axis 1 is out of bounds for array of dimension 1

[14790897]

code:

https://www.kaggle.com/code/liuweiq/coincide-separation-detectron2-training

Environment:

kaggle

error:

[07/16 13:14:44 d2.engine.defaults]: Model:
GeneralizedRCNN(
  (backbone): FPN(
    (fpn_lateral2): Conv2d(256, 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(512, 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(1024, 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(2048, 256, kernel_size=(1, 1), stride=(1, 1))
    (fpn_output5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (top_block): LastLevelMaxPool()
    (bottom_up): ResNet(
      (stem): BasicStem(
        (conv1): Conv2d(
          3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False
          (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
        )
      )
      (res2): Sequential(
        (0): BottleneckBlock(
          (shortcut): Conv2d(
            64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv1): Conv2d(
            64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
          )
          (conv2): Conv2d(
            64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
          )
          (conv3): Conv2d(
            64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
        )
        (1): BottleneckBlock(
          (conv1): Conv2d(
            256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
          )
          (conv2): Conv2d(
            64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
          )
          (conv3): Conv2d(
            64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
        )
        (2): BottleneckBlock(
          (conv1): Conv2d(
            256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
          )
          (conv2): Conv2d(
            64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
          )
          (conv3): Conv2d(
            64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
        )
      )
      (res3): Sequential(
        (0): BottleneckBlock(
          (shortcut): Conv2d(
            256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
          (conv1): Conv2d(
            256, 128, kernel_size=(1, 1), stride=(2, 2), bias=False
            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
          )
          (conv2): Conv2d(
            128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
          )
          (conv3): Conv2d(
            128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
        )
        (1): BottleneckBlock(
          (conv1): Conv2d(
            512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
          )
          (conv2): Conv2d(
            128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
          )
          (conv3): Conv2d(
            128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
        )
        (2): BottleneckBlock(
          (conv1): Conv2d(
            512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
          )
          (conv2): Conv2d(
            128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
          )
          (conv3): Conv2d(
            128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
        )
        (3): BottleneckBlock(
          (conv1): Conv2d(
            512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
          )
          (conv2): Conv2d(
            128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
          )
          (conv3): Conv2d(
            128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
        )
      )
      (res4): Sequential(
        (0): BottleneckBlock(
          (shortcut): Conv2d(
            512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
          (conv1): Conv2d(
            512, 256, kernel_size=(1, 1), stride=(2, 2), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv2): Conv2d(
            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv3): Conv2d(
            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
        )
