测试 ONNX relay 模型

测试 ONNX relay 模型#

import set_env

# path = "/media/pc/data/board/arria10/lxw/tasks/tools/npuusertools/models/test/ai_isp/mirnet_newest_RGB_SE_conv33_k1.0_opt.onnx"
path = "/media/pc/data/board/arria10/lxw/tasks/tools/npuusertools/models/test/nanotrack_head/nanotrack_head.onnx"

import onnx
from tvm import relay

onnx_model = onnx.load(path)
mod, params = relay.frontend.from_onnx(onnx_model)#shape_dict, freeze_params=True)

mod.show()

def @main(%input1: Tensor[(1, 96, 8, 8), float32] /* ty=Tensor[(1, 96, 8, 8), float32] span=Slice_4.input1:0:0 */, %input2: Tensor[(1, 96, 16, 16), float32] /* ty=Tensor[(1, 96, 16, 16), float32] span=Conv_11.input2:0:0 */) -> (Tensor[(1, 2, 15, 15), float32], Tensor[(1, 4, 15, 15), float32]) {
  %0 = nn.conv2d(%input2, meta[relay.Constant][0] /* ty=Tensor[(96, 96, 1, 1), float32] span=Conv_29.439:0:0 */, padding=[0, 0, 0, 0], channels=96, kernel_size=[1, 1]) /* ty=Tensor[(1, 96, 16, 16), float32] span=Conv_29:0:0 */;
  %1 = nn.conv2d(%input1, meta[relay.Constant][2] /* ty=Tensor[(96, 96, 1, 1), float32] span=Conv_28.436:0:0 */, padding=[0, 0, 0, 0], channels=96, kernel_size=[1, 1]) /* ty=Tensor[(1, 96, 8, 8), float32] span=Conv_28:0:0 */;
  %2 = nn.bias_add(%1, meta[relay.Constant][3] /* ty=Tensor[(96), float32] span=Conv_28.437:0:0 */) /* ty=Tensor[(1, 96, 8, 8), float32] span=Conv_28:0:0 */;
  %3 = reshape(%2, newshape=[1, 96, -1]) /* ty=Tensor[(1, 96, 64), float32] span=Reshape_30:0:0 */;
  %4 = transpose(%3, axes=[0, 2, 1]) /* ty=Tensor[(1, 64, 96), float32] span=Transpose_31:0:0 */;
  %5 = nn.bias_add(%0, meta[relay.Constant][1] /* ty=Tensor[(96), float32] span=Conv_29.440:0:0 */) /* ty=Tensor[(1, 96, 16, 16), float32] span=Conv_29:0:0 */;
  %6 = reshape(%4, newshape=[64, 96, 1, 1]) /* ty=Tensor[(64, 96, 1, 1), float32] span=Reshape_33:0:0 */;
  %7 = nn.conv2d(%5, %6, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1]) /* ty=Tensor[(1, 64, 16, 16), float32] span=Conv_34:0:0 */;
  %8 = nn.global_avg_pool2d(%7) /* ty=Tensor[(1, 64, 1, 1), float32] span=GlobalAveragePool_35:0:0 */;
  %9 = nn.conv2d(%8, meta[relay.Constant][4] /* ty=Tensor[(64, 64, 1, 1), float32] span=Conv_36.corr_pw_cls.CA_layer.fc1.weight:0:0 */, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1]) /* ty=Tensor[(1, 64, 1, 1), float32] span=Conv_36:0:0 */;
  %10 = nn.bias_add(%9, meta[relay.Constant][5] /* ty=Tensor[(64), float32] span=Conv_36.corr_pw_cls.CA_layer.fc1.bias:0:0 */) /* ty=Tensor[(1, 64, 1, 1), float32] span=Conv_36:0:0 */;
  %11 = nn.relu(%10) /* ty=Tensor[(1, 64, 1, 1), float32] span=Relu_37:0:0 */;
  %12 = nn.conv2d(%11, meta[relay.Constant][6] /* ty=Tensor[(64, 64, 1, 1), float32] span=Conv_38.corr_pw_cls.CA_layer.fc2.weight:0:0 */, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1]) /* ty=Tensor[(1, 64, 1, 1), float32] span=Conv_38:0:0 */;
