forked from mindspore-Ecosystem/mindspore
89 lines
3.1 KiB
Python
89 lines
3.1 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 random
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from functools import reduce
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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
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from mindspore.common import dtype as mstype
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import mindspore.ops as ops
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class NetArgmax(nn.Cell):
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def __init__(self, axis=0):
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super(NetArgmax, self).__init__()
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self.argmax = ops.Argmax(axis, output_type=mstype.int32)
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def construct(self, x):
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return self.argmax(x)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_argmax_1d():
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for mode in [context.PYNATIVE_MODE, context.GRAPH_MODE]:
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context.set_context(mode=mode, device_target="GPU")
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x = Tensor(np.array([1., 20., 5.]).astype(np.float32))
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Argmax = NetArgmax(axis=0)
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output = Argmax(x)
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expect = np.array([1]).astype(np.float32)
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assert (output.asnumpy() == expect).all()
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_argmax_2d():
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for mode in [context.PYNATIVE_MODE, context.GRAPH_MODE]:
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context.set_context(mode=mode, device_target="GPU")
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x = Tensor(np.array([[1., 20., 5.],
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[67., 8., 9.],
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[130., 24., 15.],
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[0.3, -0.4, -15.]]).astype(np.float32))
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Argmax_axis_0 = NetArgmax(axis=0)
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output = Argmax_axis_0(x)
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expect = np.array([2, 2, 2]).astype(np.int32)
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assert (output.asnumpy() == expect).all()
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Argmax_axis_1 = NetArgmax(axis=1)
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output = Argmax_axis_1(x)
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expect = np.array([1, 0, 0, 0]).astype(np.int32)
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assert (output.asnumpy() == expect).all()
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_argmax_high_dims():
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for mode in [context.PYNATIVE_MODE, context.GRAPH_MODE]:
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context.set_context(mode=mode, device_target="GPU")
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for dim in range(3, 10):
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shape = np.random.randint(1, 10, size=dim)
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x = np.random.randn(reduce(lambda x, y: x * y, shape)).astype(np.float32)
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x = x.reshape(shape)
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rnd_axis = random.randint(-dim + 1, dim - 1)
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Argmax = NetArgmax(axis=rnd_axis)
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ms_output = Argmax(Tensor(x))
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np_output = np.argmax(x, axis=rnd_axis)
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assert (ms_output.asnumpy() == np_output).all()
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