Right now, I think the process fails in `relay.build_module.build(mod, target=target, params=params)` That is after the code I showed above. I just verified that the layout transformation takes place by comparing the both `relay_NHWC.txt` and `relay_NCHW.txt`.
Let me create a minimal script so you can reproduce the error. I'll post it in a few, in the meantime below is the contents of `relay_NHWC.txt` as requested (had to take some text out due to limitations of the post tool) ``` free_var %input: Tensor[(1, 224, 224, 3), uint8] free_var %v_param_1: Tensor[(3, 3, 3, 32), uint8] %0 = qnn.conv2d(%input, %v_param_1, 128 /* ty=int32 */, 122 /* ty=int32 */, 0.0078125f /* ty=float32 */, 0.0339689f /* ty=float32 */, strides=[2, 2], padding=[0, 0, 1, 1], channels=32, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32") /* ty=Tensor[(1, 112, 112, 32), int32] */; free_var %v_param_2: Tensor[(32), int32] %1 = nn.bias_add(%0, %v_param_2, axis=3) /* ty=Tensor[(1, 112, 112, 32), int32] */; %2 = qnn.requantize(%1, 0.000265382f /* ty=float32 */, 0 /* ty=int32 */, 0.0235285f /* ty=float32 */, 0 /* ty=int32 */, out_dtype="uint8") /* ty=Tensor[(1, 112, 112, 32), uint8] */; free_var %v_param_3: Tensor[(3, 3, 32, 1), uint8] %3 = qnn.conv2d(%2, %v_param_3, 0 /* ty=int32 */, 165 /* ty=int32 */, 0.0235285f /* ty=float32 */, 0.343696f /* ty=float32 */, padding=[1, 1, 1, 1], groups=32, channels=32, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI", out_dtype="int32") /* ty=Tensor[(1, 112, 112, 32), int32] */; free_var %v_param_4: Tensor[(32), int32] %4 = nn.bias_add(%3, %v_param_4, axis=3) /* ty=Tensor[(1, 112, 112, 32), int32] */; %5 = qnn.requantize(%4, 0.00808663f /* ty=float32 */, 0 /* ty=int32 */, 0.0235285f /* ty=float32 */, 0 /* ty=int32 */, out_dtype="uint8") /* ty=Tensor[(1, 112, 112, 32), uint8] */; free_var %v_param_5: Tensor[(1, 1, 32, 16), uint8] %6 = qnn.conv2d(%5, %v_param_5, 0 /* ty=int32 */, 140 /* ty=int32 */, 0.0235285f /* ty=float32 */, 0.0373718f /* ty=float32 */, padding=[0, 0, 0, 0], channels=16, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32") /* ty=Tensor[(1, 112, 112, 16), int32] */; free_var %v_param_6: Tensor[(16), int32] %7 = nn.bias_add(%6, %v_param_6, axis=3) /* ty=Tensor[(1, 112, 112, 16), int32] */; %8 = qnn.requantize(%7, 0.0008793f /* ty=float32 */, 0 /* ty=int32 */, 0.354413f /* ty=float32 */, 129 /* ty=int32 */, out_dtype="uint8") /* ty=Tensor[(1, 112, 112, 16), uint8] */; free_var %v_param_7: Tensor[(1, 1, 16, 96), uint8] %9 = qnn.conv2d(%8, %v_param_7, 129 /* ty=int32 */, 127 /* ty=int32 */, 0.354413f /* ty=float32 */, 0.00975851f /* ty=float32 */, padding=[0, 0, 0, 0], channels=96, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32") /* ty=Tensor[(1, 112, 112, 96), int32] */; free_var %v_param_8: Tensor[(96), int32] %10 = nn.bias_add(%9, %v_param_8, axis=3) /* ty=Tensor[(1, 112, 112, 96), int32] */; %11 = qnn.requantize(%10, 0.00345855f /* ty=float32 */, 0 /* ty=int32 */, 0.0235285f /* ty=float32 */, 0 /* ty=int32 */, out_dtype="uint8") /* ty=Tensor[(1, 112, 112, 96), uint8] */; free_var %v_param_9: Tensor[(3, 3, 96, 1), uint8] %12 = qnn.conv2d(%11, %v_param_9, 0 /* ty=int32 */, 109 /* ty=int32 */, 0.0235285f /* ty=float32 */, 0.0209691f /* ty=float32 */, strides=[2, 2], padding=[0, 0, 1, 1], groups=96, channels=96, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI", out_dtype="int32") /* ty=Tensor[(1, 56, 56, 