This is how it looks like when I give verbose = True
thanks!
graph(%input : Float(1, 3, 240, 240, strides=[172800, 57600, 240, 1], requires_grad=0, device=cuda:0),
%blocks.0.0.se.conv_reduce.weight : Float(8, 32, 1, 1, strides=[32, 1, 1, 1], requires_grad=1, device=cuda:0),
%blocks.0.0.se.conv_reduce.bias : Float(8, strides=[1], requires_grad=1, device=cuda:0),
%blocks.0.0.se.conv_expand.weight : Float(32, 8, 1, 1, strides=[8, 1, 1, 1], requires_grad=1, device=cuda:0),
%blocks.0.0.se.conv_expand.bias : Float(32, strides=[1], requires_grad=1, device=cuda:0),
%blocks.0.1.se.conv_reduce.weight : Float(4, 16, 1, 1, strides=[16, 1, 1, 1], requires_grad=1, device=cuda:0),
%blocks.0.1.se.conv_reduce.bias : Float(4, strides=[1], requires_grad=1, device=cuda:0),
%blocks.0.1.se.conv_expand.weight : Float(16, 4, 1, 1, strides=[4, 1, 1, 1], requires_grad=1, device=cuda:0),
%blocks.0.1.se.conv_expand.bias : Float(16, strides=[1], requires_grad=1, device=cuda:0),
%blocks.1.0.se.conv_reduce.weight : Float(4, 96, 1, 1, strides=[96, 1, 1, 1], requires_grad=1, device=cuda:0),
%blocks.1.0.se.conv_reduce.bias : Float(4, strides=[1], requires_grad=1, device=cuda:0),
%blocks.1.0.se.conv_expand.weight : Float(96, 4, 1, 1, strides=[4, 1, 1, 1], requires_grad=1, device=cuda:0),
%blocks.1.0.se.conv_expand.bias : Float(96, strides=[1], requires_grad=1, device=cuda:0),
%blocks.1.1.se.conv_reduce.weight : Float(6, 144, 1, 1, strides=[144, 1, 1, 1], requires_grad=1, device=cuda:0),
%blocks.1.1.se.conv_reduce.bias : Float(6, strides=[1], requires_grad=1, device=cuda:0),
%blocks.1.1.se.conv_expand.weight : Float(144, 6, 1, 1, strides=[6, 1, 1, 1], requires_grad=1, device=cuda:0),
%blocks.1.1.se.conv_expand.bias : Float(144, strides=[1], requires_grad=1, device=cuda:0),
%blocks.1.2.se.conv_reduce.weight : Float(6, 144, 1, 1, strides=[144, 1, 1, 1], requires_grad=1, device=cuda:0),
%blocks.1.2.se.conv_reduce.bias : Float(6, strides=[1], requires_grad=1, device=cuda:0),
%blocks.1.2.se.conv_expand.weight : Float(144, 6, 1, 1, strides=[6, 1, 1, 1], requires_grad=1, device=cuda:0),
%blocks.1.2.se.conv_expand.bias : Float(144, strides=[1], requires_grad=1, device=cuda:0),
%blocks.2.0.se.conv_reduce.weight : Float(6, 144, 1, 1, strides=[144, 1, 1, 1], requires_grad=1, device=cuda:0),
%blocks.2.0.se.conv_reduce.bias : Float(6, strides=[1], requires_grad=1, device=cuda:0),
%blocks.2.0.se.conv_expand.weight : Float(144, 6, 1, 1, strides=[6, 1, 1, 1], requires_grad=1, device=cuda:0),
%blocks.2.0.se.conv_expand.bias : Float(144, strides=[1], requires_grad=1, device=cuda:0),
%blocks.2.1.se.conv_reduce.weight : Float(10, 240, 1, 1, strides=[240, 1, 1, 1], requires_grad=1, device=cuda:0),
%blocks.2.1.se.conv_reduce.bias : Float(10, strides=[1], requires_grad=1, device=cuda:0),
%blocks.2.1.se.conv_expand.weight : Float(240, 10, 1, 1, strides=[10, 1, 1, 1], requires_grad=1, device=cuda:0),
%blocks.2.1.se.conv_expand.bias : Float(240, strides=[1], requires_grad=1, device=cuda:0),
%blocks.2.2.se.conv_reduce.weight : Float(10, 240, 1, 1, strides=[240, 1, 1, 1], requires_grad=1, device=cuda:0),
%blocks.2.2.se.conv_reduce.bias : Float(10, strides=[1], requires_grad=1, device=cuda:0),
%blocks.2.2.se.conv_expand.weight : Float(240, 10, 1, 1, strides=[10, 1, 1, 1], requires_grad=1, device=cuda:0),
%blocks.2.2.se.conv_expand.bias : Float(240, strides=[1], requires_grad=1, device=cuda:0),
%blocks.3.0.se.conv_reduce.weight : Float(10, 240, 1, 1, strides=[240, 1, 1, 1], requires_grad=1, device=cuda:0),
%blocks.3.0.se.conv_reduce.bias : Float(10, strides=[1], requires_grad=1, device=cuda:0),
%blocks.3.0.se.conv_expand.weight : Float(240, 10, 1, 1, strides=[10, 1, 1, 1], requires_grad=1, device=cuda:0),
%blocks.3.0.se.conv_expand.bias : Float(240, strides=[1], requires_grad=1, device=cuda:0),
%blocks.3.1.se.conv_reduce.weight : Float(20, 480, 1, 1, strides=[480, 1, 1, 1], requires_grad=1, device=cuda:0),
%blocks.3.1.se.conv_reduce.bias : Float(20, strides=[1], requires_grad=1, device=cuda:0),
%blocks.3.1.se.conv_expand.weight : Float(480, 20, 1, 1, strides=[20, 1, 1, 1], requires_grad=1, device=cuda:0),
%blocks.3.1.se.conv_expand.bias : Float(480, strides=[1], requires_grad=1, device=cuda:0),
%blocks.3.2.se.conv_reduce.weight : Float(20, 480, 1, 1, strides=[480, 1, 1, 1], requires_grad=1, device=cuda:0),
%blocks.3.2.se.conv_reduce.bias : Float(20, strides=[1], requires_grad=1, device=cuda:0),
%blocks.3.2.se.conv_expand.weight : Float(480, 20, 1, 1, strides=[20, 1, 1, 1], requires_grad=1, device=cuda:0),
%blocks.3.2.se.conv_expand.bias : Float(480, strides=[1], requires_grad=1, device=cuda:0),
%blocks.3.3.se.conv_reduce.weight : Float(20, 480, 1, 1, strides=[480, 1, 1, 1], requires_grad=1, device=cuda:0),
%blocks.3.3.se.conv_reduce.bias : Float(20, strides=[1], requires_grad=1, device=cuda:0),
%blocks.3.3.se.conv_expand.weight : Float(480, 20, 1, 1, strides=[20, 1, 1, 1], requires_grad=1, device=cuda:0),
%blocks.3.3.se.conv_expand.bias : Float(480, strides=[1], requires_grad=1, device=cuda:0),
%blocks.4.0.se.conv_reduce.weight : Float(20, 480, 1, 1, strides=[480, 1, 1, 1], requires_grad=1, device=cuda:0),
%blocks.4.0.se.conv_reduce.bias : Float(20, strides=[1], requires_grad=1, device=cuda:0),
%blocks.4.0.se.conv_expand.weight : Float(480, 20, 1, 1, strides=[20, 1, 1, 1], requires_grad=1, device=cuda:0),
%blocks.4.0.se.conv_expand.bias : Float(480, strides=[1], requires_grad=1, device=cuda:0),
%blocks.4.1.se.conv_reduce.weight : Float(28, 672, 1, 1, strides=[672, 1, 1, 1], requires_grad=1, device=cuda:0),
%blocks.4.1.se.conv_reduce.bias : Float(28, strides=[1], requires_grad=1, device=cuda:0),
%blocks.4.1.se.conv_expand.weight : Float(672, 28, 1, 1, strides=[28, 1, 1, 1], requires_grad=1, device=cuda:0),
%blocks.4.1.se.conv_expand.bias : Float(672, strides=[1], requires_grad=1, device=cuda:0),
%blocks.4.2.se.conv_reduce.weight : Float(28, 672, 1, 1, strides=[672, 1, 1, 1], requires_grad=1, device=cuda:0),
%blocks.4.2.se.conv_reduce.bias : Float(28, strides=[1], requires_grad=1, device=cuda:0),
%blocks.4.2.se.conv_expand.weight : Float(672, 28, 1, 1, strides=[28, 1, 1, 1], requires_grad=1, device=cuda:0),
%blocks.4.2.se.conv_expand.bias : Float(672, strides=[1], requires_grad=1, device=cuda:0),
%blocks.4.3.se.conv_reduce.weight : Float(28, 672, 1, 1, strides=[672, 1, 1, 1], requires_grad=1, device=cuda:0),
%blocks.4.3.se.conv_reduce.bias : Float(28, strides=[1], requires_grad=1, device=cuda:0),
%blocks.4.3.se.conv_expand.weight : Float(672, 28, 1, 1, strides=[28, 1, 1, 1], requires_grad=1, device=cuda:0),
%blocks.4.3.se.conv_expand.bias : Float(672, strides=[1], requires_grad=1, device=cuda:0),
%blocks.5.0.se.conv_reduce.weight : Float(28, 672, 1, 1, strides=[672, 1, 1, 1], requires_grad=1, device=cuda:0),
%blocks.5.0.se.conv_reduce.bias : Float(28, strides=[1], requires_grad=1, device=cuda:0),
%blocks.5.0.se.conv_expand.weight : Float(672, 28, 1, 1, strides=[28, 1, 1, 1], requires_grad=1, device=cuda:0),
%blocks.5.0.se.conv_expand.bias : Float(672, strides=[1], requires_grad=1, device=cuda:0),
%blocks.5.1.se.conv_reduce.weight : Float(48, 1152, 1, 1, strides=[1152, 1, 1, 1], requires_grad=1, device=cuda:0),
%blocks.5.1.se.conv_reduce.bias : Float(48, strides=[1], requires_grad=1, device=cuda:0),
%blocks.5.1.se.conv_expand.weight : Float(1152, 48, 1, 1, strides=[48, 1, 1, 1], requires_grad=1, device=cuda:0),
%blocks.5.1.se.conv_expand.bias : Float(1152, strides=[1], requires_grad=1, device=cuda:0),
%blocks.5.2.se.conv_reduce.weight : Float(48, 1152, 1, 1, strides=[1152, 1, 1, 1], requires_grad=1, device=cuda:0),
%blocks.5.2.se.conv_reduce.bias : Float(48, strides=[1], requires_grad=1, device=cuda:0),
%blocks.5.2.se.conv_expand.weight : Float(1152, 48, 1, 1, strides=[48, 1, 1, 1], requires_grad=1, device=cuda:0),
%blocks.5.2.se.conv_expand.bias : Float(1152, strides=[1], requires_grad=1, device=cuda:0),
%blocks.5.3.se.conv_reduce.weight : Float(48, 1152, 1, 1, strides=[1152, 1, 1, 1], requires_grad=1, device=cuda:0),
%blocks.5.3.se.conv_reduce.bias : Float(48, strides=[1], requires_grad=1, device=cuda:0),
%blocks.5.3.se.conv_expand.weight : Float(1152, 48, 1, 1, strides=[48, 1, 1, 1], requires_grad=1, device=cuda:0),
%blocks.5.3.se.conv_expand.bias : Float(1152, strides=[1], requires_grad=1, device=cuda:0),
%blocks.5.4.se.conv_reduce.weight : Float(48, 1152, 1, 1, strides=[1152, 1, 1, 1], requires_grad=1, device=cuda:0),
%blocks.5.4.se.conv_reduce.bias : Float(48, strides=[1], requires_grad=1, device=cuda:0),
%blocks.5.4.se.conv_expand.weight : Float(1152, 48, 1, 1, strides=[48, 1, 1, 1], requires_grad=1, device=cuda:0),
%blocks.5.4.se.conv_expand.bias : Float(1152, strides=[1], requires_grad=1, device=cuda:0),
%blocks.6.0.se.conv_reduce.weight : Float(48, 1152, 1, 1, strides=[1152, 1, 1, 1], requires_grad=1, device=cuda:0),
%blocks.6.0.se.conv_reduce.bias : Float(48, strides=[1], requires_grad=1, device=cuda:0),
%blocks.6.0.se.conv_expand.weight : Float(1152, 48, 1, 1, strides=[48, 1, 1, 1], requires_grad=1, device=cuda:0),
