forked from mindspore-Ecosystem/mindspore
148 lines
4.1 KiB
Python
148 lines
4.1 KiB
Python
# Copyright 2020 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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import numpy as np
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import pytest
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore import Tensor, ms_function, ops
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from mindspore.ops import operations as P
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.ops = P.Pow()
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def construct(self, x, y):
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return self.ops(x, y)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_net():
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"""
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Feature: ALL To ALL
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Description: test cases for Pow
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Expectation: the result match to numpy
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"""
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x0_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float32)
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y0_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float32)
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x1_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float32)
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y1_np = np.array(3).astype(np.float32)
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x2_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float64)
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y2_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float64)
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x3_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float64)
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y3_np = np.array(3).astype(np.float64)
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x0 = Tensor(x0_np)
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y0 = Tensor(y0_np)
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x1 = Tensor(x1_np)
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y1 = Tensor(y1_np)
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x2 = Tensor(x2_np)
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y2 = Tensor(y2_np)
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x3 = Tensor(x3_np)
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y3 = Tensor(y3_np)
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context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
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net = Net()
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out = net(x0, y0).asnumpy()
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expect = np.power(x0_np, y0_np)
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assert np.all(out == expect)
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assert out.shape == expect.shape
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out = net(x1, y1).asnumpy()
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expect = np.power(x1_np, y1_np)
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assert np.all(out == expect)
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assert out.shape == expect.shape
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out = net(x2, y2).asnumpy()
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expect = np.power(x2_np, y2_np)
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assert np.all(out == expect)
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assert out.shape == expect.shape
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out = net(x3, y3).asnumpy()
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expect = np.power(x3_np, y3_np)
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assert np.all(out == expect)
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assert out.shape == expect.shape
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_tensor_pow_pynative():
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"""
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Feature: Tensor interface pow.
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Description: test tensor interface pow in pynative mode.
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Expectation: Success.
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"""
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x = Tensor([1, 2, 3])
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y = Tensor([1, 2, 3])
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output = x.pow(y)
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assert np.all(output.asnumpy() == np.array([1, 4, 27]))
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@ms_function
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def tensor_pow_func(x, y):
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return x.pow(y)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_tensor_pow_graph():
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"""
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Feature: Tensor interface pow.
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Description: test tensor interface pow in graph mode.
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Expectation: Success.
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"""
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x = Tensor([1, 2, 3])
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y = Tensor([1, 2, 3])
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output = tensor_pow_func(x, y)
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assert np.all(output.asnumpy() == np.array([1, 4, 27]))
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_pow_functional():
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"""
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Feature: Tensor interface pow.
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Description: test functional interface pow.
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Expectation: Success.
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"""
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x = Tensor([1, 2, 3])
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y = Tensor([1, 2, 3])
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output = ops.pow(x, y)
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assert np.all(output.asnumpy() == np.array([1, 4, 27]))
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_pow_vmap():
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"""
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Feature: Tensor interface pow.
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Description: test pow operation with vmap.
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Expectation: Success.
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"""
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x = Tensor([[1, 2, 3], [3, 2, 1]])
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y = Tensor([[1, 2, 3], [3, 2, 1]])
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pow_vmap = ops.vmap(tensor_pow_func, (0, 0), 0)
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output = pow_vmap(x, y)
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assert np.all(output.asnumpy() == np.array([[1, 4, 27], [27, 4, 1]]))
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