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fix doc typos, a new nuscenes model with 1 nds better result
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configs/nusc/voxelnet/nusc_centerpoint_voxelnet_0075voxel_fix_bn_z.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,232 @@ | ||
| import itertools | ||
| import logging | ||
|
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| from det3d.utils.config_tool import get_downsample_factor | ||
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| tasks = [ | ||
| dict(num_class=1, class_names=["car"]), | ||
| dict(num_class=2, class_names=["truck", "construction_vehicle"]), | ||
| dict(num_class=2, class_names=["bus", "trailer"]), | ||
| dict(num_class=1, class_names=["barrier"]), | ||
| dict(num_class=2, class_names=["motorcycle", "bicycle"]), | ||
| dict(num_class=2, class_names=["pedestrian", "traffic_cone"]), | ||
| ] | ||
|
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| class_names = list(itertools.chain(*[t["class_names"] for t in tasks])) | ||
|
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| # training and testing settings | ||
| target_assigner = dict( | ||
| tasks=tasks, | ||
| ) | ||
|
|
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| # model settings | ||
| model = dict( | ||
| type="VoxelNet", | ||
| pretrained=None, | ||
| reader=dict( | ||
| type="VoxelFeatureExtractorV3", | ||
| # type='SimpleVoxel', | ||
| num_input_features=5, | ||
| ), | ||
| backbone=dict( | ||
| type="SpMiddleResNetFHD", num_input_features=5, ds_factor=8 | ||
| ), | ||
| neck=dict( | ||
| type="RPN", | ||
| layer_nums=[5, 5], | ||
| ds_layer_strides=[1, 2], | ||
| ds_num_filters=[128, 256], | ||
| us_layer_strides=[1, 2], | ||
| us_num_filters=[256, 256], | ||
| num_input_features=256, | ||
| logger=logging.getLogger("RPN"), | ||
| ), | ||
| bbox_head=dict( | ||
| type="CenterHead", | ||
| in_channels=sum([256, 256]), | ||
| tasks=tasks, | ||
| dataset='nuscenes', | ||
| weight=0.25, | ||
| code_weights=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.2, 0.2, 1.0, 1.0], | ||
| common_heads={'reg': (2, 2), 'height': (1, 2), 'dim':(3, 2), 'rot':(2, 2), 'vel': (2, 2)}, | ||
| share_conv_channel=64, | ||
| dcn_head=False | ||
| ), | ||
| ) | ||
|
|
||
| assigner = dict( | ||
| target_assigner=target_assigner, | ||
| out_size_factor=get_downsample_factor(model), | ||
| dense_reg=1, | ||
| gaussian_overlap=0.1, | ||
| max_objs=500, | ||
| min_radius=2, | ||
| ) | ||
|
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||
|
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| train_cfg = dict(assigner=assigner) | ||
|
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| test_cfg = dict( | ||
| post_center_limit_range=[-61.2, -61.2, -10.0, 61.2, 61.2, 10.0], | ||
| max_per_img=500, | ||
| nms=dict( | ||
| use_rotate_nms=True, | ||
| use_multi_class_nms=False, | ||
| nms_pre_max_size=1000, | ||
| nms_post_max_size=83, | ||
| nms_iou_threshold=0.2, | ||
| ), | ||
| score_threshold=0.1, | ||
| pc_range=[-54, -54], | ||
| out_size_factor=get_downsample_factor(model), | ||
| voxel_size=[0.075, 0.075] | ||
| ) | ||
|
|
||
| # dataset settings | ||
| dataset_type = "NuScenesDataset" | ||
| nsweeps = 10 | ||
| data_root = "data/nuScenes" | ||
|
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| db_sampler = dict( | ||
| type="GT-AUG", | ||
| enable=False, | ||
| db_info_path="data/nuScenes/dbinfos_train_10sweeps_withvelo.pkl", | ||
| sample_groups=[ | ||
| dict(car=2), | ||
| dict(truck=3), | ||
| dict(construction_vehicle=7), | ||
| dict(bus=4), | ||
| dict(trailer=6), | ||
| dict(barrier=2), | ||
| dict(motorcycle=6), | ||
| dict(bicycle=6), | ||
| dict(pedestrian=2), | ||
| dict(traffic_cone=2), | ||
| ], | ||
| db_prep_steps=[ | ||
| dict( | ||
| filter_by_min_num_points=dict( | ||
| car=5, | ||
| truck=5, | ||
| bus=5, | ||
| trailer=5, | ||
| construction_vehicle=5, | ||
