Upload src/models/__init__.py with huggingface_hub

src/models/__init__.py

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+__all__ = ["build_teacher", "build_student"]
+
+from typing import Dict, Any
+
+from .vision_transformer import VisionTransformerMIM
+
+
+def build_student(cfg: Dict[str, Any]) -> VisionTransformerMIM:
+    """Builds the student ViT model from config."""
+    student_cfg = cfg["model"]["student"]
+
+    use_mask_tokens = student_cfg.get("use_mask_tokens", True)
+
+    # Sparse mode requires absolute position embeddings (variable sequence length)
+    if not use_mask_tokens:
+        default_abs_pos = True
+        default_sincos_pos = False
+        default_rel_pos = False
+        default_shared_rel = False
+    else:
+        # Dense mode can use relative position embeddings
+        default_abs_pos = False
+        default_sincos_pos = False
+        default_rel_pos = False
+        default_shared_rel = True
+
+    model = VisionTransformerMIM(
+        img_size=student_cfg["img_size"],
+        patch_size=student_cfg["patch_size"],
+        embed_dim=student_cfg["embed_dim"],
+        depth=student_cfg["depth"],
+        num_heads=student_cfg["num_heads"],
+        mlp_ratio=student_cfg.get("mlp_ratio", 4.0),
+        drop_path_rate=student_cfg.get("drop_path_rate", 0.1),
+        init_values=student_cfg.get("init_values", 0.1),
+        use_abs_pos_emb=student_cfg.get("use_abs_pos_emb", default_abs_pos),
+        use_sincos_pos_emb=student_cfg.get("use_sincos_pos_emb", default_sincos_pos),
+        use_shared_rel_pos_bias=student_cfg.get("use_shared_rel_pos_bias", default_shared_rel),
+        use_rel_pos_bias=student_cfg.get("use_rel_pos_bias", default_rel_pos),
+        use_mask_tokens=use_mask_tokens,
+    )
+    return model
+
+
+def build_teacher(cfg: Dict[str, Any]):
+    """Builds the frozen CLIP teacher model. Import is lazy to avoid slow CLIP loading at import time."""
+    from .clip_teacher import ClipTeacher
+    teacher_cfg = cfg["model"]["teacher"]
+    return ClipTeacher(
+        model_name=teacher_cfg["name"],
+        layer_extraction=teacher_cfg.get("layer_extraction", "last"),
+        num_layers_to_extract=teacher_cfg.get("num_layers_to_extract", 1),
+    )