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  1. .gitattributes +2 -0
  2. .python-version +1 -0
  3. README +13 -0
  4. README.md +0 -0
  5. cityscapes/gtFine/test/bonn/bonn_000005_000019_gtFine_color.png +0 -0
  6. cityscapes/gtFine/test/bonn/bonn_000014_000019_gtFine_labelIds.png +0 -0
  7. cityscapes/gtFine/test/bonn/bonn_000015_000019_gtFine_labelIds.png +0 -0
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  10. cityscapes/gtFine/test/bonn/bonn_000025_000019_gtFine_labelIds.png +0 -0
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  13. cityscapes/gtFine/test/bonn/bonn_000030_000019_gtFine_polygons.json +132 -0
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  26. cityscapes/leftImg8bit/train/darmstadt/darmstadt_000003_000019_leftImg8bit.png +3 -0
  27. cityscapes/splits/fold_1_train_split.txt +2316 -0
  28. cityscapes/splits/fold_1_val_split.txt +1159 -0
  29. cityscapes/splits/fold_2_train_split.txt +2317 -0
  30. cityscapes/splits/fold_2_val_split.txt +1158 -0
  31. cityscapes/splits/fold_3_train_split.txt +2317 -0
  32. cityscapes/splits/fold_3_val_split.txt +1158 -0
  33. cookies.txt +5 -0
  34. homework4.pdf +3 -0
  35. index.html +350 -0
  36. license.txt +23 -0
  37. main.py +6 -0
  38. pyproject.toml +17 -0
  39. segmentation/README.md +298 -0
  40. segmentation/deploy.py +308 -0
  41. segmentation/dist_test.sh +9 -0
  42. segmentation/dist_train.sh +9 -0
  43. segmentation/get_flops.py +120 -0
  44. segmentation/image_demo.py +79 -0
  45. segmentation/prepare_cityscapes_folds.py +146 -0
  46. segmentation/run_kfold_training.sh +82 -0
  47. segmentation/slurm_test.sh +25 -0
  48. segmentation/slurm_train.sh +24 -0
  49. segmentation/test.py +287 -0
  50. segmentation/train.py +306 -0
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+ README and scripts
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+ ------------------
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+ The README and various scripts for inspection, preparation, and evaluation can be found in our git repository:
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+ https://github.com/mcordts/cityscapesScripts
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+
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+ Contact
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+ -------
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+
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+ Marius Cordts, Mohamed Omran
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+ www.cityscapes-dataset.net
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+ [email protected]
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852
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853
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854
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855
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856
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857
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858
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859
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860
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861
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862
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863
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864
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865
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866
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867
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868
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869
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870
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871
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872
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873
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874
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875
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876
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877
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878
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879
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880
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881
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882
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883
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884
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885
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886
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887
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888
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889
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890
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891
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892
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893
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894
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895
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896
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897
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898
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899
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900
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901
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902
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903
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904
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905
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906
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907
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908
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909
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910
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911
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912
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913
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914
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915
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916
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917
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918
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919
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920
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921
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922
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923
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924
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925
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926
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927
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928
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929
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930
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931
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932
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933
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934
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935
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936
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937
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938
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939
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940
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941
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942
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943
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944
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945
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946
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947
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948
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949
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950
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951
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952
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953
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954
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955
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956
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957
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958
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959
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960
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961
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962
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963
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964
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965
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966
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967
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968
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969
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970
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971
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972
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973
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974
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975
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976
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977
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978
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979
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980
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981
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982
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983
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984
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985
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986
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987
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988
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989
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990
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991
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992
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993
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994
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995
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996
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997
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998
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999
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1000
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1001
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1002
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1003
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1004
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1005
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1006
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1007
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1008
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1009
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1010
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1011
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1012
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1013
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1014
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1015
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1016
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1017
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1018
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1019
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1020
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1021
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1022
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1023
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1024
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1025
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1026
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1027
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1028
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1029
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1030
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1031
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1032
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1033
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1034
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1035
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1036
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1037
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1038
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1039
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1040
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1041
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1042
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1043
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1044
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1045
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1046
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1047
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1048
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1049
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1050
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1051
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1052
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1053
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1054
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1055
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1056
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1057
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1058
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1059
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1060
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1061
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1062
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1063
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1064
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1065
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1066
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1067
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1068
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1069
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1070
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1071
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1072
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1073
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1074
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1075
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1076
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1077
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1078
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1079
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1080
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1081
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1082
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1083
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1084
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1085
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1086
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1087
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1088
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1089
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1090
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1091
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1092
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1093
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1094
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1095
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1096
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1097
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1098
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1099
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1100
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1101
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1102
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1103
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1104
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1105
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1106
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1107
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1108
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1109
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1110
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1111
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1112
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1113
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1114
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1115
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1116
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1117
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1118
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1119
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1120
+ train/strasbourg/strasbourg_000001_002519
1121
+ train/hanover/hanover_000000_002458
1122
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1123
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1124
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1125
+ train/ulm/ulm_000089_000019
1126
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1127
+ train/aachen/aachen_000082_000019
1128
+ val/munster/munster_000114_000019
1129
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1130
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1131
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1132
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1133
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1134
+ train/strasbourg/strasbourg_000001_057811
1135
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1136
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1137
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1138
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1139
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1140
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1141
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1142
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1143
+ train/dusseldorf/dusseldorf_000207_000019
1144
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1145
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1146
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1147
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1148
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1149
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1150
+ train/aachen/aachen_000104_000019
1151
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1152
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1153
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1154
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1155
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1156
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1157
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1158
+ train/dusseldorf/dusseldorf_000057_000019
1159
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+ <div id="tc-page-wrap" class="">
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+
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+ <a class="site-logo" href="https://www.cityscapes-dataset.com/" aria-label="Cityscapes Dataset | Semantic Understanding of Urban Street Scenes"><img src="https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2015/07/cityscapesLogo.png" alt="Back Home" width="1999" height="369" style="max-width:250px;max-height:100px" data-no-retina class=" attachment-289"/></a> </div> <!-- brand span3 -->
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+ <li class="menu-item menu-item-type-custom menu-item-object-custom menu-item-70"><a href="https://www.cityscapes-dataset.com/dataset-overview/#labeling-policy">Labeling Policy</a></li>
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+ <li class="menu-item menu-item-type-custom menu-item-object-custom menu-item-71"><a href="https://www.cityscapes-dataset.com/dataset-overview/#class-definitions">Class Definitions</a></li>
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+ <li class="menu-item menu-item-type-post_type menu-item-object-page menu-item-has-children dropdown menu-item-57"><a href="https://www.cityscapes-dataset.com/examples/">Examples <strong class="caret"></strong></a>
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+ <ul class="dropdown-menu">
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+ <h1 class="entry-title ">Download</h1><hr class="featurette-divider __before_content"> </header>
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+ <p>Below, you can find all currently available downloads. A README and various scripts for inspection, preparation, and evaluation can be found in our <a href="https://github.com/mcordts/cityscapesScripts" target="_blank" rel="noopener noreferrer">git repository</a>.</p>
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+ <style type="text/css">
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+ /* LIST #2 */
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+ /*#csFileList { width:640px; }*/
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+ /* #csFileList ul { font-style:italic; font-size:18px; color:#999; } */
