SegFormer error with segmentation map

Please provide the following information when requesting support.
• Hardware (T4/V100/Xavier/Nano/etc): T4
• Network Type (Detectnet_v2/Faster_rcnn/Yolo_v4/LPRnet/Mask_rcnn/Classification/etc): SegFormer
• TLT Version: docker tag 5.5.0-pyt
• Training spec file(If have, please share here): attached at end
• How to reproduce the issue ? (This is for errors. Please share the command line and the detailed log here.)

Command line: tao model segformer train
-e $SPECS_DIR/train_leo.yaml
results_dir=$RESULTS_DIR/leo

I am trying to use the TAO SegFormer 5.5.0 model to do semantic segmentation on 1032x772 color images. Note that I have grayscale ground truth files but am getting the following warning:

/usr/local/lib/python3.10/dist-packages/mmseg/datasets/transforms/formatting.py:81: UserWarning: Please pay attention your ground truth segmentation map, usually the segmentation map is 2D, but got (772, 1032, 3)

And later I get the following error:

File “/usr/local/lib/python3.10/dist-packages/mmseg/evaluation/metrics/iou_metric.py”, line 186, in intersect_and_union
pred_label = pred_label[mask]IndexError: too many indices for tensor of dimension 2

This is confusing because even though I have grayscale ground truth it seems like it’s treating them as color.

Any help would be appreciated.

Duane Harkness
Rendered.ai

Additional information - Here is my specs file

train:
  exp_config:
      manual_seed: 49
  checkpoint_interval: 200
  logging_interval: 50
  max_iters: 1000
  resume_training_checkpoint_path: null
  trainer:
      find_unused_parameters: True
      sf_optim:
        lr: 0.00006
model:
  input_height: 772
  input_width: 1032
  pretrained_model_path: null
  backbone:
    type: "mit_b1"
dataset:
  input_type: "rgb"
  img_norm_cfg:
        mean:
          - 127.5
          - 127.5
          - 127.5
        std:
          - 127.5
          - 127.5
          - 127.5
  data_root: /data
  train_dataset:
      img_dir:
        - /data/images/train
      ann_dir:
        - /data/masks/train
      pipeline:
        augmentation_config:
          random_crop:
            cat_max_ratio: 0.75
          resize:
            ratio_range:
              - 0.5
              - 2.0
          random_flip:
            prob: 0.5
  palette:
    - seg_class: background
      rgb:
        - 0
        - 0
        - 0
      label_id: 0
      mapping_class: background
    - seg_class: cable
      rgb:
        - 255
        - 255
        - 255
      label_id: 1
      mapping_class: cable
  repeat_data_times: 500
  batch_size: 4
  workers_per_gpu: 1

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