%blocks.6.0.se.conv_expand.bias : Float(1152, strides=[1], requires_grad=1, device=cuda:0),
%blocks.6.1.se.conv_reduce.weight : Float(80, 1920, 1, 1, strides=[1920, 1, 1, 1], requires_grad=1, device=cuda:0),
%blocks.6.1.se.conv_reduce.bias : Float(80, strides=[1], requires_grad=1, device=cuda:0),
%blocks.6.1.se.conv_expand.weight : Float(1920, 80, 1, 1, strides=[80, 1, 1, 1], requires_grad=1, device=cuda:0),
%blocks.6.1.se.conv_expand.bias : Float(1920, strides=[1], requires_grad=1, device=cuda:0),
%classifier.weight : Float(1000, 1280, strides=[1280, 1], requires_grad=1, device=cuda:0),
%classifier.bias : Float(1000, strides=[1], requires_grad=1, device=cuda:0),
%920 : Float(32, 3, 3, 3, strides=[27, 9, 3, 1], requires_grad=0, device=cuda:0),
%921 : Float(32, strides=[1], requires_grad=0, device=cuda:0),
%923 : Float(32, 1, 3, 3, strides=[9, 9, 3, 1], requires_grad=0, device=cuda:0),
%924 : Float(32, strides=[1], requires_grad=0, device=cuda:0),
%926 : Float(16, 32, 1, 1, strides=[32, 1, 1, 1], requires_grad=0, device=cuda:0),
%927 : Float(16, strides=[1], requires_grad=0, device=cuda:0),
%929 : Float(16, 1, 3, 3, strides=[9, 9, 3, 1], requires_grad=0, device=cuda:0),
%930 : Float(16, strides=[1], requires_grad=0, device=cuda:0),
%932 : Float(16, 16, 1, 1, strides=[16, 1, 1, 1], requires_grad=0, device=cuda:0),
%933 : Float(16, strides=[1], requires_grad=0, device=cuda:0),
%935 : Float(96, 16, 1, 1, strides=[16, 1, 1, 1], requires_grad=0, device=cuda:0),
%936 : Float(96, strides=[1], requires_grad=0, device=cuda:0),
%938 : Float(96, 1, 3, 3, strides=[9, 9, 3, 1], requires_grad=0, device=cuda:0),
%939 : Float(96, strides=[1], requires_grad=0, device=cuda:0),
%941 : Float(24, 96, 1, 1, strides=[96, 1, 1, 1], requires_grad=0, device=cuda:0),
%942 : Float(24, strides=[1], requires_grad=0, device=cuda:0),
%944 : Float(144, 24, 1, 1, strides=[24, 1, 1, 1], requires_grad=0, device=cuda:0),
%945 : Float(144, strides=[1], requires_grad=0, device=cuda:0),
%947 : Float(144, 1, 3, 3, strides=[9, 9, 3, 1], requires_grad=0, device=cuda:0),
%948 : Float(144, strides=[1], requires_grad=0, device=cuda:0),
%950 : Float(24, 144, 1, 1, strides=[144, 1, 1, 1], requires_grad=0, device=cuda:0),
%951 : Float(24, strides=[1], requires_grad=0, device=cuda:0),
%953 : Float(144, 24, 1, 1, strides=[24, 1, 1, 1], requires_grad=0, device=cuda:0),
%954 : Float(144, strides=[1], requires_grad=0, device=cuda:0),
%956 : Float(144, 1, 3, 3, strides=[9, 9, 3, 1], requires_grad=0, device=cuda:0),
%957 : Float(144, strides=[1], requires_grad=0, device=cuda:0),
%959 : Float(24, 144, 1, 1, strides=[144, 1, 1, 1], requires_grad=0, device=cuda:0),
%960 : Float(24, strides=[1], requires_grad=0, device=cuda:0),
%962 : Float(144, 24, 1, 1, strides=[24, 1, 1, 1], requires_grad=0, device=cuda:0),
%963 : Float(144, strides=[1], requires_grad=0, device=cuda:0),
%965 : Float(144, 1, 5, 5, strides=[25, 25, 5, 1], requires_grad=0, device=cuda:0),
%966 : Float(144, strides=[1], requires_grad=0, device=cuda:0),
%968 : Float(40, 144, 1, 1, strides=[144, 1, 1, 1], requires_grad=0, device=cuda:0),
%969 : Float(40, strides=[1], requires_grad=0, device=cuda:0),
%971 : Float(240, 40, 1, 1, strides=[40, 1, 1, 1], requires_grad=0, device=cuda:0),
%972 : Float(240, strides=[1], requires_grad=0, device=cuda:0),
%974 : Float(240, 1, 5, 5, strides=[25, 25, 5, 1], requires_grad=0, device=cuda:0),
%975 : Float(240, strides=[1], requires_grad=0, device=cuda:0),
%977 : Float(40, 240, 1, 1, strides=[240, 1, 1, 1], requires_grad=0, device=cuda:0),
%978 : Float(40, strides=[1], requires_grad=0, device=cuda:0),
%980 : Float(240, 40, 1, 1, strides=[40, 1, 1, 1], requires_grad=0, device=cuda:0),
%981 : Float(240, strides=[1], requires_grad=0, device=cuda:0),
%983 : Float(240, 1, 5, 5, strides=[25, 25, 5, 1], requires_grad=0, device=cuda:0),
%984 : Float(240, strides=[1], requires_grad=0, device=cuda:0),
%986 : Float(40, 240, 1, 1, strides=[240, 1, 1, 1], requires_grad=0, device=cuda:0),
%987 : Float(40, strides=[1], requires_grad=0, device=cuda:0),
%989 : Float(240, 40, 1, 1, strides=[40, 1, 1, 1], requires_grad=0, device=cuda:0),
%990 : Float(240, strides=[1], requires_grad=0, device=cuda:0),
%992 : Float(240, 1, 3, 3, strides=[9, 9, 3, 1], requires_grad=0, device=cuda:0),
%993 : Float(240, strides=[1], requires_grad=0, device=cuda:0),
%995 : Float(80, 240, 1, 1, strides=[240, 1, 1, 1], requires_grad=0, device=cuda:0),
%996 : Float(80, strides=[1], requires_grad=0, device=cuda:0),
%998 : Float(480, 80, 1, 1, strides=[80, 1, 1, 1], requires_grad=0, device=cuda:0),
%999 : Float(480, strides=[1], requires_grad=0, device=cuda:0),
%1001 : Float(480, 1, 3, 3, strides=[9, 9, 3, 1], requires_grad=0, device=cuda:0),
%1002 : Float(480, strides=[1], requires_grad=0, device=cuda:0),
%1004 : Float(80, 480, 1, 1, strides=[480, 1, 1, 1], requires_grad=0, device=cuda:0),
%1005 : Float(80, strides=[1], requires_grad=0, device=cuda:0),
%1007 : Float(480, 80, 1, 1, strides=[80, 1, 1, 1], requires_grad=0, device=cuda:0),
%1008 : Float(480, strides=[1], requires_grad=0, device=cuda:0),
%1010 : Float(480, 1, 3, 3, strides=[9, 9, 3, 1], requires_grad=0, device=cuda:0),
%1011 : Float(480, strides=[1], requires_grad=0, device=cuda:0),
%1013 : Float(80, 480, 1, 1, strides=[480, 1, 1, 1], requires_grad=0, device=cuda:0),
%1014 : Float(80, strides=[1], requires_grad=0, device=cuda:0),
%1016 : Float(480, 80, 1, 1, strides=[80, 1, 1, 1], requires_grad=0, device=cuda:0),
%1017 : Float(480, strides=[1], requires_grad=0, device=cuda:0),
%1019 : Float(480, 1, 3, 3, strides=[9, 9, 3, 1], requires_grad=0, device=cuda:0),
%1020 : Float(480, strides=[1], requires_grad=0, device=cuda:0),
%1022 : Float(80, 480, 1, 1, strides=[480, 1, 1, 1], requires_grad=0, device=cuda:0),
%1023 : Float(80, strides=[1], requires_grad=0, device=cuda:0),
%1025 : Float(480, 80, 1, 1, strides=[80, 1, 1, 1], requires_grad=0, device=cuda:0),
%1026 : Float(480, strides=[1], requires_grad=0, device=cuda:0),
%1028 : Float(480, 1, 5, 5, strides=[25, 25, 5, 1], requires_grad=0, device=cuda:0),
%1029 : Float(480, strides=[1], requires_grad=0, device=cuda:0),
%1031 : Float(112, 480, 1, 1, strides=[480, 1, 1, 1], requires_grad=0, device=cuda:0),
%1032 : Float(112, strides=[1], requires_grad=0, device=cuda:0),
%1034 : Float(672, 112, 1, 1, strides=[112, 1, 1, 1], requires_grad=0, device=cuda:0),
%1035 : Float(672, strides=[1], requires_grad=0, device=cuda:0),
%1037 : Float(672, 1, 5, 5, strides=[25, 25, 5, 1], requires_grad=0, device=cuda:0),
%1038 : Float(672, strides=[1], requires_grad=0, device=cuda:0),
%1040 : Float(112, 672, 1, 1, strides=[672, 1, 1, 1], requires_grad=0, device=cuda:0),
%1041 : Float(112, strides=[1], requires_grad=0, device=cuda:0),
%1043 : Float(672, 112, 1, 1, strides=[112, 1, 1, 1], requires_grad=0, device=cuda:0),
%1044 : Float(672, strides=[1], requires_grad=0, device=cuda:0),
%1046 : Float(672, 1, 5, 5, strides=[25, 25, 5, 1], requires_grad=0, device=cuda:0),
%1047 : Float(672, strides=[1], requires_grad=0, device=cuda:0),
%1049 : Float(112, 672, 1, 1, strides=[672, 1, 1, 1], requires_grad=0, device=cuda:0),
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%1052 : Float(672, 112, 1, 1, strides=[112, 1, 1, 1], requires_grad=0, device=cuda:0),
%1053 : Float(672, strides=[1], requires_grad=0, device=cuda:0),
%1055 : Float(672, 1, 5, 5, strides=[25, 25, 5, 1], requires_grad=0, device=cuda:0),
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%1076 : Float(192, 1152, 1, 1, strides=[1152, 1, 1, 1], requires_grad=0, device=cuda:0),
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%1082 : Float(1152, 1, 5, 5, strides=[25, 25, 5, 1], requires_grad=0, device=cuda:0),
%1083 : Float(1152, strides=[1], requires_grad=0, device=cuda:0),
%1085 : Float(192, 1152, 1, 1, strides=[1152, 1, 1, 1], requires_grad=0, device=cuda:0),
%1086 : Float(192, strides=[1], requires_grad=0, device=cuda:0),
%1088 : Float(1152, 192, 1, 1, strides=[192, 1, 1, 1], requires_grad=0, device=cuda:0),
%1089 : Float(1152, strides=[1], requires_grad=0, device=cuda:0),
%1091 : Float(1152, 1, 5, 5, strides=[25, 25, 5, 1], requires_grad=0, device=cuda:0),
%1092 : Float(1152, strides=[1], requires_grad=0, device=cuda:0),
%1094 : Float(192, 1152, 1, 1, strides=[1152, 1, 1, 1], requires_grad=0, device=cuda:0),
%1095 : Float(192, strides=[1], requires_grad=0, device=cuda:0),
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%1098 : Float(1152, strides=[1], requires_grad=0, device=cuda:0),
%1100 : Float(1152, 1, 5, 5, strides=[25, 25, 5, 1], requires_grad=0, device=cuda:0),
%1101 : Float(1152, strides=[1], requires_grad=0, device=cuda:0),
%1103 : Float(192, 1152, 1, 1, strides=[1152, 1, 1, 1], requires_grad=0, device=cuda:0),
%1104 : Float(192, strides=[1], requires_grad=0, device=cuda:0),
%1106 : Float(1152, 192, 1, 1, strides=[192, 1, 1, 1], requires_grad=0, device=cuda:0),
%1107 : Float(1152, strides=[1], requires_grad=0, device=cuda:0),
%1109 : Float(1152, 1, 3, 3, strides=[9, 9, 3, 1], requires_grad=0, device=cuda:0),
%1110 : Float(1152, strides=[1], requires_grad=0, device=cuda:0),