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+ #csPackageList ul { list-style-type: none; font-style:italic; font-size:18px; color:#999; }
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+ #csPackageList ul li p { padding:5px; padding-left:8px; font-style:normal; font-size:16px; color:#444; border-left: 1px solid #999; height: 80px; }
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+ #csPackageCategory h4 { padding-bottom:8px; padding-top: 12px }
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+ /*.li1 {
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+ background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/02/jena00_small.png) no-repeat no-repeat;
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+ background-size: 160px 90px;
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+ padding-left: 170px;
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+ }*/
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+ /*list-style-image: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2015/07/mohamed.jpg);*/
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+ </style>
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+ <div id='csPackageCategory'><h4>The following packages are available for download. Please download only those packages that you need for your immediate research.</h4><div id='csPackageList'><ul ><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/02/jena00_small.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px; '><p><em><a href='https://www.cityscapes-dataset.com/file-handling/?packageID=1'>gtFine_trainvaltest.zip (241MB)</a> <a href='https://www.cityscapes-dataset.com/md5-sum/?packageID=1' target='_blank'>[md5]</a></em> fine annotations for train and val sets (3475 annotated images) and dummy annotations (ignore regions) for the test set (1525 images)</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/02/erlangen00_small.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px; '><p><em><a href='https://www.cityscapes-dataset.com/file-handling/?packageID=2'>gtCoarse.zip (1.3GB)</a> <a href='https://www.cityscapes-dataset.com/md5-sum/?packageID=2' target='_blank'>[md5]</a></em> coarse annotations for train and val set (3475 annotated images) and train_extra (19998 annotated images)</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/02/berlin00056_img_small.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px; '><p><em><a href='https://www.cityscapes-dataset.com/file-handling/?packageID=3'>leftImg8bit_trainvaltest.zip (11GB)</a> <a href='https://www.cityscapes-dataset.com/md5-sum/?packageID=3' target='_blank'>[md5]</a></em> left 8-bit images - train, val, and test sets (5000 images)</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/02/berlin00056_img_small.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px; '><p><em><a href='https://www.cityscapes-dataset.com/file-handling/?packageID=4'>leftImg8bit_trainextra.zip (44GB)</a> <a href='https://www.cityscapes-dataset.com/md5-sum/?packageID=4' target='_blank'>[md5]</a></em> left 8-bit images - trainextra set (19998 images, note that the image "troisdorf_000000_000073_leftImg8bit.png" is corrupt/black)</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/02/berlin00056_img_small_stereo.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px; '><p><em><a href='https://www.cityscapes-dataset.com/file-handling/?packageID=5'>rightImg8bit_trainvaltest.zip (11GB)</a> <a href='https://www.cityscapes-dataset.com/md5-sum/?packageID=5' target='_blank'>[md5]</a></em> right 8-bit images - train, val, and test sets (5000 images)</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/02/berlin00056_img_small_stereo.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px; '><p><em><a href='https://www.cityscapes-dataset.com/file-handling/?packageID=6'>rightImg8bit_trainextra.zip (44GB)</a> <a href='https://www.cityscapes-dataset.com/md5-sum/?packageID=6' target='_blank'>[md5]</a></em> right 8-bit images - trainextra set (19998 images)</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/02/berlin00056_depth150.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px; '><p><em><a href='https://www.cityscapes-dataset.com/file-handling/?packageID=7'>disparity_trainvaltest.zip (3.5GB)</a> <a href='https://www.cityscapes-dataset.com/md5-sum/?packageID=7' target='_blank'>[md5]</a></em> precomputed depth maps using SGM for train, val, and test sets (5000 images)</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/02/berlin00056_depth150.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px; '><p><em><a href='https://www.cityscapes-dataset.com/file-handling/?packageID=22'>disparity_trainextra.zip (15GB)</a> <a href='https://www.cityscapes-dataset.com/md5-sum/?packageID=22' target='_blank'>[md5]</a></em> precomputed depth maps using SGM for trainextra set</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/02/cameraDownload.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px; '><p><em><a href='https://www.cityscapes-dataset.com/file-handling/?packageID=8'>camera_trainvaltest.zip (2MB)</a> <a href='https://www.cityscapes-dataset.com/md5-sum/?packageID=8' target='_blank'>[md5]</a></em> intrinsic and extrinsic camera parameters for train, val, and test sets</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/02/cameraDownload.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px; '><p><em><a href='https://www.cityscapes-dataset.com/file-handling/?packageID=9'>camera_trainextra.zip (8MB)</a> <a href='https://www.cityscapes-dataset.com/md5-sum/?packageID=9' target='_blank'>[md5]</a></em> intrinsic and extrinsic camera parameters for trainextra set</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/02/vehicleDownload.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px; '><p><em><a href='https://www.cityscapes-dataset.com/file-handling/?packageID=10'>vehicle_trainvaltest.zip (2MB)</a> <a href='https://www.cityscapes-dataset.com/md5-sum/?packageID=10' target='_blank'>[md5]</a></em> vehicle odometry + GPS coordinates + temperature for train, val, and test sets</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/02/vehicleDownload.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px; '><p><em><a href='https://www.cityscapes-dataset.com/file-handling/?packageID=11'>vehicle_trainextra.zip (7MB)</a> <a href='https://www.cityscapes-dataset.com/md5-sum/?packageID=11' target='_blank'>[md5]</a></em> vehicle odometry + GPS coordinates + temperature for trainextra set</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/03/demoVideoDownload.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px; '><p><em><a href='https://www.cityscapes-dataset.com/file-handling/?packageID=12'>leftImg8bit_demoVideo.zip (6.6GB)</a> <a href='https://www.cityscapes-dataset.com/md5-sum/?packageID=12' target='_blank'>[md5]</a></em> video sequences for qualitative evaluation, left 8-bit images only</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/03/demoVideoDownload.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px; '><p><em><a href='https://www.cityscapes-dataset.com/file-handling/?packageID=13'>all_demoVideo.zip (59GB)</a> <a href='https://www.cityscapes-dataset.com/md5-sum/?packageID=13' target='_blank'>[md5]</a></em> video sequences for qualitative evaluation, all types of data</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2019/01/downloadblurred.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px; '><p><em><a href='https://www.cityscapes-dataset.com/file-handling/?packageID=32'>leftImg8bit_blurred.zip (11GB)</a> <a href='https://www.cityscapes-dataset.com/md5-sum/?packageID=32' target='_blank'>[md5]</a></em> Left 8-bit images with blurred faces and license plates. Please compute results on the original images but use the blurred ones for visualization. We thank <a href="https://www.mapillary.com" target="_blank">Mapillary</a> for blurring the images.</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/02/berlin00056_img_small.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px; '><p><em><a href='https://www.cityscapes-dataset.com/file-handling/?packageID=14'>leftImg8bit_sequence_trainvaltest.zip (324GB)</a> <a href='https://www.cityscapes-dataset.com/md5-sum/?packageID=14' target='_blank'>[md5]</a></em> 30-frame snippets (17Hz) surrounding each left 8-bit image (-19 | +10) from the train, val, and test sets (150000 images)</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/03/timestampDownload.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px; '><p><em><a href='https://www.cityscapes-dataset.com/file-handling/?packageID=21'>timestamp_sequence.zip (40MB)</a> <a href='https://www.cityscapes-dataset.com/md5-sum/?packageID=21' target='_blank'>[md5]</a></em> timestamps for 30-frame snippets from train, val, and test sets</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/02/vehicleDownload.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px; '><p><em><a href='https://www.cityscapes-dataset.com/file-handling/?packageID=20'>vehicle_sequence.zip (56MB)</a> <a href='https://www.cityscapes-dataset.com/md5-sum/?packageID=20' target='_blank'>[md5]</a></em> vehicle odometry + GPS coordinates + temperature for 30-frame snippets from train, val, and test sets</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/02/berlin00056_img_small_stereo.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px; '><p><em><a href='https://www.cityscapes-dataset.com/file-handling/?packageID=15'>rightImg8bit_sequence_trainvaltest.zip (322GB)</a> <a href='https://www.cityscapes-dataset.com/md5-sum/?packageID=15' target='_blank'>[md5]</a></em> 30-frame snippets (17Hz) surrounding each right 8-bit image (-19 | +10) from the train, val, and test sets (150000 images)</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/02/berlin00056_depth150.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px; '><p><em><a href='https://www.cityscapes-dataset.com/file-handling/?packageID=26'>disparity_sequence_trainvaltest.zip (106GB)</a> <a href='https://www.cityscapes-dataset.com/md5-sum/?packageID=26' target='_blank'>[md5]</a></em> precomputed depth maps using SGM for 30-frame snippets from train, val, and test sets</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/02/berlin00056_img_small.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px; '><p><em><a href='https://www.cityscapes-dataset.com/file-handling/?packageID=23'>leftImg8bit_allFrames_frankfurt.zip (241GB)</a> <a href='https://www.cityscapes-dataset.com/md5-sum/?packageID=23' target='_blank'>[md5]</a></em> left 8-bit images for whole drive in frankfurt from val set</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/03/timestampDownload.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px; '><p><em><a href='https://www.cityscapes-dataset.com/file-handling/?packageID=24'>timestamp_allFrames_frankfurt.zip (29MB)</a> <a href='https://www.cityscapes-dataset.com/md5-sum/?packageID=24' target='_blank'>[md5]</a></em> timestamps for whole drive in frankfurt from val set</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/02/vehicleDownload.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px; '><p><em><a href='https://www.cityscapes-dataset.com/file-handling/?packageID=25'>vehicle_allFrames_frankfurt.zip (38MB)</a> <a href='https://www.cityscapes-dataset.com/md5-sum/?packageID=25' target='_blank'>[md5]</a></em> vehicle odometry + GPS coordinates + temperature for whole drive in frankfurt from val set</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/03/hdrLeftDownload.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px; '><p><em><a href='https://www.cityscapes-dataset.com/file-handling/?packageID=16'>leftImg16bit_trainvaltest.zip (37GB)</a> <a href='https://www.cityscapes-dataset.com/md5-sum/?packageID=16' target='_blank'>[md5]</a></em> left HDR images stored as 16-bit PNGs - train, val, and test sets (5000 images)</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/02/berlin00056_img_small_stereo.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px; '><p><em><a href='https://www.cityscapes-dataset.com/file-handling/?packageID=27'>rightImg8bit_allFrames_frankfurt.zip (242GB)</a> <a href='https://www.cityscapes-dataset.com/md5-sum/?packageID=27' target='_blank'>[md5]</a></em> right 8-bit images for whole drive in frankfurt from val set</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/03/hdrLeftDownload.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px; '><p><em><a href='https://www.cityscapes-dataset.com/file-handling/?packageID=17'>leftImg16bit_trainextra.zip (149GB)</a> <a href='https://www.cityscapes-dataset.com/md5-sum/?packageID=17' target='_blank'>[md5]</a></em> left HDR images stored as 16-bit PNGs - trainextra set (19998 images)</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/03/hdrRightDownload.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px; '><p><em><a href='https://www.cityscapes-dataset.com/file-handling/?packageID=18'>rightImg16bit_trainvaltest.zip (37GB)</a> <a href='https://www.cityscapes-dataset.com/md5-sum/?packageID=18' target='_blank'>[md5]</a></em> right HDR images stored as 16-bit PNGs - train, val, and test sets (5000 images)</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2016/03/hdrRightDownload.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px; '><p><em><a href='https://www.cityscapes-dataset.com/file-handling/?packageID=19'>rightImg16bit_trainextra.zip (149GB)</a> <a href='https://www.cityscapes-dataset.com/md5-sum/?packageID=19' target='_blank'>[md5]</a></em> right HDR images stored as 16-bit PNGs - trainextra set (19998 images)</p></li></ul></div></div>
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+ <div id='csPackageCategory'><hr><h4>The following packages from third party publications are available for download.</h4><p>If you use the packages in your research, please cite both publications, <a href='https://www.cityscapes-dataset.com/citation/'>Cityscapes</a> and the work listed in conjunction with the package. Please contact the respective authors directly in case of questions.</p><div id='csPackageList'><ul ><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2020/06/3dboxesDownload.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px; '><p><em><a href='https://www.cityscapes-dataset.com/file-handling/?packageID=34'>gtBbox3d_trainvaltest.zip (6.6MB)</a> <a href='https://www.cityscapes-dataset.com/md5-sum/?packageID=34' target='_blank'>[md5]</a></em> 3D bounding box annotations of vehicles for train and val sets (3475 annotated images). Citation: N. G&auml;hlert, N. Jourdan, M. Cordts, U. Franke, and J. Denzler, <a href="https://arxiv.org/abs/2006.07864" target="_blank">"Cityscapes 3D: Dataset and Benchmark for 9 DoF Vehicle Detection"</a>, CVPRW 2020.</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2017/08/citypersonsDownload.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px; '><p><em><a href='https://www.cityscapes-dataset.com/file-handling/?packageID=28'>gtBbox_cityPersons_trainval.zip (2.2MB)</a> <a href='https://www.cityscapes-dataset.com/md5-sum/?packageID=28' target='_blank'>[md5]</a></em> CityPersons bounding box annotations of people for train and val sets (3475 annotated images). Citation: S. Zhang, R. Benenson, and B. Schiele, <a href="http://openaccess.thecvf.com/content_cvpr_2017/html/Zhang_CityPersons_A_Diverse_CVPR_2017_paper.html" target="_blank">"CityPersons: A Diverse Dataset for Pedestrian Detection"</a>, CVPR 2017.</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2021/01/panopticPartsDownload.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px; '><p><em><a href='https://www.cityscapes-dataset.com/file-handling/?packageID=35'>gtFinePanopticParts_trainval.zip (474MB)</a> <a href='https://www.cityscapes-dataset.com/md5-sum/?packageID=35' target='_blank'>[md5]</a></em> Panoptic parts annotations for train and val sets (3475 annotated images). Citation: P. Meletis, X. Wen, C. Lu, D. de Geus, and G. Dubbelman, <a href="https://arxiv.org/abs/2004.07944" target="_blank">"Cityscapes-Panoptic-Parts and PASCAL-Panoptic-Parts datasets for Scene Understanding"</a>, arXiv 2020.</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2019/01/foggydbfdownload.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px; '><p><em><a href='https://www.cityscapes-dataset.com/file-handling/?packageID=31'>leftImg8bit_trainval_foggyDBF.zip (20GB)</a> <a href='https://www.cityscapes-dataset.com/md5-sum/?packageID=31' target='_blank'>[md5]</a></em> Foggy DBF images (left 8-bit) for train and val sets (3475 images). Citation: C. Sakaridis, D. Dai, S. Hecker, and L. Van Gool, <a href="https://www.vision.ee.ethz.ch/~csakarid/Model_adaptation_SFSU_dense/">"Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding"</a>, ECCV 2018.</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2017/12/foggyDownload.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px; '><p><em><a href='https://www.cityscapes-dataset.com/file-handling/?packageID=29'>leftImg8bit_trainvaltest_foggy.zip (30GB)</a> <a href='https://www.cityscapes-dataset.com/md5-sum/?packageID=29' target='_blank'>[md5]</a></em> Foggy images (left 8-bit) for train, val, and test sets (5000 images). Citation: C. Sakaridis, D. Dai, and L. Van Gool, <a href="http://people.ee.ethz.ch/~csakarid/SFSU_synthetic/" target="_blank">"Semantic Foggy Scene Understanding with Synthetic Data"</a>, IJCV 2018.</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2017/12/foggyDownload.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px; '><p><em><a href='https://www.cityscapes-dataset.com/file-handling/?packageID=30'>leftImg8bit_trainextra_foggy.zip (40GB)</a> <a href='https://www.cityscapes-dataset.com/md5-sum/?packageID=30' target='_blank'>[md5]</a></em> Foggy images (left 8-bit) for trainextra set (19997 images). Citation: C. Sakaridis, D. Dai, and L. Van Gool, <a href="http://people.ee.ethz.ch/~csakarid/SFSU_synthetic/" target="_blank">"Semantic Foggy Scene Understanding with Synthetic Data"</a>, IJCV 2018.</p></li><li id='li1' class='li1' style='background: url(https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2019/07/raindownload.png) no-repeat no-repeat; background-size: 160px 90px; padding-left: 170px; '><p><em><a href='https://www.cityscapes-dataset.com/file-handling/?packageID=33'>leftImg8bit_trainval_rain.zip (21GB)</a> <a href='https://www.cityscapes-dataset.com/md5-sum/?packageID=33' target='_blank'>[md5]</a></em> Rain images (left 8-bit) for parts of train and val sets (295 images, 36 variants each). Citation: X. Hu, C.-W. Fu, L. Zhu, and P.-A. Heng, <a href="http://openaccess.thecvf.com/content_CVPR_2019/html/Hu_Depth-Attentional_Features_for_Single-Image_Rain_Removal_CVPR_2019_paper.html" target="_blank">"Depth-Attentional Features for Single-Image Rain Removal"</a>, CVPR 2019.</p></li></ul></div></div>
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+ <a href="https://www.cityscapes-dataset.com/cityscapes-3d-benchmark-online/">Cityscapes 3D Benchmark Online</a>
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+ <span class="post-date">October 17, 2020</span>
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+ </li>
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+ <li>
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+ <a href="https://www.cityscapes-dataset.com/cityscapes-3d-dataset-released/">Cityscapes 3D Dataset Released</a>
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+ <span class="post-date">August 30, 2020</span>
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+ <a href="https://www.cityscapes-dataset.com/coming-soon-cityscapes-3d/">Coming Soon: Cityscapes 3D</a>
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+ <span class="post-date">June 16, 2020</span>
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license.txt ADDED
@@ -0,0 +1,23 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ----------------------
2
+ The Cityscapes Dataset
3
+ ----------------------
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+
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+
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+ License agreement
7
+ -----------------
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+
9
+ This dataset is made freely available to academic and non-academic entities for non-commercial purposes such as academic research, teaching, scientific publications, or personal experimentation. Permission is granted to use the data given that you agree:
10
+
11
+ 1. That the dataset comes "AS IS", without express or implied warranty. Although every effort has been made to ensure accuracy, we (Daimler AG, MPI Informatics, TU Darmstadt) do not accept any responsibility for errors or omissions.
12
+ 2. That you include a reference to the Cityscapes Dataset in any work that makes use of the dataset. For research papers, cite our preferred publication as listed on our website; for other media cite our preferred publication as listed on our website or link to the Cityscapes website.
13
+ 3. That you do not distribute this dataset or modified versions. It is permissible to distribute derivative works in as far as they are abstract representations of this dataset (such as models trained on it or additional annotations that do not directly include any of our data) and do not allow to recover the dataset or something similar in character.
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+ 4. That you may not use the dataset or any derivative work for commercial purposes as, for example, licensing or selling the data, or using the data with a purpose to procure a commercial gain.
15
+ 5. That all rights not expressly granted to you are reserved by us (Daimler AG, MPI Informatics, TU Darmstadt).
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+
17
+
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+ Contact
19
+ -------
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+
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+ Marius Cordts, Mohamed Omran
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+ www.cityscapes-dataset.net
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+ [email protected]
main.py ADDED
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+ def main():
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+ print("Hello from homework4!")
