[Bug] [Relax][ONNX] NonMaxSuppression silently replaces a runtime output limit with zero

Abierto
  • ¿Algo no está bien?
#20,898 0 comentarios 0 reacciones 0 asignados Ver en GitHub

Los mantenedores suelen responder en 1 día

Nadie ha tomado este issue todavía.

Evaluación

Dificultad
3/5
Tiempo estimado
1-2 días
Aptitud para principiantes
67/100
Tipo de issue
Error
Claridad
Bien especificado
Estado de actividad
Activo
Stack tecnológico
python
Área
machine-learning

Línea de trabajo

Start in python/tvm/relax/frontend/onnx/onnx_frontend.py at the converter lines linked in the report, and trace how a runtime max_output_boxes_per_class Var is handled when it is absent from the initializer table. Compare the dynamic-input and initializer cases using the reproduction with scalar inputs and an explicit IoU threshold. Done means the runtime limit selects the same two rows as the constant limit, with relevant tests passing.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

needs-triage type: bug
Expected behavior

A runtime max_output_boxes_per_class=2 should select the same two disjoint boxes as an initializer containing the identical value.

Actual behavior

The constant-limit control matches ORT. Making only the limit a graph input silently changes the result from two rows to no selections:

constant limit (2, 3) [[0, 0, 0], [0, 0, 1]]
runtime limit (0, 3) []

The converter substitutes zero when a limit Var is absent from the initializer table. This case uses zero-dimensional scalars and an explicit IoU threshold, unlike #19693 and #20506.

Environment

TVM 0.28.dev0, LLVM 18.1.3, Linux x86_64; Python 3.12.3, NumPy 2.5.4, ONNX 1.22.0, ORT 1.29.0. The installed binary build revision is unknown; current main was inspected statically, not rebuilt.

Steps to reproduce

Run with an LLVM-enabled TVM installation:

import numpy as np
import onnx
import onnxruntime as ort
import tvm
from onnx import TensorProto as T, helper as h, numpy_helper as nh
from tvm import relax
from tvm.relax.frontend.onnx import from_onnx

boxes = np.array([[[0, 0, 1, 1], [2, 2, 3, 3]]], dtype="float32")
scores = np.array([[[0.9, 0.8]]], dtype="float32")
limit = np.array(2, dtype="int64")
expected = np.array([[0, 0, 0], [0, 0, 1]], dtype="int64")
for dynamic in (False, True):
    inputs = [h.make_tensor_value_info("boxes", T.FLOAT, [1, 2, 4]),
              h.make_tensor_value_info("scores", T.FLOAT, [1, 1, 2])]
    constants = [nh.from_array(np.array(0.5, dtype="float32"), "iou")]
    feeds = {"boxes": boxes, "scores": scores}
    if dynamic:
        inputs.append(h.make_tensor_value_info("limit", T.INT64, []))
        feeds["limit"] = limit
    else:
        constants.append(nh.from_array(limit, "limit"))
    model = h.make_model(h.make_graph([
        h.make_node("NonMaxSuppression", ["boxes", "scores", "limit", "iou"], ["y"])],
        "nms", inputs, [h.make_tensor_value_info("y", T.INT64, [None, 3])], constants),
        opset_imports=[h.make_opsetid("", 11)], ir_version=10)
    onnx.checker.check_model(model, full_check=True)
    options = ort.SessionOptions()
    options.intra_op_num_threads = options.inter_op_num_threads = 1
    options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_DISABLE_ALL
    source = ort.InferenceSession(model.SerializeToString(), options,
        providers=["CPUExecutionProvider"]).run(None, feeds)[0]
    np.testing.assert_array_equal(source, expected)
    mod = from_onnx(model, keep_params_in_input=False)
    vm = relax.VirtualMachine(relax.build(mod, target="llvm"), tvm.cpu())
    actual = vm["main"](*[tvm.runtime.tensor(v) for v in feeds.values()]).numpy()
    print("runtime limit" if dynamic else "constant limit", actual.shape, actual.tolist())
    if not dynamic:
        np.testing.assert_array_equal(actual, expected)
Triage
  • needs-triage
Lenguaje dominante
Python
Estrellas
13.9k
Forks
4k
Merge medio
7 h 59 min
PR fusionados (30 d)
159

Preparar el entorno

Este proyecto no incluye contenedor de desarrollo, Dockerfile ni guía de contribución, así que la configuración corre por tu cuenta: empieza por su README y consulta nuestra guía para la primera contribución para los pasos generales.

Primeros pasos

  1. Lee el issue completo y luego la guía de contribución del proyecto.
  2. Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
  3. Haz un fork del repositorio y trabaja en una rama.
  4. Abre un pull request que haga referencia al número del issue.

Más de apache/tvm

Todos los issues de apache/tvm

Issues similares

Más issues de Python

Recibe los nuevos issues en tu correo

Un resumen breve de issues de GitHub para principiantes.