From 41e1191cbc47f84a0598732baa9ea1c32e315cc7 Mon Sep 17 00:00:00 2001
From: Wenwei Zhang <40779233+ZwwWayne@users.noreply.github.com>
Date: Wed, 16 Feb 2022 23:19:18 +0800
Subject: [PATCH] add unit tests of data abstract interface (#21)
* add unit tests of data abstract interface
* update
* update
* update docs of data element
* a draft of UT of datasample, to be finished
* update datasample test
* updata
* update
* fix comments
* fix comments
* fix comments
Co-authored-by: liukuikun <liukuikun@sensetime.com>
---
.../tutorials/abstract_data_interface.md | 11 +-
tests/test_data/test_data_element.py | 204 ++++++++++++
tests/test_data/test_data_sample.py | 298 ++++++++++++++++++
3 files changed, 508 insertions(+), 5 deletions(-)
create mode 100644 tests/test_data/test_data_element.py
create mode 100644 tests/test_data/test_data_sample.py
diff --git a/docs/zh_cn/tutorials/abstract_data_interface.md b/docs/zh_cn/tutorials/abstract_data_interface.md
index ce85d7d7..6f2e2905 100644
--- a/docs/zh_cn/tutorials/abstract_data_interface.md
+++ b/docs/zh_cn/tutorials/abstract_data_interface.md
@@ -113,13 +113,13 @@ gt_instances2 = gt_instances1.new()
gt_instances = BaseDataElement()
# 设置 gt_instances çš„ meta å—段，img_id å’Œ img_shape 会被作为 metainfo çš„å—段æˆä¸º gt_instances 的属性
gt_instances.set_metainfo(dict(img_id=9, img_shape=(100, 100))
-assert 'img_shape' in gt_instaces.metainfo_keys()
+assert 'img_shape' in gt_instances.metainfo_keys()
# 'img_shape' 是 gt_instances 的属性
-assert 'img_shape' in gt_instaces
+assert 'img_shape' in gt_instances
# img_shape ä¸æ˜¯ gt_instances çš„ data å—段
-assert 'img_shape' not in gt_instaces.data_keys()
+assert 'img_shape' not in gt_instances.data_keys()
# 通过 keys æ¥è®¿é—®æ‰€æœ‰å±žæ€§
-assert 'img_shape' in gt_instaces.keys()
+assert 'img_shape' in gt_instances.keys()
# è®¿é—®ç±»å±žæ€§ä¸€æ ·è®¿é—® 'img_shape'
print(gt_instances.img_shape)
@@ -179,7 +179,7 @@ gt_instances.get('bboxes', None) # 6x4 tensor
# å±žæ€§çš„åˆ é™¤
del gt_instances.img_shape
del gt_instances.bboxes
-assert 'img_shape' in gt_instances
+assert 'img_shape' not in gt_instances
assert 'bboxes' not in gt_instances
# æ供了便æ·çš„å±žæ€§åˆ é™¤å’Œè®¿é—®æ“作 pop
@@ -191,6 +191,7 @@ gt_instances.pop('bboxes', None) # None
用户å¯ä»¥åƒ torch.Tensor é‚£æ ·å¯¹ `BaseDataElement` çš„ data 进行状æ€è½¬æ¢ï¼Œç›®å‰æ”¯æŒ `cuda`， `cpu`， `to`， `numpy` ç‰æ“作。
å…¶ä¸ï¼Œ`to` 函数拥有和 `torch.Tensor.to()` 相åŒçš„接å£ï¼Œä½¿å¾—用户å¯ä»¥çµæ´»åœ°å°†è¢«å°è£…çš„ tensor 进行状æ€è½¬æ¢ã€‚
+**注æ„：** 这些接å£åªä¼šå¤„ç†ç±»åž‹ä¸º np.array，torch.Tensor，或者数å—çš„åºåˆ—，其他属性的数æ®ï¼ˆå¦‚å—符串）会被跳过处ç†ã€‚
```python
# 将所有 data 转移到 GPU 上
diff --git a/tests/test_data/test_data_element.py b/tests/test_data/test_data_element.py
new file mode 100644
index 00000000..4bf8c722
--- /dev/null
+++ b/tests/test_data/test_data_element.py
@@ -0,0 +1,204 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import random
+from unittest import TestCase
+
+import numpy as np
+import pytest
+import torch
+
+from mmengine.data import BaseDataElement
+
+
+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)))
+ data = dict(bboxes=torch.rand((5, 4)), scores=torch.rand((5, )))
+ 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
+
+ def check_data_dtype(self, instances, dtype):
+ for v in instances.data_values():
+ if isinstance(v, (torch.Tensor, np.ndarray)):
