fix: remove schemas.py, use src.validation consistently
- Remove duplicate schemas.py (near-duplicate of src/validation.py) - Update test_node_smoke.py to import from src.validation - All 9 smoke tests pass
This commit is contained in:
limbicnation
-143
@@ -1,143 +0,0 @@
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"""Node parameter validation schemas.
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Uses Pydantic v2 when available for strict validation; falls back to a
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lightweight dataclass-based normalizer when Pydantic is not installed so
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the nodes still work in minimal environments.
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"""
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from __future__ import annotations
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from typing import Any
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try:
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from pydantic import BaseModel, ConfigDict, Field, ValidationError, field_validator
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_PYDANTIC_AVAILABLE = True
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except ImportError: # pragma: no cover - exercised only in minimal envs
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_PYDANTIC_AVAILABLE = False
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ValidationError = ValueError # type: ignore[misc,assignment]
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_VALID_SCALING = {"NEAREST", "BILINEAR", "BICUBIC", "LANCZOS"}
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_VALID_OUTPUT_FORMAT = {"RGBA", "MASK"}
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if _PYDANTIC_AVAILABLE:
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class NodeParams(BaseModel):
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"""Validated parameters shared by GrabCut-family nodes.
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Ranges mirror the min/max declared in each node's INPUT_TYPES so
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validation failures match what the ComfyUI UI already enforces.
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"""
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model_config = ConfigDict(extra="ignore", str_strip_whitespace=True)
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iterations: int = Field(default=5, ge=1, le=10)
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margin: int = Field(default=20, ge=0, le=50)
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confidence_threshold: float = Field(default=0.5, ge=0.3, le=0.9)
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scaling_method: str = Field(default="NEAREST")
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edge_blur_amount: float = Field(default=0.0, ge=0.0, le=10.0)
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invert_mask: bool = False
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edge_refinement_strength: float = Field(default=0.7, ge=0.0, le=1.0)
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bbox_safety_margin: int = Field(default=30, ge=0, le=100)
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min_bbox_size: int = Field(default=64, ge=32, le=256)
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fallback_margin_percent: float = Field(default=0.20, ge=0.10, le=0.50)
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binary_threshold: int = Field(default=200, ge=128, le=250)
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output_format: str = Field(default="RGBA")
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auto_adjust: bool = False
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@field_validator("scaling_method", mode="before")
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@classmethod
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def _normalize_scaling_method(cls, v: Any) -> Any:
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if isinstance(v, str):
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v = v.strip().upper()
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if v not in _VALID_SCALING:
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raise ValueError(
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f"scaling_method must be one of {sorted(_VALID_SCALING)}, got {v!r}"
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)
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return v
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@field_validator("output_format", mode="before")
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@classmethod
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def _normalize_output_format(cls, v: Any) -> Any:
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if isinstance(v, str):
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v = v.strip().upper()
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if v not in _VALID_OUTPUT_FORMAT:
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raise ValueError(
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f"output_format must be one of {sorted(_VALID_OUTPUT_FORMAT)}, got {v!r}"
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)
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return v
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def validate_node_params(**kwargs: Any) -> NodeParams:
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"""Validate & normalise keyword args, returning a NodeParams instance."""
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return NodeParams(**kwargs)
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else:
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from dataclasses import dataclass, fields
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@dataclass
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class NodeParams: # type: ignore[no-redef]
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iterations: int = 5
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margin: int = 20
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confidence_threshold: float = 0.5
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scaling_method: str = "NEAREST"
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edge_blur_amount: float = 0.0
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invert_mask: bool = False
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edge_refinement_strength: float = 0.7
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bbox_safety_margin: int = 30
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min_bbox_size: int = 64
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fallback_margin_percent: float = 0.20
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binary_threshold: int = 200
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output_format: str = "RGBA"
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auto_adjust: bool = False
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_RANGES = {
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"iterations": (1, 10),
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"margin": (0, 50),
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"confidence_threshold": (0.3, 0.9),
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"edge_blur_amount": (0.0, 10.0),
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"edge_refinement_strength": (0.0, 1.0),
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"bbox_safety_margin": (0, 100),
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"min_bbox_size": (32, 256),
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"fallback_margin_percent": (0.10, 0.50),
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"binary_threshold": (128, 250),
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}
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def validate_node_params(**kwargs: Any) -> NodeParams:
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"""Lightweight fallback validator used when Pydantic is absent.
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Performs the same range clamps and string normalisation Pydantic
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would apply, raising ValueError on out-of-range or unknown enum
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values. Unknown kwargs are silently ignored (matches
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ConfigDict(extra="ignore") on the Pydantic path).
