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Engage in a semantic segmentation challenge for land cover description using multimodal remote sensing earth observation data, delving into real-world scenarios with a dataset comprising 70,000+ aerial imagery patches and 50,000 Sentinel-2 satellite acquisitions.

Home Page: https://codalab.lisn.upsaclay.fr/competitions/13447

License: MIT License

Python 0.18% Jupyter Notebook 99.82%

computer-vision deep-learning multiclass-segmentation cookiecutter-template deeplearning image-processing lightning multimodal multimodal-deep-learning pytorch

flair-2's Introduction

🛰️ FLAIR #2

The challenge involves a semantic segmentation task focusing on land cover description using multimodal remote sensing earth observation data. Participants will explore heterogeneous data fusion methods in a real-world scenario. Upon registration, access is granted to a dataset containing 70,000+ aerial imagery patches with pixel-based annotations and 50,000 Sentinel-2 satellite acquisitions.

This project was made possible by our compute partners 2CRSi and NVIDIA.

🏆 Challenge ranking

The score of the challenge was the mIoU.
Our solution was the 8th one (out of 30 teams) with a mIoU equal to 0.62610 🎉.

The podium:
🥇 strakajk - 0.64130
🥈 Breizhchess - 0.63550
🥉 qwerty64 - 0.63510

🖼️ Result example

Aerial input image	Multi-class label	Multi-class pred

View more results on the WandB project.

🏛️ Model architecture

#️⃣ Command lines

Launch a training

python src/models/train_model.py <hyperparams args>

Create a submission

python src/models/predict_model.py -n {model.ckpt}

🔬 References

Chen, L. C., Papandreou, G., Schroff, F., & Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587.

Garioud, A., De Wit, A., Poupée, M., Valette, M., Giordano, S., & Wattrelos, B. (2023). FLAIR# 2: textural and temporal information for semantic segmentation from multi-source optical imagery. arXiv preprint arXiv:2305.14467.

Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J. M., & Luo, P. (2021). SegFormer: Simple and efficient design for semantic segmentation with transformers. Advances in Neural Information Processing Systems, 34, 12077-12090.

📝 Citing

@misc{RebergaUrgell:2023,
  Author = {Louis Reberga and Baptiste Urgell},
  Title = {FLAIR #2},
  Year = {2023},
  Publisher = {GitHub},
  Journal = {GitHub repository},
  Howpublished = {\url{https://github.com/association-rosia/flair-2}}
}

🛡️ License

Project is distributed under MIT License

👨🏻‍💻 Contributors

Louis REBERGA

Baptiste URGELL

flair-2's People

Contributors

Stargazers

flair-2's Issues

Sentinel deal with saturated pixel

Les données sentinel sont à la base entre 1 et 10000. Il arrive cependant que cette valeur soit dépassée, dans ce cas on dit que le pixel est saturé. Il faut donc regarder la démarche à effectuer lorsque c'est le cas. De même il arrive que la valeur soit de 0, cela arrive lorsqu'il n'y a pas de valeur, on devrais donc ne pas la compter dans la moyenne.

liens intéressants :
👉 https://gis.stackexchange.com/questions/233874/what-is-the-range-of-values-of-sentinel-2-level-2a-images
👉 https://docs.digitalearthafrica.org/en/latest/data_specs/Sentinel-2_Level-2A_specs.html

Test Dataset

Le dataset ne fonctionne pas en mode test car il n'existe pas de label.

ref : rasterio.errors.RasterioIOError: data/raw/test/labels/D061_2020/Z7_AU/msk/MSK_085636.tif: No such file or directory

Improve TTA batch inference

You are using a CUDA device ('NVIDIA A40') 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

Try medium and high to compare the performances

Got 16,051 files but only 16,050 are asked

Add a wandb parameter for dry run

Labels begins to 1

https://github.com/association-rosia/flair-2/blame/af0fea4f2c0b9cfdf2751b355cf7eb15f5944b0d/src/data/make_dataset.py#L152

ref (Description section): https://ignf.github.io/FLAIR/

this will not working during training

suggestion: labels -= 1

Comment make_dataset

Ajoute des commentaire dans ton code stp je viens de passer un petit moment à me demander comment les transformations de sen fonctionnaient. Utilise des termes plus explicite pour le nommage des variables ex: channels ambiguë est-ce les bands ou les date ?. Type les variables pour savoir sur quoi on travaille. Et ajoutes des docstring (utilise chatgpt par exemple).

