return (%2750, %2789, %2828, %2867, %2906, %2945, %2984, %3023, %3077, %3154, %3231) , None, False

[yinianwujide]

model = build_model(cfg['model'])
oto = OTO(model, dummy_input)

issue：

%3630 : Float(2, 256, 80, 160, strides=[3276800, 12800, 160, 1], requires_grad=1, device=cpu) = aten::relu(%input.309) # /opt/conda/lib/python3.8/site-packages/torch/nn/functional.py:1455:0
%3513 : int[] = prim::Constantvalue=[1, 1]
%3514 : int[] = prim::Constantvalue=[0, 0]
%3515 : int[] = prim::Constantvalue=[1, 1]
%2897 : bool = prim::Constantvalue=0 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3516 : int[] = prim::Constantvalue=[0, 0]
%2901 : int = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%2902 : bool = prim::Constantvalue=0 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%2903 : bool = prim::Constantvalue=0 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%2904 : bool = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%2905 : bool = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%2906 : Float(2, 2, 80, 160, strides=[25600, 12800, 160, 1], requires_grad=0, device=cpu) = aten::_convolution(%3630, %342, %343, %3513, %3514, %3515, %2897, %3516, %2901, %2902, %2903, %2904, %2905) # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3517 : int[] = prim::Constantvalue=[1, 1]
%3518 : int[] = prim::Constantvalue=[1, 1]
%3519 : int[] = prim::Constantvalue=[1, 1]
%2916 : bool = prim::Constantvalue=0 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3520 : int[] = prim::Constantvalue=[0, 0]
%2920 : int = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%2921 : bool = prim::Constantvalue=0 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%2922 : bool = prim::Constantvalue=0 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%2923 : bool = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%2924 : bool = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%input.313 : Float(2, 256, 80, 160, strides=[3276800, 12800, 160, 1], requires_grad=0, device=cpu) = aten::_convolution(%3625, %344, %345, %3517, %3518, %3519, %2916, %3520, %2920, %2921, %2922, %2923, %2924) # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3631 : Float(2, 256, 80, 160, strides=[3276800, 12800, 160, 1], requires_grad=1, device=cpu) = aten::relu(%input.313) # /opt/conda/lib/python3.8/site-packages/torch/nn/functional.py:1455:0
%3521 : int[] = prim::Constantvalue=[1, 1]
%3522 : int[] = prim::Constantvalue=[0, 0]
%3523 : int[] = prim::Constantvalue=[1, 1]
%2936 : bool = prim::Constantvalue=0 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3524 : int[] = prim::Constantvalue=[0, 0]
%2940 : int = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%2941 : bool = prim::Constantvalue=0 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%2942 : bool = prim::Constantvalue=0 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%2943 : bool = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%2944 : bool = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%2945 : Float(2, 2, 80, 160, strides=[25600, 12800, 160, 1], requires_grad=0, device=cpu) = aten::_convolution(%3631, %346, %347, %3521, %3522, %3523, %2936, %3524, %2940, %2941, %2942, %2943, %2944) # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3525 : int[] = prim::Constantvalue=[1, 1]
%3526 : int[] = prim::Constantvalue=[1, 1]
%3527 : int[] = prim::Constantvalue=[1, 1]
%2955 : bool = prim::Constantvalue=0 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3528 : int[] = prim::Constantvalue=[0, 0]
%2959 : int = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%2960 : bool = prim::Constantvalue=0 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%2961 : bool = prim::Constantvalue=0 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%2962 : bool = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%2963 : bool = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%input.317 : Float(2, 256, 80, 160, strides=[3276800, 12800, 160, 1], requires_grad=0, device=cpu) = aten::_convolution(%3625, %348, %349, %3525, %3526, %3527, %2955, %3528, %2959, %2960, %2961, %2962, %2963) # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3632 : Float(2, 256, 80, 160, strides=[3276800, 12800, 160, 1], requires_grad=1, device=cpu) = aten::relu(%input.317) # /opt/conda/lib/python3.8/site-packages/torch/nn/functional.py:1455:0
%3529 : int[] = prim::Constantvalue=[1, 1]
%3530 : int[] = prim::Constantvalue=[0, 0]
%3531 : int[] = prim::Constantvalue=[1, 1]
%2975 : bool = prim::Constantvalue=0 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3532 : int[] = prim::Constantvalue=[0, 0]
