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a745a5e | 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 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 | import json
import os
import random
import re
from torch.utils.data import Dataset
from PIL import Image
class COCODatasetAdvanced(Dataset):
def __init__(self,
annotation_path,
image_folder,
processor,
mode="mixed",
max_length=40):
self.image_folder = image_folder
self.processor = processor
self.max_length = max_length
self.mode = mode
with open(annotation_path, "r") as f:
raw_data = [json.loads(line) for line in f]
self.annotations = []
for ann in raw_data:
filtered_captions = []
for cap in ann["captions"]:
cap = cap.strip().lower()
# ---------- QUALITY FILTERS ----------
# Remove very short captions
if len(cap.split()) < 3:
continue
# Remove repeated words
words = cap.split()
if len(set(words)) < len(words) * 0.6:
continue
# Remove non-alphabetic captions
if not re.search(r"[a-z]", cap):
continue
word_count = len(words)
# ---------- LENGTH FILTERS ----------
if self.mode == "short" and word_count <= 8:
filtered_captions.append(cap)
elif self.mode == "long" and word_count > 15:
filtered_captions.append(cap)
elif self.mode == "mixed":
filtered_captions.append(cap)
if len(filtered_captions) > 0:
self.annotations.append({
"image": ann["image"],
"captions": filtered_captions
})
def __len__(self):
return len(self.annotations)
def __getitem__(self, idx):
ann = self.annotations[idx]
file_name = ann["image"]
caption = random.choice(ann["captions"])
image_path = os.path.join(self.image_folder, file_name)
image = Image.open(image_path).convert("RGB")
encoding = self.processor(
images=image,
text=caption,
padding="max_length",
truncation=True,
max_length=self.max_length,
return_tensors="pt"
)
input_ids = encoding["input_ids"].squeeze(0)
return {
"pixel_values": encoding["pixel_values"].squeeze(0),
"input_ids": input_ids,
"attention_mask": encoding["attention_mask"].squeeze(0),
"labels": input_ids.clone()
} |