461 lines
17 KiB
Python
461 lines
17 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import random
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from unittest import TestCase
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import numpy as np
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import pytest
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import torch
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from mmengine.structures import BaseDataElement
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class DetDataSample(BaseDataElement):
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@property
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def proposals(self):
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return self._proposals
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@proposals.setter
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def proposals(self, value):
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self.set_field(value=value, name='_proposals', dtype=BaseDataElement)
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@proposals.deleter
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def proposals(self):
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del self._proposals
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@property
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def gt_instances(self):
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return self._gt_instances
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@gt_instances.setter
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def gt_instances(self, value):
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self.set_field(
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value=value, name='_gt_instances', dtype=BaseDataElement)
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@gt_instances.deleter
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def gt_instances(self):
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del self._gt_instances
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@property
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def pred_instances(self):
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return self._pred_instances
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@pred_instances.setter
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def pred_instances(self, value):
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self.set_field(
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value=value, name='_pred_instances', dtype=BaseDataElement)
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@pred_instances.deleter
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def pred_instances(self):
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del self._pred_instances
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class TestBaseDataElement(TestCase):
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def setup_data(self):
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metainfo = dict(
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img_id=random.randint(0, 100),
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img_shape=(random.randint(400, 600), random.randint(400, 600)))
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gt_instances = BaseDataElement(
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bboxes=torch.rand((5, 4)), labels=torch.rand((5, )))
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pred_instances = BaseDataElement(
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bboxes=torch.rand((5, 4)), scores=torch.rand((5, )))
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data = dict(gt_instances=gt_instances, pred_instances=pred_instances)
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return metainfo, data
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def is_equal(self, x, y):
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assert type(x) == type(y)
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if isinstance(
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x, (int, float, str, list, tuple, dict, set, BaseDataElement)):
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return x == y
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elif isinstance(x, (torch.Tensor, np.ndarray)):
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return (x == y).all()
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def check_key_value(self, instances, metainfo=None, data=None):
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# check the existence of keys in metainfo, data, and instances
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if metainfo:
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for k, v in metainfo.items():
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assert k in instances
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assert k in instances.all_keys()
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assert k in instances.metainfo_keys()
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assert k not in instances.keys()
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assert self.is_equal(instances.get(k), v)
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assert self.is_equal(getattr(instances, k), v)
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if data:
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for k, v in data.items():
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assert k in instances
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assert k in instances.keys()
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assert k not in instances.metainfo_keys()
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assert k in instances.all_keys()
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assert self.is_equal(instances.get(k), v)
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assert self.is_equal(getattr(instances, k), v)
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def check_data_device(self, instances, device):
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# assert instances.device == device
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for v in instances.values():
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if isinstance(v, torch.Tensor):
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assert v.device == torch.device(device)
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elif isinstance(v, BaseDataElement):
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self.check_data_device(v, device)
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def check_data_dtype(self, instances, dtype):
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for v in instances.values():
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if isinstance(v, (torch.Tensor, np.ndarray)):
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assert isinstance(v, dtype)
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if isinstance(v, BaseDataElement):
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self.check_data_dtype(v, dtype)
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def check_requires_grad(self, instances):
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for v in instances.values():
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if isinstance(v, torch.Tensor):
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assert v.requires_grad is False
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if isinstance(v, BaseDataElement):
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self.check_requires_grad(v)
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def test_init(self):
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# initialization with no data and metainfo
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metainfo, data = self.setup_data()
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instances = BaseDataElement()
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for k in metainfo:
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assert k not in instances
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assert instances.get(k, None) is None
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for k in data:
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assert k not in instances