%2979 : int = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%2980 : bool = prim::Constantvalue=0 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%2981 : bool = prim::Constantvalue=0 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%2982 : bool = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%2983 : bool = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%2984 : Float(2, 3, 80, 160, strides=[38400, 12800, 160, 1], requires_grad=0, device=cpu) = aten::_convolution(%3632, %350, %351, %3529, %3530, %3531, %2975, %3532, %2979, %2980, %2981, %2982, %2983) # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3533 : int[] = prim::Constantvalue=[1, 1]
%3534 : int[] = prim::Constantvalue=[1, 1]
%3535 : int[] = prim::Constantvalue=[1, 1]
%2994 : bool = prim::Constantvalue=0 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3536 : int[] = prim::Constantvalue=[0, 0]
%2998 : int = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%2999 : bool = prim::Constantvalue=0 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3000 : bool = prim::Constantvalue=0 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3001 : bool = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3002 : bool = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%input.321 : Float(2, 256, 80, 160, strides=[3276800, 12800, 160, 1], requires_grad=0, device=cpu) = aten::_convolution(%3625, %352, %353, %3533, %3534, %3535, %2994, %3536, %2998, %2999, %3000, %3001, %3002) # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3633 : Float(2, 256, 80, 160, strides=[3276800, 12800, 160, 1], requires_grad=1, device=cpu) = aten::relu(%input.321) # /opt/conda/lib/python3.8/site-packages/torch/nn/functional.py:1455:0
%3537 : int[] = prim::Constantvalue=[1, 1]
%3538 : int[] = prim::Constantvalue=[0, 0]
%3539 : int[] = prim::Constantvalue=[1, 1]
%3014 : bool = prim::Constantvalue=0 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3540 : int[] = prim::Constantvalue=[0, 0]
%3018 : int = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3019 : bool = prim::Constantvalue=0 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3020 : bool = prim::Constantvalue=0 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3021 : bool = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3022 : bool = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3023 : Float(2, 24, 80, 160, strides=[307200, 12800, 160, 1], requires_grad=0, device=cpu) = aten::_convolution(%3633, %354, %355, %3537, %3538, %3539, %3014, %3540, %3018, %3019, %3020, %3021, %3022) # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3541 : int[] = prim::Constantvalue=[1, 1]
%3542 : int[] = prim::Constantvalue=[1, 1]
%3543 : int[] = prim::Constantvalue=[1, 1]
%3033 : bool = prim::Constantvalue=0 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3544 : int[] = prim::Constantvalue=[0, 0]
%3037 : int = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3038 : bool = prim::Constantvalue=0 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3039 : bool = prim::Constantvalue=0 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3040 : bool = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3041 : bool = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%input.325 : Float(2, 256, 80, 160, strides=[3276800, 12800, 160, 1], requires_grad=0, device=cpu) = aten::_convolution(%3625, %356, %357, %3541, %3542, %3543, %3033, %3544, %3037, %3038, %3039, %3040, %3041) # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3634 : Float(2, 256, 80, 160, strides=[3276800, 12800, 160, 1], requires_grad=1, device=cpu) = aten::relu(%input.325) # /opt/conda/lib/python3.8/site-packages/torch/nn/functional.py:1455:0
%3545 : int[] = prim::Constantvalue=[1, 1]
%3546 : int[] = prim::Constantvalue=[0, 0]
%3547 : int[] = prim::Constantvalue=[1, 1]
%3053 : bool = prim::Constantvalue=0 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3548 : int[] = prim::Constantvalue=[0, 0]
%3057 : int = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3058 : bool = prim::Constantvalue=0 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3059 : bool = prim::Constantvalue=0 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3060 : bool = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3061 : bool = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3062 : Float(2, 1, 80, 160, strides=[12800, 12800, 160, 1], requires_grad=0, device=cpu) = aten::_convolution(%3634, %358, %359, %3545, %3546, %3547, %3053, %3548, %3057, %3058, %3059, %3060, %3061) # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3063 : int = prim::Constantvalue=0 # /home/uids0529/userdata/monodle0604_modo_dataname/lib/models/centernet3d.py:79:0
%3064 : int = aten::size(%3062, %3063) # /home/uids0529/userdata/monodle0604_modo_dataname/lib/models/centernet3d.py:79:0
