Hello, I'm sorry to disturb you. My config.py file is as follows, but I encountered a problem when executing $ python ./SE-SSD/tools/create_data.py. How can I solve it? thanks！

import itertools import logging from pathlib import Path

from det3d.builder import build_box_coder from det3d.utils.config_tool import get_downsample_factor

data_root_prefix = "/home/y9000k/data/"

norm_cfg = dict(type='SyncBN', eps=1e-3, momentum=0.01)

norm_cfg = None

tasks = [dict(num_class=1, class_names=["Car"],),]

class_names = list(itertools.chain(*[t["class_names"] for t in tasks]))

training and testing settings

box_coder = dict(type="ground_box3d_coder", n_dim=7, linear_dim=False, encode_angle_vector=False,)

exp_sesssd_release_v0_0: based on v1_0, remove sada

exp_sesssd_release_v1_0: default settings of sesssd

TAG = 'exp_se_ssd_v1_8'

torch.set_printoptions(precision=4, sci_mode=False)

my_paras = dict( batch_size=4, data_mode="train", # "train" or "trainval": the set to train the model; enable_ssl=True, # Ensure "False" in CIA-SSD training eval_training_set=False, # True: eval on "data_mode" set; False: eval on validation set.[Ensure "False" in training; Switch in Testing]

# unused
enable_difficulty_level=False,
remove_difficulty_points=False,  # act with neccessary condition: enable_difficulty_level=True.
gt_random_drop=-1,
data_aug_random_drop=-1,
far_points_first=False,
data_aug_with_context=-1,        # enlarged size for w and l in data aug.
gt_aug_with_context=-1,
gt_aug_similar_type=False,
min_points_in_gt=-1,
loss_iou=None,

)

model settings

model = dict( type="VoxelNet", pretrained=None, reader=dict(type="VoxelFeatureExtractorV3", num_input_features=4, norm_cfg=norm_cfg,), backbone=dict(type="SpMiddleFHD", num_input_features=4, ds_factor=8, norm_cfg=norm_cfg,), neck=dict( type="SSFA", layer_nums=[5,], ds_layer_strides=[1,], ds_num_filters=[128,], us_layer_strides=[1,], us_num_filters=[128,], num_input_features=128, norm_cfg=norm_cfg, logger=logging.getLogger("RPN"), ), bbox_head=dict( type="MultiGroupHead", mode="3d", in_channels=sum([128,]), norm_cfg=norm_cfg, tasks=tasks, weights=[1,], box_coder=build_box_coder(box_coder), encode_background_as_zeros=True, loss_norm=dict(type="NormByNumPositives", pos_cls_weight=1.0, neg_cls_weight=1.0,), loss_cls=dict(type="SigmoidFocalLoss", alpha=0.25, gamma=2.0, loss_weight=1.0,), use_sigmoid_score=True, loss_bbox=dict(type="WeightedSmoothL1Loss", sigma=3.0, code_weights=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], codewise=True, loss_weight=2.0, ), encode_rad_error_by_sin=True, loss_aux=dict(type="WeightedSoftmaxClassificationLoss", name="direction_classifier", loss_weight=0.2,), direction_offset=0.0,

loss_iou=my_paras['loss_iou'],

),

)

target_assigner = dict( type="iou", anchor_generators=[ dict( type="anchor_generator_range", sizes=[1.6, 3.9, 1.56], # w, l, h anchor_ranges=[0, -40.0, -1.0, 70.4, 40.0, -1.0], rotations=[0, 1.57], matched_threshold=0.6, unmatched_threshold=0.45, class_name="Car", ), ], sample_positive_fraction=-1, sample_size=512, region_similarity_calculator=dict(type="nearest_iou_similarity",), pos_area_threshold=-1, tasks=tasks, )

assigner = dict( box_coder=box_coder, target_assigner=target_assigner, out_size_factor=8, debug=False, enable_similar_type=True, )

train_cfg = dict(assigner=assigner)

test_cfg = dict( nms=dict( use_rotate_nms=True, use_multi_class_nms=False, nms_pre_max_size=1000, nms_post_max_size=100, nms_iou_threshold=0.01, ), score_threshold=0.3, post_center_limit_range=[0, -40.0, -5.0, 70.4, 40.0, 5.0], max_per_img=100, )

dataset settings

dataset_type = "KittiDataset"

