# -*- coding: utf-8 -*- # MegEngine is Licensed under the Apache License, Version 2.0 (the "License") # # Copyright (c) 2014-2021 Megvii Inc. All rights reserved. # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. import numpy as np import megengine.functional as F import megengine.functional.elemwise as elemwise from megengine import tensor from megengine.core.tensor import dtype from megengine.functional.elemwise import _elwise def test_abs(): np.testing.assert_allclose( F.abs(tensor([-3.0, -4.0, -5.0])).numpy(), np.abs(np.array([-3.0, -4.0, -5.0], dtype=np.float32)), ) np.testing.assert_allclose(F.abs(-3.0).numpy(), np.abs(np.float32(-3.0))) def test_elemwise_mode_string(): np.testing.assert_allclose( _elwise(tensor([-3.0, -4.0, -5.0]), mode="ABS").numpy(), np.abs(np.array([-3.0, -4.0, -5.0], dtype=np.float32)), ) np.testing.assert_allclose( _elwise(-3.0, mode="ABS").numpy(), np.abs(np.float32(-3.0)) ) def test_multiply(): np.testing.assert_allclose( F.mul(-3.0, -4.0).numpy(), np.multiply(np.float32(-3.0), np.float32(-4.0)) ) np.testing.assert_allclose( F.mul(tensor([3.0, 4.0]), 4.0).numpy(), np.multiply(np.array([3.0, 4.0], dtype=np.float32), 4.0), ) np.testing.assert_allclose( F.mul(4.0, tensor([3.0, 4.0])).numpy(), np.multiply(4.0, np.array([3.0, 4.0], dtype=np.float32)), ) np.testing.assert_allclose( F.mul(tensor([3.0, 4.0]), tensor([3.0, 4.0])).numpy(), np.multiply( np.array([3.0, 4.0], dtype=np.float32), np.array([3.0, 4.0], dtype=np.float32), ), ) def test_clamp(): """Fix an issue when `lower` or `upper` is 0, it will be recognized as `False` and `F.clip` will fall into wrong conditions unexpectedly. """ x = np.linspace(-6, 6, dtype="float32") np.testing.assert_allclose( F.clip(tensor(x) + 3, 0, 6).numpy(), np.clip(x + 3, 0, 6) ) np.testing.assert_allclose( F.clip(tensor(x) - 3, -6, 0).numpy(), np.clip(x - 3, -6, 0) ) def test_isnan(): for case in [[1, float("nan"), 0]]: np.testing.assert_allclose(F.isnan(tensor(case)).numpy(), np.isnan(case)) def test_isinf(): for case in [[1, float("inf"), 0]]: np.testing.assert_allclose(F.isinf(tensor(case)).numpy(), np.isinf(case)) def test_sign(): for case in [[1, -1, 0]]: x = tensor(case) np.testing.assert_allclose(F.sign(x).numpy(), np.sign(case).astype(x.dtype)) def test_cosh(): np.random.seed(42) x = np.random.randn(100).astype("float32") y_np = np.cosh(x) y_mge = F.cosh(tensor(x)).numpy() np.testing.assert_allclose(y_np, y_mge, rtol=1e-5) def test_sinh(): np.random.seed(42) x = np.random.randn(100).astype("float32") y_np = np.sinh(x) y_mge = F.sinh(tensor(x)).numpy() np.testing.assert_allclose(y_np, y_mge, rtol=1e-5) def test_asinh(): np.random.seed(42) x = np.random.randn(100).astype("float32") y_np = np.arcsinh(x) y_mge = F.asinh(tensor(x)).numpy() np.testing.assert_almost_equal(y_np, y_mge, decimal=5) def test_acosh(): x = np.arange(0, 10000).astype("float32") / 100 + 1 y_np = np.arccosh(x) y_mge = F.acosh(tensor(x)).numpy() np.testing.assert_almost_equal(y_np, y_mge, decimal=6) def test_atanh(): np.random.seed(42) x = np.random.rand(100).astype("float32") * 2 - 1 y_np = np.arctanh(x) y_mge = F.atanh(tensor(x)).numpy() np.testing.assert_almost_equal(y_np, y_mge, decimal=5) def test_hswish(): np.random.seed(42) x = np.random.randn(100).astype("float32") y_np = x * np.minimum(np.maximum(x + 3, 0), 6) / 6 y_mge = F.hswish(tensor(x)).numpy() np.testing.assert_almost_equal(y_np, y_mge, decimal=6) def test_hsigmoid(): np.random.seed(42) x = np.random.randn(100).astype("float32") y_np = np.minimum(np.maximum(x + 3, 0), 6) / 6 y_mge = F.hsigmoid(tensor(x)).numpy() np.testing.assert_equal(y_np, y_mge) def test_logical_oprs(): x = np.array([[True, False], [False, True]]) y = np.array([[True, True], [False, False]]) xx = tensor(x) yy = tensor(y) np.testing.assert_equal(~x, (F.logical_not(xx)).numpy()) np.testing.assert_equal(x & y, F.logical_and(xx, yy).numpy()) np.testing.assert_equal(x | y, F.logical_or(xx, yy).numpy()) np.testing.assert_equal(x ^ y, F.logical_xor(xx, yy).numpy()) def test_qadd(): inp_scale = 0.5 outp_scale = 0.2 x = np.arange(6).reshape(2, 3).astype("float32") y = np.arange(6).reshape(2, 3).astype("float32") x = tensor(x, dtype=dtype.qint8(inp_scale)) y = tensor(y, dtype=dtype.qint8(inp_scale)) result_mge = F.elemwise._elemwise_multi_type( x, y, mode="qadd", dtype=dtype.qint8(outp_scale) ) result_mge = result_mge.astype("float32").numpy() result_expect = x.astype("float32").numpy() + y.astype("float32").numpy() np.testing.assert_almost_equal(result_mge, result_expect, decimal=6) def test_int32_input(): x = tensor(np.array([1, 2, 3, 4, 5]), dtype="int32") for op_name in elemwise.__all__: op = getattr(elemwise, op_name) nargs = op.__code__.co_argcount if op_name == "clip": inp = (x, 0, 1) elif op_name.endswith("_shift"): inp = (x, 1) elif op_name.startswith("logical_"): continue else: inp = (x,) * nargs y = op(*inp) y.numpy()