%1112 : Float(320, 1152, 1, 1, strides=[1152, 1, 1, 1], requires_grad=0, device=cuda:0),
%1113 : Float(320, strides=[1], requires_grad=0, device=cuda:0),
%1115 : Float(1920, 320, 1, 1, strides=[320, 1, 1, 1], requires_grad=0, device=cuda:0),
%1116 : Float(1920, strides=[1], requires_grad=0, device=cuda:0),
%1118 : Float(1920, 1, 3, 3, strides=[9, 9, 3, 1], requires_grad=0, device=cuda:0),
%1119 : Float(1920, strides=[1], requires_grad=0, device=cuda:0),
%1121 : Float(320, 1920, 1, 1, strides=[1920, 1, 1, 1], requires_grad=0, device=cuda:0),
%1122 : Float(320, strides=[1], requires_grad=0, device=cuda:0),
%1124 : Float(1280, 320, 1, 1, strides=[320, 1, 1, 1], requires_grad=0, device=cuda:0),
%1125 : Float(1280, strides=[1], requires_grad=0, device=cuda:0)):
%919 : Float(1, 32, 120, 120, strides=[460800, 14400, 120, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[2, 2]](%input, %920, %921)
%511 : Float(1, 32, 120, 120, strides=[460800, 14400, 120, 1], device=cpu) = onnx::Sigmoid(%919)
%512 : Float(1, 32, 120, 120, strides=[460800, 14400, 120, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%919, %511) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%922 : Float(1, 32, 120, 120, strides=[460800, 14400, 120, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=32, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%512, %923, %924)
%515 : Float(1, 32, 120, 120, strides=[460800, 14400, 120, 1], device=cpu) = onnx::Sigmoid(%922)
%516 : Float(1, 32, 120, 120, strides=[460800, 14400, 120, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%922, %515) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%517 : Float(1, 32, 1, 1, strides=[32, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::ReduceMean[axes=[2, 3], keepdims=1](%516) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:44:0
%518 : Float(1, 8, 1, 1, strides=[8, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%517, %blocks.0.0.se.conv_reduce.weight, %blocks.0.0.se.conv_reduce.bias) # /usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py:395:0
%519 : Float(1, 8, 1, 1, strides=[8, 1, 1, 1], device=cpu) = onnx::Sigmoid(%518)
%520 : Float(1, 8, 1, 1, strides=[8, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%518, %519) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%521 : Float(1, 32, 1, 1, strides=[32, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%520, %blocks.0.0.se.conv_expand.weight, %blocks.0.0.se.conv_expand.bias) # /usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py:395:0
%522 : Float(1, 32, 1, 1, strides=[32, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Sigmoid(%521) # /usr/local/lib/python3.8/dist-packages/timm/models/layers/activations.py:47:0
%523 : Float(1, 32, 120, 120, strides=[460800, 14400, 120, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%516, %522) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:48:0
%925 : Float(1, 16, 120, 120, strides=[230400, 14400, 120, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%523, %926, %927)
%928 : Float(1, 16, 120, 120, strides=[230400, 14400, 120, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=16, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%925, %929, %930)
%528 : Float(1, 16, 120, 120, strides=[230400, 14400, 120, 1], device=cpu) = onnx::Sigmoid(%928)
%529 : Float(1, 16, 120, 120, strides=[230400, 14400, 120, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%928, %528) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%530 : Float(1, 16, 1, 1, strides=[16, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::ReduceMean[axes=[2, 3], keepdims=1](%529) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:44:0
%531 : Float(1, 4, 1, 1, strides=[4, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%530, %blocks.0.1.se.conv_reduce.weight, %blocks.0.1.se.conv_reduce.bias) # /usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py:395:0
%532 : Float(1, 4, 1, 1, strides=[4, 1, 1, 1], device=cpu) = onnx::Sigmoid(%531)
%533 : Float(1, 4, 1, 1, strides=[4, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%531, %532) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%534 : Float(1, 16, 1, 1, strides=[16, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%533, %blocks.0.1.se.conv_expand.weight, %blocks.0.1.se.conv_expand.bias) # /usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py:395:0
%535 : Float(1, 16, 1, 1, strides=[16, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Sigmoid(%534) # /usr/local/lib/python3.8/dist-packages/timm/models/layers/activations.py:47:0
%536 : Float(1, 16, 120, 120, strides=[230400, 14400, 120, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%529, %535) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:48:0
%931 : Float(1, 16, 120, 120, strides=[230400, 14400, 120, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%536, %932, %933)
%539 : Float(1, 16, 120, 120, strides=[230400, 14400, 120, 1], requires_grad=1, device=cuda:0) = onnx::Add(%931, %925) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:133:0
%934 : Float(1, 96, 120, 120, strides=[1382400, 14400, 120, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%539, %935, %936)
%542 : Float(1, 96, 120, 120, strides=[1382400, 14400, 120, 1], device=cpu) = onnx::Sigmoid(%934)
%543 : Float(1, 96, 120, 120, strides=[1382400, 14400, 120, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%934, %542) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%937 : Float(1, 96, 60, 60, strides=[345600, 3600, 60, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=96, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[2, 2]](%543, %938, %939)
%546 : Float(1, 96, 60, 60, strides=[345600, 3600, 60, 1], device=cpu) = onnx::Sigmoid(%937)
%547 : Float(1, 96, 60, 60, strides=[345600, 3600, 60, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%937, %546) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%548 : Float(1, 96, 1, 1, strides=[96, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::ReduceMean[axes=[2, 3], keepdims=1](%547) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:44:0
%549 : Float(1, 4, 1, 1, strides=[4, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%548, %blocks.1.0.se.conv_reduce.weight, %blocks.1.0.se.conv_reduce.bias) # /usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py:395:0
%550 : Float(1, 4, 1, 1, strides=[4, 1, 1, 1], device=cpu) = onnx::Sigmoid(%549)
%551 : Float(1, 4, 1, 1, strides=[4, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%549, %550) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%552 : Float(1, 96, 1, 1, strides=[96, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%551, %blocks.1.0.se.conv_expand.weight, %blocks.1.0.se.conv_expand.bias) # /usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py:395:0
%553 : Float(1, 96, 1, 1, strides=[96, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Sigmoid(%552) # /usr/local/lib/python3.8/dist-packages/timm/models/layers/activations.py:47:0
%554 : Float(1, 96, 60, 60, strides=[345600, 3600, 60, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%547, %553) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:48:0
%940 : Float(1, 24, 60, 60, strides=[86400, 3600, 60, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%554, %941, %942)
%943 : Float(1, 144, 60, 60, strides=[518400, 3600, 60, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%940, %944, %945)
%559 : Float(1, 144, 60, 60, strides=[518400, 3600, 60, 1], device=cpu) = onnx::Sigmoid(%943)
%560 : Float(1, 144, 60, 60, strides=[518400, 3600, 60, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%943, %559) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%946 : Float(1, 144, 60, 60, strides=[518400, 3600, 60, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=144, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%560, %947, %948)
%563 : Float(1, 144, 60, 60, strides=[518400, 3600, 60, 1], device=cpu) = onnx::Sigmoid(%946)
%564 : Float(1, 144, 60, 60, strides=[518400, 3600, 60, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%946, %563) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%565 : Float(1, 144, 1, 1, strides=[144, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::ReduceMean[axes=[2, 3], keepdims=1](%564) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:44:0
%566 : Float(1, 6, 1, 1, strides=[6, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%565, %blocks.1.1.se.conv_reduce.weight, %blocks.1.1.se.conv_reduce.bias) # /usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py:395:0
%567 : Float(1, 6, 1, 1, strides=[6, 1, 1, 1], device=cpu) = onnx::Sigmoid(%566)
%568 : Float(1, 6, 1, 1, strides=[6, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%566, %567) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%569 : Float(1, 144, 1, 1, strides=[144, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%568, %blocks.1.1.se.conv_expand.weight, %blocks.1.1.se.conv_expand.bias) # /usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py:395:0
