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facebookresearch / maskrcnn-benchmark

Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch.
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Currently care nothing about the training efficiency and just want to get training work through, but it doesn't! #1292

Open terU3760 opened 3 years ago

terU3760 commented 3 years ago

❓ Questions and Help

When I run tools/train_net.py upon the downloaded coco dataset, it reported the following error. Currently, I care nothing about the training efficiency and just want to get training work through. Could anyone please provide some opinion about how to shrink the parameters in configuration file to let me get the training process to work. Or if it is an error, please tell me how to correct it?

The error is:

pydev debugger: process 11995 is connecting

Connected to pydev debugger (build 193.5662.61) 2020-12-29 16:51:08,191 maskrcnn_benchmark INFO: Using 1 GPUs 2020-12-29 16:51:08,192 maskrcnn_benchmark INFO: Namespace(config_file='/root/maskrcnn-benchmark/configs/rpn_R_50_C4_1x_by_NgaimanChow_1st_test.yaml', distributed=False, local_rank=0, opts=[], skip_test=False) 2020-12-29 16:51:08,192 maskrcnn_benchmark INFO: Collecting env info (might take some time) 2020-12-29 16:51:08,305 maskrcnn_benchmark INFO: PyTorch version: 1.0.0.dev20190328 Is debug build: No CUDA used to build PyTorch: 9.0.176

OS: Ubuntu 16.04.7 LTS GCC version: (Ubuntu 5.4.0-6ubuntu1~16.04.12) 5.4.0 20160609 CMake version: version 3.5.1

Python version: 3.6 Is CUDA available: Yes CUDA runtime version: Could not collect GPU models and configuration: GPU 0: GeForce GTX 1080 Nvidia driver version: 396.37 cuDNN version: /usr/lib/x86_64-linux-gnu/libcudnn.so.7.6.4

Versions of relevant libraries: [pip] Could not collect [conda] Could not collect Pillow (8.0.1) 2020-12-29 16:51:08,305 maskrcnn_benchmark INFO: Loaded configuration file /root/maskrcnn-benchmark/configs/rpn_R_50_C4_1x_by_NgaimanChow_1st_test.yaml 2020-12-29 16:51:08,306 maskrcnn_benchmark INFO: MODEL: META_ARCHITECTURE: "GeneralizedRCNN" WEIGHT: "catalog://ImageNetPretrained/MSRA/R-50" RPN_ONLY: True RPN: PRE_NMS_TOP_N_TEST: 12000 POST_NMS_TOP_N_TEST: 2000 DATASETS: TRAIN: ("coco_2014_train", "coco_2014_val" ) TEST: ("coco_2014_test",) SOLVER: BASE_LR: 0.02 WEIGHT_DECAY: 0.0001 STEPS: (60000, 80000) MAX_ITER: 90000

