[02/26 14:12:58] detectron2 INFO: Rank of current process: 0. World size: 1 [02/26 14:12:59] detectron2 INFO: Environment info: ------------------------------- ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ sys.platform linux Python 3.10.13 (main, Dec 8 2023, 14:34:43) [GCC 11.4.0] numpy 1.26.4 detectron2 0.6 @/work3/s194235/python310/lib/python3.10/site-packages/detectron2 detectron2._C not built correctly: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.29' not found (required by /work3/s194235/python310/lib/python3.10/site-packages/detectron2/_C.cpython-310-x86_64-linux-gnu.so) Compiler ($CXX) c++ (GCC) 4.8.5 20150623 (Red Hat 4.8.5-44) detectron2 arch flags /work3/s194235/python310/lib/python3.10/site-packages/detectron2/_C.cpython-310-x86_64-linux-gnu.so DETECTRON2_ENV_MODULE PyTorch 2.1.2+cu121 @/work3/s194235/python310/lib/python3.10/site-packages/torch PyTorch debug build False torch._C._GLIBCXX_USE_CXX11_ABI False GPU available Yes GPU 0 NVIDIA A100-PCIE-40GB (arch=8.0) Driver version 535.154.05 CUDA_HOME None - invalid! Pillow 10.2.0 torchvision 0.16.2+cu121 @/work3/s194235/python310/lib/python3.10/site-packages/torchvision torchvision arch flags /work3/s194235/python310/lib/python3.10/site-packages/torchvision/_C.so fvcore 0.1.5.post20221221 iopath 0.1.9 cv2 4.9.0 ------------------------------- ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ PyTorch built with: - GCC 9.3 - C++ Version: 201703 - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications - Intel(R) MKL-DNN v3.1.1 (Git Hash 64f6bcbcbab628e96f33a62c3e975f8535a7bde4) - OpenMP 201511 (a.k.a. OpenMP 4.5) - LAPACK is enabled (usually provided by MKL) - NNPACK is enabled - CPU capability usage: AVX512 - CUDA Runtime 12.1 - NVCC architecture flags: -gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90 - CuDNN 8.9.2 - Magma 2.6.1 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=8.9.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-invalid-partial-specialization -Wno-unused-private-field -Wno-aligned-allocation-unavailable -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.1.2, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, [02/26 14:12:59] detectron2 INFO: Command line arguments: Namespace(config_file='configs/Base_Omni3D.yaml', resume=False, eval_only=False, num_gpus=1, num_machines=1, machine_rank=0, dist_url='tcp://127.0.0.1:50599', opts=['OUTPUT_DIR', 'output/Baseline_sgd']) [02/26 14:12:59] detectron2 INFO: Contents of args.config_file=configs/Base_Omni3D.yaml: _BASE_: "Base.yaml" SOLVER:  TYPE: "sgd"  IMS_PER_BATCH: 35 #196  BASE_LR: 0.0214 #0.12  STEPS: (17280, 23040)  MAX_ITER: 28800 #116000  WARMUP_ITERS: 900 #3625 TEST:  EVAL_PERIOD: 7200 #29000 VIS_PERIOD: 576 #2320 DATASETS:  TRAIN: ('SUNRGBD_train', 'SUNRGBD_val')  TEST: ('SUNRGBD_test',)   CATEGORY_NAMES: ('chair', 'table', 'cabinet', 'car', 'lamp', 'books', 'sofa', 'pedestrian', 'picture', 'window', 'pillow', 'truck', 'door', 'blinds', 'sink', 'shelves', 'television', 'shoes', 'cup', 'bottle', 'bookcase', 'laptop', 'desk', 'cereal box', 'floor mat', 'traffic cone', 'mirror', 'barrier', 'counter', 'camera', 'bicycle', 'toilet', 'bus', 'bed', 'refrigerator', 'trailer', 'box', 'oven', 'clothes', 'van', 'towel', 'motorcycle', 'night stand', 'stove', 'machine', 'stationery', 'bathtub', 'cyclist', 'curtain', 'bin') MODEL:  ROI_HEADS:  NUM_CLASSES: 50 [02/26 14:12:59] detectron2 INFO: Running with full config: CUDNN_BENCHMARK: false DATALOADER:  ASPECT_RATIO_GROUPING: true  BALANCE_DATASETS: false  FILTER_EMPTY_ANNOTATIONS: true  NUM_WORKERS: 4  REPEAT_THRESHOLD: 0.1  SAMPLER_TRAIN: RepeatFactorTrainingSampler DATASETS:  CATEGORY_NAMES:  - chair  - table  - cabinet  - car  - lamp  - books  - sofa  - pedestrian  - picture  - window  - pillow  - truck  - door  - blinds  - sink  - shelves  - television  - shoes  - cup  - bottle  - bookcase  - laptop  - desk  - cereal box  - floor mat  - traffic cone  - mirror  - barrier  - counter  - camera  - bicycle  - toilet  - bus  - bed  - refrigerator  - trailer  - box  - oven  - clothes  - van  - towel  - motorcycle  - night stand  - stove  - machine  - stationery  - bathtub  - cyclist  - curtain  - bin  IGNORE_NAMES:  - dontcare  - ignore  - void  MAX_DEPTH: 100000000.0  MIN_HEIGHT_THRES: 0.05  MODAL_2D_BOXES: false  PRECOMPUTED_PROPOSAL_TOPK_TEST: 1000  PRECOMPUTED_PROPOSAL_TOPK_TRAIN: 2000  PROPOSAL_FILES_TEST: []  PROPOSAL_FILES_TRAIN: []  TEST:  - SUNRGBD_test  TRAIN:  - SUNRGBD_train  - SUNRGBD_val  TRUNCATION_THRES: 0.75  TRUNC_2D_BOXES: true  VISIBILITY_THRES: 0.25 GLOBAL:  HACK: 1.0 INPUT:  CROP:  ENABLED: false  SIZE:  - 0.9  - 0.9  TYPE: relative_range  FORMAT: BGR  MASK_FORMAT: polygon  MAX_SIZE_TEST: 4096  MAX_SIZE_TRAIN: 4096  MIN_SIZE_TEST: 512  MIN_SIZE_TRAIN:  - 256  - 272  - 288  - 304  - 320  - 336  - 352  - 368  - 384  - 400  - 416  - 432  - 448  - 464  - 480  - 496  - 512  - 528  - 544  - 560  - 576  - 592  - 608  - 624  - 640  MIN_SIZE_TRAIN_SAMPLING: choice  RANDOM_FLIP: horizontal MODEL:  ANCHOR_GENERATOR:  ANGLES:  - - -90  - 0  - 90  ASPECT_RATIOS:  - - 0.5  - 1.0  - 2.0  NAME: DefaultAnchorGenerator  OFFSET: 0.0  SIZES:  - - 32  - - 64  - - 128  - - 256  - - 512  BACKBONE:  FREEZE_AT: 0  NAME: build_dla_from_vision_fpn_backbone  DEVICE: cuda  DLA:  TRICKS: false  TYPE: dla34  FPN:  FUSE_TYPE: sum  IN_FEATURES:  - p2  - p3  - p4  - p5  - p6  NORM: ''  OUT_CHANNELS: 256  KEYPOINT_ON: false  LOAD_PROPOSALS: false  MASK_ON: false  META_ARCHITECTURE: RCNN3D  PANOPTIC_FPN:  COMBINE:  ENABLED: true  INSTANCES_CONFIDENCE_THRESH: 0.5  OVERLAP_THRESH: 0.5  STUFF_AREA_LIMIT: 4096  INSTANCE_LOSS_WEIGHT: 1.0  PIXEL_MEAN:  - 103.53  - 116.28  - 123.675  PIXEL_STD:  - 57.375  - 57.12  - 58.395  PROPOSAL_GENERATOR:  MIN_SIZE: 0  NAME: RPNWithIgnore  RESNETS:  DEFORM_MODULATED: false  DEFORM_NUM_GROUPS: 1  DEFORM_ON_PER_STAGE:  - false  - false  - false  - false  DEPTH: 50  NORM: FrozenBN  NUM_GROUPS: 1  OUT_FEATURES:  - res4  RES2_OUT_CHANNELS: 256  RES5_DILATION: 1  STEM_OUT_CHANNELS: 64  STRIDE_IN_1X1: true  TORCHVISION: true  WIDTH_PER_GROUP: 64  RETINANET:  BBOX_REG_LOSS_TYPE: smooth_l1  BBOX_REG_WEIGHTS: &id002  - 1.0  - 1.0  - 1.0  - 1.0  FOCAL_LOSS_ALPHA: 0.25  FOCAL_LOSS_GAMMA: 2.0  IN_FEATURES:  - p3  - p4  - p5  - p6  - p7  IOU_LABELS:  - 0  - -1  - 1  IOU_THRESHOLDS:  - 0.4  - 0.5  NMS_THRESH_TEST: 0.5  NORM: ''  NUM_CLASSES: 80  NUM_CONVS: 4  PRIOR_PROB: 0.01  SCORE_THRESH_TEST: 0.05  SMOOTH_L1_LOSS_BETA: 0.1  TOPK_CANDIDATES_TEST: 1000  ROI_BOX_CASCADE_HEAD:  BBOX_REG_WEIGHTS:  - &id001  - 10.0  - 10.0  - 5.0  - 5.0  - - 20.0  - 20.0  - 10.0  - 10.0  - - 30.0  - 30.0  - 15.0  - 15.0  IOUS:  - 0.5  - 0.6  - 0.7  ROI_BOX_HEAD:  BBOX_REG_LOSS_TYPE: smooth_l1  BBOX_REG_LOSS_WEIGHT: 1.0  BBOX_REG_WEIGHTS: *id001  CLS_AGNOSTIC_BBOX_REG: false  CONV_DIM: 256  FC_DIM: 1024  FED_LOSS_FREQ_WEIGHT_POWER: 0.5  FED_LOSS_NUM_CLASSES: 50  NAME: FastRCNNConvFCHead  NORM: ''  NUM_CONV: 0  NUM_FC: 2  POOLER_RESOLUTION: 7  POOLER_SAMPLING_RATIO: 0  POOLER_TYPE: ROIAlignV2  SMOOTH_L1_BETA: 0.0  TRAIN_ON_PRED_BOXES: false  USE_FED_LOSS: false  USE_SIGMOID_CE: false  ROI_CUBE_HEAD:  ALLOCENTRIC_POSE: true  CHAMFER_POSE: true  CLUSTER_BINS: 1  CONV_DIM: 256  DIMS_PRIORS_ENABLED: true  DIMS_PRIORS_FUNC: exp  DISENTANGLED_LOSS: true  FC_DIM: 1024  INVERSE_Z_WEIGHT: false  LOSS_W_3D: 1.0  LOSS_W_DIMS: 1.0  LOSS_W_JOINT: 1.0  LOSS_W_POSE: 1.0  LOSS_W_XY: 1.0  LOSS_W_Z: 1.0  NAME: CubeHead  NUM_CONV: 0  NUM_FC: 2  POOLER_RESOLUTION: 7  POOLER_SAMPLING_RATIO: 0  POOLER_TYPE: ROIAlignV2  POSE_TYPE: 6d  SCALE_ROI_BOXES: 0.0  SHARED_FC: true  USE_CONFIDENCE: 1.0  VIRTUAL_DEPTH: true  VIRTUAL_FOCAL: 512.0  Z_TYPE: direct  ROI_HEADS:  BATCH_SIZE_PER_IMAGE: 512  IN_FEATURES:  - p2  - p3  - p4  - p5  - p6  IOU_LABELS:  - 0  - 1  IOU_THRESHOLDS:  - 0.5  NAME: ROIHeads3D  NMS_THRESH_TEST: 0.5  NUM_CLASSES: 50  POSITIVE_FRACTION: 0.25  PROPOSAL_APPEND_GT: true  SCORE_THRESH_TEST: 0.01  ROI_KEYPOINT_HEAD:  CONV_DIMS:  - 512  - 512  - 512  - 512  - 512  - 512  - 512  - 512  LOSS_WEIGHT: 1.0  MIN_KEYPOINTS_PER_IMAGE: 1  NAME: KRCNNConvDeconvUpsampleHead  NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS: true  NUM_KEYPOINTS: 17  POOLER_RESOLUTION: 14  POOLER_SAMPLING_RATIO: 0  POOLER_TYPE: ROIAlignV2  ROI_MASK_HEAD:  CLS_AGNOSTIC_MASK: false  CONV_DIM: 256  NAME: MaskRCNNConvUpsampleHead  NORM: ''  NUM_CONV: 0  POOLER_RESOLUTION: 14  POOLER_SAMPLING_RATIO: 0  POOLER_TYPE: ROIAlignV2  RPN:  BATCH_SIZE_PER_IMAGE: 256  BBOX_REG_LOSS_TYPE: smooth_l1  BBOX_REG_LOSS_WEIGHT: 1.0  BBOX_REG_WEIGHTS: *id002  BOUNDARY_THRESH: -1  CONV_DIMS:  - -1  HEAD_NAME: StandardRPNHead  IGNORE_THRESHOLD: 0.5  IN_FEATURES:  - p2  - p3  - p4  - p5  - p6  IOU_LABELS:  - 0  - -1  - 1  IOU_THRESHOLDS:  - 0.05  - 0.05  LOSS_WEIGHT: 1.0  NMS_THRESH: 0.7  OBJECTNESS_UNCERTAINTY: IoUness  POSITIVE_FRACTION: 1.0  POST_NMS_TOPK_TEST: 1000  POST_NMS_TOPK_TRAIN: 1000  PRE_NMS_TOPK_TEST: 1000  PRE_NMS_TOPK_TRAIN: 2000  SMOOTH_L1_BETA: 0.0  SEM_SEG_HEAD:  COMMON_STRIDE: 4  CONVS_DIM: 128  IGNORE_VALUE: 255  IN_FEATURES:  - p2  - p3  - p4  - p5  LOSS_WEIGHT: 1.0  NAME: SemSegFPNHead  NORM: GN  NUM_CLASSES: 54  STABILIZE: 0.02  USE_BN: true  WEIGHTS: ''  WEIGHTS_PRETRAIN: '' OUTPUT_DIR: output/Baseline_sgd SEED: -1 SOLVER:  AMP:  ENABLED: false  BASE_LR: 0.0214  BASE_LR_END: 0.0  BIAS_LR_FACTOR: 1.0  CHECKPOINT_PERIOD: 5000  CLIP_GRADIENTS:  CLIP_TYPE: value  CLIP_VALUE: 1.0  ENABLED: false  NORM_TYPE: 2.0  GAMMA: 0.1  IMS_PER_BATCH: 35  LR_SCHEDULER_NAME: WarmupMultiStepLR  MAX_ITER: 28800  MOMENTUM: 0.9  NESTEROV: false  NUM_DECAYS: 3  REFERENCE_WORLD_SIZE: 0  RESCALE_INTERVAL: false  STEPS:  - 17280  - 23040  TYPE: sgd  WARMUP_FACTOR: 0.001  WARMUP_ITERS: 900  WARMUP_METHOD: linear  WEIGHT_DECAY: 0.0001  WEIGHT_DECAY_BIAS: null  WEIGHT_DECAY_NORM: 0.0 TEST:  AUG:  ENABLED: false  FLIP: true  MAX_SIZE: 4000  MIN_SIZES:  - 400  - 500  - 600  - 700  - 800  - 900  - 1000  - 1100  - 1200  DETECTIONS_PER_IMAGE: 100  EVAL_PERIOD: 7200  EXPECTED_RESULTS: []  KEYPOINT_OKS_SIGMAS: []  PRECISE_BN:  ENABLED: false  NUM_ITER: 200  TRUNCATION_THRES: 0.33333333  VISIBILITY_THRES: 0.33333333 VERSION: 2 VIS_PERIOD: 576 [02/26 14:12:59] detectron2 INFO: Full config saved to output/Baseline_sgd/config.yaml [02/26 14:12:59] d2.utils.env INFO: Using a generated random seed 59727926 [02/26 14:13:04] cubercnn INFO: Preprocessing Training Datasets [02/26 14:13:09] cubercnn INFO: Available categories for SUNRGBD Train [02/26 14:13:09] cubercnn INFO: ['bicycle', 'books', 'bottle', 'chair', 'cup', 'laptop', 'shoes', 'towel', 'blinds', 'window', 'lamp', 'shelves', 'mirror', 'sink', 'cabinet', 'bathtub', 'door', 'toilet', 'desk', 'box', 'bookcase', 'picture', 'table', 'counter', 'bed', 'night stand', 'pillow', 'sofa', 'television', 'floor mat', 'curtain', 'clothes', 'stationery', 'refrigerator', 'bin', 'stove', 'oven', 'machine'] [02/26 14:13:09] cubercnn INFO: Available categories for SUNRGBD Validation [02/26 14:13:09] cubercnn INFO: ['books', 'bottle', 'chair', 'cup', 'laptop', 'towel', 'blinds', 'window', 'lamp', 'shelves', 'mirror', 'sink', 'cabinet', 'bathtub', 'door', 'toilet', 'desk', 'box', 'bookcase', 'picture', 'table', 'counter', 'bed', 'night stand', 'pillow', 'sofa', 'television', 'curtain', 'clothes', 'stationery', 'refrigerator', 'bin', 'stove', 'oven', 'machine'] [02/26 14:13:15] cubercnn INFO: Model: RCNN3D( (backbone): FPN( (fpn_lateral2): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1)) (fpn_output2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (fpn_lateral3): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1)) (fpn_output3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (fpn_lateral4): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1)) (fpn_output4): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (fpn_lateral5): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1)) (fpn_output5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (fpn_lateral6): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1)) (fpn_output6): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (bottom_up): DLABackbone( (base_layer): Sequential( (0): Conv2d(3, 16, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), bias=False) (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU(inplace=True) ) (level0): Sequential( (0): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU(inplace=True) ) (level1): Sequential( (0): Conv2d(16, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU(inplace=True) ) (level2): Tree( (tree1): BasicBlock( (conv1): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (tree2): BasicBlock( (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (root): Root( (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (downsample): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) (project): Sequential( (0): Conv2d(32, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (level3): Tree( (tree1): Tree( (tree1): BasicBlock( (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (tree2): BasicBlock( (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (root): Root( (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (downsample): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) (project): Sequential( (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (tree2): Tree( (tree1): BasicBlock( (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (tree2): BasicBlock( (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (root): Root( (conv): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) ) (downsample): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) (project): Sequential( (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (level4): Tree( (tree1): Tree( (tree1): BasicBlock( (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (tree2): BasicBlock( (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (root): Root( (conv): Conv2d(512, 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) ) (downsample): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) (project): Sequential( (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (tree2): Tree( (tree1): BasicBlock( (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (tree2): BasicBlock( (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (root): Root( (conv): Conv2d(896, 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) ) ) (downsample): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) (project): Sequential( (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (level5): Tree( (tree1): BasicBlock( (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (tree2): BasicBlock( (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (root): Root( (conv): Conv2d(1280, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (downsample): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) (project): Sequential( (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) ) ) (proposal_generator): RPNWithIgnore( (rpn_head): StandardRPNHead( (conv): Conv2d( 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1) (activation): ReLU() ) (objectness_logits): Conv2d(256, 3, kernel_size=(1, 1), stride=(1, 1)) (anchor_deltas): Conv2d(256, 12, kernel_size=(1, 1), stride=(1, 1)) ) (anchor_generator): DefaultAnchorGenerator( (cell_anchors): BufferList() ) ) (roi_heads): ROIHeads3D( (box_pooler): ROIPooler( (level_poolers): ModuleList( (0): ROIAlign(output_size=(7, 7), spatial_scale=0.25, sampling_ratio=0, aligned=True) (1): ROIAlign(output_size=(7, 7), spatial_scale=0.125, sampling_ratio=0, aligned=True) (2): ROIAlign(output_size=(7, 7), spatial_scale=0.0625, sampling_ratio=0, aligned=True) (3): ROIAlign(output_size=(7, 7), spatial_scale=0.03125, sampling_ratio=0, aligned=True) (4): ROIAlign(output_size=(7, 7), spatial_scale=0.015625, sampling_ratio=0, aligned=True) ) ) (box_head): FastRCNNConvFCHead( (flatten): Flatten(start_dim=1, end_dim=-1) (fc1): Linear(in_features=12544, out_features=1024, bias=True) (fc_relu1): ReLU() (fc2): Linear(in_features=1024, out_features=1024, bias=True) (fc_relu2): ReLU() ) (box_predictor): FastRCNNOutputs( (cls_score): Linear(in_features=1024, out_features=51, bias=True) (bbox_pred): Linear(in_features=1024, out_features=200, bias=True) ) (cube_head): CubeHead( (feature_generator): Sequential( (fc1): Linear(in_features=12544, out_features=1024, bias=True) (fc_relu1): ReLU() (fc2): Linear(in_features=1024, out_features=1024, bias=True) (fc_relu2): ReLU() ) (bbox_3D_dims): Linear(in_features=1024, out_features=150, bias=True) (bbox_3D_center_deltas): Linear(in_features=1024, out_features=100, bias=True) (bbox_3D_pose): Linear(in_features=1024, out_features=300, bias=True) (bbox_3D_center_depth): Linear(in_features=1024, out_features=50, bias=True) (bbox_3D_uncertainty): Linear(in_features=1024, out_features=50, bias=True) ) (cube_pooler): ROIPooler( (level_poolers): ModuleList( (0): ROIAlign(output_size=(7, 7), spatial_scale=0.25, sampling_ratio=0, aligned=True) (1): ROIAlign(output_size=(7, 7), spatial_scale=0.125, sampling_ratio=0, aligned=True) (2): ROIAlign(output_size=(7, 7), spatial_scale=0.0625, sampling_ratio=0, aligned=True) (3): ROIAlign(output_size=(7, 7), spatial_scale=0.03125, sampling_ratio=0, aligned=True) (4): ROIAlign(output_size=(7, 7), spatial_scale=0.015625, sampling_ratio=0, aligned=True) ) ) ) ) [02/26 14:13:15] d2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in training: [ResizeShortestEdge(short_edge_length=..., max_size=4096, sample_style='choice'), RandomFlip()] [02/26 14:13:17] cubercnn.data.datasets INFO: Loading datasets/Omni3D/SUNRGBD_train.json takes 1.58 seconds. [02/26 14:13:17] cubercnn.data.datasets INFO: Loaded 4929 images in Omni3D format from datasets/Omni3D/SUNRGBD_train.json [02/26 14:13:20] cubercnn.data.datasets INFO: Filtered out 290/4929 images without valid annotations [02/26 14:13:20] cubercnn.data.datasets INFO: Loaded 356 images in Omni3D format from datasets/Omni3D/SUNRGBD_val.json [02/26 14:13:21] cubercnn.data.datasets INFO: Filtered out 23/356 images without valid annotations [02/26 14:13:21] d2.data.build INFO: Removed 0 images with no usable annotations. 4972 images left. [02/26 14:13:21] cubercnn.data.build INFO: Using training sampler RepeatFactorTrainingSampler [02/26 14:13:21] d2.data.common INFO: Serializing the dataset using: [02/26 14:13:21] d2.data.common INFO: Serializing 4972 elements to byte tensors and concatenating them all ... [02/26 14:13:21] d2.data.common INFO: Serialized dataset takes 20.81 MiB [02/26 14:13:21] d2.data.build INFO: Making batched data loader with batch_size=35 [02/26 14:13:21] d2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from ... [02/26 14:13:21] fvcore.common.checkpoint INFO: No checkpoint found. Initializing model from scratch [02/26 14:13:21] cubercnn INFO: Starting training from iteration 0 [02/26 14:13:52] d2.utils.events INFO: iter: 19 Cube/total_3D_loss: 8.858 total_loss: 5.429 BoxHead/loss_cls: 0.3542 BoxHead/loss_box_reg: 0.04424 Cube/loss_dims: 0.002107 Cube/loss_xy: 0.002291 Cube/loss_z: 0.03866 Cube/loss_pose: 0.02973 Cube/loss_joint: 0.201 lr: 0.00047273 max_mem: 28028M [02/26 14:14:18] d2.utils.events INFO: eta: 10:28:32 iter: 39 Cube/total_3D_loss: 6.282 total_loss: 4.636 BoxHead/loss_cls: 0.5412 BoxHead/loss_box_reg: 0.111 Cube/loss_dims: 0.01558 Cube/loss_xy: 0.01168 Cube/loss_z: 0.08788 Cube/loss_pose: 0.1019 Cube/loss_joint: 0.45 lr: 0.00094781 max_mem: 28028M [02/26 14:14:44] d2.utils.events INFO: eta: 10:29:36 iter: 59 Cube/total_3D_loss: 4.818 total_loss: 3.976 BoxHead/loss_cls: 0.3573 BoxHead/loss_box_reg: 0.1586 Cube/loss_dims: 0.02329 Cube/loss_xy: 0.02132 Cube/loss_z: 0.1006 Cube/loss_pose: 0.1852 Cube/loss_joint: 0.5477 lr: 0.0014229 max_mem: 28028M [02/26 14:15:10] d2.utils.events INFO: eta: 10:21:13 iter: 79 Cube/total_3D_loss: 4.009 total_loss: 3.726 BoxHead/loss_cls: 0.3591 BoxHead/loss_box_reg: 0.1994 Cube/loss_dims: 0.03107 Cube/loss_xy: 0.0283 Cube/loss_z: 0.0912 Cube/loss_pose: 0.2237 Cube/loss_joint: 0.56 lr: 0.001898 max_mem: 28029M [02/26 14:15:36] d2.utils.events INFO: eta: 10:15:10 iter: 99 Cube/total_3D_loss: 3.717 total_loss: 3.737 BoxHead/loss_cls: 0.3603 BoxHead/loss_box_reg: 0.2162 Cube/loss_dims: 0.03046 Cube/loss_xy: 0.03275 Cube/loss_z: 0.08503 Cube/loss_pose: 0.2287 Cube/loss_joint: 0.5363 lr: 0.002373 max_mem: 28029M [02/26 14:16:02] d2.utils.events INFO: eta: 10:17:53 iter: 119 Cube/total_3D_loss: 3.564 total_loss: 3.722 BoxHead/loss_cls: 0.3856 BoxHead/loss_box_reg: 0.2586 Cube/loss_dims: 0.03047 Cube/loss_xy: 0.03449 Cube/loss_z: 0.08308 Cube/loss_pose: 0.2327 Cube/loss_joint: 0.5579 lr: 0.0028481 max_mem: 28029M [02/26 14:16:28] d2.utils.events INFO: eta: 10:17:12 iter: 139 Cube/total_3D_loss: 3.336 total_loss: 3.625 BoxHead/loss_cls: 0.3831 BoxHead/loss_box_reg: 0.2612 Cube/loss_dims: 0.03062 Cube/loss_xy: 0.03121 Cube/loss_z: 0.08435 Cube/loss_pose: 0.2234 Cube/loss_joint: 0.5311 lr: 0.0033232 max_mem: 28029M [02/26 14:16:54] d2.utils.events INFO: eta: 10:20:23 iter: 159 Cube/total_3D_loss: 2.994 total_loss: 3.627 BoxHead/loss_cls: 0.3912 BoxHead/loss_box_reg: 0.286 Cube/loss_dims: 0.03147 Cube/loss_xy: 0.03289 Cube/loss_z: 0.08041 Cube/loss_pose: 0.2416 Cube/loss_joint: 0.5393 lr: 0.0037983 max_mem: 28029M [02/26 14:17:20] d2.utils.events INFO: eta: 10:26:47 iter: 179 Cube/total_3D_loss: 2.945 total_loss: 3.57 BoxHead/loss_cls: 0.3954 BoxHead/loss_box_reg: 0.3008 Cube/loss_dims: 0.03452 Cube/loss_xy: 0.03044 Cube/loss_z: 0.07693 Cube/loss_pose: 0.2133 Cube/loss_joint: 0.5118 lr: 0.0042734 max_mem: 28029M [02/26 14:17:47] d2.utils.events INFO: eta: 10:33:03 iter: 199 Cube/total_3D_loss: 3.314 total_loss: 3.675 BoxHead/loss_cls: 0.4032 BoxHead/loss_box_reg: 0.2987 Cube/loss_dims: 0.03192 Cube/loss_xy: 0.0355 Cube/loss_z: 0.08253 Cube/loss_pose: 0.2218 Cube/loss_joint: 0.5478 lr: 0.0047484 max_mem: 28029M [02/26 14:18:14] d2.utils.events INFO: eta: 10:48:33 iter: 219 Cube/total_3D_loss: 3.047 total_loss: 3.614 BoxHead/loss_cls: 0.3983 BoxHead/loss_box_reg: 0.293 Cube/loss_dims: 0.03007 Cube/loss_xy: 0.03434 Cube/loss_z: 0.08101 Cube/loss_pose: 0.2267 Cube/loss_joint: 0.5527 lr: 0.0052235 max_mem: 28029M [02/26 14:18:41] d2.utils.events INFO: eta: 10:54:01 iter: 239 Cube/total_3D_loss: 2.974 total_loss: 3.537 BoxHead/loss_cls: 0.3892 BoxHead/loss_box_reg: 0.2968 Cube/loss_dims: 0.03176 Cube/loss_xy: 0.03748 Cube/loss_z: 0.08415 Cube/loss_pose: 0.2307 Cube/loss_joint: 0.5611 lr: 0.0056986 max_mem: 28029M [02/26 14:19:09] d2.utils.events INFO: eta: 10:55:28 iter: 259 Cube/total_3D_loss: 2.953 total_loss: 3.59 BoxHead/loss_cls: 0.3872 BoxHead/loss_box_reg: 0.2957 Cube/loss_dims: 0.0303 Cube/loss_xy: 0.0379 Cube/loss_z: 0.08276 Cube/loss_pose: 0.2168 Cube/loss_joint: 0.5602 lr: 0.0061737 max_mem: 28029M [02/26 14:19:37] d2.utils.events INFO: eta: 11:04:37 iter: 279 Cube/total_3D_loss: 4.258 total_loss: 3.888 BoxHead/loss_cls: 0.3707 BoxHead/loss_box_reg: 0.2908 Cube/loss_dims: 0.02771 Cube/loss_xy: 0.04001 Cube/loss_z: 0.08689 Cube/loss_pose: 0.1883 Cube/loss_joint: 0.5458 lr: 0.0066488 max_mem: 28029M [02/26 14:20:04] d2.utils.events INFO: eta: 10:50:45 iter: 299 Cube/total_3D_loss: 2.94 total_loss: 3.579 BoxHead/loss_cls: 0.4043 BoxHead/loss_box_reg: 0.3082 Cube/loss_dims: 0.03298 Cube/loss_xy: 0.03361 Cube/loss_z: 0.07786 Cube/loss_pose: 0.2339 Cube/loss_joint: 0.5215 lr: 0.0071238 max_mem: 28029M [02/26 14:20:32] d2.utils.events INFO: eta: 10:50:47 iter: 319 Cube/total_3D_loss: 2.89 total_loss: 3.504 BoxHead/loss_cls: 0.3822 BoxHead/loss_box_reg: 0.2903 Cube/loss_dims: 0.02949 Cube/loss_xy: 0.03048 Cube/loss_z: 0.08559 Cube/loss_pose: 0.2148 Cube/loss_joint: 0.5454 lr: 0.0075989 max_mem: 28029M [02/26 14:20:59] d2.utils.events INFO: eta: 10:50:45 iter: 339 Cube/total_3D_loss: 3.173 total_loss: 3.611 BoxHead/loss_cls: 0.3746 BoxHead/loss_box_reg: 0.29 Cube/loss_dims: 0.03075 Cube/loss_xy: 0.03613 Cube/loss_z: 0.08282 Cube/loss_pose: 0.2359 Cube/loss_joint: 0.5901 lr: 0.008074 max_mem: 28029M [02/26 14:21:27] d2.utils.events INFO: eta: 10:55:34 iter: 359 Cube/total_3D_loss: 3.068 total_loss: 3.62 BoxHead/loss_cls: 0.399 BoxHead/loss_box_reg: 0.3107 Cube/loss_dims: 0.03146 Cube/loss_xy: 0.03817 Cube/loss_z: 0.08374 Cube/loss_pose: 0.2168 Cube/loss_joint: 0.5806 lr: 0.0085491 max_mem: 28029M [02/26 14:21:54] d2.utils.events INFO: eta: 10:54:34 iter: 379 Cube/total_3D_loss: 2.777 total_loss: 3.518 BoxHead/loss_cls: 0.4038 BoxHead/loss_box_reg: 0.3257 Cube/loss_dims: 0.0339 Cube/loss_xy: 0.03486 Cube/loss_z: 0.08218 Cube/loss_pose: 0.2437 Cube/loss_joint: 0.5624 lr: 0.0090242 max_mem: 28029M [02/26 14:22:22] d2.utils.events INFO: eta: 11:00:58 iter: 399 Cube/total_3D_loss: 2.885 total_loss: 3.548 BoxHead/loss_cls: 0.3973 BoxHead/loss_box_reg: 0.3231 Cube/loss_dims: 0.03233 Cube/loss_xy: 0.03362 Cube/loss_z: 0.08596 Cube/loss_pose: 0.2238 Cube/loss_joint: 0.5482 lr: 0.0094992 max_mem: 28029M [02/26 14:22:50] d2.utils.events INFO: eta: 10:47:45 iter: 419 Cube/total_3D_loss: 2.676 total_loss: 3.436 BoxHead/loss_cls: 0.3867 BoxHead/loss_box_reg: 0.3275 Cube/loss_dims: 0.03313 Cube/loss_xy: 0.03243 Cube/loss_z: 0.08198 Cube/loss_pose: 0.2542 Cube/loss_joint: 0.5625 lr: 0.0099743 max_mem: 28029M [02/26 14:23:17] d2.utils.events INFO: eta: 10:54:59 iter: 439 Cube/total_3D_loss: 2.786 total_loss: 3.512 BoxHead/loss_cls: 0.3752 BoxHead/loss_box_reg: 0.3122 Cube/loss_dims: 0.03168 Cube/loss_xy: 0.03165 Cube/loss_z: 0.08581 Cube/loss_pose: 0.2149 Cube/loss_joint: 0.5724 lr: 0.010449 max_mem: 28029M [02/26 14:23:45] d2.utils.events INFO: eta: 10:47:18 iter: 459 Cube/total_3D_loss: 2.852 total_loss: 3.506 BoxHead/loss_cls: 0.3698 BoxHead/loss_box_reg: 0.325 Cube/loss_dims: 0.03016 Cube/loss_xy: 0.03168 Cube/loss_z: 0.08786 Cube/loss_pose: 0.2224 Cube/loss_joint: 0.5647 lr: 0.010924 max_mem: 28029M [02/26 14:24:12] d2.utils.events INFO: eta: 10:48:51 iter: 479 Cube/total_3D_loss: 2.873 total_loss: 3.526 BoxHead/loss_cls: 0.3796 BoxHead/loss_box_reg: 0.3249 Cube/loss_dims: 0.0311 Cube/loss_xy: 0.02943 Cube/loss_z: 0.08126 Cube/loss_pose: 0.222 Cube/loss_joint: 0.5277 lr: 0.0114 max_mem: 28029M [02/26 14:24:40] d2.utils.events INFO: eta: 10:45:23 iter: 499 Cube/total_3D_loss: 2.794 total_loss: 3.517 BoxHead/loss_cls: 0.3795 BoxHead/loss_box_reg: 0.3132 Cube/loss_dims: 0.03265 Cube/loss_xy: 0.03354 Cube/loss_z: 0.08012 Cube/loss_pose: 0.2227 Cube/loss_joint: 0.5408 lr: 0.011875 max_mem: 28029M [02/26 14:25:07] d2.utils.events INFO: eta: 10:46:55 iter: 519 Cube/total_3D_loss: 2.742 total_loss: 3.491 BoxHead/loss_cls: 0.3754 BoxHead/loss_box_reg: 0.3056 Cube/loss_dims: 0.03021 Cube/loss_xy: 0.03033 Cube/loss_z: 0.07987 Cube/loss_pose: 0.2222 Cube/loss_joint: 0.5276 lr: 0.01235 max_mem: 28029M [02/26 14:25:35] d2.utils.events INFO: eta: 10:45:12 iter: 539 Cube/total_3D_loss: 2.839 total_loss: 3.493 BoxHead/loss_cls: 0.3721 BoxHead/loss_box_reg: 0.3146 Cube/loss_dims: 0.03127 Cube/loss_xy: 0.03445 Cube/loss_z: 0.08187 Cube/loss_pose: 0.22 Cube/loss_joint: 0.5323 lr: 0.012825 max_mem: 28029M [02/26 14:26:02] d2.utils.events INFO: eta: 10:55:57 iter: 559 Cube/total_3D_loss: 2.831 total_loss: 3.534 BoxHead/loss_cls: 0.3679 BoxHead/loss_box_reg: 0.316 Cube/loss_dims: 0.03116 Cube/loss_xy: 0.03668 Cube/loss_z: 0.08109 Cube/loss_pose: 0.228 Cube/loss_joint: 0.5603 lr: 0.0133 max_mem: 28029M [02/26 14:26:30] d2.utils.events INFO: eta: 10:50:10 iter: 579 Cube/total_3D_loss: 2.834 total_loss: 3.5 BoxHead/loss_cls: 0.374 BoxHead/loss_box_reg: 0.3221 Cube/loss_dims: 0.03101 Cube/loss_xy: 0.03266 Cube/loss_z: 0.07735 Cube/loss_pose: 0.2411 Cube/loss_joint: 0.5513 lr: 0.013775 max_mem: 28029M [02/26 14:26:58] d2.utils.events INFO: eta: 11:02:35 iter: 599 Cube/total_3D_loss: 2.626 total_loss: 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14:36:11] d2.utils.events INFO: eta: 10:39:31 iter: 999 Cube/total_3D_loss: 2.466 total_loss: 3.298 BoxHead/loss_cls: 0.3467 BoxHead/loss_box_reg: 0.3323 Cube/loss_dims: 0.03486 Cube/loss_xy: 0.03228 Cube/loss_z: 0.08208 Cube/loss_pose: 0.2419 Cube/loss_joint: 0.5735 lr: 0.0214 max_mem: 28029M [02/26 14:36:39] d2.utils.events INFO: eta: 10:35:21 iter: 1019 Cube/total_3D_loss: 2.411 total_loss: 3.277 BoxHead/loss_cls: 0.36 BoxHead/loss_box_reg: 0.3413 Cube/loss_dims: 0.03411 Cube/loss_xy: 0.03309 Cube/loss_z: 0.07775 Cube/loss_pose: 0.241 Cube/loss_joint: 0.5572 lr: 0.0214 max_mem: 28029M [02/26 14:37:07] d2.utils.events INFO: eta: 10:48:28 iter: 1039 Cube/total_3D_loss: 2.504 total_loss: 3.309 BoxHead/loss_cls: 0.3484 BoxHead/loss_box_reg: 0.3301 Cube/loss_dims: 0.03242 Cube/loss_xy: 0.03305 Cube/loss_z: 0.07993 Cube/loss_pose: 0.2506 Cube/loss_joint: 0.5694 lr: 0.0214 max_mem: 28029M [02/26 14:37:35] d2.utils.events INFO: eta: 11:04:25 iter: 1059 Cube/total_3D_loss: 2.445 total_loss: 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Cube/loss_xy: 0.0316 Cube/loss_z: 0.08172 Cube/loss_pose: 0.2502 Cube/loss_joint: 0.5855 lr: 0.0214 max_mem: 28029M [02/26 14:39:26] d2.utils.events INFO: eta: 10:36:28 iter: 1139 Cube/total_3D_loss: 2.36 total_loss: 3.259 BoxHead/loss_cls: 0.3409 BoxHead/loss_box_reg: 0.3406 Cube/loss_dims: 0.03341 Cube/loss_xy: 0.03068 Cube/loss_z: 0.08232 Cube/loss_pose: 0.2349 Cube/loss_joint: 0.5674 lr: 0.0214 max_mem: 28029M [02/26 14:39:54] d2.utils.events INFO: eta: 10:37:49 iter: 1159 Cube/total_3D_loss: 2.308 total_loss: 3.175 BoxHead/loss_cls: 0.3438 BoxHead/loss_box_reg: 0.3232 Cube/loss_dims: 0.03513 Cube/loss_xy: 0.03015 Cube/loss_z: 0.08085 Cube/loss_pose: 0.2612 Cube/loss_joint: 0.5619 lr: 0.0214 max_mem: 28029M [02/26 14:40:22] d2.utils.events INFO: eta: 10:50:41 iter: 1179 Cube/total_3D_loss: 2.312 total_loss: 3.208 BoxHead/loss_cls: 0.333 BoxHead/loss_box_reg: 0.3315 Cube/loss_dims: 0.03363 Cube/loss_xy: 0.03211 Cube/loss_z: 0.08316 Cube/loss_pose: 0.2473 Cube/loss_joint: 0.5838 lr: 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INFO: eta: 10:26:47 iter: 1459 Cube/total_3D_loss: 2.31 total_loss: 3.173 BoxHead/loss_cls: 0.323 BoxHead/loss_box_reg: 0.3069 Cube/loss_dims: 0.03474 Cube/loss_xy: 0.02976 Cube/loss_z: 0.07965 Cube/loss_pose: 0.2467 Cube/loss_joint: 0.5675 lr: 0.0214 max_mem: 28029M [02/26 14:47:18] d2.utils.events INFO: eta: 10:33:01 iter: 1479 Cube/total_3D_loss: 2.177 total_loss: 3.041 BoxHead/loss_cls: 0.3274 BoxHead/loss_box_reg: 0.3221 Cube/loss_dims: 0.0363 Cube/loss_xy: 0.03171 Cube/loss_z: 0.08037 Cube/loss_pose: 0.244 Cube/loss_joint: 0.5868 lr: 0.0214 max_mem: 28029M [02/26 14:47:45] d2.utils.events INFO: eta: 10:24:06 iter: 1499 Cube/total_3D_loss: 2.116 total_loss: 3.021 BoxHead/loss_cls: 0.3016 BoxHead/loss_box_reg: 0.3119 Cube/loss_dims: 0.03714 Cube/loss_xy: 0.02994 Cube/loss_z: 0.07955 Cube/loss_pose: 0.247 Cube/loss_joint: 0.5786 lr: 0.0214 max_mem: 28029M [02/26 14:48:13] d2.utils.events INFO: eta: 10:36:22 iter: 1519 Cube/total_3D_loss: 2.032 total_loss: 2.976 BoxHead/loss_cls: 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[02/26 14:51:26] d2.utils.events INFO: eta: 10:30:10 iter: 1659 Cube/total_3D_loss: 2.155 total_loss: 3.01 BoxHead/loss_cls: 0.2955 BoxHead/loss_box_reg: 0.305 Cube/loss_dims: 0.03561 Cube/loss_xy: 0.03104 Cube/loss_z: 0.08081 Cube/loss_pose: 0.2486 Cube/loss_joint: 0.5779 lr: 0.0214 max_mem: 28029M [02/26 14:51:54] d2.utils.events INFO: eta: 10:22:19 iter: 1679 Cube/total_3D_loss: 2.061 total_loss: 2.963 BoxHead/loss_cls: 0.3048 BoxHead/loss_box_reg: 0.3164 Cube/loss_dims: 0.03493 Cube/loss_xy: 0.03144 Cube/loss_z: 0.07684 Cube/loss_pose: 0.2483 Cube/loss_joint: 0.5709 lr: 0.0214 max_mem: 28029M [02/26 14:52:22] d2.utils.events INFO: eta: 10:31:13 iter: 1699 Cube/total_3D_loss: 2.149 total_loss: 2.995 BoxHead/loss_cls: 0.2968 BoxHead/loss_box_reg: 0.2976 Cube/loss_dims: 0.03752 Cube/loss_xy: 0.03233 Cube/loss_z: 0.07735 Cube/loss_pose: 0.2497 Cube/loss_joint: 0.5746 lr: 0.0214 max_mem: 28029M [02/26 14:52:49] d2.utils.events INFO: eta: 10:21:05 iter: 1719 Cube/total_3D_loss: 1.974 total_loss: 2.915 BoxHead/loss_cls: 0.306 BoxHead/loss_box_reg: 0.311 Cube/loss_dims: 0.03914 Cube/loss_xy: 0.03192 Cube/loss_z: 0.07621 Cube/loss_pose: 0.2547 Cube/loss_joint: 0.5892 lr: 0.0214 max_mem: 28029M [02/26 14:53:17] d2.utils.events INFO: eta: 10:26:08 iter: 1739 Cube/total_3D_loss: 2.043 total_loss: 2.982 BoxHead/loss_cls: 0.3032 BoxHead/loss_box_reg: 0.3199 Cube/loss_dims: 0.03674 Cube/loss_xy: 0.02901 Cube/loss_z: 0.07393 Cube/loss_pose: 0.2632 Cube/loss_joint: 0.5655 lr: 0.0214 max_mem: 28029M [02/26 14:53:45] d2.utils.events INFO: eta: 10:31:27 iter: 1759 Cube/total_3D_loss: 2.115 total_loss: 2.998 BoxHead/loss_cls: 0.302 BoxHead/loss_box_reg: 0.2996 Cube/loss_dims: 0.03756 Cube/loss_xy: 0.03156 Cube/loss_z: 0.07683 Cube/loss_pose: 0.2546 Cube/loss_joint: 0.5712 lr: 0.0214 max_mem: 28029M [02/26 14:54:13] d2.utils.events INFO: eta: 10:18:54 iter: 1779 Cube/total_3D_loss: 2.08 total_loss: 2.957 BoxHead/loss_cls: 0.287 BoxHead/loss_box_reg: 0.2938 Cube/loss_dims: 0.03873 Cube/loss_xy: 0.0309 Cube/loss_z: 0.07427 Cube/loss_pose: 0.2618 Cube/loss_joint: 0.5622 lr: 0.0214 max_mem: 28029M [02/26 14:54:40] d2.utils.events INFO: eta: 10:26:41 iter: 1799 Cube/total_3D_loss: 1.955 total_loss: 2.896 BoxHead/loss_cls: 0.2846 BoxHead/loss_box_reg: 0.2979 Cube/loss_dims: 0.037 Cube/loss_xy: 0.03112 Cube/loss_z: 0.07815 Cube/loss_pose: 0.2567 Cube/loss_joint: 0.5672 lr: 0.0214 max_mem: 28029M [02/26 14:55:08] d2.utils.events INFO: eta: 10:15:40 iter: 1819 Cube/total_3D_loss: 1.969 total_loss: 2.924 BoxHead/loss_cls: 0.3125 BoxHead/loss_box_reg: 0.3098 Cube/loss_dims: 0.03666 Cube/loss_xy: 0.03062 Cube/loss_z: 0.07734 Cube/loss_pose: 0.2611 Cube/loss_joint: 0.5715 lr: 0.0214 max_mem: 28029M [02/26 14:55:35] d2.utils.events INFO: eta: 10:18:12 iter: 1839 Cube/total_3D_loss: 2.015 total_loss: 2.921 BoxHead/loss_cls: 0.2977 BoxHead/loss_box_reg: 0.3026 Cube/loss_dims: 0.03784 Cube/loss_xy: 0.0334 Cube/loss_z: 0.07382 Cube/loss_pose: 0.2491 Cube/loss_joint: 0.5823 lr: 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BoxHead/loss_box_reg: 0.2807 Cube/loss_dims: 0.03697 Cube/loss_xy: 0.03056 Cube/loss_z: 0.07945 Cube/loss_pose: 0.2512 Cube/loss_joint: 0.5818 lr: 0.0214 max_mem: 28029M [02/26 15:03:51] d2.utils.events INFO: eta: 10:06:05 iter: 2199 Cube/total_3D_loss: 1.825 total_loss: 2.806 BoxHead/loss_cls: 0.2785 BoxHead/loss_box_reg: 0.3036 Cube/loss_dims: 0.03856 Cube/loss_xy: 0.03157 Cube/loss_z: 0.0745 Cube/loss_pose: 0.2506 Cube/loss_joint: 0.5726 lr: 0.0214 max_mem: 28029M [02/26 15:04:19] d2.utils.events INFO: eta: 10:07:59 iter: 2219 Cube/total_3D_loss: 1.903 total_loss: 2.87 BoxHead/loss_cls: 0.2699 BoxHead/loss_box_reg: 0.2828 Cube/loss_dims: 0.03856 Cube/loss_xy: 0.03121 Cube/loss_z: 0.07988 Cube/loss_pose: 0.2404 Cube/loss_joint: 0.5922 lr: 0.0214 max_mem: 28029M [02/26 15:04:46] d2.utils.events INFO: eta: 10:05:57 iter: 2239 Cube/total_3D_loss: 1.992 total_loss: 2.893 BoxHead/loss_cls: 0.2794 BoxHead/loss_box_reg: 0.2896 Cube/loss_dims: 0.03919 Cube/loss_xy: 0.03173 Cube/loss_z: 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15:06:37] d2.utils.events INFO: eta: 10:16:09 iter: 2319 Cube/total_3D_loss: 1.866 total_loss: 2.806 BoxHead/loss_cls: 0.271 BoxHead/loss_box_reg: 0.2973 Cube/loss_dims: 0.03772 Cube/loss_xy: 0.03115 Cube/loss_z: 0.07918 Cube/loss_pose: 0.2437 Cube/loss_joint: 0.5757 lr: 0.0214 max_mem: 28029M [02/26 15:07:05] d2.utils.events INFO: eta: 10:19:30 iter: 2339 Cube/total_3D_loss: 1.865 total_loss: 2.778 BoxHead/loss_cls: 0.2649 BoxHead/loss_box_reg: 0.2781 Cube/loss_dims: 0.03897 Cube/loss_xy: 0.03094 Cube/loss_z: 0.07811 Cube/loss_pose: 0.2456 Cube/loss_joint: 0.5854 lr: 0.0214 max_mem: 28029M [02/26 15:07:32] d2.utils.events INFO: eta: 10:07:12 iter: 2359 Cube/total_3D_loss: 1.869 total_loss: 2.782 BoxHead/loss_cls: 0.2584 BoxHead/loss_box_reg: 0.3002 Cube/loss_dims: 0.03811 Cube/loss_xy: 0.03331 Cube/loss_z: 0.078 Cube/loss_pose: 0.248 Cube/loss_joint: 0.5758 lr: 0.0214 max_mem: 28029M [02/26 15:08:00] d2.utils.events INFO: eta: 10:01:28 iter: 2379 Cube/total_3D_loss: 1.849 total_loss: 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0.0214 max_mem: 28030M [02/26 15:35:37] d2.utils.events INFO: eta: 9:45:29 iter: 3579 Cube/total_3D_loss: 1.687 total_loss: 2.601 BoxHead/loss_cls: 0.2252 BoxHead/loss_box_reg: 0.2753 Cube/loss_dims: 0.04043 Cube/loss_xy: 0.03105 Cube/loss_z: 0.07834 Cube/loss_pose: 0.2585 Cube/loss_joint: 0.5893 lr: 0.0214 max_mem: 28030M [02/26 15:36:04] d2.utils.events INFO: eta: 9:35:20 iter: 3599 Cube/total_3D_loss: 1.623 total_loss: 2.562 BoxHead/loss_cls: 0.222 BoxHead/loss_box_reg: 0.2747 Cube/loss_dims: 0.03987 Cube/loss_xy: 0.03087 Cube/loss_z: 0.07645 Cube/loss_pose: 0.2404 Cube/loss_joint: 0.5924 lr: 0.0214 max_mem: 28030M [02/26 15:36:32] d2.utils.events INFO: eta: 9:33:59 iter: 3619 Cube/total_3D_loss: 1.581 total_loss: 2.583 BoxHead/loss_cls: 0.2302 BoxHead/loss_box_reg: 0.2862 Cube/loss_dims: 0.03956 Cube/loss_xy: 0.03063 Cube/loss_z: 0.07915 Cube/loss_pose: 0.2503 Cube/loss_joint: 0.6008 lr: 0.0214 max_mem: 28030M [02/26 15:36:59] d2.utils.events INFO: eta: 9:40:09 iter: 3639 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9:25:14 iter: 3839 Cube/total_3D_loss: 1.608 total_loss: 2.53 BoxHead/loss_cls: 0.2121 BoxHead/loss_box_reg: 0.2589 Cube/loss_dims: 0.03952 Cube/loss_xy: 0.03158 Cube/loss_z: 0.07474 Cube/loss_pose: 0.2488 Cube/loss_joint: 0.5919 lr: 0.0214 max_mem: 28030M [02/26 15:42:02] d2.utils.events INFO: eta: 9:37:04 iter: 3859 Cube/total_3D_loss: 1.645 total_loss: 2.558 BoxHead/loss_cls: 0.2212 BoxHead/loss_box_reg: 0.2601 Cube/loss_dims: 0.04048 Cube/loss_xy: 0.03209 Cube/loss_z: 0.08108 Cube/loss_pose: 0.2508 Cube/loss_joint: 0.6046 lr: 0.0214 max_mem: 28030M [02/26 15:42:29] d2.utils.events INFO: eta: 9:26:12 iter: 3879 Cube/total_3D_loss: 1.632 total_loss: 2.552 BoxHead/loss_cls: 0.2183 BoxHead/loss_box_reg: 0.2671 Cube/loss_dims: 0.0414 Cube/loss_xy: 0.03196 Cube/loss_z: 0.07473 Cube/loss_pose: 0.2501 Cube/loss_joint: 0.5939 lr: 0.0214 max_mem: 28030M [02/26 15:42:57] d2.utils.events INFO: eta: 9:30:06 iter: 3899 Cube/total_3D_loss: 1.63 total_loss: 2.589 BoxHead/loss_cls: 0.232 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d2.utils.events INFO: eta: 9:20:53 iter: 4039 Cube/total_3D_loss: 1.617 total_loss: 2.542 BoxHead/loss_cls: 0.2206 BoxHead/loss_box_reg: 0.2569 Cube/loss_dims: 0.0398 Cube/loss_xy: 0.03198 Cube/loss_z: 0.07553 Cube/loss_pose: 0.2418 Cube/loss_joint: 0.5814 lr: 0.0214 max_mem: 28030M [02/26 15:46:35] d2.utils.events INFO: eta: 9:27:04 iter: 4059 Cube/total_3D_loss: 1.566 total_loss: 2.563 BoxHead/loss_cls: 0.2302 BoxHead/loss_box_reg: 0.2747 Cube/loss_dims: 0.04056 Cube/loss_xy: 0.03188 Cube/loss_z: 0.07722 Cube/loss_pose: 0.2427 Cube/loss_joint: 0.592 lr: 0.0214 max_mem: 28030M [02/26 15:47:02] d2.utils.events INFO: eta: 9:20:01 iter: 4079 Cube/total_3D_loss: 1.57 total_loss: 2.519 BoxHead/loss_cls: 0.2203 BoxHead/loss_box_reg: 0.2797 Cube/loss_dims: 0.03851 Cube/loss_xy: 0.03296 Cube/loss_z: 0.07537 Cube/loss_pose: 0.2353 Cube/loss_joint: 0.589 lr: 0.0214 max_mem: 28030M [02/26 15:47:30] d2.utils.events INFO: eta: 9:20:31 iter: 4099 Cube/total_3D_loss: 1.594 total_loss: 2.519 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total_loss: 2.538 BoxHead/loss_cls: 0.229 BoxHead/loss_box_reg: 0.2815 Cube/loss_dims: 0.04034 Cube/loss_xy: 0.03175 Cube/loss_z: 0.07884 Cube/loss_pose: 0.2486 Cube/loss_joint: 0.5985 lr: 0.0214 max_mem: 28030M [02/26 15:52:31] d2.utils.events INFO: eta: 9:17:22 iter: 4319 Cube/total_3D_loss: 1.582 total_loss: 2.533 BoxHead/loss_cls: 0.2166 BoxHead/loss_box_reg: 0.2795 Cube/loss_dims: 0.04088 Cube/loss_xy: 0.03195 Cube/loss_z: 0.07552 Cube/loss_pose: 0.2518 Cube/loss_joint: 0.5901 lr: 0.0214 max_mem: 28030M [02/26 15:52:59] d2.utils.events INFO: eta: 9:23:19 iter: 4339 Cube/total_3D_loss: 1.495 total_loss: 2.487 BoxHead/loss_cls: 0.2211 BoxHead/loss_box_reg: 0.2792 Cube/loss_dims: 0.04057 Cube/loss_xy: 0.03137 Cube/loss_z: 0.07474 Cube/loss_pose: 0.243 Cube/loss_joint: 0.5855 lr: 0.0214 max_mem: 28030M [02/26 15:53:26] d2.utils.events INFO: eta: 9:11:39 iter: 4359 Cube/total_3D_loss: 1.542 total_loss: 2.503 BoxHead/loss_cls: 0.2144 BoxHead/loss_box_reg: 0.2713 Cube/loss_dims: 0.0406 Cube/loss_xy: 0.03218 Cube/loss_z: 0.07888 Cube/loss_pose: 0.242 Cube/loss_joint: 0.5982 lr: 0.0214 max_mem: 28030M [02/26 15:53:53] d2.utils.events INFO: eta: 9:16:35 iter: 4379 Cube/total_3D_loss: 1.539 total_loss: 2.504 BoxHead/loss_cls: 0.2206 BoxHead/loss_box_reg: 0.2747 Cube/loss_dims: 0.04056 Cube/loss_xy: 0.03119 Cube/loss_z: 0.07906 Cube/loss_pose: 0.2491 Cube/loss_joint: 0.6036 lr: 0.0214 max_mem: 28030M [02/26 15:54:20] d2.utils.events INFO: eta: 9:09:53 iter: 4399 Cube/total_3D_loss: 1.556 total_loss: 2.492 BoxHead/loss_cls: 0.1999 BoxHead/loss_box_reg: 0.2625 Cube/loss_dims: 0.0408 Cube/loss_xy: 0.03105 Cube/loss_z: 0.07314 Cube/loss_pose: 0.2552 Cube/loss_joint: 0.5734 lr: 0.0214 max_mem: 28030M [02/26 15:54:47] d2.utils.events INFO: eta: 9:14:54 iter: 4419 Cube/total_3D_loss: 1.51 total_loss: 2.448 BoxHead/loss_cls: 0.212 BoxHead/loss_box_reg: 0.2496 Cube/loss_dims: 0.04062 Cube/loss_xy: 0.03033 Cube/loss_z: 0.07819 Cube/loss_pose: 0.2465 Cube/loss_joint: 0.5956 lr: 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Cube/total_3D_loss: 1.561 total_loss: 2.52 BoxHead/loss_cls: 0.2119 BoxHead/loss_box_reg: 0.2755 Cube/loss_dims: 0.03861 Cube/loss_xy: 0.03085 Cube/loss_z: 0.07616 Cube/loss_pose: 0.2417 Cube/loss_joint: 0.5766 lr: 0.0214 max_mem: 28030M [02/26 15:57:04] d2.utils.events INFO: eta: 9:11:59 iter: 4519 Cube/total_3D_loss: 1.514 total_loss: 2.444 BoxHead/loss_cls: 0.2021 BoxHead/loss_box_reg: 0.2579 Cube/loss_dims: 0.03943 Cube/loss_xy: 0.03148 Cube/loss_z: 0.07661 Cube/loss_pose: 0.2452 Cube/loss_joint: 0.5838 lr: 0.0214 max_mem: 28030M [02/26 15:57:31] d2.utils.events INFO: eta: 9:10:34 iter: 4539 Cube/total_3D_loss: 1.54 total_loss: 2.464 BoxHead/loss_cls: 0.197 BoxHead/loss_box_reg: 0.2489 Cube/loss_dims: 0.03924 Cube/loss_xy: 0.03176 Cube/loss_z: 0.08128 Cube/loss_pose: 0.2352 Cube/loss_joint: 0.6179 lr: 0.0214 max_mem: 28030M [02/26 15:57:59] d2.utils.events INFO: eta: 9:09:30 iter: 4559 Cube/total_3D_loss: 1.549 total_loss: 2.489 BoxHead/loss_cls: 0.2081 BoxHead/loss_box_reg: 0.2627 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d2.utils.events INFO: eta: 9:01:45 iter: 4899 Cube/total_3D_loss: 1.527 total_loss: 2.454 BoxHead/loss_cls: 0.1982 BoxHead/loss_box_reg: 0.2714 Cube/loss_dims: 0.03912 Cube/loss_xy: 0.03139 Cube/loss_z: 0.0797 Cube/loss_pose: 0.2425 Cube/loss_joint: 0.6047 lr: 0.0214 max_mem: 28030M [02/26 16:06:13] d2.utils.events INFO: eta: 9:03:11 iter: 4919 Cube/total_3D_loss: 1.526 total_loss: 2.44 BoxHead/loss_cls: 0.2007 BoxHead/loss_box_reg: 0.2571 Cube/loss_dims: 0.0403 Cube/loss_xy: 0.03199 Cube/loss_z: 0.07725 Cube/loss_pose: 0.2507 Cube/loss_joint: 0.5932 lr: 0.0214 max_mem: 28030M [02/26 16:06:40] d2.utils.events INFO: eta: 9:03:06 iter: 4939 Cube/total_3D_loss: 1.483 total_loss: 2.454 BoxHead/loss_cls: 0.2007 BoxHead/loss_box_reg: 0.2643 Cube/loss_dims: 0.04021 Cube/loss_xy: 0.03102 Cube/loss_z: 0.07762 Cube/loss_pose: 0.2417 Cube/loss_joint: 0.5798 lr: 0.0214 max_mem: 28030M [02/26 16:07:07] d2.utils.events INFO: eta: 8:59:53 iter: 4959 Cube/total_3D_loss: 1.537 total_loss: 2.46 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d2.utils.events INFO: eta: 8:24:31 iter: 6479 Cube/total_3D_loss: 1.418 total_loss: 2.307 BoxHead/loss_cls: 0.1793 BoxHead/loss_box_reg: 0.2388 Cube/loss_dims: 0.0412 Cube/loss_xy: 0.03205 Cube/loss_z: 0.07529 Cube/loss_pose: 0.2439 Cube/loss_joint: 0.5937 lr: 0.0214 max_mem: 28030M [02/26 16:42:10] d2.utils.events INFO: eta: 8:24:05 iter: 6499 Cube/total_3D_loss: 1.372 total_loss: 2.343 BoxHead/loss_cls: 0.1797 BoxHead/loss_box_reg: 0.2575 Cube/loss_dims: 0.04088 Cube/loss_xy: 0.03054 Cube/loss_z: 0.07695 Cube/loss_pose: 0.2366 Cube/loss_joint: 0.5968 lr: 0.0214 max_mem: 28030M [02/26 16:42:37] d2.utils.events INFO: eta: 8:25:01 iter: 6519 Cube/total_3D_loss: 1.424 total_loss: 2.329 BoxHead/loss_cls: 0.1738 BoxHead/loss_box_reg: 0.2523 Cube/loss_dims: 0.04046 Cube/loss_xy: 0.03091 Cube/loss_z: 0.0762 Cube/loss_pose: 0.2455 Cube/loss_joint: 0.6012 lr: 0.0214 max_mem: 28030M [02/26 16:43:04] d2.utils.events INFO: eta: 8:25:21 iter: 6539 Cube/total_3D_loss: 1.464 total_loss: 2.335 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[02/26 16:46:13] d2.utils.events INFO: eta: 8:18:43 iter: 6679 Cube/total_3D_loss: 1.416 total_loss: 2.344 BoxHead/loss_cls: 0.1697 BoxHead/loss_box_reg: 0.252 Cube/loss_dims: 0.04071 Cube/loss_xy: 0.03149 Cube/loss_z: 0.07612 Cube/loss_pose: 0.2435 Cube/loss_joint: 0.5916 lr: 0.0214 max_mem: 28030M [02/26 16:46:41] d2.utils.events INFO: eta: 8:20:00 iter: 6699 Cube/total_3D_loss: 1.39 total_loss: 2.355 BoxHead/loss_cls: 0.1735 BoxHead/loss_box_reg: 0.2534 Cube/loss_dims: 0.0398 Cube/loss_xy: 0.03113 Cube/loss_z: 0.08218 Cube/loss_pose: 0.2373 Cube/loss_joint: 0.6142 lr: 0.0214 max_mem: 28030M [02/26 16:47:08] d2.utils.events INFO: eta: 8:19:40 iter: 6719 Cube/total_3D_loss: 1.434 total_loss: 2.389 BoxHead/loss_cls: 0.181 BoxHead/loss_box_reg: 0.2531 Cube/loss_dims: 0.04005 Cube/loss_xy: 0.03011 Cube/loss_z: 0.07841 Cube/loss_pose: 0.2437 Cube/loss_joint: 0.5957 lr: 0.0214 max_mem: 28030M [02/26 16:47:35] d2.utils.events INFO: eta: 8:21:35 iter: 6739 Cube/total_3D_loss: 1.421 total_loss: 2.289 BoxHead/loss_cls: 0.1654 BoxHead/loss_box_reg: 0.2439 Cube/loss_dims: 0.03993 Cube/loss_xy: 0.03095 Cube/loss_z: 0.07696 Cube/loss_pose: 0.2368 Cube/loss_joint: 0.5894 lr: 0.0214 max_mem: 28030M [02/26 16:48:02] d2.utils.events INFO: eta: 8:18:11 iter: 6759 Cube/total_3D_loss: 1.45 total_loss: 2.366 BoxHead/loss_cls: 0.161 BoxHead/loss_box_reg: 0.246 Cube/loss_dims: 0.03884 Cube/loss_xy: 0.02931 Cube/loss_z: 0.08168 Cube/loss_pose: 0.226 Cube/loss_joint: 0.5916 lr: 0.0214 max_mem: 28030M [02/26 16:48:29] d2.utils.events INFO: eta: 8:17:49 iter: 6779 Cube/total_3D_loss: 1.432 total_loss: 2.335 BoxHead/loss_cls: 0.1734 BoxHead/loss_box_reg: 0.256 Cube/loss_dims: 0.04003 Cube/loss_xy: 0.03202 Cube/loss_z: 0.07709 Cube/loss_pose: 0.2353 Cube/loss_joint: 0.6076 lr: 0.0214 max_mem: 28030M [02/26 16:48:57] d2.utils.events INFO: eta: 8:22:04 iter: 6799 Cube/total_3D_loss: 1.45 total_loss: 2.357 BoxHead/loss_cls: 0.1702 BoxHead/loss_box_reg: 0.2394 Cube/loss_dims: 0.04025 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0.0214 max_mem: 28030M [02/26 16:50:45] d2.utils.events INFO: eta: 8:14:31 iter: 6879 Cube/total_3D_loss: 1.359 total_loss: 2.267 BoxHead/loss_cls: 0.1626 BoxHead/loss_box_reg: 0.23 Cube/loss_dims: 0.04091 Cube/loss_xy: 0.03123 Cube/loss_z: 0.07559 Cube/loss_pose: 0.2465 Cube/loss_joint: 0.5928 lr: 0.0214 max_mem: 28030M [02/26 16:51:12] d2.utils.events INFO: eta: 8:10:35 iter: 6899 Cube/total_3D_loss: 1.349 total_loss: 2.3 BoxHead/loss_cls: 0.1764 BoxHead/loss_box_reg: 0.2508 Cube/loss_dims: 0.03979 Cube/loss_xy: 0.03246 Cube/loss_z: 0.07445 Cube/loss_pose: 0.239 Cube/loss_joint: 0.5987 lr: 0.0214 max_mem: 28030M [02/26 16:51:39] d2.utils.events INFO: eta: 8:23:38 iter: 6919 Cube/total_3D_loss: 1.333 total_loss: 2.247 BoxHead/loss_cls: 0.1667 BoxHead/loss_box_reg: 0.2392 Cube/loss_dims: 0.04086 Cube/loss_xy: 0.03185 Cube/loss_z: 0.07561 Cube/loss_pose: 0.2396 Cube/loss_joint: 0.5934 lr: 0.0214 max_mem: 28030M [02/26 16:52:07] d2.utils.events INFO: eta: 8:19:33 iter: 6939 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datasets/Omni3D/SUNRGBD_test.json takes 2.10 seconds. [02/26 16:58:08] cubercnn.data.datasets INFO: Loaded 5050 images in Omni3D format from datasets/Omni3D/SUNRGBD_test.json [02/26 16:58:12] cubercnn.data.datasets INFO: Filtered out 0/5050 images without valid annotations [02/26 16:58:12] d2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(512, 512), max_size=4096, sample_style='choice')] [02/26 16:58:12] d2.data.common INFO: Serializing the dataset using: [02/26 16:58:12] d2.data.common INFO: Serializing 5050 elements to byte tensors and concatenating them all ... [02/26 16:58:12] d2.data.common INFO: Serialized dataset takes 20.13 MiB [02/26 16:58:12] cubercnn.evaluation.omni3d_evaluation INFO: Start inference on 5050 batches [02/26 16:58:13] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 11/5050. Dataloading: 0.0005 s/iter. Inference: 0.0263 s/iter. Eval: 0.0010 s/iter. Total: 0.0279 s/iter. 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ETA=0:00:46 [02/26 16:59:33] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 3326/5050. Dataloading: 0.0007 s/iter. Inference: 0.0220 s/iter. Eval: 0.0015 s/iter. Total: 0.0242 s/iter. ETA=0:00:41 [02/26 16:59:38] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 3539/5050. Dataloading: 0.0007 s/iter. Inference: 0.0219 s/iter. Eval: 0.0015 s/iter. Total: 0.0241 s/iter. ETA=0:00:36 [02/26 16:59:43] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 3710/5050. Dataloading: 0.0007 s/iter. Inference: 0.0219 s/iter. Eval: 0.0017 s/iter. Total: 0.0244 s/iter. ETA=0:00:32 [02/26 16:59:48] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 3925/5050. Dataloading: 0.0007 s/iter. Inference: 0.0219 s/iter. Eval: 0.0017 s/iter. Total: 0.0243 s/iter. ETA=0:00:27 [02/26 16:59:53] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 4140/5050. Dataloading: 0.0007 s/iter. Inference: 0.0219 s/iter. Eval: 0.0016 s/iter. Total: 0.0243 s/iter. ETA=0:00:22 [02/26 16:59:58] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 4355/5050. Dataloading: 0.0007 s/iter. Inference: 0.0219 s/iter. Eval: 0.0016 s/iter. Total: 0.0242 s/iter. ETA=0:00:16 [02/26 17:00:03] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 4573/5050. Dataloading: 0.0007 s/iter. Inference: 0.0219 s/iter. Eval: 0.0016 s/iter. Total: 0.0242 s/iter. ETA=0:00:11 [02/26 17:00:08] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 4791/5050. Dataloading: 0.0007 s/iter. Inference: 0.0218 s/iter. Eval: 0.0015 s/iter. Total: 0.0241 s/iter. ETA=0:00:06 [02/26 17:00:13] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 5008/5050. Dataloading: 0.0007 s/iter. Inference: 0.0218 s/iter. Eval: 0.0015 s/iter. Total: 0.0241 s/iter. ETA=0:00:01 [02/26 17:00:14] cubercnn.evaluation.omni3d_evaluation INFO: Total inference time: 0:02:01.400973 (0.024064 s / iter per device, on 1 devices) [02/26 17:00:14] cubercnn.evaluation.omni3d_evaluation INFO: Total inference pure compute time: 0:01:50 (0.021828 s / iter per device, on 1 devices) [02/26 17:00:27] cubercnn.evaluation.omni3d_evaluation INFO: Preparing results for COCO format ... [02/26 17:00:27] cubercnn.evaluation.omni3d_evaluation INFO: Saving results to output/Baseline_sgd/inference/iter_7200/SUNRGBD_test/omni_instances_results.json [02/26 17:00:33] cubercnn.evaluation.omni3d_evaluation INFO: Evaluating predictions with official COCO API... [02/26 17:01:40] cubercnn.evaluation.omni3d_evaluation INFO: Evaluation results for bbox in 2D mode: | AP | AP50 | AP75 | AP95 | APs | APm | APl | |:------:|:------:|:------:|:------:|:-----:|:-----:|:------:| | 14.173 | 29.647 | 11.531 | 0.062 | 0.000 | 3.539 | 15.078 | [02/26 17:01:40] cubercnn.evaluation.omni3d_evaluation INFO: Per-category bbox AP in 2D mode: | category | AP | category | AP | category | AP | |:-------------|:-------|:------------|:-------|:-----------|:-------| | bicycle | 12.154 | books | 1.109 | bottle | 3.126 | | chair | 43.146 | cup | 3.949 | laptop | 5.444 | | shoes | 0.022 | towel | 2.970 | blinds | 7.473 | | window | 0.158 | lamp | 24.615 | shelves | 4.142 | | mirror | 5.077 | sink | 19.353 | cabinet | 12.637 | | bathtub | 30.937 | door | 4.398 | toilet | 42.722 | | desk | 11.537 | box | 2.369 | bookcase | 19.674 | | picture | 1.772 | table | 28.223 | counter | 15.009 | | bed | 49.992 | night stand | 32.893 | pillow | 12.910 | | sofa | 43.432 | television | 17.293 | floor mat | 5.457 | | curtain | 9.491 | clothes | 0.651 | stationery | 3.498 | | refrigerator | 19.477 | bin | 29.077 | stove | 8.436 | | oven | 3.224 | machine | 0.713 | | | [02/26 17:01:40] cubercnn.evaluation.omni3d_evaluation INFO: Evaluation results for bbox in 3D mode: | AP | AP15 | AP25 | AP50 | APn | APm | APf | |:------:|:------:|:------:|:------:|:------:|:-----:|:-----:| | 11.165 | 16.605 | 12.181 | 2.185 | 11.165 | nan | nan | [02/26 17:01:40] cubercnn.evaluation.omni3d_evaluation INFO: Some metrics cannot be computed and is shown as NaN. [02/26 17:01:40] cubercnn.evaluation.omni3d_evaluation INFO: Per-category bbox AP in 3D mode: | category | AP | category | AP | category | AP | |:-------------|:-------|:------------|:-------|:-----------|:-------| | bicycle | 12.944 | books | 0.313 | bottle | 0.296 | | chair | 44.134 | cup | 0.028 | laptop | 3.575 | | shoes | 0.022 | towel | 0.349 | blinds | 0.184 | | window | 0.000 | lamp | 8.893 | shelves | 2.564 | | mirror | 0.089 | sink | 21.724 | cabinet | 6.600 | | bathtub | 31.743 | door | 0.714 | toilet | 52.849 | | desk | 14.318 | box | 1.667 | bookcase | 4.989 | | picture | 0.049 | table | 29.164 | counter | 9.521 | | bed | 59.604 | night stand | 18.825 | pillow | 7.811 | | sofa | 43.503 | television | 3.164 | floor mat | 2.178 | | curtain | 1.413 | clothes | 0.318 | stationery | 1.370 | | refrigerator | 17.405 | bin | 9.241 | stove | 7.109 | | oven | 4.456 | machine | 1.125 | | | [02/26 17:01:54] cubercnn.evaluation.omni3d_evaluation INFO: SUNRGBD_test iter=7200 mode=2D Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.142 SUNRGBD_test iter=7200 mode=2D Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.296 SUNRGBD_test iter=7200 mode=2D Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.115 SUNRGBD_test iter=7200 mode=2D Average Precision (AP) @[ IoU=0.95 | area= all | maxDets=100 ] = 0.001 SUNRGBD_test iter=7200 mode=2D Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 SUNRGBD_test iter=7200 mode=2D Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.035 SUNRGBD_test iter=7200 mode=2D Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.151 SUNRGBD_test iter=7200 mode=2D Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.242 SUNRGBD_test iter=7200 mode=2D Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.325 SUNRGBD_test iter=7200 mode=2D Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.326 SUNRGBD_test iter=7200 mode=2D Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 SUNRGBD_test iter=7200 mode=2D Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.126 SUNRGBD_test iter=7200 mode=2D Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.343 [02/26 17:01:54] cubercnn.evaluation.omni3d_evaluation INFO: SUNRGBD_test iter=7200 mode=3D Average Precision (AP) @[ IoU=0.05:0.50 | depth= all | maxDets=100 ] = 0.112 SUNRGBD_test iter=7200 mode=3D Average Precision (AP) @[ IoU=0.15 | depth= all | maxDets=100 ] = 0.166 SUNRGBD_test iter=7200 mode=3D Average Precision (AP) @[ IoU=0.25 | depth= all | maxDets=100 ] = 0.122 SUNRGBD_test iter=7200 mode=3D Average Precision (AP) @[ IoU=0.50 | depth= all | maxDets=100 ] = 0.022 SUNRGBD_test iter=7200 mode=3D Average Precision (AP) @[ IoU=0.05:0.50 | depth= near | maxDets=100 ] = 0.112 SUNRGBD_test iter=7200 mode=3D Average Precision (AP) @[ IoU=0.05:0.50 | depth=medium | maxDets=100 ] = -1.000 SUNRGBD_test iter=7200 mode=3D Average Precision (AP) @[ IoU=0.05:0.50 | depth= far | maxDets=100 ] = -1.000 SUNRGBD_test iter=7200 mode=3D Average Recall (AR) @[ IoU=0.05:0.50 | depth= all | maxDets= 1 ] = 0.187 SUNRGBD_test iter=7200 mode=3D Average Recall (AR) @[ IoU=0.05:0.50 | depth= all | maxDets= 10 ] = 0.269 SUNRGBD_test iter=7200 mode=3D Average Recall (AR) @[ IoU=0.05:0.50 | depth= all | maxDets=100 ] = 0.271 SUNRGBD_test iter=7200 mode=3D Average Recall (AR) @[ IoU=0.05:0.50 | depth= near | maxDets=100 ] = 0.271 SUNRGBD_test iter=7200 mode=3D Average Recall (AR) @[ IoU=0.05:0.50 | depth=medium | maxDets=100 ] = -1.000 SUNRGBD_test iter=7200 mode=3D Average Recall (AR) @[ IoU=0.05:0.50 | depth= far | maxDets=100 ] = -1.000 [02/26 17:01:54] cubercnn.vis.logperf INFO: Performance for each of 38 categories on SUNRGBD_test: | category | AP2D | AP3D | category | AP2D | AP3D | category | AP2D | AP3D | |:----------:|:---------|:----------|:-----------:|:---------|:---------|:------------:|:----------|:----------| | chair | 43.1455 | 44.1344 | table | 28.2229 | 29.1643 | cabinet | 12.637 | 6.59974 | | lamp | 24.6154 | 8.89299 | books | 1.10945 | 0.312762 | sofa | 43.4319 | 43.5032 | | picture | 1.77161 | 0.0492581 | window | 0.158416 | 0 | pillow | 12.9098 | 7.81111 | | door | 4.39755 | 0.714169 | blinds | 7.47317 | 0.183977 | sink | 19.3535 | 21.7241 | | shelves | 4.14188 | 2.56413 | television | 17.2929 | 3.16422 | shoes | 0.0224151 | 0.0219289 | | cup | 3.94882 | 0.02807 | bottle | 3.12599 | 0.296353 | bookcase | 19.6736 | 4.98862 | | laptop | 5.44409 | 3.57495 | desk | 11.5367 | 14.3176 | floor mat | 5.45733 | 2.17822 | | mirror | 5.0766 | 0.0894684 | counter | 15.0091 | 9.5209 | bicycle | 12.1538 | 12.9439 | | toilet | 42.7223 | 52.8487 | bed | 49.9916 | 59.6038 | refrigerator | 19.4767 | 17.4052 | | box | 2.36942 | 1.66716 | oven | 3.22369 | 4.45563 | clothes | 0.65106 | 0.318382 | | towel | 2.97006 | 0.349173 | night stand | 32.8935 | 18.8246 | stove | 8.43561 | 7.10861 | | machine | 0.713297 | 1.12535 | stationery | 3.49826 | 1.36954 | bathtub | 30.9366 | 31.7433 | | curtain | 9.4913 | 1.41291 | bin | 29.0771 | 9.24111 | | | | [02/26 17:02:14] cubercnn INFO: SUNRGBD_testiter=7200, xy(30.25), z(0.31), whl(0.19, 0.11, 0.13), ry(2.14) [02/26 17:02:21] cubercnn.vis.logperf INFO: Performance for each of 38 categories on : | category | AP2D | AP3D | category | AP2D | AP3D | category | AP2D | AP3D | |:----------:|:---------|:----------|:-----------:|:---------|:---------|:------------:|:----------|:----------| | chair | 43.1455 | 44.1344 | table | 28.2229 | 29.1643 | cabinet | 12.637 | 6.59974 | | lamp | 24.6154 | 8.89299 | books | 1.10945 | 0.312762 | sofa | 43.4319 | 43.5032 | | picture | 1.77161 | 0.0492581 | window | 0.158416 | 0 | pillow | 12.9098 | 7.81111 | | door | 4.39755 | 0.714169 | blinds | 7.47317 | 0.183977 | sink | 19.3535 | 21.7241 | | shelves | 4.14188 | 2.56413 | television | 17.2929 | 3.16422 | shoes | 0.0224151 | 0.0219289 | | cup | 3.94882 | 0.02807 | bottle | 3.12599 | 0.296353 | bookcase | 19.6736 | 4.98862 | | laptop | 5.44409 | 3.57495 | desk | 11.5367 | 14.3176 | floor mat | 5.45733 | 2.17822 | | mirror | 5.0766 | 0.0894684 | counter | 15.0091 | 9.5209 | bicycle | 12.1538 | 12.9439 | | toilet | 42.7223 | 52.8487 | bed | 49.9916 | 59.6038 | refrigerator | 19.4767 | 17.4052 | | box | 2.36942 | 1.66716 | oven | 3.22369 | 4.45563 | clothes | 0.65106 | 0.318382 | | towel | 2.97006 | 0.349173 | night stand | 32.8935 | 18.8246 | stove | 8.43561 | 7.10861 | | machine | 0.713297 | 1.12535 | stationery | 3.49826 | 1.36954 | bathtub | 30.9366 | 31.7433 | | curtain | 9.4913 | 1.41291 | bin | 29.0771 | 9.24111 | | | | [02/26 17:02:21] cubercnn.vis.logperf INFO: Per-dataset performance analysis on test set: +--------------+----------+---------+---------+-----------+-----------+-----------+----------+----------+----------+ | Dataset | #iters | AP2D | AP3D | AP3D@15 | AP3D@25 | AP3D@50 | AP3D-N | AP3D-M | AP3D-F | +==============+==========+=========+=========+===========+===========+===========+==========+==========+==========+ | SUNRGBD_test | 7200 | 14.1726 | 11.1645 | 16.605 | 12.1811 | 2.1847 | 11.1645 | nan | nan | +--------------+----------+---------+---------+-----------+-----------+-----------+----------+----------+----------+ | | 7200 | 14.1726 | 11.1645 | 16.605 | 12.1811 | 2.1847 | 11.1645 | nan | nan | +--------------+----------+---------+---------+-----------+-----------+-----------+----------+----------+----------+ [02/26 17:02:21] cubercnn.vis.logperf INFO: Omni3D performance on test set. The numbers below should be used to compare to others approaches on Omni3D, such as Cube R-CNN [02/26 17:02:21] cubercnn.vis.logperf INFO: Performance on Omni3D: +--------------+----------+---------+---------+ | Dataset | #iters | AP2D | AP3D | +==============+==========+=========+=========+ | SUNRGBD_test | 7200 | 14.1726 | 11.1645 | +--------------+----------+---------+---------+ | Omni3D_Out | 7200 | nan | nan | +--------------+----------+---------+---------+ | Omni3D_In | 7200 | 14.1726 | 11.1645 | +--------------+----------+---------+---------+ | Omni3D | 7200 | nan | nan | +--------------+----------+---------+---------+ [02/26 17:02:23] d2.utils.events INFO: eta: 3 days, 14:50:17 iter: 7199 Cube/total_3D_loss: 1.338 total_loss: 2.236 BoxHead/loss_cls: 0.1512 BoxHead/loss_box_reg: 0.2313 Cube/loss_dims: 0.04099 Cube/loss_xy: 0.0314 Cube/loss_z: 0.07568 Cube/loss_pose: 0.2463 Cube/loss_joint: 0.5931 lr: 0.0214 max_mem: 28030M [02/26 17:02:50] d2.utils.events INFO: eta: 8:02:11 iter: 7219 Cube/total_3D_loss: 1.335 total_loss: 2.263 BoxHead/loss_cls: 0.1571 BoxHead/loss_box_reg: 0.2376 Cube/loss_dims: 0.04222 Cube/loss_xy: 0.03261 Cube/loss_z: 0.07463 Cube/loss_pose: 0.249 Cube/loss_joint: 0.5977 lr: 0.0214 max_mem: 28030M [02/26 17:03:17] d2.utils.events INFO: eta: 8:05:04 iter: 7239 Cube/total_3D_loss: 1.345 total_loss: 2.272 BoxHead/loss_cls: 0.1632 BoxHead/loss_box_reg: 0.2459 Cube/loss_dims: 0.03951 Cube/loss_xy: 0.03073 Cube/loss_z: 0.07679 Cube/loss_pose: 0.2384 Cube/loss_joint: 0.5905 lr: 0.0214 max_mem: 28030M [02/26 17:03:44] d2.utils.events INFO: eta: 8:05:12 iter: 7259 Cube/total_3D_loss: 1.346 total_loss: 2.252 BoxHead/loss_cls: 0.1633 BoxHead/loss_box_reg: 0.2461 Cube/loss_dims: 0.04147 Cube/loss_xy: 0.03221 Cube/loss_z: 0.07509 Cube/loss_pose: 0.2356 Cube/loss_joint: 0.5933 lr: 0.0214 max_mem: 28030M [02/26 17:04:11] d2.utils.events INFO: eta: 8:06:57 iter: 7279 Cube/total_3D_loss: 1.356 total_loss: 2.274 BoxHead/loss_cls: 0.1657 BoxHead/loss_box_reg: 0.2393 Cube/loss_dims: 0.04292 Cube/loss_xy: 0.03211 Cube/loss_z: 0.07627 Cube/loss_pose: 0.2471 Cube/loss_joint: 0.602 lr: 0.0214 max_mem: 28030M [02/26 17:04:38] d2.utils.events INFO: eta: 8:05:09 iter: 7299 Cube/total_3D_loss: 1.347 total_loss: 2.289 BoxHead/loss_cls: 0.1622 BoxHead/loss_box_reg: 0.2346 Cube/loss_dims: 0.04132 Cube/loss_xy: 0.03154 Cube/loss_z: 0.07583 Cube/loss_pose: 0.2447 Cube/loss_joint: 0.5953 lr: 0.0214 max_mem: 28030M [02/26 17:05:05] d2.utils.events INFO: eta: 7:59:26 iter: 7319 Cube/total_3D_loss: 1.376 total_loss: 2.27 BoxHead/loss_cls: 0.1624 BoxHead/loss_box_reg: 0.2414 Cube/loss_dims: 0.03972 Cube/loss_xy: 0.02989 Cube/loss_z: 0.08025 Cube/loss_pose: 0.2386 Cube/loss_joint: 0.6001 lr: 0.0214 max_mem: 28030M [02/26 17:05:32] d2.utils.events INFO: eta: 8:03:02 iter: 7339 Cube/total_3D_loss: 1.372 total_loss: 2.283 BoxHead/loss_cls: 0.1697 BoxHead/loss_box_reg: 0.235 Cube/loss_dims: 0.04214 Cube/loss_xy: 0.03076 Cube/loss_z: 0.07755 Cube/loss_pose: 0.2401 Cube/loss_joint: 0.6001 lr: 0.0214 max_mem: 28030M [02/26 17:05:58] d2.utils.events INFO: eta: 7:56:22 iter: 7359 Cube/total_3D_loss: 1.339 total_loss: 2.242 BoxHead/loss_cls: 0.1655 BoxHead/loss_box_reg: 0.2554 Cube/loss_dims: 0.04206 Cube/loss_xy: 0.03141 Cube/loss_z: 0.07134 Cube/loss_pose: 0.246 Cube/loss_joint: 0.5812 lr: 0.0214 max_mem: 28030M [02/26 17:06:30] d2.utils.events INFO: eta: 9:32:16 iter: 7379 Cube/total_3D_loss: 1.291 total_loss: 2.198 BoxHead/loss_cls: 0.1512 BoxHead/loss_box_reg: 0.2331 Cube/loss_dims: 0.04198 Cube/loss_xy: 0.03263 Cube/loss_z: 0.07642 Cube/loss_pose: 0.242 Cube/loss_joint: 0.6023 lr: 0.0214 max_mem: 28030M [02/26 17:06:57] d2.utils.events INFO: eta: 7:56:11 iter: 7399 Cube/total_3D_loss: 1.344 total_loss: 2.273 BoxHead/loss_cls: 0.167 BoxHead/loss_box_reg: 0.2419 Cube/loss_dims: 0.04194 Cube/loss_xy: 0.03149 Cube/loss_z: 0.07797 Cube/loss_pose: 0.2422 Cube/loss_joint: 0.5976 lr: 0.0214 max_mem: 28030M [02/26 17:07:24] d2.utils.events INFO: eta: 7:59:31 iter: 7419 Cube/total_3D_loss: 1.32 total_loss: 2.236 BoxHead/loss_cls: 0.1581 BoxHead/loss_box_reg: 0.241 Cube/loss_dims: 0.04072 Cube/loss_xy: 0.03228 Cube/loss_z: 0.0762 Cube/loss_pose: 0.2424 Cube/loss_joint: 0.5965 lr: 0.0214 max_mem: 28030M [02/26 17:07:51] d2.utils.events INFO: eta: 7:57:56 iter: 7439 Cube/total_3D_loss: 1.401 total_loss: 2.293 BoxHead/loss_cls: 0.1589 BoxHead/loss_box_reg: 0.2338 Cube/loss_dims: 0.03966 Cube/loss_xy: 0.02969 Cube/loss_z: 0.07671 Cube/loss_pose: 0.2383 Cube/loss_joint: 0.5912 lr: 0.0214 max_mem: 28030M [02/26 17:08:18] d2.utils.events INFO: eta: 8:02:29 iter: 7459 Cube/total_3D_loss: 1.322 total_loss: 2.243 BoxHead/loss_cls: 0.1651 BoxHead/loss_box_reg: 0.241 Cube/loss_dims: 0.04241 Cube/loss_xy: 0.03214 Cube/loss_z: 0.07398 Cube/loss_pose: 0.2538 Cube/loss_joint: 0.5914 lr: 0.0214 max_mem: 28030M [02/26 17:08:45] d2.utils.events INFO: eta: 8:02:12 iter: 7479 Cube/total_3D_loss: 1.356 total_loss: 2.237 BoxHead/loss_cls: 0.1674 BoxHead/loss_box_reg: 0.2595 Cube/loss_dims: 0.0409 Cube/loss_xy: 0.0312 Cube/loss_z: 0.07372 Cube/loss_pose: 0.2391 Cube/loss_joint: 0.5882 lr: 0.0214 max_mem: 28030M [02/26 17:09:12] d2.utils.events INFO: eta: 8:01:49 iter: 7499 Cube/total_3D_loss: 1.344 total_loss: 2.235 BoxHead/loss_cls: 0.1529 BoxHead/loss_box_reg: 0.2334 Cube/loss_dims: 0.04037 Cube/loss_xy: 0.03138 Cube/loss_z: 0.07517 Cube/loss_pose: 0.2352 Cube/loss_joint: 0.591 lr: 0.0214 max_mem: 28030M [02/26 17:09:40] d2.utils.events INFO: eta: 8:08:04 iter: 7519 Cube/total_3D_loss: 1.311 total_loss: 2.227 BoxHead/loss_cls: 0.1622 BoxHead/loss_box_reg: 0.2442 Cube/loss_dims: 0.04266 Cube/loss_xy: 0.03403 Cube/loss_z: 0.0731 Cube/loss_pose: 0.2488 Cube/loss_joint: 0.5914 lr: 0.0214 max_mem: 28030M [02/26 17:10:07] d2.utils.events INFO: eta: 7:55:56 iter: 7539 Cube/total_3D_loss: 1.368 total_loss: 2.277 BoxHead/loss_cls: 0.1652 BoxHead/loss_box_reg: 0.2381 Cube/loss_dims: 0.04151 Cube/loss_xy: 0.03149 Cube/loss_z: 0.07445 Cube/loss_pose: 0.24 Cube/loss_joint: 0.5914 lr: 0.0214 max_mem: 28030M [02/26 17:10:34] d2.utils.events INFO: eta: 7:57:49 iter: 7559 Cube/total_3D_loss: 1.383 total_loss: 2.285 BoxHead/loss_cls: 0.1582 BoxHead/loss_box_reg: 0.2396 Cube/loss_dims: 0.04164 Cube/loss_xy: 0.03199 Cube/loss_z: 0.0777 Cube/loss_pose: 0.241 Cube/loss_joint: 0.6064 lr: 0.0214 max_mem: 28030M [02/26 17:11:02] d2.utils.events INFO: eta: 8:13:00 iter: 7579 Cube/total_3D_loss: 1.371 total_loss: 2.256 BoxHead/loss_cls: 0.1553 BoxHead/loss_box_reg: 0.2368 Cube/loss_dims: 0.03928 Cube/loss_xy: 0.03087 Cube/loss_z: 0.07738 Cube/loss_pose: 0.2402 Cube/loss_joint: 0.5927 lr: 0.0214 max_mem: 28030M [02/26 17:11:29] d2.utils.events INFO: eta: 7:55:51 iter: 7599 Cube/total_3D_loss: 1.329 total_loss: 2.232 BoxHead/loss_cls: 0.1571 BoxHead/loss_box_reg: 0.2519 Cube/loss_dims: 0.04111 Cube/loss_xy: 0.03189 Cube/loss_z: 0.07538 Cube/loss_pose: 0.233 Cube/loss_joint: 0.5883 lr: 0.0214 max_mem: 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d2.utils.events INFO: eta: 7:45:47 iter: 8279 Cube/total_3D_loss: 1.298 total_loss: 2.208 BoxHead/loss_cls: 0.1455 BoxHead/loss_box_reg: 0.238 Cube/loss_dims: 0.04165 Cube/loss_xy: 0.03218 Cube/loss_z: 0.0769 Cube/loss_pose: 0.2431 Cube/loss_joint: 0.6095 lr: 0.0214 max_mem: 28030M [02/26 17:27:21] d2.utils.events INFO: eta: 7:42:29 iter: 8299 Cube/total_3D_loss: 1.297 total_loss: 2.187 BoxHead/loss_cls: 0.1492 BoxHead/loss_box_reg: 0.2392 Cube/loss_dims: 0.04112 Cube/loss_xy: 0.03032 Cube/loss_z: 0.07748 Cube/loss_pose: 0.2358 Cube/loss_joint: 0.6003 lr: 0.0214 max_mem: 28030M [02/26 17:27:48] d2.utils.events INFO: eta: 7:37:54 iter: 8319 Cube/total_3D_loss: 1.332 total_loss: 2.195 BoxHead/loss_cls: 0.1403 BoxHead/loss_box_reg: 0.2214 Cube/loss_dims: 0.04198 Cube/loss_xy: 0.03034 Cube/loss_z: 0.0772 Cube/loss_pose: 0.2444 Cube/loss_joint: 0.6001 lr: 0.0214 max_mem: 28030M [02/26 17:28:15] d2.utils.events INFO: eta: 7:42:14 iter: 8339 Cube/total_3D_loss: 1.294 total_loss: 2.175 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17:46:17] d2.utils.events INFO: eta: 7:20:07 iter: 9139 Cube/total_3D_loss: 1.247 total_loss: 2.164 BoxHead/loss_cls: 0.1452 BoxHead/loss_box_reg: 0.2369 Cube/loss_dims: 0.04258 Cube/loss_xy: 0.03271 Cube/loss_z: 0.07141 Cube/loss_pose: 0.2444 Cube/loss_joint: 0.5886 lr: 0.0214 max_mem: 28030M [02/26 17:46:44] d2.utils.events INFO: eta: 7:27:52 iter: 9159 Cube/total_3D_loss: 1.256 total_loss: 2.169 BoxHead/loss_cls: 0.1334 BoxHead/loss_box_reg: 0.2281 Cube/loss_dims: 0.04104 Cube/loss_xy: 0.0311 Cube/loss_z: 0.07455 Cube/loss_pose: 0.2383 Cube/loss_joint: 0.5908 lr: 0.0214 max_mem: 28030M [02/26 17:47:11] d2.utils.events INFO: eta: 7:22:51 iter: 9179 Cube/total_3D_loss: 1.286 total_loss: 2.162 BoxHead/loss_cls: 0.139 BoxHead/loss_box_reg: 0.2234 Cube/loss_dims: 0.04227 Cube/loss_xy: 0.03173 Cube/loss_z: 0.07431 Cube/loss_pose: 0.2434 Cube/loss_joint: 0.5904 lr: 0.0214 max_mem: 28030M [02/26 17:47:38] d2.utils.events INFO: eta: 7:18:48 iter: 9199 Cube/total_3D_loss: 1.271 total_loss: 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d2.utils.events INFO: eta: 7:05:58 iter: 9999 Cube/total_3D_loss: 1.226 total_loss: 2.075 BoxHead/loss_cls: 0.1322 BoxHead/loss_box_reg: 0.2268 Cube/loss_dims: 0.04209 Cube/loss_xy: 0.03111 Cube/loss_z: 0.07315 Cube/loss_pose: 0.2396 Cube/loss_joint: 0.5872 lr: 0.0214 max_mem: 28030M [02/26 18:05:46] fvcore.common.checkpoint INFO: Saving checkpoint to output/Baseline_sgd/model_recent.pth [02/26 18:06:14] d2.utils.events INFO: eta: 7:12:50 iter: 10019 Cube/total_3D_loss: 1.223 total_loss: 2.106 BoxHead/loss_cls: 0.1293 BoxHead/loss_box_reg: 0.2372 Cube/loss_dims: 0.04199 Cube/loss_xy: 0.03226 Cube/loss_z: 0.07443 Cube/loss_pose: 0.2397 Cube/loss_joint: 0.5922 lr: 0.0214 max_mem: 28030M [02/26 18:06:41] d2.utils.events INFO: eta: 7:03:38 iter: 10039 Cube/total_3D_loss: 1.247 total_loss: 2.122 BoxHead/loss_cls: 0.1396 BoxHead/loss_box_reg: 0.22 Cube/loss_dims: 0.04118 Cube/loss_xy: 0.03083 Cube/loss_z: 0.07617 Cube/loss_pose: 0.2342 Cube/loss_joint: 0.5811 lr: 0.0214 max_mem: 28030M [02/26 18:07:08] d2.utils.events INFO: eta: 7:03:47 iter: 10059 Cube/total_3D_loss: 1.262 total_loss: 2.16 BoxHead/loss_cls: 0.1445 BoxHead/loss_box_reg: 0.2229 Cube/loss_dims: 0.04311 Cube/loss_xy: 0.03125 Cube/loss_z: 0.07512 Cube/loss_pose: 0.2367 Cube/loss_joint: 0.6002 lr: 0.0214 max_mem: 28030M [02/26 18:07:35] d2.utils.events INFO: eta: 7:00:55 iter: 10079 Cube/total_3D_loss: 1.306 total_loss: 2.199 BoxHead/loss_cls: 0.1433 BoxHead/loss_box_reg: 0.228 Cube/loss_dims: 0.04129 Cube/loss_xy: 0.03061 Cube/loss_z: 0.07901 Cube/loss_pose: 0.2363 Cube/loss_joint: 0.6023 lr: 0.0214 max_mem: 28030M [02/26 18:08:02] d2.utils.events INFO: eta: 7:05:32 iter: 10099 Cube/total_3D_loss: 1.268 total_loss: 2.123 BoxHead/loss_cls: 0.1309 BoxHead/loss_box_reg: 0.223 Cube/loss_dims: 0.04187 Cube/loss_xy: 0.02987 Cube/loss_z: 0.0763 Cube/loss_pose: 0.2373 Cube/loss_joint: 0.5949 lr: 0.0214 max_mem: 28030M [02/26 18:08:30] d2.utils.events INFO: eta: 7:06:27 iter: 10119 Cube/total_3D_loss: 1.226 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INFO: eta: 6:45:12 iter: 10979 Cube/total_3D_loss: 1.173 total_loss: 2.063 BoxHead/loss_cls: 0.1338 BoxHead/loss_box_reg: 0.2295 Cube/loss_dims: 0.04266 Cube/loss_xy: 0.03129 Cube/loss_z: 0.07429 Cube/loss_pose: 0.2468 Cube/loss_joint: 0.5882 lr: 0.0214 max_mem: 28030M [02/26 18:28:21] d2.utils.events INFO: eta: 6:38:33 iter: 10999 Cube/total_3D_loss: 1.197 total_loss: 2.056 BoxHead/loss_cls: 0.1262 BoxHead/loss_box_reg: 0.2286 Cube/loss_dims: 0.04047 Cube/loss_xy: 0.03061 Cube/loss_z: 0.07573 Cube/loss_pose: 0.2397 Cube/loss_joint: 0.5892 lr: 0.0214 max_mem: 28030M [02/26 18:28:49] d2.utils.events INFO: eta: 6:42:06 iter: 11019 Cube/total_3D_loss: 1.255 total_loss: 2.101 BoxHead/loss_cls: 0.1195 BoxHead/loss_box_reg: 0.205 Cube/loss_dims: 0.04082 Cube/loss_xy: 0.02988 Cube/loss_z: 0.07817 Cube/loss_pose: 0.2243 Cube/loss_joint: 0.5977 lr: 0.0214 max_mem: 28030M [02/26 18:29:16] d2.utils.events INFO: eta: 6:39:11 iter: 11039 Cube/total_3D_loss: 1.252 total_loss: 2.118 BoxHead/loss_cls: 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[02/26 18:32:25] d2.utils.events INFO: eta: 6:32:27 iter: 11179 Cube/total_3D_loss: 1.202 total_loss: 2.033 BoxHead/loss_cls: 0.1294 BoxHead/loss_box_reg: 0.2086 Cube/loss_dims: 0.04085 Cube/loss_xy: 0.03061 Cube/loss_z: 0.07329 Cube/loss_pose: 0.235 Cube/loss_joint: 0.5878 lr: 0.0214 max_mem: 28030M [02/26 18:32:52] d2.utils.events INFO: eta: 6:34:35 iter: 11199 Cube/total_3D_loss: 1.172 total_loss: 2.012 BoxHead/loss_cls: 0.1192 BoxHead/loss_box_reg: 0.2203 Cube/loss_dims: 0.04196 Cube/loss_xy: 0.03093 Cube/loss_z: 0.07333 Cube/loss_pose: 0.2443 Cube/loss_joint: 0.5914 lr: 0.0214 max_mem: 28030M [02/26 18:33:19] d2.utils.events INFO: eta: 6:34:42 iter: 11219 Cube/total_3D_loss: 1.15 total_loss: 2.047 BoxHead/loss_cls: 0.1362 BoxHead/loss_box_reg: 0.2321 Cube/loss_dims: 0.04173 Cube/loss_xy: 0.03168 Cube/loss_z: 0.07518 Cube/loss_pose: 0.2386 Cube/loss_joint: 0.5968 lr: 0.0214 max_mem: 28030M [02/26 18:33:46] d2.utils.events INFO: eta: 6:38:11 iter: 11239 Cube/total_3D_loss: 1.234 total_loss: 2.098 BoxHead/loss_cls: 0.1302 BoxHead/loss_box_reg: 0.215 Cube/loss_dims: 0.04172 Cube/loss_xy: 0.0301 Cube/loss_z: 0.07353 Cube/loss_pose: 0.2407 Cube/loss_joint: 0.5978 lr: 0.0214 max_mem: 28030M [02/26 18:34:13] d2.utils.events INFO: eta: 6:39:08 iter: 11259 Cube/total_3D_loss: 1.192 total_loss: 2.04 BoxHead/loss_cls: 0.1271 BoxHead/loss_box_reg: 0.2256 Cube/loss_dims: 0.04176 Cube/loss_xy: 0.03116 Cube/loss_z: 0.07467 Cube/loss_pose: 0.2461 Cube/loss_joint: 0.5957 lr: 0.0214 max_mem: 28030M [02/26 18:34:40] d2.utils.events INFO: eta: 6:28:10 iter: 11279 Cube/total_3D_loss: 1.195 total_loss: 2.049 BoxHead/loss_cls: 0.1215 BoxHead/loss_box_reg: 0.2103 Cube/loss_dims: 0.04148 Cube/loss_xy: 0.0318 Cube/loss_z: 0.07201 Cube/loss_pose: 0.2388 Cube/loss_joint: 0.5848 lr: 0.0214 max_mem: 28030M [02/26 18:35:07] d2.utils.events INFO: eta: 6:31:59 iter: 11299 Cube/total_3D_loss: 1.231 total_loss: 2.119 BoxHead/loss_cls: 0.1292 BoxHead/loss_box_reg: 0.2336 Cube/loss_dims: 0.04243 Cube/loss_xy: 0.03282 Cube/loss_z: 0.07423 Cube/loss_pose: 0.2354 Cube/loss_joint: 0.5942 lr: 0.0214 max_mem: 28030M [02/26 18:35:33] d2.utils.events INFO: eta: 6:28:52 iter: 11319 Cube/total_3D_loss: 1.213 total_loss: 2.088 BoxHead/loss_cls: 0.1315 BoxHead/loss_box_reg: 0.2347 Cube/loss_dims: 0.04175 Cube/loss_xy: 0.03123 Cube/loss_z: 0.07145 Cube/loss_pose: 0.2374 Cube/loss_joint: 0.5814 lr: 0.0214 max_mem: 28030M [02/26 18:36:00] d2.utils.events INFO: eta: 6:30:24 iter: 11339 Cube/total_3D_loss: 1.203 total_loss: 2.065 BoxHead/loss_cls: 0.1278 BoxHead/loss_box_reg: 0.2186 Cube/loss_dims: 0.04134 Cube/loss_xy: 0.03245 Cube/loss_z: 0.07653 Cube/loss_pose: 0.2335 Cube/loss_joint: 0.6053 lr: 0.0214 max_mem: 28030M [02/26 18:36:28] d2.utils.events INFO: eta: 6:43:48 iter: 11359 Cube/total_3D_loss: 1.175 total_loss: 2.033 BoxHead/loss_cls: 0.1151 BoxHead/loss_box_reg: 0.2053 Cube/loss_dims: 0.03999 Cube/loss_xy: 0.03143 Cube/loss_z: 0.07448 Cube/loss_pose: 0.2303 Cube/loss_joint: 0.5936 lr: 0.0214 max_mem: 28030M [02/26 18:36:54] d2.utils.events INFO: eta: 6:22:43 iter: 11379 Cube/total_3D_loss: 1.218 total_loss: 2.082 BoxHead/loss_cls: 0.1244 BoxHead/loss_box_reg: 0.2206 Cube/loss_dims: 0.04143 Cube/loss_xy: 0.03074 Cube/loss_z: 0.07569 Cube/loss_pose: 0.2433 Cube/loss_joint: 0.5959 lr: 0.0214 max_mem: 28030M [02/26 18:37:21] d2.utils.events INFO: eta: 6:25:54 iter: 11399 Cube/total_3D_loss: 1.143 total_loss: 2.058 BoxHead/loss_cls: 0.122 BoxHead/loss_box_reg: 0.2299 Cube/loss_dims: 0.04218 Cube/loss_xy: 0.03005 Cube/loss_z: 0.07475 Cube/loss_pose: 0.2365 Cube/loss_joint: 0.5863 lr: 0.0214 max_mem: 28030M [02/26 18:37:48] d2.utils.events INFO: eta: 6:32:24 iter: 11419 Cube/total_3D_loss: 1.206 total_loss: 2.072 BoxHead/loss_cls: 0.1236 BoxHead/loss_box_reg: 0.2255 Cube/loss_dims: 0.04177 Cube/loss_xy: 0.03085 Cube/loss_z: 0.07409 Cube/loss_pose: 0.2528 Cube/loss_joint: 0.5877 lr: 0.0214 max_mem: 28030M [02/26 18:38:15] d2.utils.events INFO: eta: 6:32:56 iter: 11439 Cube/total_3D_loss: 1.255 total_loss: 2.092 BoxHead/loss_cls: 0.1274 BoxHead/loss_box_reg: 0.2258 Cube/loss_dims: 0.04104 Cube/loss_xy: 0.03084 Cube/loss_z: 0.07603 Cube/loss_pose: 0.2362 Cube/loss_joint: 0.5962 lr: 0.0214 max_mem: 28030M [02/26 18:38:46] d2.utils.events INFO: eta: 7:30:43 iter: 11459 Cube/total_3D_loss: 1.17 total_loss: 2.043 BoxHead/loss_cls: 0.1288 BoxHead/loss_box_reg: 0.223 Cube/loss_dims: 0.04121 Cube/loss_xy: 0.0317 Cube/loss_z: 0.07031 Cube/loss_pose: 0.2359 Cube/loss_joint: 0.5922 lr: 0.0214 max_mem: 28030M [02/26 18:39:15] d2.utils.events INFO: eta: 6:58:03 iter: 11479 Cube/total_3D_loss: 1.165 total_loss: 2.032 BoxHead/loss_cls: 0.1211 BoxHead/loss_box_reg: 0.2226 Cube/loss_dims: 0.04119 Cube/loss_xy: 0.03145 Cube/loss_z: 0.07114 Cube/loss_pose: 0.2382 Cube/loss_joint: 0.583 lr: 0.0214 max_mem: 28030M [02/26 18:39:42] d2.utils.events INFO: eta: 6:29:07 iter: 11499 Cube/total_3D_loss: 1.181 total_loss: 2.022 BoxHead/loss_cls: 0.1227 BoxHead/loss_box_reg: 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INFO: eta: 6:22:41 iter: 11639 Cube/total_3D_loss: 1.201 total_loss: 2.053 BoxHead/loss_cls: 0.1198 BoxHead/loss_box_reg: 0.2298 Cube/loss_dims: 0.04176 Cube/loss_xy: 0.03157 Cube/loss_z: 0.07226 Cube/loss_pose: 0.2416 Cube/loss_joint: 0.5827 lr: 0.0214 max_mem: 28030M [02/26 18:43:17] d2.utils.events INFO: eta: 6:23:12 iter: 11659 Cube/total_3D_loss: 1.224 total_loss: 2.063 BoxHead/loss_cls: 0.1239 BoxHead/loss_box_reg: 0.2034 Cube/loss_dims: 0.04247 Cube/loss_xy: 0.03053 Cube/loss_z: 0.07518 Cube/loss_pose: 0.2416 Cube/loss_joint: 0.5934 lr: 0.0214 max_mem: 28030M [02/26 18:43:44] d2.utils.events INFO: eta: 6:21:50 iter: 11679 Cube/total_3D_loss: 1.244 total_loss: 2.067 BoxHead/loss_cls: 0.1175 BoxHead/loss_box_reg: 0.2078 Cube/loss_dims: 0.04195 Cube/loss_xy: 0.03026 Cube/loss_z: 0.07731 Cube/loss_pose: 0.2338 Cube/loss_joint: 0.5975 lr: 0.0214 max_mem: 28030M [02/26 18:44:11] d2.utils.events INFO: eta: 6:22:53 iter: 11699 Cube/total_3D_loss: 1.199 total_loss: 2.095 BoxHead/loss_cls: 0.13 BoxHead/loss_box_reg: 0.2224 Cube/loss_dims: 0.0387 Cube/loss_xy: 0.0301 Cube/loss_z: 0.07702 Cube/loss_pose: 0.2277 Cube/loss_joint: 0.5895 lr: 0.0214 max_mem: 28030M [02/26 18:44:38] d2.utils.events INFO: eta: 6:22:05 iter: 11719 Cube/total_3D_loss: 1.173 total_loss: 2.054 BoxHead/loss_cls: 0.1207 BoxHead/loss_box_reg: 0.2124 Cube/loss_dims: 0.04033 Cube/loss_xy: 0.03133 Cube/loss_z: 0.07035 Cube/loss_pose: 0.234 Cube/loss_joint: 0.5889 lr: 0.0214 max_mem: 28030M [02/26 18:45:05] d2.utils.events INFO: eta: 6:27:38 iter: 11739 Cube/total_3D_loss: 1.193 total_loss: 2.047 BoxHead/loss_cls: 0.119 BoxHead/loss_box_reg: 0.2234 Cube/loss_dims: 0.04154 Cube/loss_xy: 0.03127 Cube/loss_z: 0.07631 Cube/loss_pose: 0.2382 Cube/loss_joint: 0.5935 lr: 0.0214 max_mem: 28030M [02/26 18:45:32] d2.utils.events INFO: eta: 6:21:17 iter: 11759 Cube/total_3D_loss: 1.202 total_loss: 2.041 BoxHead/loss_cls: 0.1128 BoxHead/loss_box_reg: 0.2021 Cube/loss_dims: 0.04147 Cube/loss_xy: 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max_mem: 28030M [02/26 18:47:20] d2.utils.events INFO: eta: 6:22:00 iter: 11839 Cube/total_3D_loss: 1.235 total_loss: 2.115 BoxHead/loss_cls: 0.1239 BoxHead/loss_box_reg: 0.2263 Cube/loss_dims: 0.04056 Cube/loss_xy: 0.02942 Cube/loss_z: 0.07594 Cube/loss_pose: 0.2351 Cube/loss_joint: 0.5906 lr: 0.0214 max_mem: 28030M [02/26 18:47:47] d2.utils.events INFO: eta: 6:18:41 iter: 11859 Cube/total_3D_loss: 1.143 total_loss: 1.98 BoxHead/loss_cls: 0.1087 BoxHead/loss_box_reg: 0.2047 Cube/loss_dims: 0.0421 Cube/loss_xy: 0.03052 Cube/loss_z: 0.07292 Cube/loss_pose: 0.2469 Cube/loss_joint: 0.5866 lr: 0.0214 max_mem: 28030M [02/26 18:48:13] d2.utils.events INFO: eta: 6:20:11 iter: 11879 Cube/total_3D_loss: 1.183 total_loss: 1.999 BoxHead/loss_cls: 0.1195 BoxHead/loss_box_reg: 0.203 Cube/loss_dims: 0.04053 Cube/loss_xy: 0.03079 Cube/loss_z: 0.07167 Cube/loss_pose: 0.2483 Cube/loss_joint: 0.579 lr: 0.0214 max_mem: 28030M [02/26 18:48:40] d2.utils.events INFO: eta: 6:17:47 iter: 11899 Cube/total_3D_loss: 1.162 total_loss: 1.985 BoxHead/loss_cls: 0.1175 BoxHead/loss_box_reg: 0.2102 Cube/loss_dims: 0.04137 Cube/loss_xy: 0.03133 Cube/loss_z: 0.07231 Cube/loss_pose: 0.2434 Cube/loss_joint: 0.5815 lr: 0.0214 max_mem: 28030M [02/26 18:49:07] d2.utils.events INFO: eta: 6:21:35 iter: 11919 Cube/total_3D_loss: 1.186 total_loss: 2.033 BoxHead/loss_cls: 0.1148 BoxHead/loss_box_reg: 0.2075 Cube/loss_dims: 0.04093 Cube/loss_xy: 0.02981 Cube/loss_z: 0.07646 Cube/loss_pose: 0.2404 Cube/loss_joint: 0.6011 lr: 0.0214 max_mem: 28030M [02/26 18:49:34] d2.utils.events INFO: eta: 6:12:48 iter: 11939 Cube/total_3D_loss: 1.178 total_loss: 2.034 BoxHead/loss_cls: 0.1208 BoxHead/loss_box_reg: 0.2119 Cube/loss_dims: 0.04337 Cube/loss_xy: 0.03096 Cube/loss_z: 0.07474 Cube/loss_pose: 0.237 Cube/loss_joint: 0.6013 lr: 0.0214 max_mem: 28030M [02/26 18:50:01] d2.utils.events INFO: eta: 6:15:56 iter: 11959 Cube/total_3D_loss: 1.177 total_loss: 1.995 BoxHead/loss_cls: 0.1155 BoxHead/loss_box_reg: 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Cube/loss_joint: 0.595 lr: 0.0214 max_mem: 28030M [02/26 18:51:48] d2.utils.events INFO: eta: 6:14:41 iter: 12039 Cube/total_3D_loss: 1.141 total_loss: 1.98 BoxHead/loss_cls: 0.1101 BoxHead/loss_box_reg: 0.21 Cube/loss_dims: 0.04204 Cube/loss_xy: 0.03046 Cube/loss_z: 0.07404 Cube/loss_pose: 0.2477 Cube/loss_joint: 0.5944 lr: 0.0214 max_mem: 28030M [02/26 18:52:15] d2.utils.events INFO: eta: 6:13:57 iter: 12059 Cube/total_3D_loss: 1.158 total_loss: 1.994 BoxHead/loss_cls: 0.1191 BoxHead/loss_box_reg: 0.2172 Cube/loss_dims: 0.04111 Cube/loss_xy: 0.03064 Cube/loss_z: 0.07401 Cube/loss_pose: 0.2354 Cube/loss_joint: 0.5888 lr: 0.0214 max_mem: 28030M [02/26 18:52:42] d2.utils.events INFO: eta: 6:21:09 iter: 12079 Cube/total_3D_loss: 1.172 total_loss: 2.017 BoxHead/loss_cls: 0.1156 BoxHead/loss_box_reg: 0.2078 Cube/loss_dims: 0.04243 Cube/loss_xy: 0.03032 Cube/loss_z: 0.07442 Cube/loss_pose: 0.2402 Cube/loss_joint: 0.5946 lr: 0.0214 max_mem: 28030M [02/26 18:53:10] d2.utils.events INFO: eta: 6:18:00 iter: 12099 Cube/total_3D_loss: 1.16 total_loss: 1.994 BoxHead/loss_cls: 0.1141 BoxHead/loss_box_reg: 0.2071 Cube/loss_dims: 0.04225 Cube/loss_xy: 0.03071 Cube/loss_z: 0.07449 Cube/loss_pose: 0.2344 Cube/loss_joint: 0.5902 lr: 0.0214 max_mem: 28030M [02/26 18:53:37] d2.utils.events INFO: eta: 6:22:28 iter: 12119 Cube/total_3D_loss: 1.151 total_loss: 2.005 BoxHead/loss_cls: 0.1185 BoxHead/loss_box_reg: 0.2245 Cube/loss_dims: 0.03939 Cube/loss_xy: 0.03044 Cube/loss_z: 0.0745 Cube/loss_pose: 0.2371 Cube/loss_joint: 0.5908 lr: 0.0214 max_mem: 28030M [02/26 18:54:04] d2.utils.events INFO: eta: 6:10:59 iter: 12139 Cube/total_3D_loss: 1.221 total_loss: 2.031 BoxHead/loss_cls: 0.1196 BoxHead/loss_box_reg: 0.2212 Cube/loss_dims: 0.04275 Cube/loss_xy: 0.03098 Cube/loss_z: 0.0721 Cube/loss_pose: 0.2518 Cube/loss_joint: 0.5894 lr: 0.0214 max_mem: 28030M [02/26 18:54:31] d2.utils.events INFO: eta: 6:13:06 iter: 12159 Cube/total_3D_loss: 1.129 total_loss: 1.972 BoxHead/loss_cls: 0.1153 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Cube/loss_pose: 0.2406 Cube/loss_joint: 0.5994 lr: 0.0214 max_mem: 28030M [02/26 18:56:20] d2.utils.events INFO: eta: 6:11:55 iter: 12239 Cube/total_3D_loss: 1.218 total_loss: 2.052 BoxHead/loss_cls: 0.1248 BoxHead/loss_box_reg: 0.2231 Cube/loss_dims: 0.04222 Cube/loss_xy: 0.03009 Cube/loss_z: 0.07666 Cube/loss_pose: 0.2468 Cube/loss_joint: 0.5926 lr: 0.0214 max_mem: 28030M [02/26 18:56:47] d2.utils.events INFO: eta: 6:16:54 iter: 12259 Cube/total_3D_loss: 1.176 total_loss: 2.027 BoxHead/loss_cls: 0.1178 BoxHead/loss_box_reg: 0.2109 Cube/loss_dims: 0.04018 Cube/loss_xy: 0.02966 Cube/loss_z: 0.07444 Cube/loss_pose: 0.2325 Cube/loss_joint: 0.5808 lr: 0.0214 max_mem: 28030M [02/26 18:57:14] d2.utils.events INFO: eta: 6:14:54 iter: 12279 Cube/total_3D_loss: 1.133 total_loss: 1.965 BoxHead/loss_cls: 0.1146 BoxHead/loss_box_reg: 0.2162 Cube/loss_dims: 0.04128 Cube/loss_xy: 0.0313 Cube/loss_z: 0.06952 Cube/loss_pose: 0.237 Cube/loss_joint: 0.5727 lr: 0.0214 max_mem: 28030M [02/26 18:57:41] d2.utils.events INFO: eta: 6:10:33 iter: 12299 Cube/total_3D_loss: 1.189 total_loss: 2.026 BoxHead/loss_cls: 0.1198 BoxHead/loss_box_reg: 0.2159 Cube/loss_dims: 0.0392 Cube/loss_xy: 0.03036 Cube/loss_z: 0.07218 Cube/loss_pose: 0.2368 Cube/loss_joint: 0.5812 lr: 0.0214 max_mem: 28030M [02/26 18:58:08] d2.utils.events INFO: eta: 6:09:07 iter: 12319 Cube/total_3D_loss: 1.16 total_loss: 2.032 BoxHead/loss_cls: 0.118 BoxHead/loss_box_reg: 0.2218 Cube/loss_dims: 0.04186 Cube/loss_xy: 0.03346 Cube/loss_z: 0.07242 Cube/loss_pose: 0.2421 Cube/loss_joint: 0.5912 lr: 0.0214 max_mem: 28030M [02/26 18:58:35] d2.utils.events INFO: eta: 6:07:42 iter: 12339 Cube/total_3D_loss: 1.216 total_loss: 2.076 BoxHead/loss_cls: 0.1233 BoxHead/loss_box_reg: 0.2283 Cube/loss_dims: 0.04232 Cube/loss_xy: 0.03129 Cube/loss_z: 0.07563 Cube/loss_pose: 0.2464 Cube/loss_joint: 0.5876 lr: 0.0214 max_mem: 28030M [02/26 18:59:02] d2.utils.events INFO: eta: 6:06:40 iter: 12359 Cube/total_3D_loss: 1.162 total_loss: 2.003 BoxHead/loss_cls: 0.1172 BoxHead/loss_box_reg: 0.2196 Cube/loss_dims: 0.04316 Cube/loss_xy: 0.03078 Cube/loss_z: 0.07354 Cube/loss_pose: 0.2343 Cube/loss_joint: 0.5816 lr: 0.0214 max_mem: 28030M [02/26 18:59:28] d2.utils.events INFO: eta: 6:05:04 iter: 12379 Cube/total_3D_loss: 1.133 total_loss: 2.021 BoxHead/loss_cls: 0.1243 BoxHead/loss_box_reg: 0.2237 Cube/loss_dims: 0.043 Cube/loss_xy: 0.0311 Cube/loss_z: 0.07297 Cube/loss_pose: 0.2421 Cube/loss_joint: 0.5881 lr: 0.0214 max_mem: 28030M [02/26 18:59:55] d2.utils.events INFO: eta: 6:06:44 iter: 12399 Cube/total_3D_loss: 1.164 total_loss: 2.017 BoxHead/loss_cls: 0.1168 BoxHead/loss_box_reg: 0.2073 Cube/loss_dims: 0.04115 Cube/loss_xy: 0.03098 Cube/loss_z: 0.07223 Cube/loss_pose: 0.2395 Cube/loss_joint: 0.5923 lr: 0.0214 max_mem: 28030M [02/26 19:00:23] d2.utils.events INFO: eta: 6:19:05 iter: 12419 Cube/total_3D_loss: 1.161 total_loss: 2.008 BoxHead/loss_cls: 0.11 BoxHead/loss_box_reg: 0.2042 Cube/loss_dims: 0.04102 Cube/loss_xy: 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19:12:28] d2.utils.events INFO: eta: 5:53:40 iter: 12959 Cube/total_3D_loss: 1.184 total_loss: 2.038 BoxHead/loss_cls: 0.1149 BoxHead/loss_box_reg: 0.2182 Cube/loss_dims: 0.04154 Cube/loss_xy: 0.02882 Cube/loss_z: 0.07301 Cube/loss_pose: 0.237 Cube/loss_joint: 0.5822 lr: 0.0214 max_mem: 28030M [02/26 19:12:54] d2.utils.events INFO: eta: 5:52:25 iter: 12979 Cube/total_3D_loss: 1.163 total_loss: 2.004 BoxHead/loss_cls: 0.1147 BoxHead/loss_box_reg: 0.2054 Cube/loss_dims: 0.04161 Cube/loss_xy: 0.03056 Cube/loss_z: 0.07073 Cube/loss_pose: 0.2431 Cube/loss_joint: 0.5808 lr: 0.0214 max_mem: 28030M [02/26 19:13:21] d2.utils.events INFO: eta: 5:56:22 iter: 12999 Cube/total_3D_loss: 1.108 total_loss: 1.959 BoxHead/loss_cls: 0.1169 BoxHead/loss_box_reg: 0.2153 Cube/loss_dims: 0.04112 Cube/loss_xy: 0.03106 Cube/loss_z: 0.0686 Cube/loss_pose: 0.2325 Cube/loss_joint: 0.5805 lr: 0.0214 max_mem: 28030M [02/26 19:13:48] d2.utils.events INFO: eta: 5:52:23 iter: 13019 Cube/total_3D_loss: 1.141 total_loss: 1.955 BoxHead/loss_cls: 0.1004 BoxHead/loss_box_reg: 0.1956 Cube/loss_dims: 0.04355 Cube/loss_xy: 0.03054 Cube/loss_z: 0.07485 Cube/loss_pose: 0.2368 Cube/loss_joint: 0.59 lr: 0.0214 max_mem: 28030M [02/26 19:14:16] d2.utils.events INFO: eta: 6:00:03 iter: 13039 Cube/total_3D_loss: 1.132 total_loss: 1.98 BoxHead/loss_cls: 0.1106 BoxHead/loss_box_reg: 0.2043 Cube/loss_dims: 0.03953 Cube/loss_xy: 0.03082 Cube/loss_z: 0.07587 Cube/loss_pose: 0.2247 Cube/loss_joint: 0.587 lr: 0.0214 max_mem: 28030M [02/26 19:14:43] d2.utils.events INFO: eta: 6:04:44 iter: 13059 Cube/total_3D_loss: 1.146 total_loss: 1.967 BoxHead/loss_cls: 0.1084 BoxHead/loss_box_reg: 0.2078 Cube/loss_dims: 0.04137 Cube/loss_xy: 0.03094 Cube/loss_z: 0.07298 Cube/loss_pose: 0.2355 Cube/loss_joint: 0.5861 lr: 0.0214 max_mem: 28030M [02/26 19:15:11] d2.utils.events INFO: eta: 5:56:22 iter: 13079 Cube/total_3D_loss: 1.133 total_loss: 1.941 BoxHead/loss_cls: 0.1072 BoxHead/loss_box_reg: 0.1998 Cube/loss_dims: 0.043 Cube/loss_xy: 0.0307 Cube/loss_z: 0.07341 Cube/loss_pose: 0.2548 Cube/loss_joint: 0.592 lr: 0.0214 max_mem: 28030M [02/26 19:15:38] d2.utils.events INFO: eta: 5:56:55 iter: 13099 Cube/total_3D_loss: 1.143 total_loss: 1.968 BoxHead/loss_cls: 0.1081 BoxHead/loss_box_reg: 0.2146 Cube/loss_dims: 0.04225 Cube/loss_xy: 0.03069 Cube/loss_z: 0.07074 Cube/loss_pose: 0.2373 Cube/loss_joint: 0.5817 lr: 0.0214 max_mem: 28030M [02/26 19:16:05] d2.utils.events INFO: eta: 5:52:22 iter: 13119 Cube/total_3D_loss: 1.129 total_loss: 1.998 BoxHead/loss_cls: 0.1194 BoxHead/loss_box_reg: 0.2255 Cube/loss_dims: 0.04121 Cube/loss_xy: 0.03023 Cube/loss_z: 0.07106 Cube/loss_pose: 0.2384 Cube/loss_joint: 0.5765 lr: 0.0214 max_mem: 28030M [02/26 19:16:31] d2.utils.events INFO: eta: 5:47:56 iter: 13139 Cube/total_3D_loss: 1.138 total_loss: 1.98 BoxHead/loss_cls: 0.1135 BoxHead/loss_box_reg: 0.2135 Cube/loss_dims: 0.04206 Cube/loss_xy: 0.0304 Cube/loss_z: 0.07399 Cube/loss_pose: 0.242 Cube/loss_joint: 0.5919 lr: 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Cube/total_3D_loss: 1.104 total_loss: 1.933 BoxHead/loss_cls: 0.1114 BoxHead/loss_box_reg: 0.2017 Cube/loss_dims: 0.04209 Cube/loss_xy: 0.03071 Cube/loss_z: 0.07138 Cube/loss_pose: 0.2337 Cube/loss_joint: 0.575 lr: 0.0214 max_mem: 28030M [02/26 19:18:46] d2.utils.events INFO: eta: 5:52:52 iter: 13239 Cube/total_3D_loss: 1.116 total_loss: 1.963 BoxHead/loss_cls: 0.1121 BoxHead/loss_box_reg: 0.2028 Cube/loss_dims: 0.04098 Cube/loss_xy: 0.031 Cube/loss_z: 0.07498 Cube/loss_pose: 0.2289 Cube/loss_joint: 0.5952 lr: 0.0214 max_mem: 28030M [02/26 19:19:13] d2.utils.events INFO: eta: 5:52:13 iter: 13259 Cube/total_3D_loss: 1.195 total_loss: 2.027 BoxHead/loss_cls: 0.1132 BoxHead/loss_box_reg: 0.2129 Cube/loss_dims: 0.04157 Cube/loss_xy: 0.02865 Cube/loss_z: 0.07532 Cube/loss_pose: 0.2315 Cube/loss_joint: 0.5865 lr: 0.0214 max_mem: 28030M [02/26 19:19:41] d2.utils.events INFO: eta: 5:56:32 iter: 13279 Cube/total_3D_loss: 1.128 total_loss: 1.937 BoxHead/loss_cls: 0.1133 BoxHead/loss_box_reg: 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INFO: eta: 5:42:19 iter: 13419 Cube/total_3D_loss: 1.145 total_loss: 2.034 BoxHead/loss_cls: 0.1159 BoxHead/loss_box_reg: 0.2088 Cube/loss_dims: 0.04116 Cube/loss_xy: 0.02943 Cube/loss_z: 0.07297 