%570 : Float(1, 144, 1, 1, strides=[144, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Sigmoid(%569) # /usr/local/lib/python3.8/dist-packages/timm/models/layers/activations.py:47:0
%571 : Float(1, 144, 60, 60, strides=[518400, 3600, 60, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%564, %570) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:48:0
%949 : Float(1, 24, 60, 60, strides=[86400, 3600, 60, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%571, %950, %951)
%574 : Float(1, 24, 60, 60, strides=[86400, 3600, 60, 1], requires_grad=1, device=cuda:0) = onnx::Add(%949, %940)
%952 : Float(1, 144, 60, 60, strides=[518400, 3600, 60, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%574, %953, %954)
%577 : Float(1, 144, 60, 60, strides=[518400, 3600, 60, 1], device=cpu) = onnx::Sigmoid(%952)
%578 : Float(1, 144, 60, 60, strides=[518400, 3600, 60, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%952, %577) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%955 : Float(1, 144, 60, 60, strides=[518400, 3600, 60, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=144, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%578, %956, %957)
%581 : Float(1, 144, 60, 60, strides=[518400, 3600, 60, 1], device=cpu) = onnx::Sigmoid(%955)
%582 : Float(1, 144, 60, 60, strides=[518400, 3600, 60, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%955, %581) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%583 : Float(1, 144, 1, 1, strides=[144, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::ReduceMean[axes=[2, 3], keepdims=1](%582) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:44:0
%584 : Float(1, 6, 1, 1, strides=[6, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%583, %blocks.1.2.se.conv_reduce.weight, %blocks.1.2.se.conv_reduce.bias) # /usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py:395:0
%585 : Float(1, 6, 1, 1, strides=[6, 1, 1, 1], device=cpu) = onnx::Sigmoid(%584)
%586 : Float(1, 6, 1, 1, strides=[6, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%584, %585) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%587 : Float(1, 144, 1, 1, strides=[144, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%586, %blocks.1.2.se.conv_expand.weight, %blocks.1.2.se.conv_expand.bias) # /usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py:395:0
%588 : Float(1, 144, 1, 1, strides=[144, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Sigmoid(%587) # /usr/local/lib/python3.8/dist-packages/timm/models/layers/activations.py:47:0
%589 : Float(1, 144, 60, 60, strides=[518400, 3600, 60, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%582, %588) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:48:0
%958 : Float(1, 24, 60, 60, strides=[86400, 3600, 60, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%589, %959, %960)
%592 : Float(1, 24, 60, 60, strides=[86400, 3600, 60, 1], requires_grad=1, device=cuda:0) = onnx::Add(%958, %574) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:208:0
%961 : Float(1, 144, 60, 60, strides=[518400, 3600, 60, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%592, %962, %963)
%595 : Float(1, 144, 60, 60, strides=[518400, 3600, 60, 1], device=cpu) = onnx::Sigmoid(%961)
%596 : Float(1, 144, 60, 60, strides=[518400, 3600, 60, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%961, %595) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%964 : Float(1, 144, 30, 30, strides=[129600, 900, 30, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=144, kernel_shape=[5, 5], pads=[2, 2, 2, 2], strides=[2, 2]](%596, %965, %966)
%599 : Float(1, 144, 30, 30, strides=[129600, 900, 30, 1], device=cpu) = onnx::Sigmoid(%964)
%600 : Float(1, 144, 30, 30, strides=[129600, 900, 30, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%964, %599) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%601 : Float(1, 144, 1, 1, strides=[144, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::ReduceMean[axes=[2, 3], keepdims=1](%600) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:44:0
%602 : Float(1, 6, 1, 1, strides=[6, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%601, %blocks.2.0.se.conv_reduce.weight, %blocks.2.0.se.conv_reduce.bias) # /usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py:395:0
%603 : Float(1, 6, 1, 1, strides=[6, 1, 1, 1], device=cpu) = onnx::Sigmoid(%602)
%604 : Float(1, 6, 1, 1, strides=[6, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%602, %603) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%605 : Float(1, 144, 1, 1, strides=[144, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%604, %blocks.2.0.se.conv_expand.weight, %blocks.2.0.se.conv_expand.bias) # /usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py:395:0
%606 : Float(1, 144, 1, 1, strides=[144, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Sigmoid(%605) # /usr/local/lib/python3.8/dist-packages/timm/models/layers/activations.py:47:0
%607 : Float(1, 144, 30, 30, strides=[129600, 900, 30, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%600, %606) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:48:0
%967 : Float(1, 40, 30, 30, strides=[36000, 900, 30, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%607, %968, %969)
%970 : Float(1, 240, 30, 30, strides=[216000, 900, 30, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%967, %971, %972)
%612 : Float(1, 240, 30, 30, strides=[216000, 900, 30, 1], device=cpu) = onnx::Sigmoid(%970)
%613 : Float(1, 240, 30, 30, strides=[216000, 900, 30, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%970, %612) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%973 : Float(1, 240, 30, 30, strides=[216000, 900, 30, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=240, kernel_shape=[5, 5], pads=[2, 2, 2, 2], strides=[1, 1]](%613, %974, %975)
%616 : Float(1, 240, 30, 30, strides=[216000, 900, 30, 1], device=cpu) = onnx::Sigmoid(%973)
%617 : Float(1, 240, 30, 30, strides=[216000, 900, 30, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%973, %616) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%618 : Float(1, 240, 1, 1, strides=[240, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::ReduceMean[axes=[2, 3], keepdims=1](%617) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:44:0
%619 : Float(1, 10, 1, 1, strides=[10, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%618, %blocks.2.1.se.conv_reduce.weight, %blocks.2.1.se.conv_reduce.bias) # /usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py:395:0
%620 : Float(1, 10, 1, 1, strides=[10, 1, 1, 1], device=cpu) = onnx::Sigmoid(%619)
%621 : Float(1, 10, 1, 1, strides=[10, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%619, %620) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%622 : Float(1, 240, 1, 1, strides=[240, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%621, %blocks.2.1.se.conv_expand.weight, %blocks.2.1.se.conv_expand.bias) # /usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py:395:0
%623 : Float(1, 240, 1, 1, strides=[240, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Sigmoid(%622) # /usr/local/lib/python3.8/dist-packages/timm/models/layers/activations.py:47:0
%624 : Float(1, 240, 30, 30, strides=[216000, 900, 30, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%617, %623) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:48:0
%976 : Float(1, 40, 30, 30, strides=[36000, 900, 30, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%624, %977, %978)
%627 : Float(1, 40, 30, 30, strides=[36000, 900, 30, 1], requires_grad=1, device=cuda:0) = onnx::Add(%976, %967)
%979 : Float(1, 240, 30, 30, strides=[216000, 900, 30, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%627, %980, %981)
%630 : Float(1, 240, 30, 30, strides=[216000, 900, 30, 1], device=cpu) = onnx::Sigmoid(%979)
%631 : Float(1, 240, 30, 30, strides=[216000, 900, 30, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%979, %630) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%982 : Float(1, 240, 30, 30, strides=[216000, 900, 30, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=240, kernel_shape=[5, 5], pads=[2, 2, 2, 2], strides=[1, 1]](%631, %983, %984)
%634 : Float(1, 240, 30, 30, strides=[216000, 900, 30, 1], device=cpu) = onnx::Sigmoid(%982)
%635 : Float(1, 240, 30, 30, strides=[216000, 900, 30, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%982, %634) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%636 : Float(1, 240, 1, 1, strides=[240, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::ReduceMean[axes=[2, 3], keepdims=1](%635) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:44:0
%637 : Float(1, 10, 1, 1, strides=[10, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%636, %blocks.2.2.se.conv_reduce.weight, %blocks.2.2.se.conv_reduce.bias) # /usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py:395:0
%638 : Float(1, 10, 1, 1, strides=[10, 1, 1, 1], device=cpu) = onnx::Sigmoid(%637)
%639 : Float(1, 10, 1, 1, strides=[10, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%637, %638) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%640 : Float(1, 240, 1, 1, strides=[240, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%639, %blocks.2.2.se.conv_expand.weight, %blocks.2.2.se.conv_expand.bias) # /usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py:395:0