2020-12-29 16:51:08,307 maskrcnn_benchmark INFO: Running with config: AMP_VERBOSE: False DATALOADER: ASPECT_RATIO_GROUPING: True NUM_WORKERS: 4 SIZE_DIVISIBILITY: 0 DATASETS: TEST: ('coco_2014_test',) TRAIN: ('coco_2014_train', 'coco_2014_val') DTYPE: float32 INPUT: BRIGHTNESS: 0.0 CONTRAST: 0.0 HORIZONTAL_FLIP_PROB_TRAIN: 0.5 HUE: 0.0 MAX_SIZE_TEST: 1333 MAX_SIZE_TRAIN: 1333 MIN_SIZE_TEST: 800 MIN_SIZE_TRAIN: (800,) PIXEL_MEAN: [102.9801, 115.9465, 122.7717] PIXEL_STD: [1.0, 1.0, 1.0] SATURATION: 0.0 TO_BGR255: True VERTICAL_FLIP_PROB_TRAIN: 0.0 MODEL: BACKBONE: CONV_BODY: R-50-C4 FREEZE_CONV_BODY_AT: 2 CLS_AGNOSTIC_BBOX_REG: False DEVICE: cuda FBNET: ARCH: default ARCH_DEF: BN_TYPE: bn DET_HEAD_BLOCKS: [] DET_HEAD_LAST_SCALE: 1.0 DET_HEAD_STRIDE: 0 DW_CONV_SKIP_BN: True DW_CONV_SKIP_RELU: True KPTS_HEAD_BLOCKS: [] KPTS_HEAD_LAST_SCALE: 0.0 KPTS_HEAD_STRIDE: 0 MASK_HEAD_BLOCKS: [] MASK_HEAD_LAST_SCALE: 0.0 MASK_HEAD_STRIDE: 0 RPN_BN_TYPE: RPN_HEAD_BLOCKS: 0 SCALE_FACTOR: 1.0 WIDTH_DIVISOR: 1 FPN: USE_GN: False USE_RELU: False GROUP_NORM: DIM_PER_GP: -1 EPSILON: 1e-05 NUM_GROUPS: 32 KEYPOINT_ON: False MASK_ON: False META_ARCHITECTURE: GeneralizedRCNN RESNETS: BACKBONE_OUT_CHANNELS: 1024 DEFORMABLE_GROUPS: 1 NUM_GROUPS: 1 RES2_OUT_CHANNELS: 256 RES5_DILATION: 1 STAGE_WITH_DCN: (False, False, False, False) STEM_FUNC: StemWithFixedBatchNorm STEM_OUT_CHANNELS: 64 STRIDE_IN_1X1: True TRANS_FUNC: BottleneckWithFixedBatchNorm WIDTH_PER_GROUP: 64 WITH_MODULATED_DCN: False RETINANET: ANCHOR_SIZES: (32, 64, 128, 256, 512) ANCHOR_STRIDES: (8, 16, 32, 64, 128) ASPECT_RATIOS: (0.5, 1.0, 2.0) BBOX_REG_BETA: 0.11 BBOX_REG_WEIGHT: 4.0 BG_IOU_THRESHOLD: 0.4 FG_IOU_THRESHOLD: 0.5 INFERENCE_TH: 0.05 LOSS_ALPHA: 0.25 LOSS_GAMMA: 2.0 NMS_TH: 0.4 NUM_CLASSES: 81 NUM_CONVS: 4 OCTAVE: 2.0 PRE_NMS_TOP_N: 1000 PRIOR_PROB: 0.01 SCALES_PER_OCTAVE: 3 STRADDLE_THRESH: 0 USE_C5: True RETINANET_ON: False ROI_BOX_HEAD: CONV_HEAD_DIM: 256 DILATION: 1 FEATURE_EXTRACTOR: ResNet50Conv5ROIFeatureExtractor MLP_HEAD_DIM: 1024 NUM_CLASSES: 81 NUM_STACKED_CONVS: 4 POOLER_RESOLUTION: 14 POOLER_SAMPLING_RATIO: 0 POOLER_SCALES: (0.0625,) PREDICTOR: FastRCNNPredictor USE_GN: False ROI_HEADS: BATCH_SIZE_PER_IMAGE: 512 BBOX_REG_WEIGHTS: (10.0, 10.0, 5.0, 5.0) BG_IOU_THRESHOLD: 0.5 DETECTIONS_PER_IMG: 100 FG_IOU_THRESHOLD: 0.5 NMS: 0.5 POSITIVE_FRACTION: 0.25 SCORE_THRESH: 0.05 USE_FPN: False ROI_KEYPOINT_HEAD: CONV_LAYERS: (512, 512, 512, 512, 512, 512, 512, 512) FEATURE_EXTRACTOR: KeypointRCNNFeatureExtractor MLP_HEAD_DIM: 1024 NUM_CLASSES: 17 POOLER_RESOLUTION: 14 POOLER_SAMPLING_RATIO: 0 POOLER_SCALES: (0.0625,) PREDICTOR: KeypointRCNNPredictor RESOLUTION: 14 SHARE_BOX_FEATURE_EXTRACTOR: True ROI_MASK_HEAD: CONV_LAYERS: (256, 256, 256, 256) DILATION: 1 FEATURE_EXTRACTOR: ResNet50Conv5ROIFeatureExtractor MLP_HEAD_DIM: 1024 POOLER_RESOLUTION: 14 POOLER_SAMPLING_RATIO: 0 POOLER_SCALES: (0.0625,) POSTPROCESS_MASKS: False POSTPROCESS_MASKS_THRESHOLD: 0.5 PREDICTOR: MaskRCNNC4Predictor RESOLUTION: 14 SHARE_BOX_FEATURE_EXTRACTOR: True USE_GN: False RPN: ANCHOR_SIZES: (32, 64, 128, 256, 512) ANCHOR_STRIDE: (16,) ASPECT_RATIOS: (0.5, 1.0, 2.0) BATCH_SIZE_PER_IMAGE: 256 BG_IOU_THRESHOLD: 0.3 FG_IOU_THRESHOLD: 0.7 FPN_POST_NMS_PER_BATCH: True FPN_POST_NMS_TOP_N_TEST: 2000 FPN_POST_NMS_TOP_N_TRAIN: 2000 MIN_SIZE: 0 NMS_THRESH: 0.7 POSITIVE_FRACTION: 0.5 POST_NMS_TOP_N_TEST: 2000 POST_NMS_TOP_N_TRAIN: 2000 PRE_NMS_TOP_N_TEST: 12000 PRE_NMS_TOP_N_TRAIN: 12000 RPN_HEAD: SingleConvRPNHead STRADDLE_THRESH: 0 USE_FPN: False RPN_ONLY: True WEIGHT: catalog://ImageNetPretrained/MSRA/R-50 OUTPUT_DIR: . PATHS_CATALOG: /root/maskrcnn-benchmark/maskrcnn_benchmark/config/paths_catalog.py SOLVER: BASE_LR: 0.02 BIAS_LR_FACTOR: 2 CHECKPOINT_PERIOD: 2500 GAMMA: 0.1 IMS_PER_BATCH: 16 MAX_ITER: 90000 MOMENTUM: 0.9 STEPS: (60000, 80000) TEST_PERIOD: 0 WARMUP_FACTOR: 0.3333333333333333 WARMUP_ITERS: 500 WARMUP_METHOD: linear WEIGHT_DECAY: 0.0001 WEIGHT_DECAY_BIAS: 0 TEST: BBOX_AUG: ENABLED: False H_FLIP: False MAX_SIZE: 4000 SCALES: () SCALE_H_FLIP: False DETECTIONS_PER_IMG: 100 EXPECTED_RESULTS: [] EXPECTED_RESULTS_SIGMA_TOL: 4 IMS_PER_BATCH: 8 2020-12-29 16:51:08,307 maskrcnn_benchmark INFO: Saving config into: ./config.yml Selected optimization level O0: Pure FP32 training.

