feat: train fixed-size classifiers and export class probabilities
This commit is contained in:
Shaun Zhou
@@ -1,10 +1,8 @@
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#!/usr/bin/env python
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"""Export a classifier with the input/output contract of a YOLO OpenVINO IR.
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"""Export RGB / 255 BCHW -> Softmax probabilities [1, C], in training class order.
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The sole active candidate is a full-image pseudo box, not a localization result.
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Input must already be RGB float32 BCHW in [0, 1], exactly as in deploy/cpp.
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Training normalization is embedded. No resize, pseudo boxes or background gating.
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"""
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from __future__ import annotations
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import argparse
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@@ -19,7 +17,6 @@ import yaml
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from torch import nn
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sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
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from models import build_model # noqa: E402
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@@ -36,63 +33,48 @@ def ordered_names(names) -> list[str]:
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return names
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def class_order(source: list[str], target: list[str]) -> list[int]:
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source, target = ordered_names(source), ordered_names(target)
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if set(source) != set(target):
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raise ValueError(f"Class sets differ: classifier={source}, YOLO={target}")
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return [source.index(name) for name in target]
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def input_size(config: dict) -> list[int]:
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size = config["data"]["image_size"]
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size = [size, size] if isinstance(size, int) else list(size)
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if len(size) != 2 or any(not isinstance(v, int) or v < 1 for v in size):
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raise ValueError(f"Invalid training image_size: {size}")
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return size
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def preprocessing(config: dict, size: int) -> float | None:
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"""Reject preprocessing that cannot be reproduced without resampling the input."""
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def preprocessing(config: dict, size: int | list[int]) -> float | None:
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shape = [size, size] if isinstance(size, int) else list(size)
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transforms = config["data"]["val_transforms"]
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sequence = [t["name"] for t in transforms]
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if sequence not in (["Resize", "ToTensor", "PerImageStandardize"],
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["ToTensor", "PerImageStandardize"], ["Resize", "ToTensor"], ["ToTensor"]):
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raise ValueError(f"Unsupported validation preprocessing: {sequence}")
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eps = None
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for transform in transforms:
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if transform["name"] == "Resize":
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if transform["size"] != [size, size]:
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raise ValueError("Training size differs from template; retrain at the target size")
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if transform["name"] == "Resize" and list(transform["size"]) != shape:
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raise ValueError("Training size differs from export input")
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if transform["name"] == "PerImageStandardize":
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return float(transform.get("eps", 1e-5))
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return None
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eps = float(transform.get("eps", 1e-5))
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if eps <= 0:
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raise ValueError("Standardization eps must be positive")
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return eps
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class YoloClassifierAdapter(nn.Module):
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def __init__(self, classifier: nn.Module, size: int, anchors: int,
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order: list[int], eps: float | None = 1e-5,
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background_index: int | None = None):
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class ClassifierProbabilities(nn.Module):
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def __init__(self, classifier: nn.Module, eps: float | None = None):
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super().__init__()
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if size <= 0 or anchors <= 0:
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raise ValueError("size and anchors must be positive")
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self.classifier = classifier
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self.eps = eps
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if background_index is not None and not 0 <= background_index < len(order):
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raise ValueError("background_index must be an output class index")
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self.background_index = background_index
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self.register_buffer("order", torch.tensor(order, dtype=torch.long))
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self.register_buffer("box", torch.tensor([size / 2, size / 2, size, size])
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.reshape(1, 4, 1))
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# Only candidate zero is active; do not duplicate predictions across anchors.
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self.register_buffer("inactive", torch.zeros(1, 4 + len(order), anchors - 1))
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self.classifier, self.eps = classifier, eps
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def normalize(self, x: torch.Tensor) -> torch.Tensor:
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if self.eps is None:
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return x
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dims = (1, 2, 3)
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mean = x.mean(dim=dims, keepdim=True)
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# Match datasets.transforms.PerImageStandardize: torch.std correction=1.
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# Match training torch.std(correction=1), separately for each image.