96), int32] */; free_var %v_param_10: Tensor[(96), int32] %13 = nn.bias_add(%12, %v_param_10, axis=3) /* ty=Tensor[(1, 56, 56, 96), int32] */; %14 = qnn.requantize(%13, 0.000493371f /* ty=float32 */, 0 /* ty=int32 */, 0.0235285f /* ty=float32 */, 0 /* ty=int32 */, out_dtype="uint8") /* ty=Tensor[(1, 56, 56, 96), uint8] */; free_var %v_param_11: Tensor[(1, 1, 96, 24), uint8] %15 = qnn.conv2d(%14, %v_param_11, 0 /* ty=int32 */, 156 /* ty=int32 */, 0.0235285f /* ty=float32 */, 0.022536f /* ty=float32 */, padding=[0, 0, 0, 0], channels=24, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32") /* ty=Tensor[(1, 56, 56, 24), int32] */; free_var %v_param_12: Tensor[(24), int32] %16 = nn.bias_add(%15, %v_param_12, axis=3) /* ty=Tensor[(1, 56, 56, 24), int32] */; %17 = qnn.requantize(%16, 0.000530238f /* ty=float32 */, 0 /* ty=int32 */, 0.275834f /* ty=float32 */, 119 /* ty=int32 */, out_dtype="uint8") /* ty=Tensor[(1, 56, 56, 24), uint8] */; free_var %v_param_13: Tensor[(1, 1, 24, 144), uint8] %18 = qnn.conv2d(%17, %v_param_13, 119 /* ty=int32 */, 144 /* ty=int32 */, 0.275834f /* ty=float32 */, 0.0036557f /* ty=float32 */, padding=[0, 0, 0, 0], channels=144, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32") /* ty=Tensor[(1, 56, 56, 144), int32] */; free_var %v_param_14: Tensor[(144), int32] %19 = nn.bias_add(%18, %v_param_14, axis=3) /* ty=Tensor[(1, 56, 56, 144), int32] */; %20 = qnn.requantize(%19, 0.00100837f /* ty=float32 */, 0 /* ty=int32 */, 0.0235285f /* ty=float32 */, 0 /* ty=int32 */, out_dtype="uint8") /* ty=Tensor[(1, 56, 56, 144), uint8] */; free_var %v_param_15: Tensor[(3, 3, 144, 1), uint8] %21 = qnn.conv2d(%20, %v_param_15, 0 /* ty=int32 */, 52 /* ty=int32 */, 0.0235285f /* ty=float32 */, 0.169819f /* ty=float32 */, padding=[1, 1, 1, 1], groups=144, channels=144, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI", out_dtype="int32") /* ty=Tensor[(1, 56, 56, 144), int32] */; free_var %v_param_16: Tensor[(144), int32] %22 = nn.bias_add(%21, %v_param_16, axis=3) /* ty=Tensor[(1, 56, 56, 144), int32] */; %23 = qnn.requantize(%22, 0.00399559f /* ty=float32 */, 0 /* ty=int32 */, 0.0235285f /* ty=float32 */, 0 /* ty=int32 */, out_dtype="uint8") /* ty=Tensor[(1, 56, 56, 144), uint8] */; free_var %v_param_17: Tensor[(1, 1, 144, 24), uint8] %24 = qnn.conv2d(%23, %v_param_17, 0 /* ty=int32 */, 122 /* ty=int32 */, 0.0235285f /* ty=float32 */, 0.0274089f /* ty=float32 */, padding=[0, 0, 0, 0], channels=24, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32") /* ty=Tensor[(1, 56, 56, 24), int32] */; free_var %v_param_18: Tensor[(24), int32] %25 = nn.bias_add(%24, %v_param_18, axis=3) /* ty=Tensor[(1, 56, 56, 24), int32] */; %26 = qnn.requantize(%25, 0.000644889f /* ty=float32 */, 0 /* ty=int32 */, 0.401493f /* ty=float32 */, 136 /* ty=int32 */, out_dtype="uint8") /* ty=Tensor[(1, 56, 56, 24), uint8] */; %27 = qnn.add(%26, %17, 0.401493f /* ty=float32 */, 136 /* ty=int32 */, 0.275834f /* ty=float32 */, 119 /* ty=int32 */, 0.432169f /* ty=float32 */, 133 /* ty=int32 */) /* ty=Tensor[(1, 56, 56, 24), uint8] */; free_var %v_param_19: Tensor[(1, 1, 24, 144), uint8] %28 = qnn.conv2d(%27, %v_param_19, 133 /* ty=int32 */, 104 /* ty=int32 */, 0.432169f /* ty=float32 */, 0.00299887f /* ty=float32 */, padding=[0, 0, 0, 0], channels=144, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32") /* ty=Tensor[(1, 56, 56, 144), int32] */; free_var %v_param_20: Tensor[(144), int32] %29 = nn.bias_add(%28, %v_param_20, axis=3) /* ty=Tensor[(1, 56, 56, 144), int32] */; %30 = qnn.requantize(%29, 0.00129602f /* ty=float32 */, 0 /* ty=int32 */, 0.0235285f /* ty=float32 */, 0 /* ty=int32 */, out_dtype="uint8") /* ty=Tensor[(1, 56, 56, 144), uint8] */; free_var %v_param_21: Tensor[(3, 3, 144, 1), uint8] %31 = qnn.conv2d(%30, %v_param_21, 0 /* ty=int32 */, 143 /* ty=int32 */, 0.0235285f /* ty=float32 */, 0.0172029f /* ty=float32 */, strides=[2, 2], padding=[0, 0, 1, 1], groups=144, channels=144, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI", out_dtype="int32") /* ty=Tensor[(1, 28, 28, 144), int32] */; free_var %v_param_22: Tensor[(144), int32] %32 = nn.bias_add(%31, %v_param_22, axis=3) /* ty=Tensor[(1, 28, 28, 144), int32] */; %33 = qnn.requantize(%32, 0.000404757f /* ty=float32 */, 0 /* ty=int32 */, 0.0235285f /* ty=float32 */, 0 /* ty=int32 */, out_dtype="uint8") /* ty=Tensor[(1, 28, 28, 144), uint8] */; free_var %v_param_23: Tensor[(1, 1, 144, 32), uint8] %34 = qnn.conv2d(%33, %v_param_23, 0 /* ty=int32 */, 111 /* ty=int32 */, 0.0235285f /* ty=float32 */, 0.0168447f /* ty=float32 */, padding=[0, 0, 0, 0], channels=32, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32") /* ty=Tensor[(1, 28, 28, 32), int32] */; free_var %v_param_24: Tensor[(32), int32] %35 = nn.bias_add(%34, %v_param_24, axis=3) /* ty=Tensor[(1, 28, 28, 32), int32] */; %36 = qnn.requantize(%35, 0.00039633f /* ty=float32 */, 0 /* ty=int32 */, 0.218362f /* ty=float32 */, 127 /* ty=int32 */, out_dtype="uint8") /* ty=Tensor[(1, 28, 28, 32), uint8] */; free_var %v_param_25: Tensor[(1, 1, 32, 192), uint8] %37 = qnn.conv2d(%36, %v_param_25, 127 /* ty=int32 */, 128 /* ty=int32 */, 0.218362f /* ty=float32 */, 0.00192442f /* ty=float32 */, padding=[0, 0, 0, 0], channels=192, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32") /* ty=Tensor[(1, 28, 28, 192), int32] */; free_var %v_param_26: Tensor[(192), int32] %38 = nn.bias_add(%37, %v_param_26, axis=3) /* ty=Tensor[(1, 28, 28, 192), int32] */; %39 = qnn.requantize(%38, 0.000420222f /* ty=float32 */, 0 /* ty=int32 */, 0.0235285f /* ty=float32 */, 0 /* ty=int32 */, out_dtype="uint8") /* ty=Tensor[(1, 28, 28, 192), uint8] */; free_var %v_param_27: Tensor[(3, 3, 192, 1), uint8] %40 = qnn.conv2d(%39, %v_param_27, 0 /* ty=int32 */, 118 /* ty=int32 */, 0.0235285f /* ty=float32 */, 0.0652507f /* ty=float32 */, padding=[1, 1, 1, 1], groups=192, channels=192, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI", out_dtype="int32") /* ty=Tensor[(1, 28, 28, 192), int32] */; free_var %v_param_28: Tensor[(192), int32] %41 = nn.bias_add(%40, %v_param_28, axis=3) /* ty=Tensor[(1, 28, 28, 192), int32] */; %42 = qnn.requantize(%41, 0.00153525f /* ty=float32 */, 0 /* ty=int32 */, 0.0235285f /* ty=float32 */, 0 /* ty=int32 */, out_dtype="uint8") /* ty=Tensor[(1, 28, 28, 192), uint8] */; free_var %v_param_29: Tensor[(1, 1, 192, 32), uint8] %43 = qnn.conv2d(%42, %v_param_29, 0 /* ty=int32 */, 146 /* ty=int32 */, 0.0235285f /* ty=float32 */, 0.0190629f /* ty=float32 */, padding=[0, 0, 0, 0], channels=32, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32") /* ty=Tensor[(1, 28, 28, 32), int32] */; free_var %v_param_30: Tensor[(32), int32] %44 = nn.bias_add(%43, %v_param_30, axis=3) /* ty=Tensor[(1, 28, 28, 32), int32] */; %45 = qnn.requantize(%44, 0.000448521f /* ty=float32 */, 0 /* ty=int32 */, 0.227942f /* ty=float32 */, 