Defaults for this optimization level are: enabled : True opt_level : O0 cast_model_type : torch.float32 patch_torch_functions : False keep_batchnorm_fp32 : None master_weights : False loss_scale : 1.0 Processing user overrides (additional kwargs that are not None)... After processing overrides, optimization options are: enabled : True opt_level : O0 cast_model_type : torch.float32 patch_torch_functions : False keep_batchnorm_fp32 : None master_weights : False loss_scale : 1.0 Warning: multi_tensor_applier fused unscale kernel is unavailable, possibly because apex was installed without --cuda_ext --cpp_ext. Using Python fallback. Original ImportError was: ImportError('/miniconda/envs/py36_test_install_torchvision/lib/python3.6/site-packages/apex-0.1-py3.6-linux-x86_64.egg/amp_C.cpython-36m-x86_64-linux-gnu.so: undefined symbol: _ZN3c1019ComplexCUDATensorIdEv',) 2020-12-29 16:51:10,076 maskrcnn_benchmark.utils.checkpoint INFO: Loading checkpoint from catalog://ImageNetPretrained/MSRA/R-50 2020-12-29 16:51:10,077 maskrcnn_benchmark.utils.checkpoint INFO: catalog://ImageNetPretrained/MSRA/R-50 points to https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-50.pkl 2020-12-29 16:51:10,078 maskrcnn_benchmark.utils.checkpoint INFO: url https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-50.pkl cached in /root/.torch/models/R-50.pkl 2020-12-29 16:51:10,135 maskrcnn_benchmark.utils.c2_model_loading INFO: Remapping C2 weights 2020-12-29 16:51:10,135 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: conv1_b mapped name: conv1.bias 2020-12-29 16:51:10,135 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: conv1_w mapped name: conv1.weight 2020-12-29 16:51:10,135 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: fc1000_b mapped name: fc1000.bias 2020-12-29 16:51:10,136 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: fc1000_w mapped name: fc1000.weight 2020-12-29 16:51:10,136 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_0_branch1_b mapped name: layer1.0.downsample.0.bias 2020-12-29 16:51:10,136 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_0_branch1_bn_b mapped name: layer1.0.downsample.1.bias 2020-12-29 16:51:10,136 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_0_branch1_bn_s mapped name: layer1.0.downsample.1.weight 2020-12-29 16:51:10,136 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_0_branch1_w mapped name: layer1.0.downsample.0.weight 2020-12-29 16:51:10,136 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_0_branch2a_b mapped name: layer1.0.conv1.bias 2020-12-29 16:51:10,137 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_0_branch2a_bn_b mapped name: layer1.0.bn1.bias 2020-12-29 16:51:10,137 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_0_branch2a_bn_s mapped name: layer1.0.bn1.weight 2020-12-29 16:51:10,137 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_0_branch2a_w mapped name: layer1.0.conv1.weight 2020-12-29 16:51:10,137 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_0_branch2b_b mapped name: layer1.0.conv2.bias 2020-12-29 16:51:10,137 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_0_branch2b_bn_b mapped name: layer1.0.bn2.bias 2020-12-29 16:51:10,137 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_0_branch2b_bn_s mapped name: layer1.0.bn2.weight 2020-12-29 16:51:10,138 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_0_branch2b_w mapped name: layer1.0.conv2.weight 2020-12-29 16:51:10,138 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_0_branch2c_b mapped name: layer1.0.conv3.bias 2020-12-29 16:51:10,138 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_0_branch2c_bn_b mapped name: layer1.0.bn3.bias 2020-12-29 16:51:10,138 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_0_branch2c_bn_s mapped name: layer1.0.bn3.weight 2020-12-29 16:51:10,138 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_0_branch2c_w mapped name: layer1.0.conv3.weight 2020-12-29 16:51:10,138 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_1_branch2a_b mapped name: layer1.1.conv1.bias 2020-12-29 16:51:10,138 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_1_branch2a_bn_b mapped name: layer1.1.bn1.bias 2020-12-29 16:51:10,139 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_1_branch2a_bn_s mapped name: layer1.1.bn1.weight 2020-12-29 16:51:10,139 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_1_branch2a_w mapped name: layer1.1.conv1.weight 2020-12-29 16:51:10,139 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_1_branch2b_b mapped name: layer1.1.conv2.bias 2020-12-29 16:51:10,139 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_1_branch2b_bn_b mapped name: layer1.1.bn2.bias 2020-12-29 16:51:10,139 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_1_branch2b_bn_s mapped name: layer1.1.bn2.weight 2020-12-29 16:51:10,139 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_1_branch2b_w mapped name: layer1.1.conv2.weight 2020-12-29 16:51:10,140 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_1_branch2c_b mapped name: layer1.1.conv3.bias 2020-12-29 16:51:10,140 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_1_branch2c_bn_b mapped name: layer1.1.bn3.bias 2020-12-29 16:51:10,140 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_1_branch2c_bn_s mapped name: layer1.1.bn3.weight 2020-12-29 16:51:10,140 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_1_branch2c_w mapped name: layer1.1.conv3.weight 2020-12-29 16:51:10,140 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_2_branch2a_b mapped name: layer1.2.conv1.bias 2020-12-29 16:51:10,140 