mirror of
https://github.com/open-mmlab/mmengine.git
synced 2025-06-03 21:54:44 +08:00
7e1d7af2d9
* Rename data to structure * adjust the way to import module * adjust the way to import module * rename Structure to Data Structures in docs api * rename structure to structures * support using some modules of mmengine without torch * fix circleci config * fix circleci config * fix registry ut * minor fix * move init method from model/utils to model/weight_init.py * move init method from model/utils to model/weight_init.py * move sync_bn to model * move functions depending on torch to dl_utils * format import * fix logging ut * add weight init in model/__init__.py * move get_config and get_model to mmengine/hub * move log_processor.py to mmengine/runner * fix ut * Add TimeCounter in dl_utils/__init__.py
56 lines
2.0 KiB
Python
56 lines
2.0 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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from unittest import TestCase
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import pytest
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import torch
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from mmengine.structures import LabelData
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class TestLabelData(TestCase):
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def test_label_to_onehot(self):
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item = torch.tensor([1], dtype=torch.int64)
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num_classes = 10
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onehot = LabelData.label_to_onehot(label=item, num_classes=num_classes)
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assert tuple(onehot.shape) == (num_classes, )
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assert onehot.device == item.device
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# item is not onehot
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with self.assertRaises(AssertionError):
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LabelData.label_to_onehot(label='item', num_classes=num_classes)
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# item'max bigger than num_classes
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with self.assertRaises(AssertionError):
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LabelData.label_to_onehot(
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torch.tensor([11], dtype=torch.int64), num_classes)
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onehot = LabelData.label_to_onehot(
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label=torch.tensor([], dtype=torch.int64), num_classes=num_classes)
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assert (onehot == torch.zeros((num_classes, ),
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dtype=torch.int64)).all()
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def test_onehot_to_label(self):
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# item is not onehot
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with self.assertRaisesRegex(
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ValueError,
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'input is not one-hot and can not convert to label'):
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LabelData.onehot_to_label(
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onehot=torch.tensor([2], dtype=torch.int64))
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with self.assertRaises(AssertionError):
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LabelData.onehot_to_label(onehot='item')
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item = torch.arange(0, 9)
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onehot = LabelData.label_to_onehot(item, num_classes=10)
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label = LabelData.onehot_to_label(onehot)
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assert (label == item).all()
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assert label.device == item.device
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@pytest.mark.skipif(
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not torch.cuda.is_available(), reason='GPU is required!')
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def test_cuda(self):
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item = torch.arange(0, 9).cuda()
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onehot = LabelData.label_to_onehot(item, num_classes=10)
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assert item.device == onehot.device
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label = LabelData.onehot_to_label(onehot)
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assert label.device == onehot.device
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