mirror of
https://github.com/open-mmlab/mmengine.git
synced 2025-06-03 21:54:44 +08:00
299 lines
12 KiB
Python
299 lines
12 KiB
Python
|
# Copyright (c) OpenMMLab. All rights reserved.
|
||
|
|
import random
|
||
|
|
from functools import partial
|
||
|
|
from unittest import TestCase
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
import pytest
|
||
|
|
import torch
|
||
|
|
|
||
|
|
from mmengine.data import BaseDataElement, BaseDataSample
|
||
|
|
|
||
|
|
|
||
|
|
class TestBaseDataSample(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(
|
||
|
|
data=dict(bboxes=torch.rand((5, 4)), labels=torch.rand((5, ))))
|
||
|
|
pred_instances = BaseDataElement(
|
||
|
|
data=dict(bboxes=torch.rand((5, 4)), scores=torch.rand((5, ))))
|
||
|
|
data = dict(gt_instances=gt_instances, pred_instances=pred_instances)
|
||
|
|
return metainfo, data
|
||
|
|
|
||
|
|
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.keys()
|
||
|
|
assert k in instances.metainfo_keys()
|
||
|
|
assert k not in instances.data_keys()
|
||
|
|
assert instances.get(k) == v
|
||
|
|
assert 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.data_keys()
|
||
|
|
assert instances.get(k) == v
|
||
|
|
assert getattr(instances, k) == v
|
||
|
|
|
||
|
|
def check_data_device(self, instances, device):
|
||
|
|
assert instances.device == device
|
||
|
|
for v in instances.data_values():
|
||
|
|
if isinstance(v, torch.Tensor):
|
||
|
|
assert v.device == device
|
||
|
|
elif isinstance(v, BaseDataElement):
|
||
|
|
self.check_data_device(v, device)
|
||
|
|
|
||
|
|
def check_data_dtype(self, instances, dtype):
|
||
|
|
for v in instances.data_values():
|
||
|
|
if isinstance(v, (torch.Tensor, np.ndarray)):
|
||
|
|
assert isinstance(v, dtype)
|
||
|
|
if isinstance(v, BaseDataElement):
|
||
|
|
self.check_data_dtype(v, dtype)
|
||
|
|
|
||
|
|
def test_init(self):
|
||
|
|
# initialization with no data and metainfo
|
||
|
|
metainfo, data = self.setup_data()
|
||
|
|
instances = BaseDataSample()
|
||
|
|
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 = BaseDataSample(metainfo=metainfo, data=data)
|
||
|
|
self.check_key_value(instances, metainfo, data)
|
||
|
|
|
||
|
|
# initialization with args
|
||
|
|
metainfo, data = self.setup_data()
|
||
|
|
instances = BaseDataSample(metainfo, data)
|
||
|
|
self.check_key_value(instances, metainfo, data)
|
||
|
|
|
||
|
|
# initialization with args
|
||
|
|
metainfo, data = self.setup_data()
|
||
|
|
instances = BaseDataSample(metainfo=metainfo)
|
||
|
|
self.check_key_value(instances, metainfo)
|
||
|
|
instances = BaseDataSample(data=data)
|
||
|
|
self.check_key_value(instances, data=data)
|
||
|
|
|
||
|
|
def test_new(self):
|
||
|
|
metainfo, data = self.setup_data()
|
||
|
|
instances = BaseDataSample(metainfo=metainfo, data=data)
|
||
|
|
|
||
|
|
# test new() with no arguments
|
||
|
|
new_instances = instances.new()
|
||
|
|
assert type(new_instances) == type(instances)
|
||
|
|
assert id(new_instances.data) != id(instances.data)
|
||
|
|
assert id(new_instances.bboxes) != id(data)
|
||
|
|
self.check_key_value(new_instances, metainfo, data)
|
||
|
|
|
||
|
|
# test new() with arguments
|
||
|
|