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_2_branch2a_bn_b mapped name: layer1.2.bn1.bias 2020-12-29 16:51:10,141 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_2_branch2a_bn_s mapped name: layer1.2.bn1.weight 2020-12-29 16:51:10,141 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_2_branch2a_w mapped name: layer1.2.conv1.weight 2020-12-29 16:51:10,141 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_2_branch2b_b mapped name: layer1.2.conv2.bias 2020-12-29 16:51:10,141 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_2_branch2b_bn_b mapped name: layer1.2.bn2.bias 2020-12-29 16:51:10,141 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_2_branch2b_bn_s mapped name: layer1.2.bn2.weight 2020-12-29 16:51:10,142 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_2_branch2b_w mapped name: layer1.2.conv2.weight 2020-12-29 16:51:10,142 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_2_branch2c_b mapped name: layer1.2.conv3.bias 2020-12-29 16:51:10,142 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_2_branch2c_bn_b mapped name: layer1.2.bn3.bias 2020-12-29 16:51:10,142 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_2_branch2c_bn_s mapped name: layer1.2.bn3.weight 2020-12-29 16:51:10,142 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res2_2_branch2c_w mapped name: layer1.2.conv3.weight 2020-12-29 16:51:10,142 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res3_0_branch1_b mapped name: layer2.0.downsample.0.bias 2020-12-29 16:51:10,142 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res3_0_branch1_bn_b mapped name: layer2.0.downsample.1.bias 2020-12-29 16:51:10,143 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res3_0_branch1_bn_s mapped name: layer2.0.downsample.1.weight 2020-12-29 16:51:10,143 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res3_0_branch1_w mapped name: layer2.0.downsample.0.weight 2020-12-29 16:51:10,143 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res3_0_branch2a_b mapped name: layer2.0.conv1.bias 2020-12-29 16:51:10,143 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res3_0_branch2a_bn_b mapped name: layer2.0.bn1.bias 2020-12-29 16:51:10,143 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res3_0_branch2a_bn_s mapped name: layer2.0.bn1.weight 2020-12-29 16:51:10,143 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res3_0_branch2a_w mapped name: layer2.0.conv1.weight 2020-12-29 16:51:10,144 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res3_0_branch2b_b mapped name: layer2.0.conv2.bias 2020-12-29 16:51:10,144 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res3_0_branch2b_bn_b mapped name: layer2.0.bn2.bias 2020-12-29 16:51:10,144 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res3_0_branch2b_bn_s mapped name: layer2.0.bn2.weight 2020-12-29 16:51:10,144 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res3_0_branch2b_w mapped name: layer2.0.conv2.weight 2020-12-29 16:51:10,144 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res3_0_branch2c_b mapped name: layer2.0.conv3.bias 2020-12-29 16:51:10,144 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res3_0_branch2c_bn_b mapped name: layer2.0.bn3.bias 2020-12-29 16:51:10,145 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res3_0_branch2c_bn_s mapped name: layer2.0.bn3.weight 2020-12-29 16:51:10,145 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res3_0_branch2c_w mapped name: layer2.0.conv3.weight 2020-12-29 16:51:10,145 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: 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2020-12-29 16:51:10,167 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res5_2_branch2c_bn_b mapped name: layer4.2.bn3.bias 2020-12-29 16:51:10,167 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res5_2_branch2c_bn_s mapped name: layer4.2.bn3.weight 2020-12-29 16:51:10,168 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res5_2_branch2c_w mapped name: layer4.2.conv3.weight 2020-12-29 16:51:10,168 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res_conv1_bn_b mapped name: bn1.bias 2020-12-29 16:51:10,168 maskrcnn_benchmark.utils.c2_model_loading INFO: C2 name: res_conv1_bn_s mapped name: bn1.weight 2020-12-29 16:51:10,168 maskrcnn_benchmark.utils.c2_model_loading INFO: Remapping conv weights for deformable conv weights 2020-12-29 16:51:10,210 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer1.0.bn1.bias loaded from layer1.0.bn1.bias of shape (64,) 2020-12-29 16:51:10,210 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer1.0.bn1.weight loaded from layer1.0.bn1.weight of shape (64,) 2020-12-29 16:51:10,210 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer1.0.bn2.bias loaded from layer1.0.bn2.bias of shape (64,) 2020-12-29 16:51:10,210 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer1.0.bn2.weight loaded from layer1.0.bn2.weight of shape (64,) 2020-12-29 16:51:10,210 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer1.0.bn3.bias loaded from layer1.0.bn3.bias of shape (256,) 2020-12-29 16:51:10,211 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer1.0.bn3.weight loaded from layer1.0.bn3.weight of shape (256,) 2020-12-29 16:51:10,211 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer1.0.conv1.weight loaded from layer1.0.conv1.weight of shape (64, 64, 1, 1) 2020-12-29 16:51:10,211 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer1.0.conv2.weight loaded from layer1.0.conv2.weight of shape (64, 64, 3, 3) 2020-12-29 16:51:10,211 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer1.0.conv3.weight loaded from