Comments (4)
Hi, thanks for getting in touch with this.
Can you please be a bit more specific about what you are trying here.
- Are you training with the KITTI dataset, or with a different dataset?
- Are you changing the input dimensions with
--height xx --width yy
flags, or via a different means? - And if you are setting them via these flags, what values are you using?
Thanks!
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Thanks for the response.
I'm only running the inference in the simple example code provided on my 1280x720p images, and I'm overriding the "feeder_height" and width in the following line:
input_image = input_image.resize((feed_width, feed_height), Image.LANCZOS)
If I set the values to the native resolution (w=1280, h=720) I'll get the following features generated:
torch.Size([1, 64, 360, 640])
torch.Size([1, 64, 180, 320])
torch.Size([1, 128, 90, 160])
torch.Size([1, 256, 45, 80])
torch.Size([1, 512, 23, 40])
The error occurs when the odd-dimension tensor tries to concatenate during upscaling. The model I'm working with is mono+stereo_1024x320.
from monodepth2.
Ok, thanks for the extra info. I seem to remember that the network only runs if the dimensions are a multiple of 32. Could you maybe try 1280x704
? Thanks.
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(Ah, yes I see you have identified this in your original issue – it needs to have 2^5 as a factor
. Yes. I don't think there is any easy way to avoid this. I think that you need to use multiples of 32, or elso hack the networks to pad/crop dimensions where needed)
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