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assert instances.get(k, 'abc') == 'abc'
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# initialization with kwargs
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metainfo, data = self.setup_data()
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instances = BaseDataElement(metainfo=metainfo, **data)
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self.check_key_value(instances, metainfo, data)
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# initialization with args
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metainfo, data = self.setup_data()
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instances = BaseDataElement(metainfo=metainfo)
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self.check_key_value(instances, metainfo)
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instances = BaseDataElement(**data)
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self.check_key_value(instances, data=data)
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def test_new(self):
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metainfo, data = self.setup_data()
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instances = BaseDataElement(metainfo=metainfo, **data)
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# test new() with no arguments
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new_instances = instances.new()
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assert type(new_instances) == type(instances)
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# After deepcopy, the address of new data'element will be same as
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# origin, but when change new data' element will not effect the origin
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# element and will have new address
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_, data = self.setup_data()
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new_instances.set_data(data)
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assert not self.is_equal(new_instances.gt_instances,
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instances.gt_instances)
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self.check_key_value(new_instances, metainfo, data)
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# test new() with arguments
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metainfo, data = self.setup_data()
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new_instances = instances.new(metainfo=metainfo, **data)
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assert type(new_instances) == type(instances)
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assert id(new_instances.gt_instances) != id(instances.gt_instances)
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_, new_data = self.setup_data()
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new_instances.set_data(new_data)
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assert id(new_instances.gt_instances) != id(data['gt_instances'])
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self.check_key_value(new_instances, metainfo, new_data)
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metainfo, data = self.setup_data()
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new_instances = instances.new(metainfo=metainfo)
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def test_clone(self):
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metainfo, data = self.setup_data()
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instances = BaseDataElement(metainfo=metainfo, **data)
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new_instances = instances.clone()
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assert type(new_instances) == type(instances)
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def test_set_metainfo(self):
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metainfo, _ = self.setup_data()
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instances = BaseDataElement()
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instances.set_metainfo(metainfo)
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self.check_key_value(instances, metainfo=metainfo)
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# test setting existing keys and new keys
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new_metainfo, _ = self.setup_data()
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new_metainfo.update(other=123)
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instances.set_metainfo(new_metainfo)
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self.check_key_value(instances, metainfo=new_metainfo)
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# test have the same key in data
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_, data = self.setup_data()
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instances = BaseDataElement(**data)
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_, data = self.setup_data()
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with self.assertRaises(AttributeError):
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instances.set_metainfo(data)
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with self.assertRaises(AssertionError):
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instances.set_metainfo(123)
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def test_set_data(self):
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metainfo, data = self.setup_data()
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instances = BaseDataElement()
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instances.gt_instances = data['gt_instances']
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instances.pred_instances = data['pred_instances']
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self.check_key_value(instances, data=data)
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metainfo, data = self.setup_data()
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instances = BaseDataElement()
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instances.set_data(data)
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self.check_key_value(instances, data=data)
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# a.xx only set data rather than metainfo
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instances.img_shape = metainfo['img_shape']
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instances.img_id = metainfo['img_id']
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self.check_key_value(instances, data=metainfo)
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metainfo, data = self.setup_data()
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instances = BaseDataElement(metainfo=metainfo, **data)
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with self.assertRaises(AttributeError):
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instances.img_shape = metainfo['img_shape']
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# test set '_metainfo_fields' or '_data_fields'
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with self.assertRaises(AttributeError):
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instances._metainfo_fields = 1
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with self.assertRaises(AttributeError):
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instances._data_fields = 1
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with self.assertRaises(AssertionError):
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instances.set_data(123)
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metainfo, data = self.setup_data()
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instances = BaseDataElement(metainfo=metainfo, **data)
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with pytest.raises(AttributeError):
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instances.set_data(dict(img_id=1))
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def test_update(self):
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metainfo, data = self.setup_data()
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instances = BaseDataElement(metainfo=metainfo, **data)
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proposals = BaseDataElement(
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bboxes=torch.rand((5, 4)), scores=torch.rand((5, )))
