mirror of
https://github.com/brycedrennan/imaginAIry
synced 2024-11-19 03:25:41 +00:00
135 lines
3.6 KiB
Python
135 lines
3.6 KiB
Python
"""Classes for text and image encoding"""
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import kornia
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import torch
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from einops import repeat
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from torch import nn
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from transformers import CLIPTextModel, CLIPTokenizer
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from imaginairy.utils import get_device
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from imaginairy.vendored import clip
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class FrozenCLIPEmbedder(nn.Module):
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"""Uses the CLIP transformer encoder for text (from Hugging Face)."""
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def __init__(
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self,
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version="openai/clip-vit-large-patch14",
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device=get_device(),
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max_length=77,
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):
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super().__init__()
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self.tokenizer = CLIPTokenizer.from_pretrained(version)
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self.transformer = CLIPTextModel.from_pretrained(version)
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self.device = device
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self.max_length = max_length
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self.freeze()
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def freeze(self):
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self.transformer = self.transformer.eval()
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for param in self.parameters():
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param.requires_grad = False
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def forward(self, text):
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batch_encoding = self.tokenizer(
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text,
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truncation=True,
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max_length=self.max_length,
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return_length=True,
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return_overflowing_tokens=False,
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padding="max_length",
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return_tensors="pt",
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)
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tokens = batch_encoding["input_ids"].to(self.device)
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outputs = self.transformer(input_ids=tokens)
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z = outputs.last_hidden_state
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return z
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def encode(self, text):
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return self(text)
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class FrozenCLIPTextEmbedder(nn.Module):
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"""
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Uses the CLIP transformer encoder for text.
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"""
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def __init__(
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self,
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version="ViT-L/14",
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device=get_device(),
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max_length=77,
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n_repeat=1,
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normalize=True,
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):
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super().__init__()
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self.model, _ = clip.load(version, jit=False, device=device)
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self.device = device
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self.max_length = max_length
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self.n_repeat = n_repeat
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self.normalize = normalize
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def freeze(self):
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self.model = self.model.eval()
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for param in self.parameters():
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param.requires_grad = False
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def forward(self, text):
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tokens = clip.tokenize(text).to(self.device)
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z = self.model.encode_text(tokens)
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if self.normalize:
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z = z / torch.linalg.norm(z, dim=1, keepdim=True)
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return z
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def encode(self, text):
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z = self(text)
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if z.ndim == 2:
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z = z[:, None, :]
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z = repeat(z, "b 1 d -> b k d", k=self.n_repeat)
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return z
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class FrozenClipImageEmbedder(nn.Module):
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"""
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Uses the CLIP image encoder.
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"""
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def __init__(
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self,
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model_name,
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jit=False,
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device=get_device(),
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antialias=False,
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):
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super().__init__()
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self.model, preprocess = clip.load(name=model_name, device=device, jit=jit)
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self.antialias = antialias
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self.register_buffer(
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"mean", torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False
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)
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self.register_buffer(
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"std", torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False
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)
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def preprocess(self, x):
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# normalize to [0,1]
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x = kornia.geometry.resize(
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x,
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(224, 224),
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interpolation="bicubic",
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align_corners=True,
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antialias=self.antialias,
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)
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x = (x + 1.0) / 2.0
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# renormalize according to clip
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x = kornia.enhance.normalize(x, self.mean, self.std)
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return x
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def forward(self, x):
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# x is assumed to be in range [-1,1]
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return self.model.encode_image(self.preprocess(x))
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