mirror of
https://github.com/Sygil-Dev/sygil-webui.git
synced 2024-12-15 22:42:14 +03:00
126 lines
4.6 KiB
Python
126 lines
4.6 KiB
Python
import os
|
|
import json
|
|
|
|
from torch.utils.data import Dataset
|
|
from torchvision.datasets.utils import download_url
|
|
|
|
from PIL import Image
|
|
|
|
from data.utils import pre_caption
|
|
|
|
class coco_karpathy_train(Dataset):
|
|
def __init__(self, transform, image_root, ann_root, max_words=30, prompt=''):
|
|
'''
|
|
image_root (string): Root directory of images (e.g. coco/images/)
|
|
ann_root (string): directory to store the annotation file
|
|
'''
|
|
url = 'https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_train.json'
|
|
filename = 'coco_karpathy_train.json'
|
|
|
|
download_url(url,ann_root)
|
|
|
|
self.annotation = json.load(open(os.path.join(ann_root,filename),'r'))
|
|
self.transform = transform
|
|
self.image_root = image_root
|
|
self.max_words = max_words
|
|
self.prompt = prompt
|
|
|
|
self.img_ids = {}
|
|
n = 0
|
|
for ann in self.annotation:
|
|
img_id = ann['image_id']
|
|
if img_id not in self.img_ids.keys():
|
|
self.img_ids[img_id] = n
|
|
n += 1
|
|
|
|
def __len__(self):
|
|
return len(self.annotation)
|
|
|
|
def __getitem__(self, index):
|
|
|
|
ann = self.annotation[index]
|
|
|
|
image_path = os.path.join(self.image_root,ann['image'])
|
|
image = Image.open(image_path).convert('RGB')
|
|
image = self.transform(image)
|
|
|
|
caption = self.prompt+pre_caption(ann['caption'], self.max_words)
|
|
|
|
return image, caption, self.img_ids[ann['image_id']]
|
|
|
|
|
|
class coco_karpathy_caption_eval(Dataset):
|
|
def __init__(self, transform, image_root, ann_root, split):
|
|
'''
|
|
image_root (string): Root directory of images (e.g. coco/images/)
|
|
ann_root (string): directory to store the annotation file
|
|
split (string): val or test
|
|
'''
|
|
urls = {'val':'https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_val.json',
|
|
'test':'https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_test.json'}
|
|
filenames = {'val':'coco_karpathy_val.json','test':'coco_karpathy_test.json'}
|
|
|
|
download_url(urls[split],ann_root)
|
|
|
|
self.annotation = json.load(open(os.path.join(ann_root,filenames[split]),'r'))
|
|
self.transform = transform
|
|
self.image_root = image_root
|
|
|
|
def __len__(self):
|
|
return len(self.annotation)
|
|
|
|
def __getitem__(self, index):
|
|
|
|
ann = self.annotation[index]
|
|
|
|
image_path = os.path.join(self.image_root,ann['image'])
|
|
image = Image.open(image_path).convert('RGB')
|
|
image = self.transform(image)
|
|
|
|
img_id = ann['image'].split('/')[-1].strip('.jpg').split('_')[-1]
|
|
|
|
return image, int(img_id)
|
|
|
|
|
|
class coco_karpathy_retrieval_eval(Dataset):
|
|
def __init__(self, transform, image_root, ann_root, split, max_words=30):
|
|
'''
|
|
image_root (string): Root directory of images (e.g. coco/images/)
|
|
ann_root (string): directory to store the annotation file
|
|
split (string): val or test
|
|
'''
|
|
urls = {'val':'https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_val.json',
|
|
'test':'https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_test.json'}
|
|
filenames = {'val':'coco_karpathy_val.json','test':'coco_karpathy_test.json'}
|
|
|
|
download_url(urls[split],ann_root)
|
|
|
|
self.annotation = json.load(open(os.path.join(ann_root,filenames[split]),'r'))
|
|
self.transform = transform
|
|
self.image_root = image_root
|
|
|
|
self.text = []
|
|
self.image = []
|
|
self.txt2img = {}
|
|
self.img2txt = {}
|
|
|
|
txt_id = 0
|
|
for img_id, ann in enumerate(self.annotation):
|
|
self.image.append(ann['image'])
|
|
self.img2txt[img_id] = []
|
|
for i, caption in enumerate(ann['caption']):
|
|
self.text.append(pre_caption(caption,max_words))
|
|
self.img2txt[img_id].append(txt_id)
|
|
self.txt2img[txt_id] = img_id
|
|
txt_id += 1
|
|
|
|
def __len__(self):
|
|
return len(self.annotation)
|
|
|
|
def __getitem__(self, index):
|
|
|
|
image_path = os.path.join(self.image_root, self.annotation[index]['image'])
|
|
image = Image.open(image_path).convert('RGB')
|
|
image = self.transform(image)
|
|
|
|
return image, index |