Add .idea in .gitignore

Change MIoU to val/miou on wandb logs

Use yml file for default wandb configuration

Submission request

To create the submission file, the Aerial image name is mandatory. Apply modification on dataset to return the image name

Better image selection for wandb logs

Create well formated zip file for submission

Use Test labels frequences to compute classes weights

Optimize the number of workers used in the dataloaders

TTA & Batch

Wrapper doesn't work with a batch of data :

File "/Users/titou/Documents/flair-2/src/models/make_train.py", line 82, in
main()
File "/Users/titou/Documents/flair-2/src/models/make_train.py", line 77, in main
trainer.fit(model=lightning_model)
File "/opt/homebrew/Caskroom/miniconda/base/envs/flair-2-env/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 529, in fit
call._call_and_handle_interrupt(
File "/opt/homebrew/Caskroom/miniconda/base/envs/flair-2-env/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 42, in _call_and_handle_interrupt
return trainer_fn(*args, **kwargs)
File "/opt/homebrew/Caskroom/miniconda/base/envs/flair-2-env/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 568, in _fit_impl
self._run(model, ckpt_path=ckpt_path)
File "/opt/homebrew/Caskroom/miniconda/base/envs/flair-2-env/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 973, in _run
results = self._run_stage()
File "/opt/homebrew/Caskroom/miniconda/base/envs/flair-2-env/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 1016, in _run_stage
self.fit_loop.run()
File "/opt/homebrew/Caskroom/miniconda/base/envs/flair-2-env/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py", line 201, in run
self.advance()
File "/opt/homebrew/Caskroom/miniconda/base/envs/flair-2-env/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py", line 354, in advance
self.epoch_loop.run(self._data_fetcher)
File "/opt/homebrew/Caskroom/miniconda/base/envs/flair-2-env/lib/python3.10/site-packages/pytorch_lightning/loops/training_epoch_loop.py", line 133, in run
self.advance(data_fetcher)
File "/opt/homebrew/Caskroom/miniconda/base/envs/flair-2-env/lib/python3.10/site-packages/pytorch_lightning/loops/training_epoch_loop.py", line 218, in advance
batch_output = self.automatic_optimization.run(trainer.optimizers[0], kwargs)
File "/opt/homebrew/Caskroom/miniconda/base/envs/flair-2-env/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 185, in run
self._optimizer_step(kwargs.get("batch_idx", 0), closure)
File "/opt/homebrew/Caskroom/miniconda/base/envs/flair-2-env/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 260, in _optimizer_step
call._call_lightning_module_hook(
File "/opt/homebrew/Caskroom/miniconda/base/envs/flair-2-env/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 144, in _call_lightning_module_hook
output = fn(*args, **kwargs)
File "/opt/homebrew/Caskroom/miniconda/base/envs/flair-2-env/lib/python3.10/site-packages/pytorch_lightning/core/module.py", line 1256, in optimizer_step
optimizer.step(closure=optimizer_closure)
File "/opt/homebrew/Caskroom/miniconda/base/envs/flair-2-env/lib/python3.10/site-packages/pytorch_lightning/core/optimizer.py", line 155, in step
step_output = self._strategy.optimizer_step(self._optimizer, closure, **kwargs)
File "/opt/homebrew/Caskroom/miniconda/base/envs/flair-2-env/lib/python3.10/site-packages/pytorch_lightning/strategies/strategy.py", line 225, in optimizer_step
return self.precision_plugin.optimizer_step(optimizer, model=model, closure=closure, **kwargs)
File "/opt/homebrew/Caskroom/miniconda/base/envs/flair-2-env/lib/python3.10/site-packages/pytorch_lightning/plugins/precision/precision_plugin.py", line 114, in optimizer_step
return optimizer.step(closure=closure, **kwargs)
File "/opt/homebrew/Caskroom/miniconda/base/envs/flair-2-env/lib/python3.10/site-packages/torch/optim/optimizer.py", line 280, in wrapper
out = func(*args, **kwargs)
File "/opt/homebrew/Caskroom/miniconda/base/envs/flair-2-env/lib/python3.10/site-packages/torch/optim/optimizer.py", line 33, in _use_grad
ret = func(self, *args, **kwargs)
File "/opt/homebrew/Caskroom/miniconda/base/envs/flair-2-env/lib/python3.10/site-packages/torch/optim/adamw.py", line 148, in step
loss = closure()
File "/opt/homebrew/Caskroom/miniconda/base/envs/flair-2-env/lib/python3.10/site-packages/pytorch_lightning/plugins/precision/precision_plugin.py", line 101, in _wrap_closure
closure_result = closure()
File "/opt/homebrew/Caskroom/miniconda/base/envs/flair-2-env/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 140, in call
self._result = self.closure(*args, **kwargs)
File "/opt/homebrew/Caskroom/miniconda/base/envs/flair-2-env/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 126, in closure
step_output = self._step_fn()
File "/opt/homebrew/Caskroom/miniconda/base/envs/flair-2-env/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 307, in _training_step
training_step_output = call._call_strategy_hook(trainer, "training_step", *kwargs.values())
File "/opt/homebrew/Caskroom/miniconda/base/envs/flair-2-env/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 291, in _call_strategy_hook
output = fn(*args, **kwargs)
File "/opt/homebrew/Caskroom/miniconda/base/envs/flair-2-env/lib/python3.10/site-packages/pytorch_lightning/strategies/strategy.py", line 367, in training_step
return self.model.training_step(*args, **kwargs)
File "/Users/titou/Documents/flair-2/./src/models/lightning.py", line 94, in training_step
outputs = self.forward(inputs={'aerial': aerial, 'sen': sen})
File "/Users/titou/Documents/flair-2/./src/models/lightning.py", line 86, in forward
x = self.model(inputs=inputs, step=self.step, batch_size=self.batch_size)
File "/opt/homebrew/Caskroom/miniconda/base/envs/flair-2-env/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "/Users/titou/Documents/flair-2/./src/data/tta/wrappers.py", line 23, in forward
inputs = augmentation.augment(inputs, param)
File "/Users/titou/Documents/flair-2/./src/data/tta/augmentations.py", line 77, in augment
inputs[key] = F.rotate(inputs[key], angle=angle)
File "/opt/homebrew/Caskroom/miniconda/base/envs/flair-2-env/lib/python3.10/site-packages/torchvision/transforms/functional.py", line 1140, in rotate
return F_t.rotate(img, matrix=matrix, interpolation=interpolation.value, expand=expand, fill=fill)
File "/opt/homebrew/Caskroom/miniconda/base/envs/flair-2-env/lib/python3.10/site-packages/torchvision/transforms/_functional_tensor.py", line 669, in rotate
return _apply_grid_transform(img, grid, interpolation, fill=fill)
File "/opt/homebrew/Caskroom/miniconda/base/envs/flair-2-env/lib/python3.10/site-packages/torchvision/transforms/_functional_tensor.py", line 560, in _apply_grid_transform
img = grid_sample(img, grid, mode=mode, padding_mode="zeros", align_corners=False)
File "/opt/homebrew/Caskroom/miniconda/base/envs/flair-2-env/lib/python3.10/site-packages/torch/nn/functional.py", line 4244, in grid_sample
return torch.grid_sampler(input, grid, mode_enum, padding_mode_enum, align_corners)
RuntimeError: grid_sampler(): expected grid to have size 3 in last dimension, but got grid with sizes [16, 40, 40, 2]