%3067 : int = prim::Constantvalue=-1 # /home/uids0529/userdata/monodle0604_modo_dataname/lib/models/centernet3d.py:79:0
%3068 : int[] = prim::ListConstruct(%3064, %3067)
%3069 : Float(2, 12800, strides=[12800, 1], requires_grad=1, device=cpu) = aten::view(%3062, %3068) # /home/uids0529/userdata/monodle0604_modo_dataname/lib/models/centernet3d.py:79:0
%input.329 : Float(2, 100, strides=[100, 1], requires_grad=1, device=cpu) = aten::linear(%3069, %315, %316) # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/linear.py:114:0
%3071 : bool = prim::Constantvalue=0 # /opt/conda/lib/python3.8/site-packages/torch/nn/functional.py:2446:0
%3072 : float = prim::Constantvalue=0.10000000000000001 # /opt/conda/lib/python3.8/site-packages/torch/nn/functional.py:2446:0
%3073 : float = prim::Constantvalue=1.0000000000000001e-05 # /opt/conda/lib/python3.8/site-packages/torch/nn/functional.py:2446:0
%3074 : bool = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/functional.py:2446:0
%input.331 : Float(2, 100, strides=[100, 1], requires_grad=1, device=cpu) = aten::batch_norm(%input.329, %317, %318, %319, %320, %3071, %3072, %3073, %3074) # /opt/conda/lib/python3.8/site-packages/torch/nn/functional.py:2446:0
%3076 : Float(2, 100, strides=[100, 1], requires_grad=1, device=cpu) = aten::relu(%input.331) # /opt/conda/lib/python3.8/site-packages/torch/nn/functional.py:1457:0
%3077 : Float(2, 36, strides=[36, 1], requires_grad=1, device=cpu) = aten::linear(%3076, %322, %323) # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/linear.py:114:0
%3549 : int[] = prim::Constantvalue=[1, 1]
%3550 : int[] = prim::Constantvalue=[1, 1]
%3551 : int[] = prim::Constantvalue=[1, 1]
%3087 : bool = prim::Constantvalue=0 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3552 : int[] = prim::Constantvalue=[0, 0]
%3091 : int = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3092 : bool = prim::Constantvalue=0 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3093 : bool = prim::Constantvalue=0 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3094 : bool = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3095 : bool = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%input.333 : Float(2, 256, 80, 160, strides=[3276800, 12800, 160, 1], requires_grad=0, device=cpu) = aten::_convolution(%3625, %360, %361, %3549, %3550, %3551, %3087, %3552, %3091, %3092, %3093, %3094, %3095) # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3635 : Float(2, 256, 80, 160, strides=[3276800, 12800, 160, 1], requires_grad=1, device=cpu) = aten::relu(%input.333) # /opt/conda/lib/python3.8/site-packages/torch/nn/functional.py:1455:0
%3553 : int[] = prim::Constantvalue=[2, 2]
%3554 : int[] = prim::Constantvalue=[1, 1]
%3555 : int[] = prim::Constantvalue=[1, 1]
%3107 : bool = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:956:0
%3556 : int[] = prim::Constantvalue=[1, 1]
%3111 : int = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:956:0
%3112 : bool = prim::Constantvalue=0 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:956:0
%3113 : bool = prim::Constantvalue=0 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:956:0
%3114 : bool = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:956:0
%3115 : bool = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:956:0
%input.335 : Float(2, 256, 160, 320, strides=[13107200, 51200, 320, 1], requires_grad=0, device=cpu) = aten::_convolution(%3635, %362, %363, %3553, %3554, %3555, %3107, %3556, %3111, %3112, %3113, %3114, %3115) # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:956:0
%3557 : int[] = prim::Constantvalue=[1, 1]
%3558 : int[] = prim::Constantvalue=[0, 0]
%3559 : int[] = prim::Constantvalue=[1, 1]
%3126 : bool = prim::Constantvalue=0 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3560 : int[] = prim::Constantvalue=[0, 0]
%3130 : int = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3131 : bool = prim::Constantvalue=0 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3132 : bool = prim::Constantvalue=0 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3133 : bool = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3134 : bool = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3135 : Float(2, 2, 160, 320, strides=[102400, 51200, 320, 1], requires_grad=0, device=cpu) = aten::_convolution(%input.335, %364, %365, %3557, %3558, %3559, %3126, %3560, %3130, %3131, %3132, %3133, %3134) # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3561 : int[] = prim::Constantvalue=[2, 2]
%3562 : int[] = prim::Constantvalue=[1, 1]
%3563 : int[] = prim::Constantvalue=[1, 1]
%3145 : bool = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:956:0
%3564 : int[] = prim::Constantvalue=[1, 1]