metainfo, data = self.setup_data()
|
||
|
|
new_instances = instances.new(metainfo=metainfo, data=data)
|
||
|
|
assert type(new_instances) == type(instances)
|
||
|
|
assert id(new_instances.data) != id(instances.data)
|
||
|
|
assert id(new_instances.data) != id(data)
|
||
|
|
self.check_key_value(new_instances, metainfo, data)
|
||
|
|
|
||
|
|
def test_set_metainfo(self):
|
||
|
|
metainfo, _ = self.setup_data()
|
||
|
|
instances = BaseDataSample()
|
||
|
|
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)
|
||
|
|
|
||
|
|
def test_set_data(self):
|
||
|
|
metainfo, data = self.setup_data()
|
||
|
|
instances = BaseDataSample()
|
||
|
|
|
||
|
|
instances.gt_instances = data['gt_instances']
|
||
|
|
instances.pred_instances = data['pred_instances']
|
||
|
|
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)
|
||
|
|
|
||
|
|
def test_delete_modify(self):
|
||
|
|
metainfo, data = self.setup_data()
|
||
|
|
instances = BaseDataSample(metainfo, data)
|
||
|
|
|
||
|
|
new_metainfo, new_data = self.setup_data()
|
||
|
|
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 instances.gt_instances != data['gt_instances']
|
||
|
|
assert instances.pred_instances != data['pred_instances']
|
||
|
|
assert instances.img_id != metainfo['img_id']
|
||
|
|
assert instances.img_shape != metainfo['img_shape']
|
||
|
|
|
||
|
|
del instances.gt_instances
|
||
|
|
assert instances.pop('pred_instances',
|
||
|
|
None) == new_data['pred_instances']
|
||
|
|
|
||
|
|
# pred_instances has been deleted,
|
||
|
|
# instances does not have the pred_instances
|
||
|
|
with self.assertRaises(AttributeError):
|
||
|
|
del instances.pred_instances
|
||
|
|
|
||
|
|
assert 'gt_instances' not in instances
|
||
|
|
assert 'pred_instances' not in instances
|
||
|
|
assert instances.pop('gt_instances', None) is None
|
||
|
|
assert instances.pop('pred_instances', 'abcdef') == 'abcdef'
|
||
|
|
|
||
|
|
@pytest.mark.skipif(
|
||
|
|
not torch.cuda.is_available(), reason='GPU is required!')
|
||
|
|
def test_cuda(self):
|
||
|
|
metainfo, data = self.setup_data()
|
||
|
|
instances = BaseDataSample(metainfo, data)
|
||
|
|
|
||
|
|
cuda_instances = instances.cuda()
|
||
|
|
self.check_data_device(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 = BaseDataSample(metainfo, data)
|
||
|
|
self.check_data_device(instances, 'cpu')
|
||
|
|
|
||
|
|
cpu_instances = instances.cpu()
|
||
|
|
assert cpu_instances.device == 'cpu'
|
||
|
|
assert cpu_instances.bboxes.device == 'cpu'
|
||
|
|
assert cpu_instances.scores.device == 'cpu'
|
||
|
|
|
||
|
|
def test_numpy_tensor(self):
|
||
|
|
metainfo, data = self.setup_data()
|
||
|
|
instances = BaseDataSample(metainfo, data)
|
||
|
|
|
||
|
|
np_instances = instances.numpy()
|
||
|
|
self.check_data_dtype(np_instances, np.ndarray)
|
||
|
|
|
||
|
|
tensor_instances = instances.to_tensor()
|
||
|
|
self.check_data_dtype(tensor_instances, torch.Tensor)
|
||
|
|
|
||
|
|
def test_repr(self):
|
||
|
|
metainfo = dict(img_shape=(800, 1196, 3), pad_shape=(800, 1216, 3))
|
||
|
|
gt_instances = BaseDataElement(
|
||
|
|
data=dict(
|
||
|
|
det_labels=torch.LongTensor([0, 1, 2, 3],
|
||
|
|
det_scores=torch.Tensor(
|
||
|
|