  %13 = nn.bias_add(%12, meta[relay.Constant][7] /* ty=Tensor[(64), float32] span=Conv_38.corr_pw_cls.CA_layer.fc2.bias:0:0 */) /* ty=Tensor[(1, 64, 1, 1), float32] span=Conv_38:0:0 */;
  %14 = sigmoid(%13) /* ty=Tensor[(1, 64, 1, 1), float32] span=Sigmoid_39:0:0 */;
  %15 = multiply(%7, %14) /* ty=Tensor[(1, 64, 16, 16), float32] span=Mul_40:0:0 */;
  %16 = nn.conv2d(%15, meta[relay.Constant][8] /* ty=Tensor[(64, 1, 2, 2), float32] span=Conv_41.442:0:0 */, padding=[0, 0, 0, 0], groups=64, channels=64, kernel_size=[2, 2]) /* ty=Tensor[(1, 64, 15, 15), float32] span=Conv_41:0:0 */;
  %17 = nn.bias_add(%16, meta[relay.Constant][9] /* ty=Tensor[(64), float32] span=Conv_41.443:0:0 */) /* ty=Tensor[(1, 64, 15, 15), float32] span=Conv_41:0:0 */;
  %18 = clip(%17, a_min=0f, a_max=6f) /* ty=Tensor[(1, 64, 15, 15), float32] span=Clip_44:0:0 */;
  %19 = nn.conv2d(%18, meta[relay.Constant][10] /* ty=Tensor[(64, 64, 1, 1), float32] span=Conv_45.445:0:0 */, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1]) /* ty=Tensor[(1, 64, 15, 15), float32] span=Conv_45:0:0 */;
  %20 = nn.conv2d(%input2, meta[relay.Constant][12] /* ty=Tensor[(96, 96, 1, 1), float32] span=Conv_52.457:0:0 */, padding=[0, 0, 0, 0], channels=96, kernel_size=[1, 1]) /* ty=Tensor[(1, 96, 16, 16), float32] span=Conv_52:0:0 */;
  %21 = strided_slice(%input1, begin=[2], end=[6], strides=[1], axes=[2]) /* ty=Tensor[(1, 96, 4, 8), float32] span=Slice_4:0:0 */;
  %22 = strided_slice(%21, begin=[2], end=[6], strides=[1], axes=[3]) /* ty=Tensor[(1, 96, 4, 4), float32] span=Slice_9:0:0 */;
  %23 = nn.conv2d(%22, meta[relay.Constant][14] /* ty=Tensor[(96, 96, 1, 1), float32] span=Conv_51.454:0:0 */, padding=[0, 0, 0, 0], channels=96, kernel_size=[1, 1]) /* ty=Tensor[(1, 96, 4, 4), float32] span=Conv_51:0:0 */;
  %24 = nn.bias_add(%23, meta[relay.Constant][15] /* ty=Tensor[(96), float32] span=Conv_51.455:0:0 */) /* ty=Tensor[(1, 96, 4, 4), float32] span=Conv_51:0:0 */;
  %25 = nn.bias_add(%20, meta[relay.Constant][13] /* ty=Tensor[(96), float32] span=Conv_52.458:0:0 */) /* ty=Tensor[(1, 96, 16, 16), float32] span=Conv_52:0:0 */;
  %26 = reshape(%24, newshape=[96, 1, 4, 4]) /* ty=Tensor[(96, 1, 4, 4), float32] span=Reshape_54:0:0 */;
  %27 = nn.bias_add(%19, meta[relay.Constant][11] /* ty=Tensor[(64), float32] span=Conv_45.446:0:0 */) /* ty=Tensor[(1, 64, 15, 15), float32] span=Conv_45:0:0 */;
  %28 = nn.conv2d(%25, %26, padding=[1, 1, 1, 1], groups=96, channels=96, kernel_size=[4, 4]) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_55:0:0 */;
  %29 = (%27, %28) /* ty=(Tensor[(1, 64, 15, 15), float32], Tensor[(1, 96, 15, 15), float32]) span=Concat_58:0:0 */;
  %30 = concatenate(%29, axis=1) /* ty=Tensor[(1, 160, 15, 15), float32] span=Concat_58:0:0 */;
  %31 = nn.conv2d(%30, meta[relay.Constant][16] /* ty=Tensor[(96, 160, 1, 1), float32] span=Conv_59.down_cls.0.weight:0:0 */, padding=[0, 0, 0, 0], channels=96, kernel_size=[1, 1]) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_59:0:0 */;
