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import torch
import clip
import os
import time
from PIL import Image
from typing import List
from .mlp import MLP
from .utils import download_from_url
MLP_MODEL_URL = "https://huggingface.co/Eugeoter/waifu-scorer/waifu-scorer-v1-large.pth"
class WaifuScorer:
def __init__(self, model_path: str = None, device: str = 'cuda', verbose=False):
self.verbose = verbose
if self.verbose:
tic = time.time()
print(f"loading pretrained model from `{model_path}`")
if model_path is None or not os.path.isfile(model_path):
model_path = download_from_url(MLP_MODEL_URL)
if device == 'cuda' and not torch.cuda.is_available():
device = 'cpu'
print("CUDA is not available, using CPU instead")
self.mlp = load_model(model_path, input_size=768, device=device)
self.model2, self.preprocess = load_clip_models("ViT-L/14", device=device)
self.device = self.mlp.device
self.dtype = self.mlp.dtype
self.mlp.eval()
if self.verbose:
toc = time.time()
print(f"model loaded: time_cost={toc-tic:.2f} | device={self.device} | dtype={self.dtype}")
@torch.no_grad()
def __call__(self, images: List[Image.Image]) -> List[float]:
if isinstance(images, Image.Image):
images = [images]
n = len(images)
if n == 1:
images = images*2 # batch norm
images = encode_images(images, self.model2, self.preprocess, device=self.device).to(device=self.device, dtype=self.dtype)
predictions = self.mlp(images)
scores = predictions.clamp(0, 10).cpu().numpy().reshape(-1).tolist()
if n == 1:
scores = scores[0]
return scores
def load_clip_models(name: str = "ViT-L/14", device='cuda'):
model2, preprocess = clip.load(name, device=device) # RN50x64
return model2, preprocess
def load_model(model_path: str = None, input_size=768, device: str = 'cuda', dtype=None):
model = MLP(input_size=input_size)
if model_path:
s = torch.load(model_path, map_location=device)
model.load_state_dict(s)
model.to(device)
if dtype:
model = model.to(dtype=dtype)
return model
def normalized(a: torch.Tensor, order=2, dim=-1):
l2 = a.norm(order, dim, keepdim=True)
l2[l2 == 0] = 1
return a / l2
@torch.no_grad()
def encode_images(images: List[Image.Image], model2, preprocess, device='cuda') -> torch.Tensor:
if isinstance(images, Image.Image):
images = [images]
image_tensors = [preprocess(img).unsqueeze(0) for img in images]
image_batch = torch.cat(image_tensors).to(device)
image_features = model2.encode_image(image_batch)
im_emb_arr = normalized(image_features).cpu().float()
return im_emb_arr
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