[0.01, 0.1, 0.2, 0.3]))))
|
||
|
|
data = dict(gt_instances=gt_instances)
|
||
|
|
instances = BaseDataSample(metainfo=metainfo, data=data)
|
||
|
|
assert repr(instances) == ('<BaseDataSample(\n'
|
||
|
|
' META INFORMATION\n'
|
||
|
|
'img_shape: (800, 1196, 3)\n'
|
||
|
|
'pad_shape: (800, 1216, 3)\n'
|
||
|
|
' DATA FIELDS\n'
|
||
|
|
'\tgt_instances: <BaseDataElement(\n'
|
||
|
|
'\t META INFORMATION\n'
|
||
|
|
'\timg_shape: (800, 1196, 3)\n'
|
||
|
|
'\tpad_shape: (800, 1216, 3)\n'
|
||
|
|
'\t DATA FIELDS\n'
|
||
|
|
'\tshape of det_labels: torch.Size([4])\n'
|
||
|
|
'\tshape of det_scores: torch.Size([4])\n'
|
||
|
|
'\t) at 0x7f84acd10f90>'
|
||
|
|
') at 0x7f84acd10f90>')
|
||
|
|
|
||
|
|
def test_set_get_fields(self):
|
||
|
|
metainfo, data = self.setup_data()
|
||
|
|
instances = BaseDataSample(metainfo)
|
||
|
|
for key, value in data.items():
|
||
|
|
instances._set_field(value, key, BaseDataElement)
|
||
|
|
self.check_key_value(instances, data=data)
|
||
|
|
|
||
|
|
# test type check
|
||
|
|
_, data = self.setup_data()
|
||
|
|
instances = BaseDataSample()
|
||
|
|
for key, value in data.items():
|
||
|
|
with self.assertRaises(AssertionError):
|
||
|
|
instances._set_field(value, key, BaseDataSample)
|
||
|
|
|
||
|
|
def test_del_field(self):
|
||
|
|
metainfo, data = self.setup_data()
|
||
|
|
instances = BaseDataSample(metainfo)
|
||
|
|
for key, value in data.items():
|
||
|
|
instances._set_field(value, key, BaseDataElement)
|
||
|
|
instances._del_field('gt_instances')
|
||
|
|
instances._del_field('pred_instances')
|
||
|
|
|
||
|
|
# gt_instance has been deleted, instances does not have the gt_instance
|
||
|
|
with self.assertRaises(AttributeError):
|
||
|
|
instances._del_field('gt_instances')
|
||
|
|
assert 'gt_instances' not in instances
|
||
|
|
assert 'pred_instances' not in instances
|
||
|
|
|
||
|
|
def test_inheritance(self):
|
||
|
|
|
||
|
|
class DetDataSample(BaseDataSample):
|
||
|
|
proposals = property(
|
||
|
|
fget=partial(BaseDataSample._get_field, name='_proposals'),
|
||
|
|
fset=partial(
|
||
|
|
BaseDataSample._set_field,
|
||
|
|
name='_proposals',
|
||
|
|
dtype=BaseDataElement),
|
||
|
|
fdel=partial(BaseDataSample._del_field, name='_proposals'),
|
||
|
|
doc='Region proposals of an image')
|
||
|
|
gt_instances = property(
|
||
|
|
fget=partial(BaseDataSample._get_field, name='_gt_instances'),
|
||
|
|
fset=partial(
|
||
|
|
BaseDataSample._set_field,
|
||
|
|
name='_gt_instances',
|
||
|
|
dtype=BaseDataElement),
|
||
|
|
fdel=partial(BaseDataSample._del_field, name='_gt_instances'),
|
||
|
|
doc='Ground truth instances of an image')
|
||
|
|
pred_instances = property(
|
||
|
|
fget=partial(
|
||
|
|
BaseDataSample._get_field, name='_pred_instances'),
|
||
|
|
fset=partial(
|
||
|
|
BaseDataSample._set_field,
|
||
|
|
name='_pred_instances',
|
||
|
|
dtype=BaseDataElement),
|
||
|
|
fdel=partial(
|
||
|
|
BaseDataSample._del_field, name='_pred_instances'),
|
||
|
|
doc='Predicted instances of an image')
|
||
|
|
|
||
|
|
det_sample = DetDataSample()
|
||
|
|
|
||
|
|
# test set
|
||
|
|
proposals = BaseDataElement(data=dict(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))
|