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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 id2int, pre_caption
Image.MAX_IMAGE_PIXELS = None # Disable DecompressionBombWarning
class CIRRDataModule(LightningDataModule):
def __init__(
self,
batch_size: int,
num_workers: int = 4,
pin_memory: bool = True,
annotation: 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 = CIRRDataset(
transform=self.transform_train,
annotation=annotation["train"],
img_dir=img_dirs["train"],
emb_dir=emb_dirs["train"],
split="train",
)
self.data_val = CIRRDataset(
transform=self.transform_test,
annotation=annotation["val"],
img_dir=img_dirs["val"],
emb_dir=emb_dirs["val"],
split="val",
)
def prepare_data(self):
# things to do on 1 GPU/TPU (not on every GPU/TPU in DDP)
# download data, pre-process, split, save to disk, etc...
pass
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 CIRRTestDataModule(LightningDataModule):
def __init__(
self,
batch_size: int,
annotation: 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 = CIRRDataset(
transform=self.transform_test,
annotation=annotation,
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 CIRRDataset(Dataset):
def __init__(
self,
transform,
annotation: 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"))
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.pairid2ref = {
ann["pairid"]: id2int(ann["reference"]) for ann in self.annotation
}
self.int2id = {
id2int(ann["reference"]): ann["reference"] for ann in self.annotation
}
ids = {ann["reference"] for ann in self.annotation}
assert len(self.int2id) == len(ids), "Reference ids are not unique"
self.pairid2members = {
ann["pairid"]: id2int(ann["img_set"]["members"]) for ann in self.annotation
}
if split != "test":
self.pairid2tar = {
ann["pairid"]: id2int(ann["target_hard"]) for ann in self.annotation
}
else:
self.pairid2tar = None
if split == "train":
img_pths = self.img_dir.glob("*/*.png")
emb_pths = self.emb_dir.glob("*/*.pth")
else:
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["reference"] in self.id2imgpth
), f"Path to reference {ann['reference']} not found in {self.img_dir}"
assert (
ann["reference"] in self.id2embpth
), f"Path to reference {ann['reference']} not found in {self.emb_dir}"
if split != "test":
assert (
ann["target_hard"] in self.id2imgpth
), f"Path to target {ann['target_hard']} not found"
assert (
ann["target_hard"] in self.id2embpth
), f"Path to target {ann['target_hard']} not found"
def __len__(self) -> int:
return len(self.annotation)
def __getitem__(self, index):
ann = self.annotation[index]
reference_img_pth = self.id2imgpth[ann["reference"]]
reference_img = Image.open(reference_img_pth).convert("RGB")
reference_img = self.transform(reference_img)
caption = pre_caption(ann["caption"], self.max_words)
if self.split == "test":
reference_feat = torch.load(self.id2embpth[ann["reference"]])
return reference_img, reference_feat, caption, ann["pairid"]
target_emb_pth = self.id2embpth[ann["target_hard"]]
target_feat = torch.load(target_emb_pth).cpu()
return (
reference_img,
target_feat,
caption,
ann["pairid"],
)
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