feat(dataset): add synthetic dataset generation and configuration

mini-nav/configs/models.py

@@ -1,17 +1,10 @@
 """Pydantic data models for feature compressor configuration."""
 
-from enum import Enum
 from pathlib import Path
 
 from pydantic import BaseModel, ConfigDict, Field, field_validator
 
 
-class PoolingType(str, Enum):
-    """Enum for pooling types."""
-
-    ATTENTION = "attention"
-
-
 class ModelConfig(BaseModel):
     """Configuration for the vision model and compression."""
 
@@ -42,10 +35,60 @@ class OutputConfig(BaseModel):
         return Path(__file__).parent.parent.parent / v
 
 
+class DatasetConfig(BaseModel):
+    """Configuration for synthetic dataset generation."""
+
+    model_config = ConfigDict(extra="ignore")
+
+    dataset_root: Path = (
+        Path(__file__).parent.parent.parent / "datasets" / "InsDet-FULL"
+    )
+    output_dir: Path = (
+        Path(__file__).parent.parent.parent / "datasets" / "InsDet-FULL" / "Synthesized"
+    )
+    num_objects_range: tuple[int, int] = (3, 8)
+    num_scenes: int = 1000
+    object_scale_range: tuple[float, float] = (0.1, 0.4)
+    rotation_range: tuple[int, int] = (-30, 30)
+    overlap_threshold: float = 0.3
+    seed: int = 42
+
+    @field_validator("dataset_root", "output_dir", mode="after")
+    def convert_to_absolute(cls, v: Path) -> Path:
+        """
+        Converts the path to an absolute path relative to the project root.
+        This works even if the path doesn't exist on disk.
+        """
+        if v.is_absolute():
+            return v
+        return Path(__file__).parent.parent.parent / v
+
+    @field_validator("num_objects_range", mode="after")
+    def validate_num_objects(cls, v: tuple[int, int]) -> tuple[int, int]:
+        if v[0] < 1 or v[1] < v[0]:
+            raise ValueError("num_objects_range must have min >= 1 and min <= max")
+        return v
+
+    @field_validator("object_scale_range", mode="after")
+    def validate_scale(cls, v: tuple[float, float]) -> tuple[float, float]:
+        if v[0] <= 0 or v[1] <= 0 or v[1] < v[0]:
+            raise ValueError(
+                "object_scale_range must have positive values and min <= max"
+            )
+        return v
+
+    @field_validator("overlap_threshold", mode="after")
+    def validate_overlap(cls, v: float) -> float:
+        if not 0 <= v <= 1:
+            raise ValueError("overlap_threshold must be between 0 and 1")
+        return v
+
+
 class Config(BaseModel):
     """Root configuration for the feature compressor."""
 
     model_config = ConfigDict(extra="ignore")
 
-    model: ModelConfig
-    output: OutputConfig
+    model: ModelConfig = Field(default_factory=ModelConfig)
+    output: OutputConfig = Field(default_factory=OutputConfig)
+    dataset: DatasetConfig = Field(default_factory=DatasetConfig)