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e4721a6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 | import os
import pandas as pd
from PIL import Image
import torch
from torch.utils.data import Dataset
from transformers import GPT2Tokenizer
class CaptionDataset(Dataset):
def __init__(self, root_dir, captions_file, transform=None, max_length=40):
self.root_dir = root_dir
self.transform = transform
self.max_length = max_length
# Load captions
# Format: image,caption (csv)
self.df = pd.read_csv(captions_file, delimiter=',')
# Rename columns to match expected internal names if necessary, or just use them directly
# The file has 'image' and 'caption' columns based on inspection
self.df.rename(columns={'image': 'image_name', 'caption': 'comment'}, inplace=True)
self.df['image_name'] = self.df['image_name'].str.strip()
self.df['comment'] = self.df['comment'].str.strip()
self.df = self.df.dropna()
self.captions = self.df['comment'].tolist()
self.images = self.df['image_name'].tolist()
# Initialize Tokenizer
self.tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
# GPT2 doesn't have a pad token, so we use eos_token as pad_token
self.tokenizer.pad_token = self.tokenizer.eos_token
def __len__(self):
return len(self.captions)
def __getitem__(self, idx):
caption = self.captions[idx]
img_name = self.images[idx]
img_path = os.path.join(self.root_dir, img_name)
try:
image = Image.open(img_path).convert("RGB")
except Exception:
# Fallback for missing images or errors, return next item
return self.__getitem__((idx + 1) % len(self))
if self.transform:
image = self.transform(image)
# Tokenize caption
# We add a special prefix to prompt the model if desired, but for direct captioning:
# Format: [Image Feature] -> Caption
encoding = self.tokenizer(
caption,
truncation=True,
padding='max_length',
max_length=self.max_length,
return_tensors='pt'
)
input_ids = encoding['input_ids'].squeeze()
attention_mask = encoding['attention_mask'].squeeze()
return image, input_ids, attention_mask
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