File size: 17,376 Bytes
09a3fa9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 | # Copyright (c) OpenMMLab. All rights reserved.
import random
from unittest import TestCase
import numpy as np
import pytest
import torch
from mmengine.structures import BaseDataElement
class DetDataSample(BaseDataElement):
@property
def proposals(self):
return self._proposals
@proposals.setter
def proposals(self, value):
self.set_field(value=value, name='_proposals', dtype=BaseDataElement)
@proposals.deleter
def proposals(self):
del self._proposals
@property
def gt_instances(self):
return self._gt_instances
@gt_instances.setter
def gt_instances(self, value):
self.set_field(
value=value, name='_gt_instances', dtype=BaseDataElement)
@gt_instances.deleter
def gt_instances(self):
del self._gt_instances
@property
def pred_instances(self):
return self._pred_instances
@pred_instances.setter
def pred_instances(self, value):
self.set_field(
value=value, name='_pred_instances', dtype=BaseDataElement)
@pred_instances.deleter
def pred_instances(self):
del self._pred_instances
class TestBaseDataElement(TestCase):
def setup_data(self):
metainfo = dict(
img_id=random.randint(0, 100),
img_shape=(random.randint(400, 600), random.randint(400, 600)))
gt_instances = BaseDataElement(
bboxes=torch.rand((5, 4)), labels=torch.rand((5, )))
pred_instances = BaseDataElement(
bboxes=torch.rand((5, 4)), scores=torch.rand((5, )))
data = dict(gt_instances=gt_instances, pred_instances=pred_instances)
return metainfo, data
def is_equal(self, x, y):
assert type(x) == type(y)
if isinstance(
x, (int, float, str, list, tuple, dict, set, BaseDataElement)):
return x == y
elif isinstance(x, (torch.Tensor, np.ndarray)):
return (x == y).all()
def check_key_value(self, instances, metainfo=None, data=None):
# check the existence of keys in metainfo, data, and instances
if metainfo:
for k, v in metainfo.items():
assert k in instances
assert k in instances.all_keys()
assert k in instances.metainfo_keys()
assert k not in instances.keys()
assert self.is_equal(instances.get(k), v)
assert self.is_equal(getattr(instances, k), v)
if data:
for k, v in data.items():
assert k in instances
assert k in instances.keys()
assert k not in instances.metainfo_keys()
assert k in instances.all_keys()
assert self.is_equal(instances.get(k), v)
assert self.is_equal(getattr(instances, k), v)
def check_data_device(self, instances, device):
# assert instances.device == device
for v in instances.values():
if isinstance(v, torch.Tensor):
assert v.device == torch.device(device)
elif isinstance(v, BaseDataElement):
self.check_data_device(v, device)
def check_data_dtype(self, instances, dtype):
for v in instances.values():
if isinstance(v, (torch.Tensor, np.ndarray)):
assert isinstance(v, dtype)
if isinstance(v, BaseDataElement):
self.check_data_dtype(v, dtype)
def check_requires_grad(self, instances):
for v in instances.values():
if isinstance(v, torch.Tensor):
assert v.requires_grad is False
if isinstance(v, BaseDataElement):
self.check_requires_grad(v)
def test_init(self):
# initialization with no data and metainfo
metainfo, data = self.setup_data()
instances = BaseDataElement()
for k in metainfo:
assert k not in instances
assert instances.get(k, None) is None
for k in data:
assert k not in instances
assert instances.get(k, 'abc') == 'abc'
# initialization with kwargs
metainfo, data = self.setup_data()
instances = BaseDataElement(metainfo=metainfo, **data)
self.check_key_value(instances, metainfo, data)
# initialization with args
metainfo, data = self.setup_data()
instances = BaseDataElement(metainfo=metainfo)
self.check_key_value(instances, metainfo)
instances = BaseDataElement(**data)
self.check_key_value(instances, data=data)
def test_new(self):
metainfo, data = self.setup_data()
instances = BaseDataElement(metainfo=metainfo, **data)
# test new() with no arguments
new_instances = instances.new()
assert type(new_instances) == type(instances)
# After deepcopy, the address of new data'element will be same as
# origin, but when change new data' element will not effect the origin
# element and will have new address
_, data = self.setup_data()
new_instances.set_data(data)
assert not self.is_equal(new_instances.gt_instances,
instances.gt_instances)
self.check_key_value(new_instances, metainfo, data)
# test new() with arguments
metainfo, data = self.setup_data()
new_instances = instances.new(metainfo=metainfo, **data)
assert type(new_instances) == type(instances)
assert id(new_instances.gt_instances) != id(instances.gt_instances)