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+
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+
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+ if __name__ == "__main__":
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+ main()
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+ [project]
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+ name = "homework4"
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+ version = "0.1.0"
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+ description = "Add your description here"
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+ readme = "README.md"
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+ requires-python = ">=3.13"
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+ dependencies = [
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+ "matplotlib>=3.10.3",
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+ "numpy>=2.2.5",
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+ "pillow>=11.2.1",
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+ "scikit-learn>=1.6.1",
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+ "seaborn>=0.13.2",
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+ "torch>=2.7.0",
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+ "torchvision>=0.22.0",
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+ "tqdm>=4.67.1",
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+ "visdom>=0.2.4",
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+ ]
segmentation/README.md ADDED
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+ # InternImage for Semantic Segmentation
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+
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+ This folder contains the implementation of the InternImage for semantic segmentation.
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+
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+ Our segmentation code is developed on top of [MMSegmentation v0.27.0](https://github.com/open-mmlab/mmsegmentation/tree/v0.27.0).
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+
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+ <!-- TOC -->
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+
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+ - [Installation](#installation)
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+ - [Data Preparation](#data-preparation)
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+ - [Released Models](#released-models)
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+ - [Evaluation](#evaluation)
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+ - [Training](#training)
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+ - [Manage Jobs with Slurm](#manage-jobs-with-slurm)
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+ - [Image Demo](#image-demo)
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+ - [Export](#export)
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+
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+ <!-- TOC -->
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+
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+ ## Installation
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+
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+ - Clone this repository:
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+
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+ ```bash
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+ git clone https://github.com/OpenGVLab/InternImage.git
26
+ cd InternImage
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+ ```
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+
29
+ - Create a conda virtual environment and activate it:
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+
31
+ ```bash
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+ conda create -n internimage python=3.9
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+ conda activate internimage
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+ ```
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+
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+ - Install `CUDA>=10.2` with `cudnn>=7` following
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+ the [official installation instructions](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html)
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+ - Install `PyTorch>=1.10.0` and `torchvision>=0.9.0` with `CUDA>=10.2`:
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+
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+ For examples, to install `torch==1.11` with `CUDA==11.3`:
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+
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+ ```bash
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+ pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 -f https://download.pytorch.org/whl/torch_stable.html
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+ ```
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+
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+ - Install other requirements:
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+
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+ note: conda opencv will break torchvision as not to support GPU, so we need to install opencv using pip.
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+
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+ ```bash
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+ conda install -c conda-forge termcolor yacs pyyaml scipy pip -y
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+ pip install opencv-python
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+ ```
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+
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+ - Install `timm`, `mmcv-full` and \`mmsegmentation':
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+
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+ ```bash
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+ pip install -U openmim
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+ mim install mmcv-full==1.5.0
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+ mim install mmsegmentation==0.27.0
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+ pip install timm==0.6.11 mmdet==2.28.1
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+ ```
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+
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+ - Install other requirements:
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+
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+ ```bash
67
+ pip install opencv-python termcolor yacs pyyaml scipy
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+ # Please use a version of numpy lower than 2.0
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+ pip install numpy==1.26.4
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+ pip install pydantic==1.10.13
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+ ```
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+
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+ - Compile CUDA operators
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+
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+ Before compiling, please use the `nvcc -V` command to check whether your `nvcc` version matches the CUDA version of PyTorch.
76
+
77
+ ```bash
78
+ cd ./ops_dcnv3
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+ sh ./make.sh
80
+ # unit test (should see all checking is True)
81
+ python test.py
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+ ```
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+
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+ - You can also install the operator using precompiled `.whl` files
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+ [DCNv3-1.0-whl](https://github.com/OpenGVLab/InternImage/releases/tag/whl_files)
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+
87
+ ## Data Preparation
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+
89
+ Prepare datasets according to the [guidelines](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#prepare-datasets) in MMSegmentation.
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+
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+ ## Released Models
92
+
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+ <details open>
94
+ <summary> Dataset: ADE20K </summary>
95
+ <br>
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+ <div>
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+
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+ | method | backbone | resolution | mIoU (ss/ms) | #param | FLOPs | Config | Download |
99
+ | :---------: | :------------: | :--------: | :----------: | :----: | :---: | :---------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
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+ | UperNet | InternImage-T | 512x512 | 47.9 / 48.1 | 59M | 944G | [config](./configs/ade20k/upernet_internimage_t_512_160k_ade20k.py) | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/upernet_internimage_t_512_160k_ade20k.pth) \| [log](https://huggingface.co/OpenGVLab/InternImage/raw/main/upernet_internimage_t_512_160k_ade20k.log.json) |
101
+ | UperNet | InternImage-S | 512x512 | 50.1 / 50.9 | 80M | 1017G | [config](./configs/ade20k/upernet_internimage_s_512_160k_ade20k.py) | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/upernet_internimage_s_512_160k_ade20k.pth) \| [log](https://huggingface.co/OpenGVLab/InternImage/raw/main/upernet_internimage_s_512_160k_ade20k.log.json) |
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+ | UperNet | InternImage-B | 512x512 | 50.8 / 51.3 | 128M | 1185G | [config](./configs/ade20k/upernet_internimage_b_512_160k_ade20k.py) | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/upernet_internimage_b_512_160k_ade20k.pth) \| [log](https://huggingface.co/OpenGVLab/InternImage/raw/main/upernet_internimage_b_512_160k_ade20k.log.json) |
103
+ | UperNet | InternImage-L | 640x640 | 53.9 / 54.1 | 256M | 2526G | [config](./configs/ade20k/upernet_internimage_l_640_160k_ade20k.py) | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/upernet_internimage_l_640_160k_ade20k.pth) \| [log](https://huggingface.co/OpenGVLab/InternImage/raw/main/upernet_internimage_l_640_160k_ade20k.log.json) |
104
+ | UperNet | InternImage-XL | 640x640 | 55.0 / 55.3 | 368M | 3142G | [config](./configs/ade20k/upernet_internimage_xl_640_160k_ade20k.py) | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/upernet_internimage_xl_640_160k_ade20k.pth) \| [log](https://huggingface.co/OpenGVLab/InternImage/raw/main/upernet_internimage_xl_640_160k_ade20k.log.json) |
105
+ | UperNet | InternImage-H | 896x896 | 59.9 / 60.3 | 1.12B | 3566G | [config](./configs/ade20k/upernet_internimage_h_896_160k_ade20k.py) | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/upernet_internimage_h_896_160k_ade20k.pth) \| [log](https://huggingface.co/OpenGVLab/InternImage/raw/main/upernet_internimage_h_896_160k_ade20k.log.json) |
106
+ | Mask2Former | InternImage-H | 896x896 | 62.6 / 62.9 | 1.31B | 4635G | [config](./configs/ade20k/mask2former_internimage_h_896_80k_cocostuff2ade20k_ss.py) | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/mask2former_internimage_h_896_80k_cocostuff2ade20k.pth) \| [log](https://huggingface.co/OpenGVLab/InternImage/raw/main/mask2former_internimage_h_896_80k_cocostuff2ade20k.log.json) |
107
+
108
+ </div>
109
+
110
+ </details>
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+
112
+ <details>
113
+ <summary> Dataset: Cityscapes </summary>
114
+ <br>
115
+ <div>
116
+
117
+ | method | backbone | resolution | mIoU (ss/ms) | #params | FLOPs | Config | Download |
118
+ | :-----------: | :------------: | :--------: | :-----------: | :-----: | :---: | :--------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
119
+ | UperNet | InternImage-T | 512x1024 | 82.58 / 83.40 | 59M | 1889G | [config](./configs/cityscapes/upernet_internimage_t_512x1024_160k_cityscapes.py) | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/upernet_internimage_t_512x1024_160k_cityscapes.pth) \| [log](https://huggingface.co/OpenGVLab/InternImage/raw/main/upernet_internimage_t_512x1024_160k_cityscapes.log.json) |
120
+ | UperNet | InternImage-S | 512x1024 | 82.74 / 83.45 | 80M | 2035G | [config](./configs/cityscapes/upernet_internimage_s_512x1024_160k_cityscapes.py) | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/upernet_internimage_s_512x1024_160k_cityscapes.pth) \| [log](https://huggingface.co/OpenGVLab/InternImage/raw/main/upernet_internimage_s_512x1024_160k_cityscapes.log.json) |
121
+ | UperNet | InternImage-B | 512x1024 | 83.18 / 83.97 | 128M | 2369G | [config](./configs/cityscapes/upernet_internimage_b_512x1024_160k_cityscapes.py) | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/upernet_internimage_b_512x1024_160k_cityscapes.pth) \| [log](https://huggingface.co/OpenGVLab/InternImage/raw/main/upernet_internimage_b_512x1024_160k_cityscapes.log.json) |
122
+ | UperNet | InternImage-L | 512x1024 | 83.68 / 84.41 | 256M | 3234G | [config](./configs/cityscapes/upernet_internimage_l_512x1024_160k_cityscapes.py) | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/upernet_internimage_l_512x1024_160k_cityscapes.pth) \| [log](https://huggingface.co/OpenGVLab/InternImage/raw/main/upernet_internimage_l_512x1024_160k_cityscapes.log.json) |
123
+ | UperNet\* | InternImage-L | 512x1024 | 85.94 / 86.22 | 256M | 3234G | [config](./configs/cityscapes/upernet_internimage_l_512x1024_160k_mapillary2cityscapes.py) | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/upernet_internimage_l_512x1024_160k_mapillary2cityscapes.pth) \| [log](https://huggingface.co/OpenGVLab/InternImage/raw/main/upernet_internimage_l_512x1024_160k_mapillary2cityscapes.log.json) |
124
+ | UperNet | InternImage-XL | 512x1024 | 83.62 / 84.28 | 368M | 4022G | [config](./configs/cityscapes/upernet_internimage_xl_512x1024_160k_cityscapes.py) | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/upernet_internimage_xl_512x1024_160k_cityscapes.pth) \| [log](https://huggingface.co/OpenGVLab/InternImage/raw/main/upernet_internimage_xl_512x1024_160k_cityscapes.log.json) |
125
+ | UperNet\* | InternImage-XL | 512x1024 | 86.20 / 86.42 | 368M | 4022G | [config](./configs/cityscapes/upernet_internimage_xl_512x1024_160k_mapillary2cityscapes.py) | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/upernet_internimage_xl_512x1024_160k_mapillary2cityscapes.pth) \| [log](https://huggingface.co/OpenGVLab/InternImage/raw/main/upernet_internimage_xl_512x1024_160k_mapillary2cityscapes.log.json) |
126
+ | SegFormer\* | InternImage-L | 512x1024 | 85.16 / 85.67 | 220M | 1580G | [config](./configs/cityscapes/segformer_internimage_l_512x1024_160k_mapillary2cityscapes.py) | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/segformer_internimage_l_512x1024_160k_mapillary2cityscapes.pth) \| [log](https://huggingface.co/OpenGVLab/InternImage/raw/main/segformer_internimage_l_512x1024_160k_mapillary2cityscapes.log.json) |
127
+ | SegFormer\* | InternImage-XL | 512x1024 | 85.41 / 85.93 | 330M | 2364G | [config](./configs/cityscapes/segformer_internimage_xl_512x1024_160k_mapillary2cityscapes.py) | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/segformer_internimage_xl_512x1024_160k_mapillary2cityscapes.pth) \| [log](https://huggingface.co/OpenGVLab/InternImage/raw/main/segformer_internimage_xl_512x1024_160k_mapillary2cityscapes.log.json) |
128
+ | Mask2Former\* | InternImage-H | 1024x1024 | 86.37 / 86.96 | 1094M | 7878G | [config](./configs/cityscapes/mask2former_internimage_h_1024x1024_80k_mapillary2cityscapes.py) | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/mask2former_internimage_h_1024x1024_80k_mapillary2cityscapes.pth) \| [log](https://huggingface.co/OpenGVLab/InternImage/raw/main/mask2former_internimage_h_1024x1024_80k_mapillary2cityscapes.log.json) |
129
+
130
+ \* denotes the model is trained using extra Mapillary dataset.