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new_instances = BaseDataElement(proposals=proposals)
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instances.update(new_instances)
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self.check_key_value(instances, metainfo,
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data.update(dict(proposals=proposals)))
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def test_delete_modify(self):
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random.seed(10)
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metainfo, data = self.setup_data()
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instances = BaseDataElement(metainfo=metainfo, **data)
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new_metainfo, new_data = self.setup_data()
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# avoid generating same metainfo, data
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while True:
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if new_metainfo['img_id'] == metainfo['img_id'] or new_metainfo[
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'img_shape'] == metainfo['img_shape']:
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new_metainfo, new_data = self.setup_data()
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else:
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break
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instances.gt_instances = new_data['gt_instances']
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instances.pred_instances = new_data['pred_instances']
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# a.xx only set data rather than metainfo
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instances.set_metainfo(new_metainfo)
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self.check_key_value(instances, new_metainfo, new_data)
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assert not self.is_equal(instances.gt_instances, data['gt_instances'])
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assert not self.is_equal(instances.pred_instances,
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data['pred_instances'])
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assert not self.is_equal(instances.img_id, metainfo['img_id'])
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assert not self.is_equal(instances.img_shape, metainfo['img_shape'])
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del instances.gt_instances
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del instances.img_id
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assert not self.is_equal(
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instances.pop('pred_instances', None), data['pred_instances'])
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with self.assertRaises(AttributeError):
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del instances.pred_instances
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assert 'gt_instances' not in instances
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assert 'pred_instances' not in instances
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assert 'img_id' not in instances
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assert instances.pop('gt_instances', None) is None
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# test pop not exist key without default
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with self.assertRaises(KeyError):
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instances.pop('gt_instances')
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assert instances.pop('pred_instances', 'abcdef') == 'abcdef'
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assert instances.pop('img_id', None) is None
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# test pop not exist key without default
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with self.assertRaises(KeyError):
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instances.pop('img_id')
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assert instances.pop('img_shape') == new_metainfo['img_shape']
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# test del '_metainfo_fields' or '_data_fields'
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with self.assertRaises(AttributeError):
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del instances._metainfo_fields
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with self.assertRaises(AttributeError):
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del instances._data_fields
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@pytest.mark.skipif(
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not torch.cuda.is_available(), reason='GPU is required!')
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def test_cuda(self):
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metainfo, data = self.setup_data()
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instances = BaseDataElement(metainfo=metainfo, **data)
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cuda_instances = instances.cuda()
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self.check_data_device(cuda_instances, 'cuda:0')
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# here we further test to convert from cuda to cpu
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cpu_instances = cuda_instances.cpu()
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self.check_data_device(cpu_instances, 'cpu')
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del cuda_instances
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cuda_instances = instances.to('cuda:0')
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self.check_data_device(cuda_instances, 'cuda:0')
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def test_cpu(self):
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metainfo, data = self.setup_data()
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instances = BaseDataElement(metainfo=metainfo, **data)
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self.check_data_device(instances, 'cpu')
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cpu_instances = instances.cpu()
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# assert cpu_instances.device == 'cpu'
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assert cpu_instances.gt_instances.bboxes.device == torch.device('cpu')
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assert cpu_instances.gt_instances.labels.device == torch.device('cpu')
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def test_numpy_tensor(self):
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metainfo, data = self.setup_data()
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instances = BaseDataElement(metainfo=metainfo, **data)
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np_instances = instances.numpy()
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self.check_data_dtype(np_instances, np.ndarray)
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tensor_instances = np_instances.to_tensor()
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self.check_data_dtype(tensor_instances, torch.Tensor)
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def test_detach(self):
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metainfo, data = self.setup_data()
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instances = BaseDataElement(metainfo=metainfo, **data)
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instances.detach()
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self.check_requires_grad(instances)
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def test_repr(self):
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metainfo = dict(img_shape=(800, 1196, 3))
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gt_instances = BaseDataElement(
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metainfo=metainfo, det_labels=torch.LongTensor([0, 1, 2, 3]))
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sample = BaseDataElement(metainfo=metainfo, gt_instances=gt_instances)
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address = hex(id(sample))
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address_gt_instances = hex(id(sample.gt_instances))
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assert repr(sample) == (
