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train_20240816-153231.log
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train_20240816-153231.log
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2024-08-16 15:32:31,518 INFO **********************Start logging**********************
2024-08-16 15:32:31,519 INFO CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
2024-08-16 15:32:31,519 INFO Training in distributed mode : total_batch_size: 16
2024-08-16 15:32:31,519 INFO cfg_file cfgs/nuscenes_models/bevfusion.yaml
2024-08-16 15:32:31,519 INFO batch_size 2
2024-08-16 15:32:31,519 INFO epochs 20
2024-08-16 15:32:31,519 INFO workers 4
2024-08-16 15:32:31,519 INFO extra_tag bevfusion_baseline_1_4_split_scenes
2024-08-16 15:32:31,520 INFO ckpt None
2024-08-16 15:32:31,520 INFO pretrained_model None
2024-08-16 15:32:31,520 INFO launcher pytorch
2024-08-16 15:32:31,520 INFO tcp_port 18888
2024-08-16 15:32:31,520 INFO sync_bn False
2024-08-16 15:32:31,520 INFO fix_random_seed False
2024-08-16 15:32:31,520 INFO ckpt_save_interval 1
2024-08-16 15:32:31,520 INFO local_rank 0
2024-08-16 15:32:31,520 INFO max_ckpt_save_num 30
2024-08-16 15:32:31,520 INFO merge_all_iters_to_one_epoch False
2024-08-16 15:32:31,521 INFO set_cfgs None
2024-08-16 15:32:31,521 INFO max_waiting_mins 0
2024-08-16 15:32:31,521 INFO start_epoch 0
2024-08-16 15:32:31,521 INFO num_epochs_to_eval 5
2024-08-16 15:32:31,521 INFO save_to_file True
2024-08-16 15:32:31,521 INFO use_tqdm_to_record False
2024-08-16 15:32:31,521 INFO logger_iter_interval 50
2024-08-16 15:32:31,521 INFO ckpt_save_time_interval 300
2024-08-16 15:32:31,521 INFO wo_gpu_stat False
2024-08-16 15:32:31,522 INFO use_amp False
2024-08-16 15:32:31,522 INFO split 1.0
2024-08-16 15:32:31,522 INFO cfg.ROOT_DIR: /home/songziying/workspace/OpenPCDet
2024-08-16 15:32:31,522 INFO cfg.LOCAL_RANK: 0
2024-08-16 15:32:31,522 INFO cfg.CLASS_NAMES: ['car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone']
2024-08-16 15:32:31,522 INFO ----------- DATA_CONFIG -----------
2024-08-16 15:32:31,522 INFO cfg.DATA_CONFIG.DATASET: NuScenesDataset
2024-08-16 15:32:31,522 INFO cfg.DATA_CONFIG.DATA_PATH: ../data/nuscenes
2024-08-16 15:32:31,523 INFO cfg.DATA_CONFIG.VERSION: v1.0-trainval
2024-08-16 15:32:31,523 INFO cfg.DATA_CONFIG.MAX_SWEEPS: 10
2024-08-16 15:32:31,523 INFO cfg.DATA_CONFIG.PRED_VELOCITY: True
2024-08-16 15:32:31,523 INFO cfg.DATA_CONFIG.SET_NAN_VELOCITY_TO_ZEROS: True
2024-08-16 15:32:31,523 INFO cfg.DATA_CONFIG.FILTER_MIN_POINTS_IN_GT: 1
2024-08-16 15:32:31,523 INFO ----------- DATA_SPLIT -----------
2024-08-16 15:32:31,523 INFO cfg.DATA_CONFIG.DATA_SPLIT.train: train
2024-08-16 15:32:31,523 INFO cfg.DATA_CONFIG.DATA_SPLIT.test: val
2024-08-16 15:32:31,523 INFO ----------- INFO_PATH -----------
2024-08-16 15:32:31,523 INFO cfg.DATA_CONFIG.INFO_PATH.train: ['nuscenes_infos_10sweeps_1_4_train.pkl']
2024-08-16 15:32:31,524 INFO cfg.DATA_CONFIG.INFO_PATH.test: ['nuscenes_infos_10sweeps_val.pkl']
2024-08-16 15:32:31,524 INFO cfg.DATA_CONFIG.POINT_CLOUD_RANGE: [-54.0, -54.0, -5.0, 54.0, 54.0, 3.0]
2024-08-16 15:32:31,524 INFO cfg.DATA_CONFIG.BALANCED_RESAMPLING: True
2024-08-16 15:32:31,524 INFO ----------- DATA_AUGMENTOR -----------
2024-08-16 15:32:31,524 INFO cfg.DATA_CONFIG.DATA_AUGMENTOR.DISABLE_AUG_LIST: ['placeholder']
2024-08-16 15:32:31,524 INFO cfg.DATA_CONFIG.DATA_AUGMENTOR.AUG_CONFIG_LIST: [{'NAME': 'random_world_flip', 'ALONG_AXIS_LIST': ['x', 'y']}, {'NAME': 'random_world_rotation', 'WORLD_ROT_ANGLE': [-0.78539816, 0.78539816]}, {'NAME': 'random_world_scaling', 'WORLD_SCALE_RANGE': [0.9, 1.1]}, {'NAME': 'random_world_translation', 'NOISE_TRANSLATE_STD': [0.5, 0.5, 0.5]}, {'NAME': 'imgaug', 'ROT_LIM': [-5.4, 5.4], 'RAND_FLIP': True}]
2024-08-16 15:32:31,524 INFO ----------- POINT_FEATURE_ENCODING -----------
2024-08-16 15:32:31,524 INFO cfg.DATA_CONFIG.POINT_FEATURE_ENCODING.encoding_type: absolute_coordinates_encoding
2024-08-16 15:32:31,525 INFO cfg.DATA_CONFIG.POINT_FEATURE_ENCODING.used_feature_list: ['x', 'y', 'z', 'intensity', 'timestamp']
2024-08-16 15:32:31,525 INFO cfg.DATA_CONFIG.POINT_FEATURE_ENCODING.src_feature_list: ['x', 'y', 'z', 'intensity', 'timestamp']
2024-08-16 15:32:31,525 INFO cfg.DATA_CONFIG.DATA_PROCESSOR: [{'NAME': 'mask_points_and_boxes_outside_range', 'REMOVE_OUTSIDE_BOXES': True}, {'NAME': 'shuffle_points', 'SHUFFLE_ENABLED': {'train': True, 'test': True}}, {'NAME': 'transform_points_to_voxels', 'VOXEL_SIZE': [0.075, 0.075, 0.2], 'MAX_POINTS_PER_VOXEL': 10, 'MAX_NUMBER_OF_VOXELS': {'train': 120000, 'test': 160000}}, {'NAME': 'image_calibrate'}, {'NAME': 'image_normalize', 'mean': [0.485, 0.456, 0.406], 'std': [0.229, 0.224, 0.225]}]
2024-08-16 15:32:31,525 INFO cfg.DATA_CONFIG._BASE_CONFIG_: cfgs/dataset_configs/nuscenes_dataset.yaml
2024-08-16 15:32:31,525 INFO ----------- CAMERA_CONFIG -----------
2024-08-16 15:32:31,525 INFO cfg.DATA_CONFIG.CAMERA_CONFIG.USE_CAMERA: True
2024-08-16 15:32:31,525 INFO ----------- IMAGE -----------
2024-08-16 15:32:31,526 INFO cfg.DATA_CONFIG.CAMERA_CONFIG.IMAGE.FINAL_DIM: [256, 704]
2024-08-16 15:32:31,526 INFO cfg.DATA_CONFIG.CAMERA_CONFIG.IMAGE.RESIZE_LIM_TRAIN: [0.38, 0.55]
2024-08-16 15:32:31,526 INFO cfg.DATA_CONFIG.CAMERA_CONFIG.IMAGE.RESIZE_LIM_TEST: [0.48, 0.48]
2024-08-16 15:32:31,526 INFO ----------- MODEL -----------
2024-08-16 15:32:31,526 INFO cfg.MODEL.NAME: BevFusion
2024-08-16 15:32:31,526 INFO ----------- VFE -----------
2024-08-16 15:32:31,526 INFO cfg.MODEL.VFE.NAME: MeanVFE
2024-08-16 15:32:31,526 INFO ----------- BACKBONE_3D -----------
2024-08-16 15:32:31,526 INFO cfg.MODEL.BACKBONE_3D.NAME: VoxelResBackBone8x
2024-08-16 15:32:31,526 INFO cfg.MODEL.BACKBONE_3D.USE_BIAS: False
2024-08-16 15:32:31,526 INFO ----------- MAP_TO_BEV -----------
2024-08-16 15:32:31,526 INFO cfg.MODEL.MAP_TO_BEV.NAME: HeightCompression
2024-08-16 15:32:31,526 INFO cfg.MODEL.MAP_TO_BEV.NUM_BEV_FEATURES: 256
2024-08-16 15:32:31,526 INFO ----------- IMAGE_BACKBONE -----------
2024-08-16 15:32:31,526 INFO cfg.MODEL.IMAGE_BACKBONE.NAME: SwinTransformer
2024-08-16 15:32:31,526 INFO cfg.MODEL.IMAGE_BACKBONE.EMBED_DIMS: 96
2024-08-16 15:32:31,526 INFO cfg.MODEL.IMAGE_BACKBONE.DEPTHS: [2, 2, 6, 2]
2024-08-16 15:32:31,526 INFO cfg.MODEL.IMAGE_BACKBONE.NUM_HEADS: [3, 6, 12, 24]
2024-08-16 15:32:31,526 INFO cfg.MODEL.IMAGE_BACKBONE.WINDOW_SIZE: 7
