bump

openclip.py

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+import warnings
+from dataclasses import dataclass
+from functools import cached_property
+from pathlib import Path
+from typing import Any
+
+from .util import get_model_path, save_config
+
+
+@dataclass
+class OpenCLIPModelConfig:
+    name: str
+    pretrained: str
+
+    @cached_property
+    def model_config(self) -> dict[str, Any]:
+        import open_clip
+
+        config: dict[str, Any] | None = open_clip.get_model_config(self.name)
+        if config is None:
+            raise ValueError(f"Unknown model {self.name}")
+        return config
+
+    @property
+    def image_size(self) -> int:
+        image_size: int = self.model_config["vision_cfg"]["image_size"]
+        return image_size
+
+    @property
+    def sequence_length(self) -> int:
+        context_length: int = self.model_config["text_cfg"].get("context_length", 77)
+        return context_length
+
+
+def to_onnx(
+    model_cfg: OpenCLIPModelConfig,
+    opset_version: int,
+    output_dir_visual: Path | str | None = None,
+    output_dir_textual: Path | str | None = None,
+    cache: bool = True,
+) -> tuple[Path | None, Path | None]:
+    visual_path = None
+    textual_path = None
+    if output_dir_visual is not None:
+        output_dir_visual = Path(output_dir_visual)
+        visual_path = get_model_path(output_dir_visual)
+
+    if output_dir_textual is not None:
+        output_dir_textual = Path(output_dir_textual)
+        textual_path = get_model_path(output_dir_textual)
+
+    if cache and ((textual_path is None or textual_path.exists()) and (visual_path is None or visual_path.exists())):
+        print(f"Models {textual_path} and {visual_path} already exist, skipping")
+        return visual_path, textual_path
+
+    import open_clip
+    import torch
+    from transformers import AutoTokenizer
+
+    torch.backends.mha.set_fastpath_enabled(False)
+
+    model = open_clip.create_model(
+        model_cfg.name,
+        pretrained=model_cfg.pretrained,
+        jit=False,
+        require_pretrained=True,
+    )
+
+    text_vision_cfg = open_clip.get_model_config(model_cfg.name)
+
+    model.eval()
+    for param in model.parameters():
+        param.requires_grad_(False)
+
+    if visual_path is not None and output_dir_visual is not None:
+        if not cache or not visual_path.exists():
+            save_config(
+                open_clip.get_model_preprocess_cfg(model),
+                output_dir_visual / "preprocess_cfg.json",
+            )
+            save_config(text_vision_cfg, output_dir_visual.parent / "config.json")
+            _export_image_encoder(model, model_cfg, visual_path, opset_version)
+        else:
+            print(f"Model {visual_path} already exists, skipping")
+
+    if textual_path is not None and output_dir_textual is not None:
+        if not cache or not textual_path.exists():
+            tokenizer_name = text_vision_cfg["text_cfg"].get("hf_tokenizer_name", "openai/clip-vit-base-patch32")
+            AutoTokenizer.from_pretrained(tokenizer_name).save_pretrained(output_dir_textual)
+            _export_text_encoder(model, model_cfg, textual_path, opset_version)
+        else:
+            print(f"Model {textual_path} already exists, skipping")
+    return visual_path, textual_path
+
+
+def _export_image_encoder(
+    model: Any, model_cfg: OpenCLIPModelConfig, output_path: Path | str, opset_version: int
+) -> None:
+    import torch
+
+    output_path = Path(output_path)
+
+    def encode_image(image: torch.Tensor) -> torch.Tensor:
+        output = model.encode_image(image, normalize=True)
+        assert isinstance(output, torch.Tensor)
+        return output
+
+    model.forward = encode_image
+
+    args = (torch.randn(1, 3, model_cfg.image_size, model_cfg.image_size),)
+
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore", UserWarning)
+        torch.onnx.export(
+            model,
+            args,
+            output_path.as_posix(),
+            input_names=["image"],
+            output_names=["embedding"],
+            opset_version=opset_version,
+            # dynamic_axes={"image": {0: "batch_size"}},
+        )
+
+
+def _export_text_encoder(
+    model: Any, model_cfg: OpenCLIPModelConfig, output_path: Path | str, opset_version: int
+) -> None:
+    import torch
+
+    output_path = Path(output_path)
+
+    def encode_text(text: torch.Tensor) -> torch.Tensor:
+        output = model.encode_text(text, normalize=True)
+        assert isinstance(output, torch.Tensor)
+        return output
+
+    model.forward = encode_text
+
+    args = (torch.ones(1, model_cfg.sequence_length, dtype=torch.int32),)
+
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore", UserWarning)
+        torch.onnx.export(
+            model,
+            args,
+            output_path.as_posix(),
+            input_names=["text"],
+            output_names=["embedding"],
+            opset_version=opset_version,
+            # dynamic_axes={"text": {0: "batch_size"}},
+        )