Cube/loss_pose: 0.2334 Cube/loss_joint: 0.5874 lr: 0.0214 max_mem: 28030M [02/26 19:23:15] d2.utils.events INFO: eta: 5:42:09 iter: 13439 Cube/total_3D_loss: 1.164 total_loss: 1.987 BoxHead/loss_cls: 0.1146 BoxHead/loss_box_reg: 0.2122 Cube/loss_dims: 0.04168 Cube/loss_xy: 0.02973 Cube/loss_z: 0.07407 Cube/loss_pose: 0.2286 Cube/loss_joint: 0.5886 lr: 0.0214 max_mem: 28030M [02/26 19:23:42] d2.utils.events INFO: eta: 5:43:00 iter: 13459 Cube/total_3D_loss: 1.11 total_loss: 1.937 BoxHead/loss_cls: 0.1113 BoxHead/loss_box_reg: 0.2002 Cube/loss_dims: 0.04144 Cube/loss_xy: 0.02993 Cube/loss_z: 0.07035 Cube/loss_pose: 0.2412 Cube/loss_joint: 0.5823 lr: 0.0214 max_mem: 28030M [02/26 19:24:09] d2.utils.events INFO: eta: 5:43:07 iter: 13479 Cube/total_3D_loss: 1.122 total_loss: 1.937 BoxHead/loss_cls: 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0.07926 Cube/loss_pose: 0.2417 Cube/loss_joint: 0.5994 lr: 0.0214 max_mem: 28030M [02/26 19:25:57] d2.utils.events INFO: eta: 5:44:57 iter: 13559 Cube/total_3D_loss: 1.149 total_loss: 1.972 BoxHead/loss_cls: 0.1121 BoxHead/loss_box_reg: 0.208 Cube/loss_dims: 0.04021 Cube/loss_xy: 0.03054 Cube/loss_z: 0.0737 Cube/loss_pose: 0.2306 Cube/loss_joint: 0.5935 lr: 0.0214 max_mem: 28030M [02/26 19:26:23] d2.utils.events INFO: eta: 5:38:28 iter: 13579 Cube/total_3D_loss: 1.199 total_loss: 2.016 BoxHead/loss_cls: 0.1125 BoxHead/loss_box_reg: 0.2092 Cube/loss_dims: 0.04201 Cube/loss_xy: 0.02909 Cube/loss_z: 0.07213 Cube/loss_pose: 0.237 Cube/loss_joint: 0.5825 lr: 0.0214 max_mem: 28030M [02/26 19:26:50] d2.utils.events INFO: eta: 5:41:06 iter: 13599 Cube/total_3D_loss: 1.095 total_loss: 1.97 BoxHead/loss_cls: 0.1092 BoxHead/loss_box_reg: 0.2114 Cube/loss_dims: 0.04203 Cube/loss_xy: 0.03021 Cube/loss_z: 0.07054 Cube/loss_pose: 0.2431 Cube/loss_joint: 0.5781 lr: 0.0214 max_mem: 28030M [02/26 19:27:17] d2.utils.events INFO: eta: 5:42:26 iter: 13619 Cube/total_3D_loss: 1.085 total_loss: 1.957 BoxHead/loss_cls: 0.1135 BoxHead/loss_box_reg: 0.2157 Cube/loss_dims: 0.04275 Cube/loss_xy: 0.03081 Cube/loss_z: 0.07052 Cube/loss_pose: 0.2389 Cube/loss_joint: 0.5733 lr: 0.0214 max_mem: 28030M [02/26 19:27:44] d2.utils.events INFO: eta: 5:38:05 iter: 13639 Cube/total_3D_loss: 1.117 total_loss: 1.973 BoxHead/loss_cls: 0.1097 BoxHead/loss_box_reg: 0.2047 Cube/loss_dims: 0.04171 Cube/loss_xy: 0.03081 Cube/loss_z: 0.07345 Cube/loss_pose: 0.2361 Cube/loss_joint: 0.5854 lr: 0.0214 max_mem: 28030M [02/26 19:28:11] d2.utils.events INFO: eta: 5:39:17 iter: 13659 Cube/total_3D_loss: 1.12 total_loss: 1.942 BoxHead/loss_cls: 0.1054 BoxHead/loss_box_reg: 0.204 Cube/loss_dims: 0.03997 Cube/loss_xy: 0.02867 Cube/loss_z: 0.07503 Cube/loss_pose: 0.2401 Cube/loss_joint: 0.5888 lr: 0.0214 max_mem: 28030M [02/26 19:28:38] d2.utils.events INFO: eta: 5:38:11 iter: 13679 Cube/total_3D_loss: 1.117 total_loss: 1.971 BoxHead/loss_cls: 0.11 BoxHead/loss_box_reg: 0.2077 Cube/loss_dims: 0.04167 Cube/loss_xy: 0.0318 Cube/loss_z: 0.07426 Cube/loss_pose: 0.2376 Cube/loss_joint: 0.5959 lr: 0.0214 max_mem: 28030M [02/26 19:29:05] d2.utils.events INFO: eta: 5:42:24 iter: 13699 Cube/total_3D_loss: 1.104 total_loss: 1.942 BoxHead/loss_cls: 0.1026 BoxHead/loss_box_reg: 0.2007 Cube/loss_dims: 0.04106 Cube/loss_xy: 0.03026 Cube/loss_z: 0.07261 Cube/loss_pose: 0.2328 Cube/loss_joint: 0.5921 lr: 0.0214 max_mem: 28030M [02/26 19:29:32] d2.utils.events INFO: eta: 5:37:11 iter: 13719 Cube/total_3D_loss: 1.15 total_loss: 1.988 BoxHead/loss_cls: 0.1041 BoxHead/loss_box_reg: 0.2089 Cube/loss_dims: 0.04051 Cube/loss_xy: 0.02943 Cube/loss_z: 0.07342 Cube/loss_pose: 0.2294 Cube/loss_joint: 0.5799 lr: 0.0214 max_mem: 28030M [02/26 19:29:59] d2.utils.events INFO: eta: 5:38:11 iter: 13739 Cube/total_3D_loss: 1.067 total_loss: 1.915 BoxHead/loss_cls: 0.1101 BoxHead/loss_box_reg: 0.2113 Cube/loss_dims: 0.04115 Cube/loss_xy: 0.03004 Cube/loss_z: 0.07384 Cube/loss_pose: 0.2253 Cube/loss_joint: 0.5823 lr: 0.0214 max_mem: 28030M [02/26 19:30:26] d2.utils.events INFO: eta: 5:36:39 iter: 13759 Cube/total_3D_loss: 1.134 total_loss: 1.956 BoxHead/loss_cls: 0.115 BoxHead/loss_box_reg: 0.2119 Cube/loss_dims: 0.04255 Cube/loss_xy: 0.03058 Cube/loss_z: 0.07438 Cube/loss_pose: 0.2336 Cube/loss_joint: 0.5918 lr: 0.0214 max_mem: 28030M [02/26 19:30:53] d2.utils.events INFO: eta: 5:37:25 iter: 13779 Cube/total_3D_loss: 1.124 total_loss: 1.932 BoxHead/loss_cls: 0.09934 BoxHead/loss_box_reg: 0.1927 Cube/loss_dims: 0.0403 Cube/loss_xy: 0.03008 Cube/loss_z: 0.07158 Cube/loss_pose: 0.237 Cube/loss_joint: 0.5715 lr: 0.0214 max_mem: 28030M [02/26 19:31:20] d2.utils.events INFO: eta: 5:37:23 iter: 13799 Cube/total_3D_loss: 1.095 total_loss: 1.935 BoxHead/loss_cls: 0.1051 BoxHead/loss_box_reg: 0.2038 Cube/loss_dims: 0.0403 Cube/loss_xy: 0.03047 Cube/loss_z: 0.072 Cube/loss_pose: 0.2348 Cube/loss_joint: 0.5822 lr: 0.0214 max_mem: 28030M [02/26 19:31:47] d2.utils.events INFO: eta: 5:37:09 iter: 13819 Cube/total_3D_loss: 1.173 total_loss: 1.984 BoxHead/loss_cls: 0.1098 BoxHead/loss_box_reg: 0.2053 Cube/loss_dims: 0.04187 Cube/loss_xy: 0.03085 Cube/loss_z: 0.07454 Cube/loss_pose: 0.237 Cube/loss_joint: 0.5848 lr: 0.0214 max_mem: 28030M [02/26 19:32:14] d2.utils.events INFO: eta: 5:37:43 iter: 13839 Cube/total_3D_loss: 1.164 total_loss: 1.97 BoxHead/loss_cls: 0.1142 BoxHead/loss_box_reg: 0.2057 Cube/loss_dims: 0.04256 Cube/loss_xy: 0.02903 Cube/loss_z: 0.0727 Cube/loss_pose: 0.2317 Cube/loss_joint: 0.5713 lr: 0.0214 max_mem: 28030M [02/26 19:32:41] d2.utils.events INFO: eta: 5:42:32 iter: 13859 Cube/total_3D_loss: 1.102 total_loss: 1.951 BoxHead/loss_cls: 0.1048 BoxHead/loss_box_reg: 0.2016 Cube/loss_dims: 0.04253 Cube/loss_xy: 0.03128 Cube/loss_z: 0.07064 Cube/loss_pose: 0.246 Cube/loss_joint: 0.5806 lr: 0.0214 max_mem: 28030M [02/26 19:33:08] d2.utils.events INFO: eta: 5:34:48 iter: 13879 Cube/total_3D_loss: 1.11 total_loss: 1.95 BoxHead/loss_cls: 0.1053 BoxHead/loss_box_reg: 0.2036 Cube/loss_dims: 0.04103 Cube/loss_xy: 0.03095 Cube/loss_z: 0.07153 Cube/loss_pose: 0.2323 Cube/loss_joint: 0.5836 lr: 0.0214 max_mem: 28030M [02/26 19:33:36] d2.utils.events INFO: eta: 5:39:12 iter: 13899 Cube/total_3D_loss: 1.165 total_loss: 1.995 BoxHead/loss_cls: 0.1061 BoxHead/loss_box_reg: 0.2019 Cube/loss_dims: 0.04045 Cube/loss_xy: 0.0303 Cube/loss_z: 0.07511 Cube/loss_pose: 0.2293 Cube/loss_joint: 0.5882 lr: 0.0214 max_mem: 28030M [02/26 19:34:02] d2.utils.events INFO: eta: 5:31:30 iter: 13919 Cube/total_3D_loss: 1.194 total_loss: 2.003 BoxHead/loss_cls: 0.1113 BoxHead/loss_box_reg: 0.2032 Cube/loss_dims: 0.04027 Cube/loss_xy: 0.03013 Cube/loss_z: 0.07228 Cube/loss_pose: 0.2411 Cube/loss_joint: 0.587 lr: 0.0214 max_mem: 28030M [02/26 19:34:29] d2.utils.events INFO: eta: 5:30:00 iter: 13939 Cube/total_3D_loss: 1.172 total_loss: 2.023 BoxHead/loss_cls: 0.1137 BoxHead/loss_box_reg: 0.2111 Cube/loss_dims: 0.0408 Cube/loss_xy: 0.02982 Cube/loss_z: 0.07828 Cube/loss_pose: 0.2371 Cube/loss_joint: 0.6027 lr: 0.0214 max_mem: 28030M [02/26 19:34:56] d2.utils.events INFO: eta: 5:30:04 iter: 13959 Cube/total_3D_loss: 1.099 total_loss: 1.947 BoxHead/loss_cls: 0.09703 BoxHead/loss_box_reg: 0.2047 Cube/loss_dims: 0.04007 Cube/loss_xy: 0.03131 Cube/loss_z: 0.07023 Cube/loss_pose: 0.2337 Cube/loss_joint: 0.5735 lr: 0.0214 max_mem: 28030M [02/26 19:35:22] d2.utils.events INFO: eta: 5:26:53 iter: 13979 Cube/total_3D_loss: 1.155 total_loss: 1.95 BoxHead/loss_cls: 0.1083 BoxHead/loss_box_reg: 0.2125 Cube/loss_dims: 0.04172 Cube/loss_xy: 0.03188 Cube/loss_z: 0.0746 Cube/loss_pose: 0.2426 Cube/loss_joint: 0.5961 lr: 0.0214 max_mem: 28030M [02/26 19:35:49] d2.utils.events INFO: eta: 5:31:17 iter: 13999 Cube/total_3D_loss: 1.128 total_loss: 1.959 BoxHead/loss_cls: 0.1114 BoxHead/loss_box_reg: 0.2088 Cube/loss_dims: 0.04012 Cube/loss_xy: 0.02869 Cube/loss_z: 0.07562 Cube/loss_pose: 0.2325 Cube/loss_joint: 0.5838 lr: 0.0214 max_mem: 28030M [02/26 19:36:16] d2.utils.events INFO: eta: 5:33:51 iter: 14019 Cube/total_3D_loss: 1.096 total_loss: 1.952 BoxHead/loss_cls: 0.1092 BoxHead/loss_box_reg: 0.2063 Cube/loss_dims: 0.04028 Cube/loss_xy: 0.03033 Cube/loss_z: 0.07608 Cube/loss_pose: 0.2414 Cube/loss_joint: 0.5961 lr: 0.0214 max_mem: 28030M [02/26 19:36:43] d2.utils.events INFO: eta: 5:25:41 iter: 14039 Cube/total_3D_loss: 1.095 total_loss: 1.977 BoxHead/loss_cls: 0.1116 BoxHead/loss_box_reg: 0.2069 Cube/loss_dims: 0.04068 Cube/loss_xy: 0.0297 Cube/loss_z: 0.07163 Cube/loss_pose: 0.236 Cube/loss_joint: 0.5775 lr: 0.0214 max_mem: 28030M [02/26 19:37:10] d2.utils.events INFO: eta: 5:32:25 iter: 14059 Cube/total_3D_loss: 1.125 total_loss: 1.972 BoxHead/loss_cls: 0.1051 BoxHead/loss_box_reg: 0.2064 Cube/loss_dims: 0.04091 Cube/loss_xy: 0.03056 Cube/loss_z: 0.07569 Cube/loss_pose: 0.2305 Cube/loss_joint: 0.5963 lr: 0.0214 max_mem: 28030M [02/26 19:37:36] d2.utils.events INFO: eta: 5:25:34 iter: 14079 Cube/total_3D_loss: 1.135 total_loss: 1.973 BoxHead/loss_cls: 0.1025 BoxHead/loss_box_reg: 0.2044 Cube/loss_dims: 0.04164 Cube/loss_xy: 0.03072 Cube/loss_z: 0.07187 Cube/loss_pose: 0.2313 Cube/loss_joint: 0.5933 lr: 0.0214 max_mem: 28030M [02/26 19:38:04] d2.utils.events INFO: eta: 5:35:23 iter: 14099 Cube/total_3D_loss: 1.128 total_loss: 1.95 BoxHead/loss_cls: 0.101 BoxHead/loss_box_reg: 0.1941 Cube/loss_dims: 0.04089 Cube/loss_xy: 0.02985 Cube/loss_z: 0.07871 Cube/loss_pose: 0.2303 Cube/loss_joint: 0.5977 lr: 0.0214 max_mem: 28030M [02/26 19:38:30] d2.utils.events INFO: eta: 5:26:55 iter: 14119 Cube/total_3D_loss: 1.141 total_loss: 1.973 BoxHead/loss_cls: 0.1013 BoxHead/loss_box_reg: 0.1975 Cube/loss_dims: 0.04022 Cube/loss_xy: 0.02884 Cube/loss_z: 0.07179 Cube/loss_pose: 0.2284 Cube/loss_joint: 0.5836 lr: 0.0214 max_mem: 28030M [02/26 19:38:57] d2.utils.events INFO: eta: 5:24:44 iter: 14139 Cube/total_3D_loss: 1.123 total_loss: 1.943 BoxHead/loss_cls: 0.1075 BoxHead/loss_box_reg: 0.2105 Cube/loss_dims: 0.04149 Cube/loss_xy: 0.03067 Cube/loss_z: 0.07207 Cube/loss_pose: 0.2422 Cube/loss_joint: 0.5942 lr: 0.0214 max_mem: 28030M [02/26 19:39:24] d2.utils.events INFO: eta: 5:26:20 iter: 14159 Cube/total_3D_loss: 1.129 total_loss: 1.882 BoxHead/loss_cls: 0.0937 BoxHead/loss_box_reg: 0.1879 Cube/loss_dims: 0.04276 Cube/loss_xy: 0.02918 Cube/loss_z: 0.06975 Cube/loss_pose: 0.2382 Cube/loss_joint: 0.5707 lr: 0.0214 max_mem: 28030M [02/26 19:39:51] d2.utils.events INFO: eta: 5:29:41 iter: 14179 Cube/total_3D_loss: 1.137 total_loss: 1.928 BoxHead/loss_cls: 0.1015 BoxHead/loss_box_reg: 0.1926 Cube/loss_dims: 0.04172 Cube/loss_xy: 0.03155 Cube/loss_z: 0.07182 Cube/loss_pose: 0.2368 Cube/loss_joint: 0.5893 lr: 0.0214 max_mem: 28030M [02/26 19:40:17] d2.utils.events INFO: eta: 5:26:26 iter: 14199 Cube/total_3D_loss: 1.109 total_loss: 1.937 BoxHead/loss_cls: 0.1013 BoxHead/loss_box_reg: 0.1927 Cube/loss_dims: 0.04174 Cube/loss_xy: 0.03155 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28030M [02/26 19:42:04] d2.utils.events INFO: eta: 5:24:57 iter: 14279 Cube/total_3D_loss: 1.116 total_loss: 1.979 BoxHead/loss_cls: 0.1106 BoxHead/loss_box_reg: 0.2062 Cube/loss_dims: 0.04241 Cube/loss_xy: 0.03087 Cube/loss_z: 0.07432 Cube/loss_pose: 0.2291 Cube/loss_joint: 0.5927 lr: 0.0214 max_mem: 28030M [02/26 19:42:31] d2.utils.events INFO: eta: 5:24:36 iter: 14299 Cube/total_3D_loss: 1.135 total_loss: 1.979 BoxHead/loss_cls: 0.1167 BoxHead/loss_box_reg: 0.2213 Cube/loss_dims: 0.04101 Cube/loss_xy: 0.03008 Cube/loss_z: 0.07519 Cube/loss_pose: 0.2287 Cube/loss_joint: 0.5883 lr: 0.0214 max_mem: 28030M [02/26 19:42:58] d2.utils.events INFO: eta: 5:24:41 iter: 14319 Cube/total_3D_loss: 1.131 total_loss: 1.964 BoxHead/loss_cls: 0.1072 BoxHead/loss_box_reg: 0.2096 Cube/loss_dims: 0.04202 Cube/loss_xy: 0.0302 Cube/loss_z: 0.07308 Cube/loss_pose: 0.2401 Cube/loss_joint: 0.5875 lr: 0.0214 max_mem: 28030M [02/26 19:43:25] d2.utils.events INFO: eta: 5:27:21 iter: 14339 Cube/total_3D_loss: 1.123 total_loss: 1.939 BoxHead/loss_cls: 0.105 BoxHead/loss_box_reg: 0.2058 Cube/loss_dims: 0.04005 Cube/loss_xy: 0.02938 Cube/loss_z: 0.07229 Cube/loss_pose: 0.2299 Cube/loss_joint: 0.5795 lr: 0.0214 max_mem: 28030M [02/26 19:43:52] d2.utils.events INFO: eta: 5:20:50 iter: 14359 Cube/total_3D_loss: 1.128 total_loss: 1.933 BoxHead/loss_cls: 0.1009 BoxHead/loss_box_reg: 0.2011 Cube/loss_dims: 0.04232 Cube/loss_xy: 0.03018 Cube/loss_z: 0.07284 Cube/loss_pose: 0.2414 Cube/loss_joint: 0.5834 lr: 0.0214 max_mem: 28030M [02/26 19:44:19] d2.utils.events INFO: eta: 5:20:23 iter: 14379 Cube/total_3D_loss: 1.108 total_loss: 1.912 BoxHead/loss_cls: 0.09752 BoxHead/loss_box_reg: 0.2009 Cube/loss_dims: 0.04086 Cube/loss_xy: 0.03049 Cube/loss_z: 0.07191 Cube/loss_pose: 0.2431 Cube/loss_joint: 0.5854 lr: 0.0214 max_mem: 28030M [02/26 19:44:45] cubercnn INFO: Starting test for iteration 14400 [02/26 19:44:53] cubercnn.data.datasets INFO: Loading datasets/Omni3D/SUNRGBD_test.json takes 2.14 seconds. [02/26 19:44:53] cubercnn.data.datasets INFO: Loaded 5050 images in Omni3D format from datasets/Omni3D/SUNRGBD_test.json [02/26 19:44:57] cubercnn.data.datasets INFO: Filtered out 0/5050 images without valid annotations [02/26 19:44:57] d2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(512, 512), max_size=4096, sample_style='choice')] [02/26 19:44:57] d2.data.common INFO: Serializing the dataset using: [02/26 19:44:57] d2.data.common INFO: Serializing 5050 elements to byte tensors and concatenating them all ... [02/26 19:44:57] d2.data.common INFO: Serialized dataset takes 20.13 MiB [02/26 19:44:57] cubercnn.evaluation.omni3d_evaluation INFO: Start inference on 5050 batches [02/26 19:44:58] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 11/5050. Dataloading: 0.0004 s/iter. Inference: 0.0254 s/iter. Eval: 0.0008 s/iter. Total: 0.0267 s/iter. ETA=0:02:14 [02/26 19:45:03] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 211/5050. Dataloading: 0.0007 s/iter. Inference: 0.0233 s/iter. Eval: 0.0010 s/iter. Total: 0.0251 s/iter. ETA=0:02:01 [02/26 19:45:08] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 420/5050. Dataloading: 0.0007 s/iter. Inference: 0.0228 s/iter. Eval: 0.0009 s/iter. Total: 0.0245 s/iter. ETA=0:01:53 [02/26 19:45:13] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 632/5050. Dataloading: 0.0007 s/iter. Inference: 0.0226 s/iter. Eval: 0.0009 s/iter. Total: 0.0243 s/iter. ETA=0:01:47 [02/26 19:45:18] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 843/5050. Dataloading: 0.0007 s/iter. Inference: 0.0225 s/iter. Eval: 0.0009 s/iter. Total: 0.0241 s/iter. ETA=0:01:41 [02/26 19:45:23] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 1022/5050. Dataloading: 0.0007 s/iter. Inference: 0.0224 s/iter. Eval: 0.0016 s/iter. Total: 0.0248 s/iter. ETA=0:01:39 [02/26 19:45:28] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 1233/5050. Dataloading: 0.0007 s/iter. Inference: 0.0224 s/iter. Eval: 0.0015 s/iter. Total: 0.0246 s/iter. ETA=0:01:34 [02/26 19:45:33] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 1444/5050. Dataloading: 0.0007 s/iter. Inference: 0.0223 s/iter. Eval: 0.0014 s/iter. Total: 0.0245 s/iter. ETA=0:01:28 [02/26 19:45:38] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 1656/5050. Dataloading: 0.0007 s/iter. Inference: 0.0223 s/iter. Eval: 0.0013 s/iter. Total: 0.0244 s/iter. ETA=0:01:22 [02/26 19:45:43] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 1871/5050. Dataloading: 0.0007 s/iter. Inference: 0.0222 s/iter. Eval: 0.0013 s/iter. Total: 0.0243 s/iter. ETA=0:01:17 [02/26 19:45:48] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 2042/5050. Dataloading: 0.0008 s/iter. Inference: 0.0222 s/iter. Eval: 0.0017 s/iter. Total: 0.0247 s/iter. ETA=0:01:14 [02/26 19:45:53] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 2256/5050. Dataloading: 0.0008 s/iter. Inference: 0.0221 s/iter. Eval: 0.0016 s/iter. Total: 0.0246 s/iter. ETA=0:01:08 [02/26 19:45:58] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 2473/5050. Dataloading: 0.0008 s/iter. Inference: 0.0221 s/iter. Eval: 0.0015 s/iter. Total: 0.0244 s/iter. ETA=0:01:02 [02/26 19:46:03] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 2689/5050. Dataloading: 0.0008 s/iter. Inference: 0.0220 s/iter. Eval: 0.0015 s/iter. Total: 0.0243 s/iter. ETA=0:00:57 [02/26 19:46:08] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 2890/5050. Dataloading: 0.0009 s/iter. Inference: 0.0220 s/iter. Eval: 0.0014 s/iter. Total: 0.0244 s/iter. ETA=0:00:52 [02/26 19:46:13] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 3105/5050. Dataloading: 0.0009 s/iter. Inference: 0.0220 s/iter. Eval: 0.0014 s/iter. Total: 0.0243 s/iter. ETA=0:00:47 [02/26 19:46:18] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 3320/5050. Dataloading: 0.0009 s/iter. Inference: 0.0220 s/iter. Eval: 0.0014 s/iter. Total: 0.0242 s/iter. ETA=0:00:41 [02/26 19:46:23] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 3494/5050. Dataloading: 0.0008 s/iter. Inference: 0.0220 s/iter. Eval: 0.0016 s/iter. Total: 0.0245 s/iter. ETA=0:00:38 [02/26 19:46:28] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 3708/5050. Dataloading: 0.0008 s/iter. Inference: 0.0220 s/iter. Eval: 0.0016 s/iter. Total: 0.0244 s/iter. ETA=0:00:32 [02/26 19:46:33] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 3922/5050. Dataloading: 0.0008 s/iter. Inference: 0.0220 s/iter. Eval: 0.0015 s/iter. Total: 0.0244 s/iter. ETA=0:00:27 [02/26 19:46:38] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 4138/5050. Dataloading: 0.0008 s/iter. Inference: 0.0219 s/iter. Eval: 0.0015 s/iter. Total: 0.0243 s/iter. ETA=0:00:22 [02/26 19:46:43] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 4355/5050. Dataloading: 0.0008 s/iter. Inference: 0.0219 s/iter. Eval: 0.0015 s/iter. Total: 0.0242 s/iter. ETA=0:00:16 [02/26 19:46:48] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 4568/5050. Dataloading: 0.0008 s/iter. Inference: 0.0219 s/iter. Eval: 0.0014 s/iter. Total: 0.0242 s/iter. ETA=0:00:11 [02/26 19:46:53] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 4778/5050. Dataloading: 0.0008 s/iter. Inference: 0.0219 s/iter. Eval: 0.0014 s/iter. Total: 0.0242 s/iter. ETA=0:00:06 [02/26 19:46:58] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 4994/5050. Dataloading: 0.0008 s/iter. Inference: 0.0219 s/iter. Eval: 0.0014 s/iter. Total: 0.0241 s/iter. ETA=0:00:01 [02/26 19:46:59] cubercnn.evaluation.omni3d_evaluation INFO: Total inference time: 0:02:01.810724 (0.024145 s / iter per device, on 1 devices) [02/26 19:46:59] cubercnn.evaluation.omni3d_evaluation INFO: Total inference pure compute time: 0:01:50 (0.021927 s / iter per device, on 1 devices) [02/26 19:47:09] cubercnn.evaluation.omni3d_evaluation INFO: Preparing results for COCO format ... [02/26 19:47:09] cubercnn.evaluation.omni3d_evaluation INFO: Saving results to output/Baseline_sgd/inference/iter_14400/SUNRGBD_test/omni_instances_results.json [02/26 19:47:13] cubercnn.evaluation.omni3d_evaluation INFO: Evaluating predictions with official COCO API... [02/26 19:48:07] cubercnn.evaluation.omni3d_evaluation INFO: Evaluation results for bbox in 2D mode: | AP | AP50 | AP75 | AP95 | APs | APm | APl | |:------:|:------:|:------:|:------:|:-----:|:-----:|:------:| | 14.350 | 29.201 | 12.684 | 0.157 | 0.000 | 3.340 | 15.312 | [02/26 19:48:07] cubercnn.evaluation.omni3d_evaluation INFO: Per-category bbox AP in 2D mode: | category | AP | category | AP | category | AP | |:-------------|:-------|:------------|:-------|:-----------|:-------| | bicycle | 7.145 | books | 0.853 | bottle | 3.746 | | chair | 44.366 | cup | 2.369 | laptop | 8.586 | | shoes | 0.008 | towel | 3.155 | blinds | 6.029 | | window | 0.594 | lamp | 25.103 | shelves | 4.302 | | mirror | 7.935 | sink | 19.677 | cabinet | 12.227 | | bathtub | 39.513 | door | 1.287 | toilet | 48.195 | | desk | 11.440 | box | 3.123 | bookcase | 22.193 | | picture | 2.012 | table | 29.640 | counter | 16.288 | | bed | 45.432 | night stand | 35.723 | pillow | 11.147 | | sofa | 42.646 | television | 14.947 | floor mat | 0.503 | | curtain | 8.742 | clothes | 0.390 | stationery | 1.492 | | refrigerator | 20.230 | bin | 31.827 | stove | 8.641 | | oven | 3.330 | machine | 0.453 | | | [02/26 19:48:07] cubercnn.evaluation.omni3d_evaluation INFO: Evaluation results for bbox in 3D mode: | AP | AP15 | AP25 | AP50 | APn | APm | APf | |:------:|:------:|:------:|:------:|:------:|:-----:|:-----:| | 12.697 | 18.485 | 13.547 | 3.480 | 12.697 | nan | nan | [02/26 19:48:07] cubercnn.evaluation.omni3d_evaluation INFO: Some metrics cannot be computed and is shown as NaN. [02/26 19:48:07] cubercnn.evaluation.omni3d_evaluation INFO: Per-category bbox AP in 3D mode: | category | AP | category | AP | category | AP | |:-------------|:-------|:------------|:-------|:-----------|:-------| | bicycle | 11.867 | books | 0.298 | bottle | 0.516 | | chair | 47.222 | cup | 0.829 | laptop | 5.328 | | shoes | 0.023 | towel | 1.494 | blinds | 0.223 | | window | 0.000 | lamp | 9.555 | shelves | 2.984 | | mirror | 0.405 | sink | 18.566 | cabinet | 9.861 | | bathtub | 48.422 | door | 0.272 | toilet | 61.205 | | desk | 14.736 | box | 1.302 | bookcase | 6.999 | | picture | 0.045 | table | 31.715 | counter | 19.286 | | bed | 61.153 | night stand | 25.734 | pillow | 5.388 | | sofa | 43.608 | television | 3.539 | floor mat | 0.000 | | curtain | 1.399 | clothes | 0.269 | stationery | 0.191 | | refrigerator | 17.028 | bin | 13.901 | stove | 10.568 | | oven | 6.221 | machine | 0.315 | | | [02/26 19:48:19] cubercnn.evaluation.omni3d_evaluation INFO: SUNRGBD_test iter=14400 mode=2D Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.143 SUNRGBD_test iter=14400 mode=2D Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.292 SUNRGBD_test iter=14400 mode=2D Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.127 SUNRGBD_test iter=14400 mode=2D Average Precision (AP) @[ IoU=0.95 | area= all | maxDets=100 ] = 0.002 SUNRGBD_test iter=14400 mode=2D Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 SUNRGBD_test iter=14400 mode=2D Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.033 SUNRGBD_test iter=14400 mode=2D Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.153 SUNRGBD_test iter=14400 mode=2D Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.243 SUNRGBD_test iter=14400 mode=2D Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.309 SUNRGBD_test iter=14400 mode=2D Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.310 SUNRGBD_test iter=14400 mode=2D Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 SUNRGBD_test iter=14400 mode=2D Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.117 SUNRGBD_test iter=14400 mode=2D Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.331 [02/26 19:48:19] cubercnn.evaluation.omni3d_evaluation INFO: SUNRGBD_test iter=14400 mode=3D Average Precision (AP) @[ IoU=0.05:0.50 | depth= all | maxDets=100 ] = 0.127 SUNRGBD_test iter=14400 mode=3D Average Precision (AP) @[ IoU=0.15 | depth= all | maxDets=100 ] = 0.185 SUNRGBD_test iter=14400 mode=3D Average Precision (AP) @[ IoU=0.25 | depth= all | maxDets=100 ] = 0.135 SUNRGBD_test iter=14400 mode=3D Average Precision (AP) @[ IoU=0.50 | depth= all | maxDets=100 ] = 0.035 SUNRGBD_test iter=14400 mode=3D Average Precision (AP) @[ IoU=0.05:0.50 | depth= near | maxDets=100 ] = 0.127 SUNRGBD_test iter=14400 mode=3D Average Precision (AP) @[ IoU=0.05:0.50 | depth=medium | maxDets=100 ] = -1.000 SUNRGBD_test iter=14400 mode=3D Average Precision (AP) @[ IoU=0.05:0.50 | depth= far | maxDets=100 ] = -1.000 SUNRGBD_test iter=14400 mode=3D Average Recall (AR) @[ IoU=0.05:0.50 | depth= all | maxDets= 1 ] = 0.219 SUNRGBD_test iter=14400 mode=3D Average Recall (AR) @[ IoU=0.05:0.50 | depth= all | maxDets= 10 ] = 0.283 SUNRGBD_test iter=14400 mode=3D Average Recall (AR) @[ IoU=0.05:0.50 | depth= all | maxDets=100 ] = 0.284 SUNRGBD_test iter=14400 mode=3D Average Recall (AR) @[ IoU=0.05:0.50 | depth= near | maxDets=100 ] = 0.284 SUNRGBD_test iter=14400 mode=3D Average Recall (AR) @[ IoU=0.05:0.50 | depth=medium | maxDets=100 ] = -1.000 SUNRGBD_test iter=14400 mode=3D Average Recall (AR) @[ IoU=0.05:0.50 | depth= far | maxDets=100 ] = -1.000 [02/26 19:48:19] cubercnn.vis.logperf INFO: Performance for each of 38 categories on SUNRGBD_test: | category | AP2D | AP3D | category | AP2D | AP3D | category | AP2D | AP3D | |:----------:|:---------|:----------|:-----------:|:---------|:---------|:------------:|:-----------|:----------| | chair | 44.3663 | 47.2217 | table | 29.6396 | 31.7145 | cabinet | 12.227 | 9.86082 | | lamp | 25.1034 | 9.55508 | books | 0.852513 | 0.297637 | sofa | 42.6458 | 43.6083 | | picture | 2.01191 | 0.0451782 | window | 0.594059 | 0 | pillow | 11.1475 | 5.38778 | | door | 1.28655 | 0.272158 | blinds | 6.02946 | 0.22349 | sink | 19.677 | 18.5663 | | shelves | 4.30167 | 2.98448 | television | 14.9467 | 3.53935 | shoes | 0.00780808 | 0.0228023 | | cup | 2.36916 | 0.828591 | bottle | 3.74577 | 0.516237 | bookcase | 22.1928 | 6.9993 | | laptop | 8.58599 | 5.32837 | desk | 11.4396 | 14.7357 | floor mat | 0.502666 | 0 | | mirror | 7.93483 | 0.404975 | counter | 16.2879 | 19.2859 | bicycle | 7.14496 | 11.8669 | | toilet | 48.1954 | 61.2046 | bed | 45.4324 | 61.1533 | refrigerator | 20.2297 | 17.0285 | | box | 3.12279 | 1.30223 | oven | 3.33028 | 6.22135 | clothes | 0.389581 | 0.268861 | | towel | 3.15533 | 1.49385 | night stand | 35.7227 | 25.7343 | stove | 8.64052 | 10.5684 | | machine | 0.453088 | 0.315269 | stationery | 1.49157 | 0.190945 | bathtub | 39.5126 | 48.4225 | | curtain | 8.74158 | 1.39944 | bin | 31.8269 | 13.9014 | | | | [02/26 19:48:35] cubercnn INFO: SUNRGBD_testiter=14400, xy(28.62), z(0.29), whl(0.18, 0.10, 0.12), ry(2.15) [02/26 19:48:41] cubercnn.vis.logperf INFO: Performance for each of 38 categories on : | category | AP2D | AP3D | category | AP2D | AP3D | category | AP2D | AP3D | |:----------:|:---------|:----------|:-----------:|:---------|:---------|:------------:|:-----------|:----------| | chair | 44.3663 | 47.2217 | table | 29.6396 | 31.7145 | cabinet | 12.227 | 9.86082 | | lamp | 25.1034 | 9.55508 | books | 0.852513 | 0.297637 | sofa | 42.6458 | 43.6083 | | picture | 2.01191 | 0.0451782 | window | 0.594059 | 0 | pillow | 11.1475 | 5.38778 | | door | 1.28655 | 0.272158 | blinds | 6.02946 | 0.22349 | sink | 19.677 | 18.5663 | | shelves | 4.30167 | 2.98448 | television | 14.9467 | 3.53935 | shoes | 0.00780808 | 0.0228023 | | cup | 2.36916 | 0.828591 | bottle | 3.74577 | 0.516237 | bookcase | 22.1928 | 6.9993 | | laptop | 8.58599 | 5.32837 | desk | 11.4396 | 14.7357 | floor mat | 0.502666 | 0 | | mirror | 7.93483 | 0.404975 | counter | 16.2879 | 19.2859 | bicycle | 7.14496 | 11.8669 | | toilet | 48.1954 | 61.2046 | bed | 45.4324 | 61.1533 | refrigerator | 20.2297 | 17.0285 | | box | 3.12279 | 1.30223 | oven | 3.33028 | 6.22135 | clothes | 0.389581 | 0.268861 | | towel | 3.15533 | 1.49385 | night stand | 35.7227 | 25.7343 | stove | 8.64052 | 10.5684 | | machine | 0.453088 | 0.315269 | stationery | 1.49157 | 0.190945 | bathtub | 39.5126 | 48.4225 | | curtain | 8.74158 | 1.39944 | bin | 31.8269 | 13.9014 | | | | [02/26 19:48:41] cubercnn.vis.logperf INFO: Per-dataset performance analysis on test set: +--------------+----------+---------+---------+-----------+-----------+-----------+----------+----------+----------+ | Dataset | #iters | AP2D | AP3D | AP3D@15 | AP3D@25 | AP3D@50 | AP3D-N | AP3D-M | AP3D-F | +==============+==========+=========+=========+===========+===========+===========+==========+==========+==========+ | SUNRGBD_test | 14400 | 14.3496 | 12.6966 | 18.4853 | 13.547 | 3.47967 | 12.6966 | nan | nan | +--------------+----------+---------+---------+-----------+-----------+-----------+----------+----------+----------+ | | 14400 | 14.3496 | 12.6966 | 18.4853 | 13.547 | 3.47967 | 12.6966 | nan | nan | +--------------+----------+---------+---------+-----------+-----------+-----------+----------+----------+----------+ [02/26 19:48:41] cubercnn.vis.logperf INFO: Omni3D performance on test set. The numbers below should be used to compare to others approaches on Omni3D, such as Cube R-CNN [02/26 19:48:41] cubercnn.vis.logperf INFO: Performance on Omni3D: +--------------+----------+---------+---------+ | Dataset | #iters | AP2D | AP3D | +==============+==========+=========+=========+ | SUNRGBD_test | 14400 | 14.3496 | 12.6966 | +--------------+----------+---------+---------+ | Omni3D_Out | 14400 | nan | nan | +--------------+----------+---------+---------+ | Omni3D_In | 14400 | 14.3496 | 12.6966 | +--------------+----------+---------+---------+ | Omni3D | 14400 | nan | nan | +--------------+----------+---------+---------+ [02/26 19:48:43] d2.utils.events INFO: eta: 2 days, 4:48:26 iter: 14399 Cube/total_3D_loss: 1.06 total_loss: 1.923 BoxHead/loss_cls: 0.1109 BoxHead/loss_box_reg: 0.2167 Cube/loss_dims: 0.04248 Cube/loss_xy: 0.02904 Cube/loss_z: 0.07149 Cube/loss_pose: 0.2318 Cube/loss_joint: 0.5721 lr: 0.0214 max_mem: 28030M [02/26 19:49:09] d2.utils.events INFO: eta: 5:20:34 iter: 14419 Cube/total_3D_loss: 1.127 total_loss: 1.963 BoxHead/loss_cls: 0.1155 BoxHead/loss_box_reg: 0.2154 Cube/loss_dims: 0.04264 Cube/loss_xy: 0.03055 Cube/loss_z: 0.07129 Cube/loss_pose: 0.2355 Cube/loss_joint: 0.5749 lr: 0.0214 max_mem: 28030M [02/26 19:49:36] d2.utils.events INFO: eta: 5:20:08 iter: 14439 Cube/total_3D_loss: 1.146 total_loss: 1.989 BoxHead/loss_cls: 0.1089 BoxHead/loss_box_reg: 0.2201 Cube/loss_dims: 0.0401 Cube/loss_xy: 0.03025 Cube/loss_z: 0.07633 Cube/loss_pose: 0.2324 Cube/loss_joint: 0.5871 lr: 0.0214 max_mem: 28030M [02/26 19:50:03] d2.utils.events INFO: eta: 5:18:23 iter: 14459 Cube/total_3D_loss: 1.152 total_loss: 1.971 BoxHead/loss_cls: 0.1052 BoxHead/loss_box_reg: 0.2054 Cube/loss_dims: 0.04061 Cube/loss_xy: 0.02988 Cube/loss_z: 0.07657 Cube/loss_pose: 0.2332 Cube/loss_joint: 0.582 lr: 0.0214 max_mem: 28030M [02/26 19:50:29] d2.utils.events INFO: eta: 5:17:27 iter: 14479 Cube/total_3D_loss: 1.131 total_loss: 1.962 BoxHead/loss_cls: 0.1127 BoxHead/loss_box_reg: 0.2116 Cube/loss_dims: 0.04154 Cube/loss_xy: 0.03089 Cube/loss_z: 0.07319 Cube/loss_pose: 0.2343 Cube/loss_joint: 0.5799 lr: 0.0214 max_mem: 28030M [02/26 19:50:56] d2.utils.events INFO: eta: 5:17:25 iter: 14499 Cube/total_3D_loss: 1.139 total_loss: 1.947 BoxHead/loss_cls: 0.09814 BoxHead/loss_box_reg: 0.1938 Cube/loss_dims: 0.03966 Cube/loss_xy: 0.02902 Cube/loss_z: 0.08065 Cube/loss_pose: 0.2244 Cube/loss_joint: 0.6141 lr: 0.0214 max_mem: 28030M [02/26 19:51:23] d2.utils.events INFO: eta: 5:21:01 iter: 14519 Cube/total_3D_loss: 1.149 total_loss: 1.992 BoxHead/loss_cls: 0.1068 BoxHead/loss_box_reg: 0.2138 Cube/loss_dims: 0.04061 Cube/loss_xy: 0.02979 Cube/loss_z: 0.07405 Cube/loss_pose: 0.2336 Cube/loss_joint: 0.5887 lr: 0.0214 max_mem: 28030M [02/26 19:51:50] d2.utils.events INFO: eta: 5:17:14 iter: 14539 Cube/total_3D_loss: 1.101 total_loss: 1.925 BoxHead/loss_cls: 0.1055 BoxHead/loss_box_reg: 0.2098 Cube/loss_dims: 0.04105 Cube/loss_xy: 0.03034 Cube/loss_z: 0.07074 Cube/loss_pose: 0.2347 Cube/loss_joint: 0.5776 lr: 0.0214 max_mem: 28030M [02/26 19:52:17] d2.utils.events INFO: eta: 5:22:59 iter: 14559 Cube/total_3D_loss: 1.091 total_loss: 1.89 BoxHead/loss_cls: 0.095 BoxHead/loss_box_reg: 0.1941 Cube/loss_dims: 0.04058 Cube/loss_xy: 0.0301 Cube/loss_z: 0.0706 Cube/loss_pose: 0.2414 Cube/loss_joint: 0.5826 lr: 0.0214 max_mem: 28030M [02/26 19:52:44] d2.utils.events INFO: eta: 5:16:24 iter: 14579 Cube/total_3D_loss: 1.126 total_loss: 1.916 BoxHead/loss_cls: 0.09818 BoxHead/loss_box_reg: 0.1849 Cube/loss_dims: 0.0414 Cube/loss_xy: 0.02869 Cube/loss_z: 0.07043 Cube/loss_pose: 0.2346 Cube/loss_joint: 0.5693 lr: 0.0214 max_mem: 28030M [02/26 19:53:10] d2.utils.events INFO: eta: 5:15:19 iter: 14599 Cube/total_3D_loss: 1.081 total_loss: 1.925 BoxHead/loss_cls: 0.1012 BoxHead/loss_box_reg: 0.2092 Cube/loss_dims: 0.04099 Cube/loss_xy: 0.02997 Cube/loss_z: 0.07243 Cube/loss_pose: 0.2359 Cube/loss_joint: 0.586 lr: 0.0214 max_mem: 28030M [02/26 19:53:37] d2.utils.events INFO: eta: 5:14:00 iter: 14619 Cube/total_3D_loss: 1.078 total_loss: 1.933 BoxHead/loss_cls: 0.1063 BoxHead/loss_box_reg: 0.2036 Cube/loss_dims: 0.04205 Cube/loss_xy: 0.02992 Cube/loss_z: 0.07812 Cube/loss_pose: 0.2358 Cube/loss_joint: 0.5958 lr: 0.0214 max_mem: 28030M [02/26 19:54:03] d2.utils.events INFO: eta: 5:12:57 iter: 14639 Cube/total_3D_loss: 1.091 total_loss: 1.939 BoxHead/loss_cls: 0.1064 BoxHead/loss_box_reg: 0.2088 Cube/loss_dims: 0.04198 Cube/loss_xy: 0.03018 Cube/loss_z: 0.07081 Cube/loss_pose: 0.2364 Cube/loss_joint: 0.5774 lr: 0.0214 max_mem: 28030M [02/26 19:54:30] d2.utils.events INFO: eta: 5:11:18 iter: 14659 Cube/total_3D_loss: 1.081 total_loss: 1.936 BoxHead/loss_cls: 0.108 BoxHead/loss_box_reg: 0.2122 Cube/loss_dims: 0.04261 Cube/loss_xy: 0.03057 Cube/loss_z: 0.07234 Cube/loss_pose: 0.2377 Cube/loss_joint: 0.5832 lr: 0.0214 max_mem: 28030M [02/26 19:54:56] d2.utils.events INFO: eta: 5:12:41 iter: 14679 Cube/total_3D_loss: 1.096 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Cube/loss_xy: 0.03005 Cube/loss_z: 0.07201 Cube/loss_pose: 0.2326 Cube/loss_joint: 0.5801 lr: 0.0214 max_mem: 28030M [02/26 19:56:44] d2.utils.events INFO: eta: 5:10:51 iter: 14759 Cube/total_3D_loss: 1.093 total_loss: 1.944 BoxHead/loss_cls: 0.1123 BoxHead/loss_box_reg: 0.2187 Cube/loss_dims: 0.04078 Cube/loss_xy: 0.03029 Cube/loss_z: 0.07413 Cube/loss_pose: 0.2322 Cube/loss_joint: 0.597 lr: 0.0214 max_mem: 28030M [02/26 19:57:11] d2.utils.events INFO: eta: 5:11:16 iter: 14779 Cube/total_3D_loss: 1.144 total_loss: 1.98 BoxHead/loss_cls: 0.1133 BoxHead/loss_box_reg: 0.2201 Cube/loss_dims: 0.04177 Cube/loss_xy: 0.03078 Cube/loss_z: 0.07904 Cube/loss_pose: 0.2313 Cube/loss_joint: 0.6124 lr: 0.0214 max_mem: 28030M [02/26 19:57:37] d2.utils.events INFO: eta: 5:12:52 iter: 14799 Cube/total_3D_loss: 1.132 total_loss: 1.961 BoxHead/loss_cls: 0.1081 BoxHead/loss_box_reg: 0.212 Cube/loss_dims: 0.04153 Cube/loss_xy: 0.02903 Cube/loss_z: 0.07526 Cube/loss_pose: 0.2303 Cube/loss_joint: 0.5893 lr: 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Cube/loss_joint: 0.5797 lr: 0.0214 max_mem: 28030M [02/26 20:02:06] fvcore.common.checkpoint INFO: Saving checkpoint to output/Baseline_sgd/model_recent.pth [02/26 20:02:33] d2.utils.events INFO: eta: 5:15:34 iter: 15019 Cube/total_3D_loss: 1.127 total_loss: 1.942 BoxHead/loss_cls: 0.1015 BoxHead/loss_box_reg: 0.2034 Cube/loss_dims: 0.03972 Cube/loss_xy: 0.02984 Cube/loss_z: 0.07631 Cube/loss_pose: 0.2282 Cube/loss_joint: 0.5995 lr: 0.0214 max_mem: 28030M [02/26 20:03:00] d2.utils.events INFO: eta: 5:06:45 iter: 15039 Cube/total_3D_loss: 1.139 total_loss: 1.952 BoxHead/loss_cls: 0.09977 BoxHead/loss_box_reg: 0.1945 Cube/loss_dims: 0.04192 Cube/loss_xy: 0.02914 Cube/loss_z: 0.07724 Cube/loss_pose: 0.2391 Cube/loss_joint: 0.6076 lr: 0.0214 max_mem: 28030M [02/26 20:03:27] d2.utils.events INFO: eta: 5:06:29 iter: 15059 Cube/total_3D_loss: 1.138 total_loss: 1.98 BoxHead/loss_cls: 0.1113 BoxHead/loss_box_reg: 0.2107 Cube/loss_dims: 0.04129 Cube/loss_xy: 0.03003 Cube/loss_z: 0.07267 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d2.utils.events INFO: eta: 5:03:23 iter: 15139 Cube/total_3D_loss: 1.134 total_loss: 1.926 BoxHead/loss_cls: 0.1055 BoxHead/loss_box_reg: 0.2071 Cube/loss_dims: 0.04185 Cube/loss_xy: 0.03055 Cube/loss_z: 0.07293 Cube/loss_pose: 0.2308 Cube/loss_joint: 0.5892 lr: 0.0214 max_mem: 28030M [02/26 20:05:41] d2.utils.events INFO: eta: 5:09:07 iter: 15159 Cube/total_3D_loss: 1.122 total_loss: 1.979 BoxHead/loss_cls: 0.1153 BoxHead/loss_box_reg: 0.2086 Cube/loss_dims: 0.04092 Cube/loss_xy: 0.02974 Cube/loss_z: 0.07627 Cube/loss_pose: 0.2295 Cube/loss_joint: 0.5975 lr: 0.0214 max_mem: 28030M [02/26 20:06:08] d2.utils.events INFO: eta: 5:05:27 iter: 15179 Cube/total_3D_loss: 1.078 total_loss: 1.927 BoxHead/loss_cls: 0.1033 BoxHead/loss_box_reg: 0.1989 Cube/loss_dims: 0.04083 Cube/loss_xy: 0.0302 Cube/loss_z: 0.07188 Cube/loss_pose: 0.2328 Cube/loss_joint: 0.5729 lr: 0.0214 max_mem: 28030M [02/26 20:06:35] d2.utils.events INFO: eta: 5:03:12 iter: 15199 Cube/total_3D_loss: 1.04 total_loss: 1.866 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20:20:07] d2.utils.events INFO: eta: 4:46:48 iter: 15799 Cube/total_3D_loss: 1.102 total_loss: 1.881 BoxHead/loss_cls: 0.1005 BoxHead/loss_box_reg: 0.1981 Cube/loss_dims: 0.04198 Cube/loss_xy: 0.03094 Cube/loss_z: 0.07221 Cube/loss_pose: 0.2403 Cube/loss_joint: 0.5935 lr: 0.0214 max_mem: 28031M [02/26 20:20:33] d2.utils.events INFO: eta: 4:50:00 iter: 15819 Cube/total_3D_loss: 1.139 total_loss: 1.981 BoxHead/loss_cls: 0.1053 BoxHead/loss_box_reg: 0.2043 Cube/loss_dims: 0.0409 Cube/loss_xy: 0.02839 Cube/loss_z: 0.07832 Cube/loss_pose: 0.2299 Cube/loss_joint: 0.6011 lr: 0.0214 max_mem: 28031M [02/26 20:21:00] d2.utils.events INFO: eta: 4:47:43 iter: 15839 Cube/total_3D_loss: 1.107 total_loss: 1.929 BoxHead/loss_cls: 0.09748 BoxHead/loss_box_reg: 0.1936 Cube/loss_dims: 0.0404 Cube/loss_xy: 0.02841 Cube/loss_z: 0.07183 Cube/loss_pose: 0.2283 Cube/loss_joint: 0.5797 lr: 0.0214 max_mem: 28031M [02/26 20:21:27] d2.utils.events INFO: eta: 4:47:24 iter: 15859 Cube/total_3D_loss: 1.082 total_loss: 1.906 BoxHead/loss_cls: 0.1003 BoxHead/loss_box_reg: 0.2164 Cube/loss_dims: 0.0406 Cube/loss_xy: 0.03052 Cube/loss_z: 0.07385 Cube/loss_pose: 0.2455 Cube/loss_joint: 0.5796 lr: 0.0214 max_mem: 28031M [02/26 20:21:54] d2.utils.events INFO: eta: 4:55:09 iter: 15879 Cube/total_3D_loss: 1.126 total_loss: 1.947 BoxHead/loss_cls: 0.1071 BoxHead/loss_box_reg: 0.2035 Cube/loss_dims: 0.03996 Cube/loss_xy: 0.02998 Cube/loss_z: 0.07653 Cube/loss_pose: 0.2294 Cube/loss_joint: 0.5919 lr: 0.0214 max_mem: 28031M [02/26 20:22:21] d2.utils.events INFO: eta: 4:48:01 iter: 15899 Cube/total_3D_loss: 1.12 total_loss: 1.939 BoxHead/loss_cls: 0.09976 BoxHead/loss_box_reg: 0.2031 Cube/loss_dims: 0.03962 Cube/loss_xy: 0.03138 Cube/loss_z: 0.07162 Cube/loss_pose: 0.2354 Cube/loss_joint: 0.568 lr: 0.0214 max_mem: 28031M [02/26 20:22:48] d2.utils.events INFO: eta: 4:48:15 iter: 15919 Cube/total_3D_loss: 1.066 total_loss: 1.901 BoxHead/loss_cls: 0.1016 BoxHead/loss_box_reg: 0.2064 Cube/loss_dims: 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20:30:23] d2.utils.events INFO: eta: 4:38:15 iter: 16259 Cube/total_3D_loss: 1.073 total_loss: 1.934 BoxHead/loss_cls: 0.1033 BoxHead/loss_box_reg: 0.2067 Cube/loss_dims: 0.0392 Cube/loss_xy: 0.02888 Cube/loss_z: 0.07681 Cube/loss_pose: 0.2349 Cube/loss_joint: 0.5868 lr: 0.0214 max_mem: 28031M [02/26 20:30:49] d2.utils.events INFO: eta: 4:34:57 iter: 16279 Cube/total_3D_loss: 1.057 total_loss: 1.92 BoxHead/loss_cls: 0.1067 BoxHead/loss_box_reg: 0.2163 Cube/loss_dims: 0.04112 Cube/loss_xy: 0.03 Cube/loss_z: 0.0727 Cube/loss_pose: 0.226 Cube/loss_joint: 0.5754 lr: 0.0214 max_mem: 28031M [02/26 20:31:15] d2.utils.events INFO: eta: 4:35:00 iter: 16299 Cube/total_3D_loss: 1.089 total_loss: 1.945 BoxHead/loss_cls: 0.1048 BoxHead/loss_box_reg: 0.2299 Cube/loss_dims: 0.04104 Cube/loss_xy: 0.02951 Cube/loss_z: 0.06797 Cube/loss_pose: 0.242 Cube/loss_joint: 0.5745 lr: 0.0214 max_mem: 28031M [02/26 20:31:42] d2.utils.events INFO: eta: 4:35:49 iter: 16319 Cube/total_3D_loss: 1.066 total_loss: 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Cube/loss_xy: 0.0302 Cube/loss_z: 0.07638 Cube/loss_pose: 0.2284 Cube/loss_joint: 0.5834 lr: 0.0214 max_mem: 28031M [02/26 20:33:29] d2.utils.events INFO: eta: 4:34:05 iter: 16399 Cube/total_3D_loss: 1.101 total_loss: 1.931 BoxHead/loss_cls: 0.1022 BoxHead/loss_box_reg: 0.206 Cube/loss_dims: 0.03997 Cube/loss_xy: 0.02899 Cube/loss_z: 0.0753 Cube/loss_pose: 0.2287 Cube/loss_joint: 0.5834 lr: 0.0214 max_mem: 28031M [02/26 20:33:56] d2.utils.events INFO: eta: 4:43:56 iter: 16419 Cube/total_3D_loss: 1.11 total_loss: 1.922 BoxHead/loss_cls: 0.09854 BoxHead/loss_box_reg: 0.2011 Cube/loss_dims: 0.04091 Cube/loss_xy: 0.02879 Cube/loss_z: 0.0769 Cube/loss_pose: 0.229 Cube/loss_joint: 0.5998 lr: 0.0214 max_mem: 28031M [02/26 20:34:23] d2.utils.events INFO: eta: 4:31:53 iter: 16439 Cube/total_3D_loss: 1.109 total_loss: 1.92 BoxHead/loss_cls: 0.09659 BoxHead/loss_box_reg: 0.1888 Cube/loss_dims: 0.04255 Cube/loss_xy: 0.02909 Cube/loss_z: 0.0752 Cube/loss_pose: 0.2359 Cube/loss_joint: 0.5839 lr: 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Cube/total_3D_loss: 1.087 total_loss: 1.908 BoxHead/loss_cls: 0.1004 BoxHead/loss_box_reg: 0.2089 Cube/loss_dims: 0.04026 Cube/loss_xy: 0.02928 Cube/loss_z: 0.07488 Cube/loss_pose: 0.232 Cube/loss_joint: 0.5922 lr: 0.0214 max_mem: 28031M [02/26 20:36:37] d2.utils.events INFO: eta: 4:33:16 iter: 16539 Cube/total_3D_loss: 1.064 total_loss: 1.881 BoxHead/loss_cls: 0.08688 BoxHead/loss_box_reg: 0.1955 Cube/loss_dims: 0.04139 Cube/loss_xy: 0.02914 Cube/loss_z: 0.0732 Cube/loss_pose: 0.2337 Cube/loss_joint: 0.5802 lr: 0.0214 max_mem: 28031M [02/26 20:37:03] d2.utils.events INFO: eta: 4:31:24 iter: 16559 Cube/total_3D_loss: 1.106 total_loss: 1.894 BoxHead/loss_cls: 0.09318 BoxHead/loss_box_reg: 0.2014 Cube/loss_dims: 0.04197 Cube/loss_xy: 0.02879 Cube/loss_z: 0.07372 Cube/loss_pose: 0.2325 Cube/loss_joint: 0.5731 lr: 0.0214 max_mem: 28031M [02/26 20:37:30] d2.utils.events INFO: eta: 4:27:56 iter: 16579 Cube/total_3D_loss: 1.084 total_loss: 1.913 BoxHead/loss_cls: 0.09386 BoxHead/loss_box_reg: 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INFO: eta: 4:31:01 iter: 16719 Cube/total_3D_loss: 1.115 total_loss: 1.929 BoxHead/loss_cls: 0.1002 BoxHead/loss_box_reg: 0.2042 Cube/loss_dims: 0.04065 Cube/loss_xy: 0.02905 Cube/loss_z: 0.07944 Cube/loss_pose: 0.2284 Cube/loss_joint: 0.5974 lr: 0.0214 max_mem: 28031M [02/26 20:41:04] d2.utils.events INFO: eta: 4:33:10 iter: 16739 Cube/total_3D_loss: 1.094 total_loss: 1.931 BoxHead/loss_cls: 0.102 BoxHead/loss_box_reg: 0.1996 Cube/loss_dims: 0.04181 Cube/loss_xy: 0.02893 Cube/loss_z: 0.07479 Cube/loss_pose: 0.2219 Cube/loss_joint: 0.5849 lr: 0.0214 max_mem: 28031M [02/26 20:41:31] d2.utils.events INFO: eta: 4:27:16 iter: 16759 Cube/total_3D_loss: 1.074 total_loss: 1.91 BoxHead/loss_cls: 0.1053 BoxHead/loss_box_reg: 0.2041 Cube/loss_dims: 0.0402 Cube/loss_xy: 0.03057 Cube/loss_z: 0.07438 Cube/loss_pose: 0.233 Cube/loss_joint: 0.5798 lr: 0.0214 max_mem: 28031M [02/26 20:41:58] d2.utils.events INFO: eta: 4:27:48 iter: 16779 Cube/total_3D_loss: 1.041 total_loss: 1.837 BoxHead/loss_cls: 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BoxHead/loss_box_reg: 0.1929 Cube/loss_dims: 0.04213 Cube/loss_xy: 0.02937 Cube/loss_z: 0.07403 Cube/loss_pose: 0.235 Cube/loss_joint: 0.5824 lr: 0.0214 max_mem: 28031M [02/26 20:52:39] d2.utils.events INFO: eta: 4:24:27 iter: 17259 Cube/total_3D_loss: 1.061 total_loss: 1.88 BoxHead/loss_cls: 0.09374 BoxHead/loss_box_reg: 0.2021 Cube/loss_dims: 0.04139 Cube/loss_xy: 0.02965 Cube/loss_z: 0.07026 Cube/loss_pose: 0.2373 Cube/loss_joint: 0.5709 lr: 0.0214 max_mem: 28031M [02/26 20:53:05] d2.utils.events INFO: eta: 4:14:01 iter: 17279 Cube/total_3D_loss: 1.037 total_loss: 1.829 BoxHead/loss_cls: 0.09023 BoxHead/loss_box_reg: 0.1909 Cube/loss_dims: 0.04003 Cube/loss_xy: 0.02983 Cube/loss_z: 0.0739 Cube/loss_pose: 0.2338 Cube/loss_joint: 0.5881 lr: 0.0214 max_mem: 28031M [02/26 20:53:32] d2.utils.events INFO: eta: 4:15:30 iter: 17299 Cube/total_3D_loss: 0.9856 total_loss: 1.79 BoxHead/loss_cls: 0.08367 BoxHead/loss_box_reg: 0.1741 Cube/loss_dims: 0.04026 Cube/loss_xy: 0.02832 Cube/loss_z: 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[02/26 20:55:18] d2.utils.events INFO: eta: 4:10:45 iter: 17379 Cube/total_3D_loss: 0.9174 total_loss: 1.686 BoxHead/loss_cls: 0.07785 BoxHead/loss_box_reg: 0.1858 Cube/loss_dims: 0.0426 Cube/loss_xy: 0.02819 Cube/loss_z: 0.06322 Cube/loss_pose: 0.2363 Cube/loss_joint: 0.5309 lr: 0.00214 max_mem: 28031M [02/26 20:55:45] d2.utils.events INFO: eta: 4:12:43 iter: 17399 Cube/total_3D_loss: 0.8767 total_loss: 1.647 BoxHead/loss_cls: 0.07236 BoxHead/loss_box_reg: 0.1734 Cube/loss_dims: 0.04104 Cube/loss_xy: 0.02691 Cube/loss_z: 0.05959 Cube/loss_pose: 0.2296 Cube/loss_joint: 0.5269 lr: 0.00214 max_mem: 28031M [02/26 20:56:11] d2.utils.events INFO: eta: 4:09:26 iter: 17419 Cube/total_3D_loss: 0.8654 total_loss: 1.646 BoxHead/loss_cls: 0.0806 BoxHead/loss_box_reg: 0.1726 Cube/loss_dims: 0.0423 Cube/loss_xy: 0.02758 Cube/loss_z: 0.06017 Cube/loss_pose: 0.2273 Cube/loss_joint: 0.5371 lr: 0.00214 max_mem: 28031M [02/26 20:56:38] d2.utils.events INFO: eta: 4:10:14 iter: 17439 Cube/total_3D_loss: 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d2.utils.events INFO: eta: 4:05:19 iter: 17639 Cube/total_3D_loss: 0.8161 total_loss: 1.55 BoxHead/loss_cls: 0.07134 BoxHead/loss_box_reg: 0.1643 Cube/loss_dims: 0.04264 Cube/loss_xy: 0.02646 Cube/loss_z: 0.05608 Cube/loss_pose: 0.2288 Cube/loss_joint: 0.5068 lr: 0.00214 max_mem: 28031M [02/26 21:01:31] d2.utils.events INFO: eta: 4:13:09 iter: 17659 Cube/total_3D_loss: 0.8354 total_loss: 1.6 BoxHead/loss_cls: 0.07986 BoxHead/loss_box_reg: 0.186 Cube/loss_dims: 0.04262 Cube/loss_xy: 0.02717 Cube/loss_z: 0.0562 Cube/loss_pose: 0.2324 Cube/loss_joint: 0.507 lr: 0.00214 max_mem: 28031M [02/26 21:01:57] d2.utils.events INFO: eta: 4:05:51 iter: 17679 Cube/total_3D_loss: 0.8211 total_loss: 1.558 BoxHead/loss_cls: 0.07356 BoxHead/loss_box_reg: 0.175 Cube/loss_dims: 0.04288 Cube/loss_xy: 0.02682 Cube/loss_z: 0.05558 Cube/loss_pose: 0.2237 Cube/loss_joint: 0.4975 lr: 0.00214 max_mem: 28031M [02/26 21:02:23] d2.utils.events INFO: eta: 4:03:16 iter: 17699 Cube/total_3D_loss: 0.8618 total_loss: 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[02/26 21:28:22] d2.utils.events INFO: eta: 3:37:19 iter: 18879 Cube/total_3D_loss: 0.797 total_loss: 1.523 BoxHead/loss_cls: 0.06824 BoxHead/loss_box_reg: 0.167 Cube/loss_dims: 0.04408 Cube/loss_xy: 0.02712 Cube/loss_z: 0.05068 Cube/loss_pose: 0.2285 Cube/loss_joint: 0.4859 lr: 0.00214 max_mem: 28031M [02/26 21:28:49] d2.utils.events INFO: eta: 3:36:57 iter: 18899 Cube/total_3D_loss: 0.7352 total_loss: 1.437 BoxHead/loss_cls: 0.06614 BoxHead/loss_box_reg: 0.1658 Cube/loss_dims: 0.04248 Cube/loss_xy: 0.02591 Cube/loss_z: 0.0479 Cube/loss_pose: 0.2208 Cube/loss_joint: 0.4628 lr: 0.00214 max_mem: 28031M [02/26 21:29:15] d2.utils.events INFO: eta: 3:35:19 iter: 18919 Cube/total_3D_loss: 0.7603 total_loss: 1.478 BoxHead/loss_cls: 0.0687 BoxHead/loss_box_reg: 0.179 Cube/loss_dims: 0.04311 Cube/loss_xy: 0.02634 Cube/loss_z: 0.04941 Cube/loss_pose: 0.2314 Cube/loss_joint: 0.4774 lr: 0.00214 max_mem: 28031M [02/26 21:29:41] d2.utils.events INFO: eta: 3:38:00 iter: 18939 Cube/total_3D_loss: 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max_mem: 28031M [02/26 22:08:30] d2.utils.events INFO: eta: 2:57:09 iter: 20699 Cube/total_3D_loss: 0.7059 total_loss: 1.38 BoxHead/loss_cls: 0.06148 BoxHead/loss_box_reg: 0.1534 Cube/loss_dims: 0.04129 Cube/loss_xy: 0.02501 Cube/loss_z: 0.04433 Cube/loss_pose: 0.2227 Cube/loss_joint: 0.4437 lr: 0.00214 max_mem: 28031M [02/26 22:08:56] d2.utils.events INFO: eta: 2:55:47 iter: 20719 Cube/total_3D_loss: 0.7118 total_loss: 1.439 BoxHead/loss_cls: 0.06376 BoxHead/loss_box_reg: 0.1634 Cube/loss_dims: 0.04244 Cube/loss_xy: 0.02585 Cube/loss_z: 0.04652 Cube/loss_pose: 0.2176 Cube/loss_joint: 0.4538 lr: 0.00214 max_mem: 28031M [02/26 22:09:22] d2.utils.events INFO: eta: 2:55:58 iter: 20739 Cube/total_3D_loss: 0.7105 total_loss: 1.381 BoxHead/loss_cls: 0.05953 BoxHead/loss_box_reg: 0.1603 Cube/loss_dims: 0.04164 Cube/loss_xy: 0.02523 Cube/loss_z: 0.04439 Cube/loss_pose: 0.2236 Cube/loss_joint: 0.4497 lr: 0.00214 max_mem: 28031M [02/26 22:09:49] d2.utils.events INFO: eta: 2:57:43 iter: 20759 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BoxHead/loss_box_reg: 0.159 Cube/loss_dims: 0.04079 Cube/loss_xy: 0.02549 Cube/loss_z: 0.04703 Cube/loss_pose: 0.2226 Cube/loss_joint: 0.4554 lr: 0.00214 max_mem: 28031M [02/26 22:22:59] d2.utils.events INFO: eta: 2:41:36 iter: 21359 Cube/total_3D_loss: 0.7256 total_loss: 1.434 BoxHead/loss_cls: 0.06469 BoxHead/loss_box_reg: 0.1646 Cube/loss_dims: 0.04196 Cube/loss_xy: 0.02596 Cube/loss_z: 0.04809 Cube/loss_pose: 0.2321 Cube/loss_joint: 0.4627 lr: 0.00214 max_mem: 28031M [02/26 22:23:25] d2.utils.events INFO: eta: 2:41:11 iter: 21379 Cube/total_3D_loss: 0.7072 total_loss: 1.374 BoxHead/loss_cls: 0.0615 BoxHead/loss_box_reg: 0.1567 Cube/loss_dims: 0.04214 Cube/loss_xy: 0.02574 Cube/loss_z: 0.04365 Cube/loss_pose: 0.2254 Cube/loss_joint: 0.4451 lr: 0.00214 max_mem: 28031M [02/26 22:23:51] d2.utils.events INFO: eta: 2:41:59 iter: 21399 Cube/total_3D_loss: 0.6782 total_loss: 1.329 BoxHead/loss_cls: 0.06188 BoxHead/loss_box_reg: 0.1542 Cube/loss_dims: 0.04184 Cube/loss_xy: 0.02474 Cube/loss_z: 0.04502 Cube/loss_pose: 0.2181 Cube/loss_joint: 0.4509 lr: 0.00214 max_mem: 28031M [02/26 22:24:17] d2.utils.events INFO: eta: 2:41:12 iter: 21419 Cube/total_3D_loss: 0.7052 total_loss: 1.396 BoxHead/loss_cls: 0.06357 BoxHead/loss_box_reg: 0.167 Cube/loss_dims: 0.04256 Cube/loss_xy: 0.02545 Cube/loss_z: 0.04318 Cube/loss_pose: 0.2303 Cube/loss_joint: 0.4397 lr: 0.00214 max_mem: 28031M [02/26 22:24:44] d2.utils.events INFO: eta: 2:42:44 iter: 21439 Cube/total_3D_loss: 0.7508 total_loss: 1.448 BoxHead/loss_cls: 0.06675 BoxHead/loss_box_reg: 0.1669 Cube/loss_dims: 0.04241 Cube/loss_xy: 0.02577 Cube/loss_z: 0.04538 Cube/loss_pose: 0.2294 Cube/loss_joint: 0.4626 lr: 0.00214 max_mem: 28031M [02/26 22:25:10] d2.utils.events INFO: eta: 2:41:13 iter: 21459 Cube/total_3D_loss: 0.718 total_loss: 1.387 BoxHead/loss_cls: 0.05974 BoxHead/loss_box_reg: 0.1538 Cube/loss_dims: 0.04144 Cube/loss_xy: 0.0245 Cube/loss_z: 0.04266 Cube/loss_pose: 0.2274 Cube/loss_joint: 0.4389 lr: 0.00214 max_mem: 28031M [02/26 22:25:36] d2.utils.events INFO: eta: 2:40:23 iter: 21479 Cube/total_3D_loss: 0.6837 total_loss: 1.377 BoxHead/loss_cls: 0.06275 BoxHead/loss_box_reg: 0.1527 Cube/loss_dims: 0.04142 Cube/loss_xy: 0.0253 Cube/loss_z: 0.04333 Cube/loss_pose: 0.2234 Cube/loss_joint: 0.4335 lr: 0.00214 max_mem: 28031M [02/26 22:26:03] d2.utils.events INFO: eta: 2:39:23 iter: 21499 Cube/total_3D_loss: 0.6974 total_loss: 1.37 BoxHead/loss_cls: 0.05984 BoxHead/loss_box_reg: 0.1557 Cube/loss_dims: 0.04191 Cube/loss_xy: 0.0251 Cube/loss_z: 0.04372 Cube/loss_pose: 0.2306 Cube/loss_joint: 0.4481 lr: 0.00214 max_mem: 28031M [02/26 22:26:29] d2.utils.events INFO: eta: 2:37:57 iter: 21519 Cube/total_3D_loss: 0.7077 total_loss: 1.367 BoxHead/loss_cls: 0.06058 BoxHead/loss_box_reg: 0.1582 Cube/loss_dims: 0.04246 Cube/loss_xy: 0.02538 Cube/loss_z: 0.04369 Cube/loss_pose: 0.2263 Cube/loss_joint: 0.4457 lr: 0.00214 max_mem: 28031M [02/26 22:26:55] d2.utils.events INFO: eta: 2:40:26 iter: 21539 Cube/total_3D_loss: 0.7167 total_loss: 1.426 BoxHead/loss_cls: 0.06651 BoxHead/loss_box_reg: 0.1654 Cube/loss_dims: 0.04196 Cube/loss_xy: 0.02488 Cube/loss_z: 0.04502 Cube/loss_pose: 0.2225 Cube/loss_joint: 0.4538 lr: 0.00214 max_mem: 28031M [02/26 22:27:21] d2.utils.events INFO: eta: 2:36:56 iter: 21559 Cube/total_3D_loss: 0.7218 total_loss: 1.407 BoxHead/loss_cls: 0.05859 BoxHead/loss_box_reg: 0.1648 Cube/loss_dims: 0.04165 Cube/loss_xy: 0.02568 Cube/loss_z: 0.04393 Cube/loss_pose: 0.2269 Cube/loss_joint: 0.4476 lr: 0.00214 max_mem: 28031M [02/26 22:27:47] d2.utils.events INFO: eta: 2:38:00 iter: 21579 Cube/total_3D_loss: 0.7006 total_loss: 1.383 BoxHead/loss_cls: 0.06627 BoxHead/loss_box_reg: 0.1695 Cube/loss_dims: 0.04144 Cube/loss_xy: 0.02594 Cube/loss_z: 0.04388 Cube/loss_pose: 0.2214 Cube/loss_joint: 0.4409 lr: 0.00214 max_mem: 28031M [02/26 22:28:14] cubercnn INFO: Starting test for iteration 21600 [02/26 22:28:21] cubercnn.data.datasets INFO: Loading datasets/Omni3D/SUNRGBD_test.json takes 1.46 seconds. [02/26 22:28:21] cubercnn.data.datasets INFO: Loaded 5050 images in Omni3D format from datasets/Omni3D/SUNRGBD_test.json [02/26 22:28:25] cubercnn.data.datasets INFO: Filtered out 0/5050 images without valid annotations [02/26 22:28:25] d2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(512, 512), max_size=4096, sample_style='choice')] [02/26 22:28:25] d2.data.common INFO: Serializing the dataset using: [02/26 22:28:25] d2.data.common INFO: Serializing 5050 elements to byte tensors and concatenating them all ... [02/26 22:28:25] d2.data.common INFO: Serialized dataset takes 20.13 MiB [02/26 22:28:25] cubercnn.evaluation.omni3d_evaluation INFO: Start inference on 5050 batches [02/26 22:28:26] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 11/5050. Dataloading: 0.0005 s/iter. Inference: 0.0268 s/iter. Eval: 0.0008 s/iter. Total: 0.0281 s/iter. ETA=0:02:21 [02/26 22:28:31] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 184/5050. Dataloading: 0.0049 s/iter. Inference: 0.0232 s/iter. Eval: 0.0008 s/iter. Total: 0.0290 s/iter. ETA=0:02:21 [02/26 22:28:36] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 394/5050. Dataloading: 0.0026 s/iter. Inference: 0.0227 s/iter. Eval: 0.0008 s/iter. Total: 0.0262 s/iter. ETA=0:02:02 [02/26 22:28:41] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 606/5050. Dataloading: 0.0020 s/iter. Inference: 0.0225 s/iter. Eval: 0.0008 s/iter. Total: 0.0253 s/iter. ETA=0:01:52 [02/26 22:28:46] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 816/5050. Dataloading: 0.0017 s/iter. Inference: 0.0224 s/iter. Eval: 0.0008 s/iter. Total: 0.0249 s/iter. ETA=0:01:45 [02/26 22:28:51] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 1028/5050. Dataloading: 0.0015 s/iter. Inference: 0.0224 s/iter. Eval: 0.0008 s/iter. Total: 0.0247 s/iter. ETA=0:01:39 [02/26 22:28:56] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 1239/5050. Dataloading: 0.0013 s/iter. Inference: 0.0223 s/iter. Eval: 0.0008 s/iter. Total: 0.0245 s/iter. ETA=0:01:33 [02/26 22:29:01] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 1450/5050. Dataloading: 0.0012 s/iter. Inference: 0.0223 s/iter. Eval: 0.0008 s/iter. Total: 0.0244 s/iter. ETA=0:01:27 [02/26 22:29:06] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 1662/5050. Dataloading: 0.0012 s/iter. Inference: 0.0223 s/iter. Eval: 0.0008 s/iter. Total: 0.0243 s/iter. ETA=0:01:22 [02/26 22:29:11] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 1841/5050. Dataloading: 0.0011 s/iter. Inference: 0.0227 s/iter. Eval: 0.0008 s/iter. Total: 0.0247 s/iter. ETA=0:01:19 [02/26 22:29:16] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 2054/5050. Dataloading: 0.0011 s/iter. Inference: 0.0226 s/iter. Eval: 0.0008 s/iter. Total: 0.0245 s/iter. ETA=0:01:13 [02/26 22:29:21] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 2268/5050. Dataloading: 0.0010 s/iter. Inference: 0.0225 s/iter. Eval: 0.0008 s/iter. Total: 0.0244 s/iter. ETA=0:01:07 [02/26 22:29:26] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 2484/5050. Dataloading: 0.0010 s/iter. Inference: 0.0225 s/iter. Eval: 0.0008 s/iter. Total: 0.0243 s/iter. ETA=0:01:02 [02/26 22:29:31] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 2700/5050. Dataloading: 0.0010 s/iter. Inference: 0.0224 s/iter. Eval: 0.0008 s/iter. Total: 0.0242 s/iter. ETA=0:00:56 [02/26 22:29:36] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 2918/5050. Dataloading: 0.0010 s/iter. Inference: 0.0223 s/iter. Eval: 0.0008 s/iter. Total: 0.0241 s/iter. ETA=0:00:51 [02/26 22:29:41] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 3136/5050. Dataloading: 0.0009 s/iter. Inference: 0.0223 s/iter. Eval: 0.0008 s/iter. Total: 0.0241 s/iter. ETA=0:00:46 [02/26 22:29:46] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 3354/5050. Dataloading: 0.0009 s/iter. Inference: 0.0222 s/iter. Eval: 0.0008 s/iter. Total: 0.0240 s/iter. ETA=0:00:40 [02/26 22:29:51] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 3569/5050. Dataloading: 0.0009 s/iter. Inference: 0.0222 s/iter. Eval: 0.0008 s/iter. Total: 0.0240 s/iter. ETA=0:00:35 [02/26 22:29:57] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 3760/5050. Dataloading: 0.0009 s/iter. Inference: 0.0224 s/iter. Eval: 0.0008 s/iter. Total: 0.0242 s/iter. ETA=0:00:31 [02/26 22:30:02] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 3976/5050. Dataloading: 0.0009 s/iter. Inference: 0.0224 s/iter. Eval: 0.0008 s/iter. Total: 0.0241 s/iter. ETA=0:00:25 [02/26 22:30:07] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 4192/5050. Dataloading: 0.0009 s/iter. Inference: 0.0224 s/iter. Eval: 0.0008 s/iter. Total: 0.0241 s/iter. ETA=0:00:20 [02/26 22:30:12] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 4408/5050. Dataloading: 0.0009 s/iter. Inference: 0.0223 s/iter. Eval: 0.0008 s/iter. Total: 0.0240 s/iter. ETA=0:00:15 [02/26 22:30:17] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 4624/5050. Dataloading: 0.0009 s/iter. Inference: 0.0223 s/iter. Eval: 0.0008 s/iter. Total: 0.0240 s/iter. ETA=0:00:10 [02/26 22:30:22] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 4840/5050. Dataloading: 0.0009 s/iter. Inference: 0.0223 s/iter. Eval: 0.0008 s/iter. Total: 0.0239 s/iter. ETA=0:00:05 [02/26 22:30:27] cubercnn.evaluation.omni3d_evaluation INFO: Total inference time: 0:02:00.703390 (0.023925 s / iter per device, on 1 devices) [02/26 22:30:27] cubercnn.evaluation.omni3d_evaluation INFO: Total inference pure compute time: 0:01:52 (0.022252 s / iter per device, on 1 devices) [02/26 22:30:32] cubercnn.evaluation.omni3d_evaluation INFO: Preparing results for COCO format ... [02/26 22:30:32] cubercnn.evaluation.omni3d_evaluation INFO: Saving results to output/Baseline_sgd/inference/iter_21600/SUNRGBD_test/omni_instances_results.json [02/26 22:30:35] cubercnn.evaluation.omni3d_evaluation INFO: Evaluating predictions with official COCO API... [02/26 22:31:18] cubercnn.evaluation.omni3d_evaluation INFO: Evaluation results for bbox in 2D mode: | AP | AP50 | AP75 | AP95 | APs | APm | APl | |:------:|:------:|:------:|:------:|:-----:|:-----:|:------:| | 16.304 | 32.007 | 14.588 | 0.048 | 0.000 | 3.834 | 17.354 | [02/26 22:31:18] cubercnn.evaluation.omni3d_evaluation INFO: Per-category bbox AP in 2D mode: | category | AP | category | AP | category | AP | |:-------------|:-------|:------------|:-------|:-----------|:-------| | bicycle | 11.750 | books | 0.887 | bottle | 3.259 | | chair | 45.969 | cup | 2.423 | laptop | 8.749 | | shoes | 0.029 | towel | 5.796 | blinds | 10.657 | | window | 0.149 | lamp | 25.292 | shelves | 5.622 | | mirror | 4.256 | sink | 22.278 | cabinet | 17.232 | | bathtub | 36.778 | door | 1.754 | toilet | 50.037 | | desk | 13.738 | box | 4.158 | bookcase | 23.684 | | picture | 2.059 | table | 34.014 | counter | 18.493 | | bed | 48.619 | night stand | 35.698 | pillow | 14.358 | | sofa | 49.333 | television | 23.115 | floor mat | 7.624 | | curtain | 11.410 | clothes | 1.544 | stationery | 2.735 | | refrigerator | 23.547 | bin | 33.000 | stove | 11.565 | | oven | 5.456 | machine | 2.481 | | | [02/26 22:31:18] cubercnn.evaluation.omni3d_evaluation INFO: Evaluation results for bbox in 3D mode: | AP | AP15 | AP25 | AP50 | APn | APm | APf | |:------:|:------:|:------:|:------:|:------:|:-----:|:-----:| | 15.341 | 22.116 | 16.538 | 4.839 | 15.341 | nan | nan | [02/26 22:31:18] cubercnn.evaluation.omni3d_evaluation INFO: Some metrics cannot be computed and is shown as NaN. [02/26 22:31:18] cubercnn.evaluation.omni3d_evaluation INFO: Per-category bbox AP in 3D mode: | category | AP | category | AP | category | AP | |:-------------|:-------|:------------|:-------|:-----------|:-------| | bicycle | 12.987 | books | 0.510 | bottle | 0.427 | | chair | 53.714 | cup | 3.683 | laptop | 7.580 | | shoes | 0.008 | towel | 2.018 | blinds | 0.403 | | window | 0.000 | lamp | 8.231 | shelves | 2.993 | | mirror | 0.402 | sink | 28.761 | cabinet | 15.849 | | bathtub | 34.822 | door | 0.450 | toilet | 66.774 | | desk | 19.877 | box | 2.439 | bookcase | 11.145 | | picture | 0.035 | table | 39.304 | counter | 23.007 | | bed | 64.446 | night stand | 32.831 | pillow | 7.674 | | sofa | 58.367 | television | 2.777 | floor mat | 6.535 | | curtain | 4.927 | clothes | 0.463 | stationery | 0.218 | | refrigerator | 23.402 | bin | 18.701 | stove | 19.468 | | oven | 5.363 | machine | 2.364 | | | [02/26 22:31:27] cubercnn.evaluation.omni3d_evaluation INFO: SUNRGBD_test iter=21600 mode=2D Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.163 SUNRGBD_test iter=21600 mode=2D Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.320 SUNRGBD_test iter=21600 mode=2D Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.146 