  %32 = nn.bias_add(%31, meta[relay.Constant][17] /* ty=Tensor[(96), float32] span=Conv_59.down_cls.0.bias:0:0 */) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_59:0:0 */;
  %33 = nn.conv2d(%32, meta[relay.Constant][18] /* ty=Tensor[(96, 1, 3, 3), float32] span=Conv_60.460:0:0 */, padding=[1, 1, 1, 1], groups=96, channels=96, kernel_size=[3, 3]) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_60:0:0 */;
  %34 = nn.bias_add(%33, meta[relay.Constant][19] /* ty=Tensor[(96), float32] span=Conv_60.461:0:0 */) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_60:0:0 */;
  %35 = clip(%34, a_min=0f, a_max=6f) /* ty=Tensor[(1, 96, 15, 15), float32] span=Clip_63:0:0 */;
  %36 = nn.conv2d(%35, meta[relay.Constant][20] /* ty=Tensor[(96, 96, 1, 1), float32] span=Conv_64.463:0:0 */, padding=[0, 0, 0, 0], channels=96, kernel_size=[1, 1]) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_64:0:0 */;
  %37 = nn.bias_add(%36, meta[relay.Constant][21] /* ty=Tensor[(96), float32] span=Conv_64.464:0:0 */) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_64:0:0 */;
  %38 = nn.conv2d(%37, meta[relay.Constant][22] /* ty=Tensor[(96, 1, 3, 3), float32] span=Conv_65.466:0:0 */, padding=[1, 1, 1, 1], groups=96, channels=96, kernel_size=[3, 3]) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_65:0:0 */;
  %39 = nn.bias_add(%38, meta[relay.Constant][23] /* ty=Tensor[(96), float32] span=Conv_65.467:0:0 */) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_65:0:0 */;
  %40 = clip(%39, a_min=0f, a_max=6f) /* ty=Tensor[(1, 96, 15, 15), float32] span=Clip_68:0:0 */;
  %41 = nn.conv2d(%40, meta[relay.Constant][24] /* ty=Tensor[(96, 96, 1, 1), float32] span=Conv_69.469:0:0 */, padding=[0, 0, 0, 0], channels=96, kernel_size=[1, 1]) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_69:0:0 */;
  %42 = nn.bias_add(%41, meta[relay.Constant][25] /* ty=Tensor[(96), float32] span=Conv_69.470:0:0 */) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_69:0:0 */;
  %43 = nn.conv2d(%42, meta[relay.Constant][26] /* ty=Tensor[(96, 1, 3, 3), float32] span=Conv_70.472:0:0 */, padding=[1, 1, 1, 1], groups=96, channels=96, kernel_size=[3, 3]) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_70:0:0 */;
  %44 = nn.bias_add(%43, meta[relay.Constant][27] /* ty=Tensor[(96), float32] span=Conv_70.473:0:0 */) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_70:0:0 */;
  %45 = clip(%44, a_min=0f, a_max=6f) /* ty=Tensor[(1, 96, 15, 15), float32] span=Clip_73:0:0 */;
  %46 = nn.conv2d(%45, meta[relay.Constant][28] /* ty=Tensor[(96, 96, 1, 1), float32] span=Conv_74.475:0:0 */, padding=[0, 0, 0, 0], channels=96, kernel_size=[1, 1]) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_74:0:0 */;