131
+
132
+ </div>
133
+
134
+ </details>
135
+
136
+ <details>
137
+ <summary> Dataset: COCO-Stuff-164K </summary>
138
+ <br>
139
+ <div>
140
+
141
+ | method | backbone | resolution | mIoU (ss/ms) | #params | FLOPs | Config | Download |
142
+ | :---------: | :-----------: | :--------: | :----------: | :-----: | :---: | :-----------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
143
+ | Mask2Former | InternImage-H | 896x896 | 52.6 / 52.8 | 1.31B | 4635G | [config](./configs/coco_stuff164k/mask2former_internimage_h_896_80k_cocostuff164k.py) | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/mask2former_internimage_h_896_80k_cocostuff164k.pth) \| [log](https://huggingface.co/OpenGVLab/InternImage/raw/main/mask2former_internimage_h_896_80k_cocostuff164k.log.json) |
144
+
145
+ </div>
146
+
147
+ </details>
148
+
149
+ <details>
150
+ <summary> Dataset: COCO-Stuff-10K </summary>
151
+ <br>
152
+ <div>
153
+
154
+ | method | backbone | resolution | mIoU (ss/ms) | #params | FLOPs | Config | Download |
155
+ | :---------: | :-----------: | :--------: | :----------: | :-----: | :---: | :-----------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
156
+ | Mask2Former | InternImage-H | 512x512 | 59.2 / 59.6 | 1.28B | 1528G | [config](./configs/coco_stuff10k/mask2former_internimage_h_512_40k_cocostuff164k_to_10k.py) | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/mask2former_internimage_h_512_40k_cocostuff164k_to_10k.pth) \| [log](https://huggingface.co/OpenGVLab/InternImage/raw/main/mask2former_internimage_h_512_40k_cocostuff164k_to_10k.log.json) |
157
+
158
+ </div>
159
+
160
+ </details>
161
+
162
+ <details>
163
+ <summary> Dataset: Pascal-Context-59 </summary>
164
+ <br>
165
+ <div>
166
+
167
+ | method | backbone | resolution | mIoU (ss/ms) | #param | FLOPs | Config | Download |
168
+ | :---------: | :-----------: | :--------: | :----------: | :----: | :---: | :---------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
169
+ | Mask2Former | InternImage-H | 480x480 | 69.7 / 70.3 | 1.07B | 867G | [config](./configs/pascal_context/mask2former_internimage_h_480_40k_pascal_context_59.py) | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/mask2former_internimage_h_480_40k_pascal_context_59.pth) \| [log](https://huggingface.co/OpenGVLab/InternImage/raw/main/mask2former_internimage_h_480_40k_pascal_context_59.log.json) |
170
+
171
+ </div>
172
+
173
+ </details>
174
+
175
+ <details>
176
+ <summary> Dataset: NYU-Depth-V2 </summary>
177
+ <br>
178
+ <div>
179
+
180
+ | method | backbone | resolution | mIoU (ss/ms) | #param | FLOPs | Config | Download |
181
+ | :---------: | :-----------: | :--------: | :----------: | :----: | :---: | :-----------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
182
+ | Mask2Former | InternImage-H | 480x480 | 67.1 / 68.1 | 1.07B | 867G | [config](./configs/nyu_depth_v2/mask2former_internimage_h_480_40k_nyu.py) | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/mask2former_internimage_h_480_40k_nyu.pth) \| [log](https://huggingface.co/OpenGVLab/InternImage/raw/main/mask2former_internimage_h_480_40k_nyu.log.json) |
183
+
184
+ </div>
185
+
186
+ </details>
187
+
188
+ <details>
189
+ <summary> Dataset: Mapillary </summary>
190
+ <br>
191
+ <div>
192
+
193
+ | method | backbone | resolution | #param | FLOPs | Config | Download |
194
+ | :---------: | :------------: | :--------: | :----: | :---: | :------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------: |
195
+ | UperNet | InternImage-L | 512x1024 | 256M | 3234G | [config](./configs/mapillary/upernet_internimage_l_512x1024_80k_mapillary.py) | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/upernet_internimage_l_512x1024_80k_mapillary.pth) |
196
+ | UperNet | InternImage-XL | 512x1024 | 368M | 4022G | [config](./configs/mapillary/upernet_internimage_xl_512x1024_80k_mapillary.py) | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/upernet_internimage_xl_512x1024_80k_mapillary.pth) |
197
+ | SegFormer | InternImage-L | 512x1024 | 220M | 1580G | [config](./configs/mapillary/segformer_internimage_l_512x1024_80k_mapillary.py) | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/segformer_internimage_l_512x1024_80k_mapillary.pth) |
198
+ | SegFormer | InternImage-XL | 512x1024 | 330M | 2364G | [config](./configs/mapillary/segformer_internimage_xl_512x1024_80k_mapillary.py) | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/segformer_internimage_xl_512x1024_80k_mapillary.pth) |
199
+ | Mask2Former | InternImage-H | 896x896 | 1094M | 7878G | [config](./configs/mapillary/mask2former_internimage_h_896x896_80k_mapillary.py) | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/mask2former_internimage_h_896x896_80k_mapillary.pth) |
200
+
201
+ </div>
202
+
203
+ </details>
204
+
205
+ ## Evaluation
206
+
207
+ To evaluate our `InternImage` on ADE20K val, run:
208
+
209
+ ```bash
210
+ sh dist_test.sh <config-file> <checkpoint> <gpu-num> --eval mIoU
211
+ ```
212
+
213
+ For example, to evaluate the `InternImage-T` with a single GPU:
214
+
215
+ ```bash
216
+ python test.py configs/ade20k/upernet_internimage_t_512_160k_ade20k.py pretrained/upernet_internimage_t_512_160k_ade20k.pth --eval mIoU
217
+ ```
218
+
219
+ For example, to evaluate the `InternImage-B` with a single node with 8 GPUs:
220
+
221
+ ```bash
222
+ sh dist_test.sh configs/ade20k/upernet_internimage_b_512_160k_ade20k.py pretrained/upernet_internimage_b_512_160k_ade20k.pth 8 --eval mIoU
223
+ ```
224
+
225
+ ## Training
226
+
227
+ To train an `InternImage` on ADE20K, run:
228
+
229
+ ```bash
230
+ sh dist_train.sh <config-file> <gpu-num>
231
+ ```
232
+
233
+ For example, to train `InternImage-T` with 8 GPU on 1 node (total batch size 16), run:
234
+
235
+ ```bash
236
+ sh dist_train.sh configs/ade20k/upernet_internimage_t_512_160k_ade20k.py 8
237
+ ```
238
+
239
+ ## Manage Jobs with Slurm
240
+
241
+ For example, to train `InternImage-XL` with 8 GPU on 1 node (total batch size 16), run:
242
+
243
+ ```bash
244
+ GPUS=8 sh slurm_train.sh <partition> <job-name> configs/ade20k/upernet_internimage_xl_640_160k_ade20k.py
245
+ ```
246
+
247
+ ## Image Demo
248
+
249
+ To inference a single/multiple image like this.
250
+ If you specify image containing directory instead of a single image, it will process all the images in the directory.
251
+
252
+ ```
253
+ CUDA_VISIBLE_DEVICES=0 python image_demo.py \
254
+ data/ade/ADEChallengeData2016/images/validation/ADE_val_00000591.jpg \
255
+ configs/ade20k/upernet_internimage_t_512_160k_ade20k.py \
256
+ checkpoint_dir/seg/upernet_internimage_t_512_160k_ade20k.pth \
257
+ --palette ade20k
258
+ ```
259
+
260
+ ## Export
261
+
262
+ Install `mmdeploy` at first:
263
+
264
+ ```shell
265
+ pip install mmdeploy==0.14.0
266
+ ```
267
+
268
+ To export a segmentation model from PyTorch to TensorRT, run:
269
+
270
+ ```shell
271
+ MODEL="model_name"
272
+ CKPT_PATH="/path/to/model/ckpt.pth"
273
+
274
+ python deploy.py \
275
+ "./deploy/configs/mmseg/segmentation_tensorrt_static-512x512.py" \
276
+ "./configs/ade20k/${MODEL}.py" \
277
+ "${CKPT_PATH}" \
278
+ "./deploy/demo.png" \
279
+ --work-dir "./work_dirs/mmseg/${MODEL}" \
280
+ --device cuda \
281
+ --dump-info
282
+ ```
283
+
284
+ For example, to export `upernet_internimage_t_512_160k_ade20k` from PyTorch to TensorRT, run:
285
+
286
+ ```shell
287
+ MODEL="upernet_internimage_t_512_160k_ade20k"
288
+ CKPT_PATH="/path/to/model/ckpt/upernet_internimage_t_512_160k_ade20k.pth"
289
+
290
+ python deploy.py \
291
+ "./deploy/configs/mmseg/segmentation_tensorrt_static-512x512.py" \
292
+ "./configs/ade20k/${MODEL}.py" \
293
+ "${CKPT_PATH}" \
294
+ "./deploy/demo.png" \
295
+ --work-dir "./work_dirs/mmseg/${MODEL}" \
296
+ --device cuda \
297
+ --dump-info
298
+ ```
segmentation/deploy.py ADDED
@@ -0,0 +1,308 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) OpenMMLab. All rights reserved.
2
+ import argparse
3
+ import logging
4
+ import os
5
+ import os.path as osp
6
+ from functools import partial
7
+
8
+ import mmcv
9
+ import mmcv_custom
10
+ import mmseg_custom
11
+ import torch.multiprocessing as mp
12
+ from mmdeploy.apis import (create_calib_input_data, extract_model,
13
+ get_predefined_partition_cfg, torch2onnx,
14
+ torch2torchscript, visualize_model)
15
+ from mmdeploy.apis.core import PIPELINE_MANAGER
16
+ from mmdeploy.apis.utils import to_backend
17
+ from mmdeploy.backend.sdk.export_info import export2SDK
18
+ from mmdeploy.utils import (IR, Backend, get_backend, get_calib_filename,
19
+ get_ir_config, get_partition_config,
20
+ get_root_logger, load_config, target_wrapper)
21
+ from torch.multiprocessing import Process, set_start_method
22
+
23
+
24
+ def parse_args():
25
+ parser = argparse.ArgumentParser(description='Export model to backends.')
26
+ parser.add_argument('deploy_cfg', help='deploy config path')
27
+ parser.add_argument('model_cfg', help='model config path')
28
+ parser.add_argument('checkpoint', help='model checkpoint path')
29
+ parser.add_argument('img', help='image used to convert model model')
30
+ parser.add_argument(
31
+ '--test-img',
32
+ default=None,
33
+ type=str,
34
+ nargs='+',
35
+ help='image used to test model')
36
+ parser.add_argument(
37
+ '--work-dir',
38
+ default=os.getcwd(),
39
+ help='the dir to save logs and models')
40
+ parser.add_argument(
41
+ '--calib-dataset-cfg',
42
+ help=('dataset config path used to calibrate in int8 mode. If not '
43
+ 'specified, it will use "val" dataset in model config instead.'),
44
+ default=None)
45
+ parser.add_argument(
46
+ '--device', help='device used for conversion', default='cpu')
47
+ parser.add_argument(
48
+ '--log-level',
49
+ help='set log level',
50
+ default='INFO',
51
+ choices=list(logging._nameToLevel.keys()))
52
+ parser.add_argument(
53
+ '--show', action='store_true', help='Show detection outputs')
54
+ parser.add_argument(
55
+ '--dump-info', action='store_true', help='Output information for SDK')
56
+ parser.add_argument(
57
+ '--quant-image-dir',
58
+ default=None,
59
+ help='Image directory for quantize model.')
60
+ parser.add_argument(
61
+ '--quant', action='store_true', help='Quantize model to low bit.')
62
+ parser.add_argument(
63
+ '--uri',
64
+ default='192.168.1.1:60000',
65
+ help='Remote ipv4:port or ipv6:port for inference on edge device.')
66
+ args = parser.parse_args()
67
+ return args
68
+
69
+
70
+ def create_process(name, target, args, kwargs, ret_value=None):
71
+ logger = get_root_logger()
72
+ logger.info(f'{name} start.')
73
+ log_level = logger.level
74
+
75
+ wrap_func = partial(target_wrapper, target, log_level, ret_value)
76
+
77
+ process = Process(target=wrap_func, args=args, kwargs=kwargs)
78
+ process.start()
79
+ process.join()
80
+
81
+ if ret_value is not None:
82
+ if ret_value.value != 0:
83
+ logger.error(f'{name} failed.')
84
+ exit(1)
85
+ else:
86
+ logger.info(f'{name} success.')
87
+
88
+
89
+ def torch2ir(ir_type: IR):
90
+ """Return the conversion function from torch to the intermediate
91
+ representation.
92
+
93
+ Args:
94
+ ir_type (IR): The type of the intermediate representation.