121 /* ty=int32 */, out_dtype="uint8") /* ty=Tensor[(1, 28, 28, 32), uint8] */; %46 = qnn.add(%45, %36, 0.227942f /* ty=float32 */, 121 /* ty=int32 */, 0.218362f /* ty=float32 */, 127 /* ty=int32 */, 0.25969f /* ty=float32 */, 130 /* ty=int32 */) /* ty=Tensor[(1, 28, 28, 32), uint8] */; free_var %v_param_31: Tensor[(1, 1, 32, 192), uint8] %47 = qnn.conv2d(%46, %v_param_31, 130 /* ty=int32 */, 135 /* ty=int32 */, 0.25969f /* ty=float32 */, 0.00136492f /* ty=float32 */, padding=[0, 0, 0, 0], channels=192, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32") /* ty=Tensor[(1, 28, 28, 192), int32] */; free_var %v_param_32: Tensor[(192), int32] %48 = nn.bias_add(%47, %v_param_32, axis=3) /* ty=Tensor[(1, 28, 28, 192), int32] */; %49 = qnn.requantize(%48, 0.000354455f /* ty=float32 */, 0 /* ty=int32 */, 0.0235285f /* ty=float32 */, 0 /* ty=int32 */, out_dtype="uint8") /* ty=Tensor[(1, 28, 28, 192), uint8] */; free_var %v_param_33: Tensor[(3, 3, 192, 1), uint8] %50 = qnn.conv2d(%49, %v_param_33, 0 /* ty=int32 */, 95 /* ty=int32 */, 0.0235285f /* ty=float32 */, 0.0790978f /* ty=float32 */, padding=[1, 1, 1, 1], groups=192, channels=192, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI", out_dtype="int32") /* ty=Tensor[(1, 28, 28, 192), int32] */; free_var %v_param_34: Tensor[(192), int32] %51 = nn.bias_add(%50, %v_param_34, axis=3) /* ty=Tensor[(1, 28, 28, 192), int32] */; %52 = qnn.requantize(%51, 0.00186105f /* ty=float32 */, 0 /* ty=int32 */, 0.0235285f /* ty=float32 */, 0 /* ty=int32 */, out_dtype="uint8") /* ty=Tensor[(1, 28, 28, 192), uint8] */; free_var %v_param_35: Tensor[(1, 1, 192, 32), uint8] %53 = qnn.conv2d(%52, %v_param_35, 0 /* ty=int32 */, 128 /* ty=int32 */, 0.0235285f /* ty=float32 */, 0.0182931f /* ty=float32 */, padding=[0, 0, 0, 0], channels=32, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32") /* ty=Tensor[(1, 28, 28, 32), int32] */; free_var %v_param_36: Tensor[(32), int32] %54 = nn.bias_add(%53, %v_param_36, axis=3) /* ty=Tensor[(1, 28, 28, 32), int32] */; %55 = qnn.requantize(%54, 0.000430409f /* ty=float32 */, 0 /* ty=int32 */, 0.257749f /* ty=float32 */, 124 /* ty=int32 */, out_dtype="uint8") /* ty=Tensor[(1, 28, 28, 32), uint8] */; %56 = qnn.add(%55, %46, 0.257749f /* ty=float32 */, 124 /* ty=int32 */, 0.25969f /* ty=float32 */, 130 /* ty=int32 */, 0.331715f /* ty=float32 */, 124 /* ty=int32 */) /* ty=Tensor[(1, 28, 28, 32), uint8] */; free_var %v_param_37: Tensor[(1, 1, 32, 192), uint8] %57 = qnn.conv2d(%56, %v_param_37, 124 /* ty=int32 */, 127 /* ty=int32 */, 0.331715f /* ty=float32 */, 0.00191704f /* ty=float32 */, padding=[0, 0, 0, 0], channels=192, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32") /* ty=Tensor[(1, 28, 28, 192), int32] */; free_var %v_param_38: Tensor[(192), int32] %58 = nn.bias_add(%57, %v_param_38, axis=3) /* ty=Tensor[(1, 28, 28, 192), int32] */; %59 = qnn.requantize(%58, 0.000635912f /* ty=float32 */, 0 /* ty=int32 */, 0.0235285f /* ty=float32 */, 0 /* ty=int32 */, out_dtype="uint8") /* ty=Tensor[(1, 28, 28, 192), uint8] */; free_var %v_param_39: Tensor[(3, 3, 192, 1), uint8] %60 = qnn.conv2d(%59, %v_param_39, 0 /* ty=int32 */, 127 /* ty=int32 */, 0.0235285f /* ty=float32 */, 0.0100879f /* ty=float32 */, strides=[2, 2], padding=[0, 0, 1, 1], groups=192, channels=192, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI", out_dtype="int32") /* ty=Tensor[(1, 14, 14, 192), int32] */; free_var %v_param_40: Tensor[(192), int32] %61 = nn.bias_add(%60, %v_param_40, axis=3) /* ty=Tensor[(1, 14, 14, 192), int32] */; %62 = qnn.requantize(%61, 0.000237353f /* ty=float32 */, 0 /* ty=int32 */, 0.0235285f /* ty=float32 */, 0 /* ty=int32 */, out_dtype="uint8") /* ty=Tensor[(1, 14, 14, 192), uint8] */; free_var %v_param_41: Tensor[(1, 1, 192, 64), uint8] %63 = qnn.conv2d(%62, %v_param_41, 0 /* ty=int32 */, 147 /* ty=int32 */, 0.0235285f /* ty=float32 */, 0.0146013f /* ty=float32 */, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32") /* ty=Tensor[(1, 14, 14, 64), int32] */; free_var %v_param_42: Tensor[(64), int32] %64 = nn.bias_add(%63, %v_param_42, axis=3) /* ty=Tensor[(1, 14, 14, 64), int32] */; %65 = qnn.requantize(%64, 0.000343546f /* ty=float32 */, 0 /* ty=int32 */, 0.185405f /* ty=float32 */, 126 /* ty=int32 */, out_dtype="uint8") /* ty=Tensor[(1, 14, 14, 64), uint8] */; free_var %v_param_43: Tensor[(1, 1, 64, 384), uint8] %66 = qnn.conv2d(%65, %v_param_43, 126 /* ty=int32 */, 125 /* ty=int32 */, 0.185405f /* ty=float32 */, 0.00155389f /* ty=float32 */, padding=[0, 0, 0, 0], channels=384, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32") /* ty=Tensor[(1, 14, 14, 384), int32] */; free_var %v_param_44: Tensor[(384), int32] %67 = nn.bias_add(%66, %v_param_44, axis=3) /* ty=Tensor[(1, 14, 14, 384), int32] */; %68 = qnn.requantize(%67, 0.0002881f /* ty=float32 */, 0 /* ty=int32 */, 0.0235285f /* ty=float32 */, 0 /* ty=int32 */, out_dtype="uint8") /* ty=Tensor[(1, 14, 14, 384), uint8] */; free_var %v_param_45: Tensor[(3, 3, 384, 1), uint8] %69 = qnn.conv2d(%68, %v_param_45, 0 /* ty=int32 */, 110 /* ty=int32 */, 0.0235285f /* ty=float32 */, 0.0609271f /* ty=float32 */, padding=[1, 1, 1, 1], groups=384, channels=384, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI", out_dtype="int32") /* ty=Tensor[(1, 14, 14, 384), int32] */; free_var %v_param_46: Tensor[(384), int32] %70 = nn.bias_add(%69, %v_param_46, axis=3) /* ty=Tensor[(1, 14, 14, 384), int32] */; %71 = qnn.requantize(%70, 0.00143352f /* ty=float32 */, 0 /* ty=int32 */, 0.0235285f /* ty=float32 */, 0 /* ty=int32 */, out_dtype="uint8") /* ty=Tensor[(1, 14, 14, 384), uint8] */; free_var %v_param_47: Tensor[(1, 1, 384, 64), uint8] %72 = qnn.conv2d(%71, %v_param_47, 0 /* ty=int32 */, 124 /* ty=int32 */, 0.0235285f /* ty=float32 */, 0.0167829f /* ty=float32 */, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32") /* ty=Tensor[(1, 14, 14, 64), int32] */; free_var %v_param_48: Tensor[(64), int32] %73 = nn.bias_add(%72, %v_param_48, axis=3) /* ty=Tensor[(1, 14, 14, 64), int32] */; %74 = qnn.requantize(%73, 0.000394877f /* ty=float32 */, 0 /* ty=int32 */, 0.172635f /* ty=float32 */, 109 /* ty=int32 */, out_dtype="uint8") /* ty=Tensor[(1, 14, 14, 64), uint8] */; %75 = qnn.add(%74, %65, 0.172635f /* ty=float32 */, 109 /* ty=int32 */, 0.185405f /* ty=float32 */, 126 /* ty=int32 */, 0.18911f /* ty=float32 */, 122 /* ty=int32 */) /* ty=Tensor[(1, 14, 14, 64), uint8] */; free_var %v_param_49: Tensor[(1, 1, 64, 384), uint8] %76 = qnn.conv2d(%75, %v_param_49, 122 /* ty=int32 */, 134 /* ty=int32 */, 0.18911f /* ty=float32 */, 0.0014703f /* ty=float32 */, padding=[0, 0, 0, 0], channels=384, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32") /* ty=Tensor[(1, 14, 14, 384), int32] */; free_var %v_param_50: Tensor[(384), int32] %77 = nn.bias_add(%76, %v_param_50, axis=3) /* ty=Tensor[(1, 14, 14, 