Vaihingen datamodule

Description

I would expect that with a existing Vaihingen datamodule, I only need to define a segmentation task and a pl.Trainer to train a model on this dataset (but maybe this expectation is wrong). However, the Vaihingen dataset has variable sized image dimensions, and therefore one cannot specify a batch_size>1 because otherwise tensors cannot be stacked. So either there should be a collate function for the dataloaders in the datamodule or some comment in the documentation because the default batch_size of the datamodule is 64.

Steps to reproduce

from torchgeo.datamodules import Vaihingen2DDataModule
from torchgeo.trainers import SemanticSegmentationTask
import pytorch_lightning as pl

datamodule = Vaihingen2DDataModule(root="./data/Vaihingen")

task = SemanticSegmentationTask(
    segmentation_model="unet",
    encoder_name="resnet18",
    encoder_weights="imagenet",
    in_channels=3,
    num_classes=6,
    loss="jaccard",
    ignore_index=None,
    learning_rate=0.001,
    learning_rate_schedule_patience=5
)

trainer = pl.Trainer(
    fast_dev_run=True,
    enable_progress_bar=False
)

trainer.fit(
    model=task,
    datamodule=datamodule
)

Version

0.4.0.dev0

[calebrob6]

They aren’t georeferenced

[calebrob6]

There are 16 samples in the training dataset and they are more like “tiles” or “scenes”. I think the datamodule should randomly sample fixed size crops from them.

The sizes:

torch.Size([3, 2569, 1919])
torch.Size([3, 2566, 1893])
torch.Size([3, 2558, 2818])
torch.Size([3, 2565, 1919])
torch.Size([3, 1281, 2336])
torch.Size([3, 2546, 1903])
torch.Size([3, 2546, 1903])
torch.Size([3, 1783, 2995])
torch.Size([3, 2567, 1917])
torch.Size([3, 3007, 2006])
torch.Size([3, 2563, 1934])
torch.Size([3, 2555, 1980])
torch.Size([3, 2555, 1388])
torch.Size([3, 1995, 1996])
torch.Size([3, 2557, 1887])
torch.Size([3, 2557, 1887])