[2021-03-26 16:13:12,042][root][INFO] - ===> Configurations [2021-03-26 16:13:12,049][root][INFO] - net: model: Res16UNet34C conv1_kernel_size: 3 weights: /checkpoint/jihou/data_efficient3d/rebuttal/benchmark/weights/checkpoint_Res16UNet34C_mAP.pth weights_for_inner_model: false dilations: - 1 - 1 - 1 - 1 wrapper_type: null wrapper_region_type: 1 wrapper_kernel_size: 3 wrapper_lr: 0.1 meanfield_iterations: 10 crf_spatial_sigma: 1 crf_chromatic_sigma: 12 optimizer: optimizer: SGD lr: 0.01 sgd_momentum: 0.9 sgd_dampening: 0.1 adam_beta1: 0.9 adam_beta2: 0.999 weight_decay: 0.0001 param_histogram_freq: 100 save_param_histogram: false iter_size: 1 bn_momentum: 0.02 scheduler: StepLR max_iter: 60000 step_size: 20000.0 step_gamma: 0.1 poly_power: 0.9 exp_gamma: 0.95 exp_step_size: 445 data: dataset: ScannetVoxelization2cmDataset train_file: null voxel_size: 0.05 data_dir: data sampled_inds: null temporal_dilation: 30 temporal_numseq: 3 point_lim: -1 pre_point_lim: -1 batch_size: 16 val_batch_size: 1 test_batch_size: 1 cache_data: false num_workers: 1 ignore_label: 255 return_transformation: true ignore_duplicate_class: false partial_crop: 0 train_limit_numpoints: 0 synthia_path: /home/chrischoy/datasets/Synthia/Synthia4D synthia_camera_path: /home/chrischoy/datasets/Synthia/%s/CameraParams/ synthia_camera_intrinsic_file: intrinsics.txt synthia_camera_extrinsics_file: Stereo_Right/Omni_F/%s.txt temporal_rand_dilation: false temporal_rand_numseq: false scannet_path: /checkpoint/jihou/data/scannet/pointcloud/ stanford3d_path: /home/chrischoy/datasets/Stanford3D train: is_train: false stat_freq: 40 val_freq: 5 empty_cache_freq: 1 train_phase: train val_phase: val overwrite_weights: true resume: true resume_optimizer: True, eval_upsample: false lenient_weight_loading: true distributed: distributed_world_size: 8 distributed_rank: 0 distributed_backend: nccl distributed_init_method: null distributed_port: 10010 device_id: 0 distributed_no_spawn: true ddp_backend: c10d bucket_cap_mb: 25 augmentation: use_feat_aug: true data_aug_color_trans_ratio: 0.1 data_aug_color_jitter_std: 0.05 normalize_color: true data_aug_scale_min: 0.9 data_aug_scale_max: 1.1 data_aug_hue_max: 0.5 data_aug_saturation_max: 0.2 test: test_phase: val test_stat_freq: 100 evaluate_benchmark: false misc: is_cuda: true load_path: null log_step: 50 log_level: INFO num_gpus: 1 seed: 123 log_dir: ./output load_bn: all_bn resume_config: null train_stuff: false [2021-03-26 16:13:12,050][root][INFO] - ===> Initializing dataloader [2021-03-26 16:13:12,052][root][INFO] - Loading ScannetVoxelization2cmDataset: scannetv2_train.txt [2021-03-26 16:13:12,055][root][INFO] - Loading ScannetVoxelization2cmDataset: scannetv2_val.txt [2021-03-26 16:13:12,057][root][INFO] - Loading ScannetVoxelization2cmDataset: scannetv2_val.txt [2021-03-26 16:13:12,057][root][INFO] - ===> Building model building model, 3 [2021-03-26 16:13:12,331][root][INFO] - ===> Number of trainable parameters: Res16UNet34C: 37856439 [2021-03-26 16:13:12,331][root][INFO] - Res16UNet34C( (conv0p1s1): MinkowskiConvolution(in=3, out=32, region_type=RegionType.HYPERCUBE, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1]) (bn0): MinkowskiBatchNorm(32, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True) (conv1p1s2): MinkowskiConvolution(in=32, out=32, region_type=RegionType.HYPERCUBE, kernel_size=[2, 2, 2], stride=[2, 2, 2], dilation=[1, 1, 1]) (bn1): MinkowskiBatchNorm(32, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True) (block1): Sequential( (0): BasicBlock( (conv1): MinkowskiConvolution(in=32, out=32, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1]) (norm1): MinkowskiBatchNorm(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): MinkowskiConvolution(in=32, out=32, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1]) (norm2): MinkowskiBatchNorm(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): MinkowskiReLU() ) (1): BasicBlock( (conv1): MinkowskiConvolution(in=32, out=32, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1]) (norm1): MinkowskiBatchNorm(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): MinkowskiConvolution(in=32, out=32, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1]) (norm2): MinkowskiBatchNorm(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): MinkowskiReLU() ) ) (conv2p2s2): MinkowskiConvolution(in=32, out=32, region_type=RegionType.HYPERCUBE, kernel_size=[2, 