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This directory contains few tools for HGFormer.

  • convert-torchvision-to-d2.py

Tool to convert torchvision pre-trained weights for D2.

wget https://download.pytorch.org/models/resnet101-63fe2227.pth
python tools/convert-torchvision-to-d2.py resnet101-63fe2227.pth R-101.pkl
  • convert-pretrained-swin-model-to-d2.py

Tool to convert Swin Transformer pre-trained weights for D2.

pip install timm

wget https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth
python tools/convert-pretrained-swin-model-to-d2.py swin_tiny_patch4_window7_224.pth swin_tiny_patch4_window7_224.pkl

wget https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_small_patch4_window7_224.pth
python tools/convert-pretrained-swin-model-to-d2.py swin_small_patch4_window7_224.pth swin_small_patch4_window7_224.pkl

wget https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384_22k.pth
python tools/convert-pretrained-swin-model-to-d2.py swin_base_patch4_window12_384_22k.pth swin_base_patch4_window12_384_22k.pkl

wget https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth
python tools/convert-pretrained-swin-model-to-d2.py swin_large_patch4_window12_384_22k.pth swin_large_patch4_window12_384_22k.pkl
  • evaluate_pq_for_semantic_segmentation.py

Tool to evaluate PQ (PQ-stuff) for semantic segmentation predictions.

Usage:

python tools/evaluate_pq_for_semantic_segmentation.py --dataset-name ade20k_sem_seg_val --json-file OUTPUT_DIR/inference/sem_seg_predictions.json

where OUTPUT_DIR is set in the config file.

  • evaluate_coco_boundary_ap.py

Tool to evaluate Boundary AP for instance segmentation predictions.

Usage:

python tools/coco_instance_evaluation.py --gt-json-file COCO_GT_JSON --dt-json-file COCO_DT_JSON

To install Boundary IoU API, run:

pip install git+https://github.com/bowenc0221/boundary-iou-api.git
  • analyze_model.py

Tool to analyze model parameters and flops.

Usage for semantic segmentation (ADE20K only, use with caution!):

python tools/analyze_model.py --num-inputs 1 --tasks flop --use-fixed-input-size --config-file CONFIG_FILE

Note that, for semantic segmentation (ADE20K only), we use a dummy image with fixed size that equals to cfg.INPUT.CROP.SIZE[0] x cfg.INPUT.CROP.SIZE[0]. Please do not use --use-fixed-input-size for calculating FLOPs on other datasets like Cityscapes!

Usage for panoptic and instance segmentation:

python tools/analyze_model.py --num-inputs 100 --tasks flop --config-file CONFIG_FILE

Note that, for panoptic and instance segmentation, we compute the average flops over 100 real validation images.