95
+ """
96
+ if ir_type == IR.ONNX:
97
+ return torch2onnx
98
+ elif ir_type == IR.TORCHSCRIPT:
99
+ return torch2torchscript
100
+ else:
101
+ raise KeyError(f'Unexpected IR type {ir_type}')
102
+
103
+
104
+ def main():
105
+ args = parse_args()
106
+ set_start_method('spawn', force=True)
107
+ logger = get_root_logger()
108
+ log_level = logging.getLevelName(args.log_level)
109
+ logger.setLevel(log_level)
110
+
111
+ pipeline_funcs = [
112
+ torch2onnx, torch2torchscript, extract_model, create_calib_input_data
113
+ ]
114
+ PIPELINE_MANAGER.enable_multiprocess(True, pipeline_funcs)
115
+ PIPELINE_MANAGER.set_log_level(log_level, pipeline_funcs)
116
+
117
+ deploy_cfg_path = args.deploy_cfg
118
+ model_cfg_path = args.model_cfg
119
+ checkpoint_path = args.checkpoint
120
+ quant = args.quant
121
+ quant_image_dir = args.quant_image_dir
122
+
123
+ # load deploy_cfg
124
+ deploy_cfg, model_cfg = load_config(deploy_cfg_path, model_cfg_path)
125
+
126
+ # create work_dir if not
127
+ mmcv.mkdir_or_exist(osp.abspath(args.work_dir))
128
+
129
+ if args.dump_info:
130
+ export2SDK(
131
+ deploy_cfg,
132
+ model_cfg,
133
+ args.work_dir,
134
+ pth=checkpoint_path,
135
+ device=args.device)
136
+
137
+ ret_value = mp.Value('d', 0, lock=False)
138
+
139
+ # convert to IR
140
+ ir_config = get_ir_config(deploy_cfg)
141
+ ir_save_file = ir_config['save_file']
142
+ ir_type = IR.get(ir_config['type'])
143
+ torch2ir(ir_type)(
144
+ args.img,
145
+ args.work_dir,
146
+ ir_save_file,
147
+ deploy_cfg_path,
148
+ model_cfg_path,
149
+ checkpoint_path,
150
+ device=args.device)
151
+
152
+ # convert backend
153
+ ir_files = [osp.join(args.work_dir, ir_save_file)]
154
+
155
+ # partition model
156
+ partition_cfgs = get_partition_config(deploy_cfg)
157
+
158
+ if partition_cfgs is not None:
159
+
160
+ if 'partition_cfg' in partition_cfgs:
161
+ partition_cfgs = partition_cfgs.get('partition_cfg', None)
162
+ else:
163
+ assert 'type' in partition_cfgs
164
+ partition_cfgs = get_predefined_partition_cfg(
165
+ deploy_cfg, partition_cfgs['type'])
166
+
167
+ origin_ir_file = ir_files[0]
168
+ ir_files = []
169
+ for partition_cfg in partition_cfgs:
170
+ save_file = partition_cfg['save_file']
171
+ save_path = osp.join(args.work_dir, save_file)
172
+ start = partition_cfg['start']
173
+ end = partition_cfg['end']
174
+ dynamic_axes = partition_cfg.get('dynamic_axes', None)
175
+
176
+ extract_model(
177
+ origin_ir_file,
178
+ start,
179
+ end,
180
+ dynamic_axes=dynamic_axes,
181
+ save_file=save_path)
182
+
183
+ ir_files.append(save_path)
184
+
185
+ # calib data
186
+ calib_filename = get_calib_filename(deploy_cfg)
187
+ if calib_filename is not None:
188
+ calib_path = osp.join(args.work_dir, calib_filename)
189
+ create_calib_input_data(
190
+ calib_path,
191
+ deploy_cfg_path,
192
+ model_cfg_path,
193
+ checkpoint_path,
194
+ dataset_cfg=args.calib_dataset_cfg,
195
+ dataset_type='val',
196
+ device=args.device)
197
+
198
+ backend_files = ir_files
199
+ # convert backend
200
+ backend = get_backend(deploy_cfg)
201
+
202
+ # preprocess deploy_cfg
203
+ if backend == Backend.RKNN:
204
+ # TODO: Add this to task_processor in the future
205
+ import tempfile
206
+
207
+ from mmdeploy.utils import (get_common_config, get_normalization,
208
+ get_quantization_config,
209
+ get_rknn_quantization)
210
+ quantization_cfg = get_quantization_config(deploy_cfg)
211
+ common_params = get_common_config(deploy_cfg)
212
+ if get_rknn_quantization(deploy_cfg) is True:
213
+ transform = get_normalization(model_cfg)
214
+ common_params.update(
215
+ dict(
216
+ mean_values=[transform['mean']],
217
+ std_values=[transform['std']]))
218
+
219
+ dataset_file = tempfile.NamedTemporaryFile(suffix='.txt').name
220
+ with open(dataset_file, 'w') as f:
221
+ f.writelines([osp.abspath(args.img)])
222
+ quantization_cfg.setdefault('dataset', dataset_file)
223
+ if backend == Backend.ASCEND:
224
+ # TODO: Add this to backend manager in the future
225
+ if args.dump_info:
226
+ from mmdeploy.backend.ascend import update_sdk_pipeline
227
+ update_sdk_pipeline(args.work_dir)
228
+
229
+ # convert to backend
230
+ PIPELINE_MANAGER.set_log_level(log_level, [to_backend])
231
+ if backend == Backend.TENSORRT:
232
+ PIPELINE_MANAGER.enable_multiprocess(True, [to_backend])
233
+ backend_files = to_backend(
234
+ backend,
235
+ ir_files,
236
+ work_dir=args.work_dir,
237
+ deploy_cfg=deploy_cfg,
238
+ log_level=log_level,
239
+ device=args.device,
240
+ uri=args.uri)
241
+
242
+ # ncnn quantization
243
+ if backend == Backend.NCNN and quant:
244
+ from mmdeploy.apis.ncnn import get_quant_model_file, ncnn2int8
245
+ from onnx2ncnn_quant_table import get_table
246
+ model_param_paths = backend_files[::2]
247
+ model_bin_paths = backend_files[1::2]
248
+ backend_files = []
249
+ for onnx_path, model_param_path, model_bin_path in zip(
250
+ ir_files, model_param_paths, model_bin_paths):
251
+
252
+ deploy_cfg, model_cfg = load_config(deploy_cfg_path,
253
+ model_cfg_path)
254
+ quant_onnx, quant_table, quant_param, quant_bin = get_quant_model_file( # noqa: E501
255
+ onnx_path, args.work_dir)
256
+
257
+ create_process(
258
+ 'ncnn quant table',
259
+ target=get_table,
260
+ args=(onnx_path, deploy_cfg, model_cfg, quant_onnx,
261
+ quant_table, quant_image_dir, args.device),
262
+ kwargs=dict(),
263
+ ret_value=ret_value)
264
+
265
+ create_process(
266
+ 'ncnn_int8',
267
+ target=ncnn2int8,
268
+ args=(model_param_path, model_bin_path, quant_table,
269
+ quant_param, quant_bin),
270
+ kwargs=dict(),
271
+ ret_value=ret_value)
272
+ backend_files += [quant_param, quant_bin]
273
+
274
+ if args.test_img is None:
275
+ args.test_img = args.img
276
+
277
+ extra = dict(
278
+ backend=backend,
279
+ output_file=osp.join(args.work_dir, f'output_{backend.value}.jpg'),
280
+ show_result=args.show)
281
+ if backend == Backend.SNPE:
282
+ extra['uri'] = args.uri
283
+
284
+ # get backend inference result, try render
285
+ create_process(
286
+ f'visualize {backend.value} model',
287
+ target=visualize_model,
288
+ args=(model_cfg_path, deploy_cfg_path, backend_files, args.test_img,
289
+ args.device),
290
+ kwargs=extra,
291
+ ret_value=ret_value)
292
+
293
+ # get pytorch model inference result, try visualize if possible
294
+ create_process(
295
+ 'visualize pytorch model',
296
+ target=visualize_model,
297
+ args=(model_cfg_path, deploy_cfg_path, [checkpoint_path],
298
+ args.test_img, args.device),
299
+ kwargs=dict(
300
+ backend=Backend.PYTORCH,
301
+ output_file=osp.join(args.work_dir, 'output_pytorch.jpg'),
302
+ show_result=args.show),
303
+ ret_value=ret_value)
304
+ logger.info('All process success.')
305
+
306
+
307
+ if __name__ == '__main__':
308
+ main()
segmentation/dist_test.sh ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+ #!/usr/bin/env bash
2
+
3
+ CONFIG=$1
4
+ CHECKPOINT=$2
5
+ GPUS=$3
6
+ PORT=${PORT:-29510}
7
+ PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \
8
+ python -m torch.distributed.launch --nproc_per_node=$GPUS --master_port=$PORT \
9
+ $(dirname "$0")/test.py $CONFIG $CHECKPOINT --launcher pytorch ${@:4}
segmentation/dist_train.sh ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+ #!/usr/bin/env bash
2
+
3
+ CONFIG=$1
4
+ GPUS=$2
5
+ PORT=${PORT:-29300}
6
+
7
+ PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \
8
+ python -m torch.distributed.launch --nproc_per_node=$GPUS --master_port=$PORT \
9
+ $(dirname "$0")/train.py $CONFIG --launcher pytorch ${@:3}
segmentation/get_flops.py ADDED
@@ -0,0 +1,120 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) OpenMMLab. All rights reserved.
2
+ import argparse
3
+ from functools import partial
4
+
5
+ import mmcv_custom # noqa: F401,F403
6
+ import mmseg_custom # noqa: F401,F403
7
+ import numpy as np
8
+ import torch
9
+ from mmcv import Config, DictAction
10
+ from mmseg.models import build_segmentor
11
+
12
+ try:
13
+ from mmcv.cnn import get_model_complexity_info
14
+ from mmcv.cnn.utils.flops_counter import flops_to_string, params_to_string
15
+ except ImportError:
16
+ raise ImportError('Please upgrade mmcv to >0.6.2')
17
+
18
+
19
+ def parse_args():
20
+ parser = argparse.ArgumentParser(description='Train a detector')
21
+ parser.add_argument('config', help='train config file path')
22
+ parser.add_argument(
23
+ '--shape',
24
+ type=int,
25
+ nargs='+',
26
+ default=[512, 2048],
27
+ help='input image size')
28
+ parser.add_argument(
29
+ '--cfg-options',
30
+ nargs='+',
31
+ action=DictAction,
32
+ help='override some settings in the used config, the key-value pair '
33
+ 'in xxx=yyy format will be merged into config file. If the value to '
34
+ 'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
35
+ 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
36
+ 'Note that the quotation marks are necessary and that no white space '
37
+ 'is allowed.')
38
+ parser.add_argument(
39
+ '--size-divisor',
40
+ type=int,
41
+ default=32,
42
+ help='Pad the input image, the minimum size that is divisible '
43
+ 'by size_divisor, -1 means do not pad the image.')
44
+ args = parser.parse_args()
45
+ return args
46
+
47
+
48
+ def dcnv3_flops(n, k, c):
49
+ return 5 * n * k * c
50
+
51
+
52
+ def get_flops(model, input_shape):
53
+ flops, params = get_model_complexity_info(model, input_shape, as_strings=False)
54
+
55
+ backbone = model.backbone
56
+ backbone_name = type(backbone).__name__
57
+ _, H, W = input_shape
58
+
59
+ temp = 0
60
+ if 'InternImage' in backbone_name:
61
+ depths = backbone.depths # [4, 4, 18, 4]
62
+ for idx, depth in enumerate(depths):
63
+ channels = backbone.channels * (2 ** idx)
64
+ h = H / (4 * (2 ** idx))
65
+ w = W / (4 * (2 ** idx))
66
+ temp += depth * dcnv3_flops(n=h*w, k=3*3, c=channels)
67
+
68
+ flops = flops + temp
69
+ return flops_to_string(flops), params_to_string(params)
70
+
71
+
72
+ if __name__ == '__main__':
73
+
74
+ args = parse_args()
75
+
76
+ if len(args.shape) == 1:
77
+ h = w = args.shape[0]
78
+ elif len(args.shape) == 2:
79
+ h, w = args.shape
80
+ else:
81
+ raise ValueError('invalid input shape')
82
+ orig_shape = (3, h, w)
83
+ divisor = args.size_divisor
84
+ if divisor > 0:
85
+ h = int(np.ceil(h / divisor)) * divisor
86
+ w = int(np.ceil(w / divisor)) * divisor
87
+
88
+ input_shape = (3, h, w)
89
+
90
+ cfg = Config.fromfile(args.config)
91
+ if args.cfg_options is not None:
92
+ cfg.merge_from_dict(args.cfg_options)
93
+
94
+ model = build_segmentor(
95
+ cfg.model,
96
+ train_cfg=cfg.get('train_cfg'),
97
+ test_cfg=cfg.get('test_cfg'))
98
+
99
+ if torch.cuda.is_available():
100
+ model.cuda()
101
+ model.eval()
102
+ if hasattr(model, 'forward_dummy'):
103
+ model.forward = model.forward_dummy
104
+ else:
105
+ raise NotImplementedError(
106
+ 'FLOPs counter is currently not currently supported with {}'.
107
+ format(model.__class__.__name__))
108
+
109
+ flops, params = get_flops(model, input_shape)
110
+ split_line = '=' * 30
111
+
112
+ if divisor > 0 and \
113
+ input_shape != orig_shape:
114
+ print(f'{split_line}\nUse size divisor set input shape '
115
+ f'from {orig_shape} to {input_shape}\n')
116
+ print(f'{split_line}\nInput shape: {input_shape}\n'
117
+ f'Flops: {flops}\nParams: {params}\n{split_line}')
118
+ print('!!!Please be cautious if you use the results in papers. '
119
+ 'You may need to check if all ops are supported and verify that the '
120
+ 'flops computation is correct.')
segmentation/image_demo.py ADDED
@@ -0,0 +1,79 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) OpenMMLab. All rights reserved.
2
+ import os
3
+ import os.path as osp
4
+ from argparse import ArgumentParser
5
+
6
+ import cv2
7
+ import mmcv
8
+ import mmcv_custom # noqa: F401,F403
9
+ import mmseg_custom # noqa: F401,F403
10
+ from mmcv.runner import load_checkpoint
11
+ from mmseg.apis import inference_segmentor, init_segmentor, show_result_pyplot
12
+ from mmseg.core import get_classes
13
+ from mmseg.core.evaluation import get_palette
14
+
15
+
16
+ def test_single_image(model, img_name, out_dir, color_palette, opacity):
17
+ # check img_name is an image file or not
18
+ assumed_imgformat = ('.png', '.jpg', '.jpeg', '.tiff', '.bmp', '.gif')
19
+ if (not img_name.lower().endswith(assumed_imgformat)):
20
+ print(f'Skip {img_name} because it is not an image file.')