2024-08-16 15:32:31,526 INFO cfg.MODEL.IMAGE_BACKBONE.MLP_RATIO: 4
2024-08-16 15:32:31,526 INFO cfg.MODEL.IMAGE_BACKBONE.DROP_RATE: 0.0
2024-08-16 15:32:31,526 INFO cfg.MODEL.IMAGE_BACKBONE.ATTN_DROP_RATE: 0.0
2024-08-16 15:32:31,526 INFO cfg.MODEL.IMAGE_BACKBONE.DROP_PATH_RATE: 0.2
2024-08-16 15:32:31,526 INFO cfg.MODEL.IMAGE_BACKBONE.PATCH_NORM: True
2024-08-16 15:32:31,526 INFO cfg.MODEL.IMAGE_BACKBONE.OUT_INDICES: [1, 2, 3]
2024-08-16 15:32:31,526 INFO cfg.MODEL.IMAGE_BACKBONE.WITH_CP: False
2024-08-16 15:32:31,527 INFO cfg.MODEL.IMAGE_BACKBONE.CONVERT_WEIGHTS: True
2024-08-16 15:32:31,527 INFO ----------- INIT_CFG -----------
2024-08-16 15:32:31,527 INFO cfg.MODEL.IMAGE_BACKBONE.INIT_CFG.type: Pretrained
2024-08-16 15:32:31,527 INFO cfg.MODEL.IMAGE_BACKBONE.INIT_CFG.checkpoint: /home/songziying/workspace/OpenPCDet/checkpoints/swint-nuimages-pretrained.pth
2024-08-16 15:32:31,527 INFO ----------- NECK -----------
2024-08-16 15:32:31,527 INFO cfg.MODEL.NECK.NAME: GeneralizedLSSFPN
2024-08-16 15:32:31,527 INFO cfg.MODEL.NECK.IN_CHANNELS: [192, 384, 768]
2024-08-16 15:32:31,527 INFO cfg.MODEL.NECK.OUT_CHANNELS: 256
2024-08-16 15:32:31,527 INFO cfg.MODEL.NECK.START_LEVEL: 0
2024-08-16 15:32:31,527 INFO cfg.MODEL.NECK.END_LEVEL: -1
2024-08-16 15:32:31,527 INFO cfg.MODEL.NECK.NUM_OUTS: 3
2024-08-16 15:32:31,527 INFO ----------- VTRANSFORM -----------
2024-08-16 15:32:31,527 INFO cfg.MODEL.VTRANSFORM.NAME: DepthLSSTransform
2024-08-16 15:32:31,527 INFO cfg.MODEL.VTRANSFORM.IMAGE_SIZE: [256, 704]
2024-08-16 15:32:31,527 INFO cfg.MODEL.VTRANSFORM.IN_CHANNEL: 256
2024-08-16 15:32:31,527 INFO cfg.MODEL.VTRANSFORM.OUT_CHANNEL: 80
2024-08-16 15:32:31,527 INFO cfg.MODEL.VTRANSFORM.FEATURE_SIZE: [32, 88]
2024-08-16 15:32:31,527 INFO cfg.MODEL.VTRANSFORM.XBOUND: [-54.0, 54.0, 0.3]
2024-08-16 15:32:31,527 INFO cfg.MODEL.VTRANSFORM.YBOUND: [-54.0, 54.0, 0.3]
2024-08-16 15:32:31,527 INFO cfg.MODEL.VTRANSFORM.ZBOUND: [-10.0, 10.0, 20.0]
2024-08-16 15:32:31,527 INFO cfg.MODEL.VTRANSFORM.DBOUND: [1.0, 60.0, 0.5]
2024-08-16 15:32:31,527 INFO cfg.MODEL.VTRANSFORM.DOWNSAMPLE: 2
2024-08-16 15:32:31,527 INFO cfg.MODEL.VTRANSFORM.Noise: True
2024-08-16 15:32:31,527 INFO ----------- FUSER -----------
2024-08-16 15:32:31,527 INFO cfg.MODEL.FUSER.NAME: ConvFuser
2024-08-16 15:32:31,527 INFO cfg.MODEL.FUSER.IN_CHANNEL: 336
2024-08-16 15:32:31,527 INFO cfg.MODEL.FUSER.OUT_CHANNEL: 256
2024-08-16 15:32:31,527 INFO ----------- BACKBONE_2D -----------
2024-08-16 15:32:31,528 INFO cfg.MODEL.BACKBONE_2D.NAME: BaseBEVBackbone
2024-08-16 15:32:31,528 INFO cfg.MODEL.BACKBONE_2D.LAYER_NUMS: [5, 5]
2024-08-16 15:32:31,528 INFO cfg.MODEL.BACKBONE_2D.LAYER_STRIDES: [1, 2]
2024-08-16 15:32:31,528 INFO cfg.MODEL.BACKBONE_2D.NUM_FILTERS: [128, 256]
2024-08-16 15:32:31,528 INFO cfg.MODEL.BACKBONE_2D.UPSAMPLE_STRIDES: [1, 2]
2024-08-16 15:32:31,528 INFO cfg.MODEL.BACKBONE_2D.NUM_UPSAMPLE_FILTERS: [256, 256]
2024-08-16 15:32:31,528 INFO cfg.MODEL.BACKBONE_2D.USE_CONV_FOR_NO_STRIDE: True
2024-08-16 15:32:31,528 INFO ----------- DENSE_HEAD -----------
2024-08-16 15:32:31,528 INFO cfg.MODEL.DENSE_HEAD.CLASS_AGNOSTIC: False
2024-08-16 15:32:31,528 INFO cfg.MODEL.DENSE_HEAD.NAME: TransFusionHead
2024-08-16 15:32:31,528 INFO cfg.MODEL.DENSE_HEAD.USE_BIAS_BEFORE_NORM: False
2024-08-16 15:32:31,528 INFO cfg.MODEL.DENSE_HEAD.NUM_PROPOSALS: 200
2024-08-16 15:32:31,528 INFO cfg.MODEL.DENSE_HEAD.HIDDEN_CHANNEL: 128
2024-08-16 15:32:31,528 INFO cfg.MODEL.DENSE_HEAD.NUM_CLASSES: 10
2024-08-16 15:32:31,528 INFO cfg.MODEL.DENSE_HEAD.NUM_HEADS: 8
2024-08-16 15:32:31,528 INFO cfg.MODEL.DENSE_HEAD.NMS_KERNEL_SIZE: 3
2024-08-16 15:32:31,528 INFO cfg.MODEL.DENSE_HEAD.FFN_CHANNEL: 256
2024-08-16 15:32:31,528 INFO cfg.MODEL.DENSE_HEAD.DROPOUT: 0.1
2024-08-16 15:32:31,528 INFO cfg.MODEL.DENSE_HEAD.BN_MOMENTUM: 0.1
2024-08-16 15:32:31,528 INFO cfg.MODEL.DENSE_HEAD.ACTIVATION: relu
2024-08-16 15:32:31,528 INFO cfg.MODEL.DENSE_HEAD.NUM_HM_CONV: 2
2024-08-16 15:32:31,528 INFO ----------- SEPARATE_HEAD_CFG -----------
2024-08-16 15:32:31,528 INFO cfg.MODEL.DENSE_HEAD.SEPARATE_HEAD_CFG.HEAD_ORDER: ['center', 'height', 'dim', 'rot', 'vel']
2024-08-16 15:32:31,528 INFO ----------- HEAD_DICT -----------
2024-08-16 15:32:31,528 INFO ----------- center -----------
2024-08-16 15:32:31,528 INFO cfg.MODEL.DENSE_HEAD.SEPARATE_HEAD_CFG.HEAD_DICT.center.out_channels: 2
2024-08-16 15:32:31,528 INFO cfg.MODEL.DENSE_HEAD.SEPARATE_HEAD_CFG.HEAD_DICT.center.num_conv: 2
2024-08-16 15:32:31,528 INFO ----------- height -----------
2024-08-16 15:32:31,529 INFO cfg.MODEL.DENSE_HEAD.SEPARATE_HEAD_CFG.HEAD_DICT.height.out_channels: 1
2024-08-16 15:32:31,529 INFO cfg.MODEL.DENSE_HEAD.SEPARATE_HEAD_CFG.HEAD_DICT.height.num_conv: 2
2024-08-16 15:32:31,529 INFO ----------- dim -----------
2024-08-16 15:32:31,529 INFO cfg.MODEL.DENSE_HEAD.SEPARATE_HEAD_CFG.HEAD_DICT.dim.out_channels: 3
2024-08-16 15:32:31,529 INFO cfg.MODEL.DENSE_HEAD.SEPARATE_HEAD_CFG.HEAD_DICT.dim.num_conv: 2
2024-08-16 15:32:31,529 INFO ----------- rot -----------
2024-08-16 15:32:31,529 INFO cfg.MODEL.DENSE_HEAD.SEPARATE_HEAD_CFG.HEAD_DICT.rot.out_channels: 2
2024-08-16 15:32:31,529 INFO cfg.MODEL.DENSE_HEAD.SEPARATE_HEAD_CFG.HEAD_DICT.rot.num_conv: 2
2024-08-16 15:32:31,529 INFO ----------- vel -----------
2024-08-16 15:32:31,529 INFO cfg.MODEL.DENSE_HEAD.SEPARATE_HEAD_CFG.HEAD_DICT.vel.out_channels: 2
2024-08-16 15:32:31,529 INFO cfg.MODEL.DENSE_HEAD.SEPARATE_HEAD_CFG.HEAD_DICT.vel.num_conv: 2
2024-08-16 15:32:31,529 INFO ----------- TARGET_ASSIGNER_CONFIG -----------
2024-08-16 15:32:31,529 INFO cfg.MODEL.DENSE_HEAD.TARGET_ASSIGNER_CONFIG.FEATURE_MAP_STRIDE: 8
2024-08-16 15:32:31,529 INFO cfg.MODEL.DENSE_HEAD.TARGET_ASSIGNER_CONFIG.DATASET: nuScenes
2024-08-16 15:32:31,529 INFO cfg.MODEL.DENSE_HEAD.TARGET_ASSIGNER_CONFIG.GAUSSIAN_OVERLAP: 0.1
2024-08-16 15:32:31,529 INFO cfg.MODEL.DENSE_HEAD.TARGET_ASSIGNER_CONFIG.MIN_RADIUS: 2
2024-08-16 15:32:31,529 INFO ----------- HUNGARIAN_ASSIGNER -----------
2024-08-16 15:32:31,529 INFO ----------- cls_cost -----------
2024-08-16 15:32:31,529 INFO cfg.MODEL.DENSE_HEAD.TARGET_ASSIGNER_CONFIG.HUNGARIAN_ASSIGNER.cls_cost.gamma: 2.0
2024-08-16 15:32:31,529 INFO cfg.MODEL.DENSE_HEAD.TARGET_ASSIGNER_CONFIG.HUNGARIAN_ASSIGNER.cls_cost.alpha: 0.25
2024-08-16 15:32:31,529 INFO cfg.MODEL.DENSE_HEAD.TARGET_ASSIGNER_CONFIG.HUNGARIAN_ASSIGNER.cls_cost.weight: 0.15
2024-08-16 15:32:31,529 INFO ----------- reg_cost -----------