        (1): BottleneckBlock(
          (conv1): Conv2d(
            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv2): Conv2d(
            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv3): Conv2d(
            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
        )
        (2): BottleneckBlock(
          (conv1): Conv2d(
            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv2): Conv2d(
            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv3): Conv2d(
            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
        )
        (3): BottleneckBlock(
          (conv1): Conv2d(
            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv2): Conv2d(
            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv3): Conv2d(
            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
        )
        (4): BottleneckBlock(
          (conv1): Conv2d(
            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv2): Conv2d(
            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv3): Conv2d(
            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
        )
        (5): BottleneckBlock(
          (conv1): Conv2d(
            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv2): Conv2d(
            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv3): Conv2d(
            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
        )
      )
      (res5): Sequential(
        (0): BottleneckBlock(
          (shortcut): Conv2d(
            1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False
            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)
          )
          (conv1): Conv2d(
            1024, 512, kernel_size=(1, 1), stride=(2, 2), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
          (conv2): Conv2d(
            512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
          (conv3): Conv2d(
            512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)
          )
        )
        (1): BottleneckBlock(
          (conv1): Conv2d(
            2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
          (conv2): Conv2d(
            512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
          (conv3): Conv2d(
            512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)
          )
        )
        (2): BottleneckBlock(
          (conv1): Conv2d(
            2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
          (conv2): Conv2d(
            512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
          (conv3): Conv2d(
            512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)
          )
        )
      )
    )
  )
  (proposal_generator): RPN(
    (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): StandardROIHeads(
    (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)
      )
    )
    (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): FastRCNNOutputLayers(
      (cls_score): Linear(in_features=1024, out_features=3, bias=True)
      (bbox_pred): Linear(in_features=1024, out_features=8, bias=True)
    )
    (mask_pooler): ROIPooler(
      (level_poolers): ModuleList(
        (0): ROIAlign(output_size=(14, 14), spatial_scale=0.25, sampling_ratio=0, aligned=True)
        (1): ROIAlign(output_size=(14, 14), spatial_scale=0.125, sampling_ratio=0, aligned=True)
        (2): ROIAlign(output_size=(14, 14), spatial_scale=0.0625, sampling_ratio=0, aligned=True)
        (3): ROIAlign(output_size=(14, 14), spatial_scale=0.03125, sampling_ratio=0, aligned=True)
      )
    )
    (mask_head): MaskRCNNConvUpsampleHead(
      (mask_fcn1): Conv2d(
        256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
        (activation): ReLU()
      )
      (mask_fcn2): Conv2d(
        256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
        (activation): ReLU()
      )
      (mask_fcn3): Conv2d(
        256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
        (activation): ReLU()
      )
      (mask_fcn4): Conv2d(
