forked from mindspore-Ecosystem/mindspore
!24591 Debug Timeout
Merge pull request !24591 from zuochuanyong/debug_cpu_timeout
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# Copyright 2020-2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""unit tests for numpy array operations"""
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import numpy as onp
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import mindspore.numpy as mnp
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from .utils import match_all_arrays, to_tensor
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def test_pad_inner():
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x_np = onp.random.random([2, 3, 4]).astype("float32")
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x_ms = mnp.asarray(x_np.tolist())
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# pad constant
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mnp_res = mnp.pad(x_ms, ((1, 1), (2, 2), (3, 4)))
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onp_res = onp.pad(x_np, ((1, 1), (2, 2), (3, 4)))
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match_all_arrays(mnp_res, onp_res, error=1e-5)
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mnp_res = mnp.pad(x_ms, ((1, 1), (2, 3), (4, 5)), constant_values=((3, 4), (5, 6), (7, 8)))
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onp_res = onp.pad(x_np, ((1, 1), (2, 3), (4, 5)), constant_values=((3, 4), (5, 6), (7, 8)))
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match_all_arrays(mnp_res, onp_res, error=1e-5)
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# pad statistic
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mnp_res = mnp.pad(x_ms, ((1, 1), (2, 2), (3, 4)), mode="mean", stat_length=((1, 2), (2, 10), (3, 4)))
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onp_res = onp.pad(x_np, ((1, 1), (2, 2), (3, 4)), mode="mean", stat_length=((1, 2), (2, 10), (3, 4)))
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match_all_arrays(mnp_res, onp_res, error=1e-5)
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# pad edge
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mnp_res = mnp.pad(x_ms, ((1, 1), (2, 2), (3, 4)), mode="edge")
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onp_res = onp.pad(x_np, ((1, 1), (2, 2), (3, 4)), mode="edge")
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match_all_arrays(mnp_res, onp_res, error=1e-5)
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# pad wrap
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mnp_res = mnp.pad(x_ms, ((1, 1), (2, 2), (3, 4)), mode="wrap")
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onp_res = onp.pad(x_np, ((1, 1), (2, 2), (3, 4)), mode="wrap")
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match_all_arrays(mnp_res, onp_res, error=1e-5)
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# pad linear_ramp
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mnp_res = mnp.pad(x_ms, ((1, 3), (5, 2), (3, 0)), mode="linear_ramp", end_values=((0, 10), (9, 1), (-10, 99)))
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onp_res = onp.pad(x_np, ((1, 3), (5, 2), (3, 0)), mode="linear_ramp", end_values=((0, 10), (9, 1), (-10, 99)))
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match_all_arrays(mnp_res, onp_res, error=1e-5)
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def mnp_logaddexp(x1, x2):
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return mnp.logaddexp(x1, x2)
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def onp_logaddexp(x1, x2):
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return onp.logaddexp(x1, x2)
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def mnp_logaddexp2(x1, x2):
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return mnp.logaddexp2(x1, x2)
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def onp_logaddexp2(x1, x2):
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return onp.logaddexp2(x1, x2)
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def test_logaddexp_inner():
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test_cases = [
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onp.random.randint(1, 5, (5, 6, 3, 2)).astype('float16')]
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for _, x1 in enumerate(test_cases):
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for _, x2 in enumerate(test_cases):
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expected = onp_logaddexp(x1, x2)
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actual = mnp_logaddexp(to_tensor(x1), to_tensor(x2))
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onp.testing.assert_almost_equal(actual.asnumpy().tolist(), expected.tolist(),
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decimal=2)
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def test_logaddexp2_inner():
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test_cases = [
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onp.random.randint(1, 5, (2)).astype('float16'),
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onp.random.randint(1, 5, (3, 2)).astype('float16'),
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onp.random.randint(1, 5, (1, 3, 2)).astype('float16'),
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onp.random.randint(1, 5, (5, 6, 3, 2)).astype('float16')]
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for _, x1 in enumerate(test_cases):
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for _, x2 in enumerate(test_cases):
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expected = onp_logaddexp2(x1, x2)
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actual = mnp_logaddexp2(to_tensor(x1), to_tensor(x2))
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onp.testing.assert_almost_equal(actual.asnumpy().tolist(), expected.tolist(),
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decimal=2)
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@ -14,9 +14,9 @@
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# ============================================================================
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"""unit tests for numpy array operations"""
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import os
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import pytest
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import numpy as onp
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import mindspore.numpy as mnp
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from mindspore import context
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@ -935,36 +935,9 @@ def test_empty_like_exception():
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_pad():
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x_np = onp.random.random([2, 3, 4]).astype("float32")
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x_ms = mnp.asarray(x_np.tolist())
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# pad constant
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mnp_res = mnp.pad(x_ms, ((1, 1), (2, 2), (3, 4)))
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onp_res = onp.pad(x_np, ((1, 1), (2, 2), (3, 4)))
