forked from mindspore-Ecosystem/mindspore
201 lines
7.3 KiB
Python
201 lines
7.3 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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"""Tests on mindspore.explainer.ImageClassificationRunner."""
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import os
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import shutil
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from random import random
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from unittest.mock import patch
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import numpy as np
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import pytest
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from PIL import Image
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from mindspore import context
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import mindspore as ms
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import mindspore.nn as nn
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from mindspore.dataset import GeneratorDataset
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from mindspore.explainer import ImageClassificationRunner
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from mindspore.explainer._image_classification_runner import _normalize
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from mindspore.explainer.benchmark import Faithfulness
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from mindspore.explainer.explanation import Gradient
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from mindspore.train.summary import SummaryRecord
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CONST = random()
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NUMDATA = 2
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context.set_context(mode=context.PYNATIVE_MODE)
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def image_label_bbox_generator():
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for i in range(NUMDATA):
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image = np.arange(i, i + 16 * 3).reshape((3, 4, 4)) / 50
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label = np.array(i)
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bbox = np.array([1, 1, 2, 2])
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yield (image, label, bbox)
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class SimpleNet(nn.Cell):
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"""
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Simple model for the unit test.
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"""
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def __init__(self):
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super(SimpleNet, self).__init__()
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self.reshape = ms.ops.operations.Reshape()
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def construct(self, x):
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prob = ms.Tensor([0.1, 0.9], ms.float32)
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prob = self.reshape(prob, (1, 2))
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return prob
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class ActivationFn(nn.Cell):
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"""
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Simple activation function for unit test.
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"""
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def __init__(self):
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super(ActivationFn, self).__init__()
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def construct(self, x):
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return x
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def mock_gradient_call(_, inputs, targets):
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return inputs[:, 0:1, :, :]
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def mock_faithfulness_evaluate(_, explainer, inputs, targets, saliency):
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return CONST * targets
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def mock_make_rgba(array):
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return array.asnumpy()
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class TestRunner:
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"""Test on Runner."""
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def setup_method(self):
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self.dataset = GeneratorDataset(image_label_bbox_generator, ["image", "label", "bbox"])
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self.labels = ["label_{}".format(i) for i in range(2)]
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self.network = SimpleNet()
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self.summary_dir = "summary_test_temp"
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self.explainer = [Gradient(self.network)]
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self.activation_fn = ActivationFn()
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self.benchmarkers = [Faithfulness(num_labels=len(self.labels),
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metric="NaiveFaithfulness",
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activation_fn=self.activation_fn)]
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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_run_saliency_no_benchmark(self):
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"""Test case when argument benchmarkers is not parsed."""
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res = []
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runner = ImageClassificationRunner(summary_dir=self.summary_dir, data=(self.dataset, self.labels),
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network=self.network, activation_fn=self.activation_fn)
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def mock_summary_add_value(_, plugin, name, value):
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res.append((plugin, name, value))
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with patch.object(SummaryRecord, "add_value", mock_summary_add_value), \
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patch.object(Gradient, "__call__", mock_gradient_call):
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runner.register_saliency(self.explainer)
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runner.run()
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# test on meta data
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idx = 0
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assert res[idx][0] == "explainer"
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assert res[idx][1] == "metadata"
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assert res[idx][2].metadata.label == self.labels
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assert res[idx][2].metadata.explain_method == ["Gradient"]
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# test on inference data
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for i in range(NUMDATA):
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idx += 1
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data_np = np.arange(i, i + 3 * 16).reshape((3, 4, 4)) / 50
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assert res[idx][0] == "explainer"
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assert res[idx][1] == "sample"
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assert res[idx][2].sample_id == i
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original_path = os.path.join(self.summary_dir, res[idx][2].image_path)
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with open(original_path, "rb") as f:
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image_data = np.asarray(Image.open(f)) / 255.0
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original_image = _normalize(np.transpose(data_np, [1, 2, 0]))
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assert np.allclose(image_data, original_image, rtol=3e-2, atol=3e-2)
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idx += 1
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assert res[idx][0] == "explainer"
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assert res[idx][1] == "inference"
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assert res[idx][2].sample_id == i
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assert res[idx][2].ground_truth_label == [i]
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diff = np.array(res[idx][2].inference.ground_truth_prob) - np.array([[0.1, 0.9][i]])
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assert np.max(np.abs(diff)) < 1e-6
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assert res[idx][2].inference.predicted_label == [1]
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diff = np.array(res[idx][2].inference.predicted_prob) - np.array([0.9])
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assert np.max(np.abs(diff)) < 1e-6
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# test on explanation data
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for i in range(NUMDATA):
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idx += 1
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data_np = np.arange(i, i + 3 * 16).reshape((3, 4, 4)) / 50
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saliency_np = data_np[0, :, :]
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assert res[idx][0] == "explainer"
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assert res[idx][1] == "explanation"
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assert res[idx][2].sample_id == i
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assert res[idx][2].explanation[0].explain_method == "Gradient"
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assert res[idx][2].explanation[0].label in [i, 1]
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heatmap_path = os.path.join(self.summary_dir, res[idx][2].explanation[0].heatmap_path)
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assert os.path.exists(heatmap_path)
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with open(heatmap_path, "rb") as f:
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heatmap_data = np.asarray(Image.open(f)) / 255.0
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heatmap_image = _normalize(saliency_np)
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assert np.allclose(heatmap_data, heatmap_image, atol=3e-2, rtol=3e-2)
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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_run_saliency_with_benchmark(self):
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"""Test case when argument benchmarkers is parsed."""
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res = []
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def mock_summary_add_value(_, plugin, name, value):
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res.append((plugin, name, value))
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runner = ImageClassificationRunner(summary_dir=self.summary_dir, data=(self.dataset, self.labels),
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network=self.network, activation_fn=self.activation_fn)
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with patch.object(SummaryRecord, "add_value", mock_summary_add_value), \
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patch.object(Gradient, "__call__", mock_gradient_call), \
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patch.object(Faithfulness, "evaluate", mock_faithfulness_evaluate):
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runner.register_saliency(self.explainer, self.benchmarkers)
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runner.run()
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idx = 3 * NUMDATA + 1 # start index of benchmark data
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assert res[idx][0] == "explainer"
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assert res[idx][1] == "benchmark"
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assert abs(res[idx][2].benchmark[0].total_score - 2 / 3 * CONST) < 1e-6
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diff = np.array(res[idx][2].benchmark[0].label_score) - np.array([i * CONST for i in range(NUMDATA)])
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assert np.max(np.abs(diff)) < 1e-6
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def teardown_method(self):
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shutil.rmtree(self.summary_dir)
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