SUNRGBD_test iter=21600 mode=2D Average Precision (AP) @[ IoU=0.95 | area= all | maxDets=100 ] = 0.000 SUNRGBD_test iter=21600 mode=2D Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 SUNRGBD_test iter=21600 mode=2D Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.038 SUNRGBD_test iter=21600 mode=2D Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.174 SUNRGBD_test iter=21600 mode=2D Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.247 SUNRGBD_test iter=21600 mode=2D Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.302 SUNRGBD_test iter=21600 mode=2D Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.302 SUNRGBD_test iter=21600 mode=2D Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 SUNRGBD_test iter=21600 mode=2D Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.099 SUNRGBD_test iter=21600 mode=2D Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.323 [02/26 22:31:27] cubercnn.evaluation.omni3d_evaluation INFO: SUNRGBD_test iter=21600 mode=3D Average Precision (AP) @[ IoU=0.05:0.50 | depth= all | maxDets=100 ] = 0.153 SUNRGBD_test iter=21600 mode=3D Average Precision (AP) @[ IoU=0.15 | depth= all | maxDets=100 ] = 0.221 SUNRGBD_test iter=21600 mode=3D Average Precision (AP) @[ IoU=0.25 | depth= all | maxDets=100 ] = 0.165 SUNRGBD_test iter=21600 mode=3D Average Precision (AP) @[ IoU=0.50 | depth= all | maxDets=100 ] = 0.048 SUNRGBD_test iter=21600 mode=3D Average Precision (AP) @[ IoU=0.05:0.50 | depth= near | maxDets=100 ] = 0.153 SUNRGBD_test iter=21600 mode=3D Average Precision (AP) @[ IoU=0.05:0.50 | depth=medium | maxDets=100 ] = -1.000 SUNRGBD_test iter=21600 mode=3D Average Precision (AP) @[ IoU=0.05:0.50 | depth= far | maxDets=100 ] = -1.000 SUNRGBD_test iter=21600 mode=3D Average Recall (AR) @[ IoU=0.05:0.50 | depth= all | maxDets= 1 ] = 0.236 SUNRGBD_test iter=21600 mode=3D Average Recall (AR) @[ IoU=0.05:0.50 | depth= all | maxDets= 10 ] = 0.295 SUNRGBD_test iter=21600 mode=3D Average Recall (AR) @[ IoU=0.05:0.50 | depth= all | maxDets=100 ] = 0.295 SUNRGBD_test iter=21600 mode=3D Average Recall (AR) @[ IoU=0.05:0.50 | depth= near | maxDets=100 ] = 0.295 SUNRGBD_test iter=21600 mode=3D Average Recall (AR) @[ IoU=0.05:0.50 | depth=medium | maxDets=100 ] = -1.000 SUNRGBD_test iter=21600 mode=3D Average Recall (AR) @[ IoU=0.05:0.50 | depth= far | maxDets=100 ] = -1.000 [02/26 22:31:27] cubercnn.vis.logperf INFO: Performance for each of 38 categories on SUNRGBD_test: | category | AP2D | AP3D | category | AP2D | AP3D | category | AP2D | AP3D | |:----------:|:--------|:----------|:-----------:|:---------|:---------|:------------:|:----------|:-----------| | chair | 45.9689 | 53.7141 | table | 34.0139 | 39.3042 | cabinet | 17.2319 | 15.8488 | | lamp | 25.2924 | 8.23121 | books | 0.887026 | 0.509646 | sofa | 49.3331 | 58.3668 | | picture | 2.05896 | 0.0345133 | window | 0.148515 | 0 | pillow | 14.3583 | 7.67391 | | door | 1.75428 | 0.449796 | blinds | 10.6569 | 0.403263 | sink | 22.2777 | 28.7614 | | shelves | 5.62243 | 2.99274 | television | 23.1145 | 2.77696 | shoes | 0.0291206 | 0.00808244 | | cup | 2.42342 | 3.68344 | bottle | 3.25918 | 0.426973 | bookcase | 23.684 | 11.1455 | | laptop | 8.74873 | 7.57982 | desk | 13.7376 | 19.8769 | floor mat | 7.62376 | 6.53465 | | mirror | 4.25553 | 0.402159 | counter | 18.4926 | 23.0073 | bicycle | 11.7497 | 12.9872 | | toilet | 50.0373 | 66.7744 | bed | 48.6192 | 64.4458 | refrigerator | 23.5467 | 23.4022 | | box | 4.15762 | 2.43902 | oven | 5.4563 | 5.36258 | clothes | 1.54369 | 0.462638 | | towel | 5.79562 | 2.01816 | night stand | 35.6982 | 32.8309 | stove | 11.5645 | 19.4675 | | machine | 2.48141 | 2.36378 | stationery | 2.73505 | 0.217712 | bathtub | 36.7777 | 34.8216 | | curtain | 11.4103 | 4.9273 | bin | 33.0005 | 18.7009 | | | | [02/26 22:31:42] cubercnn INFO: SUNRGBD_testiter=21600, xy(25.58), z(0.24), whl(0.18, 0.10, 0.12), ry(2.14) [02/26 22:31:46] cubercnn.vis.logperf INFO: Performance for each of 38 categories on : | category | AP2D | AP3D | category | AP2D | AP3D | category | AP2D | AP3D | |:----------:|:--------|:----------|:-----------:|:---------|:---------|:------------:|:----------|:-----------| | chair | 45.9689 | 53.7141 | table | 34.0139 | 39.3042 | cabinet | 17.2319 | 15.8488 | | lamp | 25.2924 | 8.23121 | books | 0.887026 | 0.509646 | sofa | 49.3331 | 58.3668 | | picture | 2.05896 | 0.0345133 | window | 0.148515 | 0 | pillow | 14.3583 | 7.67391 | | door | 1.75428 | 0.449796 | blinds | 10.6569 | 0.403263 | sink | 22.2777 | 28.7614 | | shelves | 5.62243 | 2.99274 | television | 23.1145 | 2.77696 | shoes | 0.0291206 | 0.00808244 | | cup | 2.42342 | 3.68344 | bottle | 3.25918 | 0.426973 | bookcase | 23.684 | 11.1455 | | laptop | 8.74873 | 7.57982 | desk | 13.7376 | 19.8769 | floor mat | 7.62376 | 6.53465 | | mirror | 4.25553 | 0.402159 | counter | 18.4926 | 23.0073 | bicycle | 11.7497 | 12.9872 | | toilet | 50.0373 | 66.7744 | bed | 48.6192 | 64.4458 | refrigerator | 23.5467 | 23.4022 | | box | 4.15762 | 2.43902 | oven | 5.4563 | 5.36258 | clothes | 1.54369 | 0.462638 | | towel | 5.79562 | 2.01816 | night stand | 35.6982 | 32.8309 | stove | 11.5645 | 19.4675 | | machine | 2.48141 | 2.36378 | stationery | 2.73505 | 0.217712 | bathtub | 36.7777 | 34.8216 | | curtain | 11.4103 | 4.9273 | bin | 33.0005 | 18.7009 | | | | [02/26 22:31:46] cubercnn.vis.logperf INFO: Per-dataset performance analysis on test set: +--------------+----------+---------+---------+-----------+-----------+-----------+----------+----------+----------+ | Dataset | #iters | AP2D | AP3D | AP3D@15 | AP3D@25 | AP3D@50 | AP3D-N | AP3D-M | AP3D-F | +==============+==========+=========+=========+===========+===========+===========+==========+==========+==========+ | SUNRGBD_test | 21600 | 16.3039 | 15.3409 | 22.1158 | 16.5375 | 4.83889 | 15.3409 | nan | nan | +--------------+----------+---------+---------+-----------+-----------+-----------+----------+----------+----------+ | | 21600 | 16.3039 | 15.3409 | 22.1158 | 16.5375 | 4.83889 | 15.3409 | nan | nan | +--------------+----------+---------+---------+-----------+-----------+-----------+----------+----------+----------+ [02/26 22:31:46] cubercnn.vis.logperf INFO: Omni3D performance on test set. The numbers below should be used to compare to others approaches on Omni3D, such as Cube R-CNN [02/26 22:31:46] cubercnn.vis.logperf INFO: Performance on Omni3D: +--------------+----------+---------+---------+ | Dataset | #iters | AP2D | AP3D | +==============+==========+=========+=========+ | SUNRGBD_test | 21600 | 16.3039 | 15.3409 | +--------------+----------+---------+---------+ | Omni3D_Out | 21600 | nan | nan | +--------------+----------+---------+---------+ | Omni3D_In | 21600 | 16.3039 | 15.3409 | +--------------+----------+---------+---------+ | Omni3D | 21600 | nan | nan | +--------------+----------+---------+---------+ [02/26 22:31:47] d2.utils.events INFO: eta: 23:56:33 iter: 21599 Cube/total_3D_loss: 0.7198 total_loss: 1.435 BoxHead/loss_cls: 0.06818 BoxHead/loss_box_reg: 0.1684 Cube/loss_dims: 0.04206 Cube/loss_xy: 0.02498 Cube/loss_z: 0.04441 Cube/loss_pose: 0.2283 Cube/loss_joint: 0.447 lr: 0.00214 max_mem: 28031M [02/26 22:32:14] d2.utils.events INFO: eta: 2:39:26 iter: 21619 Cube/total_3D_loss: 0.713 total_loss: 1.412 BoxHead/loss_cls: 0.06699 BoxHead/loss_box_reg: 0.1647 Cube/loss_dims: 0.04241 Cube/loss_xy: 0.02516 Cube/loss_z: 0.04504 Cube/loss_pose: 0.2297 Cube/loss_joint: 0.4423 lr: 0.00214 max_mem: 28031M [02/26 22:32:40] d2.utils.events INFO: eta: 2:37:09 iter: 21639 Cube/total_3D_loss: 0.7248 total_loss: 1.401 BoxHead/loss_cls: 0.06506 BoxHead/loss_box_reg: 0.1631 Cube/loss_dims: 0.04308 Cube/loss_xy: 0.02567 Cube/loss_z: 0.04377 Cube/loss_pose: 0.228 Cube/loss_joint: 0.4483 lr: 0.00214 max_mem: 28031M [02/26 22:33:07] d2.utils.events INFO: eta: 2:39:13 iter: 21659 Cube/total_3D_loss: 0.7219 total_loss: 1.392 BoxHead/loss_cls: 0.06299 BoxHead/loss_box_reg: 0.1554 Cube/loss_dims: 0.04182 Cube/loss_xy: 0.02558 Cube/loss_z: 0.04481 Cube/loss_pose: 0.2195 Cube/loss_joint: 0.4461 lr: 0.00214 max_mem: 28031M [02/26 22:33:33] d2.utils.events INFO: eta: 2:38:02 iter: 21679 Cube/total_3D_loss: 0.7219 total_loss: 1.412 BoxHead/loss_cls: 0.06852 BoxHead/loss_box_reg: 0.1634 Cube/loss_dims: 0.04288 Cube/loss_xy: 0.02537 Cube/loss_z: 0.04539 Cube/loss_pose: 0.2282 Cube/loss_joint: 0.4522 lr: 0.00214 max_mem: 28031M [02/26 22:33:59] d2.utils.events INFO: eta: 2:34:22 iter: 21699 Cube/total_3D_loss: 0.6884 total_loss: 1.353 BoxHead/loss_cls: 0.06367 BoxHead/loss_box_reg: 0.1644 Cube/loss_dims: 0.04126 Cube/loss_xy: 0.02472 Cube/loss_z: 0.04352 Cube/loss_pose: 0.2219 Cube/loss_joint: 0.4383 lr: 0.00214 max_mem: 28031M [02/26 22:34:25] d2.utils.events INFO: eta: 2:34:13 iter: 21719 Cube/total_3D_loss: 0.7155 total_loss: 1.392 BoxHead/loss_cls: 0.06145 BoxHead/loss_box_reg: 0.1631 Cube/loss_dims: 0.04102 Cube/loss_xy: 0.02535 Cube/loss_z: 0.04506 Cube/loss_pose: 0.2228 Cube/loss_joint: 0.4438 lr: 0.00214 max_mem: 28031M [02/26 22:34:52] d2.utils.events INFO: eta: 2:33:58 iter: 21739 Cube/total_3D_loss: 0.6961 total_loss: 1.39 BoxHead/loss_cls: 0.06428 BoxHead/loss_box_reg: 0.166 Cube/loss_dims: 0.04233 Cube/loss_xy: 0.02469 Cube/loss_z: 0.04352 Cube/loss_pose: 0.2218 Cube/loss_joint: 0.4435 lr: 0.00214 max_mem: 28031M [02/26 22:35:18] d2.utils.events INFO: eta: 2:32:47 iter: 21759 Cube/total_3D_loss: 0.682 total_loss: 1.353 BoxHead/loss_cls: 0.06144 BoxHead/loss_box_reg: 0.1573 Cube/loss_dims: 0.0407 Cube/loss_xy: 0.02481 Cube/loss_z: 0.04247 Cube/loss_pose: 0.2155 Cube/loss_joint: 0.4322 lr: 0.00214 max_mem: 28031M [02/26 22:35:44] d2.utils.events INFO: eta: 2:32:36 iter: 21779 Cube/total_3D_loss: 0.7016 total_loss: 1.411 BoxHead/loss_cls: 0.06757 BoxHead/loss_box_reg: 0.1715 Cube/loss_dims: 0.0427 Cube/loss_xy: 0.02573 Cube/loss_z: 0.04435 Cube/loss_pose: 0.2213 Cube/loss_joint: 0.4532 lr: 0.00214 max_mem: 28031M [02/26 22:36:10] d2.utils.events INFO: eta: 2:32:24 iter: 21799 Cube/total_3D_loss: 0.7202 total_loss: 1.375 BoxHead/loss_cls: 0.05829 BoxHead/loss_box_reg: 0.1491 Cube/loss_dims: 0.04183 Cube/loss_xy: 0.02461 Cube/loss_z: 0.04386 Cube/loss_pose: 0.2309 Cube/loss_joint: 0.442 lr: 0.00214 max_mem: 28031M [02/26 22:36:36] d2.utils.events INFO: eta: 2:33:41 iter: 21819 Cube/total_3D_loss: 0.7407 total_loss: 1.411 BoxHead/loss_cls: 0.06162 BoxHead/loss_box_reg: 0.1519 Cube/loss_dims: 0.04254 Cube/loss_xy: 0.02567 Cube/loss_z: 0.04459 Cube/loss_pose: 0.2267 Cube/loss_joint: 0.4509 lr: 0.00214 max_mem: 28031M [02/26 22:37:03] d2.utils.events INFO: eta: 2:32:37 iter: 21839 Cube/total_3D_loss: 0.7014 total_loss: 1.381 BoxHead/loss_cls: 0.06032 BoxHead/loss_box_reg: 0.1516 Cube/loss_dims: 0.04277 Cube/loss_xy: 0.02502 Cube/loss_z: 0.04133 Cube/loss_pose: 0.225 Cube/loss_joint: 0.4441 lr: 0.00214 max_mem: 28031M [02/26 22:37:29] d2.utils.events INFO: eta: 2:31:13 iter: 21859 Cube/total_3D_loss: 0.7249 total_loss: 1.4 BoxHead/loss_cls: 0.06578 BoxHead/loss_box_reg: 0.1644 Cube/loss_dims: 0.04169 Cube/loss_xy: 0.02534 Cube/loss_z: 0.04423 Cube/loss_pose: 0.2254 Cube/loss_joint: 0.4479 lr: 0.00214 max_mem: 28031M [02/26 22:37:55] d2.utils.events INFO: eta: 2:33:10 iter: 21879 Cube/total_3D_loss: 0.6963 total_loss: 1.395 BoxHead/loss_cls: 0.06537 BoxHead/loss_box_reg: 0.1701 Cube/loss_dims: 0.04223 Cube/loss_xy: 0.02503 Cube/loss_z: 0.04433 Cube/loss_pose: 0.227 Cube/loss_joint: 0.4358 lr: 0.00214 max_mem: 28031M [02/26 22:38:22] d2.utils.events INFO: eta: 2:30:54 iter: 21899 Cube/total_3D_loss: 0.6714 total_loss: 1.352 BoxHead/loss_cls: 0.06125 BoxHead/loss_box_reg: 0.1597 Cube/loss_dims: 0.04097 Cube/loss_xy: 0.02513 Cube/loss_z: 0.0427 Cube/loss_pose: 0.2222 Cube/loss_joint: 0.4331 lr: 0.00214 max_mem: 28031M [02/26 22:38:48] d2.utils.events INFO: eta: 2:31:03 iter: 21919 Cube/total_3D_loss: 0.718 total_loss: 1.398 BoxHead/loss_cls: 0.06544 BoxHead/loss_box_reg: 0.1643 Cube/loss_dims: 0.04262 Cube/loss_xy: 0.02576 Cube/loss_z: 0.04459 Cube/loss_pose: 0.2161 Cube/loss_joint: 0.4495 lr: 0.00214 max_mem: 28031M [02/26 22:39:14] d2.utils.events INFO: eta: 2:30:05 iter: 21939 Cube/total_3D_loss: 0.7111 total_loss: 1.39 BoxHead/loss_cls: 0.05908 BoxHead/loss_box_reg: 0.1536 Cube/loss_dims: 0.04155 Cube/loss_xy: 0.0261 Cube/loss_z: 0.04497 Cube/loss_pose: 0.2165 Cube/loss_joint: 0.4516 lr: 0.00214 max_mem: 28031M [02/26 22:39:41] d2.utils.events INFO: eta: 2:30:08 iter: 21959 Cube/total_3D_loss: 0.7209 total_loss: 1.408 BoxHead/loss_cls: 0.0675 BoxHead/loss_box_reg: 0.1684 Cube/loss_dims: 0.04196 Cube/loss_xy: 0.0247 Cube/loss_z: 0.04262 Cube/loss_pose: 0.2246 Cube/loss_joint: 0.4336 lr: 0.00214 max_mem: 28031M [02/26 22:40:07] d2.utils.events INFO: eta: 2:27:55 iter: 21979 Cube/total_3D_loss: 0.7458 total_loss: 1.421 BoxHead/loss_cls: 0.0621 BoxHead/loss_box_reg: 0.1593 Cube/loss_dims: 0.04391 Cube/loss_xy: 0.02533 Cube/loss_z: 0.04522 Cube/loss_pose: 0.2278 Cube/loss_joint: 0.4483 lr: 0.00214 max_mem: 28031M [02/26 22:40:33] d2.utils.events INFO: eta: 2:27:34 iter: 21999 Cube/total_3D_loss: 0.6938 total_loss: 1.362 BoxHead/loss_cls: 0.06106 BoxHead/loss_box_reg: 0.1604 Cube/loss_dims: 0.04114 Cube/loss_xy: 0.02452 Cube/loss_z: 0.04399 Cube/loss_pose: 0.2201 Cube/loss_joint: 0.4439 lr: 0.00214 max_mem: 28031M [02/26 22:40:59] d2.utils.events INFO: eta: 2:27:39 iter: 22019 Cube/total_3D_loss: 0.7007 total_loss: 1.398 BoxHead/loss_cls: 0.06201 BoxHead/loss_box_reg: 0.1659 Cube/loss_dims: 0.04257 Cube/loss_xy: 0.02553 Cube/loss_z: 0.04516 Cube/loss_pose: 0.2259 Cube/loss_joint: 0.4489 lr: 0.00214 max_mem: 28031M [02/26 22:41:25] d2.utils.events INFO: eta: 2:27:23 iter: 22039 Cube/total_3D_loss: 0.701 total_loss: 1.404 BoxHead/loss_cls: 0.06817 BoxHead/loss_box_reg: 0.1685 Cube/loss_dims: 0.04194 Cube/loss_xy: 0.02537 Cube/loss_z: 0.04564 Cube/loss_pose: 0.2241 Cube/loss_joint: 0.4535 lr: 0.00214 max_mem: 28031M [02/26 22:41:51] d2.utils.events INFO: eta: 2:26:25 iter: 22059 Cube/total_3D_loss: 0.7161 total_loss: 1.385 BoxHead/loss_cls: 0.06314 BoxHead/loss_box_reg: 0.1657 Cube/loss_dims: 0.04304 Cube/loss_xy: 0.02451 Cube/loss_z: 0.04292 Cube/loss_pose: 0.23 Cube/loss_joint: 0.4405 lr: 0.00214 max_mem: 28031M [02/26 22:42:18] d2.utils.events INFO: eta: 2:29:05 iter: 22079 Cube/total_3D_loss: 0.678 total_loss: 1.354 BoxHead/loss_cls: 0.06446 BoxHead/loss_box_reg: 0.1593 Cube/loss_dims: 0.04001 Cube/loss_xy: 0.0247 Cube/loss_z: 0.04412 Cube/loss_pose: 0.2235 Cube/loss_joint: 0.4377 lr: 0.00214 max_mem: 28031M [02/26 22:42:44] d2.utils.events INFO: eta: 2:26:07 iter: 22099 Cube/total_3D_loss: 0.6929 total_loss: 1.377 BoxHead/loss_cls: 0.06262 BoxHead/loss_box_reg: 0.1585 Cube/loss_dims: 0.04155 Cube/loss_xy: 0.0254 Cube/loss_z: 0.04373 Cube/loss_pose: 0.212 Cube/loss_joint: 0.4395 lr: 0.00214 max_mem: 28031M [02/26 22:43:10] d2.utils.events INFO: eta: 2:23:53 iter: 22119 Cube/total_3D_loss: 0.6948 total_loss: 1.356 BoxHead/loss_cls: 0.06084 BoxHead/loss_box_reg: 0.1606 Cube/loss_dims: 0.04195 Cube/loss_xy: 0.02425 Cube/loss_z: 0.04299 Cube/loss_pose: 0.2189 Cube/loss_joint: 0.4339 lr: 0.00214 max_mem: 28031M [02/26 22:43:36] d2.utils.events INFO: eta: 2:24:47 iter: 22139 Cube/total_3D_loss: 0.7043 total_loss: 1.4 BoxHead/loss_cls: 0.0629 BoxHead/loss_box_reg: 0.1685 Cube/loss_dims: 0.04197 Cube/loss_xy: 0.0257 Cube/loss_z: 0.04629 Cube/loss_pose: 0.2252 Cube/loss_joint: 0.4571 lr: 0.00214 max_mem: 28031M [02/26 22:44:02] d2.utils.events INFO: eta: 2:24:13 iter: 22159 Cube/total_3D_loss: 0.7119 total_loss: 1.415 BoxHead/loss_cls: 0.06373 BoxHead/loss_box_reg: 0.1614 Cube/loss_dims: 0.04213 Cube/loss_xy: 0.02472 Cube/loss_z: 0.04434 Cube/loss_pose: 0.2297 Cube/loss_joint: 0.4467 lr: 0.00214 max_mem: 28031M [02/26 22:44:28] d2.utils.events INFO: eta: 2:24:20 iter: 22179 Cube/total_3D_loss: 0.6826 total_loss: 1.374 BoxHead/loss_cls: 0.06408 BoxHead/loss_box_reg: 0.1603 Cube/loss_dims: 0.04156 Cube/loss_xy: 0.02526 Cube/loss_z: 0.04373 Cube/loss_pose: 0.2096 Cube/loss_joint: 0.4441 lr: 0.00214 max_mem: 28031M [02/26 22:44:54] d2.utils.events INFO: eta: 2:24:09 iter: 22199 Cube/total_3D_loss: 0.6979 total_loss: 1.368 BoxHead/loss_cls: 0.06207 BoxHead/loss_box_reg: 0.1722 Cube/loss_dims: 0.04268 Cube/loss_xy: 0.02562 Cube/loss_z: 0.04295 Cube/loss_pose: 0.2241 Cube/loss_joint: 0.4446 lr: 0.00214 max_mem: 28031M [02/26 22:45:20] d2.utils.events INFO: eta: 2:23:54 iter: 22219 Cube/total_3D_loss: 0.7087 total_loss: 1.378 BoxHead/loss_cls: 0.06656 BoxHead/loss_box_reg: 0.168 Cube/loss_dims: 0.04236 Cube/loss_xy: 0.02449 Cube/loss_z: 0.04459 Cube/loss_pose: 0.2245 Cube/loss_joint: 0.4465 lr: 0.00214 max_mem: 28031M [02/26 22:45:47] d2.utils.events INFO: eta: 2:23:29 iter: 22239 Cube/total_3D_loss: 0.7053 total_loss: 1.386 BoxHead/loss_cls: 0.06375 BoxHead/loss_box_reg: 0.1648 Cube/loss_dims: 0.04257 Cube/loss_xy: 0.02502 Cube/loss_z: 0.04381 Cube/loss_pose: 0.2237 Cube/loss_joint: 0.4424 lr: 0.00214 max_mem: 28031M [02/26 22:46:13] d2.utils.events INFO: eta: 2:22:23 iter: 22259 Cube/total_3D_loss: 0.7133 total_loss: 1.379 BoxHead/loss_cls: 0.06086 BoxHead/loss_box_reg: 0.1584 Cube/loss_dims: 0.041 Cube/loss_xy: 0.02535 Cube/loss_z: 0.04486 Cube/loss_pose: 0.2164 Cube/loss_joint: 0.4414 lr: 0.00214 max_mem: 28031M [02/26 22:46:39] d2.utils.events INFO: eta: 2:20:52 iter: 22279 Cube/total_3D_loss: 0.6952 total_loss: 1.37 BoxHead/loss_cls: 0.06335 BoxHead/loss_box_reg: 0.1625 Cube/loss_dims: 0.04168 Cube/loss_xy: 0.0254 Cube/loss_z: 0.0432 Cube/loss_pose: 0.2197 Cube/loss_joint: 0.4445 lr: 0.00214 max_mem: 28031M [02/26 22:47:05] d2.utils.events INFO: eta: 2:23:17 iter: 22299 Cube/total_3D_loss: 0.6592 total_loss: 1.32 BoxHead/loss_cls: 0.06032 BoxHead/loss_box_reg: 0.1535 Cube/loss_dims: 0.04029 Cube/loss_xy: 0.02494 Cube/loss_z: 0.04301 Cube/loss_pose: 0.2047 Cube/loss_joint: 0.4388 lr: 0.00214 max_mem: 28031M [02/26 22:47:31] d2.utils.events INFO: eta: 2:22:03 iter: 22319 Cube/total_3D_loss: 0.7342 total_loss: 1.421 BoxHead/loss_cls: 0.0661 BoxHead/loss_box_reg: 0.1712 Cube/loss_dims: 0.04203 Cube/loss_xy: 0.02474 Cube/loss_z: 0.04326 Cube/loss_pose: 0.2326 Cube/loss_joint: 0.4451 lr: 0.00214 max_mem: 28031M [02/26 22:47:57] d2.utils.events INFO: eta: 2:19:59 iter: 22339 Cube/total_3D_loss: 0.6817 total_loss: 1.373 BoxHead/loss_cls: 0.06357 BoxHead/loss_box_reg: 0.1579 Cube/loss_dims: 0.04181 Cube/loss_xy: 0.02528 Cube/loss_z: 0.04416 Cube/loss_pose: 0.2208 Cube/loss_joint: 0.4427 lr: 0.00214 max_mem: 28031M [02/26 22:48:24] d2.utils.events INFO: eta: 2:20:03 iter: 22359 Cube/total_3D_loss: 0.6912 total_loss: 1.364 BoxHead/loss_cls: 0.06048 BoxHead/loss_box_reg: 0.165 Cube/loss_dims: 0.04266 Cube/loss_xy: 0.02514 Cube/loss_z: 0.04231 Cube/loss_pose: 0.2298 Cube/loss_joint: 0.437 lr: 0.00214 max_mem: 28031M [02/26 22:48:50] d2.utils.events INFO: eta: 2:19:44 iter: 22379 Cube/total_3D_loss: 0.6932 total_loss: 1.352 BoxHead/loss_cls: 0.05924 BoxHead/loss_box_reg: 0.1567 Cube/loss_dims: 0.04003 Cube/loss_xy: 0.0254 Cube/loss_z: 0.0446 Cube/loss_pose: 0.2096 Cube/loss_joint: 0.4487 lr: 0.00214 max_mem: 28031M [02/26 22:49:16] d2.utils.events INFO: eta: 2:19:14 iter: 22399 Cube/total_3D_loss: 0.7256 total_loss: 1.374 BoxHead/loss_cls: 0.06246 BoxHead/loss_box_reg: 0.1613 Cube/loss_dims: 0.04246 Cube/loss_xy: 0.02464 Cube/loss_z: 0.04387 Cube/loss_pose: 0.2296 Cube/loss_joint: 0.4396 lr: 0.00214 max_mem: 28031M [02/26 22:49:42] d2.utils.events INFO: eta: 2:19:07 iter: 22419 Cube/total_3D_loss: 0.6699 total_loss: 1.363 BoxHead/loss_cls: 0.064 BoxHead/loss_box_reg: 0.1672 Cube/loss_dims: 0.04025 Cube/loss_xy: 0.02425 Cube/loss_z: 0.04336 Cube/loss_pose: 0.2126 Cube/loss_joint: 0.4326 lr: 0.00214 max_mem: 28031M [02/26 22:50:08] d2.utils.events INFO: eta: 2:18:21 iter: 22439 Cube/total_3D_loss: 0.722 total_loss: 1.396 BoxHead/loss_cls: 0.061 BoxHead/loss_box_reg: 0.1651 Cube/loss_dims: 0.04232 Cube/loss_xy: 0.02508 Cube/loss_z: 0.0433 Cube/loss_pose: 0.2159 Cube/loss_joint: 0.4392 lr: 0.00214 max_mem: 28031M [02/26 22:50:35] d2.utils.events INFO: eta: 2:20:29 iter: 22459 Cube/total_3D_loss: 0.6965 total_loss: 1.368 BoxHead/loss_cls: 0.05899 BoxHead/loss_box_reg: 0.1654 Cube/loss_dims: 0.04138 Cube/loss_xy: 0.02518 Cube/loss_z: 0.04171 Cube/loss_pose: 0.227 Cube/loss_joint: 0.4329 lr: 0.00214 max_mem: 28031M [02/26 22:51:01] d2.utils.events INFO: eta: 2:17:59 iter: 22479 Cube/total_3D_loss: 0.7094 total_loss: 1.387 BoxHead/loss_cls: 0.05998 BoxHead/loss_box_reg: 0.1521 Cube/loss_dims: 0.04217 Cube/loss_xy: 0.02567 Cube/loss_z: 0.04407 Cube/loss_pose: 0.227 Cube/loss_joint: 0.4453 lr: 0.00214 max_mem: 28031M [02/26 22:51:29] d2.utils.events INFO: eta: 2:25:41 iter: 22499 Cube/total_3D_loss: 0.6991 total_loss: 1.383 BoxHead/loss_cls: 0.05436 BoxHead/loss_box_reg: 0.1518 Cube/loss_dims: 0.04115 Cube/loss_xy: 0.02477 Cube/loss_z: 0.04302 Cube/loss_pose: 0.218 Cube/loss_joint: 0.4398 lr: 0.00214 max_mem: 28031M [02/26 22:51:55] d2.utils.events INFO: eta: 2:16:22 iter: 22519 Cube/total_3D_loss: 0.7083 total_loss: 1.401 BoxHead/loss_cls: 0.06186 BoxHead/loss_box_reg: 0.1609 Cube/loss_dims: 0.04295 Cube/loss_xy: 0.02478 Cube/loss_z: 0.04335 Cube/loss_pose: 0.2368 Cube/loss_joint: 0.4376 lr: 0.00214 max_mem: 28031M [02/26 22:52:21] d2.utils.events INFO: eta: 2:16:01 iter: 22539 Cube/total_3D_loss: 0.6964 total_loss: 1.361 BoxHead/loss_cls: 0.06209 BoxHead/loss_box_reg: 0.1624 Cube/loss_dims: 0.04137 Cube/loss_xy: 0.02557 Cube/loss_z: 0.04332 Cube/loss_pose: 0.2227 Cube/loss_joint: 0.445 lr: 0.00214 max_mem: 28031M [02/26 22:52:47] d2.utils.events INFO: eta: 2:15:34 iter: 22559 Cube/total_3D_loss: 0.7049 total_loss: 1.386 BoxHead/loss_cls: 0.06398 BoxHead/loss_box_reg: 0.1653 Cube/loss_dims: 0.04239 Cube/loss_xy: 0.02542 Cube/loss_z: 0.04388 Cube/loss_pose: 0.2205 Cube/loss_joint: 0.4422 lr: 0.00214 max_mem: 28031M [02/26 22:53:13] d2.utils.events INFO: eta: 2:16:40 iter: 22579 Cube/total_3D_loss: 0.6871 total_loss: 1.366 BoxHead/loss_cls: 0.06248 BoxHead/loss_box_reg: 0.1583 Cube/loss_dims: 0.04113 Cube/loss_xy: 0.02546 Cube/loss_z: 0.04368 Cube/loss_pose: 0.2199 Cube/loss_joint: 0.4408 lr: 0.00214 max_mem: 28031M [02/26 22:53:39] d2.utils.events INFO: eta: 2:14:41 iter: 22599 Cube/total_3D_loss: 0.6888 total_loss: 1.365 BoxHead/loss_cls: 0.06168 BoxHead/loss_box_reg: 0.1681 Cube/loss_dims: 0.04084 Cube/loss_xy: 0.02479 Cube/loss_z: 0.04455 Cube/loss_pose: 0.2179 Cube/loss_joint: 0.4405 lr: 0.00214 max_mem: 28031M [02/26 22:54:06] d2.utils.events INFO: eta: 2:16:58 iter: 22619 Cube/total_3D_loss: 0.7141 total_loss: 1.39 BoxHead/loss_cls: 0.06474 BoxHead/loss_box_reg: 0.1657 Cube/loss_dims: 0.04275 Cube/loss_xy: 0.02571 Cube/loss_z: 0.04393 Cube/loss_pose: 0.2255 Cube/loss_joint: 0.4428 lr: 0.00214 max_mem: 28031M [02/26 22:54:32] d2.utils.events INFO: eta: 2:15:44 iter: 22639 Cube/total_3D_loss: 0.7134 total_loss: 1.387 BoxHead/loss_cls: 0.06168 BoxHead/loss_box_reg: 0.1667 Cube/loss_dims: 0.04174 Cube/loss_xy: 0.02503 Cube/loss_z: 0.0439 Cube/loss_pose: 0.2264 Cube/loss_joint: 0.444 lr: 0.00214 max_mem: 28031M [02/26 22:54:58] d2.utils.events INFO: eta: 2:12:36 iter: 22659 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[02/26 22:59:21] d2.utils.events INFO: eta: 2:09:26 iter: 22859 Cube/total_3D_loss: 0.684 total_loss: 1.372 BoxHead/loss_cls: 0.0596 BoxHead/loss_box_reg: 0.1576 Cube/loss_dims: 0.04168 Cube/loss_xy: 0.02443 Cube/loss_z: 0.04277 Cube/loss_pose: 0.222 Cube/loss_joint: 0.4368 lr: 0.00214 max_mem: 28031M [02/26 22:59:47] d2.utils.events INFO: eta: 2:09:00 iter: 22879 Cube/total_3D_loss: 0.6867 total_loss: 1.38 BoxHead/loss_cls: 0.06357 BoxHead/loss_box_reg: 0.163 Cube/loss_dims: 0.04039 Cube/loss_xy: 0.0245 Cube/loss_z: 0.04322 Cube/loss_pose: 0.2199 Cube/loss_joint: 0.4352 lr: 0.00214 max_mem: 28031M [02/26 23:00:13] d2.utils.events INFO: eta: 2:08:30 iter: 22899 Cube/total_3D_loss: 0.7164 total_loss: 1.356 BoxHead/loss_cls: 0.05807 BoxHead/loss_box_reg: 0.1629 Cube/loss_dims: 0.04139 Cube/loss_xy: 0.02452 Cube/loss_z: 0.0429 Cube/loss_pose: 0.2246 Cube/loss_joint: 0.4336 lr: 0.00214 max_mem: 28031M [02/26 23:00:39] d2.utils.events INFO: eta: 2:09:13 iter: 22919 Cube/total_3D_loss: 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d2.utils.events INFO: eta: 2:03:35 iter: 23119 Cube/total_3D_loss: 0.6744 total_loss: 1.343 BoxHead/loss_cls: 0.06248 BoxHead/loss_box_reg: 0.1723 Cube/loss_dims: 0.0416 Cube/loss_xy: 0.02395 Cube/loss_z: 0.0408 Cube/loss_pose: 0.2181 Cube/loss_joint: 0.4187 lr: 0.000214 max_mem: 28031M [02/26 23:05:29] d2.utils.events INFO: eta: 2:04:34 iter: 23139 Cube/total_3D_loss: 0.7109 total_loss: 1.374 BoxHead/loss_cls: 0.06032 BoxHead/loss_box_reg: 0.1606 Cube/loss_dims: 0.04199 Cube/loss_xy: 0.02448 Cube/loss_z: 0.04201 Cube/loss_pose: 0.2213 Cube/loss_joint: 0.4298 lr: 0.000214 max_mem: 28031M [02/26 23:05:55] d2.utils.events INFO: eta: 2:02:43 iter: 23159 Cube/total_3D_loss: 0.6649 total_loss: 1.335 BoxHead/loss_cls: 0.06223 BoxHead/loss_box_reg: 0.1597 Cube/loss_dims: 0.04013 Cube/loss_xy: 0.024 Cube/loss_z: 0.04026 Cube/loss_pose: 0.2163 Cube/loss_joint: 0.4206 lr: 0.000214 max_mem: 28031M [02/26 23:06:21] d2.utils.events INFO: eta: 2:02:40 iter: 23179 Cube/total_3D_loss: 0.6616 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d2.utils.events INFO: eta: 1:46:34 iter: 23899 Cube/total_3D_loss: 0.6731 total_loss: 1.356 BoxHead/loss_cls: 0.06111 BoxHead/loss_box_reg: 0.1601 Cube/loss_dims: 0.04206 Cube/loss_xy: 0.02529 Cube/loss_z: 0.04161 Cube/loss_pose: 0.2206 Cube/loss_joint: 0.432 lr: 0.000214 max_mem: 28031M [02/26 23:22:31] d2.utils.events INFO: eta: 1:48:33 iter: 23919 Cube/total_3D_loss: 0.6811 total_loss: 1.381 BoxHead/loss_cls: 0.06319 BoxHead/loss_box_reg: 0.1664 Cube/loss_dims: 0.04187 Cube/loss_xy: 0.02464 Cube/loss_z: 0.03955 Cube/loss_pose: 0.2237 Cube/loss_joint: 0.4277 lr: 0.000214 max_mem: 28031M [02/26 23:22:57] d2.utils.events INFO: eta: 1:45:54 iter: 23939 Cube/total_3D_loss: 0.6936 total_loss: 1.343 BoxHead/loss_cls: 0.05994 BoxHead/loss_box_reg: 0.1584 Cube/loss_dims: 0.04182 Cube/loss_xy: 0.02498 Cube/loss_z: 0.04037 Cube/loss_pose: 0.2228 Cube/loss_joint: 0.4315 lr: 0.000214 max_mem: 28031M [02/26 23:23:24] d2.utils.events INFO: eta: 1:45:33 iter: 23959 Cube/total_3D_loss: 0.6736 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d2.utils.events INFO: eta: 1:40:52 iter: 24159 Cube/total_3D_loss: 0.6575 total_loss: 1.31 BoxHead/loss_cls: 0.06011 BoxHead/loss_box_reg: 0.1477 Cube/loss_dims: 0.04155 Cube/loss_xy: 0.02455 Cube/loss_z: 0.03918 Cube/loss_pose: 0.2228 Cube/loss_joint: 0.4199 lr: 0.000214 max_mem: 28031M [02/26 23:28:12] d2.utils.events INFO: eta: 1:44:11 iter: 24179 Cube/total_3D_loss: 0.687 total_loss: 1.365 BoxHead/loss_cls: 0.06109 BoxHead/loss_box_reg: 0.1642 Cube/loss_dims: 0.04248 Cube/loss_xy: 0.0239 Cube/loss_z: 0.03949 Cube/loss_pose: 0.2251 Cube/loss_joint: 0.4177 lr: 0.000214 max_mem: 28031M [02/26 23:28:38] d2.utils.events INFO: eta: 1:40:22 iter: 24199 Cube/total_3D_loss: 0.6728 total_loss: 1.343 BoxHead/loss_cls: 0.06152 BoxHead/loss_box_reg: 0.1563 Cube/loss_dims: 0.04204 Cube/loss_xy: 0.02499 Cube/loss_z: 0.04268 Cube/loss_pose: 0.2304 Cube/loss_joint: 0.43 lr: 0.000214 max_mem: 28031M [02/26 23:29:10] d2.utils.events INFO: eta: 1:59:29 iter: 24219 Cube/total_3D_loss: 0.692 total_loss: 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23:44:54] d2.utils.events INFO: eta: 1:23:40 iter: 24939 Cube/total_3D_loss: 0.6625 total_loss: 1.296 BoxHead/loss_cls: 0.05876 BoxHead/loss_box_reg: 0.1606 Cube/loss_dims: 0.04198 Cube/loss_xy: 0.02397 Cube/loss_z: 0.03804 Cube/loss_pose: 0.2221 Cube/loss_joint: 0.4131 lr: 0.000214 max_mem: 28031M [02/26 23:45:20] d2.utils.events INFO: eta: 1:23:23 iter: 24959 Cube/total_3D_loss: 0.6728 total_loss: 1.321 BoxHead/loss_cls: 0.0605 BoxHead/loss_box_reg: 0.153 Cube/loss_dims: 0.0406 Cube/loss_xy: 0.02358 Cube/loss_z: 0.03892 Cube/loss_pose: 0.2189 Cube/loss_joint: 0.4197 lr: 0.000214 max_mem: 28031M [02/26 23:45:47] d2.utils.events INFO: eta: 1:23:26 iter: 24979 Cube/total_3D_loss: 0.6676 total_loss: 1.318 BoxHead/loss_cls: 0.06426 BoxHead/loss_box_reg: 0.1631 Cube/loss_dims: 0.04124 Cube/loss_xy: 0.02411 Cube/loss_z: 0.03841 Cube/loss_pose: 0.2143 Cube/loss_joint: 0.4018 lr: 0.000214 max_mem: 28031M [02/26 23:46:13] d2.utils.events INFO: eta: 1:22:38 iter: 24999 Cube/total_3D_loss: 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max_mem: 28031M [02/26 23:57:36] d2.utils.events INFO: eta: 1:11:19 iter: 25519 Cube/total_3D_loss: 0.6801 total_loss: 1.316 BoxHead/loss_cls: 0.0583 BoxHead/loss_box_reg: 0.1474 Cube/loss_dims: 0.0411 Cube/loss_xy: 0.02384 Cube/loss_z: 0.03894 Cube/loss_pose: 0.2184 Cube/loss_joint: 0.4135 lr: 0.000214 max_mem: 28031M [02/26 23:58:02] d2.utils.events INFO: eta: 1:10:57 iter: 25539 Cube/total_3D_loss: 0.6642 total_loss: 1.326 BoxHead/loss_cls: 0.06053 BoxHead/loss_box_reg: 0.1619 Cube/loss_dims: 0.04101 Cube/loss_xy: 0.0247 Cube/loss_z: 0.04043 Cube/loss_pose: 0.2166 Cube/loss_joint: 0.4144 lr: 0.000214 max_mem: 28031M [02/26 23:58:28] d2.utils.events INFO: eta: 1:10:14 iter: 25559 Cube/total_3D_loss: 0.6282 total_loss: 1.32 BoxHead/loss_cls: 0.06054 BoxHead/loss_box_reg: 0.1618 Cube/loss_dims: 0.04093 Cube/loss_xy: 0.02389 Cube/loss_z: 0.03894 Cube/loss_pose: 0.2074 Cube/loss_joint: 0.4169 lr: 0.000214 max_mem: 28031M [02/26 23:58:55] d2.utils.events INFO: eta: 1:11:11 iter: 25579 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Cube/loss_z: 0.03742 Cube/loss_pose: 0.2185 Cube/loss_joint: 0.4059 lr: 0.000214 max_mem: 28031M [02/27 00:58:54] d2.utils.events INFO: eta: 0:10:30 iter: 28319 Cube/total_3D_loss: 0.6671 total_loss: 1.329 BoxHead/loss_cls: 0.06018 BoxHead/loss_box_reg: 0.1577 Cube/loss_dims: 0.04125 Cube/loss_xy: 0.02423 Cube/loss_z: 0.03882 Cube/loss_pose: 0.2241 Cube/loss_joint: 0.4147 lr: 0.000214 max_mem: 28031M [02/27 00:59:20] d2.utils.events INFO: eta: 0:09:59 iter: 28339 Cube/total_3D_loss: 0.6735 total_loss: 1.325 BoxHead/loss_cls: 0.05757 BoxHead/loss_box_reg: 0.1573 Cube/loss_dims: 0.04114 Cube/loss_xy: 0.02368 Cube/loss_z: 0.03864 Cube/loss_pose: 0.2186 Cube/loss_joint: 0.4113 lr: 0.000214 max_mem: 28031M [02/27 