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RuntimeError #4
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Hi, the model supports 640x360 input resolution as input. Since you use a different resolution, I presume that the error is at the concatenation of an interpolation layer output with the features of the corresponding skip connection. This happens because the interpolation layers utilize the default h and w (360 and 640, see models/shallow_partial.py), while the skip connections feed the decoder with feature maps of your h' and w', where h' < h and w' < w (i.e. 15 < 90 in your case). |
Hi @spthermo. Thank for the reply. |
@spthermo yes,i also found input the png image will output the all black images,how to solve this? |
@kearcer try to download another image viewer, like ImageJ |
After loading a file with the load_depth function, the result should be an image tensor containing the depth values in meters (the scale parameter is responsible for that scaling, with the default value being Typical formats for depth images would be:
It should be noted that If you need more help with your files, please upload a sample that we can check. |
Hello,
I am receiving errors when I test (using inference.py) with some custom images of different size and the error is as follows -
RuntimeError: invalid argument 0: Sizes of tensors must match except in dimension 1. Got 15 and 90 in dimension 2 at c:\a\w\1\s\tmp_conda_3.7_104535\conda\conda-bld\pytorch_1550400486030\work\aten\src\thc\generic/THCTensorMath.cu:83
This particular moment I had an image with dimensions
216*120
but I have had similar error for different sized images as well which were comparatively bigger.Will you kindly tell what is causing this error and how can I get rid of it?
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