_, new_data = self.setup_data()
new_instances.set_data(new_data)
assert id(new_instances.gt_instances) != id(data['gt_instances'])
self.check_key_value(new_instances, metainfo, new_data)
metainfo, data = self.setup_data()
new_instances = instances.new(metainfo=metainfo)
def test_clone(self):
metainfo, data = self.setup_data()
instances = BaseDataElement(metainfo=metainfo, **data)
new_instances = instances.clone()
assert type(new_instances) == type(instances)
def test_set_metainfo(self):
metainfo, _ = self.setup_data()
instances = BaseDataElement()
instances.set_metainfo(metainfo)
self.check_key_value(instances, metainfo=metainfo)
# test setting existing keys and new keys
new_metainfo, _ = self.setup_data()
new_metainfo.update(other=123)
instances.set_metainfo(new_metainfo)
self.check_key_value(instances, metainfo=new_metainfo)
# test have the same key in data
_, data = self.setup_data()
instances = BaseDataElement(**data)
_, data = self.setup_data()
with self.assertRaises(AttributeError):
instances.set_metainfo(data)
with self.assertRaises(AssertionError):
instances.set_metainfo(123)
def test_set_data(self):
metainfo, data = self.setup_data()
instances = BaseDataElement()
instances.gt_instances = data['gt_instances']
instances.pred_instances = data['pred_instances']
self.check_key_value(instances, data=data)
metainfo, data = self.setup_data()
instances = BaseDataElement()
instances.set_data(data)
self.check_key_value(instances, data=data)
# a.xx only set data rather than metainfo
instances.img_shape = metainfo['img_shape']
instances.img_id = metainfo['img_id']
self.check_key_value(instances, data=metainfo)
metainfo, data = self.setup_data()
instances = BaseDataElement(metainfo=metainfo, **data)
with self.assertRaises(AttributeError):
instances.img_shape = metainfo['img_shape']
# test set '_metainfo_fields' or '_data_fields'
with self.assertRaises(AttributeError):
instances._metainfo_fields = 1
with self.assertRaises(AttributeError):
instances._data_fields = 1
with self.assertRaises(AssertionError):
instances.set_data(123)
metainfo, data = self.setup_data()
instances = BaseDataElement(metainfo=metainfo, **data)
with pytest.raises(AttributeError):
instances.set_data(dict(img_id=1))
def test_update(self):
metainfo, data = self.setup_data()
instances = BaseDataElement(metainfo=metainfo, **data)
proposals = BaseDataElement(
bboxes=torch.rand((5, 4)), scores=torch.rand((5, )))
new_instances = BaseDataElement(proposals=proposals)
instances.update(new_instances)
self.check_key_value(instances, metainfo,
data.update(dict(proposals=proposals)))
def test_delete_modify(self):
random.seed(10)
metainfo, data = self.setup_data()
instances = BaseDataElement(metainfo=metainfo, **data)
new_metainfo, new_data = self.setup_data()
# avoid generating same metainfo, data
while True:
if new_metainfo['img_id'] == metainfo['img_id'] or new_metainfo[
'img_shape'] == metainfo['img_shape']:
new_metainfo, new_data = self.setup_data()
else:
break
instances.gt_instances = new_data['gt_instances']
instances.pred_instances = new_data['pred_instances']
# a.xx only set data rather than metainfo
instances.set_metainfo(new_metainfo)
self.check_key_value(instances, new_metainfo, new_data)
assert not self.is_equal(instances.gt_instances, data['gt_instances'])
assert not self.is_equal(instances.pred_instances,
data['pred_instances'])
assert not self.is_equal(instances.img_id, metainfo['img_id'])
assert not self.is_equal(instances.img_shape, metainfo['img_shape'])
del instances.gt_instances
del instances.img_id
assert not self.is_equal(
instances.pop('pred_instances', None), data['pred_instances'])
with self.assertRaises(AttributeError):
del instances.pred_instances
assert 'gt_instances' not in instances
assert 'pred_instances' not in instances
assert 'img_id' not in instances
assert instances.pop('gt_instances', None) is None
# test pop not exist key without default
with self.assertRaises(KeyError):
instances.pop('gt_instances')
assert instances.pop('pred_instances', 'abcdef') == 'abcdef'
assert instances.pop('img_id', None) is None
# test pop not exist key without default
with self.assertRaises(KeyError):
instances.pop('img_id')
assert instances.pop('img_shape') == new_metainfo['img_shape']
# test del '_metainfo_fields' or '_data_fields'
with self.assertRaises(AttributeError):
del instances._metainfo_fields
with self.assertRaises(AttributeError):
del instances._data_fields
@pytest.mark.skipif(
not torch.cuda.is_available(), reason='GPU is required!')
