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]