  %47 = nn.bias_add(%46, meta[relay.Constant][29] /* ty=Tensor[(96), float32] span=Conv_74.476:0:0 */) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_74:0:0 */;
  %48 = nn.conv2d(%47, meta[relay.Constant][30] /* ty=Tensor[(96, 1, 3, 3), float32] span=Conv_75.478:0:0 */, padding=[1, 1, 1, 1], groups=96, channels=96, kernel_size=[3, 3]) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_75:0:0 */;
  %49 = nn.bias_add(%48, meta[relay.Constant][31] /* ty=Tensor[(96), float32] span=Conv_75.479:0:0 */) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_75:0:0 */;
  %50 = clip(%49, a_min=0f, a_max=6f) /* ty=Tensor[(1, 96, 15, 15), float32] span=Clip_78:0:0 */;
  %51 = nn.conv2d(%50, meta[relay.Constant][32] /* ty=Tensor[(96, 96, 1, 1), float32] span=Conv_79.481:0:0 */, padding=[0, 0, 0, 0], channels=96, kernel_size=[1, 1]) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_79:0:0 */;
  %52 = nn.bias_add(%51, meta[relay.Constant][33] /* ty=Tensor[(96), float32] span=Conv_79.482:0:0 */) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_79:0:0 */;
  %53 = nn.conv2d(%52, meta[relay.Constant][34] /* ty=Tensor[(96, 1, 3, 3), float32] span=Conv_80.484:0:0 */, padding=[1, 1, 1, 1], groups=96, channels=96, kernel_size=[3, 3]) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_80:0:0 */;
  %54 = nn.bias_add(%53, meta[relay.Constant][35] /* ty=Tensor[(96), float32] span=Conv_80.485:0:0 */) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_80:0:0 */;
  %55 = clip(%54, a_min=0f, a_max=6f) /* ty=Tensor[(1, 96, 15, 15), float32] span=Clip_83:0:0 */;
  %56 = nn.conv2d(%55, meta[relay.Constant][36] /* ty=Tensor[(96, 96, 1, 1), float32] span=Conv_84.487:0:0 */, padding=[0, 0, 0, 0], channels=96, kernel_size=[1, 1]) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_84:0:0 */;
  %57 = nn.bias_add(%56, meta[relay.Constant][37] /* ty=Tensor[(96), float32] span=Conv_84.488:0:0 */) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_84:0:0 */;
  %58 = nn.conv2d(%57, meta[relay.Constant][38] /* ty=Tensor[(96, 1, 3, 3), float32] span=Conv_85.490:0:0 */, padding=[1, 1, 1, 1], groups=96, channels=96, kernel_size=[3, 3]) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_85:0:0 */;
  %59 = nn.bias_add(%58, meta[relay.Constant][39] /* ty=Tensor[(96), float32] span=Conv_85.491:0:0 */) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_85:0:0 */;
  %60 = clip(%59, a_min=0f, a_max=6f) /* ty=Tensor[(1, 96, 15, 15), float32] span=Clip_88:0:0 */;
  %61 = nn.conv2d(%60, meta[relay.Constant][40] /* ty=Tensor[(96, 96, 1, 1), float32] span=Conv_89.493:0:0 */, padding=[0, 0, 0, 0], channels=96, kernel_size=[1, 1]) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_89:0:0 */;
  %62 = nn.bias_add(%61, meta[relay.Constant][41] /* ty=Tensor[(96), float32] span=Conv_89.494:0:0 */) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_89:0:0 */;