2, 2], stride=[2, 2, 2], dilation=[1, 1, 1]) (bn2): MinkowskiBatchNorm(32, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True) (block2): Sequential( (0): BasicBlock( (conv1): MinkowskiConvolution(in=32, out=64, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1]) (norm1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): MinkowskiConvolution(in=64, out=64, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1]) (norm2): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): MinkowskiReLU() (downsample): Sequential( (0): MinkowskiConvolution(in=32, out=64, region_type=RegionType.HYPERCUBE, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1]) (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True) ) ) (1): BasicBlock( (conv1): MinkowskiConvolution(in=64, out=64, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1]) (norm1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): MinkowskiConvolution(in=64, out=64, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1]) (norm2): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): MinkowskiReLU() ) (2): BasicBlock( (conv1): MinkowskiConvolution(in=64, out=64, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1]) (norm1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): MinkowskiConvolution(in=64, out=64, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1]) (norm2): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): MinkowskiReLU() ) ) (conv3p4s2): MinkowskiConvolution(in=64, out=64, region_type=RegionType.HYPERCUBE, kernel_size=[2, 2, 2], stride=[2, 2, 2], dilation=[1, 1, 1]) (bn3): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True) (block3): Sequential( (0): BasicBlock( (conv1): MinkowskiConvolution(in=64, out=128, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1]) (norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): MinkowskiConvolution(in=128, out=128, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1]) (norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): MinkowskiReLU() (downsample): Sequential( (0): MinkowskiConvolution(in=64, out=128, region_type=RegionType.HYPERCUBE, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1]) (1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True) ) ) (1): BasicBlock( (conv1): MinkowskiConvolution(in=128, out=128, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1]) (norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): MinkowskiConvolution(in=128, out=128, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1]) (norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): MinkowskiReLU() ) (2): BasicBlock( (conv1): MinkowskiConvolution(in=128, out=128, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1]) (norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): MinkowskiConvolution(in=128, out=128, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1]) (norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): MinkowskiReLU() ) (3): BasicBlock( (conv1): MinkowskiConvolution(in=128, out=128, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1]) (norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): MinkowskiConvolution(in=128, out=128, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1]) (norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): MinkowskiReLU() ) ) (conv4p8s2): MinkowskiConvolution(in=128, out=128, region_type=RegionType.HYPERCUBE, kernel_size=[2, 2, 2], stride=[2, 2, 2], dilation=[1, 1, 