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Commit 1aa10524 authored by VVsssssk's avatar VVsssssk Committed by ChaimZhu
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[Fix]Fix datasample problem

parent 0b147600
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Showing with 96 additions and 85 deletions
mmdet3d/core/__init__.py
View file @ 1aa10524
# Copyright (c) OpenMMLab. All rights reserved.
from .anchor import * # noqa: F401, F403
from .bbox import * # noqa: F401, F403
from .data_structers import * # noqa: F401, F403
from .data_structures import * # noqa: F401, F403
from .evaluation import * # noqa: F401, F403
from .points import * # noqa: F401, F403
from .post_processing import * # noqa: F401, F403
......
mmdet3d/core/data_structers/det3d_data_sample.py → mmdet3d/core/data_structures/det3d_data_sample.py
View file @ 1aa10524
# Copyright (c) OpenMMLab. All rights reserved.
# TODO: will use real PixelData once it is added in mmengine
from mmengine.data import BaseDataElement
from mmengine.data import BaseDataElement as PixelData
from mmengine.data import InstanceData
from mmengine.data import BaseDataElement, InstanceData, PixelData
class Det3DDataSample(BaseDataElement):
......@@ -28,91 +25,105 @@ class Det3DDataSample(BaseDataElement):
- ``pred_pts_panoptic_seg``(PixelData): Predicted of point cloud
panoptic segmentation.
Examples:
>>> import torch
>>> import numpy as np
>>> from mmengine.data import InstanceData
# TODO: will use real PixelData once it is added in mmengine
>>> from mmengine.data import BaseDataElement as PixelData
>>> from mmdet3d.core import Det3DDataSample
>>> data_sample = Det3DDataSample()
>>> img_meta = dict(img_shape=(800, 1196, 3),
... pad_shape=(800, 1216, 3))
>>> gt_instances_3d = InstanceData(metainfo=img_meta)
>>> gt_instances_3d.bboxes = torch.rand((5, 4))
>>> gt_instances_3d.labels = torch.rand((5,))
>>> data_sample.gt_instances_3d = gt_instances_3d
>>> assert 'img_shape' in data_sample.gt_instances_3d.metainfo_keys()
>>> len(data_sample.gt_instances_3d)
5
>>> print(data_sample)
<Det3DDataSample(
>>> from mmengine.data import InstanceData, PixelData
>>> from mmdet3d.core import Det3DDataSample
>>> from mmdet3d.core.bbox import BaseInstance3DBoxes
>>> data_sample = Det3DDataSample()
>>> meta_info = dict(img_shape=(800, 1196, 3),
... pad_shape=(800, 1216, 3))
>>> gt_instances_3d = InstanceData(metainfo=meta_info)
>>> gt_instances_3d.bboxes = BaseInstance3DBoxes(torch.rand((5, 7)))
>>> gt_instances_3d.labels = torch.randint(0,3,(5, ))
>>> data_sample.gt_instances_3d = gt_instances_3d
>>> assert 'img_shape' in data_sample.gt_instances_3d.metainfo_keys()
>>> print(data_sample)
<Det3DDataSample(
META INFORMATION
DATA FIELDS
_gt_instances_3d: <InstanceData(
META INFORMATION
pad_shape: (800, 1216, 3)
img_shape: (800, 1196, 3)
DATA FIELDS
gt_instances_3d: <InstanceData(
META INFORMATION
pad_shape: (800, 1216, 3)
img_shape: (800, 1196, 3)
DATA FIELDS
bboxes: tensor([[0.4247, 0.9994, 0.3259, 0.7683],
[0.4324, 0.6514, 0.9889, 0.7974],
[0.0928, 0.0344, 0.9114, 0.2769],
[0.2408, 0.8446, 0.5631, 0.2750],
[0.5813, 0.9661, 0.6281, 0.9755]])
labels: tensor([0.7416, 0.3896, 0.9580, 0.6292, 0.3588])
) at 0x7f43a23c7460>
) at 0x7f43a23c7fa0>
>>> pred_instances_3d = InstanceData(metainfo=img_meta)
>>> pred_instances_3d.bboxes = torch.rand((5, 4))
>>> pred_instances_3d.scores = torch.rand((5,))
>>> data_sample = Det3DDataSample(pred_instances_3d=pred_instances_3d)
>>> assert 'pred_instances_3d' in data_sample
>>> data_sample = Det3DDataSample()
>>> gt_instances_3d_data = dict(
... bboxes=torch.rand(2, 4),
... labels=torch.rand(2),
... masks=np.random.rand(2, 2, 2))
>>> gt_instances_3d = InstanceData(**gt_instances_3d_data)
