jlamperez commented on issue #8683: URL: https://github.com/apache/tvm/issues/8683#issuecomment-899759896
I've been able to get some log traces: ``` ************************************************** * RELAY IR TO PYXIR ************************************************** DEBUG:pyxir:free_var %inputs: Tensor[(1, 3, 416, 416), float32]; %0 = dyn.strided_slice(%inputs, meta[relay.Constant][0] /* ty=Tensor[(4), int64] */, meta[relay.Constant][1] /* ty=Tensor[(4), int64] */, meta[relay.Constant][2] /* ty=Tensor[(4), int64] */, begin=None, end=None, strides=None, axes=None) /* ty=Tensor[(?, ?, ?, ?), float32] */; %1 = shape_of(%0, dtype="int32") /* ty=Tensor[(4), int32] */; %2 = cast_like(%1, meta[relay.Constant][4] /* ty=Tensor[(4), int64] */) /* ty=Tensor[(4), int64] */; %3 = slice_like(%2, meta[relay.Constant][4] /* ty=Tensor[(4), int64] */, axes=None) /* ty=Tensor[(4), int64] */; %4 = add(meta[relay.Constant][4] /* ty=Tensor[(4), int64] */, %3) /* ty=Tensor[(4), int64] */; %5 = where(meta[relay.Constant][3] /* ty=Tensor[(4), bool] */, %4, meta[relay.Constant][4] /* ty=Tensor[(4), int64] */) /* ty=Tensor[(4), int64] */; %6 = greater_equal(%5, %3) /* ty=Tensor[(4), bool] */; %7 = shape_of(%0, dtype="int64") /* ty=Tensor[(4), int64] */; %8 = where(%6, %3, %5) /* ty=Tensor[(4), int64] */; %9 = scatter(%7, meta[relay.Constant][5] /* ty=Tensor[(1), int64] */, meta[relay.Constant][6] /* ty=Tensor[(1), int64] */, meta[relay.attrs.ScatterAttrs][0]) /* ty=Tensor[(4), int64] */; %10 = dyn.strided_slice(%inputs, meta[relay.Constant][8] /* ty=Tensor[(4), int64] */, meta[relay.Constant][9] /* ty=Tensor[(4), int64] */, meta[relay.Constant][10] /* ty=Tensor[(4), int64] */, begin=None, end=None, strides=None, axes=None) /* ty=Tensor[(?, ?, ?, ?), float32] */; %11 = shape_of(%10, dtype="int32") /* ty=Tensor[(4), int32] */; %12 = cast_like(%11, meta[relay.Constant][12] /* ty=Tensor[(4), int64] */) /* ty=Tensor[(4), int64] */; %13 = slice_like(%12, meta[relay.Constant][12] /* ty=Tensor[(4), int64] */, axes=None) /* ty=Tensor[(4), int64] */; %14 = add(meta[relay.Constant][12] /* ty=Tensor[(4), int64] */, %13) /* ty=Tensor[(4), int64] */; %15 = where(meta[relay.Constant][11] /* ty=Tensor[(4), bool] */, %14, meta[relay.Constant][12] /* ty=Tensor[(4), int64] */) /* ty=Tensor[(4), int64] */; %16 = greater_equal(%15, %13) /* ty=Tensor[(4), bool] */; %17 = shape_of(%10, dtype="int64") /* ty=Tensor[(4), int64] */; %18 = where(%16, %13, %15) /* ty=Tensor[(4), int64] */; %19 = scatter(%17, meta[relay.Constant][5] /* ty=Tensor[(1), int64] */, meta[relay.Constant][6] /* ty=Tensor[(1), int64] */, meta[relay.attrs.ScatterAttrs][1]) /* ty=Tensor[(4), int64] */; %20 = dyn.strided_slice(%inputs, meta[relay.Constant][14] /* ty=Tensor[(4), int64] */, meta[relay.Constant][15] /* ty=Tensor[(4), int64] */, meta[relay.Constant][16] /* ty=Tensor[(4), int64] */, begin=None, end=None, strides=None, axes=None) /* ty=Tensor[(?, ?, ?, ?), float32] */; %21 = shape_of(%20, dtype="int32") /* ty=Tensor[(4), int32] */; %22 = cast_like(%21, meta[relay.Constant][18] /* ty=Tensor[(4), int64] */) /* ty=Tensor[(4), int64] */; %23 = slice_like(%22, meta[relay.Constant][18] /* ty=Tensor[(4), int64] */, axes=None) /* ty=Tensor[(4), int64] */; %24 = add(meta[relay.Constant][18] /* ty=Tensor[(4), int64] */, %23) /* ty=Tensor[(4), int64] */; %25 = where(meta[relay.Constant][17] /* ty=Tensor[(4), bool] */, %24, meta[relay.Constant][18] /* ty=Tensor[(4), int64] */) /* ty=Tensor[(4), int64] */; %26 = greater_equal(%25, %23) /* ty=Tensor[(4), bool] */; %27 = shape_of(%20, dtype="int64") /* ty=Tensor[(4), int64] */; %28 = where(%26, %23, %25) /* ty=Tensor[(4), int64] */; %29 = scatter(%27, meta[relay.Constant][5] /* ty=Tensor[(1), int64] */, meta[relay.Constant][6] /* ty=Tensor[(1), int64] */, meta[relay.attrs.ScatterAttrs][2]) /* ty=Tensor[(4), int64] */; %30 = dyn.strided_slice(%inputs, meta[relay.Constant][20] /* ty=Tensor[(4), int64] */, meta[relay.Constant][21] /* ty=Tensor[(4), int64] */, meta[relay.Constant][22] /* ty=Tensor[(4), int64] */, begin=None, end=None, strides=None, axes=None) /* ty=Tensor[(?, ?, ?, ?), float32] */; %31 = shape_of(%30, dtype="int32") /* ty=Tensor[(4), int32] */; %32 = cast_like(%31, meta[relay.Constant][24] /* ty=Tensor[(4), int64] */) /* ty=Tensor[(4), int64] */; %33 = slice_like(%32, meta[relay.Constant][24] /* ty=Tensor[(4), int64] */, axes=None) /* ty=Tensor[(4), int64] */; %34 = add(meta[relay.Constant][24] /* ty=Tensor[(4), int64] */, %33) /* ty=Tensor[(4), int64] */; %35 = where(meta[relay.Constant][23] /* ty=Tensor[(4), bool] */, %34, meta[relay.Constant][24] /* ty=Tensor[(4), int64] */) /* ty=Tensor[(4), int64] */; %36 = greater_equal(%35, %33) /* ty=Tensor[(4), bool] */; %37 = shape_of(%30, dtype="int64") /* ty=Tensor[(4), int64] */; %38 = where(%36, %33, %35) /* ty=Tensor[(4), int64] */; %39 = scatter(%37, meta[relay.Constant][5] /* ty=Tensor[(1), int64] */, meta[relay.Constant][6] /* ty=Tensor[(1), int64] */, meta[relay.attrs.ScatterAttrs][3]) /* ty=Tensor[(4), int64] */; %40 = dyn.strided_slice(%0, %8, %9, meta[relay.Constant][7] /* ty=Tensor[(4), int64] */, begin=None, end=None, strides=None, axes=None) /* ty=Tensor[(?, ?, ?, ?), float32] */; %41 = dyn.strided_slice(%10, %18, %19, meta[relay.Constant][13] /* ty=Tensor[(4), int64] */, begin=None, end=None, strides=None, axes=None) /* ty=Tensor[(?, ?, ?, ?), float32] */; %42 = dyn.strided_slice(%20, %28, %29, meta[relay.Constant][19] /* ty=Tensor[(4), int64] */, begin=None, end=None, strides=None, axes=None) /* ty=Tensor[(?, ?, ?, ?), float32] */; %43 = dyn.strided_slice(%30, %38, %39, meta[relay.Constant][25] /* ty=Tensor[(4), int64] */, begin=None, end=None, strides=None, axes=None) /* ty=Tensor[(?, ?, ?, ?), float32] */; %44 = (%40, %41, %42, %43); %45 = concatenate(%44, axis=1) /* ty=Tensor[(?, ?, ?, ?), float32] */; %46 = layout_transform(%45, src_layout="NCHW", dst_layout="NHWC") /* ty=Tensor[(?, ?, ?, ?), float32] */; %47 = nn.conv2d(%46, meta[relay.Constant][26] /* ty=Tensor[(3, 3, 12, 16), float32] */, padding=[1, 1, 1, 1], kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %48 = add(%47, meta[relay.Constant][27] /* ty=Tensor[(1, 1, 1, 16), float32] */) /* ty=Tensor[(?, ?, ?, 16), float32] */; %49 = sigmoid(%48) /* ty=Tensor[(?, ?, ?, 16), float32] */; %50 = multiply(%48, %49) /* ty=Tensor[(?, ?, ?, 16), float32] */; %51 = nn.conv2d(%50, meta[relay.Constant][28] /* ty=Tensor[(3, 3, 16, 1), float32] */, strides=[2, 2], padding=[1, 1, 1, 1], groups=16, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI") /* ty=Tensor[(?, ?, ?, ?), float32] */; %52 = add(%51, meta[relay.Constant][29] /* ty=Tensor[(1, 1, 1, 16), float32] */) /* ty=Tensor[(?, ?, ?, 16), float32] */; %53 = sigmoid(%52) /* ty=Tensor[(?, ?, ?, 16), float32] */; %54 = multiply(%52, %53) /* ty=Tensor[(?, ?, ?, 16), float32] */; %55 = nn.conv2d(%54, meta[relay.Constant][30] /* ty=Tensor[(1, 1, 16, 32), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %56 = add(%55, meta[relay.Constant][31] /* ty=Tensor[(1, 1, 1, 32), float32] */) /* ty=Tensor[(?, ?, ?, 32), float32] */; %57 = sigmoid(%56) /* ty=Tensor[(?, ?, ?, 32), float32] */; %58 = multiply(%56, %57) /* ty=Tensor[(?, ?, ?, 32), float32] */; %59 = nn.conv2d(%58, meta[relay.Constant][32] /* ty=Tensor[(1, 1, 32, 16), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %60 = add(%59, meta[relay.Constant][33] /* ty=Tensor[(1, 1, 1, 16), float32] */) /* ty=Tensor[(?, ?, ?, 16), float32] */; %61 = sigmoid(%60) /* ty=Tensor[(?, ?, ?, 16), float32] */; %62 = multiply(%60, %61) /* ty=Tensor[(?, ?, ?, 16), float32] */; %63 = nn.conv2d(%62, meta[relay.Constant][34] /* ty=Tensor[(1, 1, 16, 16), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %64 = add(%63, meta[relay.Constant][35] /* ty=Tensor[(1, 1, 1, 16), float32] */) /* ty=Tensor[(?, ?, ?, 16), float32] */; %65 = sigmoid(%64) /* ty=Tensor[(?, ?, ?, 16), float32] */; %66 = multiply(%64, %65) /* ty=Tensor[(?, ?, ?, 16), float32] */; %67 = nn.conv2d(%66, meta[relay.Constant][36] /* ty=Tensor[(3, 3, 16, 1), float32] */, padding=[1, 1, 1, 1], groups=16, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI") /* ty=Tensor[(?, ?, ?, ?), float32] */; %68 = add(%67, meta[relay.Constant][37] /* ty=Tensor[(1, 1, 1, 16), float32] */) /* ty=Tensor[(?, ?, ?, 16), float32] */; %69 = sigmoid(%68) /* ty=Tensor[(?, ?, ?, 16), float32] */; %70 = multiply(%68, %69) /* ty=Tensor[(?, ?, ?, 16), float32] */; %71 = nn.conv2d(%70, meta[relay.Constant][38] /* ty=Tensor[(1, 1, 16, 16), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %72 = add(%71, meta[relay.Constant][39] /* ty=Tensor[(1, 1, 1, 16), float32] */) /* ty=Tensor[(?, ?, ?, 16), float32] */; %73 = sigmoid(%72) /* ty=Tensor[(?, ?, ?, 16), float32] */; %74 = multiply(%72, %73) /* ty=Tensor[(?, ?, ?, 16), float32] */; %75 = nn.conv2d(%58, meta[relay.Constant][40] /* ty=Tensor[(1, 1, 32, 16), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %76 = add(%75, meta[relay.Constant][41] /* ty=Tensor[(1, 1, 1, 16), float32] */) /* ty=Tensor[(?, ?, ?, 16), float32] */; %77 = sigmoid(%76) /* ty=Tensor[(?, ?, ?, 16), float32] */; %78 = add(%74, %62) /* ty=Tensor[(?, ?, ?, 16), float32] */; %79 = multiply(%76, %77) /* ty=Tensor[(?, ?, ?, 16), float32] */; %80 = (%78, %79); %81 = concatenate(%80, axis=3) /* ty=Tensor[(?, ?, ?, 32), float32] */; %82 = nn.conv2d(%81, meta[relay.Constant][42] /* ty=Tensor[(1, 1, 32, 32), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %83 = add(%82, meta[relay.Constant][43] /* ty=Tensor[(1, 1, 1, 32), float32] */) /* ty=Tensor[(?, ?, ?, 32), float32] */; %84 = sigmoid(%83) /* ty=Tensor[(?, ?, ?, 32), float32] */; %85 = multiply(%83, %84) /* ty=Tensor[(?, ?, ?, 32), float32] */; %86 = nn.conv2d(%85, meta[relay.Constant][44] /* ty=Tensor[(3, 3, 32, 1), float32] */, strides=[2, 2], padding=[1, 1, 1, 1], groups=32, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI") /* ty=Tensor[(?, ?, ?, ?), float32] */; %87 = add(%86, meta[relay.Constant][45] /* ty=Tensor[(1, 1, 1, 32), float32] */) /* ty=Tensor[(?, ?, ?, 32), float32] */; %88 = sigmoid(%87) /* ty=Tensor[(?, ?, ?, 32), float32] */; %89 = multiply(%87, %88) /* ty=Tensor[(?, ?, ?, 32), float32] */; %90 = nn.conv2d(%89, meta[relay.Constant][46] /* ty=Tensor[(1, 1, 32, 64), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %91 = add(%90, meta[relay.Constant][47] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %92 = sigmoid(%91) /* ty=Tensor[(?, ?, ?, 64), float32] */; %93 = multiply(%91, %92) /* ty=Tensor[(?, ?, ?, 64), float32] */; %94 = nn.conv2d(%93, meta[relay.Constant][48] /* ty=Tensor[(1, 1, 64, 32), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %95 = add(%94, meta[relay.Constant][49] /* ty=Tensor[(1, 1, 1, 32), float32] */) /* ty=Tensor[(?, ?, ?, 32), float32] */; %96 = sigmoid(%95) /* ty=Tensor[(?, ?, ?, 32), float32] */; %97 = multiply(%95, %96) /* ty=Tensor[(?, ?, ?, 32), float32] */; %98 = nn.conv2d(%97, meta[relay.Constant][50] /* ty=Tensor[(1, 1, 32, 32), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %99 = add(%98, meta[relay.Constant][51] /* ty=Tensor[(1, 1, 1, 32), float32] */) /* ty=Tensor[(?, ?, ?, 32), float32] */; %100 = sigmoid(%99) /* ty=Tensor[(?, ?, ?, 32), float32] */; %101 = multiply(%99, %100) /* ty=Tensor[(?, ?, ?, 32), float32] */; %102 = nn.conv2d(%101, meta[relay.Constant][52] /* ty=Tensor[(3, 3, 32, 1), float32] */, padding=[1, 1, 1, 1], groups=32, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI") /* ty=Tensor[(?, ?, ?, ?), float32] */; %103 = add(%102, meta[relay.Constant][53] /* ty=Tensor[(1, 1, 1, 32), float32] */) /* ty=Tensor[(?, ?, ?, 32), float32] */; %104 = sigmoid(%103) /* ty=Tensor[(?, ?, ?, 32), float32] */; %105 = multiply(%103, %104) /* ty=Tensor[(?, ?, ?, 32), float32] */; %106 = nn.conv2d(%105, meta[relay.Constant][54] /* ty=Tensor[(1, 1, 32, 32), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %107 = add(%106, meta[relay.Constant][55] /* ty=Tensor[(1, 1, 1, 32), float32] */) /* ty=Tensor[(?, ?, ?, 32), float32] */; %108 = sigmoid(%107) /* ty=Tensor[(?, ?, ?, 32), float32] */; %109 = multiply(%107, %108) /* ty=Tensor[(?, ?, ?, 32), float32] */; %110 = add(%109, %97) /* ty=Tensor[(?, ?, ?, 32), float32] */; %111 = nn.conv2d(%110, meta[relay.Constant][56] /* ty=Tensor[(1, 1, 32, 32), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %112 = add(%111, meta[relay.Constant][57] /* ty=Tensor[(1, 1, 1, 32), float32] */) /* ty=Tensor[(?, ?, ?, 32), float32] */; %113 = sigmoid(%112) /* ty=Tensor[(?, ?, ?, 32), float32] */; %114 = multiply(%112, %113) /* ty=Tensor[(?, ?, ?, 32), float32] */; %115 = nn.conv2d(%114, meta[relay.Constant][58] /* ty=Tensor[(3, 3, 32, 1), float32] */, padding=[1, 1, 1, 1], groups=32, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI") /* ty=Tensor[(?, ?, ?, ?), float32] */; %116 = add(%115, meta[relay.Constant][59] /* ty=Tensor[(1, 1, 1, 32), float32] */) /* ty=Tensor[(?, ?, ?, 32), float32] */; %117 = sigmoid(%116) /* ty=Tensor[(?, ?, ?, 32), float32] */; %118 = multiply(%116, %117) /* ty=Tensor[(?, ?, ?, 32), float32] */; %119 = nn.conv2d(%118, meta[relay.Constant][60] /* ty=Tensor[(1, 1, 32, 32), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %120 = add(%119, meta[relay.Constant][61] /* ty=Tensor[(1, 1, 1, 32), float32] */) /* ty=Tensor[(?, ?, ?, 32), float32] */; %121 = sigmoid(%120) /* ty=Tensor[(?, ?, ?, 32), float32] */; %122 = multiply(%120, %121) /* ty=Tensor[(?, ?, ?, 32), float32] */; %123 = add(%122, %110) /* ty=Tensor[(?, ?, ?, 32), float32] */; %124 = nn.conv2d(%123, meta[relay.Constant][62] /* ty=Tensor[(1, 1, 32, 32), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %125 = add(%124, meta[relay.Constant][63] /* ty=Tensor[(1, 1, 1, 32), float32] */) /* ty=Tensor[(?, ?, ?, 32), float32] */; %126 = sigmoid(%125) /* ty=Tensor[(?, ?, ?, 32), float32] */; %127 = multiply(%125, %126) /* ty=Tensor[(?, ?, ?, 32), float32] */; %128 = nn.conv2d(%127, meta[relay.Constant][64] /* ty=Tensor[(3, 3, 32, 1), float32] */, padding=[1, 1, 1, 1], groups=32, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI") /* ty=Tensor[(?, ?, ?, ?), float32] */; %129 = add(%128, meta[relay.Constant][65] /* ty=Tensor[(1, 1, 1, 32), float32] */) /* ty=Tensor[(?, ?, ?, 32), float32] */; %130 = sigmoid(%129) /* ty=Tensor[(?, ?, ?, 32), float32] */; %131 = multiply(%129, %130) /* ty=Tensor[(?, ?, ?, 