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import copy |
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import random |
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from os.path import dirname, exists, join |
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import numpy as np |
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import torch |
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from mmengine.structures import InstanceData |
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from mmdet3d.structures import (CameraInstance3DBoxes, DepthInstance3DBoxes, |
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Det3DDataSample, LiDARInstance3DBoxes, |
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PointData) |
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def setup_seed(seed): |
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torch.manual_seed(seed) |
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torch.cuda.manual_seed_all(seed) |
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np.random.seed(seed) |
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random.seed(seed) |
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torch.backends.cudnn.deterministic = True |
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def _get_config_directory(): |
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"""Find the predefined detector config directory.""" |
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try: |
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repo_dpath = dirname(dirname(dirname(__file__))) |
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except NameError: |
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import mmdet3d |
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repo_dpath = dirname(dirname(mmdet3d.__file__)) |
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config_dpath = join(repo_dpath, 'configs') |
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if not exists(config_dpath): |
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raise Exception('Cannot find config path') |
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return config_dpath |
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def _get_config_module(fname): |
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"""Load a configuration as a python module.""" |
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from mmengine import Config |
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config_dpath = _get_config_directory() |
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config_fpath = join(config_dpath, fname) |
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config_mod = Config.fromfile(config_fpath) |
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return config_mod |
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def get_model_cfg(fname): |
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"""Grab configs necessary to create a model. |
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These are deep copied to allow for safe modification of parameters without |
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influencing other tests. |
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""" |
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config = _get_config_module(fname) |
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model = copy.deepcopy(config.model) |
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return model |
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def get_detector_cfg(fname): |
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"""Grab configs necessary to create a detector. |
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These are deep copied to allow for safe modification of parameters without |
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influencing other tests. |
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""" |
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import mmengine |
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config = _get_config_module(fname) |
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model = copy.deepcopy(config.model) |
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train_cfg = mmengine.Config(copy.deepcopy(config.model.train_cfg)) |
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test_cfg = mmengine.Config(copy.deepcopy(config.model.test_cfg)) |
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model.update(train_cfg=train_cfg) |
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model.update(test_cfg=test_cfg) |
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return model |
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def create_detector_inputs(seed=0, |
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with_points=True, |
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with_img=False, |
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img_size=10, |
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num_gt_instance=20, |
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num_points=10, |
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points_feat_dim=4, |
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num_classes=3, |
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gt_bboxes_dim=7, |
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with_pts_semantic_mask=False, |
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with_pts_instance_mask=False, |
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with_eval_ann_info=False, |
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bboxes_3d_type='lidar'): |
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setup_seed(seed) |
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assert bboxes_3d_type in ('lidar', 'depth', 'cam') |
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bbox_3d_class = { |
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'lidar': LiDARInstance3DBoxes, |
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'depth': DepthInstance3DBoxes, |
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'cam': CameraInstance3DBoxes |
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} |
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meta_info = dict() |
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meta_info['depth2img'] = np.array( |
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[[5.23289349e+02, 3.68831943e+02, 6.10469439e+01], |
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[1.09560138e+02, 1.97404735e+02, -5.47377738e+02], |
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[1.25930002e-02, 9.92229998e-01, -1.23769999e-01]]) |
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meta_info['lidar2img'] = np.array( |
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[[5.23289349e+02, 3.68831943e+02, 6.10469439e+01], |
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[1.09560138e+02, 1.97404735e+02, -5.47377738e+02], |
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[1.25930002e-02, 9.92229998e-01, -1.23769999e-01]]) |
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inputs_dict = dict() |
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if with_points: |
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points = torch.rand([num_points, points_feat_dim]) |
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inputs_dict['points'] = [points] |
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if with_img: |
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if isinstance(img_size, tuple): |
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img = torch.rand(3, img_size[0], img_size[1]) |
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meta_info['img_shape'] = img_size |
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meta_info['ori_shape'] = img_size |
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else: |
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img = torch.rand(3, img_size, img_size) |
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meta_info['img_shape'] = (img_size, img_size) |
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meta_info['ori_shape'] = (img_size, img_size) |
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meta_info['scale_factor'] = np.array([1., 1.]) |
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inputs_dict['img'] = [img] |
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gt_instance_3d = InstanceData() |
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gt_instance_3d.bboxes_3d = bbox_3d_class[bboxes_3d_type]( |
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torch.rand([num_gt_instance, gt_bboxes_dim]), box_dim=gt_bboxes_dim) |
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gt_instance_3d.labels_3d = torch.randint(0, num_classes, [num_gt_instance]) |
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data_sample = Det3DDataSample( |
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metainfo=dict(box_type_3d=bbox_3d_class[bboxes_3d_type])) |
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data_sample.set_metainfo(meta_info) |
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data_sample.gt_instances_3d = gt_instance_3d |
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gt_instance = InstanceData() |
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gt_instance.labels = torch.randint(0, num_classes, [num_gt_instance]) |
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gt_instance.bboxes = torch.rand(num_gt_instance, 4) |
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gt_instance.bboxes[:, |
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2:] = gt_instance.bboxes[:, :2] + gt_instance.bboxes[:, |
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2:] |
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data_sample.gt_instances = gt_instance |
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data_sample.gt_pts_seg = PointData() |
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if with_pts_instance_mask: |
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pts_instance_mask = torch.randint(0, num_gt_instance, [num_points]) |
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data_sample.gt_pts_seg['pts_instance_mask'] = pts_instance_mask |
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if with_pts_semantic_mask: |
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pts_semantic_mask = torch.randint(0, num_classes, [num_points]) |
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data_sample.gt_pts_seg['pts_semantic_mask'] = pts_semantic_mask |
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if with_eval_ann_info: |
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data_sample.eval_ann_info = dict() |
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else: |
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data_sample.eval_ann_info = None |
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return dict(inputs=inputs_dict, data_samples=[data_sample]) |
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