mirror of
https://github.com/finegrain-ai/refiners.git
synced 2024-11-13 00:28:14 +00:00
66 lines
2.7 KiB
Python
66 lines
2.7 KiB
Python
from pathlib import Path
|
|
|
|
import numpy as np
|
|
import piq # type: ignore
|
|
import torch
|
|
import torch.nn as nn
|
|
from PIL import Image
|
|
from transformers import T5EncoderModel, T5Tokenizer # type: ignore
|
|
|
|
|
|
def compare_images(img_1: Image.Image, img_2: Image.Image) -> tuple[int, float]:
|
|
x1, x2 = (
|
|
torch.tensor(np.array(x).astype(np.float32)).permute(2, 0, 1).unsqueeze(0) / 255.0 for x in (img_1, img_2)
|
|
)
|
|
return (piq.psnr(x1, x2), piq.ssim(x1, x2).item()) # type: ignore
|
|
|
|
|
|
def ensure_similar_images(img_1: Image.Image, img_2: Image.Image, min_psnr: int = 45, min_ssim: float = 0.99):
|
|
psnr, ssim = compare_images(img_1, img_2)
|
|
assert (psnr >= min_psnr) and (
|
|
ssim >= min_ssim
|
|
), f"PSNR {psnr} / SSIM {ssim}, expected at least {min_psnr} / {min_ssim}"
|
|
|
|
|
|
class T5TextEmbedder(nn.Module):
|
|
def __init__(
|
|
self, pretrained_path: Path = Path("tests/weights/QQGYLab/T5XLFP16"), max_length: int | None = None
|
|
) -> None:
|
|
super().__init__() # type: ignore[reportUnknownMemberType]
|
|
self.model: nn.Module = T5EncoderModel.from_pretrained(pretrained_path, local_files_only=True) # type: ignore
|
|
self.tokenizer: transformers.T5Tokenizer = T5Tokenizer.from_pretrained(pretrained_path, local_files_only=True) # type: ignore
|
|
self.max_length = max_length
|
|
|
|
def forward(
|
|
self,
|
|
caption: str,
|
|
text_input_ids: torch.Tensor | None = None,
|
|
attention_mask: torch.Tensor | None = None,
|
|
max_length: int | None = None,
|
|
) -> torch.Tensor:
|
|
if max_length is None:
|
|
max_length = self.max_length
|
|
|
|
if text_input_ids is None or attention_mask is None:
|
|
if max_length is not None:
|
|
text_inputs = self.tokenizer( # type: ignore
|
|
caption,
|
|
return_tensors="pt",
|
|
add_special_tokens=True,
|
|
max_length=max_length,
|
|
padding="max_length",
|
|
truncation=True,
|
|
)
|
|
else:
|
|
text_inputs = self.tokenizer(caption, return_tensors="pt", add_special_tokens=True) # type: ignore
|
|
_text_input_ids: torch.Tensor = text_inputs.input_ids.to(self.model.device) # type: ignore
|
|
_attention_mask: torch.Tensor = text_inputs.attention_mask.to(self.model.device) # type: ignore
|
|
else:
|
|
_text_input_ids: torch.Tensor = text_input_ids.to(self.model.device) # type: ignore
|
|
_attention_mask: torch.Tensor = attention_mask.to(self.model.device) # type: ignore
|
|
|
|
outputs = self.model(_text_input_ids, attention_mask=_attention_mask)
|
|
|
|
embeddings = outputs.last_hidden_state
|
|
return embeddings
|