# Copyright (c) OpenMMLab. All rights reserved. import copy import unittest import numpy as np from mmocr.datasets.pipelines import PyramidRescale class TestPyramidRescale(unittest.TestCase): def setUp(self): self.data_info = dict(img=np.random.random((128, 100, 3))) def test_init(self): # factor is int transform = PyramidRescale(factor=4, randomize_factor=False) self.assertEqual(transform.factor, 4) # factor is float with self.assertRaisesRegex(TypeError, '`factor` should be an integer'): PyramidRescale(factor=4.0) # invalid base_shape with self.assertRaisesRegex(TypeError, '`base_shape` should be a list or tuple'): PyramidRescale(base_shape=128) with self.assertRaisesRegex( ValueError, '`base_shape` should contain two integers'): PyramidRescale(base_shape=(128, )) with self.assertRaisesRegex( ValueError, '`base_shape` should contain two integers'): PyramidRescale(base_shape=(128.0, 2.0)) # invalid randomize_factor with self.assertRaisesRegex(TypeError, '`randomize_factor` should be a bool'): PyramidRescale(randomize_factor=None) def test_transform(self): # test if the rescale keeps the original size transform = PyramidRescale() results = transform(copy.deepcopy(self.data_info)) self.assertEqual(results['img'].shape, (128, 100, 3)) # test factor = 0 transform = PyramidRescale(factor=0, randomize_factor=False) results = transform(copy.deepcopy(self.data_info)) self.assertTrue(np.all(results['img'] == self.data_info['img'])) def test_repr(self): transform = PyramidRescale( factor=4, base_shape=(128, 512), randomize_factor=False) print(repr(transform)) self.assertEqual( repr(transform), ('PyramidRescale(factor = 4, randomize_factor = False, ' 'base_w = 128, base_h = 512)'))