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revert set GLOG_v=0 for debugging

This commit is contained in:
zuochuanyong 2021-10-14 20:18:39 +08:00
parent ec981124d0
commit 662c7174e8
3 changed files with 65 additions and 103 deletions
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92
tests/st/numpy_native/debug.py
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@ -1,92 +0,0 @@
# Copyright 2020-2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""unit tests for numpy array operations"""
import numpy as onp
import mindspore.numpy as mnp
from .utils import match_all_arrays, to_tensor
def test_pad_inner():
x_np = onp.random.random([2, 3, 4]).astype("float32")
x_ms = mnp.asarray(x_np.tolist())
# pad constant
mnp_res = mnp.pad(x_ms, ((1, 1), (2, 2), (3, 4)))
onp_res = onp.pad(x_np, ((1, 1), (2, 2), (3, 4)))
match_all_arrays(mnp_res, onp_res, error=1e-5)
mnp_res = mnp.pad(x_ms, ((1, 1), (2, 3), (4, 5)), constant_values=((3, 4), (5, 6), (7, 8)))
onp_res = onp.pad(x_np, ((1, 1), (2, 3), (4, 5)), constant_values=((3, 4), (5, 6), (7, 8)))
match_all_arrays(mnp_res, onp_res, error=1e-5)
# pad statistic
mnp_res = mnp.pad(x_ms, ((1, 1), (2, 2), (3, 4)), mode="mean", stat_length=((1, 2), (2, 10), (3, 4)))
onp_res = onp.pad(x_np, ((1, 1), (2, 2), (3, 4)), mode="mean", stat_length=((1, 2), (2, 10), (3, 4)))
match_all_arrays(mnp_res, onp_res, error=1e-5)
# pad edge
mnp_res = mnp.pad(x_ms, ((1, 1), (2, 2), (3, 4)), mode="edge")
onp_res = onp.pad(x_np, ((1, 1), (2, 2), (3, 4)), mode="edge")
match_all_arrays(mnp_res, onp_res, error=1e-5)
# pad wrap
mnp_res = mnp.pad(x_ms, ((1, 1), (2, 2), (3, 4)), mode="wrap")
onp_res = onp.pad(x_np, ((1, 1), (2, 2), (3, 4)), mode="wrap")
match_all_arrays(mnp_res, onp_res, error=1e-5)
# pad linear_ramp
mnp_res = mnp.pad(x_ms, ((1, 3), (5, 2), (3, 0)), mode="linear_ramp", end_values=((0, 10), (9, 1), (-10, 99)))
onp_res = onp.pad(x_np, ((1, 3), (5, 2), (3, 0)), mode="linear_ramp", end_values=((0, 10), (9, 1), (-10, 99)))
match_all_arrays(mnp_res, onp_res, error=1e-5)
def mnp_logaddexp(x1, x2):
return mnp.logaddexp(x1, x2)
def onp_logaddexp(x1, x2):
return onp.logaddexp(x1, x2)
def mnp_logaddexp2(x1, x2):
return mnp.logaddexp2(x1, x2)
def onp_logaddexp2(x1, x2):
return onp.logaddexp2(x1, x2)
def test_logaddexp_inner():
test_cases = [
onp.random.randint(1, 5, (5, 6, 3, 2)).astype('float16')]
for _, x1 in enumerate(test_cases):
for _, x2 in enumerate(test_cases):
expected = onp_logaddexp(x1, x2)
actual = mnp_logaddexp(to_tensor(x1), to_tensor(x2))
onp.testing.assert_almost_equal(actual.asnumpy().tolist(), expected.tolist(),
decimal=2)
def test_logaddexp2_inner():
test_cases = [
onp.random.randint(1, 5, (2)).astype('float16'),
onp.random.randint(1, 5, (3, 2)).astype('float16'),
onp.random.randint(1, 5, (1, 3, 2)).astype('float16'),
onp.random.randint(1, 5, (5, 6, 3, 2)).astype('float16')]
for _, x1 in enumerate(test_cases):
for _, x2 in enumerate(test_cases):
expected = onp_logaddexp2(x1, x2)
actual = mnp_logaddexp2(to_tensor(x1), to_tensor(x2))
onp.testing.assert_almost_equal(actual.asnumpy().tolist(), expected.tolist(),
decimal=2)