  %63 = nn.conv2d(%62, meta[relay.Constant][42] /* ty=Tensor[(2, 96, 1, 1), float32] span=Conv_90.cls_logits.0.weight:0:0 */, padding=[0, 0, 0, 0], channels=2, kernel_size=[1, 1]) /* ty=Tensor[(1, 2, 15, 15), float32] span=Conv_90:0:0 */;
  %64 = nn.conv2d(%input2, meta[relay.Constant][44] /* ty=Tensor[(96, 96, 1, 1), float32] span=Conv_11.427:0:0 */, padding=[0, 0, 0, 0], channels=96, kernel_size=[1, 1]) /* ty=Tensor[(1, 96, 16, 16), float32] span=Conv_11:0:0 */;
  %65 = nn.conv2d(%input1, meta[relay.Constant][46] /* ty=Tensor[(96, 96, 1, 1), float32] span=Conv_10.424:0:0 */, padding=[0, 0, 0, 0], channels=96, kernel_size=[1, 1]) /* ty=Tensor[(1, 96, 8, 8), float32] span=Conv_10:0:0 */;
  %66 = nn.bias_add(%65, meta[relay.Constant][47] /* ty=Tensor[(96), float32] span=Conv_10.425:0:0 */) /* ty=Tensor[(1, 96, 8, 8), float32] span=Conv_10:0:0 */;
  %67 = reshape(%66, newshape=[1, 96, -1]) /* ty=Tensor[(1, 96, 64), float32] span=Reshape_12:0:0 */;
  %68 = transpose(%67, axes=[0, 2, 1]) /* ty=Tensor[(1, 64, 96), float32] span=Transpose_13:0:0 */;
  %69 = nn.bias_add(%64, meta[relay.Constant][45] /* ty=Tensor[(96), float32] span=Conv_11.428:0:0 */) /* ty=Tensor[(1, 96, 16, 16), float32] span=Conv_11:0:0 */;
  %70 = reshape(%68, newshape=[64, 96, 1, 1]) /* ty=Tensor[(64, 96, 1, 1), float32] span=Reshape_15:0:0 */;
  %71 = nn.conv2d(%69, %70, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1]) /* ty=Tensor[(1, 64, 16, 16), float32] span=Conv_16:0:0 */;
  %72 = nn.global_avg_pool2d(%71) /* ty=Tensor[(1, 64, 1, 1), float32] span=GlobalAveragePool_17:0:0 */;
  %73 = nn.conv2d(%72, meta[relay.Constant][48] /* ty=Tensor[(64, 64, 1, 1), float32] span=Conv_18.corr_pw_reg.CA_layer.fc1.weight:0:0 */, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1]) /* ty=Tensor[(1, 64, 1, 1), float32] span=Conv_18:0:0 */;
  %74 = nn.bias_add(%73, meta[relay.Constant][49] /* ty=Tensor[(64), float32] span=Conv_18.corr_pw_reg.CA_layer.fc1.bias:0:0 */) /* ty=Tensor[(1, 64, 1, 1), float32] span=Conv_18:0:0 */;
  %75 = nn.relu(%74) /* ty=Tensor[(1, 64, 1, 1), float32] span=Relu_19:0:0 */;
  %76 = nn.conv2d(%75, meta[relay.Constant][50] /* ty=Tensor[(64, 64, 1, 1), float32] span=Conv_20.corr_pw_reg.CA_layer.fc2.weight:0:0 */, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1]) /* ty=Tensor[(1, 64, 1, 1), float32] span=Conv_20:0:0 */;
  %77 = nn.bias_add(%76, meta[relay.Constant][51] /* ty=Tensor[(64), float32] span=Conv_20.corr_pw_reg.CA_layer.fc2.bias:0:0 */) /* ty=Tensor[(1, 64, 1, 1), float32] span=Conv_20:0:0 */;
  %78 = sigmoid(%77) /* ty=Tensor[(1, 64, 1, 1), float32] span=Sigmoid_21:0:0 */;
  %79 = multiply(%71, %78) /* ty=Tensor[(1, 64, 16, 16), float32] span=Mul_22:0:0 */;
  %80 = nn.conv2d(%79, meta[relay.Constant][52] /* ty=Tensor[(64, 1, 2, 2), float32] span=Conv_23.430:0:0 */, padding=[0, 0, 0, 0], groups=64, channels=64, kernel_size=[2, 2]) /* ty=Tensor[(1, 64, 15, 15), float32] span=Conv_23:0:0 */;