        256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)
        (activation): ReLU()
      )
      (deconv): ConvTranspose2d(256, 256, kernel_size=(2, 2), stride=(2, 2))
      (deconv_relu): ReLU()
      (predictor): Conv2d(256, 2, kernel_size=(1, 1), stride=(1, 1))
    )
  )
)
[07/16 13:14:44 d2.data.datasets.coco]: Loaded 161 images in COCO format from /kaggle/input/coco-data/instances.json
[07/16 13:14:44 d2.data.build]: Removed 0 images with no usable annotations. 161 images left.
[07/16 13:14:44 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in training: [ResizeShortestEdge(short_edge_length=(640, 672, 704, 736, 768, 800), max_size=1333, sample_style='choice'), RandomFlip()]
[07/16 13:14:44 d2.data.build]: Using training sampler TrainingSampler
[07/16 13:14:44 d2.data.common]: Serializing the dataset using: <class 'detectron2.data.common._TorchSerializedList'>
[07/16 13:14:44 d2.data.common]: Serializing 161 elements to byte tensors and concatenating them all ...
[07/16 13:14:44 d2.data.common]: Serialized dataset takes 0.11 MiB
[07/16 13:14:44 d2.data.build]: Making batched data loader with batch_size=2
[07/16 13:14:44 d2.checkpoint.detection_checkpoint]: [DetectionCheckpointer] Loading from https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl ...
[07/16 13:14:44 d2.engine.train_loop]: Starting training from iteration 0
[07/16 13:14:49 d2.utils.events]:  eta: 0:08:08  iter: 19  total_loss: 2.388  loss_cls: 0.5612  loss_box_reg: 0.5152  loss_mask: 0.6743  loss_rpn_cls: 0.2725  loss_rpn_loc: 0.234    time: 0.2471  last_time: 0.2480  data_time: 0.0182  last_data_time: 0.0076   lr: 0.0005  max_mem: 2758M
[07/16 13:14:54 d2.utils.events]:  eta: 0:08:10  iter: 39  total_loss: 1.896  loss_cls: 0.3904  loss_box_reg: 0.7147  loss_mask: 0.5105  loss_rpn_cls: 0.0675  loss_rpn_loc: 0.2052    time: 0.2511  last_time: 0.2110  data_time: 0.0075  last_data_time: 0.0074   lr: 0.0005  max_mem: 2758M
[07/16 13:14:59 d2.utils.events]:  eta: 0:08:03  iter: 59  total_loss: 1.697  loss_cls: 0.3442  loss_box_reg: 0.682  loss_mask: 0.3977  loss_rpn_cls: 0.0367  loss_rpn_loc: 0.187    time: 0.2512  last_time: 0.2547  data_time: 0.0082  last_data_time: 0.0085   lr: 0.0005  max_mem: 2760M
[07/16 13:15:04 d2.utils.events]:  eta: 0:08:00  iter: 79  total_loss: 1.581  loss_cls: 0.311  loss_box_reg: 0.681  loss_mask: 0.3736  loss_rpn_cls: 0.02267  loss_rpn_loc: 0.1788    time: 0.2510  last_time: 0.2624  data_time: 0.0079  last_data_time: 0.0081   lr: 0.0005  max_mem: 2760M
[07/16 13:15:09 d2.utils.events]:  eta: 0:07:54  iter: 99  total_loss: 1.489  loss_cls: 0.3167  loss_box_reg: 0.6183  loss_mask: 0.3583  loss_rpn_cls: 0.01854  loss_rpn_loc: 0.1595    time: 0.2506  last_time: 0.2538  data_time: 0.0079  last_data_time: 0.0074   lr: 0.0005  max_mem: 2760M
[07/16 13:15:15 d2.utils.events]:  eta: 0:07:51  iter: 119  total_loss: 1.481  loss_cls: 0.3209  loss_box_reg: 0.6435  loss_mask: 0.3739  loss_rpn_cls: 0.01431  loss_rpn_loc: 0.1781    time: 0.2525  last_time: 0.2795  data_time: 0.0081  last_data_time: 0.0086   lr: 0.0005  max_mem: 2760M
[07/16 13:15:20 d2.utils.events]:  eta: 0:07:48  iter: 139  total_loss: 1.421  loss_cls: 0.2869  loss_box_reg: 0.6089  loss_mask: 0.3471  loss_rpn_cls: 0.009652  loss_rpn_loc: 0.1751    time: 0.2532  last_time: 0.2746  data_time: 0.0076  last_data_time: 0.0076   lr: 0.0005  max_mem: 2760M
[07/16 13:15:25 d2.utils.events]:  eta: 0:07:42  iter: 159  total_loss: 1.42  loss_cls: 0.297  loss_box_reg: 0.6114  loss_mask: 0.3572  loss_rpn_cls: 0.01175  loss_rpn_loc: 0.1626    time: 0.2536  last_time: 0.2537  data_time: 0.0074  last_data_time: 0.0081   lr: 0.0005  max_mem: 2760M
[07/16 13:15:30 d2.utils.events]:  eta: 0:07:36  iter: 179  total_loss: 1.343  loss_cls: 0.2586  loss_box_reg: 0.5469  loss_mask: 0.3461  loss_rpn_cls: 0.009276  loss_rpn_loc: 0.1687    time: 0.2530  last_time: 0.2379  data_time: 0.0082  last_data_time: 0.0081   lr: 0.0005  max_mem: 2760M
