|
|
|
|
|
import copy |
|
|
import os.path as osp |
|
|
import unittest |
|
|
|
|
|
import numpy as np |
|
|
import torch |
|
|
from mmengine.structures import InstanceData, PixelData |
|
|
|
|
|
from mmdet.datasets.transforms import PackDetInputs |
|
|
from mmdet.structures import DetDataSample |
|
|
from mmdet.structures.mask import BitmapMasks |
|
|
|
|
|
|
|
|
class TestPackDetInputs(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 = { |
|
|
'img_id': 1, |
|
|
'img_path': img_path, |
|
|
'ori_shape': (300, 400), |
|
|
'img_shape': (600, 800), |
|
|
'scale_factor': 2.0, |
|
|
'flip': False, |
|
|
'img': rng.rand(300, 400), |
|
|
'gt_seg_map': rng.rand(300, 400), |
|
|
'gt_masks': |
|
|
BitmapMasks(rng.rand(3, 300, 400), height=300, width=400), |
|
|
'gt_bboxes_labels': rng.rand(3, ), |
|
|
'gt_ignore_flags': np.array([0, 0, 1], dtype=bool), |
|
|
'proposals': rng.rand(2, 4), |
|
|
'proposals_scores': rng.rand(2, ) |
|
|
} |
|
|
self.results2 = { |
|
|
'img_id': 1, |
|
|
'img_path': img_path, |
|
|
'ori_shape': (300, 400), |
|
|
'img_shape': (600, 800), |
|
|
'scale_factor': 2.0, |
|
|
'flip': False, |
|
|
'img': rng.rand(300, 400), |
|
|
'gt_seg_map': rng.rand(300, 400), |
|
|
'gt_masks': |
|
|
BitmapMasks(rng.rand(3, 300, 400), height=300, width=400), |
|
|
'gt_bboxes_labels': rng.rand(3, ), |
|
|
'proposals': rng.rand(2, 4), |
|
|
'proposals_scores': rng.rand(2, ) |
|
|
} |
|
|
self.meta_keys = ('img_id', 'img_path', 'ori_shape', 'scale_factor', |
|
|
'flip') |
|
|
|
|
|
def test_transform(self): |
|
|
transform = PackDetInputs(meta_keys=self.meta_keys) |
|
|
results = transform(copy.deepcopy(self.results1)) |
|
|
self.assertIn('data_samples', results) |
|
|
self.assertIsInstance(results['data_samples'], DetDataSample) |
|
|
self.assertIsInstance(results['data_samples'].gt_instances, |
|
|
InstanceData) |
|
|
self.assertIsInstance(results['data_samples'].ignored_instances, |
|
|
InstanceData) |
|
|
self.assertEqual(len(results['data_samples'].gt_instances), 2) |
|
|
self.assertEqual(len(results['data_samples'].ignored_instances), 1) |
|
|
self.assertIsInstance(results['data_samples'].gt_sem_seg, PixelData) |
|
|
self.assertIsInstance(results['data_samples'].proposals, InstanceData) |
|
|
self.assertEqual(len(results['data_samples'].proposals), 2) |
|
|
self.assertIsInstance(results['data_samples'].proposals.bboxes, |
|
|
torch.Tensor) |
|
|
self.assertIsInstance(results['data_samples'].proposals.scores, |
|
|
torch.Tensor) |
|
|
|
|
|
def test_transform_without_ignore(self): |
|
|
transform = PackDetInputs(meta_keys=self.meta_keys) |
|
|
results = transform(copy.deepcopy(self.results2)) |
|
|
self.assertIn('data_samples', results) |
|
|
self.assertIsInstance(results['data_samples'], DetDataSample) |
|
|
self.assertIsInstance(results['data_samples'].gt_instances, |
|
|
InstanceData) |
|
|
self.assertIsInstance(results['data_samples'].ignored_instances, |
|
|
InstanceData) |
|
|
self.assertEqual(len(results['data_samples'].gt_instances), 3) |
|
|
self.assertEqual(len(results['data_samples'].ignored_instances), 0) |
|
|
self.assertIsInstance(results['data_samples'].gt_sem_seg, PixelData) |
|
|
self.assertIsInstance(results['data_samples'].proposals, InstanceData) |
|
|
self.assertEqual(len(results['data_samples'].proposals), 2) |
|
|
self.assertIsInstance(results['data_samples'].proposals.bboxes, |
|
|
torch.Tensor) |
|
|
self.assertIsInstance(results['data_samples'].proposals.scores, |
|
|
torch.Tensor) |
|
|
|
|
|
def test_repr(self): |
|
|
transform = PackDetInputs(meta_keys=self.meta_keys) |
|
|
self.assertEqual( |
|
|
repr(transform), f'PackDetInputs(meta_keys={self.meta_keys})') |
|
|
|
