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mirror of https://github.com/open-mmlab/mmsegmentation.git synced 2025-06-03 22:03:48 +08:00
Miao Zheng 8de0050f25
[Refactor] data flow ()
* [WIP] Refactor data flow

* model return

* [WIP] Refactor data flow

* support data_samples is optional

* fix benchmark

* fix base

* minors

* rebase

* fix api

* ut

* fix api inference

* comments

* docstring

* docstring

* docstring

* fix bug of slide inference

* add assert c > 1
2022-08-26 15:54:23 +08:00

53 lines
1.9 KiB
Python

# Copyright (c) OpenMMLab. All rights reserved.
import copy
import os.path as osp
import unittest
import numpy as np
from mmengine.structures import BaseDataElement
from mmseg.datasets.transforms import PackSegInputs
from mmseg.structures import SegDataSample
class TestPackSegInputs(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.results = {
'img_path': img_path,
'ori_shape': (300, 400),
'pad_shape': (600, 800),
'img_shape': (600, 800),
'scale_factor': 2.0,
'flip': False,
'flip_direction': 'horizontal',
'img_norm_cfg': None,
'img': rng.rand(300, 400),
'gt_seg_map': rng.rand(300, 400),
}
self.meta_keys = ('img_path', 'ori_shape', 'img_shape', 'pad_shape',
'scale_factor', 'flip', 'flip_direction')
def test_transform(self):
transform = PackSegInputs(meta_keys=self.meta_keys)
results = transform(copy.deepcopy(self.results))
self.assertIn('data_samples', results)
self.assertIsInstance(results['data_samples'], SegDataSample)
self.assertIsInstance(results['data_samples'].gt_sem_seg,
BaseDataElement)
self.assertEqual(results['data_samples'].ori_shape,
results['data_samples'].gt_sem_seg.shape)
def test_repr(self):
transform = PackSegInputs(meta_keys=self.meta_keys)
self.assertEqual(
repr(transform), f'PackSegInputs(meta_keys={self.meta_keys})')