forked from mindspore-Ecosystem/mindspore
[Fallback] Add some fallback numpy test cases
This commit is contained in:
Margaret_wangrui
parent
fcfed2ae2a
commit
c6582b9390
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# Copyright 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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""" test graph fallback """
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import pytest
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import numpy as np
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from mindspore import ms_function, context
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context.set_context(mode=context.GRAPH_MODE)
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@pytest.mark.skip(reason='Not support graph fallback feature yet')
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def test_np_array_1():
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"""
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Feature: JIT Fallback
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Description: Test numpy with ndarray in graph mode.
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Expectation: No exception.
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"""
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@ms_function
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def np_array_1():
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a = np.array([1, 2, 3])
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return a
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res = np_array_1()
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assert res == (1, 2, 3)
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@pytest.mark.skip(reason='Not support graph fallback feature yet')
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def test_np_array_2():
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"""
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Feature: JIT Fallback
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Description: Test numpy with ndarray in graph mode.
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Expectation: No exception.
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"""
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@ms_function
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def np_array_2():
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a = np.array([[1, 2], [3, 4]])
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return a
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res = np_array_2()
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assert res == ([1, 2], [3, 4])
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@pytest.mark.skip(reason='Not support graph fallback feature yet')
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def test_np_array_3():
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"""
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Feature: JIT Fallback
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Description: Test numpy with ndarray in graph mode.
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Expectation: No exception.
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"""
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@ms_function
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def np_array_3():
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a = np.array([1, 2, 3, 4, 5], ndmin=2)
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return a
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res = np_array_3()
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assert res == ([1, 2, 3, 4, 5],)
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@pytest.mark.skip(reason='Not support graph fallback feature yet')
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def test_np_array_4():
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"""
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Feature: JIT Fallback
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Description: Test numpy with ndarray in graph mode.
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Expectation: No exception.
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"""
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@ms_function
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def np_array_4():
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a = np.array([1, 2, 3], dtype=complex)
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return a
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res = np_array_4()
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assert res == ((1+0j), (2+0j), (3+0j))
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@pytest.mark.skip(reason='Not support graph fallback feature yet')
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def test_np_dtype_1():
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"""
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Feature: JIT Fallback
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Description: Test numpy with dtype in graph mode.
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Expectation: No exception.
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"""
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@ms_function
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def np_dtype_1():
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t = np.dtype(np.int32)
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return t
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res = np_dtype_1()
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print("res:", res)
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@pytest.mark.skip(reason='Not support graph fallback feature yet')
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def test_np_dtype_2():
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"""
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Feature: JIT Fallback
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Description: Test numpy with dtype in graph mode.
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Expectation: No exception.
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"""
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@ms_function
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def np_dtype_2():
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t = np.dtype('i4')
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return t
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res = np_dtype_2()
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print("res:", res)
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@pytest.mark.skip(reason='Not support graph fallback feature yet')
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def test_np_dtype_3():
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"""
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Feature: JIT Fallback
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Description: Test numpy with dtype in graph mode.
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Expectation: No exception.
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"""
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@ms_function
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def np_dtype_3():
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t = np.dtype([('age', np.int8)])
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return t
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res = np_dtype_3()
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print("res:", res)
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@pytest.mark.skip(reason='Not support graph fallback feature yet')
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def test_np_dtype_4():
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"""
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Feature: JIT Fallback
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Description: Test numpy with dtype in graph mode.
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Expectation: No exception.
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"""
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@ms_function
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def np_dtype_4():
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student = np.dtype([('name', 'S20'), ('age', 'i1'), ('marks', 'f4')])
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a = np.array([('abc', 21, 50), ('xyz', 18, 75)], dtype=student)
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return a
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res = np_dtype_4()
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print("res:", res)
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@pytest.mark.skip(reason='Not support graph fallback feature yet')
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def test_np_array_ndim():
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"""
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Feature: JIT Fallback
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Description: Test numpy with array ndim in graph mode.
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Expectation: No exception.
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"""
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@ms_function
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def np_array_ndim():
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a = np.arange(24)
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return a.ndim
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res = np_array_ndim()
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print("res:", res)
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@pytest.mark.skip(reason='Not support graph fallback feature yet')
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def test_np_array_reshape_1():
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"""
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Feature: JIT Fallback
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Description: Test numpy with array reshape in graph mode.
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Expectation: No exception.
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"""
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@ms_function
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def np_array_reshape_1():
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a = np.array([[1, 2, 3], [4, 5, 6]])
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b = a.reshape(3, 2)
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return b.ndim
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res = np_array_reshape_1()
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print("res:", res)
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@pytest.mark.skip(reason='Not support graph fallback feature yet')
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def test_np_array_reshape_2():
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"""
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Feature: JIT Fallback
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Description: Test numpy with array reshape in graph mode.