[07/16 13:15:35 d2.utils.events]:  eta: 0:07:31  iter: 199  total_loss: 1.45  loss_cls: 0.2805  loss_box_reg: 0.6134  loss_mask: 0.3579  loss_rpn_cls: 0.008294  loss_rpn_loc: 0.173    time: 0.2525  last_time: 0.2530  data_time: 0.0081  last_data_time: 0.0081   lr: 0.0005  max_mem: 2760M
[07/16 13:15:40 d2.utils.events]:  eta: 0:07:26  iter: 219  total_loss: 1.382  loss_cls: 0.2553  loss_box_reg: 0.5704  loss_mask: 0.3324  loss_rpn_cls: 0.009328  loss_rpn_loc: 0.1638    time: 0.2528  last_time: 0.2514  data_time: 0.0082  last_data_time: 0.0073   lr: 0.0005  max_mem: 2760M
[07/16 13:15:45 d2.utils.events]:  eta: 0:07:22  iter: 239  total_loss: 1.34  loss_cls: 0.2805  loss_box_reg: 0.5728  loss_mask: 0.3491  loss_rpn_cls: 0.01016  loss_rpn_loc: 0.1435    time: 0.2536  last_time: 0.2491  data_time: 0.0078  last_data_time: 0.0079   lr: 0.0005  max_mem: 2760M
[07/16 13:15:49 d2.data.datasets.coco]: Loaded 135 images in COCO format from /kaggle/input/coco-data-val/instances.json
[07/16 13:15:49 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]
[07/16 13:15:49 d2.data.common]: Serializing the dataset using: <class 'detectron2.data.common._TorchSerializedList'>
[07/16 13:15:49 d2.data.common]: Serializing 135 elements to byte tensors and concatenating them all ...
[07/16 13:15:49 d2.data.common]: Serialized dataset takes 0.02 MiB
[07/16 13:15:49 d2.data.datasets.coco]: Loaded 135 images in COCO format from /kaggle/input/coco-data-val/instances.json
[07/16 13:15:49 d2.evaluation.evaluator]: Start inference on 135 batches
ERROR [07/16 13:15:49 d2.engine.train_loop]: Exception during training:
Traceback (most recent call last):
  File "/opt/conda/lib/python3.10/site-packages/detectron2/engine/train_loop.py", line 156, in train
    self.after_step()
  File "/opt/conda/lib/python3.10/site-packages/detectron2/engine/train_loop.py", line 190, in after_step
    h.after_step()
  File "/opt/conda/lib/python3.10/site-packages/detectron2/engine/hooks.py", line 556, in after_step
    self._do_eval()
  File "/opt/conda/lib/python3.10/site-packages/detectron2/engine/hooks.py", line 529, in _do_eval
    results = self._func()
  File "/opt/conda/lib/python3.10/site-packages/detectron2/engine/defaults.py", line 457, in test_and_save_results
    self._last_eval_results = self.test(self.cfg, self.model)
  File "/opt/conda/lib/python3.10/site-packages/detectron2/engine/defaults.py", line 621, in test
    results_i = inference_on_dataset(model, data_loader, evaluator)
  File "/opt/conda/lib/python3.10/site-packages/detectron2/evaluation/evaluator.py", line 172, in inference_on_dataset
    evaluator.process(inputs, outputs)
  File "/tmp/ipykernel_34/3164767280.py", line 37, in process
    self.scores.append(score(out, targ))
  File "/tmp/ipykernel_34/3164767280.py", line 18, in score
    tp, fp, fn = precision_at(t, ious)
  File "/tmp/ipykernel_34/3164767280.py", line 6, in precision_at
    true_positives = np.sum(matches, axis=1) == 1  # Correct objects
  File "/opt/conda/lib/python3.10/site-packages/numpy/core/fromnumeric.py", line 2313, in sum
    return _wrapreduction(a, np.add, 'sum', axis, dtype, out, keepdims=keepdims,
  File "/opt/conda/lib/python3.10/site-packages/numpy/core/fromnumeric.py", line 88, in _wrapreduction
    return ufunc.reduce(obj, axis, dtype, out, **passkwargs)
numpy.exceptions.AxisError: axis 1 is out of bounds for array of dimension 1
[07/16 13:15:49 d2.engine.hooks]: Overall training speed: 247 iterations in 0:01:02 (0.2544 s / it)
[07/16 13:15:49 d2.engine.hooks]: Total training time: 0:01:04 (0:00:01 on hooks)
[07/16 13:15:49 d2.utils.events]:  eta: 0:07:19  iter: 249  total_loss: 1.29  loss_cls: 0.2537  loss_box_reg: 0.5709  loss_mask: 0.3331  loss_rpn_cls: 0.009182  loss_rpn_loc: 0.1365    time: 0.2534  last_time: 0.2509  data_time: 0.0077  last_data_time: 0.0082   lr: 0.0005  max_mem: 2760M
---------------------------------------------------------------------------
AxisError                                 Traceback (most recent call last)
Cell In[9], line 34
     32 trainer = Trainer(cfg)  # without data augmentation
     33 trainer.resume_or_load(resume=False)
---> 34 trainer.train()