%641 : Float(1, 240, 1, 1, strides=[240, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Sigmoid(%640) # /usr/local/lib/python3.8/dist-packages/timm/models/layers/activations.py:47:0
%642 : Float(1, 240, 30, 30, strides=[216000, 900, 30, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%635, %641) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:48:0
%985 : Float(1, 40, 30, 30, strides=[36000, 900, 30, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%642, %986, %987)
%645 : Float(1, 40, 30, 30, strides=[36000, 900, 30, 1], requires_grad=1, device=cuda:0) = onnx::Add(%985, %627) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:208:0
%988 : Float(1, 240, 30, 30, strides=[216000, 900, 30, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%645, %989, %990)
%648 : Float(1, 240, 30, 30, strides=[216000, 900, 30, 1], device=cpu) = onnx::Sigmoid(%988)
%649 : Float(1, 240, 30, 30, strides=[216000, 900, 30, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%988, %648) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%991 : Float(1, 240, 15, 15, strides=[54000, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=240, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[2, 2]](%649, %992, %993)
%652 : Float(1, 240, 15, 15, strides=[54000, 225, 15, 1], device=cpu) = onnx::Sigmoid(%991)
%653 : Float(1, 240, 15, 15, strides=[54000, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%991, %652) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%654 : Float(1, 240, 1, 1, strides=[240, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::ReduceMean[axes=[2, 3], keepdims=1](%653) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:44:0
%655 : Float(1, 10, 1, 1, strides=[10, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%654, %blocks.3.0.se.conv_reduce.weight, %blocks.3.0.se.conv_reduce.bias) # /usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py:395:0
%656 : Float(1, 10, 1, 1, strides=[10, 1, 1, 1], device=cpu) = onnx::Sigmoid(%655)
%657 : Float(1, 10, 1, 1, strides=[10, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%655, %656) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%658 : Float(1, 240, 1, 1, strides=[240, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%657, %blocks.3.0.se.conv_expand.weight, %blocks.3.0.se.conv_expand.bias) # /usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py:395:0
%659 : Float(1, 240, 1, 1, strides=[240, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Sigmoid(%658) # /usr/local/lib/python3.8/dist-packages/timm/models/layers/activations.py:47:0
%660 : Float(1, 240, 15, 15, strides=[54000, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%653, %659) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:48:0
%994 : Float(1, 80, 15, 15, strides=[18000, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%660, %995, %996)
%997 : Float(1, 480, 15, 15, strides=[108000, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%994, %998, %999)
%665 : Float(1, 480, 15, 15, strides=[108000, 225, 15, 1], device=cpu) = onnx::Sigmoid(%997)
%666 : Float(1, 480, 15, 15, strides=[108000, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%997, %665) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%1000 : Float(1, 480, 15, 15, strides=[108000, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=480, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%666, %1001, %1002)
%669 : Float(1, 480, 15, 15, strides=[108000, 225, 15, 1], device=cpu) = onnx::Sigmoid(%1000)
%670 : Float(1, 480, 15, 15, strides=[108000, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%1000, %669) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%671 : Float(1, 480, 1, 1, strides=[480, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::ReduceMean[axes=[2, 3], keepdims=1](%670) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:44:0
%672 : Float(1, 20, 1, 1, strides=[20, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%671, %blocks.3.1.se.conv_reduce.weight, %blocks.3.1.se.conv_reduce.bias) # /usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py:395:0
%673 : Float(1, 20, 1, 1, strides=[20, 1, 1, 1], device=cpu) = onnx::Sigmoid(%672)
%674 : Float(1, 20, 1, 1, strides=[20, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%672, %673) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%675 : Float(1, 480, 1, 1, strides=[480, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%674, %blocks.3.1.se.conv_expand.weight, %blocks.3.1.se.conv_expand.bias) # /usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py:395:0
%676 : Float(1, 480, 1, 1, strides=[480, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Sigmoid(%675) # /usr/local/lib/python3.8/dist-packages/timm/models/layers/activations.py:47:0
%677 : Float(1, 480, 15, 15, strides=[108000, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%670, %676) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:48:0
%1003 : Float(1, 80, 15, 15, strides=[18000, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%677, %1004, %1005)
%680 : Float(1, 80, 15, 15, strides=[18000, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Add(%1003, %994)
%1006 : Float(1, 480, 15, 15, strides=[108000, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%680, %1007, %1008)
%683 : Float(1, 480, 15, 15, strides=[108000, 225, 15, 1], device=cpu) = onnx::Sigmoid(%1006)
%684 : Float(1, 480, 15, 15, strides=[108000, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%1006, %683) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%1009 : Float(1, 480, 15, 15, strides=[108000, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=480, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%684, %1010, %1011)
%687 : Float(1, 480, 15, 15, strides=[108000, 225, 15, 1], device=cpu) = onnx::Sigmoid(%1009)
%688 : Float(1, 480, 15, 15, strides=[108000, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%1009, %687) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%689 : Float(1, 480, 1, 1, strides=[480, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::ReduceMean[axes=[2, 3], keepdims=1](%688) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:44:0
%690 : Float(1, 20, 1, 1, strides=[20, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%689, %blocks.3.2.se.conv_reduce.weight, %blocks.3.2.se.conv_reduce.bias) # /usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py:395:0
%691 : Float(1, 20, 1, 1, strides=[20, 1, 1, 1], device=cpu) = onnx::Sigmoid(%690)
%692 : Float(1, 20, 1, 1, strides=[20, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%690, %691) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%693 : Float(1, 480, 1, 1, strides=[480, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%692, %blocks.3.2.se.conv_expand.weight, %blocks.3.2.se.conv_expand.bias) # /usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py:395:0
%694 : Float(1, 480, 1, 1, strides=[480, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Sigmoid(%693) # /usr/local/lib/python3.8/dist-packages/timm/models/layers/activations.py:47:0
%695 : Float(1, 480, 15, 15, strides=[108000, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%688, %694) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:48:0
%1012 : Float(1, 80, 15, 15, strides=[18000, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%695, %1013, %1014)
%698 : Float(1, 80, 15, 15, strides=[18000, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Add(%1012, %680)
%1015 : Float(1, 480, 15, 15, strides=[108000, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%698, %1016, %1017)
%701 : Float(1, 480, 15, 15, strides=[108000, 225, 15, 1], device=cpu) = onnx::Sigmoid(%1015)
%702 : Float(1, 480, 15, 15, strides=[108000, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%1015, %701) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%1018 : Float(1, 480, 15, 15, strides=[108000, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=480, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%702, %1019, %1020)
%705 : Float(1, 480, 15, 15, strides=[108000, 225, 15, 1], device=cpu) = onnx::Sigmoid(%1018)
%706 : Float(1, 480, 15, 15, strides=[108000, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%1018, %705) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%707 : Float(1, 480, 1, 1, strides=[480, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::ReduceMean[axes=[2, 3], keepdims=1](%706) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:44:0
%708 : Float(1, 20, 1, 1, strides=[20, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%707, %blocks.3.3.se.conv_reduce.weight, %blocks.3.3.se.conv_reduce.bias) # /usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py:395:0
%709 : Float(1, 20, 1, 1, strides=[20, 1, 1, 1], device=cpu) = onnx::Sigmoid(%708)