def test_cuda(self):
metainfo, data = self.setup_data()
instances = BaseDataElement(metainfo=metainfo, **data)
cuda_instances = instances.cuda()
self.check_data_device(cuda_instances, 'cuda:0')
# here we further test to convert from cuda to cpu
cpu_instances = cuda_instances.cpu()
self.check_data_device(cpu_instances, 'cpu')
del cuda_instances
cuda_instances = instances.to('cuda:0')
self.check_data_device(cuda_instances, 'cuda:0')
def test_cpu(self):
metainfo, data = self.setup_data()
instances = BaseDataElement(metainfo=metainfo, **data)
self.check_data_device(instances, 'cpu')
cpu_instances = instances.cpu()
# assert cpu_instances.device == 'cpu'
assert cpu_instances.gt_instances.bboxes.device == torch.device('cpu')
assert cpu_instances.gt_instances.labels.device == torch.device('cpu')
def test_numpy_tensor(self):
metainfo, data = self.setup_data()
instances = BaseDataElement(metainfo=metainfo, **data)
np_instances = instances.numpy()
self.check_data_dtype(np_instances, np.ndarray)
tensor_instances = np_instances.to_tensor()
self.check_data_dtype(tensor_instances, torch.Tensor)
def test_detach(self):
metainfo, data = self.setup_data()
instances = BaseDataElement(metainfo=metainfo, **data)
instances.detach()
self.check_requires_grad(instances)
def test_repr(self):
metainfo = dict(img_shape=(800, 1196, 3))
gt_instances = BaseDataElement(
metainfo=metainfo, det_labels=torch.LongTensor([0, 1, 2, 3]))
sample = BaseDataElement(metainfo=metainfo, gt_instances=gt_instances)
address = hex(id(sample))
address_gt_instances = hex(id(sample.gt_instances))
assert repr(sample) == (
'<BaseDataElement(\n\n'
' META INFORMATION\n'
' img_shape: (800, 1196, 3)\n\n'
' DATA FIELDS\n'
' gt_instances: <BaseDataElement(\n \n'
' META INFORMATION\n'
' img_shape: (800, 1196, 3)\n \n'
' DATA FIELDS\n'
' det_labels: tensor([0, 1, 2, 3])\n'
f' ) at {address_gt_instances}>\n'
f') at {address}>')
def test_set_fields(self):
metainfo, data = self.setup_data()
instances = BaseDataElement(metainfo=metainfo)
for key, value in data.items():
instances.set_field(name=key, value=value, dtype=BaseDataElement)
self.check_key_value(instances, data=data)
# test type check
_, data = self.setup_data()
instances = BaseDataElement()
for key, value in data.items():
with self.assertRaises(AssertionError):
instances.set_field(name=key, value=value, dtype=torch.Tensor)
def test_inheritance(self):
det_sample = DetDataSample()
# test set
proposals = BaseDataElement(bboxes=torch.rand((5, 4)))
det_sample.proposals = proposals
assert 'proposals' in det_sample
# test get
assert det_sample.proposals == proposals
# test delete
del det_sample.proposals
assert 'proposals' not in det_sample
# test the data whether meet the requirements
with self.assertRaises(AssertionError):
det_sample.proposals = torch.rand((5, 4))
def test_values(self):
# test_metainfo_values
metainfo, data = self.setup_data()
instances = BaseDataElement(metainfo=metainfo, **data)
assert len(instances.metainfo_values()) == len(metainfo.values())
# test_all_values
assert len(instances.all_values()) == len(metainfo.values()) + len(
data.values())
# test_values
assert len(instances.values()) == len(data.values())
def test_keys(self):
# test_metainfo_keys
metainfo, data = self.setup_data()
instances = BaseDataElement(metainfo=metainfo, **data)
assert len(instances.metainfo_keys()) == len(metainfo.keys())
# test_all_keys
assert len(
instances.all_keys()) == len(data.keys()) + len(metainfo.keys())
# test_keys
assert len(instances.keys()) == len(data.keys())
det_sample = DetDataSample()
proposals = BaseDataElement(bboxes=torch.rand((5, 4)))
det_sample.proposals = proposals
assert '_proposals' not in det_sample.keys()
def test_items(self):
# test_metainfo_items
metainfo, data = self.setup_data()
instances = BaseDataElement(metainfo=metainfo, **data)
assert len(dict(instances.metainfo_items())) == len(
dict(metainfo.items()))
# test_all_items
assert len(dict(instances.all_items())) == len(dict(
metainfo.items())) + len(dict(data.items()))
# test_items
assert len(dict(instances.items())) == len(dict(data.items()))
def test_to_dict(self):
metainfo, data = self.setup_data()
instances = BaseDataElement(metainfo=metainfo, **data)
dict_instances = instances.to_dict()
# test convert BaseDataElement to dict
for k in instances.all_keys():
# all keys in instances should be in dict_instances
assert k in dict_instances
assert isinstance(dict_instances, dict)
# sub data element should also be converted to dict
assert isinstance(dict_instances['gt_instances'], dict)
assert isinstance(dict_instances['pred_instances'], dict)
det_sample = DetDataSample()
proposals = BaseDataElement(bboxes=torch.rand((5, 4)))
det_sample.proposals = proposals
dict_sample = det_sample.to_dict()
assert '_proposals' not in dict_sample
assert 'proposals' in dict_sample
def test_metainfo(self):
# test metainfo property
metainfo, data = self.setup_data()
instances = BaseDataElement(metainfo=metainfo, **data)
self.assertDictEqual(instances.metainfo, metainfo)
|