Model inference resulting in unknown rows and cols

I converted a Tensorflow Model to ONNX and then load it using OpenCV DNN. This model expects a [-1, 100, 100, 1] input and gives a [-1, 100, 100 ,1] output

I tested in Python, creating a dummy input and I got exactly what I expected, a 100x100 output.

params = cv2.dnn.Image2BlobParams()
params.mean = (0,0,0)
params.scalefactor = (1,1,1)
params.size = (100, 100)
params.ddepth = cv2.CV_32F
params.datalayout = cv2.dnn.DNN_LAYOUT_NHWC
params.paddingmode = cv2.dnn.DNN_PMODE_NULL
batch_size = 1
h = 100
w = 100
c = 1
x = torch.rand(batch_size, h, w, c, requires_grad=False)
x_npy = x.detach().numpy()
x_opencv = x_npy.reshape(h, w, 1)

blobPB = cv2.dnn.blobFromImageWithParams(x_opencv, params)
output = modelopenCV.forward()
print(output.shape) # = (1, 100, 100, 1)

But I’m having issue in acessing my output elements in C++, my cv::Mat output from model.foward() gets -1 rows and -1 cols. Any idea of how can I acess this elements? Why this difference between Python and C++ result? I know that OpenCV Python uses np.arrays instead of cv::Mat for data storage, but I guess it should has the same dimensions of a cv::Mat, using a input with the correct dimensions and the same model… no?

		params.scalefactor = 1.0 / 255;
		params.size = cv::Size2i::Size_(100, 100);
		params.mean = 0.0;
		params.ddepth = CV_32F;
		params.datalayout = cv::dnn::DNN_LAYOUT_NHWC;
		params.paddingmode = cv::dnn::DNN_PMODE_NULL;
        cv::dnn::blobFromImageWithParams(imgpb, blobTest, params);
        /* trust me imgpb it's in correct dimensions, imgpb.rows = 100, imgpb.cols = 100, imgpb.channels() = 1 */
	    cv::Mat outputsPB1 = pModelONNXPeB.forward();

outputsPB1 has 4 dimensions, -1 rows and -1 cols

opencv sets rows & cols to -1 for more-than-2-dim Mats.
please use outputsPB1.size (w/o braces !) in the same way you would use xxx.shape in python

Didn’t know that… thanks, Berak. Could you give me an example of the syntax for acessing an element in 4 dim struct? I’m used to

	for (int i = 0; i < outputsPB1.rows; ++i) {
					for (int j = 0; j < outputsPB1.cols; ++j) {
						float valor =<float>(i, j);

But as you said, -1 rols and -1 cols are setted for > 2 dims, I can’t use this in this case.

you can use at<float>(i,j,k) , iterate over outputsPB1.shape[0](batch dim) etc.

but in your case, you probably want to make a simple [100,100] 2d Mat from it:

Mat res2d(100, 100, CV_32F, outputsPB1.ptr<float>(0)); // batch item 0
1 Like

May be you can try this

* return a specific Mat in a blob.
* if dims blob is less or equal  to 2 (N rows x M columns) blob is return
* if dims blob is 3 blob is blob is (H x N x M array) mat at (coord(0),0, 0) is returned.
* if dims blob is r blob is blob is (T x H x N x M array) mat at (coord(0), coord(1), 0, 0) is returned.
* */
Mat getMatInBlob(Mat blob, vector<int> coord, int posChannel = DNN_LAYOUT_NCHW)
1 Like

here you should try


Really good! A generic implementation, I will save for later, thanks for the tip. I don’t know, if I can reutilize this topic for another question, but in this same context of 4d data structs, is there anyway to pass a 4d data to a blob? blobFromImages only accepts structs with dims <= 2, right? I got this 4 dimension output and I need to create a blob from it to pass as input for another model.

A blob is a Mat
So you can do :

  Mat maskInput(vector<int> {1, 1, 256, 256}, CV_32FC1);
  netMask.setInput(maskInput, "mask_input");