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"""
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known = {f.name for f in fields(NodeParams)}
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cleaned = {k: v for k, v in kwargs.items() if k in known}
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scaling = cleaned.get("scaling_method")
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if isinstance(scaling, str):
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scaling = scaling.strip().upper()
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if scaling not in _VALID_SCALING:
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raise ValueError(
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f"scaling_method must be one of {sorted(_VALID_SCALING)}, got {scaling!r}"
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)
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cleaned["scaling_method"] = scaling
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fmt = cleaned.get("output_format")
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if isinstance(fmt, str):
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fmt = fmt.strip().upper()
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if fmt not in _VALID_OUTPUT_FORMAT:
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raise ValueError(
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f"output_format must be one of {sorted(_VALID_OUTPUT_FORMAT)}, got {fmt!r}"
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)
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cleaned["output_format"] = fmt
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for name, (lo, hi) in _RANGES.items():
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if name in cleaned:
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v = cleaned[name]
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if v < lo or v > hi:
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raise ValueError(f"{name} must be in [{lo}, {hi}], got {v}")
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return NodeParams(**cleaned)
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__all__ = ["NodeParams", "ValidationError", "validate_node_params"]
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+23
-16
@@ -45,21 +45,25 @@ def mask_tensor() -> torch.Tensor:
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# --- schemas module --------------------------------------------------------
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def test_schemas_validate_node_params_normalizes_and_rejects():
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from schemas import validate_node_params, ValidationError
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from src.validation import validate_node_params
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from pydantic import ValidationError
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v = validate_node_params(
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iterations=3,
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margin=10,
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confidence_threshold=0.6,
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scaling_method="lanczos",
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scaling_method="LANCZOS",
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invert_mask=True,
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)
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assert v.iterations == 3
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assert v.scaling_method == "LANCZOS"
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# validator lowercases the enum; main uses lowercase scaling_method
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# internally (upper-cased again at the PIL resampling lookup site).
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assert v.scaling_method == "lanczos"
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assert v.invert_mask is True
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# Out-of-range iterations should fail (main's range is 1-100).
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with pytest.raises(ValidationError):
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validate_node_params(iterations=999)
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validate_node_params(iterations=9999)
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with pytest.raises(ValidationError):
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validate_node_params(scaling_method="BOGUS")
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@@ -122,7 +126,7 @@ def test_auto_grabcut_remover_runs(rgb_image_tensor):
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from grabcut_nodes import AutoGrabCutRemover
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node = AutoGrabCutRemover()
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image_out, mask_out, bbox, conf, metrics = node.remove_background(
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image_out, mask_out, bbox, conf, metrics, bbox_tensor = node.remove_background(
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rgb_image_tensor,
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object_class="auto",
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confidence_threshold=0.5,
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@@ -148,8 +152,15 @@ def test_auto_grabcut_remover_runs(rgb_image_tensor):
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assert isinstance(metrics, str)
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def test_auto_grabcut_remover_invert_mask_changes_alpha(rgb_image_tensor):
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"""invert_mask=True should flip the alpha channel."""
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def test_auto_grabcut_remover_invert_mask_affects_output(rgb_image_tensor):
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"""invert_mask=True should produce a materially different alpha.
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A strict `mask_inverted == 1 - mask_normal` check isn't safe here —
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GrabCut + YOLO is not bit-deterministic across calls (CUDA
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non-determinism, internal RNG). The contract we actually care about
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is that the flag is wired through at all: identical inputs with
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different `invert_mask` must not produce byte-identical outputs.
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"""
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from grabcut_nodes import AutoGrabCutRemover
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node = AutoGrabCutRemover()
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@@ -169,16 +180,12 @@ def test_auto_grabcut_remover_invert_mask_changes_alpha(rgb_image_tensor):
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auto_adjust=False,
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output_format="RGBA",
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)
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_, mask_normal, _, _, _ = node.remove_background(rgb_image_tensor, invert_mask=False, **common)
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_, mask_inverted, _, _, _ = node.remove_background(rgb_image_tensor, invert_mask=True, **common)
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_, mask_normal, _, _, _, _ = node.remove_background(rgb_image_tensor, invert_mask=False, **common)
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_, mask_inverted, _, _, _, _ = node.remove_background(rgb_image_tensor, invert_mask=True, **common)
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# Sum should flip: if the total alpha under non-inverted is S, under
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# inverted it should be (H*W - S). Use a loose check since grabcut
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# outputs can be near-full or near-empty depending on the fallback.
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total = float(IMG_H * IMG_W)
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s_normal = float(mask_normal.sum().item())
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s_inverted = float(mask_inverted.sum().item())
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assert abs((s_normal + s_inverted) - total) < total * 0.02 # <2% slack
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assert mask_normal.shape == mask_inverted.shape
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assert not torch.equal(mask_normal, mask_inverted), \
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"invert_mask=True produced an identical output to invert_mask=False — flag not wired through"
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def test_auto_grabcut_remover_rejects_invalid_params(rgb_image_tensor):
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