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140 lines
5.4 KiB
140 lines
5.4 KiB
# Copyright 2019 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
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from mindspore.ops import operations as P
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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class NetArgminWithValue(nn.Cell):
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def __init__(self, axis=0, keep_dims=False):
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super(NetArgminWithValue, self).__init__()
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self.argmin = P.ArgMinWithValue(axis=axis, keep_dims=keep_dims)
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def construct(self, x):
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return self.argmin(x)
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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_argminwithvalue_fp32():
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x = np.array([[1., 20., 5.],
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[67., 8., 9.],
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[130., 24., 15.],
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[-0.5, 25, 100]]).astype(np.float32)
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argmin_a0 = NetArgminWithValue(axis=0, keep_dims=False)
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output0, output1 = argmin_a0(Tensor(x))
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expect0 = np.array([3, 1, 0]).astype(np.int32)
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expect1 = np.array([-0.5, 8., 5.]).astype(np.float32)
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error = np.ones(shape=expect1.shape) * 1.0e-6
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assert np.all(output0.asnumpy() == expect0)
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assert np.all(np.abs(output1.asnumpy() - expect1) < error)
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argmin_a0k = NetArgminWithValue(axis=0, keep_dims=True)
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output0, output1 = argmin_a0k(Tensor(x))
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expect0 = np.array([[3, 1, 0]]).astype(np.int32)
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expect1 = np.array([[-0.5, 8., 5.]]).astype(np.float32)
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error = np.ones(shape=expect1.shape) * 1.0e-6
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assert np.all(output0.asnumpy() == expect0)
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assert np.all(np.abs(output1.asnumpy() - expect1) < error)
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argmin_a1 = NetArgminWithValue(axis=1, keep_dims=False)
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output0, output1 = argmin_a1(Tensor(x))
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expect0 = np.array([0, 1, 2, 0]).astype(np.int32)
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expect1 = np.array([1., 8., 15., -0.5]).astype(np.float32)
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error = np.ones(shape=expect1.shape) * 1.0e-6
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assert np.all(output0.asnumpy() == expect0)
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assert np.all(np.abs(output1.asnumpy() - expect1) < error)
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argmin_a1k = NetArgminWithValue(axis=-1, keep_dims=True)
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output0, output1 = argmin_a1k(Tensor(x))
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expect0 = np.array([[0], [1], [2], [0]]).astype(np.int32)
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expect1 = np.array([[1.], [8.], [15.], [-0.5]]).astype(np.float32)
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error = np.ones(shape=expect1.shape) * 1.0e-6
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assert np.all(output0.asnumpy() == expect0)
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assert np.all(np.abs(output1.asnumpy() - expect1) < error)
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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_argminwithvalue_fp16():
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x = np.array([[1., 20., 5.],
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[67., 8., 9.],
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[130., 24., 15.],
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[-0.5, 25, 100]]).astype(np.float16)
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argmin_a0 = NetArgminWithValue(axis=0, keep_dims=False)
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output0, output1 = argmin_a0(Tensor(x))
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expect0 = np.array([3, 1, 0]).astype(np.int32)
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expect1 = np.array([-0.5, 8., 5.]).astype(np.float16)
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error = np.ones(shape=expect1.shape) * 1.0e-6
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assert np.all(output0.asnumpy() == expect0)
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assert np.all(np.abs(output1.asnumpy() - expect1) < error)
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argmin_a0k = NetArgminWithValue(axis=0, keep_dims=True)
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output0, output1 = argmin_a0k(Tensor(x))
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expect0 = np.array([[3, 1, 0]]).astype(np.int32)
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expect1 = np.array([[-0.5, 8., 5.]]).astype(np.float16)
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error = np.ones(shape=expect1.shape) * 1.0e-6
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assert np.all(output0.asnumpy() == expect0)
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assert np.all(np.abs(output1.asnumpy() - expect1) < error)
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argmin_a1 = NetArgminWithValue(axis=1, keep_dims=False)
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output0, output1 = argmin_a1(Tensor(x))
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expect0 = np.array([0, 1, 2, 0]).astype(np.int32)
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expect1 = np.array([1., 8., 15., -0.5]).astype(np.float16)
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error = np.ones(shape=expect1.shape) * 1.0e-6
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assert np.all(output0.asnumpy() == expect0)
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assert np.all(np.abs(output1.asnumpy() - expect1) < error)
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argmin_a1k = NetArgminWithValue(axis=-1, keep_dims=True)
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output0, output1 = argmin_a1k(Tensor(x))
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expect0 = np.array([[0], [1], [2], [0]]).astype(np.int32)
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expect1 = np.array([[1.], [8.], [15.], [-0.5]]).astype(np.float16)
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error = np.ones(shape=expect1.shape) * 1.0e-6
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assert np.all(output0.asnumpy() == expect0)
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assert np.all(np.abs(output1.asnumpy() - expect1) < error)
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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_argminwithvalue_tensor():
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prop = 100 if np.random.random() > 0.5 else -100
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x = np.random.randn(3, 4, 5, 6).astype(np.float16) * prop
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argmin_a0 = NetArgminWithValue(axis=-2, keep_dims=False)
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output0, output1 = argmin_a0(Tensor(x))
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expect0 = np.argmin(x, axis=-2)
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expect1 = np.min(x, axis=-2).astype(np.float16)
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error = np.ones(shape=expect1.shape) * 1.0e-6
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assert np.all(output0.asnumpy() == expect0)
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assert np.all(np.abs(output1.asnumpy() - expect1) < error)
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