113 lines
4.6 KiB
Python
113 lines
4.6 KiB
Python
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import clip
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import torch
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from torchvision import transforms
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from ldm.models.diffusion.ddim import DDIMSampler
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class CLIPEvaluator(object):
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def __init__(self, device, clip_model='ViT-B/32') -> None:
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self.device = device
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self.model, clip_preprocess = clip.load(clip_model, device=self.device)
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self.clip_preprocess = clip_preprocess
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self.preprocess = transforms.Compose([transforms.Normalize(mean=[-1.0, -1.0, -1.0], std=[2.0, 2.0, 2.0])] + # Un-normalize from [-1.0, 1.0] (generator output) to [0, 1].
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clip_preprocess.transforms[:2] + # to match CLIP input scale assumptions
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clip_preprocess.transforms[4:]) # + skip convert PIL to tensor
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def tokenize(self, strings: list):
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return clip.tokenize(strings).to(self.device)
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@torch.no_grad()
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def encode_text(self, tokens: list) -> torch.Tensor:
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return self.model.encode_text(tokens)
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@torch.no_grad()
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def encode_images(self, images: torch.Tensor) -> torch.Tensor:
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images = self.preprocess(images).to(self.device)
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return self.model.encode_image(images)
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def get_text_features(self, text: str, norm: bool = True) -> torch.Tensor:
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tokens = clip.tokenize(text).to(self.device)
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text_features = self.encode_text(tokens).detach()
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if norm:
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text_features /= text_features.norm(dim=-1, keepdim=True)
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return text_features
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def get_image_features(self, img: torch.Tensor, norm: bool = True) -> torch.Tensor:
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image_features = self.encode_images(img)
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if norm:
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image_features /= image_features.clone().norm(dim=-1, keepdim=True)
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return image_features
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def img_to_img_similarity(self, src_images, generated_images):
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src_img_features = self.get_image_features(src_images)
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gen_img_features = self.get_image_features(generated_images)
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return (src_img_features @ gen_img_features.T).mean()
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def txt_to_img_similarity(self, text, generated_images):
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text_features = self.get_text_features(text)
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gen_img_features = self.get_image_features(generated_images)
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return (text_features @ gen_img_features.T).mean()
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class LDMCLIPEvaluator(CLIPEvaluator):
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def __init__(self, device, clip_model='ViT-B/32') -> None:
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super().__init__(device, clip_model)
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def evaluate(self, ldm_model, src_images, target_text, n_samples=64, n_steps=50):
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sampler = DDIMSampler(ldm_model)
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samples_per_batch = 8
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n_batches = n_samples // samples_per_batch
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# generate samples
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all_samples=list()
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with torch.no_grad():
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with ldm_model.ema_scope():
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uc = ldm_model.get_learned_conditioning(samples_per_batch * [""])
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for batch in range(n_batches):
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c = ldm_model.get_learned_conditioning(samples_per_batch * [target_text])
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shape = [4, 256//8, 256//8]
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samples_ddim, _ = sampler.sample(S=n_steps,
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conditioning=c,
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batch_size=samples_per_batch,
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shape=shape,
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verbose=False,
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unconditional_guidance_scale=5.0,
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unconditional_conditioning=uc,
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eta=0.0)
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x_samples_ddim = ldm_model.decode_first_stage(samples_ddim)
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x_samples_ddim = torch.clamp(x_samples_ddim, min=-1.0, max=1.0)
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all_samples.append(x_samples_ddim)
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all_samples = torch.cat(all_samples, axis=0)
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sim_samples_to_img = self.img_to_img_similarity(src_images, all_samples)
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sim_samples_to_text = self.txt_to_img_similarity(target_text.replace("*", ""), all_samples)
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return sim_samples_to_img, sim_samples_to_text
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class ImageDirEvaluator(CLIPEvaluator):
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def __init__(self, device, clip_model='ViT-B/32') -> None:
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super().__init__(device, clip_model)
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def evaluate(self, gen_samples, src_images, target_text):
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sim_samples_to_img = self.img_to_img_similarity(src_images, gen_samples)
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sim_samples_to_text = self.txt_to_img_similarity(target_text.replace("*", ""), gen_samples)
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return sim_samples_to_img, sim_samples_to_text
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