mirror of
https://github.com/finegrain-ai/refiners.git
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66 lines
2.7 KiB
Python
66 lines
2.7 KiB
Python
from pathlib import Path
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import numpy as np
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import piq # type: ignore
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import torch
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import torch.nn as nn
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from PIL import Image
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from transformers import T5EncoderModel, T5Tokenizer # type: ignore
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def compare_images(img_1: Image.Image, img_2: Image.Image) -> tuple[int, float]:
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x1, x2 = (
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torch.tensor(np.array(x).astype(np.float32)).permute(2, 0, 1).unsqueeze(0) / 255.0 for x in (img_1, img_2)
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)
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return (piq.psnr(x1, x2), piq.ssim(x1, x2).item()) # type: ignore
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def ensure_similar_images(img_1: Image.Image, img_2: Image.Image, min_psnr: int = 45, min_ssim: float = 0.99):
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psnr, ssim = compare_images(img_1, img_2)
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assert (psnr >= min_psnr) and (
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ssim >= min_ssim
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), f"PSNR {psnr} / SSIM {ssim}, expected at least {min_psnr} / {min_ssim}"
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class T5TextEmbedder(nn.Module):
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def __init__(
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self, pretrained_path: Path = Path("tests/weights/QQGYLab/T5XLFP16"), max_length: int | None = None
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) -> None:
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super().__init__() # type: ignore[reportUnknownMemberType]
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self.model: nn.Module = T5EncoderModel.from_pretrained(pretrained_path, local_files_only=True) # type: ignore
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self.tokenizer: transformers.T5Tokenizer = T5Tokenizer.from_pretrained(pretrained_path, local_files_only=True) # type: ignore
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self.max_length = max_length
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def forward(
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self,
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caption: str,
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text_input_ids: torch.Tensor | None = None,
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attention_mask: torch.Tensor | None = None,
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max_length: int | None = None,
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) -> torch.Tensor:
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if max_length is None:
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max_length = self.max_length
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if text_input_ids is None or attention_mask is None:
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if max_length is not None:
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text_inputs = self.tokenizer( # type: ignore
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caption,
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return_tensors="pt",
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add_special_tokens=True,
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max_length=max_length,
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padding="max_length",
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truncation=True,
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)
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else:
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text_inputs = self.tokenizer(caption, return_tensors="pt", add_special_tokens=True) # type: ignore
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_text_input_ids: torch.Tensor = text_inputs.input_ids.to(self.model.device) # type: ignore
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_attention_mask: torch.Tensor = text_inputs.attention_mask.to(self.model.device) # type: ignore
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else:
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_text_input_ids: torch.Tensor = text_input_ids.to(self.model.device) # type: ignore
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_attention_mask: torch.Tensor = attention_mask.to(self.model.device) # type: ignore
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outputs = self.model(_text_input_ids, attention_mask=_attention_mask)
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embeddings = outputs.last_hidden_state
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return embeddings
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