| traffic_cone=5, | ||
| barrier=5, | ||
| motorcycle=5, | ||
| bicycle=5, | ||
| pedestrian=5, | ||
| ) | ||
| ), | ||
| dict(filter_by_difficulty=[-1],), | ||
| ], | ||
| global_random_rotation_range_per_object=[0, 0], | ||
| rate=1.0, | ||
| ) | ||
| train_preprocessor = dict( | ||
| mode="train", | ||
| shuffle_points=True, | ||
| global_rot_noise=[-0.78539816, 0.78539816], | ||
| global_scale_noise=[0.95, 1.05], | ||
| global_translate_std=0.5, | ||
| db_sampler=db_sampler, | ||
| class_names=class_names, | ||
| ) | ||
|
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||
| val_preprocessor = dict( | ||
| mode="val", | ||
| shuffle_points=False, | ||
| ) | ||
|
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| voxel_generator = dict( | ||
| range=[-54, -54, -5.0, 54, 54, 3.0], | ||
| voxel_size=[0.075, 0.075, 0.2], | ||
| max_points_in_voxel=10, | ||
| max_voxel_num=[120000, 160000], | ||
| ) | ||
|
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||
| train_pipeline = [ | ||
| dict(type="LoadPointCloudFromFile", dataset=dataset_type), | ||
| dict(type="LoadPointCloudAnnotations", with_bbox=True), | ||
| dict(type="Preprocess", cfg=train_preprocessor), | ||
| dict(type="Voxelization", cfg=voxel_generator), | ||
| dict(type="AssignLabel", cfg=train_cfg["assigner"]), | ||
| dict(type="Reformat"), | ||
| # dict(type='PointCloudCollect', keys=['points', 'voxels', 'annotations', 'calib']), | ||
| ] | ||
| test_pipeline = [ | ||
| dict(type="LoadPointCloudFromFile", dataset=dataset_type), | ||
| dict(type="LoadPointCloudAnnotations", with_bbox=True), | ||
| dict(type="Preprocess", cfg=val_preprocessor), | ||
| dict(type="Voxelization", cfg=voxel_generator), | ||
| dict(type="AssignLabel", cfg=train_cfg["assigner"]), | ||
| dict(type="Reformat"), | ||
| ] | ||
|
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| train_anno = "data/nuScenes/infos_train_10sweeps_withvelo_filter_True.pkl" | ||
| val_anno = "data/nuScenes/infos_val_10sweeps_withvelo_filter_True.pkl" | ||
| test_anno = None | ||
|
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| data = dict( | ||
| samples_per_gpu=4, | ||
| workers_per_gpu=6, | ||
| train=dict( | ||
| type=dataset_type, | ||
| root_path=data_root, | ||
| info_path=train_anno, | ||
| ann_file=train_anno, | ||
| nsweeps=nsweeps, | ||
| class_names=class_names, | ||
| pipeline=train_pipeline, | ||
| ), | ||
| val=dict( | ||
| type=dataset_type, | ||
| root_path=data_root, | ||
| info_path=val_anno, | ||
| test_mode=True, | ||
| ann_file=val_anno, | ||
| nsweeps=nsweeps, | ||
| class_names=class_names, | ||
| pipeline=test_pipeline, | ||
| ), | ||
| test=dict( | ||
| type=dataset_type, | ||
| root_path=data_root, | ||
| info_path=test_anno, | ||
| ann_file=test_anno, | ||
| nsweeps=nsweeps, | ||
| class_names=class_names, | ||
| pipeline=test_pipeline, | ||
| ), | ||
| ) | ||
|
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| optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2)) | ||
| # optimizer | ||
| optimizer = dict( | ||
| type="adam", amsgrad=0.0, wd=0.01, fixed_wd=True, moving_average=False, | ||
| ) | ||
| lr_config = dict( | ||
| type="one_cycle", lr_max=0.001, moms=[0.95, 0.85], div_factor=10.0, pct_start=0.4, | ||
| ) | ||
|
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| checkpoint_config = dict(interval=1) | ||
| # yapf:disable | ||
| log_config = dict( | ||
| interval=5, | ||
| hooks=[ | ||
| dict(type="TextLoggerHook"), | ||
| # dict(type='TensorboardLoggerHook') | ||
| ], | ||
| ) | ||
| # yapf:enable | ||
| # runtime settings | ||
| total_epochs = 20 | ||
| device_ids = range(8) | ||
| dist_params = dict(backend="nccl", init_method="env://") | ||
| log_level = "INFO" | ||
| work_dir = './work_dirs/{}/'.format(__file__[__file__.rfind('/') + 1:-3]) | ||
| load_from = None | ||
| resume_from = None | ||
| workflow = [('train', 1)] |
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