+ assert isinstance(v, dtype)
+
+ 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=data)
+ self.check_key_value(instances, metainfo, data)
+
+ # initialization with args
+ metainfo, data = self.setup_data()
+ instances = BaseDataElement(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=data)
+ self.check_key_value(instances, data=data)
+
+ def test_new(self):
+ metainfo, data = self.setup_data()
+ instances = BaseDataElement(metainfo=metainfo, data=data)
+
+ # test new() with no arguments
+ new_instances = instances.new()
+ assert type(new_instances) == type(instances)
+ assert id(new_instances.bboxes) != id(instances.bboxes)
+ assert id(new_instances.bboxes) != id(data['bboxes'])
+ 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.bboxes) != id(instances.bboxes)
+ assert id(new_instances.bboxes) != id(data['bboxes'])
+ self.check_key_value(new_instances, metainfo, data)
+
+ 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)
+
+ def test_set_data(self):
+ metainfo, data = self.setup_data()
+ instances = BaseDataElement()
+
+ instances.bboxes = data['bboxes']
+ instances.scores = data['scores']
+ 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, data)
+ with self.assertRaises(AssertionError):
+ instances.img_shape = metainfo['img_shape']
+
+ def test_delete_modify(self):
+ metainfo, data = self.setup_data()
+ instances = BaseDataElement(metainfo, data)
+
+ new_metainfo, new_data = self.setup_data()
+ instances.bboxes = new_data['bboxes']
+ instances.scores = new_data['scores']
+
+ # a.xx only set data rather than metainfo
+ instances.set_metainfo(new_metainfo)
+ self.check_key_value(instances, new_metainfo, new_data)
+
+ assert instances.bboxes != data['bboxes']
+ assert instances.scores != data['scores']
+ assert instances.img_id != metainfo['img_id']
+ assert instances.img_shape != metainfo['img_shape']
+
+ del instances.bboxes
+ assert instances.pop('scores', None) == new_data['scores']
+ with self.assertRaises(AttributeError):
+ del instances.scores
+
+ assert 'bboxes' not in instances
+ assert 'scores' not in instances
+ assert instances.pop('bboxes', None) is None
+ assert instances.pop('scores', 'abcdef') == 'abcdef'
+
+ @pytest.mark.skipif(
+ not torch.cuda.is_available(), reason='GPU is required!')
+ def test_cuda(self):
+ metainfo, data = self.setup_data()
+ instances = BaseDataElement(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 = BaseDataElement(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 = BaseDataElement(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))
+ instances = BaseDataElement(metainfo=metainfo)
+ instances.det_labels = torch.LongTensor([0, 1, 2, 3])
+ instances.det_scores = torch.Tensor([0.01, 0.1, 0.2, 0.3])
+ assert repr(instances) == ('<BaseDataElement(\n'
+ ' META INFORMATION\n'
+ 'img_shape: (800, 1196, 3)\n'
+ 'pad_shape: (800, 1216, 3)\n'
+ ' DATA FIELDS\n'
+ 'shape of det_labels: torch.Size([4])\n'
+ 'shape of det_scores: torch.Size([4])\n'
+ ') at 0x7f84acd10f90>')
diff --git a/tests/test_data/test_data_sample.py b/tests/test_data/test_data_sample.py
new file mode 100644
index 00000000..0d16073b
--- /dev/null
+++ b/tests/test_data/test_data_sample.py
@@ -0,0 +1,298 @@
+# 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))
--
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