1]) (bn4): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True) (block4): Sequential( (0): BasicBlock( (conv1): MinkowskiConvolution(in=128, out=256, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1]) (norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): MinkowskiConvolution(in=256, out=256, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1]) (norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): MinkowskiReLU() (downsample): Sequential( (0): MinkowskiConvolution(in=128, out=256, region_type=RegionType.HYPERCUBE, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1]) (1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True) ) ) (1): BasicBlock( (conv1): MinkowskiConvolution(in=256, out=256, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1]) (norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): MinkowskiConvolution(in=256, out=256, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1]) (norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): MinkowskiReLU() ) (2): BasicBlock( (conv1): MinkowskiConvolution(in=256, out=256, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1]) (norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): MinkowskiConvolution(in=256, out=256, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1]) (norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): MinkowskiReLU() ) (3): BasicBlock( (conv1): MinkowskiConvolution(in=256, out=256, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1]) (norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): MinkowskiConvolution(in=256, out=256, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1]) (norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): MinkowskiReLU() ) (4): BasicBlock( (conv1): MinkowskiConvolution(in=256, out=256, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1]) (norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): MinkowskiConvolution(in=256, out=256, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1]) (norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): MinkowskiReLU() ) (5): BasicBlock( (conv1): MinkowskiConvolution(in=256, out=256, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1]) (norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): MinkowskiConvolution(in=256, out=256, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1]) (norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): MinkowskiReLU() ) ) (convtr4p16s2): MinkowskiConvolutionTranspose(in=256, out=256, region_type=RegionType.HYPERCUBE, kernel_size=[2, 2, 2], stride=[2, 2, 2], dilation=[1, 1, 1]) (bntr4): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True) (block5): Sequential( (0): BasicBlock( (conv1): MinkowskiConvolution(in=384, out=256, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1]) (norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): MinkowskiConvolution(in=256, out=256, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1]) (norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): MinkowskiReLU() (downsample): Sequential( (0): MinkowskiConvolution(in=384, out=256, region_type=RegionType.HYPERCUBE, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1]) (1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True) ) ) (1): BasicBlock( (conv1): MinkowskiConvolution(in=256, out=256, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1]) (norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): MinkowskiConvolution(in=256, out=256, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1]) (norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): MinkowskiReLU() ) ) (convtr5p8s2): MinkowskiConvolutionTranspose(in=256, out=128, region_type=RegionType.HYPERCUBE, kernel_size=[2, 2, 2], stride=[2, 2, 2], dilation=[1, 1, 