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import os
from typing import Union
import clip
import torch
import torch.nn as nn
import torch.nn.functional as F
from PIL import Image
from torchvision.datasets.utils import download_url
from transformers import AutoModel, AutoProcessor
from .siglip_v2_5 import convert_v2_5_from_siglip
# All metrics.
__all__ = ["AestheticScore", "AestheticScoreSigLIP", "CLIPScore"]
_MODELS = {
"CLIP_ViT-L/14": "https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/easyanimate/video_caption/clip/ViT-L-14.pt",
"Aesthetics_V2": "https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/easyanimate/video_caption/clip/sac%2Blogos%2Bava1-l14-linearMSE.pth",
"aesthetic_predictor_v2_5": "https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/easyanimate/video_caption/clip/aesthetic_predictor_v2_5.pth",
}
_MD5 = {
"CLIP_ViT-L/14": "096db1af569b284eb76b3881534822d9",
"Aesthetics_V2": "b1047fd767a00134b8fd6529bf19521a",
"aesthetic_predictor_v2_5": "c46eb8c29f714c9231dc630b8226842a",
}
def get_list_depth(lst):
if isinstance(lst, list):
return 1 + max(get_list_depth(item) for item in lst)
else:
return 0
def reshape_images(images: Union[list[list[Image.Image]], list[Image.Image]]):
# Check the input sanity.
depth = get_list_depth(images)
if depth == 1: # batch image input
if not isinstance(images[0], Image.Image):
raise ValueError("The item in 1D images should be Image.Image.")
num_sampled_frames = None
elif depth == 2: # batch video input
if not isinstance(images[0][0], Image.Image):
raise ValueError("The item in 2D images (videos) should be Image.Image.")
num_sampled_frames = len(images[0])
if not all(len(video_frames) == num_sampled_frames for video_frames in images):
raise ValueError("All item in 2D images should be with the same length.")
# [batch_size, num_sampled_frames, H, W, C] => [batch_size * num_sampled_frames, H, W, C].
reshaped_images = []
for video_frames in images:
reshaped_images.extend([frame for frame in video_frames])
images = reshaped_images
else:
raise ValueError("The input images should be in 1/2D list.")
return images, num_sampled_frames
def reshape_scores(scores: list[float], num_sampled_frames: int) -> list[float]:
if isinstance(scores, list):
if num_sampled_frames is not None: # Batch video input
batch_size = len(scores) // num_sampled_frames
scores = [
scores[i * num_sampled_frames:(i + 1) * num_sampled_frames]
for i in range(batch_size)
]
return scores
else:
return [scores]
# if you changed the MLP architecture during training, change it also here:
class _MLP(nn.Module):
def __init__(self, input_size):
super().__init__()
self.input_size = input_size
self.layers = nn.Sequential(
nn.Linear(self.input_size, 1024),
# nn.ReLU(),
nn.Dropout(0.2),
nn.Linear(1024, 128),
# nn.ReLU(),
nn.Dropout(0.2),
nn.Linear(128, 64),
# nn.ReLU(),
nn.Dropout(0.1),
nn.Linear(64, 16),
# nn.ReLU(),
nn.Linear(16, 1),
)
def forward(self, x):
return self.layers(x)
class AestheticScore:
"""Compute LAION Aesthetics Score V2 based on openai/clip. Note that the default
inference dtype with GPUs is fp16 in openai/clip.
Ref:
1. https://github.com/christophschuhmann/improved-aesthetic-predictor/blob/main/simple_inference.py.
2. https://github.com/openai/CLIP/issues/30.
"""
def __init__(self, root: str = "~/.cache/clip", device: str = "cpu"):
# The CLIP model is loaded in the evaluation mode.
self.root = os.path.expanduser(root)
if not os.path.exists(self.root):
os.makedirs(self.root)
filename = "ViT-L-14.pt"
download_url(_MODELS["CLIP_ViT-L/14"], self.root, filename=filename, md5=_MD5["CLIP_ViT-L/14"])
self.clip_model, self.preprocess = clip.load(os.path.join(self.root, filename), device=device)
self.device = device
self._load_mlp()
def _load_mlp(self):
filename = "sac+logos+ava1-l14-linearMSE.pth"
download_url(_MODELS["Aesthetics_V2"], self.root, filename=filename, md5=_MD5["Aesthetics_V2"])
state_dict = torch.load(os.path.join(self.root, filename))
self.mlp = _MLP(768)
self.mlp.load_state_dict(state_dict)
self.mlp.to(self.device)
self.mlp.eval()
def __call__(self, images: Union[list[list[Image.Image]], list[Image.Image]], texts=None) -> list[float]:
images, num_sampled_frames = reshape_images(images)
with torch.no_grad():
images = torch.stack([self.preprocess(image) for image in images]).to(self.device)
image_embs = F.normalize(self.clip_model.encode_image(images))
scores = self.mlp(image_embs.float()) # torch.float16 -> torch.float32, [N, 1]
scores = scores.squeeze().tolist() # scalar or list
return reshape_scores(scores, num_sampled_frames)
def __repr__(self) -> str:
return "aesthetic_score"
class AestheticScoreSigLIP:
"""Compute Aesthetics Score V2.5 based on google/siglip-so400m-patch14-384.
