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import os
import json
import random
from PIL import Image
import torch
from torch.utils.data import Dataset
from torchvision import transforms
from transformers import T5TokenizerFast
from data.transforms import build_coco_transform
class CocoCaptionDataset(Dataset):
def __init__(
self,
split="train",
image_size=224,
tokenizer_name="t5-small",
max_caption_length=64,
data_dir="data/processed",
random_caption=True,
normalize=True,
):
assert split in ["train", "val", "test"]
self.split = split
self.image_size = image_size
self.random_caption = random_caption
self.max_caption_length = max_caption_length
self.images_dir = os.path.join(data_dir, "images")
self.tokenizer = T5TokenizerFast.from_pretrained(tokenizer_name)
# Load captions.json and splits.json
captions_file = os.path.join(data_dir, "captions.json")
splits_file = os.path.join(data_dir, "splits.json")
with open(captions_file) as f:
self.captions_data = json.load(f)
with open(splits_file) as f:
self.splits = json.load(f)
# Cast IDs to strings
self.image_ids = [str(i) for i in self.splits[split]]
self.transform = build_coco_transform(image_size=image_size)
def __len__(self):
return len(self.image_ids)
def __getitem__(self, idx):
image_id = self.image_ids[idx]
img_path = os.path.join(self.images_dir, f"{int(image_id):012d}.jpg")
img = Image.open(img_path).convert("RGB")
pixel_values = self.transform(img)
captions = self.captions_data[image_id]["captions"]
if self.random_caption:
caption = random.choice(captions)
else:
caption = captions[0] # deterministic for eval
# Tokenize caption (no prefix needed for T5 small)
encoding = self.tokenizer(
caption,
padding="max_length",
truncation=True,
max_length=self.max_caption_length,
return_tensors="pt"
)
input_ids = encoding.input_ids.squeeze(0)
attention_mask = encoding.attention_mask.squeeze(0)
return {
"pixel_values": pixel_values,
"input_ids": input_ids,
"attention_mask": attention_mask,
"image_id": image_id,
}
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