Submission zip file = 650MB > 300MB (max)

/home/external-rosia/miniconda3/envs/flair-2-env/lib/python3.10/site-packages/segmentation_models_pytorch/base/modules.py:116: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument. return self.activation(x)

Fix this warning

Log val/aerial_image on epoch and not on step

Dataloader Test

Lorsqu'on met le dataset en mode is_test=True et qu'on passe le dataset dans un dataloader une erreur ressort. C'est dû au fait qu'on ne puisse pas retourner None.

ref:

Traceback (most recent call last):
File "/Users/titou/Documents/flair-2/src/data/make_dataset.py", line 199, in
for image_id, aerial, sen, labels in dataloader:
File "/opt/homebrew/Caskroom/miniconda/base/envs/flair-2-env/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 633, in next
data = self._next_data()
File "/opt/homebrew/Caskroom/miniconda/base/envs/flair-2-env/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 677, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
File "/opt/homebrew/Caskroom/miniconda/base/envs/flair-2-env/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py", line 54, in fetch
return self.collate_fn(data)
File "/opt/homebrew/Caskroom/miniconda/base/envs/flair-2-env/lib/python3.10/site-packages/torch/utils/data/_utils/collate.py", line 265, in default_collate
return collate(batch, collate_fn_map=default_collate_fn_map)
File "/opt/homebrew/Caskroom/miniconda/base/envs/flair-2-env/lib/python3.10/site-packages/torch/utils/data/_utils/collate.py", line 142, in collate
return [collate(samples, collate_fn_map=collate_fn_map) for samples in transposed] # Backwards compatibility.
File "/opt/homebrew/Caskroom/miniconda/base/envs/flair-2-env/lib/python3.10/site-packages/torch/utils/data/_utils/collate.py", line 142, in
return [collate(samples, collate_fn_map=collate_fn_map) for samples in transposed] # Backwards compatibility.
File "/opt/homebrew/Caskroom/miniconda/base/envs/flair-2-env/lib/python3.10/site-packages/torch/utils/data/_utils/collate.py", line 150, in collate
raise TypeError(default_collate_err_msg_format.format(elem_type))
TypeError: default_collate: batch must contain tensors, numpy arrays, numbers, dicts or lists; found <class 'NoneType'>

Change aerial_image to val/aerial_image

Remove list_images_test, list_images_train et list_images_val from wandb

Use the time calculatio

Add name parameter for predict model scrip

Ajouter un paramètre -n / --name lors du lancement du script predict_model pour sélectionner le modèle à utiliser

The file "data\\raw\\labels-statistics-12.csv" missing!

Dear all thanks for making the module available.

I have been trying to reproduce the results from your code about the pixelwise classification but I think the "labels-statistics-12.csv" is uploaded in this repository, could you please share it with me?

I am really interested in how you merged the features maps for both modalities (Aerial and Sentinel Time Series.)
Here is the errror message I am getting when trying to train the model:
handle = open( **FileNotFoundError: [Errno 2] No such file or directory: 'data\\raw\\labels-statistics-12.csv'** wandb: Waiting for W&B process to finish... (failed 1). Press Ctrl-C to abort syncing. wandb: View run daily-forest-1 at: https://wandb.ai/hubert10/flair-2/runs/1sn7i49o wandb: Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s) wandb: Find logs at: .\wandb\run-20240130_194359-1sn7i49o\logs

Thanks in advance!