  %81 = nn.bias_add(%80, meta[relay.Constant][53] /* ty=Tensor[(64), float32] span=Conv_23.431:0:0 */) /* ty=Tensor[(1, 64, 15, 15), float32] span=Conv_23:0:0 */;
  %82 = clip(%81, a_min=0f, a_max=6f) /* ty=Tensor[(1, 64, 15, 15), float32] span=Clip_26:0:0 */;
  %83 = nn.conv2d(%82, meta[relay.Constant][54] /* ty=Tensor[(64, 64, 1, 1), float32] span=Conv_27.433:0:0 */, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1]) /* ty=Tensor[(1, 64, 15, 15), float32] span=Conv_27:0:0 */;
  %84 = nn.conv2d(%input2, meta[relay.Constant][56] /* ty=Tensor[(96, 96, 1, 1), float32] span=Conv_47.451:0:0 */, padding=[0, 0, 0, 0], channels=96, kernel_size=[1, 1]) /* ty=Tensor[(1, 96, 16, 16), float32] span=Conv_47:0:0 */;
  %85 = nn.conv2d(%22, meta[relay.Constant][58] /* ty=Tensor[(96, 96, 1, 1), float32] span=Conv_46.448:0:0 */, padding=[0, 0, 0, 0], channels=96, kernel_size=[1, 1]) /* ty=Tensor[(1, 96, 4, 4), float32] span=Conv_46:0:0 */;
  %86 = nn.bias_add(%85, meta[relay.Constant][59] /* ty=Tensor[(96), float32] span=Conv_46.449:0:0 */) /* ty=Tensor[(1, 96, 4, 4), float32] span=Conv_46:0:0 */;
  %87 = nn.bias_add(%84, meta[relay.Constant][57] /* ty=Tensor[(96), float32] span=Conv_47.452:0:0 */) /* ty=Tensor[(1, 96, 16, 16), float32] span=Conv_47:0:0 */;
  %88 = reshape(%86, newshape=[96, 1, 4, 4]) /* ty=Tensor[(96, 1, 4, 4), float32] span=Reshape_49:0:0 */;
  %89 = nn.bias_add(%83, meta[relay.Constant][55] /* ty=Tensor[(64), float32] span=Conv_27.434:0:0 */) /* ty=Tensor[(1, 64, 15, 15), float32] span=Conv_27:0:0 */;
  %90 = nn.conv2d(%87, %88, padding=[1, 1, 1, 1], groups=96, channels=96, kernel_size=[4, 4]) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_50:0:0 */;
  %91 = (%89, %90) /* ty=(Tensor[(1, 64, 15, 15), float32], Tensor[(1, 96, 15, 15), float32]) span=Concat_56:0:0 */;
  %92 = concatenate(%91, axis=1) /* ty=Tensor[(1, 160, 15, 15), float32] span=Concat_56:0:0 */;
  %93 = nn.conv2d(%92, meta[relay.Constant][60] /* ty=Tensor[(96, 160, 1, 1), float32] span=Conv_57.down_reg.0.weight:0:0 */, padding=[0, 0, 0, 0], channels=96, kernel_size=[1, 1]) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_57:0:0 */;
  %94 = nn.bias_add(%93, meta[relay.Constant][61] /* ty=Tensor[(96), float32] span=Conv_57.down_reg.0.bias:0:0 */) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_57:0:0 */;
  %95 = nn.conv2d(%94, meta[relay.Constant][62] /* ty=Tensor[(96, 1, 3, 3), float32] span=Conv_91.496:0:0 */, padding=[1, 1, 1, 1], groups=96, channels=96, kernel_size=[3, 3]) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_91:0:0 */;
  %96 = nn.bias_add(%95, meta[relay.Constant][63] /* ty=Tensor[(96), float32] span=Conv_91.497:0:0 */) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_91:0:0 */;
  %97 = clip(%96, a_min=0f, a_max=6f) /* ty=Tensor[(1, 96, 15, 15), float32] span=Clip_94:0:0 */;
