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| 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 |