mirror of
https://github.com/immich-app/immich.git
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114 lines
3.9 KiB
Python
114 lines
3.9 KiB
Python
import tempfile
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import warnings
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from dataclasses import dataclass, field
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from pathlib import Path
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import open_clip
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import torch
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from transformers import AutoTokenizer
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from .optimize import optimize
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from .util import get_model_path, save_config
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@dataclass
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class OpenCLIPModelConfig:
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name: str
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pretrained: str
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image_size: int = field(init=False)
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sequence_length: int = field(init=False)
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def __post_init__(self) -> None:
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open_clip_cfg = open_clip.get_model_config(self.name)
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if open_clip_cfg is None:
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raise ValueError(f"Unknown model {self.name}")
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self.image_size = open_clip_cfg["vision_cfg"]["image_size"]
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self.sequence_length = open_clip_cfg["text_cfg"]["context_length"]
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def to_onnx(
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model_cfg: OpenCLIPModelConfig,
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output_dir_visual: Path | str | None = None,
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output_dir_textual: Path | str | None = None,
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) -> None:
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with tempfile.TemporaryDirectory() as tmpdir:
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model = open_clip.create_model(
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model_cfg.name,
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pretrained=model_cfg.pretrained,
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jit=False,
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cache_dir=tmpdir,
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require_pretrained=True,
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)
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text_vision_cfg = open_clip.get_model_config(model_cfg.name)
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for param in model.parameters():
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param.requires_grad_(False)
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if output_dir_visual is not None:
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output_dir_visual = Path(output_dir_visual)
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visual_path = get_model_path(output_dir_visual)
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save_config(open_clip.get_model_preprocess_cfg(model), output_dir_visual / "preprocess_cfg.json")
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save_config(text_vision_cfg, output_dir_visual.parent / "config.json")
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export_image_encoder(model, model_cfg, visual_path)
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optimize(visual_path)
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if output_dir_textual is not None:
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output_dir_textual = Path(output_dir_textual)
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textual_path = get_model_path(output_dir_textual)
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tokenizer_name = text_vision_cfg["text_cfg"].get("hf_tokenizer_name", "openai/clip-vit-base-patch32")
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AutoTokenizer.from_pretrained(tokenizer_name).save_pretrained(output_dir_textual)
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export_text_encoder(model, model_cfg, textual_path)
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optimize(textual_path)
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def export_image_encoder(model: open_clip.CLIP, model_cfg: OpenCLIPModelConfig, output_path: Path | str) -> None:
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output_path = Path(output_path)
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def encode_image(image: torch.Tensor) -> torch.Tensor:
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output = model.encode_image(image, normalize=True)
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assert isinstance(output, torch.Tensor)
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return output
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args = (torch.randn(1, 3, model_cfg.image_size, model_cfg.image_size),)
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traced = torch.jit.trace(encode_image, args) # type: ignore[no-untyped-call]
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with warnings.catch_warnings():
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warnings.simplefilter("ignore", UserWarning)
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torch.onnx.export(
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traced,
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args,
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output_path.as_posix(),
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input_names=["image"],
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output_names=["image_embedding"],
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opset_version=17,
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dynamic_axes={"image": {0: "batch_size"}},
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)
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def export_text_encoder(model: open_clip.CLIP, model_cfg: OpenCLIPModelConfig, output_path: Path | str) -> None:
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output_path = Path(output_path)
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def encode_text(text: torch.Tensor) -> torch.Tensor:
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output = model.encode_text(text, normalize=True)
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assert isinstance(output, torch.Tensor)
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return output
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args = (torch.ones(1, model_cfg.sequence_length, dtype=torch.int32),)
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traced = torch.jit.trace(encode_text, args) # type: ignore[no-untyped-call]
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with warnings.catch_warnings():
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warnings.simplefilter("ignore", UserWarning)
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torch.onnx.export(
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traced,
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args,
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output_path.as_posix(),
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input_names=["text"],
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output_names=["text_embedding"],
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opset_version=17,
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dynamic_axes={"text": {0: "batch_size"}},
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)
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