%710 : Float(1, 20, 1, 1, strides=[20, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%708, %709) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%711 : Float(1, 480, 1, 1, strides=[480, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%710, %blocks.3.3.se.conv_expand.weight, %blocks.3.3.se.conv_expand.bias) # /usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py:395:0
%712 : Float(1, 480, 1, 1, strides=[480, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Sigmoid(%711) # /usr/local/lib/python3.8/dist-packages/timm/models/layers/activations.py:47:0
%713 : Float(1, 480, 15, 15, strides=[108000, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%706, %712) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:48:0
%1021 : Float(1, 80, 15, 15, strides=[18000, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%713, %1022, %1023)
%716 : Float(1, 80, 15, 15, strides=[18000, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Add(%1021, %698) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:208:0
%1024 : Float(1, 480, 15, 15, strides=[108000, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%716, %1025, %1026)
%719 : Float(1, 480, 15, 15, strides=[108000, 225, 15, 1], device=cpu) = onnx::Sigmoid(%1024)
%720 : Float(1, 480, 15, 15, strides=[108000, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%1024, %719) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%1027 : Float(1, 480, 15, 15, strides=[108000, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=480, kernel_shape=[5, 5], pads=[2, 2, 2, 2], strides=[1, 1]](%720, %1028, %1029)
%723 : Float(1, 480, 15, 15, strides=[108000, 225, 15, 1], device=cpu) = onnx::Sigmoid(%1027)
%724 : Float(1, 480, 15, 15, strides=[108000, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%1027, %723) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%725 : Float(1, 480, 1, 1, strides=[480, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::ReduceMean[axes=[2, 3], keepdims=1](%724) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:44:0
%726 : Float(1, 20, 1, 1, strides=[20, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%725, %blocks.4.0.se.conv_reduce.weight, %blocks.4.0.se.conv_reduce.bias) # /usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py:395:0
%727 : Float(1, 20, 1, 1, strides=[20, 1, 1, 1], device=cpu) = onnx::Sigmoid(%726)
%728 : Float(1, 20, 1, 1, strides=[20, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%726, %727) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%729 : Float(1, 480, 1, 1, strides=[480, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%728, %blocks.4.0.se.conv_expand.weight, %blocks.4.0.se.conv_expand.bias) # /usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py:395:0
%730 : Float(1, 480, 1, 1, strides=[480, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Sigmoid(%729) # /usr/local/lib/python3.8/dist-packages/timm/models/layers/activations.py:47:0
%731 : Float(1, 480, 15, 15, strides=[108000, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%724, %730) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:48:0
%1030 : Float(1, 112, 15, 15, strides=[25200, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%731, %1031, %1032)
%1033 : Float(1, 672, 15, 15, strides=[151200, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%1030, %1034, %1035)
%736 : Float(1, 672, 15, 15, strides=[151200, 225, 15, 1], device=cpu) = onnx::Sigmoid(%1033)
%737 : Float(1, 672, 15, 15, strides=[151200, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%1033, %736) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%1036 : Float(1, 672, 15, 15, strides=[151200, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=672, kernel_shape=[5, 5], pads=[2, 2, 2, 2], strides=[1, 1]](%737, %1037, %1038)
%740 : Float(1, 672, 15, 15, strides=[151200, 225, 15, 1], device=cpu) = onnx::Sigmoid(%1036)
%741 : Float(1, 672, 15, 15, strides=[151200, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%1036, %740) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%742 : Float(1, 672, 1, 1, strides=[672, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::ReduceMean[axes=[2, 3], keepdims=1](%741) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:44:0
%743 : Float(1, 28, 1, 1, strides=[28, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%742, %blocks.4.1.se.conv_reduce.weight, %blocks.4.1.se.conv_reduce.bias) # /usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py:395:0
%744 : Float(1, 28, 1, 1, strides=[28, 1, 1, 1], device=cpu) = onnx::Sigmoid(%743)
%745 : Float(1, 28, 1, 1, strides=[28, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%743, %744) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%746 : Float(1, 672, 1, 1, strides=[672, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%745, %blocks.4.1.se.conv_expand.weight, %blocks.4.1.se.conv_expand.bias) # /usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py:395:0
%747 : Float(1, 672, 1, 1, strides=[672, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Sigmoid(%746) # /usr/local/lib/python3.8/dist-packages/timm/models/layers/activations.py:47:0
%748 : Float(1, 672, 15, 15, strides=[151200, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%741, %747) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:48:0
%1039 : Float(1, 112, 15, 15, strides=[25200, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%748, %1040, %1041)
%751 : Float(1, 112, 15, 15, strides=[25200, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Add(%1039, %1030)
%1042 : Float(1, 672, 15, 15, strides=[151200, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%751, %1043, %1044)
%754 : Float(1, 672, 15, 15, strides=[151200, 225, 15, 1], device=cpu) = onnx::Sigmoid(%1042)
%755 : Float(1, 672, 15, 15, strides=[151200, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%1042, %754) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%1045 : Float(1, 672, 15, 15, strides=[151200, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=672, kernel_shape=[5, 5], pads=[2, 2, 2, 2], strides=[1, 1]](%755, %1046, %1047)
%758 : Float(1, 672, 15, 15, strides=[151200, 225, 15, 1], device=cpu) = onnx::Sigmoid(%1045)
%759 : Float(1, 672, 15, 15, strides=[151200, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%1045, %758) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%760 : Float(1, 672, 1, 1, strides=[672, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::ReduceMean[axes=[2, 3], keepdims=1](%759) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:44:0
%761 : Float(1, 28, 1, 1, strides=[28, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%760, %blocks.4.2.se.conv_reduce.weight, %blocks.4.2.se.conv_reduce.bias) # /usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py:395:0
%762 : Float(1, 28, 1, 1, strides=[28, 1, 1, 1], device=cpu) = onnx::Sigmoid(%761)
%763 : Float(1, 28, 1, 1, strides=[28, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%761, %762) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%764 : Float(1, 672, 1, 1, strides=[672, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%763, %blocks.4.2.se.conv_expand.weight, %blocks.4.2.se.conv_expand.bias) # /usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py:395:0
%765 : Float(1, 672, 1, 1, strides=[672, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Sigmoid(%764) # /usr/local/lib/python3.8/dist-packages/timm/models/layers/activations.py:47:0
%766 : Float(1, 672, 15, 15, strides=[151200, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%759, %765) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:48:0
%1048 : Float(1, 112, 15, 15, strides=[25200, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%766, %1049, %1050)
%769 : Float(1, 112, 15, 15, strides=[25200, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Add(%1048, %751)
%1051 : Float(1, 672, 15, 15, strides=[151200, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%769, %1052, %1053)
%772 : Float(1, 672, 15, 15, strides=[151200, 225, 15, 1], device=cpu) = onnx::Sigmoid(%1051)
%773 : Float(1, 672, 15, 15, strides=[151200, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%1051, %772) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%1054 : Float(1, 672, 15, 15, strides=[151200, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=672, kernel_shape=[5, 5], pads=[2, 2, 2, 2], strides=[1, 1]](%773, %1055, %1056)
%776 : Float(1, 672, 15, 15, strides=[151200, 225, 15, 1], device=cpu) = onnx::Sigmoid(%1054)
%777 : Float(1, 672, 15, 15, strides=[151200, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%1054, %776) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%778 : Float(1, 672, 1, 1, strides=[672, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::ReduceMean[axes=[2, 3], keepdims=1](%777) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:44:0
%779 : Float(1, 28, 1, 1, strides=[28, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%778, %blocks.4.3.se.conv_reduce.weight, %blocks.4.3.se.conv_reduce.bias) # /usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py:395:0
%780 : Float(1, 28, 1, 1, strides=[28, 1, 1, 1], device=cpu) = onnx::Sigmoid(%779)