32), float32] */; %132 = nn.conv2d(%131, meta[relay.Constant][66] /* ty=Tensor[(1, 1, 32, 32), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %133 = add(%132, meta[relay.Constant][67] /* ty=Tensor[(1, 1, 1, 32), float32] */) /* ty=Tensor[(?, ?, ?, 32), float32] */; %134 = sigmoid(%133) /* ty=Tensor[(?, ?, ?, 32), float32] */; %135 = multiply(%133, %134) /* ty=Tensor[(?, ?, ?, 32), float32] */; %136 = nn.conv2d(%93, meta[relay.Constant][68] /* ty=Tensor[(1, 1, 64, 32), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %137 = add(%136, meta[relay.Constant][69] /* ty=Tensor[(1, 1, 1, 32), float32] */) /* ty=Tensor[(?, ?, ?, 32), float32] */; %138 = sigmoid(%137) /* ty=Tensor[(?, ?, ?, 32), float32] */; %139 = add(%135, %123) /* ty=Tensor[(?, ?, ?, 32), float32] */; %140 = multiply(%137, %138) /* ty=Tensor[(?, ?, ?, 32), float32] */; %141 = (%139, %140); %142 = concatenate(%141, axis=3) /* ty=Tensor[(?, ?, ?, 64), float32] */; %143 = nn.conv2d(%142, meta[relay.Constant][70] /* ty=Tensor[(1, 1, 64, 64), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %144 = add(%143, meta[relay.Constant][71] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %145 = sigmoid(%144) /* ty=Tensor[(?, ?, ?, 64), float32] */; %146 = multiply(%144, %145) /* ty=Tensor[(?, ?, ?, 64), float32] */; %147 = nn.conv2d(%146, meta[relay.Constant][72] /* ty=Tensor[(3, 3, 64, 1), float32] */, strides=[2, 2], padding=[1, 1, 1, 1], groups=64, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI") /* ty=Tensor[(?, ?, ?, ?), float32] */; %148 = add(%147, meta[relay.Constant][73] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %149 = sigmoid(%148) /* ty=Tensor[(?, ?, ?, 64), float32] */; %150 = multiply(%148, %149) /* ty=Tensor[(?, ?, ?, 64), float32] */; %151 = nn.conv2d(%150, meta[relay.Constant][74] /* ty=Tensor[(1, 1, 64, 128), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %152 = add(%151, meta[relay.Constant][75] /* ty=Tensor[(1, 1, 1, 128), float32] */) /* ty=Tensor[(?, ?, ?, 128), float32] */; %153 = sigmoid(%152) /* ty=Tensor[(?, ?, ?, 128), float32] */; %154 = multiply(%152, %153) /* ty=Tensor[(?, ?, ?, 128), float32] */; %155 = nn.conv2d(%154, meta[relay.Constant][76] /* ty=Tensor[(1, 1, 128, 64), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %156 = add(%155, meta[relay.Constant][77] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %157 = sigmoid(%156) /* ty=Tensor[(?, ?, ?, 64), float32] */; %158 = multiply(%156, %157) /* ty=Tensor[(?, ?, ?, 64), float32] */; %159 = nn.conv2d(%158, meta[relay.Constant][78] /* ty=Tensor[(1, 1, 64, 64), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %160 = add(%159, meta[relay.Constant][79] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %161 = sigmoid(%160) /* ty=Tensor[(?, ?, ?, 64), float32] */; %162 = multiply(%160, %161) /* ty=Tensor[(?, ?, ?, 64), float32] */; %163 = nn.conv2d(%162, meta[relay.Constant][80] /* ty=Tensor[(3, 3, 64, 1), float32] */, padding=[1, 1, 1, 1], groups=64, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI") /* ty=Tensor[(?, ?, ?, ?), float32] */; %164 = add(%163, meta[relay.Constant][81] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %165 = sigmoid(%164) /* ty=Tensor[(?, ?, ?, 64), float32] */; %166 = multiply(%164, %165) /* ty=Tensor[(?, ?, ?, 64), float32] */; %167 = nn.conv2d(%166, meta[relay.Constant][82] /* ty=Tensor[(1, 1, 64, 64), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %168 = add(%167, meta[relay.Constant][83] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %169 = sigmoid(%168) /* ty=Tensor[(?, ?, ?, 64), float32] */; %170 = multiply(%168, %169) /* ty=Tensor[(?, ?, ?, 64), float32] */; %171 = add(%170, %158) /* ty=Tensor[(?, ?, ?, 64), float32] */; %172 = nn.conv2d(%171, meta[relay.Constant][84] /* ty=Tensor[(1, 1, 64, 64), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %173 = add(%172, meta[relay.Constant][85] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %174 = sigmoid(%173) /* ty=Tensor[(?, ?, ?, 64), float32] */; %175 = multiply(%173, %174) /* ty=Tensor[(?, ?, ?, 64), float32] */; %176 = nn.conv2d(%175, meta[relay.Constant][86] /* ty=Tensor[(3, 3, 64, 1), float32] */, padding=[1, 1, 1, 1], groups=64, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI") /* ty=Tensor[(?, ?, ?, ?), float32] */; %177 = add(%176, meta[relay.Constant][87] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %178 = sigmoid(%177) /* ty=Tensor[(?, ?, ?, 64), float32] */; %179 = multiply(%177, %178) /* ty=Tensor[(?, ?, ?, 64), float32] */; %180 = nn.conv2d(%179, meta[relay.Constant][88] /* ty=Tensor[(1, 1, 64, 64), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %181 = add(%180, meta[relay.Constant][89] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %182 = sigmoid(%181) /* ty=Tensor[(?, ?, ?, 64), float32] */; %183 = multiply(%181, %182) /* ty=Tensor[(?, ?, ?, 64), float32] */; %184 = add(%183, %171) /* ty=Tensor[(?, ?, ?, 64), float32] */; %185 = nn.conv2d(%184, meta[relay.Constant][90] /* ty=Tensor[(1, 1, 64, 64), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %186 = add(%185, meta[relay.Constant][91] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %187 = sigmoid(%186) /* ty=Tensor[(?, ?, ?, 64), float32] */; %188 = multiply(%186, %187) /* ty=Tensor[(?, ?, ?, 64), float32] */; %189 = nn.conv2d(%188, meta[relay.Constant][92] /* ty=Tensor[(3, 3, 64, 1), float32] */, padding=[1, 1, 1, 1], groups=64, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI") /* ty=Tensor[(?, ?, ?, ?), float32] */; %190 = add(%189, meta[relay.Constant][93] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %191 = sigmoid(%190) /* ty=Tensor[(?, ?, ?, 64), float32] */; %192 = multiply(%190, %191) /* ty=Tensor[(?, ?, ?, 64), float32] */; %193 = nn.conv2d(%192, meta[relay.Constant][94] /* ty=Tensor[(1, 1, 64, 64), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %194 = add(%193, meta[relay.Constant][95] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %195 = sigmoid(%194) /* ty=Tensor[(?, ?, ?, 64), float32] */; %196 = multiply(%194, %195) /* ty=Tensor[(?, ?, ?, 64), float32] */; %197 = nn.conv2d(%154, meta[relay.Constant][96] /* ty=Tensor[(1, 1, 128, 64), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %198 = add(%197, meta[relay.Constant][97] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %199 = sigmoid(%198) /* ty=Tensor[(?, ?, ?, 64), float32] */; %200 = add(%196, %184) /* ty=Tensor[(?, ?, ?, 64), float32] */; %201 = multiply(%198, %199) /* ty=Tensor[(?, ?, ?, 64), float32] */; %202 = (%200, %201); %203 = concatenate(%202, axis=3) /* ty=Tensor[(?, ?, ?, 128), float32] */; %204 = nn.conv2d(%203, meta[relay.Constant][98] /* ty=Tensor[(1, 1, 128, 128), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %205 = add(%204, meta[relay.Constant][99] /* ty=Tensor[(1, 1, 1, 128), float32] */) /* ty=Tensor[(?, ?, ?, 128), float32] */; %206 = sigmoid(%205) /* ty=Tensor[(?, ?, ?, 128), float32] */; %207 = multiply(%205, %206) /* ty=Tensor[(?, ?, ?, 128), float32] */; %208 = nn.conv2d(%207, meta[relay.Constant][100] /* ty=Tensor[(3, 3, 128, 1), float32] */, strides=[2, 2], padding=[1, 1, 1, 1], groups=128, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI") /* ty=Tensor[(?, ?, ?, ?), float32] */; %209 = add(%208, meta[relay.Constant][101] /* ty=Tensor[(1, 1, 1, 128), float32] */) /* ty=Tensor[(?, ?, ?, 128), float32] */; %210 = sigmoid(%209) /* ty=Tensor[(?, ?, ?, 128), float32] */; %211 = multiply(%209, %210) /* ty=Tensor[(?, ?, ?, 128), float32] */; %212 = nn.conv2d(%211, meta[relay.Constant][102] /* ty=Tensor[(1, 1, 128, 256), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %213 = add(%212, meta[relay.Constant][103] /* ty=Tensor[(1, 1, 1, 256), float32] */) /* ty=Tensor[(?, ?, ?, 256), float32] */; %214 = sigmoid(%213) /* ty=Tensor[(?, ?, ?, 256), float32] */; %215 = multiply(%213, %214) /* ty=Tensor[(?, ?, ?, 256), float32] */; %216 = nn.conv2d(%215, meta[relay.Constant][104] /* ty=Tensor[(1, 1, 256, 128), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %217 = add(%216, meta[relay.Constant][105] /* ty=Tensor[(1, 1, 1, 128), float32] */) /* ty=Tensor[(?, ?, ?, 128), float32] */; %218 = sigmoid(%217) /* ty=Tensor[(?, ?, ?, 128), float32] */; %219 = multiply(%217, %218) /* ty=Tensor[(?, ?, ?, 128), float32] */; %220 = nn.max_pool2d(%219, pool_size=[5, 5], padding=[2, 2, 2, 2], layout="NHWC") /* ty=Tensor[(?, ?, ?, 128), float32] */; %221 = nn.max_pool2d(%219, pool_size=[9, 9], padding=[4, 4, 4, 4], layout="NHWC") /* ty=Tensor[(?, ?, ?, 128), float32] */; %222 = nn.max_pool2d(%219, pool_size=[13, 13], padding=[6, 6, 6, 6], layout="NHWC") /* ty=Tensor[(?, ?, ?, 128), float32] */; %223 = (%219, %220, %221, %222); %224 = concatenate(%223, axis=3) /* ty=Tensor[(?, ?, ?, 512), float32] */; %225 = nn.conv2d(%224, meta[relay.Constant][106] /* ty=Tensor[(1, 1, 512, 256), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %226 = add(%225, meta[relay.Constant][107] /* ty=Tensor[(1, 1, 1, 256), float32] */) /* ty=Tensor[(?, ?, ?, 256), float32] */; %227 = sigmoid(%226) /* ty=Tensor[(?, ?, ?, 256), float32] */; %228 = multiply(%226, %227) /* ty=Tensor[(?, ?, ?, 256), float32] */; %229 = nn.conv2d(%228, meta[relay.Constant][108] /* ty=Tensor[(1, 1, 256, 128), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %230 = add(%229, meta[relay.Constant][109] /* ty=Tensor[(1, 1, 1, 128), float32] */) /* ty=Tensor[(?, ?, ?, 128), float32] */; %231 = sigmoid(%230) /* ty=Tensor[(?, ?, ?, 128), float32] */; %232 = multiply(%230, %231) /* ty=Tensor[(?, ?, ?, 128), float32] */; %233 = nn.conv2d(%232, meta[relay.Constant][110] /* ty=Tensor[(1, 1, 128, 128), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %234 = add(%233, meta[relay.Constant][111] /* ty=Tensor[(1, 1, 1, 128), float32] */) /* ty=Tensor[(?, ?, ?, 128), float32] */; %235 = sigmoid(%234) /* ty=Tensor[(?, ?, ?, 128), float32] */; %236 = multiply(%234, %235) /* ty=Tensor[(?, ?, ?, 128), float32] */; %237 = nn.conv2d(%236, meta[relay.Constant][112] /* ty=Tensor[(3, 3, 128, 1), float32] */, padding=[1, 1, 1, 1], groups=128, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI") /* ty=Tensor[(?, ?, ?, ?), float32] */; %238 = add(%237, meta[relay.Constant][113] /* ty=Tensor[(1, 1, 1, 128), float32] */) /* ty=Tensor[(?, ?, ?, 128), float32] */; %239 = sigmoid(%238) /* ty=Tensor[(?, ?, ?, 128), float32] */; %240 = multiply(%238, %239) /* ty=Tensor[(?, ?, ?, 128), float32] */; %241 = nn.conv2d(%240, meta[relay.Constant][114] /* ty=Tensor[(1, 1, 128, 128), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %242 = add(%241, meta[relay.Constant][115] /* ty=Tensor[(1, 1, 1, 128), float32] */) /* ty=Tensor[(?, ?, ?, 128), float32] */; %243 = sigmoid(%242) /* ty=Tensor[(?, ?, ?, 128), float32] */; %244 = nn.conv2d(%228, meta[relay.Constant][116] /* ty=Tensor[(1, 1, 256, 128), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %245 = add(%244, meta[relay.Constant][117] /* ty=Tensor[(1, 1, 1, 128), float32] */) /* ty=Tensor[(?, ?, ?, 128), float32] */; %246 = sigmoid(%245) /* ty=Tensor[(?, ?, ?, 128), float32] */; %247 = multiply(%242, %243) /* ty=Tensor[(?, ?, ?, 128), float32] */; %248 = multiply(%245, %246) /* ty=Tensor[(?, ?, ?, 128), float32] */; %249 = (%247, %248); %250 = concatenate(%249, axis=3) /* ty=Tensor[(?, ?, ?, 256), float32] */; %251 = nn.conv2d(%250, meta[relay.Constant][118] /* ty=Tensor[(1, 1, 256, 256), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %252 = add(%251, meta[relay.Constant][119] /* ty=Tensor[(1, 1, 1, 256), float32] */) /* ty=Tensor[(?, ?, ?, 256), float32] */; %253 = sigmoid(%252) /* ty=Tensor[(?, ?, ?, 256), float32] */; %254 = multiply(%252, %253) /* ty=Tensor[(?, ?, ?, 256), float32] */; %255 = nn.conv2d(%254, meta[relay.Constant][120] /* ty=Tensor[(1, 1, 256, 128), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %256 = add(%255, meta[relay.Constant][121] /* ty=Tensor[(1, 1, 1, 128), float32] */) /* ty=Tensor[(?, ?, ?, 128), float32] */; %257 = sigmoid(%256) /* ty=Tensor[(?, ?, ?, 128), float32] */; %258 = multiply(%256, %257) /* ty=Tensor[(?, ?, ?, 128), float32] */; %259 = layout_transform(%258, src_layout="NHWC", dst_layout="NCHW") /* ty=Tensor[(?, ?, ?, ?), float32] */; %260 = shape_of(%259, dtype="int64") /* ty=Tensor[(4), int64] */; %261 = cast(%260, dtype="float32") /* ty=Tensor[(4), float32] */; %262 = multiply(%261, meta[relay.Constant][122] /* ty=Tensor[(4), float32] */) /* ty=Tensor[(4), float32] */; %263 = strided_slice(%262, begin=[2], end=[4], strides=[1], axes=None) /* ty=Tensor[(2), float32] */; %264 = dyn.image.resize(%259, %263, size=[], method="nearest_neighbor", coordinate_transformation_mode="asymmetric", rounding_method="floor", bicubic_alpha=-0.75f) /* ty=Tensor[(?, ?, ?, ?), float32] */; %265 = layout_transform(%207, src_layout="NHWC", dst_layout="NCHW") /* ty=Tensor[(?, ?, ?, ?), float32] */; %266 = (%264, %265); %267 = concatenate(%266, axis=1) /* ty=Tensor[(?, ?, ?, ?), float32] */; %268 = layout_transform(%267, src_layout="NCHW", dst_layout="NHWC") /* ty=Tensor[(?, ?, ?, ?), float32] */; %269 = nn.conv2d(%268, meta[relay.Constant][123] /* ty=Tensor[(1, 1, 256, 64), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %270 = add(%269, meta[relay.Constant][124] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %271 = sigmoid(%270) /* ty=Tensor[(?, ?, ?, 64), float32] */; %272 = multiply(%270, %271) /* ty=Tensor[(?, ?, ?, 64), float32] */; %273 = nn.conv2d(%272, meta[relay.Constant][125] /* ty=Tensor[(1, 1, 64, 64), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %274 = add(%273, meta[relay.Constant][126] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %275 = sigmoid(%274) /* ty=Tensor[(?, ?, ?, 64), float32] */; %276 = multiply(%274, %275) /* ty=Tensor[(?, ?, ?, 64), float32] */; %277 = nn.conv2d(%276, meta[relay.Constant][127] /* ty=Tensor[(3, 3, 64, 1), float32] */, padding=[1, 1, 1, 1], groups=64, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI") /* ty=Tensor[(?, ?, ?, ?), float32] */; %278 = add(%277, meta[relay.Constant][128] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %279 = sigmoid(%278) /* ty=Tensor[(?, ?, ?, 64), float32] */; %280 = multiply(%278, %279) /* ty=Tensor[(?, ?, ?, 64), float32] */; %281 = nn.conv2d(%280, meta[relay.Constant][129] /* ty=Tensor[(1, 1, 64, 64), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %282 = add(%281, meta[relay.Constant][130] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %283 = sigmoid(%282) /* ty=Tensor[(?, ?, ?, 64), float32] */; %284 = nn.conv2d(%268, meta[relay.Constant][131] /* ty=Tensor[(1, 1, 256, 64), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %285 = add(%284, meta[relay.Constant][132] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %286 = sigmoid(%285) /* ty=Tensor[(?, ?, ?, 64), float32] */; %287 = multiply(%282, %283) /* ty=Tensor[(?, ?, ?, 64), float32] */; %288 = multiply(%285, %286) /* ty=Tensor[(?, ?, ?, 64), float32] */; %289 = (%287, %288); %290 = concatenate(%289, axis=3) /* ty=Tensor[(?, ?, ?, 128), float32] */; %291 = nn.conv2d(%290, meta[relay.Constant][133] /* ty=Tensor[(1, 1, 128, 128), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %292 = add(%291, meta[relay.Constant][134] /* ty=Tensor[(1, 1, 1, 128), float32] */) /* ty=Tensor[(?, ?, ?, 128), float32] */; %293 = sigmoid(%292) /* ty=Tensor[(?, ?, ?, 128), float32] */; %294 = multiply(%292, %293) /* ty=Tensor[(?, ?, ?, 128), float32] */; %295 = nn.conv2d(%294, meta[relay.Constant][135] /* ty=Tensor[(1, 1, 128, 64), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %296 = add(%295, meta[relay.Constant][136] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %297 = sigmoid(%296) /* ty=Tensor[(?, ?, ?, 64), float32] */; %298 = multiply(%296, %297) /* ty=Tensor[(?, ?, ?, 64), float32] */; %299 = layout_transform(%298, src_layout="NHWC", dst_layout="NCHW") /* ty=Tensor[(?, ?, ?, ?), float32] */; %300 = shape_of(%299, dtype="int64") /* ty=Tensor[(4), int64] */; %301 = cast(%300, dtype="float32") /* ty=Tensor[(4), float32] */; %302 = multiply(%301, meta[relay.Constant][122] /* ty=Tensor[(4), float32] */) /* ty=Tensor[(4), float32] */; %303 = strided_slice(%302, begin=[2], end=[4], strides=[1], axes=None) /* ty=Tensor[(2), float32] */; %304 = dyn.image.resize(%299, %303, size=[], method="nearest_neighbor", coordinate_transformation_mode="asymmetric", rounding_method="floor", bicubic_alpha=-0.75f) /* ty=Tensor[(?, ?, ?, ?), float32] */; %305 = layout_transform(%146, src_layout="NHWC", dst_layout="NCHW") /* ty=Tensor[(?, ?, ?, ?), float32] */; %306 = (%304, %305); %307 = concatenate(%306, axis=1) /* ty=Tensor[(?, ?, ?, ?), float32] */; %308 = layout_transform(%307, src_layout="NCHW", dst_layout="NHWC") /* ty=Tensor[(?, ?, ?, ?), float32] */; %309 = nn.conv2d(%308, meta[relay.Constant][137] /* ty=Tensor[(1, 1, 128, 32), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %310 = add(%309, meta[relay.Constant][138] /* ty=Tensor[(1, 1, 1, 32), float32] */) /* ty=Tensor[(?, ?, ?, 32), float32] */; %311 = sigmoid(%310) /* ty=Tensor[(?, ?, ?, 32), float32] */; %312 = multiply(%310, %311) /* ty=Tensor[(?, ?, ?, 32), float32] */; %313 = nn.conv2d(%312, meta[relay.Constant][139] /* ty=Tensor[(1, 1, 32, 32), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %314 = add(%313, meta[relay.Constant][140] /* ty=Tensor[(1, 1, 1, 32), float32] */) /* ty=Tensor[(?, ?, ?, 32), float32] */; %315 = sigmoid(%314) /* ty=Tensor[(?, ?, ?, 32), float32] */; %316 = multiply(%314, %315) /* ty=Tensor[(?, ?, ?, 32), float32] */; %317 = nn.conv2d(%316, meta[relay.Constant][141] /* ty=Tensor[(3, 3, 32, 1), float32] */, padding=[1, 1, 1, 1], groups=32, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI") /* ty=Tensor[(?, ?, ?, ?), float32] */; %318 = add(%317, meta[relay.Constant][142] /* ty=Tensor[(1, 1, 1, 32), float32] */) /* ty=Tensor[(?, ?, ?, 32), float32] */; %319 = sigmoid(%318) /* ty=Tensor[(?, ?, ?, 32), float32] */; %320 = multiply(%318, %319) /* ty=Tensor[(?, ?, ?, 32), float32] */; %321 = nn.conv2d(%320, meta[relay.Constant][143] /* ty=Tensor[(1, 1, 32, 32), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %322 = add(%321, meta[relay.Constant][144] /* ty=Tensor[(1, 1, 1, 32), float32] */) /* ty=Tensor[(?, ?, ?, 32), float32] */; %323 = sigmoid(%322) /* ty=Tensor[(?, ?, ?, 32), float32] */; %324 = nn.conv2d(%308, meta[relay.Constant][145] /* ty=Tensor[(1, 1, 128, 32), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %325 = add(%324, meta[relay.Constant][146] /* ty=Tensor[(1, 1, 1, 32), float32] */) /* ty=Tensor[(?, ?, ?, 32), float32] */; %326 = sigmoid(%325) /* ty=Tensor[(?, ?, ?, 32), float32] */; %327 = multiply(%322, %323) /* ty=Tensor[(?, ?, ?, 32), float32] */; %328 = multiply(%325, %326) /* ty=Tensor[(?, ?, ?, 32), float32] */; %329 = (%327, %328); %330 = concatenate(%329, axis=3) /* ty=Tensor[(?, ?, ?, 64), float32] */; %331 = nn.conv2d(%330, meta[relay.Constant][147] /* ty=Tensor[(1, 1, 64, 64), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %332 = add(%331, meta[relay.Constant][148] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %333 = sigmoid(%332) /* ty=Tensor[(?, ?, ?, 64), float32] */; %334 = multiply(%332, %333) /* ty=Tensor[(?, ?, ?, 64), float32] */; %335 = nn.conv2d(%334, meta[relay.Constant][149] /* ty=Tensor[(1, 1, 64, 64), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %336 = add(%335, meta[relay.Constant][150] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %337 = sigmoid(%336) /* ty=Tensor[(?, ?, ?, 64), float32] */; %338 = multiply(%336, %337) /* ty=Tensor[(?, ?, ?, 64), float32] */; %339 = nn.conv2d(%338, meta[relay.Constant][151] /* ty=Tensor[(3, 3, 64, 1), float32] */, padding=[1, 1, 1, 1], groups=64, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI") /* ty=Tensor[(?, ?, ?, ?), float32] */; %340 = add(%339, meta[relay.Constant][152] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %341 = sigmoid(%340) /* ty=Tensor[(?, ?, ?, 64), float32] */; %342 = multiply(%340, %341) /* ty=Tensor[(?, ?, ?, 64), float32] */; %343 = nn.conv2d(%342, meta[relay.Constant][153] /* ty=Tensor[(1, 1, 64, 64), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %344 = add(%343, meta[relay.Constant][154] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %345 = sigmoid(%344) /* ty=Tensor[(?, ?, ?, 64), float32] */; %346 = multiply(%344, %345) /* ty=Tensor[(?, ?, ?, 64), float32] */; %347 = nn.conv2d(%346, meta[relay.Constant][155] /* ty=Tensor[(3, 3, 64, 1), float32] */, padding=[1, 1, 1, 1], groups=64, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI") /* ty=Tensor[(?, ?, ?, ?), float32] */; %348 = add(%347, meta[relay.Constant][156] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %349 = sigmoid(%348) /* ty=Tensor[(?, ?, ?, 64), float32] */; %350 = multiply(%348, %349) /* ty=Tensor[(?, ?, ?, 64), float32] */; %351 = nn.conv2d(%350, meta[relay.Constant][157] /* ty=Tensor[(1, 1, 64, 64), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %352 = add(%351, meta[relay.Constant][158] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %353 = sigmoid(%352) /* ty=Tensor[(?, ?, ?, 64), float32] */; %354 = multiply(%352, %353) /* ty=Tensor[(?, ?, ?, 64), float32] */; %355 = nn.conv2d(%354, meta[relay.Constant][159] /* ty=Tensor[(1, 1, 64, 4), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %356 = nn.conv2d(%354, meta[relay.Constant][161] /* ty=Tensor[(1, 1, 64, 1), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %357 = add(%356, meta[relay.Constant][162] /* ty=Tensor[(1, 1, 1), float32] */) /* ty=Tensor[(?, ?, ?, ?), float32] */; %358 = nn.conv2d(%338, meta[relay.Constant][163] /* ty=Tensor[(3, 