| _base_ = [ | |
| '../_base_/datasets/semantickitti.py', '../_base_/models/spvcnn.py', | |
| '../_base_/default_runtime.py' | |
| ] | |
| train_pipeline = [ | |
| dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4), | |
| dict( | |
| type='LoadAnnotations3D', | |
| with_bbox_3d=False, | |
| with_label_3d=False, | |
| with_seg_3d=True, | |
| seg_3d_dtype='np.int32', | |
| seg_offset=2**16, | |
| dataset_type='semantickitti'), | |
| dict(type='PointSegClassMapping'), | |
| dict( | |
| type='GlobalRotScaleTrans', | |
| rot_range=[0., 6.28318531], | |
| scale_ratio_range=[0.95, 1.05], | |
| translation_std=[0, 0, 0], | |
| ), | |
| dict(type='Pack3DDetInputs', keys=['points', 'pts_semantic_mask']) | |
| ] | |
| train_dataloader = dict( | |
| sampler=dict(seed=0), dataset=dict(pipeline=train_pipeline)) | |
| lr = 0.24 | |
| optim_wrapper = dict( | |
| type='AmpOptimWrapper', | |
| loss_scale='dynamic', | |
| optimizer=dict( | |
| type='SGD', lr=lr, weight_decay=0.0001, momentum=0.9, nesterov=True)) | |
| param_scheduler = [ | |
| dict( | |
| type='LinearLR', start_factor=0.008, by_epoch=False, begin=0, end=125), | |
| dict( | |
| type='CosineAnnealingLR', | |
| begin=0, | |
| T_max=15, | |
| by_epoch=True, | |
| eta_min=1e-5, | |
| convert_to_iter_based=True) | |
| ] | |
| train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=15, val_interval=1) | |
| val_cfg = dict(type='ValLoop') | |
| test_cfg = dict(type='TestLoop') | |
| default_hooks = dict(checkpoint=dict(type='CheckpointHook', interval=1)) | |
| randomness = dict(seed=0, deterministic=False, diff_rank_seed=True) | |
| env_cfg = dict(cudnn_benchmark=True) | |