91 lines
3.0 KiB
Python
91 lines
3.0 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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"""test_occlusion_sensitivity"""
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import pytest
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import numpy as np
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from mindspore import nn, context
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from mindspore.common.tensor import Tensor
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from mindspore.nn.metrics import OcclusionSensitivity
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context.set_context(mode=context.GRAPH_MODE)
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class DenseNet(nn.Cell):
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def __init__(self):
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super(DenseNet, self).__init__()
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w = np.array([[0.1, 0.8, 0.1, 0.1], [1, 1, 1, 1]]).astype(np.float32)
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b = np.array([0.3, 0.6]).astype(np.float32)
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self.dense = nn.Dense(4, 2, weight_init=Tensor(w), bias_init=Tensor(b))
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def construct(self, x):
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return self.dense(x)
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model = DenseNet()
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def test_occlusion_sensitivity():
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"""test_occlusion_sensitivity"""
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test_data = np.array([[0.1, 0.2, 0.3, 0.4]]).astype(np.float32)
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label = np.array(1).astype(np.int32)
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metric = OcclusionSensitivity()
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metric.clear()
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metric.update(model, test_data, label)
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score = metric.eval()
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assert np.allclose(score, np.array([0.2, 0.2, 0.2, 0.2]))
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def test_occlusion_sensitivity_indexes_awareness():
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"""A indexes aware version of test_occlusion_sensitivity"""
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test_data = np.array([[0.1, 0.2, 0.3, 0.4]]).astype(np.float32)
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test_data2 = np.array([[0.2, 0.3, 0.1, 0.4]]).astype(np.float32)
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label = np.array(1).astype(np.int32)
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metric = OcclusionSensitivity().set_indexes([0, 2, 3])
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metric.clear()
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metric.update(model, test_data, test_data2, label)
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score = metric.eval()
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assert np.allclose(score, np.array([0.3, 0.3, 0.3, 0.3]))
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def test_occlusion_sensitivity_update1():
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"""test_occlusion_sensitivity_update1"""
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test_data = np.array([[5, 8], [3, 2], [4, 2]])
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metric = OcclusionSensitivity()
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metric.clear()
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with pytest.raises(ValueError):
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metric.update(test_data)
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def test_occlusion_sensitivity_init1():
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"""test_occlusion_sensitivity_init1"""
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with pytest.raises(TypeError):
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OcclusionSensitivity(pad_val=False, margin=2, n_batch=128, b_box=None)
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def test_occlusion_sensitivity_init2():
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"""test_occlusion_sensitivity_init2"""
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with pytest.raises(TypeError):
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OcclusionSensitivity(pad_val=0.0, margin=True, n_batch=128, b_box=None)
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def test_occlusion_sensitivity_runtime():
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"""test_occlusion_sensitivity_runtime"""
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metric = OcclusionSensitivity()
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metric.clear()
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with pytest.raises(RuntimeError):
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metric.eval()
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