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