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match_all_arrays(mnp_res, onp_res, error=1e-5)
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mnp_res = mnp.pad(x_ms, ((1, 1), (2, 3), (4, 5)), constant_values=((3, 4), (5, 6), (7, 8)))
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onp_res = onp.pad(x_np, ((1, 1), (2, 3), (4, 5)), constant_values=((3, 4), (5, 6), (7, 8)))
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match_all_arrays(mnp_res, onp_res, error=1e-5)
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# pad statistic
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mnp_res = mnp.pad(x_ms, ((1, 1), (2, 2), (3, 4)), mode="mean", stat_length=((1, 2), (2, 10), (3, 4)))
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onp_res = onp.pad(x_np, ((1, 1), (2, 2), (3, 4)), mode="mean", stat_length=((1, 2), (2, 10), (3, 4)))
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match_all_arrays(mnp_res, onp_res, error=1e-5)
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# pad edge
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mnp_res = mnp.pad(x_ms, ((1, 1), (2, 2), (3, 4)), mode="edge")
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onp_res = onp.pad(x_np, ((1, 1), (2, 2), (3, 4)), mode="edge")
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match_all_arrays(mnp_res, onp_res, error=1e-5)
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# pad wrap
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mnp_res = mnp.pad(x_ms, ((1, 1), (2, 2), (3, 4)), mode="wrap")
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onp_res = onp.pad(x_np, ((1, 1), (2, 2), (3, 4)), mode="wrap")
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match_all_arrays(mnp_res, onp_res, error=1e-5)
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# pad linear_ramp
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mnp_res = mnp.pad(x_ms, ((1, 3), (5, 2), (3, 0)), mode="linear_ramp", end_values=((0, 10), (9, 1), (-10, 99)))
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onp_res = onp.pad(x_np, ((1, 3), (5, 2), (3, 0)), mode="linear_ramp", end_values=((0, 10), (9, 1), (-10, 99)))
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match_all_arrays(mnp_res, onp_res, error=1e-5)
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os.putenv('GLOG_v', '0')
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ret = os.system('pytest -s ./debug.py::test_pad_inner --disable-warnings')
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assert ret == 0
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def pad_with_msfunc(vector, pad_width, iaxis, kwargs):
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# ============================================================================
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"""unit tests for numpy math operations"""
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import os
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import pytest
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import numpy as onp
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import mindspore.numpy as mnp
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from mindspore import context
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from mindspore.common.dtype import dtype_to_nptype
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@ -823,14 +823,6 @@ def test_log1p():
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run_unary_test(mnp_log1p, onp_log1p, test_case, error=1e-5)
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def mnp_logaddexp(x1, x2):
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return mnp.logaddexp(x1, x2)
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def onp_logaddexp(x1, x2):
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return onp.logaddexp(x1, x2)
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@ -838,14 +830,9 @@ def onp_logaddexp(x1, x2):
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_logaddexp():
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test_cases = [
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onp.random.randint(1, 5, (5, 6, 3, 2)).astype('float16')]
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for _, x1 in enumerate(test_cases):
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for _, x2 in enumerate(test_cases):
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expected = onp_logaddexp(x1, x2)
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actual = mnp_logaddexp(to_tensor(x1), to_tensor(x2))
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onp.testing.assert_almost_equal(actual.asnumpy().tolist(), expected.tolist(),
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decimal=2)
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os.putenv('GLOG_v', '0')
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ret = os.system('pytest -s ./debug.py::test_logaddexp_inner --disable-warnings')
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assert ret == 0
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def mnp_log2(x):
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@ -866,14 +853,6 @@ def test_log2():
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run_unary_test(mnp_log2, onp_log2, test_case, error=1e-5)
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def mnp_logaddexp2(x1, x2):
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return mnp.logaddexp2(x1, x2)
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def onp_logaddexp2(x1, x2):
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return onp.logaddexp2(x1, x2)
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@pytest.mark.level1
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@ -881,17 +860,9 @@ def onp_logaddexp2(x1, x2):
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_logaddexp2():
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test_cases = [
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onp.random.randint(1, 5, (2)).astype('float16'),
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onp.random.randint(1, 5, (3, 2)).astype('float16'),
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onp.random.randint(1, 5, (1, 3, 2)).astype('float16'),
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onp.random.randint(1, 5, (5, 6, 3, 2)).astype('float16')]
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for _, x1 in enumerate(test_cases):
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for _, x2 in enumerate(test_cases):
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expected = onp_logaddexp2(x1, x2)
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actual = mnp_logaddexp2(to_tensor(x1), to_tensor(x2))
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onp.testing.assert_almost_equal(actual.asnumpy().tolist(), expected.tolist(),
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decimal=2)
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os.putenv('GLOG_v', '0')
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ret = os.system('pytest -s ./debug.py::test_logaddexp2_inner --disable-warnings')
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assert ret == 0
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def mnp_log10(x):
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