34
tests/st/numpy_native/test_array_creations.py
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@ -14,7 +14,6 @@
# ============================================================================
"""unit tests for numpy array operations"""
import os
import pytest
import numpy as onp
import mindspore.numpy as mnp
@ -935,9 +934,36 @@ def test_empty_like_exception():
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_pad():
os.putenv('GLOG_v', '0')
ret = os.system('pytest -s ./debug.py::test_pad_inner --disable-warnings')
assert ret == 0
x_np = onp.random.random([2, 3, 4]).astype("float32")
x_ms = mnp.asarray(x_np.tolist())
# pad constant
mnp_res = mnp.pad(x_ms, ((1, 1), (2, 2), (3, 4)))
onp_res = onp.pad(x_np, ((1, 1), (2, 2), (3, 4)))
match_all_arrays(mnp_res, onp_res, error=1e-5)
mnp_res = mnp.pad(x_ms, ((1, 1), (2, 3), (4, 5)), constant_values=((3, 4), (5, 6), (7, 8)))
onp_res = onp.pad(x_np, ((1, 1), (2, 3), (4, 5)), constant_values=((3, 4), (5, 6), (7, 8)))
match_all_arrays(mnp_res, onp_res, error=1e-5)
# pad statistic
mnp_res = mnp.pad(x_ms, ((1, 1), (2, 2), (3, 4)), mode="mean", stat_length=((1, 2), (2, 10), (3, 4)))
onp_res = onp.pad(x_np, ((1, 1), (2, 2), (3, 4)), mode="mean", stat_length=((1, 2), (2, 10), (3, 4)))
match_all_arrays(mnp_res, onp_res, error=1e-5)
# pad edge
mnp_res = mnp.pad(x_ms, ((1, 1), (2, 2), (3, 4)), mode="edge")
onp_res = onp.pad(x_np, ((1, 1), (2, 2), (3, 4)), mode="edge")
match_all_arrays(mnp_res, onp_res, error=1e-5)
# pad wrap
mnp_res = mnp.pad(x_ms, ((1, 1), (2, 2), (3, 4)), mode="wrap")
onp_res = onp.pad(x_np, ((1, 1), (2, 2), (3, 4)), mode="wrap")
match_all_arrays(mnp_res, onp_res, error=1e-5)
# pad linear_ramp
mnp_res = mnp.pad(x_ms, ((1, 3), (5, 2), (3, 0)), mode="linear_ramp", end_values=((0, 10), (9, 1), (-10, 99)))
onp_res = onp.pad(x_np, ((1, 3), (5, 2), (3, 0)), mode="linear_ramp", end_values=((0, 10), (9, 1), (-10, 99)))
match_all_arrays(mnp_res, onp_res, error=1e-5)
def pad_with_msfunc(vector, pad_width, iaxis, kwargs):

42
tests/st/numpy_native/test_math_ops.py
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@ -14,7 +14,6 @@
# ============================================================================
"""unit tests for numpy math operations"""
import os
import pytest
import numpy as onp
import mindspore.numpy as mnp
@ -823,6 +822,14 @@ def test_log1p():
run_unary_test(mnp_log1p, onp_log1p, test_case, error=1e-5)
def mnp_logaddexp(x1, x2):
return mnp.logaddexp(x1, x2)
def onp_logaddexp(x1, x2):
return onp.logaddexp(x1, x2)
@pytest.mark.level0
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@ -830,9 +837,14 @@ def test_log1p():
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_logaddexp():
os.putenv('GLOG_v', '0')
ret = os.system('pytest -s ./debug.py::test_logaddexp_inner --disable-warnings')
assert ret == 0
test_cases = [
onp.random.randint(1, 5, (5, 6, 3, 2)).astype('float16')]
for _, x1 in enumerate(test_cases):
for _, x2 in enumerate(test_cases):
expected = onp_logaddexp(x1, x2)
actual = mnp_logaddexp(to_tensor(x1), to_tensor(x2))
onp.testing.assert_almost_equal(actual.asnumpy().tolist(), expected.tolist(),
decimal=2)
def mnp_log2(x):
@ -853,6 +865,14 @@ def test_log2():
run_unary_test(mnp_log2, onp_log2, test_case, error=1e-5)
def mnp_logaddexp2(x1, x2):
return mnp.logaddexp2(x1, x2)
def onp_logaddexp2(x1, x2):
return onp.logaddexp2(x1, x2)
@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@ -860,9 +880,17 @@ def test_log2():
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_logaddexp2():
os.putenv('GLOG_v', '0')
ret = os.system('pytest -s ./debug.py::test_logaddexp2_inner --disable-warnings')
assert ret == 0
test_cases = [
onp.random.randint(1, 5, (2)).astype('float16'),
onp.random.randint(1, 5, (3, 2)).astype('float16'),
onp.random.randint(1, 5, (1, 3, 2)).astype('float16'),
onp.random.randint(1, 5, (5, 6, 3, 2)).astype('float16')]
for _, x1 in enumerate(test_cases):
for _, x2 in enumerate(test_cases):
expected = onp_logaddexp2(x1, x2)
actual = mnp_logaddexp2(to_tensor(x1), to_tensor(x2))
onp.testing.assert_almost_equal(actual.asnumpy().tolist(), expected.tolist(),
decimal=2)
def mnp_log10(x):