2024-08-16 15:32:31,529 INFO cfg.MODEL.DENSE_HEAD.TARGET_ASSIGNER_CONFIG.HUNGARIAN_ASSIGNER.reg_cost.weight: 0.25
2024-08-16 15:32:31,529 INFO ----------- iou_cost -----------
2024-08-16 15:32:31,529 INFO cfg.MODEL.DENSE_HEAD.TARGET_ASSIGNER_CONFIG.HUNGARIAN_ASSIGNER.iou_cost.weight: 0.25
2024-08-16 15:32:31,529 INFO ----------- LOSS_CONFIG -----------
2024-08-16 15:32:31,530 INFO ----------- LOSS_WEIGHTS -----------
2024-08-16 15:32:31,530 INFO cfg.MODEL.DENSE_HEAD.LOSS_CONFIG.LOSS_WEIGHTS.cls_weight: 1.0
2024-08-16 15:32:31,530 INFO cfg.MODEL.DENSE_HEAD.LOSS_CONFIG.LOSS_WEIGHTS.bbox_weight: 0.25
2024-08-16 15:32:31,530 INFO cfg.MODEL.DENSE_HEAD.LOSS_CONFIG.LOSS_WEIGHTS.hm_weight: 1.0
2024-08-16 15:32:31,530 INFO cfg.MODEL.DENSE_HEAD.LOSS_CONFIG.LOSS_WEIGHTS.code_weights: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.2, 0.2]
2024-08-16 15:32:31,530 INFO ----------- LOSS_CLS -----------
2024-08-16 15:32:31,530 INFO cfg.MODEL.DENSE_HEAD.LOSS_CONFIG.LOSS_CLS.use_sigmoid: True
2024-08-16 15:32:31,530 INFO cfg.MODEL.DENSE_HEAD.LOSS_CONFIG.LOSS_CLS.gamma: 2.0
2024-08-16 15:32:31,530 INFO cfg.MODEL.DENSE_HEAD.LOSS_CONFIG.LOSS_CLS.alpha: 0.25
2024-08-16 15:32:31,530 INFO ----------- POST_PROCESSING -----------
2024-08-16 15:32:31,530 INFO cfg.MODEL.DENSE_HEAD.POST_PROCESSING.SCORE_THRESH: 0.0
2024-08-16 15:32:31,530 INFO cfg.MODEL.DENSE_HEAD.POST_PROCESSING.POST_CENTER_RANGE: [-61.2, -61.2, -10.0, 61.2, 61.2, 10.0]
2024-08-16 15:32:31,530 INFO ----------- POST_PROCESSING -----------
2024-08-16 15:32:31,530 INFO cfg.MODEL.POST_PROCESSING.RECALL_THRESH_LIST: [0.3, 0.5, 0.7]
2024-08-16 15:32:31,530 INFO cfg.MODEL.POST_PROCESSING.SCORE_THRESH: 0.1
2024-08-16 15:32:31,530 INFO cfg.MODEL.POST_PROCESSING.OUTPUT_RAW_SCORE: False
2024-08-16 15:32:31,530 INFO cfg.MODEL.POST_PROCESSING.EVAL_METRIC: kitti
2024-08-16 15:32:31,530 INFO ----------- OPTIMIZATION -----------
2024-08-16 15:32:31,530 INFO cfg.OPTIMIZATION.BATCH_SIZE_PER_GPU: 1
2024-08-16 15:32:31,530 INFO cfg.OPTIMIZATION.NUM_EPOCHS: 20
2024-08-16 15:32:31,530 INFO cfg.OPTIMIZATION.OPTIMIZER: adam_cosineanneal
2024-08-16 15:32:31,530 INFO cfg.OPTIMIZATION.LR: 0.0001
2024-08-16 15:32:31,530 INFO cfg.OPTIMIZATION.WEIGHT_DECAY: 0.01
2024-08-16 15:32:31,530 INFO cfg.OPTIMIZATION.MOMENTUM: 0.9
2024-08-16 15:32:31,530 INFO cfg.OPTIMIZATION.BETAS: [0.9, 0.999]
2024-08-16 15:32:31,530 INFO cfg.OPTIMIZATION.MOMS: [0.9, 0.8052631]
2024-08-16 15:32:31,530 INFO cfg.OPTIMIZATION.PCT_START: 0.4
2024-08-16 15:32:31,530 INFO cfg.OPTIMIZATION.WARMUP_ITER: 500
2024-08-16 15:32:31,530 INFO cfg.OPTIMIZATION.DECAY_STEP_LIST: [35, 45]
2024-08-16 15:32:31,530 INFO cfg.OPTIMIZATION.LR_WARMUP: False
2024-08-16 15:32:31,530 INFO cfg.OPTIMIZATION.WARMUP_EPOCH: 1
2024-08-16 15:32:31,531 INFO cfg.OPTIMIZATION.GRAD_NORM_CLIP: 35
2024-08-16 15:32:31,531 INFO cfg.OPTIMIZATION.LOSS_SCALE_FP16: 32
2024-08-16 15:32:31,531 INFO cfg.TAG: bevfusion
2024-08-16 15:32:31,531 INFO cfg.EXP_GROUP_PATH: nuscenes_models
2024-08-16 15:32:31,553 INFO ----------- Create dataloader & network & optimizer -----------
2024-08-16 15:32:31,554 INFO Loading NuScenes dataset
2024-08-16 15:32:32,895 INFO Total samples for NuScenes dataset: 7034
2024-08-16 15:32:32,969 INFO Total samples after balanced resampling: 30950
2024-08-16 15:32:37,932 INFO ----------- Model BevFusion created, param count: 40800087 -----------
2024-08-16 15:32:37,933 INFO DistributedDataParallel(
(module): BevFusion(
(vfe): MeanVFE()
(backbone_3d): VoxelResBackBone8x(
(conv_input): SparseSequential(
(0): SubMConv3d(5, 16, kernel_size=[3, 3, 3], stride=[1, 1, 1], padding=[1, 1, 1], dilation=[1, 1, 1], output_padding=[0, 0, 0], bias=False, algo=ConvAlgo.MaskImplicitGemm)
(1): BatchNorm1d(16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU()
)
(conv1): SparseSequential(
(0): SparseBasicBlock(
(conv1): SubMConv3d(16, 16, kernel_size=[3, 3, 3], stride=[1, 1, 1], padding=[1, 1, 1], dilation=[1, 1, 1], output_padding=[0, 0, 0], bias=False, algo=ConvAlgo.MaskImplicitGemm)
(bn1): BatchNorm1d(16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(relu): ReLU()
(conv2): SubMConv3d(16, 16, kernel_size=[3, 3, 3], stride=[1, 1, 1], padding=[1, 1, 1], dilation=[1, 1, 1], output_padding=[0, 0, 0], bias=False, algo=ConvAlgo.MaskImplicitGemm)
(bn2): BatchNorm1d(16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
)
(1): SparseBasicBlock(
(conv1): SubMConv3d(16, 16, kernel_size=[3, 3, 3], stride=[1, 1, 1], padding=[1, 1, 1], dilation=[1, 1, 1], output_padding=[0, 0, 0], bias=False, algo=ConvAlgo.MaskImplicitGemm)
(bn1): BatchNorm1d(16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(relu): ReLU()
(conv2): SubMConv3d(16, 16, kernel_size=[3, 3, 3], stride=[1, 1, 1], padding=[1, 1, 1], dilation=[1, 1, 1], output_padding=[0, 0, 0], bias=False, algo=ConvAlgo.MaskImplicitGemm)
(bn2): BatchNorm1d(16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
)
)
(conv2): SparseSequential(
(0): SparseSequential(
(0): SparseConv3d(16, 32, kernel_size=[3, 3, 3], stride=[2, 2, 2], padding=[1, 1, 1], dilation=[1, 1, 1], output_padding=[0, 0, 0], bias=False, algo=ConvAlgo.MaskImplicitGemm)
(1): BatchNorm1d(32, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU()
)
(1): SparseBasicBlock(
(conv1): SubMConv3d(32, 32, kernel_size=[3, 3, 3], stride=[1, 1, 1], padding=[1, 1, 1], dilation=[1, 1, 1], output_padding=[0, 0, 0], bias=False, algo=ConvAlgo.MaskImplicitGemm)
(bn1): BatchNorm1d(32, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(relu): ReLU()
(conv2): SubMConv3d(32, 32, kernel_size=[3, 3, 3], stride=[1, 1, 1], padding=[1, 1, 1], dilation=[1, 1, 1], output_padding=[0, 0, 0], bias=False, algo=ConvAlgo.MaskImplicitGemm)
(bn2): BatchNorm1d(32, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
)
(2): SparseBasicBlock(
(conv1): SubMConv3d(32, 32, kernel_size=[3, 3, 3], stride=[1, 1, 1], padding=[1, 1, 1], dilation=[1, 1, 1], output_padding=[0, 0, 0], bias=False, algo=ConvAlgo.MaskImplicitGemm)
(bn1): BatchNorm1d(32, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(relu): ReLU()
(conv2): SubMConv3d(32, 32, kernel_size=[3, 3, 3], stride=[1, 1, 1], padding=[1, 1, 1], dilation=[1, 1, 1], output_padding=[0, 0, 0], bias=False, algo=ConvAlgo.MaskImplicitGemm)
(bn2): BatchNorm1d(32, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
)
)
(conv3): SparseSequential(
(0): SparseSequential(
