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import json
from pathlib import Path
import torch
from lightning import LightningDataModule
from PIL import Image
from torch.utils.data import DataLoader, Dataset
from src.data.transforms import transform_test, transform_train
from src.data.utils import pre_caption
Image.MAX_IMAGE_PIXELS = None # Disable DecompressionBombWarning
class FashionIQDataModule(LightningDataModule):
def __init__(
self,
batch_size: int,
num_workers: int = 4,
pin_memory: bool = True,
annotation: dict = {"train": "", "val": ""},
targets: dict = {"train": "", "val": ""},
img_dirs: dict = {"train": "", "val": ""},
emb_dirs: dict = {"train": "", "val": ""},
image_size: int = 384,
**kwargs, # type: ignore
) -> None:
super().__init__()
self.save_hyperparameters(logger=False)
self.batch_size = batch_size
self.num_workers = num_workers
self.pin_memory = pin_memory
self.transform_train = transform_train(image_size)
self.transform_test = transform_test(image_size)
self.data_train = FashionIQDataset(
transform=self.transform_train,
annotation=annotation["train"],
targets=targets["train"],
img_dir=img_dirs["train"],
emb_dir=emb_dirs["train"],
split="train",
)
self.data_val = FashionIQDataset(
transform=self.transform_test,
annotation=annotation["val"],
targets=targets["val"],
img_dir=img_dirs["val"],
emb_dir=emb_dirs["val"],
split="val",
)
def train_dataloader(self):
return DataLoader(
dataset=self.data_train,
batch_size=self.batch_size,
num_workers=self.num_workers,
pin_memory=self.pin_memory,
shuffle=True,
drop_last=True,
)
def val_dataloader(self):
return DataLoader(
dataset=self.data_val,
batch_size=self.batch_size,
num_workers=self.num_workers,
pin_memory=self.pin_memory,
shuffle=False,
drop_last=False,
)
class FashionIQTestDataModule(LightningDataModule):
def __init__(
self,
batch_size: int,
annotation: str,
targets: str,
img_dirs: str,
emb_dirs: str,
num_workers: int = 4,
pin_memory: bool = True,
image_size: int = 384,
**kwargs, # type: ignore
) -> None:
super().__init__()
self.save_hyperparameters(logger=False)
self.batch_size = batch_size
self.num_workers = num_workers
self.pin_memory = pin_memory
self.transform_test = transform_test(image_size)
self.data_test = FashionIQDataset(
transform=self.transform_test,
annotation=annotation,
targets=targets,
img_dir=img_dirs,
emb_dir=emb_dirs,
split="test",
)
def test_dataloader(self):
return DataLoader(
dataset=self.data_test,
batch_size=self.batch_size,
num_workers=self.num_workers,
pin_memory=self.pin_memory,
shuffle=False,
drop_last=False,
)
class FashionIQDataset(Dataset):
def __init__(
self,
transform,
annotation: str,
targets: str,
img_dir: str,
emb_dir: str,
split: str,
max_words: int = 30,
) -> None:
super().__init__()
self.transform = transform
self.annotation_pth = annotation
assert Path(annotation).exists(), f"Annotation file {annotation} does not exist"
self.annotation = json.load(open(annotation, "r"))
assert Path(targets).exists(), f"Targets file {targets} does not exist"
self.targets = json.load(open(targets, "r"))
self.target_ids = list(set(self.targets))
self.target_ids.sort()
self.split = split
self.max_words = max_words
self.img_dir = Path(img_dir)
self.emb_dir = Path(emb_dir)
assert split in [
"train",
"val",
"test",
], f"Invalid split: {split}, must be one of train, val, or test"
assert self.img_dir.exists(), f"Image directory {img_dir} does not exist"
assert self.emb_dir.exists(), f"Embedding directory {emb_dir} does not exist"
self.id2int = {id: i for i, id in enumerate(self.target_ids)}
self.int2id = {i: id for i, id in enumerate(self.target_ids)}
self.pairid2ref = {
id: self.id2int[ann["candidate"]] for id, ann in enumerate(self.annotation)
}
self.pairid2tar = {
id: self.id2int[ann["target"]] for id, ann in enumerate(self.annotation)
}
img_pths = self.img_dir.glob("*.png")
emb_pths = self.emb_dir.glob("*.pth")
self.id2imgpth = {img_pth.stem: img_pth for img_pth in img_pths}
self.id2embpth = {emb_pth.stem: emb_pth for emb_pth in emb_pths}
for ann in self.annotation:
assert (
ann["candidate"] in self.id2imgpth
), f"Path to candidate {ann['candidate']} not found in {self.img_dir}"
assert (
ann["candidate"] in self.id2embpth
), f"Path to candidate {ann['candidate']} not found in {self.emb_dir}"
assert (
ann["target"] in self.id2imgpth
), f"Path to target {ann['target']} not found"
assert (
ann["target"] in self.id2embpth
), f"Path to target {ann['target']} not found"
def __len__(self) -> int:
return len(self.annotation)
def __getitem__(self, index):
ann = self.annotation[index]
reference_img_pth = self.id2imgpth[ann["candidate"]]
reference_img = Image.open(reference_img_pth).convert("RGB")
reference_img = self.transform(reference_img)
cap1, cap2 = ann["captions"]
caption = f"{cap1} and {cap2}"
caption = pre_caption(caption, self.max_words)
target_emb_pth = self.id2embpth[ann["target"]]
target_feat = torch.load(target_emb_pth).cpu()
return (reference_img, target_feat, caption, index)
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