00:59:46] d2.utils.events INFO: eta: 0:09:33 iter: 28359 Cube/total_3D_loss: 0.6687 total_loss: 1.328 BoxHead/loss_cls: 0.06409 BoxHead/loss_box_reg: 0.1611 Cube/loss_dims: 0.04174 Cube/loss_xy: 0.02491 Cube/loss_z: 0.03997 Cube/loss_pose: 0.2188 Cube/loss_joint: 0.4188 lr: 0.000214 max_mem: 28031M [02/27 01:00:14] d2.utils.events INFO: eta: 0:09:32 iter: 28379 Cube/total_3D_loss: 0.6669 total_loss: 1.338 BoxHead/loss_cls: 0.05952 BoxHead/loss_box_reg: 0.1611 Cube/loss_dims: 0.04172 Cube/loss_xy: 0.02459 Cube/loss_z: 0.03809 Cube/loss_pose: 0.2207 Cube/loss_joint: 0.4164 lr: 0.000214 max_mem: 28031M [02/27 01:00:40] d2.utils.events INFO: eta: 0:08:41 iter: 28399 Cube/total_3D_loss: 0.6862 total_loss: 1.352 BoxHead/loss_cls: 0.06078 BoxHead/loss_box_reg: 0.1634 Cube/loss_dims: 0.04259 Cube/loss_xy: 0.02485 Cube/loss_z: 0.04108 Cube/loss_pose: 0.2186 Cube/loss_joint: 0.4264 lr: 0.000214 max_mem: 28031M [02/27 01:01:06] d2.utils.events INFO: eta: 0:08:12 iter: 28419 Cube/total_3D_loss: 0.6811 total_loss: 1.362 BoxHead/loss_cls: 0.06056 BoxHead/loss_box_reg: 0.1664 Cube/loss_dims: 0.04254 Cube/loss_xy: 0.02446 Cube/loss_z: 0.04024 Cube/loss_pose: 0.228 Cube/loss_joint: 0.4237 lr: 0.000214 max_mem: 28031M [02/27 01:01:32] d2.utils.events INFO: eta: 0:07:48 iter: 28439 Cube/total_3D_loss: 0.6677 total_loss: 1.347 BoxHead/loss_cls: 0.06104 BoxHead/loss_box_reg: 0.1583 Cube/loss_dims: 0.04283 Cube/loss_xy: 0.02469 Cube/loss_z: 0.03887 Cube/loss_pose: 0.2216 Cube/loss_joint: 0.424 lr: 0.000214 max_mem: 28031M [02/27 01:01:58] d2.utils.events INFO: eta: 0:07:31 iter: 28459 Cube/total_3D_loss: 0.6489 total_loss: 1.313 BoxHead/loss_cls: 0.05875 BoxHead/loss_box_reg: 0.1514 Cube/loss_dims: 0.04173 Cube/loss_xy: 0.02368 Cube/loss_z: 0.03863 Cube/loss_pose: 0.2194 Cube/loss_joint: 0.4056 lr: 0.000214 max_mem: 28031M [02/27 01:02:24] d2.utils.events INFO: eta: 0:06:56 iter: 28479 Cube/total_3D_loss: 0.6584 total_loss: 1.319 BoxHead/loss_cls: 0.06141 BoxHead/loss_box_reg: 0.1612 Cube/loss_dims: 0.04071 Cube/loss_xy: 0.02446 Cube/loss_z: 0.03827 Cube/loss_pose: 0.2131 Cube/loss_joint: 0.4154 lr: 0.000214 max_mem: 28031M [02/27 01:02:50] d2.utils.events INFO: eta: 0:06:31 iter: 28499 Cube/total_3D_loss: 0.6416 total_loss: 1.315 BoxHead/loss_cls: 0.06056 BoxHead/loss_box_reg: 0.1649 Cube/loss_dims: 0.04074 Cube/loss_xy: 0.024 Cube/loss_z: 0.03902 Cube/loss_pose: 0.2126 Cube/loss_joint: 0.4155 lr: 0.000214 max_mem: 28031M [02/27 01:03:17] d2.utils.events INFO: eta: 0:06:06 iter: 28519 Cube/total_3D_loss: 0.6587 total_loss: 1.333 BoxHead/loss_cls: 0.06021 BoxHead/loss_box_reg: 0.1578 Cube/loss_dims: 0.04179 Cube/loss_xy: 0.02381 Cube/loss_z: 0.03982 Cube/loss_pose: 0.2158 Cube/loss_joint: 0.4162 lr: 0.000214 max_mem: 28031M [02/27 01:03:43] d2.utils.events INFO: eta: 0:05:40 iter: 28539 Cube/total_3D_loss: 0.6663 total_loss: 1.337 BoxHead/loss_cls: 0.05671 BoxHead/loss_box_reg: 0.1578 Cube/loss_dims: 0.04189 Cube/loss_xy: 0.02407 Cube/loss_z: 0.03861 Cube/loss_pose: 0.2243 Cube/loss_joint: 0.4061 lr: 0.000214 max_mem: 28031M [02/27 01:04:09] d2.utils.events INFO: eta: 0:05:10 iter: 28559 Cube/total_3D_loss: 0.6379 total_loss: 1.281 BoxHead/loss_cls: 0.0609 BoxHead/loss_box_reg: 0.1513 Cube/loss_dims: 0.04084 Cube/loss_xy: 0.02346 Cube/loss_z: 0.03777 Cube/loss_pose: 0.217 Cube/loss_joint: 0.402 lr: 0.000214 max_mem: 28031M [02/27 01:04:35] d2.utils.events INFO: eta: 0:04:47 iter: 28579 Cube/total_3D_loss: 0.6686 total_loss: 1.319 BoxHead/loss_cls: 0.06244 BoxHead/loss_box_reg: 0.1593 Cube/loss_dims: 0.04135 Cube/loss_xy: 0.0246 Cube/loss_z: 0.03922 Cube/loss_pose: 0.2214 Cube/loss_joint: 0.4206 lr: 0.000214 max_mem: 28031M [02/27 01:05:01] d2.utils.events INFO: eta: 0:04:23 iter: 28599 Cube/total_3D_loss: 0.6457 total_loss: 1.296 BoxHead/loss_cls: 0.05849 BoxHead/loss_box_reg: 0.1586 Cube/loss_dims: 0.03946 Cube/loss_xy: 0.0236 Cube/loss_z: 0.0383 Cube/loss_pose: 0.2129 Cube/loss_joint: 0.4082 lr: 0.000214 max_mem: 28031M [02/27 01:05:28] d2.utils.events INFO: eta: 0:03:58 iter: 28619 Cube/total_3D_loss: 0.6643 total_loss: 1.338 BoxHead/loss_cls: 0.06046 BoxHead/loss_box_reg: 0.1647 Cube/loss_dims: 0.04095 Cube/loss_xy: 0.0245 Cube/loss_z: 0.03894 Cube/loss_pose: 0.2126 Cube/loss_joint: 0.4152 lr: 0.000214 max_mem: 28031M [02/27 01:05:54] d2.utils.events INFO: eta: 0:03:29 iter: 28639 Cube/total_3D_loss: 0.6638 total_loss: 1.334 BoxHead/loss_cls: 0.06099 BoxHead/loss_box_reg: 0.1556 Cube/loss_dims: 0.04092 Cube/loss_xy: 0.02428 Cube/loss_z: 0.0395 Cube/loss_pose: 0.2202 Cube/loss_joint: 0.4166 lr: 0.000214 max_mem: 28031M [02/27 01:06:20] d2.utils.events INFO: eta: 0:03:01 iter: 28659 Cube/total_3D_loss: 0.6584 total_loss: 1.31 BoxHead/loss_cls: 0.05884 BoxHead/loss_box_reg: 0.1512 Cube/loss_dims: 0.04091 Cube/loss_xy: 0.02437 Cube/loss_z: 0.0388 Cube/loss_pose: 0.2233 Cube/loss_joint: 0.4117 lr: 0.000214 max_mem: 28031M [02/27 01:06:46] d2.utils.events INFO: eta: 0:02:37 iter: 28679 Cube/total_3D_loss: 0.6735 total_loss: 1.352 BoxHead/loss_cls: 0.06498 BoxHead/loss_box_reg: 0.168 Cube/loss_dims: 0.04207 Cube/loss_xy: 0.02488 Cube/loss_z: 0.04067 Cube/loss_pose: 0.2189 Cube/loss_joint: 0.4269 lr: 0.000214 max_mem: 28031M [02/27 01:07:13] d2.utils.events INFO: eta: 0:02:11 iter: 28699 Cube/total_3D_loss: 0.6363 total_loss: 1.282 BoxHead/loss_cls: 0.05575 BoxHead/loss_box_reg: 0.1569 Cube/loss_dims: 0.04032 Cube/loss_xy: 0.02391 Cube/loss_z: 0.03699 Cube/loss_pose: 0.2166 Cube/loss_joint: 0.4016 lr: 0.000214 max_mem: 28031M [02/27 01:07:39] d2.utils.events INFO: eta: 0:01:44 iter: 28719 Cube/total_3D_loss: 0.6606 total_loss: 1.334 BoxHead/loss_cls: 0.06057 BoxHead/loss_box_reg: 0.1635 Cube/loss_dims: 0.04215 Cube/loss_xy: 0.0241 Cube/loss_z: 0.03866 Cube/loss_pose: 0.2179 Cube/loss_joint: 0.4176 lr: 0.000214 max_mem: 28031M [02/27 01:08:05] d2.utils.events INFO: eta: 0:01:18 iter: 28739 Cube/total_3D_loss: 0.6708 total_loss: 1.326 BoxHead/loss_cls: 0.05814 BoxHead/loss_box_reg: 0.1558 Cube/loss_dims: 0.04122 Cube/loss_xy: 0.02385 Cube/loss_z: 0.03846 Cube/loss_pose: 0.2246 Cube/loss_joint: 0.4138 lr: 0.000214 max_mem: 28031M [02/27 01:08:31] d2.utils.events INFO: eta: 0:00:52 iter: 28759 Cube/total_3D_loss: 0.6645 total_loss: 1.31 BoxHead/loss_cls: 0.05934 BoxHead/loss_box_reg: 0.1502 Cube/loss_dims: 0.04186 Cube/loss_xy: 0.02439 Cube/loss_z: 0.0377 Cube/loss_pose: 0.226 Cube/loss_joint: 0.4131 lr: 0.000214 max_mem: 28031M [02/27 01:08:57] d2.utils.events INFO: eta: 0:00:26 iter: 28779 Cube/total_3D_loss: 0.668 total_loss: 1.321 BoxHead/loss_cls: 0.05967 BoxHead/loss_box_reg: 0.1525 Cube/loss_dims: 0.04237 Cube/loss_xy: 0.02387 Cube/loss_z: 0.03981 Cube/loss_pose: 0.2228 Cube/loss_joint: 0.4162 lr: 0.000214 max_mem: 28031M [02/27 01:09:24] d2.utils.events INFO: eta: 0:00:00 iter: 28799 Cube/total_3D_loss: 0.6487 total_loss: 1.306 BoxHead/loss_cls: 0.05699 BoxHead/loss_box_reg: 0.1577 Cube/loss_dims: 0.04053 Cube/loss_xy: 0.02454 Cube/loss_z: 0.03929 Cube/loss_pose: 0.2099 Cube/loss_joint: 0.422 lr: 0.000214 max_mem: 28031M [02/27 01:09:24] fvcore.common.checkpoint INFO: Saving checkpoint to output/Baseline_sgd/model_final.pth [02/27 01:09:32] cubercnn.data.datasets INFO: Loading datasets/Omni3D/SUNRGBD_test.json takes 1.48 seconds. [02/27 01:09:32] cubercnn.data.datasets INFO: Loaded 5050 images in Omni3D format from datasets/Omni3D/SUNRGBD_test.json [02/27 01:09:37] cubercnn.data.datasets INFO: Filtered out 0/5050 images without valid annotations [02/27 01:09:37] d2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(512, 512), max_size=4096, sample_style='choice')] [02/27 01:09:37] d2.data.common INFO: Serializing the dataset using: [02/27 01:09:37] d2.data.common INFO: Serializing 5050 elements to byte tensors and concatenating them all ... [02/27 01:09:37] d2.data.common INFO: Serialized dataset takes 20.13 MiB [02/27 01:09:37] cubercnn.evaluation.omni3d_evaluation INFO: Start inference on 5050 batches [02/27 01:09:38] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 11/5050. Dataloading: 0.0004 s/iter. Inference: 0.0259 s/iter. Eval: 0.0008 s/iter. Total: 0.0271 s/iter. ETA=0:02:16 [02/27 01:09:43] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 215/5050. Dataloading: 0.0007 s/iter. Inference: 0.0230 s/iter. Eval: 0.0008 s/iter. Total: 0.0246 s/iter. ETA=0:01:59 [02/27 01:09:48] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 423/5050. Dataloading: 0.0008 s/iter. Inference: 0.0227 s/iter. Eval: 0.0008 s/iter. Total: 0.0243 s/iter. ETA=0:01:52 [02/27 01:09:53] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 636/5050. Dataloading: 0.0008 s/iter. Inference: 0.0225 s/iter. Eval: 0.0008 s/iter. Total: 0.0241 s/iter. ETA=0:01:46 [02/27 01:09:58] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 849/5050. Dataloading: 0.0007 s/iter. Inference: 0.0223 s/iter. Eval: 0.0008 s/iter. Total: 0.0239 s/iter. ETA=0:01:40 [02/27 01:10:03] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 1062/5050. Dataloading: 0.0007 s/iter. Inference: 0.0223 s/iter. Eval: 0.0008 s/iter. Total: 0.0239 s/iter. ETA=0:01:35 [02/27 01:10:08] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 1273/5050. Dataloading: 0.0008 s/iter. Inference: 0.0222 s/iter. Eval: 0.0008 s/iter. Total: 0.0239 s/iter. ETA=0:01:30 [02/27 01:10:13] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 1486/5050. Dataloading: 0.0008 s/iter. Inference: 0.0222 s/iter. Eval: 0.0008 s/iter. Total: 0.0238 s/iter. ETA=0:01:24 [02/27 01:10:18] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 1700/5050. Dataloading: 0.0008 s/iter. Inference: 0.0222 s/iter. Eval: 0.0008 s/iter. Total: 0.0238 s/iter. ETA=0:01:19 [02/27 01:10:23] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 1916/5050. Dataloading: 0.0007 s/iter. Inference: 0.0221 s/iter. Eval: 0.0008 s/iter. Total: 0.0237 s/iter. ETA=0:01:14 [02/27 01:10:28] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 2104/5050. Dataloading: 0.0007 s/iter. Inference: 0.0221 s/iter. Eval: 0.0012 s/iter. Total: 0.0241 s/iter. ETA=0:01:10 [02/27 01:10:33] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 2317/5050. Dataloading: 0.0007 s/iter. Inference: 0.0221 s/iter. Eval: 0.0012 s/iter. Total: 0.0240 s/iter. ETA=0:01:05 [02/27 01:10:38] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 2535/5050. Dataloading: 0.0007 s/iter. Inference: 0.0220 s/iter. Eval: 0.0011 s/iter. Total: 0.0239 s/iter. ETA=0:01:00 [02/27 01:10:43] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 2754/5050. Dataloading: 0.0007 s/iter. Inference: 0.0220 s/iter. Eval: 0.0011 s/iter. Total: 0.0238 s/iter. ETA=0:00:54 [02/27 01:10:48] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 2974/5050. Dataloading: 0.0007 s/iter. Inference: 0.0219 s/iter. Eval: 0.0011 s/iter. Total: 0.0238 s/iter. ETA=0:00:49 [02/27 01:10:53] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 3193/5050. Dataloading: 0.0007 s/iter. Inference: 0.0219 s/iter. Eval: 0.0010 s/iter. Total: 0.0237 s/iter. ETA=0:00:44 [02/27 01:10:58] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 3411/5050. Dataloading: 0.0007 s/iter. Inference: 0.0219 s/iter. Eval: 0.0010 s/iter. Total: 0.0237 s/iter. ETA=0:00:38 [02/27 01:11:03] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 3629/5050. Dataloading: 0.0007 s/iter. Inference: 0.0219 s/iter. Eval: 0.0010 s/iter. Total: 0.0236 s/iter. ETA=0:00:33 [02/27 01:11:08] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 3848/5050. Dataloading: 0.0007 s/iter. Inference: 0.0218 s/iter. Eval: 0.0010 s/iter. Total: 0.0236 s/iter. ETA=0:00:28 [02/27 01:11:13] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 4066/5050. Dataloading: 0.0007 s/iter. Inference: 0.0218 s/iter. Eval: 0.0010 s/iter. Total: 0.0235 s/iter. ETA=0:00:23 [02/27 01:11:18] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 4284/5050. Dataloading: 0.0007 s/iter. Inference: 0.0218 s/iter. Eval: 0.0010 s/iter. Total: 0.0235 s/iter. ETA=0:00:18 [02/27 01:11:23] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 4463/5050. Dataloading: 0.0007 s/iter. Inference: 0.0220 s/iter. Eval: 0.0010 s/iter. Total: 0.0237 s/iter. ETA=0:00:13 [02/27 01:11:28] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 4680/5050. Dataloading: 0.0007 s/iter. Inference: 0.0220 s/iter. Eval: 0.0009 s/iter. Total: 0.0237 s/iter. ETA=0:00:08 [02/27 01:11:33] cubercnn.evaluation.omni3d_evaluation INFO: Inference done 4898/5050. Dataloading: 0.0007 s/iter. Inference: 0.0220 s/iter. Eval: 0.0009 s/iter. Total: 0.0236 s/iter. ETA=0:00:03 [02/27 01:11:37] cubercnn.evaluation.omni3d_evaluation INFO: Total inference time: 0:01:59.202128 (0.023628 s / iter per device, on 1 devices) [02/27 01:11:37] cubercnn.evaluation.omni3d_evaluation INFO: Total inference pure compute time: 0:01:50 (0.021944 s / iter per device, on 1 devices) [02/27 01:11:42] cubercnn.evaluation.omni3d_evaluation INFO: Preparing results for COCO format ... [02/27 01:11:42] cubercnn.evaluation.omni3d_evaluation INFO: Saving results to output/Baseline_sgd/inference/iter_final/SUNRGBD_test/omni_instances_results.json [02/27 01:11:44] cubercnn.evaluation.omni3d_evaluation INFO: Evaluating predictions with official COCO API... [02/27 01:12:27] cubercnn.evaluation.omni3d_evaluation INFO: Evaluation results for bbox in 2D mode: | AP | AP50 | AP75 | AP95 | APs | APm | APl | |:------:|:------:|:------:|:------:|:-----:|:-----:|:------:| | 16.508 | 31.799 | 15.493 | 0.041 | 0.000 | 3.604 | 17.549 | [02/27 01:12:27] cubercnn.evaluation.omni3d_evaluation INFO: Per-category bbox AP in 2D mode: | category | AP | category | AP | category | AP | |:-------------|:-------|:------------|:-------|:-----------|:-------| | bicycle | 14.929 | books | 0.929 | bottle | 2.954 | | chair | 45.937 | cup | 1.583 | laptop | 9.308 | | shoes | 0.034 | towel | 5.210 | blinds | 13.973 | | window | 0.264 | lamp | 24.808 | shelves | 5.687 | | mirror | 5.096 | sink | 21.150 | cabinet | 16.369 | | bathtub | 38.699 | door | 1.663 | toilet | 50.041 | | desk | 13.372 | box | 4.014 | bookcase | 24.159 | | picture | 1.997 | table | 34.598 | counter | 18.574 | | bed | 49.485 | night stand | 35.597 | pillow | 13.491 | | sofa | 49.235 | television | 23.282 | floor mat | 8.713 | | curtain | 12.511 | clothes | 0.604 | stationery | 2.604 | | refrigerator | 23.442 | bin | 32.478 | stove | 12.030 | | oven | 5.256 | machine | 3.207 | | | [02/27 01:12:27] cubercnn.evaluation.omni3d_evaluation INFO: Evaluation results for bbox in 3D mode: | AP | AP15 | AP25 | AP50 | APn | APm | APf | |:------:|:------:|:------:|:------:|:------:|:-----:|:-----:| | 15.079 | 21.345 | 16.226 | 4.561 | 15.079 | nan | nan | [02/27 01:12:27] cubercnn.evaluation.omni3d_evaluation INFO: Some metrics cannot be computed and is shown as NaN. [02/27 01:12:27] cubercnn.evaluation.omni3d_evaluation INFO: Per-category bbox AP in 3D mode: | category | AP | category | AP | category | AP | |:-------------|:-------|:------------|:-------|:-----------|:-------| | bicycle | 14.307 | books | 0.600 | bottle | 0.440 | | chair | 53.491 | cup | 1.270 | laptop | 8.059 | | shoes | 0.007 | towel | 1.553 | blinds | 0.505 | | window | 0.000 | lamp | 7.677 | shelves | 3.074 | | mirror | 0.401 | sink | 27.422 | cabinet | 14.088 | | bathtub | 38.296 | door | 0.478 | toilet | 65.496 | | desk | 19.174 | box | 2.589 | bookcase | 11.937 | | picture | 0.027 | table | 39.777 | counter | 22.378 | | bed | 64.361 | night stand | 30.437 | pillow | 7.531 | | sofa | 57.965 | television | 3.831 | floor mat | 2.178 | | curtain | 4.268 | clothes | 0.574 | stationery | 0.292 | | refrigerator | 21.592 | bin | 18.780 | stove | 18.114 | | oven | 6.032 | machine | 3.987 | | | [02/27 01:12:34] cubercnn.evaluation.omni3d_evaluation INFO: SUNRGBD_test iter=final mode=2D Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.165 SUNRGBD_test iter=final mode=2D Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.318 SUNRGBD_test iter=final mode=2D Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.155 SUNRGBD_test iter=final mode=2D Average Precision (AP) @[ IoU=0.95 | area= all | maxDets=100 ] = 0.000 SUNRGBD_test iter=final mode=2D Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 SUNRGBD_test iter=final mode=2D Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.036 SUNRGBD_test iter=final mode=2D Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.175 SUNRGBD_test iter=final mode=2D Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.248 SUNRGBD_test iter=final mode=2D Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.299 SUNRGBD_test iter=final mode=2D Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.300 SUNRGBD_test iter=final mode=2D Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 SUNRGBD_test iter=final mode=2D Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.094 SUNRGBD_test iter=final mode=2D Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.320 [02/27 01:12:34] cubercnn.evaluation.omni3d_evaluation INFO: SUNRGBD_test iter=final mode=3D Average Precision (AP) @[ IoU=0.05:0.50 | depth= all | maxDets=100 ] = 0.151 SUNRGBD_test iter=final mode=3D Average Precision (AP) @[ IoU=0.15 | depth= all | maxDets=100 ] = 0.213 SUNRGBD_test iter=final mode=3D Average Precision (AP) @[ IoU=0.25 | depth= all | maxDets=100 ] = 0.162 SUNRGBD_test iter=final mode=3D Average Precision (AP) @[ IoU=0.50 | depth= all | maxDets=100 ] = 0.046 SUNRGBD_test iter=final mode=3D Average Precision (AP) @[ IoU=0.05:0.50 | depth= near | maxDets=100 ] = 0.151 SUNRGBD_test iter=final mode=3D Average Precision (AP) @[ IoU=0.05:0.50 | depth=medium | maxDets=100 ] = -1.000 SUNRGBD_test iter=final mode=3D Average Precision (AP) @[ IoU=0.05:0.50 | depth= far | maxDets=100 ] = -1.000 SUNRGBD_test iter=final mode=3D Average Recall (AR) @[ IoU=0.05:0.50 | depth= all | maxDets= 1 ] = 0.232 SUNRGBD_test iter=final mode=3D Average Recall (AR) @[ IoU=0.05:0.50 | depth= all | maxDets= 10 ] = 0.288 SUNRGBD_test iter=final mode=3D Average Recall (AR) @[ IoU=0.05:0.50 | depth= all | maxDets=100 ] = 0.289 SUNRGBD_test iter=final mode=3D Average Recall (AR) @[ IoU=0.05:0.50 | depth= near | maxDets=100 ] = 0.289 SUNRGBD_test iter=final mode=3D Average Recall (AR) @[ IoU=0.05:0.50 | depth=medium | maxDets=100 ] = -1.000 SUNRGBD_test iter=final mode=3D Average Recall (AR) @[ IoU=0.05:0.50 | depth= far | maxDets=100 ] = -1.000 [02/27 01:12:34] cubercnn.vis.logperf INFO: Performance for each of 38 categories on SUNRGBD_test: | category | AP2D | AP3D | category | AP2D | AP3D | category | AP2D | AP3D | |:----------:|:--------|:----------|:-----------:|:---------|:---------|:------------:|:----------|:-----------| | chair | 45.9374 | 53.4913 | table | 34.5982 | 39.7769 | cabinet | 16.3693 | 14.0878 | | lamp | 24.8081 | 7.67653 | books | 0.928978 | 0.599711 | sofa | 49.2354 | 57.9649 | | picture | 1.99692 | 0.0266823 | window | 0.264026 | 0 | pillow | 13.4912 | 7.53125 | | door | 1.66293 | 0.478073 | blinds | 13.9732 | 0.504756 | sink | 21.1498 | 27.4216 | | shelves | 5.6873 | 3.0737 | television | 23.2818 | 3.83121 | shoes | 0.0344382 | 0.00707214 | | cup | 1.58328 | 1.2698 | bottle | 2.95419 | 0.440334 | bookcase | 24.1593 | 11.9371 | | laptop | 9.30793 | 8.05885 | desk | 13.3718 | 19.1737 | floor mat | 8.71287 | 2.17822 | | mirror | 5.09589 | 0.400501 | counter | 18.5745 | 22.3782 | bicycle | 14.9293 | 14.307 | | toilet | 50.0415 | 65.4959 | bed | 49.4855 | 64.3606 | refrigerator | 23.4416 | 21.5922 | | box | 4.01353 | 2.58869 | oven | 5.25583 | 6.03184 | clothes | 0.603895 | 0.574156 | | towel | 5.20953 | 1.5534 | night stand | 35.5967 | 30.437 | stove | 12.0301 | 18.1145 | | machine | 3.20712 | 3.98669 | stationery | 2.60406 | 0.291896 | bathtub | 38.6992 | 38.2965 | | curtain | 12.5111 | 4.26754 | bin | 32.4777 | 18.7804 | | | | [02/27 01:12:48] cubercnn INFO: SUNRGBD_testiter=final, xy(25.54), z(0.24), whl(0.18, 0.10, 0.12), ry(2.14) [02/27 01:12:51] cubercnn.vis.logperf INFO: Performance for each of 38 categories on : | category | AP2D | AP3D | category | AP2D | AP3D | category | AP2D | AP3D | |:----------:|:--------|:----------|:-----------:|:---------|:---------|:------------:|:----------|:-----------| | chair | 45.9374 | 53.4913 | table | 34.5982 | 39.7769 | cabinet | 16.3693 | 14.0878 | | lamp | 24.8081 | 7.67653 | books | 0.928978 | 0.599711 | sofa | 49.2354 | 57.9649 | | picture | 1.99692 | 0.0266823 | window | 0.264026 | 0 | pillow | 13.4912 | 7.53125 | | door | 1.66293 | 0.478073 | blinds | 13.9732 | 0.504756 | sink | 21.1498 | 27.4216 | | shelves | 5.6873 | 3.0737 | television | 23.2818 | 3.83121 | shoes | 0.0344382 | 0.00707214 | | cup | 1.58328 | 1.2698 | bottle | 2.95419 | 0.440334 | bookcase | 24.1593 | 11.9371 | | laptop | 9.30793 | 8.05885 | desk | 13.3718 | 19.1737 | floor mat | 8.71287 | 2.17822 | | mirror | 5.09589 | 0.400501 | counter | 18.5745 | 22.3782 | bicycle | 14.9293 | 14.307 | | toilet | 50.0415 | 65.4959 | bed | 49.4855 | 64.3606 | refrigerator | 23.4416 | 21.5922 | | box | 4.01353 | 2.58869 | oven | 5.25583 | 6.03184 | clothes | 0.603895 | 0.574156 | | towel | 5.20953 | 1.5534 | night stand | 35.5967 | 30.437 | stove | 12.0301 | 18.1145 | | machine | 3.20712 | 3.98669 | stationery | 2.60406 | 0.291896 | bathtub | 38.6992 | 38.2965 | | curtain | 12.5111 | 4.26754 | bin | 32.4777 | 18.7804 | | | | [02/27 01:12:51] cubercnn.vis.logperf INFO: Per-dataset performance analysis on test set: +--------------+----------+---------+---------+-----------+-----------+-----------+----------+----------+----------+ | Dataset | #iters | AP2D | AP3D | AP3D@15 | AP3D@25 | AP3D@50 | AP3D-N | AP3D-M | AP3D-F | +==============+==========+=========+=========+===========+===========+===========+==========+==========+==========+ | SUNRGBD_test | final | 16.5075 | 15.0786 | 21.3453 | 16.2259 | 4.56052 | 15.0786 | nan | nan | +--------------+----------+---------+---------+-----------+-----------+-----------+----------+----------+----------+ | | final | 16.5075 | 15.0786 | 21.3453 | 16.2259 | 4.56052 | 15.0786 | nan | nan | +--------------+----------+---------+---------+-----------+-----------+-----------+----------+----------+----------+ [02/27 01:12:51] cubercnn.vis.logperf INFO: Omni3D performance on test set. The numbers below should be used to compare to others approaches on Omni3D, such as Cube R-CNN [02/27 01:12:51] cubercnn.vis.logperf INFO: Performance on Omni3D: +--------------+----------+---------+---------+ | Dataset | #iters | AP2D | AP3D | +==============+==========+=========+=========+ | SUNRGBD_test | final | 16.5075 | 15.0786 | +--------------+----------+---------+---------+ | Omni3D_Out | final | nan | nan | +--------------+----------+---------+---------+ | Omni3D_In | final | 16.5075 | 15.0786 | +--------------+----------+---------+---------+ | Omni3D | final | nan | nan | +--------------+----------+---------+---------+ [03/07 11:26:00] detectron2 INFO: Full config saved to output/Baseline_sgd\config.yaml [03/07 11:26:00] cubercnn INFO: Preprocessing Training Datasets [03/07 11:26:15] cubercnn INFO: Available categories for SUNRGBD Train [03/07 11:26:15] cubercnn INFO: ['bicycle', 'books', 'bottle', 'chair', 'cup', 'laptop', 'shoes', 'towel', 'blinds', 'window', 'lamp', 'shelves', 'mirror', 'sink', 'cabinet', 'bathtub', 'door', 'toilet', 'desk', 'box', 'bookcase', 'picture', 'table', 'counter', 'bed', 'night stand', 'pillow', 'sofa', 'television', 'floor mat', 'curtain', 'clothes', 'stationery', 'refrigerator', 'bin', 'stove', 'oven', 'machine'] [03/07 11:26:15] cubercnn INFO: Available categories for SUNRGBD Validation [03/07 11:26:15] cubercnn INFO: ['books', 'bottle', 'chair', 'cup', 'laptop', 'towel', 'blinds', 'window', 'lamp', 'shelves', 'mirror', 'sink', 'cabinet', 'bathtub', 'door', 'toilet', 'desk', 'box', 'bookcase', 'picture', 'table', 'counter', 'bed', 'night stand', 'pillow', 'sofa', 'television', 'curtain', 'clothes', 'stationery', 'refrigerator', 'bin', 'stove', 'oven', 'machine'] [03/07 11:26:22] d2.solver.build WARNING: SOLVER.STEPS contains values larger than SOLVER.MAX_ITER. These values will be ignored. [03/07 11:26:22] d2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in training: [ResizeShortestEdge(short_edge_length=..., max_size=4096, sample_style='choice'), RandomFlip()] [03/07 11:26:25] cubercnn.data.datasets INFO: Loading datasets\Omni3D\SUNRGBD_train.json takes 2.79 seconds. [03/07 11:26:25] cubercnn.data.datasets INFO: Loaded 4929 images in Omni3D format from datasets\Omni3D\SUNRGBD_train.json [03/07 11:26:35] cubercnn.data.datasets INFO: Filtered out 290/4929 images without valid annotations [03/07 11:26:35] cubercnn.data.datasets INFO: Loaded 356 images in Omni3D format from datasets\Omni3D\SUNRGBD_val.json [03/07 11:26:36] cubercnn.data.datasets INFO: Filtered out 23/356 images without valid annotations [03/07 11:26:36] d2.data.build INFO: Removed 0 images with no usable annotations. 4972 images left. [03/07 11:26:36] cubercnn.data.build INFO: Using training sampler RepeatFactorTrainingSampler [03/07 11:26:36] d2.data.common INFO: Serializing the dataset using: [03/07 11:26:36] d2.data.common INFO: Serializing 4972 elements to byte tensors and concatenating them all ... [03/07 11:26:36] d2.data.common INFO: Serialized dataset takes 20.81 MiB [03/07 11:26:36] d2.data.build INFO: Making batched data loader with batch_size=2 [03/07 11:26:37] d2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/Baseline_sgd\model_final.pth ... [03/07 11:26:37] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/Baseline_sgd\model_final.pth ... [03/07 11:26:37] fvcore.common.checkpoint WARNING: The checkpoint state_dict contains keys that are not used by the model: roi_heads.priors_z_scales [03/07 11:26:37] fvcore.common.checkpoint INFO: Loading optimizer from output/Baseline_sgd\model_final.pth ... [03/07 11:30:25] detectron2 INFO: Full config saved to output/Baseline_sgd\config.yaml [03/07 11:30:26] cubercnn INFO: Preprocessing Training Datasets [03/07 11:30:40] cubercnn INFO: Available categories for SUNRGBD Train [03/07 11:30:40] cubercnn INFO: ['bicycle', 'books', 'bottle', 'chair', 'cup', 'laptop', 'shoes', 'towel', 'blinds', 'window', 'lamp', 'shelves', 'mirror', 'sink', 'cabinet', 'bathtub', 'door', 'toilet', 'desk', 'box', 'bookcase', 'picture', 'table', 'counter', 'bed', 'night stand', 'pillow', 'sofa', 'television', 'floor mat', 'curtain', 'clothes', 'stationery', 'refrigerator', 'bin', 'stove', 'oven', 'machine'] [03/07 11:30:40] cubercnn INFO: Available categories for SUNRGBD Validation [03/07 11:30:40] cubercnn INFO: ['books', 'bottle', 'chair', 'cup', 'laptop', 'towel', 'blinds', 'window', 'lamp', 'shelves', 'mirror', 'sink', 'cabinet', 'bathtub', 'door', 'toilet', 'desk', 'box', 'bookcase', 'picture', 'table', 'counter', 'bed', 'night stand', 'pillow', 'sofa', 'television', 'curtain', 'clothes', 'stationery', 'refrigerator', 'bin', 'stove', 'oven', 'machine'] [03/07 11:30:47] d2.solver.build WARNING: SOLVER.STEPS contains values larger than SOLVER.MAX_ITER. These values will be ignored. [03/07 11:30:47] d2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in training: [ResizeShortestEdge(short_edge_length=..., max_size=4096, sample_style='choice'), RandomFlip()] [03/07 11:30:50] cubercnn.data.datasets INFO: Loading datasets\Omni3D\SUNRGBD_train.json takes 2.78 seconds. [03/07 11:30:50] cubercnn.data.datasets INFO: Loaded 4929 images in Omni3D format from datasets\Omni3D\SUNRGBD_train.json [03/07 11:31:00] cubercnn.data.datasets INFO: Filtered out 290/4929 images without valid annotations [03/07 11:31:00] cubercnn.data.datasets INFO: Loaded 356 images in Omni3D format from datasets\Omni3D\SUNRGBD_val.json [03/07 11:31:01] cubercnn.data.datasets INFO: Filtered out 23/356 images without valid annotations [03/07 11:31:01] d2.data.build INFO: Removed 0 images with no usable annotations. 4972 images left. [03/07 11:31:01] cubercnn.data.build INFO: Using training sampler RepeatFactorTrainingSampler [03/07 11:31:01] d2.data.common INFO: Serializing the dataset using: [03/07 11:31:01] d2.data.common INFO: Serializing 4972 elements to byte tensors and concatenating them all ... [03/07 11:31:01] d2.data.common INFO: Serialized dataset takes 20.81 MiB [03/07 11:31:01] d2.data.build INFO: Making batched data loader with batch_size=2 [03/07 11:31:02] d2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/Baseline_sgd\model_final.pth ... [03/07 11:31:02] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/Baseline_sgd\model_final.pth ... [03/07 11:31:02] fvcore.common.checkpoint INFO: Loading optimizer from output/Baseline_sgd\model_final.pth ... [03/07 11:31:02] fvcore.common.checkpoint INFO: Loading scheduler from output/Baseline_sgd\model_final.pth ... [03/07 11:31:02] cubercnn INFO: Starting training from iteration 28800 [03/07 11:33:13] detectron2 INFO: Full config saved to output/Baseline_sgd\config.yaml [03/07 11:33:14] cubercnn INFO: Preprocessing Training Datasets [03/07 11:33:27] cubercnn INFO: Available categories for SUNRGBD Train [03/07 11:33:27] cubercnn INFO: ['bicycle', 'books', 'bottle', 'chair', 'cup', 'laptop', 'shoes', 'towel', 'blinds', 'window', 'lamp', 'shelves', 'mirror', 'sink', 'cabinet', 'bathtub', 'door', 'toilet', 'desk', 'box', 'bookcase', 'picture', 'table', 'counter', 'bed', 'night stand', 'pillow', 'sofa', 'television', 'floor mat', 'curtain', 'clothes', 'stationery', 'refrigerator', 'bin', 'stove', 'oven', 'machine'] [03/07 11:33:27] cubercnn INFO: Available categories for SUNRGBD Validation [03/07 11:33:27] cubercnn INFO: ['books', 'bottle', 'chair', 'cup', 'laptop', 'towel', 'blinds', 'window', 'lamp', 'shelves', 'mirror', 'sink', 'cabinet', 'bathtub', 'door', 'toilet', 'desk', 'box', 'bookcase', 'picture', 'table', 'counter', 'bed', 'night stand', 'pillow', 'sofa', 'television', 'curtain', 'clothes', 'stationery', 'refrigerator', 'bin', 'stove', 'oven', 'machine'] [03/07 11:33:35] d2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in training: [ResizeShortestEdge(short_edge_length=..., max_size=4096, sample_style='choice'), RandomFlip()] [03/07 11:33:37] cubercnn.data.datasets INFO: Loading datasets\Omni3D\SUNRGBD_train.json takes 2.67 seconds. [03/07 11:33:37] cubercnn.data.datasets INFO: Loaded 4929 images in Omni3D format from datasets\Omni3D\SUNRGBD_train.json [03/07 11:33:46] cubercnn.data.datasets INFO: Filtered out 290/4929 images without valid annotations [03/07 11:33:46] cubercnn.data.datasets INFO: Loaded 356 images in Omni3D format from datasets\Omni3D\SUNRGBD_val.json [03/07 11:33:47] cubercnn.data.datasets INFO: Filtered out 23/356 images without valid annotations [03/07 11:33:47] d2.data.build INFO: Removed 0 images with no usable annotations. 