mmclassification/tests/test_data/test_pipelines/test_formatting.py

# Copyright (c) OpenMMLab. All rights reserved.
import copy
import os.path as osp
import unittest

import numpy as np
import torch
from mmengine.data import LabelData

from mmcls.engine import ClsDataSample
from mmcls.datasets.pipelines import PackClsInputs


class TestPackClsInputs(unittest.TestCase):

    def setUp(self):
        """Setup the model and optimizer which are used in every test method.

        TestCase calls functions in this order: setUp() -> testMethod() ->
        tearDown() -> cleanUp()
        """
        data_prefix = osp.join(osp.dirname(__file__), '../../data')
        img_path = osp.join(data_prefix, 'color.jpg')
        rng = np.random.RandomState(0)
        self.results1 = {
            'sample_idx': 1,
            'img_path': img_path,
            'ori_height': 300,
            'ori_width': 400,
            'height': 600,
            'width': 800,
            'scale_factor': 2.0,
            'flip': False,
            'img': rng.rand(300, 400),
            'gt_label': rng.randint(3, )
        }
        self.meta_keys = ('sample_idx', 'img_path', 'ori_shape', 'img_shape',
                          'scale_factor', 'flip')

    def test_transform(self):
        transform = PackClsInputs(meta_keys=self.meta_keys)
        results = transform(copy.deepcopy(self.results1))
        self.assertIn('inputs', results)
        self.assertIsInstance(results['inputs'], torch.Tensor)
        self.assertIn('data_sample', results)
        self.assertIsInstance(results['data_sample'], ClsDataSample)

        data_sample = results['data_sample']
        self.assertIsInstance(data_sample.gt_label, LabelData)

    def test_repr(self):
        transform = PackClsInputs(meta_keys=self.meta_keys)
        self.assertEqual(
            repr(transform), f'PackClsInputs(meta_keys={self.meta_keys})')