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120 lines
3.7 KiB
120 lines
3.7 KiB
# 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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"""Unit test on mindspore.explainer._utils."""
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import numpy as np
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import pytest
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import mindspore as ms
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import mindspore.nn as nn
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from mindspore.explainer._utils import (
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ForwardProbe,
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rank_pixels,
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retrieve_layer,
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retrieve_layer_by_name)
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from mindspore.explainer.explanation._attribution._backprop.backprop_utils import GradNet, get_bp_weights
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class CustomNet(nn.Cell):
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"""Simple net for test."""
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def __init__(self):
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super(CustomNet, self).__init__()
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self.fc1 = nn.Dense(10, 10)
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self.fc2 = nn.Dense(10, 10)
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self.fc3 = nn.Dense(10, 10)
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self.fc4 = nn.Dense(10, 10)
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def construct(self, inputs):
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out = self.fc1(inputs)
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out = self.fc2(out)
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out = self.fc3(out)
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out = self.fc4(out)
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return out
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_rank_pixels():
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"""Test on rank_pixels."""
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saliency = np.array([[4., 3., 1.], [5., 9., 1.]])
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descending_target = np.array([[0, 1, 2], [1, 0, 2]])
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ascending_target = np.array([[2, 1, 0], [1, 2, 0]])
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descending_rank = rank_pixels(saliency)
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ascending_rank = rank_pixels(saliency, descending=False)
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assert (descending_rank - descending_target).any() == 0
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assert (ascending_rank - ascending_target).any() == 0
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_retrieve_layer_by_name():
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"""Test on rank_pixels."""
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model = CustomNet()
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target_layer_name = 'fc3'
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target_layer = retrieve_layer_by_name(model, target_layer_name)
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assert target_layer is model.fc3
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_retrieve_layer_by_name_no_name():
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"""Test on retrieve layer."""
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model = CustomNet()
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target_layer = retrieve_layer_by_name(model, '')
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assert target_layer is model
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_forward_probe():
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"""Test case for ForwardProbe."""
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model = CustomNet()
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model.set_grad()
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inputs = np.random.random((1, 10))
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inputs = ms.Tensor(inputs, ms.float32)
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gt_activation = model.fc3(model.fc2(model.fc1(inputs))).asnumpy()
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targets = 1
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weights = get_bp_weights(model, inputs, targets=targets)
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gradnet = GradNet(model)
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grad_before_probe = gradnet(inputs, weights).asnumpy()
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# Probe forward tensor
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saliency_layer = retrieve_layer(model, 'fc3')
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with ForwardProbe(saliency_layer) as probe:
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grad_after_probe = gradnet(inputs, weights).asnumpy()
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activation = probe.value.asnumpy()
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grad_after_unprobe = gradnet(inputs, weights).asnumpy()
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assert np.array_equal(gt_activation, activation)
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assert np.array_equal(grad_before_probe, grad_after_probe)
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assert np.array_equal(grad_before_probe, grad_after_unprobe)
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assert probe.value is None
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