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clip_interrogator.py
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clip_interrogator.py
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import hashlib
import inspect
import math
import numpy as np
import open_clip
import os
import pickle
import time
import torch
from dataclasses import dataclass
from models.blip import blip_decoder, BLIP_Decoder
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode
from tqdm import tqdm
from typing import List
@dataclass
class Config:
# models can optionally be passed in directly
blip_model: BLIP_Decoder = None
clip_model = None
clip_preprocess = None
# blip settings
blip_image_eval_size: int = 384
blip_max_length: int = 32
blip_model_url: str = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_large_caption.pth'
blip_num_beams: int = 8
blip_offload: bool = False
# clip settings
clip_model_name: str = 'ViT-L-14/openai'
clip_model_path: str = None
# interrogator settings
cache_path: str = 'cache'
chunk_size: int = 2048
data_path: str = os.path.join(os.path.dirname(__file__), 'data')
device: str = 'cuda' if torch.cuda.is_available() else 'cpu'
flavor_intermediate_count: int = 2048
quiet: bool = False # when quiet progress bars are not shown
class Interrogator():
def __init__(self, config: Config):
self.config = config
self.device = config.device
if config.blip_model is None:
if not config.quiet:
print("Loading BLIP model...")
blip_path = os.path.dirname(inspect.getfile(blip_decoder))
configs_path = os.path.join(os.path.dirname(blip_path), 'configs')
med_config = os.path.join(configs_path, 'med_config.json')
blip_model = blip_decoder(
pretrained=config.blip_model_url,
image_size=config.blip_image_eval_size,
vit='large',
med_config=med_config
)
blip_model.eval()
blip_model = blip_model.to(config.device)
self.blip_model = blip_model
else:
self.blip_model = config.blip_model
self.load_clip_model()
def load_clip_model(self):
start_time = time.time()
config = self.config
if config.clip_model is None:
if not config.quiet:
print("Loading CLIP model...")
clip_model_name, clip_model_pretrained_name = config.clip_model_name.split('/', 2)
self.clip_model, _, self.clip_preprocess = open_clip.create_model_and_transforms(
clip_model_name,
pretrained=clip_model_pretrained_name,
precision='fp16' if config.device == 'cuda' else 'fp32',
device=config.device,
jit=False,
cache_dir=config.clip_model_path
)
self.clip_model.to(config.device).eval()
else:
self.clip_model = config.clip_model
self.clip_preprocess = config.clip_preprocess
self.tokenize = open_clip.get_tokenizer(clip_model_name)
sites = ['Artstation', 'behance', 'cg society', 'cgsociety', 'deviantart', 'dribble', 'flickr', 'instagram', 'pexels', 'pinterest', 'pixabay', 'pixiv', 'polycount', 'reddit', 'shutterstock', 'tumblr', 'unsplash', 'zbrush central']
trending_list = [site for site in sites]
trending_list.extend(["trending on "+site for site in sites])
trending_list.extend(["featured on "+site for site in sites])
trending_list.extend([site+" contest winner" for site in sites])
raw_artists = _load_list(config.data_path, 'artists.txt')
artists = [f"by {a}" for a in raw_artists]
artists.extend([f"inspired by {a}" for a in raw_artists])
self.artists = LabelTable(artists, "artists", self.clip_model, self.tokenize, config)
self.flavors = LabelTable(_load_list(config.data_path, 'flavors.txt'), "flavors", self.clip_model, self.tokenize, config)
self.mediums = LabelTable(_load_list(config.data_path, 'mediums.txt'), "mediums", self.clip_model, self.tokenize, config)
self.movements = LabelTable(_load_list(config.data_path, 'movements.txt'), "movements", self.clip_model, self.tokenize, config)
self.trendings = LabelTable(trending_list, "trendings", self.clip_model, self.tokenize, config)
end_time = time.time()
if not config.quiet:
print(f"Loaded CLIP model and data in {end_time-start_time:.2f} seconds.")
