Hello,
Thank you for the paper, code and pretrained weights, it is great work!
I am confused by the output of enc_preprocess() in ModFlows_demo_batched.ipynb.
My understanding is that it should output a [3 x H x W] tensor of the preprocessed image, and my assumption is the "3" corresponds to the three color channels RGB.
However, when I try to visualize the output (in this case, the red channel), I get a scrambled image:
ff = '../../modflows/img/2.jpg'
x = enc_preprocess(Image.open(ff).resize((2000,2000)))
plt.imshow(x[0]) #plot red color channel
I believe this is due to the numpy reshape command being used in line 38 of ModFlows_demo_batched.ipynb: im = im.reshape(enc_shape) , which is scrambling the pixel-order. I think typically numpy transpose is used in this situation to change the dimension ordering, instead of reshape.
Is this expected behavior of the enc_preprocess, or am I doing something wrong, or possibly wrong dependencies? I checked on a few versions of numpy and it gives similar results.
Thanks!
Hello,
Thank you for the paper, code and pretrained weights, it is great work!
I am confused by the output of
enc_preprocess()inModFlows_demo_batched.ipynb.My understanding is that it should output a [3 x H x W] tensor of the preprocessed image, and my assumption is the "3" corresponds to the three color channels RGB.
However, when I try to visualize the output (in this case, the red channel), I get a scrambled image:
I believe this is due to the numpy reshape command being used in line 38 of
ModFlows_demo_batched.ipynb:im = im.reshape(enc_shape), which is scrambling the pixel-order. I think typically numpy transpose is used in this situation to change the dimension ordering, instead of reshape.Is this expected behavior of the enc_preprocess, or am I doing something wrong, or possibly wrong dependencies? I checked on a few versions of numpy and it gives similar results.
Thanks!