db_sampler = dict( type="GT-AUG", enable=True, db_info_path=data_rootprefix + "/KITTI/object/dbinfos" + my_paras['data_mode'] +".pkl", sample_groups=[dict(Car=15,),], db_prep_steps=[ dict(filter_by_min_num_points=dict(Car=5,)), dict(filter_by_difficulty=[-1],), # todo: need to check carefully ], global_random_rotation_range_per_object=[0, 0], rate=1.0, gt_random_drop=my_paras['gt_random_drop'], gt_aug_with_context=my_paras['gt_aug_with_context'], gt_aug_similar_type=my_paras['gt_aug_similar_type'], ) train_preprocessor = dict( mode="train", shuffle_points=True, gt_loc_noise=[1.0, 1.0, 0.5], gt_rot_noise=[-0.785, 0.785], global_rot_noise=[-0.785, 0.785], global_scale_noise=[0.95, 1.05], global_rot_per_obj_range=[0, 0], global_trans_noise=[0.0, 0.0, 0.0], remove_points_after_sample=True, gt_drop_percentage=0.0, gt_drop_max_keep_points=15, remove_environment=False, remove_unknown_examples=my_paras.get("remove_difficulty_points", False), db_sampler=db_sampler, class_names=class_names, # 'Car' symmetry_intensity=False, enable_similar_type=True, min_points_in_gt=my_paras["min_points_in_gt"], data_aug_with_context=my_paras["data_aug_with_context"], data_aug_random_drop=my_paras["data_aug_random_drop"], )

val_preprocessor = dict( mode="val", shuffle_points=False, remove_environment=False, remove_unknown_examples=False, )

voxel_generator = dict( range=[0, -40.0, -3.0, 70.4, 40.0, 1.0], voxel_size=[0.05, 0.05, 0.1], max_points_in_voxel=5, max_voxel_num=20000, far_points_first=my_paras['far_points_first'], )

train_pipeline = [ dict(type="LoadPointCloudFromFile"), dict(type="LoadPointCloudAnnotations", with_bbox=True, enable_difficulty_level=my_paras.get("enable_difficulty_level", False)), dict(type="Preprocess", cfg=train_preprocessor), dict(type="Voxelization", cfg=voxel_generator), dict(type="AssignTarget", cfg=train_cfg["assigner"]), dict(type="Reformat"),

dict(type='PointCloudCollect', keys=['points', 'voxels', 'annotations', 'calib']),

] test_pipeline = [ dict(type="LoadPointCloudFromFile"), dict(type="LoadPointCloudAnnotations", with_bbox=True), dict(type="Preprocess", cfg=val_preprocessor), dict(type="Voxelization", cfg=voxel_generator), dict(type="AssignTarget", cfg=train_cfg["assigner"]), dict(type="Reformat"), ] training_pipeline = test_pipeline if my_paras['eval_training_set'] else train_pipeline

data_root = data_root_prefix + "/KITTI/object" train_anno = data_root_prefix + "/KITTI/object/kittiinfos" + my_paras['data_mode'] + ".pkl" val_anno = data_root_prefix + "/KITTI/object/kitti_infos_val.pkl" test_anno = data_root_prefix + "/KITTI/object/kitti_infos_test.pkl" trainval_anno = data_root_prefix + "/KITTI/object/kitti_infos_trainval.pkl"

data = dict( samples_per_gpu=my_paras['batch_size'], # batch_size: 4 workers_per_gpu=2, # default: 2 train=dict( type=dataset_type, root_path=data_root, info_path=train_anno, class_names=class_names, pipeline=training_pipeline, ), val=dict( type=dataset_type, root_path=data_root, info_path=val_anno, class_names=class_names, pipeline=test_pipeline, ), test=dict( type=dataset_type, root_path=data_root, info_path=test_anno, class_names=class_names, pipeline=test_pipeline, ), trainval=dict( type=dataset_type, root_path=data_root, info_path=trainval_anno, class_names=class_names, pipeline=test_pipeline, ), train_unlabel_val=dict( type=dataset_type, root_path=data_root, info_path=val_anno, class_names=class_names, pipeline=train_pipeline, labeled=False, ), train_unlabel_test=dict( type=dataset_type, root_path=data_root, info_path=test_anno, class_names=class_names, pipeline=train_pipeline, labeled=False, ), )

for cia optimizer

optimizer = dict(type="adam", amsgrad=0.0, wd=0.01, fixed_wd=True, moving_average=False,) optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2)) lr_config = dict(type="one_cycle", lr_max=0.003, moms=[0.95, 0.85], div_factor=10.0, pct_start=0.4,) # learning policy in training hooks

checkpoint_config = dict(interval=1) log_config = dict(interval=10,hooks=[dict(type="TextLoggerHook"),],) # dict(type='TensorboardLoggerHook')

runtime settings

total_epochs = 60 device_ids = range(8) dist_params = dict(backend="nccl", init_method="env://") log_level = "INFO" work_dir = "/home/y9000k/SE-SSD/output/" + TAG

load_from: "path of pre-trained checkpoint to initialize both teacher & student, e.g., CIA-SSD pre-trained model"

load_from = "/xxx/xxx/xxx/epoch_60.pth"

load_from = "/home/y9000k/SE-SSD/se-ssd-model.pth" resume_from = None workflow = [("train", 60), ("val", 1)] if my_paras['enable_ssl'] else [("train", 60), ("val", 1)] save_file = False if TAG == "debug" or TAG == "exp_debug" or Path(work_dir, "Det3D").is_dir() else True

Screenshot from 2021-11-04 09-45-20

Vegeta2020 / SE-SSD

How to use our code to generate ground truth data #57