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Expectation: No exception.
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"""
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@ms_function
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def np_array_reshape_2():
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a = np.array([[1, 2, 3], [4, 5, 6]])
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a.shape = (3, 2)
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return a
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res = np_array_reshape_2()
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print("res:", res)
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@pytest.mark.skip(reason='Not support graph fallback feature yet')
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def test_np_array_itemsize():
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"""
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Feature: JIT Fallback
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Description: Test numpy with array reshape in graph mode.
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Expectation: No exception.
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"""
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@ms_function
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def np_array_itemsize():
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a = np.array([1, 2, 3, 4, 5], dtype=np.int8)
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return a.itemsize
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res = np_array_itemsize()
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print("res:", res)
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@pytest.mark.skip(reason='Not support graph fallback feature yet')
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def test_np_array_flags():
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"""
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Feature: JIT Fallback
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Description: Test numpy with array flags in graph mode.
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Expectation: No exception.
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"""
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@ms_function
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def np_array_flags():
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a = np.array([1, 2, 3, 4, 5])
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return a.flags
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res = np_array_flags()
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print("res:", res)
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@pytest.mark.skip(reason='Not support graph fallback feature yet')
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def test_np_empty_zeros_ones():
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"""
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Feature: JIT Fallback
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Description: Test numpy with array empty, zeros, ones in graph mode.
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Expectation: No exception.
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"""
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@ms_function
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def np_empty_zeros_ones():
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x = np.empty([3, 2], dtype=np.int)
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y = np.zeros(x.shape, dtype=np.int)
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z = np.ones(x.shape, dtype=np.int)
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return y + z
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res = np_empty_zeros_ones()
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print("res:", res)
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@pytest.mark.skip(reason='Not support graph fallback feature yet')
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def test_np_asarray_list():
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"""
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Feature: JIT Fallback
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Description: Test numpy with list to array in graph mode.
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Expectation: No exception.
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"""
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@ms_function
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def np_asarray_list():
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x = [1, 2, 3]
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y = np.asarray(x)
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return y
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res = np_asarray_list()
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print("res:", res)
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@pytest.mark.skip(reason='Not support graph fallback feature yet')
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def test_np_asarray_tuple():
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"""
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Feature: JIT Fallback
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Description: Test numpy with tuple to array in graph mode.
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Expectation: No exception.
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"""
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@ms_function
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def np_asarray_tuple():
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x = (1, 2, 3)
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y = np.asarray(x)
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return y
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res = np_asarray_tuple()
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print("res:", res)
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@pytest.mark.skip(reason='Not support graph fallback feature yet')
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def test_np_asarray_tuple_list():
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"""
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Feature: JIT Fallback
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Description: Test numpy with tuple list to array in graph mode.
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Expectation: No exception.
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"""
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@ms_function
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def np_asarray_tuple_list():
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x = [(1, 2, 3), (4, 5)]
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y = np.asarray(x)
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return y
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res = np_asarray_tuple_list()
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print("res:", res)
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@pytest.mark.skip(reason='Not support graph fallback feature yet')
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def test_np_frombuffer():
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"""
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Feature: JIT Fallback
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Description: Test numpy with frombuffer in graph mode.
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Expectation: No exception.
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"""
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@ms_function
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def np_frombuffer():
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s = b'Hello World'
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a = np.frombuffer(s, dtype='S1')
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return a
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res = np_frombuffer()
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print("res:", res)
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@pytest.mark.skip(reason='Not support graph fallback feature yet')
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def test_np_fromiter():
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"""
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Feature: JIT Fallback
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Description: Test numpy with fromiter in graph mode.
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Expectation: No exception.
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"""
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@ms_function
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def np_fromiter():
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l = range(5)
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it = iter(l)
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x = np.fromiter(it, dtype=float)
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return x
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res = np_fromiter()
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print("res:", res)
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@pytest.mark.skip(reason='Not support graph fallback feature yet')
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def test_np_arange():
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"""
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Feature: JIT Fallback
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Description: Test numpy with arange in graph mode.
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Expectation: No exception.
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"""
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@ms_function
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def np_arange():
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x = np.arange(5, dtype=float)
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y = np.arange(10, 20, 2)
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return x, y
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res1, res2 = np_arange()
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print("res1:", res1)
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print("res2:", res2)
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@pytest.mark.skip(reason='Not support graph fallback feature yet')
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def test_np_linspace():
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"""
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Feature: JIT Fallback
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Description: Test numpy with linspace in graph mode.
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Expectation: No exception.