>>> data_sample.gt_instances_3d = gt_instances_3d
>>> assert 'gt_instances_3d' in data_sample
>>> assert 'masks' in data_sample.gt_instances_3d
>>> data_sample = Det3DDataSample()
>>> gt_pts_panoptic_seg_data = dict(panoptic_seg=torch.rand(2, 4))
>>> gt_pts_panoptic_seg = PixelData(**gt_pts_panoptic_seg_data)
>>> data_sample.gt_pts_panoptic_seg = gt_pts_panoptic_seg
>>> print(data_sample)
<Det3DDataSample(
labels: tensor([0, 0, 1, 0, 2])
bboxes: BaseInstance3DBoxes(
tensor([[0.2874, 0.3078, 0.8368, 0.2326, 0.9845, 0.6199, 0.9944],
[0.6222, 0.8778, 0.7306, 0.3320, 0.3973, 0.7662, 0.7326],
[0.8547, 0.6082, 0.1660, 0.1676, 0.9810, 0.3092, 0.0917],
[0.4686, 0.7007, 0.4428, 0.0672, 0.3319, 0.3033, 0.8519],
[0.9693, 0.5315, 0.4642, 0.9079, 0.2481, 0.1781, 0.9557]]))
) at 0x7fb0d9354280>
gt_instances_3d: <InstanceData(
META INFORMATION
pad_shape: (800, 1216, 3)
img_shape: (800, 1196, 3)
DATA FIELDS
_gt_pts_panoptic_seg: <BaseDataElement(
META INFORMATION
DATA FIELDS
panoptic_seg: tensor([[0.4109, 0.1415, 0.8463, 0.9587],
[0.3188, 0.3690, 0.1366, 0.3860]])
) at 0x7f43a23d5700>
gt_pts_panoptic_seg: <BaseDataElement(
META INFORMATION
DATA FIELDS
panoptic_seg: tensor([[0.4109, 0.1415, 0.8463, 0.9587],
[0.3188, 0.3690, 0.1366, 0.3860]])
) at 0x7f43a23d5700>
) at 0x7f44ee39b160>
>>> data_sample = Det3DDataSample()
>>> gt_pts_sem_seg_data = dict(segm_seg=torch.rand(2, 2, 2))
>>> gt_pts_sem_seg = PixelData(**gt_pts_sem_seg_data)
>>> data_sample.gt_pts_sem_seg = gt_pts_sem_seg
>>> assert 'gt_pts_sem_seg' in data_sample
>>> assert 'segm_seg' in data_sample.gt_pts_sem_seg
labels: tensor([0, 0, 1, 0, 2])
bboxes: BaseInstance3DBoxes(
tensor([[0.2874, 0.3078, 0.8368, 0.2326, 0.9845, 0.6199, 0.9944],
[0.6222, 0.8778, 0.7306, 0.3320, 0.3973, 0.7662, 0.7326],
[0.8547, 0.6082, 0.1660, 0.1676, 0.9810, 0.3092, 0.0917],
[0.4686, 0.7007, 0.4428, 0.0672, 0.3319, 0.3033, 0.8519],
[0.9693, 0.5315, 0.4642, 0.9079, 0.2481, 0.1781, 0.9557]]))
) at 0x7fb0d9354280>
) at 0x7fb0d93543d0>
>>> pred_instances_3d = InstanceData(metainfo=meta_info)
>>> pred_instances_3d.bboxes = BaseInstance3DBoxes(torch.rand((5, 7)))
>>> pred_instances_3d.scores = torch.rand((5, ))
>>> data_sample = Det3DDataSample(pred_instances_3d=pred_instances_3d)
>>> assert 'pred_instances_3d' in data_sample
>>> data_sample = Det3DDataSample()
>>> gt_instances_3d_data = dict(
... bboxes=BaseInstance3DBoxes(torch.rand((2, 7))),
... labels=torch.rand(2))
>>> gt_instances_3d = InstanceData(**gt_instances_3d_data)
>>> data_sample.gt_instances_3d = gt_instances_3d
>>> assert 'gt_instances_3d' in data_sample
>>> assert 'bboxes' in data_sample.gt_instances_3d
>>> data_sample = Det3DDataSample()
>>> gt_pts_panoptic_seg_data = dict(panoptic_seg=torch.rand(1, 2, 4))
>>> gt_pts_panoptic_seg = PixelData(**gt_pts_panoptic_seg_data)
>>> data_sample.gt_pts_panoptic_seg = gt_pts_panoptic_seg
>>> print(data_sample)
<Det3DDataSample(
META INFORMATION
DATA FIELDS
_gt_pts_panoptic_seg: <PixelData(
META INFORMATION
DATA FIELDS
panoptic_seg: tensor([[[0.9875, 0.3012, 0.5534, 0.9593],
[0.1251, 0.1911, 0.8058, 0.2566]]])
) at 0x7fb0d93543d0>
gt_pts_panoptic_seg: <PixelData(
META INFORMATION
DATA FIELDS
panoptic_seg: tensor([[[0.9875, 0.3012, 0.5534, 0.9593],
[0.1251, 0.1911, 0.8058, 0.2566]]])
) at 0x7fb0d93543d0>
) at 0x7fb0d9354280>
>>> data_sample = Det3DDataSample()
>>> gt_pts_sem_seg_data = dict(segm_seg=torch.rand(2, 2, 2))
>>> gt_pts_sem_seg = PixelData(**gt_pts_sem_seg_data)
>>> data_sample.gt_pts_sem_seg = gt_pts_sem_seg
>>> assert 'gt_pts_sem_seg' in data_sample
>>> assert 'segm_seg' in data_sample.gt_pts_sem_seg
"""
@property
......
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