forked from mindspore-Ecosystem/mindspore
141 lines
5.3 KiB
Python
141 lines
5.3 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.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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class InTopKNet(nn.Cell):
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def __init__(self, k):
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super(InTopKNet, self).__init__()
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self.in_top_k = P.InTopK(k)
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def construct(self, predictions, targets):
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return self.in_top_k(predictions, targets)
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def in_top_k(nptype):
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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predictions = Tensor(np.array([[4, 1, 2, 0, 0, 0, 0, 0, 0],
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[7, 9, 9, 0, 0, 0, 0, 0, 0],
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[3, 3, 3, 0, 0, 0, 0, 0, 0]]).astype(nptype))
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k = 165
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in_top_k_net = InTopKNet(k)
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targets = Tensor(np.array([0, 1, 0]).astype(np.int32))
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output = in_top_k_net(predictions, targets)
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expected_output = np.array([True, True, True])
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np.testing.assert_array_equal(output.asnumpy(), expected_output)
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k = -2
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in_top_k_net = InTopKNet(k)
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targets = Tensor(np.array([0, 1, 0]).astype(np.int32))
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output = in_top_k_net(predictions, targets)
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expected_output = np.array([False, False, False])
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np.testing.assert_array_equal(output.asnumpy(), expected_output)
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k = 1
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in_top_k_net = InTopKNet(k)
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targets = Tensor(np.array([0, 1, 0]).astype(np.int32))
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output = in_top_k_net(predictions, targets)
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expected_output = np.array([True, True, True])
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np.testing.assert_array_equal(output.asnumpy(), expected_output)
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targets = Tensor(np.array([1, 0, 2]).astype(np.int32))
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output = in_top_k_net(predictions, targets)
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expected_output = np.array([False, False, True])
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np.testing.assert_array_equal(output.asnumpy(), expected_output)
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targets = Tensor(np.array([2, 2, 1]).astype(np.int32))
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output = in_top_k_net(predictions, targets)
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expected_output = np.array([False, True, True])
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np.testing.assert_array_equal(output.asnumpy(), expected_output)
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k = 2
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in_top_k_net = InTopKNet(k)
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targets = Tensor(np.array([0, 1, 2]).astype(np.int32))
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output = in_top_k_net(predictions, targets)
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expected_output = np.array([True, True, True])
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np.testing.assert_array_equal(output.asnumpy(), expected_output)
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targets = Tensor(np.array([2, 2, 0]).astype(np.int32))
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output = in_top_k_net(predictions, targets)
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expected_output = np.array([True, True, True])
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np.testing.assert_array_equal(output.asnumpy(), expected_output)
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targets = Tensor(np.array([1, 0, 1]).astype(np.int32))
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output = in_top_k_net(predictions, targets)
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expected_output = np.array([False, False, True])
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np.testing.assert_array_equal(output.asnumpy(), expected_output)
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k = 3
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in_top_k_net = InTopKNet(k)
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targets = Tensor(np.array([2, 2, 2]).astype(np.int32))
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output = in_top_k_net(predictions, targets)
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expected_output = np.array([True, True, True])
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np.testing.assert_array_equal(output.asnumpy(), expected_output)
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targets = Tensor(np.array([1, 1, 0]).astype(np.int32))
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output = in_top_k_net(predictions, targets)
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expected_output = np.array([True, True, True])
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np.testing.assert_array_equal(output.asnumpy(), expected_output)
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targets = Tensor(np.array([0, 0, 1]).astype(np.int32))
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output = in_top_k_net(predictions, targets)
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expected_output = np.array([True, True, True])
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np.testing.assert_array_equal(output.asnumpy(), expected_output)
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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_in_top_k_float16():
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in_top_k(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_in_top_k_float32():
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in_top_k(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_in_top_k_invalid_input():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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# predictions must be 2d
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with pytest.raises(ValueError):
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in_top_k_net = InTopKNet(1)
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predictions = Tensor(np.zeros(4).astype(np.float32))
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targets = Tensor(np.zeros(4).astype(np.int32))
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_ = in_top_k_net(predictions, targets)
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# targets must be 1d
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with pytest.raises(ValueError):
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in_top_k_net = InTopKNet(1)
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predictions = Tensor(np.zeros(4).astype(np.float32))
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targets = Tensor(np.zeros(4).reshape(2, 2).astype(np.int32))
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_ = in_top_k_net(predictions, targets)
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# predictions.shape[1] must be equal to targets.shape[0]
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with pytest.raises(ValueError):
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in_top_k_net = InTopKNet(1)
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predictions = Tensor(np.zeros(4).reshape(2, 2).astype(np.float32))
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targets = Tensor(np.zeros(4).astype(np.int32))
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_ = in_top_k_net(predictions, targets)
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