%781 : Float(1, 28, 1, 1, strides=[28, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%779, %780) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%782 : Float(1, 672, 1, 1, strides=[672, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%781, %blocks.4.3.se.conv_expand.weight, %blocks.4.3.se.conv_expand.bias) # /usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py:395:0
%783 : Float(1, 672, 1, 1, strides=[672, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Sigmoid(%782) # /usr/local/lib/python3.8/dist-packages/timm/models/layers/activations.py:47:0
%784 : Float(1, 672, 15, 15, strides=[151200, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%777, %783) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:48:0
%1057 : Float(1, 112, 15, 15, strides=[25200, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%784, %1058, %1059)
%787 : Float(1, 112, 15, 15, strides=[25200, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Add(%1057, %769) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:208:0
%1060 : Float(1, 672, 15, 15, strides=[151200, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%787, %1061, %1062)
%790 : Float(1, 672, 15, 15, strides=[151200, 225, 15, 1], device=cpu) = onnx::Sigmoid(%1060)
%791 : Float(1, 672, 15, 15, strides=[151200, 225, 15, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%1060, %790) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%1063 : Float(1, 672, 8, 8, strides=[43008, 64, 8, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=672, kernel_shape=[5, 5], pads=[2, 2, 2, 2], strides=[2, 2]](%791, %1064, %1065)
%794 : Float(1, 672, 8, 8, strides=[43008, 64, 8, 1], device=cpu) = onnx::Sigmoid(%1063)
%795 : Float(1, 672, 8, 8, strides=[43008, 64, 8, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%1063, %794) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%796 : Float(1, 672, 1, 1, strides=[672, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::ReduceMean[axes=[2, 3], keepdims=1](%795) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:44:0
%797 : Float(1, 28, 1, 1, strides=[28, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%796, %blocks.5.0.se.conv_reduce.weight, %blocks.5.0.se.conv_reduce.bias) # /usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py:395:0
%798 : Float(1, 28, 1, 1, strides=[28, 1, 1, 1], device=cpu) = onnx::Sigmoid(%797)
%799 : Float(1, 28, 1, 1, strides=[28, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%797, %798) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%800 : Float(1, 672, 1, 1, strides=[672, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%799, %blocks.5.0.se.conv_expand.weight, %blocks.5.0.se.conv_expand.bias) # /usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py:395:0
%801 : Float(1, 672, 1, 1, strides=[672, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Sigmoid(%800) # /usr/local/lib/python3.8/dist-packages/timm/models/layers/activations.py:47:0
%802 : Float(1, 672, 8, 8, strides=[43008, 64, 8, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%795, %801) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:48:0
%1066 : Float(1, 192, 8, 8, strides=[12288, 64, 8, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%802, %1067, %1068)
%1069 : Float(1, 1152, 8, 8, strides=[73728, 64, 8, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%1066, %1070, %1071)
%807 : Float(1, 1152, 8, 8, strides=[73728, 64, 8, 1], device=cpu) = onnx::Sigmoid(%1069)
%808 : Float(1, 1152, 8, 8, strides=[73728, 64, 8, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%1069, %807) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%1072 : Float(1, 1152, 8, 8, strides=[73728, 64, 8, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1152, kernel_shape=[5, 5], pads=[2, 2, 2, 2], strides=[1, 1]](%808, %1073, %1074)
%811 : Float(1, 1152, 8, 8, strides=[73728, 64, 8, 1], device=cpu) = onnx::Sigmoid(%1072)
%812 : Float(1, 1152, 8, 8, strides=[73728, 64, 8, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%1072, %811) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%813 : Float(1, 1152, 1, 1, strides=[1152, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::ReduceMean[axes=[2, 3], keepdims=1](%812) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:44:0
%814 : Float(1, 48, 1, 1, strides=[48, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%813, %blocks.5.1.se.conv_reduce.weight, %blocks.5.1.se.conv_reduce.bias) # /usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py:395:0
%815 : Float(1, 48, 1, 1, strides=[48, 1, 1, 1], device=cpu) = onnx::Sigmoid(%814)
%816 : Float(1, 48, 1, 1, strides=[48, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%814, %815) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%817 : Float(1, 1152, 1, 1, strides=[1152, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%816, %blocks.5.1.se.conv_expand.weight, %blocks.5.1.se.conv_expand.bias) # /usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py:395:0
%818 : Float(1, 1152, 1, 1, strides=[1152, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Sigmoid(%817) # /usr/local/lib/python3.8/dist-packages/timm/models/layers/activations.py:47:0
%819 : Float(1, 1152, 8, 8, strides=[73728, 64, 8, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%812, %818) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:48:0
%1075 : Float(1, 192, 8, 8, strides=[12288, 64, 8, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%819, %1076, %1077)
%822 : Float(1, 192, 8, 8, strides=[12288, 64, 8, 1], requires_grad=1, device=cuda:0) = onnx::Add(%1075, %1066)
%1078 : Float(1, 1152, 8, 8, strides=[73728, 64, 8, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%822, %1079, %1080)
%825 : Float(1, 1152, 8, 8, strides=[73728, 64, 8, 1], device=cpu) = onnx::Sigmoid(%1078)
%826 : Float(1, 1152, 8, 8, strides=[73728, 64, 8, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%1078, %825) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%1081 : Float(1, 1152, 8, 8, strides=[73728, 64, 8, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1152, kernel_shape=[5, 5], pads=[2, 2, 2, 2], strides=[1, 1]](%826, %1082, %1083)
%829 : Float(1, 1152, 8, 8, strides=[73728, 64, 8, 1], device=cpu) = onnx::Sigmoid(%1081)
%830 : Float(1, 1152, 8, 8, strides=[73728, 64, 8, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%1081, %829) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%831 : Float(1, 1152, 1, 1, strides=[1152, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::ReduceMean[axes=[2, 3], keepdims=1](%830) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:44:0
%832 : Float(1, 48, 1, 1, strides=[48, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%831, %blocks.5.2.se.conv_reduce.weight, %blocks.5.2.se.conv_reduce.bias) # /usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py:395:0
%833 : Float(1, 48, 1, 1, strides=[48, 1, 1, 1], device=cpu) = onnx::Sigmoid(%832)
%834 : Float(1, 48, 1, 1, strides=[48, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%832, %833) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%835 : Float(1, 1152, 1, 1, strides=[1152, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%834, %blocks.5.2.se.conv_expand.weight, %blocks.5.2.se.conv_expand.bias) # /usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py:395:0
%836 : Float(1, 1152, 1, 1, strides=[1152, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Sigmoid(%835) # /usr/local/lib/python3.8/dist-packages/timm/models/layers/activations.py:47:0
%837 : Float(1, 1152, 8, 8, strides=[73728, 64, 8, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%830, %836) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:48:0
%1084 : Float(1, 192, 8, 8, strides=[12288, 64, 8, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%837, %1085, %1086)
%840 : Float(1, 192, 8, 8, strides=[12288, 64, 8, 1], requires_grad=1, device=cuda:0) = onnx::Add(%1084, %822)
%1087 : Float(1, 1152, 8, 8, strides=[73728, 64, 8, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%840, %1088, %1089)
%843 : Float(1, 1152, 8, 8, strides=[73728, 64, 8, 1], device=cpu) = onnx::Sigmoid(%1087)
%844 : Float(1, 1152, 8, 8, strides=[73728, 64, 8, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%1087, %843) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%1090 : Float(1, 1152, 8, 8, strides=[73728, 64, 8, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1152, kernel_shape=[5, 5], pads=[2, 2, 2, 2], strides=[1, 1]](%844, %1091, %1092)
%847 : Float(1, 1152, 8, 8, strides=[73728, 64, 8, 1], device=cpu) = onnx::Sigmoid(%1090)
%848 : Float(1, 1152, 8, 8, strides=[73728, 64, 8, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%1090, %847) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%849 : Float(1, 1152, 1, 1, strides=[1152, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::ReduceMean[axes=[2, 3], keepdims=1](%848) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:44:0
%850 : Float(1, 48, 1, 1, strides=[48, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%849, %blocks.5.3.se.conv_reduce.weight, %blocks.5.3.se.conv_reduce.bias) # /usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py:395:0