4972 images left. [03/07 11:33:47] cubercnn.data.build INFO: Using training sampler RepeatFactorTrainingSampler [03/07 11:33:47] d2.data.common INFO: Serializing the dataset using: [03/07 11:33:47] d2.data.common INFO: Serializing 4972 elements to byte tensors and concatenating them all ... [03/07 11:33:47] d2.data.common INFO: Serialized dataset takes 20.81 MiB [03/07 11:33:47] d2.data.build INFO: Making batched data loader with batch_size=2 [03/07 11:33:47] d2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/Baseline_sgd\model_final.pth ... [03/07 11:33:47] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/Baseline_sgd\model_final.pth ... [03/07 11:33:48] fvcore.common.checkpoint INFO: Loading optimizer from output/Baseline_sgd\model_final.pth ... [03/07 11:33:48] fvcore.common.checkpoint INFO: Loading scheduler from output/Baseline_sgd\model_final.pth ... [03/07 11:33:48] cubercnn INFO: Starting training from iteration 28800 [03/07 11:34:47] d2.utils.events INFO: iter: 28800 Cube/total_3D_loss: 0.7917 total_loss: 1.497 BoxHead/loss_cls: 0.0569 BoxHead/loss_box_reg: 0.1216 Cube/loss_dims: 0.04741 Cube/loss_xy: 0.026 Cube/loss_z: 0.09222 Cube/loss_pose: 0.1662 Cube/loss_joint: 0.7055 lr: 0.000214 [03/07 13:30:12] detectron2 INFO: Full config saved to output/Baseline_sgd\config.yaml [03/07 13:30:12] cubercnn INFO: Preprocessing Training Datasets [03/07 13:30:20] cubercnn INFO: Available categories for SUNRGBD Train [03/07 13:30:20] cubercnn INFO: ['bicycle', 'books', 'bottle', 'chair', 'cup', 'laptop', 'shoes', 'towel', 'blinds', 'window', 'lamp', 'shelves', 'mirror', 'sink', 'cabinet', 'bathtub', 'door', 'toilet', 'desk', 'box', 'bookcase', 'picture', 'table', 'counter', 'bed', 'night stand', 'pillow', 'sofa', 'television', 'floor mat', 'curtain', 'clothes', 'stationery', 'refrigerator', 'bin', 'stove', 'oven', 'machine'] [03/07 13:30:20] cubercnn INFO: Available categories for SUNRGBD Validation [03/07 13:30:20] cubercnn INFO: ['books', 'bottle', 'chair', 'cup', 'laptop', 'towel', 'blinds', 'window', 'lamp', 'shelves', 'mirror', 'sink', 'cabinet', 'bathtub', 'door', 'toilet', 'desk', 'box', 'bookcase', 'picture', 'table', 'counter', 'bed', 'night stand', 'pillow', 'sofa', 'television', 'curtain', 'clothes', 'stationery', 'refrigerator', 'bin', 'stove', 'oven', 'machine'] [03/07 13:30:23] d2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in training: [ResizeShortestEdge(short_edge_length=..., max_size=4096, sample_style='choice'), RandomFlip()] [03/07 13:30:25] cubercnn.data.datasets INFO: Loading datasets\Omni3D\SUNRGBD_train.json takes 1.80 seconds. [03/07 13:30:25] cubercnn.data.datasets INFO: Loaded 4929 images in Omni3D format from datasets\Omni3D\SUNRGBD_train.json [03/07 13:30:30] cubercnn.data.datasets INFO: Filtered out 290/4929 images without valid annotations [03/07 13:30:30] cubercnn.data.datasets INFO: Loaded 356 images in Omni3D format from datasets\Omni3D\SUNRGBD_val.json [03/07 13:30:30] cubercnn.data.datasets INFO: Filtered out 23/356 images without valid annotations [03/07 13:30:30] d2.data.build INFO: Removed 0 images with no usable annotations. 4972 images left. [03/07 13:30:30] cubercnn.data.build INFO: Using training sampler RepeatFactorTrainingSampler [03/07 13:30:31] d2.data.common INFO: Serializing the dataset using: [03/07 13:30:31] d2.data.common INFO: Serializing 4972 elements to byte tensors and concatenating them all ... [03/07 13:30:31] d2.data.common INFO: Serialized dataset takes 20.81 MiB [03/07 13:30:31] d2.data.build INFO: Making batched data loader with batch_size=2 [03/07 13:30:31] d2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/Baseline_sgd\model_final.pth ... [03/07 13:30:31] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/Baseline_sgd\model_final.pth ... [03/07 13:30:31] fvcore.common.checkpoint INFO: Loading optimizer from output/Baseline_sgd\model_final.pth ... [03/07 13:30:31] fvcore.common.checkpoint INFO: Loading scheduler from output/Baseline_sgd\model_final.pth ... [03/07 13:30:31] cubercnn INFO: Starting training from iteration 28800 [03/07 14:02:06] detectron2 INFO: Full config saved to output/Baseline_sgd\config.yaml [03/07 14:02:07] cubercnn INFO: Preprocessing Training Datasets [03/07 14:02:21] cubercnn INFO: Available categories for SUNRGBD Train [03/07 14:02:21] cubercnn INFO: ['bicycle', 'books', 'bottle', 'chair', 'cup', 'laptop', 'shoes', 'towel', 'blinds', 'window', 'lamp', 'shelves', 'mirror', 'sink', 'cabinet', 'bathtub', 'door', 'toilet', 'desk', 'box', 'bookcase', 'picture', 'table', 'counter', 'bed', 'night stand', 'pillow', 'sofa', 'television', 'floor mat', 'curtain', 'clothes', 'stationery', 'refrigerator', 'bin', 'stove', 'oven', 'machine'] [03/07 14:02:21] cubercnn INFO: Available categories for SUNRGBD Validation [03/07 14:02:21] cubercnn INFO: ['books', 'bottle', 'chair', 'cup', 'laptop', 'towel', 'blinds', 'window', 'lamp', 'shelves', 'mirror', 'sink', 'cabinet', 'bathtub', 'door', 'toilet', 'desk', 'box', 'bookcase', 'picture', 'table', 'counter', 'bed', 'night stand', 'pillow', 'sofa', 'television', 'curtain', 'clothes', 'stationery', 'refrigerator', 'bin', 'stove', 'oven', 'machine'] [03/07 14:02:29] d2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in training: [ResizeShortestEdge(short_edge_length=..., max_size=4096, sample_style='choice'), RandomFlip()] [03/07 14:02:32] cubercnn.data.datasets INFO: Loading datasets\Omni3D\SUNRGBD_train.json takes 2.77 seconds. [03/07 14:02:32] cubercnn.data.datasets INFO: Loaded 4929 images in Omni3D format from datasets\Omni3D\SUNRGBD_train.json [03/07 14:02:41] cubercnn.data.datasets INFO: Filtered out 290/4929 images without valid annotations [03/07 14:02:42] cubercnn.data.datasets INFO: Loaded 356 images in Omni3D format from datasets\Omni3D\SUNRGBD_val.json [03/07 14:02:42] cubercnn.data.datasets INFO: Filtered out 23/356 images without valid annotations [03/07 14:02:42] d2.data.build INFO: Removed 0 images with no usable annotations. 4972 images left. [03/07 14:02:42] cubercnn.data.build INFO: Using training sampler RepeatFactorTrainingSampler [03/07 14:02:42] d2.data.common INFO: Serializing the dataset using: [03/07 14:02:42] d2.data.common INFO: Serializing 4972 elements to byte tensors and concatenating them all ... [03/07 14:02:43] d2.data.common INFO: Serialized dataset takes 20.81 MiB [03/07 14:02:43] d2.data.build INFO: Making batched data loader with batch_size=2 [03/07 14:02:43] d2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/Baseline_sgd\model_final.pth ... [03/07 14:02:43] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/Baseline_sgd\model_final.pth ... [03/07 14:02:43] fvcore.common.checkpoint INFO: Loading optimizer from output/Baseline_sgd\model_final.pth ... [03/07 14:02:43] fvcore.common.checkpoint INFO: Loading scheduler from output/Baseline_sgd\model_final.pth ... [03/07 14:02:43] cubercnn INFO: Starting training from iteration 28800 [03/07 14:15:09] detectron2 INFO: Full config saved to output/Baseline_sgd\config.yaml [03/07 14:15:10] cubercnn INFO: Preprocessing Training Datasets [03/07 14:15:23] cubercnn INFO: Available categories for SUNRGBD Train [03/07 14:15:23] cubercnn INFO: ['bicycle', 'books', 'bottle', 'chair', 'cup', 'laptop', 'shoes', 'towel', 'blinds', 'window', 'lamp', 'shelves', 'mirror', 'sink', 'cabinet', 'bathtub', 'door', 'toilet', 'desk', 'box', 'bookcase', 'picture', 'table', 'counter', 'bed', 'night stand', 'pillow', 'sofa', 'television', 'floor mat', 'curtain', 'clothes', 'stationery', 'refrigerator', 'bin', 'stove', 'oven', 'machine'] [03/07 14:15:23] cubercnn INFO: Available categories for SUNRGBD Validation [03/07 14:15:23] cubercnn INFO: ['books', 'bottle', 'chair', 'cup', 'laptop', 'towel', 'blinds', 'window', 'lamp', 'shelves', 'mirror', 'sink', 'cabinet', 'bathtub', 'door', 'toilet', 'desk', 'box', 'bookcase', 'picture', 'table', 'counter', 'bed', 'night stand', 'pillow', 'sofa', 'television', 'curtain', 'clothes', 'stationery', 'refrigerator', 'bin', 'stove', 'oven', 'machine'] [03/07 14:15:31] d2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in training: [ResizeShortestEdge(short_edge_length=..., max_size=4096, sample_style='choice'), RandomFlip()] [03/07 14:15:33] cubercnn.data.datasets INFO: Loading datasets\Omni3D\SUNRGBD_train.json takes 2.66 seconds. [03/07 14:15:33] cubercnn.data.datasets INFO: Loaded 4929 images in Omni3D format from datasets\Omni3D\SUNRGBD_train.json [03/07 14:15:42] cubercnn.data.datasets INFO: Filtered out 290/4929 images without valid annotations [03/07 14:15:42] cubercnn.data.datasets INFO: Loaded 356 images in Omni3D format from datasets\Omni3D\SUNRGBD_val.json [03/07 14:15:43] cubercnn.data.datasets INFO: Filtered out 23/356 images without valid annotations [03/07 14:15:43] d2.data.build INFO: Removed 0 images with no usable annotations. 4972 images left. [03/07 14:15:43] cubercnn.data.build INFO: Using training sampler RepeatFactorTrainingSampler [03/07 14:15:43] d2.data.common INFO: Serializing the dataset using: [03/07 14:15:43] d2.data.common INFO: Serializing 4972 elements to byte tensors and concatenating them all ... [03/07 14:15:44] d2.data.common INFO: Serialized dataset takes 20.81 MiB [03/07 14:15:44] d2.data.build INFO: Making batched data loader with batch_size=2 [03/07 14:15:44] d2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/Baseline_sgd\model_final.pth ... [03/07 14:15:44] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/Baseline_sgd\model_final.pth ... [03/07 14:15:44] fvcore.common.checkpoint INFO: Loading optimizer from output/Baseline_sgd\model_final.pth ... [03/07 14:15:44] fvcore.common.checkpoint INFO: Loading scheduler from output/Baseline_sgd\model_final.pth ... [03/07 14:15:44] cubercnn INFO: Starting training from iteration 28800 [03/07 14:22:30] detectron2 INFO: Full config saved to output/Baseline_sgd\config.yaml [03/07 14:22:31] cubercnn INFO: Preprocessing Training Datasets [03/07 14:22:45] cubercnn INFO: Available categories for SUNRGBD Train [03/07 14:22:45] cubercnn INFO: ['bicycle', 'books', 'bottle', 'chair', 'cup', 'laptop', 'shoes', 'towel', 'blinds', 'window', 'lamp', 'shelves', 'mirror', 'sink', 'cabinet', 'bathtub', 'door', 'toilet', 'desk', 'box', 'bookcase', 'picture', 'table', 'counter', 'bed', 'night stand', 'pillow', 'sofa', 'television', 'floor mat', 'curtain', 'clothes', 'stationery', 'refrigerator', 'bin', 'stove', 'oven', 'machine'] [03/07 14:22:45] cubercnn INFO: Available categories for SUNRGBD Validation [03/07 14:22:45] cubercnn INFO: ['books', 'bottle', 'chair', 'cup', 'laptop', 'towel', 'blinds', 'window', 'lamp', 'shelves', 'mirror', 'sink', 'cabinet', 'bathtub', 'door', 'toilet', 'desk', 'box', 'bookcase', 'picture', 'table', 'counter', 'bed', 'night stand', 'pillow', 'sofa', 'television', 'curtain', 'clothes', 'stationery', 'refrigerator', 'bin', 'stove', 'oven', 'machine'] [03/07 14:22:52] d2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in training: [ResizeShortestEdge(short_edge_length=..., max_size=4096, sample_style='choice'), RandomFlip()] [03/07 14:22:55] cubercnn.data.datasets INFO: Loading datasets\Omni3D\SUNRGBD_train.json takes 2.89 seconds. [03/07 14:22:55] cubercnn.data.datasets INFO: Loaded 4929 images in Omni3D format from datasets\Omni3D\SUNRGBD_train.json [03/07 14:23:04] cubercnn.data.datasets INFO: Filtered out 290/4929 images without valid annotations [03/07 14:23:04] cubercnn.data.datasets INFO: Loaded 356 images in Omni3D format from datasets\Omni3D\SUNRGBD_val.json [03/07 14:23:05] cubercnn.data.datasets INFO: Filtered out 23/356 images without valid annotations [03/07 14:23:05] d2.data.build INFO: Removed 0 images with no usable annotations. 4972 images left. [03/07 14:23:05] cubercnn.data.build INFO: Using training sampler RepeatFactorTrainingSampler [03/07 14:23:05] d2.data.common INFO: Serializing the dataset using: [03/07 14:23:05] d2.data.common INFO: Serializing 4972 elements to byte tensors and concatenating them all ... [03/07 14:23:05] d2.data.common INFO: Serialized dataset takes 20.81 MiB [03/07 14:23:05] d2.data.build INFO: Making batched data loader with batch_size=2 [03/07 14:23:05] d2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/Baseline_sgd\model_final.pth ... [03/07 14:23:05] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/Baseline_sgd\model_final.pth ... [03/07 14:23:06] fvcore.common.checkpoint INFO: Loading optimizer from output/Baseline_sgd\model_final.pth ... [03/07 14:23:06] fvcore.common.checkpoint INFO: Loading scheduler from output/Baseline_sgd\model_final.pth ... [03/07 14:23:06] cubercnn INFO: Starting training from iteration 28800 [03/07 14:29:20] detectron2 INFO: Full config saved to output/Baseline_sgd\config.yaml [03/07 14:29:21] cubercnn INFO: Preprocessing Training Datasets [03/07 14:29:35] cubercnn INFO: Available categories for SUNRGBD Train [03/07 14:29:35] cubercnn INFO: ['bicycle', 'books', 'bottle', 'chair', 'cup', 'laptop', 'shoes', 'towel', 'blinds', 'window', 'lamp', 'shelves', 'mirror', 'sink', 'cabinet', 'bathtub', 'door', 'toilet', 'desk', 'box', 'bookcase', 'picture', 'table', 'counter', 'bed', 'night stand', 'pillow', 'sofa', 'television', 'floor mat', 'curtain', 'clothes', 'stationery', 'refrigerator', 'bin', 'stove', 'oven', 'machine'] [03/07 14:29:35] cubercnn INFO: Available categories for SUNRGBD Validation [03/07 14:29:35] cubercnn INFO: ['books', 'bottle', 'chair', 'cup', 'laptop', 'towel', 'blinds', 'window', 'lamp', 'shelves', 'mirror', 'sink', 'cabinet', 'bathtub', 'door', 'toilet', 'desk', 'box', 'bookcase', 'picture', 'table', 'counter', 'bed', 'night stand', 'pillow', 'sofa', 'television', 'curtain', 'clothes', 'stationery', 'refrigerator', 'bin', 'stove', 'oven', 'machine'] [03/07 14:29:43] d2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in training: [ResizeShortestEdge(short_edge_length=..., max_size=4096, sample_style='choice'), RandomFlip()] [03/07 14:29:46] cubercnn.data.datasets INFO: Loading datasets\Omni3D\SUNRGBD_train.json takes 2.89 seconds. [03/07 14:29:46] cubercnn.data.datasets INFO: Loaded 4929 images in Omni3D format from datasets\Omni3D\SUNRGBD_train.json [03/07 14:29:55] cubercnn.data.datasets INFO: Filtered out 290/4929 images without valid annotations [03/07 14:29:55] cubercnn.data.datasets INFO: Loaded 356 images in Omni3D format from datasets\Omni3D\SUNRGBD_val.json [03/07 14:29:56] cubercnn.data.datasets INFO: Filtered out 23/356 images without valid annotations [03/07 14:29:56] d2.data.build INFO: Removed 0 images with no usable annotations. 4972 images left. [03/07 14:29:56] cubercnn.data.build INFO: Using training sampler RepeatFactorTrainingSampler [03/07 14:29:56] d2.data.common INFO: Serializing the dataset using: [03/07 14:29:56] d2.data.common INFO: Serializing 4972 elements to byte tensors and concatenating them all ... [03/07 14:29:56] d2.data.common INFO: Serialized dataset takes 20.81 MiB [03/07 14:29:56] d2.data.build INFO: Making batched data loader with batch_size=2 [03/07 14:29:56] d2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/Baseline_sgd\model_final.pth ... [03/07 14:29:56] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/Baseline_sgd\model_final.pth ... [03/07 14:29:57] fvcore.common.checkpoint INFO: Loading optimizer from output/Baseline_sgd\model_final.pth ... [03/07 14:29:57] fvcore.common.checkpoint INFO: Loading scheduler from output/Baseline_sgd\model_final.pth ... [03/07 14:29:57] cubercnn INFO: Starting training from iteration 28800 [03/13 12:06:35] detectron2 INFO: Full config saved to output/Baseline_sgd\config.yaml [03/13 12:06:36] cubercnn INFO: Preprocessing Training Datasets [03/14 11:29:36] detectron2 INFO: Full config saved to output/Baseline_sgd\config.yaml [03/14 11:29:37] cubercnn INFO: Preprocessing Training Datasets [03/14 11:29:51] cubercnn INFO: Available categories for SUNRGBD Train [03/14 11:29:51] cubercnn INFO: ['bicycle', 'books', 'bottle', 'chair', 'cup', 'laptop', 'shoes', 'towel', 'blinds', 'window', 'lamp', 'shelves', 'mirror', 'sink', 'cabinet', 'bathtub', 'door', 'toilet', 'desk', 'box', 'bookcase', 'picture', 'table', 'counter', 'bed', 'night stand', 'pillow', 'sofa', 'television', 'floor mat', 'curtain', 'clothes', 'stationery', 'refrigerator', 'bin', 'stove', 'oven', 'machine'] [03/14 11:29:51] cubercnn INFO: Available categories for SUNRGBD Validation [03/14 11:29:51] cubercnn INFO: ['books', 'bottle', 'chair', 'cup', 'laptop', 'towel', 'blinds', 'window', 'lamp', 'shelves', 'mirror', 'sink', 'cabinet', 'bathtub', 'door', 'toilet', 'desk', 'box', 'bookcase', 'picture', 'table', 'counter', 'bed', 'night stand', 'pillow', 'sofa', 'television', 'curtain', 'clothes', 'stationery', 'refrigerator', 'bin', 'stove', 'oven', 'machine'] [03/14 11:29:59] d2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in training: [ResizeShortestEdge(short_edge_length=..., max_size=4096, sample_style='choice'), RandomFlip()] [03/14 11:30:02] cubercnn.data.datasets INFO: Loading datasets\Omni3D\SUNRGBD_train.json takes 2.83 seconds. [03/14 11:30:02] cubercnn.data.datasets INFO: Loaded 4929 images in Omni3D format from datasets\Omni3D\SUNRGBD_train.json [03/14 11:30:09] cubercnn.data.datasets INFO: Filtered out 290/4929 images without valid annotations [03/14 11:30:09] cubercnn.data.datasets INFO: Loaded 356 images in Omni3D format from datasets\Omni3D\SUNRGBD_val.json [03/14 11:30:10] cubercnn.data.datasets INFO: Filtered out 23/356 images without valid annotations [03/14 11:30:10] d2.data.build INFO: Removed 0 images with no usable annotations. 4972 images left. [03/14 11:30:10] cubercnn.data.build INFO: Using training sampler RepeatFactorTrainingSampler [03/14 11:30:10] d2.data.common INFO: Serializing the dataset using: [03/14 11:30:10] d2.data.common INFO: Serializing 4972 elements to byte tensors and concatenating them all ... [03/14 11:30:10] d2.data.common INFO: Serialized dataset takes 21.03 MiB [03/14 11:30:10] d2.data.build INFO: Making batched data loader with batch_size=2 [03/14 11:30:10] d2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/Baseline_sgd\model_final.pth ... [03/14 11:30:10] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/Baseline_sgd\model_final.pth ... [03/14 11:30:11] fvcore.common.checkpoint INFO: Loading optimizer from output/Baseline_sgd\model_final.pth ... [03/14 11:30:11] fvcore.common.checkpoint INFO: Loading scheduler from output/Baseline_sgd\model_final.pth ... [03/14 11:30:11] cubercnn INFO: Starting training from iteration 28800 [03/14 13:21:16] detectron2 INFO: Full config saved to output/Baseline_sgd\config.yaml [03/14 13:21:17] cubercnn INFO: Preprocessing Training Datasets [03/14 13:21:31] cubercnn INFO: Available categories for SUNRGBD Train [03/14 13:21:31] cubercnn INFO: ['bicycle', 'books', 'bottle', 'chair', 'cup', 'laptop', 'shoes', 'towel', 'blinds', 'window', 'lamp', 'shelves', 'mirror', 'sink', 'cabinet', 'bathtub', 'door', 'toilet', 'desk', 'box', 'bookcase', 'picture', 'table', 'counter', 'bed', 'night stand', 'pillow', 'sofa', 'television', 'floor mat', 'curtain', 'clothes', 'stationery', 'refrigerator', 'bin', 'stove', 'oven', 'machine'] [03/14 13:21:31] cubercnn INFO: Available categories for SUNRGBD Validation [03/14 13:21:31] cubercnn INFO: ['books', 'bottle', 'chair', 'cup', 'laptop', 'towel', 'blinds', 'window', 'lamp', 'shelves', 'mirror', 'sink', 'cabinet', 'bathtub', 'door', 'toilet', 'desk', 'box', 'bookcase', 'picture', 'table', 'counter', 'bed', 'night stand', 'pillow', 'sofa', 'television', 'curtain', 'clothes', 'stationery', 'refrigerator', 'bin', 'stove', 'oven', 'machine'] [03/14 13:21:39] d2.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in training: [ResizeShortestEdge(short_edge_length=..., max_size=4096, sample_style='choice'), RandomFlip()] [03/14 13:21:42] cubercnn.data.datasets INFO: Loading datasets\Omni3D\SUNRGBD_train.json takes 3.07 seconds. [03/14 13:21:42] cubercnn.data.datasets INFO: Loaded 4929 images in Omni3D format from datasets\Omni3D\SUNRGBD_train.json [03/14 13:21:52] cubercnn.data.datasets INFO: Filtered out 290/4929 images without valid annotations [03/14 13:21:52] cubercnn.data.datasets INFO: Loaded 356 images in Omni3D format from datasets\Omni3D\SUNRGBD_val.json [03/14 13:21:53] cubercnn.data.datasets INFO: Filtered out 23/356 images without valid annotations [03/14 13:21:53] d2.data.build INFO: Removed 0 images with no usable annotations. 4972 images left. [03/14 13:21:53] cubercnn.data.build INFO: Using training sampler RepeatFactorTrainingSampler [03/14 13:21:53] d2.data.common INFO: Serializing the dataset using: [03/14 13:21:53] d2.data.common INFO: Serializing 4972 elements to byte tensors and concatenating them all ... [03/14 13:21:54] d2.data.common INFO: Serialized dataset takes 21.03 MiB [03/14 13:21:54] d2.data.build INFO: Making batched data loader with batch_size=2 [03/14 13:21:54] d2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/Baseline_sgd\model_final.pth ... [03/14 13:21:54] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/Baseline_sgd\model_final.pth ... [03/14 13:21:54] fvcore.common.checkpoint WARNING: The checkpoint state_dict contains keys that are not used by the model: roi_heads.priors_z_scales [03/14 13:21:54] fvcore.common.checkpoint INFO: Loading optimizer from output/Baseline_sgd\model_final.pth ... [03/27 15:05:56] detectron2 INFO: Full config saved to output/Baseline_sgd\config.yaml [03/27 15:05:56] cubercnn INFO: Preprocessing Training Datasets [03/27 15:05:56] cubercnn INFO: Available categories for SUNRGBD Train [03/27 15:05:56] cubercnn INFO: ['bicycle', 'books', 'bottle', 'chair', 'cup', 'laptop', 'shoes', 'towel', 'blinds', 'window', 'lamp', 'shelves', 'mirror', 'sink', 'cabinet', 'bathtub', 'door', 'toilet', 'desk', 'box', 'bookcase', 'picture', 'table', 'counter', 'bed', 'night stand', 'pillow', 'sofa', 'television', 'floor mat', 'curtain', 'clothes', 'stationery', 'refrigerator', 'bin', 'stove', 'oven', 'machine'] [03/27 15:05:56] cubercnn INFO: Available categories for SUNRGBD Validation [03/27 15:05:56] cubercnn INFO: ['books', 'bottle', 'chair', 'cup', 'laptop', 'towel', 'blinds', 'window', 'lamp', 'shelves', 'mirror', 'sink', 'cabinet', 'bathtub', 'door', 'toilet', 'desk', 'box', 'bookcase', 'picture', 'table', 'counter', 'bed', 'night stand', 'pillow', 'sofa', 'television', 'curtain', 'clothes', 'stationery', 'refrigerator', 'bin', 'stove', 'oven', 'machine'] [03/27 15:05:57] cubercnn.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in training: [ResizeShortestEdge(short_edge_length=..., max_size=4096, sample_style='choice'), RandomFlip()] [03/27 15:05:57] cubercnn.data.datasets INFO: Loaded 10 images in Omni3D format from datasets\Omni3D\SUNRGBD_train_mini.json [03/27 15:05:58] cubercnn.data.datasets INFO: Filtered out 0/10 images without valid annotations [03/27 15:05:58] cubercnn.data.datasets INFO: Loaded 10 images in Omni3D format from datasets\Omni3D\SUNRGBD_val_mini.json [03/27 15:05:58] cubercnn.data.datasets INFO: Filtered out 0/10 images without valid annotations [03/27 15:05:58] d2.data.build INFO: Removed 0 images with no usable annotations. 20 images left. [03/27 15:05:58] cubercnn.data.build INFO: Using training sampler RepeatFactorTrainingSampler [03/27 15:05:58] d2.data.common INFO: Serializing the dataset using: [03/27 15:05:58] d2.data.common INFO: Serializing 20 elements to byte tensors and concatenating them all ... [03/27 15:05:58] d2.data.common INFO: Serialized dataset takes 0.08 MiB [03/27 15:05:58] d2.data.build INFO: Making batched data loader with batch_size=2 [03/27 15:05:58] d2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/Baseline_sgd\model_final.pth ... [03/27 15:05:58] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/Baseline_sgd\model_final.pth ... [03/27 15:05:58] fvcore.common.checkpoint INFO: Loading optimizer from output/Baseline_sgd\model_final.pth ... [03/27 15:05:58] fvcore.common.checkpoint INFO: Loading scheduler from output/Baseline_sgd\model_final.pth ... [03/27 15:05:58] cubercnn INFO: Starting training from iteration 28800 [03/27 15:29:25] detectron2 INFO: Full config saved to output/Baseline_sgd\config.yaml [03/27 15:29:26] cubercnn INFO: Preprocessing Training Datasets [03/27 15:29:26] cubercnn INFO: Available categories for SUNRGBD Train [03/27 15:29:26] cubercnn INFO: ['bicycle', 'books', 'bottle', 'chair', 'cup', 'laptop', 'shoes', 'towel', 'blinds', 'window', 'lamp', 'shelves', 'mirror', 'sink', 'cabinet', 'bathtub', 'door', 'toilet', 'desk', 'box', 'bookcase', 'picture', 'table', 'counter', 'bed', 'night stand', 'pillow', 'sofa', 'television', 'floor mat', 'curtain', 'clothes', 'stationery', 'refrigerator', 'bin', 'stove', 'oven', 'machine'] [03/27 15:29:26] cubercnn INFO: Available categories for SUNRGBD Validation [03/27 15:29:26] cubercnn INFO: ['books', 'bottle', 'chair', 'cup', 'laptop', 'towel', 'blinds', 'window', 'lamp', 'shelves', 'mirror', 'sink', 'cabinet', 'bathtub', 'door', 'toilet', 'desk', 'box', 'bookcase', 'picture', 'table', 'counter', 'bed', 'night stand', 'pillow', 'sofa', 'television', 'curtain', 'clothes', 'stationery', 'refrigerator', 'bin', 'stove', 'oven', 'machine'] [03/27 15:29:27] cubercnn.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in training: [ResizeShortestEdge(short_edge_length=..., max_size=4096, sample_style='choice'), RandomFlip()] [03/27 15:29:27] cubercnn.data.datasets INFO: Loaded 10 images in Omni3D format from datasets\Omni3D\SUNRGBD_train_mini.json [03/27 15:29:27] cubercnn.data.datasets INFO: Filtered out 0/10 images without valid annotations [03/27 15:29:27] cubercnn.data.datasets INFO: Loaded 10 images in Omni3D format from datasets\Omni3D\SUNRGBD_val_mini.json [03/27 15:29:27] cubercnn.data.datasets INFO: Filtered out 0/10 images without valid annotations [03/27 15:29:27] d2.data.build INFO: Removed 0 images with no usable annotations. 20 images left. [03/27 15:29:27] cubercnn.data.build INFO: Using training sampler RepeatFactorTrainingSampler [03/27 15:29:27] d2.data.common INFO: Serializing the dataset using: [03/27 15:29:27] d2.data.common INFO: Serializing 20 elements to byte tensors and concatenating them all ... [03/27 15:29:27] d2.data.common INFO: Serialized dataset takes 0.08 MiB [03/27 15:29:27] d2.data.build INFO: Making batched data loader with batch_size=2 [03/27 15:29:27] d2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/Baseline_sgd\model_final.pth ... [03/27 15:29:27] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/Baseline_sgd\model_final.pth ... [03/27 15:29:27] fvcore.common.checkpoint INFO: Loading optimizer from output/Baseline_sgd\model_final.pth ... [03/27 15:29:27] fvcore.common.checkpoint INFO: Loading scheduler from output/Baseline_sgd\model_final.pth ... [03/27 15:29:27] cubercnn INFO: Starting training from iteration 28800 [03/27 15:29:53] detectron2 INFO: Full config saved to output/Baseline_sgd\config.yaml [03/27 15:29:54] cubercnn INFO: Preprocessing Training Datasets [03/27 15:29:54] cubercnn INFO: Available categories for SUNRGBD Train [03/27 15:29:54] cubercnn INFO: ['bicycle', 'books', 'bottle', 'chair', 'cup', 'laptop', 'shoes', 'towel', 'blinds', 'window', 'lamp', 'shelves', 'mirror', 'sink', 'cabinet', 'bathtub', 'door', 'toilet', 'desk', 'box', 'bookcase', 'picture', 'table', 'counter', 'bed', 'night stand', 'pillow', 'sofa', 'television', 'floor mat', 'curtain', 'clothes', 'stationery', 'refrigerator', 'bin', 'stove', 'oven', 'machine'] [03/27 15:29:54] cubercnn INFO: Available categories for SUNRGBD Validation [03/27 15:29:54] cubercnn INFO: ['books', 'bottle', 'chair', 'cup', 'laptop', 'towel', 'blinds', 'window', 'lamp', 'shelves', 'mirror', 'sink', 'cabinet', 'bathtub', 'door', 'toilet', 'desk', 'box', 'bookcase', 'picture', 'table', 'counter', 'bed', 'night stand', 'pillow', 'sofa', 'television', 'curtain', 'clothes', 'stationery', 'refrigerator', 'bin', 'stove', 'oven', 'machine'] [03/27 15:29:55] cubercnn.data.dataset_mapper INFO: [DatasetMapper] Augmentations used in training: [ResizeShortestEdge(short_edge_length=..., max_size=4096, sample_style='choice'), RandomFlip()] [03/27 15:29:55] cubercnn.data.datasets INFO: Loaded 10 images in Omni3D format from datasets\Omni3D\SUNRGBD_train_mini.json [03/27 15:29:55] cubercnn.data.datasets INFO: Filtered out 0/10 images without valid annotations [03/27 15:29:55] cubercnn.data.datasets INFO: Loaded 10 images in Omni3D format from datasets\Omni3D\SUNRGBD_val_mini.json [03/27 15:29:55] cubercnn.data.datasets INFO: Filtered out 0/10 images without valid annotations [03/27 15:29:55] d2.data.build INFO: Removed 0 images with no usable annotations. 20 images left. [03/27 15:29:55] cubercnn.data.build INFO: Using training sampler RepeatFactorTrainingSampler [03/27 15:29:55] d2.data.common INFO: Serializing the dataset using: [03/27 15:29:55] d2.data.common INFO: Serializing 20 elements to byte tensors and concatenating them all ... [03/27 15:29:55] d2.data.common INFO: Serialized dataset takes 0.08 MiB [03/27 15:29:55] d2.data.build INFO: Making batched data loader with batch_size=2 [03/27 15:29:55] d2.checkpoint.detection_checkpoint INFO: [DetectionCheckpointer] Loading from output/Baseline_sgd\model_final.pth ... [03/27 15:29:55] fvcore.common.checkpoint INFO: [Checkpointer] Loading from output/Baseline_sgd\model_final.pth ... [03/27 15:29:55] fvcore.common.checkpoint INFO: Loading optimizer from output/Baseline_sgd\model_final.pth ... [03/27 15:29:55] fvcore.common.checkpoint INFO: Loading scheduler from output/Baseline_sgd\model_final.pth ... [03/27 15:29:55] cubercnn INFO: Starting training from iteration 28800