1]) (bntr5): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True) (block6): Sequential( (0): BasicBlock( (conv1): MinkowskiConvolution(in=192, out=128, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1]) (norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): MinkowskiConvolution(in=128, out=128, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1]) (norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): MinkowskiReLU() (downsample): Sequential( (0): MinkowskiConvolution(in=192, out=128, region_type=RegionType.HYPERCUBE, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1]) (1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True) ) ) (1): BasicBlock( (conv1): MinkowskiConvolution(in=128, out=128, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1]) (norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): MinkowskiConvolution(in=128, out=128, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1]) (norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): MinkowskiReLU() ) ) (convtr6p4s2): MinkowskiConvolutionTranspose(in=128, out=96, region_type=RegionType.HYPERCUBE, kernel_size=[2, 2, 2], stride=[2, 2, 2], dilation=[1, 1, 1]) (bntr6): MinkowskiBatchNorm(96, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True) (block7): Sequential( (0): BasicBlock( (conv1): MinkowskiConvolution(in=128, out=96, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1]) (norm1): MinkowskiBatchNorm(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): MinkowskiConvolution(in=96, out=96, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1]) (norm2): MinkowskiBatchNorm(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): MinkowskiReLU() (downsample): Sequential( (0): MinkowskiConvolution(in=128, out=96, region_type=RegionType.HYPERCUBE, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1]) (1): MinkowskiBatchNorm(96, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True) ) ) (1): BasicBlock( (conv1): MinkowskiConvolution(in=96, out=96, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1]) (norm1): MinkowskiBatchNorm(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): MinkowskiConvolution(in=96, out=96, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1]) (norm2): MinkowskiBatchNorm(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): MinkowskiReLU() ) ) (convtr7p2s2): MinkowskiConvolutionTranspose(in=96, out=96, region_type=RegionType.HYPERCUBE, kernel_size=[2, 2, 2], stride=[2, 2, 2], dilation=[1, 1, 1]) (bntr7): MinkowskiBatchNorm(96, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True) (block8): Sequential( (0): BasicBlock( (conv1): MinkowskiConvolution(in=128, out=96, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1]) (norm1): MinkowskiBatchNorm(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): MinkowskiConvolution(in=96, out=96, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1]) (norm2): MinkowskiBatchNorm(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): MinkowskiReLU() (downsample): Sequential( (0): MinkowskiConvolution(in=128, out=96, region_type=RegionType.HYPERCUBE, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1]) (1): MinkowskiBatchNorm(96, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True) ) ) (1): BasicBlock( (conv1): MinkowskiConvolution(in=96, out=96, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1]) (norm1): MinkowskiBatchNorm(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): MinkowskiConvolution(in=96, out=96, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1]) (norm2): MinkowskiBatchNorm(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): MinkowskiReLU() ) ) (final): MinkowskiConvolution(in=96, out=20, region_type=RegionType.HYPERCUBE, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1]) (relu): MinkowskiReLU() (offsets_pre): MinkowskiConvolution(in=96, out=96, region_type=RegionType.HYPERCUBE, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1]) (bntr_offset): MinkowskiBatchNorm(96, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True) (offsets): MinkowskiConvolution(in=96, out=3, region_type=RegionType.HYPERCUBE, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1]) ) [2021-03-26 16:13:12,334][root][INFO] - ===> Loading weights: /checkpoint/jihou/data_efficient3d/rebuttal/benchmark/weights/checkpoint_Res16UNet34C_mAP.pth [2021-03-26 16:13:12,542][root][INFO] - Loading weights:conv0p1s1.kernel, bn0.bn.weight, bn0.bn.bias, bn0.bn.running_mean, bn0.bn.running_var, bn0.bn.num_batches_tracked, conv1p1s2.kernel, bn1.bn.weight, bn1.bn.bias, bn1.bn.running_mean, bn1.bn.running_var, bn1.bn.num_batches_tracked, block1.0.conv1.kernel, block1.0.norm1.bn.weight, block1.0.norm1.bn.bias, block1.0.norm1.bn.running_mean, block1.0.norm1.bn.running_var, block1.0.norm1.bn.num_batches_tracked, block1.0.conv2.kernel, block1.0.norm2.bn.weight, block1.0.norm2.bn.bias, block1.0.norm2.bn.running_mean, block1.0.norm2.bn.running_var, block1.0.norm2.bn.num_batches_tracked, block1.1.conv1.kernel, block1.1.norm1.bn.weight, block1.1.norm1.bn.bias, block1.1.norm1.bn.running_mean, block1.1.norm1.bn.running_var, block1.1.norm1.bn.num_batches_tracked, block1.1.conv2.kernel, block1.1.norm2.bn.weight, block1.1.norm2.bn.bias, block1.1.norm2.bn.running_mean, block1.1.norm2.bn.running_var, block1.1.norm2.bn.num_batches_tracked, conv2p2s2.kernel, bn2.bn.weight, bn2.bn.bias, bn2.bn.running_mean, bn2.bn.running_var, bn2.bn.num_batches_tracked, block2.0.conv1.kernel, block2.0.norm1.bn.weight, block2.0.norm1.bn.bias, block2.0.norm1.bn.running_mean, block2.0.norm1.bn.running_var, block2.0.norm1.bn.num_batches_tracked, block2.0.conv2.kernel, block2.0.norm2.bn.weight, block2.0.norm2.bn.bias, block2.0.norm2.bn.running_mean, block2.0.norm2.bn.running_var, block2.0.norm2.bn.num_batches_tracked, block2.0.downsample.0.kernel, block2.0.downsample.1.bn.weight, block2.0.downsample.1.bn.bias, block2.0.downsample.1.bn.running_mean, block2.0.downsample.1.bn.running_var, block2.0.downsample.1.bn.num_batches_tracked, block2.1.conv1.kernel, block2.1.norm1.bn.weight, block2.1.norm1.bn.bias, block2.1.norm1.bn.running_mean, block2.1.norm1.bn.running_var, block2.1.norm1.bn.num_batches_tracked, block2.1.conv2.kernel, block2.1.norm2.bn.weight, block2.1.norm2.bn.bias, block2.1.norm2.bn.running_mean, block2.1.norm2.bn.running_var, block2.1.norm2.bn.num_batches_tracked, block2.2.conv1.kernel, block2.2.norm1.bn.weight, block2.2.norm1.bn.bias, block2.2.norm1.bn.running_mean, block2.2.norm1.bn.running_var, block2.2.norm1.bn.num_batches_tracked, block2.2.conv2.kernel, block2.2.norm2.bn.weight, block2.2.norm2.bn.bias, block2.2.norm2.bn.running_mean, block2.2.norm2.bn.running_var, block2.2.norm2.bn.num_batches_tracked, conv3p4s2.kernel, bn3.bn.weight, bn3.bn.bias, bn3.bn.running_mean, bn3.bn.running_var, bn3.bn.num_batches_tracked, block3.0.conv1.kernel, block3.0.norm1.bn.weight, block3.0.norm1.bn.bias, block3.0.norm1.bn.running_mean, block3.0.norm1.bn.running_var, block3.0.norm1.bn.num_batches_tracked, block3.0.conv2.kernel, block3.0.norm2.bn.weight, block3.0.norm2.bn.bias, block3.0.norm2.bn.running_mean, block3.0.norm2.bn.running_var, block3.0.norm2.bn.num_batches_tracked, block3.0.downsample.0.kernel, block3.0.downsample.1.bn.weight, block3.0.downsample.1.bn.bias, block3.0.downsample.1.bn.running_mean, block3.0.downsample.1.bn.running_var, block3.0.downsample.1.bn.num_batches_tracked, block3.1.conv1.kernel, block3.1.norm1.bn.weight, block3.1.norm1.bn.bias, block3.1.norm1.bn.running_mean, block3.1.norm1.bn.running_var, block3.1.norm1.bn.num_batches_tracked, block3.1.conv2.kernel, block3.1.norm2.bn.weight, block3.1.norm2.bn.bias, block3.1.norm2.bn.running_mean, block3.1.norm2.bn.running_var, block3.1.norm2.bn.num_batches_tracked, block3.2.conv1.kernel, block3.2.norm1.bn.weight, block3.2.norm1.bn.bias, block3.2.norm1.bn.running_mean, block3.2.norm1.bn.running_var, block3.2.norm1.bn.num_batches_tracked, block3.2.conv2.kernel, block3.2.norm2.bn.weight, block3.2.norm2.bn.bias, block3.2.norm2.bn.running_mean, block3.2.norm2.bn.running_var, block3.2.norm2.bn.num_batches_tracked, block3.3.conv1.kernel, block3.3.norm1.bn.weight, block3.3.norm1.bn.bias, block3.3.norm1.bn.running_mean, block3.3.norm1.bn.running_var, block3.3.norm1.bn.num_batches_tracked, block