3, 64, 1), float32] */, padding=[1, 1, 1, 1], groups=64, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI") /* ty=Tensor[(?, ?, ?, ?), float32] */; %359 = add(%358, meta[relay.Constant][164] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %360 = sigmoid(%359) /* ty=Tensor[(?, ?, ?, 64), float32] */; %361 = multiply(%359, %360) /* ty=Tensor[(?, ?, ?, 64), float32] */; %362 = nn.conv2d(%361, meta[relay.Constant][165] /* ty=Tensor[(1, 1, 64, 64), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %363 = add(%362, meta[relay.Constant][166] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %364 = sigmoid(%363) /* ty=Tensor[(?, ?, ?, 64), float32] */; %365 = multiply(%363, %364) /* ty=Tensor[(?, ?, ?, 64), float32] */; %366 = nn.conv2d(%365, meta[relay.Constant][167] /* ty=Tensor[(3, 3, 64, 1), float32] */, padding=[1, 1, 1, 1], groups=64, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI") /* ty=Tensor[(?, ?, ?, ?), float32] */; %367 = add(%366, meta[relay.Constant][168] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %368 = sigmoid(%367) /* ty=Tensor[(?, ?, ?, 64), float32] */; %369 = multiply(%367, %368) /* ty=Tensor[(?, ?, ?, 64), float32] */; %370 = nn.conv2d(%369, meta[relay.Constant][169] /* ty=Tensor[(1, 1, 64, 64), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %371 = add(%370, meta[relay.Constant][170] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %372 = sigmoid(%371) /* ty=Tensor[(?, ?, ?, 64), float32] */; %373 = multiply(%371, %372) /* ty=Tensor[(?, ?, ?, 64), float32] */; %374 = nn.conv2d(%373, meta[relay.Constant][171] /* ty=Tensor[(1, 1, 64, 80), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %375 = add(%374, meta[relay.Constant][172] /* ty=Tensor[(1, 1, 1, 80), float32] */) /* ty=Tensor[(?, ?, ?, 80), float32] */; %376 = add(%355, meta[relay.Constant][160] /* ty=Tensor[(1, 1, 1, 4), float32] */) /* ty=Tensor[(?, ?, ?, 4), float32] */; %377 = sigmoid(%357) /* ty=Tensor[(?, ?, ?, ?), float32] */; %378 = sigmoid(%375) /* ty=Tensor[(?, ?, ?, 80), float32] */; %379 = (%376, %377, %378); %380 = concatenate(%379, axis=3) /* ty=Tensor[(?, ?, ?, ?), float32] */; %381 = layout_transform(%380, src_layout="NHWC", dst_layout="NCHW") /* ty=Tensor[(?, ?, ?, ?), float32] */; %382 = nn.conv2d(%334, meta[relay.Constant][173] /* ty=Tensor[(3, 3, 64, 1), float32] */, strides=[2, 2], padding=[1, 1, 1, 1], groups=64, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI") /* ty=Tensor[(?, ?, ?, ?), float32] */; %383 = add(%382, meta[relay.Constant][174] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %384 = sigmoid(%383) /* ty=Tensor[(?, ?, ?, 64), float32] */; %385 = multiply(%383, %384) /* ty=Tensor[(?, ?, ?, 64), float32] */; %386 = nn.conv2d(%385, meta[relay.Constant][175] /* ty=Tensor[(1, 1, 64, 64), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %387 = add(%386, meta[relay.Constant][176] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %388 = sigmoid(%387) /* ty=Tensor[(?, ?, ?, 64), float32] */; %389 = multiply(%387, %388) /* ty=Tensor[(?, ?, ?, 64), float32] */; %390 = (%389, %298); %391 = concatenate(%390, axis=3) /* ty=Tensor[(?, ?, ?, 128), float32] */; %392 = nn.conv2d(%391, meta[relay.Constant][177] /* ty=Tensor[(1, 1, 128, 64), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %393 = add(%392, meta[relay.Constant][178] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %394 = sigmoid(%393) /* ty=Tensor[(?, ?, ?, 64), float32] */; %395 = multiply(%393, %394) /* ty=Tensor[(?, ?, ?, 64), float32] */; %396 = nn.conv2d(%395, meta[relay.Constant][179] /* ty=Tensor[(1, 1, 64, 64), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %397 = add(%396, meta[relay.Constant][180] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %398 = sigmoid(%397) /* ty=Tensor[(?, ?, ?, 64), float32] */; %399 = multiply(%397, %398) /* ty=Tensor[(?, ?, ?, 64), float32] */; %400 = nn.conv2d(%399, meta[relay.Constant][181] /* ty=Tensor[(3, 3, 64, 1), float32] */, padding=[1, 1, 1, 1], groups=64, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI") /* ty=Tensor[(?, ?, ?, ?), float32] */; %401 = add(%400, meta[relay.Constant][182] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %402 = sigmoid(%401) /* ty=Tensor[(?, ?, ?, 64), float32] */; %403 = multiply(%401, %402) /* ty=Tensor[(?, ?, ?, 64), float32] */; %404 = nn.conv2d(%403, meta[relay.Constant][183] /* ty=Tensor[(1, 1, 64, 64), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %405 = add(%404, meta[relay.Constant][184] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %406 = sigmoid(%405) /* ty=Tensor[(?, ?, ?, 64), float32] */; %407 = nn.conv2d(%391, meta[relay.Constant][185] /* ty=Tensor[(1, 1, 128, 64), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %408 = add(%407, meta[relay.Constant][186] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %409 = sigmoid(%408) /* ty=Tensor[(?, ?, ?, 64), float32] */; %410 = multiply(%405, %406) /* ty=Tensor[(?, ?, ?, 64), float32] */; %411 = multiply(%408, %409) /* ty=Tensor[(?, ?, ?, 64), float32] */; %412 = (%410, %411); %413 = concatenate(%412, axis=3) /* ty=Tensor[(?, ?, ?, 128), float32] */; %414 = nn.conv2d(%413, meta[relay.Constant][187] /* ty=Tensor[(1, 1, 128, 128), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %415 = add(%414, meta[relay.Constant][188] /* ty=Tensor[(1, 1, 1, 128), float32] */) /* ty=Tensor[(?, ?, ?, 128), float32] */; %416 = sigmoid(%415) /* ty=Tensor[(?, ?, ?, 128), float32] */; %417 = multiply(%415, %416) /* ty=Tensor[(?, ?, ?, 128), float32] */; %418 = nn.conv2d(%417, meta[relay.Constant][189] /* ty=Tensor[(1, 1, 128, 64), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %419 = add(%418, meta[relay.Constant][190] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %420 = sigmoid(%419) /* ty=Tensor[(?, ?, ?, 64), float32] */; %421 = multiply(%419, %420) /* ty=Tensor[(?, ?, ?, 64), float32] */; %422 = nn.conv2d(%421, meta[relay.Constant][191] /* ty=Tensor[(3, 3, 64, 1), float32] */, padding=[1, 1, 1, 1], groups=64, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI") /* ty=Tensor[(?, ?, ?, ?), float32] */; %423 = add(%422, meta[relay.Constant][192] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %424 = sigmoid(%423) /* ty=Tensor[(?, ?, ?, 64), float32] */; %425 = multiply(%423, %424) /* ty=Tensor[(?, ?, ?, 64), float32] */; %426 = nn.conv2d(%425, meta[relay.Constant][193] /* ty=Tensor[(1, 1, 64, 64), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %427 = add(%426, meta[relay.Constant][194] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %428 = sigmoid(%427) /* ty=Tensor[(?, ?, ?, 64), float32] */; %429 = multiply(%427, %428) /* ty=Tensor[(?, ?, ?, 64), float32] */; %430 = nn.conv2d(%429, meta[relay.Constant][195] /* ty=Tensor[(3, 3, 64, 1), float32] */, padding=[1, 1, 1, 1], groups=64, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI") /* ty=Tensor[(?, ?, ?, ?), float32] */; %431 = add(%430, meta[relay.Constant][196] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %432 = sigmoid(%431) /* ty=Tensor[(?, ?, ?, 64), float32] */; %433 = multiply(%431, %432) /* ty=Tensor[(?, ?, ?, 64), float32] */; %434 = nn.conv2d(%433, meta[relay.Constant][197] /* ty=Tensor[(1, 