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from tensorboardX import TorchVis |
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import numpy as np |
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import pytest |
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import unittest |
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true_positive_counts = [75, 64, 21, 5, 0] |
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false_positive_counts = [150, 105, 18, 0, 0] |
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true_negative_counts = [0, 45, 132, 150, 150] |
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false_negative_counts = [0, 11, 54, 70, 75] |
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precision = [0.3333333, 0.3786982, 0.5384616, 1.0, 0.0] |
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recall = [1.0, 0.8533334, 0.28, 0.0666667, 0.0] |
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class VisdomTest(unittest.TestCase): |
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def test_TorchVis(self): |
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w = TorchVis('visdom') |
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w.add_scalar('scalar_visdom', 1, 0) |
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w.add_scalar('scalar_visdom', 2, 1) |
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w.add_histogram('histogram_visdom', np.array([1, 2, 3, 4, 5]), 1) |
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w.add_image('image_visdom', np.ndarray((3, 20, 20)), 2) |
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w.add_audio('audio_visdom', [1, 2, 3, 4, 5]) |
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w.add_text('text_visdom', 'mystring') |
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w.add_pr_curve('pr_curve_visdom', np.random.randint(2, size=100), np.random.rand(100), 10) |
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w.add_pr_curve_raw('prcurve with raw data', |
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true_positive_counts, |
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false_positive_counts, |
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true_negative_counts, |
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false_negative_counts, |
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precision, |
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recall, 20) |
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del w |
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