ETCI2021 Img/Label pairing

I think there might be a bug based on visualizing some of the training patches. Shown below are VV, VH, Water mask, Flood mask for some sample:

Run the following a few times to reproduce:

from torchgeo.datasets import ETCI2021

import numpy as np
import matplotlib.pyplot as plt

ds = ETCI2021("data/", split="train", download=False)

for i in np.random.choice(len(ds), size=10, replace=False):
    
    img1 = np.rollaxis(ds[i]["image"][:3].numpy(),0,3)
    img2 = np.rollaxis(ds[i]["image"][3:].numpy(),0,3)
    mask1 = ds[i]["mask"][0].numpy()
    mask2 = ds[i]["mask"][1].numpy()
        
    fig, axs = plt.subplots(1,4,figsize=(12,3))
    axs[0].imshow(img1)
    axs[0].axis("off")
    axs[1].imshow(img2)
    axs[1].axis("off")
    axs[2].imshow(mask1)
    axs[2].axis("off")
    axs[3].imshow(mask2)
    axs[3].axis("off")
    plt.tight_layout()
    plt.show()
    plt.close()

@adamjstewart – this is another example of a dataset that will need a custom trainer. You can interpret the imagery as a 6 band input, however there are two label masks (you want to predict the flood mask, and you are always given the water mask).

[calebrob6]

Moving forward, let’s visualize what comes out of a dataset before we merge them. These semantic bugs are the real killers. Like, this dataset is fully tested but takes me several hours of debugging to actually use – not a great experience for any users.

[calebrob6]

The problem above is caused by glob.glob not returning files in sorted order and is fixed by putting sorted(...) everywhere. I’m going to commit this fix directly to main.