# -*- coding: utf-8 -*- from ..core._imperative_rt.core2 import apply from ..core._imperative_rt.core2 import sync as _sync from ..core.ops.builtin import AssertEqual from ..tensor import Tensor from ..utils.deprecation import deprecated_func from .elemwise import abs, maximum, minimum from .tensor import ones, zeros __all__ = ["topk_accuracy"] def _assert_equal( expect: Tensor, actual: Tensor, *, maxerr: float = 0.0001, verbose: bool = False ): r"""Asserts two tensors equal and returns expected value (first input). It is a variant of python assert which is symbolically traceable (similar to ``numpy.testing.assert_equal``). If we want to verify the correctness of model, just ``assert`` its states and outputs. While sometimes we need to verify the correctness at different backends for *dumped* model (or in :class:`~jit.trace` context), and no python code could be executed in that case. Thus we have to use :func:`~functional.utils._assert_equal` instead. Args: expect: expected tensor value actual: tensor to check value maxerr: max allowed error; error is defined as the minimal of absolute and relative error verbose: whether to print maxerr to stdout during opr exec Examples: >>> x = Tensor([1, 2, 3], dtype="float32") >>> y = Tensor([1, 2, 3], dtype="float32") >>> F.utils._assert_equal(x, y, maxerr=0) Tensor([1. 2. 3.], device=xpux:0) """ err = ( abs(expect - actual) / maximum( minimum(abs(expect), abs(actual)), Tensor(1.0, dtype="float32", device=expect.device), ) ).max() result = apply(AssertEqual(maxerr=maxerr, verbose=verbose), expect, actual, err)[0] _sync() # sync interpreter to get exception return result def _simulate_error(): x1 = zeros(100) x2 = ones(100) (ret,) = apply(AssertEqual(maxerr=0, verbose=False), x1, x2, x2) return ret topk_accuracy = deprecated_func( "1.3", "megengine.functional.metric", "topk_accuracy", True ) copy = deprecated_func("1.3", "megengine.functional.tensor", "copy", True)