layer1.0.conv3.weight of shape (256, 64, 1, 1) 2020-12-29 16:51:10,211 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer1.0.downsample.0.weight loaded from layer1.0.downsample.0.weight of shape (256, 64, 1, 1) 2020-12-29 16:51:10,211 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer1.0.downsample.1.bias loaded from layer1.0.downsample.1.bias of shape (256,) 2020-12-29 16:51:10,211 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer1.0.downsample.1.weight loaded from layer1.0.downsample.1.weight of shape (256,) 2020-12-29 16:51:10,212 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer1.1.bn1.bias loaded from layer1.1.bn1.bias of shape (64,) 2020-12-29 16:51:10,212 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer1.1.bn1.weight loaded from layer1.1.bn1.weight of shape (64,) 2020-12-29 16:51:10,212 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer1.1.bn2.bias loaded from layer1.1.bn2.bias of shape (64,) 2020-12-29 16:51:10,212 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer1.1.bn2.weight loaded from layer1.1.bn2.weight of shape (64,) 2020-12-29 16:51:10,212 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer1.1.bn3.bias loaded from layer1.1.bn3.bias of shape (256,) 2020-12-29 16:51:10,213 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer1.1.bn3.weight loaded from layer1.1.bn3.weight of shape (256,) 2020-12-29 16:51:10,213 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer1.1.conv1.weight loaded from layer1.1.conv1.weight of shape (64, 256, 1, 1) 2020-12-29 16:51:10,213 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer1.1.conv2.weight loaded from 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16:51:10,214 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer1.2.bn3.weight loaded from layer1.2.bn3.weight of shape (256,) 2020-12-29 16:51:10,214 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer1.2.conv1.weight loaded from layer1.2.conv1.weight of shape (64, 256, 1, 1) 2020-12-29 16:51:10,214 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer1.2.conv2.weight loaded from layer1.2.conv2.weight of shape (64, 64, 3, 3) 2020-12-29 16:51:10,214 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer1.2.conv3.weight loaded from layer1.2.conv3.weight of shape (256, 64, 1, 1) 2020-12-29 16:51:10,214 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer2.0.bn1.bias loaded from layer2.0.bn1.bias of shape (128,) 2020-12-29 16:51:10,214 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer2.0.bn1.weight loaded from layer2.0.bn1.weight of shape (128,) 2020-12-29 16:51:10,215 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16:51:10,216 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer2.1.bn2.bias loaded from layer2.1.bn2.bias of shape (128,) 2020-12-29 16:51:10,216 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer2.1.bn2.weight loaded from layer2.1.bn2.weight of shape (128,) 2020-12-29 16:51:10,217 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer2.1.bn3.bias loaded from layer2.1.bn3.bias of shape (512,) 2020-12-29 16:51:10,217 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer2.1.bn3.weight loaded from layer2.1.bn3.weight of shape (512,) 2020-12-29 16:51:10,217 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer2.1.conv1.weight loaded from layer2.1.conv1.weight of shape (128, 512, 1, 1) 2020-12-29 16:51:10,217 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer2.1.conv2.weight loaded from layer2.1.conv2.weight of shape (128, 128, 3, 3) 2020-12-29 16:51:10,217 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backbone.body.layer2.2.bn3.weight loaded from layer2.2.bn3.weight of shape (512,) 2020-12-29 16:51:10,218 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer2.2.conv1.weight loaded from layer2.2.conv1.weight of shape (128, 512, 1, 1) 2020-12-29 16:51:10,218 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer2.2.conv2.weight loaded from layer2.2.conv2.weight of shape (128, 128, 3, 3) 2020-12-29 16:51:10,218 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer2.2.conv3.weight loaded from layer2.2.conv3.weight of shape (512, 128, 1, 1) 2020-12-29 16:51:10,218 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer2.3.bn1.bias loaded from layer2.3.bn1.bias of shape (128,) 2020-12-29 16:51:10,218 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer2.3.bn1.weight loaded from layer2.3.bn1.weight of shape (128,) 2020-12-29 16:51:10,219 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer2.3.bn2.bias loaded from layer2.3.bn2.bias of shape (128,) 2020-12-29 16:51:10,219 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer2.3.bn2.weight loaded from layer2.3.bn2.weight of shape (128,) 2020-12-29 16:51:10,219 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer2.3.bn3.bias loaded from layer2.3.bn3.bias of shape (512,) 2020-12-29 16:51:10,219 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer2.3.bn3.weight loaded from layer2.3.bn3.weight of shape (512,) 2020-12-29 16:51:10,219 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer2.3.conv1.weight loaded from layer2.3.conv1.weight of shape (128, 512, 1, 1) 2020-12-29 16:51:10,219 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer2.3.conv2.weight loaded from layer2.3.conv2.weight of shape (128, 128, 3, 3) 2020-12-29 16:51:10,219 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer2.3.conv3.weight loaded from layer2.3.conv3.weight of shape (512, 128, 1, 1) 2020-12-29 16:51:10,219 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer3.0.bn1.bias loaded from layer3.0.bn1.bias of shape (256,) 2020-12-29 16:51:10,219 