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dnn resize_layer assertion 'getmemoryshapes'

asked 2018-11-05 12:46:15 -0500

aguila gravatar image

hello! i was testing a model with the opencv dnn module to then implement the whole algorithm from https://github.com/wywu/LAB but i get the following error:

opencv-master/modules/dnn/src/layers/resize_layer.cpp:200: error: (-215:Assertion failed) inputs.size() == 1 in function 'getMemoryShapes'

I can read the network and print the details but while forwarding it crashes.. Has anyone encountered something similar?

Thanks and greetz!

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hi, aguila, can you be a bit more concise about what you are doing here ? there must be a prebuilt model, code, etc.

berak gravatar imageberak ( 2018-11-05 12:59:47 -0500 )edit
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Hi @berak, the prebuilt model i downloaded from the repo https://wywu.github.io/projects/LAB/s... and there is an implementation in caffe https://github.com/wywu/LAB/blob/mast... i based myself in that one and this one https://github.com/HandsomeHans/Easy-LAB

I started something like this:

Net net_lab = readNet("rel.prototxt", "model.bin");
Mat imge = imread("faceImg.jpg",0);

resize(imge, imge, Size(256,256));
imge.convertTo(imge, CV_32F);
Mat inputBlob = blobFromImage(imge, 1.0, Size(), Scalar(127,127,127), false, false);
net_lab.setInput(inputBlob);
Mat outputBlob = net_lab.forward();
aguila gravatar imageaguila ( 2018-11-05 13:39:27 -0500 )edit

Author uses customized Interp layer (see https://github.com/wywu/LAB/blob/mast...). It resizes the first input blob to size of the second one. If your model always works with 1x1x256x256 input you can easy compute a destination shape and use an origin Interp layer introduced in https://github.com/cdmh/deeplab-public.

dkurt gravatar imagedkurt ( 2018-11-06 01:10:08 -0500 )edit

Hello @dkurt thank you for your help, sorry i did not checked in the past week the two different layers. I am looking now the differences between the Interp Layer from opencv (the one from deeplab) and the custom layer from the author.

In my case, i will always have the same input size 256x256.

For this, if i am correct, i should explicit give the output size as input in the Interp Layer no? something like:

layer {
  name: "hourglass_1/upper_4/interp"
  type: "Interp"
  bottom: "hourglass_1/lower_4_3/res"
  bottom: "hourglass_1/lower_3/res"
  top: "hourglass_1/upper_4/interp"
  param {
  width:  XX        <--- My inputs
  height: XX
  }
}
aguila gravatar imageaguila ( 2018-11-12 06:34:06 -0500 )edit

@aguila, Try this:

layer {
  name: "hourglass_1/upper_4/interp"
  type: "Interp"
  bottom: "hourglass_1/lower_4_3/res"
  # bottom: "hourglass_1/lower_3/res"  Now we don't need it with explicit output sizes
  top: "hourglass_1/upper_4/interp"
  interp_param {
    width:  256
    height: 256
  }
}
dkurt gravatar imagedkurt ( 2018-11-12 07:46:45 -0500 )edit

ok i will try it later today and post my results here.. thanks!

aguila gravatar imageaguila ( 2018-11-12 07:59:40 -0500 )edit

@benjamin, please do not post answers here, if you have a question or a comment, thank you

(apart from that, questons like yours never work here, leave older questions alone, noone's here anymore)

berak gravatar imageberak ( 2019-04-17 02:22:33 -0500 )edit

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answered 2018-12-10 17:16:02 -0500

aguila gravatar image

Hello, i forgot to finish this example.. thanks for the hints @dkurt . I just made the modifications to the network. The trick is to find the correct size for the "Interp" Layers (using the opencv Interp Layer with 1 Input).

As in the example, the size of the layer "hourglass_1/lower_3/res" must be given as parameter, in that case would be:

layer {
  name: "hourglass_1/upper_4/interp"
  type: "Interp"
  bottom: "hourglass_1/lower_4_3/res"
  # bottom: "hourglass_1/lower_3/res"  Now we don't need it with explicit output sizes
  top: "hourglass_1/upper_4/interp"
  interp_param {
    width:  8 ---> Size of "hourglass_1/lower_3/res" layer
    height: 8
  }
}

The same process is applied to all the "Interp" layers.

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interp_param { width: 8 height: 8 } Where do the parameters come from?

benjamin gravatar imagebenjamin ( 2019-04-17 03:26:36 -0500 )edit
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Asked: 2018-11-05 12:46:15 -0500

Seen: 505 times

Last updated: Dec 10 '18