55 lines
1.8 KiB
Python
55 lines
1.8 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import copy
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import os.path as osp
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import unittest
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import numpy as np
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import torch
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from mmengine.data import LabelData
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from mmcls.engine import ClsDataSample
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from mmcls.datasets.pipelines import PackClsInputs
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class TestPackClsInputs(unittest.TestCase):
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def setUp(self):
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"""Setup the model and optimizer which are used in every test method.
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TestCase calls functions in this order: setUp() -> testMethod() ->
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tearDown() -> cleanUp()
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"""
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data_prefix = osp.join(osp.dirname(__file__), '../../data')
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img_path = osp.join(data_prefix, 'color.jpg')
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rng = np.random.RandomState(0)
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self.results1 = {
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'sample_idx': 1,
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'img_path': img_path,
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'ori_height': 300,
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'ori_width': 400,
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'height': 600,
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'width': 800,
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'scale_factor': 2.0,
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'flip': False,
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'img': rng.rand(300, 400),
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'gt_label': rng.randint(3, )
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}
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self.meta_keys = ('sample_idx', 'img_path', 'ori_shape', 'img_shape',
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'scale_factor', 'flip')
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def test_transform(self):
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transform = PackClsInputs(meta_keys=self.meta_keys)
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results = transform(copy.deepcopy(self.results1))
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self.assertIn('inputs', results)
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self.assertIsInstance(results['inputs'], torch.Tensor)
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self.assertIn('data_sample', results)
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self.assertIsInstance(results['data_sample'], ClsDataSample)
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data_sample = results['data_sample']
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self.assertIsInstance(data_sample.gt_label, LabelData)
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def test_repr(self):
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transform = PackClsInputs(meta_keys=self.meta_keys)
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self.assertEqual(
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repr(transform), f'PackClsInputs(meta_keys={self.meta_keys})')
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