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# Authors: Hui Ren (rhfeiyang.github.io)
from transformers import CLIPProcessor, CLIPModel
import torch
import numpy as np
import os
os.environ["TOKENIZERS_PARALLELISM"] = "false"
from tqdm import tqdm
class Caption_filter:
def __init__(self, filter_prompts=["painting", "paintings", "art", "artwork", "drawings", "sketch", "sketches", "illustration", "illustrations",
"sculpture","sculptures", "installation", "printmaking", "digital art", "conceptual art", "mosaic", "tapestry",
"abstract", "realism", "surrealism", "impressionism", "expressionism", "cubism", "minimalism", "baroque", "rococo",
"pop art", "art nouveau", "art deco", "futurism", "dadaism",
"stamp", "stamps", "advertisement", "advertisements","logo", "logos"
],):
self.filter_prompts = filter_prompts
self.total_count=0
self.filter_count=[0]*len(filter_prompts)
def reset(self):
self.total_count=0
self.filter_count=[0]*len(self.filter_prompts)
def filter(self, captions):
filter_result = []
for caption in captions:
words = caption[0]
if words == None:
filter_result.append((True, "None"))
continue
words = words.lower()
words = words.split()
filt = False
reason=None
for i, filter_keyword in enumerate(self.filter_prompts):
key_len = len(filter_keyword.split())
for j in range(len(words)-key_len+1):
if " ".join(words[j:j+key_len]) == filter_keyword:
self.filter_count[i] += 1
filt = True
reason = filter_keyword
break
if filt:
break
filter_result.append((filt, reason))
self.total_count += 1
return filter_result
class Clip_filter:
prompt_threshold = {
"painting": 17,
"art": 17.5,
"artwork": 19,
"drawing": 15.8,
"sketch": 17,
"illustration": 15,
"sculpture": 19.2,
"installation art": 20,
"printmaking art": 16.3,
"digital art": 15,
"conceptual art": 18,
"mosaic art": 19,
"tapestry": 16,
"abstract art":16.5,
"realism art": 16,
"surrealism art": 15,
"impressionism art": 17,
"expressionism art": 17,
"cubism art": 15,
"minimalism art": 16,
"baroque art": 17.5,
"rococo art": 17,
"pop art": 16,
"art nouveau": 19,
"art deco": 19,
"futurism art": 16.5,
"dadaism art": 16.5,
"stamp": 18,
"advertisement": 16.5,
"logo": 15.5,
}
@torch.no_grad()
def __init__(self, positive_prompt=["painting", "art", "artwork", "drawing", "sketch", "illustration",
"sculpture", "installation art", "printmaking art", "digital art", "conceptual art", "mosaic art", "tapestry",
"abstract art", "realism art", "surrealism art", "impressionism art", "expressionism art", "cubism art",
"minimalism art", "baroque art", "rococo art",
"pop art", "art nouveau", "art deco", "futurism art", "dadaism art",
"stamp", "advertisement",
"logo"
],
device="cuda"):
self.device = device
self.model = (CLIPModel.from_pretrained("openai/clip-vit-large-patch14")).to(device)
self.processor = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14")
self.positive_prompt = positive_prompt
self.text = self.positive_prompt
self.tokenizer = self.processor.tokenizer
self.image_processor = self.processor.image_processor
self.text_encoding = self.tokenizer(self.text, return_tensors="pt", padding=True).to(device)
self.text_features = self.model.get_text_features(**self.text_encoding)
self.text_features = self.text_features / self.text_features.norm(p=2, dim=-1, keepdim=True)
@torch.no_grad()
def similarity(self, image):
# inputs = self.processor(text=self.text, images=image, return_tensors="pt", padding=True)
image_processed = self.image_processor(image, return_tensors="pt", padding=True).to(self.device, non_blocking=True)
inputs = {**self.text_encoding, **image_processed}
outputs = self.model(**inputs)
logits_per_image = outputs.logits_per_image
return logits_per_image
def get_logits(self, image):
logits_per_image = self.similarity(image)
return logits_per_image.cpu()
def get_image_features(self, image):
image_processed = self.image_processor(image, return_tensors="pt", padding=True).to(self.device, non_blocking=True)
image_features = self.model.get_image_features(**image_processed)
return image_features
class Art_filter:
def __init__(self):
self.caption_filter = Caption_filter()
self.clip_filter = Clip_filter()
def caption_filt(self, dataloader):
self.caption_filter.reset()
dataloader.dataset.get_img = False
dataloader.dataset.get_cap = True
remain_ids = []
filtered_ids = []
for i, batch in tqdm(enumerate(dataloader)):
captions = batch["text"]
filter_result = self.caption_filter.filter(captions)
for j, (filt, reason) in enumerate(filter_result):
if filt:
filtered_ids.append((batch["ids"][j], reason))
if i%10==0:
print(f"Filtered caption: {captions[j]}, reason: {reason}")
else:
remain_ids.append(batch["ids"][j])
return {"remain_ids":remain_ids, "filtered_ids":filtered_ids, "total_count":self.caption_filter.total_count, "filter_count":self.caption_filter.filter_count, "filter_prompts":self.caption_filter.filter_prompts}
def clip_filt(self, clip_logits_ckpt:dict):
logits = clip_logits_ckpt["clip_logits"]
ids = clip_logits_ckpt["ids"]
text = clip_logits_ckpt["text"]
filt_mask = torch.zeros(logits.shape[0], dtype=torch.bool)
for i, prompt in enumerate(text):
threshold = Clip_filter.prompt_threshold[prompt]
filt_mask = filt_mask | (logits[:,i] >= threshold)
filt_ids = []
remain_ids = []
for i, id in enumerate(ids):
if filt_mask[i]:
filt_ids.append(id)
else:
remain_ids.append(id)
return {"remain_ids":remain_ids, "filtered_ids":filt_ids}
def clip_feature(self, dataloader):
dataloader.dataset.get_img = True
dataloader.dataset.get_cap = False
clip_features = []
ids = []
for i, batch in enumerate(dataloader):
images = batch["images"]
features = self.clip_filter.get_image_features(images).cpu()
clip_features.append(features)
ids.extend(batch["ids"])
clip_features = torch.cat(clip_features)
return {"clip_features":clip_features, "ids":ids}
def clip_logit(self, dataloader):
dataloader.dataset.get_img = True
dataloader.dataset.get_cap = False
clip_features = []
clip_logits = []
ids = []
for i, batch in enumerate(dataloader):
images = batch["images"]
# logits = self.clip_filter.get_logits(images)
feature = self.clip_filter.get_image_features(images)
logits = self.clip_logit_by_feat(feature)["clip_logits"]
clip_features.append(feature)
clip_logits.append(logits)
ids.extend(batch["ids"])
clip_features = torch.cat(clip_features)
clip_logits = torch.cat(clip_logits)
return {"clip_features":clip_features, "clip_logits":clip_logits, "ids":ids, "text": self.clip_filter.text}
def clip_logit_by_feat(self, feature):
feature = feature.clone().to(self.clip_filter.device)
feature = feature / feature.norm(p=2, dim=-1, keepdim=True)
logit_scale = self.clip_filter.model.logit_scale.exp()
logits = ((feature @ self.clip_filter.text_features.T) * logit_scale).cpu()
return {"clip_logits":logits, "text": self.clip_filter.text}
if __name__ == "__main__":
import pickle
with open("/vision-nfs/torralba/scratch/jomat/sam_dataset/filt_result/sa_000000/clip_logits_result.pickle","rb") as f:
result=pickle.load(f)
feat = result['clip_features']
logits =Art_filter().clip_logit_by_feat(feat)
print(logits)
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