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variance = ((x - mean) ** 2).sum(dim=dims, keepdim=True) / (x[0].numel() - 1)
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return (x - mean) / (torch.sqrt(variance) + self.eps)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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scores = self.classifier(self.normalize(x)).softmax(dim=1)[:, self.order]
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if self.background_index is not None:
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foreground = (scores.argmax(dim=1) != self.background_index).unsqueeze(1)
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scores = scores * foreground.to(scores.dtype)
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active = torch.cat((self.box.expand(x.shape[0], -1, -1), scores.unsqueeze(2)), dim=1)
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return torch.cat((active, self.inactive.expand(x.shape[0], -1, -1)), dim=2)
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return self.classifier(self.normalize(x)).softmax(dim=1)
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def main() -> int:
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@@ -100,97 +82,71 @@ def main() -> int:
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument("--checkpoint", default="runs/classify/best.pth")
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parser.add_argument("--template", required=True, help="Production YOLO .xml (metadata.yaml beside it)")
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parser.add_argument("--classes", help="Classifier class order: classes.txt or eval_report.json")
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parser.add_argument("--background-class", default="normal", help="Top1 class to emit no boxes for")
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parser.add_argument("--keep-background-box", action="store_true",
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help="Opt out of background suppression (legacy classification behavior)")
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parser.add_argument("--classes", help="Training class order: classes.txt or eval_report.json")
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parser.add_argument("--fp32-weights", action="store_true", help="Keep full-precision weights")
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parser.add_argument("--out", required=True, help="New directory; existing directories are rejected")
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args = parser.parse_args()
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checkpoint, template, out = Path(args.checkpoint), Path(args.template), Path(args.out)
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checkpoint, out = Path(args.checkpoint), Path(args.out)
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if out.exists():
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raise FileExistsError(f"Refusing to overwrite {out}")
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state = torch.load(checkpoint, map_location="cpu", weights_only=False)
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config = state["config"]
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names = config["data"].get("classes")
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if args.classes:
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path = Path(args.classes)
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source = (json.loads(path.read_text())["classes"] if path.suffix == ".json"
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else path.read_text().splitlines())
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else:
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source = config["data"].get("classes")
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if not source:
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report = checkpoint.parent / "eval_report.json"
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if not report.exists():
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raise ValueError("No recorded class order; provide --classes (do not guess folder order)")
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source = json.loads(report.read_text())["classes"]
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source = ordered_names(source)
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metadata = yaml.safe_load((template.parent / "metadata.yaml").read_text())
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if metadata.get("end2end") or metadata.get("args", {}).get("nms"):
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raise ValueError("Expected raw [1, 4+nc, anchors] output, not end-to-end/NMS output")
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target = ordered_names(metadata["names"])
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order = class_order(source, target)
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if not args.keep_background_box and args.background_class not in target:
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raise ValueError(f"Background class {args.background_class!r} is not in {target}")
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background_index = None if args.keep_background_box else target.index(args.background_class)
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core = ov.Core()
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reference = core.read_model(str(template))
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if len(reference.inputs) != 1 or len(reference.outputs) != 1:
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raise ValueError("Expected one input and one output")
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input_shape, output_shape = list(reference.input().shape), list(reference.output().shape)
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if (len(input_shape) != 4 or input_shape[:2] != [1, 3]
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or input_shape[2] != input_shape[3] or len(output_shape) != 3
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or output_shape[:2] != [1, 4 + len(target)] or output_shape[2] < 1):
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raise ValueError(f"Unsupported template shapes: {input_shape}, {output_shape}")
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if (reference.input().element_type != ov.Type.f32
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or reference.output().element_type != ov.Type.f32):
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raise ValueError("Only float32 input/output are supported")
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size = input_shape[2]
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supplied = (json.loads(path.read_text())["classes"] if path.suffix == ".json"
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else path.read_text().splitlines())
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if names and ordered_names(supplied) != ordered_names(names):
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raise ValueError("--classes contradicts checkpoint class order")
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names = supplied
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if not names:
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report = checkpoint.parent / "eval_report.json"
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if not report.exists():
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raise ValueError("No recorded class order; provide --classes (do not guess folder order)")
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names = json.loads(report.read_text())["classes"]
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names = ordered_names(names)
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size = input_size(config)
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eps = preprocessing(config, size)
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model_config = copy.deepcopy(config["model"])
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model_config["backbone"]["pretrained"] = False
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model_config["init_from"] = None
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model_config["num_classes"] = len(source)
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model_config["num_classes"] = len(names)
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model = build_model(model_config).eval()
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model.load_state_dict(state["model"], strict=True)
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adapter = YoloClassifierAdapter(model, size, output_shape[2], order, eps,
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background_index=background_index).eval()
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wrapper = ClassifierProbabilities(model, eps).eval()
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shape = [1, model_config["backbone"].get("in_channels", 3), *size]
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torch.manual_seed(42)
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example = torch.rand(input_shape)
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converted = ov.convert_model(adapter, example_input=example, input=input_shape)
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converted.input().get_tensor().set_names(reference.input().get_names())
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converted.output().get_tensor().set_names(reference.output().get_names())
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# Preserve any interface metadata used by OpenVINO consumers, then mark pseudo boxes.
