Spaces:
Runtime error
Runtime error
File size: 8,796 Bytes
fc0ff8f |
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 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 |
import json
import os
from collections import Counter
import torch
from PIL import Image
from torch.utils.data import Dataset
from torchvision.datasets import ImageFolder
from open_flamingo.eval.classification_utils import IMAGENET_1K_CLASS_ID_TO_LABEL
class CaptionDataset(Dataset):
def __init__(
self,
image_train_dir_path,
annotations_path,
is_train,
dataset_name,
image_val_dir_path=None,
which_gt=None,
best_gt_caption_path=None,
):
self.image_train_dir_path = image_train_dir_path
self.image_val_dir_path = image_val_dir_path
self.annotations = []
self.is_train = is_train
self.dataset_name = dataset_name
full_annotations = json.load(open(annotations_path))["images"]
for i in range(len(full_annotations)):
if self.is_train and full_annotations[i]["split"] != "train":
continue
elif not self.is_train and full_annotations[i]["split"] != "test":
continue
self.annotations.append(full_annotations[i])
if isinstance(which_gt, str):
self.which_gt = int(which_gt) if which_gt.isdigit() else which_gt
else:
self.which_gt = which_gt
if best_gt_caption_path is not None:
with open(best_gt_caption_path, 'r') as f:
self.best_gt_captions = json.load(f)
else:
self.best_gt_captions = None
def __len__(self):
return len(self.annotations)
def __getitem__(self, idx):
if self.dataset_name == "coco":
image = Image.open(
os.path.join(
self.image_train_dir_path, self.annotations[idx]["filename"]
)
if self.annotations[idx]["filepath"] == "train2014"
else os.path.join(
self.image_val_dir_path, self.annotations[idx]["filename"]
)
)
elif self.dataset_name == "flickr":
image = Image.open(
os.path.join(
self.image_train_dir_path, self.annotations[idx]["filename"]
)
)
image.load()
image_id = self.annotations[idx]["cocoid"] if self.dataset_name == "coco" else self.annotations[idx]["filename"].split(".")[0]
if isinstance(self.which_gt, int):
cpt_idx = self.which_gt
elif isinstance(self.which_gt, dict):
cpt_idx = self.which_gt[image_id]
elif self.which_gt == "best":
cpt_idx = self.best_gt_captions[str(image_id)]
else:
assert self.which_gt is None
cpt_idx = 0
caption = self.annotations[idx]["sentences"][cpt_idx]["raw"]
return {
"image": image,
"caption": caption,
"image_id": image_id,
}
class VQADataset(Dataset):
def __init__(
self, image_dir_path, question_path, annotations_path, is_train, dataset_name, which_gt='all', is_tensor=False
):
self.questions = json.load(open(question_path, "r"))["questions"]
if annotations_path is not None:
self.answers = json.load(open(annotations_path, "r"))["annotations"]
else:
self.answers = None
self.image_dir_path = image_dir_path
self.is_train = is_train
self.dataset_name = dataset_name
if self.dataset_name in {"vqav2", "ok_vqa"}:
self.img_coco_split = self.image_dir_path.strip("/").split("/")[-1]
assert self.img_coco_split in {"train2014", "val2014", "test2015"}
self.which_gt = which_gt
self.is_tensor = is_tensor
def __len__(self):
return len(self.questions)
def get_img_path(self, question):
if self.dataset_name in {"vqav2", "ok_vqa"}:
return os.path.join(
self.image_dir_path,
f"COCO_{self.img_coco_split}_{question['image_id']:012d}.jpg"
if self.is_train
else f"COCO_{self.img_coco_split}_{question['image_id']:012d}.jpg",
)
elif self.dataset_name == "vizwiz":
return os.path.join(self.image_dir_path, question["image_id"])
elif self.dataset_name == "textvqa":
return os.path.join(self.image_dir_path, f"{question['image_id']}.jpg")
else:
raise Exception(f"Unknown VQA dataset {self.dataset_name}")
def get_from_id(self, question_id):
assert not self.is_train
assert self.dataset_name == "textvqa"
prefix = ''
image_path = f"{self.image_dir_path}/{prefix}{str(question_id).zfill(12)}.pt"
image = torch.load(image_path)
return image
def __getitem__(self, idx):
question = self.questions[idx]
img_path = self.get_img_path(question)
if self.is_tensor:
image_path = img_path.replace("jpg", "pt")
image = torch.load(image_path)
else:
image = Image.open(img_path)
image.load()
results = {
"image": image,
"question": question["question"],
"question_id": question["question_id"],
}
if self.answers is not None:
answers = self.answers[idx]
answers = [a["answer"] for a in answers["answers"]]
if self.which_gt in ["all", None]:
results["answers"] = answers
elif isinstance(self.which_gt, int) or isinstance(self.which_gt, dict):
which_gt = self.which_gt[question["question_id"]] if isinstance(self.which_gt, dict) else self.which_gt
# return the nth most common answer
counter = Counter(answers)
most_common = counter.most_common()
if which_gt >= len(most_common):
results["answers"] = []
else:
results["answers"] = [most_common[which_gt][0]]
else:
raise ValueError(f"Unknown which_gt: {self.which_gt}")
return results
class ImageNetDataset(ImageFolder):
"""Class to represent the ImageNet1k dataset."""
def __init__(self, root, **kwargs):
super().__init__(root=root, **kwargs)
def __getitem__(self, idx):
sample, target = super().__getitem__(idx)
target_label = IMAGENET_1K_CLASS_ID_TO_LABEL[target]
return {
"id": idx,
"image": sample,
"class_id": target, # numeric ID of the ImageNet class
"class_name": target_label, # human-readable name of ImageNet class
}
class HatefulMemesDataset(Dataset):
def __init__(self, image_dir_path, annotations_path):
self.image_dir_path = image_dir_path
with open(annotations_path, "r") as f:
self.annotations = [json.loads(line) for line in f]
def __len__(self):
return len(self.annotations)
def __getitem__(self, idx):
annotation = self.annotations[idx]
img_path = os.path.join(self.image_dir_path, annotation["img"].split("/")[-1])
image = Image.open(img_path)
image.load()
return {
"id": annotation["id"],
"image": image,
"ocr": annotation["text"],
"class_name": "yes" if annotation["label"] == 1 else "no",
"class_id": annotation["label"],
}
class TensorCaptionDataset(CaptionDataset):
def get_from_id(self, image_id):
assert self.dataset_name == "coco"
assert not self.is_train
# prefix = 'COCO_val2014_'
prefix = ''
image_path = f"{self.image_val_dir_path}/{prefix}{str(image_id).zfill(12)}.pt"
image = torch.load(image_path)
return image
def __getitem__(self, idx):
if self.dataset_name == "coco":
image_path = os.path.join(
self.image_train_dir_path if self.annotations[idx]["filepath"] == "train2014" else self.image_val_dir_path,
self.annotations[idx]["filename"]
)
image_path = image_path.replace("jpg", "pt")
image = torch.load(image_path)
elif self.dataset_name == "flickr":
raise NotImplementedError
image = Image.open(
os.path.join(
self.image_train_dir_path, self.annotations[idx]["filename"]
)
)
caption = self.annotations[idx]["sentences"][0]["raw"]
return {
"image": image,
"caption": caption,
"image_id": self.annotations[idx]["cocoid"]
if self.dataset_name == "coco"
else self.annotations[idx]["filename"].split(".")[0],
} |