  %98 = nn.conv2d(%97, meta[relay.Constant][64] /* ty=Tensor[(96, 96, 1, 1), float32] span=Conv_95.499:0:0 */, padding=[0, 0, 0, 0], channels=96, kernel_size=[1, 1]) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_95:0:0 */;
  %99 = nn.bias_add(%98, meta[relay.Constant][65] /* ty=Tensor[(96), float32] span=Conv_95.500:0:0 */) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_95:0:0 */;
  %100 = nn.conv2d(%99, meta[relay.Constant][66] /* ty=Tensor[(96, 1, 3, 3), float32] span=Conv_96.502:0:0 */, padding=[1, 1, 1, 1], groups=96, channels=96, kernel_size=[3, 3]) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_96:0:0 */;
  %101 = nn.bias_add(%100, meta[relay.Constant][67] /* ty=Tensor[(96), float32] span=Conv_96.503:0:0 */) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_96:0:0 */;
  %102 = clip(%101, a_min=0f, a_max=6f) /* ty=Tensor[(1, 96, 15, 15), float32] span=Clip_99:0:0 */;
  %103 = nn.conv2d(%102, meta[relay.Constant][68] /* ty=Tensor[(96, 96, 1, 1), float32] span=Conv_100.505:0:0 */, padding=[0, 0, 0, 0], channels=96, kernel_size=[1, 1]) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_100:0:0 */;
  %104 = nn.bias_add(%103, meta[relay.Constant][69] /* ty=Tensor[(96), float32] span=Conv_100.506:0:0 */) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_100:0:0 */;
  %105 = nn.conv2d(%104, meta[relay.Constant][70] /* ty=Tensor[(96, 1, 3, 3), float32] span=Conv_101.508:0:0 */, padding=[1, 1, 1, 1], groups=96, channels=96, kernel_size=[3, 3]) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_101:0:0 */;
  %106 = nn.bias_add(%105, meta[relay.Constant][71] /* ty=Tensor[(96), float32] span=Conv_101.509:0:0 */) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_101:0:0 */;
  %107 = clip(%106, a_min=0f, a_max=6f) /* ty=Tensor[(1, 96, 15, 15), float32] span=Clip_104:0:0 */;
  %108 = nn.conv2d(%107, meta[relay.Constant][72] /* ty=Tensor[(96, 96, 1, 1), float32] span=Conv_105.511:0:0 */, padding=[0, 0, 0, 0], channels=96, kernel_size=[1, 1]) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_105:0:0 */;
  %109 = nn.bias_add(%108, meta[relay.Constant][73] /* ty=Tensor[(96), float32] span=Conv_105.512:0:0 */) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_105:0:0 */;
  %110 = nn.conv2d(%109, meta[relay.Constant][74] /* ty=Tensor[(96, 1, 3, 3), float32] span=Conv_106.514:0:0 */, padding=[1, 1, 1, 1], groups=96, channels=96, kernel_size=[3, 3]) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_106:0:0 */;
  %111 = nn.bias_add(%110, meta[relay.Constant][75] /* ty=Tensor[(96), float32] span=Conv_106.515:0:0 */) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_106:0:0 */;
  %112 = clip(%111, a_min=0f, a_max=6f) /* ty=Tensor[(1, 96, 15, 15), float32] span=Clip_109:0:0 */;
  %113 = nn.conv2d(%112, meta[relay.Constant][76] /* ty=Tensor[(96, 96, 1, 1), float32] span=Conv_110.517:0:0 */, padding=[0, 0, 0, 0], channels=96, kernel_size=[1, 1]) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_110:0:0 */;