def generate_caption(self, pil_image: Image) -> str:
if self.config.blip_offload:
self.blip_model = self.blip_model.to(self.device)
size = self.config.blip_image_eval_size
gpu_image = transforms.Compose([
transforms.Resize((size, size), interpolation=InterpolationMode.BICUBIC),
transforms.ToTensor(),
transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
])(pil_image).unsqueeze(0).to(self.device)
with torch.no_grad():
caption = self.blip_model.generate(
gpu_image,
sample=False,
num_beams=self.config.blip_num_beams,
max_length=self.config.blip_max_length,
min_length=5
)
if self.config.blip_offload:
self.blip_model = self.blip_model.to("cpu")
return caption[0]
def image_to_features(self, image: Image) -> torch.Tensor:
images = self.clip_preprocess(image).unsqueeze(0).to(self.device)
with torch.no_grad(), torch.cuda.amp.autocast():
image_features = self.clip_model.encode_image(images)
image_features /= image_features.norm(dim=-1, keepdim=True)
return image_features
def interrogate_classic(self, image: Image, max_flavors: int=3) -> str:
caption = self.generate_caption(image)
image_features = self.image_to_features(image)
medium = self.mediums.rank(image_features, 1)[0]
artist = self.artists.rank(image_features, 1)[0]
trending = self.trendings.rank(image_features, 1)[0]
movement = self.movements.rank(image_features, 1)[0]
flaves = ", ".join(self.flavors.rank(image_features, max_flavors))
if caption.startswith(medium):
prompt = f"{caption} {artist}, {trending}, {movement}, {flaves}"
else:
prompt = f"{caption}, {medium} {artist}, {trending}, {movement}, {flaves}"
return _truncate_to_fit(prompt, self.tokenize)
def interrogate_fast(self, image: Image, max_flavors: int = 32) -> str:
caption = self.generate_caption(image)
image_features = self.image_to_features(image)
merged = _merge_tables([self.artists, self.flavors, self.mediums, self.movements, self.trendings], self.config)
tops = merged.rank(image_features, max_flavors)
return _truncate_to_fit(caption + ", " + ", ".join(tops), self.tokenize)
def interrogate(self, image: Image, max_flavors: int=32) -> str:
caption = self.generate_caption(image)
image_features = self.image_to_features(image)
flaves = self.flavors.rank(image_features, self.config.flavor_intermediate_count)
best_medium = self.mediums.rank(image_features, 1)[0]
best_artist = self.artists.rank(image_features, 1)[0]
best_trending = self.trendings.rank(image_features, 1)[0]
best_movement = self.movements.rank(image_features, 1)[0]
best_prompt = caption
best_sim = self.similarity(image_features, best_prompt)
def check(addition: str) -> bool:
nonlocal best_prompt, best_sim
prompt = best_prompt + ", " + addition
sim = self.similarity(image_features, prompt)
if sim > best_sim:
best_sim = sim
best_prompt = prompt
return True
return False
def check_multi_batch(opts: List[str]):
nonlocal best_prompt, best_sim
prompts = []
for i in range(2**len(opts)):
prompt = best_prompt
for bit in range(len(opts)):
if i & (1 << bit):
prompt += ", " + opts[bit]
prompts.append(prompt)
t = LabelTable(prompts, None, self.clip_model, self.tokenize, self.config)
best_prompt = t.rank(image_features, 1)[0]
best_sim = self.similarity(image_features, best_prompt)
check_multi_batch([best_medium, best_artist, best_trending, best_movement])
extended_flavors = set(flaves)
for _ in tqdm(range(max_flavors), desc="Flavor chain", disable=self.config.quiet):
best = self.rank_top(image_features, [f"{best_prompt}, {f}" for f in extended_flavors])
flave = best[len(best_prompt)+2:]
if not check(flave):
break
if _prompt_at_max_len(best_prompt, self.tokenize):
break
extended_flavors.remove(flave)
return best_prompt
def rank_top(self, image_features: torch.Tensor, text_array: List[str]) -> str:
text_tokens = self.tokenize([text for text in text_array]).to(self.device)
with torch.no_grad(), torch.cuda.amp.autocast():
text_features = self.clip_model.encode_text(text_tokens)
text_features /= text_features.norm(dim=-1, keepdim=True)
similarity = text_features @ image_features.T
return text_array[similarity.argmax().item()]