%851 : Float(1, 48, 1, 1, strides=[48, 1, 1, 1], device=cpu) = onnx::Sigmoid(%850)
%852 : Float(1, 48, 1, 1, strides=[48, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%850, %851) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%853 : Float(1, 1152, 1, 1, strides=[1152, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%852, %blocks.5.3.se.conv_expand.weight, %blocks.5.3.se.conv_expand.bias) # /usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py:395:0
%854 : Float(1, 1152, 1, 1, strides=[1152, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Sigmoid(%853) # /usr/local/lib/python3.8/dist-packages/timm/models/layers/activations.py:47:0
%855 : Float(1, 1152, 8, 8, strides=[73728, 64, 8, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%848, %854) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:48:0
%1093 : Float(1, 192, 8, 8, strides=[12288, 64, 8, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%855, %1094, %1095)
%858 : Float(1, 192, 8, 8, strides=[12288, 64, 8, 1], requires_grad=1, device=cuda:0) = onnx::Add(%1093, %840)
%1096 : Float(1, 1152, 8, 8, strides=[73728, 64, 8, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%858, %1097, %1098)
%861 : Float(1, 1152, 8, 8, strides=[73728, 64, 8, 1], device=cpu) = onnx::Sigmoid(%1096)
%862 : Float(1, 1152, 8, 8, strides=[73728, 64, 8, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%1096, %861) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%1099 : Float(1, 1152, 8, 8, strides=[73728, 64, 8, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1152, kernel_shape=[5, 5], pads=[2, 2, 2, 2], strides=[1, 1]](%862, %1100, %1101)
%865 : Float(1, 1152, 8, 8, strides=[73728, 64, 8, 1], device=cpu) = onnx::Sigmoid(%1099)
%866 : Float(1, 1152, 8, 8, strides=[73728, 64, 8, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%1099, %865) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%867 : Float(1, 1152, 1, 1, strides=[1152, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::ReduceMean[axes=[2, 3], keepdims=1](%866) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:44:0
%868 : Float(1, 48, 1, 1, strides=[48, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%867, %blocks.5.4.se.conv_reduce.weight, %blocks.5.4.se.conv_reduce.bias) # /usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py:395:0
%869 : Float(1, 48, 1, 1, strides=[48, 1, 1, 1], device=cpu) = onnx::Sigmoid(%868)
%870 : Float(1, 48, 1, 1, strides=[48, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%868, %869) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%871 : Float(1, 1152, 1, 1, strides=[1152, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%870, %blocks.5.4.se.conv_expand.weight, %blocks.5.4.se.conv_expand.bias) # /usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py:395:0
%872 : Float(1, 1152, 1, 1, strides=[1152, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Sigmoid(%871) # /usr/local/lib/python3.8/dist-packages/timm/models/layers/activations.py:47:0
%873 : Float(1, 1152, 8, 8, strides=[73728, 64, 8, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%866, %872) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:48:0
%1102 : Float(1, 192, 8, 8, strides=[12288, 64, 8, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%873, %1103, %1104)
%876 : Float(1, 192, 8, 8, strides=[12288, 64, 8, 1], requires_grad=1, device=cuda:0) = onnx::Add(%1102, %858) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:208:0
%1105 : Float(1, 1152, 8, 8, strides=[73728, 64, 8, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%876, %1106, %1107)
%879 : Float(1, 1152, 8, 8, strides=[73728, 64, 8, 1], device=cpu) = onnx::Sigmoid(%1105)
%880 : Float(1, 1152, 8, 8, strides=[73728, 64, 8, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%1105, %879) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%1108 : Float(1, 1152, 8, 8, strides=[73728, 64, 8, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1152, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%880, %1109, %1110)
%883 : Float(1, 1152, 8, 8, strides=[73728, 64, 8, 1], device=cpu) = onnx::Sigmoid(%1108)
%884 : Float(1, 1152, 8, 8, strides=[73728, 64, 8, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%1108, %883) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%885 : Float(1, 1152, 1, 1, strides=[1152, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::ReduceMean[axes=[2, 3], keepdims=1](%884) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:44:0
%886 : Float(1, 48, 1, 1, strides=[48, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%885, %blocks.6.0.se.conv_reduce.weight, %blocks.6.0.se.conv_reduce.bias) # /usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py:395:0
%887 : Float(1, 48, 1, 1, strides=[48, 1, 1, 1], device=cpu) = onnx::Sigmoid(%886)
%888 : Float(1, 48, 1, 1, strides=[48, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%886, %887) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%889 : Float(1, 1152, 1, 1, strides=[1152, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%888, %blocks.6.0.se.conv_expand.weight, %blocks.6.0.se.conv_expand.bias) # /usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py:395:0
%890 : Float(1, 1152, 1, 1, strides=[1152, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Sigmoid(%889) # /usr/local/lib/python3.8/dist-packages/timm/models/layers/activations.py:47:0
%891 : Float(1, 1152, 8, 8, strides=[73728, 64, 8, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%884, %890) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:48:0
%1111 : Float(1, 320, 8, 8, strides=[20480, 64, 8, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%891, %1112, %1113)
%1114 : Float(1, 1920, 8, 8, strides=[122880, 64, 8, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%1111, %1115, %1116)
%896 : Float(1, 1920, 8, 8, strides=[122880, 64, 8, 1], device=cpu) = onnx::Sigmoid(%1114)
%897 : Float(1, 1920, 8, 8, strides=[122880, 64, 8, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%1114, %896) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%1117 : Float(1, 1920, 8, 8, strides=[122880, 64, 8, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1920, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%897, %1118, %1119)
%900 : Float(1, 1920, 8, 8, strides=[122880, 64, 8, 1], device=cpu) = onnx::Sigmoid(%1117)
%901 : Float(1, 1920, 8, 8, strides=[122880, 64, 8, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%1117, %900) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%902 : Float(1, 1920, 1, 1, strides=[1920, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::ReduceMean[axes=[2, 3], keepdims=1](%901) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:44:0
%903 : Float(1, 80, 1, 1, strides=[80, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%902, %blocks.6.1.se.conv_reduce.weight, %blocks.6.1.se.conv_reduce.bias) # /usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py:395:0
%904 : Float(1, 80, 1, 1, strides=[80, 1, 1, 1], device=cpu) = onnx::Sigmoid(%903)
%905 : Float(1, 80, 1, 1, strides=[80, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%903, %904) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%906 : Float(1, 1920, 1, 1, strides=[1920, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%905, %blocks.6.1.se.conv_expand.weight, %blocks.6.1.se.conv_expand.bias) # /usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py:395:0
%907 : Float(1, 1920, 1, 1, strides=[1920, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::Sigmoid(%906) # /usr/local/lib/python3.8/dist-packages/timm/models/layers/activations.py:47:0
%908 : Float(1, 1920, 8, 8, strides=[122880, 64, 8, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%901, %907) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:48:0
%1120 : Float(1, 320, 8, 8, strides=[20480, 64, 8, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%908, %1121, %1122)
%911 : Float(1, 320, 8, 8, strides=[20480, 64, 8, 1], requires_grad=1, device=cuda:0) = onnx::Add(%1120, %1111) # /usr/local/lib/python3.8/dist-packages/timm/models/efficientnet_blocks.py:208:0
%1123 : Float(1, 1280, 8, 8, strides=[81920, 64, 8, 1], requires_grad=1, device=cuda:0) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%911, %1124, %1125)
%914 : Float(1, 1280, 8, 8, strides=[81920, 64, 8, 1], device=cpu) = onnx::Sigmoid(%1123)
%915 : Float(1, 1280, 8, 8, strides=[81920, 64, 8, 1], requires_grad=1, device=cuda:0) = onnx::Mul(%1123, %914) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1795:0
%916 : Float(1, 1280, 1, 1, strides=[1280, 1, 1, 1], requires_grad=1, device=cuda:0) = onnx::GlobalAveragePool(%915) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1037:0
%917 : Float(1, 1280, strides=[1280, 1], requires_grad=1, device=cuda:0) = onnx::Flatten[axis=1](%916) # /usr/local/lib/python3.8/dist-packages/timm/models/layers/adaptive_avgmax_pool.py:109:0
%output : Float(1, 1000, strides=[1000, 1], requires_grad=1, device=cuda:0) = onnx::Gemm[alpha=1., beta=1., transB=1](%917, %classifier.weight, %classifier.bias) # /usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1753:0
return (%output)