(0): SparseConv3d(32, 64, kernel_size=[3, 3, 3], stride=[2, 2, 2], padding=[1, 1, 1], dilation=[1, 1, 1], output_padding=[0, 0, 0], bias=False, algo=ConvAlgo.MaskImplicitGemm)
(1): BatchNorm1d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU()
)
(1): SparseBasicBlock(
(conv1): SubMConv3d(64, 64, kernel_size=[3, 3, 3], stride=[1, 1, 1], padding=[1, 1, 1], dilation=[1, 1, 1], output_padding=[0, 0, 0], bias=False, algo=ConvAlgo.MaskImplicitGemm)
(bn1): BatchNorm1d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(relu): ReLU()
(conv2): SubMConv3d(64, 64, kernel_size=[3, 3, 3], stride=[1, 1, 1], padding=[1, 1, 1], dilation=[1, 1, 1], output_padding=[0, 0, 0], bias=False, algo=ConvAlgo.MaskImplicitGemm)
(bn2): BatchNorm1d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
)
(2): SparseBasicBlock(
(conv1): SubMConv3d(64, 64, kernel_size=[3, 3, 3], stride=[1, 1, 1], padding=[1, 1, 1], dilation=[1, 1, 1], output_padding=[0, 0, 0], bias=False, algo=ConvAlgo.MaskImplicitGemm)
(bn1): BatchNorm1d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(relu): ReLU()
(conv2): SubMConv3d(64, 64, kernel_size=[3, 3, 3], stride=[1, 1, 1], padding=[1, 1, 1], dilation=[1, 1, 1], output_padding=[0, 0, 0], bias=False, algo=ConvAlgo.MaskImplicitGemm)
(bn2): BatchNorm1d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
)
)
(conv4): SparseSequential(
(0): SparseSequential(
(0): SparseConv3d(64, 128, kernel_size=[3, 3, 3], stride=[2, 2, 2], padding=[0, 1, 1], dilation=[1, 1, 1], output_padding=[0, 0, 0], bias=False, algo=ConvAlgo.MaskImplicitGemm)
(1): BatchNorm1d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU()
)
(1): SparseBasicBlock(
(conv1): SubMConv3d(128, 128, kernel_size=[3, 3, 3], stride=[1, 1, 1], padding=[1, 1, 1], dilation=[1, 1, 1], output_padding=[0, 0, 0], bias=False, algo=ConvAlgo.MaskImplicitGemm)
(bn1): BatchNorm1d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(relu): ReLU()
(conv2): SubMConv3d(128, 128, kernel_size=[3, 3, 3], stride=[1, 1, 1], padding=[1, 1, 1], dilation=[1, 1, 1], output_padding=[0, 0, 0], bias=False, algo=ConvAlgo.MaskImplicitGemm)
(bn2): BatchNorm1d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
)
(2): SparseBasicBlock(
(conv1): SubMConv3d(128, 128, kernel_size=[3, 3, 3], stride=[1, 1, 1], padding=[1, 1, 1], dilation=[1, 1, 1], output_padding=[0, 0, 0], bias=False, algo=ConvAlgo.MaskImplicitGemm)
(bn1): BatchNorm1d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(relu): ReLU()
(conv2): SubMConv3d(128, 128, kernel_size=[3, 3, 3], stride=[1, 1, 1], padding=[1, 1, 1], dilation=[1, 1, 1], output_padding=[0, 0, 0], bias=False, algo=ConvAlgo.MaskImplicitGemm)
(bn2): BatchNorm1d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
)
)
(conv_out): SparseSequential(
(0): SparseConv3d(128, 128, kernel_size=[3, 1, 1], stride=[2, 1, 1], padding=[0, 0, 0], dilation=[1, 1, 1], output_padding=[0, 0, 0], bias=False, algo=ConvAlgo.MaskImplicitGemm)
(1): BatchNorm1d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU()
)
)
(map_to_bev_module): HeightCompression()
(pfe): None
(image_backbone): SwinTransformer(
(patch_embed): PatchEmbed(
(adap_padding): AdaptivePadding()
(projection): Conv2d(3, 96, kernel_size=(4, 4), stride=(4, 4))
(norm): LayerNorm((96,), eps=1e-05, elementwise_affine=True)
)
(drop_after_pos): Dropout(p=0.0, inplace=False)
(stages): ModuleList(
(0): SwinBlockSequence(
(blocks): ModuleList(
(0): SwinBlock(
(norm1): LayerNorm((96,), eps=1e-05, elementwise_affine=True)
(attn): ShiftWindowMSA(
(w_msa): WindowMSA(
(qkv): Linear(in_features=96, out_features=288, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=96, out_features=96, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop): DropPath()
)
(norm2): LayerNorm((96,), eps=1e-05, elementwise_affine=True)
(ffn): FFN(
(activate): GELU(approximate=none)
(layers): Sequential(
(0): Sequential(
(0): Linear(in_features=96, out_features=384, bias=True)
(1): GELU(approximate=none)
(2): Dropout(p=0.0, inplace=False)
)
(1): Linear(in_features=384, out_features=96, bias=True)
(2): Dropout(p=0.0, inplace=False)
)
(dropout_layer): DropPath()
)
)
(1): SwinBlock(
(norm1): LayerNorm((96,), eps=1e-05, elementwise_affine=True)
(attn): ShiftWindowMSA(
(w_msa): WindowMSA(
(qkv): Linear(in_features=96, out_features=288, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=96, out_features=96, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop): DropPath()
)
(norm2): LayerNorm((96,), eps=1e-05, elementwise_affine=True)
(ffn): FFN(
(activate): GELU(approximate=none)
(layers): Sequential(
(0): Sequential(
(0): Linear(in_features=96, out_features=384, bias=True)
(1): GELU(approximate=none)
(2): Dropout(p=0.0, inplace=False)
)
(1): Linear(in_features=384, out_features=96, bias=True)
(2): Dropout(p=0.0, inplace=False)
)
(dropout_layer): DropPath()
)
)
)
(downsample): PatchMerging(
(adap_padding): AdaptivePadding()
(sampler): Unfold(kernel_size=(2, 2), dilation=(1, 1), padding=(0, 0), stride=(2, 2))
(norm): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
(reduction): Linear(in_features=384, out_features=192, bias=False)
)
)
(1): SwinBlockSequence(
(blocks): ModuleList(
(0): SwinBlock(
(norm1): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
(attn): ShiftWindowMSA(
(w_msa): WindowMSA(
(qkv): Linear(in_features=192, out_features=576, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=192, out_features=192, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop): DropPath()
)
(norm2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
(ffn): FFN(
(activate): GELU(approximate=none)
(layers): Sequential(
(0): Sequential(
(0): Linear(in_features=192, out_features=768, bias=True)
(1): GELU(approximate=none)
(2): Dropout(p=0.0, inplace=False)
)
(1): Linear(in_features=768, out_features=192, bias=True)
(2): Dropout(p=0.0, inplace=False)
)
(dropout_layer): DropPath()
)
)
(1): SwinBlock(
(norm1): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
(attn): ShiftWindowMSA(
(w_msa): WindowMSA(
(qkv): Linear(in_features=192, out_features=576, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=192, out_features=192, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop): DropPath()
)
(norm2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
(ffn): FFN(
(activate): GELU(approximate=none)
(layers): Sequential(
(0): Sequential(
(0): Linear(in_features=192, out_features=768, bias=True)
(1): GELU(approximate=none)
