Is there implicit limit for DNN input size of OpenCV Android?

edit

I trained a Caffe model which does NOT specify input sizes. Then I ran it on Android using OpenCV. The code is like below.

private void runEntireImageTest(Mat inY) {
    float scaleFactor = 1.0f / 255.0f;
    Scalar mean = new Scalar(0);
    Mat resized = new Mat();

    // (550, 441) runs OK. (smaller sized runs also fine)
    // (551, 441), (550, 442) crash.
    // (441, 550) crashes

    Imgproc.resize(inY, resized, new Size(550, 441));
    Mat segBlob = Dnn.blobFromImage(resized, scaleFactor, resized.size(), mean, false, false);
    mNet.setInput(segBlob);

    // if input size is above some value, crash will happen here
    Mat lastLayer = mNet.forward();
    Mat outY = lastLayer.reshape(1, 1);
}

As it is written in the comment, there seems to be some intrinsic, or implicit limitation for the input size. (550, 441) ran okay, but (551, 441) will cause SIGSEGV:

A/libc: Fatal signal 11 (SIGSEGV), code 1, fault addr 0x6d494bd744 in tid 19693 (myappname), pid 19661 (myappname)

I think it's not a memory problem because (550, 441) runs fine but (441, 550) will crash. What is the cause of this problem?

ADDED

Below is the prototxt file. It is a variant of FSRCNN paper (link to the paper). Although the input shape is written as { dim: 1 dim: 1 dim:678 dim:1014}, it does not have any effect.

name: "FSRCNN"
layer {
  name: "data"
  type: "Input"
  top: "data"
  input_param { shape: { dim: 1 dim: 1 dim:678 dim:1014} }
}

layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    lr_mult: 0.1
  }
  param {
    lr_mult: 0.1
  }
  convolution_param {
    num_output: 32
    kernel_size: 5
    stride: 1
    pad: 0
    weight_filler {
      type: "gaussian"
      std: 0.05
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "relu1"
  type: "PReLU"
  bottom: "conv1"
  top: "conv1"
  prelu_param {
    channel_shared: false
  }
}

layer {
  name: "conv2"
  type: "Convolution"
  bottom: "conv1"
  top: "conv2"

  param {
    lr_mult: 0.1
  }
  param {
    lr_mult: 0.1
  }
  convolution_param {
    num_output: 5
    kernel_size: 1
    stride: 1
    group: 1
    pad: 0
    weight_filler {
      type: "gaussian"
      std: 0.6325
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "relu2"
  type: "PReLU"
  bottom: "conv2"
  top: "conv2"
  prelu_param {
    channel_shared: false
  }
}

layer {
  name: "conv22"
  type: "Convolution"
  bottom: "conv2"
  top: "conv22"

  param {
    lr_mult: 0.1
  }
  param {
    lr_mult: 0.1
  }
  convolution_param {
    num_output: 5
    kernel_size: 3
    stride: 1
    group: 1
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.2108
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "relu22"
  type: "PReLU"
  bottom: "conv22"
  top: "conv22"
  prelu_param {
    channel_shared: false
  }
}

layer {
  name: "conv23"
  type: "Convolution"
  bottom: "conv22"
  top: "conv23"

  param {
    lr_mult: 0.1
  }
  param {
    lr_mult: 0.1
  }
  convolution_param {
    num_output: 32
    kernel_size: 1
    stride: 1
    group: 1
    pad: 0
    weight_filler {
      type: "gaussian"
      std: 0.25
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "relu23"
  type: "PReLU"
  bottom: "conv23"
  top: "conv23"
  prelu_param {
    channel_shared: false
  }
}

layer {
  name: "conv3"
  type: "Deconvolution"
  bottom: "conv23"
  top: "conv3"

  param {
    lr_mult: 0.1
  }
  convolution_param {
    num_output: 1
    kernel_size: 9
    stride: 3
    pad: 3
    weight_filler {
      type: "gaussian"
      std: 0.001
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}