3.3.conv2.kernel, block3.3.norm2.bn.weight, block3.3.norm2.bn.bias, block3.3.norm2.bn.running_mean, block3.3.norm2.bn.running_var, block3.3.norm2.bn.num_batches_tracked, conv4p8s2.kernel, bn4.bn.weight, bn4.bn.bias, bn4.bn.running_mean, bn4.bn.running_var, bn4.bn.num_batches_tracked, block4.0.conv1.kernel, block4.0.norm1.bn.weight, block4.0.norm1.bn.bias, block4.0.norm1.bn.running_mean, block4.0.norm1.bn.running_var, block4.0.norm1.bn.num_batches_tracked, block4.0.conv2.kernel, block4.0.norm2.bn.weight, block4.0.norm2.bn.bias, block4.0.norm2.bn.running_mean, block4.0.norm2.bn.running_var, block4.0.norm2.bn.num_batches_tracked, block4.0.downsample.0.kernel, block4.0.downsample.1.bn.weight, block4.0.downsample.1.bn.bias, block4.0.downsample.1.bn.running_mean, block4.0.downsample.1.bn.running_var, block4.0.downsample.1.bn.num_batches_tracked, block4.1.conv1.kernel, block4.1.norm1.bn.weight, block4.1.norm1.bn.bias, block4.1.norm1.bn.running_mean, block4.1.norm1.bn.running_var, block4.1.norm1.bn.num_batches_tracked, block4.1.conv2.kernel, block4.1.norm2.bn.weight, block4.1.norm2.bn.bias, block4.1.norm2.bn.running_mean, block4.1.norm2.bn.running_var, block4.1.norm2.bn.num_batches_tracked, block4.2.conv1.kernel, block4.2.norm1.bn.weight, block4.2.norm1.bn.bias, block4.2.norm1.bn.running_mean, block4.2.norm1.bn.running_var, block4.2.norm1.bn.num_batches_tracked, block4.2.conv2.kernel, block4.2.norm2.bn.weight, block4.2.norm2.bn.bias, block4.2.norm2.bn.running_mean, block4.2.norm2.bn.running_var, block4.2.norm2.bn.num_batches_tracked, block4.3.conv1.kernel, block4.3.norm1.bn.weight, block4.3.norm1.bn.bias, block4.3.norm1.bn.running_mean, block4.3.norm1.bn.running_var, block4.3.norm1.bn.num_batches_tracked, block4.3.conv2.kernel, block4.3.norm2.bn.weight, block4.3.norm2.bn.bias, block4.3.norm2.bn.running_mean, block4.3.norm2.bn.running_var, block4.3.norm2.bn.num_batches_tracked, block4.4.conv1.kernel, block4.4.norm1.bn.weight, block4.4.norm1.bn.bias, block4.4.norm1.bn.running_mean, block4.4.norm1.bn.running_var, block4.4.norm1.bn.num_batches_tracked, block4.4.conv2.kernel, block4.4.norm2.bn.weight, block4.4.norm2.bn.bias, block4.4.norm2.bn.running_mean, block4.4.norm2.bn.running_var, block4.4.norm2.bn.num_batches_tracked, block4.5.conv1.kernel, block4.5.norm1.bn.weight, block4.5.norm1.bn.bias, block4.5.norm1.bn.running_mean, block4.5.norm1.bn.running_var, block4.5.norm1.bn.num_batches_tracked, block4.5.conv2.kernel, block4.5.norm2.bn.weight, block4.5.norm2.bn.bias, block4.5.norm2.bn.running_mean, block4.5.norm2.bn.running_var, block4.5.norm2.bn.num_batches_tracked, convtr4p16s2.kernel, bntr4.bn.weight, bntr4.bn.bias, bntr4.bn.running_mean, bntr4.bn.running_var, bntr4.bn.num_batches_tracked, block5.0.conv1.kernel, block5.0.norm1.bn.weight, block5.0.norm1.bn.bias, block5.0.norm1.bn.running_mean, block5.0.norm1.bn.running_var, block5.0.norm1.bn.num_batches_tracked, block5.0.conv2.kernel, block5.0.norm2.bn.weight, block5.0.norm2.bn.bias, block5.0.norm2.bn.running_mean, block5.0.norm2.bn.running_var, block5.0.norm2.bn.num_batches_tracked, block5.0.downsample.0.kernel, block5.0.downsample.1.bn.weight, block5.0.downsample.1.bn.bias, block5.0.downsample.1.bn.running_mean, block5.0.downsample.1.bn.running_var, block5.0.downsample.1.bn.num_batches_tracked, block5.1.conv1.kernel, block5.1.norm1.bn.weight, block5.1.norm1.bn.bias, block5.1.norm1.bn.running_mean, block5.1.norm1.bn.running_var, block5.1.norm1.bn.num_batches_tracked, block5.1.conv2.kernel, block5.1.norm2.bn.weight, block5.1.norm2.bn.bias, block5.1.norm2.bn.running_mean, block5.1.norm2.bn.running_var, block5.1.norm2.bn.num_batches_tracked, convtr5p8s2.kernel, bntr5.bn.weight, bntr5.bn.bias, bntr5.bn.running_mean, bntr5.bn.running_var, bntr5.bn.num_batches_tracked, block6.0.conv1.kernel, block6.0.norm1.bn.weight, block6.0.norm1.bn.bias, block6.0.norm1.bn.running_mean, block6.0.norm1.bn.running_var, block6.0.norm1.bn.num_batches_tracked, block6.0.conv2.kernel, block