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for key, value in reference.get_rt_info().items():
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if key != "conversion_parameters":
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converted.set_rt_info(value, [key])
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converted.set_rt_info("classifier_pseudo_box", ["adapter_kind"])
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example = torch.rand(shape)
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converted = ov.convert_model(wrapper, example_input=example, input=shape)
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converted.input().get_tensor().set_names({"images"})
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converted.output().get_tensor().set_names({"probabilities"})
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converted.set_rt_info("classify", ["model_info", "task"])
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converted.set_rt_info(json.dumps(dict(enumerate(names))), ["model_info", "names"])
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out.mkdir(parents=True)
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xml = out / template.name
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ov.save_model(converted, str(xml), compress_to_fp16=True)
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compiled = core.compile_model(str(xml), "CPU", {"PERFORMANCE_HINT": "LATENCY"})
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arch = model_config["backbone"].get("arch", model_config["backbone"]["name"])
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xml = out / f"{arch}.xml"
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ov.save_model(converted, str(xml), compress_to_fp16=not args.fp32_weights)
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compiled = ov.Core().compile_model(str(xml), "CPU", {"PERFORMANCE_HINT": "LATENCY"})
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errors = []
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with torch.inference_mode():
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for sample in (example, torch.zeros_like(example), torch.ones_like(example)):
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expected = adapter(sample).numpy()
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expected = wrapper(sample).numpy()
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actual = compiled(sample.numpy())[compiled.output()]
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assert actual.shape == (1, len(names))
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assert np.isfinite(actual).all() and (actual >= 0).all() and (actual <= 1).all()
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np.testing.assert_allclose(actual.sum(axis=1), 1, atol=1e-5)
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np.testing.assert_allclose(actual, expected, atol=0.02, rtol=0.02)
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assert np.argmax(actual[0, 4:, 0]) == np.argmax(expected[0, 4:, 0])
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assert np.count_nonzero(actual[:, :, 1:]) == 0
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assert actual.argmax(axis=1).tolist() == expected.argmax(axis=1).tolist()
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errors.append(float(np.max(np.abs(expected - actual))))
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metadata.update({"names": dict(enumerate(target)), "adapter_kind": "classifier_pseudo_box",
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"description": "Classifier with one full-image pseudo box; no localization",
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"background_class": None if background_index is None else args.background_class,
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"end2end": False, "args": {"nms": False}, "imgsz": [size, size]})
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metadata = {"task": "classify", "names": dict(enumerate(names)), "imgsz": size,
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"channels": shape[1], "layout": "BCHW", "input_range": [0, 1],
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"output": "softmax_probabilities", "input_shape": shape,
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"output_shape": [1, len(names)], "standardize_eps": eps,
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"weight_storage": "fp32" if args.fp32_weights else "fp16"}
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(out / "metadata.yaml").write_text(yaml.safe_dump(metadata, allow_unicode=True))
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(out / "classes.txt").write_text("\n".join(target) + "\n")
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summary = {"checkpoint": str(checkpoint.resolve()), "template": str(template.resolve()),
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"classifier_classes": source, "output_classes": target, "class_order": order,
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"input_shape": input_shape, "output_shape": output_shape,
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"standardize_eps": eps, "max_abs_errors": errors,
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"background_class": None if background_index is None else args.background_class,
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"background_index": background_index,
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"pseudo_box": [size / 2, size / 2, size, size]}
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(out / "adapter.json").write_text(json.dumps(summary, ensure_ascii=False, indent=2))
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(out / "classes.txt").write_text("\n".join(names) + "\n")
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summary = {"checkpoint": str(checkpoint.resolve()), **metadata, "max_abs_errors": errors}
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(out / "export.json").write_text(json.dumps(summary, ensure_ascii=False, indent=2))
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print(json.dumps({"model": str(xml), **summary}, ensure_ascii=False, indent=2))
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return 0
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