Custom dimension input to model about monodepth2 HOT 4 CLOSED

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!

from monodepth2.

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.

from monodepth2.

(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)

from monodepth2.