Ref:
1. https://github.com/discus0434/aesthetic-predictor-v2-5.
2. https://github.com/discus0434/aesthetic-predictor-v2-5/issues/2.
"""
def __init__(
self,
root: str = "~/.cache/clip",
device: str = "cpu",
torch_dtype=torch.float16
):
self.root = os.path.expanduser(root)
if not os.path.exists(self.root):
os.makedirs(self.root)
filename = "aesthetic_predictor_v2_5.pth"
download_url(_MODELS["aesthetic_predictor_v2_5"], self.root, filename=filename, md5=_MD5["aesthetic_predictor_v2_5"])
self.model, self.preprocessor = convert_v2_5_from_siglip(
predictor_name_or_path=os.path.join(self.root, filename),
low_cpu_mem_usage=True,
trust_remote_code=True,
)
self.model = self.model.to(device=device, dtype=torch_dtype)
self.device = device
self.torch_dtype = torch_dtype
def __call__(self, images: Union[list[list[Image.Image]], list[Image.Image]], texts=None) -> list[float]:
images, num_sampled_frames = reshape_images(images)
pixel_values = self.preprocessor(images, return_tensors="pt").pixel_values
pixel_values = pixel_values.to(self.device, self.torch_dtype)
with torch.no_grad():
scores = self.model(pixel_values).logits.squeeze().float().cpu().numpy()
scores = scores.squeeze().tolist() # scalar or list
return reshape_scores(scores, num_sampled_frames)
def __repr__(self) -> str:
return "aesthetic_score_siglip"
class CLIPScore:
"""Compute CLIP scores for image-text pairs based on huggingface/transformers."""
def __init__(
self,
model_name_or_path: str = "openai/clip-vit-large-patch14",
torch_dtype=torch.float16,
device: str = "cpu",
):
self.model = AutoModel.from_pretrained(model_name_or_path, torch_dtype=torch_dtype).eval().to(device)
self.processor = AutoProcessor.from_pretrained(model_name_or_path)
self.torch_dtype = torch_dtype
self.device = device
def __call__(self, images: Union[list[list[Image.Image]], list[Image.Image]], texts: list[str]) -> list[float]:
assert len(images) == len(texts)
images, num_sampled_frames = reshape_images(images)
# Expand texts in the batch video input case.
if num_sampled_frames is not None:
texts = [[text] * num_sampled_frames for text in texts]
texts = [item for sublist in texts for item in sublist]
image_inputs = self.processor(images=images, return_tensors="pt") # {"pixel_values": }
if self.torch_dtype == torch.float16:
image_inputs["pixel_values"] = image_inputs["pixel_values"].half()
text_inputs = self.processor(text=texts, return_tensors="pt", padding=True, truncation=True) # {"inputs_id": }
image_inputs, text_inputs = image_inputs.to(self.device), text_inputs.to(self.device)
with torch.no_grad():
image_embs = F.normalize(self.model.get_image_features(**image_inputs))
text_embs = F.normalize(self.model.get_text_features(**text_inputs))
scores = text_embs @ image_embs.T # [N, N]
scores = scores.squeeze().tolist() # scalar or list
return reshape_scores(scores, num_sampled_frames)
def __repr__(self) -> str:
return "clip_score"
if __name__ == "__main__":
from torch.utils.data import DataLoader
from tqdm import tqdm
from .video_dataset import VideoDataset, collate_fn
aesthetic_score = AestheticScore(device="cuda")
aesthetic_score_siglip = AestheticScoreSigLIP(device="cuda")
# clip_score = CLIPScore(device="cuda")
paths = ["your_image_path"] * 3
# texts = ["a joker", "a woman", "a man"]
images = [Image.open(p).convert("RGB") for p in paths]
print(aesthetic_score(images))
# print(clip_score(images, texts))
test_dataset = VideoDataset(
dataset_inputs={"video_path": ["your_video_path"] * 3},
sample_method="mid",
num_sampled_frames=2
)
test_loader = DataLoader(test_dataset, batch_size=1, num_workers=1, collate_fn=collate_fn)
for idx, batch in enumerate(tqdm(test_loader)):
batch_frame = batch["sampled_frame"]
print(aesthetic_score_siglip(batch_frame)) |