From 11b38b12d64bfdcc58de30d357ef51038b85d0aa Mon Sep 17 00:00:00 2001
From: liukuikun <24622904+Harold-lkk@users.noreply.github.com>
Date: Sat, 5 Mar 2022 15:02:54 +0800
Subject: [PATCH] [Fix] fix random may generate same value (#85)
---
mmengine/data/base_data_element.py | 71 ++++++++---------
mmengine/data/base_data_sample.py | 119 +++++++++++++---------------
tests/test_data/test_data_sample.py | 8 ++
3 files changed, 99 insertions(+), 99 deletions(-)
diff --git a/mmengine/data/base_data_element.py b/mmengine/data/base_data_element.py
index 5ab96f6f..ac5870bc 100644
--- a/mmengine/data/base_data_element.py
+++ b/mmengine/data/base_data_element.py
@@ -25,27 +25,26 @@ class BaseDataElement:
types of ground truth labels or predictions.
They are used as interfaces between different commopenets.
-
The attributes in ``BaseDataElement`` are divided into two parts,
the ``metainfo`` and the ``data`` respectively.
- - ``metainfo``: Usually contains the
- information about the image such as filename,
- image_shape, pad_shape, etc. The attributes can be accessed or
- modified by dict-like or object-like operations, such as
- ``.``(for data access and modification) , ``in``, ``del``,
- ``pop(str)``, ``get(str)``, ``metainfo_keys()``,
- ``metainfo_values()``, ``metainfo_items()``, ``set_metainfo()``(for
- set or change key-value pairs in metainfo).
-
- - ``data``: Annotations or model predictions are
- stored. The attributes can be accessed or modified by
- dict-like or object-like operations, such as
- ``.`` , ``in``, ``del``, ``pop(str)`` ``get(str)``, ``data_keys()``,
- ``data_values()``, ``data_items()``. Users can also apply tensor-like
- methods to all obj:``torch.Tensor`` in the ``data_fileds``,
- such as ``.cuda()``, ``.cpu()``, ``.numpy()``, , ``.to()``
- ``to_tensor()``, ``.detach()``, ``.numpy()``
+ - ``metainfo``: Usually contains the
+ information about the image such as filename,
+ image_shape, pad_shape, etc. The attributes can be accessed or
+ modified by dict-like or object-like operations, such as
+ ``.``(for data access and modification) , ``in``, ``del``,
+ ``pop(str)``, ``get(str)``, ``metainfo_keys()``,
+ ``metainfo_values()``, ``metainfo_items()``, ``set_metainfo()``(for
+ set or change key-value pairs in metainfo).
+
+ - ``data``: Annotations or model predictions are
+ stored. The attributes can be accessed or modified by
+ dict-like or object-like operations, such as
+ ``.`` , ``in``, ``del``, ``pop(str)`` ``get(str)``, ``data_keys()``,
+ ``data_values()``, ``data_items()``. Users can also apply tensor-like
+ methods to all obj:``torch.Tensor`` in the ``data_fileds``,
+ such as ``.cuda()``, ``.cpu()``, ``.numpy()``, , ``.to()``
+ ``to_tensor()``, ``.detach()``, ``.numpy()``
Args:
meta_info (dict, optional): A dict contains the meta information
@@ -57,24 +56,24 @@ class BaseDataElement:
Examples:
>>> from mmengine.data import BaseDataElement
>>> gt_instances = BaseDataElement()
-
>>> bboxes = torch.rand((5, 4))
>>> scores = torch.rand((5,))
>>> img_id = 0
>>> img_shape = (800, 1333)
>>> gt_instances = BaseDataElement(
- metainfo=dict(img_id=img_id, img_shape=img_shape),
- data=dict(bboxes=bboxes, scores=scores))
+ ... metainfo=dict(img_id=img_id, img_shape=img_shape),
+ ... data=dict(bboxes=bboxes, scores=scores))
>>> gt_instances = BaseDataElement(dict(img_id=img_id,
- img_shape=(H, W)))
- # new
+ ... img_shape=(H, W)))
+
+ >>> # new
>>> gt_instances1 = gt_instance.new(
- metainfo=dict(img_id=1, img_shape=(640, 640)),
- data=dict(bboxes=torch.rand((5, 4)),
- scores=torch.rand((5,))))
+ ... metainfo=dict(img_id=1, img_shape=(640, 640)),
+ ... data=dict(bboxes=torch.rand((5, 4)),
+ ... scores=torch.rand((5,))))
>>> gt_instances2 = gt_instances1.new()
- # add and process property
+ >>> # add and process property
>>> gt_instances = BaseDataElement()
>>> gt_instances.set_metainfo(dict(img_id=9, img_shape=(100, 100))
>>> assert 'img_shape' in gt_instances.metainfo_keys()
@@ -82,14 +81,12 @@ class BaseDataElement:
