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