130 lines
3.7 KiB
Python
130 lines
3.7 KiB
Python
# 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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import numpy as np
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import pytest
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import mindspore
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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, ops, jit
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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.round = ops.Round()
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def construct(self, x):
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return self.round(x)
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def generate_testcases(nptype):
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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x = np.array([0.9920, -0.4077, 0.9734, -1.0362, 1.5, -2.5, 4.5]).astype(nptype)
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net = Net()
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output = net(Tensor(x))
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expect = np.round(x).astype(nptype)
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np.testing.assert_almost_equal(output.asnumpy(), expect)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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x = np.array([0.9920, -0.4077, 0.9734, -1.0362, 1.5, -2.5, 4.5]).astype(nptype)
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net = Net()
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output = net(Tensor(x))
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expect = np.round(x).astype(nptype)
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np.testing.assert_almost_equal(output.asnumpy(), expect)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_round_float32():
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generate_testcases(np.float32)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_round_float16():
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generate_testcases(np.float16)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_round_functional():
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"""
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Feature: functional round.
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Description: Test functional interface round.
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Expectation: success.
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"""
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x = Tensor(np.array([1.1, 2.6, 4.5]), mindspore.float32)
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output = ops.round(x)
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assert np.all(output.asnumpy() == np.array([1, 3, 4]))
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@jit
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def round_fn_graph(x):
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return x.round()
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_tensor_round_graph():
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"""
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Feature: tensor round interface.
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Description: Test tensor round interface in graph mode.
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Expectation: success
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"""
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context.set_context(mode=context.GRAPH_MODE)
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x = Tensor(np.array([1.1, 2.6, 4.5]), mindspore.float32)
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output = round_fn_graph(x)
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assert np.all(output.asnumpy() == np.array([1, 3, 4]))
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def round_fn_pynative(x):
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return x.round()
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_tensor_round_pynative():
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"""
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Feature: tensor round interface.
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Description: Test tensor round interface in pynative mode.
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Expectation: success
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"""
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context.set_context(mode=context.PYNATIVE_MODE)
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x = Tensor(np.array([1.1, 2.6, 4.5]), mindspore.float32)
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output = round_fn_pynative(x)
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assert np.all(output.asnumpy() == np.array([1, 3, 4]))
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_round_vmap():
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"""
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Feature: vmap for ops Round.
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Description: Test operation Round with vmap.
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Expectation: success
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"""
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x = Tensor(np.array([[1.1, 2.2], [3.3, 4.4]]), mindspore.float32)
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vmap_round = ops.vmap(round_fn_graph, 0, 1)
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output = vmap_round(x)
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assert np.all(output.asnumpy() == np.array([[1, 3], [2, 4]]))
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