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'<BaseDataElement(\n\n'
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' META INFORMATION\n'
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' img_shape: (800, 1196, 3)\n\n'
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' DATA FIELDS\n'
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' gt_instances: <BaseDataElement(\n \n'
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' META INFORMATION\n'
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' img_shape: (800, 1196, 3)\n \n'
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' DATA FIELDS\n'
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' det_labels: tensor([0, 1, 2, 3])\n'
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f' ) at {address_gt_instances}>\n'
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f') at {address}>')
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def test_set_fields(self):
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metainfo, data = self.setup_data()
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instances = BaseDataElement(metainfo=metainfo)
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for key, value in data.items():
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instances.set_field(name=key, value=value, dtype=BaseDataElement)
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self.check_key_value(instances, data=data)
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# test type check
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_, data = self.setup_data()
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instances = BaseDataElement()
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for key, value in data.items():
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with self.assertRaises(AssertionError):
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instances.set_field(name=key, value=value, dtype=torch.Tensor)
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def test_inheritance(self):
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det_sample = DetDataSample()
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# test set
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proposals = BaseDataElement(bboxes=torch.rand((5, 4)))
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det_sample.proposals = proposals
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assert 'proposals' in det_sample
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# test get
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assert det_sample.proposals == proposals
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# test delete
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del det_sample.proposals
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assert 'proposals' not in det_sample
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# test the data whether meet the requirements
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with self.assertRaises(AssertionError):
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det_sample.proposals = torch.rand((5, 4))
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def test_values(self):
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# test_metainfo_values
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metainfo, data = self.setup_data()
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instances = BaseDataElement(metainfo=metainfo, **data)
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assert len(instances.metainfo_values()) == len(metainfo.values())
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# test_all_values
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assert len(instances.all_values()) == len(metainfo.values()) + len(
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data.values())
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# test_values
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assert len(instances.values()) == len(data.values())
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def test_keys(self):
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# test_metainfo_keys
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metainfo, data = self.setup_data()
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instances = BaseDataElement(metainfo=metainfo, **data)
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assert len(instances.metainfo_keys()) == len(metainfo.keys())
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# test_all_keys
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assert len(
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instances.all_keys()) == len(data.keys()) + len(metainfo.keys())
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# test_keys
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assert len(instances.keys()) == len(data.keys())
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det_sample = DetDataSample()
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proposals = BaseDataElement(bboxes=torch.rand((5, 4)))
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det_sample.proposals = proposals
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assert '_proposals' not in det_sample.keys()
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def test_items(self):
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# test_metainfo_items
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metainfo, data = self.setup_data()
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instances = BaseDataElement(metainfo=metainfo, **data)
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assert len(dict(instances.metainfo_items())) == len(
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dict(metainfo.items()))
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# test_all_items
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assert len(dict(instances.all_items())) == len(dict(
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metainfo.items())) + len(dict(data.items()))
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# test_items
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assert len(dict(instances.items())) == len(dict(data.items()))
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def test_to_dict(self):
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metainfo, data = self.setup_data()
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instances = BaseDataElement(metainfo=metainfo, **data)
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dict_instances = instances.to_dict()
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# test convert BaseDataElement to dict
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for k in instances.all_keys():
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# all keys in instances should be in dict_instances
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assert k in dict_instances
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assert isinstance(dict_instances, dict)
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# sub data element should also be converted to dict
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assert isinstance(dict_instances['gt_instances'], dict)
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assert isinstance(dict_instances['pred_instances'], dict)
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det_sample = DetDataSample()
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proposals = BaseDataElement(bboxes=torch.rand((5, 4)))
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det_sample.proposals = proposals
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dict_sample = det_sample.to_dict()
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assert '_proposals' not in dict_sample
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assert 'proposals' in dict_sample
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def test_metainfo(self):
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# test metainfo property
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metainfo, data = self.setup_data()
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instances = BaseDataElement(metainfo=metainfo, **data)
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self.assertDictEqual(instances.metainfo, metainfo)
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