Spaces:
Running
on
Zero
Running
on
Zero
File size: 12,854 Bytes
262b155 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 |
import os.path
from typing import Any, Callable, List, Optional, Tuple
from PIL import Image
from torchvision.datasets.vision import VisionDataset
import pickle
import csv
import pandas as pd
import torch
import torchvision
import re
# from torchvision.datasets import CocoDetection
# from utils.clip_filter import Clip_filter
from tqdm import tqdm
from .mypath import MyPath
class CocoDetection(VisionDataset):
"""`MS Coco Detection <https://cocodataset.org/#detection-2016>`_ Dataset.
It requires the `COCO API to be installed <https://github.com/pdollar/coco/tree/master/PythonAPI>`_.
Args:
root (string): Root directory where images are downloaded to.
annFile (string): Path to json annotation file.
transform (callable, optional): A function/transform that takes in an PIL image
and returns a transformed version. E.g, ``transforms.PILToTensor``
target_transform (callable, optional): A function/transform that takes in the
target and transforms it.
transforms (callable, optional): A function/transform that takes input sample and its target as entry
and returns a transformed version.
"""
def __init__(
self,
root: str ,
annFile: str,
transform: Optional[Callable] = None,
target_transform: Optional[Callable] = None,
transforms: Optional[Callable] = None,
get_img=True,
get_cap=True
) -> None:
super().__init__(root, transforms, transform, target_transform)
from pycocotools.coco import COCO
self.coco = COCO(annFile)
self.ids = list(sorted(self.coco.imgs.keys()))
self.column_names = ["image", "text"]
self.get_img = get_img
self.get_cap = get_cap
def _load_image(self, id: int) -> Image.Image:
path = self.coco.loadImgs(id)[0]["file_name"]
with open(os.path.join(self.root, path), 'rb') as f:
img = Image.open(f).convert("RGB")
return img
def _load_target(self, id: int) -> List[Any]:
return self.coco.loadAnns(self.coco.getAnnIds(id))
def __getitem__(self, index: int) -> Tuple[Any, Any]:
id = self.ids[index]
ret={"id":id}
if self.get_img:
image = self._load_image(id)
ret["image"] = image
if self.get_cap:
target = self._load_target(id)
ret["caption"] = [target]
if self.transforms is not None:
ret = self.transforms(ret)
return ret
def subsample(self, n: int = 10000):
if n is None or n == -1:
return self
ori_len = len(self)
assert n <= ori_len
# equal interval subsample
ids = self.ids[::ori_len // n][:n]
self.ids = ids
print(f"COCO dataset subsampled from {ori_len} to {len(self)}")
return self
def with_transform(self, transform):
self.transforms = transform
return self
def __len__(self) -> int:
# return 100
return len(self.ids)
class CocoCaptions(CocoDetection):
"""`MS Coco Captions <https://cocodataset.org/#captions-2015>`_ Dataset.
It requires the `COCO API to be installed <https://github.com/pdollar/coco/tree/master/PythonAPI>`_.
Args:
root (string): Root directory where images are downloaded to.
annFile (string): Path to json annotation file.
transform (callable, optional): A function/transform that takes in an PIL image
and returns a transformed version. E.g, ``transforms.PILToTensor``
target_transform (callable, optional): A function/transform that takes in the
target and transforms it.
transforms (callable, optional): A function/transform that takes input sample and its target as entry
and returns a transformed version.
Example:
.. code:: python
import torchvision.datasets as dset
import torchvision.transforms as transforms
cap = dset.CocoCaptions(root = 'dir where images are',
annFile = 'json annotation file',
transform=transforms.PILToTensor())
print('Number of samples: ', len(cap))
img, target = cap[3] # load 4th sample
print("Image Size: ", img.size())
print(target)
Output: ::
Number of samples: 82783
Image Size: (3L, 427L, 640L)
[u'A plane emitting smoke stream flying over a mountain.',
u'A plane darts across a bright blue sky behind a mountain covered in snow',
u'A plane leaves a contrail above the snowy mountain top.',
u'A mountain that has a plane flying overheard in the distance.',
u'A mountain view with a plume of smoke in the background']
"""
def _load_target(self, id: int) -> List[str]:
return [ann["caption"] for ann in super()._load_target(id)]
class CocoCaptions_clip_filtered(CocoCaptions):
positive_prompt=["painting", "drawing", "graffiti",]
def __init__(
self,
root: str ,
annFile: str,
transform: Optional[Callable] = None,
target_transform: Optional[Callable] = None,
transforms: Optional[Callable] = None,
regenerate: bool = False,
id_file: Optional[str] = "/afs/csail.mit.edu/u/h/huiren/code/diffusion/stable_diffusion/data/coco/coco_clip_filtered_ids.pickle"
) -> None:
super().__init__(root, annFile, transform, target_transform, transforms)
os.makedirs(os.path.dirname(id_file), exist_ok=True)
if os.path.exists(id_file) and not regenerate:
with open(id_file, "rb") as f:
self.ids = pickle.load(f)
else:
self.ids, naive_filtered_num = self.naive_filter()
self.ids, clip_filtered_num = self.clip_filter(0.7)
print(f"naive Filtered {naive_filtered_num} images")
print(f"Clip Filtered {clip_filtered_num} images")
with open(id_file, "wb") as f:
pickle.dump(self.ids, f)
print(f"Filtered ids saved to {id_file}")
print(f"COCO filtered dataset size: {len(self)}")
def naive_filter(self, filter_prompt="painting"):
new_ids = []
naive_filtered_num = 0
for id in self.ids:
target = self._load_target(id)
filtered = False
for prompt in target:
if filter_prompt in prompt.lower():
filtered = True
naive_filtered_num += 1
break
# if "artwork" in prompt.lower():
# pass
if not filtered:
new_ids.append(id)
return new_ids, naive_filtered_num
# def clip_filter(self, threshold=0.7):
#
# def collate_fn(examples):
# # {"image": image, "text": [target], "id":id}
# pixel_values = [example["image"] for example in examples]
# prompts = [example["text"] for example in examples]
# id = [example["id"] for example in examples]
# return {"images": pixel_values, "prompts": prompts, "ids": id}
#
#
# clip_filtered_num = 0
# clip_filter = Clip_filter(positive_prompt=self.positive_prompt)
# clip_logs={"positive_prompt":clip_filter.positive_prompt, "negative_prompt":clip_filter.negative_prompt,
# "ids":torch.Tensor([]),"logits":torch.Tensor([])}
# clip_log_file = "data/coco/clip_logs.pth"
# new_ids = []
# batch_size = 128
# dataloader = torch.utils.data.DataLoader(self, batch_size=batch_size, num_workers=10, shuffle=False,
# collate_fn=collate_fn)
# for i, batch in enumerate(tqdm(dataloader)):
# images = batch["images"]
# filter_result, logits = clip_filter.filter(images, threshold=threshold)
# ids = torch.IntTensor(batch["ids"])
# clip_logs["ids"] = torch.cat([clip_logs["ids"], ids])
# clip_logs["logits"] = torch.cat([clip_logs["logits"], logits])
#
# new_ids.extend(ids[~filter_result].tolist())
# clip_filtered_num += filter_result.sum().item()
# if i % 50 == 0:
# torch.save(clip_logs, clip_log_file)
# torch.save(clip_logs, clip_log_file)
#
# return new_ids, clip_filtered_num
class CustomCocoCaptions(CocoCaptions):
def __init__(self, root: str=MyPath.db_root_dir("coco_val"), annFile: str=MyPath.db_root_dir("coco_caption_val"), custom_file:str="/afs/csail.mit.edu/u/h/huiren/code/diffusion/stable_diffusion/jomat-code/filtering/ms_coco_captions_testset100.txt",transform: Optional[Callable] = None, target_transform: Optional[Callable] = None, transforms: Optional[Callable] = None) -> None:
super().__init__(root, annFile, transform, target_transform, transforms)
self.column_names = ["image", "text"]
self.custom_file = custom_file
self.load_custom_data(custom_file)
self.transforms = transforms
def load_custom_data(self, custom_file):
self.custom_data = []
with open(custom_file, "r") as f:
data = f.readlines()
head = data[0].strip().split(",")
self.head = head
for line in data[1:]:
sub_data = line.strip().split(",")
if len(sub_data) > len(head):
sub_data_new = [sub_data[0]]
sub_data_new+=[",".join(sub_data[1:-1])]
sub_data_new.append(sub_data[-1])
sub_data = sub_data_new
assert len(sub_data) == len(head)
self.custom_data.append(sub_data)
# to pd
self.custom_data = pd.DataFrame(self.custom_data, columns=head)
def __len__(self) -> int:
return len(self.custom_data)
def __getitem__(self, index: int) -> Tuple[Any, Any]:
data = self.custom_data.iloc[index]
id = int(data["image_id"])
ret={"id":id}
if self.get_img:
image = self._load_image(id)
ret["image"] = image
if self.get_cap:
caption = data["caption"]
ret["caption"] = [caption]
ret["seed"] = int(data["random_seed"])
if self.transforms is not None:
ret = self.transforms(ret)
return ret
def get_validation_set():
coco_instance = CocoDetection(root="/afs/csail.mit.edu/u/h/huiren/code/diffusion/stable_diffusion/.datasets/coco_2017/train2017/", annFile="/afs/csail.mit.edu/u/h/huiren/code/diffusion/stable_diffusion/.datasets/coco_2017/annotations/instances_train2017.json")
discard_cat_id = coco_instance.coco.getCatIds(supNms=["person", "animal"])
discard_img_id = []
for cat_id in discard_cat_id:
discard_img_id += coco_instance.coco.catToImgs[cat_id]
coco_clip_filtered = CocoCaptions_clip_filtered(root=MyPath.db_root_dir("coco_train"), annFile=MyPath.db_root_dir("coco_caption_train"),
regenerate=False)
coco_clip_filtered_ids = coco_clip_filtered.ids
new_ids = set(coco_clip_filtered_ids) - set(discard_img_id)
new_ids = list(new_ids)
new_ids = random.sample(new_ids, 100)
with open("/afs/csail.mit.edu/u/h/huiren/code/diffusion/stable_diffusion/data/coco/coco_clip_filtered_subset100.pickle", "wb") as f:
pickle.dump(new_ids, f)
if __name__ == "__main__":
from mypath import MyPath
import random
# get_validation_set()
# coco_filtered_remian_id = pickle.load(open("data/coco/coco_clip_filtered_ids.pickle", "rb"))
#
# coco_filtered_subset100 = random.sample(coco_filtered_remian_id, 100)
# save_path = "data/coco/coco_clip_filtered_subset100.pickle"
# with open(save_path, "wb") as f:
# pickle.dump(coco_filtered_subset100, f)
# dataset = CocoCaptions_clip_filtered(root=MyPath.db_root_dir("coco_train"), annFile=MyPath.db_root_dir("coco_caption_train"),
# regenerate=False)
dataset = CustomCocoCaptions(root=MyPath.db_root_dir("coco_val"), annFile=MyPath.db_root_dir("coco_caption_val"),
custom_file="/afs/csail.mit.edu/u/h/huiren/code/diffusion/stable_diffusion/jomat-code/filtering/ms_coco_captions_testset100.txt")
dataset[0]
|