File /opt/conda/lib/python3.10/site-packages/detectron2/engine/defaults.py:488, in DefaultTrainer.train(self)
    481 def train(self):
    482     """
    483     Run training.
    484 
    485     Returns:
    486         OrderedDict of results, if evaluation is enabled. Otherwise None.
    487     """
--> 488     super().train(self.start_iter, self.max_iter)
    489     if len(self.cfg.TEST.EXPECTED_RESULTS) and comm.is_main_process():
    490         assert hasattr(
    491             self, "_last_eval_results"
    492         ), "No evaluation results obtained during training!"

File /opt/conda/lib/python3.10/site-packages/detectron2/engine/train_loop.py:156, in TrainerBase.train(self, start_iter, max_iter)
    154     self.before_step()
    155     self.run_step()
--> 156     self.after_step()
    157 # self.iter == max_iter can be used by `after_train` to
    158 # tell whether the training successfully finished or failed
    159 # due to exceptions.
    160 self.iter += 1

File /opt/conda/lib/python3.10/site-packages/detectron2/engine/train_loop.py:190, in TrainerBase.after_step(self)
    188 def after_step(self):
    189     for h in self._hooks:
--> 190         h.after_step()

File /opt/conda/lib/python3.10/site-packages/detectron2/engine/hooks.py:556, in EvalHook.after_step(self)
    553 if self._period > 0 and next_iter % self._period == 0:
    554     # do the last eval in after_train
    555     if next_iter != self.trainer.max_iter:
--> 556         self._do_eval()

File /opt/conda/lib/python3.10/site-packages/detectron2/engine/hooks.py:529, in EvalHook._do_eval(self)
    528 def _do_eval(self):
--> 529     results = self._func()
    531     if results:
    532         assert isinstance(
    533             results, dict
    534         ), "Eval function must return a dict. Got {} instead.".format(results)

File /opt/conda/lib/python3.10/site-packages/detectron2/engine/defaults.py:457, in DefaultTrainer.build_hooks.<locals>.test_and_save_results()
    456 def test_and_save_results():
--> 457     self._last_eval_results = self.test(self.cfg, self.model)
    458     return self._last_eval_results

File /opt/conda/lib/python3.10/site-packages/detectron2/engine/defaults.py:621, in DefaultTrainer.test(cls, cfg, model, evaluators)
    619         results[dataset_name] = {}
    620         continue
--> 621 results_i = inference_on_dataset(model, data_loader, evaluator)
    622 results[dataset_name] = results_i
    623 if comm.is_main_process():

File /opt/conda/lib/python3.10/site-packages/detectron2/evaluation/evaluator.py:172, in inference_on_dataset(model, data_loader, evaluator, callbacks)
    169 total_compute_time += time.perf_counter() - start_compute_time
    171 start_eval_time = time.perf_counter()
--> 172 evaluator.process(inputs, outputs)
    173 total_eval_time += time.perf_counter() - start_eval_time
    175 iters_after_start = idx + 1 - num_warmup * int(idx >= num_warmup)

Cell In[7], line 37, in MAPIOUEvaluator.process(self, inputs, outputs)
     35 else:
     36     targ = self.annotations_cache[inp['image_id']]
---> 37     self.scores.append(score(out, targ))

Cell In[7], line 18, in score(pred, targ)
     16 prec = []
     17 for t in np.arange(0.5, 1.0, 0.05):
---> 18     tp, fp, fn = precision_at(t, ious)
     19     p = tp / (tp + fp + fn)
     20     prec.append(p)

Cell In[7], line 6, in precision_at(threshold, iou)
      4 def precision_at(threshold, iou):
      5     matches = iou > threshold
----> 6     true_positives = np.sum(matches, axis=1) == 1  # Correct objects
      7     false_positives = np.sum(matches, axis=0) == 0  # Missed objects
      8     false_negatives = np.sum(matches, axis=1) == 0  # Extra objects

File /opt/conda/lib/python3.10/site-packages/numpy/core/fromnumeric.py:2313, in sum(a, axis, dtype, out, keepdims, initial, where)
   2310         return out
   2311     return res
-> 2313 return _wrapreduction(a, np.add, 'sum', axis, dtype, out, keepdims=keepdims,
   2314                       initial=initial, where=where)

File /opt/conda/lib/python3.10/site-packages/numpy/core/fromnumeric.py:88, in _wrapreduction(obj, ufunc, method, axis, dtype, out, **kwargs)
     85         else:
     86             return reduction(axis=axis, out=out, **passkwargs)
---> 88 return ufunc.reduce(obj, axis, dtype, out, **passkwargs)

AxisError: axis 1 is out of bounds for array of dimension 1

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