384), int32] */; %78 = qnn.requantize(%77, 0.000278048f /* ty=float32 */, 0 /* ty=int32 */, 0.0235285f /* ty=float32 */, 0 /* ty=int32 */, out_dtype="uint8") /* ty=Tensor[(1, 14, 14, 384), uint8] */; free_var %v_param_51: Tensor[(3, 3, 384, 1), uint8] %79 = qnn.conv2d(%78, %v_param_51, 0 /* ty=int32 */, 133 /* ty=int32 */, 0.0235285f /* ty=float32 */, 0.0524078f /* ty=float32 */, padding=[1, 1, 1, 1], groups=384, channels=384, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI", out_dtype="int32") /* ty=Tensor[(1, 14, 14, 384), int32] */; free_var %v_param_52: Tensor[(384), int32] %80 = nn.bias_add(%79, %v_param_52, axis=3) /* ty=Tensor[(1, 14, 14, 384), int32] */; %81 = qnn.requantize(%80, 0.00123308f /* ty=float32 */, 0 /* ty=int32 */, 0.0235285f /* ty=float32 */, 0 /* ty=int32 */, out_dtype="uint8") /* ty=Tensor[(1, 14, 14, 384), uint8] */; free_var %v_param_53: Tensor[(1, 1, 384, 64), uint8] %82 = qnn.conv2d(%81, %v_param_53, 0 /* ty=int32 */, 125 /* ty=int32 */, 0.0235285f /* ty=float32 */, 0.0128983f /* ty=float32 */, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32") /* ty=Tensor[(1, 14, 14, 64), int32] */; free_var %v_param_54: Tensor[(64), int32] %83 = nn.bias_add(%82, %v_param_54, axis=3) /* ty=Tensor[(1, 14, 14, 64), int32] */; %84 = qnn.requantize(%83, 0.000303476f /* ty=float32 */, 0 /* ty=int32 */, 0.147155f /* ty=float32 */, 123 /* ty=int32 */, out_dtype="uint8") /* ty=Tensor[(1, 14, 14, 64), uint8] */; %85 = qnn.add(%84, %75, 0.147155f /* ty=float32 */, 123 /* ty=int32 */, 0.18911f /* ty=float32 */, 122 /* ty=int32 */, 0.199681f /* ty=float32 */, 124 /* ty=int32 */) /* ty=Tensor[(1, 14, 14, 64), uint8] */; free_var %v_param_55: Tensor[(1, 1, 64, 384), uint8] %86 = qnn.conv2d(%85, %v_param_55, 124 /* ty=int32 */, 127 /* ty=int32 */, 0.199681f /* ty=float32 */, 0.00137335f /* ty=float32 */, padding=[0, 0, 0, 0], channels=384, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32") /* ty=Tensor[(1, 14, 14, 384), int32] */; free_var %v_param_56: Tensor[(384), int32] %87 = nn.bias_add(%86, %v_param_56, axis=3) /* ty=Tensor[(1, 14, 14, 384), int32] */; %88 = qnn.requantize(%87, 0.000274232f /* ty=float32 */, 0 /* ty=int32 */, 0.0235285f /* ty=float32 */, 0 /* ty=int32 */, out_dtype="uint8") /* ty=Tensor[(1, 14, 14, 384), uint8] */; free_var %v_param_57: Tensor[(3, 3, 384, 1), uint8] %89 = qnn.conv2d(%88, %v_param_57, 0 /* ty=int32 */, 155 /* ty=int32 */, 0.0235285f /* ty=float32 */, 0.0407789f /* ty=float32 */, padding=[1, 1, 1, 1], groups=384, channels=384, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI", out_dtype="int32") /* ty=Tensor[(1, 14, 14, 384), int32] */; free_var %v_param_58: Tensor[(384), int32] %90 = nn.bias_add(%89, %v_param_58, axis=3) /* ty=Tensor[(1, 14, 14, 384), int32] */; %91 = qnn.requantize(%90, 0.000959465f /* ty=float32 */, 0 /* ty=int32 */, 0.0235285f /* ty=float32 */, 0 /* ty=int32 */, out_dtype="uint8") /* ty=Tensor[(1, 14, 14, 384), uint8] */; free_var %v_param_59: Tensor[(1, 1, 384, 64), uint8] %92 = qnn.conv2d(%91, %v_param_59, 0 /* ty=int32 */, 144 /* ty=int32 */, 0.0235285f /* ty=float32 */, 0.0195615f /* ty=float32 */, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32") /* ty=Tensor[(1, 14, 14, 64), int32] */; free_var %v_param_60: Tensor[(64), int32] %93 = nn.bias_add(%92, %v_param_60, axis=3) /* ty=Tensor[(1, 14, 14, 64), int32] */; %94 = qnn.requantize(%93, 0.000460252f /* ty=float32 */, 0 /* ty=int32 */, 0.156276f /* ty=float32 */, 122 /* ty=int32 */, out_dtype="uint8") /* ty=Tensor[(1, 14, 14, 64), uint8] */; %95 = qnn.add(%94, %85, 0.156276f /* ty=float32 */, 122 /* ty=int32 */, 0.199681f /* ty=float32 */, 124 /* ty=int32 */, 0.220273f /* ty=float32 */, 120 /* ty=int32 */) /* ty=Tensor[(1, 14, 14, 64), uint8] */; free_var %v_param_61: Tensor[(1, 1, 64, 384), uint8] %96 = qnn.conv2d(%95, %v_param_61, 120 /* ty=int32 */, 131 /* ty=int32 */, 0.220273f /* ty=float32 */, 0.00162825f /* ty=float32 */, padding=[0, 0, 0, 0], channels=384, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32") /* ty=Tensor[(1, 14, 14, 384), int32] */; free_var %v_param_62: Tensor[(384), int32] %97 = nn.bias_add(%96, %v_param_62, axis=3) /* ty=Tensor[(1, 14, 14, 384), int32] */; %98 = qnn.requantize(%97, 0.00035866f /* ty=float32 */, 0 /* ty=int32 */, 0.0235285f /* ty=float32 */, 0 /* ty=int32 */, out_dtype="uint8") /* ty=Tensor[(1, 14, 14, 384), uint8] */; free_var %v_param_63: Tensor[(3, 3, 384, 1), uint8] %99 = qnn.conv2d(%98, %v_param_63, 0 /* ty=int32 */, 143 /* ty=int32 */, 0.0235285f /* ty=float32 */, 0.0311078f /* ty=float32 */, padding=[1, 1, 1, 1], groups=384, channels=384, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI", out_dtype="int32") /* ty=Tensor[(1, 14, 14, 384), int32] */; free_var %v_param_64: Tensor[(384), int32] %100 = nn.bias_add(%99, %v_param_64, axis=3) /* ty=Tensor[(1, 14, 14, 384), int32] */; %101 = qnn.requantize(%100, 0.00073192f /* ty=float32 */, 0 /* ty=int32 */, 0.0235285f /* ty=float32 */, 0 /* ty=int32 */, out_dtype="uint8") /* ty=Tensor[(1, 14, 14, 384), uint8] */; free_var %v_param_65: Tensor[(1, 1, 384, 96), uint8] %102 = qnn.conv2d(%101, %v_param_65, 0 /* ty=int32 */, 129 /* ty=int32 */, 0.0235285f /* ty=float32 */, 0.00743631f /* ty=float32 */, padding=[0, 0, 0, 0], channels=96, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32") /* ty=Tensor[(1, 14, 14, 96), int32] */; free_var %v_param_66: Tensor[(96), int32] %103 = nn.bias_add(%102, %v_param_66, axis=3) /* ty=Tensor[(1, 14, 14, 96), int32] */; %104 = qnn.requantize(%103, 0.000174965f /* ty=float32 */, 0 /* ty=int32 */, 0.170611f /* ty=float32 */, 129 /* ty=int32 */, out_dtype="uint8") /* ty=Tensor[(1, 14, 14, 96), uint8] */; free_var %v_param_67: Tensor[(1, 1, 96, 576), uint8] %105 = qnn.conv2d(%104, %v_param_67, 129 /* ty=int32 */, 134 /* ty=int32 */, 0.170611f /* ty=float32 */, 0.00163099f /* ty=float32 */, padding=[0, 0, 0, 0], channels=576, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32") /* ty=Tensor[(1, 14, 14, 576), int32] */; free_var %v_param_68: Tensor[(576), int32] %106 = nn.bias_add(%105, %v_param_68, axis=3) /* ty=Tensor[(1, 14, 14, 576), int32] */; %107 = qnn.requantize(%106, 0.000278264f /* ty=float32 */, 0 /* ty=int32 */, 0.0235285f /* ty=float32 */, 0 /* ty=int32 */, out_dtype="uint8") /* ty=Tensor[(1, 14, 14, 576), uint8] */; free_var %v_param_69: Tensor[(3, 3, 576, 1), uint8] %108 = qnn.conv2d(%107, %v_param_69, 0 /* ty=int32 */, 66 /* ty=int32 */, 0.0235285f /* ty=float32 */, 0.0708081f /* ty=float32 */, padding=[1, 1, 1, 1], groups=576, channels=576, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWOI", out_dtype="int32") /* ty=Tensor[(1, 14, 14, 576), int32] */; free_var %v_param_70: Tensor[(576), int32] %109 = nn.bias_add(%108, %v_param_70, axis=3) /* ty=Tensor[(1, 14, 14, 576), int32] */; %110 = qnn.requantize(%109, 0.00166601f /* ty=float32 */, 0 /* ty=int32 */, 0.0235285f /* ty=float32 */, 0 /* ty=int32 */, out_dtype="uint8") /* ty=Tensor[(1, 14, 14, 576), uint8] */; free_var %v_param_71: Tensor[(1, 1, 576, 96), uint8] %111 = qnn.conv2d(%110, %v_param_71, 0 /* ty=int32 */, 136 /* ty=int32 */, 0.0235285f /* ty=float32 */, 0.00838223f /* ty=float32 */, padding=[0, 0, 0, 0], channels=96, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32") /* ty=Tensor[(1, 14, 14, 96), int32] */; free_var %v_param_72: Tensor[(96), int32] %112 = nn.bias_add(%111, %v_param_72, axis=3) /* ty=Tensor[(1, 14, 14, 96), int32] */; %113 = qnn.requantize(%112, 0.000197221f /* ty=float32 */, 0 /* ty=int32 */, 0.123328f /* ty=float32 */, 127 /* ty=int32 */, out_dtype="uint8") /* ty=Tensor[(1, 14, 14, 96), uint8] */; %114 = qnn.add(%113, %104, 0.123328f /* ty=float32 */, 127 /* ty=int32 */, 0.170611f /* ty=float32 */, 129 /* ty=int32 */, 0.176158f /* ty=float32 */, 127 /* ty=int32 */) /* ty=Tensor[(1, 14, 14, 96), uint8] */; free_var %v_param_73: Tensor[(1, 1, 96, 576), uint8] %115 = qnn.conv2d(%114, %v_param_73, 127 /* ty=int32 */, 138 /* ty=int32 */, 0.176158f /* ty=float32 */, 0.00182588f /* ty=float32 */, padding=[0, 0, 0, 0], channels=576, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32") /* ty=Tensor[(1, 14, 14, 576), int32] */; free_var %v_param_74: Tensor[(576), int32] ... %161 = nn.bias_add(%160, %v_param_102, axis=3) /* ty=Tensor[(1, 7, 7, 320), int32] */; %162 = qnn.requantize(%161, 0.000188446f /* ty=float32 */, 0 /* ty=int32 */, 0.116945f /* ty=float32 */, 130 /* ty=int32 */, out_dtype="uint8") /* ty=Tensor[(1, 7, 7, 320), uint8] */; free_var %v_param_103: Tensor[(1, 1, 320, 1280), uint8] %163 = qnn.conv2d(%162, %v_param_103, 130 /* ty=int32 */, 125 /* ty=int32 */, 0.116945f /* ty=float32 */, 0.00516707f /* ty=float32 */, padding=[0, 0, 0, 0], channels=1280, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32") /* ty=Tensor[(1, 7, 7, 1280), int32] */; free_var %v_param_104: Tensor[(1280), int32] %164 = nn.bias_add(%163, %v_param_104, axis=3) /* ty=Tensor[(1, 7, 7, 1280), int32] */; %165 = qnn.requantize(%164, 0.000604263f /* ty=float32 */, 0 /* ty=int32 */, 0.0235285f /* ty=float32 */, 0 /* ty=int32 */, out_dtype="uint8") /* ty=Tensor[(1, 7, 7, 1280), uint8] */; %166 = cast(%165, dtype="int32") /* ty=Tensor[(1, 7, 7, 1280), int32] */; %167 = nn.avg_pool2d(%166, pool_size=[7, 7], layout="NHWC") /* ty=Tensor[(1, 1, 1, 1280), int32] */; %168 = cast(%167, dtype="uint8") /* ty=Tensor[(1, 1, 1, 1280), uint8] */; free_var %v_param_105: Tensor[(1, 1, 1280, 1001), uint8] %169 = qnn.conv2d(%168, %v_param_105, 0 /* ty=int32 */, 113 /* ty=int32 */, 0.0235285f /* ty=float32 */, 0.00169108f /* ty=float32 */, padding=[0, 0, 0, 0], channels=1001, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO", out_dtype="int32") /* ty=Tensor[(1, 1, 1, 1001), int32] */; free_var %v_param_106: Tensor[(1001), int32] %170 = nn.bias_add(%169, %v_param_106, axis=3) /* ty=Tensor[(1, 1, 1, 1001), int32] */; %171 = qnn.requantize(%170, 3.97886e-05f /* ty=float32 */, 0 /* ty=int32 */, 0.0988925f /* ty=float32 */, 58 /* ty=int32 */, out_dtype="uint8") /* ty=Tensor[(1, 1, 1, 1001), uint8] */; reshape(%171, newshape=[1, 1001]) /* ty=Tensor[(1, 1001), uint8] */ ``` --- [Visit Topic](https://discuss.tvm.ai/t/autotvm-task-extract-from-program-in-tflite/6578/16) to respond. 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