21
+ return
22
+
23
+ result = inference_segmentor(model, img_name)
24
+
25
+ # show the results
26
+ if hasattr(model, 'module'):
27
+ model = model.module
28
+ img = model.show_result(img_name, result,
29
+ palette=color_palette,
30
+ show=False, opacity=opacity)
31
+
32
+ # save the results
33
+ mmcv.mkdir_or_exist(out_dir)
34
+ out_path = osp.join(out_dir, osp.basename(img_name))
35
+ cv2.imwrite(out_path, img)
36
+ print(f'Result is save at {out_path}')
37
+
38
+
39
+ def main():
40
+ parser = ArgumentParser()
41
+ parser.add_argument(
42
+ 'img', help='Image file or a directory contains images')
43
+ parser.add_argument('config', help='Config file')
44
+ parser.add_argument('checkpoint', help='Checkpoint file')
45
+ parser.add_argument('--out', type=str, default='demo', help='out dir')
46
+ parser.add_argument(
47
+ '--device', default='cuda:0', help='Device used for inference')
48
+ parser.add_argument(
49
+ '--palette',
50
+ default='ade20k',
51
+ choices=['ade20k', 'cityscapes', 'cocostuff'],
52
+ help='Color palette used for segmentation map')
53
+ parser.add_argument(
54
+ '--opacity',
55
+ type=float,
56
+ default=0.5,
57
+ help='Opacity of painted segmentation map. In (0, 1] range.')
58
+ args = parser.parse_args()
59
+
60
+ # build the model from a config file and a checkpoint file
61
+ model = init_segmentor(args.config, checkpoint=None, device=args.device)
62
+ checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu')
63
+ if 'CLASSES' in checkpoint.get('meta', {}):
64
+ model.CLASSES = checkpoint['meta']['CLASSES']
65
+ else:
66
+ model.CLASSES = get_classes(args.palette)
67
+
68
+ # check arg.img is directory of a single image.
69
+ if osp.isdir(args.img):
70
+ for img in sorted(os.listdir(args.img)):
71
+ test_single_image(model, osp.join(args.img, img),
72
+ args.out, get_palette(args.palette), args.opacity)
73
+ else:
74
+ test_single_image(model, args.img, args.out,
75
+ get_palette(args.palette), args.opacity)
76
+
77
+
78
+ if __name__ == '__main__':
79
+ main()
segmentation/prepare_cityscapes_folds.py ADDED
@@ -0,0 +1,146 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import argparse
2
+ import glob
3
+ import os
4
+ import random
5
+
6
+ from sklearn.model_selection import KFold
7
+
8
+
9
+ def prepare_folds(cityscapes_path, output_dir, n_splits=3):
10
+ """
11
+ Prepares k-fold cross-validation splits for the Cityscapes dataset.
12
+
13
+ Args:
14
+ cityscapes_path (str): Path to the root Cityscapes directory
15
+ (containing leftImg8bit and gtFine).
16
+ output_dir (str): Directory to save the split files.
17
+ n_splits (int): Number of folds for cross-validation.
18
+ """
19
+ leftimg8bit_path = os.path.join(cityscapes_path, "leftImg8bit")
20
+ train_img_dir = os.path.join(leftimg8bit_path, "train")
21
+ val_img_dir = os.path.join(leftimg8bit_path, "val")
22
+
23
+ train_files = []
24
+ # Check if train_img_dir exists before listing its contents
25
+ if not os.path.exists(train_img_dir):
26
+ print(f"Error: Training image directory not found: {train_img_dir}")
27
+ print(
28
+ f"Please ensure '{cityscapes_path}' is the correct root and contains 'leftImg8bit/train'."
29
+ )
30
+ return
31
+ for city_folder in os.listdir(train_img_dir):
32
+ city_path = os.path.join(train_img_dir, city_folder)
33
+ if os.path.isdir(city_path):
34
+ train_files.extend(glob.glob(os.path.join(city_path, "*.png")))
35
+
36
+ val_files = []
37
+ # Check if val_img_dir exists
38
+ if not os.path.exists(val_img_dir):
39
+ print(f"Error: Validation image directory not found: {val_img_dir}")
40
+ print(
41
+ f"Please ensure '{cityscapes_path}' is the correct root and contains 'leftImg8bit/val'."
42
+ )
43
+ pass
44
+ elif os.path.exists(val_img_dir):
45
+ for city_folder in os.listdir(val_img_dir):
46
+ city_path = os.path.join(val_img_dir, city_folder)
47
+ if os.path.isdir(city_path):
48
+ val_files.extend(glob.glob(os.path.join(city_path, "*.png")))
49
+
50
+ if not train_files and not val_files:
51
+ print(f"Error: No image files found in {train_img_dir} or {val_img_dir}.")
52
+ print("Please check your Cityscapes dataset structure and path.")
53
+ return
54
+
55
+ all_files = train_files + val_files
56
+
57
+ # 关键修改：生成正确的相对路径格式
58
+ all_files_relative = []
59
+ for f in all_files:
60
+ rel_path = os.path.relpath(f, leftimg8bit_path)
61
+ # 去掉 _leftImg8bit.png 后缀
62
+ if rel_path.endswith("_leftImg8bit.png"):
63
+ rel_path = rel_path[: -len("_leftImg8bit.png")]
64
+ all_files_relative.append(rel_path)
65
+
66
+ all_files_relative = sorted(all_files_relative)
67
+
68
+ # Ensure consistent shuffling for reproducibility if needed
69
+ random.seed(42)
70
+ random.shuffle(all_files_relative)
71
+
72
+ kf = KFold(n_splits=n_splits, shuffle=False) # Shuffle is already done
73
+
74
+ os.makedirs(output_dir, exist_ok=True)
75
+
76
+ for i, (train_index, val_index) in enumerate(kf.split(all_files_relative)):
77
+ fold_train_files = [all_files_relative[k] for k in train_index]
78
+ fold_val_files = [all_files_relative[k] for k in val_index]
79
+
80
+ train_file_path = os.path.join(output_dir, f"fold_{i + 1}_train_split.txt")
81
+ val_file_path = os.path.join(output_dir, f"fold_{i + 1}_val_split.txt")
82
+
83
+ with open(train_file_path, "w") as f:
84
+ for item in fold_train_files:
85
+ f.write(f"{item}\n") # 修复：使用单个 \n
86
+
87
+ with open(val_file_path, "w") as f:
88
+ for item in fold_val_files:
89
+ f.write(f"{item}\n") # 修复：使用单个 \n
90
+
91
+ print(f"Fold {i + 1}: {len(fold_train_files)} train, {len(fold_val_files)} val")
92
+
93
+ # 添加调试信息
94
+ print("Sample train files:")
95
+ for sample in fold_train_files[:3]:
96
+ print(f" {sample}")
97
+ print("Sample val files:")
98
+ for sample in fold_val_files[:3]:
99
+ print(f" {sample}")
100
+
101
+ print(f"Split files saved to: {os.path.abspath(output_dir)}")
102
+
103
+
104
+ if __name__ == "__main__":
105
+ parser = argparse.ArgumentParser(description="Prepare Cityscapes k-fold splits.")
106
+ parser.add_argument(
107
+ "cityscapes_path",
108
+ type=str,
109
+ help="Absolute path to the Cityscapes dataset directory.",
110
+ )
111
+ parser.add_argument(
112
+ "--output_dir",
113
+ type=str,
114
+ default=None,
115
+ help="Directory to save the split files. If not provided, a 'splits' folder will be created inside the cityscapes_path.",
116
+ )
117
+ parser.add_argument(
118
+ "--n_splits", type=int, default=3, help="Number of folds (default: 3)."
119
+ )
120
+
121
+ args = parser.parse_args()
122
+
123
+ if args.output_dir is None:
124
+ effective_output_dir = os.path.join(args.cityscapes_path, "splits")
125
+ else:
126
+ effective_output_dir = args.output_dir
127
+
128
+ abs_cityscapes_path = os.path.abspath(args.cityscapes_path)
129
+ if not os.path.isdir(abs_cityscapes_path):
130
+ print(
131
+ f"Error: Cityscapes path not found or is not a directory: {abs_cityscapes_path}"
132
+ )
133
+ exit(1)
134
+
135
+ prepare_folds(abs_cityscapes_path, effective_output_dir, args.n_splits)
136
+
137
+ script_name = os.path.basename(__file__)
138
+ print("\nTo run this script again, for example:")
139
+ print(f"python {script_name} /path/to/your/cityscapes")
140
+ if args.output_dir is not None:
141
+ print(
142
+ f"python {script_name} /path/to/your/cityscapes --output_dir {args.output_dir}"
143
+ )
144
+ print(
145
+ "Replace '/path/to/your/cityscapes' with the actual path to your Cityscapes dataset."
146
+ )
segmentation/run_kfold_training.sh ADDED
@@ -0,0 +1,82 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/bin/bash
2
+
3
+ # Script to run 3-fold cross-validation training for Cityscapes
4
+
5
+ # Ensure the script exits if any command fails
6
+ set -e
7
+
8
+ # --- Configuration ---
9
+ # Absolute path to your Cityscapes dataset
10
+ CITYSCAPES_ROOT="/pasteur/u/yiming/homework4/cityscapes"
11
+
12
+ # Path to your main MMSegmentation configuration file
13
+ CONFIG_FILE="configs/cityscapes/segformer_internimage_xl_512x1024_160k_mapillary2cityscapes.py"
14
+
15
+ # Number of folds
16
+ N_FOLDS=3
17
+
18
+ # Number of GPUs (as in your original command)
19
+ GPUS=8
20
+
21
+ # Workspace root (where dist_train.sh is located, assuming this script is in segmentation/)
22
+ WORKSPACE_ROOT=$(pwd) # This script should be in the segmentation directory
23
+
24
+ # --- Script Logic ---
25
+
26
+ echo "Starting 3-fold cross-validation training..."
27
+
28
+ for FOLD in $(seq 1 $N_FOLDS)
29
+ do
30
+ echo "----------------------------------------------------"
31
+ echo "Starting Training for Fold $FOLD of $N_FOLDS"
32
+ echo "----------------------------------------------------"
33
+
34
+ # Define the work directory for this specific fold
35
+ WORK_DIR="work_dirs/cityscapes_kfold/mask2former_internimage_h_fold_${FOLD}"
36
+
37
+ # Define paths to the split files for the current fold
38
+ TRAIN_SPLIT_FILE="splits/fold_${FOLD}_train_split.txt"
39
+ VAL_SPLIT_FILE="splits/fold_${FOLD}_val_split.txt"
40
+
41
+ # Construct the override options for mmsegmentation
42
+ # Note: We point img_dir and ann_dir to the parent directories
43
+ # because the split files contain paths relative to them (e.g., 'train/aachen/aachen_000000_000019_leftImg8bit.png')
44
+ CFG_OPTIONS="\
45
+ data.train.data_root='${CITYSCAPES_ROOT}' \
46
+ data.train.data_root='${CITYSCAPES_ROOT}' \
47
+ data.train.img_dir='leftImg8bit/' \
48
+ data.train.ann_dir='gtFine/' \
49
+ data.train.split='${TRAIN_SPLIT_FILE}' \
50
+ data.val.data_root='${CITYSCAPES_ROOT}' \
51
+ data.val.img_dir='leftImg8bit/' \
52
+ data.val.ann_dir='gtFine/' \
53
+ data.val.split='${VAL_SPLIT_FILE}' \
54
+ data.test.data_root='${CITYSCAPES_ROOT}' \
55
+ data.test.img_dir='leftImg8bit/val/' \
56
+ data.test.ann_dir='gtFine/val/' \
57
+ work_dir='${WORK_DIR}'"
58
+
59
+ # Define the training command
60
+ # Assuming dist_train.sh is in the parent directory of where this script (run_kfold_training.sh) is located
61
+ TRAIN_CMD="bash ./dist_train.sh ${CONFIG_FILE} ${GPUS} --cfg-options ${CFG_OPTIONS}"
62
+
63
+ echo "Training command for Fold $FOLD:"
64
+ echo "${TRAIN_CMD}"
65
+ echo "Output will be in: ${WORK_DIR}"
66
+
67
+ # Execute the training command
68
+ eval ${TRAIN_CMD}
69
+
70
+ echo "----------------------------------------------------"
71
+ echo "Finished Training for Fold $FOLD"
72
+ echo "----------------------------------------------------"
73
+ done
74
+
75
+ echo "3-fold cross-validation training complete."
76
+ echo "Check work_dirs/cityscapes_kfold/ for outputs of each fold."
77
+
78
+ # To aggregate results or find the best model, you would typically:
79
+ # 1. Look at the mIoU scores in the log files for each fold's validation set.
80
+ # 2. Potentially average the performance, or pick the model from the best performing fold.