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"""
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@ms_function
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def np_linspace():
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a = np.linspace(1, 10, 10)
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b = np.linspace(1, 1, 10)
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c = np.linspace(10, 20, 5, endpoint=False)
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d = np.linspace(10, 20, 5, endpoint=True)
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e = np.linspace(1, 10, 10, retstep=True)
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f = np.linspace(1, 10, 10).reshape([10, 1])
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return a, b, c, d, e, f
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a, b, c, d, e, f = np_linspace()
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print("a:", a)
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print("b:", b)
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print("c:", c)
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print("d:", d)
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print("e:", e)
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print("f:", f)
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@pytest.mark.skip(reason='Not support graph fallback feature yet')
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def test_np_logspace():
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"""
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Feature: JIT Fallback
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Description: Test numpy with logspace in graph mode.
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Expectation: No exception.
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"""
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@ms_function
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def np_logspace():
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a = np.logspace(1.0, 2.0, num=10)
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b = np.logspace(0, 9, 10, base=2)
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return a, b
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a, b = np_logspace()
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print("a:", a)
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print("b:", b)
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@pytest.mark.skip(reason='Not support graph fallback feature yet')
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def test_np_arange_slice_1():
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"""
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Feature: JIT Fallback
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Description: Test numpy with arange slice in graph mode.
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Expectation: No exception.
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"""
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@ms_function
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def np_arange_slice_1():
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x = np.arange(10)
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index = slice(2, 7, 2)
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a = x[index]
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b = x[2:7:2]
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c = x[5]
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d = x[2:]
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e = x[2:5]
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return a, b, c, d, e
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a, b, c, d, e = np_arange_slice_1()
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print("a:", a)
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print("b:", b)
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print("c:", c)
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print("d:", d)
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print("e:", e)
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@pytest.mark.skip(reason='Not support graph fallback feature yet')
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def test_np_arange_slice_2():
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"""
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Feature: JIT Fallback
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Description: Test numpy with arange slice in graph mode.
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Expectation: No exception.
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"""
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@ms_function
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def np_arange_slice_2():
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x = np.array([[1, 2, 3], [3, 4, 5], [4, 5, 6]])
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a = x[1:]
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b = x[..., 1]
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c = x[1, ...]
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d = x[..., 1:]
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return a, b, c, d
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a, b, c, d = np_arange_slice_2()
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print("a:", a)
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print("b:", b)
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print("c:", c)
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print("d:", d)
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@pytest.mark.skip(reason='Not support graph fallback feature yet')
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def test_np_array_advanced_index_1():
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"""
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Feature: JIT Fallback
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Description: Test numpy with array advanced index in graph mode.
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Expectation: No exception.
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"""
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@ms_function
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def np_array_advanced_index_1():
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x = np.array([[0, 1, 2], [3, 4, 5], [6, 7, 8], [9, 10, 11]])
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a = x[[0, 1, 2], [0, 1, 0]]
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rows = np.array([[0, 0], [3, 3]])
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cols = np.array([[0, 2], [0, 2]])
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b = x[rows, cols]
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c = x[1:3, 1:3]
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d = x[1:3, [1, 2]]
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e = x[..., 1:]
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return a, b, c, d, e
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a, b, c, d, e = np_array_advanced_index_1()
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print("a:", a)
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print("b:", b)
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print("c:", c)
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print("d:", d)
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print("e:", e)
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@pytest.mark.skip(reason='Not support graph fallback feature yet')
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def test_np_array_advanced_index_2():
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"""
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Feature: JIT Fallback
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Description: Test numpy with array advanced index in graph mode.
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Expectation: No exception.
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"""
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@ms_function
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def np_array_advanced_index_2():
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x = np.array([[0, 1, 2], [3, 4, 5], [6, 7, 8], [9, 10, 11]])
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y = np.array([np.nan, 1, 2, np.nan, 3, 4, 5])
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z = np.array([1, 2 + 6j, 5, 3.5 + 5j])
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a = x[x > 5]
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b = y[~np.isnan(y)]
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c = z[np.iscomplex(z)]
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return a, b, c
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a, b, c = np_array_advanced_index_2()
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print("a:", a)
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print("b:", b)
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print("c:", c)
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@pytest.mark.skip(reason='Not support graph fallback feature yet')
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def test_np_array_advanced_index_3():
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"""
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Feature: JIT Fallback
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Description: Test numpy with array advanced index in graph mode.
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Expectation: No exception.
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"""
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@ms_function
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def np_array_advanced_index_3():
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x = np.arange(32).reshape((8, 4))
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a = x[[4, 2, 1, 7]]
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y = np.arange(32).reshape((8, 4))
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b = y[[-4, -2, -1, -7]]
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z = np.arange(32).reshape((8, 4))
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c = z[np.ix_([1, 5, 7, 2], [0, 3, 1, 2])]
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return a, b, c
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a, b, c = np_array_advanced_index_3()
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print("a:", a)
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print("b:", b)
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print("c:", c)
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