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer3.0.bn1.weight loaded from layer3.0.bn1.weight of shape (256,) 2020-12-29 16:51:10,220 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer3.0.bn2.bias loaded from layer3.0.bn2.bias of shape (256,) 2020-12-29 16:51:10,220 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer3.0.bn2.weight loaded from layer3.0.bn2.weight of shape (256,) 2020-12-29 16:51:10,220 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer3.0.bn3.bias loaded from layer3.0.bn3.bias of shape (1024,) 2020-12-29 16:51:10,220 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer3.0.bn3.weight loaded from layer3.0.bn3.weight of shape (1024,) 2020-12-29 16:51:10,220 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer3.0.conv1.weight loaded from layer3.0.conv1.weight of shape (256, 512, 1, 1) 2020-12-29 16:51:10,220 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer3.0.conv2.weight loaded from layer3.0.conv2.weight of shape (256, 256, 3, 3) 2020-12-29 16:51:10,220 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer3.0.conv3.weight loaded from layer3.0.conv3.weight of shape (1024, 256, 1, 1) 2020-12-29 16:51:10,220 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer3.0.downsample.0.weight loaded from layer3.0.downsample.0.weight of shape (1024, 512, 1, 1) 2020-12-29 16:51:10,221 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer3.0.downsample.1.bias loaded from layer3.0.downsample.1.bias of shape (1024,) 2020-12-29 16:51:10,221 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer3.0.downsample.1.weight loaded from layer3.0.downsample.1.weight of shape (1024,) 2020-12-29 16:51:10,221 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer3.1.bn1.bias loaded from layer3.1.bn1.bias of shape (256,) 2020-12-29 16:51:10,221 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer3.1.bn1.weight loaded from layer3.1.bn1.weight of shape (256,) 2020-12-29 16:51:10,221 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer3.1.bn2.bias loaded from layer3.1.bn2.bias of shape (256,) 2020-12-29 16:51:10,221 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer3.1.bn2.weight loaded from layer3.1.bn2.weight of shape (256,) 2020-12-29 16:51:10,221 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer3.1.bn3.bias loaded from layer3.1.bn3.bias of shape (1024,) 2020-12-29 16:51:10,221 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer3.1.bn3.weight loaded from layer3.1.bn3.weight of shape (1024,) 2020-12-29 16:51:10,222 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer3.1.conv1.weight loaded from layer3.1.conv1.weight of shape (256, 1024, 1, 1) 2020-12-29 16:51:10,222 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer3.1.conv2.weight loaded from layer3.1.conv2.weight of shape (256, 256, 3, 3) 2020-12-29 16:51:10,222 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer3.1.conv3.weight loaded from layer3.1.conv3.weight of shape (1024, 256, 1, 1) 2020-12-29 16:51:10,222 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer3.2.bn1.bias loaded from layer3.2.bn1.bias of shape (256,) 2020-12-29 16:51:10,222 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer3.2.bn1.weight loaded from layer3.2.bn1.weight of shape (256,) 2020-12-29 16:51:10,222 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer3.2.bn2.bias loaded from layer3.2.bn2.bias of shape (256,) 2020-12-29 16:51:10,222 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backbone.body.layer3.3.conv1.weight loaded from layer3.3.conv1.weight of shape (256, 1024, 1, 1) 2020-12-29 16:51:10,224 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer3.3.conv2.weight loaded from layer3.3.conv2.weight of shape (256, 256, 3, 3) 2020-12-29 16:51:10,224 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer3.3.conv3.weight loaded from layer3.3.conv3.weight of shape (1024, 256, 1, 1) 2020-12-29 16:51:10,224 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer3.4.bn1.bias loaded from layer3.4.bn1.bias of shape (256,) 2020-12-29 16:51:10,224 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer3.4.bn1.weight loaded from layer3.4.bn1.weight of shape (256,) 2020-12-29 16:51:10,224 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer3.4.bn2.bias loaded from layer3.4.bn2.bias of shape (256,) 2020-12-29 16:51:10,225 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer3.4.bn2.weight loaded from layer3.4.bn2.weight of shape (256,) 2020-12-29 16:51:10,225 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer3.4.bn3.bias loaded from layer3.4.bn3.bias of shape (1024,) 2020-12-29 16:51:10,225 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer3.4.bn3.weight loaded from layer3.4.bn3.weight of shape (1024,) 2020-12-29 16:51:10,225 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer3.4.conv1.weight loaded from layer3.4.conv1.weight of shape (256, 1024, 1, 1) 2020-12-29 16:51:10,225 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer3.4.conv2.weight loaded from layer3.4.conv2.weight of shape (256, 256, 3, 3) 2020-12-29 16:51:10,225 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer3.4.conv3.weight loaded from layer3.4.conv3.weight of shape (1024, 256, 1, 1) 2020-12-29 16:51:10,225 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer3.5.bn1.bias loaded from layer3.5.bn1.bias of shape (256,) 2020-12-29 16:51:10,225 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer3.5.bn1.weight loaded from layer3.5.bn1.weight of shape (256,) 2020-12-29 16:51:10,226 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer3.5.bn2.bias loaded from layer3.5.bn2.bias of shape (256,) 2020-12-29 