81
+ # 3. If needed, re-train a final model on ALL combined train+val data from all folds,
82
+ # or use the best single fold's model for final testing on the actual test set.
segmentation/slurm_test.sh ADDED
@@ -0,0 +1,25 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/usr/bin/env bash
2
+
3
+ set -x
4
+
5
+ PARTITION=$1
6
+ JOB_NAME=$2
7
+ CONFIG=$3
8
+ CHECKPOINT=$4
9
+ GPUS=${GPUS:-8}
10
+ GPUS_PER_NODE=${GPUS_PER_NODE:-8}
11
+ CPUS_PER_TASK=${CPUS_PER_TASK:-5}
12
+ PY_ARGS=${@:5}
13
+ SRUN_ARGS=${SRUN_ARGS:-""}
14
+
15
+ PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \
16
+ srun -p ${PARTITION} \
17
+ --job-name=${JOB_NAME} \
18
+ --gres=gpu:${GPUS_PER_NODE} \
19
+ --ntasks=${GPUS} \
20
+ --ntasks-per-node=${GPUS_PER_NODE} \
21
+ --cpus-per-task=${CPUS_PER_TASK} \
22
+ --kill-on-bad-exit=1 \
23
+ --quotatype=auto \
24
+ ${SRUN_ARGS} \
25
+ python -u test.py ${CONFIG} ${CHECKPOINT} --launcher="slurm" ${PY_ARGS}
segmentation/slurm_train.sh ADDED
@@ -0,0 +1,24 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/usr/bin/env bash
2
+
3
+ set -x
4
+
5
+ PARTITION=$1
6
+ JOB_NAME=$2
7
+ CONFIG=$3
8
+ GPUS=${GPUS:-8}
9
+ GPUS_PER_NODE=${GPUS_PER_NODE:-8}
10
+ CPUS_PER_TASK=${CPUS_PER_TASK:-5}
11
+ SRUN_ARGS=${SRUN_ARGS:-""}
12
+ PY_ARGS=${@:4}
13
+
14
+ PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \
15
+ srun -p ${PARTITION} \
16
+ --job-name=${JOB_NAME} \
17
+ --gres=gpu:${GPUS_PER_NODE} \
18
+ --ntasks=${GPUS} \
19
+ --ntasks-per-node=${GPUS_PER_NODE} \
20
+ --cpus-per-task=${CPUS_PER_TASK} \
21
+ --quotatype=spot \
22
+ --kill-on-bad-exit=1 \
23
+ ${SRUN_ARGS} \
24
+ python -u train.py ${CONFIG} --launcher="slurm" ${PY_ARGS}
segmentation/test.py ADDED
@@ -0,0 +1,287 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # --------------------------------------------------------
2
+ # InternImage
3
+ # Copyright (c) 2022 OpenGVLab
4
+ # Licensed under The MIT License [see LICENSE for details]
5
+ # --------------------------------------------------------
6
+
7
+ import argparse
8
+ import os
9
+ import os.path as osp
10
+ import shutil
11
+ import time
12
+ import warnings
13
+
14
+ import mmcv
15
+ import mmcv_custom # noqa: F401,F403
16
+ import mmseg_custom # noqa: F401,F403
17
+ import torch
18
+ from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
19
+ from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,
20
+ load_state_dict, wrap_fp16_model)
21
+ from mmcv.utils import DictAction
22
+ from mmseg.apis import multi_gpu_test, single_gpu_test
23
+ from mmseg.datasets import build_dataloader, build_dataset
24
+ from mmseg.models import build_segmentor
25
+
26
+
27
+ def parse_args():
28
+ parser = argparse.ArgumentParser(
29
+ description='mmseg test (and eval) a model')
30
+ parser.add_argument('config', help='test config file path')
31
+ parser.add_argument('checkpoint', help='checkpoint file')
32
+ parser.add_argument(
33
+ '--work-dir',
34
+ help=('if specified, the evaluation metric results will be dumped'
35
+ 'into the directory as json'))
36
+ parser.add_argument(
37
+ '--aug-test', action='store_true', help='Use Flip and Multi scale aug')
38
+ parser.add_argument('--out', help='output result file in pickle format')
39
+ parser.add_argument('--dir-name', help='dir name')
40
+ parser.add_argument(
41
+ '--format-only',
42
+ action='store_true',
43
+ help='Format the output results without perform evaluation. It is'
44
+ 'useful when you want to format the result to a specific format and '
45
+ 'submit it to the test server')
46
+ parser.add_argument(
47
+ '--eval',
48
+ type=str,
49
+ nargs='+',
50
+ help='evaluation metrics, which depends on the dataset, e.g., "mIoU"'
51
+ ' for generic datasets, and "cityscapes" for Cityscapes')
52
+ parser.add_argument('--show', action='store_true', help='show results')
53
+ parser.add_argument(
54
+ '--show-dir', help='directory where painted images will be saved')
55
+ parser.add_argument(
56
+ '--gpu-collect',
57
+ action='store_true',
58
+ help='whether to use gpu to collect results.')
59
+ parser.add_argument(
60
+ '--tmpdir',
61
+ help='tmp directory used for collecting results from multiple '
62
+ 'workers, available when gpu_collect is not specified')
63
+ parser.add_argument(
64
+ '--options',
65
+ nargs='+',
66
+ action=DictAction,
67
+ help="--options is deprecated in favor of --cfg_options' and it will "
68
+ 'not be supported in version v0.22.0. Override some settings in the '
69
+ 'used config, the key-value pair in xxx=yyy format will be merged '
70
+ 'into config file. If the value to be overwritten is a list, it '
71
+ 'should be like key="[a,b]" or key=a,b It also allows nested '
72
+ 'list/tuple values, e.g. key="[(a,b),(c,d)]" Note that the quotation '
73
+ 'marks are necessary and that no white space is allowed.')
74
+ parser.add_argument(
75
+ '--cfg-options',
76
+ nargs='+',
77
+ action=DictAction,
78
+ help='override some settings in the used config, the key-value pair '
79
+ 'in xxx=yyy format will be merged into config file. If the value to '
80
+ 'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
81
+ 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
82
+ 'Note that the quotation marks are necessary and that no white space '
83
+ 'is allowed.')
84
+ parser.add_argument(
85
+ '--eval-options',
86
+ nargs='+',
87
+ action=DictAction,
88
+ help='custom options for evaluation')
89
+ parser.add_argument(
90
+ '--launcher',
91
+ choices=['none', 'pytorch', 'slurm', 'mpi'],
92
+ default='none',
93
+ help='job launcher')
94
+ parser.add_argument(
95
+ '--opacity',
96
+ type=float,
97
+ default=0.5,
98
+ help='Opacity of painted segmentation map. In (0, 1] range.')
99
+ parser.add_argument('--local_rank', type=int, default=0)
100
+ args = parser.parse_args()
101
+ if 'LOCAL_RANK' not in os.environ:
102
+ os.environ['LOCAL_RANK'] = str(args.local_rank)
103
+
104
+ if args.options and args.cfg_options:
105
+ raise ValueError(
106
+ '--options and --cfg-options cannot be both '
107
+ 'specified, --options is deprecated in favor of --cfg-options. '
108
+ '--options will not be supported in version v0.22.0.')
109
+ if args.options:
110
+ warnings.warn('--options is deprecated in favor of --cfg-options. '
111
+ '--options will not be supported in version v0.22.0.')
112
+ args.cfg_options = args.options
113
+
114
+ return args
115
+
116
+
117
+ def main():
118
+ args = parse_args()
119
+ assert args.out or args.eval or args.format_only or args.show \
120
+ or args.show_dir, \
121
+ ('Please specify at least one operation (save/eval/format/show the '
122
+ 'results / save the results) with the argument "--out", "--eval"'
123
+ ', "--format-only", "--show" or "--show-dir"')
124
+
125
+ if args.eval and args.format_only:
126
+ raise ValueError('--eval and --format_only cannot be both specified')
127
+
128
+ if args.out is not None and not args.out.endswith(('.pkl', '.pickle')):
129
+ raise ValueError('The output file must be a pkl file.')
130
+
131
+ cfg = mmcv.Config.fromfile(args.config)
132
+ if args.cfg_options is not None:
133
+ cfg.merge_from_dict(args.cfg_options)
134
+ # set cudnn_benchmark
135
+ if cfg.get('cudnn_benchmark', False):
136
+ torch.backends.cudnn.benchmark = True
137
+ if args.aug_test:
138
+ # hard code index
139
+ cfg.data.test.pipeline[1].img_ratios = [
140
+ 0.5, 0.75, 1.0, 1.25, 1.5, 1.75
141
+ ]
142
+ cfg.data.test.pipeline[1].flip = True
143
+ cfg.model.pretrained = None
144
+ cfg.data.test.test_mode = True
145
+
146
+ # init distributed env first, since logger depends on the dist info.
147
+ if args.launcher == 'none':
148
+ distributed = False
149
+ else:
150
+ distributed = True
151
+ init_dist(args.launcher, **cfg.dist_params)
152
+
153
+ rank, _ = get_dist_info()
154
+ # allows not to create
155
+ if args.work_dir is not None and rank == 0:
156
+ mmcv.mkdir_or_exist(osp.abspath(args.work_dir))
157
+ timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
158
+ if args.aug_test:
159
+ json_file = osp.join(args.work_dir,
160
+ f'eval_multi_scale_{timestamp}.json')
161
+ else:
162
+ json_file = osp.join(args.work_dir,
163
+ f'eval_single_scale_{timestamp}.json')
164
+ elif rank == 0:
165
+ work_dir = osp.join('./work_dirs',
166
+ osp.splitext(osp.basename(args.config))[0])
167
+ mmcv.mkdir_or_exist(osp.abspath(work_dir))
168
+ timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
169
+ if args.aug_test:
170
+ json_file = osp.join(work_dir,
171
+ f'eval_multi_scale_{timestamp}.json')
172
+ else:
173
+ json_file = osp.join(work_dir,
174
+ f'eval_single_scale_{timestamp}.json')
175
+
176
+ # build the dataloader
177
+ # TODO: support multiple images per gpu (only minor changes are needed)
178
+ dataset = build_dataset(cfg.data.test)
179
+ data_loader = build_dataloader(
180
+ dataset,
181
+ samples_per_gpu=1,
182
+ workers_per_gpu=cfg.data.workers_per_gpu,
183
+ dist=distributed,
184
+ shuffle=False)
185
+
186
+ # build the model and load checkpoint
187
+ cfg.model.train_cfg = None
188
+ model = build_segmentor(cfg.model, test_cfg=cfg.get('test_cfg'))
189
+
190
+ fp16_cfg = cfg.get('fp16', None)
191
+ if fp16_cfg is not None:
192
+ wrap_fp16_model(model)
193
+ checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu')
194
+
195
+ if hasattr(model, 'module'):
196
+ load_state_dict(model.module, checkpoint['state_dict'], strict=False)
197
+ else:
198
+ load_state_dict(model, checkpoint['state_dict'], strict=False)
199
+
200
+ if 'CLASSES' in checkpoint.get('meta', {}):
201
+ model.CLASSES = checkpoint['meta']['CLASSES']
202
+ else:
203
+ print('"CLASSES" not found in meta, use dataset.CLASSES instead')
204
+ model.CLASSES = dataset.CLASSES
205
+ if 'PALETTE' in checkpoint.get('meta', {}):
206
+ model.PALETTE = checkpoint['meta']['PALETTE']
207
+ else:
208
+ print('"PALETTE" not found in meta, use dataset.PALETTE instead')
209
+ model.PALETTE = dataset.PALETTE
210
+
211
+ # clean gpu memory when starting a new evaluation.
212
+ torch.cuda.empty_cache()
213
+ eval_kwargs = {} if args.eval_options is None else args.eval_options
214
+
215
+ # Deprecated
216
+ efficient_test = eval_kwargs.get('efficient_test', False)
217
+ if efficient_test:
218
+ warnings.warn(
219
+ '``efficient_test=True`` does not have effect in tools/test.py, '
220
+ 'the evaluation and format results are CPU memory efficient by '
221
+ 'default')
222
+
223
+ eval_on_format_results = (
224
+ args.eval is not None and 'cityscapes' in args.eval)
225
+ if eval_on_format_results:
226
+ assert len(args.eval) == 1, 'eval on format results is not ' \
227
+ 'applicable for metrics other than ' \
228
+ 'cityscapes'
229
+ if args.format_only or eval_on_format_results:
230
+ if 'imgfile_prefix' in eval_kwargs:
231
+ tmpdir = eval_kwargs['imgfile_prefix']
232
+ else:
233
+ tmpdir = '.format_cityscapes'
234
+ eval_kwargs.setdefault('imgfile_prefix', tmpdir)
235
+ mmcv.mkdir_or_exist(tmpdir)
236
+ else:
237
+ tmpdir = None
238
+
239
+ if not distributed:
240
+ model = MMDataParallel(model, device_ids=[0])
241
+ results = single_gpu_test(
242
+ model,
243
+ data_loader,
244
+ args.show,
245
+ args.show_dir,
246
+ False,
247
+ args.opacity,
248
+ pre_eval=args.eval is not None and not eval_on_format_results,
249
+ format_only=args.format_only or eval_on_format_results,
250
+ format_args=eval_kwargs)
251
+ else:
252
+ model = MMDistributedDataParallel(
253
+ model.cuda(),
254
+ device_ids=[torch.cuda.current_device()],
255
+ broadcast_buffers=False)
256
+ results = multi_gpu_test(
257
+ model,
258
+ data_loader,
259
+ args.tmpdir,
260
+ args.gpu_collect,
261
+ False,
262
+ pre_eval=args.eval is not None and not eval_on_format_results,
263
+ format_only=args.format_only or eval_on_format_results,
264
+ format_args=eval_kwargs)
265
+
266
+ rank, _ = get_dist_info()
267
+ if rank == 0:
268
+ if args.out:
269
+ warnings.warn(
270
+ 'The behavior of ``args.out`` has been changed since MMSeg '
271
+ 'v0.16, the pickled outputs could be seg map as type of '
272
+ 'np.array, pre-eval results or file paths for '
273
+ '``dataset.format_results()``.')