def similarity(self, image_features: torch.Tensor, text: str) -> float:
text_tokens = self.tokenize([text]).to(self.device)
with torch.no_grad(), torch.cuda.amp.autocast():
text_features = self.clip_model.encode_text(text_tokens)
text_features /= text_features.norm(dim=-1, keepdim=True)
similarity = text_features @ image_features.T
return similarity[0][0].item()
class LabelTable():
def __init__(self, labels:List[str], desc:str, clip_model, tokenize, config: Config):
self.chunk_size = config.chunk_size
self.config = config
self.device = config.device
self.embeds = []
self.labels = labels
self.tokenize = tokenize
hash = hashlib.sha256(",".join(labels).encode()).hexdigest()
cache_filepath = None
if config.cache_path is not None and desc is not None:
os.makedirs(config.cache_path, exist_ok=True)
sanitized_name = config.clip_model_name.replace('/', '_').replace('@', '_')
cache_filepath = os.path.join(config.cache_path, f"{sanitized_name}_{desc}.pkl")
if desc is not None and os.path.exists(cache_filepath):
with open(cache_filepath, 'rb') as f:
try:
data = pickle.load(f)
if data.get('hash') == hash:
self.labels = data['labels']
self.embeds = data['embeds']
except Exception as e:
print(f"Error loading cached table {desc}: {e}")
if len(self.labels) != len(self.embeds):
self.embeds = []
chunks = np.array_split(self.labels, max(1, len(self.labels)/config.chunk_size))
for chunk in tqdm(chunks, desc=f"Preprocessing {desc}" if desc else None, disable=self.config.quiet):
text_tokens = self.tokenize(chunk).to(self.device)
with torch.no_grad(), torch.cuda.amp.autocast():
text_features = clip_model.encode_text(text_tokens)
text_features /= text_features.norm(dim=-1, keepdim=True)
text_features = text_features.half().cpu().numpy()
for i in range(text_features.shape[0]):
self.embeds.append(text_features[i])
if cache_filepath is not None:
with open(cache_filepath, 'wb') as f:
pickle.dump({
"labels": self.labels,
"embeds": self.embeds,
"hash": hash,
"model": config.clip_model_name
}, f)
if self.device == 'cpu' or self.device == torch.device('cpu'):
self.embeds = [e.astype(np.float32) for e in self.embeds]
def _rank(self, image_features: torch.Tensor, text_embeds: torch.Tensor, top_count: int=1) -> str:
top_count = min(top_count, len(text_embeds))
text_embeds = torch.stack([torch.from_numpy(t) for t in text_embeds]).to(self.device)
with torch.cuda.amp.autocast():
similarity = image_features @ text_embeds.T
_, top_labels = similarity.float().cpu().topk(top_count, dim=-1)
return [top_labels[0][i].numpy() for i in range(top_count)]
def rank(self, image_features: torch.Tensor, top_count: int=1) -> List[str]:
if len(self.labels) <= self.chunk_size:
tops = self._rank(image_features, self.embeds, top_count=top_count)
return [self.labels[i] for i in tops]
num_chunks = int(math.ceil(len(self.labels)/self.chunk_size))
keep_per_chunk = int(self.chunk_size / num_chunks)
top_labels, top_embeds = [], []
for chunk_idx in tqdm(range(num_chunks), disable=self.config.quiet):
start = chunk_idx*self.chunk_size
stop = min(start+self.chunk_size, len(self.embeds))
tops = self._rank(image_features, self.embeds[start:stop], top_count=keep_per_chunk)
top_labels.extend([self.labels[start+i] for i in tops])
top_embeds.extend([self.embeds[start+i] for i in tops])
tops = self._rank(image_features, top_embeds, top_count=top_count)
return [top_labels[i] for i in tops]
def _load_list(data_path: str, filename: str) -> List[str]:
with open(os.path.join(data_path, filename), 'r', encoding='utf-8', errors='replace') as f:
items = [line.strip() for line in f.readlines()]
return items
def _merge_tables(tables: List[LabelTable], config: Config) -> LabelTable:
m = LabelTable([], None, None, None, config)
for table in tables:
m.labels.extend(table.labels)
m.embeds.extend(table.embeds)
return m
def _prompt_at_max_len(text: str, tokenize) -> bool:
tokens = tokenize([text])
return tokens[0][-1] != 0
def _truncate_to_fit(text: str, tokenize) -> str:
parts = text.split(', ')
new_text = parts[0]
for part in parts[1:]:
if _prompt_at_max_len(new_text + part, tokenize):
break
new_text += ', ' + part
return new_text