%3149 : int = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:956:0
%3150 : bool = prim::Constantvalue=0 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:956:0
%3151 : bool = prim::Constantvalue=0 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:956:0
%3152 : bool = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:956:0
%3153 : bool = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:956:0
%3154 : Float(2, 2, 320, 640, strides=[409600, 204800, 640, 1], requires_grad=0, device=cpu) = aten::_convolution(%3135, %366, %367, %3561, %3562, %3563, %3145, %3564, %3149, %3150, %3151, %3152, %3153) # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:956:0
%3565 : int[] = prim::Constantvalue=[1, 1]
%3566 : int[] = prim::Constantvalue=[1, 1]
%3567 : int[] = prim::Constantvalue=[1, 1]
%3164 : bool = prim::Constantvalue=0 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3568 : int[] = prim::Constantvalue=[0, 0]
%3168 : int = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3169 : bool = prim::Constantvalue=0 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3170 : bool = prim::Constantvalue=0 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3171 : bool = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3172 : bool = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%input.337 : Float(2, 256, 80, 160, strides=[3276800, 12800, 160, 1], requires_grad=0, device=cpu) = aten::_convolution(%3625, %368, %369, %3565, %3566, %3567, %3164, %3568, %3168, %3169, %3170, %3171, %3172) # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3636 : Float(2, 256, 80, 160, strides=[3276800, 12800, 160, 1], requires_grad=1, device=cpu) = aten::relu(%input.337) # /opt/conda/lib/python3.8/site-packages/torch/nn/functional.py:1455:0
%3569 : int[] = prim::Constantvalue=[2, 2]
%3570 : int[] = prim::Constantvalue=[1, 1]
%3571 : int[] = prim::Constantvalue=[1, 1]
%3184 : bool = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:956:0
%3572 : int[] = prim::Constantvalue=[1, 1]
%3188 : int = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:956:0
%3189 : bool = prim::Constantvalue=0 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:956:0
%3190 : bool = prim::Constantvalue=0 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:956:0
%3191 : bool = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:956:0
%3192 : bool = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:956:0
%input : Float(2, 256, 160, 320, strides=[13107200, 51200, 320, 1], requires_grad=0, device=cpu) = aten::_convolution(%3636, %370, %371, %3569, %3570, %3571, %3184, %3572, %3188, %3189, %3190, %3191, %3192) # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:956:0
%3573 : int[] = prim::Constantvalue=[1, 1]
%3574 : int[] = prim::Constantvalue=[0, 0]
%3575 : int[] = prim::Constantvalue=[1, 1]
%3203 : bool = prim::Constantvalue=0 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3576 : int[] = prim::Constantvalue=[0, 0]
%3207 : int = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3208 : bool = prim::Constantvalue=0 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3209 : bool = prim::Constantvalue=0 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3210 : bool = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3211 : bool = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3212 : Float(2, 2, 160, 320, strides=[102400, 51200, 320, 1], requires_grad=0, device=cpu) = aten::_convolution(%input, %372, %373, %3573, %3574, %3575, %3203, %3576, %3207, %3208, %3209, %3210, %3211) # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:459:0
%3577 : int[] = prim::Constantvalue=[2, 2]
%3578 : int[] = prim::Constantvalue=[1, 1]
%3579 : int[] = prim::Constantvalue=[1, 1]
%3222 : bool = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:956:0
%3580 : int[] = prim::Constantvalue=[1, 1]
%3226 : int = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:956:0
%3227 : bool = prim::Constantvalue=0 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:956:0
%3228 : bool = prim::Constantvalue=0 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:956:0
%3229 : bool = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:956:0
%3230 : bool = prim::Constantvalue=1 # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:956:0
%3231 : Float(2, 2, 320, 640, strides=[409600, 204800, 640, 1], requires_grad=0, device=cpu) = aten::_convolution(%3212, %374, %375, %3577, %3578, %3579, %3222, %3580, %3226, %3227, %3228, %3229, %3230) # /opt/conda/lib/python3.8/site-packages/torch/nn/modules/conv.py:956:0
return (%2750, %2789, %2828, %2867, %2906, %2945, %2984, %3023, %3077, %3154, %3231)
, None, False

This interruption occurred during the OTO.