(2): Dropout(p=0.0, inplace=False)
)
(1): Linear(in_features=768, out_features=192, bias=True)
(2): Dropout(p=0.0, inplace=False)
)
(dropout_layer): DropPath()
)
)
)
(downsample): PatchMerging(
(adap_padding): AdaptivePadding()
(sampler): Unfold(kernel_size=(2, 2), dilation=(1, 1), padding=(0, 0), stride=(2, 2))
(norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(reduction): Linear(in_features=768, out_features=384, bias=False)
)
)
(2): SwinBlockSequence(
(blocks): ModuleList(
(0): SwinBlock(
(norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
(attn): ShiftWindowMSA(
(w_msa): WindowMSA(
(qkv): Linear(in_features=384, out_features=1152, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=384, out_features=384, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop): DropPath()
)
(norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
(ffn): FFN(
(activate): GELU(approximate=none)
(layers): Sequential(
(0): Sequential(
(0): Linear(in_features=384, out_features=1536, bias=True)
(1): GELU(approximate=none)
(2): Dropout(p=0.0, inplace=False)
)
(1): Linear(in_features=1536, out_features=384, bias=True)
(2): Dropout(p=0.0, inplace=False)
)
(dropout_layer): DropPath()
)
)
(1): SwinBlock(
(norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
(attn): ShiftWindowMSA(
(w_msa): WindowMSA(
(qkv): Linear(in_features=384, out_features=1152, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=384, out_features=384, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop): DropPath()
)
(norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
(ffn): FFN(
(activate): GELU(approximate=none)
(layers): Sequential(
(0): Sequential(
(0): Linear(in_features=384, out_features=1536, bias=True)
(1): GELU(approximate=none)
(2): Dropout(p=0.0, inplace=False)
)
(1): Linear(in_features=1536, out_features=384, bias=True)
(2): Dropout(p=0.0, inplace=False)
)
(dropout_layer): DropPath()
)
)
(2): SwinBlock(
(norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
(attn): ShiftWindowMSA(
(w_msa): WindowMSA(
(qkv): Linear(in_features=384, out_features=1152, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=384, out_features=384, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop): DropPath()
)
(norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
(ffn): FFN(
(activate): GELU(approximate=none)
(layers): Sequential(
(0): Sequential(
(0): Linear(in_features=384, out_features=1536, bias=True)
(1): GELU(approximate=none)
(2): Dropout(p=0.0, inplace=False)
)
(1): Linear(in_features=1536, out_features=384, bias=True)
(2): Dropout(p=0.0, inplace=False)
)
(dropout_layer): DropPath()
)
)
(3): SwinBlock(
(norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
(attn): ShiftWindowMSA(
(w_msa): WindowMSA(
(qkv): Linear(in_features=384, out_features=1152, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=384, out_features=384, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop): DropPath()
)
(norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
(ffn): FFN(
(activate): GELU(approximate=none)
(layers): Sequential(
(0): Sequential(
(0): Linear(in_features=384, out_features=1536, bias=True)
(1): GELU(approximate=none)
(2): Dropout(p=0.0, inplace=False)
)
(1): Linear(in_features=1536, out_features=384, bias=True)
(2): Dropout(p=0.0, inplace=False)
)
(dropout_layer): DropPath()
)
)
(4): SwinBlock(
(norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
(attn): ShiftWindowMSA(
(w_msa): WindowMSA(
(qkv): Linear(in_features=384, out_features=1152, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=384, out_features=384, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop): DropPath()
)
(norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
(ffn): FFN(
(activate): GELU(approximate=none)
(layers): Sequential(
(0): Sequential(
(0): Linear(in_features=384, out_features=1536, bias=True)
(1): GELU(approximate=none)
(2): Dropout(p=0.0, inplace=False)
)
(1): Linear(in_features=1536, out_features=384, bias=True)
(2): Dropout(p=0.0, inplace=False)
)
(dropout_layer): DropPath()
)
)
(5): SwinBlock(
(norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
(attn): ShiftWindowMSA(
(w_msa): WindowMSA(
(qkv): Linear(in_features=384, out_features=1152, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=384, out_features=384, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop): DropPath()
)
(norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
(ffn): FFN(
(activate): GELU(approximate=none)
(layers): Sequential(
(0): Sequential(
(0): Linear(in_features=384, out_features=1536, bias=True)
(1): GELU(approximate=none)
(2): Dropout(p=0.0, inplace=False)
)
(1): Linear(in_features=1536, out_features=384, bias=True)
(2): Dropout(p=0.0, inplace=False)
)
(dropout_layer): DropPath()
)
)
)
(downsample): PatchMerging(
(adap_padding): AdaptivePadding()
(sampler): Unfold(kernel_size=(2, 2), dilation=(1, 1), padding=(0, 0), stride=(2, 2))
(norm): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)
(reduction): Linear(in_features=1536, out_features=768, bias=False)
)
)
(3): SwinBlockSequence(
(blocks): ModuleList(
(0): SwinBlock(
(norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(attn): ShiftWindowMSA(
(w_msa): WindowMSA(
(qkv): Linear(in_features=768, out_features=2304, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=768, out_features=768, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop): DropPath()
)
(norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(ffn): FFN(
(activate): GELU(approximate=none)
(layers): Sequential(
(0): Sequential(
(0): Linear(in_features=768, out_features=3072, bias=True)
(1): GELU(approximate=none)
(2): Dropout(p=0.0, inplace=False)
)
(1): Linear(in_features=3072, out_features=768, bias=True)
(2): Dropout(p=0.0, inplace=False)
)
(dropout_layer): DropPath()
)
)
(1): SwinBlock(
(norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(attn): ShiftWindowMSA(
(w_msa): WindowMSA(
(qkv): Linear(in_features=768, out_features=2304, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=768, out_features=768, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop): DropPath()
)
(norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(ffn): FFN(
(activate): GELU(approximate=none)
(layers): Sequential(
(0): Sequential(
(0): Linear(in_features=768, out_features=3072, bias=True)
(1): GELU(approximate=none)
(2): Dropout(p=0.0, inplace=False)
)
(1): Linear(in_features=3072, out_features=768, bias=True)
