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import unittest |
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import torch |
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from tensorboardX import SummaryWriter |
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class EmbeddingTest(unittest.TestCase): |
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def test_embedding(self): |
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w = SummaryWriter() |
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all_features = torch.Tensor([[1, 2, 3], [5, 4, 1], [3, 7, 7]]) |
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all_labels = torch.Tensor([33, 44, 55]) |
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all_images = torch.zeros(3, 3, 5, 5) |
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w.add_embedding(all_features, |
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metadata=all_labels, |
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label_img=all_images, |
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global_step=2) |
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dataset_label = ['test'] * 2 + ['train'] * 2 |
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all_labels = list(zip(all_labels, dataset_label)) |
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w.add_embedding(all_features, |
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metadata=all_labels, |
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label_img=all_images, |
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metadata_header=['digit', 'dataset'], |
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global_step=2) |
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def test_embedding_64(self): |
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w = SummaryWriter() |
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all_features = torch.Tensor([[1, 2, 3], [5, 4, 1], [3, 7, 7]]) |
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all_labels = torch.Tensor([33, 44, 55]) |
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all_images = torch.zeros((3, 3, 5, 5), dtype=torch.float64) |
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w.add_embedding(all_features, |
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metadata=all_labels, |
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label_img=all_images, |
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global_step=2) |
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dataset_label = ['test'] * 2 + ['train'] * 2 |
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all_labels = list(zip(all_labels, dataset_label)) |
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w.add_embedding(all_features, |
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metadata=all_labels, |
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label_img=all_images, |
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metadata_header=['digit', 'dataset'], |
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global_step=2) |
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def test_embedding_square(self): |
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w = SummaryWriter(comment='sq') |
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all_features = torch.rand(228,256) |
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all_images = torch.rand(228, 3, 32, 32) |
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for i in range(all_images.shape[0]): |
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all_images[i] *= (float(i)+60)/(all_images.shape[0]+60) |
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w.add_embedding(all_features, |
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label_img=all_images, |
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global_step=2) |
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def test_embedding_fail(self): |
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with self.assertRaises(AssertionError): |
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w = SummaryWriter(comment='shouldfail') |
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all_features = torch.rand(228,256) |
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all_images = torch.rand(228, 3, 16, 32) |
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for i in range(all_images.shape[0]): |
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all_images[i] *= (float(i)+60)/(all_images.shape[0]+60) |
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w.add_embedding(all_features, |
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label_img=all_images, |
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global_step=2) |
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