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import pytest |
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3
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4
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@pytest.fixture |
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def nst_observer(disk): |
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"""An instance object of StylingObserver class.""" |
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from artificial_artwork.styling_observer import StylingObserver |
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from artificial_artwork.image.image_operations import convert_to_uint8 |
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return StylingObserver(disk.save_image, convert_to_uint8) |
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12
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@pytest.fixture |
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def subject_container(tmpdir): |
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import numpy as np |
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class TestSubject: |
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def __init__(self, subject): |
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self.subject = subject |
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def build_state(self, runtime_data): |
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return { |
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'output_path': tmpdir, |
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'content_image_path': 'c', |
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'style_image_path': 's', |
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'metrics': { |
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'iterations': runtime_data |
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}, |
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'matrix': np.random.randint(0, high=255, size=(30, 40), dtype=np.uint8) |
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} |
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def notify(self, runtime_data): |
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self.subject.state = type('Subject', (), self.build_state(runtime_data)) |
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self.subject.notify() |
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return TestSubject |
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@pytest.fixture |
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def styling_observer_data(subject_container, nst_observer): |
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from artificial_artwork.utils.notification import Subject |
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return type('TestData', (), { |
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'broadcaster': subject_container(Subject()), |
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'observer': nst_observer, |
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}) |
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def test_styling_observer(styling_observer_data, subscribe, tmpdir): |
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d = styling_observer_data |
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# Subscribe observer/listener to subject/broadcaster at runtime |
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subscribe( |
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d.broadcaster.subject, |
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[d.observer] |
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) |
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import os |
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d.broadcaster.notify(1) |
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assert os.path.isfile(os.path.join(tmpdir, 'c+s-1.png')) |
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d.broadcaster.notify(2) |
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assert os.path.isfile(os.path.join(tmpdir, 'c+s-2.png')) |
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boromir674 /
neural-style-transfer