6.0.norm2.bn.weight, block6.0.norm2.bn.bias, block6.0.norm2.bn.running_mean, block6.0.norm2.bn.running_var, block6.0.norm2.bn.num_batches_tracked, block6.0.downsample.0.kernel, block6.0.downsample.1.bn.weight, block6.0.downsample.1.bn.bias, block6.0.downsample.1.bn.running_mean, block6.0.downsample.1.bn.running_var, block6.0.downsample.1.bn.num_batches_tracked, block6.1.conv1.kernel, block6.1.norm1.bn.weight, block6.1.norm1.bn.bias, block6.1.norm1.bn.running_mean, block6.1.norm1.bn.running_var, block6.1.norm1.bn.num_batches_tracked, block6.1.conv2.kernel, block6.1.norm2.bn.weight, block6.1.norm2.bn.bias, block6.1.norm2.bn.running_mean, block6.1.norm2.bn.running_var, block6.1.norm2.bn.num_batches_tracked, convtr6p4s2.kernel, bntr6.bn.weight, bntr6.bn.bias, bntr6.bn.running_mean, bntr6.bn.running_var, bntr6.bn.num_batches_tracked, block7.0.conv1.kernel, block7.0.norm1.bn.weight, block7.0.norm1.bn.bias, block7.0.norm1.bn.running_mean, block7.0.norm1.bn.running_var, block7.0.norm1.bn.num_batches_tracked, block7.0.conv2.kernel, block7.0.norm2.bn.weight, block7.0.norm2.bn.bias, block7.0.norm2.bn.running_mean, block7.0.norm2.bn.running_var, block7.0.norm2.bn.num_batches_tracked, block7.0.downsample.0.kernel, block7.0.downsample.1.bn.weight, block7.0.downsample.1.bn.bias, block7.0.downsample.1.bn.running_mean, block7.0.downsample.1.bn.running_var, block7.0.downsample.1.bn.num_batches_tracked, block7.1.conv1.kernel, block7.1.norm1.bn.weight, block7.1.norm1.bn.bias, block7.1.norm1.bn.running_mean, block7.1.norm1.bn.running_var, block7.1.norm1.bn.num_batches_tracked, block7.1.conv2.kernel, block7.1.norm2.bn.weight, block7.1.norm2.bn.bias, block7.1.norm2.bn.running_mean, block7.1.norm2.bn.running_var, block7.1.norm2.bn.num_batches_tracked, convtr7p2s2.kernel, bntr7.bn.weight, bntr7.bn.bias, bntr7.bn.running_mean, bntr7.bn.running_var, bntr7.bn.num_batches_tracked, block8.0.conv1.kernel, block8.0.norm1.bn.weight, block8.0.norm1.bn.bias, block8.0.norm1.bn.running_mean, block8.0.norm1.bn.running_var, block8.0.norm1.bn.num_batches_tracked, block8.0.conv2.kernel, block8.0.norm2.bn.weight, block8.0.norm2.bn.bias, block8.0.norm2.bn.running_mean, block8.0.norm2.bn.running_var, block8.0.norm2.bn.num_batches_tracked, block8.0.downsample.0.kernel, block8.0.downsample.1.bn.weight, block8.0.downsample.1.bn.bias, block8.0.downsample.1.bn.running_mean, block8.0.downsample.1.bn.running_var, block8.0.downsample.1.bn.num_batches_tracked, block8.1.conv1.kernel, block8.1.norm1.bn.weight, block8.1.norm1.bn.bias, block8.1.norm1.bn.running_mean, block8.1.norm1.bn.running_var, block8.1.norm1.bn.num_batches_tracked, block8.1.conv2.kernel, block8.1.norm2.bn.weight, block8.1.norm2.bn.bias, block8.1.norm2.bn.running_mean, block8.1.norm2.bn.running_var, block8.1.norm2.bn.num_batches_tracked, final.kernel, final.bias, offsets_pre.kernel, offsets_pre.bias, bntr_offset.bn.weight, bntr_offset.bn.bias, bntr_offset.bn.running_mean, bntr_offset.bn.running_var, bntr_offset.bn.num_batches_tracked, offsets.kernel, offsets.bias [2021-03-26 16:13:14,601][root][INFO] - => no weights.pth [2021-03-26 16:13:14,619][root][INFO] - ===> Start testing [2021-03-26 16:14:56,664][root][INFO] - 101/312: Data time: 0.0144, Iter time: 0.6419 Loss 0.124 (AVG: 0.113) Score 94.986 (AVG: 96.057) mIOU 88.208 mAP 85.576 mAcc 93.959 IOU: 93.109 96.951 92.215 95.338 94.665 91.507 83.779 87.021 86.173 96.048 67.222 83.007 80.749 92.180 90.114 82.769 94.406 79.760 90.334 86.811 mAP: 85.046 96.034 86.314 90.909 93.042 86.444 79.623 83.522 84.040 62.257 80.828 58.410 76.767 95.087 93.951 96.033 97.509 89.246 98.006 78.448 mAcc: 96.673 98.061 95.731 97.261 97.016 94.755 90.089 93.417 91.575 98.248 81.847 90.227 93.864 95.439 95.203 97.079 95.947 88.562 94.158 94.029 [2021-03-26 16:16:13,510][root][INFO] - 201/312: Data time: 0.0067, Iter time: 0.2019 Loss 0.078 (AVG: 0.103) Score 97.061 (AVG: 96.461) mIOU 88.883 mAP 86.720 mAcc 94.281 IOU: 93.768 97.103 90.755 93.883 95.169 