(2): Dropout(p=0.0, inplace=False)
)
(dropout_layer): DropPath()
)
)
)
)
)
(norm1): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
(norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
(norm3): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
)
(neck): GeneralizedLSSFPN(
(lateral_convs): ModuleList(
(0): BasicBlock2D(
(conv): Conv2d(448, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(1): BasicBlock2D(
(conv): Conv2d(1152, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
)
(fpn_convs): ModuleList(
(0): BasicBlock2D(
(conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(1): BasicBlock2D(
(conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
)
)
(vtransform): DepthLSSTransform(
(dtransform): Sequential(
(0): Conv2d(1, 8, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(8, 32, kernel_size=(5, 5), stride=(4, 4), padding=(2, 2))
(4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU(inplace=True)
(6): Conv2d(32, 64, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2))
(7): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(8): ReLU(inplace=True)
)
(depthnet): Sequential(
(0): Conv2d(320, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU(inplace=True)
(6): Conv2d(256, 198, kernel_size=(1, 1), stride=(1, 1))
)
(downsample): Sequential(
(0): Conv2d(80, 80, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(80, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(80, 80, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(4): BatchNorm2d(80, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU(inplace=True)
(6): Conv2d(80, 80, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(7): BatchNorm2d(80, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(8): ReLU(inplace=True)
)
)
(fuser): ConvFuser(
(conv): Sequential(
(0): Conv2d(336, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
)
(backbone_2d): BaseBEVBackbone(
(blocks): ModuleList(
(0): Sequential(
(0): ZeroPad2d((1, 1, 1, 1))
(1): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), bias=False)
(2): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(3): ReLU()
(4): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(5): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(6): ReLU()
(7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(8): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(9): ReLU()
(10): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(11): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(12): ReLU()
(13): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(14): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(15): ReLU()
(16): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(17): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(18): ReLU()
)
(1): Sequential(
(0): ZeroPad2d((1, 1, 1, 1))
(1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), bias=False)
(2): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(3): ReLU()
(4): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(5): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(6): ReLU()
(7): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(8): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(9): ReLU()
(10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(11): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(12): ReLU()
(13): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(14): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(15): ReLU()
(16): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(17): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(18): ReLU()
)
)
(deblocks): ModuleList(
(0): Sequential(
(0): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU()
)
(1): Sequential(
(0): ConvTranspose2d(256, 256, kernel_size=(2, 2), stride=(2, 2), bias=False)
(1): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU()
)
)
)
(dense_head): TransFusionHead(
(loss_cls): SigmoidFocalClassificationLoss()
(loss_bbox): L1Loss()
(loss_heatmap): GaussianFocalLoss()
(custom_loss): CustomLoss()
(shared_conv): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(heatmap_head): Sequential(
(0): BasicBlock2D(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(1): Conv2d(128, 10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(class_encoding): Conv1d(10, 128, kernel_size=(1,), stride=(1,))
(decoder): TransformerDecoderLayer(
(self_attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)
)
(multihead_attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)
)
(linear1): Linear(in_features=128, out_features=256, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
(linear2): Linear(in_features=256, out_features=128, bias=True)
(norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)
(norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)
(norm3): LayerNorm((128,), eps=1e-05, elementwise_affine=True)
(dropout1): Dropout(p=0.1, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
(dropout3): Dropout(p=0.1, inplace=False)
(self_posembed): PositionEmbeddingLearned(
(position_embedding_head): Sequential(
(0): Conv1d(2, 128, kernel_size=(1,), stride=(1,))
(1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv1d(128, 128, kernel_size=(1,), stride=(1,))
)
)
(cross_posembed): PositionEmbeddingLearned(
(position_embedding_head): Sequential(
(0): Conv1d(2, 128, kernel_size=(1,), stride=(1,))
(1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv1d(128, 128, kernel_size=(1,), stride=(1,))
)
)
)
(prediction_head): SeparateHead_Transfusion(
(center): Sequential(
(0): Sequential(
(0): Conv1d(128, 64, kernel_size=(1,), stride=(1,), bias=False)
(1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(1): Conv1d(64, 2, kernel_size=(1,), stride=(1,))
)
(height): Sequential(
(0): Sequential(
(0): Conv1d(128, 64, kernel_size=(1,), stride=(1,), bias=False)
(1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(1): Conv1d(64, 1, kernel_size=(1,), stride=(1,))
)
(dim): Sequential(
(0): Sequential(
(0): Conv1d(128, 64, kernel_size=(1,), stride=(1,), bias=False)
(1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(1): Conv1d(64, 3, kernel_size=(1,), stride=(1,))
)
(rot): Sequential(
(0): Sequential(
(0): Conv1d(128, 64, kernel_size=(1,), stride=(1,), bias=False)
(1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(1): Conv1d(64, 2, kernel_size=(1,), stride=(1,))
)
(vel): Sequential(
(0): Sequential(
(0): Conv1d(128, 64, kernel_size=(1,), stride=(1,), bias=False)
