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from torch.utils.data import Dataset
from torch.utils.data import DataLoader
import os
import json
from PIL import Image
class MS_COCO_dataset(Dataset):
def __init__(self, base_dir, annotation_file=None):
self.data= []
self.img_dir = base_dir + '/images'
self.annotation_file = base_dir + annotation_file
with open(self.annotation_file, 'r') as file:
for line in file:
self.data.append(json.loads(line))
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
# Extract the relevant info from the JSONL entry
img_name = os.path.join(self.img_dir, f"{self.data[idx]['image_name']}")
caption = self.data[idx]['caption']
sample_id = self.data[idx]['image_id']
# Load the image using PIL
img = Image.open(img_name)
return {"id": sample_id,
"image": img,
"caption": caption
}
class COCO_CF_dataset(Dataset):
def __init__(self, base_dir):
self.data= []
self.img_dir = base_dir + '/images'
self.annotation_file = base_dir + "/examples.jsonl"
with open(self.annotation_file, 'r') as file:
for line in file:
self.data.append(json.loads(line))
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
# Extract the relevant info from the JSONL entry
img_0_name = os.path.join(self.img_dir, f"{self.data[idx]['image_0']}.jpg")
img_1_name = os.path.join(self.img_dir, f"{self.data[idx]['image_1']}.jpg")
caption_0 = self.data[idx]['caption_0']
caption_1 = self.data[idx]['caption_1']
sample_id = self.data[idx]['id']
# Load the image using PIL
img_0 = Image.open(img_0_name)
img_1 = Image.open(img_1_name)
return {"id": sample_id,
"caption_0": caption_0,
"caption_1": caption_1,
"image_0": img_0,
"image_1": img_1}
def custom_collate_fn(batch):
collated_batch = {}
for key in batch[0].keys():
collated_batch[key] = [item[key] for item in batch]
return collated_batch
if __name__ == "__main__":
base_dir = '/home/htc/kchitranshi/SCRATCH/MS_COCO/'
data = MS_COCO_dataset(base_dir=base_dir)
data_loader = DataLoader(data, batch_size=10,collate_fn=custom_collate_fn)
for batch in data_loader:
print(batch)
break
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