274
+ print(f'\nwriting results to {args.out}')
275
+ mmcv.dump(results, args.out)
276
+ if args.eval:
277
+ eval_kwargs.update(metric=args.eval)
278
+ metric = dataset.evaluate(results, **eval_kwargs)
279
+ metric_dict = dict(config=args.config, metric=metric)
280
+ mmcv.dump(metric_dict, json_file, indent=4)
281
+ if tmpdir is not None and eval_on_format_results:
282
+ # remove tmp dir when cityscapes evaluation
283
+ shutil.rmtree(tmpdir)
284
+
285
+
286
+ if __name__ == '__main__':
287
+ main()
segmentation/train.py ADDED
@@ -0,0 +1,306 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # --------------------------------------------------------
2
+ # InternImage
3
+ # Copyright (c) 2022 OpenGVLab
4
+ # Licensed under The MIT License [see LICENSE for details]
5
+ # --------------------------------------------------------
6
+
7
+ import argparse
8
+ import copy
9
+ import os
10
+ import os.path as osp
11
+ import time
12
+ import warnings
13
+
14
+ import mmcv
15
+ import mmcv_custom # noqa: F401,F403
16
+ import mmseg_custom # noqa: F401,F403
17
+ import torch
18
+ import torch.distributed as dist
19
+ from mmcv.cnn.utils import revert_sync_batchnorm
20
+ from mmcv.runner import get_dist_info, init_dist
21
+ from mmcv.utils import Config, DictAction, get_git_hash
22
+ from mmseg import __version__
23
+ from mmseg.apis import init_random_seed, set_random_seed, train_segmentor
24
+ from mmseg.datasets import build_dataset
25
+ from mmseg.models import build_segmentor
26
+ from mmseg.utils import collect_env, get_device, get_root_logger, setup_multi_processes
27
+
28
+
29
+ def parse_args():
30
+ parser = argparse.ArgumentParser(description="Train a segmentor")
31
+ parser.add_argument("config", help="train config file path")
32
+ parser.add_argument("--work-dir", help="the dir to save logs and models")
33
+ parser.add_argument("--load-from", help="the checkpoint file to load weights from")
34
+ parser.add_argument("--resume-from", help="the checkpoint file to resume from")
35
+ parser.add_argument(
36
+ "--no-validate",
37
+ action="store_true",
38
+ help="whether not to evaluate the checkpoint during training",
39
+ )
40
+ group_gpus = parser.add_mutually_exclusive_group()
41
+ group_gpus.add_argument(
42
+ "--gpus",
43
+ type=int,
44
+ help="(Deprecated, please use --gpu-id) number of gpus to use "
45
+ "(only applicable to non-distributed training)",
46
+ )
47
+ group_gpus.add_argument(
48
+ "--gpu-ids",
49
+ type=int,
50
+ nargs="+",
51
+ help="(Deprecated, please use --gpu-id) ids of gpus to use "
52
+ "(only applicable to non-distributed training)",
53
+ )
54
+ group_gpus.add_argument(
55
+ "--gpu-id",
56
+ type=int,
57
+ default=0,
58
+ help="id of gpu to use (only applicable to non-distributed training)",
59
+ )
60
+ parser.add_argument("--seed", type=int, default=None, help="random seed")
61
+ parser.add_argument(
62
+ "--diff_seed",
63
+ action="store_true",
64
+ help="Whether or not set different seeds for different ranks",
65
+ )
66
+ parser.add_argument(
67
+ "--deterministic",
68
+ action="store_true",
69
+ help="whether to set deterministic options for CUDNN backend.",
70
+ )
71
+ parser.add_argument(
72
+ "--options",
73
+ nargs="+",
74
+ action=DictAction,
75
+ help="--options is deprecated in favor of --cfg_options' and it will "
76
+ "not be supported in version v0.22.0. Override some settings in the "
77
+ "used config, the key-value pair in xxx=yyy format will be merged "
78
+ "into config file. If the value to be overwritten is a list, it "
79
+ 'should be like key="[a,b]" or key=a,b It also allows nested '
80
+ 'list/tuple values, e.g. key="[(a,b),(c,d)]" Note that the quotation '
81
+ "marks are necessary and that no white space is allowed.",
82
+ )
83
+ parser.add_argument(
84
+ "--cfg-options",
85
+ nargs="+",
86
+ action=DictAction,
87
+ help="override some settings in the used config, the key-value pair "
88
+ "in xxx=yyy format will be merged into config file. If the value to "
89
+ 'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
90
+ 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
91
+ "Note that the quotation marks are necessary and that no white space "
92
+ "is allowed.",
93
+ )
94
+ parser.add_argument(
95
+ "--launcher",
96
+ choices=["none", "pytorch", "slurm", "mpi"],
97
+ default="none",
98
+ help="job launcher",
99
+ )
100
+ parser.add_argument("--local_rank", type=int, default=0)
101
+ parser.add_argument(
102
+ "--auto-resume",
103
+ action="store_true",
104
+ help="resume from the latest checkpoint automatically.",
105
+ )
106
+ args = parser.parse_args()
107
+ if "LOCAL_RANK" not in os.environ:
108
+ os.environ["LOCAL_RANK"] = str(args.local_rank)
109
+
110
+ if args.options and args.cfg_options:
111
+ raise ValueError(
112
+ "--options and --cfg-options cannot be both "
113
+ "specified, --options is deprecated in favor of --cfg-options. "
114
+ "--options will not be supported in version v0.22.0."
115
+ )
116
+ if args.options:
117
+ warnings.warn(
118
+ "--options is deprecated in favor of --cfg-options. "
119
+ "--options will not be supported in version v0.22.0."
120
+ )
121
+ args.cfg_options = args.options
122
+
123
+ return args
124
+
125
+
126
+ def main():
127
+ args = parse_args()
128
+
129
+ cfg = Config.fromfile(args.config)
130
+ if args.cfg_options is not None:
131
+ cfg.merge_from_dict(args.cfg_options)
132
+ # set cudnn_benchmark
133
+ if cfg.get("cudnn_benchmark", False):
134
+ torch.backends.cudnn.benchmark = True
135
+
136
+ # work_dir is determined in this priority: CLI > segment in file > filename
137
+ if args.work_dir is not None:
138
+ # update configs according to CLI args if args.work_dir is not None
139
+ cfg.work_dir = args.work_dir
140
+ elif cfg.get("work_dir", None) is None:
141
+ # use config filename as default work_dir if cfg.work_dir is None
142
+ cfg.work_dir = osp.join(
143
+ "./work_dirs", osp.splitext(osp.basename(args.config))[0]
144
+ )
145
+ if args.load_from is not None:
146
+ cfg.load_from = args.load_from
147
+ if args.resume_from is not None:
148
+ cfg.resume_from = args.resume_from
149
+ if args.gpus is not None:
150
+ cfg.gpu_ids = range(1)
151
+ warnings.warn(
152
+ "`--gpus` is deprecated because we only support "
153
+ "single GPU mode in non-distributed training. "
154
+ "Use `gpus=1` now."
155
+ )
156
+ if args.gpu_ids is not None:
157
+ cfg.gpu_ids = args.gpu_ids[0:1]
158
+ warnings.warn(
159
+ "`--gpu-ids` is deprecated, please use `--gpu-id`. "
160
+ "Because we only support single GPU mode in "
161
+ "non-distributed training. Use the first GPU "
162
+ "in `gpu_ids` now."
163
+ )
164
+ if args.gpus is None and args.gpu_ids is None:
165
+ cfg.gpu_ids = [args.gpu_id]
166
+
167
+ cfg.auto_resume = args.auto_resume
168
+
169
+ # init distributed env first, since logger depends on the dist info.
170
+ if args.launcher == "none":
171
+ distributed = False
172
+ else:
173
+ distributed = True
174
+ init_dist(args.launcher, **cfg.dist_params)
175
+ # gpu_ids is used to calculate iter when resuming checkpoint
176
+ _, world_size = get_dist_info()
177
+ cfg.gpu_ids = range(world_size)
178
+
179
+ # create work_dir
180
+ mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
181
+ # dump config
182
+ cfg.dump(osp.join(cfg.work_dir, osp.basename(args.config)))
183
+ # init the logger before other steps
184
+ timestamp = time.strftime("%Y%m%d_%H%M%S", time.localtime())
185
+ log_file = osp.join(cfg.work_dir, f"{timestamp}.log")
186
+ logger = get_root_logger(log_file=log_file, log_level=cfg.log_level)
187
+
188
+ # set multi-process settings
189
+ setup_multi_processes(cfg)
190
+
191
+ # init the meta dict to record some important information such as
192
+ # environment info and seed, which will be logged
193
+ meta = dict()
194
+ # log env info
195
+ env_info_dict = collect_env()
196
+ env_info = "\n".join([f"{k}: {v}" for k, v in env_info_dict.items()])
197
+ dash_line = "-" * 60 + "\n"
198
+ logger.info("Environment info:\n" + dash_line + env_info + "\n" + dash_line)
199
+ meta["env_info"] = env_info
200
+
201
+ # log some basic info
202
+ logger.info(f"Distributed training: {distributed}")
203
+ logger.info(f"Config:\n{cfg.pretty_text}")
204
+
205
+ # set random seeds
206
+ cfg.device = get_device()
207
+ seed = init_random_seed(args.seed, device=cfg.device)
208
+ seed = seed + dist.get_rank() if args.diff_seed else seed
209
+ logger.info(f"Set random seed to {seed}, deterministic: {args.deterministic}")
210
+ set_random_seed(seed, deterministic=args.deterministic)
211
+ cfg.seed = seed
212
+ meta["seed"] = seed
213
+ meta["exp_name"] = osp.basename(args.config)
214
+
215
+ model = build_segmentor(
216
+ cfg.model, train_cfg=cfg.get("train_cfg"), test_cfg=cfg.get("test_cfg")
217
+ )
218
+ model.init_weights()
219
+
220
+ # SyncBN is not support for DP
221
+ if not distributed:
222
+ warnings.warn(
223
+ "SyncBN is only supported with DDP. To be compatible with DP, "
224
+ "we convert SyncBN to BN. Please use dist_train.sh which can "
225
+ "avoid this error."
226
+ )
227
+ model = revert_sync_batchnorm(model)
228
+
229
+ # logger.info(model)
230
+
231
+ datasets = [build_dataset(cfg.data.train)]
232
+ logger.info(f"Built training dataset from config: {cfg.data.train}")
233
+ logger.info(f"Number of samples in training dataset: {len(datasets[0])}")
234
+
235
+ if len(datasets[0]) == 0:
236
+ logger.error("Training dataset is EMPTY! Please check the following:")
237
+ logger.error(
238
+ f"1. Cityscapes root path (exists?): {cfg.data.train.get('data_root', 'Not Set')}"
239
+ )
240
+ logger.error(
241
+ f"2. Image directory (resolved path should exist): {cfg.data.train.get('img_dir', 'Not Set')}"
242
+ )
243
+ logger.error(
244
+ f"3. Annotation directory (resolved path should exist): {cfg.data.train.get('ann_dir', 'Not Set')}"
245
+ )
246
+ logger.error(
247
+ f"4. Split file path (exists and readable?): {cfg.data.train.get('split', 'Not Set')}"
248
+ )
249
+ logger.error(
250
+ " Ensure the paths in the split file are correct relative to img_dir and ann_dir."
251
+ )
252
+ logger.error(
253
+ " And that the actual image and annotation files exist at the fully resolved paths."
254
+ )
255
+ # Attempt to log resolved paths if the dataset object has them
256
+ # This might not work if the object is a simple list wrapper or if attributes are private
257
+ try:
258
+ dataset_obj = datasets[0]
259
+ if hasattr(dataset_obj, "data_root"):
260
+ logger.error(f" Dataset effective data_root: {dataset_obj.data_root}")
261
+ if hasattr(dataset_obj, "img_dir"):
262
+ logger.error(f" Dataset effective img_dir: {dataset_obj.img_dir}")
263
+ if hasattr(dataset_obj, "ann_dir"):
264
+ logger.error(f" Dataset effective ann_dir: {dataset_obj.ann_dir}")
265
+ if hasattr(dataset_obj, "split"):
266
+ logger.error(f" Dataset effective split file: {dataset_obj.split}")
267
+ except Exception as e:
268
+ logger.error(
269
+ f" Could not retrieve effective paths from dataset object: {e}"
270
+ )
271
+ return # Exit if dataset is empty
272
+
273
+ if len(cfg.workflow) == 2:
274
+ val_dataset = copy.deepcopy(cfg.data.val)
275
+ val_dataset.pipeline = cfg.data.train.pipeline
276
+ datasets.append(build_dataset(val_dataset))
277
+ if cfg.checkpoint_config is not None:
278
+ # save mmseg version, config file content and class names in
279
+ # checkpoints as meta data
280
+ cfg.checkpoint_config.meta = dict(
281
+ mmseg_version=f"{__version__}+{get_git_hash()[:7]}",
282
+ config=cfg.pretty_text,
283
+ CLASSES=datasets[0].CLASSES,
284
+ PALETTE=datasets[0].PALETTE,
285
+ )
286
+ # add an attribute for visualization convenience
287
+ model.CLASSES = datasets[0].CLASSES
288
+ # passing checkpoint meta for saving best checkpoint
289
+ meta.update(cfg.checkpoint_config.meta)
290
+
291
+ torch.backends.cuda.matmul.allow_tf32 = True
292
+ torch.backends.cudnn.allow_tf32 = True
293
+
294
+ train_segmentor(
295
+ model,
296
+ datasets,
297
+ cfg,
298
+ distributed=distributed,
299
+ validate=(not args.no_validate),
300
+ timestamp=timestamp,
301
+ meta=meta,
302
+ )
303
+
304
+
305
+ if __name__ == "__main__":
306
+ main()