  %114 = nn.bias_add(%113, meta[relay.Constant][77] /* ty=Tensor[(96), float32] span=Conv_110.518:0:0 */) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_110:0:0 */;
  %115 = nn.conv2d(%114, meta[relay.Constant][78] /* ty=Tensor[(96, 1, 3, 3), float32] span=Conv_111.520:0:0 */, padding=[1, 1, 1, 1], groups=96, channels=96, kernel_size=[3, 3]) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_111:0:0 */;
  %116 = nn.bias_add(%115, meta[relay.Constant][79] /* ty=Tensor[(96), float32] span=Conv_111.521:0:0 */) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_111:0:0 */;
  %117 = clip(%116, a_min=0f, a_max=6f) /* ty=Tensor[(1, 96, 15, 15), float32] span=Clip_114:0:0 */;
  %118 = nn.conv2d(%117, meta[relay.Constant][80] /* ty=Tensor[(96, 96, 1, 1), float32] span=Conv_115.523:0:0 */, padding=[0, 0, 0, 0], channels=96, kernel_size=[1, 1]) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_115:0:0 */;
  %119 = nn.bias_add(%118, meta[relay.Constant][81] /* ty=Tensor[(96), float32] span=Conv_115.524:0:0 */) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_115:0:0 */;
  %120 = nn.conv2d(%119, meta[relay.Constant][82] /* ty=Tensor[(96, 1, 3, 3), float32] span=Conv_116.526:0:0 */, padding=[1, 1, 1, 1], groups=96, channels=96, kernel_size=[3, 3]) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_116:0:0 */;
  %121 = nn.bias_add(%120, meta[relay.Constant][83] /* ty=Tensor[(96), float32] span=Conv_116.527:0:0 */) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_116:0:0 */;
  %122 = clip(%121, a_min=0f, a_max=6f) /* ty=Tensor[(1, 96, 15, 15), float32] span=Clip_119:0:0 */;
  %123 = nn.conv2d(%122, meta[relay.Constant][84] /* ty=Tensor[(96, 96, 1, 1), float32] span=Conv_120.529:0:0 */, padding=[0, 0, 0, 0], channels=96, kernel_size=[1, 1]) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_120:0:0 */;
  %124 = nn.bias_add(%123, meta[relay.Constant][85] /* ty=Tensor[(96), float32] span=Conv_120.530:0:0 */) /* ty=Tensor[(1, 96, 15, 15), float32] span=Conv_120:0:0 */;
  %125 = nn.conv2d(%124, meta[relay.Constant][86] /* ty=Tensor[(4, 96, 1, 1), float32] span=Conv_121.bbox_pred.0.weight:0:0 */, padding=[0, 0, 0, 0], channels=4, kernel_size=[1, 1]) /* ty=Tensor[(1, 4, 15, 15), float32] span=Conv_121:0:0 */;
  %126 = nn.bias_add(%125, meta[relay.Constant][87] /* ty=Tensor[(4), float32] span=Conv_121.bbox_pred.0.bias:0:0 */) /* ty=Tensor[(1, 4, 15, 15), float32] span=Conv_121:0:0 */;
  %127 = nn.bias_add(%63, meta[relay.Constant][43] /* ty=Tensor[(2), float32] span=Conv_90.cls_logits.0.bias:0:0 */) /* ty=Tensor[(1, 2, 15, 15), float32] span=Conv_90:0:0 */;
  %128 = exp(%126) /* ty=Tensor[(1, 4, 15, 15), float32] span=Exp_122:0:0 */;
  (%127, %128) /* ty=(Tensor[(1, 2, 15, 15), float32], Tensor[(1, 4, 15, 15), float32]) */
}