forked from mindspore-Ecosystem/mindspore
103 lines
3.2 KiB
Python
103 lines
3.2 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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"""test topk"""
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import math
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import numpy as np
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import pytest
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from mindspore import Tensor
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from mindspore.nn.metrics import TopKCategoricalAccuracy, Top1CategoricalAccuracy, Top5CategoricalAccuracy
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def test_type_topk():
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with pytest.raises(TypeError):
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TopKCategoricalAccuracy(2.1)
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def test_value_topk():
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with pytest.raises(ValueError):
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TopKCategoricalAccuracy(-1)
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def test_input_topk():
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x = Tensor(np.array([[0.2, 0.5, 0.3, 0.6, 0.2],
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[0.3, 0.1, 0.5, 0.1, 0.],
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[0.9, 0.6, 0.2, 0.01, 0.3]]))
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topk = TopKCategoricalAccuracy(3)
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topk.clear()
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with pytest.raises(ValueError):
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topk.update(x)
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def test_topk():
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"""test_topk"""
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x = Tensor(np.array([[0.2, 0.5, 0.3, 0.6, 0.2],
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[0.1, 0.35, 0.5, 0.2, 0.],
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[0.9, 0.6, 0.2, 0.01, 0.3]]))
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y = Tensor(np.array([2, 0, 1]))
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y2 = Tensor(np.array([[0, 0, 1, 0, 0],
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[1, 0, 0, 0, 0],
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[0, 1, 0, 0, 0]]))
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topk = TopKCategoricalAccuracy(3)
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topk.clear()
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topk.update(x, y)
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result = topk.eval()
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result2 = topk(x, y2)
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assert math.isclose(result, 2 / 3)
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assert math.isclose(result2, 2 / 3)
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def test_zero_topk():
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topk = TopKCategoricalAccuracy(3)
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topk.clear()
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with pytest.raises(RuntimeError):
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topk.eval()
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def test_top1():
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"""test_top1"""
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x = Tensor(np.array([[0.2, 0.5, 0.2, 0.1, 0.],
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[0.1, 0.35, 0.25, 0.2, 0.1],
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[0.9, 0.1, 0, 0., 0]]))
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y = Tensor(np.array([2, 0, 0]))
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y2 = Tensor(np.array([[0, 0, 1, 0, 0],
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[1, 0, 0, 0, 0],
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[1, 0, 0, 0, 0]]))
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topk = Top1CategoricalAccuracy()
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topk.clear()
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topk.update(x, y)
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result = topk.eval()
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result2 = topk(x, y2)
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assert math.isclose(result, 1 / 3)
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assert math.isclose(result2, 1 / 3)
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def test_top5():
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"""test_top5"""
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x = Tensor(np.array([[0.15, 0.4, 0.1, 0.05, 0., 0.2, 0.1],
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[0.1, 0.35, 0.25, 0.2, 0.1, 0., 0.],
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[0., 0.5, 0.2, 0.1, 0.1, 0.1, 0.]]))
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y = Tensor(np.array([2, 0, 0]))
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y2 = Tensor(np.array([[0, 0, 1, 0, 0],
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[1, 0, 0, 0, 0],
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[1, 0, 0, 0, 0]]))
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topk = Top5CategoricalAccuracy()
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topk.clear()
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topk.update(x, y)
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result = topk.eval()
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result2 = topk(x, y2)
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assert math.isclose(result, 2 / 3)
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assert math.isclose(result2, 2 / 3)
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