(1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(1): Conv1d(64, 2, kernel_size=(1,), stride=(1,))
)
(heatmap): Sequential(
(0): Sequential(
(0): Conv1d(128, 64, kernel_size=(1,), stride=(1,), bias=False)
(1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(1): Conv1d(64, 10, kernel_size=(1,), stride=(1,))
)
)
)
(point_head): None
(roi_head): None
)
)
2024-08-16 15:32:37,941 INFO **********************Start training nuscenes_models/bevfusion(bevfusion_baseline_1_4_split_scenes)**********************
2024-08-16 15:34:22,313 INFO Train: 1/20 ( 5%) [ 0/1935 ( 0%)] Loss: 1829. (1.83e+03) LR: 3.333e-05 Time cost: 00:19/10:43:33 [01:44/214:31:19] Acc_iter 1 Data time: 2.93(2.93) Forward time: 18.79(18.79) Batch time: 21.73(21.73)
2024-08-16 15:36:30,770 INFO Train: 1/20 ( 5%) [ 49/1935 ( 3%)] Loss: 378.5 (1.33e+03) LR: 3.987e-05 Time cost: 02:28/1:33:18 [03:52/31:52:05] Acc_iter 50 Data time: 0.20(0.49) Forward time: 1.47(2.51) Batch time: 1.67(3.00)
2024-08-16 15:38:34,387 INFO Train: 1/20 ( 5%) [ 99/1935 ( 5%)] Loss: 54.38 (758.) LR: 4.653e-05 Time cost: 04:32/1:23:14 [05:56/29:10:06] Acc_iter 100 Data time: 0.02(0.42) Forward time: 0.76(2.32) Batch time: 0.78(2.74)
2024-08-16 15:39:04,258 INFO Save latest model to /home/songziying/workspace/OpenPCDet/output/nuscenes_models/bevfusion/bevfusion_baseline_1_4_split_scenes/ckpt/latest_model
2024-08-16 15:40:15,873 INFO Train: 1/20 ( 5%) [ 149/1935 ( 8%)] Loss: 15.29 (515.) LR: 5.320e-05 Time cost: 06:13/1:14:07 [07:37/26:39:56] Acc_iter 150 Data time: 0.02(0.36) Forward time: 0.78(2.14) Batch time: 0.80(2.50)
2024-08-16 15:40:16,421 INFO sugon-X640-G40 Fri Aug 16 15:40:16 2024 535.183.01
[0] NVIDIA GeForce RTX 3090 | 50'C, 0 % | 15676 / 24576 MB | songziying(15330M) gdm(9M) gdm(6M)
[1] NVIDIA GeForce RTX 3090 | 52'C, 100 % | 20091 / 24576 MB | xiayongchao(3810M) songziying(15946M) gdm(4M)
[2] NVIDIA GeForce RTX 3090 | 56'C, 100 % | 19357 / 24576 MB | xiayongchao(3294M) songziying(15728M) gdm(4M)
[3] NVIDIA GeForce RTX 3090 | 57'C, 100 % | 21679 / 24576 MB | xiayongchao(5508M) songziying(15836M) gdm(4M)
[4] NVIDIA GeForce RTX 3090 | 47'C, 100 % | 19987 / 24576 MB | xiayongchao(4352M) songziying(15300M) gdm(4M)
[5] NVIDIA GeForce RTX 3090 | 54'C, 100 % | 20321 / 24576 MB | xiayongchao(4248M) songziying(15738M) gdm(4M)
[6] NVIDIA GeForce RTX 3090 | 57'C, 100 % | 21107 / 24576 MB | xiayongchao(4770M) songziying(16002M) gdm(4M)
[7] NVIDIA GeForce RTX 3090 | 50'C, 0 % | 17024 / 24576 MB | songziying(16692M) gdm(4M)
2024-08-16 15:41:48,549 INFO Train: 1/20 ( 5%) [ 199/1935 ( 10%)] Loss: 18.29 (390.) LR: 5.987e-05 Time cost: 07:46/1:07:26 [09:10/24:55:44] Acc_iter 200 Data time: 0.11(0.32) Forward time: 1.39(2.02) Batch time: 1.50(2.34)
2024-08-16 15:43:06,604 INFO Train: 1/20 ( 5%) [ 249/1935 ( 13%)] Loss: 15.30 (314.) LR: 6.653e-05 Time cost: 09:04/1:01:10 [10:28/23:15:07] Acc_iter 250 Data time: 0.03(0.29) Forward time: 0.75(1.90) Batch time: 0.79(2.18)
2024-08-16 15:44:07,099 INFO Save latest model to /home/songziying/workspace/OpenPCDet/output/nuscenes_models/bevfusion/bevfusion_baseline_1_4_split_scenes/ckpt/latest_model
2024-08-16 15:44:17,139 INFO Train: 1/20 ( 5%) [ 299/1935 ( 15%)] Loss: 9.428 (264.) LR: 7.320e-05 Time cost: 10:14/55:52 [11:39/21:51:34] Acc_iter 300 Data time: 0.33(0.26) Forward time: 2.83(1.80) Batch time: 3.17(2.06)
2024-08-16 15:44:17,727 INFO sugon-X640-G40 Fri Aug 16 15:44:17 2024 535.183.01
[0] NVIDIA GeForce RTX 3090 | 52'C, 0 % | 15678 / 24576 MB | songziying(15332M) gdm(9M) gdm(6M)
[1] NVIDIA GeForce RTX 3090 | 53'C, 100 % | 21101 / 24576 MB | xiayongchao(4820M) songziying(15946M) gdm(4M)
[2] NVIDIA GeForce RTX 3090 | 57'C, 100 % | 21081 / 24576 MB | xiayongchao(5018M) songziying(15728M) gdm(4M)
[3] NVIDIA GeForce RTX 3090 | 58'C, 100 % | 21047 / 24576 MB | xiayongchao(4872M) songziying(15840M) gdm(4M)
[4] NVIDIA GeForce RTX 3090 | 48'C, 100 % | 19013 / 24576 MB | xiayongchao(3378M) songziying(15300M) gdm(4M)
[5] NVIDIA GeForce RTX 3090 | 56'C, 100 % | 21101 / 24576 MB | xiayongchao(5028M) songziying(15738M) gdm(4M)
[6] NVIDIA GeForce RTX 3090 | 58'C, 96 % | 19201 / 24576 MB | xiayongchao(2864M) songziying(16002M) gdm(4M)
[7] NVIDIA GeForce RTX 3090 | 52'C, 100 % | 17024 / 24576 MB | songziying(16692M) gdm(4M)
2024-08-16 15:45:22,009 INFO Train: 1/20 ( 5%) [ 349/1935 ( 18%)] Loss: 9.229 (227.) LR: 7.987e-05 Time cost: 11:19/51:19 [12:44/20:41:12] Acc_iter 350 Data time: 0.02(0.24) Forward time: 0.74(1.70) Batch time: 0.76(1.95)
2024-08-16 15:46:20,225 INFO Train: 1/20 ( 5%) [ 399/1935 ( 21%)] Loss: 6.134 (200.) LR: 8.653e-05 Time cost: 12:17/47:13 [13:42/19:37:32] Acc_iter 400 Data time: 0.12(0.22) Forward time: 0.78(1.63) Batch time: 0.90(1.85)
2024-08-16 15:47:06,548 INFO Train: 1/20 ( 5%) [ 449/1935 ( 23%)] Loss: 5.448 (178.) LR: 9.320e-05 Time cost: 13:04/43:09 [14:28/18:30:57] Acc_iter 450 Data time: 0.11(0.21) Forward time: 0.73(1.54) Batch time: 0.84(1.75)
2024-08-16 15:47:07,207 INFO sugon-X640-G40 Fri Aug 16 15:47:07 2024 535.183.01
[0] NVIDIA GeForce RTX 3090 | 54'C, 0 % | 15678 / 24576 MB | songziying(15332M) gdm(9M) gdm(6M)
[1] NVIDIA GeForce RTX 3090 | 56'C, 100 % | 21005 / 24576 MB | xiayongchao(4724M) songziying(15946M) gdm(4M)
[2] NVIDIA GeForce RTX 3090 | 59'C, 100 % | 19133 / 24576 MB | xiayongchao(3070M) songziying(15728M) gdm(4M)
[3] NVIDIA GeForce RTX 3090 | 61'C, 100 % | 21617 / 24576 MB | xiayongchao(5442M) songziying(15840M) gdm(4M)
[4] NVIDIA GeForce RTX 3090 | 51'C, 100 % | 19571 / 24576 MB | xiayongchao(3936M) songziying(15300M) gdm(4M)
[5] NVIDIA GeForce RTX 3090 | 57'C, 100 % | 21503 / 24576 MB | xiayongchao(5430M) songziying(15738M) gdm(4M)
[6] NVIDIA GeForce RTX 3090 | 61'C, 100 % | 21617 / 24576 MB | xiayongchao(5280M) songziying(16002M) gdm(4M)
[7] NVIDIA GeForce RTX 3090 | 54'C, 100 % | 17026 / 24576 MB | songziying(16694M) gdm(4M)
2024-08-16 15:47:53,236 INFO Train: 1/20 ( 5%) [ 499/1935 ( 26%)] Loss: 6.216 (161.) LR: 9.987e-05 Time cost: 13:50/39:46 [15:15/17:38:00] Acc_iter 500 Data time: 0.05(0.19) Forward time: 0.71(1.47) Batch time: 0.76(1.67)
2024-08-16 15:48:36,693 INFO Train: 1/20 ( 5%) [ 549/1935 ( 28%)] Loss: 3.930 (147.) LR: 1.000e-04 Time cost: 14:34/36:43 [15:58/16:50:48] Acc_iter 550 Data time: 0.09(0.18) Forward time: 0.76(1.41) Batch time: 0.85(1.59)
