircraft(root=aircraft_root, transform=val_transform, split='trainval')
test_dataset = FGVCAircraft(root=aircraft_root, transform=val_transform, split='test')
targets = list(set([s[1] for s in train_dataset.samples]))
else:
raise NotImplementedError
# ----------------------
# DATALOADER
# ----------------------
train_loader = DataLoader(train_dataset, batch_size=args.batch_size, shuffle=False, num_workers=args.num_workers)
test_loader = DataLoader(test_dataset, batch_size=args.batch_size, shuffle=False, num_workers=args.num_workers)
print('Creating base directories...')
# ----------------------
# INIT SAVE DIRS
# Create a directory for each class
# ----------------------
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
for fold in ('train', 'test'):
fold_dir = os.path.join(args.save_dir, fold)
if not os.path.exists(fold_dir):
os.mkdir(fold_dir)
for t in targets:
target_dir = os.path.join(fold_dir, f'{t}')
if not os.path.exists(target_dir):
os.mkdir(target_dir)
# ----------------------
# EXTRACT FEATURES
# ----------------------
# Extract train features
train_save_dir = os.path.join(args.save_dir, 'train')
print('Extracting features from train split...')
extract_features_func(model=model, loader=train_loader, save_dir=train_save_dir)
# Extract test features
test_save_dir = os.path.join(args.save_dir, 'test')
print('Extracting features from test split...')
extract_features_func(model=model, loader=test_loader, save_dir=test_save_dir)
print('Done!')