Upload modeling_exaonepath_slide_encoder.py with huggingface_hub

modeling_exaonepath_slide_encoder.py

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+from __future__ import annotations
+
+"""Remote-code modeling file for EXAONE-Path Slide/WSI encoder.
+
+This file is imported by Transformers when using `trust_remote_code=True`.
+
+Important:
+- This file acts as a *thin AutoModel entrypoint*.
+- The actual implementation lives in `exaonepath.models.slide_encoder_hf`.
+- At runtime, the repository snapshot is downloaded via `snapshot_download`
+    and added to `sys.path` so that `exaonepath/` can be imported.
+- Do NOT import sibling modules like `configuration_exaonepath_slide_encoder` here.
+    Transformers' remote-code dependency checker treats those imports as missing
+    third-party packages (e.g. it suggests `pip install configuration_exaonepath_slide_encoder`).
+"""
+
+from typing import Any, Dict, Optional
+import importlib
+import sys
+
+from huggingface_hub import snapshot_download
+from torch import Tensor, nn
+from transformers import PretrainedConfig, PreTrainedModel
+
+
+class ExaonePathSlideEncoderConfig(PretrainedConfig):
+    """Self-contained Transformers config for EXAONE-Path Slide/WSI encoder.
+
+    Keep it here (in the modeling file) so we don't need a separate
+    `configuration_exaonepath_slide_encoder.py` on the Hub.
+    """
+
+    model_type = "exaonepath_slide_encoder"
+
+    def __init__(self, wsi_cfg: Dict[str, Any] | None = None, **kwargs: Any):
+        self.wsi_cfg = dict(wsi_cfg or {})
+        super().__init__(**kwargs)
+
+
+class ExaonePathSlideEncoderModel(PreTrainedModel):
+    config_class = ExaonePathSlideEncoderConfig
+    base_model_prefix = "slide_encoder"
+
+    def __init__(self, config: ExaonePathSlideEncoderConfig):
+        super().__init__(config)
+
+        # Ensure the repo code (including `exaonepath/`) is available at runtime.
+        # NOTE: config._name_or_path is usually the repo id when loaded from Hub.
+        repo_id = getattr(config, "_name_or_path", None) or getattr(config, "name_or_path", None)
+        if isinstance(repo_id, str) and repo_id:
+            local_root = snapshot_download(repo_id)
+            if local_root not in sys.path:
+                sys.path.insert(0, local_root)
+
+        WSIEncoder = getattr(importlib.import_module("exaonepath.models.slide_encoder_hf"), "WSIEncoder")
+        self.slide_encoder: nn.Module = WSIEncoder.from_wsi_config(wsi_cfg=config.wsi_cfg)
+
+        self.post_init()
+
+    def forward(
+        self,
+        patch_features: Tensor,
+        patch_mask: Tensor,
+        patch_coords: Optional[Tensor] = None,
+        patch_contour_index: Optional[Tensor] = None,
+        **kwargs: Any,
+    ) -> Dict[str, Tensor]:
+        """Return patch- and slide-level embeddings.
+
+        Returns a dict with exactly two keys:
+        - "patch_embedding": [B, N, C_in + D]
+        - "slide_embedding": [B, C_in + D]
+
+        Note: We intentionally return a plain dict (instead of a ModelOutput)
+        to make the remote-code API explicit and easy to use.
+        """
+
+        out: Dict[str, Tensor] = self.slide_encoder(
+            patch_features=patch_features,
+            patch_mask=patch_mask,
+            patch_coords=patch_coords,
+            patch_contour_index=patch_contour_index,
+        )
+        return out
+
+
+__all__ = ["ExaonePathSlideEncoderModel"]