94.432 87.902 87.049 88.422 96.198 62.465 83.050 84.246 91.780 91.947 87.498 94.744 77.479 91.805 87.956 mAP: 85.381 96.847 85.097 85.477 92.829 85.123 85.248 82.315 85.615 75.707 83.187 65.376 78.676 95.382 94.306 95.041 97.361 89.774 97.328 78.334 mAcc: 97.120 98.158 95.119 96.452 97.383 96.805 92.926 92.641 92.539 98.301 82.979 89.700 93.559 96.224 95.981 97.816 96.493 85.141 95.317 94.957 [2021-03-26 16:17:45,871][root][INFO] - 301/312: Data time: 0.0078, Iter time: 0.2685 Loss 0.102 (AVG: 0.103) Score 96.247 (AVG: 96.502) mIOU 89.425 mAP 86.050 mAcc 94.182 IOU: 93.656 97.045 90.906 94.649 95.210 94.621 89.152 86.526 88.529 96.042 69.474 77.893 85.515 90.592 91.951 91.267 95.197 75.856 92.405 92.006 mAP: 84.139 96.411 85.117 86.160 92.198 85.949 83.139 83.103 85.090 80.062 81.870 63.293 75.735 92.773 94.522 95.259 97.773 90.885 89.980 77.539 mAcc: 97.227 98.116 95.683 96.934 97.377 97.019 94.081 91.489 92.544 98.175 85.402 84.850 93.412 95.153 96.001 98.545 96.914 82.576 95.419 96.715 [2021-03-26 16:17:52,849][root][INFO] - 312/312: Data time: 0.0036, Iter time: 0.2681 Loss 0.068 (AVG: 0.103) Score 97.658 (AVG: 96.501) mIOU 89.456 mAP 86.203 mAcc 94.222 IOU: 93.646 96.999 90.903 94.649 95.219 94.936 89.214 86.416 88.757 96.082 69.324 78.385 85.644 90.331 91.951 90.381 95.409 75.856 93.052 91.960 mAP: 84.120 96.404 85.178 86.160 92.057 86.344 83.235 83.400 85.224 80.293 81.941 63.665 75.700 92.915 94.522 95.598 97.860 90.885 90.640 77.916 mAcc: 97.238 98.073 95.645 96.934 97.394 97.373 94.139 91.280 92.731 98.229 85.046 85.320 93.405 94.866 96.001 98.572 97.071 82.576 95.813 96.726 [2021-03-26 16:17:52,849][root][INFO] - Finished test. 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288 scans processed: 289 scans processed: 290 scans processed: 291 scans processed: 292 scans processed: 293 scans processed: 294 scans processed: 295 scans processed: 296 scans processed: 297 scans processed: 298 scans processed: 299 scans processed: 300 scans processed: 301 scans processed: 302 scans processed: 303 scans processed: 304 scans processed: 305 scans processed: 306 scans processed: 307 scans processed: 308 scans processed: 309 scans processed: 310 scans processed: 311 scans processed: 312 [2021-03-26 16:18:02,646][root][INFO] - [2021-03-26 16:18:02,646][root][INFO] - ################################################################ [2021-03-26 16:18:02,646][root][INFO] - what : AP AP_50% AP_25% [2021-03-26 16:18:02,646][root][INFO] - ################################################################ [2021-03-26 16:18:02,646][root][INFO] - cabinet : 0.477 0.838 0.926 [2021-03-26 16:18:02,646][root][INFO] - bed : 0.490 0.888 1.000 [2021-03-26 16:18:02,646][root][INFO] - chair : 0.752 0.895 0.937 [2021-03-26 16:18:02,646][root][INFO] - sofa : 0.659 0.937 0.958 [2021-03-26 16:18:02,646][root][INFO] - table : 0.554 0.815 0.922 [2021-03-26 16:18:02,646][root][INFO] - door : 0.341 0.577 0.688 [2021-03-26 16:18:02,647][root][INFO] - window : 0.442 0.740 0.893 [2021-03-26 16:18:02,647][root][INFO] - bookshelf : 0.315 0.778 0.936 [2021-03-26 16:18:02,647][root][INFO] - picture : 0.284 0.510 0.599 [2021-03-26 16:18:02,647][root][INFO] - counter : 0.075 0.260 0.687 [2021-03-26 16:18:02,647][root][INFO] - desk : 0.180 0.556 0.907 [2021-03-26 16:18:02,647][root][INFO] - curtain : 0.501 0.766 0.860 [2021-03-26 16:18:02,647][root][INFO] - refrigerator : 0.724 0.947 0.982 [2021-03-26 16:18:02,647][root][INFO] - shower curtain : 0.694 0.937 0.937 [2021-03-26 16:18:02,647][root][INFO] - toilet : 0.869 1.000 1.000 [2021-03-26 16:18:02,647][root][INFO] - sink : 0.299 0.642 0.799 [2021-03-26 16:18:02,647][root][INFO] - bathtub : 0.582 0.935 1.000 [2021-03-26 16:18:02,647][root][INFO] - otherfurniture : 0.511 0.720 0.844 [2021-03-26 16:18:02,647][root][INFO] - ---------------------------------------------------------------- [2021-03-26 16:18:02,647][root][INFO] - average : 0.486 0.764 0.882 [2021-03-26 16:18:02,647][root][INFO] -