>>> assert 'img_shape' not in gt_instances.data_keys()
>>> assert 'img_shape' in gt_instances.keys()
>>> print(gt_instances.img_shape)
-
>>> gt_instances.scores = torch.rand((5,))
>>> assert 'scores' in gt_instances.data_keys()
>>> assert 'scores' in gt_instances
>>> assert 'scores' in gt_instances.keys()
>>> assert 'scores' not in gt_instances.metainfo_keys()
>>> print(gt_instances.scores)
-
>>> gt_instances.bboxes = torch.rand((5, 4))
>>> assert 'bboxes' in gt_instances.data_keys()
>>> assert 'bboxes' in gt_instances
@@ -97,14 +94,14 @@ class BaseDataElement:
>>> assert 'bboxes' not in gt_instances.metainfo_keys()
>>> print(gt_instances.bboxes)
- # delete and change property
+ >>> # delete and change property
>>> gt_instances = BaseDataElement(
- metainfo=dict(img_id=0, img_shape=(640, 640))，
- data=dict(bboxes=torch.rand((6, 4)), scores=torch.rand((6,))))
+ ... metainfo=dict(img_id=0, img_shape=(640, 640)),
+ ... data=dict(bboxes=torch.rand((6, 4)), scores=torch.rand((6,))))
>>> gt_instances.img_shape = (1280, 1280)
>>> gt_instances.img_shape # (1280, 1280)
>>> gt_instances.bboxes = gt_instances.bboxes * 2
- >>> gt_instances.get('img_shape', None) # (640， 640)
+ >>> gt_instances.get('img_shape', None) # (640, 640)
>>> gt_instances.get('bboxes', None) # 6x4 tensor
>>> del gt_instances.img_shape
>>> del gt_instances.bboxes
@@ -113,18 +110,18 @@ class BaseDataElement:
>>> gt_instances.pop('img_shape', None) # None
>>> gt_instances.pop('bboxes', None) # None
- # Tensor-like
+ >>> # Tensor-like
>>> cuda_instances = gt_instances.cuda()
>>> cuda_instances = gt_instancess.to('cuda:0')
>>> cpu_instances = cuda_instances.cpu()
>>> cpu_instances = cuda_instances.to('cpu')
>>> fp16_instances = cuda_instances.to(
- device=None, dtype=torch.float16, non_blocking=False, copy=False,
- memory_format=torch.preserve_format)
+ ... device=None, dtype=torch.float16, non_blocking=False, copy=False,
+ ... memory_format=torch.preserve_format)
>>> cpu_instances = cuda_instances.detach()
>>> np_instances = cpu_instances.numpy()
- # print
+ >>> # print
>>> img_meta = dict(img_shape=(800, 1196, 3), pad_shape=(800, 1216, 3))
>>> instance_data = BaseDataElement(metainfo=img_meta)
>>> instance_data.det_labels = torch.LongTensor([0, 1, 2, 3])
diff --git a/mmengine/data/base_data_sample.py b/mmengine/data/base_data_sample.py
index 4c6a2607..dd6fafd1 100644
--- a/mmengine/data/base_data_sample.py
+++ b/mmengine/data/base_data_sample.py
@@ -54,55 +54,52 @@ class BaseDataSample:
Examples:
>>> from mmengine.data import BaseDataElement, BaseDataSample
>>> gt_instances = BaseDataSample()
-
>>> bboxes = torch.rand((5, 4))
>>> scores = torch.rand((5,))
>>> img_id = 0
>>> img_shape = (800, 1333)
>>> gt_instances = BaseDataElement(
- metainfo=dict(img_id=img_id, img_shape=img_shape),
- data=dict(bboxes=bboxes, scores=scores))
+ ... metainfo=dict(img_id=img_id, img_shape=img_shape),
+ ... data=dict(bboxes=bboxes, scores=scores))
>>> data = dict(gt_instances=gt_instances)
>>> sample = BaseDataSample(
- metainfo=dict(img_id=img_id, img_shape=img_shape),
- data=data)
+ ... metainfo=dict(img_id=img_id, img_shape=img_shape),
+ ... data=data)
>>> sample = BaseDataSample(dict(img_id=img_id,
- img_shape=(H, W)))
- # new
+ ... img_shape=(H, W)))
+
+ >>> # new
>>> data1 = dict(bboxes=torch.rand((5, 4)),
scores=torch.rand((5,)))
>>> metainfo1 = dict(img_id=1, img_shape=(640, 640)),
>>> gt_instances1 = BaseDataElement(
- metainfo=metainfo1,
- data=data1)
+ ... metainfo=metainfo1,
+ ... data=data1)
>>> sample1 = sample.new(
- metainfo=metainfo1
- data=dict(gt_instances1=gt_instances1)),
-
+ ... metainfo=metainfo1
+ ... data=dict(gt_instances1=gt_instances1)),
>>> gt_instances2 = gt_instances1.new()
- # property add and access
+ >>> # property add and access
>>> sample = BaseDataSample()
>>> gt_instances = BaseDataElement(
- metainfo=dict(img_id=9, img_shape=(100, 100)),
- data=dict(bboxes=torch.rand((5, 4)), scores=torch.rand((5,)))
+ ... metainfo=dict(img_id=9, img_shape=(100, 100)),
+ ... data=dict(bboxes=torch.rand((5, 4)), scores=torch.rand((5,)))