1, 64, 64), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %435 = add(%434, meta[relay.Constant][198] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %436 = sigmoid(%435) /* ty=Tensor[(?, ?, ?, 64), float32] */; %437 = multiply(%435, %436) /* ty=Tensor[(?, ?, ?, 64), float32] */; %438 = nn.conv2d(%437, meta[relay.Constant][199] /* ty=Tensor[(1, 1, 64, 4), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %439 = nn.conv2d(%437, meta[relay.Constant][201] /* ty=Tensor[(1, 1, 64, 1), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %440 = add(%439, meta[relay.Constant][202] /* ty=Tensor[(1, 1, 1), float32] */) /* ty=Tensor[(?, ?, ?, ?), float32] */; %441 = nn.conv2d(%421, meta[relay.Constant][203] /* ty=Tensor[(3, 3, 64, 1), float32] */, padding=[1, 1, 1, 1], groups=64, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI") /* ty=Tensor[(?, ?, ?, ?), float32] */; %442 = add(%441, meta[relay.Constant][204] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %443 = sigmoid(%442) /* ty=Tensor[(?, ?, ?, 64), float32] */; %444 = multiply(%442, %443) /* ty=Tensor[(?, ?, ?, 64), float32] */; %445 = nn.conv2d(%444, meta[relay.Constant][205] /* ty=Tensor[(1, 1, 64, 64), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %446 = add(%445, meta[relay.Constant][206] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %447 = sigmoid(%446) /* ty=Tensor[(?, ?, ?, 64), float32] */; %448 = multiply(%446, %447) /* ty=Tensor[(?, ?, ?, 64), float32] */; %449 = nn.conv2d(%448, meta[relay.Constant][207] /* ty=Tensor[(3, 3, 64, 1), float32] */, padding=[1, 1, 1, 1], groups=64, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI") /* ty=Tensor[(?, ?, ?, ?), float32] */; %450 = add(%449, meta[relay.Constant][208] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %451 = sigmoid(%450) /* ty=Tensor[(?, ?, ?, 64), float32] */; %452 = multiply(%450, %451) /* ty=Tensor[(?, ?, ?, 64), float32] */; %453 = nn.conv2d(%452, meta[relay.Constant][209] /* ty=Tensor[(1, 1, 64, 64), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %454 = add(%453, meta[relay.Constant][210] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %455 = sigmoid(%454) /* ty=Tensor[(?, ?, ?, 64), float32] */; %456 = multiply(%454, %455) /* ty=Tensor[(?, ?, ?, 64), float32] */; %457 = nn.conv2d(%456, meta[relay.Constant][211] /* ty=Tensor[(1, 1, 64, 80), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %458 = add(%457, meta[relay.Constant][212] /* ty=Tensor[(1, 1, 1, 80), float32] */) /* ty=Tensor[(?, ?, ?, 80), float32] */; %459 = add(%438, meta[relay.Constant][200] /* ty=Tensor[(1, 1, 1, 4), float32] */) /* ty=Tensor[(?, ?, ?, 4), float32] */; %460 = sigmoid(%440) /* ty=Tensor[(?, ?, ?, ?), float32] */; %461 = sigmoid(%458) /* ty=Tensor[(?, ?, ?, 80), float32] */; %462 = (%459, %460, %461); %463 = concatenate(%462, axis=3) /* ty=Tensor[(?, ?, ?, ?), float32] */; %464 = layout_transform(%463, src_layout="NHWC", dst_layout="NCHW") /* ty=Tensor[(?, ?, ?, ?), float32] */; %465 = nn.conv2d(%417, meta[relay.Constant][213] /* ty=Tensor[(3, 3, 128, 1), float32] */, strides=[2, 2], padding=[1, 1, 1, 1], groups=128, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI") /* ty=Tensor[(?, ?, ?, ?), float32] */; %466 = add(%465, meta[relay.Constant][214] /* ty=Tensor[(1, 1, 1, 128), float32] */) /* ty=Tensor[(?, ?, ?, 128), float32] */; %467 = sigmoid(%466) /* ty=Tensor[(?, ?, ?, 128), float32] */; %468 = multiply(%466, %467) /* ty=Tensor[(?, ?, ?, 128), float32] */; %469 = nn.conv2d(%468, meta[relay.Constant][215] /* ty=Tensor[(1, 1, 128, 128), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %470 = add(%469, meta[relay.Constant][216] /* ty=Tensor[(1, 1, 1, 128), float32] */) /* ty=Tensor[(?, ?, ?, 128), float32] */; %471 = sigmoid(%470) /* ty=Tensor[(?, ?, ?, 128), float32] */; %472 = multiply(%470, %471) /* ty=Tensor[(?, ?, ?, 128), float32] */; %473 = (%472, %258); %474 = concatenate(%473, axis=3) /* ty=Tensor[(?, ?, ?, 256), float32] */; %475 = nn.conv2d(%474, meta[relay.Constant][217] /* ty=Tensor[(1, 1, 256, 128), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %476 = add(%475, meta[relay.Constant][218] /* ty=Tensor[(1, 1, 1, 128), float32] */) /* ty=Tensor[(?, ?, ?, 128), float32] */; %477 = sigmoid(%476) /* ty=Tensor[(?, ?, ?, 128), float32] */; %478 = multiply(%476, %477) /* ty=Tensor[(?, ?, ?, 128), float32] */; %479 = nn.conv2d(%478, meta[relay.Constant][219] /* ty=Tensor[(1, 1, 128, 128), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %480 = add(%479, meta[relay.Constant][220] /* ty=Tensor[(1, 1, 1, 128), float32] */) /* ty=Tensor[(?, ?, ?, 128), float32] */; %481 = sigmoid(%480) /* ty=Tensor[(?, ?, ?, 128), float32] */; %482 = multiply(%480, %481) /* ty=Tensor[(?, ?, ?, 128), float32] */; %483 = nn.conv2d(%482, meta[relay.Constant][221] /* ty=Tensor[(3, 3, 128, 1), float32] */, padding=[1, 1, 1, 1], groups=128, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI") /* ty=Tensor[(?, ?, ?, ?), float32] */; %484 = add(%483, meta[relay.Constant][222] /* ty=Tensor[(1, 1, 1, 128), float32] */) /* ty=Tensor[(?, ?, ?, 128), float32] */; %485 = sigmoid(%484) /* ty=Tensor[(?, ?, ?, 128), float32] */; %486 = multiply(%484, %485) /* ty=Tensor[(?, ?, ?, 128), float32] */; %487 = nn.conv2d(%486, meta[relay.Constant][223] /* ty=Tensor[(1, 1, 128, 128), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %488 = add(%487, meta[relay.Constant][224] /* ty=Tensor[(1, 1, 1, 128), float32] */) /* ty=Tensor[(?, ?, ?, 128), float32] */; %489 = sigmoid(%488) /* ty=Tensor[(?, ?, ?, 128), float32] */; %490 = nn.conv2d(%474, meta[relay.Constant][225] /* ty=Tensor[(1, 1, 256, 128), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %491 = add(%490, meta[relay.Constant][226] /* ty=Tensor[(1, 1, 1, 128), float32] */) /* ty=Tensor[(?, ?, ?, 128), float32] */; %492 = sigmoid(%491) /* ty=Tensor[(?, ?, ?, 128), float32] */; %493 = multiply(%488, %489) /* ty=Tensor[(?, ?, ?, 128), float32] */; %494 = multiply(%491, %492) /* ty=Tensor[(?, ?, ?, 128), float32] */; %495 = (%493, %494); %496 = concatenate(%495, axis=3) /* ty=Tensor[(?, ?, ?, 256), float32] */; %497 = nn.conv2d(%496, meta[relay.Constant][227] /* ty=Tensor[(1, 1, 256, 256), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %498 = add(%497, meta[relay.Constant][228] /* ty=Tensor[(1, 1, 1, 256), float32] */) /* ty=Tensor[(?, ?, ?, 256), float32] */; %499 = sigmoid(%498) /* ty=Tensor[(?, ?, ?, 256), float32] */; %500 = multiply(%498, %499) /* ty=Tensor[(?, ?, ?, 256), float32] */; %501 = nn.conv2d(%500, meta[relay.Constant][229] /* ty=Tensor[(1, 1, 256, 64), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %502 = add(%501, meta[relay.Constant][230] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %503 = sigmoid(%502) /* ty=Tensor[(?, ?, ?, 64), float32] */; %504 = multiply(%502, %503) /* ty=Tensor[(?, ?, ?, 64), float32] */; %505 = nn.conv2d(%504, meta[relay.Constant][231] /* ty=Tensor[(3, 3, 64, 1), float32] */, padding=[1, 1, 1, 1], groups=64, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI") /* ty=Tensor[(?, ?, ?, ?), float32] */; %506 = add(%505, meta[relay.Constant][232] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %507 = sigmoid(%506) /* ty=Tensor[(?, ?, ?, 64), float32] */; %508 = multiply(%506, %507) /* ty=Tensor[(?, ?, ?, 64), float32] */; %509 = nn.conv2d(%508, meta[relay.Constant][233] /* ty=Tensor[(1, 1, 64, 64), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %510 = add(%509, meta[relay.Constant][234] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %511 = sigmoid(%510) /* ty=Tensor[(?, ?, ?, 64), float32] */; %512 = multiply(%510, %511) /* ty=Tensor[(?, ?, ?, 64), float32] */; %513 = nn.conv2d(%512, meta[relay.Constant][235] /* ty=Tensor[(3, 3, 64, 1), float32] */, padding=[1, 1, 1, 1], groups=64, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI") /* ty=Tensor[(?, ?, ?, ?), float32] */; %514 = add(%513, meta[relay.Constant][236] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %515 = sigmoid(%514) /* ty=Tensor[(?, ?, ?, 64), float32] */; %516 = multiply(%514, %515) /* ty=Tensor[(?, ?, ?, 64), float32] */; %517 = nn.conv2d(%516, meta[relay.Constant][237] /* ty=Tensor[(1, 1, 64, 64), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %518 = add(%517, meta[relay.Constant][238] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %519 = sigmoid(%518) /* ty=Tensor[(?, ?, ?, 64), float32] */; %520 = multiply(%518, %519) /* ty=Tensor[(?, ?, ?, 64), float32] */; %521 = nn.conv2d(%520, meta[relay.Constant][239] /* ty=Tensor[(1, 1, 64, 4), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %522 = nn.conv2d(%520, meta[relay.Constant][241] /* ty=Tensor[(1, 1, 64, 1), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %523 = add(%522, meta[relay.Constant][242] /* ty=Tensor[(1, 1, 1), float32] */) /* ty=Tensor[(?, ?, ?, ?), float32] */; %524 = nn.conv2d(%504, meta[relay.Constant][243] /* ty=Tensor[(3, 3, 64, 1), float32] */, padding=[1, 1, 1, 1], groups=64, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI") /* ty=Tensor[(?, ?, ?, ?), float32] */; %525 = add(%524, meta[relay.Constant][244] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %526 = sigmoid(%525) /* ty=Tensor[(?, ?, ?, 64), float32] */; %527 = multiply(%525, %526) /* ty=Tensor[(?, ?, ?, 64), float32] */; %528 = nn.conv2d(%527, meta[relay.Constant][245] /* ty=Tensor[(1, 1, 64, 64), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %529 = add(%528, meta[relay.Constant][246] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %530 = sigmoid(%529) /* ty=Tensor[(?, ?, ?, 64), float32] */; %531 = multiply(%529, %530) /* ty=Tensor[(?, ?, ?, 64), float32] */; %532 = nn.conv2d(%531, meta[relay.Constant][247] /* ty=Tensor[(3, 3, 64, 1), float32] */, padding=[1, 1, 1, 1], groups=64, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI") /* ty=Tensor[(?, ?, ?, ?), float32] */; %533 = add(%532, meta[relay.Constant][248] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %534 = sigmoid(%533) /* ty=Tensor[(?, ?, ?, 64), float32] */; %535 = multiply(%533, %534) /* ty=Tensor[(?, ?, ?, 64), float32] */; %536 = nn.conv2d(%535, meta[relay.Constant][249] /* ty=Tensor[(1, 1, 64, 64), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %537 = add(%536, meta[relay.Constant][250] /* ty=Tensor[(1, 1, 1, 64), float32] */) /* ty=Tensor[(?, ?, ?, 64), float32] */; %538 = sigmoid(%537) /* ty=Tensor[(?, ?, ?, 64), float32] */; %539 = multiply(%537, %538) /* ty=Tensor[(?, ?, ?, 64), float32] */; %540 = nn.conv2d(%539, meta[relay.Constant][251] /* ty=Tensor[(1, 1, 64, 80), float32] */, padding=[0, 0, 0, 0], kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO") /* ty=Tensor[(?, ?, ?, ?), float32] */; %541 = add(%540, meta[relay.Constant][252] /* ty=Tensor[(1, 1, 1, 80), float32] */) /* ty=Tensor[(?, ?, ?, 80), float32] */; %542 = add(%521, meta[relay.Constant][240] /* ty=Tensor[(1, 1, 1, 4), float32] */) /* ty=Tensor[(?, ?, ?, 4), float32] */; %543 = sigmoid(%523) /* ty=Tensor[(?, ?, ?, ?), float32] */; %544 = sigmoid(%541) /* ty=Tensor[(?, ?, ?, 80), float32] */; %545 = (%542, %543, %544); %546 = concatenate(%545, axis=3) /* ty=Tensor[(?, ?, ?, ?), float32] */; %547 = layout_transform(%546, src_layout="NHWC", dst_layout="NCHW") /* ty=Tensor[(?, ?, ?, ?), float32] */; %548 = reshape(%381, newshape=[1, 85, -1]) /* ty=Tensor[(1, 85, ?), float32] */; %549 = reshape(%464, newshape=[1, 85, -1]) /* ty=Tensor[(1, 85, ?), float32] */; %550 = reshape(%547, newshape=[1, 85, -1]) /* ty=Tensor[(1, 85, ?), float32] */; %551 = (%548, %549, %550); %552 = concatenate(%551, axis=-1) /* ty=Tensor[(1, 85, ?), float32] */; transpose(%552, axes=[0, 2, 1]) /* ty=Tensor[(1, ?, 85), float32] */ ``` ``` DEBUG:pyxir:Call: transpose DEBUG:pyxir:Call: concatenate DEBUG:pyxir:Concatenate: 94191941064416 DEBUG:pyxir:-- Call DEBUG:pyxir:Call: reshape DEBUG:pyxir:Call: layout_transform DEBUG:pyxir:Call: concatenate DEBUG:pyxir:Concatenate: 94191941295472 DEBUG:pyxir:-- Call DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: concatenate DEBUG:pyxir:Concatenate: 94191938793392 DEBUG:pyxir:-- Call DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: layout_transform DEBUG:pyxir:Call: concatenate DEBUG:pyxir:Concatenate: 94191939987696 DEBUG:pyxir:-- Call DEBUG:pyxir:Call: dyn.image.resize DEBUG:pyxir:Call: layout_transform DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: concatenate DEBUG:pyxir:Concatenate: 94191940823856 DEBUG:pyxir:-- Call DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: layout_transform DEBUG:pyxir:Call: concatenate DEBUG:pyxir:Concatenate: 94191939175600 DEBUG:pyxir:-- Call DEBUG:pyxir:Call: dyn.image.resize DEBUG:pyxir:Call: layout_transform DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: concatenate DEBUG:pyxir:Concatenate: 94191939353744 DEBUG:pyxir:-- Call DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: concatenate DEBUG:pyxir:Concatenate: 94191931856784 DEBUG:pyxir:-- Call DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: concatenate DEBUG:pyxir:Concatenate: 94191982487968 DEBUG:pyxir:-- Call DEBUG:pyxir:Call: add DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: add DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: add DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: concatenate DEBUG:pyxir:Concatenate: 94191940522224 DEBUG:pyxir:-- Call DEBUG:pyxir:Call: add DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: add DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: add DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: concatenate DEBUG:pyxir:Concatenate: 94191940934400 DEBUG:pyxir:-- Call DEBUG:pyxir:Call: add DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: multiply DEBUG:pyxir:Call: add DEBUG:pyxir:Call: nn.conv2d DEBUG:pyxir:Call: layout_transform DEBUG:pyxir:Call: concatenate DEBUG:pyxir:Concatenate: 94191939381456 DEBUG:pyxir:-- Call DEBUG:pyxir:Call: dyn.strided_slice DEBUG:pyxir:Call: dyn.strided_slice DEBUG:pyxir:var: DEBUG:pyxir:-- name: inputs DEBUG:pyxir:-- shape: [1, 3, 416, 416] DEBUG:pyxir:constant: DEBUG:pyxir:constant: DEBUG:pyxir:constant: DEBUG:pyxir:RelayOp: DEBUG:pyxir:-- op_name: RelayOp-94191981978496 DEBUG:pyxir:-- expr: dyn.strided_slice ``` So it seems that the problem is related to `dyn.strided_slice` expression that has no attribute value. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