16:51:10,226 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer3.5.bn2.weight loaded from layer3.5.bn2.weight of shape (256,) 2020-12-29 16:51:10,226 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer3.5.bn3.bias loaded from layer3.5.bn3.bias of shape (1024,) 2020-12-29 16:51:10,226 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer3.5.bn3.weight loaded from layer3.5.bn3.weight of shape (1024,) 2020-12-29 16:51:10,226 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer3.5.conv1.weight loaded from layer3.5.conv1.weight of shape (256, 1024, 1, 1) 2020-12-29 16:51:10,226 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer3.5.conv2.weight loaded from layer3.5.conv2.weight of shape (256, 256, 3, 3) 2020-12-29 16:51:10,226 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.layer3.5.conv3.weight loaded from layer3.5.conv3.weight of shape (1024, 256, 1, 1) 2020-12-29 16:51:10,226 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.stem.bn1.bias loaded from bn1.bias of shape (64,) 2020-12-29 16:51:10,227 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.stem.bn1.weight loaded from bn1.weight of shape (64,) 2020-12-29 16:51:10,227 maskrcnn_benchmark.utils.model_serialization INFO: backbone.body.stem.conv1.weight loaded from conv1.weight of shape (64, 3, 7, 7) 2020-12-29 16:51:10,245 maskrcnn_benchmark.data.build WARNING: When using more than one image per GPU you may encounter an out-of-memory (OOM) error if your GPU does not have sufficient memory. If this happens, you can reduce SOLVER.IMS_PER_BATCH (for training) or TEST.IMS_PER_BATCH (for inference). For training, you must also adjust the learning rate and schedule length according to the linear scaling rule. See for example: https://github.com/facebookresearch/Detectron/blob/master/configs/getting_started/tutorial_1gpu_e2e_faster_rcnn_R-50-FPN.yaml#L14 loading annotations into memory... Done (t=6.74s) creating index... index created! loading annotations into memory... Done (t=3.82s) creating index... index created! 2020-12-29 16:51:24,816 maskrcnn_benchmark.utils.miscellaneous WARNING: Dataset [ConcatDataset] has no categories attribute, labels.json file won't be created 2020-12-29 16:51:25,556 maskrcnn_benchmark.trainer INFO: Start training Expected: %s to exist. NoneType: None Expected: %s to exist. NoneType: None Expected: %s to exist. NoneType: None Expected: %s to exist. NoneType: None Traceback (most recent call last): File "/root/.pycharm_helpers/pydev/pydevd.py", line 1434, in _exec pydev_imports.execfile(file, globals, locals) # execute the script File "/root/.pycharm_helpers/pydev/_pydev_imps/_pydev_execfile.py", line 18, in execfile exec(compile(contents+"\n", file, 'exec'), glob, loc) File "/root/maskrcnn-benchmark/tools/train_net.py", line 201, in main() File "/root/maskrcnn-benchmark/tools/train_net.py", line 194, in main model = train(cfg, args.local_rank, args.distributed) File "/root/maskrcnn-benchmark/tools/train_net.py", line 94, in train arguments, File "/root/maskrcnn-benchmark/maskrcnn_benchmark/engine/trainer.py", line 72, in dotrain for iteration, (images, targets, ) in enumerate(data_loader, start_iter): File "/miniconda/envs/py36_test_install_torchvision/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 551, in next return self._process_next_batch(batch) File "/miniconda/envs/py36_test_install_torchvision/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 572, in _process_next_batch raise batch.exc_type(batch.exc_msg) IndexError: Traceback (most recent call last): File "/miniconda/envs/py36_test_install_torchvision/lib/python3.6/site-packages/torch/utils/data/_utils/worker.py", line 99, in _worker_loop samples = collate_fn([dataset[i] for i in batch_indices]) File "/miniconda/envs/py36_test_install_torchvision/lib/python3.6/site-packages/torch/utils/data/_utils/worker.py", line 99, in samples = collate_fn([dataset[i] for i in batch_indices]) File "/miniconda/envs/py36_test_install_torchvision/lib/python3.6/site-packages/torch/utils/data/dataset.py", line 85, in getitem return self.datasets[dataset_idx][sample_idx] File "/root/maskrcnn-benchmark/maskrcnn_benchmark/data/datasets/coco.py", line 94, in getitem target = target.clip_to_image(remove_empty=True) File "/root/maskrcnn-benchmark/maskrcnn_benchmark/structures/bounding_box.py", line 223, in clip_to_image return self[keep] File "/root/maskrcnn-benchmark/maskrcnn_benchmark/structures/bounding_box.py", line 208, in getitem bbox.add_field(k, v[item]) File "/root/maskrcnn-benchmark/maskrcnn_benchmark/structures/segmentation_mask.py", line 555, in getitem selected_instances = self.instances.getitem(item) File "/root/maskrcnn-benchmark/maskrcnn_benchmark/structures/segmentation_mask.py", line 464, in getitem selected_polygons.append(self.polygons[i]) IndexError: list index out of range

ERROR: Unexpected bus error encountered in worker. This might be caused by insufficient shared memory (shm). We've got an error while stopping in post-mortem: <class 'IndexError'>

Process finished with exit code 1

MishMick commented 3 years ago

If you are runnning via docker, then give --shm-size 24G in docker run command(For eg : docker run -it --shm-size=24G docker_name .....) or higher if required to make it work. It indicates the shared memory between the docker and system to pass data between the processes.

Also as error is due to list index out of range, there is a possibility that self.polygons array might be empty. Just print that and see whether it has the info needed.

terU3760 commented 3 years ago

@MishMick So many thanks for your advice!