>>> sample.set_metainfo(dict(img_id=9, img_shape=(100, 100))
>>> assert 'img_shape' in sample.metainfo_keys()
>>> assert 'img_shape' in sample
>>> assert 'img_shape' not in sample.data_keys()
>>> assert 'img_shape' in sample.keys()
>>> print(sample.img_shape)
-
>>> gt_instances.gt_instances = gt_instances
>>> assert 'gt_instances' in sample.data_keys()
>>> assert 'gt_instances' in sample
>>> assert 'gt_instances' in sample.keys()
>>> assert 'gt_instances' not in sample.metainfo_keys()
>>> print(sample.gt_instances)
-
>>> pred_instances = BaseDataElement(
- metainfo=dict(img_id=9, img_shape=(100, 100)),
- data=dict(bboxes=torch.rand((5, 4)), scores=torch.rand((5,))
+ ... metainfo=dict(img_id=9, img_shape=(100, 100)),
+ ... data=dict(bboxes=torch.rand((5, 4)), scores=torch.rand((5,))
>>> sample.pred_instances = pred_instances
>>> assert 'pred_instances' in sample.data_keys()
>>> assert 'pred_instances' in sample
@@ -110,17 +107,17 @@ class BaseDataSample:
>>> assert 'pred_instances' not in sample.metainfo_keys()
>>> print(sample.pred_instances)
- # property delete and change
+ >>> # property delete and change
>>> metainfo=dict(img_id=0, img_shape=(640, 640)
>>> gt_instances = BaseDataElement(
- metainfo=metainfo)，
- data=dict(bboxes=torch.rand((6, 4)), scores=torch.rand((6,))))
+ ... metainfo=metainfo),
+ ... data=dict(bboxes=torch.rand((6, 4)), scores=torch.rand((6,))))
>>> sample = BaseDataSample(metainfo=metainfo,
- data=dict(gt_instances=gt_instances))
+ ... data=dict(gt_instances=gt_instances))
>>> sample.img_shape = (1280, 1280)
>>> sample.img_shape # (1280, 1280)
>>> sample.gt_instances = gt_instances
- >>> sample.get('img_shape', None) # (640， 640)
+ >>> sample.get('img_shape', None) # (640, 640)
>>> sample.get('gt_instances', None)
>>> del sample.img_shape
>>> del sample.gt_instances
@@ -129,23 +126,22 @@ class BaseDataSample:
>>> sample.pop('img_shape', None) # None
>>> sample.pop('gt_instances', None) # None
- # Tensor-like
+ >>> # Tensor-like
>>> cuda_sample = gt_instasamplences.cuda()
>>> cuda_sample = gt_sample.to('cuda:0')
>>> cpu_sample = cuda_sample.cpu()
>>> cpu_sample = cuda_sample.to('cpu')
>>> fp16_sample = cuda_sample.to(
- device=None, dtype=torch.float16, non_blocking=False, copy=False,
- memory_format=torch.preserve_format)
+ ... device=None, dtype=torch.float16, non_blocking=False, copy=False,
+ ... memory_format=torch.preserve_format)
>>> cpu_sample = cuda_sample.detach()
>>> np_sample = cpu_sample.numpy()
- # print
+ >>> # print
>>> metainfo = dict(img_shape=(800, 1196, 3))
>>> gt_instances = BaseDataElement(
- metainfo=metainfo,
- data=dict(det_labels=torch.LongTensor([0, 1, 2, 3])))
-
+ ... metainfo=metainfo,
+ ... data=dict(det_labels=torch.LongTensor([0, 1, 2, 3])))
>>> data = dict(gt_instances=gt_instances)
>>> sample = BaseDataSample(metainfo=metainfo, data=data)
>>> print(sample)
@@ -161,37 +157,36 @@ class BaseDataSample:
) at 0x7f9705daecd0>'
) at 0x7f981e41c550>'
- # inheritance
+ >>> # inheritance
>>> 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')
-
+ ... 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()
>>> proposals = BaseDataElement(data=dict(bboxes=torch.rand((5, 4))))
>>> det_sample.proposals = proposals
@@ -200,7 +195,7 @@ class BaseDataSample:
>>> del det_sample.proposals
>>> assert 'proposals' not in det_sample
>>> with self.assertRaises(AssertionError):
- det_sample.proposals = torch.rand((5, 4))
+ ... det_sample.proposals = torch.rand((5, 4))
"""
def __init__(self,
diff --git a/tests/test_data/test_data_sample.py b/tests/test_data/test_data_sample.py
index cc940073..e03508fb 100644
--- a/tests/test_data/test_data_sample.py
+++ b/tests/test_data/test_data_sample.py
@@ -185,6 +185,14 @@ class TestBaseDataSample(TestCase):
instances = BaseDataSample(metainfo, data)
new_metainfo, new_data = self.setup_data()
+ # avoid generating same metainfo
+ 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']
--
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