2024-08-16 15:49:04,170 INFO Save latest model to /home/songziying/workspace/OpenPCDet/output/nuscenes_models/bevfusion/bevfusion_baseline_1_4_split_scenes/ckpt/latest_model
2024-08-16 15:49:19,944 INFO Train: 1/20 ( 5%) [ 599/1935 ( 31%)] Loss: 6.858 (135.) LR: 1.000e-04 Time cost: 15:17/34:03 [16:41/16:11:08] Acc_iter 600 Data time: 0.05(0.17) Forward time: 0.84(1.36) Batch time: 0.89(1.53)
2024-08-16 15:49:20,546 INFO sugon-X640-G40 Fri Aug 16 15:49:20 2024 535.183.01
[0] NVIDIA GeForce RTX 3090 | 56'C, 0 % | 15678 / 24576 MB | songziying(15332M) gdm(9M) gdm(6M)
[1] NVIDIA GeForce RTX 3090 | 58'C, 100 % | 21695 / 24576 MB | xiayongchao(5414M) songziying(15946M) gdm(4M)
[2] NVIDIA GeForce RTX 3090 | 61'C, 100 % | 20151 / 24576 MB | xiayongchao(4088M) songziying(15728M) gdm(4M)
[3] NVIDIA GeForce RTX 3090 | 62'C, 100 % | 21181 / 24576 MB | xiayongchao(5006M) songziying(15840M) gdm(4M)
[4] NVIDIA GeForce RTX 3090 | 52'C, 100 % | 19331 / 24576 MB | xiayongchao(3696M) songziying(15300M) gdm(4M)
[5] NVIDIA GeForce RTX 3090 | 59'C, 100 % | 18323 / 24576 MB | xiayongchao(2250M) songziying(15738M) gdm(4M)
[6] NVIDIA GeForce RTX 3090 | 62'C, 100 % | 20933 / 24576 MB | xiayongchao(4596M) songziying(16002M) gdm(4M)
[7] NVIDIA GeForce RTX 3090 | 55'C, 100 % | 17026 / 24576 MB | songziying(16694M) gdm(4M)
2024-08-16 15:50:01,591 INFO Train: 1/20 ( 5%) [ 649/1935 ( 34%)] Loss: 6.456 (125.) LR: 1.000e-04 Time cost: 15:59/31:37 [17:23/15:35:53] Acc_iter 650 Data time: 0.05(0.16) Forward time: 0.79(1.32) Batch time: 0.83(1.48)
2024-08-16 15:50:42,144 INFO Train: 1/20 ( 5%) [ 699/1935 ( 36%)] Loss: 4.959 (117.) LR: 1.000e-04 Time cost: 16:39/29:25 [18:04/15:04:35] Acc_iter 700 Data time: 0.07(0.15) Forward time: 0.76(1.28) Batch time: 0.83(1.43)
2024-08-16 15:51:23,805 INFO Train: 1/20 ( 5%) [ 749/1935 ( 39%)] Loss: 5.011 (109.) LR: 1.000e-04 Time cost: 17:21/27:26 [18:45/14:38:18] Acc_iter 750 Data time: 0.03(0.15) Forward time: 0.71(1.24) Batch time: 0.73(1.39)
2024-08-16 15:51:24,344 INFO sugon-X640-G40 Fri Aug 16 15:51:24 2024 535.183.01
[0] NVIDIA GeForce RTX 3090 | 56'C, 0 % | 15678 / 24576 MB | songziying(15332M) gdm(9M) gdm(6M)
[1] NVIDIA GeForce RTX 3090 | 58'C, 100 % | 19977 / 24576 MB | xiayongchao(3696M) songziying(15946M) gdm(4M)
[2] NVIDIA GeForce RTX 3090 | 61'C, 100 % | 19521 / 24576 MB | xiayongchao(3458M) songziying(15728M) gdm(4M)
[3] NVIDIA GeForce RTX 3090 | 63'C, 100 % | 20119 / 24576 MB | xiayongchao(3944M) songziying(15840M) gdm(4M)
[4] NVIDIA GeForce RTX 3090 | 52'C, 100 % | 19747 / 24576 MB | xiayongchao(4112M) songziying(15300M) gdm(4M)
[5] NVIDIA GeForce RTX 3090 | 59'C, 100 % | 20127 / 24576 MB | xiayongchao(4054M) songziying(15738M) gdm(4M)
[6] NVIDIA GeForce RTX 3090 | 62'C, 100 % | 19719 / 24576 MB | xiayongchao(3382M) songziying(16002M) gdm(4M)
[7] NVIDIA GeForce RTX 3090 | 55'C, 100 % | 17026 / 24576 MB | songziying(16694M) gdm(4M)
2024-08-16 15:52:04,895 INFO Train: 1/20 ( 5%) [ 799/1935 ( 41%)] Loss: 4.588 (103.) LR: 1.000e-04 Time cost: 18:02/25:37 [19:26/14:14:46] Acc_iter 800 Data time: 0.05(0.14) Forward time: 0.75(1.21) Batch time: 0.81(1.36)
2024-08-16 15:52:45,100 INFO Train: 1/20 ( 5%) [ 849/1935 ( 44%)] Loss: 3.699 (96.9) LR: 1.000e-04 Time cost: 18:42/23:54 [20:07/13:53:16] Acc_iter 850 Data time: 0.02(0.14) Forward time: 0.77(1.19) Batch time: 0.79(1.32)
2024-08-16 15:53:25,885 INFO Train: 1/20 ( 5%) [ 899/1935 ( 46%)] Loss: 4.475 (91.7) LR: 1.000e-04 Time cost: 19:23/22:19 [20:47/13:34:29] Acc_iter 900 Data time: 0.09(0.13) Forward time: 0.71(1.16) Batch time: 0.80(1.29)
2024-08-16 15:53:26,381 INFO sugon-X640-G40 Fri Aug 16 15:53:26 2024 535.183.01
[0] NVIDIA GeForce RTX 3090 | 56'C, 0 % | 15678 / 24576 MB | songziying(15332M) gdm(9M) gdm(6M)
[1] NVIDIA GeForce RTX 3090 | 58'C, 100 % | 23067 / 24576 MB | xiayongchao(6786M) songziying(15946M) gdm(4M)
[2] NVIDIA GeForce RTX 3090 | 61'C, 99 % | 21041 / 24576 MB | xiayongchao(4978M) songziying(15728M) gdm(4M)
[3] NVIDIA GeForce RTX 3090 | 63'C, 100 % | 19857 / 24576 MB | xiayongchao(3682M) songziying(15840M) gdm(4M)
[4] NVIDIA GeForce RTX 3090 | 52'C, 100 % | 20311 / 24576 MB | xiayongchao(4674M) songziying(15302M) gdm(4M)
[5] NVIDIA GeForce RTX 3090 | 59'C, 100 % | 19391 / 24576 MB | xiayongchao(3318M) songziying(15738M) gdm(4M)
[6] NVIDIA GeForce RTX 3090 | 62'C, 100 % | 20025 / 24576 MB | xiayongchao(3686M) songziying(16004M) gdm(4M)
[7] NVIDIA GeForce RTX 3090 | 56'C, 100 % | 17026 / 24576 MB | songziying(16694M) gdm(4M)
2024-08-16 15:54:04,386 INFO Save latest model to /home/songziying/workspace/OpenPCDet/output/nuscenes_models/bevfusion/bevfusion_baseline_1_4_split_scenes/ckpt/latest_model
2024-08-16 15:54:08,429 INFO Train: 1/20 ( 5%) [ 949/1935 ( 49%)] Loss: 4.058 (87.1) LR: 1.000e-04 Time cost: 20:06/20:51 [21:30/13:18:46] Acc_iter 950 Data time: 0.07(0.13) Forward time: 0.75(1.14) Batch time: 0.83(1.27)
2024-08-16 15:54:48,931 INFO Train: 1/20 ( 5%) [ 999/1935 ( 52%)] Loss: 3.808 (83.0) LR: 1.000e-04 Time cost: 20:46/19:26 [22:10/13:03:17] Acc_iter 1000 Data time: 0.04(0.12) Forward time: 0.72(1.12) Batch time: 0.76(1.25)
2024-08-16 15:55:29,353 INFO Train: 1/20 ( 5%) [1049/1935 ( 54%)] Loss: 3.271 (79.2) LR: 1.000e-04 Time cost: 21:26/18:05 [22:51/12:49:09] Acc_iter 1050 Data time: 0.02(0.12) Forward time: 0.75(1.11) Batch time: 0.77(1.23)
2024-08-16 15:55:29,878 INFO sugon-X640-G40 Fri Aug 16 15:55:29 2024 535.183.01
[0] NVIDIA GeForce RTX 3090 | 56'C, 0 % | 15678 / 24576 MB | songziying(15332M) gdm(9M) gdm(6M)
[1] NVIDIA GeForce RTX 3090 | 58'C, 100 % | 19295 / 24576 MB | xiayongchao(3014M) songziying(15946M) gdm(4M)
[2] NVIDIA GeForce RTX 3090 | 61'C, 100 % | 19891 / 24576 MB | xiayongchao(3828M) songziying(15728M) gdm(4M)
[3] NVIDIA GeForce RTX 3090 | 63'C, 100 % | 20279 / 24576 MB | xiayongchao(4104M) songziying(15840M) gdm(4M)
[4] NVIDIA GeForce RTX 3090 | 52'C, 100 % | 20501 / 24576 MB | xiayongchao(4864M) songziying(15302M) gdm(4M)
[5] NVIDIA GeForce RTX 3090 | 59'C, 100 % | 20131 / 24576 MB | xiayongchao(4058M) songziying(15738M) gdm(4M)
[6] NVIDIA GeForce RTX 3090 | 62'C, 100 % | 19841 / 24576 MB | xiayongchao(3502M) songziying(16004M) gdm(4M)
[7] NVIDIA GeForce RTX 3090 | 55'C, 100 % | 17026 / 24576 MB | songziying(16694M) gdm(4M)
2024-08-16 15:56:10,442 INFO Train: 1/20 ( 5%) [1099/1935 ( 57%)] Loss: 4.448 (75.8) LR: 1.000e-04 Time cost: 22:08/16:49 [23:32/12:36:37] Acc_iter 1100 Data time: 0.06(0.12) Forward time: 0.77(1.09) Batch time: 0.83(1.21)