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data_lp.py
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import utils
import random
import torch as th
from PIL import Image
from pathlib import Path
import pytorch_lightning as pl
from torch.utils.data import Dataset, DataLoader
from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
from torchvision.transforms import InterpolationMode
try:
from torchvision.transforms import InterpolationMode
BICUBIC = InterpolationMode.BICUBIC
except ImportError:
BICUBIC = Image.BICUBIC
def _convert_image_to_rgb(image):
return image.convert("RGB")
def _transform(n_px):
return Compose([
Resize(n_px, interpolation=BICUBIC),
CenterCrop(n_px),
_convert_image_to_rgb,
ToTensor(),
Normalize((0.48145466, 0.4578275, 0.40821073),
(0.26862954, 0.26130258, 0.27577711)),
])
preprocess = _transform(224)
class LinearProbeDataset(Dataset):
"""
Provide (image, label) pair for association matrix optimization,
where image is a tensor representing image after transformation
"""
def __init__(self, cls2img, processed_path, n_shots, cls_names, img_ext='jpg'):
self.data_path = processed_path
self.images = []
self.labels = []
self.img_ext = img_ext
for cls_name, imgs in cls2img.items():
if cls_name not in cls_names:
continue
label = cls_names.index(cls_name)
if n_shots != 'all':
imgs = random.sample(imgs, n_shots)
self.images.extend(imgs)
self.labels += [label] * len(imgs)
def __len__(self):
return len(self.images)
def __getitem__(self, idx):
image = preprocess(
Image.open(
self.data_path.joinpath('{}{}'.format(self.images[idx],
self.img_ext))))
return image, self.labels[idx]
class DataModule(pl.LightningDataModule):
def __init__(self,
data_root,
batch_size,
data_split_path,
img_path,
n_shots,
cls_names,
num_workers=0,
img_ext='.jpg'):
super().__init__()
self.data_root = Path(data_root)
self.data_root.mkdir(exist_ok=True)
self.bs = batch_size
self.num_workers = num_workers
self.cls2train = utils.pickle_load(
Path(data_split_path).joinpath('class2images_train.p'))
self.cls2val = utils.pickle_load(
Path(data_split_path).joinpath('class2images_val.p'))
self.cls2test = utils.pickle_load(
Path(data_split_path).joinpath('class2images_test.p'))
self.data_split_path = data_split_path
self.img_path = Path(img_path)
self.cls_names = cls_names
self.img_ext = img_ext
self.n_shots = n_shots
def compute_img_feat(self, cls2img, n_shots):
labels = []
all_img_paths = []
for i, (cls_name, img_names) in enumerate(cls2img.items()):
if n_shots != 'all':
img_names = img_names[:n_shots]
labels.extend([self.cls_names.index(cls_name)] * len(img_names))
all_img_paths.extend(
[self.img_path.joinpath('{}{}'.format(img_name, self.img_ext))\
for img_name in img_names])
img_feat = utils.prepare_img_feat(all_img_paths)
return img_feat, th.tensor(labels)
def prepare_img_feat_for_splits(self):
train_img_data_path = self.data_root.joinpath('train_img_data.pth')
val_img_data_path = self.data_root.joinpath('val_img_data.pth')
test_img_data_path = self.data_root.joinpath('test_img_data.pth')
if not train_img_data_path.exists():
img_feat_train, label_train = self.compute_img_feat(self.cls2train, self.n_shots)
th.save({
"img_feat": img_feat_train,
"label": label_train
}, train_img_data_path)
else:
img_data = th.load(train_img_data_path)
img_feat_train, label_train = img_data['img_feat'], img_data[
'label']
if not val_img_data_path.exists():
img_feat_val, label_val = self.compute_img_feat(self.cls2val, self.n_shots)
th.save({
"img_feat": img_feat_val,
"label": label_val
}, val_img_data_path)
else:
img_data = th.load(val_img_data_path)
img_feat_val, label_val = img_data['img_feat'], img_data['label']
if not test_img_data_path.exists():
img_feat_test, label_test = self.compute_img_feat(self.cls2test, self.n_shots)
th.save({
"img_feat": img_feat_test,
"label": label_test
}, test_img_data_path)
else:
img_data = th.load(test_img_data_path)
img_feat_test, label_test = img_data['img_feat'], img_data['label']
self.img_feat_train = img_feat_train
self.label_train = label_train
self.img_feat_val = img_feat_val
self.label_val = label_val
self.img_feat_test = img_feat_test
self.label_test = label_test
def setup(self, stage=None):
self.train_dataset = self.val_dataset = self.test_dataset = None
raise NotImplementedError
def train_dataloader(self):
return DataLoader(self.train_dataset,
batch_size=self.bs,
shuffle=True,
num_workers=self.num_workers,
pin_memory=True)
def val_dataloader(self):
return DataLoader(self.val_dataset,
batch_size=self.bs,
num_workers=self.num_workers,
pin_memory=True)
def test_dataloader(self):
return DataLoader(self.test_dataset,
batch_size=self.bs,
num_workers=self.num_workers,
pin_memory=True)
def predict_dataloader(self):
return self.test_dataloader()
class LinearProbeDataModule(DataModule):
def __init__(self,
data_root,
batch_size,
data_split_path,
img_path,
n_shots,
cls_names,
num_workers=0,
img_ext='jpg'):
super().__init__(data_root, batch_size, data_split_path, img_path, n_shots, cls_names,
num_workers)
self.n_shots = n_shots
self.img_ext = img_ext
def setup(self, stage=None):
self.train_dataset = LinearProbeDataset(self.cls2train, self.img_path,
self.n_shots, self.cls_names, img_ext=self.img_ext)
self.val_dataset = LinearProbeDataset(self.cls2val, self.img_path,
'all', self.cls_names, img_ext=self.img_ext)
self.test_dataset = LinearProbeDataset(self.cls2test, self.img_path,
'all', self.cls_names, img_ext=self.img_ext)