LEVIR-CD+ example.ipynb

Running with torchgeo 0.5.0 will give:

TypeError: SemanticSegmentationTask.__init__() got an unexpected keyword argument 'segmentation_model'

The necessary update:

task = CustomSemanticSegmentationTask(
    model="unet",
    backbone="resnet18",
    weights=True,
    in_channels=6,
    num_classes=2,
    loss="ce",
    ignore_index=None,
    lr=learning_rate,
    patience=10
)

Then on running trainer.fit you will get

TypeError: `model` must be a `LightningModule` or `torch._dynamo.OptimizedModule`, got `CustomSemanticSegmentationTask`

It is necessary to use the new lightning format for imports:

from lightning.pytorch.callbacks import EarlyStopping, ModelCheckpoint
from lightning.pytorch import Trainer, seed_everything
from lightning.pytorch.loggers import TensorBoardLogger, WandbLogger
from lightning.pytorch import LightningDataModule

You will next get:

AttributeError: 'CustomSemanticSegmentationTask' object has no attribute 'loss'

It is necessary in the custom trainer to use:

    loss: Tensor = self.criterion(y_hat, y)

| Name | Type | Params

0 | criterion | CrossEntropyLoss | 0
1 | train_metrics | MetricCollection | 0
2 | val_metrics | MetricCollection | 0
3 | test_metrics | MetricCollection | 0
4 | model | Unet | 14.3 M

14.3 M Trainable params
0 Non-trainable params
14.3 M Total params
57.351 Total estimated model params size (MB)
Sanity Checking: | | 0/? [00:00<?, ?it/s]

It remains at this stage, what could be the problem?

Hey @mustafaemre2 -- are you running on the GPU?

Updated with @robmarkcole's fixes (and ensured that the notebook runs end-to-end) for torchgeo 0.5.1 (thanks Robin!)

Yes, I used your codes exactly
My GPU's RTX 3060 laptop @calebrob6

You are using a CUDA device ('NVIDIA GeForce RTX 3060 Laptop GPU') that has Tensor Cores. To properly utilize them, you should set torch.set_float32_matmul_precision('medium' | 'high') which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]

i get this message @calebrob6

Download the dataset and see how many images are in the train and test splits

train_dataset = LEVIRCDPlus(root="data/LEVIRCDPlus", split="train", download=True, checksum=True)
test_dataset = LEVIRCDPlus(root="data/LEVIRCDPlus", split="test", download=True, checksum=True)
len(train_dataset), len(test_dataset)

Its give error :-
RuntimeError: The MD5 checksum of the download file data/LEVIRCDPlus/LEVIR-CD+.zip does not match the one on record.Please delete the file and try again. If the issue persists, please report this to torchvision at https://github.com/pytorch/vision/issues.

i get this msg @calebrob6

@nadeem-git-coder Have you found any solution for that?

@ProtikBose I have downloaded the dataset mannually and use it .
Have you encountered the error?
What the error